[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Pediatr</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Pediatr</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Pediatr.</journal-id><journal-title-group><journal-title>Frontiers in Pediatrics</journal-title></journal-title-group><issn pub-type=\"epub\">2296-2360</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32850558</article-id><article-id pub-id-type=\"pmc\">PMC7431947</article-id><article-id pub-id-type=\"doi\">10.3389/fped.2020.00449</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Pediatrics</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>Euphrasia Eye Drops in Preterm Neonates With Ocular Discharge: A Randomized Double-Blind Placebo-Controlled Trial</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Meier-Girard</surname><given-names>Delphine</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><xref ref-type=\"author-notes\" rid=\"fn002\"><sup>†</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/836979/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Gerstenberg</surname><given-names>Gisa</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"author-notes\" rid=\"fn002\"><sup>†</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/933101/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Stoffel</surname><given-names>Liliane</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Kohler</surname><given-names>Therese</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Klein</surname><given-names>Sabine D.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/537822/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Eschenmoser</surname><given-names>Marco</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/901104/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Mitter</surname><given-names>Vera Ruth</given-names></name><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Nelle</surname><given-names>Mathias</given-names></name><xref ref-type=\"aff\" rid=\"aff5\"><sup>5</sup></xref><xref ref-type=\"author-notes\" rid=\"fn003\"><sup>‡</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1043775/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Wolf</surname><given-names>Ursula</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"author-notes\" rid=\"fn003\"><sup>‡</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/121116/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Anthroposophically Extended Medicine, Institute of Complementary and Integrative Medicine, University of Bern</institution>, <addr-line>Bern</addr-line>, <country>Switzerland</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Department of Neonatology, Inselspital, Bern University Hospital, University of Bern</institution>, <addr-line>Bern</addr-line>, <country>Switzerland</country></aff><aff id=\"aff3\"><sup>3</sup><institution>Department of Pharmacy, Hospital of Freiburg</institution>, <addr-line>Freiburg</addr-line>, <country>Switzerland</country></aff><aff id=\"aff4\"><sup>4</sup><institution>Department of Obstetrics and Gynecology, Inselspital, Bern University Hospital, University of Bern</institution>, <addr-line>Bern</addr-line>, <country>Switzerland</country></aff><aff id=\"aff5\"><sup>5</sup><institution>Neonatology Division, University Hospital of Zurich</institution>, <addr-line>Zurich</addr-line>, <country>Switzerland</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Gunnar Naulaers, KU Leuven, Belgium</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Kazumichi Fujioka, Kobe University, Japan; Catherine Cassiman, University Hospitals Leuven, Belgium</p></fn><corresp id=\"c001\">*Correspondence: Delphine Meier-Girard <email>delphine.meier@ikim.unibe.ch</email></corresp><fn fn-type=\"other\" id=\"fn001\"><p>This article was submitted to Neonatology, a section of the journal Frontiers in Pediatrics</p></fn><fn fn-type=\"other\" id=\"fn002\"><p>†These authors have contributed equally to this work and share first authorship</p></fn><fn fn-type=\"other\" id=\"fn003\"><p>‡These authors have contributed equally to this work and share last authorship</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>8</volume><elocation-id>449</elocation-id><history><date date-type=\"received\"><day>29</day><month>10</month><year>2019</year></date><date date-type=\"accepted\"><day>29</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Meier-Girard, Gerstenberg, Stoffel, Kohler, Klein, Eschenmoser, Mitter, Nelle and Wolf.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Meier-Girard, Gerstenberg, Stoffel, Kohler, Klein, Eschenmoser, Mitter, Nelle and Wolf</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p><bold>Aim:</bold> To investigate whether the early administration of Euphrasia eye drops® in preterm neonates presenting with ocular discharge fosters the resolution of the ocular discharge and reduces the need for topical antibiotic therapy, as compared to placebo.</p><p><bold>Methods:</bold> We conducted a randomized double-blind placebo-controlled trial at the University Children's Hospital Bern, Switzerland. Preterm neonates with white, yellow, or green ocular discharge were included. Infants were randomly assigned (1:1) to the Euphrasia arm (Euphrasia eye drops®, Weleda AG, Arlesheim) or the placebo arm (NaCl 0.9%). Euphrasia or placebo was administrated at a dose of one drop in each eye four times a day over a period of 96 h. The primary outcome was the treatment success, defined as no ocular discharge at 96 h and no use of topical antibiotic therapy during the 96-h intervention.</p><p><bold>Results:</bold> A total of 114 neonates were screened and 84 were randomized. Among neonates in the Euphrasia arm, 22 (55.0%) achieved our primary outcome compared to 21 (51.2%) in the placebo arm (<italic>p</italic> = 0.85). In the Euphrasia arm, time to resolution of reddening tended to fall within the shorter bracket of 24 to 48 h (24 (92.3%) vs. 12 (80.0%) in the placebo arm, <italic>p</italic> = 0.34) and relapse or first signs of reddening during the 96-h intervention tended to be lower [3 (7.9%) eyes vs. 8 (18.2%) eyes in the placebo arm, <italic>p</italic> = 0.17]. Tearing at 96 h tended to be lower in the Euphrasia arm [5 (12.8%) eyes in the Euphrasia arm vs. 12 (27.3%) eyes in the placebo arm, <italic>p</italic> = 0.10].</p><p><bold>Discussion:</bold> Euphrasia did not significantly improve treatment success, defined as no ocular discharge at 96 h and no use of topical antibiotic therapy during the 96-h intervention. However, results suggest that Euphrasia may be of benefit for symptoms such as reddening and tearing, and thus improve the comfort of patients.</p><p><bold>Trial Registration:</bold> The trial is registered at the US National Institutes of Health (<ext-link ext-link-type=\"uri\" xlink:href=\"https://ClinicalTrials.gov\">ClinicalTrials.gov</ext-link>) NCT04122300 and at the portal for human research in Switzerland SNCTP000003490.</p></abstract><kwd-group><kwd>ocular discharge</kwd><kwd>congenital nasolacrimal duct obstruction</kwd><kwd>preterm neonate</kwd><kwd>Euphrasia drops</kwd><kwd>complementary medicine</kwd></kwd-group><counts><fig-count count=\"1\"/><table-count count=\"5\"/><equation-count count=\"0\"/><ref-count count=\"26\"/><page-count count=\"9\"/><word-count count=\"5859\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>Neonatal ocular discharge is mostly related to ophthalmia neonatorum (neonatal conjunctivitis) or to congenital nasolacrimal duct obstruction (CNLDO).</p><p>Ophthalmia neonatorum is a relatively common illness, defined as conjunctivitis occurring within the first month of life (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>). Ophthalmia neonatorum can be caused by the sexually transmitted pathogens of <italic>Neisseria gonorrhoeae</italic> and <italic>Chlamydia trachomatis</italic>, by bacteria such as <italic>Staphylococcus</italic> species, <italic>Streptococcus</italic> species, <italic>Haemophilus</italic> species and other gram-negative bacterial species, or much less commonly by viral infections (herpes simplex and adenovirus virus). The organisms inducing ophthalmia neonatorum are usually acquired during the birth process, from the mother's birth canal, or after birth from the immediate surroundings (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>–<xref rid=\"B4\" ref-type=\"bibr\">4</xref>). Epidemiology of organisms that induce ophtalmia neonatorum substantially differs between developing countries and developed countries (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>). In the United States <italic>Neisseria gonorrhoeae</italic> counts for <1% of cases and <italic>C. trachomatis</italic> for 2–40% of cases, while 30–50% of cases are caused by other bacteria (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>).</p><p>Ophthalmia neonatorum must be distinguished from ocular discharge related to CNLDO. CNLDO occurs in ~10–20% of all term newborns, and is the most common cause of persistent tearing and ocular discharge in children. It results from a congenital abnormality of the lacrimal drainage system in the form of a membranous obstruction of the nasolacrimal duct of one or both eyes (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>). Spontaneous resolution, through the spontaneous perforation of the membrane, occurs by 6 months of age in ~90% of infants (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>). The first clinical signs appear during the first month of life in 95% of cases (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>) and usually consist of tearing and debris on the eyelashes (“mattering”). Mucopurulent eye discharge occurs commonly in infants with CNLDO and, in the absence of other signs of infection, suggests bacterial overgrowth in the stagnant tear pool of the lacrimal sac (“chronic dacryocystitis”) (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>).</p><p>In most instances, ocular discharge is a mild illness. Complication such as microbial keratitis and acute dacryocystitis are rare but can be serious, resulting in severe visual impairment (e.g., corneal diseases), or blindness (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>–<xref rid=\"B9\" ref-type=\"bibr\">9</xref>). In consequence, topical antibiotic therapy is recommended in case of significant mucopurulent discharge in order to control the bacterial overgrowth, to avoid further inflammatory damage, and to prevent serious infective complications (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>, <xref rid=\"B6\" ref-type=\"bibr\">6</xref>, <xref rid=\"B10\" ref-type=\"bibr\">10</xref>, <xref rid=\"B11\" ref-type=\"bibr\">11</xref>). This therapy should be started just after bacterial/viral and chlamydial swabs have been taken. However, the use of antibiotics as the first-line treatment in infants who present with ocular discharge has be questioned in light of other possible treatment strategies (conservative management). At the time of the study it was generally recommended to apply NaCl 0.9% locally in neonates presenting with a white or yellow ocular discharge, and to introduce a topical antibiotic therapy should symptoms become stronger.</p><p>A previous pilot study suggested that early treatment with Euphrasia eye drops in 24 neonates with ocular discharge with or without tearing and reddened eye might reduce the antibiotic consumption (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>). Euphrasia (eyebright) eye drops (from <italic>Euphrasia officinalis</italic>) are a frequently prescribed medication for the treatment of irritative, infectious, or allergic conjunctivitis and other affections of the eye (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>–<xref rid=\"B15\" ref-type=\"bibr\">15</xref>). It is also effective against hyperemia. It has been used for more than 70 years for the structuring of the fluid organism in the eye, especially in inflammatory and catarrhal conjunctivitis (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>). Euphrasia has an anti-inflammatory effect through the aucubin (inhibition of prostaglandin synthesis), as well as an antibacterial effect through phenolic carboxylic acids and flavonoids. Previous studies have reported excellent tolerance of Euphrasia eye drops in adults and children (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>, <xref rid=\"B13\" ref-type=\"bibr\">13</xref>, <xref rid=\"B15\" ref-type=\"bibr\">15</xref>).</p><p>The present study—a randomized double-blind placebo-controlled trial—follows our previous pilot study (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>) and aimed to investigate whether early administration of Euphrasia eye drops (Weleda AG, Arlesheim) in preterm neonates presenting with ocular discharge with or without tearing and reddened eye fosters the resolution of the ocular discharge and reduces the need for topical antibiotic therapy.</p></sec><sec sec-type=\"methods\" id=\"s2\"><title>Methods</title><sec><title>Study Design</title><p>We conducted a randomized double-blind placebo-controlled trial between May 2011 and December 2016 at the Department of Neonatology at the Children's University Hospital, Bern, Switzerland. The study was approved by the Ethics Commission of the Canton of Bern, Switzerland (215/08) and written informed consent was obtained from parents or legal guardians of each neonate before any procedures were conducted. The trial was registered at the US National Institutes of Health (<ext-link ext-link-type=\"uri\" xlink:href=\"https://ClinicalTrials.gov\">ClinicalTrials.gov</ext-link>) NCT04122300 and at the portal for human research in Switzerland SNCTP000003490.</p></sec><sec><title>Study Population</title><p>Eligible patients were preterm neonates (with a gestational age of 24 to 37 weeks) diagnosed with white, yellow, or green ocular discharge with or without tearing and reddened eye. The criteria for exclusion were congenital abnormalities of the eye, severe asphyxia, sepsis, or intracranial bleeding (intraventricular hemorrhage ≥ grade III).</p></sec><sec><title>Study Interventions</title><sec><title>Randomization and Blinding</title><p>Infants were randomly assigned (1:1) to receive either Euphrasia or placebo. We used a randomization program (DatInf RandList version 1.0) to generate the randomization lists. Euphrasia or placebo was administrated at a dose of one drop in each eye four times a day over a period of 96 h.</p><p>Study investigators, research coordinators, attending care teams and the infants' legual guardian were blinded to treatment allocation. The hospital pharmacy provided the blinded study medication: 0.2 ml of Euphrasia eye drops (Weleda AG, Arlesheim) or 0.2 ml of placebo (sodium chloride 0.9%, Bichsel). The Euphrasia eye drops and placebo were filled in neutral tuberculin syringes of 1 ml under aseptic conditions according to the Good Manufacturing Practices (2010<xref ref-type=\"fn\" rid=\"fn0001\"><sup>1</sup></xref>).</p></sec><sec><title>Treatment Regimen and Microbial Evaluation</title><p>At inclusion, before the start of the therapy, a bacterial/viral and chlamydial conjunctival swab was conducted. Afterwards, both eyes of neonates were washed four times a day (i.e., every 6 h) with NaCl 0.9%. Subsequently, a drop of Euphrasia or placebo was placed into the lower conjunctival sac of each eye, and followed by a lacrimal sac digital massage. In case of worsening of symptoms or a positive swab without any improvement of symptoms an antibiotic therapy was initiated: bacitracin, neomycin, polymyxin B (Neosporin ointment, HeliDerm) or tobramycin (Tobrex 0.3% eye drops, Novartis Pharma Schweiz AG) after the Neosporin withdrawal in September 2013. An additional swab was performed at 96 h (i.e., at the end of the study).</p></sec></sec><sec><title>Study Outcomes</title><p>The primary outcome was the treatment success, defined as no ocular discharge at 96 h and no use of topical antibiotic therapy during the 96-h intervention period. If a neonate presented a bilateral affection, the therapy was defined as successful only if ocular discharge had disappeared in both eyes at 96 h.</p><p>As secondary outcomes, the type of ocular discharge (white, yellow, or green) and the presence of tearing or reddening were recorded at baseline, and at 24, 72, and 96 h, as was the use of topical antibiotic therapy during the 96-h intervention period.</p></sec><sec><title>Sample Size and Statistical Analysis</title><p>We calculated that a total of 84 infants would be needed to detect a difference between groups, with a two-tailed α of 0.05 and a power of 80%, for a comparison of two independent proportions if there was an absolute increase of 30% in the primary outcome measure (treatment success). Our initial estimate of sample size calculation included an assumption of treatment success rate of 30% in the placebo arm. Assumptions were based on the results of our pilot study and communication with clinicians (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>).</p><p>Our primary analysis was conducted applying an intention-to-treat approach, and therefore included all randomized infants. Baseline characteristics of patients in the two treatment groups were reported using frequency distribution and descriptive statistics. Baseline characteristics included demographic characteristics of neonates (i.e., gender, age, and weight), birth characteristics of neonates, and age of mother at birth. Birth characteristics included gestational age at birth (<28 weeks of gestation (WG) was defined as extremely preterm, 28 to 32 WG as very preterm, and 32 to 37 WG as moderate to late preterm, in accordance with the WHO definitions), birthweight and birth procedure (spontaneous birth, instrumental vaginal delivery, or cesarean section).</p><p>The principal analysis of our primary outcome was an unadjusted chi-square test comparing the proportion of events in each treatment group.</p><p>A per-protocol analysis of infants who filled all inclusion criteria was also conducted to examine the robustness of our primary estimates.</p><p>All analyses were conducted with R, Version 3.5.1 (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>).</p></sec></sec><sec sec-type=\"results\" id=\"s3\"><title>Results</title><sec><title>Study Population</title><p>A total of 114 neonates were screened for eligibility and 84 were randomized between May 2011 and December 2016 into the Euphrasia arm (<italic>n</italic> = 42) and the placebo arm (<italic>n</italic> = 42) (<xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>). Three neonates were randomized though they did not fill the inclusion criteria (one neonate was born at full-term and two neonates did not present any ocular discharge at inclusion). These three neonates were allocated to the Euphrasia arm. All randomized infants completed the trial with the exception of three infants who were discharged before the end of the study. The primary outcome was consequently missing for these three infants. In total, 48 (60%) were boys, the mean postnatal age was 21 ± 16 days, 42 (52.5%) were moderate to late preterm, 24 (30.0%) were very preterm, and 14 (17.5%) were extremely preterm. Baseline demographic and clinical characteristics were similar in both treatment arms (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>).</p><fig id=\"F1\" position=\"float\"><label>Figure 1</label><caption><p>Participant flow.</p></caption><graphic xlink:href=\"fped-08-00449-g0001\"/></fig><table-wrap id=\"T1\" position=\"float\"><label>Table 1</label><caption><p>Baseline characteristics (<italic>n</italic> = 84 neonates).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"1\" colspan=\"1\"/><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Euphrasia (<italic>n</italic> = 42 neonates)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Missing data</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Placebo (<italic>n</italic> = 42 neonates)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Missing data</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" colspan=\"5\" rowspan=\"1\"><bold>Demographic characteristics</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Sex, male</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">27 (64.3%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24 (57.1%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Postnatal age, days</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12 [10–22]</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17 [12–27]</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Weight, <italic>g</italic></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1807 ± 523</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1722 ± 443</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"5\" rowspan=\"1\"><bold>Birth characteristics</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Gestational age at birth, week of gestation<xref ref-type=\"table-fn\" rid=\"TN1\"><sup>a</sup></xref></td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Extremely preterm <28</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7 (16.7%)</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8 (19.0%)</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Very preterm [28;31(6/7)]</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13 (31.0%)</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12 (28.6%)</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Moderate to late preterm [32;36(6/7)]</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">21 (50.0%)</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22 (52.4%)</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Full-term ≥37<xref ref-type=\"table-fn\" rid=\"TN2\"><sup>b</sup></xref></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1 (2.4%)</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0 (0.0%)</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Birthweight, <italic>g</italic></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1,608 [1,226–1,834]</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1,498 [988–1,699]</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Age of mother at delivery, years</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">35.0 [30.2–37.0]</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">32.5 [29.0–36.7]</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Birth procedure</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Spontaneous birth</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10 (23.8%)</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5 (12.5%)</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Instrumental vaginal delivery</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2 (4.8%)</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3 (7.5%)</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Cesarean section</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30 (71.4%)</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">32 (80.0%)</td><td rowspan=\"1\" colspan=\"1\"/></tr></tbody></table><table-wrap-foot><p><italic>Data are expressed as mean ± standard deviation, median [interquartile range], and number (percentage)</italic>.</p><fn id=\"TN1\"><label>a</label><p><italic>In accordance with the WHO definitions</italic>.</p></fn><fn id=\"TN2\"><label>b</label><p><italic>One neonate was randomized though he was born at full-term</italic>.</p></fn></table-wrap-foot></table-wrap></sec><sec><title>Ocular Health at Baseline</title><p>Laterality of the ocular discharge (i.e., unilateral or bilateral) was similar in both treatment arms (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>). In the Euphrasia arm, 10 (25.0%) neonates presented a bilateral ocular discharge at baseline, resulting in 50 eyes with ocular discharge among the 42 neonates (59.5%). In the placebo arm, 17 (40.5%) neonates presented a bilateral ocular discharge, resulting in 59 eyes with ocular discharge among the 42 neonates (70.2%).</p><table-wrap id=\"T2\" position=\"float\"><label>Table 2</label><caption><p>Laterality of ocular discharge at baseline according to treatment arm.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"1\" colspan=\"1\"/><th valign=\"top\" align=\"center\" colspan=\"2\" style=\"border-bottom: thin solid #000000;\" rowspan=\"1\"><bold>All neonates (</bold><italic><bold>n</bold></italic>\n<bold>=</bold>\n<bold>84 neonates)</bold></th><th rowspan=\"1\" colspan=\"1\"/><th valign=\"top\" align=\"center\" colspan=\"2\" style=\"border-bottom: thin solid #000000;\" rowspan=\"1\"><bold>No topical antibiotic therapy (</bold><italic><bold>n</bold></italic>\n<bold>=</bold>\n<bold>68 neonates)</bold></th><th rowspan=\"1\" colspan=\"1\"/></tr><tr><th rowspan=\"1\" colspan=\"1\"/><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Euphrasia</bold><break/><bold> (<italic>n</italic> = 42 neonates)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Placebo</bold><break/><bold> (<italic>n</italic> = 42 neonates)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>MD</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Euphrasia</bold><break/><bold> (<italic>n</italic> = 34 neonates)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Placebo</bold><break/><bold> (<italic>n</italic> = 34 neonates)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>MD</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">At least one eye with ocular discharge</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">40 (95.2%)<xref ref-type=\"table-fn\" rid=\"TN3\"><sup>a</sup></xref></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">42 (100.0%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">32 (94.1%)<xref ref-type=\"table-fn\" rid=\"TN3\"><sup>a</sup></xref></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">34 (100.0%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Laterality of ocular discharge</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">One eye</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30 (75.0%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25 (59.5%)</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25 (78.1%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22 (64.7%)</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Both eyes</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10 (25.0%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17 (40.5%)</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7 (21.9%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12 (35.3%)</td><td rowspan=\"1\" colspan=\"1\"/></tr></tbody></table><table-wrap-foot><p><italic>Data are expressed as number (percentage)</italic>.</p><fn id=\"TN3\"><label>a</label><p><italic>Two neonates were randomized though they did not present with ocular discharge at baseline</italic>.</p></fn></table-wrap-foot></table-wrap><p>Symptoms at baseline were similar in both treatment arms with the exception of the reddening (<xref rid=\"T3\" ref-type=\"table\">Table 3</xref>). In both treatment arms, neonates presented mainly with yellow ocular discharge, and slight tearing. More neonates presented with green ocular discharge in the Euphrasia arm. In the Euphrasia arm, ocular discharge was accompanied by reddening in more eyes than in the placebo arm. The reddening was bilateral for 5 neonates in the Euphrasia arm, while this was the case for only one neonate in the placebo arm.</p><table-wrap id=\"T3\" position=\"float\"><label>Table 3</label><caption><p>Ocular health at baseline according to treatment arm (<italic>n</italic> = 84 neonates, 111 affected eyes).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"1\" colspan=\"1\"/><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Euphrasia (<italic>n</italic> = 52 eyes)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Missing data</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Placebo (<italic>n</italic> = 59 eyes)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Missing data</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" colspan=\"5\" rowspan=\"1\"><bold>Symptoms at baseline</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Ocular discharge</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">50 (96.2%)<xref ref-type=\"table-fn\" rid=\"TN4\"><sup>a</sup></xref></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">59 (100%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Type of ocular discharge</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">White</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5 (10.2%)</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8 (13.6%)</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Yellow</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">40 (81.6%)</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">50 (84.7%)</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Green</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4 (8.2%)</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1 (1.7%)</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Reddening</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">33 (63.5%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">21 (36.2%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Tearing</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">34 (66.6%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">36 (63.2%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td></tr></tbody></table><table-wrap-foot><p><italic>Data are expressed as number (percentage)</italic>.</p><fn id=\"TN4\"><label>a</label><p><italic>Two neonates were randomized though they did not present with ocular discharge at baseline</italic>.</p></fn></table-wrap-foot></table-wrap></sec><sec><title>Therapy Outcome</title><sec><title>Primary Outcome</title><p>A total of 43 (53.1%) infants met our primary outcome of treatment success. Treatment success did not significantly differ between treatment arms [22 (55.0%) in the Euphrasia arm vs. 21 (51.2%) in the placebo arm, <italic>p</italic> = 0.85] (<xref rid=\"T4\" ref-type=\"table\">Table 4</xref>).</p><table-wrap id=\"T4\" position=\"float\"><label>Table 4</label><caption><p>Treatment success at 96 h (primary outcome) and use of topical antibiotic therapy according to treatment arm (<italic>n</italic> =8 4 neonates).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"1\" colspan=\"1\"/><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Euphrasia (<italic>n</italic> = 42 neonates)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Missing data</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Placebo (<italic>n</italic> = 42 neonates)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Missing data</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>p</italic>-value<xref ref-type=\"table-fn\" rid=\"TN5\"><sup>a</sup></xref></bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Success of treatment at 96 h</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22 (55.0%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">21 (51.2%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.85</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Topical antibiotic therapy</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8 (19.0%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8 (19.0%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.99</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Time to initiation of antibiotic therapy<xref ref-type=\"table-fn\" rid=\"TN6\"><sup>b</sup></xref></td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.99</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Within 48 h</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5 (62.5%)</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4 (50.0%)</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Between 48 and 96 h</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3 (37.5%)</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4 (50.0%)</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Decreasing of symptoms after topical antibiotic therapy</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6 (75.0%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6 (75.0%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>-</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.99</td></tr></tbody></table><table-wrap-foot><p><italic>Data are expressed as number (percentage)</italic>.</p><p><italic>Treatment success was defined as no ocular discharge at 96 h and no use of topical antibiotic therapy during the 96-h intervention period. If a neonate presented a bilateral affection, the treatment was defined as successful only if ocular discharge had disappeared in both eyes at 96 h</italic>.</p><fn id=\"TN5\"><label>a</label><p><italic>Chi-square test or Fischer test, as appropriate</italic>.</p></fn><fn id=\"TN6\"><label>b</label><p><italic>In the 8 neonates who received a topical antibiotic therapy</italic>.</p></fn></table-wrap-foot></table-wrap></sec><sec><title>Topical Antibiotic Therapy</title><p>The use of topical antibiotic therapy was similar in both treatment arms (<xref rid=\"T4\" ref-type=\"table\">Table 4</xref>). Within the 96-h intervention period, 8 (19.0%) neonates in the Euphrasia arm and 8 (19.0%) neonates in the placebo arm received a topical antibiotic therapy (<italic>p</italic> = 0.99). The time to initiation of the antibiotic therapy did not significantly differ between treatment arms and symptoms were observed to decrease similarly in both groups after introduction of topical antibiotic therapy. Neonates who received topical antibiotic therapy presented at inclusion significantly more tearing as compared to neonates who did not receive topical antibiotic therapy [21 (87.5%) eyes with tearing vs. 49 (58.3%) eyes with tearing, respectively, <italic>p</italic> = 0.008] (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Table E1</xref>).</p><p>Among the 16 neonates who received a topical antibiotic therapy, results of swabs performed at inclusion were similar in both groups (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Table E2</xref>). Swabs were positive for <italic>Staphylococcus aureus (S. aureus)</italic> in 9 neonates. Swabs were negative at inclusion in three neonates. Due to a substantial worsening of symptoms in these three neonates, it had not been possible to wait for the results of the swabs before initiating the antibiotic therapy.</p><p>Among the 68 (81.0%) neonates who did not receive any topical antibiotic therapy, results of swabs performed at inclusion were similar in both groups (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Table E2</xref>). Swabs were positive in 31 (45.6%) neonates [16 (47.1%) in the Euphrasia arm vs. 15 (44.1%) in the placebo arm, <italic>p</italic> = 0.81]. The swabs were mainly positive to <italic>S. aureus</italic> [16/31 (51.2%) swabs]. At 96 h, 21 (70.0%) of the neonates with a positive swab at inclusion and 22 (61.1%) of the neonates with a negative swab at inclusion were free of ocular discharge (<italic>p</italic> = 0.45) (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Table E3</xref>). Therefore, no significant difference in the success of treatment at 96 h was observed based on the presence of microbiological agents at the beginning of the affection. No significant difference was observed between treatment arms (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Table E3</xref>).</p><p>None of the neonates tested positive for <italic>Neisseria gonorrhoeae</italic> and <italic>C. trachomatis</italic>.</p></sec><sec><title>Resolution of Ocular Discharge and Evolution of Other Symptoms</title><p>Among the 68 (81.0%) neonates who did not receive any topical antibiotic therapy, in the Euphrasia arm, 7 (21.9%) neonates presented a bilateral ocular discharge at baseline, resulting in 39 eyes with ocular discharge among 34 neonates (57.4%) (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>). In the placebo arm, 12 (35.3%) neonates presented a bilateral ocular discharge, resulting in 46 eyes with ocular discharge among the 34 neonates (67.6%). <xref rid=\"T5\" ref-type=\"table\">Table 5</xref> provides an overview according to treatment arm of how symptoms (i.e., ocular discharge, reddening, tearing) evolved in neonates who did not receive any topical antibiotic therapy over the 96 h of intervention.</p><list list-type=\"bullet\"><list-item><p>Resolution of ocular discharge was observed in 34 (89.5%) eyes in the Euphrasia arm and in 39 (86.7%) in the placebo arm (<italic>p</italic> = 0.83). Time to resolution of ocular discharge did not differ between arms (<italic>p</italic> = 0.16). Relapse of ocular discharge was significantly higher in the Euphrasia arm as compared to the placebo arm [13 (38.2%) eyes vs. 6 (15.8%) eyes, <italic>p</italic> = 0.03]. Resolution at 96 h was similar in both treatment arms [ocular discharge in 12 (30.8%) eyes in the Euphrasia arm vs. 12 (27.7%) eyes in the placebo arm, <italic>p</italic> = 0.73].</p></list-item><list-item><p>Resolution of reddening was observed in 26 (96.3%) eyes in the Euphrasia arm and in 15 (100.0%) eyes in the placebo arm (<italic>p</italic> = 0.99). In the Euphrasia arm, time to resolution tended to be shorter [24 (92.3%) vs. 12 (80.0%) in the placebo arm, <italic>p</italic> = 0.34] and relapse or first signs of reddening during the 96-h intervention tended to be lower [3 (7.9%) eyes vs. 8 (18.2%) eyes in the placebo arm, <italic>p</italic> = 0.17]. Reddening at 96 h was similar in both treatment arms [reddening in 1 (2.6%) eyes in the Euphrasia arm vs. 2 (4.5%) eyes in the placebo arm, <italic>p</italic> = 0.99].</p></list-item><list-item><p>Resolution of tearing was observed in 23 (100.0%) eyes in the Euphrasia arm and in 24 (96.0%) eyes in the placebo arm (<italic>p</italic> = 0.99). Time to resolution did not differ between arms (<italic>p</italic> = 0.99). Relapse of first signs of tearing during the 96-h intervention was similar in both treatment arms (<italic>p</italic> = 0.73). Tearing at 96 h tended to be lower in the Euphrasia arm [5 (12.8%) eyes in the Euphrasia arm vs. 12 (27.3%) eyes in the placebo arm, <italic>p</italic> = 0.10].</p></list-item></list><table-wrap id=\"T5\" position=\"float\"><label>Table 5</label><caption><p>Evolution of symptoms in neonates who did not go on to receive a topical antibiotic therapy according to treatment arm (<italic>n</italic> = 68 neonates, 87 affected eyes).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"1\" colspan=\"1\"/><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Euphrasia (<italic>n</italic> = 41 eyes)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Missing data</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Placebo (<italic>n</italic> = 46 eyes)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Missing data</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>p</italic>-value<xref ref-type=\"table-fn\" rid=\"TN7\"><sup>a</sup></xref></bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" colspan=\"6\" rowspan=\"1\"><bold>Ocular discharge</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Ocular discharge at baseline</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">39 (95.1%)<xref ref-type=\"table-fn\" rid=\"TN8\"><sup>b</sup></xref></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">46 (100.0%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.99</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Resolution during intervention</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">34 (89.5%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">39 (86.7%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.83</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Time to resolution</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.16</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">24–48 h</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22 (64.7%)</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">31 (79.5%)</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">72–96 h</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12 (35.3%)</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8 (20.5%)</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Relapse during intervention</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13 (38.2%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6 (15.8%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.03</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Ocular discharge at 96 h</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12 (30.8%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12 (27.7%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.73</td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"6\" rowspan=\"1\"><bold>Reddening</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Reddening at baseline</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">27 (65.9%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15 (33.3%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.003</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Resolution during intervention</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">26 (96.3%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15 (100.0%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.99</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Time to resolution</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.34</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">24–48 h</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24 (92.3%)</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12 (80.0%)</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">72–96 h</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2 (7.7%)</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3 (20.0%)</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Relapse or first signs during intervention</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3 (7.9%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8 (18.2%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.17</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Reddening at 96 h</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1 (2.6%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2 (4.5%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.99</td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"6\" rowspan=\"1\"><bold>Tearing</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Tearing at baseline</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24 (60.0%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25 (56.8%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.77</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Resolution during intervention</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">23 (100.0%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24 (96.0%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.99</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Time to resolution</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.99</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">24–48 h</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19 (82.6%)</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19 (79.2%)</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">72–96 h</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4 (17.4%)</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5 (20.8%)</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Relapse or first signs during intervention</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18 (46.2%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22 (50.0%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.73</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Tearing at 96 h</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5 (12.8%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12 (27.3%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.10</td></tr></tbody></table><table-wrap-foot><p><italic>Data are expressed as number (percentage)</italic>.</p><fn id=\"TN7\"><label>a</label><p><italic>Chi-square test or Fischer test, as appropriate</italic>.</p></fn><fn id=\"TN8\"><label>b</label><p><italic>Two neonates did not fill the inclusion criteria (intention-to-treat approach)</italic>.</p></fn></table-wrap-foot></table-wrap></sec></sec><sec><title>Sensitivity Analyses</title><p>A per-protocol analysis of infants who met all inclusion criteria did not alter the conclusions on the primary (treatment success was 19 (52.8%) in the Euphrasia arm vs. 21 (51.2%), <italic>p</italic> = 0.89) and secondary outcomes (results not shown).</p><p>The analysis of the treatment success according to treatment arm and preterm status (extremely and very preterm vs. moderate to late preterm and full-term) did not show any significant difference (results not shown).</p></sec></sec><sec sec-type=\"discussion\" id=\"s4\"><title>Discussion</title><sec><title>Main Results</title><p>In this study with 84 preterm neonates with ocular discharge treated with either Euphrasia eye drops or placebo, Euphrasia did not significantly improve treatment success as compared to the placebo arm, defined as no ocular discharge at 96 h and no use of topical antibiotic therapy during the 96-h intervention.</p><p>However, this study shows a remarkably high treatment success rate at 96 h [i.e., no ocular discharge at 96 h and no use of topical antibiotic therapy: <italic>n</italic> = 43 (53.1%)] and a low rate of topical antibiotic use [<italic>n</italic> = 16 (19.0%)]. At the time of the study, treatment success was expected in 3 to 5 days, with 50 to 80% of neonates receiving a topical antibiotic therapy. Treatment success and use of topical antibiotic therapy were similar in both treatment arms. We explain these results by the systematic eye washing and lacrimal sac massage, newly introduced in the Department of Neonatology at the University Children's Hospital, Bern, Switzerland, as part of this study. The massage of the lacrimal sac has been recognized as a very efficient therapy (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>), and is recommended as the first-line treatment in CNLDO (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>, <xref rid=\"B19\" ref-type=\"bibr\">19</xref>).</p><p>We observed a high rate of treatment success at 96 h in neonates with a positive bacteriological swab at inclusion who did not go on to receive a topical antibiotic therapy. No relationship was observed between the microbiological agent identified and the success of treatment. These results support that topical antibiotics should not be used systematically as the first-line treatment of preterm neonates presenting with ocular discharge. Rather, the first-line treatment of lacrimal sac massage should be applied first.</p><p>In the Euphrasia arm, a trend was observed for shorter time to reddening resolution as well as lower rate of relapse, suggesting that Euphrasia might be of benefit against reddening. However, the difference was not statistically significant, possibly due to a lack of power.</p><p>The rate of relapse of ocular discharge in the subgroup of neonates who did not go on to receive a topical antibiotic therapy was significantly higher in the Euphrasia arm. Some of the relapses in the Euphrasia arm were observed in neonates initially presenting a green ocular discharge, indicating a more severe affection, while no green ocular discharge was observed in the placebo arm. Moreover, in this subgroup, significantly more infants presented with a reddening at baseline in the Euphrasia arm, also indicating a potentially more severe affection. At 96 h, the ocular discharge relapses had resolved in the Euphrasia arm such that there was no significant difference between treatment arms.</p><p>Finally, the pattern of tearing fluctuated during the intervention period with high rates of resolution and relapse. This is to be expected given the nature of the mechanical mechanism underlying this symptom. At the end of the intervention, we observed a trend for less tearing in the Euphrasia arm.</p></sec><sec><title>Strengths and Weaknesses of the Study</title><p>To the best of our knowledge, this is the first double-blind randomized controlled trial comparing Euphrasia eye drops to placebo in the first-line treatment of ocular discharge. Also, this trial investigated preterm neonates, a vulnerable population that is highly under-represented in clinical trials.</p><p>A limitation of the study is the absence of grading of the symptoms (i.e., ocular discharge, tearing, and reddening). We limited our analysis to the presence or absence of symptoms to avoid any subjectivity in the assessment of the symptoms. A second limitation is the small sample size with respect to the assessment of the secondary endpoints (e.g., tearing and reddening) which may have prevented some reasonably strong associations from coming up as significant. A third limitation is the non-independence of eyes in neonates with a bilateral affection.</p></sec><sec><title>Generalizability of the Trial Findings</title><p>Clinical characteristics observed in the present study were consistent with those reported in prior studies. Laterality of symptoms occurred in approximately one-third of cases, consistent with prior reports (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>–<xref rid=\"B23\" ref-type=\"bibr\">23</xref>). Ocular discharge was accompanied by tearing in 63% of neonates at baseline. Tearing is described also in ~80–90% of patients with CNLDO but is not systematically present in preterm neonates because the tear production is not fully developed (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>, <xref rid=\"B22\" ref-type=\"bibr\">22</xref>–<xref rid=\"B25\" ref-type=\"bibr\">25</xref>). In general, ocular discharge can also be a first sign of CNLDO, which cannot definitively be ruled out at first stage of illness.</p><p>We found a success rate at 96 h of 53.1% which is in accordance with Stolovitch et al. who showed a success rate (defined as no epiphora or discharge) of 56% after the first attempt of lacrimal sac massage in children aged <2 months with CNLDO (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>).</p><p>None of the neonates tested positive for <italic>Neisseria gonorrhoeae</italic> or <italic>C. trachomatis</italic>. This supports the efficacy of the preventive measures applied in Switzerland. No association was observed between the microbiological flora and the success of therapy. This underlines the different possible evolutions for the same pathogen and thus supports the use of conservative management as a first-line treatment. Furthermore, as preterm neonates have a premature immune system, the use of antibiotic therapy in these individuals might facilitate the overgrowth of resistant bacteria in the nasolacrimal system (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>).</p><p>We observed a trend for a shorter time to reddening resolution and less relapse of reddening with Euphrasia, as well as a trend for less tearing at 96 h. This is in the line with previous studies showing promising results regarding the use of Euphrasia in the symptomatic treatment of conjunctivitis of any etiology (i.e., reddening, burning, and veiling of vision) (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>).</p></sec><sec><title>Conclusions and Implications</title><p>In this study, Euphrasia did not improve ocular discharge at 96 h and did not decrease the use of topical antibiotic therapy. However, this study provides evidence of the clinical efficacy of conservative therapy (i.e., lacrimal sac massage) as a first-line treatment in preterm neonates with ocular discharge. No relationship between the microbiological agent identified and the success of treatment was observed. These results support that topical antibiotics should not be used systematically as the first-line treatment of ocular discharge, including in preterm neonates. Finally, results suggest that Euphrasia may be of benefit for symptoms such as reddening and tearing, and thus improve the comfort of patients.</p></sec></sec><sec sec-type=\"data-availability\" id=\"s5\"><title>Data Availability Statement</title><p>The datasets generated for this study are available on request to the corresponding author.</p></sec><sec id=\"s6\"><title>Ethics Statement</title><p>The studies involving human participants were reviewed and approved by Ethics Commission of the Canton of Bern, Switzerland (215/08). Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.</p></sec><sec id=\"s7\"><title>Author Contributions</title><p>UW and MN conceived the study. UW supervised the research team. LS and ME participated in designing the study. UW, SK, LS, and TK participated in operationalizing procedures, participant recruitment, project coordination, and study execution. ME and VM provided the study medication and participated in operationalizing medication procedures. GG and DM-G wrote the first draft of the manuscript. All authors participated in the revision of subsequent drafts and all approved the final version of the manuscript.</p></sec><sec id=\"s8\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><ack><p>We thank the study participants and their legal guardians as well as the medical staff. We thank Rachel Scholkmann for proofreading.</p></ack><fn-group><fn id=\"fn0001\"><p><sup>1</sup>(2010). “EudraLex - The rules governing medicinal products in the european union.”</p></fn></fn-group><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> Funding for this study was provided by the Institute of Complementary and Integrative Medicine, University of Bern, Bern, Switzerland and the Department of Neonatology, Children's University Hospital, Bern, University of Bern, Bern, Switzerland.</p></fn></fn-group><sec sec-type=\"supplementary-material\" id=\"s9\"><title>Supplementary Material</title><p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.frontiersin.org/articles/10.3389/fped.2020.00449/full#supplementary-material\">https://www.frontiersin.org/articles/10.3389/fped.2020.00449/full#supplementary-material</ext-link></p><supplementary-material content-type=\"local-data\" id=\"SM1\"><media xlink:href=\"Data_Sheet_1.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec><ref-list><title>References</title><ref id=\"B1\"><label>1.</label><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Moore</surname><given-names>DL</given-names></name><name><surname>MacDonald</surname><given-names>NE</given-names></name></person-group>\n<article-title>Canadian Paediatric Society Infectious Diseases and Immunization Committee. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Immunol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Immunol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Immunol.</journal-id><journal-title-group><journal-title>Frontiers in Immunology</journal-title></journal-title-group><issn pub-type=\"epub\">1664-3224</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849653</article-id><article-id pub-id-type=\"pmc\">PMC7431948</article-id><article-id pub-id-type=\"doi\">10.3389/fimmu.2020.01945</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Immunology</subject><subj-group><subject>Review</subject></subj-group></subj-group></article-categories><title-group><article-title>Molecular Regulation of NK Cell Maturation</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Bi</surname><given-names>Jiacheng</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/436012/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Wang</surname><given-names>Xuefu</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"corresp\" rid=\"c002\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/837387/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences</institution>, <addr-line>Shenzhen</addr-line>, <country>China</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Anhui Provincial Laboratory of Inflammatory and Immunity Disease, School of Pharmacy, Anhui Institute of Innovative Drugs, Anhui Medical University</institution>, <addr-line>Hefei</addr-line>, <country>China</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Zhigang Tian, University of Science and Technology of China, China</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Xi Yang, University of Manitoba, Canada; Jian Zhang, Shandong University, China</p></fn><corresp id=\"c001\">*Correspondence: Jiacheng Bi, <email>jc.bi@siat.ac.cn</email></corresp><corresp id=\"c002\">Xuefu Wang, <email>wangxuefu@ahmu.edu.cn</email></corresp><fn fn-type=\"other\" id=\"fn004\"><p>This article was submitted to NK and Innate Lymphoid Cell Biology, a section of the journal Frontiers in Immunology</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>1945</elocation-id><history><date date-type=\"received\"><day>01</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>20</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Bi and Wang.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Bi and Wang</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>Natural killer (NK) cells are innate lymphocytes specialized in immune surveillance against tumors and infections. To reach their optimal functional status, NK cells must undergo a process of maturation from immature to mature NK cells. Genetically modified mice, as well as <italic>in vivo</italic> and <italic>in vitro</italic> NK cell differentiation assays, have begun to reveal the landscape of the regulatory network involved in NK cell maturation, in which a balance of cytokine signaling pathways leads to an optimal coordination of transcription factor activity. An increased understanding of NK cell maturation will greatly promote the development and application of NK cell-based clinical therapy. Thus, in this review, we summarize the dynamics of NK cell maturation, describe recently identified factors involved in the regulation of the NK cell maturation process, including cytokines and transcription factors, and discuss the importance of NK cell maturation in health and disease.</p></abstract><kwd-group><kwd>NK cells</kwd><kwd>maturation</kwd><kwd>development</kwd><kwd>cytokines</kwd><kwd>transcriptional regulation</kwd></kwd-group><funding-group><award-group><funding-source id=\"cn001\">National Natural Science Foundation of China<named-content content-type=\"fundref-id\">10.13039/501100001809</named-content></funding-source></award-group></funding-group><counts><fig-count count=\"2\"/><table-count count=\"1\"/><equation-count count=\"0\"/><ref-count count=\"118\"/><page-count count=\"12\"/><word-count count=\"0\"/></counts></article-meta></front><body><sec id=\"S1\"><title>Introduction</title><p>Natural killer (NK) cells are innate lymphocytes that play an important role in immune surveillance against tumors and virus-infected cells, and are an emerging target for tumor immunotherapy. NK cell immune surveillance is mediated by direct effector functions (such as IFN-γ production and cytotoxicity), as well as immune-regulatory functions [such as interactions with dendritic cells (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>–<xref rid=\"B3\" ref-type=\"bibr\">3</xref>)] of NK cells. NK cells develop from precursors generated from hematopoietic stem cells (HSCs) in the bone marrow, and then begin the process of maturation before they egress to the periphery for immune surveillance. Since the initial discovery of NK cells in 1975, enormous progress has been made in our understanding of NK cell development and maturation, as well as in identifying the cytokines and transcription factors that are important for these processes. NK cells that reach maturation represent the optimal functional status of the NK cell population at steady-state, and also represent the optimal functional status at the single-cell level, as evidenced by the upregulation of genes encoding cytotoxicity-related effector molecules during NK cell maturation. Compromised NK-dependent immune surveillance usually accompanies impaired NK cell maturation (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>, <xref rid=\"B5\" ref-type=\"bibr\">5</xref>). Therefore, it is important to understand the molecular regulatory mechanisms underlying NK cell maturation, which could potentially be exploited for the development of novel therapeutic strategies. In this review, we aim to provide a framework of the current knowledge of NK cell maturation and the factors that regulate this process, including transcription factors and cytokines, and to discuss the importance of NK cell maturation in health and disease.</p></sec><sec id=\"S2\"><title>An Overview of NK Cell Maturation</title><sec id=\"S2.SS1\"><title>NK Cell Maturation in Mice</title><p>In adult mice, NK cells begin their development in the bone marrow, and go through a stepwise cell differentiation process, including HSCs, common lymphoid progenitors (CLPs), preNK cell progenitors (preNKPs), NK cell precursors (NKPs), immature NK cells (iNKs), and mature NK cells (mNKs) (<xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>). CLPs are Lin<sup>–</sup>IL-7Rα<sup>+</sup>c-Kit<sup>+</sup>SCA-1<sup>+</sup>FLT-3<sup>+</sup> and can generate pro-B and pre-T cells, as well as the earliest common innate lymphoid cell (ILC) precursor (CILCP). PreNKPs, representing an intermediate stage between CLPs and NKPs, are Lin<sup>–</sup>CD244<sup>+</sup>c-Kit<sup>low</sup>IL-7Rα<sup>+</sup>FLT-3<sup>–</sup>CD122<sup>–</sup> and comprise both early NK-committed precursors and IL. NKPs, also called refined NKPs (rNKPs), are Lin<sup>–</sup>NK1.1<sup>–</sup>DX5<sup>–</sup>IL-7Rα<sup>+</sup>CD122<sup>+</sup>NKG2D<sup>+</sup> and differentiate into NK cells (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>, <xref rid=\"B7\" ref-type=\"bibr\">7</xref>). Before the final NK lineage commitment, NKPs progress to an iNK stage, which is characterized by the acquisition of NK1.1 and natural cytotoxicity receptor (NCR) expression. However, NK cells at the iNK stage are not yet functionally mature, and their maturation continues in the bone marrow and the periphery. The acquisition of DX5 and Ly49 expression marks the maturation of NK cells (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>). To acquire optimized capacity, NK cells continue a late maturation program, which is accompanied by an increase in their effector function and changes in the expression of phenotype markers, such as upregulation of CD11b, CD43, and KLRG1, or downregulation of CD27. Therefore, based on the expression levels of CD27/CD11b, NK cell maturation can be divided into CD27<sup>+</sup>CD11b<sup>–</sup>, CD27<sup>+</sup>CD11b<sup>+</sup>, and CD27<sup>–</sup>CD11b<sup>+</sup> stages (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>). NK cells mature from the CD27<sup>+</sup>CD11b<sup>–</sup>iNK or M1 stage, and then progress to the CD27<sup>+</sup>CD11b<sup>+</sup> transitional NK cell (tNK) or M2 stage, and finally to the CD27<sup>–</sup>CD11b<sup>+</sup> terminally mNK or M3 stage (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>). During maturation, NK cells gradually increase their cytotoxic capacity but decrease their potential for homeostatic expansion (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>).</p><fig id=\"F1\" position=\"float\"><label>FIGURE 1</label><caption><p>Dynamics of NK cell development and maturation in mice and human. Hematopoietic stem cells (HSCs) differentiate into common lymphoid progenitors (CLPs), and then differentiate into NK cell progenitors (NKPs). The acquisition of CD122 marks NK cell lineage commitment from HSCs. iNKs generated from NKPs continue to mature in the bone marrow and periphery for fully functional acquisition. In mice, CD27/CD11b divides NK cell maturation into three stages. In human, CD56/CD57 divides NK cell maturation into three stages.</p></caption><graphic xlink:href=\"fimmu-11-01945-g001\"/></fig></sec><sec id=\"S2.SS2\"><title>NK Cell Maturation in Humans</title><p>Human NK cells have developmental trajectories similar to those of mice (<xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>). HSCs differentiate into CLPs, and then NKPs (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>). In humans, the expression of CD122 on NKPs is critical for NK cell lineage commitment. The appearance of CD56 represents the final step in the differentiation of NKPs into NK cells. The maturation of human NK cells can be divided into a CD56<sup><italic>bright</italic></sup> stage and a CD56<sup><italic>dim</italic></sup> stage based on the expression levels of CD56. CD56<sup><italic>bright</italic></sup> NK cells are thought to be immature, and can differentiate into CD56<sup><italic>dim</italic></sup> NK cells with the acquisition of CD16 (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>). While two subsets produce inflammatory cytokines, CD56<sup><italic>dim</italic></sup> NK cells have more potent cytolytic activity. CD56<sup><italic>dim</italic></sup> NK cells can further progress into late-maturation stages, with changes in their surface markers and function (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>). The terminal maturation of CD56<sup><italic>dim</italic></sup> NK cells with highest cytolytic activity can be defined by the expression of CD57. Approximately 30–60% of all CD56<sup><italic>dim</italic></sup> NK cells in healthy adults express CD57 on their surface (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>). Interestingly, high-dimensional, single-cell analysis can identify the high similarity between mouse CD27<sup>–</sup>CD11b<sup>+</sup> NK cells and human CD56<sup><italic>dim</italic></sup> NK cells and between mouse CD27<sup>+</sup>CD11b<sup>–</sup> NK cells and human CD56<sup><italic>bright</italic></sup> NK cells (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>). Additionally, Fu et al. has showed that CD27 and CD11b can reflect distinct populations of human NK cells from different tissues, functionally similar with their counterparts in mice (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>).</p><p>Similar to the differentiation process of other innate lymphocytes (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>), the maturation of NK cells includes multiple physiological processes. To attain an optimal NK cell population size, the maturation process usually requires the optimal egress of NK cells from the bone marrow, and a finely tuned balance between survival, proliferation, and apoptosis at the steady-state. Meanwhile, optimal NK cell functional status at the single-cell level requires a dedicated transcriptional program dictated by an optimal level of transcriptional factor activity.</p></sec><sec id=\"S2.SS3\"><title>Models Used for Investigation of NK Cell Maturation</title><p>Based on the above parameters, several systems are available to investigate the factors involved in the regulation of NK cell maturation:</p><list list-type=\"simple\"><list-item><label>(1)</label><p>Knockout mouse models provide a powerful tool to determine the effects of a gene-of-interest on NK cell maturation. Importantly, an increasing number of studies have employed NK cell-specific conditional knockout mouse models, in which Cre recombination-directed gene deletion occurs soon after the acquisition of NKp46 (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>, <xref rid=\"B16\" ref-type=\"bibr\">16</xref>–<xref rid=\"B19\" ref-type=\"bibr\">19</xref>). This model allows gene deletion that is restricted to NK cells and group 1 innate lymphoid cells (ILC1s) (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>); importantly, it also allows the dissection of stage-dependent effects elicited by the gene-of-interest on NK cell maturation.</p></list-item><list-item><label>(2)</label><p>Adoptive transfer of NK cells into immune-deficient (e.g., <italic>Il2rg</italic><sup>–/–</sup><italic>Rag2</italic><sup>–/–</sup>) mice or reconstitution of the bone marrow in lethally irradiated mice can recapitulate the maturation of NK cells under physiological conditions (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>, <xref rid=\"B21\" ref-type=\"bibr\">21</xref>). Different groups of NK cells can be monitored in a competitive manner to assess cell-intrinsic effects through the use of congenic markers such as CD45.1/2.</p></list-item><list-item><label>(3)</label><p><italic>In vitro</italic> NK cell differentiation assays using OP9 stromal cells provide an <italic>in vitro</italic> model to mimic cytokine-driven physiological NK cell differentiation from NK precursors (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>, <xref rid=\"B23\" ref-type=\"bibr\">23</xref>); this model also allows the determination of cell-specific effects associated with a gene-of-interest.</p></list-item></list><p>Several factors and pathways that play a role in NK cell maturation have been identified using the above-mentioned approaches. The results have demonstrated that NK cell maturation is dependent on several critical signaling pathways, and is triggered by a balance between extracellular signals (cytokines) and dictated by an optimal coordination of transcription factor activity. Although NK cell maturation has been extensively studied in mice, knowledge about the factors that control human NK cell maturation remains limited. Nevertheless, advances in gene editing, humanized mice models, single-cell sequencing, mass cytometry, and genome-wide association studies have led to a deeper understanding of how NK cell maturation is regulated in humans.</p></sec></sec><sec id=\"S3\"><title>Cytokines that Regulate NK Cell Maturation</title><p>Increasing evidence suggests that multiple cytokines are involved in NK cell development (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). For instance, IL-7, SCF, and FLT3L are critical for CD122<sup>+</sup> NKP generation from HSCs, while IL-15 is essential for NK cell lineage commitment and maturation from CD122<sup>+</sup> NKPs to mNK cells. Additionally, multiple cytokines have been found to be involved in NK cell maturation by modulating IL-15 signaling.</p><table-wrap id=\"T1\" position=\"float\"><label>TABLE 1</label><caption><p>Factors involved in NK cell maturation.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Factors</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Effect (Stages affected)</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Note</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>References</bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Transcription factors</bold></td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">E4BP4</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">+ (CLP, NKP)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Required for IL-15 responsiveness</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B54\" ref-type=\"bibr\">54</xref>, <xref rid=\"B55\" ref-type=\"bibr\">55</xref>, <xref rid=\"B57\" ref-type=\"bibr\">57</xref>)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">TCF-1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">− (NKP, iNK, mNK)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Essential for development but limits maturation</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B59\" ref-type=\"bibr\">59</xref>)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ETS1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">+ (NKP, iNK, mNK)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Induce expression of T-bet, GATA3, Blimp1 and ID2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B62\" ref-type=\"bibr\">62</xref>, <xref rid=\"B64\" ref-type=\"bibr\">64</xref>)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">STAT5</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">+ (NKP, iNK, mNK)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Downstream of IL-15 receptor</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B16\" ref-type=\"bibr\">16</xref>, <xref rid=\"B66\" ref-type=\"bibr\">66</xref>–<xref rid=\"B69\" ref-type=\"bibr\">69</xref>)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ID2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">+ (NKP, iNK, mNK)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Suppress E-box genes; suppress SOCS3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B70\" ref-type=\"bibr\">70</xref>–<xref rid=\"B72\" ref-type=\"bibr\">72</xref>)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">T-bet</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">+ (iNK, mNK)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Promote S1pr5, IFNG; repress Eomes</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B4\" ref-type=\"bibr\">4</xref>, <xref rid=\"B5\" ref-type=\"bibr\">5</xref>, <xref rid=\"B21\" ref-type=\"bibr\">21</xref>, <xref rid=\"B73\" ref-type=\"bibr\">73</xref>, <xref rid=\"B74\" ref-type=\"bibr\">74</xref>)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Eomes</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">+ (iNK, mNK)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Repress T-bet</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B21\" ref-type=\"bibr\">21</xref>)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">TOX1/2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">+ (iNK, mNK)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Promotes T-bet expression</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B75\" ref-type=\"bibr\">75</xref>–<xref rid=\"B77\" ref-type=\"bibr\">77</xref>)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PRDM1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">+ (iNK, mNK)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">induced by T-bet</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B22\" ref-type=\"bibr\">22</xref>, <xref rid=\"B78\" ref-type=\"bibr\">78</xref>)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Zeb2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">+ (iNK, mNK)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Induced by T-bet; Zeb2 KO phenocopy T-bet KO</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B5\" ref-type=\"bibr\">5</xref>)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GATA3</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">+ (iNK, mNK)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Dispensable for early NK cell development</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B79\" ref-type=\"bibr\">79</xref>–<xref rid=\"B84\" ref-type=\"bibr\">84</xref>)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SMAD4</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">+ (iNK, mNK)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">TGF-β -independent</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B19\" ref-type=\"bibr\">19</xref>)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Foxo1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">± (iNK)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Promote autophagy (+) or suppress T-bet (−)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B17\" ref-type=\"bibr\">17</xref>, <xref rid=\"B85\" ref-type=\"bibr\">85</xref>)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Cytokines</bold></td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">IL-7, SCF, FLT-3L</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">+ (NKP)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Critical for NKP generation from HSC</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B24\" ref-type=\"bibr\">24</xref>–<xref rid=\"B32\" ref-type=\"bibr\">32</xref>)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Lymphotoxin</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">+ (NKP)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Essential for early-stage NK development</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B33\" ref-type=\"bibr\">33</xref>–<xref rid=\"B35\" ref-type=\"bibr\">35</xref>)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">IL-15</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">+ (NKP, iNK, mNK)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Promote NK development, maturation, activation, survival and homeostasis</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B36\" ref-type=\"bibr\">36</xref>–<xref rid=\"B45\" ref-type=\"bibr\">45</xref>)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">IL-17</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">− (iNK, mNK)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Induce SOCS3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B47\" ref-type=\"bibr\">47</xref>)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">TGF-β</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">− (NKP, iNK, mNK)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Cell cycle arrest before mature stage</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B49\" ref-type=\"bibr\">49</xref>–<xref rid=\"B53\" ref-type=\"bibr\">53</xref>)</td></tr></tbody></table><table-wrap-foot><attrib><italic>This table lists transcriptional factors or cytokines that either promote (“+”) or suppress (“−”) NK cell maturation, the stages affected (CLP, NKP, iNK, or mNK), as well as the functions or features of these molecules.</italic></attrib></table-wrap-foot></table-wrap><sec id=\"S3.SS1\"><title>IL-7, SCF, and FLT-3L</title><p>Stroma-free culture has shown that pre-culture in IL-7, SCF, and FLT3L is necessary for NK cell development by inducing IL-15 responsiveness in progenitors (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>). CLPs normally exhibit high levels of IL-7 receptor, SCF receptor and FLT-3L receptor; however, the expression of these receptors is gradually lost during the process of NK cell maturation. Nevertheless, these cytokines have distinct roles in NK cell development. For instance, NK cell maturation is almost normal in IL-7Rα- or IL-7-deficient mice (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>, <xref rid=\"B26\" ref-type=\"bibr\">26</xref>), whereas the homeostasis of thymus-derived CD127<sup>+</sup> NK cells is exclusively dependent on IL-7, showing characteristics similar to those of human CD56<sup><italic>high</italic></sup>CD16<sup>–</sup> NK cells (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>). IL-7 promotes the survival of CD56<sup><italic>bright</italic></sup> NK cells by increasing BCL2 expression, although it does not increase NK cell cytotoxicity, interferon-gamma (IFN-γ) production, or the expression of activation markers (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>). IL-7 alone is not sufficient to support human NK cell development, as evidenced by the findings in human IL-7 knock-in NOD scid gamma (NSG) mice (<xref rid=\"B29\" ref-type=\"bibr\">29</xref>). SCF promotes the survival of peripheral c-Kit<sup>+</sup> NK cells and the absence of c-Kit signaling reduces the generation of NK cells from fetal liver precursors (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>). Additionally, a marked deficiency of NK cells has been observed in the spleen of mice lacking FLT3L (<xref rid=\"B31\" ref-type=\"bibr\">31</xref>). FLT3L not only potently induces IL-15 responsiveness in progenitors, but can also significantly expand NK cells by increasing the number of IL-15-expressing CD11c<sup><italic>hi</italic></sup> dendritic cells (DCs) (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>), indicating that FLT3L is important for the early differentiation of NK cells and NK cell expansion.</p></sec><sec id=\"S3.SS2\"><title>Lymphotoxin</title><p>Lymphotoxins alpha (LTα) and beta (LTβ) belong to the tumor necrosis factor (TNF) ligand superfamily. LTα bound to the LTβ receptor (LTβR) constitutes a membrane-bound LTα1β2 heterotrimer that is essential for secondary lymphoid tissue organogenesis, as evidenced by defective lymph node development and altered splenic micro-architecture in <italic>Lta</italic><sup>–/–</sup>, <italic>Ltb</italic><sup>–/–</sup>, and <italic>Ltbr</italic><sup>–/–</sup> mice. LTα and LTβ are expressed in activated lymphocytes, including T, B, and NK cells, whereas LTβR is expressed exclusively in non-lymphoid tissues, including in bone marrow stromal cells. The ablation of LTα or LTβR leads to impaired early-stage development of NK cells (<xref rid=\"B33\" ref-type=\"bibr\">33</xref>–<xref rid=\"B35\" ref-type=\"bibr\">35</xref>). Bone marrow stromal cells from <italic>Lta</italic><sup>–/–</sup> or <italic>Ltbr</italic><sup>–/–</sup> mice cannot efficiently support early NK cell progenitors (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>). IL-15 can overcome the arrest of <italic>Lta</italic><sup>–/–</sup>NK cell development <italic>in vitro</italic> (<xref rid=\"B33\" ref-type=\"bibr\">33</xref>); however, NK cell progenitors from wild-type mice generated more NK1.1<sup>+</sup> cells than NK cell progenitors from <italic>Lta</italic><sup>–/–</sup> mice in the presence of IL-15 (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>). These findings suggest that the interaction between LTα on NK precursors and LTβR on stromal cells creates a permissive microenvironment that is essential for the early-stage development of NK cells.</p></sec><sec id=\"S3.SS3\"><title>IL-15</title><p>IL-15 belongs to the γc family of cytokines, sharing a γc chain with IL-2, -4, -7, -9, and -21. IL-15 signals through IL-15Rβ/γc (encoded by <italic>IL2RG</italic>) heterodimers, either alone with intermediate affinity, or as a complex with IL-15Rα with high affinity (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>, <xref rid=\"B37\" ref-type=\"bibr\">37</xref>). A phenotype with a near-complete loss of NK cells is observed in the absence of either IL-15Rα, IL-15Rβ, γc chain, or IL-15 (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>–<xref rid=\"B40\" ref-type=\"bibr\">40</xref>). In contrast, mice deficient for IL-2, -4, or -7 have normal NK cell development and maturation. During NK cell development, IL-15 acts from CD122<sup>+</sup>NKPs until the terminal maturation of NK cells. Indeed, the ablation of IL-15 does not affect the production of pre-NKPs or NKPs. Although IL-15 is widely expressed in various tissues and by many cell types, IRF-1-dependent IL-15 production in the bone marrow microenvironment is critical for NK cell generation (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>). Importantly, the effects of IL-15 on NK cell development and maturation require <italic>trans-</italic>presentation of IL-15 by IL-15α on bone marrow-derived DCs (<xref rid=\"B42\" ref-type=\"bibr\">42</xref>–<xref rid=\"B44\" ref-type=\"bibr\">44</xref>) in an IL-15α-dose-dependent manner (<xref rid=\"B45\" ref-type=\"bibr\">45</xref>). In addition, IL-15 transgenic mice or mice that overexpress IL-15 have a dramatic increase in the number of mNK cells (<xref rid=\"B46\" ref-type=\"bibr\">46</xref>). IL-15 is also needed to support mNK cell survival and promote mNK cell expansion in the steady-state. Similar with the observations in mice, IL-15 determines human NK cell maturation and homeostasis, which is supported by evidence from patients with γc mutation and humanized mice, as well as by experiments involving NK cell <italic>ex vivo</italic> development.</p></sec><sec id=\"S3.SS4\"><title>IL-17</title><p>IL-17A is a pro-inflammatory cytokine that also plays an immunosuppressive role in some settings (such as in tumors) by recruiting myeloid-derived suppressor cells, promoting angiogenesis, or suppressing CD8<sup>+</sup> T cells. In addition, our group recently found that IL-17 signaling negatively regulates NK cell maturation and function. IFN-γ production and the cytolytic activity of NK cells, as well as NK cell antitumor and antiviral immune activity, are enhanced in <italic>Il17a</italic><sup>–/–</sup>mice (<xref rid=\"B47\" ref-type=\"bibr\">47</xref>). Although total NK cell numbers are comparable with those in wild-type mice, increased percentages of terminal mature CD27<sup>–</sup>CD11b<sup>+</sup> NK cells were observed in <italic>Il17a</italic><sup>–/–</sup>, <italic>Il17f</italic><sup>–/–</sup>, <italic>Il17a</italic><sup>–/–</sup><italic>Il17f</italic><sup>–/–</sup>, and <italic>Il17ra</italic><sup>–/–</sup> mice. Overexpression of IL-17A <italic>in vivo</italic> reduces CD27<sup>–</sup>CD11b<sup>+</sup> NK cell subsets (<xref rid=\"B47\" ref-type=\"bibr\">47</xref>). Mechanistically, IL-17A signaling suppresses IL-15-induced phosphorylation of STAT5 <italic>via</italic> the upregulation of SOCS-3 in NK cells, leading to inhibition of NK cell terminal maturation (<xref rid=\"B47\" ref-type=\"bibr\">47</xref>). On the other hand, IL-17A exerts context-dependent effects on NK cells. IL-17 signaling is required for IFN-γ production and the cytolytic activity of NK cells derived from lipopolysaccharide (LPS)-primed mice, as well as for optimal production of granulocyte-macrophage colony-stimulating factor (GM-CSF) by NK cells in fungal infection, which plays a non-redundant role in activating neutrophils for fungal control (<xref rid=\"B48\" ref-type=\"bibr\">48</xref>).</p></sec><sec id=\"S3.SS5\"><title>TGF-β</title><p>Transforming growth factor beta (TGF-β) is a major immunosuppressive cytokine. Many cell types express TGF-β, and nearly all lymphocyte populations express the TGF-β receptor (TGFβR). Deletion of the TGF-β receptor subunit TGFβ RII enhances mTOR activity and the cytotoxic activity of NK cells in response to IL-15 (<xref rid=\"B49\" ref-type=\"bibr\">49</xref>). In the steady-state, NK-specific deletion of TGFβRII in <italic>Ncr1</italic><sup><italic>Cre/+</italic></sup><italic>; Tgfbr2</italic><sup><italic>fl/fl</italic></sup> mice has a minimal effect on conventional NK cell maturation and homeostasis (<xref rid=\"B49\" ref-type=\"bibr\">49</xref>). In contrast, constitutive TGF-β signaling arrests NK cell maturation (<xref rid=\"B49\" ref-type=\"bibr\">49</xref>). Moreover, TGF-β blocks IL-15-induced mTOR activation, thereby suppressing the activation and functions of NK cells (<xref rid=\"B49\" ref-type=\"bibr\">49</xref>). Consistent with the observations in mice, treatment with TGF-β <italic>in vitro</italic> inhibits cytokine-stimulated metabolism of human NK cells <italic>via</italic> canonical TGF-β signaling (<xref rid=\"B50\" ref-type=\"bibr\">50</xref>). In contrast, transgenic mice expressing a dominant-negative form of TGFβRII under the control of the CD11c promoter (CD11c<sup><italic>dnTGF</italic>β</sup>\n<sup><italic>RII</italic></sup>) exhibit an increase in the number of mature NK cells in the periphery (<xref rid=\"B51\" ref-type=\"bibr\">51</xref>). In an <italic>in vitro</italic> assay for the differentiation of NK cell progenitors to less mature CD122<sup>+</sup>NK1.1<sup>+</sup> and more mature NK1.1<sup>+</sup>DX5<sup>+</sup> NK cells in the presence of IL-15 and OP9 stromal cells, the addition of TGF-β markedly blocked the derivation of mature NK cells from wild-type precursors, whereas precursors from the CD11c<sup><italic>dnTGF</italic>β</sup>\n<sup><italic>RII</italic></sup> mice were not affected (<xref rid=\"B51\" ref-type=\"bibr\">51</xref>). Mechanistically, TGF-β was found to mediate NK cell immaturity during ontogeny by arresting the cell cycle of NK cells at the least mature CD11b<sup>–</sup>CD43<sup>–</sup> and intermediate CD11b<sup>+</sup>CD43<sup>–</sup> stages, as well as by limiting NK cell transition at the terminally mature CD11b<sup>+</sup>CD43<sup>+</sup> stage (<xref rid=\"B51\" ref-type=\"bibr\">51</xref>). As a result of resistance to TGF-β, infant CD11c<sup><italic>dnTGF</italic>β</sup>\n<sup><italic>RII</italic></sup> mice are protected against murine cytomegalovirus (MCMV) infection (<xref rid=\"B51\" ref-type=\"bibr\">51</xref>). In addition, SMAD3 has been found to suppress E4BP4-mediated NK cell development and effector functions, as evidenced by the increased number of mNK cells in <italic>Smad3</italic><sup>–/–</sup> mice and the increased levels of granzyme B, IL-2, and IFN-γ in <italic>Smad3</italic><sup>–/–</sup>NK cells (<xref rid=\"B52\" ref-type=\"bibr\">52</xref>). Meanwhile, the silencing of <italic>Smad3</italic> in the human NK-92 cell line allows for the upregulation of E4BP4, which subsequently promotes IFN-γ production (<xref rid=\"B53\" ref-type=\"bibr\">53</xref>). The differences in phenotypes between CD11c<sup>+</sup> cells and NKp46<sup>+</sup> cells following blockade of TGFβR signaling might be due to the abrogation of the TGFβR signal at different stages of NK cell development/maturation. Further studies are required to reveal the temporal regulation of NK cell maturation by TGF-β.</p></sec></sec><sec id=\"S4\"><title>Transcription Factors that Regulate NK Cell Maturation</title><p>Natural killer cell development is also regulated by sequential and coordinated transcription factor activity (<xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref>). For instance, transcription factors such as E4BP4, TOX, and ID2 are required for NK cell lineage commitment, while ID2, T-bet, Eomes, and ZEB2 are required for NK cell maturation. Transcription factors that mediate NK cell maturation usually promote the expression of genes coding for effector molecules, receptors responsible for egress, and cell-surface maturation markers.</p><fig id=\"F2\" position=\"float\"><label>FIGURE 2</label><caption><p>Transcriptional regulation of NK cell development and maturation. Multiple transcriptional factors mediate regulation at distinct stages during NK cell development and maturation. E4BP4, TCF1 and ID2 are essential for NK cell lineage commitment. T-BET, EOMES, TOX, PRDM1, GATA3, ZEB2, and SMAD4 are critical for NK cell maturation. The discrepancy of FOXO1 in NK cell maturation needs further confirmation.</p></caption><graphic xlink:href=\"fimmu-11-01945-g002\"/></fig><sec id=\"S4.SS1\"><title>E4BP4</title><p>E4BP4 is expressed in CLPs (<xref rid=\"B54\" ref-type=\"bibr\">54</xref>). <italic>E4bp4</italic><sup>–/–</sup> mice specifically lack NK cells (<xref rid=\"B55\" ref-type=\"bibr\">55</xref>). Indeed, the numbers of pre-NKPs, rNKPs, iNK cells, and mNK cells, but not those of CLPs, are reduced in the bone marrow of <italic>E4bp4</italic><sup>–/–</sup> mice. Ectopic expression of E4BP4 in <italic>E4bp4</italic><sup>–/–</sup>CLPs can rescue NK cell production (<xref rid=\"B54\" ref-type=\"bibr\">54</xref>). These findings indicate that E4BP4 acts at the CLP stage and is required for NK cell lineage commitment. Pre-NKPs and rNKPs are not affected in the absence of IL-15 signaling; however, the absence of E4BP4 results in the failure of IL-15-responsive NKP production. Specifically, IL-15 cannot rescue NK cell production in <italic>E4bp4</italic><sup>–/–</sup> bone marrow (<xref rid=\"B55\" ref-type=\"bibr\">55</xref>). In contrast, ectopic expression of E4BP4 enables limited NK cell production, even in the absence of IL-15 signaling (<xref rid=\"B55\" ref-type=\"bibr\">55</xref>). However, ectopic expression of Eomes, ID2, or T-bet cannot rescue NK cell production in the absence of E4BP4. Furthermore, E4BP4 was found to directly regulate the expression of Eomes and ID2 (<xref rid=\"B54\" ref-type=\"bibr\">54</xref>), while the histone H2A deubiquitinase MYSM1 has been found to be critical for the recruitment of E4BP4 to the <italic>ID2</italic> locus and be required for NK cell maturation, but not NK lineage commitment (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>). In addition, <italic>NOTCH1</italic> has been identified as an E4BP4 target gene in NK cells (<xref rid=\"B56\" ref-type=\"bibr\">56</xref>). The abrogation of Notch signaling blocked NK cell production, similar to that observed in <italic>E4bp4</italic><sup>–/–</sup>mice. Exposure to Notch peptide ligands at an early stage rescued the defective NK cell development from <italic>E4bp4</italic><sup>–/–</sup> progenitors (<xref rid=\"B56\" ref-type=\"bibr\">56</xref>). Moreover, SUMOylation and phosphorylation can regulate E4BP4 activity and then influence NK cell development (<xref rid=\"B56\" ref-type=\"bibr\">56</xref>). The ablation of either SUMOylation or phosphorylation sites significantly increased the transcriptional activity of E4BP4 and promoted the production of NK cells. Therefore, not only the expression level but also the activity of E4BP4 is critical for NK cell development. However, the regulation of E4BP4 activity by post-translational modifications needs further investigation. Firth et al. reported that specific ablation of E4BP4 in either iNK or mNK cells had no effect on NK cell lineage maintenance and homeostasis (<xref rid=\"B57\" ref-type=\"bibr\">57</xref>). Therefore, these findings confirm that E4BP4 is required for NK lineage commitment, but not for the maturation from the iNK to the mNK stage or the survival or maintenance of mNK cells. Although it has been reported that the upregulation of E4BP4 can enhance IFN-γ production in NK-92 cells (<xref rid=\"B53\" ref-type=\"bibr\">53</xref>), the roles of E4BP4 in human NK lineage commitment and development are not well defined.</p></sec><sec id=\"S4.SS2\"><title>TCF1</title><p>TCF1 (encoded by Tcf7 gene) is a member of the high-mobility group (HMG) of proteins, and is important for T cell development and function. TCF1 was initially found to bind to the <italic>Ly49A</italic> promoter and be required for the acquisition of Ly49A expression during NK cell development (<xref rid=\"B58\" ref-type=\"bibr\">58</xref>). Loss of TCF1 results in a greater than 50% reduction in the levels of LY49D on NK cells (<xref rid=\"B59\" ref-type=\"bibr\">59</xref>). Subsequently, studies on a TCF1 reporter mouse strain demonstrated that TCF1 was expressed by pre-NKPs, NKPs, iNK cells, and mNK cells, but not CLPs, in the bone marrow (<xref rid=\"B60\" ref-type=\"bibr\">60</xref>). Pre-NKPs, NKPs, and mNK cell numbers were reduced in <italic>Tcf7</italic><sup>–/–</sup> mice. Moreover, detailed analysis in splenic NK cells showed that TCF1 expression was high in CD27<sup>+</sup>CD11b<sup>–</sup> NK cells, declined with NK cell maturation, and disappeared in CD27<sup>–</sup>CD11b<sup>+</sup> NK cells. Surprisingly, the number of terminally mature NK cells in <italic>Tcf7</italic><sup>–/–</sup> mice is significantly increased. These findings indicate that TCF1 is essential for NK cell development, but limits NK cell terminal maturation. In humans, TCF1 has been found to be uniquely expressed in circulating CD56<sup><italic>bright</italic></sup> NK cells and not in CD56<sup><italic>dim</italic></sup> NK cells (<xref rid=\"B61\" ref-type=\"bibr\">61</xref>). The effects of TCF1 on human NK cell development and maturation, as well as the underlying mechanisms, need further investigation.</p></sec><sec id=\"S4.SS3\"><title>ETS1</title><p>ETS1 is the founding member of the ETS family of winged helix-turn-helix transcription factors and is highly evolutionarily conserved. ETS1 has been shown to function from the pre-NKP stage and is required for the expression of various transcription factors, including T-bet and ID2, activating NKRs, including NKp46, Ly49D, and Ly49H, and signaling molecules (<xref rid=\"B62\" ref-type=\"bibr\">62</xref>). The levels of mNK cells are significantly decreased in the bone marrow and spleen of <italic>Ets1</italic><sup>–/–</sup>mice (<xref rid=\"B62\" ref-type=\"bibr\">62</xref>). Moreover, <italic>Ets1</italic><sup>–/–</sup> mNK cells display impaired effector function and decreased expression of activating NKRs, but increased expression of inhibitory NKRs (<xref rid=\"B62\" ref-type=\"bibr\">62</xref>, <xref rid=\"B63\" ref-type=\"bibr\">63</xref>). Meanwhile, in human NK cells, Taveirne et al. revealed that ETS1 induced the expression of transcription factors such as T-bet, GATA3, and BLIMP-1 that determine NK cell development, suggesting that ETS1 might play a similar role as in humans (<xref rid=\"B64\" ref-type=\"bibr\">64</xref>). Moreover, IL-2 and IL-15 can increase ETS1 expression through ERK1/2 signaling in human NK cells (<xref rid=\"B65\" ref-type=\"bibr\">65</xref>). These observations indicate that ETS1 is essential for NK cell development. However, the stage-specific requirement of ETS1 for NK cell maturation requires further investigation.</p></sec><sec id=\"S4.SS4\"><title>STAT5</title><p>Downstream of IL-15 receptor, JAK-1 associates with IL-2Rβ, leading to STAT3 phosphorylation, while JAK-3 associates with IL-2Rγc, resulting in STAT5 phosphorylation (<xref rid=\"B66\" ref-type=\"bibr\">66</xref>). The JAK-1/3–STAT5 pathway has been identified as being essential for NK cell maturation. Germ-line deletion of <italic>Jak1</italic> or <italic>Stat5</italic> and conditional deletion of <italic>Jak1</italic> or <italic>Stat5</italic> in NKp46<sup>+</sup> cells both lead to a marked reduction in NK cell levels, as well as inhibition of NK cell maturation in the bone marrow and the periphery (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>, <xref rid=\"B67\" ref-type=\"bibr\">67</xref>). Consistent with the reduced levels of mNK cells, <italic>Jak1</italic><sup><italic>flox/flox</italic></sup><italic>Ncr1</italic>-iCre Tg or <italic>Stat5</italic><sup><italic>flox/flox</italic></sup><italic>Ncr1</italic>-iCre Tg mice showed defective NK cell-dependent tumor surveillance. Moreover, STAT5 tetramers are required for the maturation of NK cells from the iNK to the mNK state in bone marrow and spleen, as evidenced by the substantial decrease in mNK cell numbers, but normal levels of NKPs and iNK cells, in STAT5A-STAT5B tetramer-deficient double knock-in mice (<xref rid=\"B68\" ref-type=\"bibr\">68</xref>). STAT5b mutation also leads to defective human NK cell maturation and impaired lytic function (<xref rid=\"B69\" ref-type=\"bibr\">69</xref>). These observations indicate that JAK-1/3-STAT5 is critical for NK cell maturation.</p></sec><sec id=\"S4.SS5\"><title>ID2</title><p>ID2 is a member of the inhibitor of DNA-binding (ID) protein family, which acts by preventing E-proteins, such as E2A, E2-2, and HEB, from binding E-box (CANNTG)-containing target genes through heterodimerization. ID2 is expressed at high levels after NK cell lineage commitment and in all subsequent stages (<xref rid=\"B70\" ref-type=\"bibr\">70</xref>). ID2 was first reported to play an essential role in the generation of peripheral lymphoid organs and NK cells through the use of <italic>Id2</italic><sup>–/–</sup> mice (<xref rid=\"B71\" ref-type=\"bibr\">71</xref>). Although <italic>Id2</italic> deficiency does not reduce Lin<sup>–</sup>CD122<sup>+</sup> NK cell progenitors in the bone marrow, it does lead to reduced NK cell numbers and impaired maturation of NK cells, as evidenced by the decreased expression of CD43 and CD11b maturation markers, as well as granzyme expression (<xref rid=\"B72\" ref-type=\"bibr\">72</xref>). A more recent study showed that the hematopoietic deletion and acute deletion of ID2, or ID2 deletion in NKp46<sup>+</sup> cells, results in the loss of most NK cells (<xref rid=\"B70\" ref-type=\"bibr\">70</xref>). This indicates that ID2 is required for NK cell maintenance at all stages of development. ID2 suppresses E-box genes required for NK cell maturation, suppresses SOCS-3 expression, and is required for normal IL-15 receptor signaling. Strong IL-15 receptor stimulation by IL-2 pre-ligated to an anti-IL-2 antibody, or SOCS-3 deficiency, partially restores homeostasis in <italic>Id2</italic>-deficient NK cells.</p><p>In support of the role of ID2 in NK cell maturation, deficiency of MYSM1, which mediates the recruitment of E4BP4 to the <italic>Id2</italic> locus, leads to defects in NK cell maturation, but not NK lineage specification or commitment (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>). MYSM1 also plays a role in maintaining an open chromatin structure at the <italic>Id2</italic> locus.</p></sec><sec id=\"S4.SS6\"><title>T-Bet and Eomes</title><p>T-bet (T-box expressed in T cells, encoded by <italic>TBX21</italic>) is a member of the T-box family of transcription factors that are primarily expressed by T cells and NK cells. In <italic>Tbet</italic><sup>–/–</sup> mice, the number of NK cells is significantly decreased in the spleen, liver, and peripheral blood, but modestly increased in bone marrow NK cells (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>). These defects result from the impaired egress of NK cells from the bone marrow. T-bet directly regulates S1pr5 by binding to its locus (<xref rid=\"B73\" ref-type=\"bibr\">73</xref>), and loss of T-bet leads to the decreased expression of S1pr5 (<xref rid=\"B74\" ref-type=\"bibr\">74</xref>). In addition, T-bet-deficient NK cells express lower levels of the maturation markers CD43, CD11b, and DX5, and higher levels of c-Kit and integrin alpha-v, which reflects the immaturity of NK cells (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>). Therefore, T-bet is essential for NK cell maturation.</p><p>Another T-box transcription factor, Eomes (eomesodermin), also plays non-redundant roles in NK cell maturation. In mice, most spleen NK cells express Eomes. Expression of Eomes marks NK cell maturation, and Eomes<sup>+</sup> NK cells are characterized by the expression of DX5 and the absence of TRAIL expression (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>). Eomes is required for NK cell maturation. Hematopoietic deletion of Eomes in <italic>Eomes</italic><sup><italic>flox/flox</italic></sup><italic>Vav</italic>-Cre<sup>+</sup> mice results in a substantial reduction in the number of NK cells in the spleen and blood, and to a lesser extent in the liver, lymph nodes, and bone marrow. Loss of Eomes also renders NK cells phenotypically similar to Eomes<sup>–</sup> NK cells derived from wild-type mice, with expression of TRAIL but lacking DX5, as well as decreased levels of Ly49 receptors and CD11b. In addition, TRAIL<sup>+</sup> iNKs require Eomes for conversion to DX5<sup>+</sup> mature NK cells following their transfer into <italic>Il2rg</italic><sup>–/–</sup><italic>Rag2</italic><sup>–/–</sup> mice. Eomes is also required to maintain NK cell maturity, as temporal deletion of Eomes in mature NK cells results in the loss of the maturation marker DX5 and upregulation of TRAIL.</p><p>Despite the critical roles of Eomes and T-bet in NK cell maturation, loss of either transcription factor does not markedly impair the effector functions of NK cells at the single-cell level <italic>in vitro</italic>, further confirming that these factors have roles in NK cell maturation, but not in NK cell identity.</p></sec><sec id=\"S4.SS7\"><title>TOX</title><p>The TOX (thymocyte selection-associated HMG box) protein belongs to a family of evolutionarily conserved DNA-binding proteins. Besides TOX, family members include TOX2, TOX3, and TOX4. The TOX protein is highly expressed in iNK and mNK cells in the bone marrow (<xref rid=\"B75\" ref-type=\"bibr\">75</xref>). The loss of TOX significantly reduces the frequency and number of mNK cells in the periphery and bone marrow, with a severe block in the transition from the iNK to mNK stage and, to a lesser extent, from the NKP to the iNK stage. ID2 expression is significantly reduced in the NK cells that remain in <italic>Tox</italic><sup>–/–</sup>mice; however, ID2 expression does not rescue the defective maturation of <italic>Tox</italic><sup>–/–</sup>NK cells. In addition, Yun et al. demonstrated that TOX enhances the maturation of human NK cells, and affects, though not directly regulates, T-bet expression during NK cell maturation (<xref rid=\"B76\" ref-type=\"bibr\">76</xref>). This indicates that TOX is required for NK cell maturation both in humans and in mice. However, the regulatory circuit downstream of TOX during NK cell maturation requires further investigations. Besides TOX, TOX2 has been found to play a critical role in human NK cell maturation. Vong et al. found that TOX2 is preferentially expressed in human NK cells among several immune cell populations and is upregulated during human NK cell maturation. TOX2 is essential for human NK cell maturation and cytotoxicity. TOX2 directly upregulates T-bet expression, and T-bet overexpression can rescue the TOX2 knockdown-mediated NK cell maturation defects (<xref rid=\"B77\" ref-type=\"bibr\">77</xref>). These results imply that TOX2 functions upstream of ID2 in human NK cells. However, the role of TOX2 in murine NK cell maturation remains to be elucidated. Combined, these results show TOX and TOX2 play a crucial role in NK cell maturation.</p></sec><sec id=\"S4.SS8\"><title>PRDM1</title><p>The expression of the transcriptional repressor B lymphocyte-induced maturation protein 1 (Blimp-1, also known as PRDM1) can be induced by IL-15 stimulation (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>), and its expression is T-bet-dependent (<xref rid=\"B78\" ref-type=\"bibr\">78</xref>). Blimp-1 is constitutively expressed by NK cells and is upregulated during NK cell maturation in both humans and mice. Loss of Blimp-1 affects NK cell homeostasis, leading to increased NK cell numbers in the bone marrow and lymph nodes and reduced NK cell numbers in the liver and the lungs. Loss of Blimp-1 also leads to impaired NK cell maturation, as shown by the decreased levels of the CD27<sup>–</sup>CD11b<sup>+</sup> NK cell subset and expression of the maturation markers CD43 and KLRG1. However, Blimp-1 is dispensable for most effector functions of murine NK cells, and even suppresses the production of IFN-γ, TNF, and LTαin human NK cells by directly binding to multiple conserved regulatory regions (<xref rid=\"B78\" ref-type=\"bibr\">78</xref>). An impaired response to IL-15 in the absence of Blimp-1 might be the cause of the aberrant maturation of Blimp-1<sup>–/–</sup> NK cells.</p></sec><sec id=\"S4.SS9\"><title>Zeb2</title><p>T-bet also synergizes with ZEB2 to promote NK cell maturation. T-bet is necessary and sufficient to induce ZEB2 expression, and ZEB2 expression positively correlates with NK cell maturation (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>). ZEB2 is required for normal NK cell homeostasis. Deletion of ZEB2 in NK cells results in the retention of NK cells in the bone marrow, and reduced NK cell numbers in the periphery. ZEB2 overexpression leads to a reduced number of NK cells in the bone marrow. Moreover, mice with NK-specific <italic>Zeb2</italic> deletion lack mature CD27<sup>–</sup> NK cells, while ZEB2 overexpression increases the numbers of mature CD27<sup>–</sup> NK cells. The reduced numbers of mature NK cells might be due to poor survival and response to IL-15, as well as the downregulation of S1pr5 expression in ZEB2-deficient NK cells. Additionally, the levels of the maturation markers KLRG1 and CD146 are also proportional to the levels of ZEB2 expression. This implies that ZEB2 promotes NK cell maturation. Interestingly, although the absence of ZEB2 in NK cells compromises the overall NK cell-mediated immune response, as evidenced by the increased susceptibility of NK-<italic>Zeb2</italic><sup>–/–</sup> mice to B16F10 lung metastasis, NK cell effector functions at the single-cell level <italic>in vitro</italic> are mostly preserved, or even increased.</p><p>ZEB2-deficient NK cells phenocopy T-bet-deficient NK cells in terms of reduced numbers of mature NK cells, higher rates of apoptosis, impaired egress from the bone marrow, and the expression of a series of surface molecules (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>). Impaired maturation of ZEB2- or T-bet-deficient NK cells is not further aggravated in ZEB2/T-bet double-deficient NK cells. Furthermore, overexpression of ZEB2 partially rescues the defects of T-bet-deficient NK cells related to cell numbers, mature NK cell levels, expression of maturation markers such as KLRG1, and surveillance against tumor metastasis.</p></sec><sec id=\"S4.SS10\"><title>GATA3</title><p>GATA3 belongs to a family of six transcription factors termed GATA binding proteins (<xref rid=\"B79\" ref-type=\"bibr\">79</xref>). GATA3 is expressed in the hematopoietic system and throughout the NK lineage, from NKPs to iNK and mNK cells (<xref rid=\"B80\" ref-type=\"bibr\">80</xref>). Deletion of GATA3 in all hematopoietic cells at the early stage of development does not result in the loss of conventional NK cells, indicating that GATA3 is dispensable for early NK cell development (<xref rid=\"B81\" ref-type=\"bibr\">81</xref>–<xref rid=\"B83\" ref-type=\"bibr\">83</xref>). However, <italic>NKp46</italic>-Cre-<italic>Gata3</italic><sup><italic>flox/flox</italic></sup> mice show a decrease in NK cell numbers in the spleen and liver, and an increase in the bone marrow (<xref rid=\"B84\" ref-type=\"bibr\">84</xref>). The maturation markers CD11b and KLRG1 are also expressed at lower levels on NK cells in mice with either hematopoietic or <italic>NKp46</italic> promoter-mediated deletion of <italic>Gata</italic>3 (<xref rid=\"B81\" ref-type=\"bibr\">81</xref>, <xref rid=\"B84\" ref-type=\"bibr\">84</xref>). In addition, although NK cell cytolytic activity is preserved in the absence of GATA3, both from the early stage of development and in the immature NK cell stage, IFN-γ production by NK cells is impaired in <italic>NKp46</italic>-Cre-<italic>Gata3</italic><sup><italic>flox/flox</italic></sup> mice. Accordingly, control of early Listeria infection is compromised in mice lacking GATA3. This indicates that GATA3 is required for NK cell maturation.</p></sec><sec id=\"S4.SS11\"><title>SMAD3 and SMAD4</title><p>The binding of TGF-β to its tetrameric receptor phosphorylates the transcription factors Smad2/3, which then binds to Smad4 to regulate the expression of target genes. Consistently, Smad3 has been also found to suppress NK cell E4BP4-mediated development and effector functions, as evidenced by the increased number of mNK cells in <italic>Smad3</italic><sup>–/–</sup> mice and the increased levels of granzyme B, IL-2,and IFN-γ in <italic>Smad3</italic><sup>–/–</sup> NK cells (<xref rid=\"B52\" ref-type=\"bibr\">52</xref>). Meanwhile, the silencing of <italic>Smad3</italic> was found to result in the upregulation of E4BP4, leading to the subsequent promotion of IFN-γ production in human NK-92 cells (<xref rid=\"B53\" ref-type=\"bibr\">53</xref>). SMAD4 generally acts as a mediator of the TGF-β signaling pathway; however, a recent study found that SMAD4 promotes NK cell homeostasis and maturation in a TGF-β-independent manner (<xref rid=\"B19\" ref-type=\"bibr\">19</xref>). SMAD4 deficiency in NK cells compromises NK cell granzyme B expression, as well as NK cell antitumor and antiviral immunity. SMAD4 cooperates with JUNB to transactivate <italic>Gzmb</italic> gene expression. SMAD4 is also required to maintain NK cell homeostasis, possibly by promoting c-Kit expression. SMAD4 deficiency impairs NK cell maturation, as evidenced by the decreased percentage of KLRG1<sup>+</sup> cells and CD27<sup>–</sup>CD11b<sup>+</sup> mature subsets among NK cells, possibly due to the decreased expression of Blimp-1 in these NK cells. Interestingly, these effects of SMAD4 on NK cells are independent of TGF-β signaling, as SMAD4 deficiency also results in decreased KLRG1<sup>+</sup> and CD27<sup>–</sup>CD11b<sup>+</sup> NK cells, and in decreased expression of the <italic>Gzmb</italic> gene in a Tgfbr2- deficient background.</p></sec><sec id=\"S4.SS12\"><title>FOXO1</title><p>To date, forkhead box O1 (FOXO1) is the only transcription factor reported to negatively regulate NK cell maturation. FOXO1 is abundantly expressed in iNKs and its expression subsequently decreases during the NK cell maturation process (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>), opposite to that observed for T-bet. FOXO1 is normally localized in the nucleus; however, following cellular activation (e.g., by stimulatory cytokines), it is phosphorylated and translocates to the cytoplasm. In the nucleus, FOXO1 directly represses <italic>Tbx21</italic> transcription. Accordingly, conditional FOXO1 deletion in NKp46<sup>+</sup> cells promotes NK cell maturation and effector functions. However, it has also been reported that FOXO1 interacts with ATG7 to induce autophagy, which is important for immature NK cell viability, and that conditional deletion of FOXO1 in NKp46<sup>+</sup> cells compromises NK cell development and MCMV clearance (<xref rid=\"B85\" ref-type=\"bibr\">85</xref>). These observations highlight the complexity associated with the activity of this molecule.</p></sec></sec><sec id=\"S5\"><title>Defective NK Cell Maturation and Disease</title><p>Natural killer cells play critical roles in immune surveillance against viral infections and cancers. They can be directly activated without priming and have the potential to kill infected or transformed cells. However, the compromised functional competence of NK cells can lead to immune escape of viruses and cancer cells. NK cell maturation is critical for the acquisition of full functional competence. Consequently, defective maturation of NK cells is associated with chronic viral infection and cancer progression.</p><sec id=\"S5.SS1\"><title>Defective NK Cell Maturation and Infection</title><p>Natural killer cells play critical roles in defending against viral infection. Evidence indicates that hosts without circulating NK cells suffer multiple severe viral infections, including varicella-zoster virus (VZV), cytomegalovirus (CMV), Epstein–Barr virus (EBV), and herpes simplex virus (HSV), and have a higher risk of death from these infections (<xref rid=\"B86\" ref-type=\"bibr\">86</xref>). Mutations in <italic>GATA2</italic> might result in NK cell deficiency (<xref rid=\"B87\" ref-type=\"bibr\">87</xref>). The highest expression of GATA2 was found in the CD56<sup><italic>bright</italic></sup> subset. CD56<sup><italic>bright</italic></sup> NK cells were absent in patients with <italic>GATA2</italic> mutations, indicating that GATA2 is critical for human NK cell development. Further experimental work may provide insights into these patients and the particular mutation to better understand the role of GATA2 in NK cell biology. In addition, patients with MCM4 deficiency have a selective loss of CD56<sup><italic>dim</italic></sup> in NK cells, indicating that MCM4 mutation may interrupt the maturational transition of CD56<sup><italic>bright</italic></sup> NK cells to CD56<sup><italic>dim</italic></sup> NK cells. Notably, mutations in the IL-15 receptor, JAK-3, or STAT5 in humans results in the defective development and maturation of NK cells and increases the risk of viral infection (<xref rid=\"B86\" ref-type=\"bibr\">86</xref>). Functional NK cell deficiency resulting from gene mutations in humans also leads to increased susceptibility to multiple infections. For instance, IL-21 promotes the functional maturation of both murine and human NK cells (<xref rid=\"B88\" ref-type=\"bibr\">88</xref>, <xref rid=\"B89\" ref-type=\"bibr\">89</xref>). The loss-of-function of human IL-21 or its receptor leads to impaired NK cell cytotoxicity and recurrent infections (<xref rid=\"B90\" ref-type=\"bibr\">90</xref>, <xref rid=\"B91\" ref-type=\"bibr\">91</xref>). Meanwhile, viral chronic infection, such as that associated with hepatitis B virus (HBV), hepatitis C virus (HCV), human immunodeficiency virus (HIV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), results in NK cell depletion or dysfunction, characterized by reduced numbers, proliferation, activity, and expression of activating receptors, as well as increased apoptosis and expression of inhibitory receptors (<xref rid=\"B92\" ref-type=\"bibr\">92</xref>, <xref rid=\"B93\" ref-type=\"bibr\">93</xref>). Defective maturation of NK cells affects the outcomes of viral infections. For instance, the frequency of the CD11b<sup>+</sup>CD27<sup>–</sup>NK subset is higher in <italic>Il1r8</italic><sup>–/–</sup> mice compared with intact ones. Enhanced NK cell maturation in <italic>Il1r8</italic><sup>–/–</sup> mice promotes NK-cell-mediated resistance to CMV infection (<xref rid=\"B94\" ref-type=\"bibr\">94</xref>). Together, these results indicate that NK cells are critical for host defense against viral infections and NK cell maturation is required for the acquisition of their full functional competence.</p></sec><sec id=\"S5.SS2\"><title>Defective NK Cell Maturation and Cancer</title><p>Cancer immune surveillance by NK cells contributes to the exploration and application of NK cells in clinical cancer treatment. Hosts with dysregulated NK cells have an increased cancer risk (<xref rid=\"B95\" ref-type=\"bibr\">95</xref>). NK cell defects have been observed in a variety of patients with different types of advanced cancer, and contribute to defective immunity against cancer progression (<xref rid=\"B96\" ref-type=\"bibr\">96</xref>). Immunotherapy based on NK cells has displayed robust anticancer efficacy in preclinical and clinical trials (<xref rid=\"B97\" ref-type=\"bibr\">97</xref>). There is some evidence to show that NK cell maturation is also blocked during tumor growth. It has been demonstrated that multiple lineages of tumors, including breast cancer, colon cancer, and melanoma cell lines, interrupt NK cell functional maturation and impair the antitumor capacity of NK cells (<xref rid=\"B98\" ref-type=\"bibr\">98</xref>). The maturation defects in these cancer hosts are associated with a significant reduction in the number of IL-15Rα<sup>+</sup> cells in the bone marrow, and these defects can be rescued by overexpression of IL-15 (<xref rid=\"B98\" ref-type=\"bibr\">98</xref>). Interestingly, it has been reported that GSK3 kinase inhibition in human NK cells that were expanded with IL-15 enhances CD57 acquisition and late-stage maturation, which is accompanied by higher TNF and IFN-γ production, elevated natural cytotoxicity, and increased antibody-dependent cellular cytotoxicity (<xref rid=\"B99\" ref-type=\"bibr\">99</xref>). Furthermore, the adoptive transfer of NK cells expanded in the presence of a GSK3 kinase inhibitor led to a stronger and more durable antitumor effect in a xenogeneic ovarian cancer model. Mechanistically, GSK3 kinase inhibition increases the expression of T-bet, ZEB2, and BLIMP-1, transcription factors that are associated with late-stage NK cell maturation. This demonstrates that NK cell functional maturation is required for the efficacy of NK cell-based cancer immunotherapy.</p></sec></sec><sec id=\"S6\"><title>Perspectives</title><p>As discussed above, NK cell maturation is a process coordinated by a network that consists of transcription factors, signaling pathways, and cytokines. Through the use of stage-specific, gene-modified mouse models, as well as other complementary systems, additional molecules that play a role in the NK cell maturation process are being identified, further expanding our knowledge of this basic topic in NK cell biology.</p><p>On the other hand, NK cells are composed of different subsets with different maturation status. For example, mouse NK cells with a self-MHC-specific Ly49 inhibitory receptor (e.g., the Ly49I/C<sup>+</sup> NK cell subset) are tolerant toward self-MHC –expressing cells, but are better producers of IFN-γ in response to cross-linking of activation receptors (<xref rid=\"B100\" ref-type=\"bibr\">100</xref>). These better-educated Ly49I/C<sup>+</sup> NK cells display a more mature phenotype, compared with Ly49I/C<sup>–</sup> NK cells (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>). Another example is that NK cells can be divided into two distinct subsets: CD49a<sup>+</sup> and CD49a<sup>–</sup>. Different from conventional CD49a- NK cells, CD49a<sup>+</sup> NK cells are tissue resident. Tissue-resident NK (trNK) cells have been observed in diverse organs and tissues, including thymus, uterus, skin, adipose, liver, and salivary. The transcriptional factors that instruct the development of trNK cells are different from those of cNK cells. For instance, mice with <italic>E4bp4</italic> deficiency lack conventional NK (cNK) cells, but have trNK cells in liver and uterus (<xref rid=\"B101\" ref-type=\"bibr\">101</xref>). In addition, the phenotype markers and necessary factors for the development of trNK cells have been found to display notable tissue-specific properties. For example, the development of trNK cells in the thymus with CD127<sup>+</sup>CD49b<sup>+</sup>CD49a<sup>–</sup> phenotype requires IL-7 and GATA3 (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>). The origin, developmental process, transcriptional factor, and functional competence of trNK cells in mice and human remain less-studied, though some recent studies have started shedding insights into the comparison between the cNK and trNK (<xref rid=\"B102\" ref-type=\"bibr\">102</xref>, <xref rid=\"B103\" ref-type=\"bibr\">103</xref>). Interestingly, CD49a<sup>+</sup> liver-resident NK cells display an immature phenotype, compared with CD49a<sup>–</sup> conventional NK cells. The impacts of maturation status on the differentiation programs and specific features (such as liver-residency of liver-resident NK cells) of these NK cell subsets still remains to be investigated.</p><p>Another conceptual breakthrough in NK cell biology relates to the immunological memory and adaption of NK cells (<xref rid=\"B104\" ref-type=\"bibr\">104</xref>, <xref rid=\"B105\" ref-type=\"bibr\">105</xref>). Increasing evidence indicates that NK cells recall haptens, viruses, and cytokines (<xref rid=\"B106\" ref-type=\"bibr\">106</xref>). For antigen-specific NK cell memory responses in a murine model of hapten-induced contact hypersensitivity (CHS), NK cells require CXCR6 expression for such process (<xref rid=\"B107\" ref-type=\"bibr\">107</xref>). Also, NK cell-mediated CHS is dependent on IL-12, IFN-α, and IFN-γ (<xref rid=\"B108\" ref-type=\"bibr\">108</xref>). For generation of protective memory NK cell response during MCMV infection, pro-inflammatory cytokines (e.g., IL-12 and downstream STAT4) (<xref rid=\"B109\" ref-type=\"bibr\">109</xref>) and co-stimulatory receptors (e.g., CD226) (<xref rid=\"B110\" ref-type=\"bibr\">110</xref>) play crucial roles. Moreover, in order to form NK cell memory, NK cells require Zbtb32 to undergo a proliferative burst and clonal expansion in response to infection (<xref rid=\"B111\" ref-type=\"bibr\">111</xref>), require BNIP3 and BNIP3L -dependent clearance of damaged mitochondria by autophagy (<xref rid=\"B112\" ref-type=\"bibr\">112</xref>), and require pro-apoptotic factor Bim for contraction (<xref rid=\"B113\" ref-type=\"bibr\">113</xref>). Considering the above-discussed effects of NK cell maturation on NK cell effector functions, the question arises as to whether adaptive NK cell memory also depends on NK cell maturation status. An appropriate model of NK cell memory should be employed that reflects the relationship between an immunological memory and maturation of NK cells (<xref rid=\"B114\" ref-type=\"bibr\">114</xref>). Although molecular mechanisms on the differentiation of adaptive NK cells remains to be fully revealed, the immunological memory of NK cells support the potential for NK cells in promoting better clinical outcomes during disease treatment.</p><p>More efforts are required, however, before knowledge in this area can be exploited for disease treatment. For example, NK cell activity is dysregulated in autoimmune diseases (<xref rid=\"B115\" ref-type=\"bibr\">115</xref>), which potentially affects the maturation process in the bone marrow even if the disease is confined to a region away from the bone marrow. However, relatively few studies have investigated the maturation status of NK cells under such conditions. On the other hand, NK cells are functionally exhausted in the tumor microenvironment (<xref rid=\"B116\" ref-type=\"bibr\">116</xref>, <xref rid=\"B117\" ref-type=\"bibr\">117</xref>). The levels of CD27<sup>–</sup>CD11b<sup>–</sup> tumor-infiltrating NK cells are associated with tumor progression in a murine Lewis lung cancer (LLC) model (<xref rid=\"B118\" ref-type=\"bibr\">118</xref>), indicating that the tumor microenvironment displays region-specific immune features, and further suggests that NK cell maturation is differentially regulated when compared with the normal condition; however, this idea requires further investigation.</p></sec><sec id=\"S7\"><title>Author Contributions</title><p>JB and XW conceived and wrote the manuscript, and designed the figures. Both authors contributed to the article and approved the submitted version.</p></sec><sec id=\"conf1\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> This work was supported by the National Natural Science Foundation of China (#31872741), Young Top Talents Program of Anhui Medical University, and Research Improvement Program of Anhui Medical University.</p></fn></fn-group><ref-list><title>References</title><ref id=\"B1\"><label>1.</label><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Thomas</surname><given-names>R</given-names></name><name><surname>Yang</surname><given-names>X.</given-names></name></person-group>\n<article-title>NK-DC Crosstalk in immunity to microbial 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Oncol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Oncol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Oncol.</journal-id><journal-title-group><journal-title>Frontiers in Oncology</journal-title></journal-title-group><issn pub-type=\"epub\">2234-943X</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32850418</article-id><article-id pub-id-type=\"pmc\">PMC7431949</article-id><article-id pub-id-type=\"doi\">10.3389/fonc.2020.01319</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Oncology</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>2-O-Methylmagnolol, a Magnolol Derivative, Suppresses Hepatocellular Carcinoma Progression via Inhibiting Class I Histone Deacetylase Expression</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Chen</surname><given-names>Chi-Yuan</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"author-notes\" rid=\"fn002\"><sup>†</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/722213/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Fang</surname><given-names>Jia-You</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref><xref ref-type=\"author-notes\" rid=\"fn002\"><sup>†</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/121193/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Chen</surname><given-names>Chin-Chuan</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><xref ref-type=\"author-notes\" rid=\"fn002\"><sup>†</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/442953/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Chuang</surname><given-names>Wen-Yu</given-names></name><xref ref-type=\"aff\" rid=\"aff5\"><sup>5</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Leu</surname><given-names>Yann-Lii</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><xref ref-type=\"aff\" rid=\"aff6\"><sup>6</sup></xref><xref ref-type=\"aff\" rid=\"aff7\"><sup>7</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/912140/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Ueng</surname><given-names>Shir-Hwa</given-names></name><xref ref-type=\"aff\" rid=\"aff5\"><sup>5</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Wei</surname><given-names>Li-Shan</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Cheng</surname><given-names>Shu-Fang</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Hsueh</surname><given-names>Chuen</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff5\"><sup>5</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Wang</surname><given-names>Tong-Hong</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff8\"><sup>8</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/796003/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Tissue Bank, Chang Gung Memorial Hospital</institution>, <addr-line>Taoyuan City</addr-line>, <country>Taiwan</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Graduate Institute of Health Industry Technology, Research Center for Food and Cosmetic Safety, Research Center for Chinese Herbal Medicine, College of Human Ecology, Chang Gung University of Science and Technology</institution>, <addr-line>Taoyuan City</addr-line>, <country>Taiwan</country></aff><aff id=\"aff3\"><sup>3</sup><institution>Graduate Institute of Natural Products, Chang Gung University</institution>, <addr-line>Taoyuan City</addr-line>, <country>Taiwan</country></aff><aff id=\"aff4\"><sup>4</sup><institution>Department of Anesthesiology, Chang Gung Memorial Hospital</institution>, <addr-line>Taoyuan City</addr-line>, <country>Taiwan</country></aff><aff id=\"aff5\"><sup>5</sup><institution>Department of Anatomic Pathology, Chang Gung Memorial Hospital, Chang Gung University School of Medicine</institution>, <addr-line>Taoyuan City</addr-line>, <country>Taiwan</country></aff><aff id=\"aff6\"><sup>6</sup><institution>Chinese Herbal Medicine Research Team, Healthy Aging Research Center, Chang Gung University</institution>, <addr-line>Taoyuan City</addr-line>, <country>Taiwan</country></aff><aff id=\"aff7\"><sup>7</sup><institution>Center for Traditional Chinese Medicine, Chang Gung Memorial Hospital</institution>, <addr-line>Taoyuan City</addr-line>, <country>Taiwan</country></aff><aff id=\"aff8\"><sup>8</sup><institution>Department of Hepato-Gastroenterology, Liver Research Center, Chang Gung Memorial Hospital</institution>, <addr-line>Taoyuan City</addr-line>, <country>Taiwan</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Ângela Sousa, University of Beira Interior, Portugal</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Chen Ling, Fudan University, China; Samuel Silvestre, University of Beira Interior, Portugal</p></fn><corresp id=\"c001\">*Correspondence: Tong-Hong Wang <email>cellww@adm.cgmh.org.tw</email></corresp><fn fn-type=\"other\" id=\"fn001\"><p>This article was submitted to Pharmacology of Anti-Cancer Drugs, a section of the journal Frontiers in Oncology</p></fn><fn fn-type=\"other\" id=\"fn002\"><p>†These authors have contributed equally to this work</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>10</volume><elocation-id>1319</elocation-id><history><date date-type=\"received\"><day>14</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>24</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Chen, Fang, Chen, Chuang, Leu, Ueng, Wei, Cheng, Hsueh and Wang.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Chen, Fang, Chen, Chuang, Leu, Ueng, Wei, Cheng, Hsueh and Wang</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p><italic>Magnolia officinalis</italic> is widely used in Southeast Asian countries for the treatment of fever, headache, diarrhea, and stroke. Magnolol is a phenolic compound extracted from <italic>M. officinalis</italic>, with proven antibacterial, antioxidant, anti-inflammatory, and anticancer activities. In this study, we modified magnolol to synthesize a methoxylated derivative, 2-O-methylmagnolol (MM1), and investigated the use of MM1, and magnolol in the treatment of liver cancer. We found that both magnolol and MM1 exhibited inhibitory effects on the growth, migration, and invasion of hepatocellular carcinoma (HCC) cell lines and halted the cell cycle at the G1 phase. MM1 also demonstrated a substantially better tumor-suppressive effect than magnolol. Further analysis suggested that by inhibiting class I histone deacetylase expression in HCC cell lines, magnolol and MM1 induced p21 expression and p53 activation, thereby causing cell cycle arrest and inhibiting HCC cell growth, migration, and invasion. Subsequently, we verified the significant tumor-suppressive effects of magnolol and MM1 in an animal model. Collectively, these findings demonstrate the anti-HCC activities of magnolol and MM1 and their potential for clinical use.</p></abstract><kwd-group><kwd>magnolol</kwd><kwd>2-O-methylmagnolol (MM1)</kwd><kwd>histone deacetylase (HDAC)</kwd><kwd>hepatocellular carcinoma (HCC)</kwd><kwd>p21</kwd><kwd>p53</kwd></kwd-group><funding-group><award-group><funding-source id=\"cn001\">Chang Gung Medical Foundation<named-content content-type=\"fundref-id\">10.13039/501100004606</named-content></funding-source></award-group></funding-group><counts><fig-count count=\"7\"/><table-count count=\"0\"/><equation-count count=\"0\"/><ref-count count=\"69\"/><page-count count=\"13\"/><word-count count=\"7677\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>The World Health Organization (WHO) indicated that liver cancer was the sixth most common cancer and the fourth leading cause of cancer deaths worldwide in 2018, with a global death toll of 782,000 (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>). The risk factors for liver cancer include hepatitis B, hepatitis C, alcoholic liver disease, non-alcoholic fatty liver disease, and cirrhosis (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>, <xref rid=\"B3\" ref-type=\"bibr\">3</xref>). At present, surgery remains the first line of treatment for liver cancer; however, chemotherapy or radiation therapy is the preferred choice for patients with advanced liver cancer who cannot undergo surgical resection (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>). Most chemotherapeutic drugs, however, often have large side effects and significantly impact patients' quality of life (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>). Therefore, the development of effective therapeutic drugs with minimal side effects has been at the forefront of liver cancer research.</p><p>Due to its advantages, such as high specificity and low side effects, targeted therapy has become the main modality of cancer treatment (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>, <xref rid=\"B7\" ref-type=\"bibr\">7</xref>). However, carcinogenic factors are multifactorial and often complicated. This complexity is further aggravated by tumor heterogeneity (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>, <xref rid=\"B9\" ref-type=\"bibr\">9</xref>). Therefore, drugs against a single target often demonstrate limited efficacy. Even sorafenib, which is recognized as the most effective targeted drug against liver cancer, only prolongs patient survival by ~3 months (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>, <xref rid=\"B11\" ref-type=\"bibr\">11</xref>). Thus, in clinical practice, targeted therapy is often used in conjunction with other treatment modalities, such as chemotherapy and radiation therapy, to improve therapeutic outcomes (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>, <xref rid=\"B13\" ref-type=\"bibr\">13</xref>).</p><p>Recent studies have shown that the occurrence of liver cancer is closely associated with genetic and epigenetic variations (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>, <xref rid=\"B15\" ref-type=\"bibr\">15</xref>). Common epigenetic regulatory mechanisms include DNA methylation, histone modification, and non-coding RNA regulation (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>). Previous studies have reported that histone deacetylase (HDAC) overexpression is common in hepatitis B virus (HBV)-infected liver cancer patients (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>, <xref rid=\"B18\" ref-type=\"bibr\">18</xref>) and could lead to carcinogenesis, as HDACs regulate the deacetylation of histone and non-histone proteins, thereby coordinating gene expression or protein activation. Histone protein deacetylation leads to its tighter binding of the surrounding DNA, consequently inhibiting gene expression in the bound region. Alternatively, non-histone protein acetylation not only is closely associated with its protein activity but also affects its ability to bind other proteins or DNA, thereby indirectly regulating the expression of other genes and their encoded proteins (<xref rid=\"B19\" ref-type=\"bibr\">19</xref>, <xref rid=\"B20\" ref-type=\"bibr\">20</xref>). The 18 known HDAC types found in humans can be categorized into four classes: class I (HDAC1, HDAC2, HDAC3, and HDAC8), class IIa (HDAC4, HDAC5, HDAC7, and HDAC9), class IIb (HDAC6 and HDAC10), class III Sir2-like enzymes (comprising seven sirtuins), and class IV (HDAC11). Among these, class I HDAC overexpression is observed in most cancer types, including liver cancer (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>–<xref rid=\"B24\" ref-type=\"bibr\">24</xref>). Class I HDAC overexpression can inhibit the expression of multiple tumor-suppressor genes, such as p21 and p53, thereby promoting carcinogenesis (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>–<xref rid=\"B27\" ref-type=\"bibr\">27</xref>). Moreover, these HDACs are therapeutic targets for multiple anticancer treatments. HDAC inhibitors, including trichostatin A, vorinostat (suberoylanilide hydroxamic acid, SAHA), trapoxin A, and valproic acid, are effective in the treatment of lung, breast, and esophageal cancers, whereas, SAHA has been approved by the Food and Drug Administration (FDA) for the treatment of T-cell lymphoma (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>–<xref rid=\"B31\" ref-type=\"bibr\">31</xref>). Furthermore, recent studies have found that the combined use of an HDAC inhibitor with sorafenib can substantially improve the treatment efficacy of sorafenib in liver cancer (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>, <xref rid=\"B33\" ref-type=\"bibr\">33</xref>). However, most HDAC inhibitors have significant side effects, which are the reason for the primary bottleneck to their clinical use.</p><p>The application of traditional Chinese herbal medicine in disease treatment has become increasingly popular in recent years. Compared to Western medicine, Chinese herbal medicine is an alternative treatment option that can be effective and introduces fewer side effects (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>–<xref rid=\"B36\" ref-type=\"bibr\">36</xref>). Owing to the development of component separation technologies, the active ingredients of traditional Chinese medicines have been extracted and their functions identified. These compounds can act at lower effective doses and produce more specific therapeutic effects. Among them, artemisinin and curcumin are used and have shown good outcomes in cancer treatment (<xref rid=\"B37\" ref-type=\"bibr\">37</xref>–<xref rid=\"B39\" ref-type=\"bibr\">39</xref>). Other extracts, such as resveratrol and chrysin, exert an anti-cancer stem cell (CSC) effect and may provide an alternative approach to manage cancers (<xref rid=\"B40\" ref-type=\"bibr\">40</xref>).</p><p><italic>Magnolia officinalis</italic> is a traditional Chinese medicinal plant commonly used in Southeast Asian countries. Its extract, magnolol, a phenolic compound, has proven antibacterial, antioxidant, and anti-inflammatory activities, and its anticancer and antiangiogenic activities have also been recently verified (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>, <xref rid=\"B42\" ref-type=\"bibr\">42</xref>). However, the mechanism of its anticancer effects is yet to be elucidated. In the present study, we modified magnolol and synthesized a methoxylated derivative, 2-O-methylmagnolol (MM1). In addition to testing the anti-hepatocellular carcinoma (HCC) activities of magnolol and its derivative MM1, we also used cell and animal models to clarify their modes of action, thereby elucidating the feasibility of their clinical applications.</p></sec><sec sec-type=\"materials and methods\" id=\"s2\"><title>Materials and Methods</title><sec><title>Cell Culture</title><p>Human HCC cell lines Huh7 and HepG2 were purchased from the American Type Culture Collection (Manassas, VA, USA) and donated by Dr. Chau-Ting Yeh of Chang Gung Memorial Hospital, respectively. Human skin fibroblasts (HFBs) were kindly provided by Dr. Pan-Chyr Yang of Taiwan University. The cells were maintained in Dulbecco's Modified Eagle Medium (Gibco, Gaithersburg, MD, USA) containing 10% fetal bovine serum (FBS) and cultured at 37°C with 5% carbon dioxide in a humidified incubator. Culture medium, chemical compounds, and FBS were purchased from Life Technologies (Grand Island, NY, USA).</p></sec><sec><title>Compounds and Antibodies</title><p>Magnolol was purchased from Shanghai BS Bio-Tech Co., Ltd. (Shanghai, China). MM1 was prepared as described by Lin et al. (<xref rid=\"B43\" ref-type=\"bibr\">43</xref>). The purity of magnolol and MM1 was <99%, as determined by high-precision liquid chromatography (HPLC) analysis. Magnolol and MM1 were each dissolved in dimethyl sulfoxide (DMSO) to obtain a stock concentration of 100 mM, which was then stored at −20°C before use. DMSO 0.1% v/v was used as the vehicle control. Sorafenib was purchased from Sigma-Aldrich (St. Louis, MO, USA). Antibodies against human class I HDACs (HDAC1, HDAC2, HDAC3, and HDAC8), acetyl-histone H3, acetyl-p53, p53, p21, Ki-67, E-cadherin, N-cadherin, vimentin, Snail, Slug, and β-actin were purchased from GeneTex (Irvine, CA, USA) and Cell Signaling Technology (Beverly, MA, USA). The antibody to cyclin D1 was purchased from ABclonal Technology (Woburn, MA, USA), and the antibodies against CDK4 were purchased from Proteintech (Rosemont, IL, USA). Secondary antibodies were purchased from Santa Cruz Biotechnology (Santa Cruz, CA, USA).</p></sec><sec><title>Real-Time Reverse Transcription-Polymerase Chain Reaction Analysis</title><p>Total RNA from Huh7 and HepG2 cells were extracted using TOOLSmart RNA extractor (BIOTOOLS Co., Ltd., Taiwan) and RNeasy Mini Kit (QIAGEN, Gaithersburg, MD, USA) according to the manufacturer's instructions. Complementary DNA was synthesized using a ToolScript MMLV RT Kit (BIOTOOLS Co., Ltd., Taiwan). Quantitative real-time polymerase chain reaction (PCR) assays using the TaqMan Gene Expression Kit (Applied Biosystems, Foster City, CA, USA), TOOLS 2 × SYBR qPCR Mix (BIOTOOLS Co., Ltd., Taiwan), and an ABI StepOnePlus™ System (Applied Biosystems) were used to detect p21 expression, using glyceraldehyde 3-phosphate dehydrogenase as an internal control.</p></sec><sec><title>Western Blot Analysis</title><p>Huh7 and HepG2 cells were treated with magnolol, MM1, or dimethyl sulfoxide (DMSO) for 48 h, followed by lysis in RIPA lysis buffer (BIOTOOLS Co., Ltd., Taiwan) containing protease inhibitors. Cell lysates (30-μg protein) were subjected to Western blotting as described previously, using β-actin as a loading control. The relative intensities of the protein bands were quantified using ImageQuant 5.2 software (GE Healthcare, Piscataway, NJ, USA).</p></sec><sec><title><italic>In vitro</italic> Cell Proliferation Assay</title><p>The proliferation capacity of magnolol-/MM1-treated cells was examined using an xCELLigence Real-Time Cell Analyzer (Roche Life Science, Indianapolis, IN, USA) according to the manufacturer's standard protocol.</p></sec><sec><title>Transwell Migration and Invasion Assay</title><p>The migration and invasion capacities of magnolol-/MM1-treated cells were analyzed using a Transwell migration assay, as described previously (<xref rid=\"B44\" ref-type=\"bibr\">44</xref>).</p></sec><sec><title>Cell Cycle Analysis</title><p>Cells were trypsinized, washed twice, and fixed with 70% ethanol at −20°C for 1 h. The fixed cells were subsequently incubated in phosphate-buffered saline containing 0.12% Triton X-100, 0.12 mmol/L ethylenediaminetetraacetic acid, and 100 mg/mL ribonuclease A at 37°C for 1 h. Cells were stained with propidium iodide (50 μg/mL) at 4°C for 20 min, and cell cycle distribution was measured using a BD FACS caliber.</p></sec><sec><title>Cell Apoptosis Assay</title><p>The apoptosis status of Huh7 cells was determined using a DeadEnd™ Fluorometric terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay kit (Promega, Madison, WI, USA) according to the manufacturer's protocol. In summary, Huh7 cells were grown on chamber slides and treated with different concentrations of magnolol or MM1 for 48 h. The cells were fixed with 4% paraformaldehyde for 15 min at room temperature and subsequently subjected to the TUNEL assay. Apoptotic cells were examined using a fluorescence microscope (magnification × 100). Images of five random fields per dish were examined for each experiment.</p></sec><sec><title>Tumor Formation Assay in Nude Mice</title><p>Six-week-old male BALB/c nude mice were purchased from the National Laboratory Animal Center (Taipei, Taiwan), and maintained under specific pathogen-free conditions. Animal experiments were performed under an approved protocol in accordance with the guidelines for the Animal Care and Ethics Commission of Chang Gung Memorial Hospital (IACUC Approval No. 2018031301; approval date: 6/19/2018). The mice were injected subcutaneously with 5 × 10<sup>6</sup> Huh7 cells (in 100 μL of saline with 50% Matrigel [BD Biosciences]) into both flanks. All tumors were allowed to grow for 1 week before the initiation of drug treatment. At the start of the second week, mice with tumors were intraperitoneally injected three times a week with 100 μL of magnolol or MM1 (0.1 μmol in 100 μL of DMSO) or an equal volume of DMSO, which served as a control. Twenty-eight days after drug administration, the mice were euthanized and the tumors were subjected to immunohistochemical staining.</p></sec><sec><title>Immunohistochemistry</title><p>The tumors from the mice were fixed in 4% paraformaldehyde overnight, dehydrated, and embedded in paraffin. Paraffin blocks were sliced into 2-mm-thick sections and floated onto glass slides. The tissue sections were deparaffinized, and the expression of HDAC1, HDAC2, p21, cyclin D1, CDK4, Ki-67, E-cadherin, N-cadherin, vimentin, and Snail in the tissues were detected as described previously (<xref rid=\"B45\" ref-type=\"bibr\">45</xref>).</p></sec><sec><title>Statistical Analyses</title><p>Comparisons between groups were analyzed using Student's <italic>t</italic>-tests. The results are expressed as the mean ± standard deviation. The half-maximal inhibitory concentration (IC<sub>50</sub>) values were determined by non-linear regression analysis using GraphPad Prism version 8.0 (GraphPad Software Inc., La Jolla, CA, USA). All statistical analyses were performed using the Statistical Package for the Social Sciences version 16.0 and Microsoft Excel 2007. All <italic>p</italic>-values were two-sided, with <italic>p</italic> < 0.05 considered to indicate a statistically significant difference.</p></sec></sec><sec sec-type=\"results\" id=\"s3\"><title>Results</title><sec><title>2-O-Methylmagnolol (MM1) Has Superior Inhibitory Effects on Hepatocellular Carcinoma (Hcc) Cell Growth, Metastasis, and Invasion</title><p>To determine whether magnolol and MM1 exhibited anticancer activities against liver cancer (<xref ref-type=\"fig\" rid=\"F1\">Figure 1A</xref>), HCC cell lines, HepG2, and Huh7, were treated with different concentrations of magnolol and MM1 to analyze their effects on cell growth. The results suggested that both magnolol and MM1 significantly inhibited HCC cell growth. Compared to the control group treated with DMSO, magnolol inhibited the growth of the two cell lines from 25 μM onward, with increasing effects in a dose-dependent manner. However, MM1 displayed a significantly stronger inhibitory effect on cell growth than magnolol at similar concentrations, indicating a greater tumor-suppressive activity than that of magnolol (<xref ref-type=\"fig\" rid=\"F1\">Figures 1C–F</xref>). The half-maximal inhibitory concentration (IC<sub>50</sub>) of magnolol toward Huh7 and HepG2 cells was ~97 and 65 μM, respectively, which is similar to results from other studies (<xref rid=\"B46\" ref-type=\"bibr\">46</xref>–<xref rid=\"B48\" ref-type=\"bibr\">48</xref>), while the IC<sub>50</sub> of MM1 in Huh7 and HepG2 cells was 48 and 61 μM, respectively. Moreover, only a slight inhibitory effect was observed on the growth of the HFB cell line at the highest concentrations of magnolol and MM1 (<xref ref-type=\"fig\" rid=\"F1\">Figure 1B</xref>). This finding indicated that magnolol and MM1 selectively inhibited HCC cell growth with low toxicity to normal cells.</p><fig id=\"F1\" position=\"float\"><label>Figure 1</label><caption><p>Compared to magnolol, 2-O-methylmagnolol (MM1) demonstrates a greater ability to inhibit hepatocellular carcinoma (HCC) cell growth. <bold>(A)</bold> Chemical structures of MM1 and magnolol. <bold>(B–F)</bold> Human skin fibroblasts, Huh 7, and HepG2 cells were treated with different concentrations (0, 25, 50, 75, and 100 μM) of magnolol or MM1, and the cell proliferation status was analyzed using an xCELLigence Real-Time Cell Analyzer. The results are shown as the mean ± standard deviation of three independent experiments. Significant differences compared with the vehicle groups, **<italic>p</italic> < 0.01, ***<italic>p</italic> < 0.001. <bold>(G)</bold> Effect of magnolol and MM1 on cell cycle progression in Huh7 cells. Cells were treated with the indicated concentrations of magnolol or MM1 for 48 h. Cell cycle distribution was measured by propidium iodide staining and quantified by flow cytometry. The quantitative results are shown in <bold>(H)</bold>. <bold>(I)</bold> Effects of magnolol and MM1 on apoptosis in Huh7 cells. Terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) staining was used to observe the apoptotic cells under a fluorescence microscope (magnification × 100). Green punctate staining represents TUNEL-positive cells (white arrow).</p></caption><graphic xlink:href=\"fonc-10-01319-g0001\"/></fig><p>Flow cytometry analysis to further understand the potential influences of magnolol and MM1 on the cell cycle showed that treatment with magnolol and MM1 caused cells to stagnate at the G1 phase (<xref ref-type=\"fig\" rid=\"F1\">Figures 1G,H</xref>). Additionally, even at high concentrations, magnolol, and MM1 treatment did not cause apoptosis (<xref ref-type=\"fig\" rid=\"F1\">Figure 1I</xref>). These findings suggest that magnolol and MM1 inhibited cell growth by causing cell cycle arrest.</p><p>One of the primary reasons that liver cancer is difficult to cure is the strong invasion and metastasis ability of tumor cells. To investigate the effects of magnolol and MM1 on the metastasis and invasion ability of HCC cells, we performed a transwell migration assay. The results indicated that both magnolol and MM1 had inhibitory effects on cell migration ability (<xref ref-type=\"fig\" rid=\"F2\">Figures 2A,B</xref>). Similar inhibitory effects were also observed on the invasion abilities of HCC cells at similar concentrations (<xref ref-type=\"fig\" rid=\"F2\">Figures 2C,D</xref>). Consistent with the results of the cell growth analysis, MM1 displayed higher inhibitory effects on the migration and invasion capacities of HCC cells, compared to those of magnolol at similar concentrations.</p><fig id=\"F2\" position=\"float\"><label>Figure 2</label><caption><p>2-O-methylmagnolol (MM1) and magnolol inhibit hepatocellular carcinoma cell migration and invasion by suppressing epithelial-mesenchymal transition (EMT). <bold>(A,B)</bold> Comparisons of migration capacities of Huh7 and HepG2 cells treated with magnolol or MM1 in transwell assays. <bold>(C,D)</bold> Invasion assays using Matrigel-coated polyethylene terephthalate membrane inserts. <bold>(E,G)</bold> Western blotting showing the expression of EMT-related proteins in Huh7 and HepG2 cells after treatment with magnolol and MM1. Quantitative results are shown in <bold>(F,H)</bold>. The results are shown as the mean of three independent experiments. Significant differences compared with the vehicle control groups, *<italic>p</italic> < 0.05, **<italic>p</italic> < 0.01, ***<italic>p</italic> < 0.001.</p></caption><graphic xlink:href=\"fonc-10-01319-g0002\"/></fig><p>Considering that the epithelial-mesenchymal transition (EMT) is an important process for tumor metastasis, we also measured the expression levels of EMT-related proteins such as N-cadherin, E-cadherin, and slug to determine whether magnolol and MM1 inhibited HCC migration and invasion by regulating EMT. The results showed significantly lower expression of EMT-promoting proteins, N-cadherin, and slug in magnolol and MM1-treated cells compared to that in the control group (<xref ref-type=\"fig\" rid=\"F2\">Figures 2E–H</xref>). These findings suggested that magnolol and MM1 inhibited HCC migration and invasion by suppressing EMT.</p></sec><sec><title>Magnolol and MM1 Inhibit Class I Histone Deacetylase Expression in HCC Cells</title><p>Previous studies suggest that magnolol could inhibit non-small cell lung cancer progression by inhibiting class I HDAC expression (<xref rid=\"B49\" ref-type=\"bibr\">49</xref>). Additionally, the overexpression of class I HDACs commonly observed in liver cancer patients is associated with liver cancer progression (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>). To determine whether the anti-HCC effects of magnolol and MM1 were exerted by inhibiting class I HDACs, Western blot analysis was performed to examine the expression of class I HDACs in HCC cells treated with magnolol and MM1. The results indicated that treatment with magnolol and MM1 considerably inhibited the expression of HDAC 1, 2, 3, and 8 proteins. Additionally, the inhibitory effect of MM1 on class I HDACs was significantly higher than that of magnolol at similar concentrations (<xref ref-type=\"fig\" rid=\"F3\">Figures 3A–D</xref>). Another Western blot analysis performed to investigate the association between magnolol or MM1 treatment and the degree of acetylation of histone H3 in HCC cell lines showed substantially higher histone H3 acetylation in cells treated with magnolol and MM1 compared to that in the control group (<xref ref-type=\"fig\" rid=\"F3\">Figures 3E,F</xref>). These findings indicated that magnolol and MM1 promoted histone acetylation by inhibiting HDAC expression.</p><fig id=\"F3\" position=\"float\"><label>Figure 3</label><caption><p>2-O-methylmagnolol (MM1) and magnolol inhibit class I histone deacetylase (HDAC) expression in hepatocellular carcinoma cell lines. <bold>(A,C)</bold> Huh7 and HepG2 cells were treated with magnolol, MM1, or vehicle for 48 h. The expression levels of HDACs 1, 2, 3, and 8 were determined by Western blotting. Quantitative results are shown <bold>(B,D)</bold>. <bold>(E)</bold> The levels of acetylated histone H3 in HepG2 cells were examined by Western blotting. The quantitative results are shown in <bold>(F)</bold>. The measurement data are expressed as the mean ± standard deviation of three independent experiments. *<italic>p</italic> < 0.05, **<italic>p</italic> < 0.01, ***<italic>p</italic> < 0.001.</p></caption><graphic xlink:href=\"fonc-10-01319-g0003\"/></fig></sec><sec><title>Magnolol and MM1 Induce p21 Expression and p53 Acetylation</title><p>HDACs can regulate the degree of deacetylation of histone and non-histone proteins, thereby suppressing gene expression. Previous studies have reported that class I HDACs induce carcinogenesis by inhibiting the expression of the tumor-suppressor gene p21 and activating the tumor-suppressor protein p53 (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>, <xref rid=\"B27\" ref-type=\"bibr\">27</xref>). Thus, real-time RT-PCR and Western blot analyses were performed to identify the effects of magnolol and MM1 on the expression and activation of p21 and p53. The results showed substantially higher expression of p21 mRNA and protein in Huh7 and HepG2 cells treated with magnolol and MM1 and lower expression of cell cycle regulatory proteins such as CDK4 and cyclin D1 than in the control group (<xref ref-type=\"fig\" rid=\"F4\">Figures 4A–C</xref>). These findings suggested that magnolol and MM1 could induce p21 gene expression, thereby impeding cell cycle progression. Furthermore, the fact that the degree of p53 protein acetylation increased with increasing magnolol and MM1 concentrations (<xref ref-type=\"fig\" rid=\"F4\">Figures 4D–E</xref>) suggested that magnolol and MM1 could promote the activation of p53 tumor-suppressor proteins.</p><fig id=\"F4\" position=\"float\"><label>Figure 4</label><caption><p>2-O-methylmagnolol (MM1) and magnolol induce the expression of the tumor-suppressor gene p21 and the acetylation of p53. <bold>(A)</bold> Huh7 and HepG2 cells were treated with the indicated concentrations of magnolol or MM1 for 48 h. The p21 RNA levels were examined by quantitative real-time reverse transcription-polymerase chain reaction. <bold>(B)</bold> Expression levels of p21 and downstream proteins in Huh7 cells were analyzed by Western blot using β-actin as an internal control. Quantitative results are shown in <bold>(C)</bold>. *<italic>p</italic> < 0.05, **<italic>p</italic> < 0.01, ***<italic>p</italic> < 0.001. <bold>(D)</bold> HepG2 cells were treated with magnolol or MM1 for 48 h, and the levels of acetylated p53 were examined by Western blot. Quantitative results are shown in <bold>(E)</bold>. Error bars represent mean ± standard deviation from three independent experiments. *<italic>p</italic> < 0.05, **<italic>p</italic> < 0.01, ***<italic>p</italic> < 0.001.</p></caption><graphic xlink:href=\"fonc-10-01319-g0004\"/></fig></sec><sec><title>Magnolol and MM1 Enhance the Anti-HCC Effect of Sorafenib</title><p>Previous studies suggest that the combined use of HDAC inhibitors and sorafenib could enhance the antitumor effect of sorafenib (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>, <xref rid=\"B33\" ref-type=\"bibr\">33</xref>). To understand whether the combined use of magnolol/MM1 and sorafenib showed compounded effects, magnolol/MM1 and sorafenib were administered individually and concurrently to HCC cell lines. The cell proliferation assay and flow cytometry were performed to analyze cell proliferation and cell cycle progression. The results indicated that individual treatment with magnolol/MM1 or sorafenib led to cell stagnation at the G1 phase and induced cell apoptosis. In contrast, the concurrent administration of magnolol/MM1 and sorafenib substantially improved the toxic effect on HCC cell lines (<xref ref-type=\"fig\" rid=\"F5\">Figures 5A–C</xref>). These findings verified that the combined use of magnolol and sorafenib could enhance the efficacy of anti-liver cancer treatment.</p><fig id=\"F5\" position=\"float\"><label>Figure 5</label><caption><p>2-O-methylmagnolol (MM1)/magnolol and sorafenib show a synergistic anti-hepatocellular carcinoma effect. <bold>(A,B)</bold> Huh7 and HepG2 cells were treated with 40 μM magnolol/MM1 and 3.5 μM sorafenib, individually, or in combination. The cell proliferation status was analyzed using an xCELLigence Real-Time Cell Analyzer. The data are expressed as the mean ± standard deviation from three independent experiments. <bold>(C)</bold> The cell cycle status in Huh7 cells treated with magnolol/MM1/sorafenib was examined by flow cytometry. <bold>(D)</bold> Effects of magnolol/MM1 and sorafenib alone or in combination on cell proliferation in human fibroblasts.</p></caption><graphic xlink:href=\"fonc-10-01319-g0005\"/></fig><p>To understand the safety of the combined use of magnolol/MM1 and sorafenib, the effects of the above compounds on human fibroblasts HFB alone or in combination were tested (<xref ref-type=\"fig\" rid=\"F5\">Figure 5D</xref>). We found that magnolol and MM1 did not significantly affect the growth of HFB, whereas sorafenib slightly inhibited the growth of HFB. However, when MM1 or magnolol are used in combination with sorafenib, it can reduce the toxicity of sorafenib to HFB.</p></sec><sec><title>Magnolol and MM1 Inhibit Tumor Growth in Mice</title><p>To confirm that magnolol and MM1 demonstrated the same inhibitory effects on HCC cells <italic>in vivo</italic> and verified the abovementioned regulatory mechanism, a mouse xenograft model was established by injecting Huh7 cells into the backs of mice and subsequently administering magnolol or MM1 periodically via intraperitoneal injection. The results suggested that, compared to the control group that only received DMSO, the administration of either magnolol or MM1 significantly inhibited tumor growth in mice. In addition, the inhibitory effect of MM1 was superior to that of magnolol (<xref ref-type=\"fig\" rid=\"F6\">Figures 6A–C</xref>). Furthermore, the weights and liver tissue morphology of mice treated with magnolol or MM1 did not change considerably, nor were there any significant abnormalities in the serological test results for the two groups of mice (<xref ref-type=\"fig\" rid=\"F6\">Figures 6D–F</xref>), indicating that neither treatment was toxic to the mice.</p><fig id=\"F6\" position=\"float\"><label>Figure 6</label><caption><p>2-O-methylmagnolol (MM1) and magnolol inhibit tumor growth in mice. <bold>(A)</bold> A total of 5 × 10<sup>6</sup> Huh7 cells were injected into the dorsal flanks of nude mice (<italic>n</italic> = 5 per group). Subsequently, the mice were intraperitoneally injected three times per week with 100 μL of magnolol or MM1 [0.1 μmol in 100 μL of dimethyl sulfoxide (DMSO)] or an equal volume of DMSO. Representative images show the tumor xenografts at 5 weeks post-implantation. <bold>(B)</bold> Tumor tissues were collected at the end point. <bold>(C)</bold> Tumor weights at end point. <bold>(D)</bold> Body weights measured during the experiment. ***<italic>p</italic> < 0.001. <bold>(E)</bold> Serological test results of the three groups of mice. <bold>(F)</bold> Hematoxylin and eosin (H&E) staining of mouse liver tissue sections. Magnification: 400×. <bold>(G,H)</bold> Immunohistochemical staining showing the effect of magnolol or MM1 on class I histone deacetylases, p21, CDK4, cyclin D1, Ki-67, and EMT-related protein expression in mouse xenograft tumors. Magnification: 400×.</p></caption><graphic xlink:href=\"fonc-10-01319-g0006\"/></fig><p>Mouse tumor tissues were sectioned and subjected to immunohistochemical staining to analyze the expression of class I HDACs and p21, CDK4, cyclin D1, Ki-67, and EMT-related genes. Our results were consistent with those from <italic>in vivo</italic> experiments, that is, dramatic decreases in class I HDACs, CDK4, cyclin D1, Ki-67, and EMT-promoted protein expression and increased p21 and E-cadherin expression in tumor tissues of mice treated with magnolol or MM1 (<xref ref-type=\"fig\" rid=\"F6\">Figures 6G,H</xref>). These findings confirm that magnolol and MM1 induce the expression of the above tumor-suppressor genes by inhibiting class I HDACs, thereby inhibiting HCC growth and metastasis (<xref ref-type=\"fig\" rid=\"F7\">Figure 7</xref>).</p><fig id=\"F7\" position=\"float\"><label>Figure 7</label><caption><p>Schematic representation summarizing the anti-hepatocellular carcinoma mechanisms of magnolol or 2-O-methylmagnolol (MM1). MM1 and magnolol inhibited cell cycle progression and tumor growth by inhibiting class I histone deacetylase expression and promoting p21 expression and p53 acetylation.</p></caption><graphic xlink:href=\"fonc-10-01319-g0007\"/></fig></sec></sec><sec sec-type=\"discussion\" id=\"s4\"><title>Discussion</title><p>In the present study, we tested the anti-HCC effects of magnolol and its methoxylated derivative MM1 and elucidated their modes of action. Both the cell and the animal models showed that magnolol and MM1 inhibited HCC cell and tumor growth, although the inhibitory effect of MM1 was superior to that of magnolol at similar concentrations. Additionally, we found that magnolol and MM1 inhibited cell cycle progression and tumor growth by inhibiting class I HDAC expression and promoting p21 expression and p53 acetylation. To the best of our knowledge, this is the first study to report the anti-HCC activity of MM1 and its superior potential for liver cancer treatment compared to that of magnolol.</p><p>Due to their extensive range of gene regulation, HDACs affect multiple physiological processes, including cell growth, differentiation, and apoptosis. Previous studies have suggested that abnormal HDAC expression is closely associated with the occurrence of various diseases, including cancer, and therefore identified HDACs as key therapeutic targets (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>, <xref rid=\"B50\" ref-type=\"bibr\">50</xref>, <xref rid=\"B51\" ref-type=\"bibr\">51</xref>). Among HDAC types, substantial expression of class I HDACs is commonly observed in most cancer types, including HDACs 1 and 2 in breast cancer (<xref rid=\"B52\" ref-type=\"bibr\">52</xref>, <xref rid=\"B53\" ref-type=\"bibr\">53</xref>); HDAC 1 in lung cancer (<xref rid=\"B54\" ref-type=\"bibr\">54</xref>); HDACs 2 and 3 in colorectal cancer (<xref rid=\"B55\" ref-type=\"bibr\">55</xref>); and HDACs 1, 2, and 3 in liver cancer (<xref rid=\"B56\" ref-type=\"bibr\">56</xref>, <xref rid=\"B57\" ref-type=\"bibr\">57</xref>). Considering this, we focused on the inhibitory effects of magnolol and MM1 on class I HDACs to investigate the feasibility of their clinical applications. However, due to the overexpression of other types of HDACs in other cancer types, it is necessary to analyze the inhibitory effects of these compounds on other types of HDACs to determine whether they can be used for the treatment of other cancers. Furthermore, although we discussed the effects of magnolol and MM1 on the tumor-suppressor genes p21 and p53, these results may only partially explain the anticancer mechanism of magnolol and MM1. Future studies will continue to investigate the effects of these two compounds on the regulation of other tumor-suppressor pathways to better understand the mechanisms by which they act to suppress tumors.</p><p>We observed that magnolol and MM1 enhanced p21 expression by inducing histone acetylation, thereby inhibiting cyclin D1 and CDK4 activities, as well as cell cycle progression. Additionally, magnolol and MM1 also induce p53 protein acetylation, which not only enhances its stability but also improves its ability to bind to the target gene promoter, thereby upregulating the expression of downstream tumor-suppressor genes such as p21 and BAX (<xref rid=\"B58\" ref-type=\"bibr\">58</xref>). These results indicated that magnolol and MM1 could regulate p21 expression via both direct and indirect pathways and consequently inhibit tumor growth.</p><p>Our previous studies confirmed that replacing the hydroxyl functional group of magnolol with a methoxy group could increase the lipophilicity of the methoxylated derivative and improve its skin delivery ability and anti-inflammatory activity (<xref rid=\"B43\" ref-type=\"bibr\">43</xref>). In another study, we also confirmed that the same concentration of MM1 could induce increased expression of the tumor suppressor long non-coding RNA, GAS5, compared to magnolol, and exert a greater inhibitory effect on skin cancer cells (<xref rid=\"B59\" ref-type=\"bibr\">59</xref>). Consistent with previous research, we found that the same concentration of MM1 could yield better anti-liver cancer activity compared to that of magnolol. This may be due to the better lipophilicity and cell uptake efficiency of MM1 compared to those of magnolol, as it has higher efficacy at the same concentration. This methoxylation could also increase the mucosal absorption rate of the compound, enhancing the flexibility of the route of administration and its clinical applicability.</p><p>Multiple clinical trials have shown excellent outcomes for HDAC inhibitors, including chidamide, panobinostat, vorinostat, and SAHA, in the treatment of many cancers (<xref rid=\"B60\" ref-type=\"bibr\">60</xref>, <xref rid=\"B61\" ref-type=\"bibr\">61</xref>). Among these, SAHA was the first HDAC inhibitor approved by the FDA for the treatment of T-cell lymphoma. It can specifically bind to the zinc-containing catalytic domains of class I, II, and VI HDACs, thus inhibiting their enzymatic activities. In addition to T-cell lymphoma, SAHA has shown promise in treating cancers of the breast, lungs, and prostate. Additionally, the combined use of HDAC inhibitors with other clinical anticancer medications shows compounded effects (<xref rid=\"B62\" ref-type=\"bibr\">62</xref>–<xref rid=\"B64\" ref-type=\"bibr\">64</xref>). For example, the combined use of SAHA and bortezomib promotes nasopharyngeal cancer cell apoptosis (<xref rid=\"B65\" ref-type=\"bibr\">65</xref>), and the combined use of romidepsin with cisplatin and gemcitabine enhances their therapeutic effects against triple-negative breast cancer (<xref rid=\"B66\" ref-type=\"bibr\">66</xref>). In the present study, we found that magnolol and MM1 inhibit the growth of HCC cells by suppressing the expression of class I HDAC, which is different from the mechanism of action of SAHA. However, we also observed that the combined use of magnolol/MM1 and sorafenib substantially enhanced their antiproliferative effects on HCC cells. The findings indicate the potential of using magnolol/MM1 as an adjuvant in combination with sorafenib in liver cancer treatment. Future studies will continue to investigate the optimal combination and dosage of magnolol/MM1 and existing clinical drugs including sorafenib and SAHA.</p><p>Sorafenib is an FDA-approved kinase inhibitor that inhibits the activation of tyrosine kinases such as VEGFR, PDGFR, and RAF family kinases (<xref rid=\"B67\" ref-type=\"bibr\">67</xref>). It has also been reported to induce the expression of p21 and p53 (<xref rid=\"B68\" ref-type=\"bibr\">68</xref>, <xref rid=\"B69\" ref-type=\"bibr\">69</xref>), which is the main tumor suppressor regulatory pathway of magnolol and MM1. Before fully elucidating the interaction between these drugs and molecules, we cannot assume that the additive anti-HCC effect of magnolol/MM1 and sorafenib is entirely due to the activation of the p21 and p53 tumor suppression pathways. However, we believe that these molecules should play an important regulatory role. In addition, we observed that the combined use of magnolol/MM1 and sorafenib not only substantially enhanced their antiproliferative effects on HCC cells but also reduced the toxicity of sorafenib monotherapy in normal cells. Further studies are also required to determine the mechanisms by which magnolol/MM1 reduces the physiological toxicity of sorafenib.</p><p>In conclusion, although HDAC inhibitors have been used extensively for the treatment of various cancers, their side effects remain a bottleneck to their clinical application. In this study, we synthesized a methoxylated derivative of magnolol, MM1, and verified its superior anti-HCC activity over magnolol. Additionally, it can enhance the therapeutic effect of sorafenib, when used in conjunction, and does not present physiological toxicity. Thus, MM1 is a suitable combination therapeutic adjuvant to improve the therapeutic efficacy of anticancer drugs.</p></sec><sec sec-type=\"data-availability\" id=\"s5\"><title>Data Availability Statement</title><p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.</p></sec><sec id=\"s6\"><title>Ethics Statement</title><p>This animal study was reviewed and approved by the Animal Care Ethics Commission of Chang Gung Memorial Hospital (IACUC Approval No. 2018031301, Approval Date: 6/19/2018).</p></sec><sec id=\"s7\"><title>Author Contributions</title><p>T-HW, J-YF, and C-YC: conceptualization. C-YC and T-HW: data curation. C-CC, W-YC, and Y-LL: formal analysis. T-HW, L-SW, and S-FC: investigation. CH and J-YF: methodology. T-HW, C-CC, and C-YC: project administration. Y-LL and J-YF: resources. T-HW: supervision. S-HU: validation. S-HU, W-YC, and CH: visualization. T-HW and C-YC: writing of the original manuscript draft and manuscript review and editing. All authors contributed to manuscript revision and have read and approved the submitted version.</p></sec><sec id=\"s8\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><ack><p>We would like to extend our gratitude to the Tissue Bank at the Chang Gung Memorial Hospital, Lin-Kou, Taiwan, for tissue processing, as well as Ying Chen, Szu-Chi Chen, Ya-Min Shih, and Yao-Fei Chang for their technical assistance inthis project.</p></ack><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> This study was partially supported by the Ministry of Science and Technology of Taiwan (Grant No. MOST 107-2314-B-182A-140-MY3) and the Chang Gung Medical Research Program, Taiwan (Grant No. CMRPG3J0861).</p></fn></fn-group><ref-list><title>References</title><ref id=\"B1\"><label>1.</label><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Bray</surname><given-names>F</given-names></name><name><surname>Ferlay</surname><given-names>J</given-names></name><name><surname>Soerjomataram</surname><given-names>I</given-names></name><name><surname>Siegel</surname><given-names>RL</given-names></name><name><surname>Torre</surname><given-names>LA</given-names></name><name><surname>Jemal</surname><given-names>A</given-names></name></person-group>. <article-title>Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries</article-title>. <source>CA Cancer J Clin</source>. 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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Psychol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Psychol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Psychol.</journal-id><journal-title-group><journal-title>Frontiers in Psychology</journal-title></journal-title-group><issn pub-type=\"epub\">1664-1078</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849047</article-id><article-id pub-id-type=\"pmc\">PMC7431950</article-id><article-id pub-id-type=\"doi\">10.3389/fpsyg.2020.01767</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Psychology</subject><subj-group><subject>Mini Review</subject></subj-group></subj-group></article-categories><title-group><article-title>Molecules of Silence: Effects of Meditation on Gene Expression and Epigenetics</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Venditti</surname><given-names>Sabrina</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/557490/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Verdone</surname><given-names>Loredana</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/595729/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Reale</surname><given-names>Anna</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/984979/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Vetriani</surname><given-names>Valerio</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/659446/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Caserta</surname><given-names>Micaela</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/592222/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Zampieri</surname><given-names>Michele</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/198765/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Department of Biology and Biotechnology “Charles Darwin”, Sapienza University</institution>, <addr-line>Rome</addr-line>, <country>Italy</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Institute of Molecular Biology and Pathology, National Council of Research (CNR)</institution>, <addr-line>Rome</addr-line>, <country>Italy</country></aff><aff id=\"aff3\"><sup>3</sup><institution>Department of Experimental Medicine, Sapienza University</institution>, <addr-line>Rome</addr-line>, <country>Italy</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Joseph Glicksohn, Bar-Ilan University, Israel</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Henriette van Praag, Florida Atlantic University, United States; Rachel Tomer, University of Haifa, Israel</p></fn><corresp id=\"c001\">*Correspondence: Sabrina Venditti, <email>sabrina.venditti@uniroma1.it</email></corresp><fn fn-type=\"other\" id=\"fn004\"><p>This article was submitted to Consciousness Research, a section of the journal Frontiers in Psychology</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>1767</elocation-id><history><date date-type=\"received\"><day>20</day><month>3</month><year>2020</year></date><date date-type=\"accepted\"><day>26</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Venditti, Verdone, Reale, Vetriani, Caserta and Zampieri.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Venditti, Verdone, Reale, Vetriani, Caserta and Zampieri</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>Many studies have consistently demonstrated an epigenetic link between environmental stimuli and physiological as well as cognitive responses. Epigenetic mechanisms represent a way to regulate gene activity in real time without modifying the DNA sequence, thus allowing the genome to adapt its functions to changing environmental contexts. Factors such as lifestyle, behavior, and the practice of sitting and moving mindful activities have been shown to be important means of environmental enrichment. Such practices, which include mindfulness meditation, Vipassana, Yoga, Tai Chi, and Quadrato Motor Training, have been reported to positively impact well-being. In fact, they can be considered emotional and attentional regulatory activities, which, by inducing a state of greater inner silence, allow the development of increased self-awareness. Inner silence can therefore be considered a powerful tool to counteract the negative effects of overabundant environmental noise, thanks to its power to relieve stress-related symptoms. Since all these positive outcomes rely on physiological and biochemical activities, the molecular and epigenetic mechanisms influenced by different mindful practices have recently started to be investigated. Here, we review some of the findings that could allow us to uncover the mechanisms by which specific practices influence well-being.</p></abstract><kwd-group><kwd>meditation</kwd><kwd>silence</kwd><kwd>epigenetics</kwd><kwd>epigenetic marks</kwd><kwd>gene-expression</kwd><kwd>mindfulness</kwd></kwd-group><funding-group><award-group><funding-source id=\"cn001\">Seventh Framework Programme<named-content content-type=\"fundref-id\">10.13039/100011102</named-content></funding-source></award-group></funding-group><counts><fig-count count=\"0\"/><table-count count=\"1\"/><equation-count count=\"0\"/><ref-count count=\"90\"/><page-count count=\"9\"/><word-count count=\"0\"/></counts></article-meta></front><body><sec id=\"S1\"><title>Introduction</title><p>According to the most recent definition given by <xref rid=\"B21\" ref-type=\"bibr\">Cavalli and Heard (2019)</xref>, epigenetics refers to “the study of molecules and mechanisms that perpetuate alternative gene activity states in the context of the same DNA sequence” (<xref rid=\"B21\" ref-type=\"bibr\">Cavalli and Heard, 2019</xref>, p. 489). The activity of genomes is deeply impacted by environmental and lifestyle factors that interface with the genetic information. Epigenetic mechanisms do not change the DNA sequence; instead, they generate different and interchangeable structural states that modify gene activity. These mechanisms include interdependent modifications of DNA and of chromatin, the histone protein structure that compacts DNA in the cell nucleus. These modifications can be grouped into three main categories: DNA methylation, modifications of histones, and small non-coding RNAs. Placement of these epigenetic marks is spatially and temporally controlled and exerts gene-expression regulatory functions. For instance, the addition of methyl groups to cytosines causes the condensation of chromatin, which renders the DNA unavailable to the transcriptional machinery leading to gene silencing. Similarly, acetylation and deacetylation of histones loosen and tighten chromatin, respectively, thus creating “open” or “closed” domains of gene activity along the genome. Finally, microRNAs can control the stability and access of mRNAs to the translation machinery, with an impact on protein production (<xref rid=\"B21\" ref-type=\"bibr\">Cavalli and Heard, 2019</xref>).</p><p>Mindful practices have long been suggested to promote well-being by producing a state of body relaxation and inner silence, i.e., a state of quiet mind and emotions characterized by the absence of recurring thoughts, images, and emotional fluctuations (<xref rid=\"B5\" ref-type=\"bibr\">Ben-Soussan et al., 2019</xref>). Inner silence therefore counteracts the negative effects of the increasing environmental noise reported by the <xref rid=\"B89\" ref-type=\"bibr\">World Health Organization (2011)</xref>. Consequently, a wide variety of mindful practices stemming from oriental traditions have been introduced in western societies following the impelling demand to increase self-awareness, improve health, and ameliorate the quality of daily life. These practices include a spectrum of meditations, both sitting (i.e., mindfulness meditation, Vipassana, breathing attention) and moving (Yoga, Tai Chi, and Quadrato Motor Training), all of which share the common goal of achieving a state of silence of mind, with positive repercussions on emotional regulation and health. The evidence for their effectiveness is growing and promising mind–body practices are emerging as complementary to more conventional therapeutic interventions. In fact, a considerable amount of literature suggests that mind–body activities can alleviate stress-dependent symptoms of various diseases including psychological disorders (e.g., mood and anxiety disorders), inflammatory diseases, aging, and cancer (<xref rid=\"B1\" ref-type=\"bibr\">Abbott and Lavretsky, 2013</xref>; <xref rid=\"B14\" ref-type=\"bibr\">Bower et al., 2015</xref>; <xref rid=\"B25\" ref-type=\"bibr\">Chételat et al., 2018</xref>). However, although it is likely that related outcomes might be mediated by changes in the levels of some humoral, immune, and neurological factors, the molecular mechanisms underlying the benefits of mind–body interventions remain poorly understood. The analysis of human peripheral tissues (e.g., blood and saliva) has started to show that various types of meditation can reduce levels of the stress hormone cortisol and of reactive oxygen species (ROS), as well as stimulate anti-inflammatory cytokines, endorphins, and neurotrophins (<xref rid=\"B50\" ref-type=\"bibr\">Kasala et al., 2014</xref>; <xref rid=\"B68\" ref-type=\"bibr\">Pascoe et al., 2017</xref>). In addition, some authors have traced the effect of meditation on such effector molecules back to expression changes of the corresponding genes and, more recently, to specific mechanisms that regulate gene expression (<xref rid=\"B16\" ref-type=\"bibr\">Buric et al., 2017</xref>; <xref rid=\"B48\" ref-type=\"bibr\">Kaliman, 2019</xref>). The above observations raise the intriguing idea that mindful practices influence the body by means of epigenetics. However, at present, because of the relative novelty of the field, a unifying view of the molecular pathways underlying the benefits conveyed by meditation and a direct correlation between inner silence and specific epigenetic signatures is still lacking. In the current mini-review, we intend to give a summary of the most recent advances in the field of molecular and epigenetic effects of inner silence-inducing activities.</p></sec><sec id=\"S2\"><title>Epigenetics, Stress, and the Importance of Mindful Activities</title><p>During the past 15 years, many studies have correlated alterations of epigenetic marks with conditions of physiological and psychological stress (<xref rid=\"B80\" ref-type=\"bibr\">Szyf, 2012</xref>; <xref rid=\"B70\" ref-type=\"bibr\">Provençal et al., 2013</xref>; <xref rid=\"B83\" ref-type=\"bibr\">Turecki and Meaney, 2016</xref>). Altered profiles of whole-genome DNA methylation were found in brain specimens associated with early-life adverse experiences and in different conditions, such as post-traumatic stress disorder (PTSD; <xref rid=\"B77\" ref-type=\"bibr\">Roth et al., 2011</xref>; <xref rid=\"B76\" ref-type=\"bibr\">Roth and Sweatt, 2011</xref>) and altered parental care (<xref rid=\"B88\" ref-type=\"bibr\">Weaver et al., 2004</xref>; <xref rid=\"B62\" ref-type=\"bibr\">Naumova et al., 2012</xref>) in animal models, and mood disorders in humans (<xref rid=\"B59\" ref-type=\"bibr\">McGowan and Kato, 2008</xref>). In addition, alterations of histone acetylation profiles and of small non-coding RNAs activity have been found in depression (reviewed in <xref rid=\"B60\" ref-type=\"bibr\">Misztak et al., 2018</xref>; <xref rid=\"B90\" ref-type=\"bibr\">Yuan et al., 2018</xref>, respectively). Genes affected by differential epigenetic regulation range from modulators of the immune response (i.e., cytokines; <xref rid=\"B70\" ref-type=\"bibr\">Provençal et al., 2013</xref>) and glucocorticoids in the hypothalamic–pituitary–adrenal (HPA) axis (<xref rid=\"B85\" ref-type=\"bibr\">Tyrka et al., 2016</xref>; <xref rid=\"B2\" ref-type=\"bibr\">Argentieri et al., 2017</xref>) to neurotrophins such as brain-derived neurotrophic factor (BDNF) and nerve growth factor (NGF) involved in neuroplasticity, learning, and memory (<xref rid=\"B76\" ref-type=\"bibr\">Roth and Sweatt, 2011</xref>).</p><p>One key feature of epigenetic information is its potential reversibility. For example, interventions aimed at improving life conditions and behavior (i.e., education, exercise, diet, sleep) were associated with changes in DNA methylation profiles (<xref rid=\"B62\" ref-type=\"bibr\">Naumova et al., 2012</xref>; <xref rid=\"B72\" ref-type=\"bibr\">Quach et al., 2017</xref>). Early studies of environmental enrichment in rodents (i.e., a combination of multisensory/cognitive stimulation and increased physical activity that enhances social interactions and explorative behavior in mice) showed positive association with improved social behavior and coping with stress, elicited by increased histone acetylation in the hippocampus and neocortex as well as increased production of BDNF (<xref rid=\"B4\" ref-type=\"bibr\">Baroncelli et al., 2010</xref>). Moreover, environmental enrichment was associated with decreased DNA methylation at glucocorticoid receptor gene promoter (<xref rid=\"B38\" ref-type=\"bibr\">Gapp et al., 2016</xref>) and microRNA-mediated up-regulation of BDNF (<xref rid=\"B58\" ref-type=\"bibr\">McCreary et al., 2016</xref>).</p><p>In humans, in addition to the beneficial effects of an appropriate lifestyle as reported above, mental and emotional health can be ameliorated by the experience of inner silence, which induces a state of equanimity and leads to improved attention and increased relaxation. To this end, many types of meditation techniques that help reach awareness and reflectivity have been proven to be highly effective. It is therefore of great relevance to investigate whether these practices could reproduce in humans the epigenetic effects elicited by environmental enrichment in rodents and, more importantly, whether it would be possible to identify a specific epigenetic signature for the state of inner silence. In fact, identification of meditation-induced epigenetic marks on the genome may provide critical information on epigenetically modified genes and pathways underlying the association between meditation and mental/emotional health amelioration and may help uncover new targets for therapeutic intervention.</p><sec id=\"S2.SS1\"><title>Molecular and Epigenetic Effects of Movement Meditations</title><sec id=\"S2.SS1.SSS1\"><title>Yoga, Tai Chi, and Qigong</title><p>Yoga, Tai Chi, and Qigong can improve attention, self-control, and mindfulness by helping to achieve inner silence through movement. Several studies have addressed the physiological effects of these practices at the molecular level.</p><p>Yoga was shown to improve the redox state of the body by reducing ROS levels, known to cause inflammation and accelerated aging (<xref rid=\"B27\" ref-type=\"bibr\">Dada et al., 2015</xref>; <xref rid=\"B61\" ref-type=\"bibr\">Mohammad et al., 2019</xref>). Moreover, Yoga can help cope with stress conditions as shown by reduction of serum cortisol <italic>via</italic> the HPA axis (<xref rid=\"B82\" ref-type=\"bibr\">Tolahunase et al., 2017</xref>). However, some authors consider the Cortisol Awakening Response (CAR) a more appropriate measure of stress resilience. In the study by <xref rid=\"B17\" ref-type=\"bibr\">Cahn et al. (2017)</xref>, CAR appeared significantly increased after training. Importantly BDNF, a central regulator of neuroplasticity, was also found to increase following practice both in healthy (<xref rid=\"B17\" ref-type=\"bibr\">Cahn et al., 2017</xref>; <xref rid=\"B82\" ref-type=\"bibr\">Tolahunase et al., 2017</xref>) and depressed (<xref rid=\"B63\" ref-type=\"bibr\">Naveen et al., 2013</xref>, <xref rid=\"B64\" ref-type=\"bibr\">2016</xref>) subjects. Since BDNF can cross the blood–brain barrier, it is possible to assume that peripheral BDNF levels may reflect those in the brain (<xref rid=\"B20\" ref-type=\"bibr\">Cattaneo et al., 2016</xref>). These results suggest that Yoga may counteract neurodegenerative processes triggered by various types of stress by reducing cellular aging and preserving neuroplasticity in the brain. Finally, reduced plasma cortisol levels and increased BDNF were associated with cardiovascular health (<xref rid=\"B65\" ref-type=\"bibr\">Pal et al., 2014</xref>). Similarly, the practice of Tai Chi and Qigong was shown to improve immune function by reducing plasma inflammatory cytokines (<xref rid=\"B18\" ref-type=\"bibr\">Campo et al., 2015</xref>). Moreover, increased blood levels of endorphins and reduced levels of adrenocorticotropic hormone (ACTH) and cortisol (<xref rid=\"B78\" ref-type=\"bibr\">Ryu et al., 1996</xref>; <xref rid=\"B56\" ref-type=\"bibr\">Lee et al., 2004</xref>) indicate a positive impact on the HPA axis.</p><p>Genome-wide approaches to gene activity, using both plasma and blood samples, have started to be employed to elucidate the effects of mind–body activities (reviewed in <xref rid=\"B16\" ref-type=\"bibr\">Buric et al., 2017</xref>) on gene modulation. The first microarray analysis of global mRNAs was carried out in a cross-sectional pilot study of Qigong long-term practitioners. Results showed modulation of several sets of genes having common functions, related to enhanced immunity, lower cellular metabolism, and delayed cell death (<xref rid=\"B57\" ref-type=\"bibr\">Li et al., 2005</xref>). Subsequent longitudinal observations revealed that Yoga was able to induce rapid gene expression changes in PBMCs (peripheral blood mononuclear cells, <xref rid=\"B71\" ref-type=\"bibr\">Qu et al., 2013</xref>). Similarly, differential expression of genes related to type I interferon response and inflammation was reported following daily Yoga in a population of breast cancer survivors (<xref rid=\"B15\" ref-type=\"bibr\">Bower et al., 2014</xref>). In addition, a number of microarray studies revealed transcription profile changes following Tai Chi, involving pathways of inflammation, antiviral response, energy and adrenergic activation, in PBMCs (<xref rid=\"B45\" ref-type=\"bibr\">Irwin et al., 2014</xref>, <xref rid=\"B46\" ref-type=\"bibr\">2015</xref>; <xref rid=\"B51\" ref-type=\"bibr\">Kinney et al., 2019</xref>).</p><p>While differential gene expression and protein levels appear, in most cases, correlated and coherent with the observed biological effects, the mechanisms involved are mostly unknown. However, the involvement of epigenetic regulation seems to be the most likely scenario. Nevertheless, research on epigenetic profiles following mindful movement practices is rare, with only two published studies involving Yoga and Tai Chi practitioners. DNA methylation occurs predominantly at CpG sites located at promoter regions that are known to undergo age-related changes (<xref rid=\"B36\" ref-type=\"bibr\">Fraga et al., 2005</xref>; <xref rid=\"B40\" ref-type=\"bibr\">Hannum et al., 2013</xref>). The first cross-sectional study, by <xref rid=\"B74\" ref-type=\"bibr\">Ren et al. (2012)</xref>, analyzed the epigenetic effects of Tai Chi on the methylation of 66 sites using saliva samples from experienced practitioners. They observed that 6 CpG sites on different chromosomes showed significant differences between the trainees and controls. Interestingly, these authors reported that the age-related methylation trend at those CpGs was slower in the Tai Chi group <italic>vs</italic> the controls. Since the age-related decline of DNA methylation reflects the gradual deterioration of important regulatory functions of the genome, the authors speculated that the practice of Tai Chi might protect against the age-related decay of the epigenome. Subsequently, a similar cross-sectional study was conducted by <xref rid=\"B41\" ref-type=\"bibr\">Harkess et al. (2016)</xref> who focused on the CpG methylation levels of candidate genes involved in immune function, namely, TNF, IL-6, and CRP, in blood samples of chronically stressed women practicing Yoga compared to a waitlist group. Their main result was that Yoga was associated with hypomethylation of the TNF gene while IL-6 and CRP appeared unaffected. Interestingly, the same result was observed when the waitlist group that later participated in the Yoga intervention was analyzed. However, because of the pilot approach of these studies, further research is required to validate the link between DNA methylation and these practices.</p></sec><sec id=\"S2.SS1.SSS2\"><title>Quadrato Motor Training</title><p>Here, we treat Quadrato Motor Training (QMT) as a separate case because its unique feature, to the best of our knowledge not present in other movement meditations (i.e., Aikido; <xref rid=\"B5\" ref-type=\"bibr\">Ben-Soussan et al., 2019</xref>), is the requirement for second-by-second response inhibition. QMT is a specifically structured walking meditation, developed with the purpose of balancing the three fundamental components of the human being – body, cognition, and emotion (<xref rid=\"B33\" ref-type=\"bibr\">Dotan Ben-Soussan et al., 2013</xref>; <xref rid=\"B67\" ref-type=\"bibr\">Paoletti et al., 2017</xref>). QMT consists of a set of recorded oral instructions that guide the individual to move within the corners of a 50 × 50 cm square drawn on the floor, by making 12 possible movements. QMT was shown to increase attention, reflectivity and creativity, and these outcomes were correlated with enhanced neuroplasticity as detailed below (<xref rid=\"B33\" ref-type=\"bibr\">Dotan Ben-Soussan et al., 2013</xref>; <xref rid=\"B6\" ref-type=\"bibr\">Ben-Soussan et al., 2014</xref>). Reflectivity is the capacity to exercise introspection consciously and requires suspension from the habitual thought, inward attention, and receptivity toward the experience (<xref rid=\"B31\" ref-type=\"bibr\">Depraz et al., 2000</xref>). Each step taken in the QMT square is followed by a time of waiting for the next recorded instruction, obliging the subject to suspend the tendency for habitual movement (response inhibition). Therefore, the practice requires and reinforces a state of sustained divided attention between the cognitive processing and the motor response, to take the correct direction at each step. During the period of silent waiting between two movements, this unique type of attention brings a state of awareness into the experience of movement itself. Therefore, QMT can be conceived as a form of “mindful movement” (<xref rid=\"B6\" ref-type=\"bibr\">Ben-Soussan et al., 2014</xref>, <xref rid=\"B5\" ref-type=\"bibr\">2019</xref>; <xref rid=\"B30\" ref-type=\"bibr\">De Fano et al., 2019</xref>; <xref rid=\"B32\" ref-type=\"bibr\">Diamond and Ling, 2019</xref>).</p><p>Electrophysiological and magnetic resonance studies have suggested that QMT can improve reflectivity and creativity by stimulating neuroplasticity processes leading to increased inter- and intra-hemispheric connectivity (<xref rid=\"B33\" ref-type=\"bibr\">Dotan Ben-Soussan et al., 2013</xref>; <xref rid=\"B6\" ref-type=\"bibr\">Ben-Soussan et al., 2014</xref>, <xref rid=\"B7\" ref-type=\"bibr\">2015a</xref>; <xref rid=\"B54\" ref-type=\"bibr\">Lasaponara et al., 2017</xref>; <xref rid=\"B69\" ref-type=\"bibr\">Piervincenzi et al., 2017</xref>). Subsequent studies have suggested a link between the QMT-driven neuroplasticity processes and changes in the salivary levels of proBDNF and proNGF (<xref rid=\"B8\" ref-type=\"bibr\">Ben-Soussan et al., 2015b</xref>; <xref rid=\"B86\" ref-type=\"bibr\">Venditti et al., 2015</xref>; <xref rid=\"B19\" ref-type=\"bibr\">Caserta et al., 2019</xref>). In fact, proNGF was found to decrease following 1 month of practice in both adults and children, in association with an improvement of cognitive and metacognitive functions, such as creativity (<xref rid=\"B86\" ref-type=\"bibr\">Venditti et al., 2015</xref>). A parallel pilot study reported increased proBDNF following 3 months of practice, and this variation was correlated with an increase in white matter volume in several brain areas, including the corpus callosum, suggesting improved inter-hemispheric connectivity (<xref rid=\"B8\" ref-type=\"bibr\">Ben-Soussan et al., 2015b</xref>). A more recent study comparing levels of proBDNF and proNGF showed significantly correlated increases of both neurotrophins after 3 months of practice (<xref rid=\"B19\" ref-type=\"bibr\">Caserta et al., 2019</xref>). It is conceivable that these changes represent the link between the cognitive and psychological outcomes and the ongoing QMT-driven increased neuroplasticity. Further studies are now being carried out to answer the question whether these changes reflect differential expression and/or variations of DNA methylation of the corresponding genes.</p></sec></sec><sec id=\"S2.SS2\"><title>Effects of Sitting Meditations</title><p>There are multiple approaches to meditation, including Transcendental Meditation (TM), Zen meditation, Vipassana, Buddhist meditation, Sudarshan Kriya (SK), Kirtan Kriya (KK), Pranayama, and others. Notwithstanding this variety, they share the common goal of achieving mindfulness, a state of moment-to-moment non-judgmental awareness of the actual experience, possibly reached through a state of inner silence.</p><p>The abovementioned meditations have been widely studied from the molecular point of view. They influence the levels of several metabolites and biomarkers like hormones and neurotransmitters, as well as immune and neuroendocrine factors affected by stress and relevant to disease development and progression (<xref rid=\"B29\" ref-type=\"bibr\">Daube and Jakobsche, 2015</xref>; <xref rid=\"B75\" ref-type=\"bibr\">Robert-McComb et al., 2015</xref>; <xref rid=\"B84\" ref-type=\"bibr\">Twal et al., 2016</xref>; <xref rid=\"B43\" ref-type=\"bibr\">Househam et al., 2017</xref>). For example, TM, SK, and Zen meditation were shown to influence levels of cortisol, serotonin, melatonin, epinephrine and norepinephrine, gamma amino butyric acid (GABA), glutamate, and dehydroepiandrosterone (DHEA; reviewed in <xref rid=\"B50\" ref-type=\"bibr\">Kasala et al., 2014</xref>). Nevertheless, although several imaging studies demonstrated that meditation promotes neurogenesis in brain areas involved in cognitive and emotional functions (<xref rid=\"B81\" ref-type=\"bibr\">Tang and Posner, 2014</xref>; <xref rid=\"B55\" ref-type=\"bibr\">Lee et al., 2018</xref>), studies of neurotrophin levels following meditation are scarce. One report shows increased serum BDNF levels, related to antidepressant activity in subjects practicing SK (<xref rid=\"B66\" ref-type=\"bibr\">Pan et al., 2006</xref>). Another study reports on increased salivary NGF following Pranayama (<xref rid=\"B3\" ref-type=\"bibr\">Balasubramanian et al., 2015</xref>).</p><p>Cross-sectional and longitudinal studies of SK, Pranayama, and KK, using mostly blood samples, in both healthy and clinical populations, found transcriptional changes in common pathways involved in oxidative stress, cell death, aging, cell cycle regulation, and immune response (<xref rid=\"B79\" ref-type=\"bibr\">Sharma et al., 2008</xref>; <xref rid=\"B52\" ref-type=\"bibr\">Kumar and Balkrishna, 2009</xref>; <xref rid=\"B13\" ref-type=\"bibr\">Black et al., 2013</xref>). One interesting case study involving two lifelong expert meditators, able to achieve higher states of consciousness, again revealed differential expression of genes involved in metabolism and cell cycle regulation, immune response, stress response, and cell death (<xref rid=\"B73\" ref-type=\"bibr\">Ravnik-Glavač et al., 2012</xref>).</p><p>Few epigenetic studies involving experienced meditators were conducted to evaluate possible changes in genome-wide DNA methylation profiling at CpG sites. In the first study, <xref rid=\"B22\" ref-type=\"bibr\">Chaix et al. (2017)</xref>, using PBMCs, analyzed 353 CpG sites whose methylation level is highly correlated with chronological age across tissues and cell types and represent a measure of epigenetic age (DNAm age; <xref rid=\"B44\" ref-type=\"bibr\">Hovarth, 2013</xref>). The deviation between the DNAm age and the chronological age provides information regarding the epigenetic aging rate of an individual (<xref rid=\"B24\" ref-type=\"bibr\">Chen et al., 2016</xref>). The study, focused on subjects practicing mindfulness and compassion meditation, revealed that the epigenetic aging rate in meditators is significantly decreased as a function of the practice duration (<xref rid=\"B22\" ref-type=\"bibr\">Chaix et al., 2017</xref>). In a follow up of this study, the same authors showed that short meditation interventions can influence the methylome of experienced meditators rapidly, at genes associated with immune metabolism and aging (<xref rid=\"B23\" ref-type=\"bibr\">Chaix et al., 2020</xref>). The second methylomic approach, by <xref rid=\"B39\" ref-type=\"bibr\">García-Campayo et al. (2018)</xref>, compared the methylation profiles obtained from circulating lymphocytes of experienced meditators with more than 10 years of experience with those of meditation-naïve subjects, and identified 64 differentially methylated regions, corresponding to 43 genes involved in glucose homeostasis, lipid metabolism, protein folding, neurotransmission, and modulation of inflammatory pathways.</p></sec><sec id=\"S2.SS3\"><title>Effects of Multiple-Meditation Protocols: Mindfulness-Based Stress Reduction</title><p>Following the introduction of meditation in Western countries, many combined protocols of mindful practices have been developed and introduced in several clinical contexts. The first ones were the Relaxation Response (RR, <xref rid=\"B87\" ref-type=\"bibr\">Wallace et al., 1971</xref>) and the Mindfulness-Based Stress Reduction (MBSR, <xref rid=\"B47\" ref-type=\"bibr\">Kabat-Zinn, 1982</xref>). This latter is an 8-week integrated approach, an amalgam of mindfulness meditation, concentrative meditation, breathing exercises, Yoga, autogenic training, and Buddhist philosophy. In recent years many such protocols, combining different techniques to be applied for defined periods of time to various clinical settings, have been developed under various acronyms, such as Mindful Awareness Practices (MAP; <xref rid=\"B15\" ref-type=\"bibr\">Bower et al., 2014</xref>), Mindfulness-Based Movement (MBM; <xref rid=\"B75\" ref-type=\"bibr\">Robert-McComb et al., 2015</xref>) and more generally standardized as Mindfulness-Based Interventions (MBI; <xref rid=\"B12\" ref-type=\"bibr\">Black and Slavich, 2016</xref>). All protocols have become the subject of psychological, physiological, and molecular investigations (<xref rid=\"B12\" ref-type=\"bibr\">Black and Slavich, 2016</xref>). Neurotrophins were studied in only a few cases (i.e., BDNF; <xref rid=\"B28\" ref-type=\"bibr\">Dada et al., 2018</xref>; <xref rid=\"B37\" ref-type=\"bibr\">Gagrani et al., 2018</xref>). Transcriptomic analyses were performed in several longitudinal and mixed design studies in both healthy and clinical populations combining diverse MBI activities (<xref rid=\"B34\" ref-type=\"bibr\">Dusek et al., 2008</xref>; <xref rid=\"B10\" ref-type=\"bibr\">Bhasin et al., 2013</xref>; <xref rid=\"B15\" ref-type=\"bibr\">Bower et al., 2014</xref>; <xref rid=\"B53\" ref-type=\"bibr\">Kuo et al., 2015</xref>; <xref rid=\"B35\" ref-type=\"bibr\">Epel et al., 2016</xref>). Results showed that, in both long and short-term practitioners, differential transcription occurs in genes involved in metabolism, inflammatory processes, oxidative stress, and DNA damage response. In most cases, these results were correlated with reduced stress and fatigue, decreased depression symptoms, and improved immune response. A few studies dedicated to the transcriptomic analysis of subjects involved in MBSR protocols obtained similar results (<xref rid=\"B26\" ref-type=\"bibr\">Creswell et al., 2012</xref>; <xref rid=\"B42\" ref-type=\"bibr\">Ho et al., 2016</xref>).</p><p>MBSR was also the subject of two epigenetic studies, one focused on histone acetylation levels following the last day-long session of the MBSR program (<xref rid=\"B49\" ref-type=\"bibr\">Kaliman et al., 2014</xref>), and the other focused on the CpG methylation of two specific genes (SLC6A4 and FKBP5) as potential biomarkers for depression (<xref rid=\"B11\" ref-type=\"bibr\">Bishop et al., 2018</xref>). Kaliman and collaborators showed lower expression of histone deacetylase genes (HDAC 2, 3, and 9) in PBMCs, as well as alterations of global histone H4 acetylation levels, and proposed that the reduction of HDAC expression may represent a potential therapeutic effect of MBSR in depression. Bishop and colleagues reported hypomethylation of the FKBP5 gene in subjects with PTSD responding to the MBSR intervention compared to non-responders who, in contrast, showed increased methylation. These authors speculated that FKBP5 methylation could be a predictive biomarker of the response to MBSR in PTSD.</p></sec></sec><sec id=\"S3\"><title>Conclusion and Perspectives</title><p>Growing evidence suggests that epigenetic changes are a key mechanism by which a stressful environment acts on the genome, causing stable changes in gene expression and in behavior that can mediate maladaptive responses. On the other end, the voluntary practice of meditation can be considered a form of environmental enrichment, equivalent to positive external stimulation. Hence, it appears fundamental to understand whether meditation can elicit epigenetic events able to prevent disease and promote health. Relevant examples of stress-related targets of epigenetic deregulation are genes involved in glucocorticoid signaling, serotonergic signaling, and neurotrophins. Surprisingly, meditation practices seem to act on the same gene targets, such as FKBP5, SLC6A4, and BDNF, and promote endocrinal, neuronal, and behavioral functions. This suggests that the achievement of a state of inner silence through the practice of meditation can prevent or reverse the detrimental effects of a stressful environment (see <xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). However, it is unclear whether stress and meditation act antagonistically on shared epigenetic mechanisms and, because of the relative novelty of the field, molecular and epigenetic evidence of the effects of mindful activities is still not sufficient to demonstrate a cause–effect relationship. It is conceivable that, by improving the immune system, metabolism, and stress–response pathways, and by promoting neuroplasticity, meditations of several kinds could affect mechanisms of energy saving, promote homeostasis, and potentiate the reciprocal mind and body’s relaxation abilities, with a positive impact on psychology.</p><table-wrap id=\"T1\" position=\"float\"><label>TABLE 1</label><caption><p>Side-by-side summary of the effects of different meditations.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" colspan=\"4\" rowspan=\"1\">Moving meditations<hr/></td><td valign=\"top\" align=\"center\" colspan=\"2\" rowspan=\"1\">Sitting meditations<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Multiple meditation protocols<hr/></td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Yoga</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Tai Chi</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Qigong</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">QMT</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">TM/SK/KK Pranayama</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mindfulness</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MBSR</td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Epigenetics</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↓ DNAmet at CpGs of TNF gene</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Slower age-related DNAmet at CpGs.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">N/A</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">N/A</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">N/A</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↓ DNAm age at CpGs ≠ DMR</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↓HDAC 2,3, 9 ↑DNA met at FKBP5 gene</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CS, Blood</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CS Saliva</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">L, CS PBMCs Lymphocytes</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">L, CS PBMCs,</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B41\" ref-type=\"bibr\">Harkess et al., 2016</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B74\" ref-type=\"bibr\">Ren et al., 2012</xref></td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B22\" ref-type=\"bibr\">Chaix et al., 2017</xref>, <xref rid=\"B23\" ref-type=\"bibr\">2020</xref>; <xref rid=\"B39\" ref-type=\"bibr\">García-Campayo et al., 2018</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B49\" ref-type=\"bibr\">Kaliman et al., 2014</xref>; <xref rid=\"B11\" ref-type=\"bibr\">Bishop et al., 2018</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Differential gene expression (Pathways affected)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Inflammation pathways</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Inflammation pathways, antiviral response</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↑Immunity, ↓Cell metabolism, delayed cell death,</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">N/A</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Oxidative stress, cell death, aging, cell cycle, immune response</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">N/A</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Inflammation pathways, metabolism, oxidative stress, DNA damage</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">L PBMCs</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">L PBMCs</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CS PBMCs</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">L, CS Blood</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">L, CS</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B71\" ref-type=\"bibr\">Qu et al., 2013</xref>; <xref rid=\"B15\" ref-type=\"bibr\">Bower et al., 2014</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B45\" ref-type=\"bibr\">Irwin et al., 2014</xref>, <xref rid=\"B46\" ref-type=\"bibr\">2015</xref>; <xref rid=\"B51\" ref-type=\"bibr\">Kinney et al., 2019</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B57\" ref-type=\"bibr\">Li et al., 2005</xref></td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B79\" ref-type=\"bibr\">Sharma et al., 2008</xref>; <xref rid=\"B52\" ref-type=\"bibr\">Kumar and Balkrishna, 2009</xref>; <xref rid=\"B13\" ref-type=\"bibr\">Black et al., 2013</xref></td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B26\" ref-type=\"bibr\">Creswell et al., 2012</xref>; <xref rid=\"B42\" ref-type=\"bibr\">Ho et al., 2016</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Biomolecules</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↓ROS levels ↓Cortisol ↓Inflammation markers</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↓Inflammatory cytokines ↓Cortisol</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↓ACTH ↓Cortisol ↑Endorphins</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">N/A</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↓Cortisol ↑DHEA ↑Serotonin ↑Melatonin</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Markers of inflammation, Markers of stress, Cytokines</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Markers of inflammation, Markers of stress, Cytokines</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↓Epinephrine</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↓Norepinephrine</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CS Blood</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">L Saliva</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CS, L Plasma</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">L, CS Various fluids</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">L, CS Various fluids</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">L, CS Various fluids</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Reviewed in: <xref rid=\"B27\" ref-type=\"bibr\">Dada et al., 2015</xref>; <xref rid=\"B61\" ref-type=\"bibr\">Mohammad et al., 2019</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B18\" ref-type=\"bibr\">Campo et al., 2015</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B78\" ref-type=\"bibr\">Ryu et al., 1996</xref>; <xref rid=\"B56\" ref-type=\"bibr\">Lee et al., 2004</xref></td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Reviewed in: <xref rid=\"B29\" ref-type=\"bibr\">Daube and Jakobsche, 2015</xref><xref rid=\"B50\" ref-type=\"bibr\">Kasala et al., 2014</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Reviewed in <xref rid=\"B12\" ref-type=\"bibr\">Black and Slavich, 2016</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Reviewed in <xref rid=\"B12\" ref-type=\"bibr\">Black and Slavich, 2016</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Neurotrophins</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↑BDNF</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">N/A</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">N/A</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↑BDNF (3 mo.) ↓NGF (1 mo.) ↑NGF (3 mo.)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↑BDNF ↑NGF</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">N/A</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↑BDNF</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">L Serum <xref rid=\"B63\" ref-type=\"bibr\">Naveen et al., 2013</xref>, <xref rid=\"B64\" ref-type=\"bibr\">2016</xref>; <xref rid=\"B17\" ref-type=\"bibr\">Cahn et al., 2017</xref>; <xref rid=\"B82\" ref-type=\"bibr\">Tolahunase et al., 2017</xref></td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">L Saliva <xref rid=\"B8\" ref-type=\"bibr\">Ben-Soussan et al., 2015b</xref>; <xref rid=\"B86\" ref-type=\"bibr\">Venditti et al., 2015</xref>; <xref rid=\"B19\" ref-type=\"bibr\">Caserta et al., 2019</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CS, L Saliva, Serum <xref rid=\"B66\" ref-type=\"bibr\">Pan et al., 2006</xref> (SK/KK) <xref rid=\"B3\" ref-type=\"bibr\">Balasubramanian et al., 2015</xref> (Pranayama)</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CS Serum <xref rid=\"B28\" ref-type=\"bibr\">Dada et al., 2018</xref>; <xref rid=\"B37\" ref-type=\"bibr\">Gagrani et al., 2018</xref></td></tr></tbody></table><table-wrap-foot><attrib><italic>MBSR, Mindfulness-based stress reduction; QMT, Quadrato Motor Training; TM, Transcendental Meditation; SK, Sudarshan Kriya; KK, Kirtan Kriya; L, Longitudinal; CS, Cross-sectional; DMR, Differentially methylated regions; HDAC, Histone deacetylase; PBMC, Peripheral blood mononuclear cell; ROS, Reactive oxygen species.</italic></attrib></table-wrap-foot></table-wrap><p>We are still far from identifying specific epigenetic markers associated with the state of inner silence, but the pioneering studies conducted so far suggest that this possibility deserves to be further explored. More epigenetics-focused studies will be necessary to understand the mechanistic details of meditative techniques. An increased, thorough understanding of these techniques and their molecular and epigenetic bases will bring us closer to the possibility of introducing them as non-pharmacological approaches to stress-related diseases and psychological disorders.</p></sec><sec id=\"S4\"><title>Author Contributions</title><p>SV wrote the manuscript. MZ, AR, VV, MC, and LV read and revised the manuscript. All authors contributed to the article and approved the submitted version.</p></sec><sec id=\"conf1\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> This work was supported by the European Union’s Seventh Framework Program (Grant No: HEALTH-F4-2008-200880 MARK-AGE), granted to MZ.</p></fn></fn-group><ack><p>We are grateful to Dr. Barbara Xella for reading the manuscript and for the careful revision of the language.</p></ack><ref-list><title>References</title><ref id=\"B1\"><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Abbott</surname><given-names>R. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Oncol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Oncol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Oncol.</journal-id><journal-title-group><journal-title>Frontiers in Oncology</journal-title></journal-title-group><issn pub-type=\"epub\">2234-943X</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32850456</article-id><article-id pub-id-type=\"pmc\">PMC7431951</article-id><article-id pub-id-type=\"doi\">10.3389/fonc.2020.01414</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Oncology</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>Supraclavicular Recurrence in Completely Resected (y)pN2 Non-Small Cell Lung Cancer: Implications for Postoperative Radiotherapy</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Liu</surname><given-names>Liang</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"author-notes\" rid=\"fn002\"><sup>†</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Zheng</surname><given-names>Zhiqin</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><xref ref-type=\"author-notes\" rid=\"fn002\"><sup>†</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1006052/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Li</surname><given-names>Juan</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Li</surname><given-names>Yuan</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Ni</surname><given-names>Jianjiao</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/978524/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Department of Radiation Oncology, Fudan University Shanghai Cancer Center</institution>, <addr-line>Shanghai</addr-line>, <country>China</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Department of Oncology, Shanghai Medical College, Fudan University</institution>, <addr-line>Shanghai</addr-line>, <country>China</country></aff><aff id=\"aff3\"><sup>3</sup><institution>Department of Radiation Oncology, Minhang Branch Hospital, Fudan University Shanghai Cancer Center</institution>, <addr-line>Shanghai</addr-line>, <country>China</country></aff><aff id=\"aff4\"><sup>4</sup><institution>Department of Pathology, Fudan University Shanghai Cancer Center</institution>, <addr-line>Shanghai</addr-line>, <country>China</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Stephen V. Liu, Georgetown University Medical Center, United States</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Tim Kruser, Northwestern Medicine, United States; Heloisa De Andrade Carvalho, University of São Paulo, Brazil</p></fn><corresp id=\"c001\">*Correspondence: Jianjiao Ni <email>nijianjiao8@sina.com</email></corresp><fn fn-type=\"other\" id=\"fn001\"><p>This article was submitted to Thoracic Oncology, a section of the journal Frontiers in Oncology</p></fn><fn fn-type=\"other\" id=\"fn002\"><p>†These authors have contributed equally to this work</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>10</volume><elocation-id>1414</elocation-id><history><date date-type=\"received\"><day>21</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>06</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Liu, Zheng, Li, Li and Ni.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Liu, Zheng, Li, Li and Ni</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p><bold>Background:</bold> The clinical value and delineation of clinical target volume (CTV) of postoperative radiotherapy (PORT) in completely resected (y)pN2 non-small cell lung cancer (NSCLC) remain controversial. Investigations specifically focusing on the cumulative incidence and prognostic significance of initial disease recurrence at the supraclavicular region (SCR) in this disease population are seldom reported.</p><p><bold>Methods:</bold> Consecutive patients with curatively resected (y)pN2 NSCLC who received adjuvant chemotherapy from January 2013 to December 2018 at our cancer center were retrospectively examined. Disease recurrence at the surgical margin, ipsilateral hilum, and/or mediastinum was defined as loco-regional recurrence (LRR). Disease recurrence beyond LRR and SCR, was defined as distant metastasis (DM). Overall survival (OS1 and OS2) were calculated from surgery and disease recurrence to death of any cause, in the entire cohort and in patients with recurrent disease, respectively.</p><p><bold>Results:</bold> Among the 311 patients enrolled, PORT without elective supraclavicular nodal irradiation (ESRT) was performed in 94 patients and neoadjuvant chemotherapy was administered in 31 patients. With a median follow-up of 26 months, 203 patients developed recurrent disease, including 27 SCRs, among which 16 were without DM and 22 involved the ipsilateral supraclavicular region. The 1, 3, and 5-year cumulative incidence of SCR were 6.53, 13.0, and 24.7%, respectively. Chosen DM as a competing event, cN2, ypN2, not receiving lobectomy, and negative expression of CK7 were significantly associated with SCR using the univariate competing risk analysis, while ypN2 was identified as the only independent risk factor of SCR (<italic>p</italic> = 0.012). PORT significantly reduced LRR (<italic>p</italic> = 0.031) and prolonged OS1 (<italic>p</italic> = 0.018), but didn't impact SCR (<italic>p</italic> = 0.254). Pattern of failure analyses indicated that the majority of LRRs developed within the actuarial or virtual CTV of PORT, and 15 of the 22 ipsilateral SCRs could be covered by the virtual CTV of proposed ESRT. In terms of OS2, patients who developed SCR but without DM had intermediate prognosis, compared with those who had DM (<italic>p</italic> = 0.009) and those who had only LRR (<italic>p</italic> = 0.048).</p><p><bold>Conclusions:</bold> SCR is not uncommon and has important prognostic significance in completely resected (y)pN2 NSCLC. The clinical value of PORT and ESRT in such patients need to be further investigated.</p></abstract><kwd-group><kwd>supraclavicular recurrence</kwd><kwd>postoperative radiotherapy</kwd><kwd>non-small cell lung cancer</kwd><kwd>overall survival</kwd><kwd>clinical target volume</kwd></kwd-group><funding-group><award-group><funding-source id=\"cn001\">National Natural Science Foundation of China<named-content content-type=\"fundref-id\">10.13039/501100001809</named-content></funding-source></award-group><award-group><funding-source id=\"cn002\">Shanghai Municipal Health and Family Planning Commission<named-content content-type=\"fundref-id\">10.13039/501100014175</named-content></funding-source></award-group></funding-group><counts><fig-count count=\"3\"/><table-count count=\"4\"/><equation-count count=\"0\"/><ref-count count=\"36\"/><page-count count=\"9\"/><word-count count=\"6618\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>Stage III non-small cell lung cancer (NSCLC) is a heterogeneous disease and surgical resection with or without neoadjuvant therapy could be carried out in selected patients (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>, <xref rid=\"B2\" ref-type=\"bibr\">2</xref>). After curative resection, disease recurrence poses a considerable threat and it has been demonstrated that platinum-based adjuvant chemotherapy could significantly reduce postoperative recurrence and improve 5-year survival (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>, <xref rid=\"B4\" ref-type=\"bibr\">4</xref>). However, although numerous retrospective studies and several population-based investigations (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>–<xref rid=\"B9\" ref-type=\"bibr\">9</xref>) have suggested a beneficial role of postoperative radiotherapy (PORT) in reducing loco-regional recurrence (LRR), prolonging disease-free survival (DFS) and even improving overall survival (OS) among patients with completely resected (y)pN2 NSCLC (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>, <xref rid=\"B10\" ref-type=\"bibr\">10</xref>, <xref rid=\"B11\" ref-type=\"bibr\">11</xref>), the clinical value of PORT is still controversial due to a lack of convincing data from large randomized clinical trials (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>, <xref rid=\"B13\" ref-type=\"bibr\">13</xref>).</p><p>Moreover, there is no definite agreement on the delineation of clinical target volume (CTV) during PORT for completely resected (y)pN2 NSCLC, and it varies between different institutions and clinical trials (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>, <xref rid=\"B15\" ref-type=\"bibr\">15</xref>). The rationales of CTV delineation are mostly based on the patterns of disease recurrence in surgical resected patients who don't receive PORT and patterns of treatment failure in those who receive PORT. In these studies, cumulative incidence, anatomic locations, and risk factors of LRR were extensively examined. However, the definitions of LRR are different, some of which include the initial disease recurrence developed in the supraclavicular region (SCR) (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>, <xref rid=\"B16\" ref-type=\"bibr\">16</xref>), while others don't (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>, <xref rid=\"B17\" ref-type=\"bibr\">17</xref>). Investigations specifically focused on SCR are seldom reported and elective supraclavicular nodal irradiation (ESRT) is not routinely performed.</p><p>In the current study, we investigated the cumulative incidence, risk factor, and prognostic significance of SCR in completely resected (y)pN2 NSCLC. Additionally, our recent study finds crucial prognostic value of routine immunohistochemical (IHC) markers in completely resected NSCLC (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>). Hence, besides common clinic-pathological variables, a list of routine IHC markers were examined when investigating the risk factors of SCR.</p></sec><sec sec-type=\"materials and methods\" id=\"s2\"><title>Materials and Methods</title><sec><title>Patients</title><p>Lung cancer patients who received surgery at Fudan University Shanghai Cancer Center (FUSCC) from January 2013 to December 2018 were retrospectively reviewed. Patients who underwent complete surgical resection (<xref rid=\"B19\" ref-type=\"bibr\">19</xref>), with pathologically confirmed N2 disease and received standard adjuvant chemotherapy, were included in the study. Patients received PORT or not, as well as neoadjuvant chemotherapy or not, were both allowed to be included. Exclusion criteria included a second primary tumor, compromised resection, positive surgical margins, neoadjuvant radiotherapy, receiving no adjuvant chemotherapy, death due to surgical complications, and postoperative follow up <3 months.</p><p>For each patient, common clinic-pathological parameters were gathered from the electronic medical records, including age, sex, smoking history, the Eastern Corporative Oncology Group (ECOG) performance score, clinical TNM stage, pathological TNM stage, primary tumor size, tumor differentiation, tumor histology, tumor location, lymphovascular invasion, visceral pleural invasion, perineural invasion, and type of surgery. Pathologic TNM stage was in accordance with the eighth edition Lung Cancer Stage Classification (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>). Tumor differentiation and tumor histology were determined on the basis of the 2015 World Health Organization Classification of Tumors of the Lung, Pleura, Thymus, and Heart (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>). Besides, the expression status of 12 IHC markers (i.e., HER2, TTF1, ERCC1, CK20, CK5/6, CK7, P63, NapsinA, Syn, RRM1, EGFR, and Ki67) were collected. The IHC staining and evaluation were routinely performed in the Immunohistochemistry Diagnostic Laboratory of our cancer center. Our study followed The Declaration of Helsinki. The institutional review board of FUSCC approved the study. Informed consent was waived by the institutional review board because this was a retrospective study.</p></sec><sec><title>Treatment</title><p>Pretreatment evaluation generally included clinical assessment, blood test, bronchoscopy, contrast-enhanced chest computed tomography (CT) scan, ultrasonographic examination or CT scan of the abdomen, brain magnetic resonance imaging (MRI) and bone scans. Patients with mediastinal lymph node enlargement (>1 cm) in the short axis on CT scan or pathologically proven to be malignant, were defined as harboring clinical N2 (cN2) disease. Of note, positron emission tomography (PET)/CT, as well as invasive staging of the mediastinum, was strongly recommended for patients with cN2 disease at our cancer center.</p><p>Neoadjuvant therapy generally consisted of 3–4 cycles of platinum-based doublet regimen and surgical treatment included lobectomy, sublobectomy, and pneumonectomy, with systematic multilevel mediastinal lymph node dissection or adequate mediastinal sampling (no <3 N2 stations, must include the subcarinal station). PORT was performed according to our institutional protocol (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>), using the intensity-modulated radiation therapy technique employing a linear accelerator with 6-MV X-rays. Briefly, the CTV for left lung cancers included the bronchial stump and 2R, 2L, 4R, 4L, 5, 6, 7, 10, and 11L lymph node stations, while the CTV for right lung cancers included the bronchial stump and 2R, 4R, 7, 10, and 11R stations. ESRT was not performed. The total radiation dose prescibed to the planning target volume (PTV) was generally 50.4 Gy, administered daily at 1.8 Gy per fraction, 5 days per week.</p></sec><sec><title>Follow Up</title><p>Follow-ups were at the discretion of the treating physicians and were generally scheduled at regular intervals: every 3 months after surgery in the first 2 years, every 6 months for the next 3 years and annually thereafter. During follow-up, blood tests, chest CT scans, and CT scans or ultrasonographic examination of abdominal and cervical regions, were routinely performed, while brain MRI and bone scans were not mandatory. Telephone calls were also implemented when necessary.</p><p>Postoperative recurrence was diagnosed considering all the evidence provided by imaging scans and pathologic confirmation. Initial disease recurrence in the supraclavicular region was defined as SCR and first relapse developed at the surgical margin, ipsilateral hilum, and/or mediastinum was considered LRR. Initial disease recurrence beyond LRR and SCR, was categorized as distant metastasis (DM).</p></sec><sec><title>Pattern of Failure Analyses</title><p>For patients with LRR, the PTVs were restored for those who received PORT and virtual PTVs were created for those who didn't receive PORT by independent radiation oncologist, according to our institutional protocol mentioned above. Meanwhile, for patients with SCR, individual virtual PTVs were created for ipsilateral ESRT (PTV-sc) by independent radiation oncologist, according to the CT atlas proposed by Lynch et al. (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>). Then, we plotted the sites of LRRs and/or SCRs, and overlaid them with restored or created PTVs. Coverage of the LRRs and SCRs by the PTVs were investigated.</p></sec><sec><title>Statistical Analyses</title><p>Recurrence free survival (RFS) was calculated from surgery to initial disease recurrence. Overall survival (OS1) was calculated from surgery to death of any cause in the entire cohort and OS2 was calculated from initial disease recurrence to death of any cause in patients with recurrent disease. Differences between clinical parameters were compared using the χ<sup>2</sup> and Fisher's exact tests. The predictors of SCR were selected using competing risk methodology and Stata version 13.1 software (StataCorp, College Station, TX, USA). The associations between clinic-pathological parameters and OS were identified using the Cox proportional hazard regression model. The hazard ratio (HR) and the 95% confidence interval (CI) were calculated using coefficients from the model. Kaplan–Meier method was used to estimate survival, and differences among groups were investigated by the log-rank test. Statistical analysis was performed using SPSS 21.0 (SPSS, Chicago, IL, USA). All assessment is considered to be significant when two-sided <italic>p</italic>-value is <0.05.</p></sec></sec><sec sec-type=\"results\" id=\"s3\"><title>Results</title><sec><title>Patients Characteristics</title><p>A total of 311 patients were finally enrolled and a flowchart for patient selection was presented in <xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure 1</xref>. Detailed baseline disease characteristics of the 311 patients were summarized in <xref rid=\"T1\" ref-type=\"table\">Table 1</xref>. The majority of patients had a histology of non-squamous NSCLC and received lobectomy. The positive rate of HER2, TTF1, ERCC1, CK20, CK5/6, CK7, P63, NapsinA, Syn, RRM1, and EGFR, was 31.8, 64.0, 39.9, 6.4, 25.4, 80.4, 38.6, 54.3, 12.5, 45.0, and 60.8%, respectively. Additionally, Ki67 ≥ 50% was detected in 43.7% of the patients. Pretreatment PET/CT was performed in 237 (76.2%) patients and invasive staging of the mediastinum was underwent in 35 (11.3%) patients. One hundred and sixty-four patients were found to have cN2 disease, among whom 148 (90.2%) patients received pretreatment PET/CT and 30 (18.3%) patients had invasive staging of the mediastinum. A total of 31 (18.9%) patients received neoadjuvant chemotherapy.</p><table-wrap id=\"T1\" position=\"float\"><label>Table 1</label><caption><p>Disease characteristics.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Variables</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Number of patients (%)</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" colspan=\"2\" rowspan=\"1\"><bold>Age at diagnosis (years)</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">    ≤ 65</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">151 (48.6)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">   >65</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">160 (51.4)</td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"2\" rowspan=\"1\"><bold>Sex</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">   Female</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">127 (40.8)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">   Male</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">184 (59.2)</td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"2\" rowspan=\"1\"><bold>Smoking history</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">   Ever smoker</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">144 (46.3)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">   Never smoker</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">167 (53.7)</td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"2\" rowspan=\"1\"><bold>ECOG performance score</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">   0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">252 (81.0)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">   1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">59 (19.0)</td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"2\" rowspan=\"1\"><bold>Clinical N stage</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">   cN0–1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">147 (47.3)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">   cN2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">164 (52.7)</td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"2\" rowspan=\"1\"><bold>Neoadjuvant chemotherapy</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">   Yes</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">31 (10.0)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">   No</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">280 (90.0)</td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"2\" rowspan=\"1\"><bold>Surgery type</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">   Sublobar</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18 (5.8)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">   Lobectomy</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">276 (88.7)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">   Pneumonectomy</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17 (5.5)</td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"2\" rowspan=\"1\"><bold>Pathological T stage</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">   pT0–2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">262 (84.2)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">   pT3–4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">49 (15.8)</td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"2\" rowspan=\"1\"><bold>Lymphovascular invasion</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">   Absent</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">150 (48.2)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">   Present</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">161 (51.8)</td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"2\" rowspan=\"1\"><bold>Visceral pleural invasion</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">   Absent</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">195 (62.7)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">   Present</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">116 (37.3)</td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"2\" rowspan=\"1\"><bold>Tumor location</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">   Left lower lobe</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">44 (14.1)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">   Left upper lobe</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">90 (29.0)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">   Right lower lobe</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">53 (17.0)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">   Right middle lobe</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">44 (14.1)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">   Right upper lobe</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">80 (25.7)</td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"2\" rowspan=\"1\"><bold>Histology</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">   Squamous cell carcinoma</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">61 (19.6)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">   Non-squamous non-small cell lung cancer</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">250 (80.4)</td></tr></tbody></table><table-wrap-foot><p><italic>ECOG, Eastern Corporative Oncology Group</italic>.</p></table-wrap-foot></table-wrap></sec><sec><title>Cumulative Incidence and Risk Factors of SCR</title><p>Post surgery, 94 patients received PORT and with a median follow up of 26 (range, 3–78) months, 203 patients developed recurrent disease, including 27 SCRs. Of note, 17 of the 27 SCRs were pathologically confirmed and the rest 10 were diagnosed by clinical assessments and radiographic findings. The 1, 3, and 5-year RFS were 56.9, 23.9, and 9.0%, in patients without PORT, respectively, and were 71.5, 42.7, and 27.4%, in patients with PORT, respectively. Among the 27 patients with SCR, 16 (59.3%) patients developed SCR without DM (<xref ref-type=\"fig\" rid=\"F1\">Figure 1A</xref>) and 22 (81.5%) patients developed SCR involving the ipsilateral supraclavicular region (<xref ref-type=\"fig\" rid=\"F1\">Figure 1B</xref>). Moreover, among the 12 patients with left-lung cancer who developed SCR, seven were ipsilateral, three bilateral, and two contralateral. Among the 15 patients with right-lung cancer who developed SCR, nine were ipsilateral, three bilateral, and three contralateral.</p><fig id=\"F1\" position=\"float\"><label>Figure 1</label><caption><p>Patterns of supraclavicular recurrence. <bold>(A)</bold> Venn diagram demonstrating the distribution of initial postoperative recurrence. <bold>(B)</bold> Pie chart demonstrating the distribution of SCR. SCR, supraclavicular recurrence; LRR, loco-regional recurrence; DM, distant metastasis.</p></caption><graphic xlink:href=\"fonc-10-01414-g0001\"/></fig><p>The 1, 3, and 5-year cumulative incidence of SCR were 6.53, 13.0, and 24.7%, respectively (<xref ref-type=\"fig\" rid=\"F2\">Figure 2A</xref>), and the dynamic of hazard ratio of SCR was presented in <xref ref-type=\"fig\" rid=\"F2\">Figure 2B</xref>. Chosen DM as a competing event, cN2 disease, ypN2, lobectomy, and CK7 were identified as significant risk factors of SCR using the univariate competing risk analysis (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>). Since there was a significant association between cN2 disease and receiving neoadjuvant chemotherapy (<italic>p</italic> <0.001, χ<sup>2</sup> test), we excluded cN2 disease and included the other three significant risk factors in the multivariate competing risk analyses. The result showed that only ypN2 were identified as an independent risk factor of SCR (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>). The 1, 3, and 5-year cumulative incidence of SCR were 24.90, 33.24, and 33.24% among ypN2 patients, respectively, and were 4.46, 10.67, and 22.69% among pN2 patients, respectively (<xref ref-type=\"fig\" rid=\"F2\">Figure 2A</xref>).</p><fig id=\"F2\" position=\"float\"><label>Figure 2</label><caption><p>Cumulative incidence and dynamics of supraclavicular recurrence. <bold>(A)</bold> Cumulative incidence of supraclavicular recurrence in the entire cohort and stratified by pathological status (ypN2 vs. pN2). <bold>(B)</bold> The dynamics of hazard ratio of supraclavicular recurrence.</p></caption><graphic xlink:href=\"fonc-10-01414-g0002\"/></fig><table-wrap id=\"T2\" position=\"float\"><label>Table 2</label><caption><p>Competing risk analyses of clinical-pathological variables associated with supraclavicular recurrence.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Variables</bold></th><th valign=\"top\" align=\"center\" colspan=\"2\" style=\"border-bottom: thin solid #000000;\" rowspan=\"1\"><bold>Univariate Analyses</bold></th><th valign=\"top\" align=\"center\" colspan=\"2\" style=\"border-bottom: thin solid #000000;\" rowspan=\"1\"><bold>Multivariate Analyses</bold></th></tr><tr><th rowspan=\"1\" colspan=\"1\"/><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>HR (95%CI)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>p</italic></bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>HR (95%CI)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>p</italic></bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Age (>65 vs. ≤ 65)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.85 (0.40–1.81)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.671</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Sex (Male vs. Female)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.23 (0.56–2.69)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.604</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Smoking (Never vs. Ever)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.77 (0.35–1.69)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.577</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ECOG (1 vs. 0)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.81 (0.62–5.26)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.279</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">cN2 (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.58 (1.09–6.11)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.031</bold></td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">pT stage (T3–4 vs. T0–2)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.72 (0.22–2.41)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.599</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">pN1 (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.83 (0.39–1.80)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.643</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Multiple levels of pN2 (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.13 (0.53–2.42)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.745</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Histology (SCC vs. Non-SCC)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.24 (0.50–3.07)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.646</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Differentiation (P vs. W/M)<xref ref-type=\"table-fn\" rid=\"TN1\"><sup>&</sup></xref></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.23 (0.57–2.65)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.603</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">LVI (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.54 (0.72–3.31)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.271</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VPI (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.09 (0.49–2.43)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.836</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PNI (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.86 (0.20–3.64)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.834</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ypN2 vs. pN2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.61 (1.89–11.22)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.001</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.32 (1.30–6.50)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.012</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Tumor Location (Left vs. Right)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.81 (0.38–1.73)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.582</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Tumor Lobe (Upper vs. Others)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.25 (0.78–5.23)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.374</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">TLN (≥16 vs. <16)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.60 (0.28–1.29)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.190</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PLN (≥3 vs. <3)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.95 (0.44–2.02)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.885</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">LNR (≥0.2 vs. <0.2)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.88 (0.84–4.19)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.123</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Surgery (Others vs. Lobectomy)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.68 (1.61–8.42)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.002</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.32 (0.76–2.39)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.319</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PORT (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.62 (0.27–1.43)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.260</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ERCC1 (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.90 (0.53–1.52)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.695</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Her2 (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.10 (0.67–1.78)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.715</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Ki67 (≥50 vs. <50%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.81 (0.84–3.88)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.129</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">TTF1 (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.64 (0.33–1.24)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.181</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CK20 (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.07 (0.67–1.70)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.784</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CK7 (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.32 (0.15–0.68)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.003</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.46 (0.19–1.13)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.090</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CK5/6 (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.24 (0.77–2.01)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.180</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">P63 (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.13 (0.66–1.93)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.651</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NapsinA (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.91 (0.50–1.64)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.754</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Syn (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.23 (0.71–2.57)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.326</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RRM1 (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.99 (0.58–1.69)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.955</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">EGFR (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.98 (0.54–1.79)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.946</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr></tbody></table><table-wrap-foot><p>HR, hazard ratios; CI, confidence intervals; SCC, squamous cell carcinoma; LVI, Lymphovascular invasion; VPI, Visceral pleural invasion; PI, perineural invasion; ECOG, the Eastern Corporative Oncology Group; TLN, total lymph node examined; PLN, positive lymph node; LNR, positive lymph node ratio; PORT, postoperative radiotherapy;</p><fn id=\"TN1\"><label>&</label><p><italic>W/M, well/moderate; P, poor. Bold values indicates statistical significant</italic>.</p></fn></table-wrap-foot></table-wrap></sec><sec><title>Pattern of Failure Analyses</title><p>In the entire cohort, 6 (6.38%) of the 94 patients who received PORT developed LRR, while 24 (11.1%) of the 217 patients who did not receive PORT developed LRR. PORT significantly reduced the risk of LRR (<xref ref-type=\"fig\" rid=\"F3\">Figure 3A</xref>). Among the six patients who received PORT and subsequently developed LRR, five developed LRR only within the PTV and the rest one developed LRR both within and outside the PTV. Among the 24 patients who did not receive PORT and subsequently developed LRR, 20 developed LRR only within the proposed PTV, three developed LRR both within and outside the proposed PTV, and the rest one developed LRR outside the proposed PTV. That patient had adenocarcinoma in the middle lobe of right lung, with pathologically proven metastatic lymph node in the right hilum and station 7, but developed recurrent disease at mediastinal lymph node stations 5 and 6.</p><fig id=\"F3\" position=\"float\"><label>Figure 3</label><caption><p>Prognostic significance of postoperative radiotherapy and supraclavicular recurrence. The impact of postoperative radiotherapy (PORT) on loco-regional recurrence (LRR) <bold>(A)</bold>, supraclavicular recurrence (SCR) <bold>(B)</bold>, overall survival (OS1) <bold>(C)</bold>, in the entire cohort. Kaplan–Meier survival curve stratified by the disease recurrence patterns among patients with recurrent disease <bold>(D)</bold>.</p></caption><graphic xlink:href=\"fonc-10-01414-g0003\"/></fig><p>On the other hand, 8 (8.51%) of the 94 patients who received PORT developed SCR, while 19 (8.76%) of the 217 patients who did not receive PORT developed SCR in the entire cohort. PORT without ESRT didn't reduce the incidence of SCR (<xref ref-type=\"fig\" rid=\"F3\">Figure 3B</xref>). Fifteen of the 16 ipsilateral SCRs could be covered by the proposed PTV-sc and the ipsilateral parts of the six bilateral SCRs could all be covered by the proposed PTV-sc.</p></sec><sec><title>Survival Analyses</title><p>By the time of data cut-off, 125 patients had died and the median OS1 was 49.0 (95CI 40.5–57.6) months. PORT was found to significant prolong OS1 in the entire cohort (<xref ref-type=\"fig\" rid=\"F3\">Figure 3C</xref>). Age, sex, ECOG score, lymphovascular invasion, total number of positive lymph node, positive lymph node ratio, PORT, and Ki67, were found to be significantly associated with OS1 in univariate Cox analyses, while age, ECOG score, PORT, and Ki67 were identified to be independent indicators of OS1 in multivariate Cox analyses (<xref rid=\"T3\" ref-type=\"table\">Table 3</xref>). Among the 203 patients with recurrent disease, the median OS2 was 19.0 (95CI 14.7–23.3) months. Age, sex, ECOG score, and DM were revealed to be significantly associated with OS1 in univariate and multivariate Cox analyses (<xref rid=\"T4\" ref-type=\"table\">Table 4</xref>).</p><table-wrap id=\"T3\" position=\"float\"><label>Table 3</label><caption><p>Cox analyses of clinical-pathological variables associated with overall survival (OS1).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Variables</bold></th><th valign=\"top\" align=\"center\" colspan=\"2\" style=\"border-bottom: thin solid #000000;\" rowspan=\"1\"><bold>Univariate Analyses</bold></th><th valign=\"top\" align=\"center\" colspan=\"2\" style=\"border-bottom: thin solid #000000;\" rowspan=\"1\"><bold>Multivariate Analyses</bold></th></tr><tr><th rowspan=\"1\" colspan=\"1\"/><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>HR (95%CI)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>p</italic></bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>HR (95%CI)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>p</italic></bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Age (>65 vs. ≤ 65)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.64 (1.14–2.15)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.008</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.46 (1.01–2.12)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.048</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Sex (Male vs. Female)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.49 (1.03–2.17)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.035</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.40 (0.94–2.08)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.097</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Smoking (Never vs. Ever)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.21 (0.85–1.73)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.285</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ECOG (1 vs. 0)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.67 (1.71–4.19)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.001</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.22 (1.41–3.52)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.001</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">cN2 (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.11 (0.78–1.57)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.575</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">pT stage (T3–4 vs. T0–2)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.19 (0.74–1.90)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.473</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">pN1 (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.51 (1.01–2.25)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.042</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Multiple levels of pN2 (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.18 (0.83–1.67)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.366</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Histology (SCC vs. Non-SCC)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.35 (0.88–2.08)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.167</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Differentiation (P vs. W/M)<xref ref-type=\"table-fn\" rid=\"TN2\"><sup>&</sup></xref></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.05 (0.80–1.24)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.899</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">LVI (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.49 (1.04–2.12)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.028</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.21 (0.84–1.75)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.297</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VPI (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.29 (0.90–1.85)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.168</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PNI (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.22 (0.70–2.14)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.477</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ypN2 vs. pN2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.76 (0.37–1.55)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.445</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Tumor Location (Left vs. Right)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.80 (0.56–1.14)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.217</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Tumor Lobe (Upper vs. Others)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.17 (0.89–1.25)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.913</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">TLN (≥16 vs. <16)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.92 (0.63–1.33)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.644</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PLN (≥3 vs. <3)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.46 (1.02–2.10)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.041</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.33 (0.80–2.21)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.274</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">LNR (≥0.2 vs. <0.2)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.44 (1.01–2.07)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.048</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.29 (0.78–2.13)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.324</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Surgery (Others vs. Lobectomy)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.80 (0.43–1.49)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.483</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PORT (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.63 (0.42–0.93)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.020</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.61 (0.40–0.92)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.018</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ERCC1 (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.03 (0.80–1.32)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.825</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Her2 (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.92 (0.72–1.16)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.464</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Ki67 (≥50 vs. <50%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.65 (1.16–2.34)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.006</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.55 (1.06–2.25)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.023</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">TTF1 (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.81 (0.59–1.10)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.181</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CK20 (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.96 (0.76–1.20)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.704</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CK7 (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.75 (0.49–1.14)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.178</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CK5/6 (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.07 (0.85–1.35)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.545</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">P63 (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.02 (0.80–1.32)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.857</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NapsinA (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.79 (0.60–1.05)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.103</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Syn (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.521 (0.23–1.41)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.324</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RRM1 (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.891 (0.62–1.42)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.897</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">EGFR (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.82 (0.62–1.08)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.155</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr></tbody></table><table-wrap-foot><p>HR, hazard ratios; CI, confidence intervals; SCC, squamous cell carcinoma; LVI, Lymphovascular invasion; VPI, Visceral pleural invasion; PI, perineural invasion; ECOG, the Eastern Corporative Oncology Group; TLN, total lymph node examined; PLN, positive lymph node; LNR, positive lymph node ratio; PORT, postoperative radiotherapy;</p><fn id=\"TN2\"><label>&</label><p><italic>W/M, well/moderate; P, poor. Bold values indicates statistical significant</italic>.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"T4\" position=\"float\"><label>Table 4</label><caption><p>Cox analyses of clinical-pathological variables associated with OS2 in patients with recurrent disease.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Variables</bold></th><th valign=\"top\" align=\"center\" colspan=\"2\" style=\"border-bottom: thin solid #000000;\" rowspan=\"1\"><bold>Univariate Analyses</bold></th><th valign=\"top\" align=\"center\" colspan=\"2\" style=\"border-bottom: thin solid #000000;\" rowspan=\"1\"><bold>Multivariate Analyses</bold></th></tr><tr><th rowspan=\"1\" colspan=\"1\"/><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>HR (95%CI)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>p</italic></bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>HR (95%CI)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>p</italic></bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Age (>65 vs. ≤ 65)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.70 (1.17–2.47)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.005</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.49 (1.02–2.19)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.040</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Sex (Male vs. Female)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.57 (1.07–2.30)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.020</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.74 (1.19–2.56)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.005</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Smoking (Never vs. Ever)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.23 (0.86–1.77)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.260</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ECOG (1 vs. 0)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.95 (1.22–3.10)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.005</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.02 (1.25–3.26)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.004</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">cN2 (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.12 (0.78–1.61)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.534</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">pT stage (T3–4 vs. T0–2)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.15 (0.71–1.86)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.572</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">pN1 (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.11 (0.74–1.65)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.622</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Multiple levels of pN2 (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.89 (0.62–1.27)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.517</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Histology (SCC vs. Non-SCC)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.56 (0.99–2.42)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.051</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Differentiation (P vs. W/M)<xref ref-type=\"table-fn\" rid=\"TN3\"><sup>&</sup></xref></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.04 (0.71–1.51)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.851</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">LVI (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.35 (0.94–1.94)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.104</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VPI (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.07 (0.74–1.55)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.732</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PNI (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.96 (0.55–1.67)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.875</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ypN2 vs. pN2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.56 (0.28–1.16)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.118</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Tumor Location (Left vs. Right)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.05 (0.73–1.50)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.810</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Tumor Lobe (Upper vs. Others)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.03 (0.79–2.17)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.874</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">TLN (≥16 vs. <16)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.85 (0.65–1.42)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.849</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PLN (≥3 vs. <3)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.14 (0.78–1.65)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.485</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">LNR (≥0.2 vs. <0.2)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.03 (0.72–1.50)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.361</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Surgery (Others vs. Lobectomy)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.45 (0.76–2.77)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.266</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PORT (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.85 (0.66–1.23)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.414</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">DM (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.49 (2.36–17.85)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold> <0.001</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.43 (2.67–20.68)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold> <0.001</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ERCC1 (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.04 (0.81–1.33)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.765</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Her2 (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.83 (0.65–1.07)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.152</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Ki67 (≥50 vs. <50%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.19 (0.83–1.70)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.356</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">TTF1 (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.73(0.53–1.02)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.061</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CK20 (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.92 (0.72–1.16)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.472</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CK7 (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.87 (0.63–1.20)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.403</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CK5/6 (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.01 (0.80–1.27)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.955</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">P63 (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.91 (0.71–1.18)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.484</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NapsinA (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.76 (0.57–1.02)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.063</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Syn (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.37 (0.92–2.35)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.781</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RRM1 (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.97 (0.71–1.42)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.971</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">EGFR (+ vs. –)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.84 (0.64–1.11)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.226</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr></tbody></table><table-wrap-foot><p>OS, overall survival; HR, hazard ratios; CI, confidence intervals; SCC, squamous cell carcinoma; LVI, Lymphovascular invasion; VPI, Visceral pleural invasion; PI, perineural invasion; ECOG, the Eastern Corporative Oncology Group; TLN, total lymph node examined; PLN, positive lymph node; LNR, positive lymph node ratio; PORT, postoperative radiotherapy; DM, distant metastasis;</p><fn id=\"TN3\"><label>&</label><p><italic>W/M, well/moderate; P, poor. Bold values indicates statistical significant</italic>.</p></fn></table-wrap-foot></table-wrap><p>In order to investigate the prognostic significance of SCR, patients with recurrent disease were further divided into three groups: Group A consisted of patients who had DM (<italic>n</italic> = 178), Group B consisted of patients who did not have DM but have SCR (<italic>n</italic> = 16), and Group C consisted of patients who only had LRR (<italic>n</italic> = 9). In terms of OS2, patients in Group B had an intermediate prognosis, when compared with patients in Group A and Group C (<xref ref-type=\"fig\" rid=\"F3\">Figure 3D</xref>).</p></sec></sec><sec sec-type=\"discussion\" id=\"s4\"><title>Discussion</title><p>To the best of our knowledge, this is the first comprehensive study specifically focusing on SCR in completely resected (y)pN2 NSCLC with a relatively large sample size, in the era of modern radiation technique. SCR was not uncommon and had imperative prognostic significance, indicating that treatment modalities able to reduce the incidence of SCR may be beneficial. Additionally, PORT without ESRT significantly reduced LRR and prolonged OS, but did not decrease SCR in our study, suggesting that the clinical value of ESRT may be reconsidered in selected patients with high risks of SCR.</p><p>SCR is not uncommon in completely resected (y)pN2 NSCLC, especially among those with extra risk factors. Although there was limited historical data published that could be directly compared, the incidence of SCR in our study was reliable, since the overall recurrence rate and the percentage of SCR among patients with recurrent disease were in accordance with previous findings. The cumulative incidence of postoperative recurrence in the PORT group and non-PORT group, were generally comparable with recent studies (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>, <xref rid=\"B9\" ref-type=\"bibr\">9</xref>, <xref rid=\"B17\" ref-type=\"bibr\">17</xref>, <xref rid=\"B23\" ref-type=\"bibr\">23</xref>). Furthermore, studies from our institution (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>) and others (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>, <xref rid=\"B24\" ref-type=\"bibr\">24</xref>) had reported a similar percentage of SCR among patients with recurrent disease (7.7–11.6% in the literature, 13.3% in our study). Compared with their counterpart, patients staged cN2 or ypN2 generally had a more advanced and aggressive disease, and thus it was reasonable for them to have a higher risk developing disease recurrence, including SCR (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>, <xref rid=\"B25\" ref-type=\"bibr\">25</xref>–<xref rid=\"B27\" ref-type=\"bibr\">27</xref>). Compared with those receiving lobectomy, patients receiving pneumonectomy generally had a higher tumor burden and those receiving sublobectomy commonly had unfavorable prognostic factors, such as more comorbidities and poorer preoperative lung functions, that made them unsuitable for lobectomy (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>, <xref rid=\"B28\" ref-type=\"bibr\">28</xref>). Therefore, patients who didn't receive lobectomy were also at a higher risk developing postoperative recurrence, which is generally consistent with a recent retrospective study using the SEER database (<xref rid=\"B29\" ref-type=\"bibr\">29</xref>). In addition, two recent studies found that positive expression of CK7 were associated with more advanced disease and shorter overall survival (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>, <xref rid=\"B31\" ref-type=\"bibr\">31</xref>). In our study, distant metastasis was chosen as a competing event and negative expression of CK7 was identified as a risk factor of SCR, which need to be further verified.</p><p>Compared with patients developing only LRR and those developing DM, patients developing SCR but without DM had intermediate OS2, highlighting the vital prognostic significance of SCR in curatively resected (y)pN2 NSCLC. The TNM staging system is one of the most powerful indicators of patient's prognosis in NSCLC, among which patients having supraclavicular lymph node metastasis (N3) generally have intermediate prognosis when compared with those having distant metastasis (M1) and those harboring metastatic tumor lesions limited to the ipsilateral hilar (N1) or mediastinal (N2) lymph nodes (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>). Similarly, SCR represented an unfavorable sign of subsequent disease metastasis to distant organs and thus was reasonable to have worse prognosis when compared with those who had only LRR. On the other hand, when compared with those who already had DM, patients who had recurrent disease limited to the thoracic region (i.e., LRR and SCR) could be considered as harboring loco-regional disease and may benefit from aggressive loco-regional treatment, as well as systematic therapies, and thus may still have a chance of long-term survival (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>). In fact, among the 16 patients with SCR but without DM, the 3-year survival rate exceeded 70% in our study (<xref ref-type=\"fig\" rid=\"F3\">Figure 3D</xref>). However, due to the advancement of adjuvant chemotherapy and PORT, the number of patients who developed localized recurrent disease (i.e., LRR and SCR) was small (16 patients in group B and 9 patients in group C), although a total of 311 patients were enrolled and followed up for a median of 26 months. Hence, the prognostic significance of SCR needed to be interpreted with caution and future investigations with larger sample size and prospective design are warranted.</p><p>The clinical value of PORT in completely resected (y)pN2 NSCLC was demonstrated again in our study, but the delineation of CTV remain controversial. In the current study, PORT significantly reduced LRR and improved OS1, which have been demonstrated in various studies (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>, <xref rid=\"B6\" ref-type=\"bibr\">6</xref>, <xref rid=\"B8\" ref-type=\"bibr\">8</xref>–<xref rid=\"B11\" ref-type=\"bibr\">11</xref>, <xref rid=\"B16\" ref-type=\"bibr\">16</xref>, <xref rid=\"B17\" ref-type=\"bibr\">17</xref>, <xref rid=\"B23\" ref-type=\"bibr\">23</xref>). However, since ESRT was not routinely performed in our cancer institution (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>), PORT failed to reduce SCR, indicating that the majority of SCRs represented the outgrowth of subclinical tumor lesions already in the supraclavicular region and were not originated from the loco-regional recurrent disease through lymphatic metastasis. In fact, 19 (70.4%) of the 27 patients with SCR had no LRR in the current study. These data indicated a potential role of ESRT in selected patients with high risks. Actually, for locally advanced NSCLC receiving chemo-radiotherapy, there is no significant difference of patient's survival between those with or without N3 disease (<xref rid=\"B33\" ref-type=\"bibr\">33</xref>, <xref rid=\"B34\" ref-type=\"bibr\">34</xref>), highlighting that the treatment efficacy of chemo-radiotherapy in locally advanced NSCLC was largely dependent on the intrinsic biology of the tumor and the prognosis of patients with or without macroscopic supraclavicular tumor lesions seemed similar. PORT with adjuvant chemotherapy has been repeatedly shown to significantly reduce LRR, indicating the beneficial role of adjuvant chemo-radiotherapy in treating microscopic N1/N2 disease. It is possible that adjuvant chemo-radiotherapy (i.e., adjuvant chemotherapy in combination with ESRT) may also play a role in reducing SCR and subsequently improve patient's survival. Furthermore, nearly 70% of the ipsilateral SCRs could be covered with the virtual CTV of ESRT in our study. However, there are also evidence against the use of ESRT for patients with completely resected NSCLC. Elective irradiation of mediastinal, contralateral hilar and supraclavicular lymph nodes failed to improve patient's survival in unresectable stage III NSCLC without clinical N3 disease (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>). And pattern of failure analyses of a prospective trial of PORT without ESRT suggested that the use of limited CTV including only the involved lymph node stations and those with a risk of invasion >10%, was associated with acceptable risk of geographic miss (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>). Taken together, PORT without ESRT provided significant clinical benefit for patients with completely resected (y)pN2 NSCLC, and the clinical value of ESRT in highly selected patients, for example those with persistent N2 (ypN2) disease after neoadjuvant chemotherapy, need to be further investigated.</p><p>Our study also has some limitations. Firstly, since ESRT is not routinely performed in our cancer center, we could not directly examine the clinical value and prognostic significance of ESRT. Secondly, as this was a retrospectively study, treatment decisions and follow-up strategies were at the discretion of the treating physicians. Different neoadjuvant and adjuvant chemotherapy regimens were used and the protocols of follow-up were not identical. Moreover, since brain MRI and bone scans were not mandatory, asymptomatic brain and/or bone metastasis may be underestimated. Despite these limitations, we believe our study provided valuable information about the cumulative incidence and prognostic significance of SCR in completely resected (y)pN2 NSCLC, which may guide better design of adjuvant treatment modalities and individualized surveillance strategies.</p></sec><sec sec-type=\"data-availability\" id=\"s5\"><title>Data Availability Statement</title><p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.</p></sec><sec id=\"s6\"><title>Ethics Statement</title><p>The studies involving human participants were reviewed and approved by the institutional review board of Fudan University Shanghai Cancer Center. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.</p></sec><sec id=\"s7\"><title>Author Contributions</title><p>LL, ZZ, and JN: conceptualization. LL and ZZ: methodology, validation, and writing—original draft preparation. LL, ZZ, and JL: formal analysis and investigation. LL, ZZ, and YL: resources and data curation. LL and JN: writing—review and editing. All the authors have approved the final manuscript.</p></sec><sec id=\"s8\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> This study was supported by the National Natural Science Foundation of China (No. 81903253 to JN) and grant provided by the Shanghai Municipal Health Commission (No. 20194Y0501 to JN).</p></fn></fn-group><sec sec-type=\"supplementary-material\" id=\"s9\"><title>Supplementary Material</title><p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.frontiersin.org/articles/10.3389/fonc.2020.01414/full#supplementary-material\">https://www.frontiersin.org/articles/10.3389/fonc.2020.01414/full#supplementary-material</ext-link></p><supplementary-material content-type=\"local-data\" id=\"SM1\"><label>Supplementary Figure 1</label><caption><p>Flowchart of patient enrollment. 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Genet.</journal-id><journal-title-group><journal-title>Frontiers in Genetics</journal-title></journal-title-group><issn pub-type=\"epub\">1664-8021</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849773</article-id><article-id pub-id-type=\"pmc\">PMC7431952</article-id><article-id pub-id-type=\"doi\">10.3389/fgene.2020.00711</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Genetics</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>A Potential Endurance Algorithm Prediction in the Field of Sports Performance</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>de la Iglesia</surname><given-names>Rocio</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/800293/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Espinosa-Salinas</surname><given-names>Isabel</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/899005/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Lopez-Silvarrey</surname><given-names>F. Javier</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Ramos-Alvarez</surname><given-names>J. Jose</given-names></name><xref ref-type=\"aff\" rid=\"aff5\"><sup>5</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/705862/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Segovia</surname><given-names>J. Carlos</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Colmenarejo</surname><given-names>Gonzalo</given-names></name><xref ref-type=\"aff\" rid=\"aff6\"><sup>6</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/685892/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Borregon-Rivilla</surname><given-names>Elena</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Marcos-Pasero</surname><given-names>Helena</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Aguilar-Aguilar</surname><given-names>Elena</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1011745/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Loria-Kohen</surname><given-names>Viviana</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1006073/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Reglero</surname><given-names>Guillermo</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff7\"><sup>7</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/916716/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Ramirez-de Molina</surname><given-names>Ana</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/163599/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Departamento de Ciencias Farmacéuticas y de la Salud, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities</institution>, <addr-line>Alcorcón</addr-line>, <country>Spain</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Nutrition and Clinical Trials Unit, GENYAL Platform IMDEA-Food Institute, CEI UAM + CSIC</institution>, <addr-line>Madrid</addr-line>, <country>Spain</country></aff><aff id=\"aff3\"><sup>3</sup><institution>Facultad de Ciencias de la Salud, Universidad Camilo José Cela</institution>, <addr-line>Madrid</addr-line>, <country>Spain</country></aff><aff id=\"aff4\"><sup>4</sup><institution>Sannus Clinic</institution>, <addr-line>Madrid</addr-line>, <country>Spain</country></aff><aff id=\"aff5\"><sup>5</sup><institution>Departamento de Radiología, Rehabilitación y Fisioterapia, Universidad Complutense de Madrid</institution>, <addr-line>Madrid</addr-line>, <country>Spain</country></aff><aff id=\"aff6\"><sup>6</sup><institution>Biostatistics and Bioinformatics Unit, IMDEA Food CEI UAM + CSIC</institution>, <addr-line>Madrid</addr-line>, <country>Spain</country></aff><aff id=\"aff7\"><sup>7</sup><institution>Department of Production and Characterization of Novel Foods, Institute of Food Science Research (CIAL) CEI UAM + CSIC</institution>, <addr-line>Madrid</addr-line>, <country>Spain</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Georgia Theocharopoulou, Ionian University, Greece</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Girish Kumar Gupta, Sri Sai Group of Institutes Badhani, India; Stylianos Chatzichronis, National and Kapodistrian University of Athens, Greece</p></fn><corresp id=\"c001\">*Correspondence: Ana Ramirez-de Molina, <email>ana.ramirez@imdea.org</email></corresp><fn fn-type=\"other\" id=\"fn004\"><p>This article was submitted to ELSI in Science and Genetics, a section of the journal Frontiers in Genetics</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>711</elocation-id><history><date date-type=\"received\"><day>05</day><month>3</month><year>2020</year></date><date date-type=\"accepted\"><day>11</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 de la Iglesia, Espinosa-Salinas, Lopez-Silvarrey, Ramos-Alvarez, Segovia, Colmenarejo, Borregon-Rivilla, Marcos-Pasero, Aguilar-Aguilar, Loria-Kohen, Reglero and Ramirez-de Molina.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>de la Iglesia, Espinosa-Salinas, Lopez-Silvarrey, Ramos-Alvarez, Segovia, Colmenarejo, Borregon-Rivilla, Marcos-Pasero, Aguilar-Aguilar, Loria-Kohen, Reglero and Ramirez-de Molina</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>Sport performance is influenced by several factors, including genetic susceptibility. In the past years, specific single nucleotide polymorphisms have been associated to sport performance; however, these effects should be considered in multivariable prediction systems since they are related to a polygenic inheritance. The aim of this study was to design a genetic endurance prediction score (GES) of endurance performance and analyze its association with anthropometric, nutritional and sport efficiency variables in a cross-sectional study within fifteen male cyclists. A statistically significant positive relationship between GES and the VO<sub>2</sub> maximum (<italic>P</italic> = 0.033), VO<sub>2</sub> VT1 (<italic>P</italic> = 0.049) and VO<sub>2</sub> VT2 (<italic>P</italic> < 0.001) was observed. Moreover, additional remarkable associations between genotype and the anthropometric, nutritional and sport performance variables, were achieved. In addition, an interesting link between the habit of consuming caffeinated beverages and the GES was observed. The outcomes of the present study indicate a potential use of this genetic prediction algorithm in the sports’ field, which may facilitate the finding of genetically talented athletes, improve their training and food habits, as well as help in the improvement of physical conditions of amateurs.</p></abstract><kwd-group><kwd>SNP</kwd><kwd>genetics</kwd><kwd>exercise</kwd><kwd>functional validation</kwd><kwd>nutrition</kwd></kwd-group><counts><fig-count count=\"4\"/><table-count count=\"4\"/><equation-count count=\"0\"/><ref-count count=\"58\"/><page-count count=\"11\"/><word-count count=\"0\"/></counts></article-meta></front><body><sec id=\"S1\"><title>Introduction</title><p>Athletic performance can be influenced by several factors. These can include extrinsic factors such as the quality of training, the dietary habits, the technology used or the weather conditions (<xref rid=\"B45\" ref-type=\"bibr\">Sacha and Quinn, 2011</xref>). Besides, intrinsic factors such as individual genetic profile also play an important role (<xref rid=\"B42\" ref-type=\"bibr\">Peveler and Green, 2010</xref>). In fact, the study of the genetic influence on sports performance has become a leading field of research.</p><p>Single nucleotide polymorphisms (SNPs) are among the genetic variants implicated in the phenotypic differences that can influence individual physical abilities (<xref rid=\"B48\" ref-type=\"bibr\">Santos et al., 2016</xref>). To date, various links between several genetic variants and favorable phenotypes for certain sports have been established (<xref rid=\"B48\" ref-type=\"bibr\">Santos et al., 2016</xref>). This suggests that the presence of specific genotypes can predispose an individual to competitive advantages in a particular sport (<xref rid=\"B58\" ref-type=\"bibr\">Zilberman-Schapira et al., 2012</xref>).</p><p>For example, backed by a meta-analysis, the II genotype of the angiotensin converting enzyme <italic>ACE</italic> insertion/deletion (I/D) rs4340 has been significantly associated with endurance’s discipline as compared with the ID and DD genotypes (<xref rid=\"B37\" ref-type=\"bibr\">Ma et al., 2013</xref>). Specifically, it is suggested that the I allele implies higher blood flow and sugar utilization by muscles, that facilitates endurance performance (<xref rid=\"B56\" ref-type=\"bibr\">Woods et al., 2000</xref>). Additionally, considering reported difficulties in genotyping <italic>ACE</italic> I/D, the <italic>ACE</italic> rs4343 A and G alleles are admitted as equivalents to the <italic>ACE</italic> rs4340 I and D alleles, respectively, and considered an alternative method for genotyping of the <italic>ACE</italic> I/D polymorphism (<xref rid=\"B27\" ref-type=\"bibr\">Glenn et al., 2009</xref>).</p><p>Moreover, the different genotypes of the peroxisome proliferator activated receptor alpha PPARA rs4253778 have also been associated with athlete’s performance. Concretely, in a meta-analysis carried out by <xref rid=\"B36\" ref-type=\"bibr\">Lopez-Leon et al. (2016)</xref> the GG genotype and G allele were significantly more common in endurance athletes as compared to controls.</p><p>Besides, the α-actinin 3 (ACTN3) is a sarcomeric actin-binding protein specifically expressed in fast twitch myofibers of the skeletal muscle, required for explosive muscle contraction (<xref rid=\"B55\" ref-type=\"bibr\">Wilson et al., 2012</xref>). But the gene encoding this protein also seems to be associated with endurance capacities. In particular, TT rs1815739 carriers were found to be more common in endurance athletes as compared with sedentary individuals in a sample of 395 Israeli (<xref rid=\"B23\" ref-type=\"bibr\">Eynon et al., 2009</xref>). They were also described to obtain better results in an endurance test (<xref rid=\"B40\" ref-type=\"bibr\">Pasqua et al., 2016</xref>), to exhibit a higher proportion of endurance-associated type I myofibers and to prefer to skate long- than short-distance races (<xref rid=\"B2\" ref-type=\"bibr\">Ahmetov et al., 2011</xref>).</p><p>Another gene with a potential association with endurance capacities is the Aquaporin 1 <italic>AQP1</italic> rs1049305 where C allele carriers were faster in running performances than carriers of the GG genotype (<xref rid=\"B39\" ref-type=\"bibr\">Martinez et al., 2009</xref>; <xref rid=\"B44\" ref-type=\"bibr\">Rivera et al., 2011</xref>; <xref rid=\"B49\" ref-type=\"bibr\">Saunders et al., 2015</xref>).</p><p>Moreover, the CC genotype of the peroxisome proliferator activated receptor gamma, coactivator 1 alpha <italic>PPARGC1A</italic> rs8192678 has been described to be associated with high values of aerobic performance (<xref rid=\"B4\" ref-type=\"bibr\">Akhmetov et al., 2007</xref>; <xref rid=\"B52\" ref-type=\"bibr\">Stefan et al., 2007</xref>; <xref rid=\"B22\" ref-type=\"bibr\">Eynon et al., 2010b</xref>).</p><p>And finally, other SNPs such as the beta-3 adrenergic receptor <italic>ADRB3</italic> rs4994 (<xref rid=\"B47\" ref-type=\"bibr\">Santiago et al., 2011</xref>), the GA-binding protein transcription factor, subunit beta 1 <italic>GABPB1</italic> rs12594956 (<xref rid=\"B21\" ref-type=\"bibr\">Eynon et al., 2010a</xref>, <xref rid=\"B24\" ref-type=\"bibr\">2013</xref>), the collagen type V alpha 1 chain <italic>COL5A1</italic> rs12722 (<xref rid=\"B11\" ref-type=\"bibr\">Brown et al., 2011</xref>; <xref rid=\"B43\" ref-type=\"bibr\">Posthumus et al., 2011</xref>) and the hemochromatosis <italic>HFE</italic> rs1799945 (<xref rid=\"B28\" ref-type=\"bibr\">Grealy et al., 2015</xref>) have also been associated with better endurance performance, although the scientific evidence support for these associations is still scarce.</p><p>Thus, it is clear that there is a genetic influence, but there is still a weak scientific evidence for most of the reported associations. Besides, most studies that associate genetics with physical capacities have focused on individual genes. However, as a polygenic inheritance, various genes can make a contribution to the overall outcome. For these reasons, we consider that it is necessary to create more complex prediction algorithms, including different genetic factors together. Therefore, in the present study, we have developed a predictive algorithm of endurance performance including 11 genes. In this report, we present the associations of the predictive algorithm and each of the SNPs with anthropometric, nutritional and sport performance variables, in a group of 15 semi-professional cyclists.</p></sec><sec sec-type=\"materials|methods\" id=\"S2\"><title>Materials and Methods</title><sec id=\"S2.SS1\"><title>Subjects</title><p>Fifteen healthy male members of the Spanish Cycling Federation, body mass index (BMI) 22.3 ± 2.5 aged 40.7 ± 7.0, with at least 5 years of experience in national-level competitions were recruited by the Sports Medicine University Center (Complutense University of Madrid, Madrid, Spain) to participate in the present study. All participants were non-smokers. This research was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving human subjects were approved by the Research Ethics Committee of the IMDEA Food Foundation (PI-0031). Written informed consent to participate in the study was obtained from all subjects.</p></sec><sec id=\"S2.SS2\"><title>Design</title><p>This was a cross-sectional clinical study where volunteers attended two different centers to complete the study:</p><list list-type=\"simple\"><list-item><label>1)</label><p>The Sports Medicine University Center to carry out a maximal incremental treadmill test.</p></list-item><list-item><label>2)</label><p>The Research Institute on Food and Health Sciences “IMDEA Food” (Madrid, Spain) for anthropometric measurements, body composition analysis, dietary records, DNA collection and genotyping.</p></list-item></list></sec><sec id=\"S2.SS3\"><title>Methodology</title><sec id=\"S2.SS3.SSS1\"><title>Cardiopulmonary Exercise Test</title><p>All participants carried out an incremental (30 w/min) exercise testing on a cycle ergometer (Cardgirus, Barcelona, Spain) after 2 h of fasting. During the test, the heart rate (HR) was measured using a 12-lead wireless electrocardiograph (Norav, Wiesbaden, Germany).</p><p>Oxygen uptake (VO<sub>2</sub>), carbon dioxide output (VCO<sub>2</sub>) and minute ventilation (VE) were assessed using the analyzer Jaeger Oxycon-Pro (Hoechberg, Germany). The respiratory exchange ratio (RER) was calculated as VCO<sub>2</sub>/VO<sub>2</sub>, while the VO<sub>2</sub> pulse as VO<sub>2</sub>/HR (<xref rid=\"B8\" ref-type=\"bibr\">Bergh et al., 2000</xref>). Every 2 min cyclists had to estimate their feelings of exertion and pain using the Borg Rating of Perceived Exertion (RPE) Scale (<xref rid=\"B9\" ref-type=\"bibr\">Borg et al., 1985</xref>).</p><p>All participants achieved maximal exercise criteria: VO<sub>2</sub> plateau (considered as VO<sub>2</sub> max) which was estimated as a VO<sub>2</sub> increase lower than 150 mL/min for two consecutive periods (<xref rid=\"B41\" ref-type=\"bibr\">Petot et al., 2012</xref>), RPE higher than 16 (<xref rid=\"B8\" ref-type=\"bibr\">Bergh et al., 2000</xref>), RER above 1.10 and a HR upper 90% of the theoretical maximum HR (<xref rid=\"B33\" ref-type=\"bibr\">Howley et al., 1995</xref>). The maximum HR was calculated as [(208.75−(0.73 × age)] (<xref rid=\"B53\" ref-type=\"bibr\">Tanaka et al., 2001</xref>).</p><p>The aerobic ventilatory threshold (VT1) was estimated using the criteria of the ventilatory equivalent for VO<sub>2</sub> (VE/VO<sub>2</sub> ratio), corresponding to the rupture of the linearity in the increment of VE. Finally, the anaerobic ventilatory threshold (VT2) was calculated by the increase of the VCO<sub>2</sub> equivalent principles (VE/VCO<sub>2</sub>), as the second rupture of the linearity in the increment of VE (<xref rid=\"B7\" ref-type=\"bibr\">Beaver et al., 1986</xref>).</p></sec><sec id=\"S2.SS3.SSS2\"><title>Anthropometry and Lifestyle Parameters</title><p>Anthropometric measurements were determined while subjects were wearing light clothing and no shoes. Height was assessed to the nearest 0.1 cm using a stadiometer (Leicester-Biological Medical Technology SL, Barcelona). Body weight, fat mass and muscle mass percentages were evaluated using a BF511 Body Composition Monitor (BF511- OMRON Healthcare UK, LT, Kyoto, Japan). Brachial, contracted arm, waist, hip and leg circumferences were measured with an inextensible tape (KaWe Kirchner & Wilhelm GmbH, Asperg, Germany; range 0–150 cm, 1 mm of precision). A caliper (Holtain Ltd., Crymych, United Kingdom; 10 g/mm<sup>2</sup> constant pressure; range 0–39 mm and 0.1 mm of precision) was used for biceps, triceps, subscapular, abdominal, supraspinal, front thigh and medial calf skinfolds determinations. Moreover, the diameters of the femur and humerus were also assessed using a small bone caliper (Nonio sliding Bicondyleo, Holtain Ltd., United Kingdom). Systolic and diastolic blood pressures were evaluated using an automatic digital blood pressure monitor Model M3 (OMRON Healthcare UK, LT, Kyoto, Japan) in the right arm, with the patient seated and relaxed. Measurements were taken three times after a 5-min resting period, following World Health Organization (WHO) criteria (<xref rid=\"B54\" ref-type=\"bibr\">Whitworth and Chalmers, 2004</xref>). Finally, with the different anthropometric data, the BMI was calculated as the body weight divided by the squared height (kg/m<sup>2</sup>) and somatotype values (endomorphic, mesomorphic, and ectomorphic values) accordingly to Heath-Carter method (<xref rid=\"B13\" ref-type=\"bibr\">Carter and Heath, 1990</xref>).</p><p>The food habits of each participant were recorded using a validated 3-day dietary food record and a food frequency questionnaire (<xref rid=\"B1\" ref-type=\"bibr\">Aguirre-Jaime et al., 2008</xref>). Subsequently, the composition of the different dietary records was analyzed using the DIAL software (2.16 version Alce Ingeniería, Madrid, Spain). For the calculation of the Healthy Eating Index score (<xref rid=\"B29\" ref-type=\"bibr\">Guenther et al., 2013</xref>), the DIAL program gives different values ranging from 0 to 100 considering the daily servings of cereals, vegetables, fruits, dairy products, and meat; the percentage of energy provided by total and saturated fats; the amount of cholesterol and sodium per day and the number of different foods consumed. The final score is classified into five categories: an “excellent diet” (>80 points), a “very good diet” (71–80 points), a “good diet” (61–70 points), an “acceptable diet” (51–60 points), or an “inadequate diet” (0–50 points).</p></sec><sec id=\"S2.SS3.SSS3\"><title>Genotyping of the Population</title><p>A sample of 500 μl of peripheral capillary blood of each volunteer was drawn for DNA extraction. To perform the subsequent analysis of the samples, genomic DNA was extracted from the cellular fraction collected by the Genomic QIAamp DNA Blood Kit Mini Kit (QIAGEN, Spain). The samples were genotyped with TaqMan Assays by the high-performance QuantStudio Real-Time PCR (Applied Biosystem, United States).</p></sec></sec><sec id=\"S2.SS4\"><title>Statistical Analysis</title><p>Data were analyzed using the R Statistical Software Version 3.4.1<sup><xref ref-type=\"fn\" rid=\"footnote1\">1</xref></sup>. The description of the qualitative data was made in the form of absolute frequencies and percentages and the quantitative data by mean and standard deviation. The Mann–Whitney U test was used to check for significant differences in the continuous variables (not always normally distributed) for the different genotypes. The Spearman correlation coefficient was used for the association between the algorithm and the other variables. The Bonferroni correction was also applied to control against type-I errors for multiple tests. All the statistical tests were two-tailed. Statistical significance was assumed when <italic>P</italic> < 0.05.</p></sec><sec id=\"S2.SS5\"><title>Selection of SNPs and Design of the GES</title><p>An exhaustive literature review of the scientific databases (Pubmed, Medline, Web of Sciences) was carried out to identify all studies that analyzed the relationship between one or more SNP and sports performance. A selection of 11 SNPs was made considering the European frequencies of each SNP according to Ensembl database, the scientific evidence of each association and its availability for TaqMan SNP Genotyping Assay. Among them, nine SNPs were associated with endurance capacities and two SNPs were mainly related to power abilities.</p><p>Once the SNPs selection was completed, an algorithm to predict endurance capacities based on the nine SNPs associated with endurance performance was established (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). Depending on the scientific evidence, each SNP was given a different weight to the total GES. Therefore, the two SNPs with the highest scientific evidence based on meta-analysis, <italic>ACE</italic> and <italic>PPAR</italic>α, were given the highest normalized weight of the GES, contributing 22.2% each of the total value of the algorithm. On the other hand, those SNPs whose evidence was based on at least three studies with positive and conclusive results (<italic>ACTN3</italic>, <italic>AQP1</italic>, and <italic>PPARGC1A</italic>) were given a weight of 11.1% each, of the total value of the algorithm. Besides, to the remaining 4 SNPs, with less scientific evidence and some contradictory results, each one was given a weight of 5.6% to the final GES. Finally, based on the literature, the three possible genotypes of each SNP were classified into three categories: “favorable genotypes” (GES weight × 1), which were those associated with better endurance performance; “intermediate genotypes” (GES weight × 0.5), which were those with a neutral effect on endurance abilities, and “unfavorable genotypes” (GES weight × 0), which were those with a negative effect. For example, an individual with all favorable SNPs, would have a GES value of 100: 22.2 × 1 (<italic>ACE</italic>) + 22.2 × 1 (<italic>PPAR</italic>α) + 11.1 × 1 (<italic>ACTN3</italic>) + 11.1 × 1 (<italic>AQP1</italic>) + 11.1 × 1 (<italic>PPARGC1A</italic>) + 5.6 × 1 (<italic>ADRB3</italic>) + 5.6 × 1 (<italic>GABPB1</italic>) + 5.6 × 1 (<italic>COL5A1</italic>) + 5.6 × 1 (<italic>HFE</italic>) = 100. However, an individual with unfavorable <italic>ACE</italic> and <italic>PPAR</italic>α SNPs and all the other SNPs favorable, would have a score of 56: 22.2 × 0 (<italic>ACE</italic>) + 22.2 × 0 (<italic>PPAR</italic>α) + 11.1 × 1 (<italic>ACTN3</italic>) + 11.1 × 1 (<italic>AQP1</italic>) + 11.1 × 1 (<italic>PPARGC1A</italic>) + 5.6 × 1 (<italic>ADRB3</italic>) + 5.6 × 1 (<italic>GABPB1</italic>) + 5.6 × 1 (<italic>COL5A1</italic>) + 5.6 × 1 (<italic>HFE</italic>) = 56.</p><table-wrap id=\"T1\" position=\"float\"><label>TABLE 1</label><caption><p>Genes included in the genetic endurance prediction score (GES).</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Gene (Complete name)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Gene (Acronym) SNP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Specific association</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Genotypes classification</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Frequency (%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Contribution (%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">References</td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Angiotensin converting enzyme</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>ACE</italic> rs4343</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Blood pressure regulation</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">AA favorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19.1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22.2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Metanalysis (<xref rid=\"B37\" ref-type=\"bibr\">Ma et al., 2013</xref>)</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">AG intermediate</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">48.9</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GG unfavorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">32.0</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Alpha-actinin-3</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>ACTN3</italic> rs1815739</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Muscle contraction</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">TT favorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17.9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11.1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B23\" ref-type=\"bibr\">Eynon et al., 2009</xref>; <xref rid=\"B2\" ref-type=\"bibr\">Ahmetov et al., 2011</xref>; <xref rid=\"B40\" ref-type=\"bibr\">Pasqua et al., 2016</xref></td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CT intermediate</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">51.1</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CC intermediate</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">31.0</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Beta-3 adrenergic receptor</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>ADRB3</italic> rs4994</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Lipolysis and thermogenesis stimulation</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GG favorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.6</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B47\" ref-type=\"bibr\">Santiago et al., 2011</xref></td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">AG favorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14.7</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">AA unfavorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">84.5</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Aquaporin 1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>AQP1</italic> rs1049305</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Osmotic balance by water transport</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CC favorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15.5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11.1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B39\" ref-type=\"bibr\">Martinez et al., 2009</xref>; <xref rid=\"B44\" ref-type=\"bibr\">Rivera et al., 2011</xref>; <xref rid=\"B49\" ref-type=\"bibr\">Saunders et al., 2015</xref></td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CG favorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">46.9</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GG unfavorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">37.6</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GA-binding protein transcription factor, β subunit 1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>GABPB1</italic> rs12594956</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Energy synthesis in mitochondria</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">AA favorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">38.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.6</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B21\" ref-type=\"bibr\">Eynon et al., 2010a</xref>, <xref rid=\"B24\" ref-type=\"bibr\">2013</xref></td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">AC intermediate</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">44.9</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CC intermediate</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17.1</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Collagen type V alpha 1 chain</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>COL5A1</italic> rs12722</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Fibrillogenesis in ligaments and tendons</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">TT favorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">35.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.6</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B11\" ref-type=\"bibr\">Brown et al., 2011</xref>; <xref rid=\"B43\" ref-type=\"bibr\">Posthumus et al., 2011</xref></td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CT intermediate</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">45.9</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CC intermediate</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18.5</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Hemochromatosis</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>HFE rs1799945</italic></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Iron absorption</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CG favorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">27.2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.6</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B28\" ref-type=\"bibr\">Grealy et al., 2015</xref></td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GG intermediate</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.6</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CC intermediate</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">69.2</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Peroxisome proliferator activated receptor alpha</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>PPAR</italic>α <italic>rs4253778</italic></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Metabolism of energy, lipids and carbohydrates</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GG favorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">65.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22.2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Metanalysis (<xref rid=\"B36\" ref-type=\"bibr\">Lopez-Leon et al., 2016</xref>)</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CG intermediate</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30.4</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CC unfavorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.0</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Peroxisome proliferator activated receptor, gamma, coactivator 1, alpha</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>PPARGC1A rs8192678</italic></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Glucose transportation and lipid and glucose oxidation</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CC favorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">41.2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11.1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B4\" ref-type=\"bibr\">Akhmetov et al., 2007</xref>; <xref rid=\"B52\" ref-type=\"bibr\">Stefan et al., 2007</xref>; <xref rid=\"B22\" ref-type=\"bibr\">Eynon et al., 2010b</xref></td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CT unfavorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">45.5</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">TT unfavorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13.3</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr></tbody></table><table-wrap-foot><attrib><italic>Genetic endurance prediction score (GES): 2.22 ACE + 2.22 PPARα + 1.11 ACTN3 + 1.11 AQP1 + 1.11 PPARGC1A + 5.6 ADRB3 + 5.6 GABPB1 + 5.6 COL5A1 + 5.6 HFE.</italic></attrib></table-wrap-foot></table-wrap><p>The selected power-related genotypes were:</p><list list-type=\"simple\"><list-item><label>•</label><p>Hypoxia inducible factor 1 alpha subunit <italic>HIF1A</italic> rs11549465: CC (81.1%) unfavorable, CT (17.7%) intermediate, TT (1.2%) favorable.</p></list-item><list-item><label>•</label><p>Muscle-specific creatine kinase <italic>CKM</italic> rs8111989: TT (48.3%) unfavorable, CT (43.3%) intermediate, CC (8.3%) favorable.</p></list-item></list></sec></sec><sec id=\"S3\"><title>Results</title><sec id=\"S3.SS1\"><title>Descriptive Analysis</title><p>Mean values of anthropometric, body composition, somatotype and blood pressure of all cyclists are shown in <xref rid=\"T2\" ref-type=\"table\">Table 2</xref>. As expected, the mean BMI and fat mass percentage values were in the range of “normal weight” [18.5–24.9 BMI, 12–20% fat mass percentage according to the Spanish Society for the Study of Obesity (<xref rid=\"B46\" ref-type=\"bibr\">Salas-Salvado et al., 2007</xref>)]. The three somatotype components were around the moderate 3–5 rate (<xref rid=\"B13\" ref-type=\"bibr\">Carter and Heath, 1990</xref>).</p><table-wrap id=\"T2\" position=\"float\"><label>TABLE 2</label><caption><p>Anthropometric, body composition, somatotype and blood pressure variables.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Variables</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Mean (SD)</td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Age (years)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">40.67(6.97)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Body weight (kg)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">70.49(8.09)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Height (cm)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">177.99(4.71)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Body mass index</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22.27(2.47)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Fat mass (%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14.43(5.27)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Muscle mass (%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">40.61(3.63)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Visceral fat classification</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.43(2.59)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Waist circumference (cm)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">78.95(5.28)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Waist to hip ratio</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.82(0.04)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Relaxed arm circumference (cm)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">29.73(2.23)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Flexed and tensed arm circumference (cm)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">31.58(2.21)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Biepicondylar humerus diameter (cm)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.25(0.29)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Biepicondylar femur diameter (cm)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.92(0.40)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Biceps skinfold (mm)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.43(0.90)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Triceps skinfold (mm)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.55(4.09)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Subscapular skinfold (mm)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.05(2.36)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Supraspinal skinfold (mm)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.4(6.03)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Abdominal skinfold (mm)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14.61(9.24)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Front thigh skinfold (mm)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.42(4.98)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Calf circumference (cm)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">36.89(2.08)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Medial calf skinfold (mm)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.98(1.81)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Systolic blood pressure (mmHg)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">126.8(7.04)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Diastolic blood pressure (mmHg)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">76.4(6.38)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Endomorphic value</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.98(1.24)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mesomorphic value</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.79(1.07)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Ectomorphic value</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.06(1.03)</td></tr></tbody></table></table-wrap><p>Moreover, the results of the analysis of the 3-day dietary records can be observed in <xref rid=\"T3\" ref-type=\"table\">Table 3</xref>. Here, it can be appreciated how the energy requirements of the cyclists were slightly higher than the energy intakes, these lasts with a high variability between the participants. Moreover, the mean Healthy Eating Index resulted in a “good diet” which, as explained in the methodology, is considered an intermediate value (61–70 points). This table also shows the average of servings per group of food consumed according to the Validated Food Frequency Questionnaire, where we can observe that vegetables and fruits are the most consumed.</p><table-wrap id=\"T3\" position=\"float\"><label>TABLE 3</label><caption><p>Dietary data.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Variables</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Mean (SD)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Variables</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Mean (SD)</td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Energy requirements (kcal)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3370(348)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Energy intake (kcal)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3192(1123)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Proteins (% TCV)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16.35(3.67)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Carbohydrates (% TCV)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">43.5(10.62)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Sugars (% TCV)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19.62(7.40)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Lipids (% TCV)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">36.91(9.22)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Saturated fatty acids (% TCV)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11.39(3.31)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Healthy Eating Index</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">65.19(14.06)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Glycemic index</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">48.96(11.58)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Vegetables (s/d)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.66(1.32)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Fish and seafood (s/d)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.92(0.56)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Fruits (s/d)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.98(2.10)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Legumes (s/d)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.39(0.28)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Nuts (s/d)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.68(1.26)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Dairy (s/d)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.53(1.20)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Eggs (s/d)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.53(0.25)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Red meat (s/d)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.45(0.21)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">White meat (s/d)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.48(0.21)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Processed meat (s/d)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.09(0.94)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Animal fats (s/d)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.26(0.40)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Viscera (s/d)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.02(0.04)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Ready to eat foods (s/d)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.19(0.13)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Salty snacks (s/d)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.38(0.34)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Sauces (s/d)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.23(0.15)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Alcohol (s/d)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.32(0.28)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Coffee and tea (s/d)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.28(1.33)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Garlic and spices (s/d)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.57(0.52)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Pastries and sweets (s/d)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.00(0.98)</td></tr></tbody></table><table-wrap-foot><attrib><italic>s/d, servings per day; TCV, total caloric value.</italic></attrib></table-wrap-foot></table-wrap><p>Finally, <xref rid=\"T4\" ref-type=\"table\">Table 4</xref> describes the mean results of the cardiopulmonary exercise test carried out at the Sports Medicine University Center.</p><table-wrap id=\"T4\" position=\"float\"><label>TABLE 4</label><caption><p>Cardiopulmonary exercise test output.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Variables</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Mean (SD)</td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Maximal heart rate (bpm)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">176.20  (15.23)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Resting heart rate (bpm)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">51.40  (7.03)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Heart rate in VT1 (bpm)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">131.67  (15.25)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Heart rate in VT2 (bpm)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">157.67  (14.07)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VO<sub>2</sub> max (mL/min)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4116  (565)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VO<sub>2</sub> max (mL/kg/min)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">59.16  (6.50)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VO<sub>2</sub> in VT1 (mL/kg/min)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">39.59  (8.08)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VO<sub>2</sub> in VT2 (mL/kg/min)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">50.61  (6.19)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VCO<sub>2</sub> max (mL/min)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5136  (777)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VCO<sub>2</sub> max (mL/kg/min)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">73.85  (10.41)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Maximum minute ventilation (L/min)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">169.80  (23.99)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Minute ventilation in VT1 (L/min)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">58.21  (13.78)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">% VO<sub>2</sub> in VT1 in relation to VO<sub>2</sub> max</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">66.94  (9.13)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">% VO<sub>2</sub> in VT2 in relation to VO<sub>2</sub> max</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">85.95  (5.53)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Minute ventilation in VT2 (L/min)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">93.07  (16.13)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">O<sub>2</sub> pulse max</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">23.53  (3.36)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">O<sub>2</sub> pulse in VT1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20.87  (3.28)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">O<sub>2</sub> pulse in VT2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22.47  (3.41)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Highest workload achieved</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">329.00  (42.74)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Workload in VT1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">186.27  (35.20)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Workload in VT2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">261.67  (38.94)</td></tr></tbody></table><table-wrap-foot><attrib><italic>VCO<sub>2</sub> max, maximal carbon dioxide output; O<sub>2</sub> pulse, VO<sub>2</sub>/heart rate; VO<sub>2</sub> max, maximal oxygen consumption; VT1, aerobic ventilatory threshold; VT2, anaerobic ventilatory threshold.</italic></attrib></table-wrap-foot></table-wrap></sec><sec id=\"S3.SS2\"><title>Association Analyses</title><sec id=\"S3.SS2.SSS1\"><title>The GES Correlates With the Cardiovascular Exercise Test</title><p>Once established the prediction algorithm (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>), it was related to the results obtained from the functional cardiovascular exercise test (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>). The association analysis of the GES and the variables obtained in the cardiovascular exercise test, revealed a statistically significant positive relationship between the GES results and the VO<sub>2</sub> max (<italic>P</italic> = 0.033, <xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>), VO<sub>2</sub> in VT1 (<italic>P</italic> = 0.049, ρ = 0.516) and VO<sub>2</sub> in VT2 (<italic>P</italic> < 0.001, ρ = 0.813) values.</p><fig id=\"F1\" position=\"float\"><label>FIGURE 1</label><caption><p>Levels of VO<sub>2</sub> max (mL/kg/min) during the cardiopulmonary exercise test according to the genetic endurance prediction score (GES).</p></caption><graphic xlink:href=\"fgene-11-00711-g001\"/></fig><p>As expected, SNPs included in the GES were also individually associated with specific parameters related to individual performance. The analysis of the association of the different genotypes individually and the cardiopulmonary exercise test showed that GG genotypes for <italic>AQP1</italic> showed lower levels of VO<sub>2</sub> in VT1 (32.1 ± 4.6 mL/kg/min vs 42.3 ± 7.4 mL/kg/min) and fewer values of% VO<sub>2</sub> in VT1 in relation to VO<sub>2</sub> max (58.2 ± 7.0% vs 70.1 ± 7.7%) than CC + CG (<italic>P</italic> = 0.020 and <italic>P</italic> = 0.030, respectively) (<xref ref-type=\"fig\" rid=\"F2\">Figures 2A,B</xref>). On the other hand, GG genotypes for the <italic>PPAR</italic>α presented higher values of VO<sub>2</sub> in VT2 than CC + CG (52.6 ± 4.6 mL/kg/min vs 42.8 ± 5.9 mL/kg/min, <italic>P</italic> = 0.030), as shown in <xref ref-type=\"fig\" rid=\"F2\">Figure 2C</xref>.</p><fig id=\"F2\" position=\"float\"><label>FIGURE 2</label><caption><p>SNPs included in the prediction algorithm GES individually associated to specific parameters related to individual performance. <bold>(A,B)</bold> Levels of VO<sub>2</sub> and% VO<sub>2</sub> in VT1 during the cardiopulmonary exercise test according to <italic>AQP1</italic> rs1049305. <bold>(C)</bold> Levels of VO<sub>2</sub> in VT2 during the cardiopulmonary exercise test according to <italic>PPAR</italic>α rs4253778. <bold>(D)</bold> Maximal heart rate during the cardiopulmonary exercise test according <italic>GABPB1</italic> rs12594956. <bold>(E)</bold> Maximum minute ventilation (VE) during the cardiopulmonary exercise test according <italic>ACE</italic> rs4343.</p></caption><graphic xlink:href=\"fgene-11-00711-g002\"/></fig><p>Regarding the <italic>GABPB1</italic>, AA genotypes had significantly lower maximal HR (<xref ref-type=\"fig\" rid=\"F2\">Figure 2D</xref>) and minor HR in VT1 and VT2 than AC genotypes (167.8 ± 16.5 bpm vs 185.9 ± 4.5 bpm, 120.8 ± 11.0 bpm vs 144.1 ± 7.8 bpm and 149.0 ± 13.3 bpm vs 167.6 ± 6.3 bpm; <italic>P</italic> < 0.001, <italic>P</italic>-adjusted for Bonferroni = 0.040 and <italic>P</italic> = 0.001, respectively). There were no CC genotypes in the studied sample. Moreover, a statistically significant difference was found between AA genotypes for the <italic>ACE</italic> gene and the AG + GG with respect to the maximum VE (200 ± 16 L/min vs 162 ± 19 L/min, <italic>P</italic> = 0.040, <xref ref-type=\"fig\" rid=\"F2\">Figure 2E</xref>).</p></sec><sec id=\"S3.SS2.SSS2\"><title>HIF1A Genetic Variant Is Associated With Somatotype</title><p>When we studied the relationship between the different SNPs and the results of the somatotype, we found a statistically significant association between <italic>HIF1A</italic> genotypes and the mesomorphic component. Precisely, among the CC cyclists, 8.33% presented a low mesomorphic value, 91.7% a moderate value and 0% a high value; while amongst the CT individuals, 33.33% presented a moderate result and the rest 66.7% a high value (<italic>P</italic> = 0.029). There were no TT individuals in the studied sample.</p></sec><sec id=\"S3.SS2.SSS3\"><title>Genes Associated With Dietary Records</title><p>When analyzing the association of the different genotypes of each SNP and the results of the dietary variables, we found different interesting associations.</p><list list-type=\"simple\"><list-item><label>•</label><p>Subjects GG for the <italic>AQP1</italic> presented a lower intake of carbohydrates [% total caloric value (TCV)] than CG + CC individuals (34.2 ± 11.4% vs 46.9 ± 8.5%, <italic>P</italic> = 0.040), as shown in <xref ref-type=\"fig\" rid=\"F3\">Figure 3A</xref>.\n<fig id=\"F3\" position=\"float\"><label>FIGURE 3</label><caption><p>Association of SNPs included in the prediction algorithm GES and specific dietary variables. <bold>(A)</bold> Consumption of% total caloric value (TCV) carbohydrates according to <italic>AQP1</italic> rs1049305. <bold>(B)</bold> Consumption of% TCV sugars according to <italic>CKM</italic> rs8111989. <bold>(C)</bold> Mean glycemic index values according to <italic>ACE</italic> rs4343.</p></caption><graphic xlink:href=\"fgene-11-00711-g003\"/></fig></p></list-item><list-item><label>•</label><p>Participants TT for the <italic>CKM</italic> had a higher consumption of sugars (% TCV) than CC + CT individuals (25.9 ± 9.2% vs 16.5 ± 3.9%, <italic>P</italic> = 0.010, <xref ref-type=\"fig\" rid=\"F3\">Figure 3B</xref>).</p></list-item><list-item><label>•</label><p>Cyclists AA for the <italic>ACE</italic> presented a higher mean glycemic index consumption than AG + GG (58.9 ± 1.1 vs 46.5 ± 11.7, <italic>P</italic> = 0.010, <xref ref-type=\"fig\" rid=\"F3\">Figure 3C</xref>).</p></list-item></list><p>On the other hand, with regard to the food frequency questionnaire, and inverse correlation between the number of coffee and tea rations consumed per day and the result of the GES was observed (<italic>P</italic> = 0.004), so that the higher the consumption the lower the GES (<xref ref-type=\"fig\" rid=\"F4\">Figure 4</xref>).</p><fig id=\"F4\" position=\"float\"><label>FIGURE 4</label><caption><p>Rations of tea and coffee consumed according to the genetic endurance prediction score (GES).</p></caption><graphic xlink:href=\"fgene-11-00711-g004\"/></fig></sec></sec></sec><sec id=\"S4\"><title>Discussion</title><p>The present research provides new information regarding the link between genetics and sport performance from different angles.</p><p>The most important result was the potential validation of an algorithm prediction of genetic susceptibility to endurance abilities. At present, cardiovascular exercise test is considered the gold standard assessment of endurance performance (<xref rid=\"B31\" ref-type=\"bibr\">Hausen et al., 2018</xref>). We observed that those subjects with a higher value in the GES, presented significantly better results in the cardiovascular exercise test according to VO<sub>2</sub> in VT1, VO<sub>2</sub> in VT2 and most importantly, VO<sub>2</sub> max which is internationally considered the key measure of aerobic capacity (<xref rid=\"B31\" ref-type=\"bibr\">Hausen et al., 2018</xref>). Actually, VO<sub>2</sub> max obtained in this kind of test represents the highest capacity of oxygen consumption during a maximal exercise (<xref rid=\"B51\" ref-type=\"bibr\">Shete et al., 2014</xref>). Additionally, VT1 is the point at which the aerobic metabolism is no longer the major energy source and the anaerobic metabolism begins to be used. The exercise intensity at which VT1 occurs is considered the highest submaximal level tolerated by an individual for long time periods (<xref rid=\"B32\" ref-type=\"bibr\">Herdy et al., 2016</xref>). Hence, we hypothesized that higher values of VO<sub>2</sub> at VT1 may predict higher VO<sub>2</sub> max, which leads to better endurance conditions. Concerning VT2, it is the point where the demand of oxygen by muscles exceeds the mitochondrial supplies and consequently, energy production begins predominantly anaerobic. It begins when lactate starts to accumulate in blood which is usually at 60–70% VO<sub>2</sub> max (<xref rid=\"B5\" ref-type=\"bibr\">Albouaini et al., 2007</xref>). The VO<sub>2</sub> at VT2 is considered a submaximal index of exercise capacity and endurance performance, so that the higher VO<sub>2</sub> at VT2, the better endurance results (<xref rid=\"B15\" ref-type=\"bibr\">Coyle et al., 1988</xref>; <xref rid=\"B32\" ref-type=\"bibr\">Herdy et al., 2016</xref>). These achievements may complement the association found between a genetic score based on a GWAS study and the improvement of the VO<sub>2</sub> max in a sedentary population carried out by previous researchers (<xref rid=\"B10\" ref-type=\"bibr\">Bouchard et al., 2011</xref>).</p><p>When we analyzed the association between the different genotypes individually and the cardiovascular test results, we also found some interesting correlations. In this regard, individuals presenting the favorable genotypes for endurance sports according to <italic>AQP1</italic> (<xref rid=\"B39\" ref-type=\"bibr\">Martinez et al., 2009</xref>; <xref rid=\"B49\" ref-type=\"bibr\">Saunders et al., 2015</xref>), showed better results in the cardiovascular exercise test (higher levels of VO<sub>2</sub> in VT1 and % of VO<sub>2</sub> in VT1 in relation to VO<sub>2</sub> max), than GG homozygous. The physiological explanation for this association may lie in the <italic>AQP1</italic> encoded membrane protein role. The aquaporin 1 water channel is implicated in the transport of water, maintaining the osmotic balance between the blood and the cells (<xref rid=\"B26\" ref-type=\"bibr\">Frigeri et al., 2004</xref>). During prolonged exercise, body temperature is regulated by controlling the distribution of body fluid; water passes from the intracellular to the extracellular spaces and evaporates by sweating. An inadequate loss of sweat during exercise, especially in hot climates, will negatively affect athletic performance (<xref rid=\"B34\" ref-type=\"bibr\">Kenny, 2014</xref>). Thus, we hypothesize that CC genotype is associated with an efficient state of cellular hydration and body temperature regulation which leads to a better endurance performance.</p><p>Moreover, favorable genotypes for endurance performance of the <italic>PPAR</italic>α (<xref rid=\"B36\" ref-type=\"bibr\">Lopez-Leon et al., 2016</xref>) presented significantly higher values of VO<sub>2</sub> in VT2 than CC + CG. This gene regulates the expression of other genes involved in the metabolism of energy, fats and sugars in the skeletal muscle among other tissues (<xref rid=\"B20\" ref-type=\"bibr\">Duval et al., 2004</xref>). Given its involvement in these processes, it is hypothesized that it is activated during endurance exercise (<xref rid=\"B36\" ref-type=\"bibr\">Lopez-Leon et al., 2016</xref>). Another suggested explanation for the association of <italic>PPAR</italic>α genotypes with endurance performance has to do with the type of fibers in the skeletal muscle. In this sense, in a cohort of 786 Russian athletes it was observed that GG homozygous presented significantly higher percentage of type I muscle fibers than the other genotypes (<xref rid=\"B3\" ref-type=\"bibr\">Ahmetov et al., 2006</xref>). These skeletal muscle fibers are classified into two types: type I or slow contraction and type II or rapid contraction fibers. Type I fibers have greater resistance to fatigue and predominate in resistance athletes, while the type II ones are adapted to strong and explosive muscle contractions and predominate in athletes who perform power sports (<xref rid=\"B12\" ref-type=\"bibr\">Cartee et al., 2016</xref>).</p><p>Regarding the <italic>GABPB1</italic>, the individuals presenting the AA genotype showed a lower maximal HR and lower HR in VT1 and VT2 than AC genotypes. This result is in accordance with other studies that also consider this genotype favorable for endurance sports (<xref rid=\"B24\" ref-type=\"bibr\">Eynon et al., 2013</xref>) as it has been described that endurance athletes present lower HR in maximum aerobic traits (<xref rid=\"B57\" ref-type=\"bibr\">Zaniqueli et al., 2014</xref>). The <italic>GABPB1</italic> gene encodes the GA-binding protein transcription factor, which is implicated in the regulation of the mitochondrial function generating ATP energy (<xref rid=\"B16\" ref-type=\"bibr\">Dinkova-Kostova and Abramov, 2015</xref>) what might explain the implication of this gene in the individual endurance capacity.</p><p>Additionally, AA genotypes for the <italic>ACE</italic> gene presented higher maximum VE than the AG + GG genotypes. The AA genotype of the rs4343 is considered equivalent to the II genotype of the <italic>ACE</italic> I/D rs4340 (<xref rid=\"B27\" ref-type=\"bibr\">Glenn et al., 2009</xref>) which at the same time is the most studied favorable genotype for endurance performance (<xref rid=\"B37\" ref-type=\"bibr\">Ma et al., 2013</xref>). Maximal VE has been reported to be directly correlated with VO<sub>2</sub> max (<xref rid=\"B35\" ref-type=\"bibr\">Keramidas et al., 2010</xref>; <xref rid=\"B38\" ref-type=\"bibr\">Malekmohammad et al., 2012</xref>). Accordingly, it is suggested that muscles of endurance athletes require higher values of VO<sub>2</sub> so that their VE during exercise is higher.</p><p>Hypoxia-inducible factor-1 (HIF1) regulates oxygen homeostasis in mammalian cells and in particular, it seems to have a role during high intensity exercise, helping the skeletal muscle to adapt to low oxygen concentrations (<xref rid=\"B25\" ref-type=\"bibr\">Freyssenet, 2007</xref>). According to the association between somatotype variables and the different genotypes, individuals carrying the T allele of the <italic>HIF1A</italic> SNP presented moderate or high mesomorphic values, while none of the CC homozygous had a high mesomorphic component. Precisely, allele T is associated with power-oriented athletes (<xref rid=\"B17\" ref-type=\"bibr\">Drozdovska et al., 2013</xref>). This makes sense as high mesomorphic individuals are characterized by high skeletal muscle mass, needed for power anaerobic exercises (<xref rid=\"B30\" ref-type=\"bibr\">Gutnik et al., 2015</xref>). Besides, as cycling is more an endurance-oriented sport than a power sport, it also makes sense that the mesomorph was the component with the lowest value among the sample.</p><p>When we analyzed the association of the different genotypes and the dietary records, we also found diverse significant associations. The cyclists with a favorable genotype for endurance sports regarding the <italic>AQP1</italic> presented a higher intake of carbohydrates which we hypothesize that would probably be due to maintain the glycogen stores needed for long distance exercise (<xref rid=\"B6\" ref-type=\"bibr\">Alghannam et al., 2018</xref>). Similarly, individuals with an unfavorable genotype for power sports according to <italic>CKM</italic> consumed higher amounts of sugars (<xref rid=\"B14\" ref-type=\"bibr\">Chen et al., 2017</xref>). Commonly, it is considered that genotypes unfavorable for power are favorable for endurance sports and vice versa, which might explain this association. To our knowledge, this is the first time that <italic>AQP1</italic> and <italic>CKM</italic> genotypes have been associated with dietary intake.</p><p>Moreover, individuals presenting a favorable genotype for endurance sports according to <italic>ACE</italic> gene presented a higher mean glycemic index intake than the other genotypes. Although it seems clear that carbohydrate consumption is needed to maintain glycogen stores, whether these macronutrients are preferable to be complex or with a high glycemic index appears to be controversial, as there are studies that point out that moderate glycemic index diets improves exercise performance (<xref rid=\"B19\" ref-type=\"bibr\">Durkalec-Michalski et al., 2017</xref>) while others support the low glycemic index (<xref rid=\"B18\" ref-type=\"bibr\">Durkalec-Michalski et al., 2018</xref>). A possible explanation of this controversy may lie in the fact that the unfavorable <italic>ACE</italic> genotype for endurance performance has also been linked to lower glucose tolerance (<xref rid=\"B50\" ref-type=\"bibr\">Schuler et al., 2017</xref>). Thus, we hypothesize that <italic>ACE</italic> AA individuals might take advantage in endurance sports by consuming a moderate glycemic index diet as they can metabolize glucose in a better way. However, we are aware that more studies in this field are needed to confirm this hypothesis.</p><p>Finally, an interesting association between the habit of consuming caffeinated beverages and the result of the GES was observed. A possible explanation for this inverse correlation might be that athletes less genetically predisposed to endurance sports where energy is a key factor, are more likely to use ergogenic aids such as caffeine. However, we have to consider that the food frequency questionnaire used gives overage data of the last year, but we do not have data on dietary intakes in specific time frames such as competitions.</p></sec><sec id=\"S5\"><title>Conclusion</title><p>The outcomes of the present study confirm a positive relationship between an endurance prediction algorithm and the results of a cardiopulmonary exercise test. Moreover, <italic>AQP1</italic>, <italic>PPAR</italic>α, <italic>GABPB1</italic>, and <italic>ACE</italic> genes were individually related with endurance performance. Besides, <italic>HIF1A</italic> showed an association with the somatotype and <italic>AQP1</italic>, <italic>CKM</italic>, and <italic>ACE</italic> genes were associated with the athletes’ dietary intake. In addition, an inverse association between the habit of consuming caffeinated beverages and the GES was observed.</p><p>This information may facilitate the design of larger studies implicated in the prediction of sports capacities, which may facilitate the finding of genetically talented athletes, improve their training and dietary habits, as well as help in the improvement of physical conditions of amateur athletes.</p></sec><sec sec-type=\"data-availability\" id=\"S6\"><title>Data Availability Statement</title><p>The datasets presented in this article are not readily available because are part of the GENYAL Platform for clinical trials in nutrition and health (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.food.imdea.org/services/Platform-Clinical-Trials-Nutrition-and-Health\">https://www.food.imdea.org/services/Platform-Clinical-Trials-Nutrition-and-Health</ext-link>) database. This is a database that is currently registered as a collection under the Spanish rules which by policy of the Center will be public afterwards once the data of the entire expected population is gathered. Requests to access the datasets should be directed to <email>ana.ramirez@imdea.org</email>.</p></sec><sec id=\"S7\"><title>Ethics Statement</title><p>The studies involving human participants were reviewed and approved by the Research Ethics Committee of the IMDEA Food Foundation. The patients/participants provided their written informed consent to participate in this study.</p></sec><sec id=\"S8\"><title>Author Contributions</title><p>AR, GR, JS, and VL-K proposed, funded, and designed the research. FL-S, JR-A, and JS did the recruitment of the sample and the monitoring of the cardiopulmonary exercise test. RI, IE-S, EB-R, HM-P, and EA-A performed the anthropometric measurements, body composition analysis, dietary records and, DNA collection and genotyping. GC performed the statistical analysis. RI wrote the first draft of the manuscript. IE-S, FL-S, and JR-A wrote sections of the manuscript. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Genet</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Genet</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Genet.</journal-id><journal-title-group><journal-title>Frontiers in Genetics</journal-title></journal-title-group><issn pub-type=\"epub\">1664-8021</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849827</article-id><article-id pub-id-type=\"pmc\">PMC7431953</article-id><article-id pub-id-type=\"doi\">10.3389/fgene.2020.00844</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Genetics</subject><subj-group><subject>Review</subject></subj-group></subj-group></article-categories><title-group><article-title>Circulating Cell-Free Nucleic Acids as Epigenetic Biomarkers in Precision Medicine</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Rahat</surname><given-names>Beenish</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/656823/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Ali</surname><given-names>Taqveema</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1046682/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Sapehia</surname><given-names>Divika</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/737859/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Mahajan</surname><given-names>Aatish</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/746823/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Kaur</surname><given-names>Jyotdeep</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/659345/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>National Institute of Child Health and Human Development, National Institutes of Health</institution>, <addr-line>Bethesda, MD</addr-line>, <country>United States</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Postgraduate Institute of Medical Education and Research</institution>, <addr-line>Chandigarh</addr-line>, <country>India</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Rui Henrique, Portuguese Oncology Institute, Portugal</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Naoko Hattori, National Cancer Center Research Institute, Japan; Igor Kovalchuk, University of Lethbridge, Canada</p></fn><corresp id=\"c001\">*Correspondence: Jyotdeep Kaur, <email>jyotdeep2001@yahoo.co.in</email></corresp><fn fn-type=\"other\" id=\"fn004\"><p>This article was submitted to Epigenomics and Epigenetics, a section of the journal Frontiers in Genetics</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>844</elocation-id><history><date date-type=\"received\"><day>27</day><month>2</month><year>2020</year></date><date date-type=\"accepted\"><day>13</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Rahat, Ali, Sapehia, Mahajan and Kaur.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Rahat, Ali, Sapehia, Mahajan and Kaur</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>The circulating cell-free nucleic acids (ccfNAs) are a mixture of single- or double-stranded nucleic acids, released into the blood plasma/serum by different tissues via apoptosis, necrosis, and secretions. Under healthy conditions, ccfNAs originate from the hematopoietic system, whereas under various clinical scenarios, the concomitant tissues release ccfNAs into the bloodstream. These ccfNAs include DNA, RNA, microRNA (miRNA), long non-coding RNA (lncRNA), fetal DNA/RNA, and mitochondrial DNA/RNA, and act as potential biomarkers in various clinical conditions. These are associated with different epigenetic modifications, which show disease-related variations and so finding their role as epigenetic biomarkers in clinical settings. This field has recently emerged as the latest advance in precision medicine because of its clinical relevance in diagnostic, prognostic, and predictive values. DNA methylation detected in ccfDNA has been widely used in personalized clinical diagnosis; furthermore, there is also the emerging role of ccfRNAs like miRNA and lncRNA as epigenetic biomarkers. This review focuses on the novel approaches for exploring ccfNAs as epigenetic biomarkers in personalized clinical diagnosis and prognosis, their potential as therapeutic targets and disease progression monitors, and reveals the tremendous potential that epigenetic biomarkers present to improve precision medicine. We explore the latest techniques for both quantitative and qualitative detection of epigenetic modifications in ccfNAs. The data on epigenetic modifications on ccfNAs are complex and often milieu-specific posing challenges for its understanding. Artificial intelligence and deep networks are the novel approaches for decoding complex data and providing insight into the decision-making in precision medicine.</p></abstract><kwd-group><kwd>autoimmune diseases</kwd><kwd>cancer diagnosis</kwd><kwd>precision medicine</kwd><kwd>epigenetic biomarkers</kwd><kwd>circulating nucleic acids in plasma/serum</kwd><kwd>prenatal and genetic diagnostics</kwd><kwd>circulating cell free nucleic acids</kwd></kwd-group><counts><fig-count count=\"1\"/><table-count count=\"4\"/><equation-count count=\"0\"/><ref-count count=\"253\"/><page-count count=\"20\"/><word-count count=\"0\"/></counts></article-meta></front><body><sec id=\"S1\"><title>Introduction</title><p>The diagnostic platform utilizing the detection of biomarkers in various body fluids called “liquid biopsy” can revolutionize precision medicine. Precision medicine is aimed at attaining better-personalized care by the development of the latest diagnostic and prognostic methods that consider individual variability (<xref rid=\"B104\" ref-type=\"bibr\">Kaur et al., 2017</xref>). Liquid biopsy is being utilized for non-invasive prognostic and predictive purposes. Efficient and reliable markers within the body fluids can help in personalized treatment decisions for monitoring disease and survival. ccfNAs have emerged as such markers for screening, diagnosis, prognosis, management, and treatment of various cancers; autoimmune, neurological, and mitochondrial diseases; prenatal diagnosis; diagnosis of pregnancy-related complications (<xref rid=\"B161\" ref-type=\"bibr\">Pos et al., 2018</xref>); diabetes; inflammation; rheumatoid arthritis; stroke; and trauma (<xref rid=\"B199\" ref-type=\"bibr\">Swarup and Rajeswari, 2007</xref>). An increased amount of ccfNAs is observed in these disorders, making liquid biopsies more sensitive, rapid, accurate, and preferable alternatives for various invasive diagnostic methods (<xref rid=\"B161\" ref-type=\"bibr\">Pos et al., 2018</xref>).</p><p>ccfNAs present in blood circulation include cell-free genomic DNAs (ccfDNAs) and cell-free mtDNA (<xref rid=\"B111\" ref-type=\"bibr\">Kohler et al., 2009</xref>; <xref rid=\"B206\" ref-type=\"bibr\">Thierry et al., 2016</xref>) and cell-free RNAs (ccfRNAs) including protein-coding messenger RNA (mRNA), regulatory non-coding RNAs like microRNAs (miRNAs), long non-coding RNAs (lncRNAs), circular RNAs, and RNAs involved in translation like transfer RNAs (tRNAs) and ribosomal RNAs (rRNAs) (<xref rid=\"B161\" ref-type=\"bibr\">Pos et al., 2018</xref>).</p><p>The ccfNAs (DNAs and RNAs) are generally released into the blood circulation either by apoptosis, necrosis, or active secretion. In healthy persons, the origin of ccfNAs is mainly attributed to lymphoid and myeloid tissues (<xref rid=\"B192\" ref-type=\"bibr\">Snyder et al., 2016</xref>), while in the case of various clinical conditions, the associated or the affected tissues would release the extra amount of ccfNAs into blood (<xref rid=\"B199\" ref-type=\"bibr\">Swarup and Rajeswari, 2007</xref>; <xref rid=\"B54\" ref-type=\"bibr\">Devonshire et al., 2014</xref>) in a pattern specific to the pathophysiological condition (<xref rid=\"B91\" ref-type=\"bibr\">Hunter et al., 2008</xref>; <xref rid=\"B150\" ref-type=\"bibr\">Noferesti et al., 2015</xref>).</p><p>Various genetic as well as epigenetic biomarkers have been explored for ccfNA-based liquid biopsy. As genetic biomarkers are less consistent and provide more variability across studies, epigenetic markers, which are more generalized between samples, present as a promising alternative for early diagnosis and monitoring of the diseases. These epigenetic marks are tissue specific and reflect the pattern of disease progression (<xref rid=\"B243\" ref-type=\"bibr\">Zeng et al., 2019</xref>). Furthermore, epigenetic biomarkers are dynamic with most techniques required for analysis of these biomarkers that are already available in clinical laboratories.</p><p>The use of epigenetic marks has revolutionized the field of non-invasive molecular diagnosis replacing traditional screening and treatment methods. These assays have great potential in future precise patient care. The epigenetic marks for ccfNAs reflect the pattern specific for the tissue contributing to these ccfNAs. Therefore, the use of epigenetic markers can help in the diagnosis of various diseases even before the onset of actual symptoms and hence help in better management of the disease. Precision medicine has improved health care by the identification of different stages/subsets of diseases, precise diagnosis, and treatment. Furthermore, the development of advanced analytical software techniques like machine learning and artificial intelligence can enhance precision medicine (<xref rid=\"B4\" ref-type=\"bibr\">Ahlquist, 2018</xref>; <xref rid=\"B22\" ref-type=\"bibr\">Beltran-Garcia et al., 2019</xref>). These are used in combination with next-generation sequencing to identify novel ccfNA-based epigenetic markers.</p></sec><sec id=\"S2\"><title>Epigenetic Biomarkers in ccfNAs</title><p>Reliable markers are required to guide personalized treatment decisions for monitoring disease progression and survival. The presence of epigenetic marks on ccfNAs specific to a particular clinical condition is widely being explored to advance personalized medicine. A perfect epigenetic marker for precision medicine should be able to detect the disease with high sensitivity, predict the risk of disease development and its progression, and monitor the therapeutic response of the patient (<xref rid=\"B22\" ref-type=\"bibr\">Beltran-Garcia et al., 2019</xref>). ccfDNAs are associated with various epigenetic marks (<xref rid=\"B181\" ref-type=\"bibr\">Schwarzenbach et al., 2011</xref>) like DNA methylation, hydroxymethylcytosine (5hmC), and posttranslational modifications of histones. In addition, nucleosome positioning and occupancy on ccfDNAs have exhibited high sensitivity and specificity in liquid biopsy-based methods for disease detection and classification.</p><p>The 5-methylcytosine (5mC) modification at CpG dinucleotides is the most abundant form of DNA methylation. It plays an important role in the regulation of gene expression and is widely used as an epigenetic biomarker for ccfDNA-based assays. DNA methylation has replaced many genetic mutation- or protein-based markers. These 5mC biomarkers are also valuable in identifying tissue-specific methylation to estimate tumor burden and tissue of origin in ccfDNAs. In addition to 5mC, 5-hydroxymethylcytosine (5hmC) is also used as an epigenetic mark on ccfDNAs (<xref rid=\"B243\" ref-type=\"bibr\">Zeng et al., 2019</xref>). 5hmC is created by the oxidation of 5mC by 10–11 translocation (Tet) proteins. Although 5hmC is far less abundant compared to 5mC, it is more distinctly distributed among different transcriptionally active regions, which emphasizes its potential as a diagnostic marker. Genome-wide analysis of 5hmC pattern can provide more information about the potential of this epigenetic marker for ccfDNAs (<xref rid=\"B243\" ref-type=\"bibr\">Zeng et al., 2019</xref>).</p><p>Nucleosome positioning has emerged as a recent biomarker to distinguish the tissue of origin of ccfDNA based on derived nucleosome maps. <xref rid=\"B192\" ref-type=\"bibr\">Snyder et al., 2016</xref> performed deep sequencing on ccfDNAs and observed a distinct pattern of nucleosome positioning between healthy persons and cancer patients correlating with the tissues of origin (<xref rid=\"B192\" ref-type=\"bibr\">Snyder et al., 2016</xref>). This emphasizes the use of nucleosome maps, which consist of occupancy of transcription factor and nucleosome as the epigenetic marks to distinguish normal versus cancer ccfDNAs. Hence, nucleosome positioning can also be used to identify various cancers that generally require invasive biopsies for definitive diagnosis. Moreover, genome-wide nucleosome positioning of ccfDNAs is utilized to infer pathological states of multiple disease types. A comprehensive public database called cell-free epigenome atlas (CFEA) provides the epigenome profile of ccfDNAs from various human diseases and can help in a better understanding of collected data (<xref rid=\"B240\" ref-type=\"bibr\">Yu et al., 2020</xref>). ccfDNA are generally associated with nucleosomes and histone proteins. Histone proteins are posttranslationally modified at amino acid residues located on their N- and C-terminal tails. These modifications act as epigenetic marks that can specifically distinguish disease-related ccfDNAs in blood samples. Various types of histone modifications are associated with the development and pathogenesis of human diseases (<xref rid=\"B249\" ref-type=\"bibr\">Zhao and Shilatifard, 2019</xref>).</p><p>In addition to DNA markers, RNA markers like mRNAs, miRNAs, lncRNAs, and circRNAs are also getting attention in the focus of clinical research (<xref rid=\"B161\" ref-type=\"bibr\">Pos et al., 2018</xref>).</p><p>Most of the currently available diagnostic tests based on ccfNAs use either DNA methylation markers or the differential expression of miRNAs. These biomarkers are relatively easily detected and estimated using accessible techniques like methyLight, methyl-specific PCR, methylation-sensitive high-resolution melting, and pyrosequencing (<xref rid=\"B67\" ref-type=\"bibr\">García-Giménez et al., 2017</xref>). DNA methylation specific to fetal and tumor DNA has been reported in pregnant women and cancer patients, respectively (<xref rid=\"B235\" ref-type=\"bibr\">Wong et al., 1999</xref>; <xref rid=\"B160\" ref-type=\"bibr\">Poon et al., 2002</xref>). The pattern of the methylation in these ccfDNAs has been traced back to their tissue of origin (<xref rid=\"B133\" ref-type=\"bibr\">Lun et al., 2013</xref>; <xref rid=\"B198\" ref-type=\"bibr\">Sun et al., 2017</xref>). Differentially methylated markers have been reported in ccfDNAs like INS promoter 1 in diabetes and <italic>REG1A</italic> and <italic>CUX2</italic> genes in pancreatic cancer (<xref rid=\"B115\" ref-type=\"bibr\">Lehmann-Werman et al., 2016</xref>). Promoter methylation of <italic>SERPINB5</italic>, <italic>RASSF1A</italic>, and <italic>STAT5A</italic> act as epigenetic fetal markers in maternal blood (<xref rid=\"B46\" ref-type=\"bibr\">Chim et al., 2005</xref>; <xref rid=\"B41\" ref-type=\"bibr\">Chan et al., 2006</xref>; <xref rid=\"B168\" ref-type=\"bibr\">Rahat et al., 2016a</xref>).</p></sec><sec id=\"S3\"><title>Diagnostic Approach for Epigenetic Modifications in ccfNA</title><p>The various diagnostic approaches to study the epigenetic modifications in the nucleic acids include methylated CpG island recovery assay (MIRA) and MethylCap that rely on methyl-CpG-binding domains (MBD) to capture methylated DNA after DNA fractionation either by restriction digestion or sonication (<xref rid=\"B142\" ref-type=\"bibr\">Mitchell et al., 2011</xref>). These methods can also be combined with microarray or NGS technologies (MethylCap-seq) to identify biomarkers for cancer diagnosis and DNA methylation maps of cancer genomes (<xref rid=\"B189\" ref-type=\"bibr\">Simmer et al., 2012</xref>). Reduced representation bisulfite sequencing (RRBS) (<xref rid=\"B139\" ref-type=\"bibr\">Meissner et al., 2005</xref>) is an efficient method for absolute quantification of the methylation status of more than one million CpG sites at single base-pair resolution, covering regions of moderate to high CpG density (<xref rid=\"B114\" ref-type=\"bibr\">Lee et al., 2014</xref>). New techniques such as whole-genome bisulfite sequencing (WGBS) allows for an unbiased assessment of DNA methylation at single-base resolution with full coverage of more than 28 million CpG sites in the human genome, and by using this technique in the clinical settings, relevant biomarkers were identified in colorectal and breast cancers and certain types of leukemia (<xref rid=\"B23\" ref-type=\"bibr\">Berman et al., 2011</xref>).</p><p>Some of the techniques are used in clinical settings, like parallel shotgun sequencing and targeted sequencing (<xref rid=\"B151\" ref-type=\"bibr\">Norwitz and Levy, 2013</xref>) for non-invasive prenatal testing, WGS for fetal gene detection (<xref rid=\"B126\" ref-type=\"bibr\">Lo et al., 2010</xref>), and cancer personalized profiling by deep sequencing (CAPP-seq) to quantify circulating tumor DNA (<xref rid=\"B147\" ref-type=\"bibr\">Newman et al., 2014</xref>).</p><p>Despite the advancement of the techniques to study epigenetic modifications, the use of epigenetic biomarkers present on ccfNAs is limited due to their lower levels in the blood circulation. In the case of cancer, WGS is applied to only 5–10% of cell-free tumor DNA depending on the copy number. Mostly targeted methylation sequencing is carried out in such cases, which has a greater potential for the detection of lower levels of ccfNA in patients with early-stage disease.</p><p>Chromatin-based ChIP-seq experiments are revolutionizing our understanding of the complexes associated with chromatin dynamics. Ongoing advances such as nano-ChIP-seq allow ChIP-seq to be analyzed from far fewer cells necessary for embryology and development studies (<xref rid=\"B145\" ref-type=\"bibr\">Nakato and Shirahige, 2017</xref>). The emergence of ChIP-exo that digests the ends of DNA fragments not bound to protein is quite promising (<xref rid=\"B66\" ref-type=\"bibr\">Furey, 2012</xref>). However, the application of these techniques to identify biomarkers is limited due to the expertise and cost associated.</p><p>ChIP-seq also provides critical information on other chromatin modifiers, such as histone marks and the enzymes that modify these marks in diseases such as cancer. ChIP-seq has identified the role of aberrant H3K79 methylation by the methyltransferase DOT1L in mixed lineage leukemia (MLL)-rearranged leukemias (<xref rid=\"B24\" ref-type=\"bibr\">Bernt et al., 2011</xref>). In addition to ChiP-seq, different techniques like ChIP-PCR, ELISA-based assays, or mass spectrometry are used to detect and quantify histone modifications on ccfNAs in serum or plasma (<xref rid=\"B2\" ref-type=\"bibr\">Adli and Bernstein, 2011</xref>).</p></sec><sec id=\"S4\"><title>ccfNAs as Epigenetic Biomarkers in Various Diseases</title><p>The detection and quantification of ccfNAs, viz. RNA, DNA, fetal DNA, fetal RNA, mtDNA, and mitochondrial RNA and miRNA levels in body fluids are of clinical significance. These ccfNAs have the potential to act as biomarkers for diagnosis as well as prognosis of various diseases (<xref rid=\"B64\" ref-type=\"bibr\">Fleischhacker and Schmidt, 2007</xref>; <xref rid=\"B29\" ref-type=\"bibr\">Breitbach et al., 2012</xref>), such as different cancers, obstetric, autoimmune, neurological, and mitochondrial diseases, as well as prenatal diagnosis, etc. (<xref rid=\"B102\" ref-type=\"bibr\">Kandel, 2012</xref>; <xref rid=\"B184\" ref-type=\"bibr\">Shaw et al., 2012</xref>). Although the most studied area of epigenetics is DNA methylation, yet in the clinical setting, there are only a few methylation markers. Various blood- or tissue-based cohort well-powered studies have recently shown that changes in the DNA methylation are not only observed frequently in cancers but also in other broad range of complex diseases including neurodegenerative, metabolic, autoimmune, and inflammatory diseases although at a lower frequency (<xref rid=\"B210\" ref-type=\"bibr\">Tost, 2016</xref>).</p><p>DNA methylation analysis of ccfDNA might provide a valuable option in some cases when the blood–brain barrier is temporarily disrupted. It was recently demonstrated by the detection of unmethylated fragments of <italic>MBP3</italic> and <italic>WM1</italic>, specific for oligodendrocytes in about 75% of patients with relapsing multiple sclerosis (<xref rid=\"B242\" ref-type=\"bibr\">Zachariah et al., 2009</xref>). cfRNAs are also present in the patient’s serum/plasma in addition to ccfDNAs. Higher levels of circulatory RNases were observed in cancers and various diseases like cerebral attack, preeclampsia, etc., and surprisingly, RNA found in the circulation was found to be stable (<xref rid=\"B216\" ref-type=\"bibr\">Umu et al., 2018</xref>). Changes in the expression of intracellular miRNA have been causally linked with many diseases that include cancer (<xref rid=\"B59\" ref-type=\"bibr\">Esquela-Kerscher and Slack, 2006</xref>), cardiovascular diseases (<xref rid=\"B146\" ref-type=\"bibr\">Navickas et al., 2016</xref>), neurodegenerative diseases (<xref rid=\"B75\" ref-type=\"bibr\">Gupta et al., 2017</xref>), etc. Such changes in expression of miRNA are either similar or distinct in the serum of a particular set of patients, thus enabling miRNA detection in serum as biomarkers of human diseases (<xref rid=\"B14\" ref-type=\"bibr\">Backes et al., 2016</xref>). Therefore, ccfNAs play a prominent role in the pathogenesis and diagnosis of various diseases. Further research is required in this field to ensure the widespread application of these markers in clinical settings.</p></sec><sec id=\"S5\"><title>ccfNAs in Cancer</title><p>Every year, about 14 million new cases of cancer are reported (excluding skin cancer other than melanoma) that cause about 8.8 million deaths, accounting for 15.7% of deaths in a year (<xref rid=\"B61\" ref-type=\"bibr\">Ferlay et al., 2019</xref>). An estimated number of more than 1.8 million new cancer cases are likely to be diagnosed, and 606,520 cancer deaths are expected in the United States in 2020, which deciphers almost 1,660 deaths per day (<xref rid=\"B187\" ref-type=\"bibr\">Siegel et al., 2020</xref>). The six major hallmarks of cancer (<xref rid=\"B78\" ref-type=\"bibr\">Hanahan and Weinberg, 2000</xref>) are uncontrolled cell growth and division, programmed cell death avoidance, invasion, metastasis, and angiogenesis. The diagnosis of cancer usually occurs following the appearance of signs or symptoms or through screening and investigations like X-rays, blood tests, endoscopy, CT scans, etc. Biopsy tissue examination indicates the type of proliferating cells, genetic abnormalities, and histological grade, and other characteristics. Therefore, advanced measures such as estimating prognosis, risk assessment for early diagnosis, biomarkers, and observing the response to therapy can lead to successful treatment, positive outcomes, and improvement of the quality of life for patients. The tissue biopsy-matched ccfDNA is considered as surrogate marker due to its release from the tumor sites (<xref rid=\"B53\" ref-type=\"bibr\">De Mattos-Arruda et al., 2013</xref>). It is proven to be a non-invasive, rapid, and sensitive marker for diagnosis, prognosis, and therapy response monitoring in different cancers (<xref rid=\"B220\" ref-type=\"bibr\">Volik et al., 2016</xref>). In addition, the integrity of ccfDNA (extent of ccfDNA fragmentation) may be utilized as a promising biomarker for diagnosis and prognosis of cancer (<xref rid=\"B136\" ref-type=\"bibr\">Madhavan et al., 2014</xref>).</p><sec id=\"S5.SS1\"><title>ccfNAs as Diagnostic and Prognostic Biomarkers for Cancer</title><p>Serum or plasma ccfNA serves as a “liquid biopsy,” which is useful for various applications in diagnostics and avoids the necessity for biopsy of tumor tissue. The levels of ccfNA in blood and lymphatic circulation are affected by degradation, clearance, and various other physiological events. Liver and kidney clear nucleic acids from the blood, and they have a half-life of different time intervals in the circulation that varies from 15 min to several hours (<xref rid=\"B64\" ref-type=\"bibr\">Fleischhacker and Schmidt, 2007</xref>). miRNAs appear to be extremely stable, but their rate of clearance from the blood is not well studied in cancer patients thus owing to the uniqueness of this research area.</p><sec id=\"S5.SS1.SSS1\"><title>ccfDNAs in Cancer</title><p>ccfDNAs consists of both genomic DNA (gDNA) as well as mtDNA. There is a production of longer uneven fragments of DNA by necrosis in cancer patients and shorter DNA fragments from apoptosis. Hence, increased levels of longer DNA fragments in the bloodstream have been targeted as a potential marker for the presence of malignant tumor DNA (<xref rid=\"B13\" ref-type=\"bibr\">Arko-Boham et al., 2019</xref>). Tumor cells are the origin of ccfDNA in the blood of cancer patients (<xref rid=\"B195\" ref-type=\"bibr\">Stroun et al., 1989</xref>). Aberrations specific to tumors like oncogene and tumor suppressor gene mutations (<xref rid=\"B229\" ref-type=\"bibr\">Wang et al., 2004</xref>), methylation of DNA (<xref rid=\"B65\" ref-type=\"bibr\">Fujiwara et al., 2005</xref>), and instability of microsatellite DNA (<xref rid=\"B185\" ref-type=\"bibr\">Shaw et al., 2000</xref>) were recognized in ccfDNA. Tumorigenesis and its progression are monitored by the change in various epigenetic modifications. Patients with different types of malignancies have methylated DNA in their serum or plasma. One of the most important methods for analyzing malignancy is by detecting the presence of methylated ccfDNA in cancer patients.</p><p>For early diagnosis of colorectal cancer (CRC), analysis of promoter hypermethylation in blood and fecal DNA has the potential to be used as a non-invasive test, and efforts are made for clinical application of these molecular markers. Various studies have observed <italic>MGMT</italic>, <italic>RASSF2A</italic>, <italic>Wif-1</italic>, <italic>NGFR</italic>, and <italic>SEPT9</italic> as aberrantly methylated genes used as diagnostic biomarkers in patients with CRC (<xref rid=\"B112\" ref-type=\"bibr\">Lee et al., 2009</xref>; <xref rid=\"B162\" ref-type=\"bibr\">Powrozek et al., 2014</xref>). Several potential methylation biomarkers have been found that differentiate plasma from breast cancer patients and that from control subjects (<xref rid=\"B86\" ref-type=\"bibr\">Hoque et al., 2006</xref>). Remarkably, two independent studies recognized <italic>CST6</italic> as being methylated differentially between breast cancer and control plasma samples (<xref rid=\"B163\" ref-type=\"bibr\">Radpour et al., 2011</xref>; <xref rid=\"B47\" ref-type=\"bibr\">Chimonidou et al., 2013</xref>). For lung cancer, an early focus was to search methylated <italic>CDKN2A</italic> as a plasma diagnostic biomarker. Studies observed hypermethylation of <italic>CDKN2A</italic> in the plasma of patients with lung cancer as compared to cancer-free controls (<xref rid=\"B246\" ref-type=\"bibr\">Zhang et al., 2011</xref>). <italic>SHOX2</italic> was identified as a potential biomarker in a retrospective study done by researchers from the Theracode, a diagnostic firm (<xref rid=\"B109\" ref-type=\"bibr\">Kneip et al., 2011</xref>). A recent study, by a group, as part of the Australian Pancreatic Cancer Genome Initiative (APGI), has observed elevated levels of aberrant methylation in the important cell signaling pathways, thus suggesting its possibility as a disease biomarker. They worked on a group of six candidate genes, <italic>NPTX2</italic>, <italic>SARP2</italic>, <italic>UCHL1</italic>, <italic>ppENK</italic>, <italic>CDKN2A</italic>, and <italic>RASSF1A</italic>, and observed differential methylation in the promoters of all the genes in pancreatic cancer and healthy controls except in <italic>CDKN2A</italic> promoter, which was methylated differentially between pancreatic cancer patients and those having chronic pancreatitis (<xref rid=\"B157\" ref-type=\"bibr\">Park et al., 2012</xref>). Epigenetic events in the progression of cancer include the promoter region hypermethylation of the genes, pi-class <italic>GSTP1</italic>, and <italic>APC</italic>, which are the most common somatic genome abnormalities in colorectal and prostate cancer (<xref rid=\"B55\" ref-type=\"bibr\">Ellinger et al., 2008</xref>). <italic>RASSF1A</italic>, <italic>RARB</italic>, <italic>SEPT9</italic>, <italic>ESR1</italic>, and <italic>CDKN2A</italic> are the important methylated genes that have shown utility in prognosis using ccfDNA assays in many patients. Methylation of histones is an active process with vital roles in differentiation and development. Tumorigenesis also occurs due to aberrant levels of histone methylation. The promoters associated with H3K4 are primarily trimethylated by <italic>SET1A</italic> and <italic>SET1B</italic>. <italic>SET1A</italic> plays a vital role in oncogenic function in breast cancer metastasis, lung cancer, and colorectal cancer (<xref rid=\"B249\" ref-type=\"bibr\">Zhao and Shilatifard, 2019</xref>). <xref rid=\"T1\" ref-type=\"table\">Table 1</xref> presents the frequently hypermethylated genes in various cancer types.</p><table-wrap id=\"T1\" position=\"float\"><label>TABLE 1</label><caption><p>Frequently hypermethylated genes in various cancer types.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Gene</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Cancer Type</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>References</bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>ITIH5, DKK3, BRCA1, ER-beta, APC, GSTP1, ESR-b</italic></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Breast cancer</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B108\" ref-type=\"bibr\">Kloten et al., 2013</xref>; <xref rid=\"B44\" ref-type=\"bibr\">Cheuk et al., 2017</xref>; <xref rid=\"B221\" ref-type=\"bibr\">Vu et al., 2018</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>RASSF1A</italic></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Prostate cancer</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B125\" ref-type=\"bibr\">Liu et al., 2002</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>P16</italic></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Esophageal, liver, and pancreas</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B87\" ref-type=\"bibr\">House et al., 2003</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>ARF, BAX, BCL2, CDH1, DAPK, EDNRB, EOMES, FADD, PCDH17, POU4F2</italic></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bladder cancer</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B1\" ref-type=\"bibr\">Abern et al., 2014</xref>; <xref rid=\"B230\" ref-type=\"bibr\">Wang et al., 2016</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>SEPT9, HLTF, NELL1, CEA, TAC1</italic></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Colorectal cancer</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B204\" ref-type=\"bibr\">Tham et al., 2014</xref>; <xref rid=\"B182\" ref-type=\"bibr\">Semaan et al., 2016</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>VHL</italic></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Kidney tumors</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B135\" ref-type=\"bibr\">Ma et al., 2017</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>RB</italic></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Retinoblastoma</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B152\" ref-type=\"bibr\">Ohtani-Fujita et al., 1997</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>TMEFF2, PRDM1,3OST2, MGMT</italic></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Lung cancer</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B153\" ref-type=\"bibr\">Palmisano et al., 2000</xref>; <xref rid=\"B113\" ref-type=\"bibr\">Lee et al., 2012</xref>; <xref rid=\"B197\" ref-type=\"bibr\">Su et al., 2016</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>APC, GSTP1</italic></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Renal cell carcinoma</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B81\" ref-type=\"bibr\">Hauser et al., 2013</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>ST6GALNAC3, ZNF660</italic></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Prostate</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B77\" ref-type=\"bibr\">Haldrup et al., 2018</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>BRCA1, RASSF1A, RASSF2A</italic></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Ovarian cancer</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B68\" ref-type=\"bibr\">Giannopoulou et al., 2017</xref>; <xref rid=\"B129\" ref-type=\"bibr\">Lonning et al., 2018</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>hTERT</italic></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Leptomeningeal carcinomatosis in CSF</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B28\" ref-type=\"bibr\">Bougel et al., 2013</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>p16INK4a, TIMP-3, THBS1</italic></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Glioma</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B123\" ref-type=\"bibr\">Liu et al., 2010</xref></td></tr></tbody></table></table-wrap></sec><sec id=\"S5.SS1.SSS2\"><title>CcfmiRNAs in Cancer</title><p>In various cancers, miRNA expression dysregulation has been observed that suggests its role in many processes necessary for the progression of cancer like proliferation, cell death, metastasis, and resistance to treatment (<xref rid=\"B93\" ref-type=\"bibr\">Iorio and Croce, 2012</xref>). During the development of the liver, miRNA expression changes dynamically. <italic>miR-500</italic> is one such oncofetal miRNA that is important for the diagnosis of hepatocellular carcinoma (<xref rid=\"B239\" ref-type=\"bibr\">Yamamoto et al., 2009</xref>). Lately, in non-small cell lung cancer (NSCLC), <italic>miR-1246</italic> and <italic>miR-1290</italic> were recognized as tumor-initiating and cell-specific miRNAs (<xref rid=\"B245\" ref-type=\"bibr\">Zhang et al., 2016</xref>). <italic>miR-1290</italic> was found to be a significant prognostic factor for OSCC patients based on Cox regression analysis. In addition, <italic>miR-1290</italic> could serve as a valuable biomarker in OSCC patients to predict the clinical response to chemoradiotherapy (<xref rid=\"B120\" ref-type=\"bibr\">Lin et al., 2018</xref>). A study by <xref rid=\"B6\" ref-type=\"bibr\">Alhasan et al., 2016</xref> showed a serum signature of 5-miRNAs (<italic>miR-135a</italic>, <italic>miR-106a</italic>, <italic>miR-200c</italic>, <italic>miR-605</italic>, and <italic>miR-433</italic>) predicted a very high-risk prostate cancer (<xref rid=\"B6\" ref-type=\"bibr\">Alhasan et al., 2016</xref>). Expression levels of <italic>miR-21</italic>, <italic>miR-23b</italic>, <italic>miR-200c</italic>, and <italic>miR-200b</italic> were upregulated in metastatic breast cancer when compared to early breast cancer patients, therefore supporting the notion that ccfmiRNAs presents a tool with the crucial diagnostic and prognostic implication in breast cancer (<xref rid=\"B154\" ref-type=\"bibr\">Papadaki et al., 2019</xref>). Furthermore, a study discovered that increased <italic>miR-122</italic> expression was significantly associated with a reduction in the overall survival as well as progression-free survival in breast cancer patients (<xref rid=\"B177\" ref-type=\"bibr\">Saleh et al., 2019</xref>). Elevation in the levels of serum <italic>miR-29</italic>, <italic>miR-122</italic>, <italic>miR-155</italic>, and <italic>miR-192</italic> was observed in cholangiocarcinoma. Although miRNAs levels before surgery were inappropriate as survival prognostic marker; however, postsurgery decrease in the serum <italic>miR-122</italic> levels was significantly linked with better patient prognosis (<xref rid=\"B130\" ref-type=\"bibr\">Loosen et al., 2019</xref>).</p></sec></sec><sec id=\"S5.SS2\"><title>ccfNAs in Treatment and Cancer Progression</title><p>ccfDNA analysis is a non-invasive process that allows day to day patient follow-up and monitoring of response toward treatment (<xref rid=\"B69\" ref-type=\"bibr\">Gorges et al., 2012</xref>). Both genetic and epigenetic changes are exhibited by ccfDNA (<xref rid=\"B196\" ref-type=\"bibr\">Stroun et al., 2001</xref>). The study of these changes might provide valuable information to mold the choice of treatment by clinicians given the limitations of the novel targeted therapies.</p><p>Abnormal hypermethylation at CpG islands occurs rarely in non-malignant and normally differentiated cells, so the release of DNA from tumor cells can be found with a prominent extent of sensitivity, even when the excess of DNA is released from normal cells, and this characterizes its potential clinical application (<xref rid=\"B234\" ref-type=\"bibr\">Wong et al., 2001</xref>). In this context, promoter region hypermethylation of <italic>INK4A</italic> occurs very early in the progression of hepatocellular carcinoma (HCC), and hence, it serves as a valuable biomarker for non-invasive diagnosis as well as prediction of response to therapy (<xref rid=\"B89\" ref-type=\"bibr\">Huang et al., 2014</xref>).</p><p>In the <italic>MYCN</italic>-amplified neuroblastoma progression, <italic>MYCN</italic> is detected in circulating DNA. This phenomenon was found to be associated strongly with the quick progression of tumors and poor outcomes (<xref rid=\"B51\" ref-type=\"bibr\">Combaret et al., 2002</xref>). Loss of heterozygosity (LOH) and abnormal methylation at the promoter region of <italic>MYCN</italic> were detected using ccfDNA, which showed elevated levels in patients of high-grade glioma. Detection of promoter region hypermethylation of <italic>MYOD1</italic> in serum may serve as a potential prognostic marker for discriminating patients of cervical cancer at high risk for lymph node metastasis or relapse (<xref rid=\"B232\" ref-type=\"bibr\">Widschwendter et al., 2004</xref>).</p><p>Moreover, the investigation of circulating miRNAs presents great potential in revealing new insights into their role in therapy and diagnosis. miRNA serum signatures (<italic>miR-345 -5p</italic>, <italic>miR-330 -3p</italic>, and <italic>miR-9 -3p</italic>) were found to be significantly upregulated in patients of prostate cancer (PCa) when compared to healthy individuals. The role of <italic>miR-345-5p</italic> to act as an oncomir through <italic>CDKN1A</italic> targeting makes it a potential target for PCa therapeutically (<xref rid=\"B207\" ref-type=\"bibr\">Tinay et al., 2018</xref>).</p><p>Immunotherapy is a rapidly developing therapy in many cancers because of various advantages over standard chemotherapy. Identification of significant miRNAs that provides a foresight of response in cancer immunotherapy would enable better patient selection and enhancement of therapeutic efficacy and provide a novel target (<xref rid=\"B12\" ref-type=\"bibr\">Antonia et al., 2004</xref>; <xref rid=\"B43\" ref-type=\"bibr\">Chen et al., 2008</xref>). <italic>miRNA-21</italic> is a cell-free oncogenic miRNA, which has been known as a potential regulator of <italic>STAT3</italic>, and thus, it could be detected in various tumors (<xref rid=\"B96\" ref-type=\"bibr\">Ji et al., 2009</xref>). Thus, circulating <italic>miRNA-21</italic> can act as a biomarker for response in cancer immunotherapy (<xref rid=\"B236\" ref-type=\"bibr\">Wu et al., 2012</xref>).</p><p>ccfDNAs in glioma were associated with differential methylation levels of <italic>MGMT</italic>, cyclin-dependent kinase inhibitor 2A, multiple tumor suppressor 1 <italic>p16/</italic>(<italic>INK4a</italic>), <italic>p73</italic>, and retinoic acid receptor beta (<italic>RARb</italic>) (<xref rid=\"B16\" ref-type=\"bibr\">Balana et al., 2003</xref>; <xref rid=\"B231\" ref-type=\"bibr\">Weaver et al., 2006</xref>; <xref rid=\"B222\" ref-type=\"bibr\">Wakabayashi et al., 2009</xref>). All these studies propose a crucial role of epigenetic marks in ccfNAs in cancer-targeted therapy as well as pathogenesis.</p></sec><sec id=\"S5.SS3\"><title>ccfNAs in Cancer Precision Medicine</title><p>Precision oncology is an approach that includes the molecular profiling of tumors to identify effective therapeutic strategies. A clinical research program initiated by The Englander Institute for Precision Medicine (EIPM) in 2013 uses whole-exome sequencing of metastatic and primary tumors to identify individualized therapeutic options and to help guide clinical decision making, by prospective follow-up of patients (<xref rid=\"B175\" ref-type=\"bibr\">Rennert et al., 2016</xref>). Oncology is the obvious choice for heightening the impact of precision medicine. Several targeted therapies have been developed that have shown profound benefits. Recently, novel immunological approaches produced insightful responses (<xref rid=\"B191\" ref-type=\"bibr\">Snyder et al., 2014</xref>).</p><p>In addition, the identification of epigenetic biomarkers leads to more precise disease prognosis, especially in therapeutic areas that are linked with a high degree of variability concerning survival (<xref rid=\"B218\" ref-type=\"bibr\">Van Neste et al., 2017</xref>). Research carried out in several cancers like glioblastoma reveals that levels of 5hmC are critical in the regulation of genes having a crucial role in disease and show that global reduction in 5hmC over the genome leads to poor clinical outcomes in these patients (<xref rid=\"B100\" ref-type=\"bibr\">Johnson et al., 2016</xref>).</p><p>Epigenetic changes introduced common genetic mutations in an <italic>in vitro</italic> model of lung cancer (<xref rid=\"B219\" ref-type=\"bibr\">Vaz et al., 2017</xref>). Epigenetic-based diagnostics can detect early disease signals and thus can provide possibilities for clinical intervention before the progression of symptoms.</p><p>The detection of ccfNAs could be exploited by targeted therapies approved lately and eventually benefit the patients. Scrutinizing cancers by analyzing ccfNA dynamics in blood or serum is an innovative and emerging research area. As far as the existing research advancement and the growth of the medical industry are concerned, we consider that ccfNA assays may be employed for real-time personalized treatments in the future for cancer patients, based on their ccfNAs or ccfDNA methylation levels, for diagnosis and prognosis. Nevertheless, there is much scope for improvement before the application of this technology in clinical settings.</p></sec></sec><sec id=\"S6\"><title>Use of ccf-Fetal-NAs in Prenatal Diagnosis and Pregnancy-Related Disorders</title><p>During pregnancy, the apoptosis/necrosis of trophoblasts arising from syncytiotrophoblast is the prime source of the release of ccf-fetal-NAs into the maternal blood (<xref rid=\"B122\" ref-type=\"bibr\">Litton et al., 2009</xref>). The presence of ccf-fetal-NAs has paved the way for non-invasive prenatal diagnosis and early prediction of pregnancy-related complications (<xref rid=\"B127\" ref-type=\"bibr\">Lo et al., 1997</xref>, <xref rid=\"B128\" ref-type=\"bibr\">1998</xref>). The use of ccf-fetal-NAs has gradually replaced invasive techniques like amniocentesis or chorionic villus sampling (<xref rid=\"B183\" ref-type=\"bibr\">Serr et al., 2017</xref>). ccf-fetal-DNA comprises 10–15% of the maternal ccfDNA (<xref rid=\"B224\" ref-type=\"bibr\">Wang et al., 2013</xref>) and can be efficiently detected at the fifth week of gestation (<xref rid=\"B74\" ref-type=\"bibr\">Guibert et al., 2003</xref>). The amount of ccf-fetal-DNA in maternal blood increases progressively throughout pregnancy (<xref rid=\"B27\" ref-type=\"bibr\">Birch et al., 2005</xref>).</p><sec id=\"S6.SS1\"><title>ccfNAs in Prenatal Diagnosis</title><p>Prenatal diagnosis is an established practice for the management of pregnancy as well as avoidance of prenatal/neonatal deaths. The leading causes for such deaths are genetic disorder, birth defects, congenital malformations, and chromosomal abnormalities like trisomy 21 (Down’s syndrome), 18 (Edward’s syndrome), and 13 (Patau syndrome), and sex chromosome aneuploidies like monosomy X (Turner syndrome) (<xref rid=\"B36\" ref-type=\"bibr\">Carlson and Vora, 2017</xref>). Therefore, successful management of pregnancy demands efficient and timely prenatal diagnosis to determine the outcome of pregnancy. Timely detection of neural tube defects is already providing early prenatal treatment resulting in better neonatal outcomes (<xref rid=\"B3\" ref-type=\"bibr\">Adzick et al., 2011</xref>).</p><p>ccf-fetal-DNA is clinically used for the detection of fetal sex and multiple anomalies based on paternally inherited mutations (<xref rid=\"B26\" ref-type=\"bibr\">Bianchi, 1998</xref>). Recent studies have discovered many fetal epigenetic biomarkers for ccf-fetal-NA-based liquid biopsies in clinical samples that have demonstrated high clinical potential in disease diagnosis, prognosis, and pregnancy management. These epigenetic modifications are specific to the fetus and help to distinguish fetal nucleic acids from maternal nucleic acids (<xref rid=\"B101\" ref-type=\"bibr\">Jones and Takai, 2001</xref>). Clinical testing of recently developed fetal epigenetic markers can help in the proper management of personalized care. The first reported use of fetal-derived epigenetic marker in maternal body fluids had come from <xref rid=\"B160\" ref-type=\"bibr\">Poon et al. (2002)</xref> who utilized an imprinted <italic>H19/Igf2</italic> locus based on parent-of-origin-specific methylation, and the maternal and the paternal copies of the gene were distinguished in maternal blood (<xref rid=\"B160\" ref-type=\"bibr\">Poon et al., 2002</xref>). Based on the placental origin of ccf-fetal-DNA having placenta-specific DNA methylation pattern, the genomic regions that show differential methylation between the placenta and the maternal blood cells can act as a marker for fetal DNA in maternal blood. The promoter region of <italic>maspin</italic> (<italic>SERPINB5</italic>) is the first such reported universal fetal DNA marker, with detectable hypomethylation, in the background of hypermethylated maternal sequences. The fetal origin of these hypomethylated <italic>maspin</italic> has been confirmed by the clearance of these sequences within 24 h of delivery (<xref rid=\"B46\" ref-type=\"bibr\">Chim et al., 2005</xref>). The clinical use of hypomethylated <italic>maspin</italic> is limited by the required bisulfite treatment of ccf-fetal-DNA, as this treatment can degrade around 95% of the DNA (<xref rid=\"B71\" ref-type=\"bibr\">Grunau et al., 2001</xref>), thus decreasing the amount of already low levels of fetal DNA in maternal blood. Such limitation was overcome by the detection of fetal-derived hypermethylated <italic>RASSF1A</italic> in maternal blood for prenatal diagnosis (<xref rid=\"B41\" ref-type=\"bibr\">Chan et al., 2006</xref>; <xref rid=\"B92\" ref-type=\"bibr\">Hyland et al., 2009</xref>; <xref rid=\"B212\" ref-type=\"bibr\">Tounta et al., 2011b</xref>). The maternal hypomethylated <italic>RASSF1A</italic> ccfDNA can be removed by treatment with methylation-sensitive restriction enzyme digestion, leaving behind fetal hypermethylated <italic>RASSF1A</italic> ccf-fetal-DNA (<xref rid=\"B41\" ref-type=\"bibr\">Chan et al., 2006</xref>). Various other fetal-derived differentially methylated sequences have also shown a similar potential to act as fetal DNA epigenetic markers in maternal blood (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>).</p><table-wrap id=\"T2\" position=\"float\"><label>TABLE 2</label><caption><p>Clinical application of ccf-fetal nucleic acids in prenatal diagnosis and pregnancy-related diseases.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Clinical application</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Gene</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Detection method</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>References</bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Fetal DNA marker</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SERPINB5</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Hypomethylated ccf-fetal DNA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B46\" ref-type=\"bibr\">Chim et al., 2005</xref></td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RASSF1A; APC and PRKCDBP; MEST and SNRPN</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Hypermethylated ccf-fetal DNA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B41\" ref-type=\"bibr\">Chan et al., 2006</xref>; <xref rid=\"B169\" ref-type=\"bibr\">Rahat et al., 2016b</xref>; <xref rid=\"B166\" ref-type=\"bibr\">Rahat et al., 2017a</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Fetal Rh status</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RASSF1A</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Hypermethylated ccf-fetal DNA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B92\" ref-type=\"bibr\">Hyland et al., 2009</xref>; <xref rid=\"B212\" ref-type=\"bibr\">Tounta et al., 2011b</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Trisomy 21</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">HLCS</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Fetal DNA allelic ratio</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B209\" ref-type=\"bibr\">Tong et al., 2010</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Trisomy 18</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SERPINB5</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B208\" ref-type=\"bibr\">Tong et al., 2006</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Congenital heart diseases</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">miR-19b, miR-22, miR-29c, and miR-375, miR-99a</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↑ level of fetal miRNAs</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B252\" ref-type=\"bibr\">Zhu et al., 2013</xref>; <xref rid=\"B105\" ref-type=\"bibr\">Kehler et al., 2015</xref></td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ENST00000436681, ENST00000422826, AA584040, AA706223, and BX478947</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">De-regulated lncRNAs</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B73\" ref-type=\"bibr\">Gu et al., 2016</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Preeclampsia</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SERPINB5</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↑ level of hypomethylated ccf-fetal DNA.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B46\" ref-type=\"bibr\">Chim et al., 2005</xref></td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RASSF1A</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↑ level of hypermethylated ccf-fetal DNA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B213\" ref-type=\"bibr\">Tsui et al., 2007</xref></td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">c-myc</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Hypermethylated ccf-fetal DNA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B165\" ref-type=\"bibr\">Rahat et al., 2014</xref></td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VEGF</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Hypermethylated ccf-fetal DNA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B167\" ref-type=\"bibr\">Rahat et al., 2017b</xref></td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Corticotrophin-releasing hormone</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↑ level of fetal mRNA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B76\" ref-type=\"bibr\">Hahn et al., 2011</xref></td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">miR-1233, miR-520, miR-210, miR-155</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↑ level of fetal miRNAs</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B217\" ref-type=\"bibr\">Ura et al., 2014</xref>; <xref rid=\"B144\" ref-type=\"bibr\">Nagy, 2019</xref></td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">miR-144</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↓ level of fetal miRNA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B217\" ref-type=\"bibr\">Ura et al., 2014</xref></td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">miR-24, miR-26a, miR-103, miR-130b, miR-181a, miR-342-3p, and miR-574-5p</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↑ level of fetal miRNAs</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B236\" ref-type=\"bibr\">Wu et al., 2012</xref>; <xref rid=\"B18\" ref-type=\"bibr\">Barchitta et al., 2017</xref></td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">miR-26a and miR-342-3p</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↑ level of fetal miRNAs</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B48\" ref-type=\"bibr\">Choi et al., 2011</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">IUGR</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">miR-518b and miR-519a</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↑ level of fetal miRNAs</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B223\" ref-type=\"bibr\">Wang et al., 2014</xref></td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↑ level of ccf-fetal DNA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B176\" ref-type=\"bibr\">Romao et al., 1992</xref>; <xref rid=\"B188\" ref-type=\"bibr\">Sifakis et al., 2015</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Preterm birth</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">miR-143 and miR-145</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↑ level of fetal miRNAs</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B57\" ref-type=\"bibr\">Elovitz et al., 2014</xref></td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">miR-200a, miR-4695-5P, miR-665, and miR88</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Altered structure of fetal miRNAs</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B56\" ref-type=\"bibr\">Elovitz et al., 2015</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Hyperemesis gravidarum</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↑ level of ccf-fetal DNA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B176\" ref-type=\"bibr\">Romao et al., 1992</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Placenta accrete/inccreta</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↑ levels of ccf-fetal DNA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B176\" ref-type=\"bibr\">Romao et al., 1992</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Gestational diabetes mellitus</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">miR-518d, miR-508-3p, miR-27a, miR-9, miR-137, miR-92a, miR-33a, miR-30d, miR-362-5p, and miR-502-5p</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↑↓ level of fetal miRNAs</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B70\" ref-type=\"bibr\">Grissa et al., 2010</xref>; <xref rid=\"B18\" ref-type=\"bibr\">Barchitta et al., 2017</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Low birth weight infants</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">mir-517a</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↑ level of fetal miRNAs</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B193\" ref-type=\"bibr\">Song et al., 2013</xref></td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↑ level of ccf-fetal DNA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B188\" ref-type=\"bibr\">Sifakis et al., 2015</xref></td></tr></tbody></table><table-wrap-foot><attrib><italic><italic>IUGR, intrauterine growth restriction</italic>.</italic></attrib></table-wrap-foot></table-wrap><p>ccf-fetal-DNA methylation markers have the potential of being used as both quantitative as well as qualitative markers in prenatal diagnosis. As qualitative markers, these are used to estimate the false positives during the determination of fetal gender, Rh status, and paternally inherited polymorphisms (<xref rid=\"B41\" ref-type=\"bibr\">Chan et al., 2006</xref>), while as quantitative markers, these can estimate the levels of ccf-fetal-DNA in maternal plasma. Such an application of ccf-fetal-DNA finds its use in the detection of chromosomal aneuploidies (<xref rid=\"B134\" ref-type=\"bibr\">Lun et al., 2008</xref>). Based on the location of the <italic>maspin</italic> gene on chromosome 18, hypomethylated fetal <italic>maspin</italic> has been used to calculate the allelic ratio to diagnose trisomy 18 with 100% sensitivity (<xref rid=\"B208\" ref-type=\"bibr\">Tong et al., 2006</xref>). Fetal trisomy 21 was detected by analyzing chromosomal dosage via targeting of fetal hypermethylated <italic>HLCS</italic> sequences in the combination of microfluidics digital PCR. <italic>RASSF1A</italic> on chromosome 3 and <italic>ZFY</italic> on the Y chromosome were used as references (<xref rid=\"B209\" ref-type=\"bibr\">Tong et al., 2010</xref>). Fragmentation pattern of ccf-fetal-DNA in maternal plasma has been successfully used for enrichment method in size separation manner on agarose gel electrophoresis (<xref rid=\"B172\" ref-type=\"bibr\">Ramezanzadeh et al., 2017</xref>).</p><p>Various next-generation sequencing and high-throughput techniques have catalyzed the identification of newer and novel fetal epigenetic markers further advancing non-invasive prenatal diagnosis. The microarray-based approach has identified many fetal epigenetic markers with differential methylation between chorionic villus samples and maternal blood, on chromosomes 21, 13, and 18 for aneuploidy detection (<xref rid=\"B49\" ref-type=\"bibr\">Chu et al., 2009</xref>). Combining high-resolution tiling oligonucleotide array with methylated DNA immunoprecipitation (MeDiP) has helped in a genome-wide screen for detecting the differential methylated sites between placental tissue and maternal blood cells. It has detected various new fetal epigenetic markers on chromosomes 21, 13, and 18 (<xref rid=\"B155\" ref-type=\"bibr\">Papageorgiou et al., 2009</xref>). Whole-genome bisulfite sequencing has further identified many clinically useful novel fetal-specific methylated CpG sites (<xref rid=\"B133\" ref-type=\"bibr\">Lun et al., 2013</xref>). Latest techniques like high-resolution methylation-specific bead chip microarray (<xref rid=\"B80\" ref-type=\"bibr\">Hatt et al., 2015</xref>) and GeneChip Human Promoter 1.0R Array (<xref rid=\"B228\" ref-type=\"bibr\">Wang et al., 2017</xref>) identified many differentially methylated CpG sites between maternal blood cells and chorionic villi, which can help in better and efficient prenatal diagnosis and the expansion of its application in other disorders.</p><p>More recently, non-coding RNAs like miRNAs, lncRNAs, and circRNAs are in the focus of the clinical research for prenatal diagnosis. Several placenta-specific miRNAs are differentially expressed within the placenta and are also secreted during pregnancy from the trophoblast layer of the placenta (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>). These are located in clusters on chromosomes 14 and 19 (<italic>C14MC</italic>, <italic>C19MC</italic>, and <italic>miR-371-3</italic>) (<xref rid=\"B144\" ref-type=\"bibr\">Nagy, 2019</xref>). Highly stable placental miRNAs were detected in maternal plasma. These can help in tracking gene regulation in the placenta (<xref rid=\"B45\" ref-type=\"bibr\">Chim et al., 2008</xref>). ccf-fetal miRNAs in the maternal blood act as expression-based novel epigenetic markers in prenatal diagnosis. miRNA microarray-based screen has detected many differentially expressed ccf-fetal miRNAs in maternal serum to diagnose congenital heart defects, which can be especially helpful in personalized care (<xref rid=\"B72\" ref-type=\"bibr\">Gu et al., 2019</xref>). Circular RNA and lncRNA are used in prenatal diagnosis for congenital heart diseases (<xref rid=\"B144\" ref-type=\"bibr\">Nagy, 2019</xref>). Early diagnosis of congenital heart diseases is beneficial to reduce morbidity and mortality. Sequencing by Oligonucleotide Ligation and Detection (SOLiD) has been used to identify congenital heart disease-related miRNAs in maternal blood (<xref rid=\"B252\" ref-type=\"bibr\">Zhu et al., 2013</xref>; <xref rid=\"B105\" ref-type=\"bibr\">Kehler et al., 2015</xref>). <xref rid=\"B73\" ref-type=\"bibr\">Gu et al. (2016)</xref> has reported many lncRNAs related to congenital heart diseases. The clinical use of these lncRNAs is extensively studied (<xref rid=\"B73\" ref-type=\"bibr\">Gu et al., 2016</xref>).</p><p>The use of artificial intelligence platforms and machine learning in combination to analyze genome-wide DNA methylation data has helped to identify epigenomic predictors for cerebral palsy in newborns with better sensitivity and specificity. These epigenetic predictors provided more mechanistic information about the pathogenesis of cerebral palsy (<xref rid=\"B15\" ref-type=\"bibr\">Bahado-Singh et al., 2019</xref>).</p></sec><sec id=\"S6.SS2\"><title>ccfNAs in Pregnancy-Related Disorders</title><p>Certain pregnancy-related complications are associated with poor placental growth and development, which is usually also accompanied by aggravated trophoblastic apoptosis, resulting in the release of increased amounts of ccf-fetal-NAs into maternal blood. ccf-fetal-NAs are especially important for precision medicine being useful for clinical diagnosis and management of these complications, as these precede the actual clinical symptoms of the disease (<xref rid=\"B188\" ref-type=\"bibr\">Sifakis et al., 2015</xref>). The quantification of ccf-fetal-DNA levels in maternal blood might serve as an indicator of some developing abnormality; however, the absolute concentration of ccf-fetal-DNA varies with maternal weight and ethnicity and fluctuates throughout pregnancy (<xref rid=\"B211\" ref-type=\"bibr\">Tounta et al., 2011a</xref>), which warrants the need for a disease-specific qualitative marker. Aberrations in the levels of epigenetic marks present in these fetal DNA fragments serve as a valuable alternative for the diagnosis and management of pregnancy-related complications. The ccf-fetal-DNA present in maternal blood has been explored to predict such placental abnormality-linked pregnancies, like intrauterine growth restriction (IUGR); preeclampsia; hemolysis, elevated liver enzyme levels, and low platelet levels (HELLP) syndrome; preterm labor; hyperemesis gravidarum (severe morning sickness); placenta accrete; and placenta inccreta (<xref rid=\"B176\" ref-type=\"bibr\">Romao et al., 1992</xref>).</p><p>IUGR, defined by less than fifth percentile fetal weight, may or may not be associated with preeclampsia. Early detection of preeclampsia is highly beneficial for the proper management of preeclampsia, which is highly important for both the developing fetus and the mother (<xref rid=\"B21\" ref-type=\"bibr\">Bauer et al., 2006</xref>). The development of preeclampsia is also depicted by the increased level of unmethylated fetal <italic>SERPINB5</italic> (<xref rid=\"B46\" ref-type=\"bibr\">Chim et al., 2005</xref>) in maternal blood. Similarly, hypermethylated fetal <italic>RASSF1A</italic> (<xref rid=\"B213\" ref-type=\"bibr\">Tsui et al., 2007</xref>) sequences are also elevated in preeclamptic blood. Hypermethylated <italic>c-myc</italic> and <italic>VEGF</italic> observed specifically in ccf-fetal-DNA in preeclampsia patients are the epigenetic markers, which can diagnose preeclampsia without the requirement of quantitative estimation of ccf-fetal-DNA levels. These sequences can be used both for the diagnosis as well as prognosis of preeclampsia (<xref rid=\"B165\" ref-type=\"bibr\">Rahat et al., 2014</xref>, <xref rid=\"B167\" ref-type=\"bibr\">2017b</xref>). Such ccf-fetal-DNA-based epigenetic markers can be beneficial for early prediction and the personalized management of the disease. Fetal DNA epigenetic markers are likely to show potential as diagnostic markers in other complicated pregnancies accompanied by quantitative aberrations of ccf-fetal-DNA (<xref rid=\"B214\" ref-type=\"bibr\">Tsui et al., 2010</xref>).</p><p>Several fetal-derived mRNAs and miRNAs also as serve as diagnostic and prognostic markers for preeclampsia, preterm births, IUGR, spontaneous abortions, and low birth weight infants. A list of clinical applications of ccf-fetal nucleic acids in prenatal diagnosis and pregnancy-related diseases are given in <xref rid=\"T2\" ref-type=\"table\">Table 2</xref>. In addition, miRNAs involved in impaired trophoblast migration and invasion (<italic>miR-195</italic>, <italic>miR-276C</italic>, <italic>miR-278a-5p</italic>, and <italic>miR-210</italic>), impaired angiogenesis (<italic>miR-210</italic>, <italic>miR-21</italic>, and <italic>miR-22</italic>), and dysregulation of the maternal immune system are associated with preeclampsia (<xref rid=\"B190\" ref-type=\"bibr\">Skalis et al., 2019</xref>). Aberrant expression of circular miRNAs reported in gestational diabetes mellitus can serve as potential biomarkers for early diagnosis (<xref rid=\"B18\" ref-type=\"bibr\">Barchitta et al., 2017</xref>). Microarray analysis has also identified many miRNAs related to gestational diabetes mellitus (<xref rid=\"B70\" ref-type=\"bibr\">Grissa et al., 2010</xref>).</p><p>The use of microarray and next-generation sequencing can help to identify more ccf-fetal-RNA markers (<xref rid=\"B62\" ref-type=\"bibr\">Ferrari et al., 2008</xref>). Extensive research is required on different non-coding RNAs to be utilized in clinical settings for early diagnosis of pregnancy-related disorders.</p><p>The major obstacles in the field of ccf-fetal-NAs for diagnosis of prenatal and pregnancy-related complications are the requirements for proper standardized protocols for sample processing, detection methods, data analysis, and appropriate quality controls. Low concentration and fragmented pattern of ccf-fetal-NAs further demand the development of novel technologies for the proper utilization of ccf-fetal-NAs for diagnostics. The combined use of next-generation sequencing and bio-informative analysis could facilitate large-scale comprehensible screening and identification of promising next-generation non-invasive epigenetic biomarker in ccf-fetal-NAs. The screening for novel and disease-specific epigenetic markers on ccf-fetal-NAs in maternal blood will not only help in early diagnosis but also in providing proper personalized care. The ccf-fetal-NA-based diagnostic techniques provide new highly sensitive and specific avenues in clinical settings. These have already replaced invasive diagnostic sampling reducing pregnancy risks. ccf-fetal DNA is already being used in clinical settings, while the use of lncRNAs, circular RNAs, and miRNAs are in the research phase and could soon be used for clinical diagnosis of many fetal- and pregnancy-related disorders.</p><p>Intensive research is required in this area based on large populations to develop new clinical applications of fetal epigenetic marks in maternal blood. Additional mechanistic studies are required to identify the epigenetic changes behind the fetal–maternal complications, which can provide more insight into the possible epigenetic marks for non-invasive diagnosis.</p></sec></sec><sec id=\"S7\"><title>ccfNAs in Autoimmune Diseases</title><p>In autoimmune diseases, there are abnormal immune responses to healthy body tissues. In the United States, about 8% of the population (24 million) are affected by autoimmune diseases, where women are more commonly affected as compared to men (<xref rid=\"B60\" ref-type=\"bibr\">Fairweather and Rose, 2004</xref>). Nearly 80 different types of autoimmune diseases are known (<xref rid=\"B82\" ref-type=\"bibr\">Hayter and Cook, 2012</xref>). The appearance of disease symptoms in adulthood makes the diagnosis of autoimmune immune diseases difficult. Celiac disease, Graves’ disease rheumatoid arthritis, systemic lupus erythematosus, diabetes mellitus type 1, inflammatory bowel disease, and multiple sclerosis are some well-known autoimmune diseases (<xref rid=\"B84\" ref-type=\"bibr\">Hohlfeld et al., 2016</xref>).</p><sec id=\"S7.SS1\"><title>ccfNAs as Epigenetic Markers in Diagnosis, Progression, and Treatment of Autoimmune Diseases</title><p><xref rid=\"B233\" ref-type=\"bibr\">Witebsky’s et al. (1957)</xref> postulates were formulated for the first time for the diagnosis of autoimmune diseases. Accumulating evidence has shown that there is a significant role of epigenetic modifications in the development and progression of autoimmune diseases. With the benefits of ease of detection and the ability to analyze disease activity, specific epigenetic modifications can be proposed as novel biomarkers in autoimmune diseases.</p><p>In autoimmune diseases like other several pathological conditions, the presence of ccfDNA, has been observed, thus developing the interest of using them as a potential biomarker. Many pieces of evidence have shown that there is the presence of abnormal DNA demethylation in peripheral blood mononuclear cells (PBMCs) and CD4+ T cells of lupus patients (<xref rid=\"B94\" ref-type=\"bibr\">Javierre et al., 2010</xref>; <xref rid=\"B95\" ref-type=\"bibr\">Jeffries et al., 2011</xref>; <xref rid=\"B83\" ref-type=\"bibr\">Hewagama et al., 2013</xref>). Hypomethylation status of two sites, CpG site1 (Chr1: 79,085,222) and CpG site 2 (Chr1: 79,085,250; cg06872964), within the promoter region of IFI44L (IFN-induced protein 44-like) were identified as biomarkers for the diagnosis of SLE and further validated in the Chinese population consisting of 1,144 lupus patients, 1,350 healthy subjects, 429 RA, patients and 199 patients of primary Sjögren’s syndrome (pSS), as well as in a European cohort (<xref rid=\"B247\" ref-type=\"bibr\">Zhao et al., 2016</xref>). DNA methylation levels thus can not only distinguish active patients from inactive ones but importantly also indicate the activity of autoimmune diseases.</p><p>DNA methylation might be a good parameter, different from genetic and protein biomarkers, to serve as a predictive biomarker. Reduced DNA methylation at the <italic>IL-6</italic> and <italic>ERa</italic> promoters in PBMCs in RA patients is associated with overexpressed <italic>IL-6</italic> and hyperactive <italic>ERa</italic> signaling (<xref rid=\"B148\" ref-type=\"bibr\">Nile et al., 2008</xref>; <xref rid=\"B119\" ref-type=\"bibr\">Liao et al., 2012</xref>; <xref rid=\"B124\" ref-type=\"bibr\">Liu et al., 2014</xref>). Aberrant epigenetic modifications also have been evidenced to be associated with systemic sclerosis (SSc) disease. In this context, abnormal global and gene-specific DNA demethylation (e.g., at <italic>CD11a</italic>, <italic>CD70</italic>, and <italic>CD40L</italic>) and several hypermethylated genes (<italic>PRF1</italic>, <italic>CD11a</italic>, <italic>FoxP3</italic>, <italic>CD70</italic>, and <italic>CDKN2A</italic>) in whole blood have been observed from South Africans with SSc (<xref rid=\"B137\" ref-type=\"bibr\">Matatiele et al., 2015</xref>). Besides, the Th17-related genes, like <italic>RORC1</italic> and <italic>RORC2</italic>, are hypomethylated from SSc patients in PBMCs and further correlated with inflammatory parameters (<xref rid=\"B10\" ref-type=\"bibr\">Almanzar et al., 2016</xref>). However, these aforementioned alterations are not validated as predicted biomarkers as yet, so extensive work is required in this direction in the future. Overall, these studies suggest that changes in circulating DNA methylation levels, observed in autoimmune diseases (<xref rid=\"B171\" ref-type=\"bibr\">Rainer et al., 2003</xref>), can act as an important tool to monitor the response of the treatment and predict progression of the disease and patient’s stratification according to different stages in the disease.</p><p>Like cell-free DNA, a few studies have also reported the role of cell-free RNAs in autoimmune diseases. Recent studies have revealed that there are ∼600 circulatory RNAs differentially expressed in the PBMCs in patients suffering from rheumatoid arthritis (<xref rid=\"B250\" ref-type=\"bibr\">Zheng et al., 2017</xref>). Similarly, over 200 circulatory RNAs in the plasma of SLE patients and 400 in the PBMCs of relapsing–remitting multiple sclerosis patients (RR-MS) were found in comparison to healthy controls (<xref rid=\"B35\" ref-type=\"bibr\">Cardamone et al., 2017</xref>). Such an abundance of different circulatory RNAs can help improve the efficiency of clinical diagnosis by their combined detection with other transitional markers.</p><p>Apart from these two types of ccfNAs, other forms like miRNA, long non-coding RNA, and mtDNA also have tremendous potential in future screening, diagnosis, and prognosis of autoimmune diseases. Many studies have identified miRNAs that are abnormally expressed in lupus. However, target genes have been identified only for a few of them. In a recent study, circulating miRNA profile was identified in patients with autoimmune disease as well as in Treg-depleted mice model. Results of this study from quantitative reverse transcription PCR (qRT-PCR) quantification and analysis of receiver operating characteristic (ROC) curve were able to determine a total of six miRNAs (<italic>miR-551b</italic>, <italic>miR-34cmiR-448</italic>, <italic>miR-9</italic>, <italic>miR-148</italic>, and <italic>miR-124</italic>) in the mouse models with T-reg depletion and three miRNA (<italic>miR-448</italic>, <italic>miR-124</italic>, and <italic>miR-551b</italic>) in patients with RA, SLE, Sjogren’s syndrome (SS), and ulcerative colitis (UC), leading to a conclusion that they could serve as valuable specific biomarkers in these diseases.</p><p>As the optimal source of biomarkers, many other circulating miRNAs also have been identified to be correlated with lupus. Among them, <italic>miR-146a</italic> and <italic>miR-155</italic> are the first miRNAs that have been described as decreased in lupus serum (<xref rid=\"B225\" ref-type=\"bibr\">Wang et al., 2010</xref>). In subsequent studies, the serum levels of <italic>miR-200a</italic>, <italic>miR-200b</italic>, <italic>miR-200c</italic>, <italic>miR-429</italic>, <italic>miR-205</italic>, <italic>miR-192</italic>, <italic>miR-126</italic>, <italic>miR-16</italic>, <italic>miR-451</italic>, <italic>miR-223</italic>, <italic>miR-21</italic>, and <italic>miR-125a-3p</italic> (<xref rid=\"B226\" ref-type=\"bibr\">Wang et al., 2011</xref>, <xref rid=\"B227\" ref-type=\"bibr\">2012</xref>) were found to be abnormally expressed in SLE and correlated with disease activity.</p><p>Another inspiring observation is that <italic>miR-126</italic> has been reported to regulate DNA methylation in lupus T cells by targeting DNMT1 (<xref rid=\"B248\" ref-type=\"bibr\">Zhao et al., 2011</xref>), supporting the idea that lupus T cells are switched on by DNA hypomethylation via miRNAs (<xref rid=\"B39\" ref-type=\"bibr\">Ceribelli et al., 2011</xref>). Future studies in this direction can establish that not only circulatory DNA or RNA but also circulating miRNAs can also represent potential universal epigenetic biomarkers for autoimmune diseases (<xref rid=\"B99\" ref-type=\"bibr\">Jin et al., 2013</xref>).</p><p>At present, the treatment for autoimmune diseases is primarily based on immunosuppressive as well as anti-inflammatory agents, which mostly include humanized monoclonal antibodies, engineered biologics, and fusion proteins. Such treatment options are particularly generated against some signaling pathways or are selective for a certain subset of cells in the immune systems. The effectiveness of such types of treatments is for short duration only and not even antigen-specific in some cases. Moreover, chronic administration of these agents leads to the common side effects of increased incidence of infections and general immunosuppression (<xref rid=\"B203\" ref-type=\"bibr\">Tavakolpour, 2017</xref>). By utilizing the disease-specific epigenetic marks on the ccfNAs, for non-invasive detection, monitoring, and screening of autoimmune disorders, it will become feasible to offer personalized medicine to manage the autoimmune diseases in the future. A list of ccfNAs in autoimmune diseases is presented in <xref rid=\"T3\" ref-type=\"table\">Table 3</xref>.</p><table-wrap id=\"T3\" position=\"float\"><label>TABLE 3</label><caption><p>Cell-free nucleic acids in autoimmune diseases.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Autoimmune disease</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Cell-free/circular nucleic acids</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Source for diagnosis</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>No. of patients involved in study</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>References</bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SLE</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">cfDNA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Serum</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">95</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B201\" ref-type=\"bibr\">Tan and Kunkel, 1966</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RA and SLE</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">cfDNA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Synovial fluid and serum</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B19\" ref-type=\"bibr\">Barnett, 1968</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RA and SLE</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">cfDNA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Derum</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">114</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B110\" ref-type=\"bibr\">Koffler et al., 1973</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">cfDNA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Serum</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">70</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B116\" ref-type=\"bibr\">Leon et al., 1977</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SLE</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">cfDNA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Serum and plasma</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B42\" ref-type=\"bibr\">Chen et al., 2007</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SLE, RA, and SS</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">cfDNA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Plasma</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">112</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B20\" ref-type=\"bibr\">Bartoloni et al., 2011</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CircRNA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PBMCs from blood</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B250\" ref-type=\"bibr\">Zheng et al., 2017</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SLE</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CircRNA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Plasma</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B118\" ref-type=\"bibr\">Li et al., 2018</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RR-MS</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CircRNA and DNA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Blood and tissues</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30 (DNA) 10 (RNA)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B35\" ref-type=\"bibr\">Cardamone et al., 2017</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RA, SLE, SS, and UC</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Circulating miRNA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Serum</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">103</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B98\" ref-type=\"bibr\">Jin et al., 2018</xref></td></tr></tbody></table><table-wrap-foot><attrib><italic><italic>RA, rheumatoid arthritis; RRMS, relapsing–remitting multiple sclerosis; SLE, systemic lupus erythematosus; SS, Sjogren’s syndrome; LN, lupus nephritis; UC, ulcerative colitis; circ, circular</italic>.</italic></attrib></table-wrap-foot></table-wrap></sec></sec><sec id=\"S8\"><title>ccfNAs in Neurological Diseases</title><p>Neurological disorders include diseases associated with central as well as the peripheral nervous system. These disorders include Alzheimer’s disease (AD), epilepsy, other dementias, Parkinson’s disease (PD), and traumatic disorders of the nervous system (<xref rid=\"B11\" ref-type=\"bibr\">Amor et al., 2014</xref>).</p><p>Symptoms associated with chronic neurodegenerative diseases occur late after the beginning of the pathology due to the compensatory potential of the brain that has been demonstrated in various studies. Any treatment is difficult in the later stages of neurodegenerative diseases due to the massive death of neuronal cells (<xref rid=\"B194\" ref-type=\"bibr\">Sperling et al., 2011</xref>; <xref rid=\"B159\" ref-type=\"bibr\">Pillai and Cummings, 2013</xref>). The recent ability to detect neurological biomarkers in the blood is due to technological advances in detection. The advances related to the use of ccfNA in neurological disorders are as follows.</p><sec id=\"S8.SS1\"><title>ccfNAs in Diagnosis, Progression, and Treatment of Neurological Diseases</title><p>Cell-free DNA acts as a marker for traumatic brain injury (TBI) and neurodegenerative diseases. The blood–brain barrier gets disrupted and leaky after TBI and neurodegenerative diseases (<xref rid=\"B37\" ref-type=\"bibr\">Carvey et al., 2009</xref>), which makes ccfNAs as potential markers for disease as well as injury (<xref rid=\"B115\" ref-type=\"bibr\">Lehmann-Werman et al., 2016</xref>). This includes changes in levels of ccfNA overall and also ccfNA markers specifically associated with the brain (<xref rid=\"B253\" ref-type=\"bibr\">Zlokovic, 2011</xref>).</p><p>Specific cell-free miRNA levels (<italic>mir-34c</italic>) involved in apoptosis and survival caspase cascade in plasma of Alzheimer’s patients are known for the prediction of disease (<xref rid=\"B25\" ref-type=\"bibr\">Bhatnagar et al., 2014</xref>). An analysis presented by <xref rid=\"B202\" ref-type=\"bibr\">Tan et al. (2014)</xref> showed that miRNAs are involved in the processes in pathogenesis associated with AD: amyloid-β accumulation, toxicity associated with tau proteins, inflammation, as well as neuronal cell death (<xref rid=\"B202\" ref-type=\"bibr\">Tan et al., 2014</xref>). In AD patients, activation, as well as inhibition of expression of <italic>miR-9</italic>, was found, which is enriched in the brain (<xref rid=\"B99\" ref-type=\"bibr\">Jin et al., 2013</xref>). <italic>miR-133b</italic> was downregulated in the midbrain of PD patients (<xref rid=\"B107\" ref-type=\"bibr\">Kim et al., 2007</xref>) as well as in mouse models of PD (<xref rid=\"B79\" ref-type=\"bibr\">Harraz et al., 2011</xref>; <xref rid=\"B63\" ref-type=\"bibr\">Filatova et al., 2012</xref>). However, studies are required to ascertain the presence of these miRNAs in circulation in these patients and their evaluation for potential biomarkers.</p><p>DNA methylation has a significant involvement in several neurodegenerative diseases (<xref rid=\"B8\" ref-type=\"bibr\">Al-Mahdawi et al., 2016</xref>). Increases and decrease in both 5mC and 5hmC at global levels have been identified in different diseases including AD (<xref rid=\"B9\" ref-type=\"bibr\">Al-Mahdawi et al., 2014</xref>). 5mC and 5hmC are identified as potential epigenetic markers in various neurodegenerative diseases both at global and locus-specific levels (<xref rid=\"B237\" ref-type=\"bibr\">Xiao et al., 2012</xref>; <xref rid=\"B179\" ref-type=\"bibr\">Sandi et al., 2013</xref>). However, further investigations are required for using 5mC and 5hmC as epigenetic biomarkers in cell-free circulating nucleic acids.</p><p>Previous studies have identified increased brain-derived ccfDNA in the serum of patients after traumatic brain injury (<xref rid=\"B115\" ref-type=\"bibr\">Lehmann-Werman et al., 2016</xref>). Rhomboid 5 Homolog 2 (<italic>RHBDF2</italic>) was found to be differentially methylated in the central nervous system (CNS) in Alzheimer’s disease (<xref rid=\"B52\" ref-type=\"bibr\">De Jager et al., 2014</xref>). In addition, a differentially methylated region located in the promoter–enhancer region of the <italic>RHBDF2</italic> gene was identified in amyotrophic lateral sclerosis (ALS) patients in ccfDNA in the plasma (<xref rid=\"B140\" ref-type=\"bibr\">Mendioroz et al., 2018</xref>). Thus, liquid biopsy may be applied to living patients as a source of potential epigenetic biomarkers for neurodegenerative disorders.</p></sec><sec id=\"S8.SS2\"><title>Cell-Free miRNA in CSF and CNS Disorders</title><p>Levels of <italic>miR-146a</italic> and <italic>miR-155</italic>, the proinflammatory miRNAs, were found to be high in cerebrospinal fluid (CSF) of AD patients along with <italic>miR-9</italic> and <italic>miR-125b</italic> that are enriched in neurons (<xref rid=\"B5\" ref-type=\"bibr\">Alexandrov et al., 2012</xref>). Differential expression of <italic>miR-15b</italic> and <italic>miR-21</italic> was found in CSF from patients with primary CNS lymphoma, gliomas, and brain metastasis (<xref rid=\"B17\" ref-type=\"bibr\">Baraniskin et al., 2012</xref>). <italic>miR-451</italic> was detected in CSF microparticles after brain injury (<xref rid=\"B158\" ref-type=\"bibr\">Pigati et al., 2010</xref>). A list of ccfNAs studied in neurological diseases is provided in <xref rid=\"T4\" ref-type=\"table\">Table 4</xref>.</p><table-wrap id=\"T4\" position=\"float\"><label>TABLE 4</label><caption><p>Circulating cell-free nucleic acids (ccfNAs) in neurological diseases.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Disease</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Cell-free nucleic acids</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Source</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Potential diagnostic/prognostic biomarkers</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>References</bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Traumatic brain injury (TBI)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Cell-free DNA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Brain</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">S100B, C-tau, NSE, and Hsp 70</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B131\" ref-type=\"bibr\">Lorente, 2017</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Alzheimer’s and Parkinson’s disease</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Cell-free miRNA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Plasma</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">mir-34c</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B25\" ref-type=\"bibr\">Bhatnagar et al., 2014</xref></td></tr><tr><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Brain</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">miR-9</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B99\" ref-type=\"bibr\">Jin et al., 2013</xref></td></tr><tr><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mid-brain</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">miR-133b</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B107\" ref-type=\"bibr\">Kim et al., 2007</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Friedreich’s ataxia (FRDA)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">DNA methylation</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Blood and buccal cells</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5mC and 5hmC</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B38\" ref-type=\"bibr\">Castaldo et al., 2008</xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Primary CNS lymphoma, gliomas, and brain metastasis</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Cell-free miRNA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CSF</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">miR-146a, miR-155, miR-9, and miR-125b</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B5\" ref-type=\"bibr\">Alexandrov et al., 2012</xref></td></tr><tr><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CSF</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">miR-15b and miR-21</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B17\" ref-type=\"bibr\">Baraniskin et al., 2012</xref></td></tr><tr><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CSF microparticles</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">miR-451</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B158\" ref-type=\"bibr\">Pigati et al., 2010</xref></td></tr><tr><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ctDNA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CSF</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Methylation of MGMT, p16/(INK4a), p73, and RARb</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><xref rid=\"B16\" ref-type=\"bibr\">Balana et al., 2003</xref>; <xref rid=\"B231\" ref-type=\"bibr\">Weaver et al., 2006</xref>; <xref rid=\"B222\" ref-type=\"bibr\">Wakabayashi et al., 2009</xref></td></tr></tbody></table><table-wrap-foot><attrib><italic><italic>NSE, neuron-specific enolase; MGMT, O-6-methylguanine-DNA methyltransferase; p16/(INK4a), cyclin-dependent kinase inhibitor 2A, multiple tumor suppressor 1; RARb, retinoic acid receptor beta.</italic></italic></attrib></table-wrap-foot></table-wrap></sec></sec><sec id=\"S9\"><title>ccfNAs in Mitochondrial Diseases</title><p>In mitochondrial disorders, mitochondria fail to function properly and are not able to generate enough energy required for the body. These diseases are the chronic ones due to genetic causes, often inherited from the previous generation (<xref rid=\"B106\" ref-type=\"bibr\">Khan et al., 2015</xref>). Mitochondrial diseases can affect multiple organs of the body (<xref rid=\"B7\" ref-type=\"bibr\">Al-Enezi et al., 2008</xref>) and, in many conditions, can lead to secondary mitochondrial dysfunction like Lou Gehrig’s disease, diabetes, muscular dystrophy, Alzheimer’s disease, and cancer (<xref rid=\"B149\" ref-type=\"bibr\">Niyazov et al., 2016</xref>).</p><p>Commonly known mitochondrial diseases are mitochondrial myopathy, Leigh syndrome, Leber’s hereditary optic neuropathy (LHON), myoclonic epilepsy with ragged red fibers (MERRFs), myoneurogenic gastrointestinal encephalopathy (MNGIE), mitochondrial neurogastrointestinal encephalomyopathy (MNGIE), mitochondrial myopathy encephalomyopathy lactic acidosis stroke-like symptoms (MELAS), neuropathy ataxia retinitis pigmentosa and ptosis (NARP), and Friedreich’s ataxia (<xref rid=\"B106\" ref-type=\"bibr\">Khan et al., 2015</xref>).</p><sec id=\"S9.SS1\"><title>Potential of ccfNAs as Epigenetic Markers in Diagnosis, Progression, and Treatment of Mitochondrial Diseases</title><p>The diagnosis of mitochondrial diseases is difficult because it affects multiple organs, and hence, patients exhibit a variety of symptoms. Moreover, there is no single diagnostic or laboratory test that can accurately confirm a mitochondrial disease (<xref rid=\"B156\" ref-type=\"bibr\">Parikh et al., 2015</xref>). Therefore, investigation of ccfDNA or mtDNA in the plasma of patients independently can be a better biomarker.</p><p>Unlike nuclear DNA, mtDNA molecules are arranged in clusters, called nucleoids, which are tethered to the mitochondrial membrane and are devoid of histones (<xref rid=\"B85\" ref-type=\"bibr\">Holt, 2010</xref>). Earlier observations had suggested that mitochondria lacked the machinery required for DNA methylation. However, several strands of evidence later, including lower frequency of CG dinucleotides by bioinformatic analysis and modulation of mtDNA methylation in response to oxidative stress (<xref rid=\"B173\" ref-type=\"bibr\">Rebelo et al., 2009</xref>), suggested the other way. Additionally, DNA methylation of nuclear mitochondrial genes may play an important role in the understanding of mitochondrial disorders.</p><p>There has been some evidence that some types of histones do localize to the mitochondrial membrane, where mtDNA is tethered (<xref rid=\"B48\" ref-type=\"bibr\">Choi et al., 2011</xref>). In the nuclear DNA, histone modifications are important in transcriptional control that can be altered in diseases affecting nuclear-encoded mitochondrial proteins, of which Friedreich ataxia is an example. This disorder is caused by transcriptional silencing due to certain histone modifications of the <italic>FXN</italic> gene, which encodes a mitochondrial protein involved in the biosynthesis of iron–sulfur clusters (<xref rid=\"B170\" ref-type=\"bibr\">Rai et al., 2010</xref>). In one of the studies, total mtDNA in plasma was quantified and found to be high in Friedreich’s ataxia patients, which opened up its possible role as a blood-based biomarker (<xref rid=\"B200\" ref-type=\"bibr\">Swarup et al., 2011</xref>).</p><p>Serum microRNAs like <italic>miR-1275</italic>, <italic>miR-149</italic>, <italic>miR-1</italic>, <italic>miR-133a</italic>, <italic>miR-133b</italic>, <italic>miR-145</italic>, <italic>miR-206</italic>, <italic>miR-208a</italic>, <italic>miR-208b</italic>, <italic>miR499</italic>, and <italic>miR-206</italic> are reported in some of the studies to diagnose the muscle mitochondrial dysfunction (<xref rid=\"B30\" ref-type=\"bibr\">Cacchiarelli et al., 2011</xref>; <xref rid=\"B58\" ref-type=\"bibr\">Endo et al., 2013</xref>; <xref rid=\"B88\" ref-type=\"bibr\">Hu et al., 2014</xref>). In addition, in some metabolic diseases, where the mitochondria are not functioning properly like brown adipogenesis (<xref rid=\"B244\" ref-type=\"bibr\">Zhang et al., 2015</xref>), non-alcoholic fatty liver disease (<xref rid=\"B117\" ref-type=\"bibr\">Leti et al., 2015</xref>), and diabetes (<xref rid=\"B164\" ref-type=\"bibr\">Raffort et al., 2015</xref>), distinctive patterns of microRNA have been observed. In another study, mutations in cybrid cells identified the role of <italic>microRNA-9/9</italic><sup>∗</sup> pattern in different mitochondrial disorders, e.g., mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes (MELAS) and myoclonic epilepsy with ragged-red fiber (MERRF) (<xref rid=\"B141\" ref-type=\"bibr\">Meseguer et al., 2015</xref>).</p><p>Hence, apart from the quantification of mtDNA in FRDA and serum microRNA profiles to assess mitochondrial myopathies, there is no available data on ccfNA epigenetic marker in mitochondrial diseases. Working on circulating mtDNA poses many challenges. mtDNA is highly polymorphic, which makes introducing targeted mutations into the mitochondria and generation of cellular or animal models of mitochondrial disorders quite challenging.</p><p>In addition, the gene panels required for the molecular diagnosis of mitochondrial disorders constitute an expensive approach. Therefore, the determination of epigenetic marks on ccfNA in plasma can have a diagnostic as well as prognostic potential in mitochondrial diseases. Thus, once the technical hurdles to study circulating mtDNA are taken care of by technology advancement, there is a strong motivation to explore the role of epigenetic mechanisms in mtDNA disease, as epigenetic factors may serve to explain the observed phenotypic heterogeneity, variable penetrance, and pronounced environmental triggers in this group of disorders.</p></sec></sec><sec id=\"S10\"><title>Artificial Intelligence/Machine Learning in ccfNA-Based Precision Medicine</title><p>There is a high impact of technologies such as high-performance computing as well as biological databases, like artificial intelligence (AI), machine learning (ML), and neural network, in the field of health care. Epigenetic data have traits like chemical and biological stability over time that make it open to ML (<xref rid=\"B238\" ref-type=\"bibr\">Xiong et al., 2015</xref>). Large-scale data-rich repositories such as The Cancer Genome Atlas (TCGA), BLUEPRINT, and the ENCODE association provide large amounts of samples to employ comprehensive, high-throughput statistical analysis of differentially methylated regions with biological relevance (<xref rid=\"B241\" ref-type=\"bibr\">Yuan et al., 2011</xref>; <xref rid=\"B143\" ref-type=\"bibr\">Munsell et al., 2015</xref>). Artificial intelligence and machine learning tools have especially found scope in cancer precision medicine by offering cancer patients personalized care. These methods help to decipher weak signals in the blood circulation at the early stages of cancer and provide a real-time assessment of cancer treatment. Nearly all datasets consist of DNA methylation profiles derived from peripheral blood, meaning that patients will only be required to provide a small blood sample. These can detect minute quantities of tumor DNA in blood and analyze their epigenetic marks for cancer monitoring.</p><p>Various programs have been generated to provide useful information for proper diagnosis. The <italic>Graphite</italic> is a bioconductor package to convert pathway topology to gene network (<xref rid=\"B178\" ref-type=\"bibr\">Sales et al., 2012</xref>). The <italic>micrographite</italic> package, for instance, provides a process to amalgamate mRNA and microRNA data via their association to canonical pathways (<xref rid=\"B32\" ref-type=\"bibr\">Calura et al., 2014</xref>). This approach has been beneficial in recognizing key microRNAs in primary myelofibrosis (<xref rid=\"B34\" ref-type=\"bibr\">Calura et al., 2016c</xref>), myeloma (<xref rid=\"B31\" ref-type=\"bibr\">Calura et al., 2016a</xref>), and ovarian cancer (<xref rid=\"B33\" ref-type=\"bibr\">Calura et al., 2016b</xref>). Another program, <italic>Mergeomics</italic> (multidimensional data integration to identify pathogenic perturbations to biological systems), combines data from epigenetic, genomic, and transcriptional association studies through a process of functional enrichment, which is used as the base for network construction; nevertheless, this tool has not been used in the context of cancer (<xref rid=\"B186\" ref-type=\"bibr\">Shu et al., 2016</xref>). Based on the multiomics data, <italic>Netboost</italic> is a network reconstruction method having statistical dependency and employs a commutable approach to lessen dimensionality. This system has been utilized for the categorization and survival study of acute myeloid leukemia data (<xref rid=\"B180\" ref-type=\"bibr\">Schlosser et al., 2020</xref>). Pair-wise relationships among various omics layers are identified by <italic>AMARETTO</italic>, which decides on cancer driver genes by taking into consideration frequently altered genes at the genome or epigenome level with functional consequences (<xref rid=\"B40\" ref-type=\"bibr\">Champion et al., 2018</xref>). Another tool <italic>MAGIA</italic> is used for the rebuilding of microRNA and transcription factor regulatory routes and has been employed for the scrutiny of expression and regulatory mechanisms in the NCI60 cell panel. As personalized genomic medicine pierces the age of “Big Data,” these would lead to uncovering of novel biomarkers on cancer indicators in the blood linked to particular disease states offered by machine learning algorithms. ML algorithms will assist with assessing the effects of various biomarkers concurrently and reveal top order interactions between biomarkers that would not be feasible to devise manually. A cancer genomics company, GRAIL, has launched a large-scale study, the Circulating Cell-Free Genome Atlas (CCGA), which uses machine learning to create a huge, representative library of cancer mutations and healthy mutations using data from ccfDNA and white blood cell genomes, to train their cancer screening algorithms (<xref rid=\"B50\" ref-type=\"bibr\">Cohn et al., 2019</xref>). With the availability of more data from clinical trials, this system can fine tune its algorithm to improve its diagnostic acumen.</p><p>Furthermore, artificial neural networks, which mimic the neurons of the brain, are functioning to interpret the data and provide the basis of machine learning. Disorders of neurodevelopment start early in childhood and have an impact on a diversity of functional domains as well as executive and cognitive function, social and language function, also behavior control, and motor function (<xref rid=\"B215\" ref-type=\"bibr\">Uddin et al., 2016</xref>; <xref rid=\"B205\" ref-type=\"bibr\">Thapar et al., 2017</xref>). Various other diagnoses include autism spectrum disorder (ASD) (<xref rid=\"B97\" ref-type=\"bibr\">Jiang et al., 2013</xref>), intellectual disability (ID) (<xref rid=\"B138\" ref-type=\"bibr\">Maulik et al., 2011</xref>), attention-deficit hyperactivity disorder (ADHD) (<xref rid=\"B121\" ref-type=\"bibr\">Lionel et al., 2011</xref>), and movement disorders (<xref rid=\"B90\" ref-type=\"bibr\">Huisman-Van Dijk et al., 2016</xref>). Consequently, the initiation of technologies dependent on transcriptome sequencing led to the foundation of the Allen developmental human brain atlas, profiling of the non-coding elements in the human genome by ENCODE database and the Human Cell Atlas (<xref rid=\"B103\" ref-type=\"bibr\">Kang et al., 2011</xref>; <xref rid=\"B174\" ref-type=\"bibr\">Regev et al., 2017</xref>). Recently, AI approaches have revealed by far reasonable success in neurodegenerative diseases (NDDs) by improving genetic diagnostics. The implementation of the Human Splicing Code is one of the first ML algorithms that demonstrate persuasive evidence of correctly categorizing disease-causing variants as well as those that are intronic. This process applies a Bayesian ML algorithm and has been illustrated in spinal muscular atrophy and pathogenic missense variants in ASD (<xref rid=\"B251\" ref-type=\"bibr\">Zhou and Troyanskaya, 2015</xref>). AI approaches are vital to explain the hidden arrangement in phenotype and genetic heterogeneity. NDDs are characterized by both phenotypic and genetic heterogeneity. For instance, cognitive function is impacted by 15q13.3 microdeletion syndrome and is found to be linked with heterogenous phenotypes that include speech delay (16%), epilepsy/seizure (57%), and ASD (11%) (<xref rid=\"B132\" ref-type=\"bibr\">Lowther et al., 2015</xref>). Although, biology enlightens this nosological evolution; however, more stress needs to be given on using AI approaches on major-scale datasets to authenticate or confront existing categorization paradigms.</p></sec><sec id=\"S11\"><title>Conclusion and Future Perspective</title><p>The utility of epigenetic alterations in ccfNA in various diseases as diagnostic and prognostic markers as well as therapeutic targets has been summarized in <xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>. Liquid biopsy is fast replacing the invasive methods for the diagnosis and prognosis of various diseases. This method offers the possibility to separate and identify ccfNAs for their utility for screening, diagnostic, prognosis, or for selecting therapeutic options. The different types of ccfNAs being evaluated are ccfDNA, ccfRNA, ccfmtDNA, ccfmiRNA, ccflncRNA, etc. With the development of newer technologies for isolation of small amounts of ccfNAs and detection of the specific signatures on these, ccfDNA are already in clinical practice for a few diseases. Furthermore, recent advances in the field have shifted the focus from determining the quantity, SNPs, mutations of the ccfNAs to analyzing the epigenetic signatures like methylated sequences and nucleosome positioning, which are specific to the particular clinical condition. The epigenetic markers on the ccfNAs are widely being explored to further advance the field of personalized medicine. However, the genetic or epigenetic markers related to ccfNAs have paved the way in clinical practice mostly in cancer and prenatal screening only. For various other diseases like neurological, autoimmune, and mitochondrial diseases, there are limited data, most of which are limited to research findings only. There is a need to have a comprehensive data analysis of the epigenetic markers in ccfNAs in different physiological and pathological conditions and further testing of the selected markers in large population-based studies and disease cohorts. The field of epigenetic markers in ccfNAs holds tremendous potential in the field of precision medicine.</p><fig id=\"F1\" position=\"float\"><label>FIGURE 1</label><caption><p>Under various clinical conditions such as neurological disorder, cancer, autoimmune diseases, mitochondrial diseases, and pregnancy and fetal disorders, circulating cell-free nucleic acids (ccfNAs) are released into body fluids like serum, plasma, and cerebrospinal fluids by apoptosis and necrosis. ccfNAs are of various types. The important ones are DNA, RNA, long non-coding RNAs (lncRNAs), and microRNA (miRNA) and have been observed to have disease-specific epigenetic modifications. These act as diagnostic and prognostic markers as well as therapeutic targets providing significant clinical benefits.</p></caption><graphic xlink:href=\"fgene-11-00844-g001\"/></fig></sec><sec id=\"S12\"><title>Author Contributions</title><p>All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.</p></sec><sec id=\"conf1\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><ref-list><title>References</title><ref id=\"B1\"><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Abern</surname><given-names>M. R.</given-names></name><name><surname>Owusu</surname><given-names>R.</given-names></name><name><surname>Inman</surname><given-names>B. 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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Immunol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Immunol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Immunol.</journal-id><journal-title-group><journal-title>Frontiers in Immunology</journal-title></journal-title-group><issn pub-type=\"epub\">1664-3224</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849657</article-id><article-id pub-id-type=\"pmc\">PMC7431954</article-id><article-id pub-id-type=\"doi\">10.3389/fimmu.2020.01968</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Immunology</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>Cytoplasmic Citrate Flux Modulates the Immune Stimulatory NKG2D Ligand MICA in Cancer Cells</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Møller</surname><given-names>Sofie H.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/965035/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Mellergaard</surname><given-names>Maiken</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1044813/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Madsen</surname><given-names>Mikkel</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1044789/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Bermejo</surname><given-names>Amaia V.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/965168/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Jepsen</surname><given-names>Stine D.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1015169/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Hansen</surname><given-names>Marie H.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Høgh</surname><given-names>Rikke I.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1045087/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Aldana</surname><given-names>Blanca I.</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1007542/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Desler</surname><given-names>Claus</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1045166/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Rasmussen</surname><given-names>Lene Juel</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/99382/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Sustarsic</surname><given-names>Elahu G.</given-names></name><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/139040/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Gerhart-Hines</surname><given-names>Zachary</given-names></name><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/239672/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Daskalaki</surname><given-names>Evangelia</given-names></name><xref ref-type=\"aff\" rid=\"aff5\"><sup>5</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1044938/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Wheelock</surname><given-names>Craig E.</given-names></name><xref ref-type=\"aff\" rid=\"aff5\"><sup>5</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/401560/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Hiron</surname><given-names>Thomas K.</given-names></name><xref ref-type=\"aff\" rid=\"aff6\"><sup>6</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1037044/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Lin</surname><given-names>Da</given-names></name><xref ref-type=\"aff\" rid=\"aff6\"><sup>6</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1044814/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>O’Callaghan</surname><given-names>Christopher A.</given-names></name><xref ref-type=\"aff\" rid=\"aff6\"><sup>6</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/91087/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Wandall</surname><given-names>Hans H.</given-names></name><xref ref-type=\"aff\" rid=\"aff7\"><sup>7</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/260150/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Andresen</surname><given-names>Lars</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1015275/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Skov</surname><given-names>Søren</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/492564/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Department of Veterinary and Animal Sciences, University of Copenhagen</institution>, <addr-line>Frederiksberg</addr-line>, <country>Denmark</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Department of Drug Design and Pharmacology, University of Copenhagen</institution>, <addr-line>Copenhagen</addr-line>, <country>Denmark</country></aff><aff id=\"aff3\"><sup>3</sup><institution>Center for Healthy Aging, Department of Cellular and Molecular Medicine, University of Copenhagen</institution>, <addr-line>Copenhagen</addr-line>, <country>Denmark</country></aff><aff id=\"aff4\"><sup>4</sup><institution>Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen</institution>, <addr-line>Copenhagen</addr-line>, <country>Denmark</country></aff><aff id=\"aff5\"><sup>5</sup><institution>Department of Medical Biochemistry and Biophysics, Karolinska Institutet</institution>, <addr-line>Stockholm</addr-line>, <country>Sweden</country></aff><aff id=\"aff6\"><sup>6</sup><institution>Wellcome Trust Centre for Human Genetics, University of Oxford</institution>, <addr-line>Oxford</addr-line>, <country>United Kingdom</country></aff><aff id=\"aff7\"><sup>7</sup><institution>Copenhagen Center for Glycomics, Department of Cellular and Molecular Medicine, University of Copenhagen</institution>, <addr-line>Copenhagen</addr-line>, <country>Denmark</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Qiongzhu Dong, Fudan University, China</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Chia-Wei Li, Academia Sinica, Taiwan; Alessandro Carrer, Veneto Institute of Molecular Medicine (VIMM), Italy; Georg F. Weber, University of Cincinnati, United States; Ahmad Bakur Mahmoud, Taibah University, Saudi Arabia</p></fn><corresp id=\"c001\">*Correspondence: Søren Skov, <email>sosk@sund.ku.dk</email></corresp><fn fn-type=\"other\" id=\"fn004\"><p>This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Immunology</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>1968</elocation-id><history><date date-type=\"received\"><day>28</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>21</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Møller, Mellergaard, Madsen, Bermejo, Jepsen, Hansen, Høgh, Aldana, Desler, Rasmussen, Sustarsic, Gerhart-Hines, Daskalaki, Wheelock, Hiron, Lin, O’Callaghan, Wandall, Andresen and Skov.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Møller, Mellergaard, Madsen, Bermejo, Jepsen, Hansen, Høgh, Aldana, Desler, Rasmussen, Sustarsic, Gerhart-Hines, Daskalaki, Wheelock, Hiron, Lin, O’Callaghan, Wandall, Andresen and Skov</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>Immune surveillance of cancer cells is facilitated by the Natural Killer Group 2D (NKG2D) receptor expressed by different lymphocyte subsets. It recognizes NKG2D ligands that are rarely expressed on healthy cells, but upregulated by tumorigenesis, presenting a target for immunological clearance. The molecular mechanisms responsible for NKG2D ligand regulation remain complex. Here we report that cancer cell metabolism supports constitutive surface expression of the NKG2D ligand MHC class I chain-related proteins A (MICA). Knockout of the <italic>N</italic>-glycosylation gene <italic>N</italic>-acetylglucosaminyltransferase V (MGAT5) in HEK293 cells induced altered metabolism and continuous high MICA surface expression. MGAT5 knockout cells were used to examine the association of cell metabolism and MICA expression through genetic, pharmacological and metabolic assays. Findings were verified in cancer cell lines. Cells with constitutive high MICA expression showed enhanced spare respiratory capacity and elevated mitochondrial efflux of citrate, determined by extracellular flux analysis and metabolomics. MICA expression was reduced by inhibitors of mitochondrial function, FCCP and etomoxir e.g., and depended on conversion of citrate to acetyl-CoA and oxaloacetate by ATP citrate lyase, which was also observed in several cancer cell types. Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) analysis revealed that upregulated MICA transcription was associated with an open chromatin structure at the MICA transcription start site. We identify mitochondria and cytoplasmic citrate as key regulators of constitutive MICA expression and we propose that metabolic reprogramming of certain cancer cells facilitates MICA expression and NKG2D-mediated immune recognition.</p></abstract><abstract abstract-type=\"graphical\" id=\"G1\"><title>Graphical Abstract</title><p>MGAT5 knockout (KO) in HEK293 cells induces metabolic changes resulting in increased intracellular UDP-GlcNAc, increased glycolysis, enhanced spare respiratory capacity and higher citrate flux from the mitochondria. MGAT5 KO cells express constitutively high MICA, mainly regulated on the transcriptional level through opening of the chromatin at the MICA promoter. MICA expression in MGAT5 KO cells is dependent on citrate turnover and histone acetylation. Blocking citrate flux inhibits MICA expression in numerous cancer cell lines, and we propose that this is a central metabolic regulation of MICA and immune surveillance.</p><p><graphic xlink:href=\"fimmu-11-01968-g001a.jpg\" position=\"float\"/></p></abstract><kwd-group><kwd>cancer metabolism</kwd><kwd>tumor immunology</kwd><kwd>MHC class I chain-related proteins A</kwd><kwd>citrate</kwd><kwd>ATP citrate lyase</kwd><kwd>Natural Killer Group 2D</kwd></kwd-group><funding-group><award-group><funding-source id=\"cn001\">Novo Nordisk Fonden<named-content content-type=\"fundref-id\">10.13039/501100009708</named-content></funding-source></award-group><award-group><funding-source id=\"cn002\">Det Frie Forskningsråd<named-content content-type=\"fundref-id\">10.13039/501100004836</named-content></funding-source></award-group><award-group><funding-source id=\"cn003\">H2020 European Research Council<named-content content-type=\"fundref-id\">10.13039/100010663</named-content></funding-source></award-group><award-group><funding-source id=\"cn004\">Danmarks Grundforskningsfond<named-content content-type=\"fundref-id\">10.13039/501100001732</named-content></funding-source></award-group><award-group><funding-source id=\"cn005\">Dagmar Marshalls Fond<named-content content-type=\"fundref-id\">10.13039/100007403</named-content></funding-source></award-group></funding-group><counts><fig-count count=\"7\"/><table-count count=\"0\"/><equation-count count=\"0\"/><ref-count count=\"99\"/><page-count count=\"22\"/><word-count count=\"0\"/></counts></article-meta></front><body><sec id=\"S1\"><title>Introduction</title><p>Natural killer (NK) and CD8<sup>+</sup> T cells monitor autologous cells for markers of tumorigenesis and stress. These immune cells express the NKG2D receptor that recognizes NKG2D ligands (NKG2DLs) upregulated on the surface of transformed cells (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>). NKG2DL expression is in many ways a double-edged sword. Upregulation of NKG2DLs on cancer cells enhance NK cell infiltration and promote cancer cytotoxicity (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>, <xref rid=\"B3\" ref-type=\"bibr\">3</xref>). Conversely, numerous cancer cells maintain chronic NKG2DL expression and evade immune elimination by down-modulating and impairing NKG2D receptor signaling (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>–<xref rid=\"B7\" ref-type=\"bibr\">7</xref>).</p><p>Cancer cells that block NKG2DL surface expression to evade immune recognition and clearance, can be treated with stress-inducers such as histone deacetylase inhibitors (HDACi’s), heat-shock or short-chain fatty acids (SCFAs) that upregulate NKG2DLs (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>). To date, studies have primarily focused on delineating transient NKG2DL induction whereas not much is known about regulation of their constitutive expression.</p><p>Metabolic reprogramming is a central hallmark of cancer. Cancer cells use aerobic glycolysis that was initially believed to be a result of dysfunctional mitochondria (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>); however, later advances have shown that cancer cells often use aerobic glycolysis alongside mitochondrial oxidative phosphorylation (OXPHOS) (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>). Mitochondria are not merely the powerhouse of the cell, but also provide metabolites for anabolic pathways necessary for cell growth. Citrate can be exported from the tricarboxylic acid (TCA) cycle for biosynthetic purposes. In the cytosol, citrate is cleaved by ATP citrate lyase (ACLY) to generate acetyl-CoA and oxaloacetate (OAA) (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>, <xref rid=\"B12\" ref-type=\"bibr\">12</xref>). Citrate is an inhibitor of glycolysis, thus to maintain high aerobic glycolysis, cancer cells require low cytoplasmic citrate (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>). Moreover, conversion of citrate by ACLY is a critical regulator of gene transcription by producing acetyl-CoA for histone acetylation (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>). Several of these cancer-associated metabolic properties are shared with other highly proliferating cells, such as activated T cells.</p><p>Expression of NKG2DLs is associated with hyperproliferation (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>) and thus with highly active metabolism. Two studies have linked NKG2DL expression to active glycolysis (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>, <xref rid=\"B16\" ref-type=\"bibr\">16</xref>), whereas one study reports that inhibition of glycolysis increased basal NKG2DL expression in breast cancer cell lines (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>, <xref rid=\"B18\" ref-type=\"bibr\">18</xref>). These studies emphasize a link to proliferative cell metabolism, and suggest that the role of glycolysis in NKG2DL regulation is context-specific.</p><p>NKG2DLs fall into two groups, the UL-16 binding protein 1-6 (ULBP1-6) and the MHC class I chain-related proteins A and B (MICA and MICB). Surface expression of each NKG2DL is regulated individually and at all levels of protein biogenesis (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>). We have previously shown that surface expression of specific MICA alleles depends on <italic>N</italic>-glycosylation (<xref rid=\"B19\" ref-type=\"bibr\">19</xref>, <xref rid=\"B20\" ref-type=\"bibr\">20</xref>). <italic>N</italic>-acetylglucosaminyltransferase V (MGAT5) is an oncoprotein catalyzing the formation of β<italic>-1,6-</italic>branched <italic>N</italic>-glycans that promote surface retention of glycoproteins (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>), but it is not known if MGAT5 regulates surface expression of MICA. Growth factor receptors are examples of MGAT5 substrates, and MGAT5 overexpression is associated with growth, adhesion, invasion and metastasis of cancer (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>–<xref rid=\"B26\" ref-type=\"bibr\">26</xref>). Inhibition of MGAT5 reduces tumor growth, enhances the anti-tumor responses by CD4<sup>+</sup> T cells and macrophages, and promotes Th1 differentiation (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>, <xref rid=\"B28\" ref-type=\"bibr\">28</xref>).</p><p>In this study we examine the metabolic regulation of the NKG2DL MICA. We discover that MICA was increased after MGAT5 knockout (KO) in a metabolically dependent way, and use this as a model to investigate the regulatory mechanisms of constitutive MICA expression. We find that glycolysis and mitochondrial export of citrate promotes constitutive MICA transcription in MGAT5 KO cells, a regulation that was also shown in several MICA-expressing cancer cells. In particular, increased MICA transcription was associated with altered chromatin accessibility of the MICA promoter. Our findings suggest that citrate drives a metabolic stress that modulates chromatin accessibility to facilitate basal MICA transcription and thereby regulate immune surveillance.</p></sec><sec sec-type=\"materials|methods\" id=\"S2\"><title>Materials and Methods</title><sec id=\"S2.SS1\"><title>Animals</title><p>Female NMRI mice 6 to 10-weeks old (Taconic, Lille Skensved, Denmark) were used, and all studies were performed in accordance with the Danish Act on Animal Experimentation, which implements Directive 2010/63/EU on the protection of animals in scientific research. The studies were approved by the Animal Experimentation Inspectorate, Ministry of Environment and Food, Denmark (License No. 2017-15-0201-01262). Health monitoring was carried out in accordance with Federation for Laboratory Animal Science Associations guidelines.</p></sec><sec id=\"S2.SS2\"><title>Reagents, Pharmacological Inhibitors, and DNA Constructs</title><p>Pharmacological compounds from Sigma-Aldrich were <italic>N</italic>-acetyl-D-glucosamine (GlcNAc, A3286), PUGNAc (A7229), 2-deoxy-D-glucose (2DG, D6134), carbonyl cyanide 4-(trifluoromethoxy)phenylhydrazone (FCCP, C2920), UK-5099 (PZ0160), bis-2-(5-phenylacetamido-1,3,4-thiadiazol-2-yl)ethyl sulfide (BPTES, SML0601), potassium hydroxycitrate tribasic monohydrate (HC, 59847), sodium dihydrogencitrate (234265), sodium acetate (S5636), oxaloacetic acid (OAA, O4126), 6-Mercaptopurine monohydrate (6-MP, 852678), azaserine (A4142), 5-aminoimidazole-4-carboxamide ribonucleotide (AICA-R, A9978), <italic>N</italic>-Acetylcysteine (NAc, A9165), sodium propionate (P1880), sodium butyrate (B5887), DMSO (D2438), PBS (D8537). Etomoxir sodium salt was purchased from Cayman Chemicals (Ann Arbor, MI, United States, 828934-41-4). BMS303141 was from Tocris Bioscience (Bristol, United Kingdom, 4609).</p><p>The GFP-Myc-MICA<sup>∗</sup>018 and MICA<sup>∗</sup>008 vectors, containing the coding sequences of MICA<sup>∗</sup>018 or MICA<sup>∗</sup>008 allele downstream of a generic leader, a GFP cassette, and a myc tag, were provided by Dr. M. Wills (University of Cambridge, Cambridge, United Kingdom) (<xref rid=\"B29\" ref-type=\"bibr\">29</xref>). pGL3-Basic (pGL3B) luciferase vector was purchased from Promega (Promega, Madison, WI, United States, E1751). MICA-<italic>firefly</italic> luciferase promoter vectors and SV40-<italic>renilla</italic> luciferase promoter vector were provided by Prof. C. O’Callaghan (University of Oxford, Oxford, United Kingdom) (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>).</p></sec><sec id=\"S2.SS3\"><title>Purification of Peripheral Blood Lymphocytes</title><p>Human peripheral blood mononuclear cells (PBMCs) were isolated by Histopaque-1077 (Sigma-Aldrich, St. Louis, MO, United States, 10771) separation from buffy coats obtained from healthy blood donors (The Capital Region Blood Bank, Copenhagen University Hospital, Copenhagen, Denmark). To obtain peripheral blood lymphocytes (PBLs), PBMCs were depleted from monocytes by incubation with Dynabeads (Invitrogen, Carlsbad, CA, United States, 11041), as previously described (<xref rid=\"B31\" ref-type=\"bibr\">31</xref>). PBLs were activated in RPMI1640 without glucose (Gibco, Gaithersburg, MD, United States, 11879-020) supplemented with 10% dialyzed fetal bovine serum (FBS) (F9665), 2 mM penicillin/streptomycin (P4333), 2 mM L-Glutamine (G7513), 1 mM sodium pyruvate (S8636) and either 10 mM D-glucose (G8769) or 10 mM D-galactose (G6404), all purchased from Sigma-Aldrich. PBLs were activated with CD3/CD28 beads (Invitrogen, 11132D) and 20U/mL hIL-2 (Peprotech, Rocky Hill, NJ, United States, 200-02) for 3 days. On day 3, PBLs were treated with 20 ng/mL FR901228 (National Cancer Institute, Bethesda, MD, United States) for 18 h.</p></sec><sec id=\"S2.SS4\"><title>Cell Line Cultivation and Proliferation</title><p>Human embryonic kidney-derived HEK293 cells, the prostate cancer cell line PC-3 and the keratinocyte-derived cell line HaCaT were purchased from American Type Culture Collection (ATCC, Manassas, VA, United States). NKG2D reporter cell CT312 and the 2B4 parental cell line were kindly provided by Chiwen Chang, Trowsdale Lab, Cambridge University. The breast cancer cell lines MDA-MB231 and MCF-7 were provided by Dr. José Moreira (Department for Veterinary Disease, University of Copenhagen, Denmark) and Henrik Leffers (The State Hospital, Copenhagen, Denmark), respectively. The cervical cancer cell line HeLa was provided by Jesper Jurlander (The State Hospital, Copenhagen, Denmark). The melanoma cells SK-MEL28, FM55m1, FM78 and FM86, and the human colon adenocarcinoma cell lines HT29 and SW480 were provided by Dr. Per thor Straten (Herlev University Hospital, Denmark). HEK293, MDA-MB231 and MCF-7 cells were cultured in DMEM with GlutaMAX (Gibco, 31966047). HeLa, HaCaT, PC-3, FM55m1, FM78, FM86, SK-MEL28, and SW480 were cultured in RPMI1640 (Sigma-Aldrich, R5886), and HT29 were cultured in McCoy’s 5A medium (Sigma-Aldrich, M8403). Media were supplemented with 10% FBS and 2 mM penicillin/streptomycin. 2 mM L-Glutamine was added to RPMI1640 and McCoy’s 5A. For long-term cell culture in glucose/galactose, cells were cultured in DMEM medium without glucose (Gibco, 11966025), supplemented with 10% dialyzed FBS, 2 mM penicillin/streptomycin, 1 mM sodium pyruvate, and 10 mM glucose/galactose. All cells were kept at culture conditions 37°C and 5% CO<sub>2</sub> and were passaged every 2–3 days.</p><p>For proliferation assay, WT and MGAT5 KO cells were seeded in 1 × 10<sup>5</sup> or 2 × 10<sup>5</sup> cells/well. For each experiment, cells were counted in triplicate wells after 24 and 48 h using the Bio-Rad TC20 automated cell counter (Bio-Rad, Hercules, CA, United States).</p></sec><sec id=\"S2.SS5\"><title>Gene Editing</title><p>MGAT5 KO cells were generated by zinc finger nuclease targeting in HEK293 cells and subsequent cloning and selection was performed as described previously (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>, <xref rid=\"B33\" ref-type=\"bibr\">33</xref>). HEK293 cells were transfected with mRNA (Sigma-Aldrich) or 5 μg of endotoxin free plasmid DNA using nucleofection on an Amaxa Nucleofector (Lonza, Copenhagen, Denmark). MGAT5 KO clones were selected by loss of reactivity with L-PHA, and clones were confirmed to have Mgat5 mutations using PCR and sequencing.</p><p>Lentiviral-mediated gene transfer was performed with an MGAT5 encoding vector constructed by inserting the MGAT5 sequence (generated as a blunt-end PCR product from a vector from HW, University of Copenhagen, Copenhagen, Denmark) into an entry vector system using the pENTR Directional TOPO Cloning Kit (Invitrogen, K2435-20/K3500-20) following manufacturer’s protocol. TOPO clonal reaction entry vectors were transformed into Mach-T1 chemically competent <italic>E. coli</italic> using heat-shock and S.O.C. medium followed by selection. PCR inserts were confirmed by sequencing at Eurofins MWG Operons (Luxembourg). Colonies were amplified and plasmids were purified with Nucleobond Xtra Midi kit (Macherey-Nagel, Duren, Germany, 740410). MGAT5 sequences were inserted into pLX302 lentiviral destination vector with LR CLonase II enzyme mix (Invitrogen, 11789). After proteinase K treatment constructs were transformed into DH5α using heat-shock and S.O.C. medium. Selected clones were amplified and DNA was purified using Nucleobond Xtra Midi kit. Destination vectors were checked for insertion using <italic>Bsr</italic>GI digestion at 55°C. MGAT5-coding lentiviral particles were packaged in HEK293T cells transfected with a mix of 4 μg psPAX2 vector (packaging vector), 2 μg pCMV-VSVG (envelope vector), 5 μg pLX302 vector carrying MGAT5, and 25 μl CaCl<sub>2</sub> to a final volume of 250 μl. The DNA mixture was complexed with 250 μL 2x HBS under constant air flow, and the transfection mix was added dropwise to 2 × 10<sup>6</sup> HEK293T cells in antibiotic-free medium. Cell culture medium was harvested 4 days after transfection and viral particle preparations were prepared by centrifugation at 5000 × <italic>g</italic> for 5 min. Lentiviral particles were added to cells and incubated for 24 h. Cells were cultivated in puromycin (1 μg/mL) selection medium for 2 weeks. Functional MGAT5 expression was validated by L-PHA binding.</p></sec><sec id=\"S2.SS6\"><title>Transient Transfection</title><p>Transient transfections were performed as described previously, using Amaxa Nucleofector device (Lonza) (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>). DNA was introduced to 2 × 10<sup>6</sup> cells in 100 μl Nucleofector solution V (Lonza, VCA-1003) and pulsed using the nucleofector program Q-001. For GFP-myc-tagged MICA<sup>∗</sup>008 and MICA<sup>∗</sup>018 constructs, cells were transfected with 1 μg DNA and analyzed the next day. Transfection with shRNAs or luciferase promoter constructs was carried out by calcium-phosphate transfection. Briefly, DNA/RNA were prepared in 10 μl CaCl<sub>2</sub> (2.5M) and adjusted to a final volume of 100 μl. DNA mixture was complexed with 100 μl 2x HBS (HEPES, NaCl, Na<sub>2</sub>HPO<sub>4</sub>) and added dropwise to 2 × 10<sup>6</sup> cells. Scrambled siRNA control, siIDH1 and siIDH2 ON-TARGET plus SMART pools were purchased from GE Healthcare Dharmacon (Lafayette, CO, United States).</p></sec><sec id=\"S2.SS7\"><title>Functional Assays</title><p>For NKG2D down-modulation, PBLs were isolated as described above, followed by depletion of CD4<sup>+</sup> cells using CD4 antibody (eBioscience, San Diego, CA, United States, 16-0049) and Dynabeads Mouse pan-IgG (Invitrogen, 11041). CD4<sup>+</sup>-depleted PBLs were cultured in RPMI1640 (Sigma-Aldrich, R5886) supplemented with 10% human serum (Sigma-Aldrich, H3667), 2 mM penicillin/streptomycin, 2 mM L-Glutamine and 10 ng/mL hIL-15 (Peprotech, 200-15) for 3 days to enrich for NK/CD8<sup>+</sup> T cells. NKG2D down-modulation assay was performed as previously described (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>). NKG2D ligands on effector cells (HEK293 WT or MGAT5 KO cells) were incubated with blocking NKG2D-Fc (R&D Systems, Minneapolis, MN, United States, 1299-NK) or control IgG1-Fc (R&D Systems, 110-HG) 8 μg/mL for 30 min at 4°C. Effector cells and target cells (NK/CD8<sup>+</sup> T cells) were mixed at indicated effector:target ratios and spun down 2 min 90 × <italic>g</italic> to allow conjugate formation. After 2 h co-cultivation, NK/CD8<sup>+</sup> T cells were analyzed for NKG2D surface expression by flow cytometry using Accuri C6 flow cytometer (BD Bioscience, Franklin Lakes, NJ, United States).</p><p>For the reporter cell assay, the NKG2D-reporter cell line 2B4-CT312 and the parental control 2B4 cell line (target cells) (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>) were mixed with effector cells (WT or MGAT5 KO cells) that were either blocked with NKG2D-Fc or control IgG1-Fc as described above. Effector and target cells were co-cultivated at different E:T ratios for 14–16 h. GFP expression of target cells was assessed with Accuri C6 flow cytometer. For <italic>in vivo</italic> assay, target cells were labeled with Vybrant DiD cell-labeling solution (Invitrogen, V22887) according to manufacturer’s protocol, and injected intraperitoneally together with WT or MGAT5 KO cells in a 1:1 ratio, 20 × 10<sup>6</sup> of each. 4–6 mice were used per group. Target cells were harvested after approximately 18 h with peritoneal lavage and NKG2D activation of DiD-positive reporter cells were assessed as GFP expression with Accuri C6 flow cytometer.</p></sec><sec id=\"S2.SS8\"><title>Lactate and dNTP Measurements</title><p>Concentrations of L-Lactate was measured enzymatically with Randox colorimetric assay according to manufacturer’s protocol (Randox, Crumlin, United Kingdom, LC2389). Reaction and analysis was performed on an Advia 1800 Chemistry System (Siemens, Munich, Germany).</p><p>dNTP levels were determined in 2 × 10<sup>6</sup> cells harvested with trypsinization and pelleted by centrifugation for 1200 × <italic>g</italic> for 5 min followed by resuspension of cell pellets in 60% methanol, frozen in liquid nitrogen and boiled at 100°C for 3 min. Samples were evaporated until dryness in a speedvac and whole cell levels of dTTP dATP, dCTP, and dGTP were determined using the DNA polymerase assay previously described (<xref rid=\"B37\" ref-type=\"bibr\">37</xref>).</p></sec><sec id=\"S2.SS9\"><title>LC-HRMS Metabolite Profiling</title><p>To determine intracellular metabolite levels, cell pellets from 1 × 10<sup>6</sup> cells were resuspended in 750 μl of cold methanol after 5 min sonication. Samples were prepared by 15 s vortex followed by 15 min equilibration at room temperature. After centrifugation at 13000 × <italic>g</italic> for 10 min at 4°C, 400 μl supernatants were collected, transferred to Ultrafree-MC Centrifugal filter devices (Merck Millipore Ltd., Cork, Ireland), and centrifuged at 10000 × <italic>g</italic> for 4 min at 4°C. From this, 100 μl was transferred to LC vials, and 30 μl of each sample was pooled to a mixed QC sample.</p><p>LC-HRMS was performed on a 1290 Infinity II ultra-high performance liquid chromatography (UHPLC) system coupled to a 6550 iFunnel quadrupole-time of flight (Q-TOF) mass spectrometer equipped with a dual AJS electrospray ionization source (Agilent Technologies, Santa Clara, CA, United States). Polar metabolites were separated on a SeQuant ZIC-HILIC (Merck, Darmstadt, Germany) column 100 Å (100 mm × 2.1 mm, 3.5 μm particle size) coupled to a guard column (20 mm × 2.1 mm, 3.5 μm particle size) and an inline-filter. Mobile phases consisted of 0.1% formic acid in water with (solvent A) and 0.1% formic acid in acetonitrile with (solvent B). The elution gradient used was as follows: isocratic step at 95% B for 1.5 min, 95% B to 40% B in 12 min and maintained at 40% B for 2 min, then decreasing to 25% B at 14.2 min and maintained for 2.8 min, then returned to initial conditions over 1 min, and the column was equilibrated at initial conditions for 7 min. The flow rate was 0.3 mL/min, injection volume was 2 μL and the column oven was maintained at 25°C. The acquisition was obtained with a mass range of 50–1200 <italic>m/z</italic> for, where full scan high-resolution data is acquired at three alternating collision energies (0 eV, 10 eV and 30 eV). Positive and negative raw LC-HRMS files were independently processed with an in-house developed PCDL library for polar metabolites using Profinder version B.06 (Agilent Technologies). Identification of reported compounds was assessed by accurate mass and retention time (AMRT) plus fragment identification at two collision energies (10 and 30 eV). Detailed acquisition methodology has been described previously (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>, <xref rid=\"B39\" ref-type=\"bibr\">39</xref>). UDP-GlcNAc/UDP-GalNAc detected peak (screened by expected calculated mass) could be of either compound as these two sugars could not be separated chromatographically, hence has been reported as a putative metabolite pending confirmation.</p></sec><sec id=\"S2.SS10\"><title>Extracellular Flux Analysis</title><p>The Seahorse XFe96 extracellular flux analyzer (Agilent Technologies) was used to measure OCR and ECAR on HEK293 cells. Cells were seeded at the density 2 × 10<sup>4</sup> cells/well ∼24 h before the experiment. One hour prior to assay run, cells were rinsed and switched to XF media (Agilent Technologies) with 1 mM sodium pyruvate and 10 mM glucose or galactose and incubated at 37°C CO<sub>2</sub>-free incubator. For the mitochondrial stress tests, OCR was measured under basal conditions, and during sequential injection of 1 μM oligomycin (Sigma-Aldrich, 495455), 1 μM FCCP (Sigma-Aldrich, C2920), and 0.5 μM rotenone (Rot, Sigma-Aldrich, R8875) + 0.5 μM antimycin A (AA, Sigma-Aldrich, A8674). Reported basal respiration is calculated from the third measuring point with OCR after Rot and AA subtracted, ATP-coupled respiration display OCR after oligomycin subtracted from the third measuring point, and maximal respiration is OCR after FCCP with OCR after Rot and AA subtracted.</p><p>For measuring the effect of HC, OCR was assessed 2 h after an injection of 15 mM HC.</p></sec><sec id=\"S2.SS11\"><title><sup>13</sup>C<sub>6</sub>-Glucose Tracing Experiment</title><p>1 × 10<sup>6</sup> cells were incubated for 1 h in DMEM medium without glucose supplemented with 10% FBS, 1 mM sodium pyruvate, and 25 mM uniformly labeled [U-<sup>13</sup>C]-Glucose (Cambridge Isotope Laboratories, Tewksbury, MA, United States, CLM-1396). Incubation medium samples were collected and cleared by centrifugation 300 × <italic>g</italic> for 5 min. Cells were washed and detached sterically. Intracellular metabolites were extracted in 70% ethanol, and centrifuged at 20.000 × <italic>g</italic> for 20 min (4°C) to separate the soluble extract (supernatant) from the insoluble components (pellet). Cell extracts and medium samples were lyophilized and reconstituted in water for subsequent biochemical analyses. Extract samples were adjusted to pH 1-2 with HCl and evaporated to dryness under nitrogen flow. Analytes were extracted into an organic phase (96% ethanol/benzene) followed by derivatization with 14% DMF/86% MTBSTFA with a modified procedure from (<xref rid=\"B40\" ref-type=\"bibr\">40</xref>). Standards containing unlabeled metabolites of interest and cell extracts were separated and analyzed in a gas chromatograph (Agilent Technologies 7820A chromatograph, J&W GC column HP-5MS, parts no. 19091S-433) coupled to a mass spectrometer (Agilent Technologies, 5977E). The isotopic enrichment of the metabolites of interest was corrected for natural abundance of <sup>13</sup>C using the unlabeled standards and calculated according to (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>). Data are presented as labeling (%) of M + X, where M is the mass of the unlabeled molecule and X is the number of labeled C-atoms in a given metabolite (<xref rid=\"B42\" ref-type=\"bibr\">42</xref>).</p></sec><sec id=\"S2.SS12\"><title>Western Blotting</title><p>Proteins were extracted using RIPA buffer (Thermo Scientific, Waltham, MA, United States, 89901) and proteinase/phosphatase inhibitor cocktail (Thermo Scientific, 1861281) for 30 min on ice. Lysates were sonicated 2 times for 15 s and cleared by centrifugation at 13,000 rpm for 10 min at 4°C. Protein extracts were denatured at 70°C for 10 min in NuPAGE sample buffer and DTT (Sigma-Aldrich, 646563). Proteins were resolved using 4–12% SDS-PAGE gels (Invitrogen) and transferred to nitrocellulose membranes (Invitrogen, IB301001) using the iBlot device (Invitrogen). For total protein stain, membranes were washed in ddH2O and stained with REVERT protein stain solution (LI-COR Biosciences, Lincoln, NE, United States, 926-11015) according to manufacturer’s protocol. Membranes were blocked in TBST blocking buffer (LI-COR Biosciences, 927–50000), probed with primary antibodies in TBS w. 0.1% Tween 20 and 5% BSA overnight on a shaker at 4°C, and washed in TBS + 0.1% Tween 20. Secondary antibody was from LI-COR (LICOR Biosciences, 926–32212) and signals were visualized by the Odyssey Fc Imaging System (LI-COR Biosciences). <italic>O</italic>-GlcNacylation was detected with RL2 <italic>O</italic>-GlcNAcylation antibody (Abcam, Cambridge, United Kingdom, ab2739), ATP citrate lyase (ACLY) was detected with rabbit ACLY antibody (Cell Signaling; 4332), and ACLY phosphorylation with rabbit phospho-ACLY (Ser455) antibody (Cell Signaling; 4331).</p></sec><sec id=\"S2.SS13\"><title>Flow Cytometry</title><p>Adherent cells were detached in PBS w. 1 mM EDTA (Invitrogen, 15575-038) or by pipetting. Cell surface staining was done as previously described (<xref rid=\"B43\" ref-type=\"bibr\">43</xref>), and cells were analyzed on Accuri C6 flow cytometer (BD Bioscience). Antibodies used for this study were: MICA (R&D systems, FAB1300A), ULBP2/5/6 (R&D Systems, FAB1298P), NKG2D (R&D Systems, FAB139A), ULBP1 (R&D Systems, FAB1380P), ULPB3 (R&D Systems, FAB1517A), ULBP4 (R&D Systems, FAB6285A), MICA/B (BD Bioscience, 558352), ICAM-1 (Leinco Technologies, C170), mouse IgG1 anti-myc-tag (Merck Millipore, 05724), MICB (R&D Systems; MAB1599) or IgG2b isotype control (R&D Systems; MAB004) detected with secondary anti-mouse IgG (Biolegend, San Diego, CA, United States, 405308). Binding of fluorescently labeled AF647-L-PHA (Invitrogen, L32457) and FITC-E-PHA (Vector Laboratories, Burlingame, CA, United States, FL-1121) was used to measure surface levels of complex <italic>N</italic>-glycans. All isotype controls were purchased from BD Bioscience.</p><p>For staining with mitochondrial probes, neutral lipid stains or 2-NBDG uptake, 5 × 10<sup>5</sup> cells seeded the day prior to experiment were washed once in PBS and incubated for 15 min at 37°C and 5% CO<sub>2</sub> in warm growth medium containing 25 nM tetramethylrhodamine methyl ester perchlorate (TMRM, Sigma-Aldrich, T5428), 10 nM MitoTracker Green FM (Invitrogen, M7514), or for 2 h in growth medium with 5 μM 2-NBDG (Invitrogen, N13195). Bodipy 493/503 (Invitrogen, D3922) was diluted in warm serum-free medium in a 1:7000 dilution and shaken vigorously to solubilize the lipids, immediately before loading into the cells for 15 min. Cells were washed twice in PBS + 2% FBS and detached sterically prior to analysis.</p><p>The soluble NKG2D–Fc receptor (1299-NK, R&D Systems) and IgG1–Fc (110-HG; R&D Systems) were labeled with Zenon Alexa Fluor 647 against human IgG1 (Z25408; Invitrogen) prior to staining of melanoma cells.</p><p>Data were acquired with an Accuri C6 instrument using Accuri C6 software, and analyzed in Flowlogic v7.2.1 (Inivai Technologies, Mentone, VIC, Australia) by gating on viable cells in forward-side-scatter plots (FSC/SSC) followed by single cell gating by area-height-scatter plots (FSC-A/FSC-H). Geometric mean fluorescent intensity (MFI) values are displayed in figures as MFI, or with corresponding isotype control subtracted as ΔMFI.</p></sec><sec id=\"S2.SS14\"><title>Real Time PCR Analysis</title><p>Total RNA was extracted by phase separation in TRIzol-chlorophorm and purified on Direct-zol spin-columns (Zymo Research, Irvine, CA, United States) according to manufacturer’s protocol. cDNA was generated using SuperScript cDNA synthesis kit (Invitrogen) under standard PCR conditions. Following primer sequences were used for quantitative RT-PCR with Brilliant SYBR Green qPCR Master Mix Kit: MICA (MICA_F: TGGCAGACATTCCATGTTTCTG, MICA_R: CTCGTCCCAACTGGGTGTTG), ULBP2 (ULBP 2_F: CAGAGCAACTGCGTGACATT, ULBP2_R: GGCCAC AACCTTGTCATTCT), IDH1 (IDH1_F: CTATGATGGTGA CGTGCAGTCG, IDH1_R: CCTCTGCTTCTACTGTCTTGCC), IDH2 (IDH2_F: AGATGGCAGTGGTGTCAAGGAG, IDH 2_R: CTGGATGGCATACTGGAAGCAG), GLUT1 (GLUT1_F: CTGCTCATCAACCGCAAC, GLUT1_R: CTTCTTCTCCCG CATCATCT), GLUT2 (GLUT2_F: TACATTGCGGACTTCTG TGG, GLUT2_R: AGACTTTCCTTTGGTTTCTGG), GLUT3 (GLUT3_F: CAGCGAGACCCAGAGATG, GLUT3_R: TTGG AAAGAGCCGATTGTAG), GLUT4 (GLUT4_F: TGGGCTT CTTCATCTTCACC, GLUT4_R: GTGCTGGGTTTCACCTC CT), and RPLP0 as housekeeping gene (RPLP0_F: CCTCGTGGAAGTGACATCGT, RPLP0_R: CATTCCCCC GGATATGAGGC). Real-time qPCR was performed on Bio-Rad CFX96 Real-time Thermal Cycler C1000 Touch, and all transcripts were normalized to housekeeping RPLP0 transcript.</p></sec><sec id=\"S2.SS15\"><title>Luciferase Reporter Assay</title><p>Cells were transiently transfected, using calcium-phosphate transfection as described above, with <italic>firefly</italic> luciferase promoter vectors (1 μg) and an SV40-promoter driven <italic>renilla</italic> luciferase vector (0.5 μg). Cells were harvested and snap frozen 24 h post transfection. Pellets were lysed in Dual-Glo Luciferase Reagent (Promega, E2920) and <italic>firefly</italic> luciferase activity was analyzed by luminometer Microbeta II (PerkinElmer, Waltham, MA, United States). <italic>Renilla</italic> luciferase activity was recorded by the instrument after subsequent addition of 1:1 volume Dual-Glo Stop & Glo (Promega, E2920). To correct for transfection efficiency, <italic>firefly</italic> luciferase signals were normalized to SV40 <italic>renilla</italic> luciferase signals of corresponding sample.</p></sec><sec id=\"S2.SS16\"><title>ATAC-Seq</title><p>ATAC-seq was performed as described previously (<xref rid=\"B44\" ref-type=\"bibr\">44</xref>). For each cell line, 50,000 cells were harvested from 3 separate cultures and used to prepare tagmented chromatin (3 replicates of WT and 3 replicates of MGAT5 KO cell lines, 6 samples total). Quality of PCR-amplified sequencing libraries was assessed using a Tapestation 2200 instrument with high sensitivity DNA screentapes (Agilent). Libraries were sequenced as paired end reads on a single lane of an Illumina HiSeq4000 flow cell. Resulting reads were aligned to the GRCh37/hg19 reference genome using Rsubread (<xref rid=\"B45\" ref-type=\"bibr\">45</xref>), and alignments were filtered to remove low quality, duplicate, and mitochondrial reads. Peaks were called using MACS2 (<xref rid=\"B46\" ref-type=\"bibr\">46</xref>) on merged reads from all samples, and differential peak accessibility between cell lines was determined using edgeR (<xref rid=\"B47\" ref-type=\"bibr\">47</xref>) with a threshold false discovery rate of 5%. Transcription factor binding motifs enriched in differentially accessible peaks were identified using HOMER (<xref rid=\"B48\" ref-type=\"bibr\">48</xref>). H3K4me3 ChIP-seq data were downloaded from ENCODE<sup><xref ref-type=\"fn\" rid=\"footnote1\">1</xref></sup> and are available under accession <ext-link ext-link-type=\"DDBJ/EMBL/GenBank\" xlink:href=\"ENCFF756EHF\">ENCFF756EHF</ext-link>.</p></sec><sec id=\"S2.SS17\"><title>Quantification and Statistical Analysis</title><p>Results are presented as mean ± SEM. Differences were analyzed for statistical significance using Prism 7 or 8 (GraphPad Software, La Jolla, CA, United States). Statistical analysis was performed as stated in figure legends, using unpaired <italic>t</italic>-test in 1A, 1C, 1E, 3E-F, 3H, 5C, 7A, 7E-F, paired <italic>t</italic>-test in 4F-G, 7D, multiple <italic>t</italic>-test in 1B, 1D, 3D, 4A-B, one sample <italic>t</italic>-test in 2A-C, 3C, 4C, 4E, 7G, two-way ANOVA in 3A, 5D-F, 5H-I, 6A, 6E, 7h-I, or one-way ANOVA in 5G. Level of statistical significance was determined by <sup>∗</sup><italic>p</italic> < 0.05, <sup>∗∗</sup><italic>p</italic> < 0.01 and <sup>∗∗∗</sup><italic>p</italic> < 0.001, <sup>****</sup><italic>p</italic> < 0.0001.</p></sec></sec><sec id=\"S3\"><title>Results</title><sec id=\"S3.SS1\"><title>MGAT5 Knockout Increases NKG2DL Expression and Activates NKG2D <italic>in vitro</italic> and <italic>in vivo</italic></title><p>Regulation of constitutive MICA expression remains largely unknown. Surface expression of certain MICA alleles depends on <italic>N</italic>-linked glycosylation (<xref rid=\"B19\" ref-type=\"bibr\">19</xref>, <xref rid=\"B20\" ref-type=\"bibr\">20</xref>, <xref rid=\"B49\" ref-type=\"bibr\">49</xref>). We questioned whether the cancer-associated glycosyltransferase MGAT5 is required for MICA expression. To assess the role of MGAT5 in regulation of NKG2DL surface expression, MGAT5 KO clones were generated in HEK293 cells. Remarkably, MGAT5 KO resulted in a permanently increased surface expression of the NKG2DLs MICA, MICB, and ULBP2/5/6 compared with parental wildtype (WT) cells (<xref ref-type=\"fig\" rid=\"F1\">Figure 1A</xref>). To confirm MGAT5 KO we measured binding of Leukoagglutinin from <italic>P. vulgaris</italic> (L-PHA) that binds specifically to MGAT5-modified <italic>N</italic>-glycans. As expected, L-PHA binding was reduced, whereas binding of Erythroagglutinin from <italic>P. vulgaris</italic> (E-PHA) that interacts with MGAT3-modified <italic>N</italic>-glycans, was unaffected, thus verifying functional knockout of MGAT5 (<xref ref-type=\"fig\" rid=\"F1\">Figure 1A</xref>). Modification of MGAT5 expression therefore associated with substantial changes in constitutive expression of several NKG2DLs.</p><fig id=\"F1\" position=\"float\"><label>FIGURE 1</label><caption><p>MGAT5 knockout increases NKG2DL expression and activates NKG2D <italic>in vitro</italic> and <italic>in vivo</italic>. <bold>(A)</bold> Surface expression of NKG2D ligands and binding of fluorescently labeled L-PHA (MGAT5 modifications) or E-PHA (MGAT3 modifications) on HEK293 wildtype (WT) and HEK293 MGAT5 knockout (KO) cells or isotype control staining (Iso) analyzed by flow cytometry. Data are presented as histograms representative of at least three independent experiments and in bar graphs showing mean fluorescence intensity (MFI). <bold>(B)</bold>\n<italic>In vitro</italic> NKG2D activation measured as GFP expression in NKG2D negative reporter cells (Control) and NKG2D expressing (NKG2D) reporter cells (target cells) co-cultivated with WT or KO cells (effector cells) for 14–16 h at indicated effector:target (E:T) ratios. <bold>(C)</bold> NKG2D activation <italic>in vivo</italic> measured on reporter cells as in (B) after activation by WT or KO at a 1:1 ratio in peritoneum of NMRI mice for approximately 18 h. GFP expression in DiD-labeled reporter cells signifies NKG2D activation and is shown as GFP MFI values of cells from four-six mice per group. <bold>(D)</bold> NKG2D down-modulation was assessed on NK/CD8<sup>+</sup> T cells (target cells) after co-cultivation for 2 h with WT or KO cells (effector cells) at indicated effector:target ratios (E:T). NKG2DLs on target cells were blocked with NKG2D-Fc (BL) or unblocked with IgG1-Fc (UN). The graph depicts surface expression of NKG2D presented relative to surface NKG2D expression on target cells alone. <bold>(E)</bold> MICA surface expression (left) and L-PHA/E-PHA surface binding (right) after lentiviral introduction of MGAT5 into WT or KO cells. MFI values from antibody staining were corrected for isotype background staining (ΔMFI). Statistical analysis was performed by unpaired <italic>t</italic>-tests in <bold>(A,C,E)</bold>, and multiple <italic>t</italic>-test with 5% FDR comparing WT and KO in <bold>(B,D)</bold>. *<italic>p</italic> < 0.05, **<italic>p</italic> < 0.01, ***<italic>p</italic> < 0.001, and ****<italic>p</italic> < 0.0001.</p></caption><graphic xlink:href=\"fimmu-11-01968-g001\"/></fig><p>To verify the functionality of MGAT5 KO-induced NKG2DLs, we tested NKG2D activation in a reporter cell line expressing human NKG2D coupled to DAP10-CD3ζ signaling and Nuclear factor of activated T cells (NFAT)-controlled GFP, ultimately expressing GFP in response to NKG2D activation (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>). NKG2D-GFP activation was higher after co-cultivation with MGAT5 KO cells than with WT cells (<xref ref-type=\"fig\" rid=\"F1\">Figure 1B</xref>), corresponding to the increased NKG2DL expression in MGAT5 KO cells (<xref ref-type=\"fig\" rid=\"F1\">Figure 1A</xref>). The reporter cells without NKG2D (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure S1A</xref>) remained inactivated, indicating that the activation was NKG2D-mediated (<xref ref-type=\"fig\" rid=\"F1\">Figure 1B</xref>). Moreover, blocking NKG2DLs with soluble NKG2D-Fc receptor impaired the activation, further validating NKG2D specificity (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure S1B</xref>).</p><p>To test if MGAT5 KO cells could activate NKG2D <italic>in vivo</italic>, we adoptively injected NKG2D reporter cells together with WT or MGAT5 KO cells into the peritoneum of NMRI mice and measured GFP expression in reporter cells. In line with our <italic>in vitro</italic> data, we observed a significant increase in NKG2D-GFP activation by MGAT5 KO cells compared with WT cells. The response was NKG2D-specific since the control reporter cells were unaffected (<xref ref-type=\"fig\" rid=\"F1\">Figure 1C</xref>). These data verify that MGAT5 KO-induced NKG2DLs maintain their functional integrity <italic>in vivo</italic>.</p><p>NKG2D is down-modulated upon activation (<xref rid=\"B50\" ref-type=\"bibr\">50</xref>). To further examine the functionality of NKG2DL expression caused by MGAT5 KO, we assessed NKG2D downregulation after receptor activation. NKG2D was further downregulated on CD4<sup>+</sup>-depleted peripheral blood lymphocytes (PBLs) after co-cultivation with MGAT5 KO cells than with WT cells, and this downregulation was abolished by blocking NKG2DLs with a soluble NKG2D-Fc receptor (<xref ref-type=\"fig\" rid=\"F1\">Figure 1D</xref>). Combined, these data indicate that KO of MGAT5 upregulates MICA and ULBP2/5/6, resulting in NKG2D activation <italic>in vitro</italic> and <italic>in vivo</italic>.</p><p>To ensure that the MICA upregulation was a result of MGAT5 KO, we stably transfected MGAT5 into WT and MGAT5 KO cells. L-PHA binding was restored within days after transfection, confirming expression of functional MGAT5. Interestingly, it took multiple passages for MICA expression to decrease to WT levels (<xref ref-type=\"fig\" rid=\"F1\">Figure 1E</xref> and <xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure S1C</xref>), suggesting that MICA is regulated in response to a long-term adaptation to altered MGAT5 expression.</p></sec><sec id=\"S3.SS2\"><title>UDP-GlcNAc Upregulates MICA Expression</title><p>Long-term MGAT5 deficiency will likely result in aberrant <italic>N</italic>-glycosylation and an accumulation of the MGAT5 donor substrate UDP-<italic>N</italic>-acetylglucosamine (UDP-GlcNAc). To address if MICA was regulated by a change in <italic>N</italic>-glycosylation in MGAT5 KO cells, we assessed the post-translational regulation of MICA by measuring surface expression of transgenically expressed GFP-myc-tagged MICA under a cytomegalovirus (CMV) promoter. The MICA alleles, MICA<sup>∗</sup>008 and MICA<sup>∗</sup>018 are distinctly regulated posttranslationally (<xref rid=\"B19\" ref-type=\"bibr\">19</xref>), and although MICA<sup>∗</sup>008 was upregulated in MGAT5 KO cells, the regulation was minor and unlikely to account for the profound change in endogenously expressed MICA (<xref ref-type=\"fig\" rid=\"F1\">Figures 1A</xref>, <xref ref-type=\"fig\" rid=\"F2\">2A</xref>). MICA transcripts on the other hand, were highly increased in MGAT5 KO cells (<xref ref-type=\"fig\" rid=\"F2\">Figure 2B</xref>), as well as ULBP2 mRNA (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure S2A</xref>), suggesting that NKG2DLs are transcriptionally regulated in MGAT5 KO cells. Notably, we found that the MGAT5 substrate UDP-GlcNAc, although indistinguishable from UDP-<italic>N</italic>-acetylgalactosamine (UDP-GalNAc) tended to be higher in MGAT5 KO cells compared with WT cells (<xref ref-type=\"fig\" rid=\"F2\">Figure 2C</xref>). This prompted us to address whether UDP-GlcNAc accumulation increased MICA mRNA. We found that cultivation of cells in GlcNAc-supplemented medium, which is known to increase intracellular UDP-GlcNAc through the salvage pathway (<xref rid=\"B51\" ref-type=\"bibr\">51</xref>), increased MICA transcripts and MICA surface expression, particularly in WT cells (<xref ref-type=\"fig\" rid=\"F2\">Figure 2D</xref> and <xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure S2B</xref>). Together, this suggests that MICA is upregulated due to UDP-GlcNAc accumulation in MGAT5 KO cells by transcriptional regulation, and thus independently of MICA <italic>N</italic>-glycosylation.</p><fig id=\"F2\" position=\"float\"><label>FIGURE 2</label><caption><p>UDP-GlcNAc upregulates MICA expression. <bold>(A)</bold> HEK293 wildtype (WT) and HEK293 MGAT5 knockout (KO) cells were transfected with GFP-myc-tagged MICA*008 or MICA*018 under CMV promoter and detected on the surface the following day as surface myc-tag expression on GFP-positive cells. Data are displayed as isotype-corrected mean fluorescence intensity (ΔMFI) relative to WT from three-four independent experiments. Dot plots are representative of all experiments. Grid is set to 5% of corresponding isotype controls. <bold>(B)</bold> MICA mRNAs were analyzed by quantitative RT-PCR in total RNA purified from WT and KO cells. MICA expression was normalized to housekeeping gene RPLP0 and shown as ratio relative to WT cells from six independent experiments. <bold>(C)</bold> UDP-GlcNAc/UDP-GalNAc measurements from LC-HRMS analysis of intracellular metabolites in WT and KO cells are presented as relative peak areas (a.u.) corresponding to UDP-GlcNAc or UDP-GalNAc. Log<sub>10</sub>-transformed values from three experiments are shown as individual dots. <bold>(D)</bold> MICA mRNA analyzed by quantitative RT-PCR in total RNA purified from WT and KO cells after 8 days cultivation with indicated concentrations of GlcNAc. MICA expression is normalized to housekeeping gene RPLP0 and displayed as ratio relative to untreated WT cells from two independent experiments. All data are displayed as mean ± SEM. Statistical analysis was performed using one-sample <italic>t</italic>-test in <bold>(A,B)</bold>, and unpaired <italic>t</italic>-test with Welch’s correction in <bold>(C)</bold>. **<italic>p</italic> < 0.01 and ***<italic>p</italic> < 0.001, <italic>p</italic>-value is presented in <bold>(C)</bold>.</p></caption><graphic xlink:href=\"fimmu-11-01968-g002\"/></fig></sec><sec id=\"S3.SS3\"><title>Glycolysis Supports MICA Expression in MGAT5 KO Cells</title><p>Accumulation of UDP-GlcNAc can regulate gene transcription directly as the donor substrate for <italic>O</italic>-GlcNAcylation, which competes with phosphorylation of Ser/Thr on several enzymes and transcription factors (<xref rid=\"B52\" ref-type=\"bibr\">52</xref>, <xref rid=\"B53\" ref-type=\"bibr\">53</xref>). Moreover, UDP-GlcNAc is synthesized from the hexosamine biosynthetic pathway arising from glycolysis, and can regulate cell metabolism through <italic>N</italic>-glycosylation of nutrient transporters and receptors (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>).</p><p>We first explored if UDP-GlcNAc regulated MICA expression through <italic>O</italic>-GlcNAcylation. <italic>O</italic>-GlcNAc is added enzymatically to proteins by <italic>O</italic>-GlcNAc transferase (OGT) and removed by <italic>O</italic>-GlcNAcase. Inhibition of either enzyme is known to impair functional <italic>O</italic>-GlcNAcylation (<xref rid=\"B54\" ref-type=\"bibr\">54</xref>, <xref rid=\"B55\" ref-type=\"bibr\">55</xref>). We blocked <italic>O</italic>-GlcNAcase with PUGNAc, and found that this had no effect on MICA expression (<xref ref-type=\"fig\" rid=\"F3\">Figure 3A</xref>). In agreement with this, there was no increase in total protein <italic>O</italic>-GlcNAcylation in MGAT5 KO cells (<xref ref-type=\"fig\" rid=\"F3\">Figure 3B</xref>). This indicates that <italic>O</italic>-GlcNAcylation is not a direct regulator of MICA, although altered <italic>O</italic>-GlcNAcylation could still be contributing to the long-term adaptation by modifying targets further upstream of MICA transcription.</p><fig id=\"F3\" position=\"float\"><label>FIGURE 3</label><caption><p>Glycolysis supports MICA expression in MGAT5 KO cells. <bold>(A)</bold> HEK293 wildtype (WT) and HEK293 MGAT5 knockout (KO) cells were treated with GlcNAcase inhibitor PUGNAc for 18 h prior to flow cytometry analysis of MICA surface expression. <bold>(B)</bold> O-GlcNAcylation of whole cell proteins from WT and KO cells quantified by western blotting of O-GlcNAcylation with the monoclonal RL2 antibody and total protein stain. Data are representative of three independent experiments. <bold>(C)</bold> Uptake of fluorescent glucose analog 2-NBDG in WT and KO cells after 2 h incubation with medium containing 2-NBDG (5 μM) was analyzed by flow cytometry. Mean fluorescent intensity (MFI) values are displayed as percentage of WT. <bold>(D)</bold> mRNA of glucose transporters GLUT1, 3, and 4 analyzed by quantitative RT-PCR in total RNA purified from WT and KO cells are presented as mRNA relative to housekeeping gene (HKG) RPLP0. <bold>(E)</bold> Intracellular and extracellular glucose-derived lactate in WT and KO cells were quantified by GC mass spectrometry after 1 h incubation in culture medium containing <sup>13</sup>C<sub>6</sub>-Glucose. <sup>13</sup>C incorporation in lactate is shown as percent lactate M + 3 out of total lactate fraction in cell lysates or supernatants. <bold>(F)</bold> Total extracellular lactate measurements from cell culture supernatants conditioned by WT or KO cells for 12 h. <bold>(G)</bold> Cell counts from start concentration of 1 × 10<sup>5</sup> or 2 × 10<sup>5</sup> cells/well counted after 24 and 48 h. <bold>(H)</bold> MICA surface expression on KO cells were treated with 2DG (20 mM) with or without GlcNAc (25 mM) for 22–24 h. Data of untreated (UT) samples share values from UT samples in <xref ref-type=\"fig\" rid=\"F7\">Figures 7E,F</xref>. All MFI values from antibody staining were corrected for isotype background staining (ΔMFI). All graphs are displayed as mean ± SEM from three independent experiments. Statistical analysis was performed by two-way ANOVA with Sidak’s multiple comparison test in <bold>(A)</bold>, one-sample <italic>t</italic>-test in <bold>(C)</bold>, multiple <italic>t</italic>-test in <bold>(D)</bold>, and unpaired <italic>t</italic>-test with Welch’s correction used when SD was unequal in <bold>(E,F,H)</bold>. *<italic>p</italic> < 0.05, ***<italic>p</italic> < 0.001, and ****<italic>p</italic> < 0.0001.</p></caption><graphic xlink:href=\"fimmu-11-01968-g003\"/></fig><p>Next, we approached the metabolic alterations in MGAT5 KO cells. Both MGAT5 and UDP-GlcNAc have been associated with regulation of cell metabolism (<xref rid=\"B56\" ref-type=\"bibr\">56</xref>–<xref rid=\"B59\" ref-type=\"bibr\">59</xref>). We therefore investigated whether glucose intake had changed in MGAT5 KO cells. Indeed, these cells had an increased uptake of the glucose analog 2-NBDG compared with WT cells, corresponding to a transcriptional upregulation of glucose transporters GLUT1, 3 and 4 (<xref ref-type=\"fig\" rid=\"F3\">Figures 3C,D</xref>). Furthermore, when we traced <sup>13</sup>C<sub>6</sub>-labeled glucose, we found that MGAT5 KO cells converted glucose into intracellular lactate; however, the glucose-derived lactate in MGAT5 KO cells was not secreted from the cells, resulting in overall lower extracellular lactate (<xref ref-type=\"fig\" rid=\"F3\">Figures 3E,F</xref>). The increased uptake of glucose and conversion to intracellular lactate in MGAT5 KO cells resembled elevated aerobic glycolysis, which is one of the hallmarks of cancer and supports increased growth and proliferation (<xref rid=\"B60\" ref-type=\"bibr\">60</xref>). Nevertheless, we found that WT and MGAT5 KO cells proliferated at the same rate (<xref ref-type=\"fig\" rid=\"F3\">Figure 3G</xref>). Importantly, impairing glycolysis in MGAT5 KO cells with 2-deoxy-D-glucose (2DG) reduced MICA mRNA and surface expression (<xref ref-type=\"fig\" rid=\"F3\">Figure 3H</xref> and <xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure S3A</xref>). 2DG inhibits the flux through glycolysis as well as the hexosamine biosynthetic pathway. Addition of GlcNAc or D-mannose rescues the hexosamine biosynthetic pathway (<xref rid=\"B61\" ref-type=\"bibr\">61</xref>), and we found that it also restored MICA expression after 2DG inhibition (<xref ref-type=\"fig\" rid=\"F3\">Figure 3H</xref> and <xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure S3B</xref>), suggesting that MICA expression in MGAT5 KO cells depends on glucose flux to the synthesis of UDP-GlcNAc.</p><p>Recently, it was shown that purine biosynthesis was critical for glucose-induced MICA expression (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>). We therefore tested if MICA expression in MGAT5 KO cells was caused by enhanced purine biosynthesis, but MICA expression in MGAT5 KO cells was unaffected by the purine biosynthesis inhibitor 6-mercaptopurine (6-MP) and even slightly increased by the inhibitor azaserine (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figures S3C,D</xref>). Moreover, the precursor for <italic>de novo</italic> purine biosynthesis AICA-R upregulated MICA expression in both WT and MGAT5 KO cells (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure S3E</xref>), further suggesting that the constitutive MICA expression on MGAT5 KO cells is not caused by consistently increased purine biosynthesis. Overall, these data indicate that glycolytic flux to the hexosamine biosynthetic pathway supports constitutively upregulated MICA expression in MGAT5 KO cells.</p></sec><sec id=\"S3.SS4\"><title>MICA Expression Requires Functional Adaptation of Mitochondria in MGAT5 KO Cells</title><p>The hexosamine biosynthetic pathway plays a key role in rewiring cancer metabolism (<xref rid=\"B62\" ref-type=\"bibr\">62</xref>). We therefore evaluated mitochondrial OXPHOS in an extracellular flux assay that monitors oxygen consumption rate (OCR) in response to specific inhibitors of oxidative metabolism (<xref ref-type=\"fig\" rid=\"F4\">Figures 4A,B</xref>). Basal respiration and ATP-coupled respiration were unaltered in MGAT5 KO cells (<xref ref-type=\"fig\" rid=\"F4\">Figures 4A,B</xref>). Remarkably, the maximal respiration was more than twice as high in MGAT5 KO cells compared with WT cells (<xref ref-type=\"fig\" rid=\"F4\">Figures 4A,B</xref>), demonstrating an enhanced spare respiratory capacity (SRC). To determine if mitochondrial functionality was necessary for MICA expression, we impaired the mitochondrial membrane potential with the uncoupler FCCP, and found that it reduced MICA mRNA and cell surface expression in MGAT5 KO cells (<xref ref-type=\"fig\" rid=\"F4\">Figure 4C</xref> and <xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure S4A</xref>). The TCA cycle can be supplied with metabolites from glycolysis, fatty acid oxidation and amino acid catabolism. Certain immunological functions have been associated with specific TCA cycle substrates (<xref rid=\"B63\" ref-type=\"bibr\">63</xref>). To test if MGAT5 KO cells supported MICA expression by oxidation of either of these resources, we inhibited the mitochondrial pyruvate carrier (MPC), import of fatty acids by carnitine palmitoyltransferase 1 (CPT1) or glutaminolysis by glutaminase (GLS) with the pharmacological inhibitors UK5099, etomoxir and BPTES, respectively (<xref ref-type=\"fig\" rid=\"F4\">Figure 4D</xref>). The color of the MPC inhibitor obstructed flow cytometric analysis, but we found that it had no effect on MICA transcription (<xref ref-type=\"fig\" rid=\"F4\">Figure 4D</xref>). The CPT1 inhibitor etomoxir reduced MICA transcript levels and surface expression in MGAT5 KO cells, whereas inhibition of GLS had no effect (<xref ref-type=\"fig\" rid=\"F4\">Figure 4D</xref> and <xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure S4B</xref>). MICA mRNA levels in WT cells were not affected by etomoxir, but the response in WT cells was otherwise comparable (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figures S4B,C</xref>). These data suggest that constitutive MICA expression in MGAT5 KO cells depends on fatty acid oxidation. Accordingly, MGAT5 KO cells also had increased accumulation of neutral lipids, providing plenty of substrate for fatty acid oxidation (<xref ref-type=\"fig\" rid=\"F4\">Figure 4E</xref>). However, etomoxir has recently been shown to have inhibitory effects on OXPHOS independent of fatty acid oxidation, suggesting that MICA expression may be independent of fatty acid oxidation, but supporting the importance of functional mitochondria (<xref rid=\"B64\" ref-type=\"bibr\">64</xref>). We further tested if MGAT5 KO-induced MICA expression was regulated by the mitochondrial production of reactive oxygen species (ROS), but the antioxidant <italic>N</italic>-Acetylcysteine (NAc) potentiated rather than reduced MICA expression (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure S4D</xref>). Furthermore, MGAT5 KO cells had a higher membrane potential measured with the mitochondrial directed probe TMRM, but no difference in mitochondrial mass, quantified with the fluorescent probe MitoTracker (<xref ref-type=\"fig\" rid=\"F4\">Figures 4F,G</xref>), demonstrating that the enhanced SRC was not caused by an increased mitochondrial mass.</p><fig id=\"F4\" position=\"float\"><label>FIGURE 4</label><caption><p>MICA expression requires functional adaptation of mitochondria in MGAT5 KO cells. <bold>(A,B)</bold> Mitochondrial stress test performed on HEK293 wildtype (WT) and HEK293 MGAT5 knockout (KO) cells and analyzed by Seahorse XF analysis. <bold>(A)</bold> Fluctuations in oxygen consumption rate (OCR) after addition of oligomycin (1 μM), FCCP (1 μM) and rotenone/antimycin A (Rot/AA) (0.5 μM). <bold>(B)</bold> Shows quantifications of basal respiration (third measuring point) and maximal respiration (OCR after FCCP addition) adjusted for non-mitochondrial oxygen consumption (OCR after Rot/AA), and ATP-coupled respiration (the drop in OCR after oligomycin). Data shows mean ± SEM of three independent experiments. <bold>(C)</bold> MICA mRNA analyzed by quantitative RT-PCR in total RNA purified from KO cells after 4 h treatment with DMSO or the mitochondrial uncoupler FCCP (2.5 μM). MICA expression is normalized to housekeeping gene RPLP0 and displayed as ratio relative to vehicle (DMSO) as mean ± SEM of four independent experiments. <bold>(D)</bold> MICA expression after inhibition of TCA cycle substrates: MICA mRNA analyzed by quantitative RT-PCR in total RNA purified from KO cells after 4 h treatment with DMSO or the pyruvate import inhibitor UK5099 (200 μM). MICA expression is normalized to housekeeping gene RPLP0 and displayed as ratio relative to DMSO control (left). MICA surface expression analyzed by flow cytometry in KO cells after 18 h treatment with fatty acid import inhibitor etomoxir (500 μM) (middle) or glutaminolysis inhibitor BPTES (5 μM) (right). Mean fluorescent intensity (MFI) values are corrected for isotype background staining and displayed as percent of DMSO control. All three graphs display mean ± SEM from three independent experiments. <bold>(E)</bold> Neutral lipid stain with Bodipy 493/503 probe was loaded to WT and KO cells and detected by flow cytometry. Grid is set at ∼5% according to stained WT cells. Dot plots are representative of four independent experiments. <bold>(F,G)</bold> Mitochondrial membrane potential <bold>(F)</bold> and mitochondrial mass <bold>(G)</bold> in WT and KO cells measured with the fluorescent probe TMRM (25 nM) or MitoTracker Green FM (10 nM), respectively. The bar graphs display mean ± SEM of MFI values from three-five independent experiments. Statistical analysis was performed by multiple <italic>t</italic>-test with a 5% false discovery rate in <bold>(A,B)</bold>, and one-sample <italic>t</italic>-test in <bold>(C,E)</bold>, and paired <italic>t</italic>-test in <bold>(F,G)</bold>. **<italic>p</italic> < 0.01 and ***<italic>p</italic> < 0.001.</p></caption><graphic xlink:href=\"fimmu-11-01968-g004\"/></fig><p>Together, our data indicate that mitochondrial properties are altered in MGAT5 KO cells evident by an increased membrane potential and enhanced SRC, and disrupting mitochondrial integrity reduced MICA expression. Mitochondrial function is thus critical for the increased MICA expression in MGAT5 KO cells, but not by producing ROS.</p></sec><sec id=\"S3.SS5\"><title>Mitochondrial Export of Citrate Facilitates MICA Expression in MGAT5 KO Cells</title><p>In a metabolite screen, we found that most detected and confirmed intracellular metabolites were more abundant in MGAT5 KO cells compared with WT cells (<xref ref-type=\"fig\" rid=\"F5\">Figure 5A</xref>). When measuring forward scatter by flow cytometry, we furthermore found that MGAT5 KO cells were larger in size (<xref ref-type=\"fig\" rid=\"F5\">Figure 5B</xref>). Additionally, MGAT5 KO cells had higher dNTP levels, specifically dTTP and dATP (<xref ref-type=\"fig\" rid=\"F5\">Figure 5C</xref>). Combined with the lipid accumulation (<xref ref-type=\"fig\" rid=\"F4\">Figure 4E</xref>), this indicates that MGAT5 KO cells accumulate metabolites compared with WT cells.</p><fig id=\"F5\" position=\"float\"><label>FIGURE 5</label><caption><p>Mitochondrial export of citrate facilitates MICA expression. <bold>(A)</bold> Intracellular metabolites in HEK293 wildtype (WT) and HEK293 MGAT5 knockout (KO) cells quantified by LC-HRMS. Relative peak areas (a.u.) of identified metabolites (N:35) from three experiments are pooled, expressed as ratios (KO/WT) and sorted accordingly. <bold>(B)</bold> Cell size of WT and KO cells measured as forward scatter (FSC) by flow cytometry, representative of at least three independent experiments. <bold>(C)</bold> Deoxynucleotides (NTPs) in whole cell lysates of WT and KO were quantified by DNA polymerase assay. The bar graph displays mean ± SEM from three independent experiments. <bold>(D)</bold> Intracellular citrate levels in WT and KO cells after 6 h treatment with PBS (UT) or hydroxycitrate (HC, 30 mM) quantified by LC-HRMS. The graph displays relative peak areas (a.u.) as mean ± SEM from three experiments. <bold>(E)</bold> Oxygen consumption rate (OCR) by WT or KO cells 2 h after HC (15 mM) injection by Seahorse XF instrument. Percent of OCR at the time of HC injection is presented as mean ± SEM from five independent experiments. <bold>(F)</bold> MICA surface expression on WT and KO cells analyzed by flow cytometry after 42 h treatment with indicated concentrations of HC. The bar graph displays isotype corrected MICA expression (ΔMFI) as mean ± SEM from three independent experiments. <bold>(G)</bold> MICA surface expression on WT and KO cells analyzed by flow cytometry after 18 h treatment with HC (15 mM) with or without indicated concentrations of oxaloacetate (OAA). ΔMFI is shown as percent of corresponding treatment without HC, and graph displays mean ± SEM from three independent experiments. <bold>(H)</bold> MICA surface expression on WT and KO cells 3 days post transfection with scrambled siRNA (siCtr), siIDH1 or siIDH2. The bar graph displays ΔMFI as mean ± SEM from three independent experiments. <bold>(I)</bold> MICA surface expression on WT and KO cells after 18 h treatment with indicated concentrations of citrate. The bar graph displays ΔMFI as mean ± SEM from three independent experiments. Statistical analysis was performed by unpaired <italic>t</italic>-test in <bold>(C)</bold>, two-way ANOVA with Bonferroni’s multiple comparison test in <bold>(D–F,I,H)</bold>, and one-way ANOVA with Dunnett’s multiple comparison test in <bold>(G)</bold>. *<italic>p</italic> < 0.05, **<italic>p</italic> < 0.01, and ***<italic>p</italic> < 0.001.</p></caption><graphic xlink:href=\"fimmu-11-01968-g005\"/></fig><p>In contrast, citrate was the only metabolite that was lower in MGAT5 KO cells (<xref ref-type=\"fig\" rid=\"F5\">Figure 5A</xref>). Cancer cells support increased growth and proliferation by exporting mitochondrial citrate to the cytoplasm. When citrate exits the mitochondria through the citrate/malate-exchanger, it can be converted to acetyl-CoA and OAA by the cytosolic ACLY. The citrate analog hydroxycitrate (HC) is a non-functional competitive inhibitor of ACLY. We found no difference in ACLY expression or the activating ACLY<sup><italic>S</italic>455</sup> phosphorylation (<xref rid=\"B65\" ref-type=\"bibr\">65</xref>) between WT and MGAT5 KO cells (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure S5A</xref>). However, treatment with HC resulted in citrate accumulation that was more pronounced in MGAT5 KO cells compared with WT cells (<xref ref-type=\"fig\" rid=\"F5\">Figure 5D</xref>), suggesting that the lower levels of citrate in MGAT5 KO cells is caused by a high citrate turnover (<xref ref-type=\"fig\" rid=\"F5\">Figures 5A,D</xref>). To further validate that MGAT5 KO cells had increased turnover of citrate, we blocked ACLY to inhibit citrate export from mitochondria, thereby forcing citrate to be oxidized in the TCA cycle. Indeed, only MGAT5 KO cells increased OCR significantly after HC treatment (<xref ref-type=\"fig\" rid=\"F5\">Figure 5E</xref>). Moreover, HC reduced surface MICA expression in MGAT5 KO cells (<xref ref-type=\"fig\" rid=\"F5\">Figure 5F</xref>), and this reduction was validated by another ACLY inhibitor BMS303141 (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure S5B</xref>). HC decreased MICA mRNA levels as well, suggesting a transcriptional regulation (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure S5C</xref>). OAA can readily be converted to malate and re-enter the mitochondria to keep the citrate/malate-exchanger running, and addition of OAA rescued the expression of MICA after ACLY inhibition (<xref ref-type=\"fig\" rid=\"F5\">Figure 5G</xref>), suggesting that MICA expression depends on preserved flux of citrate from the mitochondria. Supporting this, we found that knockdown of isocitrate dehydrogenase 2 (IDH2) that converts isocitrate to α-ketoglutarate in the mitochondria, potentiated MICA expression in MGAT5 KO cells (<xref ref-type=\"fig\" rid=\"F5\">Figure 5H</xref> and <xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure S5D</xref>). This indicates that increasing citrate efflux by blocking the TCA cycle downstream of citrate augments MICA expression. Furthermore, knockdown of the cytosolic IDH1 likewise upregulated MICA in MGAT5 KO cells (<xref ref-type=\"fig\" rid=\"F5\">Figure 5H</xref> and <xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure S5D</xref>). Finally, MICA expression was amplified with the addition of citrate in MGAT5 KO cells but not WT cells (<xref ref-type=\"fig\" rid=\"F5\">Figure 5I</xref> and <xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure S5E</xref>), indicating that the metabolic reprogramming in MGAT5 KO cells has provided a capacity to translate cytosolic citrate to MICA expression. Combined, this suggests that MICA expression in MGAT5 KO cells is maintained by mitochondrial efflux of citrate.</p></sec><sec id=\"S3.SS6\"><title>Increased Chromatin Accessibility at the MICA Transcription Start Site in MGAT5 KO Cells</title><p>Conversion of citrate by ACLY regulates proliferation and gene expression by providing acetyl-CoA for fatty acid synthesis and histone acetylation (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>). Inhibiting the synthesis of fatty acids did not impair MICA expression in MGAT5 KO cells (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure S6A</xref>). Similar to citrate, acetate can increase the acetyl-CoA pool and has previously been shown to induce MICA expression in cancer cells (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>). In line with this, we found that acetate potentiated MICA expression in MGAT5 KO cells (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure S6B</xref>), indicating that MICA expression in MGAT5 KO cells could be responsive to increased acetyl-CoA. To test if MICA expression depended on protein acetylation, we inhibited histone acetyl transferases (HATs) with anachardic acid, and this impaired MICA expression in MGAT5 KO cells significantly (<xref ref-type=\"fig\" rid=\"F6\">Figure 6A</xref>). Since histone acetylation regulates chromatin accessibility that modulates gene transcription, we next investigated if this was altered in MGAT5 KO cells. Indeed, ATAC-seq analysis revealed that MGAT5 KO cells had a region −196 to + 264 bp from the MICA transcription start site where the chromatin was significantly more open than in WT cells (<xref ref-type=\"fig\" rid=\"F6\">Figure 6B</xref>), indicating increased accessibility for transcriptional activity. Analysis of chromatin accessibility across the whole genome demonstrated that several of the top 10 motifs that were enriched in MGAT5 KO cells were binding sites for factors related to the activating protein-1 (AP-1) transcription factor complex (<xref ref-type=\"fig\" rid=\"F6\">Figure 6C</xref>) which is known to bind to the MICA promoter (<xref rid=\"B66\" ref-type=\"bibr\">66</xref>). Reciprocally, downregulated peaks comprised consensus motifs for the transcriptional repressor CTCC-binding factor (CTCF) and the CTCF-like Brother of the Regulator of Imprinted Sites (BORIS) that are known regulators of chromatin structure (<xref ref-type=\"fig\" rid=\"F6\">Figure 6D</xref>) (<xref rid=\"B67\" ref-type=\"bibr\">67</xref>, <xref rid=\"B68\" ref-type=\"bibr\">68</xref>). These findings suggest that CTCF-mediated chromatin condensation in WT cells may be abrogated in MGAT5 KO cells, possibly due to metabolite-mediated epigenetic changes.</p><fig id=\"F6\" position=\"float\"><label>FIGURE 6</label><caption><p>Increased chromatin accessibility at the MICA transcription start site in MGAT5 KO cells. <bold>(A)</bold> MICA surface expression analyzed by flow cytometry in HEK293 wildtype (WT) and HEK293 MGAT5 knockout (KO) cells after 24 h treatment with the HAT inhibitor anachardic acid (75 μM). The bar graph displays isotype corrected MICA expression (ΔMFI) as mean ± SEM from three independent experiments. <bold>(B–D)</bold> Chromatin accessibility in WT and KO cells analyzed by ATAC-seq. <bold>(B)</bold> Chromatin accessibility of WT and KO at chromosome 6 spanning the MICA gene. Normalized ATAC-seq data are aligned with MICA transcripts from RefSeq and ENCODE H3K4me3 ChIP-seq data from HEK293 cells. Genomic regions with significant differences in chromatin accessibility are highlighted in blue. The ATAC-seq track is representative of three independent experiments. <bold>(C)</bold> Top 10 enriched motifs in peaks upregulated in MGAT5 KO cells. <bold>(D)</bold> Top 10 enriched motifs in peaks downregulated in MGAT5 KO cells. <bold>(E)</bold> MICA promoter activity in WT and KO cells analyzed as luciferase activity 24 h after transfection with promoter-less firefly luciferase construct (pGL3B) or firefly luciferase-coupled MICA promoter (MICA). Firefly luciferase activity is normalized to an SV40 promoter-coupled renilla luciferase co-transfected into the cells. The bar graph displays mean ± SEM from four independent experiments. Statistics in <bold>(A,E)</bold> are analyzed using two-way ANOVA with Bonferroni’s multiple comparison test. **<italic>p</italic> < 0.01.</p></caption><graphic xlink:href=\"fimmu-11-01968-g006\"/></fig><p>The region of the MICA promoter that displays increased chromatin accessibility in MGAT5 KO cells is known to be involved in the transcriptional regulation of MICA and consistent with this, it is rich in H3K4me3 marks (<xref ref-type=\"fig\" rid=\"F6\">Figure 6B</xref>). This region comprises binding sites for heat shock factor, Nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), Sp1, and AP-1 transcription factor complex (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure S6C</xref>) (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>, <xref rid=\"B43\" ref-type=\"bibr\">43</xref>, <xref rid=\"B66\" ref-type=\"bibr\">66</xref>). To investigate if MICA transcription was upregulated due to increased activity of transcription factors binding to this region, we measured luciferase activity in a MICA promoter construct expressed in WT or MGAT5 KO cells. The MICA promoter was constitutively active in both WT and MGAT5 KO cells compared with the promoter-less construct pGL3B, but luciferase activity was comparable in WT and MGAT5 KO cells (<xref ref-type=\"fig\" rid=\"F6\">Figure 6E</xref>), indicating that the raised MICA transcript levels in MGAT5 KO cells are not driven by increased transcription factor activity at this site. Further, MICA promoter constructs with mutations in either NF-κB binding site, heat shock element (HSE) or both (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>), were equally expressed in WT and MGAT5 KO cells (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure S6D</xref>). Histone acetylation is associated with open transcriptionally active chromatin and the histone deacetylase inhibitor FR901228 further upregulated MICA expression in MGAT5 KO cells (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure S6E</xref>). Transient stimulation with the short-chain fatty acid propionate also upregulated MICA expression in both WT and MGAT5 KO cells, indicating that acute stress-induced MICA regulation may be distinct from regulation of constitutively expressed MICA (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure S6E</xref>). These findings suggest that constitutive MICA expression in MGAT5 KO cells is influenced by the increased openness of the chromatin structure around the MICA transcription start site.</p></sec><sec id=\"S3.SS7\"><title>Hydroxycitrate Reduces MICA Expression in Activated T Cells and Multiple Cancer Cells</title><p>An increasing variety of histone modifications has been reported to modify chromatin accessibility. A recent study demonstrated that lactate can be used for lactylation of histones and thus regulate gene transcription (<xref rid=\"B69\" ref-type=\"bibr\">69</xref>). Since MGAT5 KO cells had reduced secretion of lactate (<xref ref-type=\"fig\" rid=\"F3\">Figures 3D,E</xref>), intracellular lactate could modify chromatin structure like acetyl-CoA. We tested if MICA expression in MGAT5 KO cells was predominantly dependent on changed glycolytic or mitochondrial function. To distinguish the effects, we forced WT cells to lower glycolysis and increase mitochondrial respiration by cultivation in galactose-medium as previously described (<xref rid=\"B70\" ref-type=\"bibr\">70</xref>–<xref rid=\"B72\" ref-type=\"bibr\">72</xref>) (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure S7A</xref>). Galactose is processed at a slower rate than glucose, and cells thereby favor mitochondrial respiration for energy production. Interestingly, WT cells in galactose increased MICA mRNA levels after long-term cultivation (over 30 passages) (<xref ref-type=\"fig\" rid=\"F7\">Figure 7A</xref>). MICA surface expression; however, was only marginally upregulated (<xref ref-type=\"fig\" rid=\"F7\">Figure 7B</xref>). This indicates that increased mitochondrial function can facilitate MICA transcription, but mitochondrial activity is not sufficient for upregulating MICA surface expression to the levels seen in MGAT5 KO cells, which also have functional glycolysis. Similar to MGAT5 KO cells, we found that WT cells cultivated in galactose medium had increased SRC (<xref ref-type=\"fig\" rid=\"F7\">Figure 7C</xref>). Moreover, their MICA expression was impaired by treatment with HC (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure S7B</xref>) and amplified by treatment with citrate (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure S7C</xref>), supporting that cells cultivated in galactose have gained an enhanced capacity to express MICA, resembling MGAT5 KO cells.</p><fig id=\"F7\" position=\"float\"><label>FIGURE 7</label><caption><p>Hydroxycitrate reduces MICA expression in activated T cells and multiple cancer cells. <bold>(A)</bold> MICA mRNA analyzed by quantitative RT-PCR in total RNA purified from HEK293 cells after more than 30 passages in glucose (Glc) and galactose (Gal). MICA expression is normalized to housekeeping gene RPLP0 and displayed as mean ± SEM from six independent experiments. <bold>(B)</bold> MICA surface expression analyzed by flow cytometry of Glc and Gal cells at basal levels. Dot plots are representative of at least three independent experiments. Grid is set to ∼5% of corresponding isotype control stainings. <bold>(C)</bold> Mitochondrial stress test on HEK293 cultivated in Glc or Gal under same conditions as in <xref ref-type=\"fig\" rid=\"F4\">Figure 4A</xref>. The graph is baselined to measuring point three and displays mean ± SEM from two independent experiments. <bold>(D)</bold> MICA/B surface expression of peripheral blood lymphocytes (PBLs) activated for 3 days in Glc or Gal growth medium prior to 18 h treatment with FR901228 (20 ng/mL). Grids in dot plots are set to ∼5% of corresponding isotype control staining and dot plots are representative of seven different donors. The bar graph displays mean ± SEM of isotype-corrected MICA/B MFI (ΔMFI) from seven donors. Left panel is zoomed in on the difference between untreated Glc and Gal PBLs. <bold>(E,F)</bold> HEK293 MGAT5 knockout (KO) cells were treated with (E) 2DG (20 mM) or <bold>(F)</bold> hydroxycitrate (HC) (15 mM) in addition to PBS (UT), citrate (10 mM), or GlcNAc (25 mM) for 22–24 h. Bar graphs display MICA surface expression as mean ± SEM of ΔMFI values from three independent experiments. Data of UT samples share values with UT samples in <xref ref-type=\"fig\" rid=\"F3\">Figure 3H</xref>. <bold>(G)</bold> MICA surface expression in several cancer cell lines after 18 or 42 h treatment with HC (10 mM). ΔMFI values are normalized to UT control and shown as mean ± SEM from at least three independent experiments. <bold>(H,I)</bold> MICA surface expression <bold>(H)</bold> and NKG2D-fc binding <bold>(I)</bold> in cancer cell lines after 2.5 h treatment with HC (10 mM) prior to 18 h stimulation with FR901228 (FR, 20 ng/mL) or sodium butyrate (But, 5 mM). Bar graphs display MICA surface expression as mean ± SEM of ΔMFI values (H), or NKG2D-fc surface binding as ± SEM of ΔMFI normalized to untreated (UT) control <bold>(I)</bold>, from three independent experiments. Statistical analysis was performed by unpaired <italic>t</italic>-test with Welch’s correction in <bold>(A,E,F)</bold>, ratio paired <italic>t</italic>-test in <bold>(D)</bold>, one-sample <italic>t</italic>-test in <bold>(G)</bold>, and two-way ANOVA with Bonferroni’s multiple comparison test <bold>(H,I)</bold>. *<italic>p</italic> < 0.05, **<italic>p</italic> < 0.01, ***<italic>p</italic> < 0.001, and ****<italic>p</italic> < 0.0001.</p></caption><graphic xlink:href=\"fimmu-11-01968-g007\"/></fig><p>Activated T cells are some of the few healthy cells that are capable of expressing MICA (<xref rid=\"B73\" ref-type=\"bibr\">73</xref>, <xref rid=\"B74\" ref-type=\"bibr\">74</xref>). Furthermore, some T cell subsets shift to a cancer-like metabolism upon activation (<xref rid=\"B75\" ref-type=\"bibr\">75</xref>). We therefore investigated if expression of MICA/B by activated T cells was potentiated by enforcing mitochondrial activity with galactose-cultivation. We activated PBLs with CD3/CD28 and IL-2 for 3 days in glucose or galactose medium prior to stimulation with the HDACi FR901228. We observed an increase in basal MICA/B expression in galactose-cultivated PBLs as well as a prominent amplification of HDACi-induced MICA/B expression compared with glucose-cultivated PBLs (<xref ref-type=\"fig\" rid=\"F7\">Figure 7D</xref>). This expression was blocked by HC (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure S7D</xref>). Combined, this indicates that the metabolic properties of galactose-cultivation supports constitutive MICA transcription, much like MGAT5 KO cells, supporting that mitochondria can drive basal MICA expression even at low glycolytic conditions.</p><p>In line with this, we found that MICA inhibition after blocking glycolysis in MGAT5 KO cells could be completely annulled by simultaneous addition of citrate (<xref ref-type=\"fig\" rid=\"F7\">Figure 7E</xref>). In comparison, MICA expression after HC, could not be recovered by citrate or GlcNAc supplementation (<xref ref-type=\"fig\" rid=\"F7\">Figure 7F</xref>), highlighting that citrate turnover is critical for MICA expression and distal to glycolytic regulation of MICA in MGAT5 KO cells. Interestingly, we also discovered that the NKG2DLs ULBP2/5/6, which were also upregulated in MGAT5 KO cells (<xref ref-type=\"fig\" rid=\"F1\">Figure 1A</xref>), were not significantly regulated by modulating mitochondrial functionality, citrate metabolism, or histone acetylation, using etomoxir, HC or anachardic acid, respectively (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figures S5F–H</xref>). To elucidate if cancer cells with constitutive MICA expression shared the same dependency on citrate conversion to maintain MICA expression, we treated different cancer cell lines with HC. Indeed, MICA expression on melanoma cell lines FM55m1, FM86, FM78, and SK-MEL-28, colorectal cancer cell lines HT29 and SW480, as well as HeLa and HaCaT cells were sensitive to ACLY inhibition by HC (<xref ref-type=\"fig\" rid=\"F7\">Figure 7G</xref>). However, breast cancer cell lines MCF-7 and MDA-MB231, and prostate cancer cell lines PC-3 were either unresponsive to HC or increased MICA expression (<xref ref-type=\"fig\" rid=\"F7\">Figure 7G</xref>). MICA expression in the HC-unresponsive MDA-MB231 cells were also unaffected by cultivation in galactose (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure S7E</xref>), suggesting that some cancer cells hold metabolic properties that have uncoupled MICA expression from citrate-metabolism. Upregulation of MICA on melanoma and colorectal cancer cell lines by treatment with HDACi FR901228 or butyrate, was significantly inhibited by HC treatment (<xref ref-type=\"fig\" rid=\"F7\">Figure 7H</xref>), resulting in reduced binding of NKG2Dfc soluble receptor, as shown in FM78, FM86, and SK-MEL28 (<xref ref-type=\"fig\" rid=\"F7\">Figure 7I</xref>). HC did not impair ICAM-1 expression, except form a minor reduction in FM55m1 and HT-29 cells (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure S7F</xref>), suggesting that citrate flux is essential for MICA expression in certain cancer cells.</p><p>Altogether, our data demonstrate that constitutively increased MICA expression in MGAT5 KO cells relied on metabolic alterations, and suggest that MICA expression was upregulated through an enhanced chromatin accessibility. A similar phenotype was observed in cancer cells that share a sensitivity to the ACLY inhibitor HC, and we propose that citrate drives basal MICA expression by providing acetyl-CoA for histone acetylation that can modify chromatin structure around the MICA transcription start site. Metabolic reprogramming of cancer cells can thus be monitored by the immune system through increased NKG2DL expression.</p></sec></sec><sec id=\"S4\"><title>Discussion</title><p>Altered metabolism is a recognized hallmark of cancer cells that has promised therapeutic opportunities for more than a decade (<xref rid=\"B76\" ref-type=\"bibr\">76</xref>). We have shown that citrate exported from mitochondria drives expression of MICA, a stress-induced ligand for the immune activating NKG2D receptor in multiple cancer cell lines, and we put forward a model where constitutive MICA expression is facilitated by metabolic regulation of chromatin structure.</p><p>Through examination of glycosylation-dependent MICA, we found that KO of the <italic>N</italic>-acetylglucosaminyltransferase MGAT5 increased MICA expression and thereby increased activation of NKG2D <italic>in vitro</italic> and <italic>in vivo</italic>. However, this is caused by altered metabolism rather than changes in MICA <italic>N-</italic>glycosylation. MICA<sup>∗</sup>008 and MICA<sup>∗</sup>018 are distinctly regulated by N-glycosylation, but both were expressed on the surface of both WT and MGAT5 KO cells, indicating that neither depend on complex N-glycan formation. HEK293 cells are homozygous for the MICA<sup>∗</sup>008 allele (<xref rid=\"B77\" ref-type=\"bibr\">77</xref>), and we found that MICA<sup>∗</sup>008 was upregulated posttranslationally in HEK293 MGAT5 KO cells, suggesting that there could be a contribution of allele-specific regulation. However, the posttranslational upregulation was not sufficient to describe the vast upregulation of endogenous MICA expression in MGAT5 KO cells was predominantly regulated transcriptionally. MGAT5 expression is in many cases associated with tumor progression and metastatic potential (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>, <xref rid=\"B24\" ref-type=\"bibr\">24</xref>, <xref rid=\"B78\" ref-type=\"bibr\">78</xref>, <xref rid=\"B79\" ref-type=\"bibr\">79</xref>). Inhibition of cancer-associated MGAT5 is considered a potential therapeutic target, and has led to immune activation in a breast cancer mouse model (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>). In line with this, our data suggest that MGAT5 KO increases NKG2DLs, which would support cancer elimination in response to MGAT5 inhibition. Furthermore, MGAT5 KO has been associated with inflammatory diseases such as colitis (<xref rid=\"B80\" ref-type=\"bibr\">80</xref>), which also correlates with increased expression of NKG2DLs and thereby increased immune activation. Nevertheless, we found that MICA regulation was a long-term response to MGAT5 KO, hence, other early events may interfere and influence immunity before MICA expression is established. Further studies are required to pinpoint the direct involvement of MGAT5 and NKG2DLs in cancer and autoimmune diseases. This model; however, gives a framework for understanding constitutive regulation of NKG2DLs, and MGAT5 KO cells have here proven useful for investigating relevant cell metabolic features driving constitutive MICA expression.</p><p>Others have shown that MGAT5 overexpression increases surface expression of growth factor receptors through glycosylation and thereby regulate growth (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>, <xref rid=\"B26\" ref-type=\"bibr\">26</xref>, <xref rid=\"B56\" ref-type=\"bibr\">56</xref>, <xref rid=\"B81\" ref-type=\"bibr\">81</xref>). Notably, our MGAT5 KO cells also show increased growth and glucose uptake, possibly due to an excessive compensating feedback mechanism when adapting to the lack of MGAT5, which is likely driven by changes in the transcriptome, since glucose transporters were upregulated transcriptionally. We found that long-term UDP-GlcNAc accumulation in MGAT5 KO cells was important for MICA expression, which could contribute to the new metabolic phenotype. MGAT5 KO cells have increased aerobic glycolysis and lipid accumulation, suggesting enhanced lipid biosynthesis, compared with WT cells. These are all traits of cancer metabolism and could infer that MGAT5 KO cells resemble cancer cells metabolically (<xref rid=\"B82\" ref-type=\"bibr\">82</xref>).</p><p>Anti-cancer immune cells rely on some of the same metabolic pathways as cancer cells for exercising effector functions, and therapeutic strategies that target cancer metabolism can thus impede anti-cancer immunity (<xref rid=\"B83\" ref-type=\"bibr\">83</xref>). It is therefore critical to delineate metabolic pathways that are essential for cancer cells selectively or even promote immune surveillance. We found that metabolic changes in MGAT5 KO cells support MICA expression, since inhibition of glycolysis or disruption of mitochondrial integrity reduced chronic MICA expression. We argue that mitochondrial function is central for regulating constitutive MICA expression in MGAT5 KO cells and CD3/CD28-activated PBLs, since lowering glycolysis with culture medium deplete of glucose and supplemented with galactose raised MICA expression. Although constitutive MICA expression in MGAT5 KO cells was dependent on functional mitochondria, basal OXPHOS was similar in WT and MGAT5 KO cells, indicating that basal OXPHOS is not driving MICA expression.</p><p>Mitochondria export citrate to the cytosol for biosynthetic purposes, and this is increased in cancer cells (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>). Cytosolic citrate inhibits phosphofructokinase of the glycolysis; therefore, maintaining a low level of cytosolic citrate is crucial for sustaining cancer-related aerobic glycolysis (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>). We found that citrate flux was high in MGAT5 KO cells, maintaining low citrate levels that can support the increased aerobic glycolysis. The citrate efflux from mitochondria was critical for MICA expression in MGAT5 KO cells, and citrate supplementation could potentiate MICA expression. A cancer-associated mutation of IDH1 that generates the oncometabolite 2-hydroxyglutarate from cytoplasmic α-ketoglutarate (expected to diminish cytoplasmic citrate) has previously been shown to inhibit expression of the NKG2DLs ULBP1 and ULBP3 (<xref rid=\"B84\" ref-type=\"bibr\">84</xref>). This corresponds with our findings that cytoplasmic citrate is a driver of NKG2DL expression, and in further support of this we found that knockdown of IDH1 or IDH2 potentiated MICA expression in MGAT5 KO cells. Intracellular citrate is associated with cancer aggressiveness in prostate cancer cells and is a suggested biomarker for response to therapy (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>, <xref rid=\"B85\" ref-type=\"bibr\">85</xref>). Citrate administration sensitizes cancer cells to chemo therapy <italic>in vitro</italic> and therefore has an anti-cancer therapeutic potential (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>, <xref rid=\"B86\" ref-type=\"bibr\">86</xref>). Our data indicate that citrate may also contribute to tumor immune surveillance by upregulating NKG2DLs in metabolically reprogrammed cells, thus promoting an anti-tumor response. However, we found that MICA expression depended on citrate conversion to acetyl-CoA and OAA by ACLY. Furthermore, we found that acetate-induced MICA was impaired by ACLY inhibition (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure S6B</xref>), in line with previous findings showing that acetate can enter that TCA cycle and expand the acetyl-CoA pool through ACLY cleavage of citrate (<xref rid=\"B87\" ref-type=\"bibr\">87</xref>). ACLY is often overexpressed in cancer, and inhibition of ACLY is known to impair tumorigenesis, and impede cancer stemness and growth (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>, <xref rid=\"B12\" ref-type=\"bibr\">12</xref>, <xref rid=\"B88\" ref-type=\"bibr\">88</xref>, <xref rid=\"B89\" ref-type=\"bibr\">89</xref>). We did not detect increased expression levels of ACLY in MGAT5 KO cells compared with WT cells, suggesting that the citrate flux is driven by changes in metabolite levels possibly promoted by to the increased glucose uptake. We found that basal MICA expression in MGAT5 KO cells as well as several cancer cells, is downregulated in response to ACLY inhibition with HC. HC likewise impaired HDACi and SCFA-induced MICA in cancer cells. The consequences of constitutive MICA expression are poorly understood since it may have dual roles on anti-tumor immunity (<xref rid=\"B90\" ref-type=\"bibr\">90</xref>). Treating cancer with HC and thus reducing MICA expression can result in tumor immune evasion. Conversely, some cancers exhaust anti-cancer immunity by persistent high NKG2DL expression (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>–<xref rid=\"B7\" ref-type=\"bibr\">7</xref>, <xref rid=\"B91\" ref-type=\"bibr\">91</xref>), here HC treatment may be a selective treatment option. This hypothesis, however, needs further experimental verification, since the current study relies on <italic>in vitro</italic> findings. HC inhibition of MICA expression was selective to some tumors. Interestingly, prostate cancer cell line PC-3 was the only cell type that upregulated MICA in response to HC. Prostate cells are known to have atypical citrate metabolism because they produce and secrete large amount of citrate as a component of semen (<xref rid=\"B92\" ref-type=\"bibr\">92</xref>). Hence, to implement this in future treatment approaches it is important to define the metabolic prerequisites that couples citrate to MICA expression.</p><p>Mechanistically, ACLY regulates histone acetylation by controlling availability of acetyl-CoA that modulates chromatin accessibility and gene transcription. We show that MGAT5 KO cells have more open chromatin at the MICA transcription start site and that MICA expression was reduced by HAT inhibition, indicating that increased MICA mRNA is promoted by increased MICA transcription. Plasmid DNA does not form proper chromatin (<xref rid=\"B93\" ref-type=\"bibr\">93</xref>) And transcription of luciferase reporter plasmids was not upregulated in MGAT5 KO cells, indicating that MGAT5 KO-induced elevated MICA expression is maintained by increased chromatin accessibility rather than increased activation of transcription factors able to bind to this site. Constitutive MICA expression in MGAT5 KO cells may thus be driven by omnipresent transcription factors activating MICA due to the higher chromatin accessibility. It is well established that acetyl-CoA availability regulates global acetylation levels and gene expression (<xref rid=\"B94\" ref-type=\"bibr\">94</xref>). However, a mechanism controlling gene specificity has been unclear. After the discovery that acetyl-CoA-producing enzymes, including ACLY, can locate to the nucleus (<xref rid=\"B95\" ref-type=\"bibr\">95</xref>, <xref rid=\"B96\" ref-type=\"bibr\">96</xref>), it has been proposed that localized production of acetyl-CoA facilitates site-specific acetylation (<xref rid=\"B97\" ref-type=\"bibr\">97</xref>). This was not explored in this study, but is a relevant course for future investigations.</p><p>Differences between expression of individual NKG2DLs in different cell types complicates the studies of NKG2DLs. It remains to be understood what determines if a cell can express NKG2DLs. Here, we provide evidence for a possible instructive role of the cell metabolism in regulating MICA, proposing that the permanent metabolic shift in cancer cells and activated T cells primes cells for MICA expression. Our data indicate that citrate holds a major regulatory role in basal MICA expression, likely by regulating chromatin accessibility through acetylation (Graphical abstract). It is noteworthy that the NKG2DLs ULBP2/5/6 likewise were highly upregulated in our cell model, but regulated in a distinct manner yet to be delineated. We recently published that the TCA intermediate fumarate upregulates ULBP2/5/6 in ROS-dependent manner (<xref rid=\"B98\" ref-type=\"bibr\">98</xref>), which together emphasize that the cell metabolism can convey a stress signal through NKG2DLs. Defining the metabolic control of NKG2DLs can help us design strategies targeting cancer metabolism without impairing immune surveillance. Furthermore, constitutive MICA expression is associated with increased inflammation and autoimmune diseases (<xref rid=\"B99\" ref-type=\"bibr\">99</xref>). This study investigates metabolic control of the immune stimulatory ligand MICA with a focus on metabolism as a hallmark of cancer. It is therefore tempting to speculate that metabolic control of constitutive MICA expression also is important in cases of metabolism-associated chronic inflammation.</p></sec><sec sec-type=\"data-availability\" id=\"S5\"><title>Data Availability Statement</title><p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.</p></sec><sec id=\"S6\"><title>Ethics Statement</title><p>The animal study was reviewed and approved by the Animal Experimentation Inspectorate, Ministry of Environment and Food, Denmark.</p></sec><sec id=\"S7\"><title>Author Contributions</title><p>SM, MMe, MMa, LA, and SS: conceptualization. SM, MMe, MMa, AB, SJ, MH, BA, CD, ES, ED, TH, DL, and LA: methodology. SM, BA, CD, ES, ED, and TH: formal analysis. SM, MMe, MMa, AB, SJ, MH, RH, BA, CD, ES, ED, and TH: investigation. CW, CO’C, HW, and SS: resources. SM and SS: writing – original draft. SM, MMe, RH, MMa, AB, SJ, MH, BA, CD, LR, ES, ZG-H, ED, CW, TH, DL, CO’C, HW, LA, and SS: writing – review and editing. SM and TH: visualization. SS: supervision. CD, CW, CO’C, HW, and SS: funding acquisition. All authors contributed to the article and approved the submitted version.</p></sec><sec id=\"conf1\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> This work was supported by funding from the Novo Nordisk Foundation (NNF15CC0018346 for SS), the Independent Research Fund Denmark (DFF-FSS 4183-00389A for SS), European Commission (H2020-ERC GAP-772735 for HW), the Danish National Research Foundation (DNRF107 for HW), and Dagmar Marshalls Fond for CD.</p></fn></fn-group><ack><p>We thank Dr. Chiwen Chang (Department of Pathology, University of Cambridge, United Kingdom) for providing 2B4 NKG2D reporter cells, Thue W. Schwartz and Siv A. Hjorth for valuable inputs, and Anni Mehlsen (Department of Veterinary and Animal Sciences, University of Copenhagen, Denmark) for technical assistance.</p></ack><fn-group><fn id=\"footnote1\"><label>1</label><p><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.encodeproject.org/\">https://www.encodeproject.org/</ext-link></p></fn></fn-group><sec id=\"S10\" sec-type=\"supplementary material\"><title>Supplementary Material</title><p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.frontiersin.org/articles/10.3389/fimmu.2020.01968/full#supplementary-material\">https://www.frontiersin.org/articles/10.3389/fimmu.2020.01968/full#supplementary-material</ext-link></p><supplementary-material content-type=\"local-data\" id=\"SM1\"><media xlink:href=\"Data_Sheet_1.docx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec><ref-list><title>References</title><ref 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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"case-report\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Case Rep Endocrinol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Case Rep Endocrinol</journal-id><journal-id journal-id-type=\"publisher-id\">CRIE</journal-id><journal-title-group><journal-title>Case Reports in Endocrinology</journal-title></journal-title-group><issn pub-type=\"ppub\">2090-6501</issn><issn pub-type=\"epub\">2090-651X</issn><publisher><publisher-name>Hindawi</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32844041</article-id><article-id pub-id-type=\"pmc\">PMC7431955</article-id><article-id pub-id-type=\"doi\">10.1155/2020/8873506</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Case Report</subject></subj-group></article-categories><title-group><article-title>Falsely Elevated 25-Hydroxy-Vitamin D Levels in Patients with Hypercalcemia</article-title></title-group><contrib-group><contrib contrib-type=\"author\" corresp=\"yes\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0002-8599-0386</contrib-id><name><surname>Choi</surname><given-names>Moon Kyung</given-names></name><email>moonkyungchoi7@gmail.com</email><xref ref-type=\"aff\" rid=\"I1\">\n<sup>1</sup>\n</xref></contrib><contrib contrib-type=\"author\"><name><surname>Putthapiban</surname><given-names>Prapaipan</given-names></name><xref ref-type=\"aff\" rid=\"I1\">\n<sup>1</sup>\n</xref></contrib><contrib contrib-type=\"author\"><name><surname>Lekprasert</surname><given-names>Patamaporn</given-names></name><xref ref-type=\"aff\" rid=\"I2\">\n<sup>2</sup>\n</xref></contrib></contrib-group><aff id=\"I1\">\n<sup>1</sup>Department of Internal Medicine, Einstein Medical Center Philadelphia, 5501 Old York Road, Philadelphia, PA 19141, USA</aff><aff id=\"I2\">\n<sup>2</sup>Department of Endocrinology, Einstein Endocrine Associates, 50 East Township Line Road Medical Arts Building, Suite G01, Elkins Park, PA 19027, USA</aff><author-notes><fn fn-type=\"other\"><p>Academic Editor: Osamu Isozaki</p></fn></author-notes><pub-date pub-type=\"collection\"><year>2020</year></pub-date><pub-date pub-type=\"epub\"><day>9</day><month>8</month><year>2020</year></pub-date><volume>2020</volume><elocation-id>8873506</elocation-id><history><date date-type=\"received\"><day>29</day><month>4</month><year>2020</year></date><date date-type=\"rev-recd\"><day>13</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>18</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Moon Kyung Choi et al.</copyright-statement><copyright-year>2020</copyright-year><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><abstract><p>Symptomatic hypercalcemia is a commonly encountered clinical scenario. Though it is important to collect detailed history to find clinical clues connecting to the etiology of hypercalcemia, the diagnostic workup of hypercalcemia depends heavily on laboratory analysis. Accurate measurement of the parathyroid hormone and vitamin D levels is essential. However, commercial laboratory measurement of vitamin D levels can be erroneous in the setting of abundant paraprotein in the serum. One of the most common conditions that can cause an increased amount of paraproteins is multiple myeloma. We report 2 cases of falsely elevated 25-hydroxy-vitamin D levels in patients presenting with hypercalcemia and an underlying diagnosis of MM.</p></abstract></article-meta></front><body><sec id=\"sec1\"><title>1. Introduction</title><p>Hypercalcemia is a well-established complication in patients with multiple myeloma (MM). Hypercalcemia due to MM is mediated by increased production of osteoclast-activating paracrine molecules by the tumor cells. Even in patients with underlying MM who present with hypercalcemia, measurement of the parathyroid hormone (PTH) and 25-hydroxy-vitamin D (25 (OH)-vit D) levels is integral for complete diagnostic investigation. Unfortunately, commercial laboratory analysis of the 25-hydroxy-vitamin D level carries limitations. This possibility of laboratory error is often overlooked by clinicians which may lead to invalid interpretation of laboratory results. Here, we describe 2 cases of erroneously elevated 25-hydroxy-vitamin D levels in MM patients presenting with symptomatic hypercalcemia.</p></sec><sec id=\"sec2\"><title>2. Case Presentation</title><sec id=\"sec2.1\"><title>2.1. Case 1</title><p>A 65-year-old woman with a past medical history of hypertension and MM presented with fatigue. She was not started on treatment for MM except for recent radiation therapy to a metastatic lesion in the thoracic spine. Laboratory analysis was notable for chronic anemia (hemoglobin 7.0 g/dL, normal 12–16 g/dL), acute kidney injury (creatinine 3.1 mg/dL, normal 0.6–1.0 mg/dL), and serum calcium level of 16.6 mg/dL (corrected calcium 18.0 mg/dL, normal 8.4–10.3 mg/dL). Total protein level was elevated to 11.1 g/dL (normal 6–8.3 g/dL), and gamma globulin level was 6.0 g/dL (normal 0.6–1.6 g/dL). As there was no worsening of patient's anemia from her personal baseline, her fatigue was attributed to hypercalcemia. Further blood work revealed a PTH level of 8.0 pg/mL (normal 9–73 pg/mL). 25 (OH)-vit D level was >96.0 ng/mL via immunoassay with a reference normal range of 30 to 50 ng/mL. 1,25-Dihyroxy-vitamin D level was 12 pg/mL (normal 18–72 pg/mL). PTH-related protein (PTHrP) level was 12 pg/mL (normal 14–27 pg/mL). These laboratory findings indicated that the patient had PTH and PTHrP-independent hypercalcemia. The elevated 25 (OH)-vit D level suggested vitamin D toxicity. However, the patient was not on any vitamin D supplements, and the clinical suspicion for vitamin D intoxication was very low. To evaluate for laboratory error, 25 (OH)-vit D level was measured via liquid chromatography-tandem mass spectrometry (LC-MS/MS) which revealed a normal value of 46 ng/mL (normal 30–100 ng/mL). Based on low PTH level, normal 25 (OH)-vit D level, low 1,25-dihydroxy-vitamin D level, and low PTHrP level, the patient was diagnosed with hypercalcemia secondary to multiple myeloma. Her hypercalcemia was treated with intravenous fluids, calcitonin, and zoledronic acid. The patient's hypercalcemia showed a good response to treatment in the following days.</p></sec><sec id=\"sec2.2\"><title>2.2. Case 2</title><p>A 72-year-old man with MM receiving melphalan and dexamethasone for palliation presented with confusion. His other comorbidities were hypertension, paroxysmal atrial fibrillation, chronic systolic heart failure, and a history of ischemic stroke. The family provided medical history on the patient's behalf. The patient had complained of thirst few hours ago and then became disoriented. Family members have not observed any focal weakness or dysarthria. The patient had not mentioned chest pain or shortness of breath. Initial blood work showed chronic anemia (hemoglobin 7.7 g/dL), acute kidney injury (creatinine 1.3 mg/dL), and hypercalcemia 14.9 mg/dL (corrected calcium 16.1 mg/dL). Total protein level was increased to 12.3 g/dL. A CT head scan did not reveal any acute disease process. Further investigation revealed a PTH level of 16.2 pg/mL and 25 (OH)-vit D level >96.0 ng/mL via immunoassay. 1,25-Dihyroxy-vitamin D and PTHrP levels were not measured. Similar to the previous case, the patient was not on any vitamin D supplements, and this raised concerns of an erroneous measurement of the 25 (OH)-vit D level. 25 (OH)-vit D level was measured again by LC-MS/MS and was found to be in the normal range (68 ng/mL). Normal PTH and 25 (OH)-vit D levels supported the high clinical suspicion for hypercalcemia secondary to MM. The patient's hypercalcemia improved when treated with intravenous fluid, calcitonin, and zoledronic acid. He was also continued on a palliative regimen for MM including dexamethasone. The patient's hypercalcemia and disorientation improved, and he was safely discharged to a nursing facility.</p></sec></sec><sec id=\"sec3\"><title>3. Discussion</title><p>25 (OH)-vit D level is widely accepted as a standard value representing the vitamin D status of a patient. Various methods are utilized to measure 25 (OH)-vit D levels such as automated immunoassay, competitive protein-binding assay, and LC-MS/MS [<xref rid=\"B1\" ref-type=\"bibr\">1</xref>]. Among these different methods, automated immunoassay is the most commonly used technique in commercial laboratories [<xref rid=\"B2\" ref-type=\"bibr\">2</xref>].</p><p>The initial measurements of the 25 (OH)-vit D level for the above 2 cases were carried out by Abbott Architect i2000 automated immunoassay. In the Architect i2000 immunoassay method, sheep polyclonal anti-vitamin D IgG is incubated with patient's serum. After sheep polyclonal anti-vitamin D IgG binds to 25 (OH)-vit D in the sample, chemiluminescence-labeled 25-hydroxy-vitamin D is added. Excess sheep polyclonal anti-vitamin D IgG that was not bound to 25 (OH)-vit D in the sample will bind to this exogenous vitamin D. After washing, only sheep anti-vitamin D IgG that is bound to exogenous vitamin D will emit chemiluminescence. Thus, the amount of chemiluminescence will be inversely associated with the quantity of 25 (OH)-vit D in the sample.</p><p>The repeat measurements of 25 (OH)-vit D levels were carried out by the AB SCIEX 6500 LC-MS/MS system. LC-MS/MS is largely accepted as the most accurate method of measuring 25 (OH)-vit D levels [<xref rid=\"B1\" ref-type=\"bibr\">1</xref>]. During LC-MS/MS, 25 (OH)-vit D is separated from other potential interfering molecules via chromatography. We hypothesize that the abundant paraprotein from MM may have interfered with the binding of sheep polyclonal anti-vitamin D IgG in the immunoassay. The abundant paraprotein may have had cross-reactivity to sheep polyclonal anti-vitamin D IgG, thus decreasing the amount of chemiluminescence. Total protein level was elevated in both cases, pointing to the abundance of the paraprotein from underlying MM. Cross-reactivity is a well-described interference in all immunoassays regardless of the measured molecule [<xref rid=\"B3\" ref-type=\"bibr\">3</xref>]. Further studies are required to investigate whether the concentration and cross-reactivity of paraproteins both affect the likelihood of laboratory error in 25 (OH)-vit D quantification.</p><p>We found 2 previous case reports of laboratory interference in the immunoassay measurement of 25 (OH)-vit D levels [<xref rid=\"B2\" ref-type=\"bibr\">2</xref>, <xref rid=\"B4\" ref-type=\"bibr\">4</xref>]. Similar to our cases presented here, Ong et al. suspected that the falsely elevated measure of 25 (OH)-vit D was caused by an increased number of immunoglobulins from MM. In the second case report, immunoglobulins due to MM and rheumatoid factor were both considered as possible interfering molecules. Other serum molecules known to interfere with the 25 (OH)-vit D immunoassay include bilirubin, triglyceride, and anti-animal antibodies [<xref rid=\"B2\" ref-type=\"bibr\">2</xref>]. Our 2 patients did not have rheumatologic disease or hypertriglyceridemia. Total bilirubin levels were also normal in both patients.</p><p>In conclusion, it is important for clinicians to be aware of the limitations of the immunoassay measurement of the 25 (OH)-vit D level in the setting of MM. Careful interpretation of laboratory data in correlation with clinical manifestation is important. When there is discordance between laboratory data and clinical clues, close collaboration with the laboratory is crucial to perform analysis with alternative methods and evaluate for laboratory interference.</p></sec></body><back><sec><title>Disclosure</title><p>An earlier version of this case series was presented at the ENDO 2019 Conference, New Orleans, LA, 2019.</p></sec><sec sec-type=\"COI-statement\"><title>Conflicts of Interest</title><p>All authors report no conflicts of interest.</p></sec><ref-list><ref id=\"B1\"><label>1</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Farrell</surname><given-names>C.-J.</given-names></name><name><surname>Herrmann</surname><given-names>M.</given-names></name></person-group><article-title>Determination of vitamin D and its metabolites</article-title><source><italic toggle=\"yes\">Best Practice & Research Clinical Endocrinology & Metabolism</italic></source><year>2013</year><volume>27</volume><issue>5</issue><fpage>675</fpage><lpage>688</lpage><pub-id pub-id-type=\"doi\">10.1016/j.beem.2013.06.001</pub-id><pub-id pub-id-type=\"other\">2-s2.0-84885306302</pub-id><pub-id pub-id-type=\"pmid\">24094638</pub-id></element-citation></ref><ref id=\"B2\"><label>2</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Ong</surname><given-names>M. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Public Health</journal-id><journal-title-group><journal-title>Frontiers in Public Health</journal-title></journal-title-group><issn pub-type=\"epub\">2296-2565</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32850571</article-id><article-id pub-id-type=\"pmc\">PMC7431956</article-id><article-id pub-id-type=\"doi\">10.3389/fpubh.2020.00297</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Public Health</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>Perceived Stress and Low-Back Pain Among Healthcare Workers: A Multi-Center Prospective Cohort Study</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Vinstrup</surname><given-names>Jonas</given-names></name><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><xref ref-type=\"author-notes\" rid=\"fn002\"><sup>†</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/893261/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Jakobsen</surname><given-names>Markus D.</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Andersen</surname><given-names>Lars L.</given-names></name><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/975552/overview\"/></contrib></contrib-group><aff><institution>National Research Centre for the Working Environment</institution>, <addr-line>Copenhagen</addr-line>, <country>Denmark</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Marissa G. Baker, University of Washington, United States</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Yuke Tien Fong, Singapore General Hospital, Singapore; Evangelia Nena, Democritus University of Thrace, Greece</p></fn><corresp id=\"c001\">*Correspondence: Jonas Vinstrup <email>jov@nfa.dk</email></corresp><fn fn-type=\"other\" id=\"fn001\"><p>This article was submitted to Occupational Health and Safety, a section of the journal Frontiers in Public Health</p></fn><fn fn-type=\"other\" id=\"fn002\"><p>†ORCID: Jonas Vinstrup <ext-link ext-link-type=\"uri\" xlink:href=\"http://orcid.org/0000-0001-8430-0139\">orcid.org/0000-0001-8430-0139</ext-link></p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>8</volume><elocation-id>297</elocation-id><history><date date-type=\"received\"><day>22</day><month>1</month><year>2020</year></date><date date-type=\"accepted\"><day>04</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Vinstrup, Jakobsen and Andersen.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Vinstrup, Jakobsen and Andersen</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p><bold>Objective:</bold> This study aimed to investigate the association between perceived stress and odds of low-back pain (LBP) in a population of Danish healthcare workers.</p><p><bold>Methods:</bold> Utilizing a prospective cohort design with 1-year follow-up, a total of 1,944 healthcare workers from 389 departments at 19 hospitals responded to questionnaires containing items related to lifestyle, health, and working environment. Using Cohen's Perceived Stress Scale, associations between baseline stress levels and LBP intensity (0–10 scale) at follow-up were modeled using cumulative logistic regression, accounting for clustering at the department level and adjusting for age, sex, baseline intensity of LBP, education, seniority, number of daily patient transfers, psychosocial work environment, and lifestyle factors.</p><p><bold>Results:</bold> For the entire population, moderate and high stress (reference: low stress) at baseline increased the odds of LBP at 1-year follow-up with odds ratios (ORs) of 1.39 (95% CI 1.13–1.71) and 1.99 (95% CI 1.49–2.66), respectively. Sensitivity analyses among female nurses showed similar results [<italic>i.e</italic>., OR 1.40 (95% CI 1.08–1.80) and OR 2.08 (95% CI 1.44–3.00) for moderate and high stress, respectively], while only high stress significantly increased the odds among those without LBP at baseline.</p><p><bold>Conclusions:</bold> Psychological stress increases the odds of LBP among healthcare workers. Identifying and diminishing work-related psychosocial stressors should be included in strategies that aim to prevent musculoskeletal disorders in this population.</p></abstract><kwd-group><kwd>Cohen</kwd><kwd>psychological stress</kwd><kwd>pain</kwd><kwd>workplace</kwd><kwd>nurses</kwd><kwd>healthcare</kwd></kwd-group><counts><fig-count count=\"0\"/><table-count count=\"2\"/><equation-count count=\"0\"/><ref-count count=\"56\"/><page-count count=\"7\"/><word-count count=\"4862\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>The physiological phenomenon known as “stress” is—in animals and humans alike—most often characterized as the result of real or perceived threat to the organism, typically as a reaction to various external exposures (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>). However, while the bi-directional stress processes between the brain and the cardiovascular, autonomic, and immune systems are essential in promoting short-term allosteric adaptations to stressors with the noble, primary goal of maintaining homeostasis, exposure to long-term stressful conditions leads to a wide array of negative health consequences (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>, <xref rid=\"B2\" ref-type=\"bibr\">2</xref>).</p><p>Indeed, persistent stress has been associated with numerous indicators of poor health, including abnormal cortisol patterns (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>), depression and anxiety (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>), type II diabetes (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>), obesity (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>), poor sleep (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>, <xref rid=\"B8\" ref-type=\"bibr\">8</xref>), persistent pain (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>, <xref rid=\"B10\" ref-type=\"bibr\">10</xref>) as well as cardiovascular- and all-cause mortality (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>). Generally, greater lifetime stress severity predicts poor mental and physical health (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>, <xref rid=\"B12\" ref-type=\"bibr\">12</xref>, <xref rid=\"B13\" ref-type=\"bibr\">13</xref>), and cumulative exposure to work-related stressors has been linked to absenteeism from work, increased healthcare costs, and decreased job performance (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>–<xref rid=\"B17\" ref-type=\"bibr\">17</xref>). Following this, several work-related situational- and environmental stressors have been identified in the literature, including high job demands, poor support from colleagues, reward imbalance, job insecurity, and over-commitment (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>, <xref rid=\"B18\" ref-type=\"bibr\">18</xref>–<xref rid=\"B21\" ref-type=\"bibr\">21</xref>).</p><p>Specifically among healthcare workers, work-related stress has been associated with job (dis)satisfaction, burnout as well as poor mental- and physical health outcomes (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>). Additionally, it is likely that certain job groups within the realm of healthcare are exposed to more work-related stressors than others: For example, a recent study among healthcare workers showed that nurses in primary care experience higher levels of perceived stress and lower levels of subjective well-being compared with their colleagues (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>), indicating that this subgroup of the working population may exhibit an increased risk of negative health outcomes due to work-related stress. This notion needs to be considered in relation to the myriad of work-related factors known to influence pain among healthcare workers (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>, <xref rid=\"B25\" ref-type=\"bibr\">25</xref>), among which indicators of stress (<italic>e.g</italic>., emotional exhaustion, high emotional demands, and poor psychosocial safety climate) are known to significantly amplify the risk of musculoskeletal pain (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>, <xref rid=\"B26\" ref-type=\"bibr\">26</xref>).</p><p>However, while increases in cortisol levels have been shown to precede increases in pain levels hours later (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>), it is currently unclear to what extent stress exacerbates future pain levels in healthcare workers. Therefore, the primary aim of this study was to investigate associations between baseline stress levels and odds of low-back pain (LBP) at 1-year follow-up in a population of Danish healthcare workers. Additionally, sensitivity analyses were performed, including individuals with and without LBP at baseline.</p></sec><sec sec-type=\"methods\" id=\"s2\"><title>Methods</title><p>Utilizing a prospective cohort design with baseline- and 1-year follow-up questionnaires, this study presents associations between stress levels and LBP intensity. Specifically, using Cohen's Perceived Stress Scale (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>), we investigated associations between subjectively measured stress levels at baseline and odds of a one-point increase on the Visual Analog Scale at follow-up.</p><p>Prior to this analysis, we have previously published associations between lifestyle factors and the outcome of poor sleep and pain (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>, <xref rid=\"B29\" ref-type=\"bibr\">29</xref>), as well as data on the prospective association between patient transfers and back injury (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>).</p><sec><title>Study Design and Participants</title><p>The baseline questionnaire was sent to 7,025 hospital workers from 389 departments at 19 hospitals in Denmark, of which 4,151 (59%) responded to the full questionnaire. Comparatively, Millar and Dillman (<xref rid=\"B31\" ref-type=\"bibr\">31</xref>) found an average of 42% response rate for web-based questionnaires, while Baruch (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>)—based on 175 studies and more than 200,000 respondents—report a mean (SD) response rate of 55.6% (19.7).</p><p>In this analysis, we included healthcare workers who frequently engage with patients (<italic>i.e</italic>., nurses, healthcare assistants, nurses' aides, physical and occupational therapists, medical doctors, midwives, porters, and radiologists) and who responded to both baseline and follow-up questionnaires, yielding a final sample size of 1,944 healthcare workers. Furthermore, in subsequent sensitivity analyses, this study also includes associations specific to workers with and without LBP at baseline (<italic>n</italic> = 1,229 and <italic>n</italic> = 715, respectively), as well as to the subpopulation of nurses (<italic>n</italic> = 1,174), nurses without LBP (<italic>n</italic> = 413), and nurses with LBP at baseline (<italic>n</italic> = 761). <xref rid=\"T1\" ref-type=\"table\">Table 1</xref> shows the baseline characteristics of the included participants.</p><table-wrap id=\"T1\" position=\"float\"><label>Table 1</label><caption><p>Demographics, work-, health-, and lifestyle variables at baseline.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"1\" colspan=\"1\"/><th valign=\"top\" align=\"center\" colspan=\"3\" style=\"border-bottom: thin solid #000000;\" rowspan=\"1\"><bold>All</bold></th><th valign=\"top\" align=\"center\" colspan=\"3\" style=\"border-bottom: thin solid #000000;\" rowspan=\"1\"><bold>All, with LBP</bold></th><th valign=\"top\" align=\"center\" colspan=\"3\" style=\"border-bottom: thin solid #000000;\" rowspan=\"1\"><bold>All, without LBP</bold></th><th valign=\"top\" align=\"center\" colspan=\"3\" style=\"border-bottom: thin solid #000000;\" rowspan=\"1\"><bold>Nurses</bold></th><th valign=\"top\" align=\"center\" colspan=\"3\" style=\"border-bottom: thin solid #000000;\" rowspan=\"1\"><bold>Nurses, with LBP</bold></th><th valign=\"top\" align=\"center\" colspan=\"3\" style=\"border-bottom: thin solid #000000;\" rowspan=\"1\"><bold>Nurses, without LBP</bold></th></tr><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Variable</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Mean</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>SD</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>%</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Mean</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>SD</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>%</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Mean</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>SD</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>%</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Mean</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>SD</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>%</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Mean</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>SD</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>%</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Mean</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>SD</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>%</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>N</italic></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1,944</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1,229</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">715</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1,174</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">761</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">413</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Female</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">86.8</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">87.6</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">85.3</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Age (y)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">48.8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10.7</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">48.9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11.5</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">48.8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11.0</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">48.4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10.4</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">48.2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10.4</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">48.7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10.4</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BMI</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25.2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.6</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25.4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.6</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24.8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.4</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25.2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.7</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25.4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.7</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24.8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.6</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Smoking (yes)</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.4</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.2</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.3</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.0</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.2</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.5</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Years in profession</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11.6</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11.5</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11.6</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11.4</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11.6</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18.5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11.0</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Frequency of daily patient transfers</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.2</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.2</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.0</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.0</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.1</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.0</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" colspan=\"19\" rowspan=\"1\"><bold>Cohen's perceived stress</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Low stress (0–9)</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">32.1</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25.0</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">44.2</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">32.4</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25.2</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">45.5</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Moderate stress (10–19)</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">56.8</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">61.8</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">48.3</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">56.9</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">61.6</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">48.2</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">High stress (20–40)</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11.1</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13.1</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.6</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10.7</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13.2</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.3</td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"19\" rowspan=\"1\"><bold>Low-back pain within the previous 4 weeks (0–10)</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Low stress</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.2</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.2</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.2</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.2</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Moderate stress</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.5</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.3</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.5</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.3</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">High stress</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.9</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.3</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.6</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.2</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" colspan=\"19\" rowspan=\"1\"><bold>Leisure-time physical activity within the previous year</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Sedentary</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.7</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.4</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.7</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.4</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.7</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.1</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Light exercise >3/week</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">62.6</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">64.5</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">58.3</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">64.9</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">66.8</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">60.3</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Moderate exercise >3/week</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">27.7</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">26.1</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30.9</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">27.9</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">26.3</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">31.5</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Vigorous exercise several times per week</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.1</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.0</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.0</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.9</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.3</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.2</td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"19\" rowspan=\"1\"><bold>Education</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Nurse</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">61.7</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">63.5</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">59.2</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Doctor</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11.7</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.5</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15.2</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Healthcare assistant</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.0</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.2</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.8</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Physiotherapist</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.1</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.5</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.4</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Other</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14.5</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15.3</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12.4</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr></tbody></table></table-wrap></sec><sec><title>Outcome and Predictor Variables</title><p>The intensity of LBP was quantified by the following question:</p><p><italic>Rate your pain for the low-back within the previous 4 weeks (0–10, where 0 is no pain and 10 is worst imaginable pain)</italic>.</p><p>Stress levels of the participants were quantified using Cohen's Perceived Stress Scale, the Danish version of which exhibits satisfactory validity, reliability, and internal consistency (<xref rid=\"B33\" ref-type=\"bibr\">33</xref>). It is a widely used instrument designed to measure “the degree to which individuals appraise situations in their lives as stressful” (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>), and the scale shows well-established psychometric properties across populations (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>, <xref rid=\"B35\" ref-type=\"bibr\">35</xref>). Furthermore, it is often used as an outcome measure in intervention research (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>, <xref rid=\"B37\" ref-type=\"bibr\">37</xref>), allowing for the measurement of psychological stress among large cohorts. Presently, the 10-item questionnaire was used, with each item rated on a five-point Likert scale (ranging from “never” to “almost always”). The scores were subsequently summed, with higher scores indicative of higher levels of perceived stress. The normative values (mean ± SD) in the general population are 12.1 (5.9) and 13.7 (6.6) for men and women, respectively, with scores >20 considered as high (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>). Therefore, a summed score between 0 and 9 was considered as “low,” 10–19 as “moderate,” and >20 as “high” in the present analysis.</p></sec><sec><title>Covariates</title><p>In the “<bold>Results</bold>” section below, we report fully adjusted associations between baseline stress level and prospective odds of a one-point increase in LBP intensity. The analysis accounts for the following possible confounders relating to the individual, psychosocial, and working environment: Age, sex, LBP at baseline, education, body mass index, smoking, seniority, physical activity during leisure time, and frequency of patient transfer—as well as for factors related to the psychosocial work environment, <italic>i.e</italic>., recognition and influence at work.</p></sec><sec><title>Ethics</title><p>By agreement with the Danish Data Protection Agency, the National Research Centre for the Working Environment is allowed to register all questionnaire studies in-house. According to Danish law, questionnaire- and register-based studies need neither informed consent nor approval from ethical and scientific committees. All data were de-identified and analyzed anonymously.</p></sec><sec><title>Statistics</title><p>Associations between stress at baseline and LBP at follow-up were modeled using cumulative logistic regression (Proc Genmod, SAS), <italic>i.e</italic>., odds ratios (ORs) express the odds of LBP increasing one point on the 0–10 scale. Analyses were adjusted for the aforementioned covariates. To account for clustering, hospital department was entered in the “repeated subject” statement. Estimates are provided as ORs and 95% confidence intervals.</p></sec></sec><sec sec-type=\"results\" id=\"s3\"><title>Results</title><p>Among healthcare workers who were pain-free at baseline, we report LBP intensities of 0.65 (SD 1.57), 0.74 (SD 1.42), and 1.52 (SD 2.06) at 1-year follow-up for the low-, moderate-, and high-stress groups, respectively. For the sub-population of nurses who were pain-free at baseline, LBP intensities of 0.75 (SD 1.69), 0.74 (SD 1.44), and 1.5 (SD 1.9) for the three groups were reported at follow-up.</p><p>Furthermore, using “low stress” as reference, this study reports fully adjusted associations between stress levels and odds of an increase in LBP at follow-up among the full population of Danish healthcare workers (moderate stress: OR 1.44, 95% CI 1.12–1.86; high stress: OR 2.30, 95% CI 1.61–3.29). In the subgroup population who were pain-free at baseline, high stress—but not moderate stress—was significantly associated with increased odds (moderate stress: OR 1.25, 95% CI 0.89–1.76; high stress: OR 2.67, 95% CI 1.51–4.75). Likewise, in the sensitivity analysis including female nurses, high stress—but not moderate stress—was associated with increased odds at follow-up (moderate stress: OR 1.34, 95% CI 0.97–1.86; high stress: OR 3.17, 95% CI 1.88–5.37). Finally, in the subgroup of female nurses who were pain-free at baseline, high stress—but not moderate stress—was associated with increased odds of developing LBP (moderate stress: OR 1.09, 95% CI 0.71–1.69; high stress: OR 3.13, 95% CI 1.28–7.70) (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>).</p><table-wrap id=\"T2\" position=\"float\"><label>Table 2</label><caption><p>Stress level and odds of 1-point increase in low-back pain intensity (0–10) at 1-year follow-up.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"1\" colspan=\"1\"/><th valign=\"top\" align=\"center\" colspan=\"3\" style=\"border-bottom: thin solid #000000;\" rowspan=\"1\"><bold>All</bold></th><th valign=\"top\" align=\"center\" colspan=\"3\" style=\"border-bottom: thin solid #000000;\" rowspan=\"1\"><bold>All, without LBP at baseline</bold></th><th valign=\"top\" align=\"center\" colspan=\"3\" style=\"border-bottom: thin solid #000000;\" rowspan=\"1\"><bold>All, with LBP at baseline</bold></th></tr><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Stress score</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>OR</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>95% CI</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>p</italic>-value</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>OR</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>95% CI</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>p</italic>-value</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>OR</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>95% CI</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>p</italic>-value</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Low (0–9)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Moderate (10–19)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.39</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.13–1.71</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0016</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.25</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.89–1.76</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.1925</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.40</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.09–1.80</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0094</td></tr><tr style=\"border-bottom: thin solid #000000;\"><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">High (20–40)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.99</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.49–2.66</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><0.0001</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.67</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.51–4.75</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0008</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.89</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.33–2.70</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0004</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" colspan=\"3\" style=\"border-bottom: thin solid #000000;\" rowspan=\"1\"><bold>Nurses</bold></td><td valign=\"top\" align=\"center\" colspan=\"3\" style=\"border-bottom: thin solid #000000;\" rowspan=\"1\"><bold>Nurses, without LBP at baseline</bold></td><td valign=\"top\" align=\"center\" colspan=\"3\" style=\"border-bottom: thin solid #000000;\" rowspan=\"1\"><bold>Nurses, with LBP at baseline</bold></td></tr><tr style=\"border-bottom: thin solid #000000;\"><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Stress score</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>OR</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>95% CI</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic><bold>p</bold></italic><bold>-value</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>OR</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>95% CI</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic><bold>p</bold></italic><bold>-value</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>OR</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>95% CI</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic><bold>p</bold></italic><bold>-value</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Low (0–9)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Moderate (10–19)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.40</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.08–1.80</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0107</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.09</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.71–1.69</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.6848</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.46</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.08–1.96</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0135</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">High (20–40)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.08</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.44–3.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><0.0001</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.13</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.28–7.70</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0125</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.07</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.35–3.17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0008</td></tr></tbody></table><table-wrap-foot><p><italic>Values are presented as odds ratios and 95% confidence intervals</italic>.</p><p><italic>Adjusted for age, sex, LBP at baseline, education, body mass index, smoking, seniority, physical activity during leisure time, frequency of patient transfer as well as recognition and influence at work</italic>.</p></table-wrap-foot></table-wrap></sec><sec sec-type=\"discussion\" id=\"s4\"><title>Discussion</title><p>This study shows robust associations between perceived stress and increased odds of LBP among healthcare workers, utilizing Cohen's Perceived Stress Scale and the Visual Analog Scale, respectively. Specifically, for the whole population, a clear relationship was identified between stress levels (moderate and high) and increased odds of LBP at follow-up, while only high stress was associated with increased odds in the population free from LBP at baseline as well as for the subgroup of female nurses. This is important new knowledge in this occupational group, in which traditional preventive strategies have focused almost solely on ergonomic factors.</p><p>The main finding of this study—<italic>i.e</italic>., high stress being consistently associated with greater odds of future LBP—is in congruence with the existing literature on the relationship between psychosocial stress and pain (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>, <xref rid=\"B10\" ref-type=\"bibr\">10</xref>, <xref rid=\"B27\" ref-type=\"bibr\">27</xref>, <xref rid=\"B38\" ref-type=\"bibr\">38</xref>–<xref rid=\"B40\" ref-type=\"bibr\">40</xref>). For example, the population-based prospective cohort study by McBeth et al. (<xref rid=\"B39\" ref-type=\"bibr\">39</xref>) found that increased levels of evening-cortisol predicts the onset of persistent widespread pain at 15-months follow-up. Likewise, a Danish cross-sectional study including 4,739 adults reported strong associations between perceived stress and the use of over-the-counter analgesics (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>). In the present cohort of healthcare workers, we find that baseline stress levels are highly predictive of increases in LBP intensity 1 year later, indicating that—among this and similar populations of the workforce—perceived stress constitutes a potent risk factor for developing future musculoskeletal disorders. Considering the biopsychosocial model of health by Engel (<xref rid=\"B42\" ref-type=\"bibr\">42</xref>), it is clear that individual perception of stress can be related to (and initiated by) an array of biomedical, psychological, and social stressors, presenting themselves in all shapes and sizes. For example, job demands, job control, over-commitment at the workplace, and social support from colleagues have been shown to predict stress outcomes (<xref rid=\"B43\" ref-type=\"bibr\">43</xref>), whereas emotional exhaustion was recently identified as a strong predictor of work-related injuries among healthcare workers (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>). In relation to pain, the biopsychosocial model acknowledges potent influences such as catastrophizing, fear avoidance, expectation, meaning, context, <italic>etc</italic>.; all indicative of the substantial contribution arising from various psychosocial stressors (<xref rid=\"B44\" ref-type=\"bibr\">44</xref>). Following this, psychosocial stress in the workplace is traditionally gauged within the framework of the job <italic>demand–control model</italic> introduced by Karasek (<xref rid=\"B45\" ref-type=\"bibr\">45</xref>), describing work-related stress as an ongoing balancing act between strain/demands and control/support (<xref rid=\"B45\" ref-type=\"bibr\">45</xref>). However, in order to fully understand the dynamic bi-directional processes taking place during work-related stress (<italic>i.e</italic>., interactions between individual capacity and environmental stressors) as well as to shed light on the inconsistent relationships between various work-related stressors and performance, the <italic>conservation of resources theory</italic> (<xref rid=\"B46\" ref-type=\"bibr\">46</xref>) and the <italic>challenge–hindrance model</italic> (<xref rid=\"B47\" ref-type=\"bibr\">47</xref>) have since emerged. In combination, these frameworks emphasize the role of individual psychosocial factors (<italic>e.g</italic>., fluctuating psychological resources such as self-efficacy and the resulting difference as to whether a stressor is viewed as a challenge or hindrance), hereby further acknowledging the complexity of maneuvering a hierarchical, socially constructed workplace.</p><p>Collectively and despite historical disagreement on how to quantify stress, work-related stress is defined as “the general process in which individuals respond to and manage demands to meet multiple goals over time” (<xref rid=\"B48\" ref-type=\"bibr\">48</xref>). The results from the present study indicates that, regardless of the origin and nature of work-related stressors, high levels of perceived stress are strongly associated with increased odds of LBP, which—paradoxically—may very well turn into an additional stressor. This, in turn, initiates a rapacious circle in which the stressors and the stress response often become equally entangled and indistinguishable. This notion is echoed in the “<bold>Results</bold>” section, illustrating that participants experiencing pain at baseline are more like to suffer an increase in LBP due to moderate stress levels compared to their pain-free counterparts. Therefore, to counteract this, it appears critical that the ill-famed working environment in healthcare—known to cultivate high ratings of perceived exertion (<xref rid=\"B49\" ref-type=\"bibr\">49</xref>, <xref rid=\"B50\" ref-type=\"bibr\">50</xref>), high prevalence of musculoskeletal disorders (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>) as well as high levels of fatigue and stress (<xref rid=\"B51\" ref-type=\"bibr\">51</xref>)—undergo a substantial overhauling and reevaluation of core principles. The overarching aim of identifying and improving potent psychosocial influencers/stressors inherent to the local working environment should take precedence, as these issues arguably constitute the low-hanging fruits of this conundrum.</p><sec><title>Strengths and Limitations</title><p>Limitations of this study include the risk of underreporting of musculoskeletal disorders, recall- and non-response bias as well as ambiguity related to questions and questionnaire design; all inherent to questionnaire surveys (<xref rid=\"B52\" ref-type=\"bibr\">52</xref>–<xref rid=\"B54\" ref-type=\"bibr\">54</xref>). Additionally, although the vast majority of LBP cases are classified as “non-specific” (<xref rid=\"B55\" ref-type=\"bibr\">55</xref>, <xref rid=\"B56\" ref-type=\"bibr\">56</xref>), the quantitative method used does not allow for the identification of possible contributors to pain in this population.</p><p>Strengths of this study include the adjustment for various confounders and inclusion of sensitivity analyses specific to nurses and pain-free participants as well as using Cohen's Perceived Stress Scale as a widely used and validated indicator of perceived stress. Finally, this analysis combines the advantages of a relatively large sample size, a prospective design as well as statistical adjustments for known physical- and psychosocial confounders. Future studies aiming to elaborate on these results should, if possible, include longer follow-up periods to confirm the robustness as well as to determine the generalizability of the presented results outside the hospital environment.</p></sec></sec><sec sec-type=\"conclusions\" id=\"s5\"><title>Conclusion</title><p>In this cohort of Danish healthcare workers, perceived psychosocial stress was strongly associated with increased risk of LBP at 1-year follow-up, whereas being pain-free at baseline attenuated this relationship. Identifying and diminishing potent psychosocial stressors inherent to the local working environment are paramount in combating the high prevalence of musculoskeletal disorders seen in this population.</p></sec><sec sec-type=\"data-availability\" id=\"s6\"><title>Data Availability Statement</title><p>The datasets generated for this study are available on request to the corresponding author.</p></sec><sec id=\"s7\"><title>Ethics Statement</title><p>Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.</p></sec><sec id=\"s8\"><title>Author Contributions</title><p>JV drafted the manuscript. MJ and LA revised it critically. All authors contributed substantially to the conception of the project as well as to the analysis and the interpretation of data, and provided approval for publication.</p></sec><sec id=\"s9\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><ack><p>The authors wholeheartedly thank the healthcare workers for participating in the study.</p></ack><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> LA obtained a grant from the Danish Working Environment Research Fund for this study (Grant Number AMFF 26-2015-09).</p></fn></fn-group><ref-list><title>References</title><ref id=\"B1\"><label>1.</label><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>McEwen</surname><given-names>BS</given-names></name><name><surname>Gianaros</surname><given-names>PJ</given-names></name></person-group>. <article-title>Central role of the brain in stress and adaptation: links to socioeconomic status, health, and disease: central links between stress and SES</article-title>. <source>Ann N Y Acad Sci.</source> (<year>2010</year>) <volume>1186</volume>:<fpage>190</fpage>–<lpage>222</lpage>. <pub-id pub-id-type=\"doi\">10.1111/j.1749-6632.2009.05331.x</pub-id><pub-id pub-id-type=\"pmid\">20201874</pub-id></mixed-citation></ref><ref id=\"B2\"><label>2.</label><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Toussaint</surname><given-names>L</given-names></name><name><surname>Shields</surname><given-names>GS</given-names></name><name><surname>Dorn</surname><given-names>G</given-names></name><name><surname>Slavich</surname><given-names>GM</given-names></name></person-group>. <article-title>Effects of lifetime stress exposure on mental and physical health in young adulthood: how stress degrades and forgiveness protects health</article-title>. <source>J Health Psychol.</source> (<year>2016</year>) <volume>21</volume>:<fpage>1004</fpage>–<lpage>14</lpage>. <pub-id pub-id-type=\"doi\">10.1177/1359105314544132</pub-id><pub-id pub-id-type=\"pmid\">25139892</pub-id></mixed-citation></ref><ref id=\"B3\"><label>3.</label><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Miller</surname><given-names>GE</given-names></name><name><surname>Chen</surname><given-names>E</given-names></name><name><surname>Zhou</surname><given-names>ES</given-names></name></person-group>. <article-title>If it goes up, must it come down? 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Genet</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Genet</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Genet.</journal-id><journal-title-group><journal-title>Frontiers in Genetics</journal-title></journal-title-group><issn pub-type=\"epub\">1664-8021</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849831</article-id><article-id pub-id-type=\"pmc\">PMC7431957</article-id><article-id pub-id-type=\"doi\">10.3389/fgene.2020.00852</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Genetics</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>MiR-125a-3p-KLF15-BCAA Regulates the Skeletal Muscle Branched-Chain Amino Acid Metabolism in Nile Tilapia (<italic>Oreochromis niloticus</italic>) During Starvation</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Li</surname><given-names>Honghui</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1048243/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>An</surname><given-names>Xiaoling</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Bao</surname><given-names>Lingsheng</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/813880/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Li</surname><given-names>Yulong</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Pan</surname><given-names>Yaxiong</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>He</surname><given-names>Jinggang</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Liu</surname><given-names>Li</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Zhu</surname><given-names>Xin</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Zhang</surname><given-names>Jianshe</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Cheng</surname><given-names>Jia</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Chu</surname><given-names>Wuying</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"corresp\" rid=\"c002\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/930746/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Hunan Provincial Key Laboratory of Nutrition and Quality Control of Aquatic Animals, College of Biological and Environmental Engineering, Changsha University</institution>, <addr-line>Changsha</addr-line>, <country>China</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Guangdong Provincial Key Laboratory of Food Quality and Safety, College of Food Science, South China Agricultural University</institution>, <addr-line>Guangzhou</addr-line>, <country>China</country></aff><aff id=\"aff3\"><sup>3</sup><institution>Hunan Fisheries Science Institute</institution>, <addr-line>Changsha</addr-line>, <country>China</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Katia Aquilano, University of Rome Tor Vergata, Italy</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Fengna Li, Institute of Subtropical Agriculture, Chinese Academy of Sciences, China; Sami Dridi, University of Arkansas, United States</p></fn><corresp id=\"c001\">*Correspondence: Jia Cheng, <email>344053032@qq.com</email></corresp><corresp id=\"c002\">Wuying Chu, <email>chuwuying18@163.com</email></corresp><fn fn-type=\"other\" id=\"fn004\"><p>This article was submitted to Nutrigenomics, a section of the journal Frontiers in Genetics</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>852</elocation-id><history><date date-type=\"received\"><day>15</day><month>3</month><year>2020</year></date><date date-type=\"accepted\"><day>13</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Li, An, Bao, Li, Pan, He, Liu, Zhu, Zhang, Cheng and Chu.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Li, An, Bao, Li, Pan, He, Liu, Zhu, Zhang, Cheng and Chu</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>The branched-chain amino acids (BCAAs) play a key role in the energy metabolism of the muscle tissue and the Krüppel-like factor 15 (KLF15) as a transcription factor, which is a key regulator of BCAA metabolism in the skeletal muscle. This study assessed the effect of starvation for 0, 3, 7, and 15 days on BCAA metabolism in the skeletal muscle of Nile tilapia. The results showed that the expression of KLF15 showed a trend of increasing first and then decreasing during starvation, as well as the expression and activity of branched-chain aminotransferase 2 (BCAT2) and alanine aminotransferase (ALT). On the other hand, the content of BCAA was at first decreased and then upregulated, and it reached the lowest level after starvation for 3 days. In addition, through dual-luciferase reporter assay and injection experiments, it was found that KLF15 is the target gene of miR-125a-3p, which further verified that miR-125a-3p can regulate the BCAA metabolism by targeting KLF15 in the skeletal muscle. Thus, our work investigated the possible mechanisms of BCAA metabolism adapting to nutritional deficiency in the skeletal muscle of Nile tilapia and illustrated the regulation of BCAA metabolism through the miR-125a-3p-KLF15-BCAA pathway in the skeletal muscle.</p></abstract><kwd-group><kwd><italic>Oreochromis niloticus</italic></kwd><kwd>miR-125a-3p-KLF15-BCAA</kwd><kwd>skeletal muscle</kwd><kwd>branched-chain amino acid metabolism</kwd><kwd>starvation</kwd></kwd-group><funding-group><award-group><funding-source id=\"cn001\">National Natural Science Foundation of China<named-content content-type=\"fundref-id\">10.13039/501100001809</named-content></funding-source><award-id rid=\"cn001\">31972766</award-id></award-group><award-group><funding-source id=\"cn002\">Hunan Provincial Science and Technology Department<named-content content-type=\"fundref-id\">10.13039/501100002767</named-content></funding-source><award-id rid=\"cn002\">2018NK2074</award-id></award-group><award-group><funding-source id=\"cn003\">Scientific Research Foundation of Hunan Provincial Education Department<named-content content-type=\"fundref-id\">10.13039/100014472</named-content></funding-source><award-id rid=\"cn003\">18C0757</award-id></award-group></funding-group><counts><fig-count count=\"5\"/><table-count count=\"3\"/><equation-count count=\"0\"/><ref-count count=\"42\"/><page-count count=\"8\"/><word-count count=\"0\"/></counts></article-meta></front><body><sec id=\"S1\"><title>Introduction</title><p>Branched-chain amino acids (BCAAs) include leucine (Leu), isoleucine (Ile), and valine (Val). They are essential amino acids in humans and animals, accounting for about 35% of essential amino acids for muscle protein, which are important regulators of metabolism and metabolic health <italic>in vivo</italic>. The main metabolic site of BCAA is muscle (<xref rid=\"B41\" ref-type=\"bibr\">Yamane et al., 2017</xref>). Numerous studies have shown that the transcription factor of Krüppel-like factor (KLF) 15 (KLF15) is a key regulator of BCAA metabolism (<xref rid=\"B26\" ref-type=\"bibr\">Platell and Kong, 2001</xref>; <xref rid=\"B33\" ref-type=\"bibr\">Sun et al., 2016</xref>; <xref rid=\"B7\" ref-type=\"bibr\">Fan et al., 2018</xref>). The KLF15-BCAA signaling pathway is essential for metabolic homeostasis of the skeletal muscle. The BCAAs could not synthesize by themselves in the body. It must be obtained from food to satisfy the body’s needs. Most of amino acid metabolism in the body takes place in the liver, whereas BCAA is the only amino acid that is highly metabolized in the skeletal muscle (<xref rid=\"B3\" ref-type=\"bibr\">Brosnan and Brosnan, 2006</xref>). Studies have shown that the decomposition of BCAA is very active in the skeletal muscle. It can carry out transamination and complete oxidation at a fairly fast rate. The efficiency of ATP produced by complete oxidation of BCAA is much higher than that of other amino acids (<xref rid=\"B32\" ref-type=\"bibr\">Sperringer et al., 2017</xref>). When the body is in a resting state, about 14% of the total energy consumed by the skeletal muscle is provided by the process of BCAA oxidation. Under particular conditions such as starvation, exercise, and lactation, the increase in BCAA oxidation function is an important energy source <italic>in vivo</italic>. Studies have reported that the ability of muscle to oxidize α-ketoglutaric acid (α-KG), the transamination product of BCAA, can increase three to five times when deprived of food (<xref rid=\"B14\" ref-type=\"bibr\">Hsu et al., 2011</xref>; <xref rid=\"B38\" ref-type=\"bibr\">Wiśnik et al., 2011</xref>). However, starvation is a severe deficiency in caloric energy intake needed to maintain an organism’s life. Under fasting conditions, the pyruvate receives amino acids from glutamic acid generated by BCAAs in the skeletal muscle, which is converted into alanine by the transamination of alanine aminotransferase (ALT) and then transported to the liver. Pyruvate was generated by deamination and then converted into glucose, which provides gluconeous substrate for the liver to maintain energy metabolism and homeostasis balance (<xref rid=\"B8\" ref-type=\"bibr\">Felig, 1973</xref>; <xref rid=\"B25\" ref-type=\"bibr\">Perry et al., 2018</xref>).</p><p>The transcription factor of the KLF15 is a member of the KLF family. The family is characterized by three highly conserved transcriptional regulators of the DNA binding domains of the continuous Cys<sup>2</sup>/His<sup>2</sup> zinc finger structure at the C-terminal, which play an important role in the growth and metabolism balance of the body. KLFs regulate the target gene expression and participate in cell growth, proliferation, differentiation, and apoptosis through binding specific GC-rich sequence in the promoter region of target genes, including GC box or GT box binding elements (<xref rid=\"B24\" ref-type=\"bibr\">Pearson et al., 2008</xref>; <xref rid=\"B19\" ref-type=\"bibr\">Leenders et al., 2012</xref>). At present, 18 members of KLFs have been identified; in particular, KLF15 has a high expression in organs with active metabolism such as the liver and skeletal muscle. This indicates its potential role in skeletal muscle metabolism (<xref rid=\"B37\" ref-type=\"bibr\">Uchida et al., 2000</xref>; <xref rid=\"B23\" ref-type=\"bibr\">Oates et al., 2001</xref>; <xref rid=\"B11\" ref-type=\"bibr\">Gray et al., 2002</xref>). <xref rid=\"B12\" ref-type=\"bibr\">Gray et al. (2007)</xref> found that after KLF15 gene knockout mice, the ability of skeletal muscle BCAA to generate alanine through transamination pathway was significantly reduced, and the expression level of branched-chain aminotransferase 2 (BCAT2) messenger RNA (mRNA), a key enzyme in the catabolism of BCAA, was significantly decreased. Moreover, the activity of ALT in the liver decreased significantly after KLF15 knockout. <xref rid=\"B31\" ref-type=\"bibr\">Shimizu et al. (2011)</xref> showed that KLF15 can regulate the expression of BCAT2 at the transcriptional level of rats. <xref rid=\"B15\" ref-type=\"bibr\">Jeyaraj et al. (2012)</xref> found in studies of normal mice and KLF15-deficient mice that overexpression of KLF15 adenovirus induces the mRNA level of ALT and that BCAT2 increased in the hepatocytes and muscle, and the content of glutamic acid (Glu) was increased and the alanine (Ala) decreased. This indicates that the abnormal expression of KLF15 mRNA level changes the metabolism of BCAAs. In addition, some studies have shown that the KLF15 has become an essential regulator in the metabolism of BCAAs, and this transcription factor deficiencies can cause serious damage to the organism (<xref rid=\"B22\" ref-type=\"bibr\">Morrison-Nozik et al., 2015</xref>; <xref rid=\"B7\" ref-type=\"bibr\">Fan et al., 2018</xref>).</p><p>MicroRNAs (miRNAs) are a family of endogenous, small, and non-coding RNAs that can negatively regulate gene expression at the post-transcriptional and/or translational level by binding loosely complementary sequences to 3′-UTR of the target gene (<xref rid=\"B1\" ref-type=\"bibr\">Bartel, 2004</xref>). MicroRNA-125a (miR-125a) is a vertebrate homolog of single-strand non-coding miRNAs (lin-4), the first miRNA reported in <italic>Caenorhabditis elegans</italic> (<xref rid=\"B18\" ref-type=\"bibr\">Lee et al., 1993</xref>). It has been found that miRNAs can regulate many important biological activities, including cell differentiation, proliferation, apoptosis, and metabolic homeostasis (<xref rid=\"B4\" ref-type=\"bibr\">Calin and Croce, 2006</xref>; <xref rid=\"B2\" ref-type=\"bibr\">Bousquet et al., 2012</xref>). Furthermore, miR-125a is also involved in regulating energy metabolism processes such as glucose metabolism, adipocyte differentiation, and amino acid metabolism (<xref rid=\"B16\" ref-type=\"bibr\">Kajimoto et al., 2006</xref>; <xref rid=\"B13\" ref-type=\"bibr\">Herrera et al., 2009</xref>; <xref rid=\"B34\" ref-type=\"bibr\">Sun et al., 2013</xref>). Some studies have reported the relationship between miRNA and glutamate metabolism in animals (<xref rid=\"B5\" ref-type=\"bibr\">Dang, 2009</xref>; <xref rid=\"B9\" ref-type=\"bibr\">Gao et al., 2009</xref>). The more active the cells are <italic>in vivo</italic>, the more active the glutamine metabolism was. Glutamine was converted into <sc>L</sc>-glutamic acid by glutamine enzyme and finally decomposed into ATP in mitochondria for energy supply or directly used as a substrate for glutathione synthesis. At present, the study of the miR-125a-3p-KLF15-BCAA signaling pathway on the metabolism of BCAAs in fish skeletal muscle has not been reported. In this study, we analyzed the regulation of the miR-125a-3p-KLF15-BCAA signaling pathway on the metabolism of BCAAs in the skeletal muscle of Nile tilapia by starvation experiment, which provided new ideas and basis for the regulation of muscle nutrition metabolism in Nile tilapia.</p></sec><sec sec-type=\"materials|methods\" id=\"S2\"><title>Materials and Methods</title><sec id=\"S2.SS1\"><title>Animals and Experimental Design</title><p>The experimental fish were obtained from the National Tilapia Seed Farm (Nanning, China). A total of 180 healthy tilapias with an initial body weight of 199.86 ± 5.29 g were randomly assigned to four groups that fasted for 0, 3, 7, and 15 days with 3 replicate tanks per group and 15 fish in each tank after 2 weeks of domestication period. During the trial period, the optimal water temperature was kept at 27–30°C, and the experiment was carried out under natural light cycle. This experiment was approved by the Animal Care and Use Committee of Changsha University, and all animal experiments complied with the ARRIVE guidelines.</p></sec><sec id=\"S2.SS2\"><title>Sample Collection and Amino Acid Analysis</title><p>At the end of the experimental period, fish in each tank were weighed and measured. After they were anesthetized with MS-222 (Green Hengxing Biotech Co., Ltd., Beijing, China), blood samples were collected from the caudal vein. After letting the samples stand, they were centrifuged at 3,500 <italic>g</italic> at 4°C for 15 min; the supernatant was then taken and stored at below ultralow temperature at −80°C for analysis. The collected skeletal muscle and other tissues were immediately put in liquid nitrogen and then stored at −80°C for analysis. The content of amino acids in the sample was determined by an automatic amino acid analyzer (Hitachi Model L8900).</p></sec><sec id=\"S2.SS3\"><title>Enzyme Activity Analysis</title><p>The skeletal muscle sample was rinsed with 0.70% saline and homogenized on ice with 1:9 volume (v/w) saline. Then, the sample was centrifuged at 4,000 <italic>g</italic> for 15 min at 4°C, and the supernatant was collected for determination. According to the kit provided by Nanjing Jiancheng Bioengineering Research Institute of China, the total protein content and ALT activity of the skeletal muscle were measured. BCAT2 antibody was coated on a 96-well plate by ELISA, and its activity was measured. The optical density (OD) value was measured at 450 nm by an enzyme-labeling instrument, and the sample concentration was calculated.</p></sec><sec id=\"S2.SS4\"><title>Quantitative Real-Time PCR Analysis</title><p>Total RNA was extracted using the TRIzol method. With the extracted RNA as a template, the first-strand cDNA was synthesized using the PrimeScript RT reagent Kit (with gDNA Eraser) (Takara, China). MiRNA reverse transcription was performed by the Mir-X miRNA First-Strand Synthesis Kit (Takara, China). According to our previous studies, using the above cDNA as the template, the quantitative real-time PCR (qRT-PCR) was performed for KLF15, BCAT2, ALT, miR-125a-3p, and β-actin (housekeeping gene) by the SYBR Premix Ex Taq II kit (<xref rid=\"B20\" ref-type=\"bibr\">Li et al., 2019</xref>). The primer sequences are indicated in <xref rid=\"T1\" ref-type=\"table\">Table 1</xref>. After the amplification efficiency of the primers was verified about 100%, the relative expression level of the tested genes was calculated by using the 2<sup>–ΔΔCt</sup> method (<xref rid=\"B21\" ref-type=\"bibr\">Livak and Schmittgen, 2001</xref>).</p><table-wrap id=\"T1\" position=\"float\"><label>TABLE 1</label><caption><p>The primer sequence of real-time quantitative PCR.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Gene</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Primer (5′–3′)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Annealing temperature (°C)</td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">KLF15-F</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5′-AAGAAGAGGAACGGTGCG-3′</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">58</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">KLF15-R</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5′-TGGGATTTCTCGGGATTCTGT-3′</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BCAT2-F</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5′-CGTCATCATCAGCCCAGTCG-3′</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">58</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BCAT2-R</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5′-CGTCATCATCAGCCCAGTCG-3′</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ALT-F</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5′-AGCCAATCACTTTCTTCCGAC-3′</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">56</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ALT-R</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5′-TGTTCCCACCACAAGACTGC-3′</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MiR-125a-3p-F</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5′-ACAGGTGAGGTTCTTGGGAG-3′</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">58</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MRQ 3′primer</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5′-CTCAACTGGTGTCGTGGAGTCGGCAATTCAGTTGAGC-3′</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">β-Actin-F</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5′-CCACAGCCGAGAGGGAAAT-3′</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">58</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">β-Actin-R</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5′-CCATCTCCTGCTCGAAGTC-3′</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr></tbody></table><table-wrap-foot><attrib><italic><italic>mRQ 3’ primer was the general primer provided by TAKARA. KLF15, Krüppel-like factor 15; BCAT2, branched-chain aminotransferase 2; ALT, alanine transaminase.</italic></italic></attrib></table-wrap-foot></table-wrap></sec><sec id=\"S2.SS5\"><title>Dual-Luciferase Reporter Gene Assay</title><p>In order to determine whether miR-125a-3p can directly recognize the 3′-UTR of KLF15 mRNA, we constructed the pGL4-CMV-luc-KLF15-WT and pGL4-CMV-luc-KLF15-Mut dual-luciferase reporter gene expression vectors. 293T cells were inoculated in a 24-well culture plate and cultured to 70–90% fusion. Cells were cotransfected with 20 μM of miR-125a-3p mimics or miR-NC, 5 ng plasmid of the KLF15-WT and KLF15-Mut and 10 ng reference plasmid of pRL-CMV, respectively. Luciferase activity was measured after transfection for 48 h by using a Dual-Luciferase Assay kit (Dual-Luciferase Reporter Assay System, Promega).</p></sec><sec id=\"S2.SS6\"><title>Injection With LNA-125i in Nile Tilapia</title><p>A total of 36 fish with an initial body weight of about 200 g were randomly divided into control (injection with saline) and experimental (injection with LNA-125i) groups (6 fish in each group) fed with a commercial diet, which were obtained from the National Tilapia Seed Farm of Nanning.</p><p>Locked nucleic acid (LNA) is a novel oligonucleotide analog, and its bicyclic nucleotide derivative is locked in an RNA-mimicking sugar conformation, which can specifically inhibit miRNAs. The LNA-inhibitor-miR-125 (LNA-125i, Thermo Fisher) was dissolved in 0.65% normal saline (concentration of 0.25 mg/ml). The fish assigned to the experimental group received intraperitoneal injection of LNA-125i for a week (1 ml per fish every other day), and the control group was injected with the same volume of 0.65% normal saline.</p></sec><sec id=\"S2.SS7\"><title>Statistical Analysis</title><p>Data were analyzed by one-way ANOVA and significance test with the SPSS 18.0 software. All data were expressed by mean ± SE, and Duncan’s multiple range tests were used to determine the significant difference between groups (<italic>p</italic> < 0.05).</p></sec></sec><sec id=\"S3\"><title>Results</title><sec id=\"S3.SS1\"><title>Expression of Starvation on Krüppel-Like Factor 15 and Branched-Chain Amino Acid Metabolism-Related Enzymes Messenger RNA Level in the Skeletal Muscle</title><p>The effect of starvation on the expression of KLF15 and BCAA metabolism-related enzyme mRNA levels in the skeletal muscle of Nile tilapia is shown in <xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>. Compared with that at 0 day of the starvation group, the expression of the KLF15 mRNA level was significantly increased in the skeletal muscle after 3 days of starvation (<italic>p</italic> < 0.05). Upon starvation for 7 to 15 days, it significantly decreased and was lower than that in normal feeding fish (<italic>p</italic> < 0.05). The expression of BCAA metabolism-related enzymes BCAT2 and ALT was significantly increased in the skeletal muscle after 3 days of starvation (<italic>p</italic> < 0.05). After starvation for 7 days, the levels of BCAT2 and ALT mRNA were significantly decreased; besides, the BCAT2 mRNA was significantly lower than that at starvation for 0 day (<italic>p</italic> < 0.05). After starvation for 15 days, the effects of ALT was decreased (<italic>p</italic> < 0.05), whereas BCAT2 displayed no significant change compared with 7 days of the starvation group (<italic>p</italic> > 0.05).</p><fig id=\"F1\" position=\"float\"><label>FIGURE 1</label><caption><p>Effect of the expression of the miR-125a-3p-KLF15-BCAA- related gene mRNA level in the skeletal muscle of Nile tilapia during starvation periods. Values represent the mean ± SE (<italic>n</italic> = 9 fish, 3 fish were sampled for each tank). *<italic>p</italic> < 0.05 significantly different vs. 0 day. BCAA, branched-chain amino acid.</p></caption><graphic xlink:href=\"fgene-11-00852-g001\"/></fig></sec><sec id=\"S3.SS2\"><title>Effect of Starvation on Branched-Chain Amino Acid Metabolism-Related Enzyme Activity in the Skeletal Muscle</title><p>The change of the activity of BCAA metabolism-related enzymes in the skeletal muscle of Nile tilapia during starvation is shown in <xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref>. The activity of BCAT2 and ALT was significantly increased in the skeletal muscle after starvation for 3 days (<italic>p</italic> < 0.05). However, after 7 days of starvation, their activity decreased remarkably, and both of them were lower than those of starvation for the 0-day group (<italic>p</italic> < 0.05). Compared with that after 7 days of starvation, the BCAT2 activity continued to decrease after 15 days of starvation, whereas ALT increased significantly (<italic>p</italic> < 0.05).</p><fig id=\"F2\" position=\"float\"><label>FIGURE 2</label><caption><p>Effect of starvation on the activity of BCAT2 and ALT in the skeletal muscle of Nile tilapia. Values represent the mean ± SE (<italic>n</italic> = 9 fish, 3 fish were sampled for each tank). *<italic>p</italic> < 0.05 significantly different vs. 0 day. ALT, alanine aminotransferase.</p></caption><graphic xlink:href=\"fgene-11-00852-g002\"/></fig></sec><sec id=\"S3.SS3\"><title>Changes of Branched-Chain Amino Acid Content in the Skeletal Muscle</title><p>The change of the BCAA content in the skeletal muscle of Nile tilapia under starvation periods is shown in <xref rid=\"T2\" ref-type=\"table\">Table 2</xref>. The results showed that the content of BCAA was decreased significantly in the skeletal muscle after starvation for 3–7 days and reached the lowest level after starvation for 3 days (<italic>p</italic> < 0.05). Then, the BCAA content was increased significantly after 15 days of starvation and returned to starvation 0-day level (<italic>p</italic> > 0.05).</p><table-wrap id=\"T2\" position=\"float\"><label>TABLE 2</label><caption><p>Effect of starvation on BCAA content in the skeletal muscle of Nile tilapia (g/100 g).</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0 day</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3 days</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7 days</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10 days</td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Leu</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.62 ± 0.13</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.45 ± 0.06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.98 ± 0.06*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.63 ± 0.08</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Ile</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.87 ± 0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.53 ± 0.02*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.78 ± 0.03*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.85 ± 0.05</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Val</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.92 ± 0.04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.56 ± 0.02*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.86 ± 0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.87 ± 0.05</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BCAA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.41 ± 0.20</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.54 ± 0.10*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.62 ± 0.12*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.35 ± 0.13</td></tr></tbody></table><table-wrap-foot><attrib><italic><italic>Values represent the mean ± SE (n = 9 fish, 3 fish were sampled for each tank).</italic> *<italic>p < 0.05 significantly different vs. 0 day. BCAA, branched-chain amino acid.</italic></italic></attrib></table-wrap-foot></table-wrap></sec><sec id=\"S3.SS4\"><title>Krüppel-Like Factor 15 Is a Direct Target of miR-125a-3p</title><p>The target gene of miR-125a-3p was predicated by the TargetScan online program and verified using a luciferase reporter gene assay, and the reporter plasmid of pGL4-CMV-luc and pRL-CMV is shown in <xref ref-type=\"fig\" rid=\"F3\">Figure 3A</xref>. The results showed that there was a binding site between miR-125a-3p and KLF15 3′-UTR (<xref ref-type=\"fig\" rid=\"F3\">Figure 3B</xref>). Compared with that of the NC group, the fluorescence activity of KLF15-WT cells was significantly decreased in the miR-125a-3p group, whereas KLF15-Mut had no significant change (<xref ref-type=\"fig\" rid=\"F3\">Figure 3C</xref>). This indicates that miR-125a-3p has a negative regulatory role in the KLF15 gene expression; therefore, KLF15 is a target gene of miR-125a-3p.</p><fig id=\"F3\" position=\"float\"><label>FIGURE 3</label><caption><p>Results of dual-luciferase reporter gene assay. <bold>(A)</bold> The reporter plasmid of pGL4-CMV-luc and pRL-CMV. <bold>(B)</bold> miR-125a-3p and KLF15 3′-UTR have matching binding sites. <bold>(C)</bold> The interaction between miR-125a-3p with the KLF15 3′-UTR. *<italic>p</italic> < 0.05 significantly different vs. NC.</p></caption><graphic xlink:href=\"fgene-11-00852-g003\"/></fig></sec><sec id=\"S3.SS5\"><title>Expression of Injection With LNA-125i on Krüppel-Like Factor 15 and Branched-Chain Amino Acid Metabolism-Related Enzymes Messenger RNA Level in the Skeletal Muscle</title><p>The effect of LNA-125i injection on the expression of KLF15 and BCAA metabolism-related enzyme mRNA level in the skeletal muscle of Nile tilapia is shown in <xref ref-type=\"fig\" rid=\"F4\">Figure 4</xref>. Compared with the control, LNA-125i injection significantly inhibited the expression of miR-125a-3p and enhanced the KLF15 mRNA level in the skeletal muscle (<italic>p</italic> < 0.05). After injection, the expression of BCAT2 and ALT was increased significantly compared with that of the control (<italic>p</italic> < 0.05).</p><fig id=\"F4\" position=\"float\"><label>FIGURE 4</label><caption><p>Effect of the expression of the miR-125a-3p-KLF15-BCAA- related gene mRNA level in the skeletal muscle of Nile tilapia after injection with LNA-125i. Values represent the mean ± SE (<italic>n</italic> = 9 fish, 3 fish were sampled for each tank). *<italic>p</italic> < 0.05 significantly different vs. control. BCAA, branched-chain amino acid.</p></caption><graphic xlink:href=\"fgene-11-00852-g004\"/></fig></sec><sec id=\"S3.SS6\"><title>Effect of Injection With LNA-125i on Branched-Chain Amino Acid Metabolism-Related Enzyme Activity in the Skeletal Muscle</title><p>The change of BCAA metabolism-related enzyme activities after LNA-125i injection in the skeletal muscle of Nile tilapia is shown in <xref ref-type=\"fig\" rid=\"F5\">Figure 5</xref>. Compared with that of the control, the activity of BCAT2 and ALT was increased significantly in the skeletal muscle after LNA-125i injection (<italic>p</italic> < 0.05).</p><fig id=\"F5\" position=\"float\"><label>FIGURE 5</label><caption><p>Effect of injection with LNA-125i on the activity of BCAT2 and ALT in the skeletal muscle of Nile tilapia. Values represent the mean ± SE (<italic>n</italic> = 9 fish, 3 fish were sampled for each tank). *<italic>p</italic> < 0.05 significantly different vs. control. ALT, alanine aminotransferase.</p></caption><graphic xlink:href=\"fgene-11-00852-g005\"/></fig></sec><sec id=\"S3.SS7\"><title>Changes of Injection With LNA-125i on Branched-Chain Amino Acid Content in the Skeletal Muscle</title><p>The change of the BCAA content treated with LNA-125i in the skeletal muscle of Nile tilapia is shown in <xref rid=\"T3\" ref-type=\"table\">Table 3</xref>. The results showed that the BCAA content was decreased significantly in the skeletal muscle after injection with LNA-125i (<italic>p</italic> < 0.05).</p><table-wrap id=\"T3\" position=\"float\"><label>TABLE 3</label><caption><p>Effect of injection with LNA-125i on BCAA content in the skeletal muscle of Nile tilapia (g/100 g).</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Control</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">LNA-125i</td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Leu</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.55 ± 0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.38 ± 0.04*</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Ile</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.69 ± 0.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.56 ± 0.02*</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Val</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.19 ± 0.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.88 ± 0.03*</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BCAA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.43 ± 0.05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.82 ± 0.07*</td></tr></tbody></table><table-wrap-foot><attrib><italic><italic>Values represent the mean ± SE (n = 9 fish, 3 fish were sampled for each tank). *p < 0.05 significantly different vs. control. BCAA, branched-chain amino acid.</italic></italic></attrib></table-wrap-foot></table-wrap></sec></sec><sec id=\"S4\"><title>Discussion</title><p>The skeletal muscle is involved in many complete processes of the body. It is not only the main protein storage in the body but also conducts an active and adaptive metabolism that produces highly plastic tissue. It plays an important role in the energy metabolism of the body and is mobilized as an amino acid source of energy metabolism under stress (<xref rid=\"B17\" ref-type=\"bibr\">Lecker and Goldberg, 2002</xref>). Some studies have shown that the BCAA metabolism is mainly through intramuscular transamination, which is the primary way of endogenous synthesis of Glu, glutamine, and aspartic acid (Asp) (<xref rid=\"B29\" ref-type=\"bibr\">She et al., 2007</xref>; <xref rid=\"B30\" ref-type=\"bibr\">She et al., 2010</xref>). When the body is in a resting state, about 14% of the total energy consumed by the skeletal muscle was provided by the process of BCAA oxidation. Under particular conditions including starvation, exercise, and lactation, the increase in the BCAA oxidation function is an important energy source <italic>in vivo</italic> (<xref rid=\"B40\" ref-type=\"bibr\">Xu et al., 2009</xref>; <xref rid=\"B19\" ref-type=\"bibr\">Leenders et al., 2012</xref>; <xref rid=\"B27\" ref-type=\"bibr\">Prosdocimo et al., 2014</xref>). KLF15 plays a key role in many biological processes, which includes cell proliferation, differentiation, development, and apoptosis. Studies have confirmed that KLF15 was involved in the regulation of glucose metabolism, fatty acid metabolism, and amino acid metabolism. The study of KLF15 on the amino acid metabolism, especially on BCAA metabolism, was a new kind of research field. Our results showed that the expression of KLF15 mRNA was upregulated in the skeletal muscle of Nile tilapia after starvation for 3 days, whereas the gene expression and activities of BCAT2 and ALT increased significantly. Meanwhile, the content of BCAA decreased significantly. This may be due to the upregulation of KLF15, which stimulates the expression and activity of BCAT2 and ALT, thus promoting the catabolism of BCAA and reducing the contents. It has been reported that the overexpression of KLF15 could increase the activity of the BCAT2 promoter in the skeletal muscle and cardiomyocytes, and the content of BCAA was decreased in cells (<xref rid=\"B42\" ref-type=\"bibr\">Yoshikawa et al., 2009</xref>; <xref rid=\"B31\" ref-type=\"bibr\">Shimizu et al., 2011</xref>), which was consistent with our results. These results showed that the BCAAs in the skeletal muscle are used to maintain energy stability in Nile tilapia under starvation. Some studies have also found that catabolism of BCAAs needs to provide a carbon matrix for liver gluconeogenesis and then maintain normal blood glucose of the body during starvation (<xref rid=\"B12\" ref-type=\"bibr\">Gray et al., 2007</xref>). Additionally, KLF15 can inhibit the lipogenesis and promote gluconeogenesis by upregulating the key enzymes of BCAA decomposition at fasting state, and thus, it provides the liver with gluconeogenesis substrate (<xref rid=\"B36\" ref-type=\"bibr\">Teshigawara et al., 2005</xref>).</p><p>Then, the expression of KLF15 mRNA was downregulated in the skeletal muscle of Nile tilapia after starvation for 7 to 15 days, whereas the gene expression and activity of BCAT2 and ALT were both decreased significantly. However, the content of BCAA began to increase after 7 days of starvation and returned to starvation 0-day level at 15 days of starvation. This may be due to the fact that BCAA increases its own decomposition and provides energy for the body during starvation. In the process of starvation, the pyruvate receives amino acids from glutamic acid generated by BCAAs in the skeletal muscle, which is converted into alanine by the transamination of ALT and then transported to the liver. Pyruvate was generated by deamination and then converted into glucose, which provides gluconeous substrate for the liver to maintain energy metabolism and homeostasis balance. Perhaps after starvation, the BCAA increases its decomposition and provides energy for the body during starvation. However, when it reached a certain level, BCAA could not be decomposed in the skeletal muscle and gradually returned to the balance level. This might be due to the fact that KLF15 was involved in regulating several metabolic pathways <italic>in vivo</italic> during starvation and promoting the gluconeogenesis and provided the energy for maintaining life activities (<xref rid=\"B35\" ref-type=\"bibr\">Takeuchi et al., 2016</xref>). In addition, protein metabolism in the intestine and the liver was increased after long-term starvation, and the intake level of gluconeogenesis precursor in the liver also increased, which indicated that gluconeogenesis was increased (<xref rid=\"B6\" ref-type=\"bibr\">de Blaauw et al., 1996</xref>). The changes of BCAA metabolism in muscle after starvation might reflect the adaptive response of muscle to nutritional deficiency.</p><p>Among the most important miRNA families, miR-125 is a highly conserved miRNA throughout diverse species, and it has been validated to change, exhibiting its different roles in many different types of diseases (<xref rid=\"B34\" ref-type=\"bibr\">Sun et al., 2013</xref>). The MiR-125 family is composed of miR-125a-3p, miR-125a-5p, miR-125b-1, and miR-125b-2, which are highly conserved in evolution. Members of this family play an important role in many biological processes in cells, including proliferation, differentiation, apoptosis, and metabolism by targeting a variety of different transcription factors (<xref rid=\"B2\" ref-type=\"bibr\">Bousquet et al., 2012</xref>), matrix-metalloprotease (<xref rid=\"B28\" ref-type=\"bibr\">Qian et al., 2012</xref>; <xref rid=\"B39\" ref-type=\"bibr\">Xu et al., 2012</xref>), growth factors (<xref rid=\"B10\" ref-type=\"bibr\">Ge et al., 2011</xref>), and so on. The purpose of this study was to investigate the mechanism of the miR-125a-3p-KLF15-BCAA signaling pathway on BCAA metabolism in the skeletal muscle of Nile tilapia by <italic>in vivo</italic> experiments. It was found that KLF15 is the target gene of miR-125a-3p, and the negative regulation effect of miR-125a-3p on KLF15 was verified after LNA-125i injection. The miR-125a-3p could regulate the BCAA metabolism in the skeletal muscle of Nile tilapia by targeting the KLF15 gene. This study demonstrates that the miR-125a-3p-KLF15-BCAA signaling pathway is essential for the regulation of BCAA metabolism in the skeletal muscle of Nile tilapia.</p><p>In conclusion, the present results suggest that the miR-125a-3p-KLF15-BCAA signaling pathway plays a key role in the regulation of BCAA metabolism adaptive in the skeletal muscle of Nile tilapia under nutrition changes. In order to adapt to the change, the tilapia could activate the catabolism of BCAAs in the skeletal muscle to provide energy by the miR-125a-3p-KLF15-BCAA signaling pathway. These results provided a new idea for the regulation of muscle nutrition metabolism in Nile tilapia.</p></sec><sec sec-type=\"data-availability\" id=\"S5\"><title>Data Availability Statement</title><p>All datasets generated for this study are included in the article/supplementary material.</p></sec><sec id=\"S6\"><title>Ethics Statement</title><p>The animal study was reviewed and approved by the Animal Care and Use Committee of Changsha University.</p></sec><sec id=\"S7\"><title>Author Contributions</title><p>WC and JC carried out the conceptualization and performed the project administration. HL and XA performed the formal analysis and investigation. WC and JZ acquired the funding. HL, XA, YL, YP, JH, LL, and XZ carried out the methodology. WC performed the supervision. HL wrote the original draft. HL and WC reviewed and edited the manuscript. All authors contributed to the article and approved the submitted version.</p></sec><sec id=\"conf1\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> This research was funded by the National Natural Science Foundation of China (Grant No. 31972766), the Key Research and Development Project of Hunan Province (Grant No. 2018NK2074), and the Scientific Research Foundation of Hunan Provincial Education Department (Grant No. 18C0757).</p></fn></fn-group><ack><p>We extend the appreciation to the National Natural Science Foundation of China, the Education Department of Hunan Province and Hunan Science, and Technology Bureau for funding this work.</p></ack><ref-list><title>References</title><ref id=\"B1\"><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Bartel</surname><given-names>D. 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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Neurosci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Neurosci</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Neurosci.</journal-id><journal-title-group><journal-title>Frontiers in Neuroscience</journal-title></journal-title-group><issn pub-type=\"ppub\">1662-4548</issn><issn pub-type=\"epub\">1662-453X</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32848556</article-id><article-id pub-id-type=\"pmc\">PMC7431959</article-id><article-id pub-id-type=\"doi\">10.3389/fnins.2020.00780</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Neuroscience</subject><subj-group><subject>Brief Research Report</subject></subj-group></subj-group></article-categories><title-group><article-title>Chromatic Pupillometry Findings in Alzheimer’s Disease</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Romagnoli</surname><given-names>Martina</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/967035/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Stanzani Maserati</surname><given-names>Michelangelo</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/692389/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>De Matteis</surname><given-names>Maddalena</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1038315/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Capellari</surname><given-names>Sabina</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/592420/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Carbonelli</surname><given-names>Michele</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/493909/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Amore</surname><given-names>Giulia</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1037894/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Cantalupo</surname><given-names>Gaetano</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/109464/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Zenesini</surname><given-names>Corrado</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1009242/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Liguori</surname><given-names>Rocco</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/476690/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Sadun</surname><given-names>Alfredo A.</given-names></name><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/173864/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Carelli</surname><given-names>Valerio</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/174868/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Park</surname><given-names>Jason C.</given-names></name><xref ref-type=\"aff\" rid=\"aff5\"><sup>5</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/608184/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>La Morgia</surname><given-names>Chiara</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/139149/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>IRCCS Istituto Delle Scienze Neurologiche di Bologna, UOC Clinica Neurologica, Ospedale Bellaria</institution>, <addr-line>Bologna</addr-line>, <country>Italy</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Department of Biomedical and Neuromotor Sciences, University of Bologna</institution>, <addr-line>Bologna</addr-line>, <country>Italy</country></aff><aff id=\"aff3\"><sup>3</sup><institution>Division of Child Neuropsychiatry, Department of Surgical Sciences, Dentistry, Gynaecology and Paediatrics, University of Verona</institution>, <addr-line>Verona</addr-line>, <country>Italy</country></aff><aff id=\"aff4\"><sup>4</sup><institution>Department of Ophthalmology, Doheny Eye Institute, University of California, Los Angeles</institution>, <addr-line>Los Angeles, CA</addr-line>, <country>United States</country></aff><aff id=\"aff5\"><sup>5</sup><institution>Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago</institution>, <addr-line>Chicago, IL</addr-line>, <country>United States</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Silvia Di Angelantonio, Sapienza University of Rome, Italy</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Kwoon Y. Wong, University of Michigan, United States; Jordan M. Renna, The University of Akron, United States</p></fn><corresp id=\"c001\">*Correspondence: Chiara La Morgia, <email>chiara.lamorgia@unibo.it</email>; <email>chiaralamorgia@gmail.com</email></corresp><fn fn-type=\"other\" id=\"fn004\"><p>This article was submitted to Neurodegeneration, a section of the journal Frontiers in Neuroscience</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>14</volume><elocation-id>780</elocation-id><history><date date-type=\"received\"><day>09</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>02</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Romagnoli, Stanzani Maserati, De Matteis, Capellari, Carbonelli, Amore, Cantalupo, Zenesini, Liguori, Sadun, Carelli, Park and La Morgia.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Romagnoli, Stanzani Maserati, De Matteis, Capellari, Carbonelli, Amore, Cantalupo, Zenesini, Liguori, Sadun, Carelli, Park and La Morgia</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>Intrinsically photosensitive melanopsin retinal ganglion cells (mRGCs) are crucial for non-image forming functions of the eye, including the photoentrainment of circadian rhythms and the regulation of the pupillary light reflex (PLR). Chromatic pupillometry, using light stimuli at different wavelengths, makes possible the isolation of the contribution of rods, cones, and mRGCs to the PLR. In particular, post-illumination pupil response (PIPR) is the most reliable pupil metric of mRGC function. We have previously described, in post-mortem investigations of AD retinas, a loss of mRGCs, and in the remaining mRGCs, we demonstrated extensive morphological abnormalities. We noted dendrite varicosities, patchy distribution of melanopsin, and reduced dendrite arborization. In this study, we evaluated, with chromatic pupillometry, the PLR in a cohort of mild-moderate AD patients compared to controls. AD and controls also underwent an extensive ophthalmological evaluation. In our AD cohort, PIPR did not significantly differ from controls, even though we observed a higher variability in the AD group and 5/26 showed PIPR values outside the 2 SD from the control mean values. Moreover, we found a significant difference between AD and controls in terms of rod-mediated transient PLR amplitude. These results suggest that in the early stage of AD there are PLR abnormalities that may reflect a pathology affecting mRGC dendrites before involving the mRGC cell body. Further studies, including AD cases with more severe and longer disease duration, are needed to further explore this hypothesis.</p></abstract><kwd-group><kwd>chromatic pupillometry</kwd><kwd>Alzheimer’s disease</kwd><kwd>melanopsin retinal ganglion cells</kwd><kwd>pupillary light reflex</kwd><kwd>post-illumination pupil response</kwd><kwd>pupil</kwd></kwd-group><counts><fig-count count=\"3\"/><table-count count=\"1\"/><equation-count count=\"0\"/><ref-count count=\"57\"/><page-count count=\"10\"/><word-count count=\"0\"/></counts></article-meta></front><body><sec id=\"S1\"><title>Introduction</title><p>Melanopsin retinal ganglion cells (mRGCs) are intrinsically photosensitive RGCs because of the expression of the photopigment melanopsin (<xref rid=\"B6\" ref-type=\"bibr\">Berson et al., 2002</xref>; <xref rid=\"B23\" ref-type=\"bibr\">Hannibal et al., 2002</xref>; <xref rid=\"B27\" ref-type=\"bibr\">Hattar et al., 2002</xref>). These cells contribute to non-image forming functions of the eye including circadian photoentrainment [projecting via the retino-hypothalamic tract (RHT) to the suprachiasmatic nucleus (SCN) of the hypothalamus] and regulation of the pupillary light reflex (PLR) [via projections to the olivary pretectal nucleus (OPN)] (<xref rid=\"B47\" ref-type=\"bibr\">Sadun et al., 1984</xref>; <xref rid=\"B24\" ref-type=\"bibr\">Hannibal et al., 2004</xref>, <xref rid=\"B25\" ref-type=\"bibr\">2014</xref>; <xref rid=\"B3\" ref-type=\"bibr\">Baver et al., 2008</xref>; <xref rid=\"B9\" ref-type=\"bibr\">Chen et al., 2011</xref>; <xref rid=\"B37\" ref-type=\"bibr\">Li and Schmidt, 2018</xref>).</p><p>Neurodegenerative disorders, including Alzheimer’s disease (AD), are characterized by prominent circadian and sleep dysfunction even in the early phase of the disease (<xref rid=\"B51\" ref-type=\"bibr\">Uddin et al., 2020</xref>). Melanopsin retinal ganglion cell loss demonstrated in post-mortem AD retinas may contribute to the circadian and sleep problems documented in these patients (<xref rid=\"B35\" ref-type=\"bibr\">La Morgia et al., 2016</xref>, <xref rid=\"B36\" ref-type=\"bibr\">2017</xref>).</p><p>Amyloid plaques have been detected in AD retinas (<xref rid=\"B31\" ref-type=\"bibr\">Koronyo et al., 2017</xref>) and amyloid pathology can also affect mRGCs, suggesting a specific mechanism of neurodegeneration independent from the aging process (<xref rid=\"B35\" ref-type=\"bibr\">La Morgia et al., 2016</xref>). Moreover, extensive morphological abnormalities with dendrite varicosities, patchy distribution of melanopsin, and reduced dendrite arborization were noted in remaining mRGCs of AD retinas (<xref rid=\"B35\" ref-type=\"bibr\">La Morgia et al., 2016</xref>).</p><p>The function of mRGCs is, however, difficult to explore <italic>in vivo</italic>, since these cells represent a small subgroup (about 1%) of the regular RGCs, and also mRGCs receive some input from rods and cones (<xref rid=\"B22\" ref-type=\"bibr\">Hannibal et al., 2017</xref>). Chromatic pupillometry protocols have been developed to isolate the contribution of mRGCs to the PLR and to assess <italic>in vivo</italic> the function of mRGCs (<xref rid=\"B28\" ref-type=\"bibr\">Kardon et al., 2011</xref>; <xref rid=\"B44\" ref-type=\"bibr\">Park et al., 2011</xref>; <xref rid=\"B34\" ref-type=\"bibr\">La Morgia et al., 2018</xref>). These protocols are based on light stimuli at different wavelengths and with light adaptation conditions aimed at isolating the contribution of single photoreceptors, taking into account that mRGCs are maximally sensitive to blue light at 480 nm (<xref rid=\"B6\" ref-type=\"bibr\">Berson et al., 2002</xref>). It has been shown that the post-illumination pupil response (PIPR) is the most reliable pupil metric of mRGC function (<xref rid=\"B2\" ref-type=\"bibr\">Adhikari et al., 2015b</xref>).</p><p>Previous studies investigated the presence of pupil abnormalities in AD patients but they used different visual stimuli, heterogeneous protocols, and results were not consistent (<xref rid=\"B10\" ref-type=\"bibr\">Chougule et al., 2019</xref>). Pre-symptomatic cases (<xref rid=\"B41\" ref-type=\"bibr\">Oh et al., 2019</xref>; <xref rid=\"B52\" ref-type=\"bibr\">Van Stavern et al., 2019</xref>) and, recently, early AD cases (<xref rid=\"B29\" ref-type=\"bibr\">Kawasaki et al., 2020</xref>) were evaluated with chromatic pupillometry to isolate the mRGC contribution.</p><p>The present study was designed to evaluate the PLR, and in particular the mRGC-mediated contribution, in AD. We here report chromatic pupillometry findings using a previously published protocol (<xref rid=\"B43\" ref-type=\"bibr\">Park et al., 2017</xref>) in a cohort of 26 mild-moderate AD patients and 26 controls for which a detailed neuro-ophthalmological evaluation has been performed.</p></sec><sec id=\"S2\"><title>Methods</title><sec id=\"S2.SS1\"><title>Study Participants</title><p>This is a cross-sectional study and follows the STROBE guidelines (<xref rid=\"B54\" ref-type=\"bibr\">von Elm et al., 2007</xref>). We included AD patients and healthy controls, evaluated between June 2017 and February 2020 at the IRCCS Institute of Neurological Sciences of Bologna. All subjects gave written informed consent for the prospective collection of clinical data, data analyses, and publication. The study was conducted in agreement with the Declaration of Helsinki and approved by the local ethical committee (EC Interaziendale Bologna-Imola #16032) and within the framework of the research project supported by the Italian Ministry of Health, GR-2013-02358026 to CLM. We included patients with a diagnosis of AD according to Dubois criteria (<xref rid=\"B12\" ref-type=\"bibr\">Dubois et al., 2014</xref>) and National Institute of Neurological and Communication Disorders–Alzheimer’s Disease and Related Disorders Association criteria (NINCDS-ADRDA) (<xref rid=\"B39\" ref-type=\"bibr\">McKhann et al., 1984</xref>) at mild–moderate stage [Mini-Mental State Examination (MMSE) score between 11 and 26] (<xref rid=\"B14\" ref-type=\"bibr\">Folstein et al., 1975</xref>).</p><p>The absence of cognitive dysfunction was ascertained in the control group.</p><p>Exclusion criteria for both control and AD groups were: spherical or cylindrical refractive errors more than 3 or 2 diopters, respectively; presence of posterior pole pathology including age-related macular degeneration and known optic neuropathies (including open-angle glaucoma); ocular pressure more than 20 mmHg; severe lens opacity and/or retinal detachment and/or vascular retinal pathology (including diabetic retinopathy); history of ophthalmologic surgery, except for uncomplicated cataract surgery, performed at least 6 months previously; shift-workers in the last year; travels through more than one time zone during the last 3 months.</p><p>All study participants completed self-administered questionnaires including Epworth Sleepiness Scale (ESS), Pittsburgh Sleep Quality Index (PSQI), Berlin questionnaire, and Beck Anxiety (BAI) and Depression Inventory (BDI) (<xref rid=\"B5\" ref-type=\"bibr\">Beck et al., 1961</xref>, <xref rid=\"B4\" ref-type=\"bibr\">1988</xref>) to evaluate the possible occurrence of sleep disturbances. For the control group, exclusion criteria included also the presence of the following abnormal scores at sleep and mood questionnaires: excessive daytime sleepiness as assessed by the ESS (<xref rid=\"B53\" ref-type=\"bibr\">Vignatelli et al., 2003</xref>); presence of sleep disturbances as determined by the PSQI (<xref rid=\"B8\" ref-type=\"bibr\">Buysse et al., 1989</xref>); abnormal scores on the BAI (<xref rid=\"B4\" ref-type=\"bibr\">Beck et al., 1988</xref>) and BDI (<xref rid=\"B5\" ref-type=\"bibr\">Beck et al., 1961</xref>) tests.</p><p>All subjects underwent an extensive neuro-ophthalmological evaluation including visual acuity testing, tonometry, fundus examination, Ishihara color vision test, and OCT examination. OCT examination was performed using SS (Swept-Source)-OCT with the deep range imaging (DRI) Triton OCT (Topcon, Japan) using the 3DWide 12 × 9 mm scan protocol including segmentation analysis. We evaluated the average and 4 individual quadrants (temporal, superior, nasal, and inferior) peripapillary Retinal Nerve Fiber Layer (pRNFL) thickness, and the average and 6 individual macular sectors (superotemporal, superior, superonasal, inferonasal, inferior, and inferotemporal) Ganglion Cell-Inner Plexiform Layer thickness (GCL + defined as the thickness from the inner boundary of the GCL to the outer boundary of the inner plexiform layer [IPL]). OCT scans were acquired by the same experienced operator (MC) and poor-quality images (quality index less than 60), segmentation or centered errors, presence of any optic disk abnormalities potentially interfering with the goodness of OCT examination (presence of dysmorphic or tilted optic disk) were rejected from OCT data analysis. Moreover, AD patients performed neuropsychological evaluation and the MMSE corrected (MMSEc score) was obtained for all of them. We also collected all the clinical information available, including concomitant medications potentially impacting on pupil function (<xref rid=\"B10\" ref-type=\"bibr\">Chougule et al., 2019</xref>; <xref rid=\"B30\" ref-type=\"bibr\">Kelbsch et al., 2019</xref>), for both controls and AD patients.</p></sec><sec id=\"S2.SS2\"><title>Chromatic Pupillometric Protocol</title><sec id=\"S2.SS2.SSS1\"><title>Apparatus, Stimuli, and Procedures</title><p>A Ganzfeld ColorDome full-field stimulator (Espion V6, ColorDome Desktop Ganzfeld; Diagnosys LLC, Lowell, MA, United States) was used for the chromatic pupillometry test. Participants were dark-adapted for 10 min prior to start of the test. With the exclusion of patients for which only one eye was eligible for the study, we tested the dominant eye, and the contralateral eye was patched for monocular testing (65% of the tested eyes were right eyes). Colored light stimuli were presented to the tested eye and the pupil responses were recorded from the same eye using the Ganzfeld system equipped with an integrated pupillometer. The complete pupillometric protocol for isolating the rod-, mRGC-, and cone-contribution is described in details elsewhere (<xref rid=\"B44\" ref-type=\"bibr\">Park et al., 2011</xref>). For this study we considered the following conditions, as previously reported (<xref rid=\"B43\" ref-type=\"bibr\">Park et al., 2017</xref>):</p><list list-type=\"simple\"><list-item><label>1.</label><p>Rod-condition: low luminance (0.001 cd/m<sup>2</sup>) blue flash presented in the dark;</p></list-item><list-item><label>2.</label><p>Melanopsin-condition: photopically-matched red and blue stimuli (450 cd/m<sup>2</sup>) presented in the dark;</p></list-item><list-item><label>3.</label><p>Cone-condition: red flash (10 cd/m<sup>2</sup>) presented against the rod-suppressing blue adapting field (6 cd/m<sup>2</sup>).</p></list-item></list><p>Stimuli consisted of short wavelength (blue, dominant wavelength of 460–485 nm; mid = 472 nm) and long wavelength (red, dominant wavelength of 620–645 nm; mid = 632 nm) light-flashes of 1 s duration. The integrated pupillometer system measured the pupil diameter at a 100 Hz sampling frequency. The interstimulus interval (ISI) was 20 s for the rod- and cone-conditions (for both red and blue stimuli), while for the melanopsin-condition ISI was 30 s for red stimulus and 70 s for the blue one. All recordings were completed in the same order with the red stimulus followed by the blue. For all three conditions, each stimulus was presented three times consecutively and the individual responses were obtained by their average recording. Participants were instructed to keep their eyes open during the duration of the light stimuli as well as following the stimuli. Participants who blinked frequently during the recordings were given another opportunity to repeat the measurements. Pupil traces with excessive artifacts due to long eye blinks or eye closure were excluded from subsequent pupillometric data analysis.</p></sec><sec id=\"S2.SS2.SSS2\"><title>Data Analysis</title><p>Data were analyzed using custom scripts programmed in MATLAB (MathWorks Inc., Natick, MA, United States), which allowed for semi-automated analysis. PLR was normalized by the median steady-state (baseline) pupil size during the 2 s preceding each stimulus onset in order to minimize the effects of inter-subject differences in the baseline pupil size.</p><p>The following pupillometric parameters were calculated:</p><list list-type=\"simple\"><list-item><label>I.</label><p>Transient PLR amplitude (or Transient Peak Amplitude) was defined as the difference between the normalized baseline and the minimum normalized PLR after stimulus onset (pupil maximum constriction);</p></list-item><list-item><label>II.</label><p>For the melanopsin-condition, the PIPR was used for evaluating the mRGC sustained response. PIPR parameter was defined as the difference between the normalized baseline and the median normalized PLR measured over a 5 to 7 s time interval from stimulus offset. In particular, we evaluated PIPR from the blue and the red photopically-matched stimuli, and also the difference between the blue PIPR and the red one (PIPR<sub>Normalized</sub> = PIPR<sub>Blue</sub> - PIPR<sub>Red</sub>).</p></list-item></list></sec></sec><sec id=\"S2.SS3\"><title>Statistical Analysis</title><p>The Shapiro-Wilk and Kolmogorov–Smirnov tests were performed to assess the normal distribution and graphic inspection of the data. Chi-square and independent-<italic>t</italic> tests were used to compare variables among groups. For continuous variables (pupillometric parameters), z-scores (standard scores) were also calculated. Levene’s test was used to assess the equality of variances for mRGC sustained response for control and AD groups. Comparisons between groups for all pupillometric variables, measured under rod-, melanopsin-, and cone-mediated conditions, were computed by means of analysis of covariance (ANCOVA) with age as the covariate. Moreover, the <italic>p</italic>-value for interaction age × group was computed from the log-likelihood ratio test comparing ANCOVA models with and without the interaction term, and stratified β coefficients (95% Confidence Interval, 95% CI) for the variables turning out to be effect modifiers (<italic>p</italic>-value for interaction < 0.15) were presented.</p><p>For OCT data, we followed “one-eye” approach by evaluating the eye tested by chromatic pupillometry. Pearson correlation coefficients were used to measure the degree of association between pupillometric parameters and clinical data (OCT measures, MMSEc score, and disease duration) in control and Alzheimer’s groups. Statistical analyses were performed using SPSS (SPSS Inc., IBM, Chicago, IL, United States) and Stata SE (StataCorp, College Station, TX, United States) softwares.</p></sec></sec><sec id=\"S3\"><title>Results</title><p>This study included 26 mild-moderate AD patients from 52 to 88 years of age (69.3 ± 7 years) and 26 healthy participants (controls) from 58 to 82 years of age (70.2 ± 11 years). The demographic and clinical data of the two groups are shown in <xref rid=\"T1\" ref-type=\"table\">Table 1</xref>. Controls and AD patients did not significantly differ in terms of age and gender (gender, <italic>p</italic> = 0.58; age, <italic>p</italic> = 0.75).</p><table-wrap id=\"T1\" position=\"float\"><label>TABLE 1</label><caption><p>Sociodemographic data.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Controls</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Alzheimer’s</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>p</italic>-value</bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold><italic>N</italic></bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">26 (50%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">26 (50%)</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" colspan=\"4\" rowspan=\"1\"><bold>Gender</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Male</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11 (42.3%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14 (53.8%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.58</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Female</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15 (57.7%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12 (46.2%)</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Age | Age-class</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">69.3 ± 7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">70.2 ± 11</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.75 | 0.16</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">50–59 years</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3 (11.5%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6 (23.1%)</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">60–69 years</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10 (38.5%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4 (15.4%)</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">70–79 years</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10 (38.5%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9 (34.6%)</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">80–89 years</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3 (11.5%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7 (26.9%)</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>MMSEc</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">/</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20.7 ± 4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">(17.5–24.8)</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Disease duration</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">/</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.8 ± 2.9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">(2–4.2)</td><td rowspan=\"1\" colspan=\"1\"/></tr></tbody></table><table-wrap-foot><attrib><italic>Values are given as n (%) or mean ± standard deviation (interquartile range, Q1–Q3). MMSEc, Mini Mental State Examination corrected score. Chi-square test was performed with categorical variables and independent-t test was performed with continuous variables.</italic></attrib></table-wrap-foot></table-wrap><p>Raw pupil traces from two controls showed excessive blink artifacts under the rod- (blue flash) and melanopsin- (blue flash) conditions and were removed from data analysis. Further, four control and six AD pupil traces under the cone-condition showed too many artifacts, were not reliable, and thus were removed from data analysis.</p><p>The single normalized pupil traces (PLR curves) under all conditions are shown in <xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>. For the rod- (<xref ref-type=\"fig\" rid=\"F1\">Figures 1A,B</xref>) and cone- (<xref ref-type=\"fig\" rid=\"F1\">Figures 1G,H</xref>) conditions, the PLR is characterized by a rapid transient constriction followed by a relatively rapid return to the baseline both in controls (<xref ref-type=\"fig\" rid=\"F1\">Figures 1A,G</xref>) and in AD (<xref ref-type=\"fig\" rid=\"F1\">Figures 1B,H</xref>).</p><fig id=\"F1\" position=\"float\"><label>FIGURE 1</label><caption><p>Single and mean pupillometric waveforms for the rod, melanopsin, and cone conditions in controls and AD. Pupillometric traces obtained under the rod <bold>(A,B)</bold>, melanopsin (<bold>C,D</bold>, blue and <bold>E</bold>,<bold>F</bold>, red), and cone <bold>(G,H)</bold> conditions of the chromatic pupillometric protocol. Light blue <bold>(A–D)</bold> and red <bold>(E–H)</bold> traces represent single individuals, while black traces <bold>(A–H)</bold> represent the mean waveforms for each group (<bold>A,C,E,G</bold> for the control group; <bold>B,D,F,H</bold> for Alzheimer’s group). The vertical dotted lines indicate the time interval (5–7 s from stimulus offset) in which the melanopsin-mediated (sustained) amplitude (PIPR, Post-Illumination Pupil Response, 450 cd/m<sup>2</sup>) was measured. The light stimulus onset and offset are represented by the gray boxes along the x-axes.</p></caption><graphic xlink:href=\"fnins-14-00780-g001\"/></fig><p>Under the melanopsin-condition (blue flash), in both controls (<xref ref-type=\"fig\" rid=\"F1\">Figure 1C</xref>) and AD (<xref ref-type=\"fig\" rid=\"F1\">Figure 1D</xref>), the PLR is characterized by an initial transient constriction followed by a sustained constriction (PIPR) during the 5–7 s time interval from light-stimulus offset. Melanopsin-mediated sustained response is more variable in the AD group (<xref ref-type=\"fig\" rid=\"F1\">Figure 1D</xref>) compared to the control group (<xref ref-type=\"fig\" rid=\"F1\">Figure 1C</xref>). In fact, the estimated population variances of the PIPR at 5-s from stimulus offset of the two groups were statistically different (Levene’s test: SD control = 0.03, SD AD = 0.06; <italic>p</italic> = 0.018) and, in particular, the variability in the AD group resulted significantly greater. Moreover, five AD patients showed a PIPR lower more than 2 SD from the control mean value. Under the melanopsin-condition (red flash), in both controls (<xref ref-type=\"fig\" rid=\"F1\">Figure 1E</xref>) and AD (<xref ref-type=\"fig\" rid=\"F1\">Figure 1F</xref>), the elicited PLR is characterized by an initial transient constriction, followed by a smaller sustained response with a reduced amplitude in the AD group compared to controls.</p><p>The individual pupillometric parameters for controls and AD for the three conditions are provided in <xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref>. There was no difference between AD and control groups in terms of baseline normalized pupil size under any of the three conditions (<xref ref-type=\"fig\" rid=\"F2\">Figures 2A,C,G</xref> and <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary Table 1</xref>). PLR transient amplitude (<xref ref-type=\"fig\" rid=\"F2\">Figures 2B,E,F</xref> and <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary Table 1</xref>) was significantly decreased under rod- (<italic>p</italic> = 0.006) and melanopsin- (blue flash, <italic>p</italic> = 0.02; red flash, <italic>p</italic> = 0.006) conditions in AD compared to controls. PIPR<sub>Blue</sub> in the melanopsin-condition was not significantly different between AD and controls (<xref ref-type=\"fig\" rid=\"F2\">Figure 2D</xref> and <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary Table 1</xref>). PLR transient amplitude was not significantly different between AD and controls under the cone-condition (<xref ref-type=\"fig\" rid=\"F2\">Figure 2H</xref> and <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary Table 1</xref>).</p><fig id=\"F2\" position=\"float\"><label>FIGURE 2</label><caption><p>Pupillometric parameters for controls and AD. Panels show scatterplots with horizontal solid line represents the mean and error bars representing standard deviations for each group. <bold>(A,B)</bold> Show the results for the Rod-condition; <bold>(C–F)</bold> Show the results for the Melanopsin-condition [<bold>(C–E)</bold> 450 cd/m<sup>2</sup>, 472 nm-blue; <bold>(F)</bold> 450 cd/m<sup>2</sup>, 632 nm-red]; <bold>(G,H)</bold> Show the results for the Cone-condition. <bold>(A,C,G)</bold> Show normalized pupil size at baseline. <bold>(B,E,F,H)</bold> Show normalized transient peak amplitude. <bold>(D)</bold> Show normalized melanopsin-mediated Post-Illumination Pupil Response (PIPR<sub>Blue</sub>). Peak amplitude (transient peak amplitude) was defined as the difference between the normalized baseline pupil size and the median normalized PLR at the point of maximum pupillary constriction after stimulus onset. PIPR was defined as the difference between the normalized baseline pupil size and the median normalized PLR measured over a 5 to 7 s time interval from stimulus offset. Significant different between controls and AD patients are indicated by an asterix symbol above the groups. *<italic>p</italic> < 0.05; **<italic>p</italic> < 0.01.</p></caption><graphic xlink:href=\"fnins-14-00780-g002\"/></fig><p><xref ref-type=\"fig\" rid=\"F3\">Figure 3</xref> shows the mean normalized pupil traces of rod- (<xref ref-type=\"fig\" rid=\"F3\">Figure 3A</xref>), melanopsin- (<xref ref-type=\"fig\" rid=\"F3\">Figure 3B</xref>), and cone- (<xref ref-type=\"fig\" rid=\"F3\">Figure 3C</xref>) conditions for each subject group. We failed to observe any difference in terms of PIPR<sub>Blue</sub> and PIPR<sub>Normalized</sub> (<xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary Table 1</xref>) between AD and controls.</p><fig id=\"F3\" position=\"float\"><label>FIGURE 3</label><caption><p>Mean pupillometric waveforms obtained under the rod, melanopsin, and cone conditions in controls and AD. <bold>(A)</bold> Shows PLR measured under the rod-condition with the short- (blue) wavelength flashes (0.001 cd/m<sup>2</sup>) presented in the dark, for comparison between control (blue mean trace) and AD (azure mean traces) groups. <bold>(B)</bold> Shows PLR measured under the melanopsin-condition, including the two photopically-matched intense long- (red) and short- (blue) wavelength flashes (450 cd/m<sup>2</sup>) presented in the dark, for comparison between control (red and blue mean traces) and AD (pink and azure mean traces) groups. The vertical dotted lines indicate the time interval (5–7 s from stimulus offset) over which the melanopsin-mediated (sustained) amplitude (PIPR) was measured. <bold>(C)</bold> Shows PLR measured with the long- (red) wavelength flashes (10 cd/m<sup>2</sup>) presented against the rod-suppressing blue adapting field (6 cd/m<sup>2</sup>) for comparison between control (red mean trace) and AD (pink mean trace) groups.</p></caption><graphic xlink:href=\"fnins-14-00780-g003\"/></fig><p>To check the extent to which the control- and AD-regression lines of each pupillometric parameters with age deviate from parallel, the likelihood-ratio test was used (<xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary Tables 2</xref>, <xref ref-type=\"supplementary-material\" rid=\"TS1\">3</xref> and <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary Figure 1</xref>). Likelihood-ratio test showed the existence of interaction age × group for melanopsin-mediated PIPR<sub>Blue</sub> (Alzheimer’s group: β = -0.0042; 95% CI = -0.0073–0.0011; <italic>r</italic> = -0.5) and transient peak amplitude (Alzheimer’s group, blue flash: β = -0.0034; 95% CI = -0.0055–0.0014; <italic>r</italic> = -0.59; red flash: β = -0.0021; 95% CI = -0.0039–0.0003; <italic>r</italic> = -0.44) parameters with a significant correlation only in AD (<xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary Tables 2</xref>, <xref ref-type=\"supplementary-material\" rid=\"TS1\">3</xref> and <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary Figures 1B,D,F</xref>).</p><p>Furthermore, Pearson’s correlation analysis was used to determine if there was a relationship between the calculated pupillometric parameters and OCT measures, MMSEc score, and disease duration (the latter two only for AD). There was no significant correlation between pupillometric parameters and OCT measurements, neither with MMSEc score and disease duration for AD patients. Mean comparisons of OCT measurements for all RNFL quadrants (temporal, superior, nasal, and inferior) and macular GCL + sectors (superotemporal, superior, superonasal, inferonasal, inferior, and inferotemporal) did not show significant differences between AD and controls (data not shown).</p><p>We also retrieved information regarding oral medications that could potentially interfere with pupillary responses, i.e., cholinesterase inhibitors for AD and beta-blockers for controls. Only a few controls (<italic>n</italic> = 4, 15.5%) were on beta-blockers (atenolol/metaprolol/bisoprolol), while 16 AD (61.5%) were on cholinesterase inhibitors drugs (donepezil/rivastigmine/galantamine). In controls, there were no differences in terms of pupillometric parameters between subjects taking beta-blockers and those not taking them (data not shown). No significant differences among AD subgroups (cholinergic-treated and cholinergic-untreated) for any PLR parameters were found (data not shown).</p></sec><sec id=\"S4\"><title>Discussion</title><p>In this paper, we evaluated for the first time, using chromatic pupillometry, aimed at isolating the mRGC contribution (<xref rid=\"B43\" ref-type=\"bibr\">Park et al., 2017</xref>), the PLR in a cohort of 26 definite mild-moderate AD patients compared to a group of age- and gender-matched controls. In particular, to specifically target mRGC function, we evaluated the PIPR amplitude using intense (450 cd/m<sup>2</sup>) blue (472 nm) light stimuli. The PIPR, which is the most reliable marker of mRGC-mediated PLR, was not significantly different between AD and controls, but we found a significant difference in terms of transient PLR amplitude between AD and controls under dark-adaption. Concerning the PIPR, even though the difference between AD and controls was not significantly different, the AD group showed higher variability with five individuals having a PIPR<sub>Blue</sub> amplitude value outside the 2 SD range from the control mean. Such variability in terms of disease severity has been already reported in terms of circadian measurements and optic nerve pathology (<xref rid=\"B35\" ref-type=\"bibr\">La Morgia et al., 2016</xref>) and might depend on the severity as well as on disease duration (<xref rid=\"B26\" ref-type=\"bibr\">Hatfield et al., 2004</xref>). In this study we included AD patients in a mild-moderate stage of the disease and disease severity and duration were on average lower than previously published cohorts (<xref rid=\"B35\" ref-type=\"bibr\">La Morgia et al., 2016</xref>). In rodents, six different mRGC subtypes were characterized, and PLR was mainly regulated by the Brn3b-positive M1 and non-M1 subtypes (<xref rid=\"B9\" ref-type=\"bibr\">Chen et al., 2011</xref>; <xref rid=\"B37\" ref-type=\"bibr\">Li and Schmidt, 2018</xref>). In humans, Hannibal and colleagues also identified six subtypes of mRGCs (M1, M2, M3, M4, giant M1, and giant displaced M1), unevenly distributed across the human retina and with distinct anatomical characteristics (<xref rid=\"B22\" ref-type=\"bibr\">Hannibal et al., 2017</xref>). We previously demonstrated in post-mortem AD retinas that mRGCs are lost in AD and amyloid pathology specifically affects these cells (<xref rid=\"B35\" ref-type=\"bibr\">La Morgia et al., 2016</xref>). However, it is not known whether in AD the neurodegenerative process affects a specific mRGC subtype, and in particular those mRGC contributing to the PLR.</p><p>Interestingly, we found a significant difference in terms of transient peak amplitude both under the rod-condition, using the short duration, narrowband pulse, and low intensity (0.001 cd/m<sup>2</sup>) blue light stimulus, and under the melanopsin-condition, but not in the cone-condition, overall pointing to a prominent rod-mediated response (<xref rid=\"B38\" ref-type=\"bibr\">McDougal and Gamlin, 2010</xref>; <xref rid=\"B32\" ref-type=\"bibr\">Kostic et al., 2016</xref>; <xref rid=\"B33\" ref-type=\"bibr\">Krishnan et al., 2020</xref>). Considering that mRGCs receive synaptic input from rods and cones through bipolar cells and a direct contact of rod bipolar cells via ribbon synapses in the ON layer of the IPL with mRGCs has been demonstrated in human retinas (<xref rid=\"B22\" ref-type=\"bibr\">Hannibal et al., 2017</xref>), this difference between AD and controls could suggest that in the early stages of the disease there is no obvious cell body dysfunction but possibly a dendropathy. This suggestion is based on the presence of mRGC dendrite pathology, previously reported in AD, with extensive morphological abnormalities in the spared mRGCs showing dendrite varicosities, patchy distribution of melanopsin, and reduced dendrite arborization (<xref rid=\"B35\" ref-type=\"bibr\">La Morgia et al., 2016</xref>). Dendritic degeneration has been also documented in RGCs of AD mouse model (<xref rid=\"B57\" ref-type=\"bibr\">Williams et al., 2013</xref>), and there are other disease models such as <italic>OPA1</italic>-related optic atrophy in which dendrites are the primary site of pathology (<xref rid=\"B56\" ref-type=\"bibr\">Williams et al., 2010</xref>). The different mRGC subtypes are distinctively connected to rods and cones and specifically modulated by various light conditions (<xref rid=\"B55\" ref-type=\"bibr\">Weng et al., 2013</xref>). Furthermore, the presence of a contact from amacrine cells and directly from rod bipolar cells via ribbon synapses on M1, M2, and M4 soma membrane and dendrites has been demonstrated in human retinas (<xref rid=\"B22\" ref-type=\"bibr\">Hannibal et al., 2017</xref>). The significantly reduced transient peak amplitude in conditions exploring the rod-contribution may thus suggest an altered contact between rods and mRGCs. It is possible to hypothesize that the rod response depends more on the distal dendrites, and consequently that the subsequent reduced dendritic arborization might interfere with the rod input out of proportion to the cones. However, we cannot exclude that the rod-mediated mRGC dysfunction in AD can be due to pathology specifically affecting rod-bipolar cells while possibly sparing cone-bipolar cells.</p><p>In summary, pathology often provokes compensation. This is particularly true with the central nervous system, which has many gain control circuits in place (<xref rid=\"B42\" ref-type=\"bibr\">Ostergaard et al., 2007</xref>; <xref rid=\"B11\" ref-type=\"bibr\">Do, 2019</xref>). Therefore, if disease, injury, or aging causes a reduction of units, there are many means for restoring the overall average mass effect. However, with coarser granularity, there is increased variability (<xref rid=\"B40\" ref-type=\"bibr\">Mendell, 2014</xref>). In this case, fewer mRGCs or even fewer dendritic circuits give less granularity in the system and a tendency for larger swings in the response. Thus, variability would precede decompensation into failure.</p><p>Baseline pupil size was not significantly different between AD and controls. It must be considered, however, that 16/26 AD patients were on acetylcholinesterase inhibitor drugs, and this might have an impact on the baseline pupil size. A few, small sample studies have reported the effect on the PLR of commonly used cholinergic AD drugs (<xref rid=\"B17\" ref-type=\"bibr\">Fotiou et al., 2000</xref>; <xref rid=\"B21\" ref-type=\"bibr\">Granholm et al., 2003</xref>). In one of these studies the authors did not find an effect of cholinergic medications on baseline pupil size, but demonstrated an increase in pupil constriction latency (<xref rid=\"B21\" ref-type=\"bibr\">Granholm et al., 2003</xref>). We also compared the PLR in AD patients taking and not taking these drugs and failed to demonstrate significant differences. Additional studies are needed to conclude a real effect of cholinergic medications on PLR and to clarify if different acetylcholinesterase inhibitors could have a different impact on PLR.</p><p>Previous studies investigated PLR in AD patients documenting reduced velocity, constriction amplitude, and increased latencies of PLR. These results were interpreted as related to the acetylcholine deficiency and parasympathetic dysfunction in AD (<xref rid=\"B45\" ref-type=\"bibr\">Prettyman et al., 1997</xref>; <xref rid=\"B17\" ref-type=\"bibr\">Fotiou et al., 2000</xref>, <xref rid=\"B15\" ref-type=\"bibr\">2007</xref>, <xref rid=\"B16\" ref-type=\"bibr\">2009</xref>; <xref rid=\"B50\" ref-type=\"bibr\">Tales et al., 2001</xref>; <xref rid=\"B21\" ref-type=\"bibr\">Granholm et al., 2003</xref>; <xref rid=\"B18\" ref-type=\"bibr\">Frost S. et al., 2013</xref>; <xref rid=\"B19\" ref-type=\"bibr\">Frost S.M. et al., 2013</xref>; <xref rid=\"B20\" ref-type=\"bibr\">Frost et al., 2017</xref>; <xref rid=\"B7\" ref-type=\"bibr\">Bittner et al., 2014</xref>; <xref rid=\"B10\" ref-type=\"bibr\">Chougule et al., 2019</xref>). Unfortunately, none of these studies were based on chromatic pupillometry protocol, and these results were not confirmed by more recent studies, which focused on early and pre-clinical stages of AD (<xref rid=\"B10\" ref-type=\"bibr\">Chougule et al., 2019</xref>). One chromatic pupillometry study evaluated pre-symptomatic AD cases (<xref rid=\"B41\" ref-type=\"bibr\">Oh et al., 2019</xref>). Oh and co-authors evaluated the PLR response using a similar pupillometric protocol in a cohort of 10 pre-symptomatic AD cases, defined on the basis of the cerebrospinal fluid markers, and they did not demonstrate a significant difference between pre-symptomatic AD cases and controls (<xref rid=\"B41\" ref-type=\"bibr\">Oh et al., 2019</xref>). However, congruent to the current findings, higher variability of PLR was documented in the AD group (<xref rid=\"B41\" ref-type=\"bibr\">Oh et al., 2019</xref>). Moreover, in this study the authors used only the 2.3 log cd/m<sup>2</sup> photopically-matched red and blue stimuli (<xref rid=\"B41\" ref-type=\"bibr\">Oh et al., 2019</xref>). Similarly, Van Stavern and co-authors, using a white light stimulus, did not show any difference between preclinical AD subjects (defined by CSF markers) and normal aging controls in any of the PLR parameters examined (<xref rid=\"B52\" ref-type=\"bibr\">Van Stavern et al., 2019</xref>). Very recently, <xref rid=\"B29\" ref-type=\"bibr\">Kawasaki et al. (2020)</xref>, using a different chromatic pupillometry protocol under photopic conditions, failed to demonstrate, similarly to our results, a significant difference between early AD and controls in terms of PIPR response.</p><p>We also demonstrated a significant correlation of the PIPR amplitude and transient peak amplitude (melanopsin-condition, 450 cd/m<sup>2</sup>) with age only in the AD group, which is in line with previous results pointing to an accelerated aging process in AD (<xref rid=\"B35\" ref-type=\"bibr\">La Morgia et al., 2016</xref>). Data on PLR in relation to age are not conclusive even though the majority of papers failed to reveal a significant difference of PLR in relation to the aging process. However, the controls included in these studies were younger than 70, and this might explain the absence of significant impairment of the pupil response (<xref rid=\"B1\" ref-type=\"bibr\">Adhikari et al., 2015a</xref>; <xref rid=\"B46\" ref-type=\"bibr\">Rukmini et al., 2017</xref>). Our results are in line with the observation of mRGC loss with age (<xref rid=\"B49\" ref-type=\"bibr\">Semo et al., 2003</xref>; <xref rid=\"B35\" ref-type=\"bibr\">La Morgia et al., 2016</xref>; <xref rid=\"B13\" ref-type=\"bibr\">Esquiva et al., 2017</xref>).</p><p>We did not find a correlation between pupil metrics and OCT parameters nor with disease severity or duration. Moreover, we did not find any significant difference in terms of RNFL and GCL + thickness between AD and controls. Any effect specific to mRGC loss would have been swamped by regular RGCs in these measures. Further, these results can be explained by the inclusion of milder cases with shorter disease duration. A recent SS-OCT study evaluating a large cohort of AD cases failed to demonstrate a significant difference in terms of RNFL between AD and controls (<xref rid=\"B48\" ref-type=\"bibr\">Sanchez et al., 2018</xref>).</p><p>Overall, the current chromatic pupillometry findings in a cohort of mild-moderate AD patients did not demonstrate a clear mRGC-driven pupil dysfunction but are rather consistent with a dendropathy in the early stage of the disease, supported by our previous post-mortem studies of AD retinas. Early pathology, while still in the range of compensatory mechanisms, often manifests as variability. Further studies including more severe and with longer disease duration AD cases are needed to further explore this hypothesis. Such studies may also clarify whether the PLR can be used as a tool evaluating the progression of the disease and eventually the efficacy of therapies in AD.</p></sec><sec sec-type=\"data-availability\" id=\"S5\"><title>Data Availability Statement</title><p>The raw data supporting the conclusion of this article will be made available from the corresponding author, without undue reservation, to any qualified researcher.</p></sec><sec id=\"S6\"><title>Ethics Statement</title><p>The studies involving human participants were reviewed and approved by the EC Interaziendale Bologna-Imola #16032. The patients/participants provided their written informed consent to participate in this study.</p></sec><sec id=\"S7\"><title>Author Contributions</title><p>MR: acquisition, analysis and interpretation of data, and drafting and revising the work. MS, MD, SC, MC, GA, and CL: data acquisition and revising the work. MR and CZ: statistical data analysis. MR, MC, GC, RL, AS, VC, JP, and CL: contributed to interpretation of the data. MR, GC, VC, JP, and CL: design of the work. MS, MD, SC, MC, GA, GC, CZ, RL, AS, VC, JP, and CL: revising the work and provided approval for publication of the content. All authors contributed to the article and approved the submitted version.</p></sec><sec id=\"conf1\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> This work was supported by the Italian Ministry of Health Young Researcher Project Grant (GR-2013-02358026 to CL).</p></fn></fn-group><ack><p>We were deeply grateful to patients and their caregivers for participating to the study. We also thank Prof. Parchi for performing the CSF examination of the AD subjects included and Dr. Vincenzo Allegri for performing amyloid PET in AD patients. We also thank Luca Berti for his assistance in pupil traces analysis and Prof. Jens Hannibal for his advices.</p></ack><sec id=\"S10\" sec-type=\"supplementary material\"><title>Supplementary Material</title><p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.frontiersin.org/articles/10.3389/fnins.2020.00780/full#supplementary-material\">https://www.frontiersin.org/articles/10.3389/fnins.2020.00780/full#supplementary-material</ext-link></p><supplementary-material content-type=\"local-data\" id=\"TS1\"><media xlink:href=\"Data_Sheet_1.docx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec><ref-list><title>References</title><ref id=\"B1\"><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Adhikari</surname><given-names>P.</given-names></name><name><surname>Pearson</surname><given-names>C. 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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Immunol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Immunol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Immunol.</journal-id><journal-title-group><journal-title>Frontiers in Immunology</journal-title></journal-title-group><issn pub-type=\"epub\">1664-3224</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849662</article-id><article-id pub-id-type=\"pmc\">PMC7431960</article-id><article-id pub-id-type=\"doi\">10.3389/fimmu.2020.01985</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Immunology</subject><subj-group><subject>Mini Review</subject></subj-group></subj-group></article-categories><title-group><article-title>Chimeric Antigen Receptor T Cell Therapy for Pediatric B-ALL: Narrowing the Gap Between Early and Long-Term Outcomes</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Schultz</surname><given-names>Liora</given-names></name><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/996269/overview\"/></contrib></contrib-group><aff><institution>Division of Pediatric Hematology and Oncology, Department of Pediatrics, Stanford University</institution>, <addr-line>Palo Alto, CA</addr-line>, <country>United States</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Francisco Martin, Andalusian Autonomous Government of Genomics and Oncological Research (GENYO), Spain</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Xiuyan Wang, Cornell University, United States; David Barrett, Children’s Hospital of Philadelphia, United States</p></fn><corresp id=\"c001\">*Correspondence: Liora Schultz, <email>lioras@stanford.edu</email>; <email>liora.schultz@gmail.com</email></corresp><fn fn-type=\"other\" id=\"fn004\"><p>This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Immunology</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>1985</elocation-id><history><date date-type=\"received\"><day>05</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>22</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Schultz.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Schultz</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>Chimeric Antigen Receptor (CAR) T cell therapy targeting CD19 has introduced a paradigmatic shift in our treatment approach for advanced B cell malignancies. A major advance has been in the field of pediatric B-ALL where complete responses have been achieved across clinical trials with rates of 65–90% in the relapsed/refractory setting. These striking early response rates led to FDA approval of Tisagenlecleucel, CD19-specific CAR T cells, in August 2017. With broadened access and available longitudinal follow up, it is imperative to study the true durability of CAR-mediated responses and establish long-term relapse free and survival outcomes following CAR therapy. Phase I and II clinical trials have reported event-free survival rates of 50% at 1 year following CD19-CAR infusion in children and young adults with B-ALL. Here, we review some of the major challenges accounting for the discrepancy between early response rates and long term outcomes. In specific, relapse with CD19<sup>+</sup> or CD19<sup>–</sup> disease has emerged as a major challenge following CD19-CAR T cell therapy. Related, is the issue of CAR persistence which has been shown to correlate with long-term outcomes. We highlight select efforts to optimize clinical strategies and CAR design to promote enhanced persistence. To date, we do not have robust predictors of response durability and relapse following CAR therapy. The ability to identify patients at risk of relapse in an <italic>a priori</italic> manner may introduce an interventional window to consolidate CAR-mediated remissions and enhance response durability. This review highlights the need to bridge the gap between the remarkable early complete responses achieved with CD19-CAR T cell therapy and the long-term survival outcomes.</p></abstract><kwd-group><kwd>chimeric antigen receptor</kwd><kwd>B cell-malignancies</kwd><kwd>acute lymphoblastic leukemia</kwd><kwd>pediatrics</kwd><kwd>CD19 antigen</kwd><kwd>adoptive immunotherapy</kwd></kwd-group><counts><fig-count count=\"1\"/><table-count count=\"0\"/><equation-count count=\"0\"/><ref-count count=\"42\"/><page-count count=\"7\"/><word-count count=\"0\"/></counts></article-meta></front><body><sec id=\"S1\"><title>Introduction</title><p>Relapsed and refractory B cell Acute Lymphoblastic Leukemia (B-ALL) is accompanied by a dismal prognosis and accounts for a significant amount of cancer-related mortality, specifically in the pediatric population, where ALL remains the most common cancer subtype (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>). The development of chimeric antigen receptor (CAR) T cell therapy has introduced a new therapeutic option for this patient population and has demonstrated remarkable clinical outcomes. In specific, CAR T cells targeting the B-cell associated antigen, CD19, has achieved complete response (CR) rates of 65–90% across clinical trials spanning institutions in patients with B-cell leukemias (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>–<xref rid=\"B6\" ref-type=\"bibr\">6</xref>). These early results led to FDA approval of CD19-specific CAR T cells, Tisagennlecleucel (kymriah), in August 2017, and prompted mass efforts to permit scalability and access. The approved indication is for the treatment of pediatric and young adult patients with refractory B-ALL or B-ALL in second or greater relapse. With FDA approval, CAR T cell therapy has become available for commercial use. With broader access to CAR T cell therapies and increased experience and longitudinal follow up, it is now pertinent to understand the long-term outcomes using CAR T cell therapy (<xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>). Here we review a limited background on CAR T cell therapy, highlight successes using CD19-CAR T cells in achieving early responses in Pediatric B-cell ALL, address the importance of identifying predictors of CAR responses and resistance and highlight factors challenging long-term CAR responses.</p><fig id=\"F1\" position=\"float\"><label>FIGURE 1</label><caption><p>Schematic representation mapping the chronology of CAR commercialization. Illustration highlights practice changes characterizing the era of CAR commercialization and pertinent queries requiring further study.</p></caption><graphic xlink:href=\"fimmu-11-01985-g001\"/></fig></sec><sec id=\"S2\"><title>Background: the CAR Construct</title><p>CARs are artificial antigen receptors engineered to fuse an antibody-binding domain targeting a tumor-specific antigen to T cell derived-signaling domain/s (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>). T cells can be engineered to express CARs targeting tumor-specific antigens, endowing T cells with tumor-specific cytotoxicity. The majority of CARs designed for clinical development have targeted the B-cell associated CD19 surface antigen and have been used in B cell malignancies, including B-cell leukemias and lymphomas across ages (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>–<xref rid=\"B6\" ref-type=\"bibr\">6</xref>, <xref rid=\"B8\" ref-type=\"bibr\">8</xref>–<xref rid=\"B13\" ref-type=\"bibr\">13</xref>). To date, the majority of CARs developed for clinical trials have included a primary CD3-ζ signaling domain and a CD28 or 4-1BB secondary co-stimulatory domain, based on early pre-clinical work demonstrating independent properties of CD28 and 4-1BB in enhancing cytotoxicity beyond first-generation CARs housing a singular CD3-ζ signaling domain (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>, <xref rid=\"B14\" ref-type=\"bibr\">14</xref>, <xref rid=\"B15\" ref-type=\"bibr\">15</xref>). Physiologic T cells rely on antigen presentation within the major histocompatibility complex (MHC) and are dependent on secondary costimulatory signals to permit effective cytotoxicity. In contrast, antibodies can bind to surface antigens in an MHC-independent manner. Tumors often downregulate MHC and can lack costimulatory ligands, rendering them immune to T-cell mediated cytotoxicity. The design of a CAR permits MHC-independent antigen-binding by the inclusion of an antibody-derived binding domain. Additionally, by including both primary T cell signaling and endogenous costimulatory signals, CARs are not reliant on tumor expression of costimulatory ligands and can effectively induce T-cell mediated cytotoxicity by inducing multiple signals with the binding of a single tumor-specific antigen (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>).</p></sec><sec id=\"S3\"><title>Successes Using CD19-Specific CAR T Cells in Achieving Early Complete Responses</title><p>Efficacy of CD19-specific CAR T cell therapy was first reported in small case series in adults with indolent lymphoma and chronic leukemia (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>, <xref rid=\"B9\" ref-type=\"bibr\">9</xref>, <xref rid=\"B12\" ref-type=\"bibr\">12</xref>). Shortly after, acute B-cell lymphoblastic leukemias were found to be exquisitely sensitive to CD19-specific CAR T cell therapy (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>, <xref rid=\"B6\" ref-type=\"bibr\">6</xref>). Phase I studies in pediatric B-ALL using independent CAR constructs across institutions demonstrated conserved efficacy ranging from 65 to 90% (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>–<xref rid=\"B4\" ref-type=\"bibr\">4</xref>). The challenge of scalability required organized collaboration, merging pharmaceutical and academic efforts. A phase II global, single-cohort study using Tisagenlecleucel demonstrated that the single-institution phase I outcomes were paralleled in this multi-institutional study with a CR rate of 81% following a single infusion of CAR T cells (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>). This landmark study importantly demonstrated feasibility of centralized CAR manufacturing for scaled, global use and led to FDA approval of Tisagenlecleucel for children and young adults with B-ALL.</p></sec><sec id=\"S4\"><title>Relapse Patterns Post-CAR and Durability of CAR-Mediated Responses</title><sec id=\"S4.SS1\"><title>Post-CAR Relapse</title><p>As described, CD19-CAR T cell therapy has proven effective at achieving early complete response rates. With increasing follow up, the emergence of relapse post-CAR T cell therapy, however, poses a significant clinical challenge to the durability of CAR-mediated remissions (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>, <xref rid=\"B17\" ref-type=\"bibr\">17</xref>). The two most prominent patterns of relapse include relapse with conserved CD19<sup>+</sup> expression, most often in context of CAR loss, and relapse with CD19-negative or downregulated disease in context of ongoing CD19-targeted pressure. Studies using CD19-targeting CARs in adults and children with B cell malignancies across institutions demonstrate relapse rates of 30–60% with both CD19<sup>+</sup> and CD19<sup>–</sup> disease accounting for relapses (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>). The largest phase II pediatric study described above however, identified CD19<sup>–</sup> relapse to be a predominant pattern of relapse in their cohort of 75 infused patients with 15 of 16 evaluable relapses having CD19-downregulation (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>). A phase I single-institution study evaluating 43 pediatric patients infused with a similar 4-1BB-based CD19-specific CAR construct demonstrated a lower rate of CD19<sup>–</sup> relapse, possibly explained by decreased persistence and less ongoing targeted-pressure, with 7 of 18 relapses demonstrating CD19 negativity and 11 relapses with conserved CD19 expression (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>). Mechanistic studies describe mutated CD19 RNA isoforms with alternatively spliced CD19 and underlying genetic mutations in CD19 driving truncated protein production or dysfunctional or absent transmembrane domains, that account for absence of surface CD19 in context of CD19<sup>–</sup> relapse (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>, <xref rid=\"B19\" ref-type=\"bibr\">19</xref>). Studies are ongoing to identify subpopulations of B-ALL that predispose patients to CD19<sup>–</sup> relapse. Patients with KMT2A (mixed lineage leukemia, MLL) rearrangement have been shown to have increased risk of CD19<sup>–</sup> relapse in context of myeloid-transformation post-CAR (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>, <xref rid=\"B21\" ref-type=\"bibr\">21</xref>). To date, however, we lack extensive established predictors identifying patients at risk of relapse or at risk of specific relapse patterns. With CAR commercialization, specific relapse patterns are not captured in the form of clinical trial monitoring and tracking of CAR-mediated outcomes occurs at institutional discretion. With commercialization and extended CAR access comes a responsibility to continue to study specific relapse patterns in efforts to establish the true rate of CD19<sup>+</sup> and CD19<sup>–</sup> relapse in the pediatric CAR setting. Additionally, it is vital to study predictors of both CD19<sup>+</sup> and CD19<sup>–</sup> relapse so we can better differentiate patients likely to achieve durable responses and patients at high risk for post-CAR relapse in an <italic>a priori</italic> manner, permitting pre-emptive intervention to prevent relapse.</p></sec><sec id=\"S4.SS2\"><title>CAR T Cell Persistence and B Cell Aplasia</title><p>Duration of B cell aplasia, as a representation of ongoing CAR T cell persistence, has been associated with durability of CAR-mediated remission. Direct testing for CAR T cell persistence can be done using PCR or flow cytometry, however, these tests remain research tests at this time and the standard for clinical CAR T cell persistence monitoring remains indirect testing of B cell apalasia. In pediatric ALL, it has been demonstrated that a longer duration of CD19-CAR persistence correlates with the durability of remission (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>, <xref rid=\"B22\" ref-type=\"bibr\">22</xref>). CD28 ad 4-1BB costimulation demonstrate distinct properties <italic>in vivo</italic>, with 4-1BB mediating protection against T cell exhaustion and facilitating long-term persistence and immune memory (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>). Tisagenlecleucel, the CD19-specific CAR commercially approved for pediatric B-ALL, houses the 4-1BB costimulatory domain.</p></sec></sec><sec id=\"S5\"><title>Optimization Strategies to Enhance CAR T Cell Persistence</title><p>B cell and disease factors may impact CAR T cell persistence. In specific, low CD19 antigen load prior to lymphodepletion has been identified in one case series as a risk factor for early CAR T cell loss (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>). A phase II study investigating the role of upfront CAR T cells in pediatric patients with refractory disease as detected by flow cytometry minimal residual disease (MRD) following consolidation (NCT03876769) is ongoing and will predominantly treat patients with low disease burden and may yield insight into the expansion and persistence potential of CAR T cells in a low disease burden setting. A novel strategy to promote <italic>in vivo</italic> CAR expansion uses T cells engineered to express the CD19 antigen (T antigen-presenting cells, T-APCs) as booster cells post-initial CAR infusion. Analysis of pediatric CD19-CAR recipients with low CD19-antigen burden or rapid CAR T cell loss or contraction treated with T-APCs demonstrated early evidence of secondary CAR T cell expansion, supporting <italic>in vivo</italic> antigen delivery as a potential approach toward reinvigorating CAR expansion and enhancing CAR persistence (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>). Additionally, in efforts to augment CAR T cell persistence and reverse exhaustion, checkpoint inhibition is being clinically explored following CD19-CAR therapy, in event of incomplete-response or early CAR-T cell loss. Early data demonstrates safety of this strategy and preliminary promise, specifically in patients with early CAR-loss and bulky extramedullary disease (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>).</p><sec id=\"S5.SS1\"><title>Selecting for T Cell Subsets</title><p>T cell factors likely contribute to CAR T cell persistence. Patients treated with CAR T cell therapy are often multiply relapsed and have had significant prior exposure to leukemia therapies including cytotoxic chemotherapies and allogeneic hematopoietic stem cell transplantation. These factors likely account for significant variation in the composition and representation of naive, memory subsets and stem central memory T cells and contribute to heterogeneity of T cell fitness across patients. Pre-clinical data demonstrate that T cell subsets of early lineage, including naive and stem central memory T cells, confer improved expansion during <italic>ex vivo</italic> CAR manufacturing. Specific chemotherapy agents such as cyclophosphamide and cytarabine associate with depletion of early lineage T cells in pediatric leukemia patients, supporting the impact of prior therapy on CAR T cell subset distribution and expansion potential. Culture methods including select use of IL7 and IL15 cytokines to enrich expansion of early lineage T cells are being explored (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>). To date, with the exception of a single-institutional effort to evaluate CAR products of defined CD8 and CD4 T cell formulations (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>, <xref rid=\"B13\" ref-type=\"bibr\">13</xref>), the majority of clinical CAR-T cell products for B-ALL are made up of heterogeneous T cell subset distributions, reflecting the individual patient’s circulating T cell pool. Although pre-clinical data support use of naive and stem central memory T cell subsets, optimization of clinical CAR products enriched for select T cell lineage subsets to promote CAR persistence remains an area under study. It is vital to pursue measured study of T cell subset distribution of apheresis and CAR products across patients as they relate to CAR T cell persistence and CAR-mediated outcomes.</p></sec><sec id=\"S5.SS2\"><title>Optimizing CAR T Cell Engineering</title><p>There is active preclinical effort to identify methods to enhance CAR signaling while mitigating CAR T cell exhaustion. CAR T cell engineering for clinical trials is primarily achieved using viral-mediated transduction with random insertion. Targeted CAR insertion to the T-cell receptor α constant (TRAC) locus using CRISPR/Cas9 has been explored as a method to avert tonic signaling and defer effector-T cell differentiation and exhaustion (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>). Efforts to characterize properties driving T cell exhaustion using a tonically active CAR model identified deficiency of the AP-1 factor, c-Jun, as a driver of T cell-exhaustion. Engineering CAR-T cells to over-express c-Jun is an alternative approach to rendering CAR T cells resistant to exhaustion that has demonstrated pre-clinical promise (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>).</p><p>Although, as described, costimulatory domains such as CD28 and 4-1BB are known to have variable properties driving expansion and persistence, the CAR structure is modular, housing additional transmembrane domains and immunoreceptor tyrosine-based activation motifs (ITAMs) that can be engineered to titrate function. Manipulation of quantity and position of functional ITAMs demonstrates that CD1928ζ CARs expressing a single, proximal ITAM maintain cytotoxicity while protecting against T cell differentiation and exhaustion as compared to CD1928ζ CARs with additional or distal ITAM signaling (<xref rid=\"B29\" ref-type=\"bibr\">29</xref>). Precise modulation of a 4-1BB CAR to express a CD28-hinge/transmembrane domain and inclusion of additional ITAM domains has been shown to permit CAR activity in response to lower-antigen expression while preserving 4-1BB-mediated persistence (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>). Further understanding of T cell factors driving activation, persistence and exhaustion underpin our ability to engineer CAR constructs optimized for low-threshold activation and enhanced persistence.</p></sec></sec><sec id=\"S6\"><title>CAR-Mediated Toxicities</title><p>Early CAR translation identified CAR-mediated cytokine release (CRS) syndrome and immune effector cell-associated neurotoxicity syndrome (ICANs) to be the two most-common post-CAR toxicity syndromes, with CRS having increased frequency and generally preceding ICANs (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>, <xref rid=\"B31\" ref-type=\"bibr\">31</xref>–<xref rid=\"B33\" ref-type=\"bibr\">33</xref>). CRS is characterized by fever but can be associated with constitutional symptoms, vital sign instability including hypotension and hypoxia and in severe cases may warrant vasopressor-use, intubation, PICU level care and rarely result in fatalities. ICANs also spans severity with symptoms ranging from mild confusion, aphasia, impairment of cognitive or motor skills to seizures, loss of consciousness and in the rare case cerebral edema and death. Treatment for both these syndromes may be limited to supportive care or may include agents such as tocilizumab, an anti-IL-6 receptor antibody that abrogates IL-6 signaling and disrupts toxicity symptoms without compromise of efficacy, or steroids (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>, <xref rid=\"B34\" ref-type=\"bibr\">34</xref>). Initial concerns that disrupting CAR-mediated toxicity will in parallel disrupt efficacy have been dispelled and agents such as tocilizumab and steroids are currently used more liberally in response to toxicity. Additional agents are under investigation for treatment of CAR related toxicities including Siltuximab, a direct IL-6-targeting antibody, and Anakinra, an anti-IL-1 receptor antagonist that has been shown to have efficacy in CAR-mediated Hemophagocytic Lymphohistiocytosis (HLH), a post-CAR toxicity on the spectrum of severe CRS seen following CD19-CAR T cell therapy (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>) and recently reported to have greater frequency following CD22-CAR T cell therapy (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>). Increased disease burden has been shown to be a predictor of CAR-toxicity and studies are ongoing to establish further predictors of toxicity and understand toxicity as it relates to efficacy (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>). CAR-mediated toxicities such as CRS and ICANs generally occur concurrently with CAR T cell expansion and commonly manifest within the first 28 day window post-CAR therapy. Long-term neurocognitive effects in patients experiencing neurotoxicity have yet to be established and remains an area under study. Aplasia of physiologic B cells is an additional expected on-target, off-tumor side effect of CD19-CAR T cell therapy that is effectively managed with IVIG replacement. Durability of B cell aplasia is desirable, as it represents ongoing CAR persistence (<xref rid=\"B37\" ref-type=\"bibr\">37</xref>). In the pediatric registration trial of Tisagenlecleucel, all patients responding to CAR therapy developed B cell aplasia with a probability of ongoing B cell aplasia at 6 months post-CAR of 83% (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>). Related hypogammaglobulinemia has been shown to be higher in pediatric patients as compared to adult CD19-CAR recipients, likely due to decreased established antibody-producing-CD19<sup>–</sup>plasma cell clones in children (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>). Understanding specifics of adaptive T cell immune responses and reconstitution following combined lymphodepletion and CD19-CAR T cell therapy remains an area under study.</p></sec><sec id=\"S7\"><title>Role of Consolidative Hematopoietic Stem Cell Transplantation Following CAR Therapy</title><p>One additional outstanding question in the field is the role for consolidative allogeneic hematopoietic stem cell transplantation (HSCT) following CAR T cell therapy. In context of data demonstrating that despite achievement of early responses post-CAR, many patients will go on to relapse with CD19<sup>+</sup> or CD19<sup>–</sup> disease, HSCT has been used at many centers to consolidate CAR-mediated remissions (<xref rid=\"B39\" ref-type=\"bibr\">39</xref>). Pediatric data following use of short-lived CD19-CARs, harboring the CD28 costimulatory domain, demonstrate decreased relapse in patients consolidated with HSCT. Of 28 patients achieving minimal residual disease (MRD) negative responses by flow cytometry across pediatric CD19-CAR T cell trials at the National Cancer Institute (NCI), 2 of 21 patients consolidated with HSCT relapsed as compared to 6 relapses of 7 patients who did not undergo post-CAR HSCT (<xref rid=\"B40\" ref-type=\"bibr\">40</xref>). Long-term Phase I data using CD28-based CAR T cells in adults with ALL did not show a benefit in event-free-survival (EFS) or overall survival (OS) in patients receiving consolidative HSCT post-CAR (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>), however, Phase I/II data studying 4-1BB CD19-CAR T cells in adult ALL, showed prolonged EFS in patients undergoing HSCT post-CAR (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>). Data analyzing pediatric ALL patients receiving 4-1BB-harboring CD19-CAR T cells, support benefit in patients receiving consolidative HSCT post-CAR. Of 38 patients achieving remission following CD19-4-1BB CAR T cells, 3 of 13 consolidated with HSCT relapsed as compared to 20 relapses of 25 patients who did not undergo post-CAR HSCT (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>). Patients who are HSCT-naive prior to CAR or who experience early CAR-loss (within 63 days) post-CAR have been identified as cohorts who may specifically benefit from consolidative HSCT (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>, <xref rid=\"B42\" ref-type=\"bibr\">42</xref>).</p><p>Due to HSCT-related toxicity risks, many centers and patients/families will opt to undergo surveillance and monitor for minimal residual disease (MRD) and B cell aplasia and only proceed with HSCT in context of loss of CAR persistence or evidence of detectable MRD. In some patients, loss of B cell aplasia will may precede disease recurrence introducing a window to proceed with HSCT while the patient is in continued remission, however, some patients will relapse concurrently with CAR loss and may lose their therapeutic window for HSCT. Additionally, many patients enter CAR after prior-HSCT, and toxicity and survival outcomes of a second HSCT are significantly inferior to outcomes following initial HSCT. To date, the decision whether to pursue a consolidative HSCT has not yet been standardized and differs across institutions and depends upon individualized factors including status of prior HSCT, CAR persistence, comorbidities, donor availability and patient/family preferences. Prospective trials on the role of first or second HSCT post-CAR remissions are essential to understanding the role of HSCT in the CAR era.</p></sec><sec id=\"S8\"><title>Discussion</title><p>We have learned that CD19-specific CAR T cells are effective at achieving early remissions in relapsed/refractory pediatric and young adult B-ALL, but the rate of lasting relapse-free curative outcomes lags behind with longer follow up. A phase I intent-to-treat study of CD19-CAR T cells in pediatric and young adult patients demonstrated an event-free survival (EFS) of 50.8% at 12 months in a cohort of 45 patients. Similarly, the long-term follow up of the phase II global, multi-institutional study analyzing Tisagenlecleucel in pediatric and young adult patients demonstrated an EFS rate of 50% at 12 months in a sample size of 75 infused patients. With CAR commercialization in 2017, longitudinal data is only recently becoming available and it is imperative we understand when and if the overall and event-free survival curves flatten post-CAR T cell therapy. The promising early post-CAR responses have introduced high expectations on behalf of CD19-CAR T cell recipients. Establishing the true curative potential and limitations of CAR-T cell therapy is vital to managing clinical expectations of this therapy and identifying interventional windows to further enhance the durability of outcomes. To date, we do not have robust predictors of response durability and relapse. It is a priority to pursue further interrogation of such predictors so we can identify and differentiate patients expected to have lasting responses following CAR and patients who may achieve early remission but require further consolidative therapy to maintain responses. Additionally, extensive pre-clinical efforts are ongoing to further tailor CAR design to promote CAR-persistence and long-term immune memory.</p><p>Despite major advances with CAR clinical development and commercialization, the field remains in its infancy with many outstanding queries and areas for long-term outcome optimization. Ongoing effort is in order to narrow the gap between early response rates and long-term outcomes and further harness the power of this potent therapeutic to achieve reliable, durable CAR-mediated cures.</p></sec><sec id=\"S9\"><title>Author Contributions</title><p>LS wrote the manuscript in entirety and is accountable for the content of the work.</p></sec><sec id=\"conf1\"><title>Conflict of Interest</title><p>The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><ack><p>Although there are no direct contributions relating to authorship of the current work, I acknowledge the pediatric immunotherapy team at Stanford for ongoing interrogation of current clinical CAR-related queries pertinent to advancing the field.</p></ack><ref-list><title>References</title><ref id=\"B1\"><label>1.</label><mixed-citation publication-type=\"journal\"><person-group 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Psychol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Psychol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Psychol.</journal-id><journal-title-group><journal-title>Frontiers in Psychology</journal-title></journal-title-group><issn pub-type=\"epub\">1664-1078</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849072</article-id><article-id pub-id-type=\"pmc\">PMC7431961</article-id><article-id pub-id-type=\"doi\">10.3389/fpsyg.2020.01819</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Psychology</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>Validation of the Japanese Version of the Burnout Assessment Tool</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Sakakibara</surname><given-names>Keiko</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/835854/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Shimazu</surname><given-names>Akihito</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/332380/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Toyama</surname><given-names>Hiroyuki</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1039385/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Schaufeli</surname><given-names>Wilmar B.</given-names></name><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref><xref ref-type=\"aff\" rid=\"aff5\"><sup>5</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/702684/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Faculty of Sociology, Toyo University</institution>, <addr-line>Tokyo</addr-line>, <country>Japan</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Faculty of Policy Management, Keio University</institution>, <addr-line>Kanagawa</addr-line>, <country>Japan</country></aff><aff id=\"aff3\"><sup>3</sup><institution>Faculty of Educational Sciences, University of Helsinki</institution>, <addr-line>Helsinki</addr-line>, <country>Finland</country></aff><aff id=\"aff4\"><sup>4</sup><institution>Department of Social, Health and Organizational Psychology, Utrecht University</institution>, <addr-line>Utrecht</addr-line>, <country>Netherlands</country></aff><aff id=\"aff5\"><sup>5</sup><institution>Department of Work, Organizational and Personnel Psychology</institution>, <addr-line>KU Leuven, Leuven</addr-line>, <country>Belgium</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Renato Pisanti, University Niccolò Cusano, Italy</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Lorenzo Avanzi, University of Trento, Italy; João P. Marôco, University Institute of Psychological, Social and Life Sciences (ISPA), Portugal</p></fn><corresp id=\"c001\">*Correspondence: Keiko Sakakibara, <email>sakakibara@toyo.jp</email></corresp><fn fn-type=\"other\" id=\"fn004\"><p>This article was submitted to Organizational Psychology, a section of the journal Frontiers in Psychology</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>1819</elocation-id><history><date date-type=\"received\"><day>10</day><month>2</month><year>2020</year></date><date date-type=\"accepted\"><day>01</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Sakakibara, Shimazu, Toyama and Schaufeli.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Sakakibara, Shimazu, Toyama and Schaufeli</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>The current study aimed to validate the Japanese version of the Burnout Assessment Tool (BAT-J), a new burnout measure. We conducted an Internet survey to confirm the validity and reliability of the BAT-J, using registered monitors from a Japanese survey company. The first-wave survey was conducted in May 2018, with 1,032 monitors. Of these, 498 participated in the second-wave survey in June 2018 to confirm 1-month test–retest reliability. We examined the factorial validity of the BAT-J core symptoms (BAT-JC) and BAT-J secondary symptoms (BAT-JS), as well as their reliability (internal consistency and test–retest reliability) and construct validity. Factorial validity was examined using confirmatory factor analyses and exploratory structural equation modeling bifactor analyses. Convergent and discriminant validity were examined using multitrait–multimethod frameworks well as the average variance explained. Exploratory structural equation modeling bifactor solutions for the BAT-JC, BAT-JS, and BAT-J demonstrated the best fit to the data. They also indicated that the general factor accounted for over two-thirds of the common variance explained. Internal consistency and test–retest reliability were confirmed. Convergent and internal discriminant validity of the BAT-JC were confirmed vis-ȧ-vis burnout, as assessed with the Maslach Burnout Inventory – General Survey. Moreover, external discriminant validity of the BAT-J was demonstrated for work engagement and workaholism. Finally, both BAT scales showed significant positive relationships with job demands and turnover intention. All validity results were in line with the job demands–resources model. The results of the current study provide the first evidence for the BAT-J’s reliability and factorial and construct validity.</p></abstract><kwd-group><kwd>Burnout</kwd><kwd>assessment tool</kwd><kwd>job demands–resources model</kwd><kwd>validation</kwd><kwd>Japanese</kwd></kwd-group><counts><fig-count count=\"3\"/><table-count count=\"6\"/><equation-count count=\"0\"/><ref-count count=\"64\"/><page-count count=\"15\"/><word-count count=\"0\"/></counts></article-meta></front><body><sec id=\"S1\"><title>Introduction</title><p>Burnout has become a matter of global concern for working people. It has been defined as “a state of exhaustion in which one is cynical about the value of one’s occupation and doubtful of one’s capacity to perform” (<xref rid=\"B30\" ref-type=\"bibr\">Maslach and Jackson, 1986</xref>, p. 20). Originally, burnout was exclusively identified with human services professionals; more recently, it has also been recognized in other occupations (<xref rid=\"B46\" ref-type=\"bibr\">Schaufeli et al., 2009c</xref>). In fact, the <xref rid=\"B64\" ref-type=\"bibr\">World Health Organization (2019)</xref> includes burnout in the recent 11th revision of the International Classification of Diseases as a global occupational phenomenon that influences health (<xref rid=\"B64\" ref-type=\"bibr\">World Health Organization, 2019</xref>); it does not, however, classify burnout as a medical condition but raises awareness of the importance of early assessment and appropriate treatment for burnout.</p><p>Research has confirmed that burnout predicts physical and psychological consequences, including cardiovascular diseases (<xref rid=\"B60\" ref-type=\"bibr\">Toppinen-Tanner et al., 2009</xref>; <xref rid=\"B59\" ref-type=\"bibr\">Toker et al., 2012</xref>), type 2 diabetes, musculoskeletal disorders (<xref rid=\"B34\" ref-type=\"bibr\">Melamed, 2009</xref>; <xref rid=\"B2\" ref-type=\"bibr\">Armon et al., 2010</xref>), depressive symptoms (<xref rid=\"B1\" ref-type=\"bibr\">Ahola and Hakanen, 2007</xref>; <xref rid=\"B3\" ref-type=\"bibr\">Armon et al., 2014</xref>; <xref rid=\"B6\" ref-type=\"bibr\">Bianchi et al., 2015</xref>), and insomnia (<xref rid=\"B4\" ref-type=\"bibr\">Armon et al., 2008</xref>). Furthermore, burnout predicts occupational consequences such as job dissatisfaction (<xref rid=\"B14\" ref-type=\"bibr\">Figueiredo-Ferraz et al., 2012</xref>; <xref rid=\"B27\" ref-type=\"bibr\">Lizano and Mor Barak, 2015</xref>), sickness-related absence (<xref rid=\"B7\" ref-type=\"bibr\">Borritz et al., 2006</xref>; <xref rid=\"B44\" ref-type=\"bibr\">Schaufeli et al., 2009a</xref>; <xref rid=\"B18\" ref-type=\"bibr\">Hallsten et al., 2011</xref>), and turnover intention (<xref rid=\"B16\" ref-type=\"bibr\">Geurts et al., 1998</xref>; <xref rid=\"B40\" ref-type=\"bibr\">Schaufeli and Bakker, 2004</xref>; <xref rid=\"B26\" ref-type=\"bibr\">Lin et al., 2013</xref>).</p><p>Most studies have used the Maslach Burnout Inventory (MBI; <xref rid=\"B29\" ref-type=\"bibr\">Maslach and Jackson, 1981</xref>), originally developed for human service workers, to assess burnout. Later, as the definition was expanded to include all occupations, a general version of the MBI – the MBI-General Survey (MBI-GS; <xref rid=\"B48\" ref-type=\"bibr\">Schaufeli et al., 1996</xref>) – was developed, consisting of three dimensions: exhaustion, cynicism, and professional efficacy.</p><p>Despite its popularity, researchers have pointed out various flaws of the MBI, related to conceptualization, psychometric shortcomings, and practical applicability. First, regarding conceptualization, the MBI does not include reduced cognitive functioning such as impaired attention, concentration, and working memory, which has been reported in recent studies (for an overview, see <xref rid=\"B11\" ref-type=\"bibr\">Deligkaris et al., 2014</xref>).</p><p>Second, the MBI suffers from psychometric shortcomings. <xref rid=\"B62\" ref-type=\"bibr\">Wheeler et al. (2011)</xref> conducted a meta-analysis of reliability coefficients for the subscales of the MBI and concluded: “personal accomplishment and depersonalization mean alpha estimates were well below recommended levels for high-stakes decisions, such as the diagnosis of burnout syndrome” (<xref rid=\"B62\" ref-type=\"bibr\">Wheeler et al., 2011</xref>, p. 213). Also, a study by <xref rid=\"B10\" ref-type=\"bibr\">De Beer and Bianchi (2019)</xref> reported results of confirmatory factor analysis (CFA) of the MBI, showing that a two-factor model composed of combined exhaustion and depersonalization/cynicism factor and a personal accomplishment factor showed the best fit to the data. Thus, the role of personal accomplishment/professional efficacy in burnout is debated. This is in line with the observation that, in many cases, only the exhaustion and/or depersonalization/cynicism subscales are used to assess burnout (<xref rid=\"B51\" ref-type=\"bibr\">Schaufeli and Taris, 2005</xref>).</p><p>Third, although burnout is recognized as an occupational disease in some European countries (<xref rid=\"B25\" ref-type=\"bibr\">Lastovkova et al., 2017</xref>), and there is a great need for a burnout measure that can be used in practice, the practical use of the MBI is rather poor. A key issue is that the MBI does not produce a single burnout score that can be dichotomized for screening employees who are or are not at risk for burnout. Tellingly, the MBI test manual states: “In general, each respondent’s scale scores should be calculated and interpreted separately. Note that responses to MBI items should not be combined to form a single ‘burnout score”’ (<xref rid=\"B31\" ref-type=\"bibr\">Maslach et al., 2017</xref>, p. 44).</p><p>In addition to the MBI, there are other burnout measures, such as the Copenhagen Burnout Inventory (<xref rid=\"B24\" ref-type=\"bibr\">Kristensen et al., 2005</xref>), Oldenburg Burnout Inventory (<xref rid=\"B12\" ref-type=\"bibr\">Demerouti et al., 2003</xref>), and the Shirom–Melamed Burnout Measure (<xref rid=\"B56\" ref-type=\"bibr\">Shirom and Melamed, 2006</xref>). However, these measures have weaknesses as well. For example, the Copenhagen Burnout Inventory assesses only exhaustion. Likewise, the Shirom–Melamed Burnout Measure consists of physical fatigue, emotional exhaustion, and cognitive weariness. Hence, these measures do not include withdrawal from work (cynicism/mental distance), which is a main feature of burnout together with exhaustion (<xref rid=\"B32\" ref-type=\"bibr\">Maslach et al., 2001</xref>; <xref rid=\"B51\" ref-type=\"bibr\">Schaufeli and Taris, 2005</xref>). Finally, the Oldenburg Burnout Inventory has two dimensions, including fatigue and disengagement (equivalent to cynicism). However, it does not include cognitive impairment, which has been reported as one of the characteristics of burnout (<xref rid=\"B11\" ref-type=\"bibr\">Deligkaris et al., 2014</xref>).</p><p>To overcome the flaws of the MBI and other burnout measures, <xref rid=\"B43\" ref-type=\"bibr\">Schaufeli et al. (2019)</xref> developed a more comprehensive conceptualization of burnout and introduced a new instrument for assessing it, the Burnout Assessment Tool (BAT). The BAT is based on a combination of deductive and inductive approaches. The deductive approach comprises a theoretical description of burnout as a primary work-related syndrome of exhaustion and mental distancing (<xref rid=\"B51\" ref-type=\"bibr\">Schaufeli and Taris, 2005</xref>). Moreover, 13 burnout questionnaires were analyzed to examine which dimensions (and items) they included. Content analyses revealed that all 13 questionnaires contained an exhaustion dimension; three only included exhaustion, two included exhaustion and secondary symptoms, one included only secondary symptoms, and the remaining seven were multidimensional and included a mental distance dimension. Hence, the analyses revealed that exhaustion and mental distance were common core components of all multidimensional burnout measures.</p><p>The inductive approach included in-depth, face-to-face, semi-structured interviews. The interviews aimed to reconceptualize burnout as it appears in today’s working environments, which has changed since the introduction of the MBI (<xref rid=\"B29\" ref-type=\"bibr\">Maslach and Jackson, 1981</xref>) 40 years ago. Interviews were conducted with 49 Flemish and Dutch professionals who handle individuals with burnout on a daily basis. To identify typical symptoms of burnout, interviewees were asked to describe a typical burnout case, the specific symptoms and causes of burnout, and their own definition of burnout. Next, interviewees ranked the symptoms they mentioned following the importance of assessing burnout; the professionals identified 260 symptoms. These qualitative data were categorized into seven dimensions after two rounds of content analysis: exhaustion, mental distance, emotional impairment, cognitive impairment, depressed mood, psychological distress, and psychosomatic complaints. The seven dimensions were clustered into core dimensions and secondary dimensions based on the theorizing of <xref rid=\"B51\" ref-type=\"bibr\">Schaufeli and Taris (2005)</xref> and the interview results. The core dimensions were exhaustion, mental distance, emotional impairment, and cognitive impairment. Exhaustion was the most obvious symptom that was mentioned by all interviewees, but it is not a sufficient condition for burnout. Interviewees also pointed to mental distance, emotional impairment, and cognitive impairment, which appear along with exhaustion in those who suffer from burnout. Additionally, three secondary dimensions were identified: depressed mood, psychological distress, and psychosomatic complaints. Because these symptoms are atypical and also appear with other disorders such as mood disorder, anxiety disorder, and cancer, they were considered secondary.</p><p>From this work, burnout was reconceptualized as “a work-related state of exhaustion that occurs among employees, which is characterized by extreme tiredness, reduced ability to regulate cognitive and emotional processes, and mental distancing. These four core dimensions of burnout are accompanied by depressed mood as well as by non-specific psychological and psychosomatic complaints” (<xref rid=\"B43\" ref-type=\"bibr\">Schaufeli et al., 2019</xref>, p. 29).</p><p>Along with the new definition, a new instrument for assessing burnout, Burnout Assessment Tool (BAT) was developed. The BAT assesses four core symptoms, referred to as BAT-C (exhaustion, mental distance, emotional impairment, and cognitive impairment), and two secondary symptoms, referred to as BAT-S (psychological distress and psychosomatic complaints). The remaining secondary dimension, depressed mood, was not included in the new burnout instrument because other well-validated depression questionnaires, such as the depression subscale of the 4-Dimensional Symptom Questionnaire (<xref rid=\"B58\" ref-type=\"bibr\">Terluin et al., 2006</xref>), are available.<sup><xref ref-type=\"fn\" rid=\"footnote1\">1</xref></sup></p><p>Although burnout is not recognized as a formal diagnosis in Japan, it is still crucial to identify employees with burnout and provide appropriate prevention and treatment because burnout has adverse effects on both employees’ health and organizational effectiveness. Because there is no established procedure to assess burnout in Japan (<xref rid=\"B22\" ref-type=\"bibr\">Kitaoka et al., 2011</xref>), it is of vital importance to validate an instrument that can be used as a screening tool for burnout in occupational health settings. In this regard, the validation of the Japanese version of BAT (BAT-J) is a necessary first step. Therefore, the current study aimed to validate the BAT-J (consisting of BAT-JC for core symptoms and BAT-JS for secondary symptoms).</p><p>We analyzed the BAT-J in three steps: first, factorial validity was assessed using CFA and exploratory structural equation modeling (ESEM) bifactor analysis; second, the reliability was assessed using internal consistency and test–retest reliability; and third, construct validity was assessed by evaluating convergent and discriminant validity. For the convergent and internal discriminant validity, we compared the BAT-J with the MBI-GS, using a multitrait–multimethod (MTMM) model (<xref rid=\"B9\" ref-type=\"bibr\">Campbell and Fiske, 1959</xref>). For external discriminant validity, we compared the average variance explained (AVE; <xref rid=\"B15\" ref-type=\"bibr\">Fornell and Lacker, 1981</xref>) of the BAT-JC and BAT-JS with work engagement and workaholism, where work engagement was defined as a positive, fulfilling, work-related state of mind characterized by vigor, dedication, and absorption (<xref rid=\"B50\" ref-type=\"bibr\">Schaufeli et al., 2002</xref>), and workaholism was defined as the uncontrollable inner need to work extremely hard (<xref rid=\"B45\" ref-type=\"bibr\">Schaufeli et al., 2009b</xref>). Workaholism includes both behavioral (excessive working) and cognitive (compulsive working) dimensions. Previous studies confirmed that burnout and work engagement are negatively related, whereas burnout and workaholism are positively related (<xref rid=\"B41\" ref-type=\"bibr\">Schaufeli and Bakker, 2010</xref>). Further, the concepts can be discriminated from each other (<xref rid=\"B52\" ref-type=\"bibr\">Schaufeli et al., 2008</xref>). Also, we assessed the construct validity of the BAT-J by adopting the conceptual framework of the job demands–resources (JD-R) model (<xref rid=\"B13\" ref-type=\"bibr\">Demerouti et al., 2001</xref>). The core idea of the JD-R model is that high job demands produce high levels of stress and subsequent health impairment, whereas high job resources lead to high levels of motivation and subsequent superior job performance. Specifically, we examined the association of the BAT-J with potential antecedents (i.e., job demands) and potential consequences (i.e., performance). Previous studies confirmed that job demands are consistently found to be antecedents of burnout (<xref rid=\"B49\" ref-type=\"bibr\">Schaufeli and Salanova, 2014</xref>), and burnout predicts organizational outcomes (<xref rid=\"B16\" ref-type=\"bibr\">Geurts et al., 1998</xref>; <xref rid=\"B40\" ref-type=\"bibr\">Schaufeli and Bakker, 2004</xref>).</p></sec><sec sec-type=\"materials|methods\" id=\"S2\"><title>Materials and Methods</title><sec id=\"S2.SS1\"><title>Translation</title><p>First, the English version of the BAT was translated into Japanese by the current study authors (KS and AS). Next, a bilingual (Japanese and English) psychologist, who had not read the original items, conducted back-translation into English. We compared the original English and the back-translated versions (WS) and harmonized them. Further, we conducted cognitive interviews with corporate employees and finalized the preliminary Japanese version after some corrections for words, meanings, and item content by the authors (KS, AS, and HT).</p></sec><sec id=\"S2.SS2\"><title>Participants</title><p>The current study was based on two waves of surveys, using the registered monitors of a survey company. The first survey was conducted in May 2018, and 22,249 employed monitors were invited to participate. Participants were equally allocated by sex and generation. Because of budgetary constraints, recruitment stopped after the number of participants exceeded 1,420. Data from 982 respondents who met the inclusion criteria (full-time employment and under 64 years old) were used in the analyses. The second-wave survey was conducted in June 2018 to confirm test–retest reliability. Again, because of budget constraints, 498 of the original respondents were invited to participate. Of these, 485 completed the questionnaire, yielding a response rate of 97.4% for the second survey. <xref rid=\"T1\" ref-type=\"table\">Table 1</xref> shows the respondents’ characteristics: mean age was 39.8 years (<italic>SD</italic> = 11.3); 51.0% were male; 50.5% were married or cohabiting; 50.3% had a university degree; 83.2% were white-collar workers, and 11.1% were shift workers; the mean working time per week was 40.3 h (<italic>SD</italic> = 18.7).</p><table-wrap id=\"T1\" position=\"float\"><label>TABLE 1</label><caption><p>Demographic characteristics of the study participants (<italic>N</italic> = 982).</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>n (%)</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Mean (<italic>SD</italic>)</bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Age (year)</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">39.8 (11.3)</td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"3\" rowspan=\"1\"><bold>Gender</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Men</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">501 (51.0)</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Women</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">481 (49.0)</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" colspan=\"3\" rowspan=\"1\"><bold>Marriage</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Yes (including co-habitant)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">496 (50.5)</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">No</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">486 (49.5)</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" colspan=\"3\" rowspan=\"1\"><bold>Education</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Collage or lower</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">488 (49.7)</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">University or higher</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">494 (50.3)</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" colspan=\"3\" rowspan=\"1\"><bold>Occupation</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">White collar</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">817 (83.2)</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Blue collar</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">165 (16.8)</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" colspan=\"3\" rowspan=\"1\"><bold>Shift work</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">No</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">873 (88.9)</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Yes</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">109 (11.1)</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Working hours/week</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">40.3 (18.7)</td></tr></tbody></table></table-wrap><sec id=\"S2.SS2.SSS1\"><title>Ethical Considerations</title><p>The Ethics Review Board of Toyo University approved the procedures before starting the study. Participants had the option of not responding to any part of the questionnaire at any time and to discontinue the survey at any point. Participants’ consent was confirmed based on their completion of the questionnaire.</p></sec></sec><sec id=\"S2.SS3\"><title>Measures</title><sec id=\"S2.SS3.SSS1\"><title>Burnout</title><p>Burnout was assessed with a preliminary version of BAT-J and the Japanese version of MBI-GS (<xref rid=\"B30\" ref-type=\"bibr\">Maslach and Jackson, 1986</xref>; <xref rid=\"B23\" ref-type=\"bibr\">Kitaoka-Higashiguchi et al., 2004</xref>). The BAT-J consists of two components: BAT-JC and BAT-JS. The BAT-JC includes 23 items, measuring four core symptoms of burnout: exhaustion (eight items; e.g., <italic>“</italic>At work, I feel mentally exhausted,” α = 0.93), mental distance (five items; e.g., “I struggle to find any enthusiasm for my work,” α = 0.86), emotional impairment (five items; e.g., “At work, I feel unable to control my emotions,” α = 0.91), and cognitive impairment (five items; e.g., “At work, I have trouble staying focused,” α = 0.93). The BAT-JS includes 10 items measuring secondary symptoms: psychological distress (five items; e.g., “I have trouble falling or staying asleep,” α = 0.89) and psychosomatic complaints (five items; e.g., “I suffer from palpitations or chest pain,” α = 0.87). All items were scored on a five-point Likert scale ranging from 1 (never) to 5 (always). Responses were summed and averaged for each subscale. The MBI-GS was used to confirm the BAT-J’s convergent and discriminant validity. The MBI-GS subscales include exhaustion (five items; e.g., “I feel tired when I get up in the morning and have to face another day on the job,” α = 0.94), cynicism (five items; e.g., “I have become more cynical about whether my work contributes anything,” α = 0.78), and professional efficacy (six reverse-scored items; e.g., “I feel I am making an effective contribution to what this organization does,” α = 0.66). All items were scored on a seven-point Likert scale ranging from 0 (never) to 6 (every day). Responses were summed and averaged for each subscale.</p></sec><sec id=\"S2.SS3.SSS2\"><title>Work-Related Well-Being</title><p>Work engagement was assessed with the short form of the Utrecht Work Engagement Scale (<xref rid=\"B50\" ref-type=\"bibr\">Schaufeli et al., 2002</xref>), which has been validated in Japan (<xref rid=\"B54\" ref-type=\"bibr\">Shimazu et al., 2008</xref>). The scale includes three subscales: vigor (three items; e.g., “At my job, I feel strong and vigorous,” α = 0.90), dedication (three items; e.g., “I am enthusiastic about my job,” α = 0.88), and absorption (three items; e.g., “I am immersed in my work,” α = 0.90). All items were scored on a seven-point Likert scale ranging from 0 (never) to 6 (always). Responses were summed and averaged for each subscale, as recommended by <xref rid=\"B42\" ref-type=\"bibr\">Schaufeli et al. (2006)</xref>.</p><p>Workaholism was assessed with the Dutch Work Addiction Scale (<xref rid=\"B47\" ref-type=\"bibr\">Schaufeli et al., 2009d</xref>), which includes two subscales: working excessively (five items; e.g., “I seem to be in a hurry and racing against the clock,” α = 0.81) and working compulsively (five items; e.g., “I feel obliged to work hard, even when it’s not enjoyable,” α = 0.79). All items were scored on a four-point Likert scale from 1 (almost never) to 4 (almost always). Responses were summed and averaged for each subscale.</p></sec><sec id=\"S2.SS3.SSS3\"><title>Potential Antecedents</title><p>Quantitative and qualitative job demands were assessed using subscales of the Brief Job Stress Questionnaire (<xref rid=\"B55\" ref-type=\"bibr\">Shimomitsu et al., 2000</xref>), whereas emotional demands were assessed using a subscale of the new version of the Brief Job Stress Questionnaire (<xref rid=\"B21\" ref-type=\"bibr\">Inoue et al., 2014</xref>). Sample items of each subscale include quantitative job demands (three items; e.g., “I have an extremely large amount of work to do,” α = 0.80); qualitative job demands (three items; e.g., “My job is difficult in that it requires a high level of knowledge and technical skill,” α = 0.74); and emotional demands (three items; e.g., “My job puts emotional burden on me,” α = 0.87). All items were scored on a four-point Likert scale from 1 (disagree) to 4 (agree). Responses were summed and averaged for each subscale.</p></sec><sec id=\"S2.SS3.SSS4\"><title>Potential Consequences</title><p>We assessed turnover intention as a potential consequence and used three items developed by <xref rid=\"B16\" ref-type=\"bibr\">Geurts et al. (1998)</xref>, translated into Japanese, and validated (<xref rid=\"B61\" ref-type=\"bibr\">Tsuno et al., 2018</xref>). Originally, this scale consisted of four items – three items were negatively worded, and one was positively worded and reverse-scored. When four items were used, Cronbach’s α was low (0.46). Therefore, we excluded the positively worded item and used the remaining three items, and the Cronbach’s α increased to 0.86. Participants were asked to rate the extent to which they felt like leaving their organization over the last month (e.g., “I consider my decision to work for this employer as an obvious mistake,” α = 0.86). Items were scored on a five-point Likert scale ranging from 1 (completely agree) to 5 (completely disagree). Responses for the three items were summed and averaged.</p></sec></sec><sec id=\"S2.SS4\"><title>Data Analyses</title><sec id=\"S2.SS4.SSS1\"><title>Factorial Validity</title><p>We assessed the factorial validity of the BAT-JC, BAT-JS, and BAT-J using CFA and ESEM bifactor analysis using Mplus 8.0, based on the robust maximum likelihood estimator. We compared four models. First, a correlated CFA model was tested to examine the correlations among the latent factors. Second, a second-order CFA model was tested. This model was based on the assumption that burnout is a syndrome comprising a set of related symptoms referring to one underlying psychological condition (i.e., burnout). Another reason for examining a second-order model was that we needed to confirm whether the BAT could produce a single score. Third, a CFA bifactor model was tested. Second-order and bifactor models are similar because both examine the presence of global and specific factors corresponding to multiple items. In second-order CFA, each item is assumed to load on its particular subscale (a first-order factor), and each first-order factor is assumed to load on a second-order factor (<xref rid=\"B37\" ref-type=\"bibr\">Rindskopf and Tedd, 1988</xref>). On the other hand, a bifactor model directly tests whether a global construct exists as a common dimension of all items and multiple more specific facets, defined by the items belonging to the facets, coexist as remaining parts that are not explained by the global factor (<xref rid=\"B35\" ref-type=\"bibr\">Morin et al., 2016</xref>). Finally, the ESEM bifactor model analysis was conducted based on oblique target rotation (<xref rid=\"B5\" ref-type=\"bibr\">Asparouhov and Muthén, 2009</xref>). In the multidimensional scale, factors are usually related to each other, and it is assumed that items belonging to each factor have some association with other factors as well. However, in CFA, each item is forced to load on one target factor, and that causes inflation of the estimated factor correlations (<xref rid=\"B35\" ref-type=\"bibr\">Morin et al., 2016</xref>). ESEM provides a solution for this problem by allowing the cross-loading of items on non-target factors (<xref rid=\"B28\" ref-type=\"bibr\">Marsh et al., 2014</xref>).</p><p>For the BAT-JC, model C1 was a correlated four-factor CFA model where four different components (exhaustion, mental distance, emotional impairment, and cognitive impairment) were correlated. Model C2 was a second-order CFA model assuming that burnout is a syndrome comprising the four core dimensions mentioned earlier. Model C3 was a CFA bifactor model where each item was related to the expected specific core dimension and the global factor (burnout). Model C4 was an ESEM bifactor model where all items of BAT-JC were allowed to load on a general factor (burnout), and each item was simultaneously allowed to load on the specific target factor, as well as non-target factors.</p><p>For the BAT-JS, model S1 was a correlated two-factor CFA model where two different components (psychological distress and psychosomatic complaints) are correlated. Model S2 was a second-order CFA model assuming that secondary burnout symptoms comprise two dimensions. Model S3 was a CFA bifactor model where each item was related to the expected specific factor and a global factor (secondary burnout symptom). Model S4 was an ESEM bifactor model where all items of the BAT-JS were allowed to load on a general factor (secondary burnout symptoms), and each item was allowed to load on a specific target factor (psychological and psychosomatic symptoms) as well as a non-target factor.</p><p>The BAT-J, model J1 was a correlated six-factor CFA model where all six factors of the BAT-JC and the BAT-JS (exhaustion, mental distance, emotional impairment, cognitive impairment, psychological distress, and psychosomatic complaints) were correlated. Model J2 was a second-order CFA model where six components were first-order factors, and burnout was the higher-order factor. Model J3 was a CFA bifactor model where each item was related to the expected specific target factor and a global factor (burnout). Finally, Model J4 was an ESEM bifactor model where all items of the BAT were allowed to load on a general factor (burnout), and each item was allowed to load on a specific factor with cross-loadings on non-target factors.</p><p>The mean item loadings on the general factor, specific factors, and the explained common variance (ECV), an index of the proportion of common variance extracted explained by the general factor (<xref rid=\"B38\" ref-type=\"bibr\">Rodriguez et al., 2016</xref>), were calculated. Higher ECV values showed a strong general factor, suggesting the measurement was unidimensional, even if multiple factors were involved (<xref rid=\"B36\" ref-type=\"bibr\">Reise, 2012</xref>). Item level ECV (I-ECV) and specific factor level ECV (S-ECV) were also calculated. Following <xref rid=\"B20\" ref-type=\"bibr\">Hu and Bentler (1995)</xref>, model fit was assessed using a combination of fit indices: chi-square (χ<sup>2</sup>), Tucker–Lewis index (TLI), comparative fit index (CFI), and root mean square error of approximation (RMSEA). The model fit was evaluated using the following criteria: both TLI and CFI exceeded at least 0.90 but preferably 0.95 (<xref rid=\"B20\" ref-type=\"bibr\">Hu and Bentler, 1995</xref>), and RMSEA was < 0.08 (<xref rid=\"B8\" ref-type=\"bibr\">Byrne, 2016</xref>, p. 98). We also used several information criteria, including Akaike Information Criterion (AIC), the constant AIC, the Bayesian Information Criterion (BIC), and the sample-size adjusted BIC to compare the alternative models, with lower values being a better fit.</p></sec><sec id=\"S2.SS4.SSS2\"><title>Reliability</title><p>We evaluated the scale’s reliability by assessing the internal consistency, based on the score of Cronbach’s α of each subscale and the composite BAT-JC and BAT-JS scales. We also assessed the test–retest reliability of the BAT-J with the stability coefficients of the scores between the first and second surveys.</p></sec><sec id=\"S2.SS4.SSS3\"><title>Construct Validity</title><p>We assessed construct validity in terms of convergent and internal discriminant validity using the MTMM model (<xref rid=\"B9\" ref-type=\"bibr\">Campbell and Fiske, 1959</xref>). MTMM is an approach for examining convergent and discriminant validity by confirming how a measure relates to other measures. <xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref> shows a graphical presentation of the MTMM model, including two-method factors (measures: the BAT-JC and the MBI-GS) and five-trait factors (constructs: exhaustion, cynicism/mental distance, professional efficacy, emotional impairment, and cognitive impairment). Because the MBI-GS does not measure secondary symptoms of burnout, we focused on the BAT-JC in this analysis. We followed the guidelines by <xref rid=\"B63\" ref-type=\"bibr\">Widaman (1985)</xref> and compared four models.</p><fig id=\"F1\" position=\"float\"><label>FIGURE 1</label><caption><p>Graphical presentation of the MTMM model, including two method factors (MBI-GS and BAT-JC) and five trait factors (exhaustion, cynicism/mental distance, professional efficacy, emotional impairment, cognitive impairment). MBI-EX1, item 1 of the exhaustion subscale of the MBI-GS; MBI-CY1, item 1 of the cynicism subscale of the MBI-GS; MBI-PE1, item 1 of the professional efficacy subscale of the MBI-GS; BAT-EX1, item 1 of the exhaustion subscale of the BAT-JC; BAT-MD1, item 1 of the mental distance subscale of the BAT-JC; BAT-EC1, item 1 of the emotional impairment subscale of the BAT-JC; BAT-CC1, item 1 of the cognitive impairment subscale of the BAT-JC.</p></caption><graphic xlink:href=\"fpsyg-11-01819-g001\"/></fig><p>Model 1, the correlated traits–correlated methods (CT-CM) model, was based on the assumption that the structure of the data is determined by both trait factors (constructs) and method factors (measures). The CT-CM model served as the baseline against which all other MTMM models were compared. It was the least restrictive model in which all trait factors were free to correlate with each other, and both method factors (the BAT-JC and the MBI-GS) were free to correlate with each other. Trait and method factors were not allowed to correlate with each other.</p><p>Model 2, the no traits-correlated methods (NT-CM) model, was based on the assumption that there is no correlation between trait factors, and the structure of the data could only be described by methods (the BAT-JC and the MBI-GS). To evaluate convergent validity, we compared models 1 and 2. If model 1 fits the data better than model 2, it would suggest that the BAT-JC and the MBI-GS are independent of each other, but some traits (constructs) are correlated.</p><p>Model 3, the perfectly correlated traits-correlated methods (PCT-CM) model, assumed that the traits (constructs) correlated perfectly (fixed to 1), and the methods (measures) were freely correlated.</p><p>Model 4, the correlated traits-perfectly correlated methods (CT-PCM) model, assumed that the trait-factors (constructs) were freely correlated, and the methods (measures) correlated perfectly (fixed to 1). To test the discriminant validity, we compared model 1 with 3 in terms of traits (constructs) and model 1 with 4 in terms of method (measures). If the BAT-JC was distinct from the MBI-GS, traits and methods would be correlated but not perfectly, and if so, model 1 was assumed to have a better fit than models 3 and 4.</p><p>We assessed the external discriminant validity of the BAT concerning work engagement and workaholism using Average Variance Explained (AVE). In the current study, the AVE of the BAT-JC and BAT-JS should be greater than their squared correlations (<italic>R</italic><sup>2</sup>) with work engagement and workaholism. To test this assumption, a general CFA model was evaluated in which the BAT-JC, BAT-JS, work engagement, and workaholism were correlated with each other.</p><p>In addition, based on the JD-R model, we assessed the construct validity of the BAT in relation to possible antecedents (quantitative and qualitative job demands and emotional demands) and consequences (turnover intention), using structural equation modeling techniques. Goodness of fit, χ<sup>2</sup>, TLI, CFI, and RMSEA were used to evaluate the models. The level of significance was 0.05 (two-tailed).</p><p>We used Mplus 8.0 for the CFA and ESEM bifactor analyses. We used IBM SPSS Statistics for Windows, Version 25, and Amos 24 software to analyze MTMM, AVE, and the relations of the BAT with potential antecedents and consequences.</p></sec></sec></sec><sec id=\"S3\"><title>Results</title><sec id=\"S3.SS1\"><title>Factorial Validity</title><p><xref rid=\"T2\" ref-type=\"table\">Table 2</xref> shows the goodness-of-fit indices and information criteria of each model. For the BAT-JC, model C1, the correlated four-factor CFA, demonstrated good fit (CFI = 0.93, TLI = 0.92, RMSEA = 0.06). Correlations among the latent factors ranged from 0.65 to 0.83. Model C2, the second-order CFA, and model C3, the bifactor CFA, also demonstrated good fit (model C2; CFI = 0.93, TLI = 0.92, RMSEA = 0.06, model C3; CFI = 0.95, TLI = 0.93, RMSEA = 0.06). Model C4, the bifactor ESEM, showed best fit to the data (CFI = 0.98, TLI = 0.96, RMSEA = 0.04) and was a better presentation than the other three models based on lower scores on the information criteria.</p><table-wrap id=\"T2\" position=\"float\"><label>TABLE 2</label><caption><p>Model fit indices and information criteria for BAT-JC, BAT-JS, and BAT-J (<italic>N</italic> = 982).</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Model</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>χ<sup>2</sup></bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>p</italic></bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>df</italic></bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>RMSEA</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>(90%CI)</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>CFI</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>TLI</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>AIC</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>BIC</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>CAIC</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>ABIC</bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" colspan=\"12\" rowspan=\"1\"><bold>BAT-JC</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Correlated four-factor CFA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1054.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">224</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">[0.06 0.07]</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.93</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.92</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">50,966</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">51,332</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">51,115</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">51,094</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Second-order CFA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1076.94</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">226</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">[0.06 0.07]</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.93</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.92</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">51,000</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">51,357</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">51,146</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">51,125</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bifactor CFA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">831.73</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">208</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">[0.05 0.06]</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.95</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.93</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">50,626</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">51,071</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">50,807</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">50,782</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bifactor ESEM</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">428.08</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">148</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">[0.04 0.05]</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.98</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.96</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">50,130</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">50,869</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">50,431</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">50,389</td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"12\" rowspan=\"1\"><bold>BAT-JS</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Correlated two-factor CFA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">268.83</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">34</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.08</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">[0.08 0.09]</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.96</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.95</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25,249</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25,401</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25,311</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25,303</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Second-order CFA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">268.83</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">34</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.08</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">[0.08 0.09]</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.96</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.95</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25,249</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25,401</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25,311</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25,303</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bifactor CFA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">83.95</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">[0.04 0.06]</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.98</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.97</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25,127</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25,323</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25,207</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25,196</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bifactor ESEM</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">28.69</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.052</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">[0.00 0.04]</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.99</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25,055</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25,284</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25,148</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25,135</td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"12\" rowspan=\"1\"><bold>BAT-J</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Correlated six-factor CFA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1774.08</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">480</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">[0.05 0.06]</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.92</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.92</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">75,166</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">75,723</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">75,393</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">75,361</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Second-order CFA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1963.17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">489</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">[0.05 0.06]</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.91</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.91</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">75,433</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">75,946</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">75,642</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">75,613</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bifactor CFA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1683.85</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">462</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">[0.05 0.06]</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.93</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.92</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">75,040</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">75,685</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">75,303</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">75,266</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bifactor ESEM</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">706.05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.000</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">318</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">[0.03 0.04]</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.98</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.96</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">73,863</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">75,212</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">74,412</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">74,336</td></tr></tbody></table><table-wrap-foot><attrib><italic>TLI, Tucker–Lewis index; CFI, comparative fit index; RMSEA, root mean square error of approximation; χ<sup>2</sup>, <italic>chi-square; df, degree of freedom; p, p-value; AIC, Akaike information criterion; BIC, Bayesian information criterion; CAIC, constant AIC; ABIC, sample-size-adjusted BIC.</italic></italic></attrib></table-wrap-foot></table-wrap><p>For the BAT-JS, model S1, the correlated two-factor CFA, and model S2, the second-order CFA, both showed good fit (TLI = 0.95, CFI = 0.96, RMSEA = 0.08). For model S1, correlations among the two latent factors were 0.84. Model S3, the bifactor CFA, demonstrated better fit (CFI = 0.98, TLI = 0.97, RMSEA = 0.05). Model S4, the bifactor ESEM, showed the best fit for the data (CFI = 1.0, TLI = 0.99, RMSEA = 0.03) with the lower scores on the information criteria compared to Model S1, S2, and S3.</p><p>Finally, for the BAT-J, model J1 (correlated six-factor CFA), J2 (second-order CFA), and J3 (bifactor CFA) showed good fit (model J1: CFI = 0.92, TLI = 0.92, RMSEA = 0.05; model J2: CFI = 0.91, TLI = 0.91, RMSEA = 0.06; model J3: CFI = 0.93, TLI = 0.92, RMSEA = 0.05). For model J1, correlations among the latent factors ranged from 0.31 to 0.62. The four core dimensions were more strongly related with each other (ranging from 0.43 to 0.72) than with the two secondary dimensions (ranging from 0.41 to 0.62). Model J4, the bifactor ESEM, demonstrated the best fit to the data (CFI = 0.98, TLI = 0.96, RMSEA = 0.04) with the lower scores on the information criteria compared with other models. Thus, the bifactor ESEM provided the best presentation of the data for the BAT-JC, BAT-JS, and BAT-J.</p><p><xref rid=\"T3\" ref-type=\"table\">Table 3</xref> shows the standardized factor loadings of the bifactor ESEM for the BAT-JC, BAT-JS, and BAT-J. All items of the BAT-JC loaded substantially on the general factor (|λ| = 0.39–0.90). The ECV index showed that the general factor accounted for 70% of the common variance extracted. Regarding factor loadings on the specific factors, target loadings on exhaustion (|λ| = 0.40–0.61) and cognitive impairment (|λ| = 0.32–0.46) were all significant. Target loadings on mental distance and emotional impairment were low to moderate (|λ| = 0.05–0.55 and 0.02–0.50, respectively); four out of five possible loadings for mental distance and three out of the five possible loadings for emotional impairment were significant.</p><table-wrap id=\"T3\" position=\"float\"><label>TABLE 3</label><caption><p>Standardized factor loading for bifactor exploratory equation modeling analysis of BAT-JC, BAT-JS, and BAT-J (<italic>N</italic> = 982).</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>General factor</bold><hr/></td><td valign=\"top\" align=\"center\" colspan=\"9\" rowspan=\"1\"><bold>Bifactors</bold><break/> <hr/></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Scale</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Subscale</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Item</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>BAT-JC</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Exhaustion</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Mental distance</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Emotional impairment</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Cognitive impairment</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>I-ECV</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>S-ECV</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>ECV</bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BAT-JC</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Exhaustion</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.61***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.53</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.57</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.70</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.39***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.47</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.22***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.36</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.61***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.61</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.50</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.60***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.61</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.49</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.63***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.52</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.11***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.58</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.64***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.40</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.19***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.67</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.53***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.57</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.46</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.63***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.61</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.51</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mental distance</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.61***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.30***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.05</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.80</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.05</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.65***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.15***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.31</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.77</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.74***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.11**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.32</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.83</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.66***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.06*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.55</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.59</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.65***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.41</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.71</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Emotional impairment</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.79***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.11***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.28</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.87</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.03</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.78***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.50</bold>**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.71</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.69***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.09*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.37</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.77</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.81***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.20***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.07</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−<bold>0.02</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.94</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.90***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.16**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.19***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−<bold>0.05</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.08***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.92</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Cognitive impairment</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.77***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.35</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.82</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.05</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.80***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.07**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.34</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.84</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">21</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.73***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.44</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.73</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.75***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.05*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.08**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.46</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.72</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">23</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.74***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.32</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.83</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" colspan=\"13\" rowspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>General factor</bold><hr/></td><td valign=\"top\" align=\"center\" colspan=\"9\" rowspan=\"1\"><bold>Bifactors</bold><break/> <hr/></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Scale</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Subscale</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Item</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>BAT-JS</bold></td><td valign=\"top\" align=\"center\" colspan=\"2\" rowspan=\"1\"><bold>Psychological distress</bold></td><td valign=\"top\" align=\"center\" colspan=\"2\" rowspan=\"1\"><bold>Psychosomatic complaints</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>I-ECV</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>S-ECV</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>ECV</bold></td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"13\" rowspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BAT-JS</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Psychological distress</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.75***</td><td valign=\"top\" align=\"center\" colspan=\"2\" rowspan=\"1\"><bold>0</bold>.<bold>37</bold>***</td><td valign=\"top\" align=\"center\" colspan=\"2\" rowspan=\"1\">−0.16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.78</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.15</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.78</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.82***</td><td valign=\"top\" align=\"center\" colspan=\"2\" rowspan=\"1\"><bold>0</bold>.<bold>66</bold>***</td><td valign=\"top\" align=\"center\" colspan=\"2\" rowspan=\"1\">0.13</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.60</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">26</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.69***</td><td valign=\"top\" align=\"center\" colspan=\"2\" rowspan=\"1\"><bold>0</bold>.<bold>69</bold>***</td><td valign=\"top\" align=\"center\" colspan=\"2\" rowspan=\"1\">0.36***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.44</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">27</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.91***</td><td valign=\"top\" align=\"center\" colspan=\"2\" rowspan=\"1\"><bold>0</bold>.<bold>41</bold>***</td><td valign=\"top\" align=\"center\" colspan=\"2\" rowspan=\"1\">0.58***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.62</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">28</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.83***</td><td valign=\"top\" align=\"center\" colspan=\"2\" rowspan=\"1\"><bold>0</bold>.<bold>32</bold>***</td><td valign=\"top\" align=\"center\" colspan=\"2\" rowspan=\"1\">0.15***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.84</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Psychosomatic complaints</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">29</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.84***</td><td valign=\"top\" align=\"center\" colspan=\"2\" rowspan=\"1\">−0.12***</td><td valign=\"top\" align=\"center\" colspan=\"2\" rowspan=\"1\"><bold>0.04</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.98</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.07</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.91***</td><td valign=\"top\" align=\"center\" colspan=\"2\" rowspan=\"1\">0.01</td><td valign=\"top\" align=\"center\" colspan=\"2\" rowspan=\"1\"><bold>0.02</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.00</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">31</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.89***</td><td valign=\"top\" align=\"center\" colspan=\"2\" rowspan=\"1\">−0.01</td><td valign=\"top\" align=\"center\" colspan=\"2\" rowspan=\"1\"><bold>0</bold>.<bold>20</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.95</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">32</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.81***</td><td valign=\"top\" align=\"center\" colspan=\"2\" rowspan=\"1\">0.10***</td><td valign=\"top\" align=\"center\" colspan=\"2\" rowspan=\"1\">−<bold>0.15</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.95</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">33</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.86***</td><td valign=\"top\" align=\"center\" colspan=\"2\" rowspan=\"1\">0.05</td><td valign=\"top\" align=\"center\" colspan=\"2\" rowspan=\"1\">−<bold>0.03</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.00</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" colspan=\"13\" rowspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>General factor</bold><hr/></td><td valign=\"top\" align=\"center\" colspan=\"9\" rowspan=\"1\"><bold>Bifactors</bold><break/> <hr/></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Scale</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Subscale</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Item</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>BAT-J</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Exhaustion</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Mental distance</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Emotional impairment</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Cognitive impairment</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Psychological distress</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Psychosomatic complaints</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>I-ECV</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>S-ECV</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>ECV</bold></td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"13\" rowspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BAT-J</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Exhaustion</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.62***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.52</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.09**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-0.08*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.57</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.11</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.69</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.41***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.45</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.23***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-0.09*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.39</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.64***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.57</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.55</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.62***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.59</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.52</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.66***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.47</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.11***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.07</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.64</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.67***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.36</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.20***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.72</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.55***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.55</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.49</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.66***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.58</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.06*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.56</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mental distance</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.61***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.28***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.05</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-0.07*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.81</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.04</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.63***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.16***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.34</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.08*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-0.07</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-0.06*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.72</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.73***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.10**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.34</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-0.12***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.79</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.63***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.06*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.58</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.08**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-0.04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-0.07**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.53</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.62***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.44</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-0.06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.65</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Emotional impairment</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.77***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.15***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.31</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-0.06*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.82</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.03</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.73***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.55</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.64</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.65***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.10***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.45</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.10**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.66</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.83***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.25***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.07*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.06</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.07</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-0.15***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-0.07*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.87</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.84***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.13***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.10***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.16</bold>*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-0.10***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-0.08**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.91</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Cognitive impairment</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.76***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.08*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.38</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-0.04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.79</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.03</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.80***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.05*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.06*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.35</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-0.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.83</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">21</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.74***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.41</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-0.05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.76</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.76***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.08***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.09***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.44</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.74</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">23</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.75***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.07**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0.31</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.84</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Psychological distress</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.63***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0</bold>.<bold>24</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.16***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.82</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.03</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.74***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0</bold>.<bold>42</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.07*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.75</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">26</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.68***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.27***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.07*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0</bold>.<bold>45</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.62</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">27</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.82***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.09**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.08*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.07*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0</bold>.<bold>28</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.12**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.86</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">28</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.69***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0</bold>.<bold>21</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.19***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.85</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Psychosomatic complaints</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">29</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.68***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.19***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.10*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-0.10**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0</bold>.<bold>33</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.74</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.05</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.64***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.07**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.11***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0</bold>.<bold>47</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.63</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">31</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.60***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.06*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.06*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.08**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0</bold>.<bold>51</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.57</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">32</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.50***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.18***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.13***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0</bold>.<bold>50</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.45</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">33</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.66***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.07*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.08*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>0</bold>.<bold>46</bold>***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.66</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr></tbody></table><table-wrap-foot><attrib><italic>ECV, explained common variance; I-ECV, item level ECV; S-ECV, specific ECV. Target factor loadings are in bold. ***p < 0.001, **p < 0.01, *p < 0.05.</italic></attrib></table-wrap-foot></table-wrap><p>For the BAT-JS, all items loaded substantially on the general factor (|λ| = 0.69–0.91), and the ECV index showed that the general factor accounted for 78% of the common variance extracted. Regarding factor loadings on the specific factors, target loadings on psychological distress (|λ| = 0.32–0.69) were all significant. Target loadings on psychosomatic complaints were low (|λ| = 0.02–0.20), and only two of five possible loadings were significant.</p><p>For the BAT-J, all items loaded substantially on the general factor (|λ| = 0.41–0.84), and the ECV index showed that the general factor accounted for 69% of the common variance extracted. Regarding factor loadings on the specific factors, target loadings on exhaustion (|λ| = 0.36–0.59), cognitive impairment (|λ| = 0.31–0.44), psychological distress (|λ| = 0.21–0.45), and psychosomatic complaints (|λ| = 0.33–0.51) were moderate, and all of the possible loadings were significant. Target loading on mental distance (|λ| = 0.05–0.58) and emotional impairment (|λ| = 0.06–0.55) were low to moderate; four out of five possible loadings for each factor were significant.</p></sec><sec id=\"S3.SS2\"><title>Reliability</title><p>Internal consistencies are shown in <xref rid=\"T4\" ref-type=\"table\">Table 4</xref>. The four subscales of BAT-JC and the two subscales of BAT-JS show a high level of internal consistency. Cronbach’s α for all subscales, except mental distance, exceeded 0.90; for the composite BAT-JC scale, Cronbach’s α was 0.96. For the BAT-JS, Cronbach’s α for both subscales ranged from 0.87 to 0.89 and was 0.92 for the composite BAT-JS. As for the test–retest reliability, the BAT-JC and BAT-JS correlated 0.64 and 0.71 (<italic>p</italic> < 0.001), respectively, across a time interval of 1 month.</p><table-wrap id=\"T4\" position=\"float\"><label>TABLE 4</label><caption><p>Means, standard deviations, internal consistencies (Cronbach’s α) and correlations of the variables used in the study (<italic>N</italic> = 982).</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" colspan=\"2\" rowspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Range</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Mean</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>SD</italic></bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>α</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>1</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>2</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>3</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>4</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>5</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>6</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>7</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>8</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>9</bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BAT-JC</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1–5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.58</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.79</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.96</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Exhaustion</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1–5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.95</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.89</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.93</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.87***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mental distance</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1–5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.44</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.88</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.86</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.88***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.67***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Emotional impairment</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1–5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.35</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.93</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.91</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.87***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.61***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.74***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Cognitive impairment</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1–5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.37</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.94</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.93</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.88***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.61***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.74***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.79***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BAT-JS</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1–5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.38</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.89</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.92</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.78***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.66***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.63***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.72***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.73***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">7</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Psychological distress</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1–5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.52</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.89</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.79***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.68***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.64***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.72***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.72***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.94***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">8</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Psychosomatic complaints</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1–5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.28</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.94</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.87</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.67***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.56***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.54***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.61***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.63***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.93***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.75***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">9</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MBI-GS</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0–6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.44</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.90</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.73***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.72***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.62***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.59***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.57***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.64***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.63***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.57***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">10</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Exhaustion</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0–6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.54</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.58</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.94</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.80***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.85***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.66***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.60***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.60***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.69***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.69***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.59***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.87***</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">11</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Cynicism</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0–6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.37</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.20</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.78</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.69***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.61***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.64***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.58***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.58***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.59***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.59***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.51***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.90***</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">12</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Reduced professional efficacy</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0–6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.42</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.72</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.29***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.29***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.20***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.29***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.22***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.30***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.27***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.29***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.74***</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">13</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Work engagement</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0–6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.32</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.24</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.95</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.31***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.26***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.38***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.21***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.24***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.19***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.22***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.13***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.13***</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">14</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Vigor</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0–6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.28</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.90</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.29***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.28***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.34***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.19***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.21***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.19***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.23***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.13***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.14***</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">15</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Dedication</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0–6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.54</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.33</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.88</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.33***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.23***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.42***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.24***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.27***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.20***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.22***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.14***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.12***</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">16</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Absorption</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0–6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.23</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.36</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.90</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.25***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.21***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.32***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.15***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.20***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.14***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.17***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.09</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.10***</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">17</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Workaholism</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1–4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.63</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.88</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.41***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.42***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.29***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.38***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.34***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.38***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.38***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.32***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.42***</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">18</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Working excessively</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1–4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.71</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.81</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.41***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.44***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.26***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.36***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.32***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.36***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.37***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.30***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.43***</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">19</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Working compulsively</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1–4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.96</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.66</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.79</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.36***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.33***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.27***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.34***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.30***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.34***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.34***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.30***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.36***</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">20</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Quantitative job demand</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1–4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.58</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.73</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.80</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.37***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.46***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.22***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.29***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.25***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.27***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.28***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.22***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.30***</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">21</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Qualitative job demand</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1–4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.62</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.68</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.74</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.26***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.36***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.12***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.18***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.17***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.22***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.23***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.18***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.25***</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">22</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Emotional demand</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1–4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.37</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.80</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.87</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.67***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.66***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.54***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.59***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.52***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.55***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.59***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.44***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.54***</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">23</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Turnover intention</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1–5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.94</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.86</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.49***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.46***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.52***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.37***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.37***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.37***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.40***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.28***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.46***</td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"15\" rowspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"2\" rowspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>10</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>11</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>12</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>13</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>14</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>15</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>16</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>17</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>18</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>19</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>20</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>21</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>22</bold></td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"15\" rowspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BAT-JC</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Exhaustion</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mental distance</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Emotional impairment</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Cognitive impairment</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BAT-JS</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">7</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Psychological distress</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">8</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Psychosomatic complaints</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">9</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MBI-GS</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">10</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Exhaustion</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">11</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Cynicism</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.73***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">12</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Reduced professional efficacy</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.38***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.54***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">13</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Work engagement</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.30***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.24***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.27***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">14</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Vigor</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.31***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.23***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.26***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.93***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">15</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Dedication</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.28***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.25***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.27***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.95***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.84***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">16</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Absorption</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.25***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.21***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.24***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.94***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.80***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.84***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">17</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Workaholism</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.41***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.34***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.29***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.18***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.13***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.17***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.21***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">18</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Working excessively</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.42***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.34***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.29***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.15***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.09</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.15***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.17***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.94***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">19</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Working compulsively</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.34***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.30***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.25***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.19***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.15***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.17***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.21***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.92***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.73***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">20</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Quantitative job demand</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.36***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.21***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.15***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.07*</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.49***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.54***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.36***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">21</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Qualitative job demand</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.26***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.17***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.17***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.14***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.09**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.17***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.15***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.37***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.40***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.27***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.65***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">22</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Emotional demand</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.62***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.51***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.20***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.22***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.22***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.21***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.18***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.35***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.35***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.29***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.36***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.29***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">23</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Turnover intention</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.53***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.50***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.10**</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.41***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.39***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.38***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.38***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.17***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.18***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.13***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.16***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.11***</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.45***</td></tr></tbody></table><table-wrap-foot><attrib><italic>SD, standard deviation. ***p < 0.001, **p < 0.01, *p < 0.05.</italic></attrib></table-wrap-foot></table-wrap></sec><sec id=\"S3.SS3\"><title>Construct Validity</title><p>The results regarding the convergent and internal discriminant validity using the MTMM framework are shown in <xref rid=\"T5\" ref-type=\"table\">Table 5</xref>. Model 1 (CT-CM) had the best fit among the four models, showing a significantly better fit compared with models 2 (NT-CM), 3 (PCT-CM), and 4 (CT-PCM). This hints that the BAT-JC is discriminant and convergent from the MBI-GS. In terms of the values for parameter estimates, all items loaded significantly on the trait factors except for items 2 and 5 of the MBI-GS cynicism, as well as items 2 and 3 of the BAT-JC mental distance. All items loaded significantly on the measurement factors, except for item 1 on the MBI-GS cynicism subscale and items 4 and 5 on the MBI-GS professional efficacy subscale. The estimated correlation values between trait factors were all significant (|r| = from 0.10 to 0.94), except cynicism/mental distance and cognitive impairment (0.07). In terms of method (measurement), the latent correlation between BAT-JC and the MBI-GS was 0.87.</p><table-wrap id=\"T5\" position=\"float\"><label>TABLE 5</label><caption><p>Model fit indices for the multitrait–multimethod framework for the BAT-JC (<italic>N</italic> = 982).</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" colspan=\"2\" rowspan=\"1\"><bold>Model</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>TLI</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>CFI</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>RMSEA[90% CI]</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>χ<sup>2</sup></bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>df</italic></bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Δχ<sup>2</sup>/<italic>df</italic></bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>p</italic></bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Modelcomparison</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Δχ<sup>2</sup></bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Δ<italic>df</italic></bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>p</italic></bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" colspan=\"13\" rowspan=\"1\"><bold>BAT-C (core symptoms)</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CT-CM model</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.89</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.90</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.07 [0.07–0.08]</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3779.54</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">617.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.13</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><0.001</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NT-CM model</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.66</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.68</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.13 [0.12–0.13]</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10863.44</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">664.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16.36</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><0.001</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2 vs. 1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7083.899</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">47</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PCT-CM mode</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.78</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.81</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.10 [0.09–0.10]</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6856.83</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">627.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10.94</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><0.001</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3 vs. 1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3077.290</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CT-PCM model</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.85</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.87</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.08 [0.08–0.09]</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4809.96</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">618.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.78</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><0.001</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4 vs. 1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1030.425</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr></tbody></table><table-wrap-foot><attrib><italic>TLI, Tucker-Lewis index; CFI, comparative fit index; RMSEA, root mean square error of approximation; χ2, chi-square; df, degree of freedom; p, p-value; Δχ2, difference in chi-square; Δdf, difference in degree of freedom. CT-CM, correlated traits-correlated methods; NT-CM, no traits-correlated methods; PCT-CM, perfectly correlated traits-correlated methods; CT-PCM, correlated traits-perfectly correlated methods.</italic></attrib></table-wrap-foot></table-wrap><p><xref rid=\"T6\" ref-type=\"table\">Table 6</xref> shows the results for the external discriminant validity. The AVE of BAT-JC (0.51) was greater than its squared correlations (<italic>R</italic><sup>2</sup>) with work engagement (0.10) and workaholism (0.19). The AVE of BAT-JS (0.55) was also greater than its squared correlations (<italic>R</italic><sup>2</sup>) with work engagement (0.04) and workaholism (0.17). These results indicate that the BAT-J can be discriminated from other well-being constructs.</p><table-wrap id=\"T6\" position=\"float\"><label>TABLE 6</label><caption><p>Average variance explained (AVE) and squared latent correlations (<italic>R</italic><sup>2</sup>) for work engagement, workaholism, and burnout (BAT) (<italic>N</italic> = 982).</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Item</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>AVE</bold></td><td valign=\"top\" align=\"center\" colspan=\"2\" rowspan=\"1\"><bold><italic>R</italic><sup>2</sup></bold><hr/></td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Work engagement</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Workaholism</bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Work engagement</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.69</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Workaholism</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.43</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.04</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BAT-JC</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.51</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.19</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BAT-JS</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.55</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.17</td></tr></tbody></table></table-wrap><p>As for the relations of the BAT-J with potential antecedents and consequences, results of SEM analyses showed that the proposed models (<xref ref-type=\"fig\" rid=\"F2\">Figures 2</xref>, <xref ref-type=\"fig\" rid=\"F3\">3</xref>) fit adequately with the data. For the BAT-JC, χ<sup>2</sup>(96) = 740.72, TLI = 0.92, CFI = 0.93, and RMSEA = 0.08. For BAT-JS, χ<sup>2</sup>(70) = 370.55, TLI = 0.95, CFI = 0.96, and RMSEA = 0.07. Both the BAT-JC and BAT-JS were positively related to potential antecedents, including quantitative, qualitative, and emotional job demands. Finally, for the relationship with potential consequences, the BAT-JC and BAT-JS were also positively related to turnover intention.</p><fig id=\"F2\" position=\"float\"><label>FIGURE 2</label><caption><p>Standardized solution (Maximum Likelihood estimates) of the hypothesized model of the relations of BAT-JC with potential antecedents and consequences (<italic>N</italic> = 982). Quant D, quantitative demand; Qual D, qualitative demand; Emot, emotional demand; TI, Tuniover intention. ***<italic>p</italic> < 0.001.</p></caption><graphic xlink:href=\"fpsyg-11-01819-g002\"/></fig><fig id=\"F3\" position=\"float\"><label>FIGURE 3</label><caption><p>Standardized solution (Maximum Likelihood estimates) of the hypothesized model of the relations of BAT-JS with potential antecedents and consequences (<italic>N</italic> = 982). Quant D, quantitative demand; Qual D, qualitative demand; Emot, emotional demand; TI, Turnover intention. ***<italic>p</italic> < 0.001.</p></caption><graphic xlink:href=\"fpsyg-11-01819-g003\"/></fig></sec></sec><sec id=\"S4\"><title>Discussion</title><p>The current study aimed to validate the Japanese version of BAT by evaluating factorial validity, reliability, and construct validity, including convergent and discriminant validity.</p><p>For factorial validity, we conducted CFA and ESEM bifactor analysis. For the BAT-JC, BAT-JS, and BAT-J, we compared the goodness of fit of four models, the correlated-factor CFA model, second-order CFA model, bifactor CFA model, and ESEM bifactor model. Results showed that the bifactor ESEM model fits the best to the data. Also, bifactor ESEM analyses showed that the general factor accounted for over two-thirds of the common variance explained for the BAT-JC, BAT-JS, and the BAT-J, indicating a strong general factor. These results suggest that the BAT-JC, BAT-JS, and BAT-J can be treated as a unidimensional scale. This is consistent with the idea that burnout is a syndrome comprising a set of related symptoms referring to one underlying psychological condition, burnout. This also means that each measure may produce a single score for establishing cut-offs to be used as a screening device, which is the added value of BAT-J. Most of the BAT-J items showed significant loadings on the target factors. The exception was when testing the BAT-JS factor loadings, the psychosomatic complaints items displayed strong loadings on the general factor (>0.81) and weak loadings on the specific factor (<0.02). However, when we tested the BAT-J factor loadings, the psychosomatic complaints items showed significant loadings on the target factor (>0.33). These results confirm that the BAT-JC, BAT-JS, and BAT-J can be used as a unidimensional measurement with multidimensional characteristics. Also, all BAT subscales and the composite BAT-JC and BAT-JS showed internal consistency (Cronbach’s α) that exceeded <xref rid=\"B19\" ref-type=\"bibr\">Henson (2001)</xref> recommended criterion of 0.70. In fact, the α values of both composite scores and most BAT subscale scores exceeded 0.90.</p><p>In terms of test–retest reliability, the stability coefficients of the four BAT-JC subscales, two BAT-JS subscales, and the BAT-JC and BAT-JS composite scores all meet the stringent criterion of 0.50 (<xref rid=\"B57\" ref-type=\"bibr\">Sturman et al., 2005</xref>). Therefore, the reliability of BAT-J was confirmed, both in terms of internal consistency as well as test–retest reliability.</p><p>For construct validity, we examined the convergent and internal discriminant validity of the BAT-JC vis-ȧ-vis the MBI, using the MTMM framework. In the MTMM model, the latent correlation between the methods (BAT-JC and MBI-GS) was high (0.87), which hints at their convergent validity. This is not surprising because both instruments seek to measure burnout using self-report items scored on a Likert scale. In terms of traits (dimensions), the latent correlations were significant (|r| = from 0.10 to 0.94) except that of cynicism/mental distance and cognitive impairment (0.07); however, the correlations were not perfect, which hints at their discriminant validity. This provides evidence that burnout is a syndrome comprising multiple, interrelated dimensions.</p><p>The discriminant validity of the BAT-JC and the MBI-GS is also strengthened by the bivariate correlations (<xref rid=\"T4\" ref-type=\"table\">Table 4</xref>). The correlation between the BAT-JC and MBI-GS was high (0.73), which is not surprising, as both scales measure burnout. This could be a result of the high correlation between exhaustion subscales (0.85). However, the bivariate correlations between other subscales were low to moderate (ranging from 0.20 to 0.66), especially for reduced professional efficacy, which had a low correlation with other subscales (below 0.29). These results confirmed that, although the BAT-JC is convergent with the MBI-GS, they are not identical, as the constructs they measure differ at the subscale level.</p><p>Please note that in the MTMM model, some of the values for parameter estimates and the estimated correlation between traits were negative, suggesting the potential for multicollinearity. <xref rid=\"B33\" ref-type=\"bibr\">Mason and Perreault (1991)</xref> pointed out that multicollinearity should not be viewed in isolation, and it is important to consider other factors that influence the accuracy of estimation results and, thus, may either aggravate or mitigate the deleterious effects of multicollinearity. Also, if the measure were highly reliable, the harmful effects of multicollinearity could be largely offset (<xref rid=\"B17\" ref-type=\"bibr\">Grewal et al., 2004</xref>). The current study sample size was large enough (<italic>n</italic> = 982), and the Cronbach’s α of the subscales and the compounded BAT-JC scale exceeded 0.86; thus, our measure was reliable. We could, therefore, conclude that multicollinearity was offset.</p><p>As for external discriminant validity, the AVEs of the BAT-JC and BAT-JS were greater than their respective squared correlations (<italic>R</italic><sup>2</sup>) with work engagement and workaholism. This result confirms that the BAT-J assesses a different construct than work engagement and workaholism, confirming the external discriminant validity of the BAT-J.</p><p>For further construct validity, the BAT-JC and BAT-JS showed positive relationships with several possible antecedents and consequences. These results were in line with the JD-R model, which assumes that high job demands are associated with high levels of stress, health problems, and poor organizational outcomes (<xref rid=\"B13\" ref-type=\"bibr\">Demerouti et al., 2001</xref>). Thus, the construct validity of the BAT-J was confirmed.</p><p>These results provide evidence that burnout comprises multidimensional, inter-correlated dimensions that cannot be grasped by the MBI, indicating that the BAT-J provides a more detailed understanding of burnout characteristics and can be an alternative measure for assessing burnout.</p><sec id=\"S4.SS1\"><title>Limitations and Future Directions</title><p>The current study has three limitations that warrant future research. First, sampling bias might exist, as we included only healthy employees in our study. As the BAT was also developed for assessing severe burnout, we need to confirm its validity and reliability in a sample of employees with burnout as well.</p><p>Second, it is necessary to establish appropriate cut-offs for screening employees who are at risk of burnout. In the original study in Belgium, cut-offs had already been established (<xref rid=\"B43\" ref-type=\"bibr\">Schaufeli et al., 2019</xref>, Manual BAT. KU Leuven, Belgium: unpublished internal report 78). Because levels of burnout vary across cultures and nations (<xref rid=\"B39\" ref-type=\"bibr\">Savicki, 2002</xref>), nation-specific cut-offs should be developed (<xref rid=\"B53\" ref-type=\"bibr\">Schaufeli and Van Dierendonck, 1995</xref>), also for Japan. This study’s findings confirmed that the BAT-JC, BAT-JS, and BAT-J could produce a single score; the next step is to develop a Japan-specific cut-off to use the BAT as a screening device.</p><p>Third, we need to examine the usability of a BAT-JC and BAT-JS combination in research and practice. As explained previously, the BAT-JC represents the core burnout symptoms, and the BAT-JS represents the secondary burnout symptoms. To what extent the BAT-JS improves the assessment of burnout over and beyond the BAT-JC is an open question. Also, future research should explore whether secondary symptoms always appear simultaneously with core symptoms or only when core symptoms reach a certain level of severity.</p></sec></sec><sec id=\"S5\"><title>Conclusion</title><p>The results of the current study provide primary evidence for the factorial validity, reliability, and construct validity of the BAT-J. This tool was developed to overcome various flaws in the MBI-GS related to the conceptualization and dimensionality of burnout, as well as its practical applicability. For practical use, this study confirmed that the BAT-J can be used as a unidimensional measurement and can produce a single score for establishing a cut-off to be used as a screening device in the next step. The BAT-J may be a viable alternative to the MBI-GS in research and practice in Japan.</p></sec><sec sec-type=\"data-availability\" id=\"S6\"><title>Data Availability Statement</title><p>The datasets generated for this study are available upon request to the corresponding author.</p></sec><sec id=\"S7\"><title>Ethics Statement</title><p>The Ethics Review Board of Toyo University approved the procedures before starting the study. Participants had the option of not responding to any part of the questionnaire at any time and to discontinue the survey at any point. Participants’ consent was confirmed based on their completion of the questionnaire.</p></sec><sec id=\"S8\"><title>Author Contributions</title><p>KS was responsible for the data analysis and writing the draft of the manuscript. AS planned the research design as a principal investigator of the project and contributed to the writing of the manuscript. HT was responsible for the data collection, data analysis for CFA and ESEM bifactor analysis, and contributed to the writing of the manuscript. WS was involved in the original conceptualization of the work and reviewed various versions of the manuscript. All authors contributed to the article and approved the submitted version.</p></sec><sec id=\"conf1\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> This study was funded by KS’ individual research expenses from Toyo University and AS’ joint research expenses with Public Health Research Foundation.</p></fn></fn-group><fn-group><fn id=\"footnote1\"><label>1</label><p>For more details on the development of the new burnout instrument (see <xref rid=\"B43\" ref-type=\"bibr\">Schaufeli et al., 2019</xref>).</p></fn></fn-group><sec id=\"S10\" sec-type=\"supplementary material\"><title>Supplementary Material</title><p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.frontiersin.org/articles/10.3389/fpsyg.2020.01819/full#supplementary-material\">https://www.frontiersin.org/articles/10.3389/fpsyg.2020.01819/full#supplementary-material</ext-link></p><supplementary-material content-type=\"local-data\" id=\"SM1\"><media xlink:href=\"Data_Sheet_1.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec><ref-list><title>References</title><ref id=\"B1\"><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Ahola</surname><given-names>K.</given-names></name><name><surname>Hakanen</surname><given-names>J.</given-names></name></person-group> (<year>2007</year>). <article-title>Job strain, burnout, and depressive symptoms: a prospective study among dentists.</article-title>\n<source><italic>J. 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Measur.</italic></source>\n<volume>9</volume>\n<fpage>1</fpage>–<lpage>26</lpage>. <pub-id pub-id-type=\"doi\">10.1177/014662168500900101</pub-id></mixed-citation></ref><ref id=\"B64\"><mixed-citation publication-type=\"book\"><collab>World Health Organization</collab> (<year>2019</year>). <source><italic>Burn-Out An ‘Occupational Phenomenon’: International Classification of Diseases.</italic></source>\n<publisher-loc>Geneva</publisher-loc>: <publisher-name>World Health Organization</publisher-name>.</mixed-citation></ref></ref-list><glossary><title>Abbreviations</title><def-list id=\"DL1\"><def-item><term> AIC</term><def><p>Akaike Information Criterion</p></def></def-item><def-item><term>AVE</term><def><p>average variance explained</p></def></def-item><def-item><term>BAT</term><def><p>Burnout Assessment Tool</p></def></def-item><def-item><term>BAT-C</term><def><p>Burnout Assessment Tool – core symptoms</p></def></def-item><def-item><term>BAT-J</term><def><p>Japanese version of the Burnout Assessment Tool</p></def></def-item><def-item><term>BAT-JC</term><def><p>Japanese version of the Burnout Assessment Tool – core symptoms</p></def></def-item><def-item><term>BAT-JS</term><def><p>Japanese version of the Burnout Assessment Tool – secondary symptoms</p></def></def-item><def-item><term>BAT-S</term><def><p>Burnout Assessment Tool – secondary symptoms</p></def></def-item><def-item><term>BIC</term><def><p>the Bayesian Information Criterion</p></def></def-item><def-item><term>CFA</term><def><p>confirmatory factor analysis</p></def></def-item><def-item><term>CFI</term><def><p>comparative fit index</p></def></def-item><def-item><term>CT-CM</term><def><p>correlated traits-correlated methods</p></def></def-item><def-item><term>CT-PCM</term><def><p>correlated traits-perfectly correlated methods</p></def></def-item><def-item><term>EFA</term><def><p>exploratory factor analysis</p></def></def-item><def-item><term>ESEM</term><def><p>exploratory structural equation modeling</p></def></def-item><def-item><term>MBI</term><def><p>Maslach Burnout Inventory</p></def></def-item><def-item><term>MBI-GS</term><def><p>Maslach Burnout Inventory – General Survey</p></def></def-item><def-item><term>MTMM</term><def><p>multitrait–multimethod</p></def></def-item><def-item><term>NT-CM</term><def><p>no traits-correlated methods</p></def></def-item><def-item><term>PCT-CM</term><def><p>perfectly correlated traits-correlated methods</p></def></def-item><def-item><term>RMSEA</term><def><p>root mean square error of approximation</p></def></def-item><def-item><term>TLI</term><def><p>Tucker–Lewis Index.</p></def></def-item></def-list></glossary></back></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Immunol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Immunol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Immunol.</journal-id><journal-title-group><journal-title>Frontiers in Immunology</journal-title></journal-title-group><issn pub-type=\"epub\">1664-3224</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849661</article-id><article-id pub-id-type=\"pmc\">PMC7431962</article-id><article-id pub-id-type=\"doi\">10.3389/fimmu.2020.01983</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Immunology</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>Reduced Calcium Signaling Is Associated With Severe Graft-Versus-Host Disease: Results From Preclinical Models and From a Prospective EBMT Study</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Riesner</surname><given-names>Katarina</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><xref ref-type=\"author-notes\" rid=\"fn002\"><sup>†</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/823178/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Cordes</surname><given-names>Steffen</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"author-notes\" rid=\"fn002\"><sup>†</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/408297/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Peczynski</surname><given-names>Christophe</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Kalupa</surname><given-names>Martina</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/408248/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Schwarz</surname><given-names>Constanze</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Shi</surname><given-names>Yu</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Mertlitz</surname><given-names>Sarah</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/818933/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Mengwasser</surname><given-names>Jörg</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>van der Werf</surname><given-names>Steffie</given-names></name><xref ref-type=\"aff\" rid=\"aff5\"><sup>5</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Peric</surname><given-names>Zinaida</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff6\"><sup>6</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/858699/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Koenecke</surname><given-names>Christian</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff7\"><sup>7</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/422088/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Schoemans</surname><given-names>Helene</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff8\"><sup>8</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Duarte</surname><given-names>Rafael F.</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff9\"><sup>9</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Basak</surname><given-names>Grzegorz W.</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff10\"><sup>10</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Penack</surname><given-names>Olaf</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff11\"><sup>11</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/53952/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Department of Hematology, Oncology, and Tumor Immunology, Charité Universitätsmedizin Berlin</institution>, <addr-line>Berlin</addr-line>, <country>Germany</country></aff><aff id=\"aff2\"><sup>2</sup><institution>EBMT Transplant Complications Working Party</institution>, <addr-line>Paris</addr-line>, <country>France</country></aff><aff id=\"aff3\"><sup>3</sup><institution>EBMT Statistical Unit</institution>, <addr-line>Paris</addr-line>, <country>France</country></aff><aff id=\"aff4\"><sup>4</sup><institution>Department of Surgery, Charité Universitätsmedizin Berlin, Campus Charité Mitte/Campus Virchow Clinic</institution>, <addr-line>Berlin</addr-line>, <country>Germany</country></aff><aff id=\"aff5\"><sup>5</sup><institution>EBMT Data Office</institution>, <addr-line>Leiden</addr-line>, <country>Netherlands</country></aff><aff id=\"aff6\"><sup>6</sup><institution>University Hospital Center Rebro</institution>, <addr-line>Zagreb</addr-line>, <country>Croatia</country></aff><aff id=\"aff7\"><sup>7</sup><institution>Hannover Medical School</institution>, <addr-line>Hanover</addr-line>, <country>Germany</country></aff><aff id=\"aff8\"><sup>8</sup><institution>UZ Leuven</institution>, <addr-line>Leuven</addr-line>, <country>Belgium</country></aff><aff id=\"aff9\"><sup>9</sup><institution>Hospital Universitario Puerta de Hierro</institution>, <addr-line>Madrid</addr-line>, <country>Spain</country></aff><aff id=\"aff10\"><sup>10</sup><institution>Medical University of Warsaw</institution>, <addr-line>Warsaw</addr-line>, <country>Poland</country></aff><aff id=\"aff11\"><sup>11</sup><institution>Berlin Institute of Health</institution>, <addr-line>Berlin</addr-line>, <country>Germany</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Annalisa Ruggeri, Laboratori di Ricerca, Bambino Gesù Ospedale Pediatrico, Italy</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Bipin N. Savani, Vanderbilt University, United States; Maria Teresa Lupo Stanghellini, San Raffaele Hospital (IRCCS), Italy</p></fn><corresp id=\"c001\">*Correspondence: Katarina Riesner, <email>katarina.riesner@charite.de</email></corresp><fn fn-type=\"other\" id=\"fn002\"><p><sup>†</sup>These authors have contributed equally to this work</p></fn><fn fn-type=\"other\" id=\"fn004\"><p>This article was submitted to Alloimmunity and Transplantation, a section of the journal Frontiers in Immunology</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>1983</elocation-id><history><date date-type=\"received\"><day>15</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>22</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Riesner, Cordes, Peczynski, Kalupa, Schwarz, Shi, Mertlitz, Mengwasser, van der Werf, Peric, Koenecke, Schoemans, Duarte, Basak and Penack.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Riesner, Cordes, Peczynski, Kalupa, Schwarz, Shi, Mertlitz, Mengwasser, van der Werf, Peric, Koenecke, Schoemans, Duarte, Basak and Penack</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>Despite its involvement in various immune functions, including the allogeneic activation of T-lymphocytes, the relevance of calcium (Ca<sup>2+</sup>) for GVHD pathobiology is largely unknown. To elucidate a potential association between Ca<sup>2+</sup>and GVHD, we analyzed Ca<sup>2+</sup>-sensing G-protein coupled receptor 6a (GPRC6a) signaling in preclinical GVHD models and conducted a prospective EBMT study on Ca<sup>2+</sup> serum levels prior alloSCT including 363 matched sibling allogeneic peripheral blood stem cell transplantations (alloSCTs). In experimental models, we found decreased <italic>Gprc6a</italic> expression during intestinal GVHD. GPRC6a deficient alloSCT recipients had higher clinical and histopathological GVHD scores leading to increased mortality. As possible underlying mechanism, we found increased antigen presentation potential in GPRC6a<sup>–/–</sup> alloSCT recipients demonstrated by higher proliferation rates of T-lymphocytes. In patients with low Ca<sup>2+</sup> serum levels (≤median 2.2 mmol/l) before alloSCT, we found a higher incidence of acute GVHD grades II-IV (HR = 2.3 Cl = 1.45–3.85 <italic>p</italic> = 0.0006), severe acute GVHD grades III-IV (HR = 3.3 CI = 1.59–7.14, <italic>p</italic> = 0.002) and extensive chronic GVHD (HR = 2.0 Cl = 1.04–3.85 <italic>p</italic> = 0.04). In conclusion, experimental and clinical data suggest an association of reduced Ca<sup>2+</sup> signaling with increased severity of GVHD. Future areas of interest include the in depth analysis of involved molecular pathways and the investigation of Ca<sup>2+</sup> signaling as a therapeutic target during GVHD.</p></abstract><kwd-group><kwd>graft-versus-host-disease</kwd><kwd>stem cell transplantation</kwd><kwd>calcium</kwd><kwd>GPRC6a</kwd><kwd>GVHD mouse models</kwd><kwd>EBMT study</kwd></kwd-group><counts><fig-count count=\"6\"/><table-count count=\"2\"/><equation-count count=\"0\"/><ref-count count=\"28\"/><page-count count=\"13\"/><word-count count=\"0\"/></counts></article-meta></front><body><sec id=\"S1\"><title>Introduction</title><p>Calcium (Ca<sup>2+</sup>) signaling is involved in the regulation of various immune functions, including B- and T-cell activation, differentiation, T-lymphocyte-mediated cytotoxicity and cytokine gene expression (1). It has been shown to modulate the allogeneic activation of T-lymphocytes by antigen presenting cells (2, 3) implicating a role for graft-versus-host disease (GVHD) pathogenesis. Additionally, Ca<sup>2+</sup> is known as “danger-associated molecular pattern” (DAMP) and plays an important role in the activation of T-cells, dendritic cells and the multi-protein complex of the NOD-like receptor protein (NLRP3) inflammasome (4). During conditioning prior to allogeneic stem cell transplantation (alloSCT), with radiation and/or chemotherapy, and later during GVHD increased cell death of immune and tissue cells occurs. This increased cell death can lead, among other things, to enhanced Ca<sup>2+</sup> levels. A locally increased extracellular Ca<sup>2+</sup> concentration can induce the activation of the NLRP3 inflammasome via the calcium sensing receptor and the G-protein coupled receptor 6a (GPRC6a) (4). Several studies revealed a significant role of DAMPs and the NLRP3 inflammasome in GVHD morbidity and mortality (5–8). While NLRP3 function has been studied in GVHD, upstream regulatory functions of GPRC6a in GVHD remain to be elucidated. GPRC6a is a class C, group 6, subtype A G-protein coupled receptor which can be directly activated or positively modulated by Ca<sup>2+</sup> in concentrations above 5 mM; and was shown to be involved in the regulation of inflammation, metabolism and endocrine functions (9). In addition, GPRC6a was identified in a genome-wide association study as a novel loci associated with C-reactive protein levels, a general biomarker for systemic inflammation (10).</p><p>The role of Ca<sup>2+</sup> during GVHD is mostly unknown. Thangavelu et al. showed an effect of genetically and inhibitor-induced diminished intracellular Ca<sup>2+</sup> levels on donor T-cell survival during GVHD in preclinical models (11). Based on prior knowledge and the fact that Ca<sup>2+</sup> is a routine laboratory parameter in patients undergoing alloSCT due to frequently arising disturbances in calcium metabolism in these patients, there is a strong rationale to study Ca<sup>2+</sup> and its association to GVHD. We performed experiments in preclinical GVHD models using GPRC6A deficient mice; and the Transplant Complications Working Party (TCWP) of the European Society for Blood and Marrow Transplantation (EBMT) performed a prospective, multicenter and non-interventional clinical study following the hypothesis that Ca<sup>2+</sup> is correlated to the occurrence of GVHD.</p></sec><sec sec-type=\"materials|methods\" id=\"S2\"><title>Materials and Methods</title><sec id=\"S2.SS1\"><title>Preclinical Data</title><sec id=\"S2.SS1.SSS1\"><title>Study Design and Statistics</title><p>Sample size for GVHD experiments was calculated by estimation of time point–specific analyses based on the Student’s <italic>t</italic>-test assuming 80% power and 0.05 2-sided level of significance. Experiments with at least 5 animals per group (wildtype transplanted [WT] and GPRC6a deficient transplanted [GPRC6a<bold><sup>–</sup></bold><sup>/</sup><bold><sup>–</sup></bold>]) were performed 2 times. Animals were randomized, including equal distribution of weight status and mixed housing of different transplanted animals. For subsequent analyses, transplant conditions were encoded. All experiments were approved by the Regional Ethics Committee for Animal Research (State Office of Health and Social Affairs, Berlin).</p><p>Survival data of at least 10 animals per group (WT and GPRC6a<bold><sup>–</sup></bold><sup>/</sup><bold><sup>–</sup></bold> transplanted) were performed 2 times and analyzed using the Kaplan-Meier method and compared with the Mantel-Cox log-rank test. For all other data, the Student’s unpaired <italic>t</italic>-test (two-tailed) was used if not indicated differently. Normality tests and F test confirmed Gaussian distribution and equality of variance between different groups. Values are presented as mean ± standard deviation (SD). Values of <italic>P</italic> < 0.05 were considered statistically significant. All statistical analyses were performed using GraphPad Prism software (GraphPad Software Inc, La Jolla, CA, United States).</p></sec><sec id=\"S2.SS1.SSS2\"><title>Mice</title><p>Female C57BL/6 (B6) (H2<sup><italic>b</italic></sup>) and BALB/c (H2<sup><italic>d</italic></sup>) mice (10–12 weeks old) were purchased from Charles River Laboratories (Sulzfeld, Germany) and from Janvier (St. Berthevin Cedex, France), respectively. Female GPRC6a deficient (GPRC6a<sup>–/–</sup>) mice on B6 background were generated by Hans Bräuner-Osborne, provided by Ulf Wagner (University Hospital Leipzig, Germany) and were bred by and obtained from the central animal unit of the Charité University Medicine. Mice had access to food and water <italic>ad libitum</italic>.</p></sec><sec id=\"S2.SS1.SSS3\"><title>GVHD Experiments</title><p>Our alloSCT acute GVHD models are characterized well (12–16). We performed alloSCTs in the BALB/c→B6 model using GPRC6a<sup>–/–</sup> mice as donors and recipients. GPRC6a<sup>–/–</sup> or B6→BALB/c: Recipient mice received 800 cGy total body irradiation from a 137Cs source as a split dose with a 4-h interval and were injected intravenously (i.v.) with 5 × 10<sup>6</sup> bone marrow (BM) cells and 0.5 × 10<sup>6</sup> splenic T-cells. BALB/c→ GPRC6a<sup>–/–</sup> or B6: Recipient mice received 1200 cGy total body irradiation from a 137Cs source as a split dose with a 4-h interval and were injected i.v. with 0.75 × 10<sup>7</sup> BM cells and 1 × 10<sup>6</sup> splenic T-cells. BM was flushed from the tibia and femur, and single-cell suspension was prepared in phosphate-buffered saline (PBS)/2% fetal calf serum/1 mM EDTA by gently passing through a 23-G needle and over a 70 μm cell strainer (BD Biosciences, San Jose, CA, United States). Splenic T-cell suspension was obtained using the Pan T-cell Isolation Kit II (Miltenyi Biotec, Bergisch Gladbach, Germany). T-cell purity was analyzed by CD3 staining and FACS analysis. For <italic>Gpcr6a</italic> expression analysis, we used the established chemotherapy-based minor mismatch model LP/J→B6 as described in (16). Mice were individually scored twice a week for 5 clinical parameters (posture, activity, fur, skin, and weight loss) on a scale from 0 to 2. Clinical GVHD score was assessed by summation of these parameters. Survival was monitored daily.</p></sec><sec id=\"S2.SS1.SSS4\"><title>Quantitative Real-Time PCR</title><p>A total of 2 μg RNA from colon of syngeneic and allogeneic transplanted mice at day + 15 after alloSCT and complementary DNA, obtained using the RNeasy Mini Kit (Qiagen, Venlo, Netherlands) and the QuantiTect Reverse Transcription Kit (Qiagen) following the manufacturer’s instructions, were amplified (50°C, 2 min; 95°C, 10 min; 49 cycles of 95°C, 10 s; 60°C, 1 min) on DNA Engine Opticon (Bio-Rad, Hercules, CA, United States) using the TaqMan Gene Expression Master Mix (Life Technologies) and GPRC6a primers and probe from BioTez GmbH (Berlin, Germany). Data were analyzed with Opticon Monitor 3.1 analysis software (Bio-Rad) and the comparative CT Method (ΔΔCT Method).</p></sec><sec id=\"S2.SS1.SSS5\"><title>Flow Cytometry</title><p>Single-cell suspensions of blood, BM, lymph nodes, spleen and thymus were prepared. Lymph nodes, splenocytes and thymocytes were passed through a 40-μm cell strainer. Erythrocytes were lysed with ammonium chloride. Cells were washed twice and stained for 20 min at 4°C in PBS/0.5 mM EDTA/0.5% BSA with the following rat mAbs from BD Biosciences: anti-H2kd (SF1-1.1-FITC and PE), anti-CD8a (53-6.7-APC), anti-Ly-6G and Ly6C/Gr1 (RB6-8C5-APC), anti-CD45R/B220 (RA3-6B2-PerCP-Cy5.5), anti-CD25 (PC61-PerCP-Cy5.5), anti-CD4 (RM4-5-PE-Cy7), anti-CD80 (16-10A1-PE), anti-CD86 (GL1-PerCP-Cy5.5), anti-CD11c (HL3-PE-Cy7), anti-CD3e (145-2C11-APC-Cy7) and anti-CD11b (M1/70-APC-Cy7). For staining of regulatory T-cells, Anti-Mouse/Rat FoxP3 Staining Set APC (eBioscience, San Diego, CA, United States) was used following the manufacturer’s instructions. Samples were analyzed by BD FACS Canto II (BD Biosciences) and FlowJo 7.6.5 Software (TreeStar Inc., Ashland, OR, United States).</p></sec><sec id=\"S2.SS1.SSS6\"><title>Mixed Leukocyte Reaction</title><p>Dendritic cells were isolated from spleen of GPRC6a<sup>–/–</sup> and B6 mice using a CD11c + isolation kit and splenic T-cells from BALB/c mice were obtained using the mouse Pan T-cell isolation Kit II (Miltenyi Biotec) according to the manufacturer’s instructions. T-cells were loaded with carboxyfluorescein diacetate succinimidylester (CFSE, Thermo Fisher Scientific, Waltham, MA). 2.5 × 10<sup>4</sup> dendritic cells (activators) and 2.5 × 10<sup>5</sup> T-cell (responders) were incubated for 96 h at 37°C and 5% CO<sub>2</sub>. Total cell counts being negative for CFSE and positive for H2kd were analyzed via flow cytometry. Proliferating cells were determined as cells, showing no CFSE load compared to control samples without T-cell stimulation at time point 0 h.</p></sec><sec id=\"S2.SS1.SSS7\"><title><italic>In vivo</italic> T-Cell Proliferation Assay</title><p>GPRC6a-/- and B6 recipients received 1200 cGy total body irradiation from a 137Cs source. CD3 + lymphocytes were isolated from spleens of BALB/c donors and enriched by Pan T-Cell isolation kit (Milteny Biotec). 4 × 10<sup>6</sup> CFSE-loaded CD3 + cells were injected i.v. into the tail vein. 72 h later, mice were sacrificed and T-cells from spleen, thymus, blood and lymph nodes were isolated and analyzed for proliferation by flow cytometry. Total cell counts being negative for CFSE and positive for H2kd were analyzed via flow cytometry. Proliferating cells were determined as cells, showing no CFSE load compared to control samples without T-cell stimulation at time point 0 h.</p></sec></sec><sec id=\"S2.SS2\"><title>Clinical Data</title><sec id=\"S2.SS2.SSS1\"><title>Data Source, Study Design, and Data Collection</title><p>We asked EBMT centers performing more than 50 alloSCT per year if they were willing to participate in this prospective study. 17 centers in ten countries agreed to participate. Data collection for the EBMT registry was approved by the IRB and/or Ethics Committee in all centers. Data were prospectively collected between 6/2014 and 3/2018. Consecutive alloSCT recipients with acute leukemia, lymphoma or myelodysplastic syndrome (MDS) receiving a first matched sibling alloSCT from peripheral blood, regardless of conditioning, were eligible, provided they had signed an informed consent document that permitted sharing of clinical data according to national rules. Basic data on patient and disease characteristics as well as longer term follow up was taken from minimal essential data (MED-A) forms, which are submitted from all consecutive patients to the central EBMT registry. In addition, we designed registration and MED-B/C forms that were prospectively collected and specific to this study. The MED-B/C form contained detailed information on calcium serum levels prior to alloSCT, patient characteristics, infectious- as well as non-infectious complications, GVHD staging, morbidity and mortality. Total Calcium (including bound Ca<sup>2+</sup> to proteins, mainly albumin, and anions; and free ionized Ca<sup>2+</sup>) serum levels were determined photometrically and corrected for albumin levels according to Payne’s formula [Ca <sub><italic>corr</italic></sub> [mmol/L] = Ca <sub><italic>measured</italic></sub> [mmol/L] – [0.025 × Albumin [g/L] + 1] at time of hospital admission for alloSCT directly before start of conditioning therapy. Treatment teams completed the specific forms at the time of registration and at day + 100 after alloSCT.</p></sec><sec id=\"S2.SS2.SSS2\"><title>Endpoints and Statistical Analyses</title><p>Patient, disease, and transplant-related characteristics for the two cohorts [calcium serum levels prior to alloSCT above median (>2.2 mmol/l)/calcium levels below median (<2.2 mmol/l)] were compared by using χ2 statistics for categorical variables and the Mann-Whitney test for continuous variables. Primary endpoint was the incidence of acute GVHD. Secondary endpoints were relapse incidence (RI), non-relapse mortality (NRM), overall survival (OS), progression free survival (PFS) and the incidence of chronic GVHD. PFS was defined as survival with no evidence of relapse or progression. RI was defined as the probability of having had a relapse during follow up time. Death without experiencing a relapse was a competing event. NRM was defined as death without evidence of relapse or progression. OS was defined as the time from alloSCT to death, regardless of the cause. Acute GVHD was graded according to the modified Seattle-Glucksberg criteria (17) and chronic GVHD according to the revised Seattle criteria (18). Cumulative incidence was used to estimate the endpoints of NRM, RI, acute, and chronic GVHD to accommodate for competing risks. To study acute and chronic GVHD, we considered relapse and death to be competing events. Probabilities of OS and PFS were calculated using the Kaplan–Meier method. Univariate analyses were done using the Gray test for cumulative incidence functions and the log rank test for OS and PFS. A Cox proportional hazards model was used for multivariate regression. All variables differing significantly between the 2 groups or factors associated with one outcome in univariate analysis were included in the Cox model. The following variables entered the multivariate models as possible confounders: age, sex mismatch between recipient and donor, diagnosis, disease status, Karnofsky score, number of CD34 cells given, intensity of conditioning (EBMT definition: myeloablative conditioning (MAC) was defined as TBI > 6 Gray or oral busulfan > 8 mg/kg or intravenous busulfan > 6.4 mg/kg), type of GVHD prophylaxis, ATG use, time from diagnosis to transplant, year of transplant and CMV status. As the number of variables was too high regarding the number of events, a stepwise selection using Akaike information criterion (AIC) was run for all the confounding factors. The difference between the two cohorts was then assessed in the final selected model.</p><p>Results were expressed as the hazard ratio (HR) with the 95% confidence interval (95% CI). Proportional hazards assumptions were checked systematically for all proposed models using the Grambsch-Therneau residual-based test. All tests were 2-sided. The type I error rate was fixed at 0.05 for the determination of factors associated with time-to-event outcomes. Statistical analyses were performed in November 2018 with R 3.4.2 (R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria<sup><xref ref-type=\"fn\" rid=\"footnote1\">1</xref></sup>.</p></sec></sec></sec><sec id=\"S3\"><title>Results</title><sec id=\"S3.SS1\"><title>GPRC6a in Preclinical GVHD</title><p>In experimental acute GVHD, we found significantly decreased <italic>Gprc6a</italic> expression levels in the colon of allogeneic transplanted recipients at day + 15 after alloSCT (<xref ref-type=\"fig\" rid=\"F1\">Figure 1A</xref>) during established acute GVHD (<xref ref-type=\"fig\" rid=\"F1\">Figures 1B,C</xref>); feasibly suggesting defects in calcium induced signaling during acute GVHD. No expression in skin and liver was detectable with the available detection methods. Generally, diverse studies have shown that GPRC6a is widely expressed albeit at relatively low levels making detection challenging (9). To further analyze the effect of GPRC6a on GVHD, we performed alloSCTs in the BALB/c→B6 murine GVHD model using mice deficient for GPRC6a (GPRC6a<sup>–/–</sup>, B6 background) either as recipients or donors of alloSCT. In line with the expression data, GPRC6a<sup>–/–</sup> alloSCT recipients exhibited higher GVHD morbidity and mortality. GPRC6a<sup>–/–</sup> alloSCT recipients had significantly increased weight loss (<xref ref-type=\"fig\" rid=\"F1\">Figure 1D</xref>), GVHD scores (<xref ref-type=\"fig\" rid=\"F1\">Figure 1E</xref>) and mortality (<xref ref-type=\"fig\" rid=\"F1\">Figure 1F</xref>). GVHD typical target organ damage was also more pronounced in GPRC6a<sup>–/–</sup> alloSCT recipients, exhibiting higher histopathological scores, significantly in liver and non-significantly in colon, compared to wildtype (WT) alloSCT recipients at day + 15 after alloSCT (<xref ref-type=\"fig\" rid=\"F1\">Figure 1G</xref>). The use of GPRC6a<sup>–/–</sup> as alloSCT donors did not result in changed GVHD morbidity and mortality compared to WT donors. Weight loss, GVHD scores and survival were similar in both groups (<xref ref-type=\"fig\" rid=\"F1\">Figures 1H–J</xref>). We conclude that GPRC6a signaling in the alloSCT recipient but not in the alloSCT donor may play a role during acute GVHD.</p><fig id=\"F1\" position=\"float\"><label>FIGURE 1</label><caption><p>GPRC6a in preclinical GVHD. <bold>(A)</bold> Reduced <italic>Gprc6a</italic> expression in colon of allogeneic (allo) compared to syngeneic (syn) transplanted mice during acute GVHD at day + 15 after LP/J→B6 SCT (<italic>n</italic> = 10 per group). Gene expression level was normalized to <italic>Gapdh</italic> expression and is shown relative to gene level of a reference naïve control. <bold>(B,C)</bold> GVHD Scores and survival in LP/J→B6 alloSCT. <bold>(D–G)</bold>\n<italic>GPRC6a deficient</italic> (<sup>–/–</sup><italic>) mice as alloSCT recipients</italic>: BALB/c→GPRC6a<sup>–/–</sup> alloSCT. GPRC6a<sup>–/–</sup> mice showed more GVHD-dependent weight loss <bold>(D)</bold>, higher GVHD-Scores <bold>(E)</bold>, decreased survival <bold>(F)</bold> (<italic>n</italic> = 20 per group) and higher histopathological GVHD Scoring determined on HE sections of colon and liver at day + 15 after alloSCT <bold>(G)</bold> (<italic>n</italic> = 6 per group). B6 wildtype (WT) recipient mice served as control. <bold>(H–J)</bold>\n<italic>GPRC6a</italic><sup>–/–</sup>\n<italic>mice as alloSCT donors:</italic> GPRC6a<sup>–/–</sup>→BALB/c alloSCT. BALB/c recipients (<italic>n</italic> = 10) transplanted from GPRC6a<sup>–/–</sup> donor mice showed no differences in weight loss <bold>(H)</bold>, GVHD Score <bold>(I)</bold> and survival <bold>(J)</bold> compared to BALB/c recipients (<italic>n</italic> = 5) transplanted from B6 WT donor mice. In panels F and J, <italic>P</italic> values were calculated by using the log-rank test. Error bars indicate mean ± SD. *<italic>P</italic> < 0.05; **<italic>P</italic> < 0.01; by Student’s <italic>t</italic>-test (two-tailed). Mice experiments were performed twice.</p></caption><graphic xlink:href=\"fimmu-11-01983-g001\"/></fig></sec><sec id=\"S3.SS2\"><title>Inflammatory Cells in GPRC6a<sup>–/–</sup> alloSCT Recipients</title><p>To elucidate the higher acute GVHD morbidity and mortality in GPRC6a<sup>–/–</sup> alloSCT recipients, we analyzed peripheral blood, spleen and axillary lymph nodes for inflammatory cells during pronounced GVHD symptoms at day + 15 after alloSCT. In GPRC6a<sup>–/–</sup> alloSCT recipients, we found significantly increased CD4<sup>+</sup> T-cells in blood and lymph nodes (<xref ref-type=\"fig\" rid=\"F2\">Figures 2A,C</xref>). In spleen, no significant changes compared to WT B6 alloSCT recipients were detected (<xref ref-type=\"fig\" rid=\"F2\">Figure 2B</xref>), however, in lymph nodes, GPRC6a<sup>–/–</sup> alloSCT recipients exhibited significantly decreased B-cell and dendritic cells and significantly increased granulocytes and regulatory T-cells (<xref ref-type=\"fig\" rid=\"F2\">Figure 2C</xref>). As underlying mechanism, we found increased antigen presentation potential in GPRC6a<sup>–/–</sup> alloSCT recipients demonstrated by higher proliferation rates of T-lymphocytes. In mixed lymphocyte reaction, BALB/c T-cells showed higher proliferation after 96 h incubation with GPRC6a<sup>–/–</sup> dendritic cells as compared to WT dendritic cells (<xref ref-type=\"fig\" rid=\"F3\">Figure 3A</xref>). To analyze the proliferation potential of allogeneic T-cells <italic>in vivo</italic>, we transplanted CFSE-labeled BALB/c (H2k<sup><italic>d</italic></sup>) T-cells in either irradiated WT B6 or GPRC6a<sup>–/–</sup> mice (H2k<sup><italic>b</italic></sup>). After 72 h, allogeneic T-cells in GPRC6a<sup>–/–</sup> mice showed a significantly increased proliferation compared to WT B6 mice in spleen and thymus (<xref ref-type=\"fig\" rid=\"F3\">Figures 3B,C</xref>), a non-significant increase in axillary lymph nodes (<xref ref-type=\"fig\" rid=\"F3\">Figure 3D</xref>) and no significant changes in the peripheral blood (<xref ref-type=\"fig\" rid=\"F3\">Figure 3E</xref>).</p><fig id=\"F2\" position=\"float\"><label>FIGURE 2</label><caption><p>Inflammatory cells in GPRC6a<sup>–/–</sup> recipient alloSCT mice during GVHD. FACS analysis of CD3, CD4, CD8, B220, CD11b, CD11c, Gr1, CD25, FoxP3 was performed in peripheral blood <bold>(A)</bold>, spleen <bold>(B)</bold> and axillary lymph nodes <bold>(C)</bold>. Samples from BALB/c→GPRC6a<sup>–/–</sup> at day + 15 after alloSCT (<italic>n</italic> = 6). Wildtype (WT) B6 recipient mice served as control (<italic>n</italic> = 5). Mice experiments were performed twice. Error bars indicate mean ± SD. *<italic>P</italic> < 0.05; **<italic>P</italic> < 0.01; n.s., not significant by Student’s <italic>t</italic>-test (two-tailed).</p></caption><graphic xlink:href=\"fimmu-11-01983-g002\"/></fig><fig id=\"F3\" position=\"float\"><label>FIGURE 3</label><caption><p>Proliferation of allogeneic T-cells by GPRC6a<sup>–/–</sup> cells. <bold>(A)</bold>\n<italic>T-cell proliferation in vitro (Mixed lymphocyte reaction):</italic> Percentage of proliferated T-cells after 96 h incubation with dendritic cells analyzed via CFSE in flow cytometry. T-cells were isolated from BALB/c mice, dendritic cells from B6 wildtype (WT) and GPRC6a<sup>–/–</sup> mice (<italic>n</italic> = 3 per group). <bold>(B–E)</bold>\n<italic>T-cell proliferation in vivo:</italic> 4 × 10<sup>6</sup> CFSE labeled T-cells from BALB/c mice (H2kd +) were transplanted in irradiated B6 WT and GPRC6a<sup>–/–</sup> mice (<italic>n</italic> = 4 per group). After 72 h, donor (H2kd +) T-cell proliferation was assessed via CFSE in flow cytometry in spleen <bold>(B)</bold>, thymus <bold>(C)</bold>, axillary lymph nodes <bold>(D)</bold>, and peripheral blood <bold>(E)</bold>. Proliferating cells were determined as cells, showing no CFSE load compared to control samples without T-cell stimulation at time point 0 h. Error bars indicate mean ± SD. <italic>P</italic> < 0.05; significant by Student’s <italic>t</italic>-test (two-tailed).</p></caption><graphic xlink:href=\"fimmu-11-01983-g003\"/></fig><p>We conclude that the potential to induce allogeneic T-cell proliferation is increased in GPRC6a<sup>–/–</sup> antigen presenting cells as compared to WT antigen presenting cells, providing a possible mechanism for the observed increased acute GVHD in GPRC6a<sup>–/–</sup> alloSCT recipients.</p><p>To further elucidate the clinical significance of these preclinical findings showing a possible correlation between Ca<sup>2+</sup> and the occurrence of GVHD, we analyzed patient data from a prospective, multicenter and non-interventional clinical study performed by the Transplant Complications Working Party of the EBMT.</p></sec><sec id=\"S3.SS3\"><title>Patients, Transplant Characteristics, and Serum Calcium Measurement</title><p>The entry criteria for analysis of primary and secondary endpoints were fulfilled in 363 patients. We used the last total Ca<sup>2+</sup> serum level that was measured in the individual patients before start of conditioning. The Ca<sup>2+</sup> cut off point was determined at 2.2 mmol/l (median of measured total Ca<sup>2+</sup> levels), resulting in a high (>median 2.2mmol/l) and a low (≤median 2.2mmol/l) Ca<sup>2+</sup> serum level group. Total Ca<sup>2+</sup> serum levels were corrected for albumin levels; of note we found in our population a strong correlation between calcium and albumin values (<italic>r</italic> = 0.56, <italic>p</italic> < 0.0001). The main patients and transplant characteristics that were included in the analysis of overall survival are described in <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary Table 1</xref>. The majority of parameters were balanced between the two cohorts. However, a higher percentage of patients in complete remission (CR) was observed in the group of patients with Ca<sup>2+</sup> above median before alloSCT. The frequency of patients with reduced-intensity conditioning (RIC) versus myeloablative conditioning differed significantly between the high Ca<sup>2+</sup> group and the low Ca<sup>2+</sup> group. Additionally, the high Ca<sup>2+</sup> group showed significantly less patients treated with ATG and a significantly different Karnofsky score. Therefore subsequent multivariate analysis included these variables.</p></sec><sec id=\"S3.SS4\"><title>Incidence of Acute and Chronic GVHD</title><p>In the present study, the incidence of acute GVHD grades II-IV and grades III-IV in the whole population at 100 days were 25% and 11%, respectively. We found a higher incidence of acute GVHD grades II-IV in univariate (<italic>p</italic> = 0.0024) and multivariate (HR = 2.3 Cl = 1.45–3.85 <italic>p</italic> = 0.0006) analysis in patients with low Ca<sup>2+</sup> serum levels before alloSCT as compared to patients with high Ca<sup>2+</sup> serum levels (<xref ref-type=\"fig\" rid=\"F4\">Figure 4A</xref> and <xref rid=\"T1\" ref-type=\"table\">Tables 1</xref>, <xref rid=\"T2\" ref-type=\"table\">2</xref>). Accordingly, we found significant higher frequency of severe acute GVHD grades III-IV in univariate (<italic>p</italic> = 0.0012) and multivariate (HR = 3.3 CI = 1.59–7.14, <italic>p</italic> = 0.002) analysis in patients with low Ca<sup>2+</sup> serum levels before alloSCT as compared to patients with high Ca<sup>2+</sup> serum levels (<xref ref-type=\"fig\" rid=\"F4\">Figure 4B</xref> and <xref rid=\"T1\" ref-type=\"table\">Tables 1</xref>, <xref rid=\"T2\" ref-type=\"table\">2</xref>). Detailed numbers of aGVHD organ grades are available for 33% of patients with liver and skin aGVHD grades and 17% of patients with gut aGVHD grades (<xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary Table 2</xref>).</p><fig id=\"F4\" position=\"float\"><label>FIGURE 4</label><caption><p>Incidence of acute GVHD until 100 days after alloSCT according to Ca<sup>2+</sup> serum levels prior to alloSCT. Acute GVHD incidence of grades II-IV <bold>(A)</bold> and grades III-IV <bold>(B)</bold> was increased in patients with low Ca<sup>2+</sup> levels (≤2.2 mmol/l, red line) as compared to patients with high Ca<sup>2+</sup> levels (>2.2 mmol/l, blue line).</p></caption><graphic xlink:href=\"fimmu-11-01983-g004\"/></fig><table-wrap id=\"T1\" position=\"float\"><label>TABLE 1</label><caption><p>Univariate global comparison of acute GVHD shown at day + 100 after alloSCT.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Group</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Acute GVHDII–IV [95% CI]</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Acute GVHD III–IV [95% CI]</bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Serum Calcium ≤ 2.2 mmol/l</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">32% (25–39)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16% (11–22)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Serum Calcium > 2.2 mmol/l</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18% (13–24)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6% (3–10)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-value</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>P</italic> = 0.0024</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>P</italic> = 0.0012</td></tr></tbody></table></table-wrap><table-wrap id=\"T2\" position=\"float\"><label>TABLE 2</label><caption><p>Multivariate global comparison.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Calcium ≤ 2.2 mmol/l vs. >2.2 mmol/l</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>HR</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>95% CI</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>p</italic>-value</bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Overall survival</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1,02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0,65 1,59</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.9443</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Progression free survival</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1,28</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0,86 1,89</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.22</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Relapse incidence</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0,95</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0,60 1,52</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.84</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Non-relapse mortality</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1,75</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0,98 3,13</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.06</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Chronic GVHD</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1,52</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0,98 2,38</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.06</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Extensive chronic GVHD</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2,0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1,04 3,85</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.04</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Acute GVHD II-IV</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2,33</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1,45 3,85</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0006</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Acute GVHD III-IV</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3,33</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1,59 7,14</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.002</td></tr></tbody></table></table-wrap><p>In the whole population, the incidence of chronic GVHD at 1 year and 2 years was 26% and 39%, respectively. The incidence of severe chronic GVHD at 1 year and 2 years was 16% and 21%, respectively. Extensive chronic GVHD was significantly increased in multivariate (HR = 2.0 Cl = 1.04–3.85 <italic>p</italic> = 0.04) analysis in patients with low Ca<sup>2+</sup> serum levels before alloSCT as compared to patients with high Ca<sup>2+</sup> serum levels (<xref ref-type=\"fig\" rid=\"F5\">Figures 5A,B</xref> and <xref rid=\"T2\" ref-type=\"table\">Table 2</xref>). However, we observed a trend but no significant differences in overall incidence of chronic GVHD between the two cohorts. As expected, the chronic GVHD incidence was significantly lower in alloSCT recipients receiving anti-T-cell globulin as part of the conditioning regimen (ATG, HR = 0.25 CI = 0.13–0.5, <italic>p</italic> < 0.0001). NIH classification of chronic GVHD was available for 14% of patients shown in <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary Table 3</xref>.</p><fig id=\"F5\" position=\"float\"><label>FIGURE 5</label><caption><p>Incidence of chronic GVHD until 12 months after alloSCT according to Ca<sup>2+</sup> serum levels prior to alloSCT. Incidence of chronic GVHD <bold>(A)</bold> and extensive chronic GVHD <bold>(B)</bold> was increased in patients with low Ca<sup>2+</sup> levels (≤2.2 mmol/l, red line) as compared to patients with high Ca<sup>2+</sup> levels (>2.2 mmol/l, blue line).</p></caption><graphic xlink:href=\"fimmu-11-01983-g005\"/></fig></sec><sec id=\"S3.SS5\"><title>Survival Endpoints</title><p>The overall survival (OS) in the whole population at 1 year was 72%. We observed no significant differences in the OS and progression free survival (PFS) of alloSCT recipients with low Ca<sup>2+</sup> serum levels before alloSCT as compared to patients with high Ca<sup>2+</sup> serum levels (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref> and <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary Table 4</xref>, OS univariate <italic>p</italic> = 0.16; OS multivariate HR = 1.02 Cl = 0.65–1.59 <italic>p</italic> = 0.94 and PFS univariate <italic>p</italic> = 0.12; PFS multivariate HR = 1.28 Cl = 0.86–1.89 <italic>p</italic> = 0.22). However, non-relapse mortality (NRM) was significantly increased in alloSCT recipients with low Ca<sup>2+</sup> levels prior to start of conditioning in univariate analysis (<xref ref-type=\"fig\" rid=\"F6\">Figure 6</xref> and <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary Table 4</xref>, <italic>p</italic> = 0.0247). The same trend (but not significant) was seen in NRM multivariate analysis (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>, HR = 1.75 Cl = 0.98–3.13 <italic>p</italic> = 0.06). The causes of death in patients without relapse were mainly due to infection. A descriptive analysis of the causes of death is given in <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary Table 5</xref>.</p><fig id=\"F6\" position=\"float\"><label>FIGURE 6</label><caption><p>Non-relapse mortality (NRM) until 12 months after alloSCT according to Ca<sup>2+</sup> serum levels prior to alloSCT. NRM was increased in patients with low Ca<sup>2+</sup> levels (≤2.2 mmol/l, red line) as compared to patients with high Ca<sup>2+</sup> levels (> 2.2 mmol/l, blue line).</p></caption><graphic xlink:href=\"fimmu-11-01983-g006\"/></fig></sec><sec id=\"S3.SS6\"><title>Incidence of Relapse</title><p>No difference in the relapse incidence was detected between alloSCT recipients with low and high Ca<sup>2+</sup> levels (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref> and <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary Table 4</xref>, univariate <italic>p</italic> = 0.8408; multivariate HR = 0.95, CI = 0.6–1.52, <italic>p</italic> = 0.84).</p><p>We conclude that low Ca<sup>2+</sup> levels (below median of 2.2 mmol/l) prior to alloSCT are associated with increased incidence of acute GVHD as well as extensive chronic GVHD.</p></sec></sec><sec id=\"S4\"><title>Discussion</title><p>Ca<sup>2+</sup> signaling is involved in various biologic processes relevant for patients after alloSCT, including inflammation, anti-infectious immunity, anti-tumor effects, kidney diseases and bone homeostasis. Although our current descriptive experimental and clinical results exhibit some limitations and lacks further mechanistic insights, it gives first insights in a possible association between Ca<sup>2+</sup> and the occurrence of GVHD, deserving further investigations.</p><p>In animal experiments, GPRC6a<sup>–/–</sup> alloSCT recipients had increased GVHD morbidity and mortality. GPRC6a, a G protein coupled receptor, was previously found to be activated by increased extracellular Ca<sup>2+</sup> levels leading to an increase in the intracellular Ca<sup>2+</sup> concentration, triggering the activation of i.a. the NLRP3 inflammasome and caspase-1, mediated through the phosphatidyl inositol/Ca<sup>2+</sup> pathway. Activation of the NLRP3 inflammasome via DAMPs has already been shown to be connected to GVHD severity (7, 19). In preclinical models, inhibition of the NLRP3 inflammasome lead to improved GVHD morbidity and delayed GVHD-induced mortality (5–7). Interestingly, when using NLRP3 deficient mice, this effect was only prominent when deficiency was present in alloSCT recipients, not in donors (6). In patients, recipient single nucleotide polymorphisms of NLRP3 were found connected to the incidence of acute GVHD grades 2–4 (20). While the role of NLRP3 was studied in GVHD, potential upstream immune regulatory functions of GPRC6a are unknown. Results in GPRC6a<sup>–/–</sup> mice showing reduced inflammatory response and reduced inflammasome activation in a mouse model of carrageenan-induced footpad swelling (4), suggested that GPRC6a deficiency may also lead to reduced GVHD-associated inflammation as seen in GVHD studies analyzing the NLRP3 inflammasome. However, our data contradicts these findings, as GPRC6a deficiency in alloSCT recipients was associated to increased acute GVHD. As GPRC6a is located upstream from the NLRP3 inflammasome, influence on other pathways is likely, potentially leading to differing results. In addition, GPRC6a is widely expressed throughout the body and can recognize multiple ligands next to Ca<sup>2+</sup>, e.g., L-α-amino acids as L-arginine or L-lysin, zinc, testosterone or osteocalcin acting as positive or negative allosteric modulators (9, 21). The wide tissue expression and multiple activators of GPRC6a, let assume a wide range of functions and the entire scope of GPRC6a functions is not yet known. In line, experimental data from GPRC6a<sup>–/–</sup> mice show a diversity in physiological and pathophysiological functions, mainly involving a role in inflammation, endocrine functions and metabolism. However, functional phenotyping of these mice is enclosed by disparities, e.g., in glucose and fat or bone metabolism (9, 22). Whereas one group found glucose intolerance and insulin resistance as well as osteopenia and impaired osteoblast mediated bone formation in GPRC6a<sup>–/–</sup> mice (22), other groups could not detect such phenotypes (23, 24). This underlines the possibility that besides the Ca<sup>2+</sup> mediated GPRC6a signaling, other signaling pathways contribute to the observed phenotype in our GVHD model.</p><p>Of note, despite its regulatory functions in inflammation, GPRC6a was shown to mediate the non-genomic effects of testosterone and other steroids <italic>in vitro</italic> and in mice (25). This raises the possibility that steroid therapy used for GVHD prophylaxis may be influenced by GPRC6a expression.</p><p>GPRC6a can be directly activated by Ca<sup>2+</sup> in concentrations above 5 mM (9); and Ca<sup>2+</sup> was shown to be involved in various immune functions, including T-cell effector and regulatory functions (1) as well as the allo-activation of T-lymphocytes by antigen presenting cells (2, 3). In GPRC6a<sup>–/–</sup> mice, we also found increased antigen presentation potential demonstrated by higher proliferation rates of allogeneic T-lymphocytes in GPRC6a<sup>–/–</sup> compared to wildtype alloSCT recipients, leading to increased GVHD severity. Phenotypic differences of GPR6CA<sup>–/–</sup> dendritic cells were detected concerning reduced CD80 expression; however, further changes in costimulatory molecules are likely and need further investigation. The role of Ca<sup>2+</sup> signaling during GVHD is mostly unknown, although first results in preclinical GVHD models support our data that Ca<sup>2+</sup> may play a role in mediating GVHD pathophysiology (3, 11). However, Ca<sup>2+</sup> signaling appears to remain complex, multi-targetable and divergent and deserves further investigation. E.g., the deletion of the plasma membrane proteins ORAI1 + 2 forming release-activated Ca<sup>2+</sup> channels leads to the abolishment of store-operated Ca<sup>2+</sup> entry and a decrease of intracellular Ca<sup>2+</sup>, and protects mice from allo-immunity in models of colitis and GVHD (3). Contrasting and more coherent to our data, the inhibition of inositol 1,4,5-trisphosphate 3-kinase B (Itpkb), a negative regulator of Ca<sup>2+</sup> influx, leads to an increase of intracellular Ca<sup>2+</sup> in donor T cells, protecting mice from acute and chronic GVHD while maintaining a graft-versus-tumor-effect (11). Interestingly, high calcium containing solutions are effective to reduce mucositis after alloSCT raising the possibility that Ca<sup>2+</sup> signaling is involved in tissue repair or tissue protection (26).</p><p>Our performed prospective study suggests an association of Ca<sup>2+</sup> with the occurrence of GVHD as low Ca<sup>2+</sup> serum levels (below median of 2.2 mmol/l) prior to alloSCT were found to be a risk factor for the incidence of acute GVHD and for extensive chronic GVHD. However, a limitation of this clinical study is the lack of Ca<sup>2+</sup> serum levels after alloSCT, e.g., during occurrence of GVHD; leading to an incomplete understanding of the effects of Ca<sup>2+</sup> on post alloSCT immunity. The aim of our prospective trial was to identify biomarkers that can be measured before alloSCT. Therefore, no biomarker measurement at time of GVHD onset was planned in the trial design. Importantly, further investigations addressing the longitudinal progress of Ca<sup>2+</sup> levels post alloSCT will further shed light on the association of Ca<sup>2+</sup> and GVHD pathophysiology. In this study, we assessed total Ca<sup>2+</sup> in serum corrected to albumin levels, as approximate 45% of circulating Ca<sup>2+</sup> is bound to proteins, mainly albumin, which displays besides its high-capacity carrier role also several physiological functions. Despite this correction, subsequent analyses may address if free ionized Ca<sup>2+</sup> and/or Ca<sup>2+</sup> bound to proteins or anions show an impact on GVHD incidence. Of note, we found in our population a strong correlation between calcium and albumin values (<italic>r</italic> = 0.56, <italic>p</italic> < 0.0001). Recently, hypoalbuminemia was shown to predict worse NRM and inferior OS (27) as well as severity of aGVHD (28) in alloSCT recipients.</p><p>Our patient population was restricted to alloSCT from HLA-identical sibling donors, allowing to draw conclusions from a homogeneous population with similar GVHD prophylaxis strategies and similar incidences of GVHD and other complications. However, this includes the limitation that we are therefore unable to draw definite conclusions from these results regarding the association of Ca<sup>2+</sup> serum levels with outcome in matched unrelated donor alloSCT or in haploidentical alloSCT, which are increasingly used. The herein presented data can stimulate further research to perform analyses of Ca<sup>2+</sup> serum levels in other multiple cohorts allowing to widen conclusions to other donor types.</p></sec><sec id=\"S5\"><title>Conclusion</title><p>This study gives first insights in a possible association of Ca<sup>2+</sup> with the incidence and severity of GVHD. Future perspectives of our results will include the clinical implementation of risk-adapted GVHD prophylaxis strategies taking into account calcium levels before alloSCT. Future studies can address the question how the high risk of GVHD in the subpopulation identified in this study can be reduced. A possible trial design would be to test intensified GVHD prophylaxis regimens in a randomized trial, based on calcium levels. This intensified regimen could be the addition of a third immunosuppressive drug in addition to the standard regimens consisting of a calcineurin inhibitor and an antimetabolite. Furthermore, future research areas of interest include: 1) to further analyze the involved mechanistic and molecular pathways of Ca<sup>2+</sup>-dependent allo-activation, and 2) to test if Ca<sup>2+</sup> signaling can serve as a therapeutic target during GVHD or other inflammatory diseases.</p></sec><sec sec-type=\"data-availability\" id=\"S6\"><title>Data Availability Statement</title><p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.</p></sec><sec id=\"S7\"><title>Ethics Statement</title><p>The studies involving human participants were reviewed and approved by IRB and/or Ethics Committee in all participating centers. The patients/participants provided their written informed consent to participate in this study. The animal study was reviewed and approved by State Office of Health and Social Affairs (LAGeSo), Berlin.</p></sec><sec id=\"S8\"><title>Author Contributions</title><p>KR, SC, and OP designed the animal study and wrote the manuscript. SC, MK, SM, and JM performed experimental GVHD experiments. CS and YS performed and analyzed qPCR experiments. KR and SC analyzed experimental data. OP, ZP, CK, HS, RD, and GB designed and performed the EBMT study. CP and SW analyzed human data and performed statistical analyses. All authors contributed to the article and approved the submitted version.</p></sec><sec id=\"conf1\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> KR acknowledges the support of José Carreras Leukämie-Stiftung (DJCLS 03 FN/2017), Deutsche Krebshilfe (70113055), and DKMS (DKMS-SLS-MHG-2019-04). OP acknowledges the support of José Carreras Leukämie-Stiftung (11R2016, 3R/2019), Deutsche Krebshilfe (70113519), and Deutsche Forschungsgemeinschaft (PE 1450/7-1). The Charité Medical Library covers Article Processing Costs.</p></fn></fn-group><ack><p>We thank Hans Bräuner-Osborne for providing the GPRC6a<sup>–/–</sup> mice (University of Copenhagen, Denmark) and acknowledge technical help from Manuela Rossol and Ulf Wagner (University of Leipzig, Germany). We also thank all the participating EBMT centers and their principal investigators for organization and support: Jürgen Finke, Arnold Ganser, Helene Schoemans, Jiri Pavlu, Riitta Niittyvuopio, Wilfried Schroyens, Leylagül Kaynar, Igor W. Blau, Walter van der Velden, Jorge Sierra, Agostino Cortelezzi, Gerald Wulf, Pascal Turlure, Montserat Rovira, Zubeydenur Ozkurt, Maria J. Pascual-Cascon, Maria C. Moreira, Johannes Clausen, and Hildegard Greinix.</p></ack><fn-group><fn id=\"footnote1\"><label>1</label><p><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.R-project.org/\">https://www.R-project.org/</ext-link></p></fn></fn-group><sec id=\"S11\" sec-type=\"supplementary material\"><title>Supplementary Material</title><p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.frontiersin.org/articles/10.3389/fimmu.2020.01983/full#supplementary-material\">https://www.frontiersin.org/articles/10.3389/fimmu.2020.01983/full#supplementary-material</ext-link></p><supplementary-material content-type=\"local-data\" id=\"TS1\"><media xlink:href=\"Table_1.DOCX\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec><ref-list><title>References</title><ref id=\"B1\"><label>1.</label><mixed-citation publication-type=\"journal\"><person-group 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Genet</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Genet</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Genet.</journal-id><journal-title-group><journal-title>Frontiers in Genetics</journal-title></journal-title-group><issn pub-type=\"epub\">1664-8021</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849830</article-id><article-id pub-id-type=\"pmc\">PMC7431964</article-id><article-id pub-id-type=\"doi\">10.3389/fgene.2020.00850</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Genetics</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>Genome-Wide Transcriptional Regulation of the Long Non-coding RNA Steroid Receptor RNA Activator in Human Erythroblasts</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Sawaengdee</surname><given-names>Waritta</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/979712/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Cui</surname><given-names>Kairong</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Zhao</surname><given-names>Keji</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Hongeng</surname><given-names>Suradej</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/634490/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Fucharoen</surname><given-names>Suthat</given-names></name><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/737404/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Wongtrakoongate</surname><given-names>Patompon</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff5\"><sup>5</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/599390/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Department of Biochemistry, Faculty of Science, Mahidol University</institution>, <addr-line>Bangkok</addr-line>, <country>Thailand</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Laboratory of Epigenome Biology, Systems Biology Center, National Heart, Lung, and Blood Institute, National Institutes of Health</institution>, <addr-line>Bethesda, MD</addr-line>, <country>United States</country></aff><aff id=\"aff3\"><sup>3</sup><institution>Department of Pediatrics, Faculty of Medicine Ramathibodi Hospital, Mahidol University</institution>, <addr-line>Bangkok</addr-line>, <country>Thailand</country></aff><aff id=\"aff4\"><sup>4</sup><institution>Thalassemia Research Center, Institute of Molecular Biosciences, Mahidol University</institution>, <addr-line>Bangkok</addr-line>, <country>Thailand</country></aff><aff id=\"aff5\"><sup>5</sup><institution>Center for Neuroscience, Faculty of Science, Mahidol University</institution>, <addr-line>Bangkok</addr-line>, <country>Thailand</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Wenbo Li, The University of Texas Health Science Center at Houston, United States</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Argyris Papantonis, University Medical Center Göttingen, Germany; Punit Prasad, Institute of Life Sciences (ILS), India</p></fn><corresp id=\"c001\">*Correspondence: Patompon Wongtrakoongate, <email>patompon.won@mahidol.ac.th</email></corresp><fn fn-type=\"other\" id=\"fn004\"><p>This article was submitted to RNA, a section of the journal Frontiers in Genetics</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>850</elocation-id><history><date date-type=\"received\"><day>09</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>13</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Sawaengdee, Cui, Zhao, Hongeng, Fucharoen and Wongtrakoongate.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Sawaengdee, Cui, Zhao, Hongeng, Fucharoen and Wongtrakoongate</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>Erythropoiesis of human hematopoietic stem cells (HSCs) maintains generation of red blood cells throughout life. However, little is known how human erythropoiesis is regulated by long non-coding RNAs (lncRNAs). By using ChIRP-seq, we report here that the lncRNA steroid receptor RNA activator (SRA) occupies chromatin, and co-localizes with CTCF, H3K4me3, and H3K27me3 genome-wide in human erythroblast cell line K562. CTCF binding sites that are also occupied by SRA are enriched for either H3K4me3 or H3K27me3. Transcriptome-wide analyses reveal that SRA facilitates expression of erythroid-associated genes, while repressing leukocyte-associated genes in both K562 and CD36-positive primary human proerythroblasts derived from HSCs. We find that SRA-regulated genes are enriched by both CTCF and SRA bindings. Further, silencing of SRA decreases expression of the erythroid-specific markers TFRC and GYPA, and down-regulates expression of globin genes in both K562 and human proerythroblast cells. Taken together, our findings establish that the lncRNA SRA occupies chromatin, and promotes transcription of erythroid genes, therefore facilitating human erythroid transcriptional program.</p></abstract><kwd-group><kwd>steroid receptor RNA activator</kwd><kwd>erythroblasts</kwd><kwd>histone modification</kwd><kwd>epigenetics</kwd><kwd>stem cells</kwd></kwd-group><funding-group><award-group><funding-source id=\"cn001\">Thailand Research Fund<named-content content-type=\"fundref-id\">10.13039/501100004396</named-content></funding-source><award-id rid=\"cn001\">TRG5880105</award-id></award-group><award-group><funding-source id=\"cn002\">Faculty of Science, Mahidol University<named-content content-type=\"fundref-id\">10.13039/501100007180</named-content></funding-source></award-group></funding-group><counts><fig-count count=\"6\"/><table-count count=\"0\"/><equation-count count=\"0\"/><ref-count count=\"65\"/><page-count count=\"15\"/><word-count count=\"0\"/></counts></article-meta></front><body><sec id=\"S1\"><title>Introduction</title><p>Adult erythropoiesis is a cellular physiological process in the bone marrow which produces red blood cells (RBCs) to maintain homeostasis of the body. Through the proerythroblast stage producing transit amplifying cells, billions of RBCs are spatiotemporally generated by hematopoietic stem cells (HSCs). Intrinsic, extrinsic, and environmental factors play crucial roles in this process to precisely control a sufficient quantity of the oxygen-carrying cells that are required for oxygen transport. Among key intrinsic factors regulating erythropoiesis are chromatin binding proteins including transcriptional and epigenetic machineries. At the onset of erythropoiesis, a sequential activation of DNA-binding transcription factors such as GATA1, TAL1, and KLF1 delineates the gradual development of erythroid cells (<xref rid=\"B57\" ref-type=\"bibr\">Wickrema and Crispino, 2007</xref>). The histone modifying complexes trithorax group (TrxG) and polycomb repressive complex 2 (PRC2), which methylate H3K4, and H3K27, respectively, are also critical for normal erythropoiesis (<xref rid=\"B38\" ref-type=\"bibr\">Majewski et al., 2008</xref>; <xref rid=\"B19\" ref-type=\"bibr\">Gan et al., 2010</xref>; <xref rid=\"B41\" ref-type=\"bibr\">Mochizuki-Kashio et al., 2011</xref>). Yet, little is known about how distinct transcription and epigenetic factors are recruited or tethered to chromatin. Thus characterization of mechanisms involved in genetic-epigenetic crosstalk is essential to understand erythropoiesis.</p><p>A significant role has been discovered for long non-coding RNAs (lncRNAs) in transcriptional control (<xref rid=\"B47\" ref-type=\"bibr\">Rinn and Chang, 2012</xref>). The lncRNA steroid receptor RNA activator (SRA) was identified as a non-coding transcript which promotes transcriptional activation of the estrogen receptors (<xref rid=\"B32\" ref-type=\"bibr\">Lanz et al., 1999</xref>, <xref rid=\"B33\" ref-type=\"bibr\">2002</xref>). A role of SRA has been reported in regulation of imprinted gene expression via the chromatin architectural transcription factor CTCF and SRA-associated RNA helicase DDX5 (<xref rid=\"B62\" ref-type=\"bibr\">Yao et al., 2010</xref>). Moreover, we have also shown that SRA physically and directly interacts with NANOG, CTCF, TrxG, and PRC2, and that SRA is important for maintenance of pluripotency and transition into induced pluripotent stem cells (<xref rid=\"B59\" ref-type=\"bibr\">Wongtrakoongate et al., 2015</xref>). Whether SRA participates in regulation of erythropoiesis has been elusive. In the present study, we report a novel function of the lncRNA SRA in regulation of global gene expression through direct chromatin binding in human erythroleukemia cell line K562 and in primary human proerythroblasts derived from HSCs. We demonstrate that SRA, together with CTCF, H3K4me3, and H3K27me3, occupies various genomic regions in K562. Further, SRA facilitates transcriptome-wide expression of erythroid program and expression of erythroid markers in K562 and in primary human proerythroblasts. Hence, a possible function of the lncRNA SRA is to promote transcription of erythroid-associated genes.</p></sec><sec sec-type=\"materials|methods\" id=\"S2\"><title>Materials and Methods</title><sec id=\"S2.SS1\"><title>Cell Culture</title><p>The cell line K562 (ATCC) was cultured in RPMI 1640 medium with GlutaMAX<sup>TM</sup> (Invitrogen) supplemented with 10% fetal bovine serum at 37°C with 5% CO<sub>2</sub> and passaged every 3 days. CD36-positive human proerythroblasts were derived from bone marrow CD34-positive cells, which were purchased from Stem Cell Technologies (70002.1) and cultured in erythroid differentiation condition as previous described (<xref rid=\"B58\" ref-type=\"bibr\">Wong et al., 2008</xref>). Briefly, the CD34-positive cells at 10<sup>4</sup> cells/ml were grown in the serum-free erythroid expansion medium containing Alpha minimum essential medium (AMEM; Mediatech) and 20% BIT9500 (Stem Cell Technologies) to achieve bovine serum albumin, recombinant human insulin and iron-saturated human transferrin at 10 mg/ml, 10 μg/ml, and 200 μg/ml, respectively. In addition, 900 ng/ml ferrous sulfate (Sigma), 90 ng/ml ferric nitrate (Sigma), 1 μM hydrocortisone (Sigma), 100 ng/ml of recombinant human stem cell factor (SCF; Stem Cell Technologies), 5 ng/ml of recombinant human interleukin-3 (IL-3; R&D Systems), and 3 IU/ml of recombinant human EPO were also included. Fresh medium was added into the culture to maintain cells at 2 × 10<sup>6</sup> cells/ml. The cells were cultured for 7 days to obtain CD36-positive cells.</p></sec><sec id=\"S2.SS2\"><title>RNA Silencing</title><p>shRNA templates including luciferase shRNA scramble control (shLuc) and shRNA sequences targeting <italic>SRA</italic> transcript (shSRA-1: 5′ CCACAAGTTTCCCAGTCGAGT 3′, shSRA-2: 5′ TGCAGCCACAGCTGAGAAGAA 3′, and shSRA-3: 5′ ACTGAGGTCAGTCAGTGGAT 3′) were individually cloned into the lentiviral vector pGreenPuro (System Biosciences) at <italic>BamH</italic>1/<italic>Eco</italic>RI restriction sites according to the manufacturer’s instruction. The plasmids were transformed into <italic>Escherichia coli</italic> strain <italic>stbl3</italic> (Invitrogen) via heat shock method and propagated in LB broth supplemented with carbenicillin. All plasmids were purified by using PureLink<sup>TM</sup> HiPure Plasmid Maxiprep Kit (Invitrogen).</p><p>Lentiviral particles were produced by co-transfecting LentiX-293T cells (Clontech) with a packaging vector (psPAX2), an envelope vector (pLP/VSVG), and an shRNA plasmid (shLuc, shSRA-1, shSRA-2, or shSRA-3) using Lipofectamine 2000 (Invitrogen) as previous described (<xref rid=\"B29\" ref-type=\"bibr\">Kidder et al., 2017</xref>). Twenty four hour after the transfection, the medium was changed to the target cell medium. Then the medium containing lentiviral particles were collected and filtered through 0.22 μM filter at 48 h post-transfection. Transduction was performed by adding the medium containing lentiviral particles with 10 μg/ml polybrene into either K562 or CD36-positive proerythroblasts. The cells were then centrifuged at 1,000 <italic>g</italic> at room temperature for 2 h and incubated at 37°C with 5% CO<sub>2</sub> overnight before changing medium. Expression of GFP was examined under fluorescent microscope to validate transduction efficiency at 48 h post-transduction. The GFP-positive cells were then sorted by FACS at 96 h post-transduction and maintained in the presence of 0.5 μg/ml puromycin for further analysis.</p></sec><sec id=\"S2.SS3\"><title>RNA-Sequencing Analysis</title><p>Total RNA was extracted from the sorted cells and purified using QIAzol Lysis Reagent and miRNeasy Micro Kit (Qiagen). RNA samples for sequencing were prepared according to Smart-seq2 method (<xref rid=\"B45\" ref-type=\"bibr\">Picelli et al., 2014</xref>) with some modifications as previously described (<xref rid=\"B24\" ref-type=\"bibr\">Hu et al., 2018</xref>). RNA-seq libraries were prepared with an End-It DNA End-repair Kit (Epicenter) and a Multiplexing Sample Preparation Oligonucleotide Kit (Illumina), and the libraries submitted for single-end sequencing on the Illumina HiSeq2500 (<xref rid=\"B25\" ref-type=\"bibr\">Hu et al., 2013</xref>). Sequencing data was annotated to the human reference genome GRch38 by Tophat2 (<xref rid=\"B30\" ref-type=\"bibr\">Kim et al., 2013</xref>) with Bowtie2 (<xref rid=\"B31\" ref-type=\"bibr\">Langmead and Salzberg, 2012</xref>), and the raw read counts and FPKMs were acquired by HTseq (<xref rid=\"B4\" ref-type=\"bibr\">Anders et al., 2015</xref>) and Cufflinks (<xref rid=\"B52\" ref-type=\"bibr\">Trapnell et al., 2010</xref>), respectively. TPMs were then calculated according to their FPKMs. Using raw read count from HTseq as input, differentially expressed genes (DEGs) were determined by DEseq package (<xref rid=\"B3\" ref-type=\"bibr\">Anders and Huber, 2010</xref>) with <italic>p</italic>-value < 0.01 and with TPM > 2. Three different SRA silencing samples targeted for individual shRNA target sites were used as three biological replicates. Accession number of sequencing data associated with RNA-seq is <ext-link ext-link-type=\"DDBJ/EMBL/GenBank\" xlink:href=\"GSE151926\">GSE151926</ext-link>. Volcano plots and heatmaps were generated by R studio (<xref rid=\"B48\" ref-type=\"bibr\">RStudio Team, 2016</xref>) using EnhancedVolcano and gplots package, respectively (<xref rid=\"B8\" ref-type=\"bibr\">Blighe et al., 2020</xref>). Gene ontology (GO) enrichment and KEGG pathways were determined by DIVID software (<xref rid=\"B26\" ref-type=\"bibr\">Huang et al., 2008</xref>, <xref rid=\"B27\" ref-type=\"bibr\">2009</xref>) using the DEGs from DEseq. Network analysis of coding genes from DEGs was performed using STRING or Search Tool for the Retrieval of Interacting Genes/Proteins (<xref rid=\"B50\" ref-type=\"bibr\">Szklarczyk et al., 2015</xref>).</p></sec><sec id=\"S2.SS4\"><title>Quantitative Real-Time PCR Analysis</title><p>RNA was extracted and purified using QIAzol Lysis Reagent and miRNeasy Micro Kit (Qiagen). Reverse transcription was carried on with 1 μg RNA using iScript<sup>TM</sup> Reverse Transcription Supermix for RT-qPCR (Biorad). qRT-PCR was performed by using KAPA SYBR<sup>®</sup> FAST qPCR Master Mix (2X) Kit (Kapa Biosystems) with LightCycler<sup>®</sup> 96 system (Roche). <italic>ACTB</italic> gene was used for normalization of gene expression and the ΔΔCt method was used for analysis of relative expression level. Primer sequences are available upon requested.</p></sec><sec id=\"S2.SS5\"><title>Flow Cytometry Analysis</title><p>One million cells and five million cells were used for surface marker analysis and cell sorting, respectively. Cells were collected and resuspended in 100 μl PBS with 2% FBS. Fluorescent conjugated antibodies were added into the cell suspension and incubated at 4°C for 30 min in dark. The stained cells were washed once and resuspended in the PBS/FBS buffer before analysis or sorting with FACSAria II cell sorter (BD Biosciences). Data were analyzed with FlowJo software. Unstained wild-type cells and the Fluorescence Minus One (FMO) controls were used as negative control for gating population. The antibodies using in the experiment include APC-conjugated anti-human CD235a (eBioscience, 17-9987-41), PE-conjugated anti-human CD34 (eBioscience, 12-0349-41), and PerCP-eFluor710-conjugated anti-human CD36 (eBioscience, 46-0369-41).</p></sec><sec id=\"S2.SS6\"><title>Chromatin Isolation by RNA Purification (ChIRP)</title><p>Chromatin isolation by RNA purification (ChIRP) analysis was performed as previously described with minor modifications (<xref rid=\"B11\" ref-type=\"bibr\">Chu et al., 2011</xref>, <xref rid=\"B12\" ref-type=\"bibr\">2012</xref>; <xref rid=\"B59\" ref-type=\"bibr\">Wongtrakoongate et al., 2015</xref>). The cell line K562 harvested at 3 × 10<sup>7</sup> cells were fixed with 1% glutaraldehyde for 10 min at room temperature with a rotator and then stopped by adding glycine solution at 125 mM of its final concentration. Crosslinked cells were washed with PBS, and resuspended in 1 ml swelling buffer. Samples were incubated at 4°C for 30 min with a rotator. Cell pellet was collected by centrifugation and resuspended with 350 μL of ChIRP lysis buffer. Cell sonication was performed using a Bioruptor (Diagenode) at maximum power, 30 s ON and 30 s OFF for 7.5 min of 6 cycles to obtain chromatin fragments ranging from 100–1000 bp. Chromatin fragments was then collected by centrifugation. Two hundred micrograms of sheared chromatin samples were pre-cleared using 100 μL of Ultralink-streptavidin beads (Pierce) for 1 h at room temperature with a rotator, and supernatant was collected. The pre-cleared chromatin was used per hybridization reaction with 10 μL of 100 μM pooled 3′ Biotin TEG oligonucleotide probes (Integrated DNA Technologies) against SRA transcript (<xref rid=\"B59\" ref-type=\"bibr\">Wongtrakoongate et al., 2015</xref>). LacZ probes were employed as negative control (<xref rid=\"B11\" ref-type=\"bibr\">Chu et al., 2011</xref>). The sample and the probes were hybridized at 37°C for 4 h with a rotator. Once the hybridization was completed, 100 μL of C-1 magnetic beads (Invitrogen) was mixed with the sample to pull down the biotinylated probes. DNA was eluted in the presence of 12.5 mM D-Biotin (Invitrogen). DNA was ethanol precipitated and subjected to library preparation, which was performed using MicroPlex Library Preparation Kit (Diagenode) according to manufacturer’s instruction. Three biological triplicates were used for ChIRP-seq. Briefly, 5–10 ng of DNA starting material, which was quantified by Qubit (Invitrogen), was used for each biological sample. The DNA was end-repaired, 3′ adenylated, and ligated with adapters. Then the ligated DNA was size-selected to obtain DNA fragments at 250–300 bp by agarose gel electrophoresis. The purified DNA was amplified to enrich the library. The final PCR product was purified by Agencourt AMPure XP beads (Beckman Coulter) and was submitted for high-throughput sequencing using Illumina HiSeq2500. The sequencing was performed with the run type of single-end, 50 bp read. Data were aligned against the human genome version human_hg19, and were exported into BAM file format. Accession number of sequencing data associated with ChIRP-seq is <ext-link ext-link-type=\"DDBJ/EMBL/GenBank\" xlink:href=\"GSE153004\">GSE153004</ext-link>.</p><p>The associated-binding regions of SRA identified from ChIRP-seq and ChIP-seq data for CTCF, H3K4me3, and H3K27me3 in K562 were identified by ChIPpeakAnno package (<xref rid=\"B65\" ref-type=\"bibr\">Zhu et al., 2010</xref>; <xref rid=\"B64\" ref-type=\"bibr\">Zhu, 2013</xref>) with max gap equals to 500 bp in R studio. The ChIP-seq data were taken from the ENCODE project of K562 cells. The region-associated genes were identified by GREAT (<xref rid=\"B40\" ref-type=\"bibr\">McLean et al., 2010</xref>) using two nearest genes’ TSS within 50 kb up- and down-stream of the regulatory binding sites including curated regulatory domains. The Fisher’s Exact test to measure peak enrichment was taken from the Fisher’s exact function from the R package for statistical computing (<xref rid=\"B46\" ref-type=\"bibr\">R Core Team, 2013</xref>).</p></sec><sec id=\"S2.SS7\"><title>RNA Pull Down</title><p>RNA pull down experiments were performed as previously described (<xref rid=\"B53\" ref-type=\"bibr\">Tsai et al., 2010</xref>; <xref rid=\"B59\" ref-type=\"bibr\">Wongtrakoongate et al., 2015</xref>). The plasmid pLITMUS28i (New England Biolabs) containing full length SRA was linearized by <italic>Stu</italic>I or <italic>Bgl</italic>I to generate antisense or sense SRA transcripts, respectively (<xref rid=\"B59\" ref-type=\"bibr\">Wongtrakoongate et al., 2015</xref>). Biotinylated SRA and a maltose-binding protein transcript were <italic>in vitro</italic> transcribed using HiScribe T7 High Yield RNA Synthesis Kit (New England Biolabs) in the presence of biotin-14-CTP (Invitrogen). Transcribed RNA products were DNase-treated (Roche), and purified by ethanol precipitation. 3 μg of sense SRA, antisense SRA, and MBP RNA was individually prepared in RNA structure buffer (Tris–Cl pH 7.5, 0.1 M KCl, and 10 mM MgCl<sub>2</sub>) and incubated at 78°C for 3 min. The RNA was then gradually cooled down to 37°C. Five hundred micrograms of K562 nuclear extract, which was prepared by Nuclear Protein Extraction Kit (Pierce), was mixed with the RNA in immunoprecipitation buffer (PBS plus 0.1% Triton X-100, 1 mM DTT, protease inhibitor cocktail, PMSF, and 80 U RNase inhibitor) in a total volume of 500 μL. The reaction was incubated for 4 h at 4°C with rotation. The RNA-beads complex isolated by MyOne Streptavidin C1 beads was further incubated overnight. Beads were washed five times with immunoprecipitation buffer and boiled with SDS loading buffer for western blot analysis.</p></sec><sec id=\"S2.SS8\"><title>Statistical Analysis</title><p>For qPCR and FACS, data was analyzed using two-tailed unpaired Student <italic>t</italic>-test and shown as mean with standard deviation of three independent replicates. The significance values were determined at 95%, with <italic>p</italic> ≤ 0.05. Differential expressed genes were determined by DEseq package based on the negative binomial distribution with significant at <italic>p</italic> < 0.01. Fisher Exact test was used to determine significant enriched GO terms with <italic>p</italic> < 0.05 using DAVID software.</p></sec></sec><sec id=\"S3\"><title>Results</title><sec id=\"S3.SS1\"><title>SRA Co-localizes With CTCF, H3K4me3, or H3K27me3 Genome-Wide</title><p>The lncRNA SRA has been shown to mediate transcriptional regulation in several cellular contexts (<xref rid=\"B13\" ref-type=\"bibr\">Colley and Leedman, 2011</xref>). We have previously reported that SRA possesses genome-wide binding regions of human pluripotent stem cells (<xref rid=\"B59\" ref-type=\"bibr\">Wongtrakoongate et al., 2015</xref>). Yet, little is known for the role of SRA in transcriptional regulation in erythropoiesis. To identify SRA-binding sites of human erythroblasts, we performed SRA ChIRP-seq (<xref rid=\"B11\" ref-type=\"bibr\">Chu et al., 2011</xref>; <xref rid=\"B59\" ref-type=\"bibr\">Wongtrakoongate et al., 2015</xref>) for the human erythroblast cell line K562. Biotin-conjugated deoxyoligonucleotide probes tiling along SRA (<xref rid=\"B59\" ref-type=\"bibr\">Wongtrakoongate et al., 2015</xref>) were hybridized with the lncRNA using sheared chromatin from K562 cells. Using next generation sequencing, we identified 2,790 SRA-binding sites genome-wide; most of which are located within 50 kb upstream or downstream of transcription start site (<xref ref-type=\"supplementary-material\" rid=\"FS1\">Supplementary Figure S1</xref>). Up- or down-stream nearest genes within this 50 kb were queried. Among these 2,790 SRA-bound genomic regions, 1,742 and 1,048 regions representing 62.4 and 37.6% of total SRA binding sites are associated with 2,170 genes and not associated with any nearby genes, respectively. Gene classification analysis reveals that SRA-bound regions are associated with genes involved in cell proliferation, Wnt signaling, NF-κB signaling, regulation of protein phosphorylation, cell differentiation, and metabolisms (<xref ref-type=\"fig\" rid=\"F1\">Figure 1A</xref> and <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary Table S1</xref>). Seven genes bound by SRA were also identified as oxygen transport including <italic>HBA2</italic>, <italic>HBB</italic>, <italic>HBD</italic>, <italic>HBG1</italic>, <italic>HBE1</italic>, <italic>HBM</italic>, and <italic>CYGB</italic>. We validated the occupancy of the lncRNA SRA at alpha and beta globin loci using ChIRP followed by real-time PCR analysis, which reveals an association of the lncRNA along both alpha and beta globin loci (<xref ref-type=\"supplementary-material\" rid=\"FS2\">Supplementary Figure S2</xref>). Specifically, at the alpha locus SRA occupies the regulatory element HS40, a site upstream of <italic>HBA2</italic> and a site downstream of the locus (<xref ref-type=\"supplementary-material\" rid=\"FS2\">Supplementary Figures S2A,B</xref>). At the beta locus, SRA occupies <italic>HBB</italic>, <italic>HBG</italic>, <italic>HBE</italic> as well as the locus control region (LCR; <xref ref-type=\"supplementary-material\" rid=\"FS2\">Supplementary Figures S2C,D</xref>). The ChIRP-seq and ChIRP-PCR results therefore indicate the direct association of the lncRNA SRA at chromatin level of human erythroblasts.</p><fig id=\"F1\" position=\"float\"><label>FIGURE 1</label><caption><p>The lncRNA SRA occupies chromatin genome-wide with CTCF, H3K4me3, and H3K27me3. ChIRP-seq analysis of SRA was performed using human erythroblast cells K562. Publicly available ChIP-seq data for CTCF, H3K4me3, and H3K27me3 were derived from the ENCODE project. <bold>(A)</bold> Categories of enriched gene ontologies of SRA-associated genes (<italic>p</italic>-value < 0.05) and their enrichment scores [−log(<italic>p</italic>-value)] were analyzed using DAVID. <bold>(B)</bold> Examples of genes with the enrichment signal of SRA, LacZ, and the input. <bold>(C)</bold> Genomic regions occupied by SRA and containing CTCF binding site tend to associate with H3K4me3. Left; Genome-wide SRA-binding sites were compared with CTCF and H3K4me3. Right; Percentage of co-occupancy of H3K4me3 at SRA binding sites without or with CTCF occupancy. <bold>(D)</bold> Genomic regions occupied by SRA and containing CTCF binding site tend to associate with H3K27me3. Left; Genome-wide SRA-binding sites were compared with CTCF and H3K27me3. Right; Percentage of co-occupancy of H3K27me3 at SRA binding sites without or with CTCF occupancy.</p></caption><graphic xlink:href=\"fgene-11-00850-g001\"/></fig><p>We and others have previously reported that SRA directly forms complexes with the chromatin architectural transcription factor CTCF (<xref rid=\"B62\" ref-type=\"bibr\">Yao et al., 2010</xref>; <xref rid=\"B59\" ref-type=\"bibr\">Wongtrakoongate et al., 2015</xref>), the histone H3 lysine 4 (H3K4) methyltransferase TrxG, and the histone H3 lysine 27 (H3K27) methyltransferase PRC2 (<xref rid=\"B59\" ref-type=\"bibr\">Wongtrakoongate et al., 2015</xref>). RNA pull down in K562 cells reveals that sense SRA, but not antisense SRA or MBP transcripts, can pull down the RNA helicase DDX5, the chromatin architectural protein CTCF, the TrxG component WDR5, and the PRC2 member EZH2 (<xref ref-type=\"supplementary-material\" rid=\"FS3\">Supplementary Figure S3</xref>). This result suggests that SRA might interact with TrxG and PRC2 in the cells, supporting our previous finding of a direct physical interaction of SRA/TrxG/PRC2 <italic>in vitro</italic> (<xref rid=\"B59\" ref-type=\"bibr\">Wongtrakoongate et al., 2015</xref>). Using ChIP-seq data from ENCODE, we show here that SRA and CTCF co-occupy 540 sites representing 19.3% of SRA binding sites (<xref ref-type=\"fig\" rid=\"F1\">Figures 1B–D</xref>). Comparing SRA with profiles of H3K4me3 and H3K27me3 in K562, we find that 465 and 789 sites representing 16.7 and 28.3% of total SRA binding sites possess, respectively, either the H3K4me3 or H3K27me3 modification (<xref ref-type=\"fig\" rid=\"F1\">Figures 1C,D</xref>). When comparing SRA, CTCF and the histone modifications, 245 and 192 sites representing about 8.8% and 6.9% of SRA binding regions are also co-occupied by CTCF plus H3K4me3 and CTCF plus H3K27me3, respectively.</p><p>Since SRA has been proposed to deliver TrxG or PRC2 to SRA-associated transcription factors including CTCF (<xref rid=\"B59\" ref-type=\"bibr\">Wongtrakoongate et al., 2015</xref>), we then asked whether sites of H3K4me3 or H3K27me3 modifications might be enriched at genomic regions occupied by both CTCF and SRA relative to those occupied by CTCF alone. We observe a higher proportion (45.4% versus 20.8%) of CTCF binding sites carrying the H3K4me3 modification at genomic regions occupied by both CTCF and SRA compared with those occupied by CTCF but lacking SRA (<xref ref-type=\"fig\" rid=\"F1\">Figure 1C</xref>). Similarly, the presence of SRA at CTCF binding sites correlates with a higher proportion of H3K27me3 modification (35.6% versus 11.5%) (<xref ref-type=\"fig\" rid=\"F1\">Figure 1D</xref>). Thus the genome-wide occupancy of H3K4me3 or H3K27me3 at SRA-associated CTCF binding sites suggest a possible role for the lncRNA SRA in transcriptional control of human erythroblast cells.</p></sec><sec id=\"S3.SS2\"><title>SRA Regulates Hematopoiesis-Related Genes Transcriptome-Wide in K562</title><p>To ascertain whether the lncRNA SRA globally regulates genes of the erythroblasts K562, a lentiviral transduction carrying an shRNA cassette was introduced into the cells. The lncRNA SRA transcript was successfully depleted (<xref ref-type=\"supplementary-material\" rid=\"FS4\">Supplementary Figure S4</xref>). RNA-seq analysis of K562 was then performed upon depletion of SRA using an Illumina HiSeq platform. DEGs with at least two fold-change were subsequently identified by using DEseq. Three individual shRNA knockdown samples, which were transduced with target site-specific shRNA targeting <italic>SRA</italic> transcript, were used as biological replicates for the analysis. Silencing of SRA led to differential expression of 675 genes, with 322 and 353 genes were down- and up-regulated by SRA knockdown, respectively (<xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref> and <xref ref-type=\"supplementary-material\" rid=\"TS2\">Supplementary Table S2</xref>). Gene ontology analysis of genes positively controlled by SRA shows that erythroblast-associated pathways such as heme biosynthesis (e.g., <italic>CPOX</italic>, <italic>PPOX</italic>, and <italic>ALAS2</italic>), iron homeostasis (e.g., <italic>TF</italic>, <italic>TFRC</italic>, and <italic>SLC11A2</italic>), cell proliferation (e.g., <italic>TAL1</italic>, <italic>BMX</italic>, and <italic>ERBB3</italic>), and erythrocytes (e.g., <italic>GYPA</italic>, <italic>AQP1</italic>, and <italic>AHSP</italic>) are enriched in the gene groups induced by SRA (<xref ref-type=\"fig\" rid=\"F2\">Figure 2B</xref> and <xref ref-type=\"supplementary-material\" rid=\"TS3\">Supplementary Table S3</xref>). Genes belonging to these pathways are clustered as shown by a functional network analysis (<xref ref-type=\"supplementary-material\" rid=\"FS5\">Supplementary Figure S5</xref>). On the other hand, the classification analysis of genes negatively controlled by SRA shows that leukocyte-associated pathways such as immune response (e.g., <italic>FTH1</italic>, <italic>IL1RN</italic>, and <italic>LCP2</italic>), inflammatory response (e.g., <italic>FOS</italic>, <italic>JUN</italic>, <italic>CCL2</italic>, and <italic>AIM2</italic>), and chemotaxis (e.g., <italic>CCL2</italic>, <italic>CCR7</italic>, and <italic>CXCL2</italic>) are enriched in the gene groups repressed by SRA (<xref ref-type=\"fig\" rid=\"F2\">Figure 2B</xref> and <xref ref-type=\"supplementary-material\" rid=\"TS4\">Supplementary Table S4</xref>). Genes associated with these pathways are well clustered (<xref ref-type=\"supplementary-material\" rid=\"FS6\">Supplementary Figure S6</xref>).</p><fig id=\"F2\" position=\"float\"><label>FIGURE 2</label><caption><p>SRA regulates transcription of hematopoiesis-related genes in the human erythroblast cells K562. The lncRNA SRA controls expression of 675 genes in the human erythroblast cells K562. RNA-seq was performed for cells depleted for SRA using individual shRNA constructs, i.e., shSRA-1, shSRA-2, or shSRA-3, and the control knockdown shLuc. Following silencing of SRA in K562 cells, 322, and 353 genes are down- and up-regulated in SRA knockdown cells, respectively. <bold>(A)</bold> Volcano plot illustrating changes in gene expression upon SRA silencing. The plot represents statistical significance vs expression fold change between the two conditions. Results from three biological replicates using different shRNA targets are shown. Genes with log2 fold change > 1 or <−1 and <italic>p</italic>-value < 0.01 are considered to be differentially expressed genes (DEGs) and are shown in red dots. <bold>(B)</bold> Heat map of DEGs between SRA knockdown samples and the control was analyzed. Pseudocount of TPMs was employed for calculation of fold-change using TPM + 1. The fold-change was calculated from TPM values of knockdown per TPM values of control, and the graph was plotted by representing log2(fold-change) of DEGs. Categories of enriched gene ontologies (GO) of genes up- and down-regulated by SRA in K562 (<italic>p</italic>-value < 0.05) were analyzed using DAVID. Examples of genes within the GO terms are shown on the right.</p></caption><graphic xlink:href=\"fgene-11-00850-g002\"/></fig><p>We and others have previously shown that SRA can form complexes with the chromatin architectural protein CTCF, RNA helicases, TrxG, and PRC2, to control transcription of SRA target genes (<xref rid=\"B62\" ref-type=\"bibr\">Yao et al., 2010</xref>; <xref rid=\"B59\" ref-type=\"bibr\">Wongtrakoongate et al., 2015</xref>). To further elucidate whether genes differentially expressed by SRA knockdown are occupied by SRA and CTCF at the chromatin level, an association analysis among the DEGs, SRA-bound genes and CTCF-bound genes was performed. There are 221 genes regulated by SRA without CTCF binding compared to 454 genes regulated by SRA with CTCF binding (<xref ref-type=\"fig\" rid=\"F3\">Figure 3A</xref>). In addition, we also asked whether SRA-regulated genes might be enriched by both CTCF and SRA binding relative to those with SRA alone. Even though we do not observe genes bound by SRA alone, there is a 14.8% (14.8% versus 0.0%) increase of SRA-regulated genes occupied by both CTCF and SRA compared with those without CTCF binding (<xref ref-type=\"fig\" rid=\"F3\">Figure 3A</xref>), indicating a contribution of the transcription factor CTCF in regulation of genes controlled by SRA. To determine to what extent SRA-occupied DEGs also contain H3K4me3 or H3K27me3, we also compared DEGs occupied by SRA in association with H3K4me3 or H3K27me3 (<xref ref-type=\"fig\" rid=\"F3\">Figure 3B</xref>). Among genes occupied by SRA, we found that genes up-regulated or down-regulated by SRA tend to associate with H3K4me3 together with H3K27me3 (<xref ref-type=\"fig\" rid=\"F3\">Figure 3C</xref>). To a lesser extent, genes up-regulated or down-regulated by SRA also harbor H3K4me3 but not H3K27me3. Collectively, these results suggest the role of SRA in transcriptome-wide regulation of human erythroblast cells.</p><fig id=\"F3\" position=\"float\"><label>FIGURE 3</label><caption><p>Genes differentially expressed by SRA knockdown are occupied by SRA, CTCF and histone marks at the chromatin level. Publicly available ChIP-seq data for CTCF, H3K4me3, and H3K27me3 were derived from the ENCODE project. <bold>(A)</bold> Genes transcriptionally regulated by SRA and containing SRA binding site tend to associate with CTCF. Numbers of differentially expressed genes (DEGs) were compared with numbers of SRA-associated genes or CTCF-associated genes as shown by the Venn diagram. Percentage of DEGs with both SRA and CTCF occupancy is higher than that without CTCF. <bold>(B)</bold> A comparison of differentially expressed genes occupied by SRA in association with H3K4me3 or H3K27me3. <bold>(C)</bold> Genes down-regulated or up-regulated by SRA containing SRA occupancy were grouped into four categories depending on the presence (+) or absence (−) of H3K4me3 or H3K27me3.</p></caption><graphic xlink:href=\"fgene-11-00850-g003\"/></fig><p>Next, we confirmed whether SRA silencing affects expression of erythroblast markers of K562. Upon the silencing of SRA, expression of committed erythroid genes <italic>TFRC</italic> and <italic>GYPA</italic> was reduced as determined by real-time PCR (<xref ref-type=\"fig\" rid=\"F4\">Figures 4A,D</xref>). Flow cytometry analysis of the two erythroid markers reveals that depletion of SRA led to a decrease in the antigen expression (<xref ref-type=\"fig\" rid=\"F4\">Figures 4B,E</xref>) and in the number of cells positive for the markers (<xref ref-type=\"fig\" rid=\"F4\">Figures 4C,F</xref>). Further, expression of globin genes including <italic>HBA1/2</italic>, <italic>HBE</italic>, <italic>HBG1/2</italic>, and <italic>HBD</italic> is reduced in K562 cells following SRA depletion (<xref ref-type=\"fig\" rid=\"F4\">Figure 4G</xref>). Since the beta globin gene is not expressed in this cell line, it was not included in our analysis. These results indicate a supportive role of the lncRNA SRA in erythroid-specific transcriptional regulation of the human erythroblasts K562.</p><fig id=\"F4\" position=\"float\"><label>FIGURE 4</label><caption><p>The lncRNA SRA maintains expression of erythroid-specific genes in K562. In the human erythroblast cell line, depletion of SRA decreased <italic>TFRC</italic>\n<bold>(A)</bold> and <italic>GYPA</italic>\n<bold>(D)</bold> gene expression. <italic>ATCB</italic> was utilized as an internal control. Error bars represent SD. (<italic>n</italic> = 3; *<italic>p</italic> < 0.05; ***<italic>p</italic> < 0.01). <bold>(B,C,E,F)</bold> Flow cytometry analysis shows that both expression level and the number of cells positive for the two markers are reduced by SRA knockdown. The histograms are shown to compare percentage of positive populations and expression level. Gray: negative control staining; Blue: control knockdown; Red: SRA knockdown of three different constructs; and White: control knockdown shown as a background. <bold>(G)</bold> SRA also facilitates expression of globin genes <italic>HBA1/2</italic>, <italic>HBE</italic>, <italic>HBG1/2</italic>, and <italic>HBD</italic> in K562. <italic>ATCB</italic> was utilized as an internal control. Error bars represent SD. (<italic>n</italic> = 3; ***<italic>p</italic> < 0.01).</p></caption><graphic xlink:href=\"fgene-11-00850-g004\"/></fig></sec><sec id=\"S3.SS3\"><title>SRA Regulates Hematopoiesis-Related Genes Transcriptome-Wide in HSC-Derived Primary Human Proerythroblast Cells</title><p>To translate whether SRA controls transcriptome-wide expression of primary human proerythroblasts, CD34-positive HSCs were induced to CD36-positive proerythroblast cells. qPCR analysis revealed that expression of the lncRNA SRA is induced upon erythroblast differentiation of HSCs, although only marginally (<xref ref-type=\"supplementary-material\" rid=\"FS7\">Supplementary Figure S7</xref>). The CD36-positive proerythroblast cells were then transduced with lentiviruses to silence SRA expression. As shown in <xref ref-type=\"supplementary-material\" rid=\"FS8\">Supplementary Figure S8</xref>, SRA transcript was successfully knocked down in CD36-positive proerythroblasts. RNA-seq analysis of CD36-positive proerythroblast cells was performed upon depletion of SRA using three different shRNA constructs. Silencing of SRA led to differential expression of 515 genes, with 233 and 282 genes were down- and up-regulated by SRA knockdown, respectively (<xref ref-type=\"fig\" rid=\"F5\">Figure 5</xref> and <xref ref-type=\"supplementary-material\" rid=\"TS5\">Supplementary Table S5</xref>). Gene ontology analysis of genes positively controlled by SRA shows that cell division- and erythroblast-associated pathways such as cell cycle (e.g., <italic>CDK1</italic>, <italic>CDC6</italic>, and <italic>MCM2</italic>), telomere maintenance (e.g., <italic>RFC3, RFC4</italic>, and <italic>RPA2</italic>), heme biosynthesis (e.g., <italic>ALAS2, CPOX</italic>, and <italic>UROS</italic>), and erythrocytes (e.g., <italic>KLF1</italic>, <italic>TFRC</italic>, <italic>GYPA</italic>, <italic>HBG2</italic>, <italic>HBD</italic>, and <italic>AHSP</italic>) are enriched in the gene groups induced by SRA (<xref ref-type=\"fig\" rid=\"F5\">Figure 5B</xref> and <xref ref-type=\"supplementary-material\" rid=\"TS6\">Supplementary Table S6</xref>). Genes belonging to these pathways are clustered in two nodes as shown by a functional network analysis (<xref ref-type=\"supplementary-material\" rid=\"FS9\">Supplementary Figure S9</xref>). On the other hand, the classification analysis of genes negatively controlled by SRA shows that leukocyte-associated pathways such as inflammatory response (e.g., <italic>CD14</italic>, <italic>PYCARD</italic>, <italic>SERPINE1</italic>, <italic>PF4</italic>, <italic>IL1B</italic>, <italic>IL2RA</italic>, and <italic>LYZ</italic>), immune response (e.g., <italic>FTH1</italic>, <italic>CSF2</italic>, and <italic>IL7R</italic>), and chemokine (e.g., <italic>CCL1</italic>, <italic>CCL2</italic>, <italic>CCL4</italic>, <italic>CXCL8</italic>, and <italic>CXCR4</italic>) are enriched in the gene groups repressed by SRA (<xref ref-type=\"fig\" rid=\"F5\">Figure 5B</xref> and <xref ref-type=\"supplementary-material\" rid=\"TS7\">Supplementary Table S7</xref>). Genes associated with these pathways are also well clustered (<xref ref-type=\"supplementary-material\" rid=\"FS10\">Supplementary Figure S10</xref>). By comparing genes down- or up-regulated upon SRA knockdown, we find that 30 and 80 genes are overlapped between K562 and primary erythroblasts, respectively (<xref ref-type=\"supplementary-material\" rid=\"FS11\">Supplementary Figure S11</xref>). Examples of gene categories commonly induced by SRA between the two cell types include those involved in heme biosynthesis, hemoglobin, and erythrocyte differentiation such as <italic>TFRC</italic>, <italic>GYPA</italic>, <italic>ALAS2</italic>, <italic>AHSP</italic>, and <italic>EPB42</italic>. Examples of gene categories commonly suppressed by SRA between the two cell types include those involved in immune response, angiogenesis, and lipopolysaccharide response such as <italic>FTH1</italic>, <italic>CCL2</italic>, <italic>IL1RN</italic>, <italic>THBS1</italic>, and <italic>CXCL8</italic>. Nonetheless, the majority of DEGs are cell type-specific, either found exclusively in K562 or in primary erythroblasts. This cell type-specific gene expression might reflect differences of their origins, i.e., K562 as erythroleukemia cells and CD36-positive cells as primary erythroblasts. Specifically, 292 genes induced by SRA exclusively in K562 such as <italic>DNAJB2</italic>, <italic>GDF2</italic>, <italic>SLC11A2</italic>, <italic>TF</italic>, and <italic>LGALS3BP</italic> are functionally grouped as negative regulation of cell growth, cellular iron ion homeostasis, and platelet degranulation. Further, 203 genes suppressed by SRA exclusively in K562 such as <italic>GNG12</italic>, <italic>JUN</italic>, <italic>TNFRSF8</italic>, <italic>DGKG</italic>, and <italic>RAP2B</italic> are functionally grouped as response to lipopolysaccharide and platelet activation. For CD36-positive proerythroblasts, 273 genes induced by SRA exclusively in the cells but not in K562 such as <italic>CENPW</italic>, <italic>CENPX</italic>, <italic>MCM2</italic>, <italic>RPA2</italic>, and <italic>CDC6</italic> can be grouped within cell division and DNA replication. In addition, 202 genes suppressed by SRA exclusively in proerythroblasts such as <italic>GBP5</italic>, <italic>CCL4</italic>, <italic>CCL17</italic>, <italic>CD14</italic>, and <italic>ITGB2</italic> are functionally grouped as inflammatory response and neutrophil chemotaxis. Together, this result indicates the role of SRA in global gene regulation of erythroleukemia and in primary human proerythroblasts.</p><fig id=\"F5\" position=\"float\"><label>FIGURE 5</label><caption><p>SRA regulates transcription of hematopoiesis-related genes in the human CD36-positive erythroid progenitor cells. The lncRNA SRA controls expression of 515 genes in the human CD36-positive erythroid progenitor cells. RNA-seq was performed for cells depleted for SRA using individual shRNA constructs, i.e., shSRA-1, shSRA-2, or shSRA-3, and the control knockdown shLuc. Following silencing of SRA in the CD36-positive cells, 233 and 282 genes are down- and up-regulated in SRA knockdown cells, respectively. <bold>(A)</bold> Volcano plot illustrating changes in gene expression upon SRA silencing. The plot represents statistical significance vs expression fold change between the two conditions. Results from three biological replicates using different shRNA targets are shown. Genes with log2 fold change > 1 or <−1 and <italic>p</italic>-value < 0.01 are considered to be differentially expressed genes (DEGs) and are shown in red dots. <bold>(B)</bold> Heat map of DEGs between SRA knockdown samples and the control was analyzed. Pseudocount of TPMs was employed for calculation of fold-change using TPM + 1. The fold-change was calculated from TPM values of knockdown per TPM values of control, and the graph was plotted by representing log2(fold-change) of DEGs. Categories of enriched gene ontologies of genes up- and down-regulated by SRA in CD36-positive proerythroblasts (<italic>p</italic>-value < 0.05) and their enrichment scores [−log(<italic>p</italic>-value)] were analyzed using DAVID.</p></caption><graphic xlink:href=\"fgene-11-00850-g005\"/></fig><p>Next, we substantiated whether SRA silencing reduces expression of erythroblast markers of the primary proerythroblast cells. At the early stage of differentiation, we found that SRA depletion led to an increase in CD34-positive population, while CD36-positive population was reduced (<xref ref-type=\"supplementary-material\" rid=\"FS12\">Supplementary Figure S12</xref>). Expression of the committed erythroid marker <italic>TFRC</italic> and <italic>GYPA</italic> is reduced in erythroid-induced differentiating HSCs (<xref ref-type=\"fig\" rid=\"F6\">Figures 6A,D</xref>). However, unlike K562, flow cytometry analysis of TFRC in proerythroblasts shows that both antigen expression level and number of cells positive of TFRC are not different between the control and SRA-depleted cells (<xref ref-type=\"fig\" rid=\"F6\">Figures 6B,C</xref>). Nonetheless, depletion of SRA led to a decrease in the antigen expression and in the number of cells positive for GYPA (<xref ref-type=\"fig\" rid=\"F6\">Figures 6E,F</xref>). We also tested whether expression of the globin genes is transcriptionally controlled by SRA. Real-time PCR analysis reveals that silencing of SRA led to a decrease in globin gene expression (<xref ref-type=\"fig\" rid=\"F6\">Figure 6G</xref>). Taken together with the RNA-seq data, these results suggest that the lncRNA SRA facilitates transcriptional expression of erythroid-associated genes of primary human proerythroblast cells.</p><fig id=\"F6\" position=\"float\"><label>FIGURE 6</label><caption><p>SRA facilitates expression of erythroid-specific genes of CD36-positive erythroid progenitor cells. During erythroid-induced differentiation, depletion of SRA decreased <italic>TFRC</italic>\n<bold>(A)</bold> and <italic>GYPA</italic>\n<bold>(D)</bold> gene expression. <italic>ATCB</italic> was utilized as an internal control. Error bars represent SD. (<italic>n</italic> = 3; ***<italic>p</italic> < 0.01). However, flow cytometry analysis shows that expression level of the erythroid marker TFRC <bold>(B)</bold> and number of TFRC-positive cells <bold>(C)</bold> are not affected by SRA knockdown, whereas those of GYPA <bold>(E,F)</bold> are reduced by SRA knockdown. The histograms are shown to compare percentage of positive populations and expression level. Gray: negative control staining; Blue: control knockdown; Red: SRA knockdown of three different constructs; and White: control knockdown shown as a background. <bold>(G)</bold> SRA facilitates expression of globin genes <italic>HBA1/2</italic>, <italic>HBE</italic>, <italic>HBG1/2</italic>, and <italic>HBD</italic> in CD36-positive erythroid progenitor cells. <italic>ATCB</italic> was utilized as an internal control. Error bars represent SD. (<italic>n</italic> = 3; ***<italic>p</italic> < 0.01).</p></caption><graphic xlink:href=\"fgene-11-00850-g006\"/></fig></sec></sec><sec id=\"S4\"><title>Discussion</title><p>Delineating molecular mechanisms underlying erythroblast gene regulation is critical for understanding RBC disorders. A vast arrays of molecular and cellular pathways have been discovered to control this process (<xref rid=\"B43\" ref-type=\"bibr\">Oburoglu et al., 2016</xref>). For example, crosstalk between signal transductions and transcription factors modulates erythropoiesis in both mice and men (<xref rid=\"B44\" ref-type=\"bibr\">Perreault and Venters, 2018</xref>). Epigenetic regulators such as enzymes that modify DNA and histones also participate in regulation of erythropoiesis at chromatin level (<xref rid=\"B22\" ref-type=\"bibr\">Gnanapragasam and Bieker, 2017</xref>). In this work, we find that the lncRNA SRA occupies chromatin genome-wide in the human erythroblast cell line K562 (<xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>), and controls expression of erythroblast-associated genes transcriptome-wide in both K562 and HSC-derived primary erythroblast cells (<xref ref-type=\"fig\" rid=\"F2\">Figures 2</xref>, <xref ref-type=\"fig\" rid=\"F5\">5</xref>). Moreover, expression of the erythroid marker GYPA, and the number of GYPA-positive cells are decreased in K562 and primary erythroblasts depleted for SRA (<xref ref-type=\"fig\" rid=\"F4\">Figures 4</xref>, <xref ref-type=\"fig\" rid=\"F6\">6</xref>) suggesting that the lncRNA SRA facilitates erythroid transcriptional program. However, we observed a reduction in TFRC expression only in K562 but not in the primary erythroblasts depleted for SRA. The relatively high level of TFRC expression of primary erythroblasts is consistent with the maintenance of TFCR expression in erythroblasts <italic>in vitro</italic> (<xref rid=\"B18\" ref-type=\"bibr\">Fajtova et al., 2013</xref>; <xref rid=\"B39\" ref-type=\"bibr\">Mao et al., 2016</xref>).</p><p>Recently, more than 9,000 genes encoding lncRNAs have been identified as being transcribed from the human genome (<xref rid=\"B16\" ref-type=\"bibr\">Derrien et al., 2012</xref>). They can participate in transcriptional regulation by acting as scaffold machineries for transcription factors and epigenetic modifying enzymes (<xref rid=\"B47\" ref-type=\"bibr\">Rinn and Chang, 2012</xref>). Using ChIRP-seq and ChIRP-PCR, we show here that SRA occupies at the alpha and beta globin loci, and facilitates the expression of the globin genes including <italic>HBA1/2</italic>, <italic>HBG1/2</italic>, <italic>HBE</italic>, and <italic>HBD</italic> in K562 cells and human proerythroblasts (<xref ref-type=\"fig\" rid=\"F4\">Figures 4</xref>, <xref ref-type=\"fig\" rid=\"F6\">6</xref>). SRA can form a complex with the chromatin architectural transcription factor CTCF, whose function in transcriptional control of genes at the beta globin locus has long been appreciated (<xref rid=\"B55\" ref-type=\"bibr\">Wallace and Felsenfeld, 2007</xref>; <xref rid=\"B21\" ref-type=\"bibr\">Ghirlando et al., 2012</xref>). Specifically, CTCF has been shown to facilitate expression of the gamma globin gene (<xref rid=\"B23\" ref-type=\"bibr\">Hou et al., 2010</xref>). In addition, the DNA binding transcription factor ATF2 which interacts with SRA also induces expression of the gamma globin gene (<xref rid=\"B37\" ref-type=\"bibr\">Liu et al., 2013</xref>). In contrast to SRA, the lncRNA HMI has been shown to suppress expression of the gamma globin gene (<xref rid=\"B42\" ref-type=\"bibr\">Morrison et al., 2018</xref>). At the alpha globin locus, lncRNA-αGT controls chicken globin expression (<xref rid=\"B5\" ref-type=\"bibr\">Arriaga-Canon et al., 2014</xref>). Since reactivation of <italic>HBG</italic> is a promising strategy for sickle cell anemia (<xref rid=\"B54\" ref-type=\"bibr\">Vinjamur et al., 2018</xref>) and accumulating evidence have suggested the role of lncRNAs in transcriptional regulation of globin genes, it is pivotal to determine which chromatin-associated factor(s) brings SRA and other lncRNAs to their target sites to induce expression of the globin genes.</p><p>The lncRNA SRA has been reported to promote cell fate transition including myogenesis (<xref rid=\"B10\" ref-type=\"bibr\">Caretti et al., 2006</xref>; <xref rid=\"B28\" ref-type=\"bibr\">Hube et al., 2011</xref>) and adipogenesis (<xref rid=\"B60\" ref-type=\"bibr\">Xu et al., 2010</xref>) as well as a transition into the pluripotent state (<xref rid=\"B59\" ref-type=\"bibr\">Wongtrakoongate et al., 2015</xref>). Yet, it has been elusive whether SRA is involved in transcriptional control during erythropoiesis. Estrogen receptor and glucocorticoid receptor, which are SRA-associated nuclear receptors, have been suggested to attenuate erythroid lineage (<xref rid=\"B49\" ref-type=\"bibr\">Schroeder et al., 1993</xref>; <xref rid=\"B9\" ref-type=\"bibr\">Blobel et al., 1995</xref>; <xref rid=\"B56\" ref-type=\"bibr\">Wessely et al., 1997</xref>; <xref rid=\"B32\" ref-type=\"bibr\">Lanz et al., 1999</xref>). On the other hand, transcription factors involved in SRA-mediated transcriptional regulation such as CTCF and thyroid hormone receptor have been shown to facilitate generation of erythroid cells (<xref rid=\"B7\" ref-type=\"bibr\">Bartunek and Zenke, 1998</xref>; <xref rid=\"B61\" ref-type=\"bibr\">Xu and Koenig, 2004</xref>; <xref rid=\"B51\" ref-type=\"bibr\">Torrano et al., 2005</xref>; <xref rid=\"B62\" ref-type=\"bibr\">Yao et al., 2010</xref>; <xref rid=\"B20\" ref-type=\"bibr\">Gao et al., 2017</xref>). Apart from being associated with transcription factors, the role of SRA in supporting cell fate transition and plasticity might be in part due to its interaction with epigenetic machineries (<xref rid=\"B59\" ref-type=\"bibr\">Wongtrakoongate et al., 2015</xref>). We have previously reported that SRA interacts with CTCF, TrxG, and PRC2 (<xref rid=\"B62\" ref-type=\"bibr\">Yao et al., 2010</xref>; <xref rid=\"B59\" ref-type=\"bibr\">Wongtrakoongate et al., 2015</xref>). In addition, CTCF tends to localize nearby H3K4me3, and H3K27me3, which are established by TrxG and PRC2, respectively (<xref rid=\"B6\" ref-type=\"bibr\">Barski et al., 2007</xref>; <xref rid=\"B34\" ref-type=\"bibr\">Li et al., 2008</xref>; <xref rid=\"B14\" ref-type=\"bibr\">Cuddapah et al., 2009</xref>). This could explain the enrichment of the two histone marks at CTCF binding sites containing SRA (<xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>). However, the majority of H3K4me3 or H3K27me3 sites are associated neither with SRA nor CTCF, supporting the existence of multiple mechanisms for establishing these histone modifications. Further studies will be required to uncover possible synergistic regulation by lncRNAs and their protein binding partners in erythropoiesis.</p><p>There are growing evidence of various functions of lncRNAs in blood cells (<xref rid=\"B35\" ref-type=\"bibr\">Li et al., 2018</xref>; <xref rid=\"B15\" ref-type=\"bibr\">Dahariya et al., 2019</xref>). During mouse embryonic hematopoiesis, the lncRNA H19 promotes gene expression program of hematopoiesis transcriptome-wide via regulation of promoter DNA methylation of key HSC genes, and is therefore critical for embryonic endothelial-to-hematopoietic transition and generating embryonic HSCs in aorta-gonads-mesonephros (<xref rid=\"B63\" ref-type=\"bibr\">Zhou et al., 2019</xref>). The mouse lncRNA EC2, which is conserved in human, has been reported to facilitate expression of the erythroid marker Ter119 and enucleation of mouse erythroblasts (<xref rid=\"B1\" ref-type=\"bibr\">Alvarez-Dominguez et al., 2014</xref>). In human, a transcriptome-wide analysis of erythroid-induced human HSCs has revealed expression of approximately 1,100 genes encoding lncRNAs. Of these, the expression level of 34 lncRNAs is correlated with that of protein coding genes involved in hematopoiesis, leukocyte activation and DNA repair in erythroblasts suggesting a possible function of these lncRNAs in transcriptional regulation of the associated genes (<xref rid=\"B17\" ref-type=\"bibr\">Ding et al., 2016</xref>). Heme biosynthesis is erythroblasts mediated by the lncRNA UCA1, which is upregulated at the proerythroblast stage interacts with the ribonucleoprotein PTBP1 (<xref rid=\"B36\" ref-type=\"bibr\">Liu et al., 2018</xref>). In another study, the lncRNA HMI (also called HMI-lncRNA), which is transiently induced during human erythropoiesis, is a negative regulator of gamma globin expression (<xref rid=\"B42\" ref-type=\"bibr\">Morrison et al., 2018</xref>). Intriguingly, using RNA-seq and ChIRP-seq approaches, the enhancer-associated lncRNA Bloodlinc has been reported to facilitate enucleation of mouse RBCs by inducing erythroid-related genes and repressing non-erythroid genes through direct binding to chromatin. Interestingly, similar to SRA, Bloodlinc also forms a complex with the RNA helicase DDX5 (<xref rid=\"B2\" ref-type=\"bibr\">Alvarez-Dominguez et al., 2017</xref>), which is important for establishment of H3K4me3 (<xref rid=\"B59\" ref-type=\"bibr\">Wongtrakoongate et al., 2015</xref>). Therefore, these findings have shed light on the function of these lncRNA transcripts in regulation of erythropoiesis. In conclusion, we have suggested the role of SRA in human erythropoiesis, as well as a direct transcriptional control of SRA in regulation of proerythroblast-associated genes. Together, our work supports the roles of lncRNAs in erythroblast gene regulation.</p></sec><sec sec-type=\"data-availability\" id=\"S5\"><title>Data Availability Statement</title><p>All relevant sequencing data has been uploaded to NCBI: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE153004\">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE 153004</ext-link> and <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE151926\">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE151926</ext-link>.</p></sec><sec id=\"S6\"><title>Author Contributions</title><p>WS and PW conceived and designed the research, analyzed the data, and wrote the manuscript. WS, KC, and PW conducted the experiments. KZ, SH, and SF contributed to reagents and analytical tools. All authors read and approved the manuscript.</p></sec><sec id=\"conf1\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> This research project was supported by Mahidol University and CIF Grant, Faculty of Science, Mahidol University. WS was supported by Science Achievement Scholarship of Thailand. KZ was supported by the Intramural Research Program of the NIH, National Heart, Lung, and Blood Institute. PW was supported by the Thailand Research Fund and Faculty of Science, Mahidol University (grant no. TRG5880105).</p></fn></fn-group><ack><p>We are indebted to Gary Felsenfeld, Gregory Riddicks, Peerapat Khamwachirapitak, and PW lab members for their suggestions and comments.</p></ack><sec id=\"S9\" sec-type=\"supplementary material\"><title>Supplementary Material</title><p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.frontiersin.org/articles/10.3389/fgene.2020.00850/full#supplementary-material\">https://www.frontiersin.org/articles/10.3389/fgene.2020.00850/full#supplementary-material</ext-link></p><supplementary-material content-type=\"local-data\" id=\"FS1\"><label>FIGURE S1</label><caption><p>The lncRNA SRA preferentially occupies within 50 kb upstream or downstream of transcription start site. <bold>(A)</bold> Amount region-gene associations at different distance to TSS. Since lncRNAs, such as SRA, can function as RNA scaffolds for chromatin regulators such as CTCF, the chromatin spanning regions from TSS were therefore set into different bins ranging from 500 kb up- or down-stream of TSS to allow discovery of potential SRA occupancy beyond TSS. <bold>(B)</bold> Percent genomic regions occupied by SRA. White and red bar represents genomic regions associated with one or more genes and not associated with any genes, respectively.</p></caption><media xlink:href=\"Image_1.JPEG\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"FS2\"><label>FIGURE S2</label><caption><p>The lncRNA SRA occupies at alpha and beta globin gene loci. Chromatin Isolation by RNA Purification (ChIRP) of SRA in K562 cells was performed using deoxyoligonucleotide probes tiling along the lncRNA SRA followed by PCR. Genomic regions for PCR primer binding were selected according to DNaseI hypersensitive sites (HS), and occupancy of CTCF, H3K4me1 and H3K4me3 as shown in the histograms, where <italic>x</italic>-axis and <italic>y</italic>-axis represent physical map and occupancy levels, respectively. The numbers of primer pairs are indicated under the histograms. <bold>(A)</bold> Thirteen pairs of primers for ChIRP-PCR were designed to determine association of SRA at the alpha globin chromatin locus. <bold>(B)</bold> ChIRP-PCR analysis at the alpha locus revealed that SRA occupies the sites HS40 and numbers 7 and 12. <bold>(C)</bold> Twenty two pairs of primers for ChIRP-PCR were designed to determine association of SRA at the beta globin chromatin locus. <bold>(D)</bold> ChIRP-PCR analysis revealed that SRA occupies site numbers 3, 6, 10, 12, 13, 14, 17, and 19. Enrichment signals of SRA-occupied chromatin fragments were normalized to those of the negative control probes LacZ. Error bars represent SD. (<italic>n</italic> = 3; <sup>∗</sup><italic>p</italic> < 0.05).</p></caption><media xlink:href=\"Image_2.jpg\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"FS3\"><label>FIGURE S3</label><caption><p>RNA pull down in K562 cells. Sense and antisense of biotinylated SRA transcripts were incubated with K562 nuclear extract. Western blot were performed with pulled down proteins. Sense SRA, but not antisense SRA or MBP transcripts, can pull down the RNA helicase DDX5, the chromatin architectural protein CTCF, the TrxG component WDR5, and the PRC2 member EZH2.</p></caption><media xlink:href=\"Image_3.JPEG\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"FS4\"><label>FIGURE S4</label><caption><p>Silencing of SRA by shRNA led to a reduction of SRA lncRNA transcript. <italic>ATCB</italic> was utilized as an internal control. Error bars represent SD. (<italic>n</italic> = 5; <sup>∗∗∗</sup><italic>p</italic> < 0.01).</p></caption><media xlink:href=\"Image_4.JPEG\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"FS5\"><label>FIGURE S5</label><caption><p>Functional network analysis of genes induced by SRA in K562 was identified by the STRING (Search Tool for the Retrieval of Interacting Genes) with connected lines representing genetic/physical interaction of connected genes.</p></caption><media xlink:href=\"Image_5.JPEG\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"FS6\"><label>FIGURE S6</label><caption><p>Functional network analysis of genes repressed by SRA in K562 was identified by the STRING (Search Tool for the Retrieval of Interacting Genes) with connected lines representing genetic/physical interaction of connected genes.</p></caption><media xlink:href=\"Image_6.JPEG\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"FS7\"><label>FIGURE S7</label><caption><p>The lncRNA SRA is marginally induced during erythroblast differentiation of human HSCs. <italic>ATCB</italic> was utilized as an internal control. Error bars represent SD. (<italic>n</italic> = 3; <sup>∗</sup><italic>p</italic> < 0.05).</p></caption><media xlink:href=\"Image_7.JPEG\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"FS8\"><label>FIGURE S8</label><caption><p>Silencing of SRA by shRNA led to a reduction of SRA in human CD36-positive cells. <italic>ATCB</italic> was utilized as an internal control. Error bars represent SD. (<italic>n</italic> = 3; <sup>∗∗∗</sup><italic>p</italic> < 0.01).</p></caption><media xlink:href=\"Image_8.JPEG\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"FS9\"><label>FIGURE S9</label><caption><p>Functional network analysis of genes induced by SRA in primary erythroblasts was identified by the STRING (Search Tool for the Retrieval of Interacting Genes) with connected lines representing genetic/physical interaction of connected genes.</p></caption><media xlink:href=\"Image_9.JPEG\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"FS10\"><label>FIGURE S10</label><caption><p>Functional network analysis of genes repressed by SRA in primary erythroblasts was identified by the STRING (Search Tool for the Retrieval of Interacting Genes) with connected lines representing genetic/physical interaction of connected genes.</p></caption><media xlink:href=\"Image_10.JPEG\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"FS11\"><label>FIGURE S11</label><caption><p>Comparative analysis of differential expressed genes controlled by SRA between K562 and CD36 + proerythroblasts. Group of erythroid- and lymphoid-associated genes are consistency down-regulated <bold>(A)</bold> and up-regulated <bold>(B)</bold>, upon SRA silencing in both K562 and CD36 + proerythroblasts.</p></caption><media xlink:href=\"Image_11.jpeg\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"FS12\"><label>FIGURE S12</label><caption><p>The lncRNA SRA facilitates differentiation of CD34<sup>+</sup> human HSCs into CD36<sup>+</sup> human erythroid progenitors. Erythroid differentiation of HSCs was performed before transducing with shRNA-containing lentiviruses at day 7 of differentiation. The lentiviral-transduced cells were collected at 96 h post-transduction, and were subjected to flow cytometry analysis co-stained for the human HSC surface marker CD34 and the erythroid progenitor marker CD36. <bold>(A)</bold> Flow cytometry histograms of CD34. <bold>(B)</bold> Flow cytometry histograms of CD36. <bold>(C)</bold> Flow cytometry histograms of double staining cells. <bold>(D)</bold> Silencing of SRA increased CD34<sup>+</sup> CD36<sup>+</sup> double-positive and CD34<sup>–</sup> CD36<sup>–</sup> double-negative populations at the expense of CD34<sup>–</sup> CD36<sup>+</sup> double-positive population.</p></caption><media xlink:href=\"Image_12.jpg\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"TS1\"><media xlink:href=\"Table_1.XLSX\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"TS2\"><media xlink:href=\"Table_2.XLSX\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"TS3\"><media xlink:href=\"Table_3.XLSX\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"TS4\"><media xlink:href=\"Table_4.XLSX\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"TS5\"><media xlink:href=\"Table_5.XLSX\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"TS6\"><media xlink:href=\"Table_6.XLSX\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"TS7\"><media xlink:href=\"Table_7.XLSX\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec><ref-list><title>References</title><ref id=\"B1\"><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Alvarez-Dominguez</surname><given-names>J. 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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">F1000Res</journal-id><journal-id journal-id-type=\"iso-abbrev\">F1000Res</journal-id><journal-id journal-id-type=\"pmc\">F1000Research</journal-id><journal-title-group><journal-title>F1000Research</journal-title></journal-title-group><issn pub-type=\"epub\">2046-1402</issn><publisher><publisher-name>F1000 Research Limited</publisher-name><publisher-loc>London, UK</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32850116</article-id><article-id pub-id-type=\"pmc\">PMC7431966</article-id><article-id pub-id-type=\"doi\">10.12688/f1000research.25998.1</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Review</subject></subj-group><subj-group><subject>Articles</subject></subj-group></article-categories><title-group><article-title>Development of vaccines for SARS-CoV-2</article-title><fn-group content-type=\"pub-status\"><fn><p>[version 1; peer review: 2 approved]</p></fn></fn-group></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Ng</surname><given-names>Wern Hann</given-names></name><role content-type=\"http://credit.casrai.org/\">Conceptualization</role><role content-type=\"http://credit.casrai.org/\">Data Curation</role><role content-type=\"http://credit.casrai.org/\">Writing – Original Draft Preparation</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><xref ref-type=\"aff\" rid=\"a1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Liu</surname><given-names>Xiang</given-names></name><role content-type=\"http://credit.casrai.org/\">Data Curation</role><role content-type=\"http://credit.casrai.org/\">Writing – Original Draft Preparation</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><xref ref-type=\"aff\" rid=\"a1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Mahalingam</surname><given-names>Suresh</given-names></name><role content-type=\"http://credit.casrai.org/\">Conceptualization</role><role content-type=\"http://credit.casrai.org/\">Data Curation</role><role content-type=\"http://credit.casrai.org/\">Writing – Original Draft Preparation</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0003-3141-8410</contrib-id><xref ref-type=\"corresp\" rid=\"c1\">a</xref><xref ref-type=\"aff\" rid=\"a1\">1</xref></contrib><aff id=\"a1\">\n<label>1</label>Emerging Viruses, Inflammation and Therapeutics Group, Menzies Health Institute Queensland, Griffith University (Gold Coast Campus), Queensland, Australia</aff></contrib-group><author-notes><corresp id=\"c1\"><label>a</label><email xlink:href=\"mailto:s.mahalingam@griffith.edu.au\">s.mahalingam@griffith.edu.au</email></corresp><fn fn-type=\"COI-statement\"><p>No competing interests were disclosed.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>9</volume><elocation-id>F1000 Faculty Rev-991</elocation-id><history><date date-type=\"accepted\"><day>7</day><month>8</month><year>2020</year></date></history><permissions><copyright-statement>Copyright: © 2020 Ng WH et al.</copyright-statement><copyright-year>2020</copyright-year><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri content-type=\"pdf\" xlink:href=\"f1000research-9-28692.pdf\"/><abstract><p>COVID-19 emerged in late 2019 and has rapidly spread through many countries globally. The causative SARS-CoV-2 virus was not known until recently, and there is little or no natural immunity in human populations. There is an urgent need for vaccines and drugs to combat this new pandemic. In just a few months, huge efforts and resources by government, academia, and industry have been thrown into the race to develop a vaccine. This brief review summarizes and discusses the array of technologies being applied to vaccine development, highlighting the strengths and weaknesses of the various approaches.</p></abstract><kwd-group kwd-group-type=\"author\"><kwd>COVID-19</kwd><kwd>Vaccines</kwd><kwd>SARS-CoV-2</kwd></kwd-group><funding-group><award-group id=\"fund-1\" xlink:href=\"http://dx.doi.org/10.13039/501100000925\"><funding-source>National Health and Medical Research Council</funding-source><award-id>APP11544347</award-id></award-group><funding-statement>SM is the recipient of a National Health and Medical Research Council (NHMRC) Senior Research Fellowship (APP11544347).</funding-statement><funding-statement><italic>The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</italic></funding-statement></funding-group></article-meta><notes><sec sec-type=\"editor-note\"><title>Editorial Note on the Review Process</title><p>\n<ext-link ext-link-type=\"uri\" xlink:href=\"http://f1000research.com/browse/faculty-reviews\">F1000 Faculty Reviews</ext-link> are commissioned from members of the prestigious\n<ext-link ext-link-type=\"uri\" xlink:href=\"http://f1000.com/prime/thefaculty\">F1000 Faculty</ext-link> and are edited as a service to readers. In order to make these reviews as comprehensive and accessible as possible, the referees provide input before publication and only the final, revised version is published. The referees who approved the final version are listed with their names and affiliations but without their reports on earlier versions (any comments will already have been addressed in the published version).</p><p>The referees who approved this article are: </p><list list-type=\"simple\" list-content=\"reviewer-list\"><list-item><p>\n<named-content content-type=\"reviewer-name\">Gavin Koh</named-content>, Diseases of the Developing World, GlaxoSmithKline, Uxbridge, UB11 1BT, UK\n<fn fn-type=\"COI-statement\"><p>No competing interests were disclosed.</p></fn>\n</p></list-item><list-item><p>\n<named-content content-type=\"reviewer-name\">Shinji Makino</named-content>, Department of Microbiology and Immunology, Institute for Human Infection and Immunity, University of Texas Medical Branch, Galveston, TX, USA\n<fn fn-type=\"COI-statement\"><p>No competing interests were disclosed.</p></fn>\n</p></list-item></list></sec></notes></front><body><sec sec-type=\"intro\"><title>Introduction</title><p>The first quarter of 2020 has been plagued by the emergence of a novel coronavirus disease, COVID-19 or severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). SARS-CoV-2 was first identified in Wuhan, China, and has since spread globally at an alarming rate, with over 200 countries and territories being infected\n<sup><xref rid=\"ref-1\" ref-type=\"bibr\">1</xref>,\n<xref rid=\"ref-2\" ref-type=\"bibr\">2</xref></sup>. In March 2020, SARS-CoV-2 was declared a global pandemic by the World Health Organization (WHO)\n<sup><xref rid=\"ref-3\" ref-type=\"bibr\">3</xref></sup>, and as of 18 July 2020, there are over 14 million confirmed cases with over 594,000 deaths worldwide. These figures are expected to rise (\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports\">https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports</ext-link>)\n<sup><xref rid=\"ref-2\" ref-type=\"bibr\">2</xref></sup>. With the emergence of SARS-CoV-2, there are currently over 2,400 listed studies on the National Institute of Health database (\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://clinicaltrials.gov/ct2/results?cond=COVID-19\">https://clinicaltrials.gov/ct2/results?cond=COVID-19</ext-link>) aimed at identifying a solution to this pandemic. The development of a much-needed vaccine against SARS-CoV-2 is of utmost importance. To date, there are more than 100 vaccine candidates being developed by industry and academic institutions using a wide range of technologies including live attenuated, viral vectored, DNA/RNA-based, protein-based, and inactivated vaccines\n<sup><xref rid=\"ref-4\" ref-type=\"bibr\">4</xref>–\n<xref rid=\"ref-6\" ref-type=\"bibr\">6</xref></sup>. At the time of writing, 19 vaccines are recorded as being in clinical trials (\n<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>)\n<sup><xref rid=\"ref-4\" ref-type=\"bibr\">4</xref>–\n<xref rid=\"ref-7\" ref-type=\"bibr\">7</xref></sup>. This article highlights various technologies employed in vaccine development for COVID-19, potential hurdles, and recent advances.</p><table-wrap id=\"T1\" orientation=\"portrait\" position=\"anchor\"><label>Table 1. </label><caption><title>Vaccine platforms and their current status.</title></caption><table frame=\"hsides\" rules=\"groups\" content-type=\"article-table\"><thead><tr><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Technology\n<break/>employed</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Developer</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Type of vaccine candidate</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Current stage in\n<break/>clinical evaluation</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"23\" valign=\"top\" colspan=\"1\">\n<bold>Non-</bold>\n<break/>\n<bold>replicating</bold>\n<break/>\n<bold>viral vector</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">University of Oxford/AstraZeneca</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">ChAdOx1-S</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Phase III</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">CanSino Biologics Inc and Beijing Institute of Biotechnology</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Adenovirus type 5 vector</td><td align=\"left\" rowspan=\"2\" valign=\"top\" colspan=\"1\">Phase II</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Shenzhen Geno-Immune Medical Institute</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Lentivirus modification</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Gamaleya Research Institute</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Adeno-based</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Phase I</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">GeoVax/BravoVax</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Modified Vaccinia Ankara (MVA)-encoded virus-like particle (VLP)</td><td align=\"left\" rowspan=\"19\" valign=\"top\" colspan=\"1\">Pre-clinical</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Stabilitech Biopharma Ltd</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Oral Ad5 S</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Janssen Pharmaceutical Companies</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Ad26 (alone or witd MVA boost)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Altimmune</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Adenovirus-based NasoVAX expressing SARS-CoV-2 Spike protein</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Greffex</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Ad5 S (GREVAX™ platform)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Vaxart</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Oral vaccine platform</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">DZIF – German Center for Infection Research</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">MVA-S encoded</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">IDIBAPS-Hospital Clinic, Spain</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">MVA-S</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Greffex</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Ad5 S (GREVAX™ platform)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Centro Nacional de Biotecnología (CNB-CSIC), Spain</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">MVA expressing structural proteins</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Reitdera/LEUKOCARE/Univercells</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Replication defective Simian adenovirus (gRAd) encoding SARS-CoV-2 S</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Valo tderapeutics Ltd</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Adenovirus-based + HLA-matched peptides</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">National Center for Genetic Engineering and Biotechnology\n<break/>(BIOTEC)/GPO, Thailand</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Inactivated flu-based SARS-CoV2 vaccine + adjuvant</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">University of Manitoba</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Dendritic cell-based vaccine</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">University of Georgia/University of Iowa</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Parainfluenza virus 5 (PIV5)-based vaccine expressing tde Spike protein</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Bharat Biotech/Thomas Jefferson University</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Recombinant deactivated rabies virus containing S1</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Massachusetts Eye and Ear/Massachusetts General Hospital/AveXis</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Adeno-associated virus vector (AAVCOVID)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">ImmunityBio, Inc. and NantKwest, Inc.</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">[E1-, E2b-, E3-] hAd5-COVID19-Spike/nucleocapsid</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">National Research Centre, Egypt</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Influenza A H1N1 vector</td></tr><tr><td align=\"left\" rowspan=\"19\" valign=\"top\" colspan=\"1\">\n<bold>RNA</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Moderna/NIAID</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Lipid nanoparticle (LNP)-encapsulated mRNA</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Phase II</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">BioNTech/Fosun Pharma/Pfizer</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Three LNP-mRNAs</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Phase I/II</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">CureVac</td><td align=\"left\" rowspan=\"2\" valign=\"top\" colspan=\"1\">mRNA</td><td align=\"left\" rowspan=\"3\" valign=\"top\" colspan=\"1\">Phase I</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">People's Liberation Army (PLA) Academy of Military Sciences/\n<break/>Walvax Biotechnology</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Imperial College London</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">LNP-nCoVsaRNA</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">University of Tokyo/Daiichi-Sankyo</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">LNP-encapsulated mRNA</td><td align=\"left\" rowspan=\"14\" valign=\"top\" colspan=\"1\">Pre-clinical</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Fudan University/Shanghai JiaoTong University/RNACure\n<break/>Biopharma</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">LNP-encapsulated mRNA cocktail encoding VLP/receptor-binding\n<break/>domain (RBD)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">China CDC/Tongji University/Stermina</td><td align=\"left\" rowspan=\"5\" valign=\"top\" colspan=\"1\">mRNA</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Arcturus Therapeutics/Duke-NUS Medical School</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">FBRI SRC VB VECTOR, Rospotrebnadzor, Koltsovo</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Greenlight Biosciences</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">IDIBAPS-Hospital Clinic, Spain</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">BIOCAD</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Liposome-encapsulated mRNA</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Centro Nacional de Biotecnología (CNB-CSIC), Spain</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Replicating defective SARS-CoV-2-derived RNAs</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Translate Bio/Sanofi Pasteur</td><td align=\"left\" rowspan=\"3\" valign=\"top\" colspan=\"1\">LNP-mRNA</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">CanSino Biologics/Precision NanoSystems</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Chula Vaccine Research Center/University of Pennsylvania</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">RNAimmune, Inc.</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Several mRNA candidates</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">eTheRNA</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">mRNA in an intranasal delivery system</td></tr><tr><td align=\"left\" rowspan=\"15\" valign=\"top\" colspan=\"1\">\n<bold>DNA</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">INOVIO Pharmaceuticals</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">DNA plasmid vaccine with electroporation device</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Phase I/II</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Cadila Healthcare Limited</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">DNA plasmid vaccine</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Phase I/II (not yet\n<break/>recruiting)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Genexine Consortium</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">DNA vaccine (GX-19)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Phase I</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Takis Biotech/Applied DNA Sciences/EvviVax</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">DNA</td><td align=\"left\" rowspan=\"11\" valign=\"top\" colspan=\"1\">Pre-clinical</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">BioNet-Asia</td><td align=\"left\" rowspan=\"2\" valign=\"top\" colspan=\"1\">DNA vaccine</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Entos Pharmaceuticals</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Mediphage Bioceuticals/University of Waterloo</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">msDNA vaccine</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Zydus Cadila</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">DNA plasmid vaccine</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Karolinska Institute/Cobra Biologics (OPENCORONA Project)</td><td align=\"left\" rowspan=\"2\" valign=\"top\" colspan=\"1\">DNA with electroporation</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Chula Vaccine Research Center</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Osaka University/AnGes/Takara Bio</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">DNA plasmid vaccine</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Immunomic Therapeutics, Inc./EpiVax, Inc./PharmaJet, Inc.</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Plasmid DNA, needle-free delivery</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Symvivo</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">bacTRL-Spike</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Scancell/University of Nottingham/ Nottingham Trent University</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">DNA plasmid vaccine RBD and N</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">National Research Centre, Egypt</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">DNA plasmid vaccine S, S1, S2, RBD, and N</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"10\" valign=\"top\" colspan=\"1\">\n<bold>Inactivated</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sinovac</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Formaldehyde-inactivated + alum</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Phase III (not yet\n<break/>recruiting)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Beijing Institute of Biological Products/Sinopharm</td><td align=\"left\" rowspan=\"3\" valign=\"top\" colspan=\"1\">Inactivated</td><td align=\"left\" rowspan=\"2\" valign=\"top\" colspan=\"1\">Phase I/II</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Wuhan Institute of Biological Products/Sinopharm</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Institute of Medical Biology, Chinese Academy of Medical Sciences</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Phase I</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Osaka University/BIKEN/NIBIOHN</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Unknown</td><td align=\"left\" rowspan=\"6\" valign=\"top\" colspan=\"1\">Pre-clinical</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sinovac/Dynavax</td><td align=\"left\" rowspan=\"2\" valign=\"top\" colspan=\"1\">Inactivated + CpG 1018</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Valneva/Dynavax</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">National Research Centre, Egypt</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Inactivated whole virus</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Beijing Minhai Biotechnology Co., Ltd.</td><td align=\"left\" rowspan=\"2\" valign=\"top\" colspan=\"1\">Inactivated</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Research Institute for Biological Safety Problems, Rep of Kazakhstan</td></tr><tr><td align=\"left\" rowspan=\"3\" valign=\"top\" colspan=\"1\">\n<bold>Live</bold>\n<break/>\n<bold>attenuated</bold>\n<break/>\n<bold>virus</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Codagenix/Serum Institute of India</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Deoptimized live attenuated vaccines</td><td align=\"left\" rowspan=\"3\" valign=\"top\" colspan=\"1\">Pre-clinical</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Indian Immunologicals Limited/Griffith University</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Codon de-optimization of live attenuated vaccine</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">UMC Utrecht/Radboud University</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Recombinant BCG (rBCG) technology</td></tr><tr><td align=\"left\" rowspan=\"54\" valign=\"top\" colspan=\"1\">\n<bold>Protein</bold>\n<break/>\n<bold>subunit</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Novavax</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Full-length recombinant SARS CoV-2 glycoprotein nanoparticle\n<break/>vaccine adjuvanted with Matrix (M)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Phase I/II</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Clover Biopharmaceuticals Inc./GSK/Dynavax</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Native like trimeric subunit Spike protein vaccine</td><td align=\"left\" rowspan=\"3\" valign=\"top\" colspan=\"1\">Phase I</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Anhui Zhifei Longcom Biopharmaceutical/Institute of Microbiology,\n<break/>Chinese Academy of Sciences</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Adjuvanted recombinant protein (RBD-dimer)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Vaxine Pty Ltd/Medytox</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Recombinant Spike protein with Advax™ adjuvant</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">ExpreS2ion Biotechnologies</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Drosophila S2 insect cell expression system VLPs</td><td align=\"left\" rowspan=\"50\" valign=\"top\" colspan=\"1\">Pre-clinical</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Osaka University/BIKEN/National Institutes of Biomedical\n<break/>Innovation, Japan</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">VLP recombinant protein + adjuvant</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Chulalongkorn University/GPO, Thailand</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">RBD protein fused with Fc of immunoglobulin G + adjuvant</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">AdaptVac (PREVENT-nCoV consortium)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Capsid-like particle</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Helix Biogen Consult, Ogbomoso, and Trinity Immonoefficient\n<break/>Laboratory, Ogbomoso, Oyo State, Nigeria</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Subunit</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">WRAIR/USAMRIID</td><td align=\"left\" rowspan=\"3\" valign=\"top\" colspan=\"1\">S protein</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">AJ Vaccines</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">EpiVax/University of Georgia</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">National Institute of Infectious Diseases, Japan</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">S protein + adjuvant</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sanofi Pasteur/GSK</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">S protein (baculovirus production)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">University of Virginia</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">S subunit intranasal liposomal formulation with GLA/3M052 adjuvants</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">ImmunoPrecise/LiteVax BV</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Spike-based (epitope screening)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Vaxil Bio</td><td align=\"left\" rowspan=\"2\" valign=\"top\" colspan=\"1\">Peptide</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Flow Pharma Inc</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">IMV Inc</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Peptide antigens formulated in lipid nanoparticle formulation</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Generex/EpiVax</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Ii-Key peptide</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">EpiVax</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Protein subunit EPV-CoV-19</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">National Research Centre, Egypt</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Protein subunit S, N, M and S1 protein</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Heat Biologics/University of Miami</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">gp-96 backbone</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">University of Queensland/GSK/Dynavax</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Molecular clamp stabilized Spike protein</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Baylor College of Medicine</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">S1 or RBD protein</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">iBio/CC-Pharming</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Subunit protein, plant produced</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">VIDO-InterVac, University of Saskatchewan</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Adjuvanted microsphere peptide</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">LakePharma, Inc.</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Nanoparticle vaccine</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Baiya Phytopharm/Chula Vaccine Research Center</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Plant-based subunit\n<break/>(RBD-Fc + adjuvant)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Biological E Ltd</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Adjuvanted protein subunit (RBD)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">University of Saskatchewan</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Adjuvanted microsphere peptide</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">University of Pittsburgh</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Microneedle arrays S1 subunit</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Saint-Petersburg Scientific Research Institute of Vaccines and\n<break/>Serums</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Recombinant protein, nanoparticles (based on S-protein and other\n<break/>epitopes)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Innovax/Xiamen University/GSK</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">COVID-19 XWG-03 truncated S (Spike) proteins</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">OncoGen</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Synthetic long peptide vaccine candidate for S and M proteins</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">MIGAL Galilee Research Institute</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Oral\n<italic>Escherichia coli</italic>-based protein expression system of S and N proteins</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Lomonosov Moscow State University</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Structurally modified spherical particles of the tobacco mosaic virus\n<break/>(TMV)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">University of Alberta</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Spike-based</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">AnyGo Technology</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Recombinant S1-Fc fusion protein</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Yisheng Biopharma</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Recombinant protein</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Vabiotech</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Recombinant S protein in IC-BEVS</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Applied Biotechnology Institute, Inc.</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Orally delivered, heat-stable subunit</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Medigen Vaccine Biologics Corporation/NIAID/Dynavax</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">S-2P protein + CpG 1018</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">National University of San Martin and CONICET, Argentina</td><td align=\"left\" rowspan=\"2\" valign=\"top\" colspan=\"1\">Protein subunit</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">MOGAM Institute for Biomedical Research, GC Pharma</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Axon Neuroscience SE</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Peptides derived from Spike protein</td></tr><tr><td align=\"left\" rowspan=\"2\" valign=\"top\" colspan=\"1\">Intravacc/EpiVax</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Outer membrane vesicle (OMV) subunit</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">OMV peptide</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Neovii/Tel Aviv University</td><td align=\"left\" rowspan=\"2\" valign=\"top\" colspan=\"1\">RBD-based</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Kentucky Bioprocessing, Inc</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Quadram Institute</td><td align=\"left\" rowspan=\"2\" valign=\"top\" colspan=\"1\">OMV-based vaccine</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">BiOMViS Srl/University of Trento</td></tr><tr><td align=\"left\" rowspan=\"2\" valign=\"top\" colspan=\"1\">FBRI SRC VB VECTOR, Rospotrebnadzor, Koltsovo</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Peptide vaccine</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Subunit vaccine</td></tr><tr><td align=\"left\" rowspan=\"11\" valign=\"top\" colspan=\"1\">\n<bold>Virus-like particles</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Medicago Inc./Université Laval</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Plant-derived VLP</td><td align=\"left\" rowspan=\"11\" valign=\"top\" colspan=\"1\">Pre-clinical</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Saiba GmbH</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">VLP based on RBD displayed on VLPs</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Navarrabiomed, Oncoimmunology group</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">VLPs, lentivirus and baculovirus vehicles</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">VBI Vaccines Inc.</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Enveloped VLP (eVLP)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Mahidol University/The Government Pharmaceutical Organization\n<break/>(GPO)/Siriraj Hospital</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">VLP + adjuvant</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">IrsiCaixa AIDS Research Institute/IRTA-CReSA/Barcelona\n<break/>Supercomputing Centre/Grifols</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">S protein integrated in HIV VLPs</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Imophoron Ltd and Bristol University Max Planck Centre</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">ADDomer™ multiepitope display</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Doherty Institute</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Unknown</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">OSIVAX</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">VLP</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">University of Sao Paulo</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">VLPs/whole virus</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">ARTES Biotechnology</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">eVLP</td></tr><tr><td align=\"left\" rowspan=\"16\" valign=\"top\" colspan=\"1\">\n<bold>Replicating</bold>\n<break/>\n<bold>viral vector</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Shenzhen Geno-Immune Medical Institute</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Minigenes engineered based on multiple viral genes, using an\n<break/>efficient lentiviral vector system (NHP/TYF) to express viral proteins\n<break/>and immune modulatory genes</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Phase I</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Cadila Healthcare Limited</td><td align=\"left\" rowspan=\"3\" valign=\"top\" colspan=\"1\">Measles vector</td><td align=\"left\" rowspan=\"15\" valign=\"top\" colspan=\"1\">Pre-clinical</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Institute Pasteur/Themis/University of Pittsburg Center for Vaccine\n<break/>Research</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">FBRI SRC VB VECTOR, Rospotrebnadzor, Koltsovo</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">DZIF – German Center for Infection Research/CanVirex AG</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Measles virus (S, N targets)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Tonix Pharmaceuticals/Southern Research</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Horsepox vector expressing S protein</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">University of Hong Kong</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Influenza vector expressing RBD</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">IAVI/Merck</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Replication-competent vesicular stomatitis virus (VSV) chimeric virus\n<break/>technology (VSVΔG) delivering the SARS-CoV-2 Spike (S) glycoprotein</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">BIOCAD and IEM</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Live viral vectored vaccine based on attenuated influenza virus\n<break/>backbone (intranasal)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Lancaster University, UK</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Avian paramyxovirus vector (APMV)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">FBRI SRC VB VECTOR, Rospotrebnadzor, Koltsovo</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Recombinant vaccine based on influenza A virus for the prevention of\n<break/>COVID-19 (intranasal) VSV vector</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">University of Western Ontario</td><td align=\"left\" rowspan=\"2\" valign=\"top\" colspan=\"1\">VSV-S</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Israel Institute for Biological Research/Weizmann Institute of\n<break/>Science</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Fundação Oswaldo Cruz and Instituto Butantan</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Attenuated influenza expressing an antigenic portion of the Spike\n<break/>protein</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Intravacc/Wageningen Bioveterinary Research/Utrecht University</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Newcastle disease virus vector (NDV-SARS-CoV-2/Spike)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">UW–Madison/FluGen/Bharat Biotech</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">M2-deficient single replication (M2SR) influenza vector</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>Unknown</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Tulane University</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Unknown</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Pre-clinical</td></tr></tbody></table><table-wrap-foot><fn><p>The table was adapted from\n<sup><xref rid=\"ref-5\" ref-type=\"bibr\">5</xref>,\n<xref rid=\"ref-6\" ref-type=\"bibr\">6</xref>,\n<xref rid=\"ref-21\" ref-type=\"bibr\">21</xref></sup>.</p></fn></table-wrap-foot></table-wrap></sec><sec><title>DNA/RNA-based platforms</title><p>DNA- and RNA-based platforms present the greatest potential for speed of production since culturing and fermentation are not required for their synthesis. The potential of this approach is showcased by Moderna, whose vaccine candidate advanced to clinical testing just 2 months after sequence identification\n<sup><xref rid=\"ref-7\" ref-type=\"bibr\">7</xref></sup>. There are many other advantages associated with DNA-based vaccines. Notably, they are renowned for their safety profile since the vectors employed are non-replicating and encode and express only the target antigen. The vectors therefore are unable to revert to a disease-causing form, which is a risk with viral vectors. Another key advantage with DNA-based vaccination is the absence of vector-specific immunity, which allows these products to be utilized in prime and boost regimens with multiple products intended for the same patient\n<sup><xref rid=\"ref-8\" ref-type=\"bibr\">8</xref></sup>. RNA-based vaccines are also a promising alternative owing to their potential for low-cost manufacture and good safety profile in animal studies\n<sup><xref rid=\"ref-9\" ref-type=\"bibr\">9</xref>–\n<xref rid=\"ref-11\" ref-type=\"bibr\">11</xref></sup>. However, both DNA- and RNA-based vaccines have their own set of challenges. Both vaccines could suffer from the drawback of having low immunogenicity, as DNA vaccines could potentially integrate into the human genome, while there are concerns about the stability of RNA vaccines\n<sup><xref rid=\"ref-12\" ref-type=\"bibr\">12</xref></sup>. As there are currently no approved DNA or RNA vaccines for medical use in humans\n<sup><xref rid=\"ref-12\" ref-type=\"bibr\">12</xref>,\n<xref rid=\"ref-13\" ref-type=\"bibr\">13</xref></sup>, the question about low immunogenicity has not yet been resolved (\n<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>). The lack of currently approved DNA or RNA vaccines provides considerable regulatory uncertainty, and their progression through the regulatory process will almost certainly take significantly longer than for more conventional vaccine technologies. Currently, there is one vaccine in phase II clinical trials, three in phase I/II clinical trials, and four in phase I clinical trials for SARS-CoV-2 based on the DNA/RNA platform. Inovio Pharmaceuticals is developing a SARS-CoV-2 vaccine, INO-4800, using a DNA-based platform, with phase I clinical trials being undertaken in conjunction with pre-clinical studies in order to hasten development\n<sup><xref rid=\"ref-14\" ref-type=\"bibr\">14</xref></sup>. The International Vaccine Institute (IVI) has announced that the Coalition for Epidemic Preparedness Innovations (CEPI) has awarded $6.9 million of funding to INOVIO to work with IVI and the Korea National Institute of Health (KNIH) for a phase I/II clinical trial of INO-4800 in South Korea. The trial will be conducted in parallel with INOVIO’s phase I study, which is currently ongoing with 40 healthy adults receiving the vaccine candidate and will eventually be expanded to older adults\n<sup><xref rid=\"ref-15\" ref-type=\"bibr\">15</xref></sup>. An RNA vaccine candidate, mRNA-1273, is being developed by Moderna in collaboration with NIAID. The Biomedical Advanced Research and Development Authority (BARDA), a division of the Office of the Assistant Secretary for Preparedness and Response (ASPR) within the US Department of Health and Human Services (HHS), has agreed to cooperate to hasten the development of mRNA-1273 by pledging up to $483 million in funding\n<sup><xref rid=\"ref-16\" ref-type=\"bibr\">16</xref></sup>. Healthy adults aged 18 to 55 were recruited for phase I trials. Two injections of mRNA-1273 were given 28 days apart at a dose of 25 μg, 100 μg, or 250 μg. Dire adverse events were absent, and no trial halting rules were met, although one participant in the 25 μg group withdrew because of an unsolicited adverse event, transient urticaria, which was postulated to be linked to the first vaccination. Live virus neutralization capable of reducing SARS-CoV-2 infectivity by 80% or more was detected at day 43 post-vaccination in all participants. Of the three doses evaluated, the 100 μg dose was optimal based on its capacity to trigger high neutralization responses and Th1-skewed CD4 T cell responses. The reactogenicity profile at 100 μg was better than for the other two doses. The safety, reactogenicity, and immunogenicity of mRNA-1273 are currently being evaluated in phase II trials using two vaccinations given 28 days apart. A total of 600 participants, 300 aged 18–54 and 300 aged 55 and above, have been assigned to three groups: placebo, 50 μg of vaccine, or 100 μg of vaccine at both vaccinations\n<sup><xref rid=\"ref-17\" ref-type=\"bibr\">17</xref></sup>. A phase III trial of mRNA-1273 is expected to begin in July with an estimated 30,000 participants. With the expected final dose of 100 μg, Moderna is confident to scale up manufacturing to approximately 500 million to 1 billion doses per year\n<sup><xref rid=\"ref-18\" ref-type=\"bibr\">18</xref></sup>.</p><table-wrap id=\"T2\" orientation=\"portrait\" position=\"anchor\"><label>Table 2. </label><caption><title>Pros and cons of different vaccine formulations and examples of licensed vaccines.</title></caption><table frame=\"hsides\" rules=\"groups\" content-type=\"article-table\"><thead><tr><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Vaccine platforms</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Pros</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Cons</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Examples of licensed vaccines\n<break/>targeted for humans</th></tr></thead><tbody><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>RNA</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">•   Potential low-cost\n<break/>manufacturing\n<break/>•   Ease of manufacturing\n<break/>•   Good safety profile</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">•   May have low immunogenicity\n<break/>due to instability\n<break/>•   May require multiple doses</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>DNA</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">•   Potential low-cost\n<break/>manufacturing\n<break/>•   Ease of manufacturing\n<break/>•   Good safety profile\n<break/>•   Good stability\n<break/>•   Does not induce anti-vector\n<break/>immunity</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">•   Potential integration to human\n<break/>genome\n<break/>•   Low immunogenicity</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>Virus vectors</bold>\n<break/>\n<bold>(replicating/non-</bold>\n<break/>\n<bold>replicating viral vectors</bold>\n<break/>\n<bold>and virus-like particles)</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">•   High-efficiency gene\n<break/>transduction\n<break/>•   High specific delivery of genes\n<break/>to target cells\n<break/>•   Induction of robust immune\n<break/>responses\n<break/>•   Increased cellular immunity</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">•   Low titer production\n<break/>•   May induce anti-vector\n<break/>immunity\n<break/>•   Generation of replication-\n<break/>competent virus, which can\n<break/>induce tumorigenesis</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">•   JYNNEOS (Smallpox/\n<break/>Monkeypox)\n<break/>•   ACAM2000 (Smallpox)\n<break/>•   Adenovirus type 4 and type 7\n<break/>vaccine, live, oral (febrile acute\n<break/>respiratory)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>Inactivated</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">•   Good safety profile\n<break/>•   Can be used in\n<break/>immunocompromised patients</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">•   Requires booster doses\n<break/>•   Low production titer</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">•   Poliovax (Polio)\n<break/>•   Flucelvax Quadrivalent\n<break/>(Influenza)\n<break/>•   Ixiaro (Japanese Encephalitis)\n<break/>•   Imovax (Rabies)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>Live attenuated virus</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">•   High potency\n<break/>•   Triggers long-lasting immunity\n<break/>•   Low-cost manufacturing</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">•   Possible regression to virulence\n<break/>strain\n<break/>•   Limited use in\n<break/>immunocompromised patients</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">•   ERVEBO (Ebola virus)\n<break/>•   MMR II (Measles, Mumps, and\n<break/>Rubella)\n<break/>•   BCG vaccine (Tuberculosis)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>Protein subunit</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">•   Can be used in\n<break/>immunocompromised patients\n<break/>•   Good safety profile</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">•   Low immunogenicity\n<break/>•   Conjugation could lead to\n<break/>batch-wise variation</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">•   PedvaxHIB\n<break/>(\n<italic>Haemophilus</italic>\n<italic>influenzae</italic> type b)\n<break/>•   Engerix-B (Hepatitis B)\n<break/>•   Recombivax HB (Hepatitis B)</td></tr></tbody></table><table-wrap-foot><fn><p>This table was adapted from\n<sup><xref rid=\"ref-8\" ref-type=\"bibr\">8</xref>–\n<xref rid=\"ref-11\" ref-type=\"bibr\">11</xref>,\n<xref rid=\"ref-20\" ref-type=\"bibr\">20</xref>,\n<xref rid=\"ref-22\" ref-type=\"bibr\">22</xref>–\n<xref rid=\"ref-28\" ref-type=\"bibr\">28</xref></sup>.</p></fn></table-wrap-foot></table-wrap></sec><sec><title>Virus vectors</title><p>Virus-based vectors are powerful tools for vaccination. Their effectiveness stems from their ability to infect cells, which allows them to be highly efficient, specific, and able to trigger robust immune responses. Despite their advantages, viral vectors have several disadvantages (\n<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>). For example, the use of vaccinia virus and adenovirus may lead to immunity against the vector, which will reduce the efficacy of the vaccine. The use of retrovirus and lentivirus, on the other hand, may lead to the risk of tumorigenesis in patients, which results from the integration of viral long terminal repeats into proto-oncogenes\n<sup><xref rid=\"ref-19\" ref-type=\"bibr\">19</xref></sup>. Additionally, certain viral vectors such as adeno-associated virus may not be cost effective owing to their low titer production\n<sup><xref rid=\"ref-20\" ref-type=\"bibr\">20</xref></sup>. Ultimately, viral vectors still present themselves as a valid choice for vaccine development against SARS-CoV-2, as illustrated by their successful use in the eradication of smallpox\n<sup><xref rid=\"ref-20\" ref-type=\"bibr\">20</xref></sup>. Examples of such vaccines currently under development are ChAdOx1-S, Ad5-nCoV, aAPC, and LV-SMENP-DC. The latter two vaccines, developed by Shenzhen Geno-Immune Medical Institute, are currently in phase I (aAPC) and phase II (LV-SMENP-DC) clinical studies. The Ad5-nCoV vaccine pioneered by CanSino Biologics is the first SARS-CoV-2 vaccine to reach phase II clinical trials\n<sup><xref rid=\"ref-5\" ref-type=\"bibr\">5</xref></sup>. Phase I clinical studies for Ad5-nCoV were conducted between March 16 and March 27. No serious adverse events within 28 days of vaccination were reported in vaccine recipients. Specific antibodies, including neutralizing antibodies, increased significantly at day 14 and peaked 28 days post-vaccination. Specific T-cell response peaked at day 14 post-vaccination\n<sup><xref rid=\"ref-29\" ref-type=\"bibr\">29</xref></sup>. A phase II study is currently underway with 500 participants registered in three different groups: 250 participants will receive the vaccine, 125 participants will receive a lower vaccine dose, and 125 participants will receive placebo. The immune response will be tested at 0, 14, and 28 days and 6 months after vaccination\n<sup><xref rid=\"ref-30\" ref-type=\"bibr\">30</xref></sup>. ChAdOx1-S, developed by the University of Oxford in partnership with AstraZeneca, is the first vaccine candidate to reach phase III clinical trials\n<sup><xref rid=\"ref-1\" ref-type=\"bibr\">1</xref>,\n<xref rid=\"ref-5\" ref-type=\"bibr\">5</xref></sup>. A study has reported that a single dose of the vaccine is able to elicit a strong immune response in rhesus macaques\n<sup><xref rid=\"ref-31\" ref-type=\"bibr\">31</xref></sup>. Additionally, a trial study conducted in pigs demonstrated that ChAdOx is able to elicit a greater antibody response when given a booster shot, suggesting that a two-dose approach may give better protection in humans against SARS-CoV-2\n<sup><xref rid=\"ref-32\" ref-type=\"bibr\">32</xref></sup>. However, it should be noted that nose swabs and oropharynx and mediastinal lymph node testing revealed viral gRNA in both vaccinated and control animal groups at 3 and 5 days post-inoculation. Interestingly, viral genome was detected in cervical lymph node in the vaccinated group but not in the control group. Detection of virus in these tissues indicates the vaccine does not provide complete protection against SARS-CoV-2 infection. Viral RNA (gRNA and sgRNA) load in lung tissue was also shown to vary dramatically between individual animals but was determined to be significantly lower in the vaccinated group than in the control group. Another troubling result to note from this study is that despite observing neutralizing antibodies in vaccinated animals, the titers reported were extremely low\n<sup><xref rid=\"ref-31\" ref-type=\"bibr\">31</xref></sup>. Generally, neutralizing antibodies elicited by effective vaccines can be diluted more than a thousand-fold and still maintain their effectiveness\n<sup><xref rid=\"ref-33\" ref-type=\"bibr\">33</xref></sup>. However, the results reported by Oxford show that the serum could be diluted only 4- to 40-fold before losing its neutralizing activity\n<sup><xref rid=\"ref-31\" ref-type=\"bibr\">31</xref>,\n<xref rid=\"ref-33\" ref-type=\"bibr\">33</xref></sup>. ChAdOx1-S was well tolerated in humans, with a trial of over 320 vaccinated individuals showing no strong adverse effects\n<sup><xref rid=\"ref-34\" ref-type=\"bibr\">34</xref></sup>. The phase III clinical trials of this vaccine will involve 8,000 individuals in the United Kingdom, 5,000 individuals in Brazil, and 2,000 individuals in South Africa\n<sup><xref rid=\"ref-35\" ref-type=\"bibr\">35</xref></sup>. AstraZeneca has reached an agreement with Europe’s Inclusive Vaccines Alliance (IVA) to supply up to 400 million doses of ChAdOx1-S starting by the end of 2020 at no profit. The total manufacturing capacity of this vaccine currently stands at 2 billion doses\n<sup><xref rid=\"ref-34\" ref-type=\"bibr\">34</xref></sup>.</p></sec><sec><title>Inactivated vaccines</title><p>Inactivated vaccines have been successfully employed over the past 70 years and are widely used today\n<sup><xref rid=\"ref-36\" ref-type=\"bibr\">36</xref></sup>. Inactivated vaccines are produced using bacteria or viruses by deactivating them with heat, chemicals, or radiation. These processes terminate the pathogen’s ability to replicate, leading to them being more stable and having higher safety profiles. These attributes allow for their use in immunocompromised individuals\n<sup><xref rid=\"ref-37\" ref-type=\"bibr\">37</xref></sup>. However, the characteristics that contribute to their strengths are also weaknesses. As the pathogens are inactivated, these vaccines generally stimulate a much weaker immune response than live vaccines and require several doses for effective immunity to be established. The immune response to an inactivated vaccine is also typically humoral. Antibody titers against the targeted antigen will diminish with time, leading to the need for booster shots\n<sup><xref rid=\"ref-22\" ref-type=\"bibr\">22</xref>,\n<xref rid=\"ref-36\" ref-type=\"bibr\">36</xref></sup> (\n<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>). Regardless, inactivated vaccines are effective agents that have prevented countless deaths due to various infections in humans, notably against wild poliovirus 2, which has not been detected since 1999 and was declared eradicated in September 2015 by WHO\n<sup><xref rid=\"ref-38\" ref-type=\"bibr\">38</xref></sup>. There are currently nine inactivated vaccine candidates for SARS-CoV-2 listed by WHO. Of these, one vaccine candidate—PiCoVacc, developed by Sinovac—is listed as ready to commence phase III clinical trials and is in the midst of preparing to recruit participants. It is very promising to note that Sinovac Biotech has demonstrated protection by PiCoVacc against SARS-CoV-2 in monkeys. Monkeys were immunized three times with two different doses (3 or 6 μg per dose) of PiCoVacc at day 0, 7, and 14 before virus challenge at week 3. It was demonstrated that monkeys vaccinated with PiCoVacc produced anti-SARS-CoV-2 neutralizing antibody titers similar to those of recovered patients. This study also indicated that PiCoVacc is safe, as there was no infection enhancement or immunopathological exacerbation observed in vaccinated monkeys. PiCoVacc is currently in phase I human clinical trials as stated above. The company plans to initiate phase II and III clinical trials with PiCoVacc by the end of this year\n<sup><xref rid=\"ref-39\" ref-type=\"bibr\">39</xref></sup>. There are two other inactivated vaccine candidates being developed by the Beijing Institute of Biological Products and the Wuhan Institute of Biological Products, respectively, both of which are currently in phase I/II clinical trials. Both institutes are collaborating with Sinopharm for the development of these vaccines.</p></sec><sec><title>Live attenuated virus</title><p>Another promising approach in vaccine design is the use of live attenuated virus (LAV), developed by codon deoptimization. This technology has proven itself to be cost effective for large-scale manufacturing and has a smooth regulatory approval pathway, as it has demonstrated high efficacy and potency in both\n<italic>in vitro</italic> and\n<italic>in vivo</italic> experiments against different respiratory viruses in mice, human cells, and non-human primates, providing lasting immunity with just a single dose\n<sup><xref rid=\"ref-23\" ref-type=\"bibr\">23</xref>–\n<xref rid=\"ref-25\" ref-type=\"bibr\">25</xref></sup>. LAV have a simple production process using well-established Vero cells, which are currently the most widely accepted cell line by regulatory authorities for vaccine development, since they have been in use for vaccine manufacturing for nearly 40 years. LAVs are also proven to grow and infect microcarrier beads in large-scale fermenters up to 6,000 L and in serum free media with zero loss in production output\n<sup><xref rid=\"ref-40\" ref-type=\"bibr\">40</xref></sup>. Existing manufacturing infrastructure can be easily utilized because of its cost effectiveness for large-scale manufacturing\n<sup><xref rid=\"ref-41\" ref-type=\"bibr\">41</xref></sup>. LAV has certain drawbacks, notably the existing possibility of reversion to virulence, which has strong safety implications, especially in immunocompromised patients\n<sup><xref rid=\"ref-26\" ref-type=\"bibr\">26</xref></sup> (\n<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>). Nonetheless, codon deoptimization technology in vaccine design has promise against SARS-CoV-2. It is a tried and tested new engineering technology, and designs such as introducing point mutations have proved that the drawbacks can be overcome with relative ease\n<sup><xref rid=\"ref-42\" ref-type=\"bibr\">42</xref></sup>. One example of its recent use is in a vaccine against Ebola virus, where an estimated efficacy of 97.5% was recorded from a preliminary analysis of 90,000 individuals who were exposed for 10 days or more to Ebola virus after vaccination\n<sup><xref rid=\"ref-43\" ref-type=\"bibr\">43</xref></sup>. Codon deoptimization vaccine candidates have virtually no risk of reversion to virulence because of the large number of substitutions that are made in the coding sequence. This is a crucial safety feature of vaccines developed using codon deoptimization. Vaccines developed against SARS-CoV-2 using LAV technology are all currently in pre-clinical development. For example, Indian Immunologicals Limited is currently working together with Griffith University to develop a vaccine using codon deoptimization as a strategy against SARS-CoV-2. The vaccine candidate is expected to provide a long-lasting immunity against SARS-CoV-2 following a single vaccination and is also expected to provide cross-protection against other coronaviruses such as MERS and SARS-CoV-1\n<sup><xref rid=\"ref-44\" ref-type=\"bibr\">44</xref></sup>.</p></sec><sec><title>Protein subunit</title><p>Protein subunit vaccines are a popular choice for the design of a SARS-CoV-2 vaccine owing to their strong safety profile, which is particularly advantageous for immunocompromised patients. They are less likely to cause complications in vaccinated individuals, as the antigenic components employed in protein subunit vaccines are purified and do not involve the use of infectious viruses\n<sup><xref rid=\"ref-45\" ref-type=\"bibr\">45</xref></sup>. However, as with other vaccine platforms, protein subunit vaccines come with their own set of challenges, with the most prominent one being their lack of efficacy. A subunit vaccine presents an antigen to the immune system without the involvement of viral particles using a specific, isolated protein of the pathogen. In the likely scenario where the isolated proteins are denatured, they will become associated with other antibodies different from what was initially targeted, leading to the lack of efficacy\n<sup><xref rid=\"ref-46\" ref-type=\"bibr\">46</xref></sup> (\n<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>). Nonetheless, novel designs and delivery can overcome this limitation and demonstrate enhanced efficacy, e.g. against Zika virus in animal studies and against malaria in\n<italic>in vivo</italic> studies\n<sup><xref rid=\"ref-47\" ref-type=\"bibr\">47</xref>,\n<xref rid=\"ref-48\" ref-type=\"bibr\">48</xref></sup>. The success of protein subunit vaccines is highlighted by the hepatitis B vaccine, and we have now controlled these diseases to the point of virtual elimination\n<sup><xref rid=\"ref-49\" ref-type=\"bibr\">49</xref>,\n<xref rid=\"ref-50\" ref-type=\"bibr\">50</xref></sup>. Currently, there are many vaccines under pre-clinical studies using this platform. For example, Danish company Expression Biotechnologies has announced that it was awarded an EU horizon 2020 grant amounting to €2.7 million for the development of a SARS-CoV-2 vaccine candidate. The company aims to conduct phase I/IIa clinical trials within 12 months after preliminary studies\n<sup><xref rid=\"ref-51\" ref-type=\"bibr\">51</xref></sup>. One other notable example of a vaccine candidate designed on the basis of this platform is developed by Novavax, which is currently in phase I/II clinical trials. There are three other protein subunit vaccine candidates currently in phase I clinical trials\n<sup><xref rid=\"ref-5\" ref-type=\"bibr\">5</xref></sup>.</p></sec><sec><title>Outlook</title><p>SARS-CoV-2 has given rise to an unprecedented economic, health, and societal challenge globally. The key to tackle this pandemic is the development of safe and effective vaccines. Monumental energy has been poured into vaccine R&D; however, there are many challenges that need to be overcome before a successful marketable vaccine becomes available. Over 100 vaccine candidates are in pre-clinical trials, with 19 in clinical trials and many others in early development\n<sup><xref rid=\"ref-5\" ref-type=\"bibr\">5</xref>,\n<xref rid=\"ref-6\" ref-type=\"bibr\">6</xref></sup>. Vaccine developers are turning a blind eye to industrial benchmarks for traditional vaccine development, as speed is considered a major priority in developing a successful vaccine against SARS-CoV-2. For example, clinical trials are being conducted simultaneously instead of sequentially using adaptive designs that are optimized for speed. Researchers, manufacturers, funders, and governing parties are targeting vaccines to be available by 2021, impelling proposals to move forward with reduced numbers of study participants and cut short safety follow-up in clinical studies. A successful COVID-19 vaccine will be administered on a global scale; thus, its safety profile must be solid. Therefore, approaches for generating a COVID-19 vaccine must not compromise safety aspects and will require careful evaluation of effectiveness and safety at each step to avoid major setbacks. There are also unresolved obstacles in ensuring global access to a developed COVID-19 vaccine. For example, during the early days of the HIV and H1N1 outbreaks, even after the successful development of a vaccine, it was difficult to achieve global distribution. Similarly, for COVID-19, a vaccine approved for marketing does not necessarily lead to easy accessibility. Manufacturing capability must be enhanced so that the production of vaccine doses can be magnified while ensuring vaccine affordability for the global community. Coordinated efforts among vaccine developers, funders, and manufacturers are essential to ensure that successful vaccine candidates can be manufactured in adequate numbers and distributed evenly across the globe.</p><p>Another important issue is whether COVID-19 vaccines will sensitize humans to a phenomenon known as antibody-dependent enhancement (ADE). Although antibodies are generally protective, rare cases have been documented in which pathogen-specific antibodies enhance disease—a phenomenon known as ADE. ADE is most prominently associated with dengue virus\n<sup><xref rid=\"ref-52\" ref-type=\"bibr\">52</xref></sup>. ADE has also been observed for both MERS and SARS-CoV-1 despite evidence pointing towards the fact that coronavirus diseases in humans lack the clinical, epidemiological, biological, or pathological attributes of ADE exemplified by the dengue viruses. Therefore, ADE warrants full consideration in the safety profiling of potential vaccine candidates against SARS-CoV-2 to avoid this phenomenon being observed, as SARS-CoV-2 shares a high sequence identity with SARS-CoV-1\n<sup><xref rid=\"ref-53\" ref-type=\"bibr\">53</xref>,\n<xref rid=\"ref-54\" ref-type=\"bibr\">54</xref></sup>.</p><p>Safety aspects of the COVID-19 vaccines under development are of the utmost importance. Thorough clinical trials need to be carried out prior to marketing of the vaccine. Recent reports in India on the possibility of launching a vaccine in record time without allowing sufficient time for clinical trials are disturbing. Although COVID-19 vaccine development is of the utmost global importance, it is crucial that we do not cut corners and that vaccines are approved only after having passed through rigorous clinical trials.</p></sec><sec><title>Concluding remarks</title><p>There are an extraordinary number of COVID-19 vaccines currently under development around the world. Highly optimistic timeframes of 12–18 months to get to the distribution of approved vaccines are being touted. Vaccines typically take many years or even decades to develop, e.g. the smallpox vaccine took 25 years to develop for human use, the HPV vaccine 15 years, the rotavirus vaccine 15 years, and the influenza vaccine 28 years. The COVID-19 vaccine initiatives aspire to have vaccines ready for distribution by 2021. This is a truly remarkable and unprecedented timeframe for vaccine development. The majority of vaccines won’t progress beyond pre-clinical or early clinical testing, but, of course, we need only one effective COVID-19 vaccine to halt this pandemic. Fewer than 10% of drugs or vaccines that enter clinical trials ever progress to become available for human use. For example, 30% fail at phase I trials. Of those that progress to phase II, 69% fail and then a further 42% fail at phase III. Of those products that successfully pass through phase III trials, a further 15% do not gain regulatory approval. Because of the drastically shorter time being allotted for the pre-clinical development of COVID-19 vaccine candidates, it is conceivable that a higher proportion of these candidates may fail during clinical testing. In addition to vaccine development, a number of resource-intensive activities will need to be undertaken in parallel, particularly the construction of manufacturing facilities capable of producing at a global scale. 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content-type=\"http://credit.casrai.org/\">Conceptualization</role><role content-type=\"http://credit.casrai.org/\">Funding Acquisition</role><role content-type=\"http://credit.casrai.org/\">Investigation</role><role content-type=\"http://credit.casrai.org/\">Methodology</role><role content-type=\"http://credit.casrai.org/\">Project Administration</role><role content-type=\"http://credit.casrai.org/\">Resources</role><role content-type=\"http://credit.casrai.org/\">Supervision</role><role content-type=\"http://credit.casrai.org/\">Validation</role><role content-type=\"http://credit.casrai.org/\">Visualization</role><role content-type=\"http://credit.casrai.org/\">Writing – Original Draft Preparation</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><xref ref-type=\"corresp\" rid=\"c2\">b</xref><xref ref-type=\"aff\" rid=\"a1\">1</xref><xref ref-type=\"aff\" rid=\"a2\">2</xref><xref ref-type=\"aff\" rid=\"a3\">3</xref></contrib><aff id=\"a1\">\n<label>1</label>Escuela de Medicina, Universidad San Francisco de Quito USFQ, Quito, Pichincha, 170901, Ecuador</aff><aff id=\"a2\">\n<label>2</label>Health Research Group, Universidad San Francisco de Quito USFQ, Quito, Pichincha, 170901, Ecuador</aff><aff id=\"a3\">\n<label>3</label>SISTEMAS MÉDICOS SIME, Universidad San Francisco de Quito USFQ, Quito, Pichincha, 170157, Ecuador</aff><aff id=\"a4\">\n<label>4</label>Hôpitaux Universitaires Paris Nord Val de Seine (Bichât Claude Bernard), Paris, 75018, France</aff><aff id=\"a5\">\n<label>5</label>Azienda Ospedaliero-Universitaria di Ferrara, Ferrara, 44122, Italy</aff><aff id=\"a6\">\n<label>6</label>Centre Hospitalier Universitaire de Nantes, Nantes, 44000, France</aff><aff id=\"a7\">\n<label>7</label>Harrogate and District NHS Foundation Trust, Harrogate, HG2 7SX, UK</aff><aff id=\"a8\">\n<label>8</label>University of Western Ontario, London, N6A 5B9, Canada</aff></contrib-group><author-notes><corresp id=\"c1\"><label>a</label><email xlink:href=\"mailto:jestrellaporter@gmail.com\">jestrellaporter@gmail.com</email></corresp><corresp id=\"c2\"><label>b</label><email xlink:href=\"mailto:jrguillemot@usfq.edu.ec\">jrguillemot@usfq.edu.ec</email></corresp><fn fn-type=\"COI-statement\"><p>No competing interests were disclosed.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>26</day><month>6</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>9</volume><elocation-id>651</elocation-id><history><date date-type=\"accepted\"><day>11</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>Copyright: © 2020 Estrella Porter J et al.</copyright-statement><copyright-year>2020</copyright-year><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri content-type=\"pdf\" xlink:href=\"f1000research-9-26191.pdf\"/><abstract><p>\n<bold>Background:</bold> Acne-induced scarring is associated with a similar burden as acne, i.e. diminished quality of life, and may be avoided if patients receive appropriate and timely acne treatment. In 2017, a four item-Acne-Scar Risk Assessment Tool (4-ASRAT) was designed by Tan\n<italic>et al</italic>. to categorise patients with acne into lower-risk or higher-risk for acne scarring. Its applicability outside the initial study population (France, Brazil and United States) remains to be determined. </p><p>\n<bold>Methods:</bold> A study protocol was developed to create a systematic approach for validating and adapting 4-ASRAT to different populations, Ecuador in this case. The protocol was reviewed by 11 local and international dermatologists and pilot-tested in an Ecuadorian population using a sample of 10 participants who currently had or had had acne. Feedback from the pilot study was used to improve the study protocol. The results of the pilot study are included here, and the final study protocol is available as extended data. </p><p>\n<bold>Results:</bold> The protocol proved to be applicable. Images taken of participants were a valuable resource for dermatological evaluation about the presence or absence of acne scars. Tangential light is necessary for this evaluation. Although dermatological assessments varied, we concluded that assessment by three local dermatologists for each participant was adequate for reaching a consensus on the presence or absence of acne scars.  </p><p>\n<bold>Conclusions:</bold> Considering the morbidity related to acne and acne scars, tools designed as prevention that alert patients about risk of developing scarring are necessary. The proposed protocol shows a feasible way of validating and adapting 4-ASRAT to different populations.</p></abstract><kwd-group kwd-group-type=\"author\"><kwd>acne</kwd><kwd>acne scars</kwd><kwd>prevention tool</kwd><kwd>risk factors</kwd><kwd>scarring</kwd><kwd>scarring prevention</kwd></kwd-group><funding-group><award-group id=\"fund-1\"><funding-source>Sistemas Médicos</funding-source><award-id>11186</award-id></award-group><award-group id=\"fund-2\" xlink:href=\"http://dx.doi.org/10.13039/501100010654\"><funding-source>Universidad San Francisco de Quito</funding-source></award-group><funding-statement>This project received research funding from Escuela de Medicina, Universidad San Francisco de Quito and SIME (Sistemas Médicos) (Project HUBi ID: 11186).</funding-statement><funding-statement><italic>The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</italic></funding-statement></funding-group></article-meta></front><body><sec sec-type=\"intro\"><title>Introduction</title><p>Acne is among the most common dermatological conditions, with an estimated prevalence among the general population of 9.4%\n<sup><xref rid=\"ref-1\" ref-type=\"bibr\">1</xref></sup>. Prevalence among those aged 12 to 24 years can reach as high as 85%, with variations in different populations\n<sup><xref rid=\"ref-2\" ref-type=\"bibr\">2</xref></sup>. While acne can be perceived as a trivial condition due to its temporary nature, the emotional and psychological burden is high\n<sup><xref rid=\"ref-3\" ref-type=\"bibr\">3</xref></sup>. Patients with acne have diminished quality of life\n<sup><xref rid=\"ref-4\" ref-type=\"bibr\">4</xref></sup>, and are more likely to suffer from depression and anxiety\n<sup><xref rid=\"ref-5\" ref-type=\"bibr\">5</xref></sup>. The extent of these impacts are comparable to that of patients with chronic disability associated with asthma, epilepsy, diabetes or arthritis\n<sup><xref rid=\"ref-6\" ref-type=\"bibr\">6</xref>,\n<xref rid=\"ref-7\" ref-type=\"bibr\">7</xref></sup>.</p><p>Acne often leads to the development of scars, which can be permanent\n<sup><xref rid=\"ref-8\" ref-type=\"bibr\">8</xref></sup>. Acne scarring is also associated with a psychosocial burden similar to that seen with acne\n<sup><xref rid=\"ref-9\" ref-type=\"bibr\">9</xref></sup>. Scarring may, however, be avoided if patients receive appropriate and timely acne treatment\n<sup><xref rid=\"ref-10\" ref-type=\"bibr\"> 10</xref>–\n<xref rid=\"ref-12\" ref-type=\"bibr\">12</xref></sup>. Although risk factors for the development of acne scars are well-known, only one tool exists to predict the risk of acne scarring to support treatment initiation decision-making\n<sup><xref rid=\"ref-13\" ref-type=\"bibr\">13</xref></sup>.</p><p>In 2017, Tan\n<italic>et al.</italic>\n<sup><xref rid=\"ref-13\" ref-type=\"bibr\">13</xref></sup> described the first, and to our knowledge only, tool which assesses the risk for acne scarring in patients with acne based on four major risk factors: “severity of acne”, “family history of acne scarring”, “squeezing and picking behaviours” and “duration of acne”. For simplicity, we named this tool the Four-item Acne Scar Risk Assessment Tool (4-ASRAT). The tool is a short questionnaire, which can be either self-administered or administered by a healthcare worker. Using a score associated with each item to the questionnaire and a score threshold, 4-ASRAT provides a binary outcome on the risk of acne scarring by categorising respondents as being at “lower” or “higher risk”. 4-ASRAT was calibrated and validated using cross-validation based on a pre-existing database containing a large sample of young adults from the United States, France and Brazil, and resulted in a sensitivity of 82% and specificity of 43%. However, its applicability to other populations is uncertain.</p><p>This study presents a protocol for validating and, if necessary, adapting 4-ASRAT to any population. The proposed protocol was tested via a pilot study in an Ecuadorian population to test its applicability and obtain feedback to develop a final version. With this study, we intend to develop and disseminate a protocol for the adaptation of 4-ASRAT to other populations, thereby promoting best practices for timely acne care and acne scar prevention.</p></sec><sec sec-type=\"methods\"><title>Methods</title><p>This study consisted of two phases: first, the development of a study protocol for the evaluation and adaptation of 4-ASRAT to different populations and, second, the pilot of the said protocol in a real-world scenario in Quito, Ecuador, to obtain feedback and improve the proposed protocol. A study using the final protocol in a large Ecuadorean population will be the subject of a subsequent publication.</p><sec><title>Protocol development</title><p>The protocol aimed to provide a replicable process to evaluate and adapt 4-ASRAT to any population. It was conceived as a reviewed version of the initial study protocol proposed by Tan\n<italic>et al</italic>. To be effective, the protocol must provide a strategy to answer three questions: What set of risk factors should the adapted 4-ASRAT use? What score should be associated with each item? What score threshold should it use?</p><p>In addition to the nature of the information that it must lead to, the study protocol must address cost-effectiveness. The adaptation of 4-ASRAT must remain simple for it to be put in practice: the application of the protocol should be inexpensive, in terms of time, human resources and money. This means that the study protocol should also be readily available and provide adequate detail to ensure replicability.</p><p>As part of the protocol, a 22-question survey regarding acne scaring risk factors, epidemiology and self-perception of acne scars was developed (available as\n<italic>Extended data</italic>). Based on acne scar prevalence, the sample of respondent should be at least 250 participants\n<sup><xref rid=\"ref-14\" ref-type=\"bibr\">14</xref></sup>. This questionnaire, which was not formally assessed in this pilot study, is designed to establish the significant risk factors related to acne scars for the evaluation and possibly the adaptation of 4-ASRAT. Participants should go to a photobooth immediately after filling the questionnaire (characteristics of the photobooth details available as\n<italic>Extended data</italic>)\n<sup><xref rid=\"ref-15\" ref-type=\"bibr\">15</xref></sup>, where three pictures will be taken for each participant (front, and right and left profiles). A group of independent dermatologists then evaluate participants’ pictures to determine the presence or absence of acne scars. Their evaluation will be considered as the gold standard for data analysis.</p><p>Data will be computed and calibration and discrimination of the tool will be calculated to validate 4-ASRAT in the studied population. If 4-ASRAT proves invalid for the given population, the adaptation process then begins. Based on the questionnaire results and dermatologists’ evaluations, risk factors for acne scaring and their respective weight to determine the relevant list of risk factors, the scores associated with each response and the optimal score threshold. The complete version of the protocol can be consulted as\n<italic>Extended data</italic>\n<sup><xref rid=\"ref-16\" ref-type=\"bibr\">16</xref></sup>.</p></sec><sec><title>Piloting approach</title><p>The pilot study was conducted to evaluate the feasibility of the aforementioned study protocol and improve its design before a full-scale research conduct. Besides the study of the data collected as part of the application of the standard study protocol, additional information was collected using observation of the data collection in real-world settings and by obtaining feedback from dermatologists.</p><p>In practice, the pilot consisted of a small-scale application of the study protocol with particular attention to the data collection process and a data analysis approach benefitting from expert input.</p><p>A sample of 10 participants, which is among the range recommended for pilot studies\n<sup><xref rid=\"ref-17\" ref-type=\"bibr\">17</xref></sup>, was recruited by an open invitation through social media and flyers in Universidad San Francisco de Quito USFQ, during October 2018, using the same inclusion criteria as in the protocol:</p><list list-type=\"bullet\"><list-item><p>To be a person aged 18–25 at the time of participation.</p></list-item><list-item><p>To have suffered acne at any time point, including having active acne at the time of the study.</p></list-item><list-item><p>To belong to [sample pool population]</p></list-item><list-item><p>To accept signing the informed consent, including consent for photographs to be captured.</p></list-item><list-item><p>To have no visible facial hair at the moment of the photograph</p></list-item><list-item><p>To have no facial make-up at the moment of the photograph</p></list-item></list><p>Participants answered the 22-question survey and were then taken to the photobooth for the three necessary photographs. Data collected as part of the pilot was recorded and tabulated as planned in the study protocol. Participants photographs were showed to a total of 11 dermatological professionals (seven local dermatologists and four international experts) for the evaluation of the presence or absence of acne scars in participants. This process was conceived to determine the minimum number of dermatologists needed to reach a consensus in the evaluation of the presence or absence of acne scars. The following inclusion criteria were used for dermatologists:</p><list list-type=\"bullet\"><list-item><p>To be a medical doctor with a specialty degree in dermatology.</p></list-item><list-item><p>To have at least five years of experience in seeing patients with acne.</p></list-item><list-item><p>To have provided medical attention to at least 35 acne patients per year over the last 5 years.</p></list-item><list-item><p>To consent to the project participation an accept the workload proposed.</p></list-item><list-item><p>Preferred: to have at least one publication related to acne in a scientific journal.</p></list-item></list><p>Dermatologists provided feedback on photography quality to improve the final study protocol. Besides elementary descriptive statistics, no statistical analysis was necessary at the stage of the pilot to evaluate the applicability of the protocol.</p></sec><sec><title>Ethical considerations</title><p>The study protocol and pilot study were approved by Universidad San Francisco de Quito’s Institutional Review Board (Comité de Ética de Investigación en Seres Humanos Universidad San Francisco de Quito) on September 25\n<sup>th</sup> 2018 (2018-193IN). Informed consent was obtained from each participant.</p></sec></sec><sec sec-type=\"results\"><title>Results</title><sec><title>Piloting results</title><p>The pilot study was carried out in October 2018 with a sample of ten participants recruited from students of Universidad San Francisco de Quito USFQ (six women and four men, aged 18–25, mean age: 23 years). Results about the evaluation of the presence or absence of acne scars are shown in\n<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>.</p><table-wrap id=\"T1\" orientation=\"portrait\" position=\"anchor\"><label>Table 1. </label><caption><title>Summary of the dermatological evaluation findings in the pilot study.</title></caption><table frame=\"hsides\" rules=\"groups\" content-type=\"article-table\"><thead><tr><th align=\"center\" rowspan=\"2\" valign=\"top\" colspan=\"1\">Participant\n<break/>code</th><th align=\"center\" rowspan=\"2\" valign=\"top\" colspan=\"1\">Participant\n<break/>Evaluation</th><th align=\"center\" colspan=\"7\" valign=\"top\" rowspan=\"1\">Local dermatologist evaluation</th><th align=\"center\" colspan=\"4\" valign=\"top\" rowspan=\"1\">Expert evaluation</th></tr><tr><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">D1</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">D2</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">D3</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">D4</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">D5</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">D6</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">D7</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">D8</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">D9</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">D10</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">D11</th></tr></thead><tbody><tr><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">01-P</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">P</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">P</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">P</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td></tr><tr><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">02-P</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">P</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">P</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">P</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td></tr><tr><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">03-P</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">P</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td></tr><tr><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">04-P</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">P</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">P</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">P</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td></tr><tr><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">05-P</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">P</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td></tr><tr><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">06-P</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td></tr><tr><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">07-P</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">P</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">P</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td></tr><tr><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">08-P</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">P</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">P</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">P</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">P</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">P</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td></tr><tr><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">09-P</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">P</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">P</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">A</td></tr><tr><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">10-P</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">P</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">P</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">P</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">P</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">P</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</td></tr></tbody></table><table-wrap-foot><fn><p>D: dermatologist; P: acne scars are present, more than mild;\n<italic>p</italic>: Scars are present, but mild; A: acne scars are absent. This table presents the results of the protocol pilot with ten participants evaluated by 11 dermatologists, of whom seven were local dermatologists and four were international acne expert dermatologists (expert in table). For participant 01-P, the participant’s self-evaluation is that acne scars are present. Local dermatologist D1 evaluated that mild acne scares are present, local dermatologists D2 and D3 reported that acne scars beyond mild are present and local and expert dermatologists D4 to D11 reported that acne scars are absent. A simple majority showed to be equally effective in professional evaluation than more complex systems of determining consensus with compound majorities.</p></fn></table-wrap-foot></table-wrap><p>Participants and dermatologists were able to complete the study without issues. Scarring assessments varied widely: compared to the gold standard established by international experts – who generally agreed – local dermatologists had a little more difficulty agreeing, with consensus reached for 6 out of 10 cases vs. 9 out of 10 for international experts. When a consensus was reached among local dermatologists, the outcome always concurred with that of international experts. This suggests that local dermatologists are an adequate proxy for international experts provided a minimum number of participating local dermatologists, which we established at three.</p><p>Participant self-evaluations varied more widely from the expert gold standard: half the time self- and international expert assessments concurred. When they differed, participants were more likely to minimize the presence of scars (false negatives, 3/10) rather than exaggerate (false positives, 1/10).</p><p>Dermatologists generally validated the approach for photographing, with three main recommendations reached by consensus:</p><list list-type=\"simple\"><list-item><label>-</label><p>Tangential light is mandatory to assess scar volume and depth in photography.</p></list-item><list-item><label>-</label><p>Besides from front, left and right angle photographs, an oblique picture of the participants should be included.</p></list-item><list-item><label>-</label><p>Make-up, even if invisible, should not be allowed in participants.</p></list-item></list><p>Surveys and photographs were easily collected.</p></sec><sec><title>Final protocol</title><p>Similar to Tan\n<italic>et al</italic>.’s study, the protocol proposes a methodology for studying at a single timepoint younger adults (18–25 years), and considers their history of acne and acne scarring in order to identify acne-scarring risk factors and their respective weight. Depending on the context, incentives may be used to promote participation. The study is designed to be completed in a six-month timeframe. All of the recommendations given by dermatologists were considered for its final version, and three dermatologists were established as the minimum number to reach consensus. No further adaptations to the protocol were required based on the pilot study.</p></sec></sec><sec sec-type=\"discussion\"><title>Discussion</title><p>The proposed protocol showed applicability and ease of execution, while confirming its usefulness in obtaining the necessary data for the validation and adaptation of 4-ASRAT (epidemiologic data, self-evaluation about presence or absence of acne scars, associated risk factors, and images for dermatological evaluation). However, some limitations were found.</p><p>Although preferable for accuracy, a prospective study including younger participants (children who have not developed acne scars yet) with follow-up evaluations was avoided for practical reasons: a study with these characteristics is impractical because of the time it implies in terms of follow-up, resulting in a low probability of applying the protocol in other populations.</p><p>A single-site study was also chosen (a university) to take advantage of the concentration of eligible volunteers who were willing to participate. The study centre should be selected, if possible, based on the representativeness of the sample pool compared to the general population and should include individuals not only from a single institution (university) but rather more representative of the population’s context.</p><p>Recruiting a representative population sample is a complex and costly exercise, which seems unrealistic in the context of acne-scar prevention. Although the lack of representativeness of the sample proposed in the study protocol may be perceived as a limitation, we prefer the local and cost-effective adaptation of 4-ASRAT to the use of the non-calibrated tool. Researchers should seek aggregated groups, such as universities, which are the best proxies to the general population for the adaption study.</p><p>Participant photography will never equal the quality of face-to-face evaluation, as bidimensional imaging does not yet allow volumetric assessment, a necessary element for determining the severity of a suspected scar\n<sup><xref rid=\"ref-18\" ref-type=\"bibr\">18</xref></sup>. Tangential light must be included in the final study, to obtain a more three-dimensional image of participants, as suggested by dermatologists during the pilot study. Again, we prefer the more cost-effective approach using photography, despite its limitations, rather than professional individual evaluations, which increase the study costs significantly.</p><p>Regarding scar assessments, comparing self- to local professional assessments, the pilot suggests the need for dermatological assessments rather than reliance on self-assessment. As opposed to an earlier study, the pilot results hint towards a tendency of participants to minimise the presence of acne scars rather than overestimate, which was earlier found\n<sup><xref rid=\"ref-19\" ref-type=\"bibr\">19</xref></sup>. It was shown that a simple majority consensus is sufficient to determine the presence or absence of acne scars, so the use of three dermatologists is recommended for final evaluation (minimum number needed to reach a simple majority consensus).</p></sec><sec sec-type=\"conclusions\"><title>Conclusions</title><p>We developed and piloted a readily available study protocol to evaluate and adapt 4-ASRAT to any population. We showed this protocol to be applicable in practice, provided that certain precautions were taken, including photography quality and local dermatologist support. Due to the scarcity of tools to assess the risk of acne scaring, the use of an adapted and validated tool for prediction of acne scar risk in a particular population is a valuable public health measure.</p></sec><sec sec-type=\"data-availability\"><title>Data availability</title><sec><title>Underlying data</title><p>All data underlying the results are available as part of the article and no additional source data are required.</p></sec><sec><title>Extended data</title><p>Harvard Dataverse: Questionnaire for the validation and adaptation of a tool to estimate the risk of acne-induced scars in different population.\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/WGDWB0\">https://doi.org/10.7910/DVN/WGDWB0</ext-link>\n<sup><xref rid=\"ref-15\" ref-type=\"bibr\">15</xref></sup>.</p><p>This project contains the questionnaire to be given to participants.</p><p>Harvard Dataverse: Protocol for the validation and adaptation of a tool to estimate the risk of acne-induced scars in different populations.\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/NED0GS\">https://doi.org/10.7910/DVN/NED0GS</ext-link>\n<sup><xref rid=\"ref-16\" ref-type=\"bibr\">16</xref></sup>.</p><p>This project contains the protocol to be conducted for the main study.</p><p>Extended data are available under the terms of the\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://creativecommons.org/publicdomain/zero/1.0/\">Creative Commons Zero \"No rights reserved\" data waiver</ext-link> (CC0 1.0 Public domain dedication).</p></sec></sec></body><back><ack><title>Acknowledgements</title><p>The authors of this manuscript would like to acknowledge the support of SIME (Sistemas Médicos) and their research and administration team, Dr. Jaime Ocampo, Dr. Andrés Caicedo and Sulema Romero.</p></ack><ref-list><ref id=\"ref-1\"><label>1</label><mixed-citation 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Harvard Dataverse, V1.<year>2020</year>\n<pub-id pub-id-type=\"doi\">10.7910/DVN/NED0GS</pub-id>\n</mixed-citation></ref><ref id=\"ref-17\"><label>17</label><mixed-citation publication-type=\"journal\">\n<person-group person-group-type=\"author\"><name><surname>Johanson</surname><given-names>GA</given-names></name><name><surname>Brooks</surname><given-names>GP</given-names></name></person-group>:\n<article-title>Initial Scale Development: Sample Size for Pilot Studies.</article-title>\n<source><italic toggle=\"yes\">Educ Psychol Meas.</italic></source>\n<year>2010</year>;<volume>70</volume>(<issue>3</issue>):<fpage>394</fpage>–<lpage>400</lpage>.\n<pub-id pub-id-type=\"doi\">10.1177/0013164409355692</pub-id>\n</mixed-citation></ref><ref id=\"ref-18\"><label>18</label><mixed-citation publication-type=\"journal\">\n<person-group person-group-type=\"author\"><name><surname>Clark</surname><given-names>AK</given-names></name><name><surname>Saric</surname><given-names>S</given-names></name><name><surname>Sivamani</surname><given-names>RK</given-names></name></person-group>:\n<article-title>Acne Scars: How Do We Grade Them?</article-title>\n<source><italic toggle=\"yes\">Am J Clin Dermatol.</italic></source>\n<year>2018</year>;<volume>19</volume>(<issue>2</issue>):<fpage>139</fpage>–<lpage>144</lpage>.\n<pub-id pub-id-type=\"doi\">10.1007/s40257-017-0321-x</pub-id>\n<pub-id pub-id-type=\"pmid\">28891036</pub-id></mixed-citation></ref><ref id=\"ref-19\"><label>19</label><mixed-citation publication-type=\"journal\">\n<person-group person-group-type=\"author\"><name><surname>de Almeida</surname><given-names>H</given-names></name><name><surname>Cecconi</surname><given-names>J</given-names></name><name><surname>Duquia</surname><given-names>RP</given-names></name><etal/></person-group>:\n<article-title>Sensitivity and specificity of self-reported acne in 18-year-old adolescent males.</article-title>\n<source><italic toggle=\"yes\">Int J Dermatol.</italic></source>\n<year>2013</year>;<volume>52</volume>(<issue>8</issue>):<fpage>946</fpage>–<lpage>8</lpage>.\n<pub-id pub-id-type=\"doi\">10.1111/j.1365-4632.2011.05443.x</pub-id>\n<pub-id pub-id-type=\"pmid\">23786338</pub-id></mixed-citation></ref></ref-list></back><sub-article id=\"report68895\" article-type=\"peer-review\"><front-stub><article-id pub-id-type=\"doi\">10.5256/f1000research.26191.r68895</article-id><title-group><article-title>Reviewer response for version 1</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Bing-Rong</surname><given-names>Zhou</given-names></name><xref ref-type=\"aff\" rid=\"r68895a1\">1</xref><role>Referee</role></contrib><aff id=\"r68895a1\">\n<label>1</label>Department of Dermatology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China</aff></contrib-group><author-notes><fn fn-type=\"COI-statement\"><p>\n<bold>Competing interests: </bold>No competing interests were disclosed.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><permissions><copyright-statement>Copyright: © 2020 Bing-Rong Z</copyright-statement><copyright-year>2020</copyright-year><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><related-article related-article-type=\"peer-reviewed-article\" id=\"d38e2163\" ext-link-type=\"doi\" xlink:href=\"10.12688/f1000research.23737.1\">Version 1</related-article><custom-meta-group><custom-meta><meta-name>recommendation</meta-name><meta-value>approve</meta-value></custom-meta></custom-meta-group></front-stub><body><p>I think 4-ASRAT will be a useful and powerful tool for assessing whether acne scars occur in patients with acne lesions. As we all know, the treatment of these scars is still full of challenges. Therefore, if there is a simple and easy way to assess the risk of scar formation in acne patients and take early prevention measures, it may help to reduce the incidence of acne scars.</p><p> The current 4-ASRAT questionnaire is relatively suitable for rapid assessment of acne scar formation risk in a short period of time. Although it has good predictive sensitivity and specificity, I think the questionnaire still needs to add more specific indicators. It is better to be able to evaluate under the guidance of dermatologists. If the assessment items can include risk tools to predict the formation of acne atrophic scar, hypertrophic scar and keloid respectively, it will help doctors to deal with the corresponding problems. In addition, because in this study, according to the patient's facial lesions photos, in some cases, both dermatology experts and local dermatologists did not reach a high degree of consistency, which indicates that the definition of acne scar is still vague. Although it is very difficult to reach 100% consensus, it is important to make a more appropriate definition of acne scar before the assessment and to train the assessors for a certain period of time.</p><p>Is the work clearly and accurately presented and does it cite the current literature?</p><p>Yes</p><p>If applicable, is the statistical analysis and its interpretation appropriate?</p><p>Yes</p><p>Are all the source data underlying the results available to ensure full reproducibility?</p><p>Yes</p><p>Is the study design appropriate and is the work technically sound?</p><p>Yes</p><p>Are the conclusions drawn adequately supported by the results?</p><p>Yes</p><p>Are sufficient details of methods and analysis provided to allow replication by others?</p><p>Yes</p><p>Reviewer Expertise:</p><p>Phototherapy.</p><p>I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.</p></body></sub-article><sub-article id=\"report65623\" article-type=\"peer-review\"><front-stub><article-id pub-id-type=\"doi\">10.5256/f1000research.26191.r65623</article-id><title-group><article-title>Reviewer response for version 1</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Torres Lozada</surname><given-names>Vicente</given-names></name><xref ref-type=\"aff\" rid=\"r65623a2\">2</xref><role>Referee</role><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0003-0165-8624</contrib-id></contrib><contrib contrib-type=\"author\"><name><surname>Torres-Calderón</surname><given-names>Maria Fernanda</given-names></name><xref ref-type=\"aff\" rid=\"r65623a1\">1</xref><role>Co-referee</role></contrib><aff id=\"r65623a1\">\n<label>1</label>Instituto Tecnológico y de Estudios Superiores de Monterrey, Mexico City, Mexico</aff><aff id=\"r65623a2\">\n<label>2</label>Dermatology Department, Hospital Juarez, Mexico City, Mexico</aff></contrib-group><author-notes><fn fn-type=\"COI-statement\"><p>\n<bold>Competing interests: </bold>No competing interests were disclosed.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>3</day><month>8</month><year>2020</year></pub-date><permissions><copyright-statement>Copyright: © 2020 Torres-Calderón MF and Torres Lozada V</copyright-statement><copyright-year>2020</copyright-year><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><related-article related-article-type=\"peer-reviewed-article\" id=\"d38e2270\" ext-link-type=\"doi\" xlink:href=\"10.12688/f1000research.23737.1\">Version 1</related-article><custom-meta-group><custom-meta><meta-name>recommendation</meta-name><meta-value>approve</meta-value></custom-meta></custom-meta-group></front-stub><body><p>Regarding the 4 ASRAT tool, I believe it can be good for screening the risk of developing scars, as tools with good sensitivity and moderate specificity are. It could be helpful as early intervention to prevent the detriment of their self esteem and mental health. The extended questionnaire, if applied to patients who already have acne, it could be helpful to intervene as soon as possible, under the premise that they are prone to develop acne scars or if they already have a few, so they do not develop any more scarring. This tool could translate into better adherence to treatment and less scarring, as well as knowing they are prone to develop them, for their future offspring. </p><p> Regarding patients who already have acne scars, I think this tool will only show that if left untreated they will develop more scars.</p><p> I believe the 4 ASRAT tool could incorporate questions from the extended questionnaire, but those specifically regarding risk factors for developing inflammatory acne, and the questionnaire should be applied by either a physician or a healthcare personnel, in order to standardize and reassure the questions are adequately interpreted by the patient. This could perhaps be done at the primary care physician office, with a referral to dermatology if the patient has a high risk of developing scars. Also, the full questionnaire would take around 20 minutes, so perhaps the most relevant questions should be kept to make it a 5-7 minute questionnaire or if most questions should remain the questionnaire should be made by trained personnel to make it objective and quicker.</p><p>Is the work clearly and accurately presented and does it cite the current literature?</p><p>Yes</p><p>If applicable, is the statistical analysis and its interpretation appropriate?</p><p>Yes</p><p>Are all the source data underlying the results available to ensure full reproducibility?</p><p>Yes</p><p>Is the study design appropriate and is the work technically sound?</p><p>Yes</p><p>Are the conclusions drawn adequately supported by the results?</p><p>Yes</p><p>Are sufficient details of methods and analysis provided to allow replication by others?</p><p>Yes</p><p>Reviewer Expertise:</p><p>Acne and Rosacea, Acne scars.</p><p>We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.</p></body></sub-article></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">F1000Res</journal-id><journal-id journal-id-type=\"iso-abbrev\">F1000Res</journal-id><journal-id journal-id-type=\"pmc\">F1000Research</journal-id><journal-title-group><journal-title>F1000Research</journal-title></journal-title-group><issn pub-type=\"epub\">2046-1402</issn><publisher><publisher-name>F1000 Research Limited</publisher-name><publisher-loc>London, UK</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32850118</article-id><article-id pub-id-type=\"pmc\">PMC7431970</article-id><article-id pub-id-type=\"doi\">10.12688/f1000research.20419.1</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Review</subject></subj-group><subj-group><subject>Articles</subject></subj-group></article-categories><title-group><article-title>Pediatric SJS-TEN: Where are we now?</article-title><fn-group content-type=\"pub-status\"><fn><p>[version 1; peer review: 2 approved]</p></fn></fn-group></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Ramien</surname><given-names>Michele</given-names></name><role content-type=\"http://credit.casrai.org/\">Conceptualization</role><role content-type=\"http://credit.casrai.org/\">Writing – Original Draft Preparation</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0001-9191-3611</contrib-id><xref ref-type=\"corresp\" rid=\"c1\">a</xref><xref ref-type=\"aff\" rid=\"a1\">1</xref><xref ref-type=\"aff\" rid=\"a2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Goldman</surname><given-names>Jennifer L.</given-names></name><role content-type=\"http://credit.casrai.org/\">Supervision</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><xref ref-type=\"aff\" rid=\"a3\">3</xref><xref ref-type=\"aff\" rid=\"a4\">4</xref></contrib><aff id=\"a1\">\n<label>1</label>Department of Pediatrics, University of Calgary, Alberta Children's Hospital, 28 Oki Dr NW, Calgary, AB, Canada</aff><aff id=\"a2\">\n<label>2</label>Department of Medicine, University of Calgary, Calgary, AB, Canada</aff><aff id=\"a3\">\n<label>3</label>Division of Clinical Pharmacology, Toxicology & Therapeutic Innovation, Children’s Mercy Hospitals and Clinics, Kansas City, MO, USA</aff><aff id=\"a4\">\n<label>4</label>Division of Pediatric Infectious Diseases, Children’s Mercy Hospitals and Clinics, Kansas City, MO, USA</aff></contrib-group><author-notes><corresp id=\"c1\"><label>a</label><email xlink:href=\"mailto:michele.ramien@ucalgary.ca\">michele.ramien@ucalgary.ca</email></corresp><fn fn-type=\"COI-statement\"><p>\n<bold>Competing interests: </bold>MLR is the primary investigator on a study supported by the Pediatric Dermatology Research Alliance on pediatric SJS-TEN. JG is principal investigator on a study (Trimethoprim: an overlooked contributor of trimethoprim-sulfamethoxazole idiosyncratic adverse drug reactions) supported by the National Institute of General Medical Sciences (5R01GM129783-02).</p></fn></author-notes><pub-date pub-type=\"epub\"><day>13</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>9</volume><elocation-id>F1000 Faculty Rev-982</elocation-id><history><date date-type=\"accepted\"><day>14</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright: © 2020 Ramien M and Goldman JL</copyright-statement><copyright-year>2020</copyright-year><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri content-type=\"pdf\" xlink:href=\"f1000research-9-22442.pdf\"/><abstract><p>Stevens–Johnson syndrome and toxic epidermal necrolysis are rare severe blistering skin reactions triggered by medications or infections. Over the last 5 to 10 years, a number of important publications have advanced understanding of these diseases and their response to treatment. Importantly, a subset of patients with disease triggered by infection has been identified as having Mycoplasma pneumoniae–induced rash and mucositis, suggesting a reconsideration of the diagnostic paradigm. We present an update on pediatric Stevens–Johnson syndrome and toxic epidermal necrolysis in the broader context of cutaneous adverse drug reactions and focus on challenges and recent advances in diagnosis, management, and prevention.</p></abstract><kwd-group kwd-group-type=\"author\"><kwd>Stevens-Johnson syndrome</kwd><kwd>toxic epidermal necrolysis</kwd><kwd>severe cutaneous drug reaction</kwd><kwd>drug reaction</kwd><kwd>drug eruption</kwd><kwd>SJS</kwd><kwd>TEN</kwd><kwd>reactive infectious mucocutaneous eruption</kwd><kwd>RIME</kwd><kwd>drug-induced epidermal necrolysis</kwd><kwd>DEN</kwd></kwd-group><funding-group><funding-statement>The author(s) declared that no grants were involved in supporting this work.</funding-statement></funding-group></article-meta><notes><sec sec-type=\"editor-note\"><title>Editorial Note on the Review Process</title><p>\n<ext-link ext-link-type=\"uri\" xlink:href=\"http://f1000research.com/browse/faculty-reviews\">F1000 Faculty Reviews</ext-link> are commissioned from members of the prestigious\n<ext-link ext-link-type=\"uri\" xlink:href=\"http://f1000.com/prime/thefaculty\">F1000 Faculty</ext-link> and are edited as a service to readers. In order to make these reviews as comprehensive and accessible as possible, the referees provide input before publication and only the final, revised version is published. The referees who approved the final version are listed with their names and affiliations but without their reports on earlier versions (any comments will already have been addressed in the published version).</p><p>The referees who approved this article are: </p><list list-type=\"simple\" list-content=\"reviewer-list\"><list-item><p>\n<named-content content-type=\"reviewer-name\">Sayan Basu</named-content>, The Cornea Institute, L V Prasad Eye Institute (LVPEI), Hyderabad, India\n<fn fn-type=\"COI-statement\"><p>No competing interests were disclosed.</p></fn>\n</p></list-item><list-item><p>\n<named-content content-type=\"reviewer-name\">Teresa Bellón</named-content>, La Paz Hospital Health Research Institute-IdiPAZ, Madrid, Spain\n<fn fn-type=\"COI-statement\"><p>No competing interests were disclosed.</p></fn>\n</p></list-item></list></sec></notes></front><body><sec sec-type=\"intro\"><title>Introduction</title><p>Skin (cutaneous) reactions are among the most common types of adverse reactions to medications in children, accounting for 36% of any adverse drug reaction (ADR)\n<sup><xref rid=\"ref-1\" ref-type=\"bibr\">1</xref></sup>. Many of these reactions are mild and self-resolving, but the rare severe ADRs can be associated with significant morbidity and even mortality. The diagnosis and treatment of cutaneous ADRs (cADRs) in children are challenging for several reasons. First, children are more commonly infected with viruses as compared with adults; each year, children in their first years of life average six to 10 respiratory viral infections and older children and adolescents average three to five such illnesses\n<sup><xref rid=\"ref-2\" ref-type=\"bibr\">2</xref></sup>. Many of these viruses, such as Epstein–Barr virus, adenovirus, and enteroviruses, are frequently associated with cutaneous reactions that can be misinterpreted as cADRs. Second, triggers of severe cADRs, such as Stevens–Johnson syndrome (SJS), are different in adults and children; medications more often are implicated in adults and infections more commonly cause SJS in children\n<sup><xref rid=\"ref-3\" ref-type=\"bibr\">3</xref></sup>. Third, causality tools currently available—for example, Naranjo, algorithm of drug causality for epidermal necrolysis (ALDEN), and the US Food and Drug Administration’s division of drug experience—have been developed and studied primarily in adults and not applied to children. Finally, owing to the overall rarity of severe cADRs in children, there is little evidence to guide treatment.</p><p>ADRs can be either type A (predictable, caused by on-target drug action) or type B (heterogeneous, immunologically and metabolically mediated) (\n<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). cADRs can be classified clinically by morphology and severity on the basis of the presence (complex) or absence (simple) of fever and other systemic symptoms (\n<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>).</p><table-wrap id=\"T1\" orientation=\"portrait\" position=\"anchor\"><label>Table 1. </label><caption><title>Cutaneous adverse drug reactions mechanisms summary.</title></caption><table frame=\"hsides\" rules=\"groups\" content-type=\"article-table\"><thead><tr><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Type A - Augmented</th><th align=\"left\" colspan=\"5\" valign=\"top\" rowspan=\"1\">Type B - Bizarre</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"7\" valign=\"top\" colspan=\"1\">Predictable (example:\n<break/>overdose)</td><td align=\"left\" rowspan=\"7\" valign=\"top\" colspan=\"1\">Non-immunological\n<break/>(example:\n<break/>intolerance)</td><td align=\"left\" colspan=\"4\" valign=\"top\" rowspan=\"1\">Immunological</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Type I: IgE</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Type II: IgG\n<break/>cytotoxic</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Type III: immune\n<break/>complexes</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Type IV: delayed\n<break/>hypersensitivity</td></tr><tr><td align=\"left\" rowspan=\"5\" valign=\"top\" colspan=\"1\">Urticaria/Angioedema</td><td align=\"left\" rowspan=\"5\" valign=\"top\" colspan=\"1\">Hemolytic\n<break/>anemia</td><td align=\"left\" rowspan=\"5\" valign=\"top\" colspan=\"1\">Vasculitis</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Delayed</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">IVa: ACD</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">IVb: DiHS/DRESS</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">IVc: SJS-TEN, morbilliform</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">IVd: AGEP</td></tr></tbody></table><table-wrap-foot><fn><p>ACD, allergic contact dermatitis; AGEP, acute generalized exanthematous pustulosis; DiHS, drug-induced hypersensitivity syndrome; DRESS, drug reaction with eosinophilia and systemic symptoms; SJS-TEN, Stevens–Johnson syndrome–toxic epidermal necrolysis\n<sup><xref rid=\"ref-4\" ref-type=\"bibr\">4</xref></sup>. Adapted with permission from Noguera-Morel\n<italic>et al</italic>.\n<sup><xref rid=\"ref-1\" ref-type=\"bibr\">1</xref></sup>.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"T2\" orientation=\"portrait\" position=\"anchor\"><label>Table 2. </label><caption><title>Morphology and severity summary.</title></caption><table frame=\"hsides\" rules=\"groups\" content-type=\"article-table\"><thead><tr><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Exanthematous</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Urticarial</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Blistering</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Pustular</th></tr></thead><tbody><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Simple – no fever</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Morbilliform</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Urticaria</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">FDE, SDRIFE</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Acneiform</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Complex – fever</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Drug HSS/DRESS</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">SSLR</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">SJS-TEN</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">AGEP</td></tr></tbody></table><table-wrap-foot><fn><p>AGEP, acute generalized exanthematous pustulosis; DRESS, drug reaction with eosinophilia and systemic symptoms; FDE, fixed drug eruption; HSS, hypersensitivity syndrome; SDRIFE, symmetrical drug related intertriginous and flexural exanthem; SJS-TEN, Stevens–Johnson syndrome–toxic epidermal necrolysis; SSLR, serum sickness-like reaction.\n<sup><xref rid=\"ref-5\" ref-type=\"bibr\">5</xref></sup>.</p></fn></table-wrap-foot></table-wrap><p>Given their rarity, complex or severe cADRs are difficult to study systematically and pediatric-specific data are limited. These challenges have led to the adoption of adult paradigms for diagnosis and management in pediatric practice, where better evidence exists.</p><p>In SJS and toxic epidermal necrolysis (TEN), severe blistering of the skin and mucous membranes related to either medications or infections occurs. The differential diagnosis in early cases where diffuse erythema is common may include viral exanthems, Kawasaki disease, and acute generalized exanthematous pustulosis. As widespread blistering begins to develop, thermal burns, toxic erythema of chemotherapy, pemphigus of all types, staphylococcal scalded skin syndrome (which spares the mucous membranes), acute graft-versus-host disease, acute syndrome of apoptotic pan-epidermolysis associated with systemic lupus erythematosus, and generalized bullous fixed drug eruption become the primary considerations.</p><p>In recent years, significant developments have occurred in the category of severe blistering drug reactions—SJS and TEN—the focus of this review. The watershed evolutions in this area will be reviewed and ultimately support the need for evolution of the diagnostic categories for severe blistering cADRs in children.</p></sec><sec><title>Incidence</title><p>Until 2017, estimates of the incidence of SJS and TEN were based on retrospective surveys and databases from the 1990s and a European registry\n<sup><xref rid=\"ref-6\" ref-type=\"bibr\">6</xref>–\n<xref rid=\"ref-9\" ref-type=\"bibr\">9</xref></sup>. More recently, larger datasets have been used in attempts to better describe these rare events in pediatrics.</p><p>A recent US pediatric database cohort study suggests that SJS-TEN spectrum disorders occur in 7.5 per 100,000 hospitalized children\n<sup><xref rid=\"ref-10\" ref-type=\"bibr\">10</xref></sup>; incidences of 6.3 and 0.5 per 100,000 hospitalized children per year for SJS and TEN, respectively, were reported. A second US cross-sectional study that sourced data from the same 2009–2012 time frame found much lower rates, of 5.3 (SJS) and 0.4 (TEN) per million children per year, in the overall population\n<sup><xref rid=\"ref-11\" ref-type=\"bibr\">11</xref></sup>. Similar findings in both studies suggest that pediatric SJS-TEN results in a substantial health-care burden, although mortality is less when compared with adult data. There are many limitations to using large datasets to examine rare events such as SJS-TEN, including poor validity of International Classification of Diseases 9/10 (ICD-9/10) codes, inability to determine cause, and the lack of a standardized diagnostic approach across institutions. Given that the clinical features of severe cADRs can be challenging to interpret for diagnosis, standardization in case identification and validation is needed.</p></sec><sec><title>Clinical features and refining diagnosis: drug versus bug</title><sec><title>Stevens–Johnson syndrome and toxic epidermal necrolysis</title><p>SJS and TEN are characterized by blistering of the skin and mucous membranes. One to three days before onset of skin and mucosal lesions, prodromal symptoms start; these include fever, general malaise, non-productive cough, stinging eyes, and a sore mouth. These symptoms are often mistaken for an upper respiratory tract infection. Macules with purpuric, non-blanching centers with a predilection for the head and torso evolve quickly, often within 12 hours, into blisters that slough off, leaving large areas of denuded skin and mucosa. Painful erythema of the palms and soles is also common early in the disease. Target papules with three distinct rings, characteristic of erythema multiforme, a similar condition usually triggered by herpes simplex virus\n<sup><xref rid=\"ref-12\" ref-type=\"bibr\">12</xref></sup>, are not the main morphology in SJS and TEN, although atypical two-ringed or macular (flat) targets may occur early, before blistering starts. Mucosal involvement affects oral, ocular, genitourinary, and anal sites.</p><p>SJS and TEN are believed to exist on a spectrum; there is less than 10% body surface area (BSA) involvement in SJS and greater than 30% BSA involvement in TEN, and intermediate BSA involvement of 10 to 30% is called SJS-TEN overlap (\n<xref rid=\"T3\" ref-type=\"table\">Table 3</xref>)\n<sup><xref rid=\"ref-13\" ref-type=\"bibr\">13</xref></sup>. Involved areas include skin that is already blistered or detached and skin that is red (macular erythema) and detachable.</p><table-wrap id=\"T3\" orientation=\"portrait\" position=\"anchor\"><label>Table 3. </label><caption><title>Severe blistering cutaneous adverse drug reactions and\n<italic>Mycoplasma pneumoniae</italic>–induced rash and mucositis.</title></caption><table frame=\"hsides\" rules=\"groups\" content-type=\"article-table\"><thead><tr><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">SJS</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">SJS-TEN overlap</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">TEN</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"2\" valign=\"top\" colspan=\"1\">Body surface area affected</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"><10%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">10–30%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">>30%</td></tr><tr><td align=\"left\" colspan=\"3\" valign=\"top\" rowspan=\"1\">MIRM (usually <10% but can be\n<break/>more extensive)</td></tr></tbody></table><table-wrap-foot><fn><p>MIRM,\n<italic>Mycoplasma pneumoniae</italic>–induced rash and mucositis; SJS, Stevens–Johnson syndrome; TEN, toxic epidermal necrolysis.</p></fn></table-wrap-foot></table-wrap><p>These diagnostic categories were developed in 1993 on the basis of an expert review and synthesis of hundreds of adult cases\n<sup><xref rid=\"ref-13\" ref-type=\"bibr\">13</xref></sup>, and for many pediatric cases, a diagnostic category can be assigned. However, problems arise when patients have severe involvement of their mucous membranes but little or no skin lesions because there is no diagnostic category for them.</p></sec><sec><title>\n<italic>Mycoplasma pneumoniae</italic>–induced rash and mucositis</title><p>In 2015, Canavan\n<italic>et al.</italic>\n<sup><xref rid=\"ref-14\" ref-type=\"bibr\">14</xref></sup> described\n<italic>Mycoplasma pneumoniae</italic> (MP)-induced rash and mucositis (MIRM) as an entity distinct from SJS. Patients with MIRM have exactly the features of the previously noted patients who evaded classification; they have severe mucositis of multiple mucous membranes out of proportion to skin involvement, which typically is sparse but in some cases may be significant\n<sup><xref rid=\"ref-14\" ref-type=\"bibr\">14</xref></sup>. The characteristic constellation of features in MIRM is triggered by respiratory infection rather than medications, and pathogens other than MP have been reported\n<sup><xref rid=\"ref-15\" ref-type=\"bibr\">15</xref></sup>. Advances in technology, including respiratory polymerase chain reaction (PCR), have improved MP detection, and more specific methods to confirm infection have recently been developed\n<sup><xref rid=\"ref-16\" ref-type=\"bibr\">16</xref></sup>; these include measurement of MP-specific antibody-secreting cells\n<sup><xref rid=\"ref-17\" ref-type=\"bibr\">17</xref></sup>.</p><p>The challenge with cases attributed to infection and with the diagnosis of MIRM is that their clinical features overlap with those of SJS, creating opportunities for patients to receive multiple diagnoses and complicating more comprehensive and systematic study of these cases in the future. Furthermore, as there is no diagnostic code in the ICD-9 or ICD-10 for MIRM, these patients would likely have been, and continue to be, assigned codes for SJS, TEN, or erythema multiforme with unreliable assignment with secondary codes for mycoplasma infection.</p></sec><sec><title>Proposed revised classification</title><p>Author MLR is part of a group that recently proposed a revised classification for severe blistering cutaneous reactions in children; the revision condenses SJS and TEN into a single category of drug-induced epidermal necrolysis (DEN) that may have variable skin involvement (manuscript under revision). This is logical because SJS and TEN are considered quantity variants of the same disease and drugs are their common trigger. The proposed classification separates out the infection-related cases typified by severe mucosal and less impressive skin lesions as reactive infectious mucocutaneous eruption (RIME). Given their disparate causes and pathogeneses, DEN and RIME have diverging management strategies: the focus is on drug withdrawal and early consideration of immunosuppressive treatment in patients with DEN and on identification and treatment of infection combined with supportive care with or without immunosuppressive therapy in RIME cases. Application of this novel pediatric-specific paradigm may have far-reaching impacts on incidence and epidemiology and ultimately provide more directed and effective management.</p><p>Biomarkers will likely emerge as tools to confirm diagnosis; the granulysin rapid test, which has a turnaround time of 15 minutes, was used in a 2011 report to establish an early diagnosis of SJS in a young child\n<sup><xref rid=\"ref-18\" ref-type=\"bibr\">18</xref></sup>. Other candidate biomarkers, including eosinophilia, perforin, interferon-gamma, soluble Fas ligand, and CD69, are being evaluated, although none is validated for clinical use at this time\n<sup><xref rid=\"ref-19\" ref-type=\"bibr\">19</xref></sup>.</p></sec></sec><sec><title>Etiologies</title><p>Recent pediatric retrospective studies identify a drug as the cause of SJS and TEN in 72 to 90% of cases\n<sup><xref rid=\"ref-20\" ref-type=\"bibr\">20</xref>–\n<xref rid=\"ref-23\" ref-type=\"bibr\">23</xref></sup>. Idiopathic cases, where no cause (medication or infection) can be identified, make up 5 to 17% of cases in retrospective reviews\n<sup><xref rid=\"ref-20\" ref-type=\"bibr\">20</xref>,\n<xref rid=\"ref-21\" ref-type=\"bibr\">21</xref>,\n<xref rid=\"ref-24\" ref-type=\"bibr\">24</xref></sup>. A limited number of medications, including antibiotics, anticonvulsants, and non-steroidal anti-inflammatory drugs, are implicated in the majority of pediatric cases. As stated above, MP is a common trigger of severe mucocutaneous reactions that have been considered SJS-TEN in the past; however, the clinical presentation and outcomes suggest a unique disease process.</p></sec><sec><title>Causality and risk reduction</title><p>It is important to avoid implicating medications used to treat the prodromal symptoms of SJS and TEN as causative agents\n<sup><xref rid=\"ref-19\" ref-type=\"bibr\">19</xref></sup>. Survivors of SJS and TEN are often told to avoid all medications taken just prior to the reaction, limiting treatments that can be used for future illnesses\n<sup><xref rid=\"ref-25\" ref-type=\"bibr\">25</xref></sup> and causing patients and families concern about the use of medications in general. Evaluation of possible implicated medications requires utilization of an effective causality tool. Many available ADR causality tools, including the Naranjo and Liverpool tools, are non-specific to the ADR phenotype. The ALDEN is specific for severe cADRs. These tools are helpful when considering the timing of drug exposure to reaction onset, the probability of drug presence in the body, prior exposure to the same drug regardless of reaction at that time, the presence of drug beyond the progression phase, drug notoriety as an ADR cause, and the presence or absence of other etiologies\n<sup><xref rid=\"ref-25\" ref-type=\"bibr\">25</xref></sup>. Although these tools can be helpful, their usefulness is limited by variability in results between users and testing methods. Further advancements in diagnostics and assessment tools have the potential to enhance causality assessment in the future\n<sup><xref rid=\"ref-26\" ref-type=\"bibr\">26</xref></sup>.</p><p>Testing to identify the causative agent in SJS-TEN remains controversial and is not widely used.\n<italic>In vivo</italic> (varied methods of re-exposure of the patient to potential trigger medications) testing includes patch testing or delayed intradermal testing. These tests can be performed once the acute reaction has resolved but within a year. Potentially cross-reacting medications can also be identified with patch testing\n<sup><xref rid=\"ref-27\" ref-type=\"bibr\">27</xref></sup>. Unfortunately, the results are not reliable for all medications, limiting their clinical utility. Oral re-challenge, recommended for other types of drug reactions, is not recommended for SJS and TEN given the serious risk of a second potentially fatal episode\n<sup><xref rid=\"ref-28\" ref-type=\"bibr\">28</xref></sup>.\n<italic>In vitro</italic> testing with the lymphocyte transformation test (LTT) during the recovery phase, 4 to 8 weeks after the reaction, is controversial as LTT historically has had low sensitivity in SJS-TEN and many false-positive and -negative results\n<sup><xref rid=\"ref-29\" ref-type=\"bibr\">29</xref></sup>. Recent reports support better sensitivity (86%) and specificity (74%) in SJS-TEN, even for low-risk drugs\n<sup><xref rid=\"ref-30\" ref-type=\"bibr\">30</xref></sup>. The drug-specific interferon-gamma–releasing cell assay is highly specific (95% specificity and 79% sensitivity for allopurinol) and can be carried out in the acute phase\n<sup><xref rid=\"ref-31\" ref-type=\"bibr\">31</xref></sup>. A recent publication highlighted its advantages over LTT: higher rates of causative drug identification (73.9% versus 52.2% for LTT) and use during the acute phase (versus recovery phase for LTT)\n<sup><xref rid=\"ref-32\" ref-type=\"bibr\">32</xref></sup>.</p><p>Prevention of SJS and TEN is becoming a reality with the identification of risk factors that can be screened prior to drug initiation. Human leukocyte antigen (HLA) screening for ethnicity-specific risk alleles prior to administration of aromatic anticonvulsants (including carbamazepine), allopurinol, and abacavir reduces the risk of SJS-TEN (\n<xref rid=\"T4\" ref-type=\"table\">Table 4</xref>)\n<sup><xref rid=\"ref-33\" ref-type=\"bibr\">33</xref></sup>. Polymorphisms that reduce drug clearance can also increase the risk of severe cADRs\n<sup><xref rid=\"ref-34\" ref-type=\"bibr\">34</xref></sup> When a risk allele is identified in a patient, the medication should be avoided\n<sup><xref rid=\"ref-35\" ref-type=\"bibr\">35</xref></sup>. As a proof of concept, in Taiwan, where pre-carbamazepine screening for HLA-B*1502 was adopted in 2010, the incidence of SJS-TEN has drastically decreased\n<sup><xref rid=\"ref-36\" ref-type=\"bibr\">36</xref></sup>. In Thailand, where HLA-B*1502 pre-treatment screening is also routine, HLA test results are printed on a wallet card that patients can carry with them as a “pharmacogenomic ID card” for future health-care interactions\n<sup><xref rid=\"ref-35\" ref-type=\"bibr\">35</xref></sup>.</p><table-wrap id=\"T4\" orientation=\"portrait\" position=\"anchor\"><label>Table 4. </label><caption><title>Medication causes of SJS-TEN with strongly predictive (100%) HLA associations\n<sup><xref rid=\"ref-35\" ref-type=\"bibr\">35</xref></sup>.</title></caption><table frame=\"hsides\" rules=\"groups\" content-type=\"article-table\"><thead><tr><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Medication</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Population</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">HLA\n<break/>type</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Interpretation of positive result</th></tr></thead><tbody><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Allopurinol</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Han Chinese and European</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">B*5801</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Increased risk of SJS-TEN, do not use in naïve patients, can be\n<break/>considered in patients without reaction after more than 3 months</td></tr><tr><td align=\"left\" rowspan=\"2\" valign=\"top\" colspan=\"1\">Carbamazepine</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Han Chinese, Thai, Malaysian,\n<break/>Indian, Singaporean, and\n<break/>Vietnamese</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">B*1502</td><td align=\"left\" rowspan=\"2\" valign=\"top\" colspan=\"1\">Increased risk of SJS-TEN, do not use CBZ or ox-CBZ in naïve\n<break/>patients, can consider using if no reaction after more than 3 months\n<break/>treatment</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">European, Japanese, and Korean</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">B*3101</td></tr></tbody></table><table-wrap-foot><fn id=\"TFN4\"><p>CBZ, carbamazepine; HLA, human leukocyte antigen; ox-CBZ, oxcarbazepine; SJS-TEN, Stevens–Johnson syndrome – toxic epidermal necrolysis.\n<sup><xref rid=\"ref-35\" ref-type=\"bibr\">35</xref></sup>. Adapted with permission from Peter\n<italic>et al</italic>.\n<sup><xref rid=\"ref-19\" ref-type=\"bibr\">19</xref></sup>.</p></fn></table-wrap-foot></table-wrap></sec><sec><title>To treat or not to treat, that is the question… but we still don’t know the answer</title><p>SCORe of Toxic Epidermal Necrolysis (SCORTEN), a TEN severity-of-illness score\n<sup><xref rid=\"ref-37\" ref-type=\"bibr\">37</xref></sup>, was developed for adults but has been used to predict both morbidity and mortality in pediatric patients\n<sup><xref rid=\"ref-38\" ref-type=\"bibr\">38</xref>,\n<xref rid=\"ref-39\" ref-type=\"bibr\">39</xref></sup>. The SCORTEN has multiple factors, but three of them—age of at least 40 years, malignancy, and heart rate of at least 120 beats per minute—are less important in children because (1) they are young, (2) they have low rates of malignancy, and (3) their normal heart rate can be over 120 beats per minute if they are younger than four. The SCORTEN, or a pediatric-adapted scoring system, could be used prospectively in studies to predict treatment efficacy by comparing actual with predicted mortality and morbidity\n<sup><xref rid=\"ref-38\" ref-type=\"bibr\">38</xref></sup>. Despite the availability of a validated severity score that helps to predict which patients will have more severe outcomes, the absence of a gold-standard effective therapy for pediatric SJS-TEN means that the decision between supportive care and directed (immunosuppressive) therapy remains challenging.</p><p>In the last two years, there has been an explosion of retrospective and database publications on pediatric SJS-TEN which have come to similar conclusions on the relative lack of efficacy of corticosteroids and intravenous immunoglobulin (IVIG) on various outcomes\n<sup><xref rid=\"ref-20\" ref-type=\"bibr\">20</xref>,\n<xref rid=\"ref-22\" ref-type=\"bibr\">22</xref>,\n<xref rid=\"ref-40\" ref-type=\"bibr\">40</xref>–\n<xref rid=\"ref-43\" ref-type=\"bibr\">43</xref></sup>. For treatment of SJS-TEN, there is a consensus on the importance of rapid identification and withdrawal of the causative medication. As adjuvant therapy, corticosteroids are most frequently used, followed by IVIG both as monotherapy and in combination with corticosteroids. None of these mono- or combination therapies appears to affect time to healing or length of hospital stay. The best evidence on treatment before 2017–2018 was from a large (n = 128 cases) systematic review published in 2011 that suggested that patients who received IVIG and prednisone had better outcomes than those who received supportive care only\n<sup><xref rid=\"ref-44\" ref-type=\"bibr\">44</xref></sup>. Overall mortality rates for SJS-TEN in newer publications range from 0 to 6.6%, and rates of up to 25% were reported for TEN cases\n<sup><xref rid=\"ref-20\" ref-type=\"bibr\">20</xref>,\n<xref rid=\"ref-22\" ref-type=\"bibr\">22</xref>,\n<xref rid=\"ref-40\" ref-type=\"bibr\">40</xref>–\n<xref rid=\"ref-43\" ref-type=\"bibr\">43</xref></sup>.</p><p>Recent case reports and case series document the rapid efficacy of cyclosporine 3 mg/kg per day divided twice a day and infliximab 5 mg/kg intravenously for one dose in both drug- and infection-related cases\n<sup><xref rid=\"ref-45\" ref-type=\"bibr\">45</xref>,\n<xref rid=\"ref-46\" ref-type=\"bibr\">46</xref></sup>. There is emerging evidence from case reports for anti-tumor necrosis factor (anti-TNF) therapy in the acute phase\n<sup><xref rid=\"ref-47\" ref-type=\"bibr\">47</xref>–\n<xref rid=\"ref-50\" ref-type=\"bibr\">50</xref></sup>. Furthermore, a recent Taiwanese randomized clinical trial of etanercept in TEN included children older than 4 years of age and showed that etanercept (25 mg twice weekly <65 kg and 50 mg if >65 kg) decreased the predicted mortality rate and reduced skin healing time compared with corticosteroids (1 to 1.5 mg/kg per day intravenously) in the group as a whole\n<sup><xref rid=\"ref-51\" ref-type=\"bibr\">51</xref></sup>. Both groups received treatment until their skin lesions healed\n<sup><xref rid=\"ref-51\" ref-type=\"bibr\">51</xref></sup>. This study included five children who were 6 to 13 years old\n<sup><xref rid=\"ref-51\" ref-type=\"bibr\">51</xref></sup>. Further large-scale studies are needed to confirm these promising results in the pediatric population.</p><p>Ocular complications and sequelae are significant for pediatric patients with SJS-TEN. Aggressive initial management, including adjuvant amniotic membrane transplantation, can reduce complications\n<sup><xref rid=\"ref-52\" ref-type=\"bibr\">52</xref>,\n<xref rid=\"ref-53\" ref-type=\"bibr\">53</xref></sup>. Even in the absence of severe ocular involvement in acute SJS-TEN, children may develop progressive disease of the ocular surface and conjunctival inflammation over time\n<sup><xref rid=\"ref-54\" ref-type=\"bibr\">54</xref></sup>. Delayed lid margin keratinization\n<sup><xref rid=\"ref-55\" ref-type=\"bibr\">55</xref></sup>, vision deterioration, and corneal damage can occur after SJS-TEN\n<sup><xref rid=\"ref-56\" ref-type=\"bibr\">56</xref></sup>, emphasizing the importance of close continual follow-up by ophthalmology\n<sup><xref rid=\"ref-55\" ref-type=\"bibr\">55</xref>,\n<xref rid=\"ref-57\" ref-type=\"bibr\">57</xref></sup>.</p><p>The early introduction of psychological and social support is critical for pediatric patients with SJS-TEN to avoid long-term anxiety and depression. Ideally, the introduction should occur as soon as the patient is stable enough for a psychologist or child-life worker to visit. For children, the experience of complete loss of control of their body can be devastating, and early explanation and attention to the patient’s emotional needs are critical. Other chronic sequelae are summarized in\n<xref rid=\"T5\" ref-type=\"table\">Table 5</xref>.</p><table-wrap id=\"T5\" orientation=\"portrait\" position=\"anchor\"><label>Table 5. </label><caption><title>Potential chronic sequelae of Stevens–Johnson syndrome – toxic epidermal necrolysis.</title></caption><table frame=\"hsides\" rules=\"groups\" content-type=\"article-table\"><thead><tr><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Organ system</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sequelae</th></tr></thead><tbody><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Skin</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Dyspigmentation, eruptive nevi, milia, nail dystrophy, alopecia, scarring, and heterotopic ossification</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Ocular</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sicca symptoms, trichiasis, corneal vascularization, corneal scarring, symblepharon, keratitis, and blindness</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Oral </td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Xerostomia, synechiae, chronic gingivitis, dental caries, periodontal disease, taste abnormalities, abnormal\n<break/>dental development, and candidiasis</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Gastrointestinal</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Pancreatitis, colon necrosis, esophageal stenosis and webs, microstomia, and persistent intestinal ulcers</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Genitourinary</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Vaginal stenosis, labial fusion, hydrocolpos, hematocolpos, dyspareunia, vaginal dryness, and urethral stenosis</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Pulmonary</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Chronic obstructive bronchitis and bronchiolitis, bronchiectasis, and pharyngeal and laryngeal scarring</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Autoimmune</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sjögren syndrome, systemic lupus erythematosus, and autoimmune thyroiditis</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Psychiatric</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Anxiety and depression</td></tr></tbody></table><table-wrap-foot><fn><p>Adapted with permission from Peter\n<italic>et al.</italic>,\n<sup><xref rid=\"ref-19\" ref-type=\"bibr\">19</xref></sup>.</p></fn></table-wrap-foot></table-wrap><sec><title>Treatment of reactive infectious disease:\n<italic> Mycoplasma pneumoniae</italic> and other infections</title><p>Given the limited data on reactive infectious mucocutaneous eruptions, including MIRM and non-MP, it remains unclear whether treatment should be similar to that of SJS-TEN. A systematic review by Canavan\n<italic>et al</italic>. suggests that the majority of patients receive antibiotics and a smaller percentage receive corticosteroids, IVIGs, or supportive care only\n<sup><xref rid=\"ref-14\" ref-type=\"bibr\">14</xref></sup>. Although antibiotics are used to treat MP respiratory infections, the effect of antibiotics on the skin and mucous membrane changes of MIRM is unclear\n<sup><xref rid=\"ref-14\" ref-type=\"bibr\">14</xref></sup>. Recurrent disease is reported in up to 20% of patients\n<sup><xref rid=\"ref-23\" ref-type=\"bibr\">23</xref></sup>, suggesting a genetic susceptibility to reactive mucositis and rash which has yet to be elucidated.</p><p>MP-related SJS-TEN overlap has been reported in one pediatric patient to have rapidly re-epithelialized with cyclosporine 3 mg/kg per day over a period of 7 days without co-treatment for MP infection\n<sup><xref rid=\"ref-45\" ref-type=\"bibr\">45</xref></sup>. A second case report of TEN induced by MP responded rapidly with complete resolution within a week after a single dose of infliximab 5 mg/kg per day (the patient also received meropenem)\n<sup><xref rid=\"ref-46\" ref-type=\"bibr\">46</xref></sup>. A case series of three children whose MIRM was treated with cyclosporine suggests that the addition of cyclosporine 3 to 5 mg/kg per day for early cases may accelerate resolution compared with supportive care and antibiotics alone\n<sup><xref rid=\"ref-58\" ref-type=\"bibr\">58</xref></sup>.</p><p>Generally, the course of MIRM is less severe than for drug-related SJS-TEN, and supportive care is a reasonable option. However, the mucous membranes should be closely monitored as the same sequelae as are seen in SJS-TEN occur and can be severe. A recent publication in the ophthalmology literature suggests that these children should be followed closely during their inpatient admission similarly to SJS-TEN\n<sup><xref rid=\"ref-59\" ref-type=\"bibr\">59</xref></sup>. In their systematic review, Canavan\n<italic>et al</italic>. reported ocular sequelae in 9%, post-inflammatory dyspigmentation in 6%, and oral or genital synechiae in less than 1%\n<sup><xref rid=\"ref-14\" ref-type=\"bibr\">14</xref></sup>. Severe gynecologic sequelae requiring surgery have been reported\n<sup><xref rid=\"ref-60\" ref-type=\"bibr\">60</xref></sup>. Given the sequelae and the acute severe disease course, psychosocial support should ideally be introduced to patients and families at the time of diagnosis (as for SJS-TEN) and there should be a regular review for signs of (latent) distress at follow-up visits to their primary care provider.</p><p>The bottom line for therapy is that the identified triggering medication should be discontinued, an infection should be sought and treated if suspected, supportive care in a low-ratio nursing environment should be provided, and anti-inflammatory immunosuppressive therapy, particularly in the early acute phase, should be considered. Specialist input should be sought and coordinated. An overview of the approach to practical management of pediatric SJS-TEN is presented in\n<xref rid=\"T6\" ref-type=\"table\">Table 6</xref> and is detailed by McPherson\n<italic>et al</italic>.\n<sup><xref rid=\"ref-62\" ref-type=\"bibr\">62</xref></sup>.</p><table-wrap id=\"T6\" orientation=\"portrait\" position=\"anchor\"><label>Table 6. </label><caption><title>Practical management of pediatric Stevens–Johnson syndrome – toxic epidermal necrolysis.</title></caption><table frame=\"hsides\" rules=\"groups\" content-type=\"article-table\"><tbody><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Admission</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Determine cause based on drug history (ALDEN), infectious symptoms\n<break/>\n<bold>Baseline:</bold>\n<break/>Investigations: confirm cause, rule out contraindications to treatment\n<break/>- routine bloodwork, including complete metabolic profile, liver function tests, urinalysis\n<break/>- infectious workup, including viral serologies/PCR (Epstein–Barr virus, cytomegalovirus, HSV, human herpes virus 6),\n<break/>nasopharyngeal swab for respiratory viruses and\n<italic>Mycoplasma pneumoniae</italic> PCR, oral mucosal swab for HSV PCR, chest\n<break/>x-ray to rule out pneumonia\n<break/>- screen for HLA risk alleles if not already known (\n<xref rid=\"T4\" ref-type=\"table\">Table 4</xref>)\n<break/>- if patient severe and might need immunosuppression: consider interferon-gamma release assay for tuberculosis,\n<break/>hepatitis and HIV serology,\n<italic>Strongyloides serology</italic>\n<break/>Document severity: SCORTEN, BSA, photography\n<break/>\n<bold>Treatment:</bold>\n<break/>Discontinue potential causative medications\n<break/>Treat for infection if present with directed antibiotics\n<break/>Supportive care: sterile wound care, fluid replacement and nutritional supplementation as for burns, airway management,\n<break/>pain control\n<break/>\n<bold>Plan:</bold>\n<break/>Assess need for transfer to specialized experienced center for severe cases (SCORTEN >1, BSA >10%, comorbidities,\n<break/>requiring ventilation)\n<break/>Consult dermatology, ophthalmology, gynecology, urology, infectious disease, pharmacy/clinical pharmacology urgently\n<break/>Consider anti-inflammatory/immunosuppressive treatment: consider contraindications, risk-benefit</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Monitoring</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Frequent vital signs, monitor for fever\n<break/>Frequent swabs to identify infection early, prophylactic antibiotics not recommended\n<break/>Document progression with SCORTEN, photography\n<break/>Supportive care, including early physiotherapy</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Follow-up</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Identify a primary contact for the patient after discharge, either a pediatrician or specialist amongst the following:\n<break/>    •    Dermatology\n<break/>    •    Ophthalmology\n<break/>    •    Gastroenterology\n<break/>    •    Gynecology (female) and urology (male)\n<break/>    •    Psychiatry/Psychology for post-traumatic stress disorder\n<break/>    •    Genetics to review HLA testing and counsel family\n<break/>    •    Respirology if needed\n<break/>Consider\n<italic>in vitro</italic> testing with lymphocyte transformation test or ELISpot (controversial)\n<break/>Give patient a wallet card that identifies their history of SJS-TEN and HLA screening result for future medical encounters. Please refer to Figure 4 in Sukasem\n<italic>et al</italic>.\n<sup><xref rid=\"ref-35\" ref-type=\"bibr\">35</xref></sup> for an example.</td></tr></tbody></table><table-wrap-foot><fn><p>ALDEN, algorithm of drug causality for epidermal necrolysis; BSA, body surface area; HLA, human leukocyte antigen; HSV, herpes simplex virus; PCR, polymerase chain reaction; SCORTEN, SCORe of Toxic Epidermal Necrolysis; SJS-TEN, Stevens–Johnson syndrome – toxic epidermal necrolysis.\n<sup><xref rid=\"ref-61\" ref-type=\"bibr\">61</xref>,\n<xref rid=\"ref-62\" ref-type=\"bibr\">62</xref></sup>.</p></fn></table-wrap-foot></table-wrap></sec></sec><sec><title>How redefining diagnosis of pediatric Stevens–Johnson syndrome – toxic epidermal necrolysis can improve treatment</title><p>The literature on the treatment of pediatric SJS and TEN is disappointing as larger studies suggest no impact of any intervention other than drug withdrawal. In contrast, case series and reports document the effectiveness of various anti-inflammatory immunosuppressive treatments. Could it be that the way that past cases are classified results in groups that are too heterogeneous (that is, triggered by infection or drugs or idiopathic) to respond similarly to a given therapy? Considering a shift in diagnostic paradigm as discussed above may be a way to interpret the existing literature with a lens focused from cause/trigger to treatment. Adding to this a precision medicine approach that compares subpopulations of responders with non-responders to identify characteristics that permit early recognition and takes advantage of biomarkers like the granulysin rapid test may lead to a future in which we can select the right treatment for the right patient every time.</p></sec><sec sec-type=\"conclusions\"><title>Conclusions</title><p>The future is bright for pediatric SJS-TEN. Initiatives are under way to improve our understanding of this spectrum of disorders, specifically in children. A British guideline on management was recently published\n<sup><xref rid=\"ref-61\" ref-type=\"bibr\">61</xref></sup>. With increasing accessibility and validation of risk factor screening (HLA and metabolism variants) and biomarkers, we may soon be able to prevent SJS-TEN in predisposed individuals and diagnose and treat it early when it occurs by chance.</p></sec><sec><title>Abbreviations</title><p>ADR, adverse drug reaction; ALDEN, algorithm of drug causality for epidermal necrolysis; BSA, body surface area; cADR, cutaneous adverse drug reaction; DEN, drug-induced epidermal necrolysis; HLA, human leukocyte antigen; ICD, International Classification of Diseases; IVIG, intravenous immunoglobulin; LTT, lymphocyte transformation test; MIRM,\n<italic>Mycoplasma pneumoniae</italic>–induced rash and mucositis; MP,\n<italic>Mycoplasma pneumoniae</italic>; RIME, reactive infectious mucocutaneous eruption; SCORTEN, SCORe of Toxic Epidermal Necrolysis; SJS, Stevens–Johnson syndrome; TEN, toxic epidermal necrolysis</p></sec></body><back><ref-list><ref id=\"ref-1\"><label>1</label><mixed-citation publication-type=\"journal\">\n<person-group person-group-type=\"author\"><name><surname>Noguera-Morel</surname><given-names>L</given-names></name><name><surname>Hernández-Martín</surname><given-names>Á</given-names></name><name><surname>Torrelo</surname><given-names>A</given-names></name></person-group>:\n<article-title>Cutaneous drug reactions in the pediatric population.</article-title>\n<source><italic toggle=\"yes\">Pediatr Clin North Am.</italic></source>\n<year>2014</year>;<volume>61</volume>(<issue>2</issue>):<fpage>403</fpage>–<lpage>26</lpage>.\n<pub-id pub-id-type=\"doi\">10.1016/j.pcl.2013.12.001</pub-id>\n<pub-id pub-id-type=\"pmid\">24636653</pub-id></mixed-citation></ref><ref id=\"ref-2\"><label>2</label><mixed-citation publication-type=\"journal\">\n<person-group person-group-type=\"author\"><name><surname>Pavia</surname><given-names>AT</given-names></name></person-group>:\n<article-title>Viral infections of the lower respiratory tract: Old viruses, new viruses, and the role of diagnosis.</article-title>\n<source><italic toggle=\"yes\">Clin Infect Dis.</italic></source>\n<year>2011</year>;<volume>52 Suppl 4</volume>(<issue>Suppl 4</issue>):<fpage>S284</fpage>–<lpage>9</lpage>.\n<pub-id pub-id-type=\"doi\">10.1093/cid/cir043</pub-id>\n<!--<pub-id pub-id-type=\"pmcid\">3106235</pub-id>-->\n<pub-id pub-id-type=\"pmid\">21460286</pub-id></mixed-citation></ref><ref id=\"ref-3\"><label>3</label><mixed-citation publication-type=\"journal\">\n<person-group person-group-type=\"author\"><name><surname>Paulmann</surname><given-names>M</given-names></name><name><surname>Mockenhaupt</surname><given-names>M</given-names></name></person-group>:\n<article-title>Fever in Stevens-Johnson Syndrome and Toxic Epidermal Necrolysis in Pediatric Cases: Laboratory Work-up and Antibiotic Therapy.</article-title>\n<source><italic toggle=\"yes\">Pediatr Infect Dis J.</italic></source>\n<year>2017</year>;<volume>36</volume>(<issue>5</issue>):<fpage>513</fpage>–<lpage>515</lpage>.\n<pub-id pub-id-type=\"doi\">10.1097/INF.0000000000001571</pub-id>\n<pub-id pub-id-type=\"pmid\">28403057</pub-id></mixed-citation><note><p>\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://facultyopinions.com/prime/727381476\">Faculty Opinions Recommendation</ext-link>\n</p></note></ref><ref id=\"ref-4\"><label>4</label><mixed-citation publication-type=\"journal\">\n<person-group person-group-type=\"author\"><name><surname>Coombs</surname><given-names>P</given-names></name><name><surname>Gell</surname><given-names>PGH</given-names></name></person-group>:\n<article-title>Classification of Allergic Reactions Responsible for Clinical Hypersensitivity and Disease.</article-title>In: Clinical Aspects of Immunology. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">HRB Open Res</journal-id><journal-id journal-id-type=\"iso-abbrev\">HRB Open Res</journal-id><journal-id journal-id-type=\"pmc\">HRB Open Res</journal-id><journal-title-group><journal-title>HRB Open Research</journal-title></journal-title-group><issn pub-type=\"epub\">2515-4826</issn><publisher><publisher-name>F1000 Research Limited</publisher-name><publisher-loc>London, UK</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32832849</article-id><article-id pub-id-type=\"pmc\">PMC7431971</article-id><article-id pub-id-type=\"doi\">10.12688/hrbopenres.12973.2</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Study Protocol</subject></subj-group><subj-group><subject>Articles</subject></subj-group></article-categories><title-group><article-title>Health inequities in unscheduled healthcare for children with intellectual disabilities in Ireland: a study protocol</article-title><fn-group content-type=\"pub-status\"><fn><p>[version 2; peer review: 2 approved]</p></fn></fn-group></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Nicholson</surname><given-names>Emma</given-names></name><role content-type=\"http://credit.casrai.org/\">Conceptualization</role><role content-type=\"http://credit.casrai.org/\">Methodology</role><role content-type=\"http://credit.casrai.org/\">Project Administration</role><role content-type=\"http://credit.casrai.org/\">Writing – Original Draft Preparation</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0002-6652-2552</contrib-id><xref ref-type=\"corresp\" rid=\"c1\">a</xref><xref ref-type=\"aff\" rid=\"a1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Doherty</surname><given-names>Edel</given-names></name><role content-type=\"http://credit.casrai.org/\">Conceptualization</role><role content-type=\"http://credit.casrai.org/\">Methodology</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><xref ref-type=\"aff\" rid=\"a2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Somanadhan</surname><given-names>Suja</given-names></name><role content-type=\"http://credit.casrai.org/\">Conceptualization</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0002-0590-0126</contrib-id><xref ref-type=\"aff\" rid=\"a3\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Guerin</surname><given-names>Suzanne</given-names></name><role content-type=\"http://credit.casrai.org/\">Conceptualization</role><xref ref-type=\"aff\" rid=\"a4\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Schreiber</surname><given-names>James</given-names></name><role content-type=\"http://credit.casrai.org/\">Data Curation</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0001-9232-3410</contrib-id><xref ref-type=\"aff\" rid=\"a5\">5</xref></contrib><contrib contrib-type=\"author\"><name><surname>Bury</surname><given-names>Gerard</given-names></name><role content-type=\"http://credit.casrai.org/\">Conceptualization</role><role content-type=\"http://credit.casrai.org/\">Methodology</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><xref ref-type=\"aff\" rid=\"a6\">6</xref></contrib><contrib contrib-type=\"author\"><name><surname>Kroll</surname><given-names>Thilo</given-names></name><role content-type=\"http://credit.casrai.org/\">Methodology</role><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0003-2082-5117</contrib-id><xref ref-type=\"aff\" rid=\"a1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Raley</surname><given-names>Meredith</given-names></name><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><xref ref-type=\"aff\" rid=\"a7\">7</xref></contrib><contrib contrib-type=\"author\"><name><surname>McAuliffe</surname><given-names>Eilish</given-names></name><role content-type=\"http://credit.casrai.org/\">Conceptualization</role><role content-type=\"http://credit.casrai.org/\">Methodology</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><xref ref-type=\"aff\" rid=\"a1\">1</xref></contrib><aff id=\"a1\">\n<label>1</label>Centre for Interdisciplinary Research, Education and Innovation in Health Systems (IRIS), University College Dublin, Belfield, Dublin 4, Ireland</aff><aff id=\"a2\">\n<label>2</label>J.E. Cairnes School of Business and Economics, National University of Ireland, Galway, University Road, Galway, Ireland</aff><aff id=\"a3\">\n<label>3</label>UCD School of Nursing, Midwifery & Health Systems, University College Dublin, Belfield, Dublin 4, Ireland</aff><aff id=\"a4\">\n<label>4</label>UCD Centre for Disability Studies, University College Dublin, Belfield, Dublin 4, Ireland</aff><aff id=\"a5\">\n<label>5</label>School of Nursing, Duquesne University, Pittsburgh, PA, USA</aff><aff id=\"a6\">\n<label>6</label>UCD School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland</aff><aff id=\"a7\">\n<label>7</label>Disability Federation of Ireland, Fumbally Court, Dublin 8, Ireland</aff></contrib-group><author-notes><corresp id=\"c1\"><label>a</label><email xlink:href=\"mailto:emma.nicholson@ucd.ie\">emma.nicholson@ucd.ie</email></corresp><fn fn-type=\"COI-statement\"><p>No competing interests were disclosed.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>16</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>3</volume><elocation-id>3</elocation-id><history><date date-type=\"accepted\"><day>6</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright: © 2020 Nicholson E et al.</copyright-statement><copyright-year>2020</copyright-year><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri content-type=\"pdf\" xlink:href=\"hrbopenres-3-14203.pdf\"/><abstract><p>\n<bold>Background:</bold> Health inequities for children with intellectual disabilities  are prevalent within different health systems, and children with intellectual disabilites  have shorter life expectancies than the general population, higher mortality rates before the age of 17 and have a greater risk of potentially preventable hospitalisations. A health systems approach to research in this area provides a useful means through which research can inform policy and practice to ensure people with intellectual disabilities receive equitable healthcare; however, there is a paucity of evidence regarding how to address differences that have been described in the literature to date. The overall aim of this research is to establish the extent of health inequities for children with intellectual disabilities  in Ireland compared to children without intellectual disabilities with respect to their utilisation of primary care and rates of hospitalisation, and to gain a better understanding of what influences utilisation of primary care and emergency department services in this population.</p><p>\n<bold>Methods and analysis: </bold>The design of this research adopts a multi-methods approach: statistical analysis of health data to determine the extent of health inequities in relation to healthcare utilisation; discrete choice experiments to explore General Practitioners’ decision making and parental preferences for optimal care; and concept mapping to develop consensus between stakeholders on how to address current healthcare inequities.</p><p>\n<bold>Discussion: </bold>By applying a systems lens to the issue of health inequities for children with intellectual disabilities, the research hopes to gain a thorough understanding of the varying components that can contribute to the maintenance of such healthcare inequities. A key output from the research will be a set of feasible solutions and interventions that can address health inequities for this population.</p></abstract><kwd-group kwd-group-type=\"author\"><kwd>health inequities</kwd><kwd>intellectual disability</kwd><kwd>children</kwd><kwd>unscheduled healthcare</kwd></kwd-group><funding-group><award-group id=\"fund-1\" xlink:href=\"http://dx.doi.org/10.13039/100010414\"><funding-source>Health Research Board</funding-source><award-id>ARPP-A-2018-003</award-id></award-group><funding-statement>Health Research Board, Ireland [Applying Research into Policy and Practice Fellowship; ARPP-A-2018-003; to EN).</funding-statement><funding-statement><italic>The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</italic></funding-statement></funding-group></article-meta><notes notes-type=\"version-changes\"><sec sec-type=\"version-changes\"><label>Revised</label><title>Amendments from Version 1</title><p>Further detail has been provided in the Methods section to provide clarity and allow for replication based on the protocol and a Figure has been included to illustrate the project. The Introduction and Discussion have been edited to expand on the potential impact of the study.</p></sec></notes></front><body><sec sec-type=\"intro\"><title>Introduction</title><p>The health needs of people with intellectual disabilities are often complex and this population are known to utilise health services more often than people without intellectual disabilities ; an effect that remains generally stable cross-culturally\n<sup><xref rid=\"ref-1\" ref-type=\"bibr\">1</xref>,\n<xref rid=\"ref-2\" ref-type=\"bibr\">2</xref></sup>. Children with intellectual disabilities have poorer reported health status than children without and such health disparities are more marked in children and young people compared to other age groups\n<sup><xref rid=\"ref-3\" ref-type=\"bibr\">3</xref></sup>. This population has a shorter life expectancy than the general population, with higher mortality rates before the age of 17 in children with intellectual disabilities compared to those without\n<sup><xref rid=\"ref-4\" ref-type=\"bibr\">4</xref></sup>. In Ireland, the mortality rate of people with intellectual disabilities under the age of 19 is seven times higher than the general population\n<sup><xref rid=\"ref-5\" ref-type=\"bibr\">5</xref></sup>. Rates of hospitalisation also tend to be higher for this population\n<sup><xref rid=\"ref-3\" ref-type=\"bibr\">3</xref></sup> with findings from Australia showing that children with Down syndrome were hospitalised at a rate five times that of the general population\n<sup><xref rid=\"ref-6\" ref-type=\"bibr\">6</xref></sup> and, in Canada, they have higher ambulatory physician visits and a greater risk of hospitalisation due to injury, respiratory illness and diabetes\n<sup><xref rid=\"ref-4\" ref-type=\"bibr\">4</xref></sup>. A greater risk of further ill-health is exacerbated by an increased risk of socioeconomic disadvantage experienced by children with intellectual disabilities\n<sup><xref rid=\"ref-7\" ref-type=\"bibr\">7</xref></sup>.</p><p>Access to healthcare constitutes the fit between the individual and the health system\n<sup><xref rid=\"ref-8\" ref-type=\"bibr\">8</xref></sup> and is influenced by a myriad of complex factors such as availability, utilisation, effectiveness and equity\n<sup><xref rid=\"ref-9\" ref-type=\"bibr\">9</xref></sup>. Access to quality primary care is associated with improved patient outcomes and reduced hospitalisation rates, however, universal access to primary care is not synonymous with equity of access\n<sup><xref rid=\"ref-10\" ref-type=\"bibr\">10</xref></sup>. General practitioners (GPs) experience challenges while treating people with intellectual disabilities related to communication difficulties, deriving incomplete medical histories, lack of knowledge regarding existing supports, and a lack of training\n<sup><xref rid=\"ref-1\" ref-type=\"bibr\">1</xref>,\n<xref rid=\"ref-11\" ref-type=\"bibr\">11</xref></sup>. GPs have also expressed that time restrictions may impact upon the quality of care that they provide for patients with intellectual disabilities\n<sup><xref rid=\"ref-11\" ref-type=\"bibr\">11</xref></sup>. Ambulatory care sensitive conditions are conditions which can be managed with access to timely and appropriate outpatient care\n<sup><xref rid=\"ref-12\" ref-type=\"bibr\">12</xref></sup>. Better access to primary care may decrease utilisation of emergency departments and rates of hospitalisation for such conditions\n<sup><xref rid=\"ref-12\" ref-type=\"bibr\">12</xref>,\n<xref rid=\"ref-13\" ref-type=\"bibr\">13</xref></sup>.</p><p>There is a significant lack of information related to accessing healthcare services for children with intellectual disabilities in comparison to the general population in an Irish context. Ireland has a national database that records details pertaining to service provision for people with intellectual disabilities, which aims to elucidate the service needs of people with intellectual disabilities in Ireland. While it is a valuable resource, not all people with intellectual disabilities are registered with the database and needs pertaining to access and utilisation of primary care and hospitalisation rates are not recorded. Healthcare provision, planning and coordination tend to be poorer for people within the intellectual disabilities population compared to the wider population\n<sup><xref rid=\"ref-3\" ref-type=\"bibr\">3</xref></sup>, and such inequities are amenable to change by improvement in quality of healthcare\n<sup><xref rid=\"ref-14\" ref-type=\"bibr\">14</xref></sup>. The UN Convention on the Rights of People with Disabilities (UNCRPD) sets out that the guiding principles of the convention will need to be considered in relation to existing policy and practice. Article 25 of the convention states that health professionals are required to provide the same standard of care for people with disabilities and outlines the importance of specialist training and appropriate ethical standards in order to meet the needs of people with disabilities. Moreover, Article 31 recommends that state parties use statistical and research data to support policy planning that will give effect to the Convention and are obliged to identify and address barriers that affect the rights of people with disabilities. While it is important to establish any health disparities that exist for this population, it is also vital that models and strategies for reducing any existing inequalities are also addressed\n<sup><xref rid=\"ref-1\" ref-type=\"bibr\">1</xref></sup>. Previous research has suggested that the quality of care GPs can provide people with intellectual disabilities is limited due to factors such as time and lack of knowledge\n<sup><xref rid=\"ref-11\" ref-type=\"bibr\">11</xref>,\n<xref rid=\"ref-15\" ref-type=\"bibr\">15</xref></sup>. However, to the best of our knowledge, no previous research has sought to systematically examine and model the trade-offs that influence GP referral practices when treating children with intellectual disabilities.</p><p>While international evidence suggests that people with intellectual disabilities, including children, experience inequality in accessing healthcare\n<sup><xref rid=\"ref-16\" ref-type=\"bibr\">16</xref></sup>, there is a paucity of evidence relating to the decision-making by frontline staff and parents in relation to this population. Decision-making is critical within healthcare where limited resources are an ongoing concern and ultimately result in a complex interplay between stakeholder choices and behaviours, which often dictate where competing resources are allocated\n<sup><xref rid=\"ref-17\" ref-type=\"bibr\">17</xref>,\n<xref rid=\"ref-18\" ref-type=\"bibr\">18</xref></sup>. Recognising the complexity of the factors that lead to health disparities for this population, beyond establishing the differences in utilisation, will be critical to identifying avoidable determinants of health disparities and how these can be modified to improve healthcare provision for this population\n<sup><xref rid=\"ref-1\" ref-type=\"bibr\">1</xref></sup>. For instance, continuity of care has been highlighted as a preference for parents of children with developmental disabilities\n<sup><xref rid=\"ref-4\" ref-type=\"bibr\">4</xref></sup>, whereas socioeconomic disadvantages may influence access to care for this population\n<sup><xref rid=\"ref-8\" ref-type=\"bibr\">8</xref></sup>. Moreover, risk factors for emergency department utilisation and hospitalisation may be unique for this population\n<sup><xref rid=\"ref-19\" ref-type=\"bibr\">19</xref></sup>. Within health services research, public and patient preferences can inform policy and practice, as ensuring the consideration of all viewpoints will increase the likelihood of demands being adequately met when planning for service provision\n<sup><xref rid=\"ref-17\" ref-type=\"bibr\">17</xref></sup>.</p><p>There are significant gaps in the evidence base around health inequalities for people with intellectual disabilities whereby, the evidence was generally of low quality and heavily skewed towards psychiatric interventions\n<sup><xref rid=\"ref-20\" ref-type=\"bibr\">20</xref></sup>. A systems level approach to research would strengthen primary care and improve equitable service for people with intellectual disabilities\n<sup><xref rid=\"ref-15\" ref-type=\"bibr\">15</xref></sup>. Much of the research makes use of health records and administrative datasets to describe healthcare utilization which can be highly informative and cost-effective\n<sup><xref rid=\"ref-21\" ref-type=\"bibr\">21</xref></sup>, however, children with intellectual disabilities are frequently under-represented in such research due to misclassification and poor coding\n<sup><xref rid=\"ref-13\" ref-type=\"bibr\">13</xref>,\n<xref rid=\"ref-22\" ref-type=\"bibr\">22</xref>–\n<xref rid=\"ref-24\" ref-type=\"bibr\">24</xref></sup>. National longitudinal data can provide appropriate evidence to address the limitations of administrative datasets for assessing healthcare utilisation\n<sup><xref rid=\"ref-22\" ref-type=\"bibr\">22</xref></sup>. There is a small evidence base on rates of hospitalisation for physical health conditions for this population\n<sup><xref rid=\"ref-3\" ref-type=\"bibr\">3</xref></sup> but there is a paucity of evidence related to the decision-making that drives or inhibits this access. Emergency or unplanned admissions for children with intellectual disability create challenges for the provision of reasonable adjustments to support good quality care\n<sup><xref rid=\"ref-25\" ref-type=\"bibr\">25</xref></sup>. In a review examining the factors that influenced access to secondary healthcare for people with intellectual disabilities and found that a myriad of issues affected a persons’ experience of care, including poor communication by staff, lack of skills and knowledge about working with people with intellectual disabilities, and poor signage and layout in hospitals\n<sup><xref rid=\"ref-26\" ref-type=\"bibr\">26</xref></sup>.</p><p>The proposed research herein will provide information about access and utilisation of primary and emergency care of children with intellectual disabilities, particularly in relation to inequalities compared to children without an intellectual disability. Modelling the health status and service utilisation of children with intellectual disabilities at a population level is critical to determine their needs and priorities and to build a robust evidence base for policy, planning and service provision\n<sup><xref rid=\"ref-4\" ref-type=\"bibr\">4</xref></sup>. Rigorous mixed-methods research, which adopts a systems approach, will support the identification of targeted strategies and interventions that will strengthen service provision through policy and practice\n<sup><xref rid=\"ref-15\" ref-type=\"bibr\">15</xref></sup>. The current research project will aim to examine the utilisation of first-contact healthcare for children with intellectual disabilities in Ireland compared to children without intellectual disabilities, gain greater understanding of parental preferences and GP decision-making that drives this utilisation and finally, devise strategies for improved healthcare for this population during a time of large scale changes within the Irish health system</p></sec><sec sec-type=\"methods\"><title>Methods and analysis</title><p>The proposed research will employ a multi-methods approach over three work packages to address the research objectives. Work package 1 will establish the extent of health inequities between children with and without intellectual disabilities with respect to access to health status, attendance at primary and emergency care, as well as rates of emergency hospitalisations. Work package 2 will examine the decision making and referral practices of GPs and elicit parental preferences for unscheduled healthcare for their children. Finally, work package 3 will seek to utilise the evidence that emerges from the first two work packages to develop strategies for improving access to unscheduled healthcare. The culmination of work packages 1 and 2 will seek to provide a comprehensive picture of the multifaceted issues that impact access and utilisation of healthcare for this population, while work package 3 aims to provide a model of strategies generated by stakeholders in order to foster meaningful impact of the research findings (see\n<xref ref-type=\"fig\" rid=\"f1\">Figure 1</xref>).</p><fig fig-type=\"figure\" id=\"f1\" orientation=\"portrait\" position=\"anchor\"><label>Figure 1. </label><caption><title>Outline of three work packages.</title></caption><graphic xlink:href=\"hrbopenres-3-14203-g0000\"/></fig><sec><title>Work Package 1. Describing differences in healthcare utilisation and hospitalisation between children with and without intellectual disabilities</title><p>The aim of this work package will be to compare the use of unscheduled health services and hospitalisation of children under 16 years of age with intellectual disabilities to children under 16 years of age without intellectual disabilities. A cross-sectional population-based study with a matched-group design will be conducted and there will be two main data sources for this work package:</p><list list-type=\"simple\"><list-item><label>1. </label><p>Existing datasets from an aligned study\n<sup><xref rid=\"ref-27\" ref-type=\"bibr\">27</xref></sup>: These datasets will contain attendance data for paediatric populations from approximately 35 primary care practices and five emergency departments (approximately n = 367,405 pediatric patients) across Ireland, as well as in-patient data from children admitted to the hospital through the five emergency departments (approximately n = 96, 394 patients). The dataset documents attendances from 1\n<sup>st</sup> of July 2013 to the 30th of June 2018 and children with intellectual disability will be identified using International Classification of Diseases 10\n<sup>th</sup> edition (ICD-10) coding\n<sup><xref rid=\"ref-28\" ref-type=\"bibr\">28</xref>,\n<xref rid=\"ref-29\" ref-type=\"bibr\">29</xref></sup>, where possible. The prevalence of intellectual disability in Ireland is 1.4% of the entire population\n<sup><xref rid=\"ref-30\" ref-type=\"bibr\">30</xref></sup>, however, it is expected that the prevalence of children with intellectual disabilities in these datasets will be lower than this figure due to the challenges of using administrative datasets for research purposes, as discussed above. Given these potential limitations, a second database will be used to ensure the research questions can be addressed if children with intellectual disabilities cannot be adequately identified using ICD-10 coding.</p></list-item><list-item><label>2. </label><p>Growing Up in Ireland (GUI) study: GUI is a national longitudinal study of children and young people in Ireland which collects data from an Infant Cohort (n = 10, 000) and a Child Cohort (n = 8, 000). The survey collects data on health conditions, disabilities as well as attendance at and utilisation of health services including primary care, emergency departments and overnight stays in hospital\n<sup><xref rid=\"ref-31\" ref-type=\"bibr\">31</xref></sup>. Prevalence of learning and intellectual disabilities in the child sample have been estimated at approximately 8%, however, it should be noted that the authors adopted a broad definition of intellectual disability\n<sup><xref rid=\"ref-32\" ref-type=\"bibr\">32</xref>,\n<xref rid=\"ref-33\" ref-type=\"bibr\">33</xref></sup>.</p></list-item></list><p>Children with intellectual disabilities will be matched by propensity score matching on specific variables (e.g., age, gender, medical card status, co-morbidities) with children without intellectual disabilities to allow for any differences between the two populations to be established. Given the greater number of males with intellectual disabilities in Ireland\n<sup><xref rid=\"ref-30\" ref-type=\"bibr\">30</xref></sup>, it is likely that there will be more males in the intellectual disability group in both datasets.</p><p>The research questions for this work package are as follows:</p><list list-type=\"simple\"><list-item><label>1. </label><p>Do children with intellectual disabilities have more attendances at primary care and the emergency department compared to those without in Ireland?</p></list-item><list-item><label>2. </label><p>Do children with intellectual disabilities have a greater risk of emergency hospitalisations compared to those without ID?</p></list-item><list-item><label>3. </label><p>What is the profile of children with intellectual disabilities who experience unscheduled hospitalisations in Ireland (i.e., age, gender, reasons for emergency hospitalisation [e.g., primary diagnosis], co-morbidities and length of stay)?</p></list-item><list-item><label>4. </label><p>What is the relative risk of emergency hospitalisations for children with intellectual disabilities compared to those without?</p></list-item></list><p>\n<bold><italic>Data analysis plan:</italic></bold> Propensity score matching will be used to match the children with intellectual disabilities to those without intellectual disabilities on specific covariates that may influence health outcomes. Imputation methods will be used where appropriate to address any missing data values. Descriptive statistics will be used to profile the patients, while\n<italic>chi</italic>-square tests and\n<italic>t</italic>-tests will allow comparisons to be made between the children with and without intellectual disabilities on the aforementioned factors and test the statistical significance of any variability. Relative risk (RR) will be used to determine if having an intellectual disability is a risk factor for emergency hospitalisations and 95% confidence intervals will be used to establish statistical significance.</p></sec><sec><title>Work Package 2. Exploring and modelling the decision-making factors that influence referral practices when treating children with ID and eliciting parental preferences for care</title><p>A discrete choice experiment (DCE) is a survey-based methodology that elucidates the relative importance of certain factors or attributes that influence decision-making and preferences. They are increasingly being used in healthcare research as they provide real-world clinically-relevant scenarios to model decision-making at a more granular level by exploring the trade-offs that typically occur when multiple factors are considered during the decision making processes\n<sup><xref rid=\"ref-17\" ref-type=\"bibr\">17</xref>,\n<xref rid=\"ref-34\" ref-type=\"bibr\">34</xref></sup>. The underlying assumption of this methodology is that services, such as healthcare provision, can be broken into numerous characteristics, with individuals assigning differing values to each\n<sup><xref rid=\"ref-34\" ref-type=\"bibr\">34</xref></sup>. For example, they have been used to examine preferences for access to primary care which highlighted that waiting time for an appointment was only important to patients when attending a new health concern and, from a parental perspective, if the appointment was for a child\n<sup><xref rid=\"ref-8\" ref-type=\"bibr\">8</xref></sup>. DCEs have adequate external validity and have been shown to accurately mimic real-world decisions for choices within healthcare\n<sup><xref rid=\"ref-35\" ref-type=\"bibr\">34</xref>,\n<xref rid=\"ref-35\" ref-type=\"bibr\">35</xref></sup>. The proposed research will utilise DCEs in order to generate an understanding of the factors that influence GP decision making and referral practices when working with children with intellectual disabilities and to elicit parental preferences for unscheduled healthcare for their children:</p><list list-type=\"simple\"><list-item><label>1. </label><p>\n<bold>Exploring decision-making and referral practices of GPs when treating children with ID</bold>. The attributes and factors that influence GP decision making and practices regarding referral to the emergency department and wider paediatric services will be examined using the DCE methodology.</p></list-item><list-item><label>2. </label><p>\n<bold>Eliciting parental preference for primary care for their children</bold>. Using the above DCE methodology, the research will seek to model parents’ preferences regarding primary care for children with intellectual disabilities</p></list-item></list><p>In keeping with best practice in DCE design, the following four-step approach will be taken to conduct the DCEs:</p><p>\n<bold>Step 1. Attribute development</bold>: An exploratory step is crucial within a DCE design to establish the attributes that potentially influence decision making and preferences and to define the levels of each attribute\n<sup><xref rid=\"ref-36\" ref-type=\"bibr\">36</xref>–\n<xref rid=\"ref-39\" ref-type=\"bibr\">39</xref></sup>. Attributes may be patient, service or clinician-focused and can have numerous levels within them. For instance, in a DCE examining preferences for access to primary care, one key attribute was waiting time for an appointment with two levels being within 48 hours and in 4 days\n<sup><xref rid=\"ref-8\" ref-type=\"bibr\">8</xref></sup>. An iterative approach is necessary to select attributes and levels that contribute to most variation in decision making\n<sup><xref rid=\"ref-38\" ref-type=\"bibr\">38</xref></sup>. This will include a systematic review to identify the relevant literature in this area and qualitative inquiry\n<sup><xref rid=\"ref-39\" ref-type=\"bibr\">39</xref></sup> to explore factors that influence GP referral practices with this population and parent preferences related to their child’s healthcare.</p><p>The qualitative inquiry to develop the attributes will be an iterative two-step process consisting of conceptual development to establish the attributes and then refining the language used to ensure it is meaningful for the intended population\n<sup><xref rid=\"ref-38\" ref-type=\"bibr\">38</xref></sup>. Interviews and focus groups\n<sup><xref rid=\"ref-38\" ref-type=\"bibr\">38</xref></sup> will be utilised with participants including both GPs and parents of children with ID. Purposive sampling will be used to ensure maximum variation of viewpoints are obtained. Topic guides for the qualitative data collection will be developed from the results of the systematic review. The qualitative data will be analysed using the constant comparison approach\n<sup><xref rid=\"ref-40\" ref-type=\"bibr\">40</xref></sup>, which will allow for questions to be adapted in response to emerging data, which is particularly valuable within DCE\n<sup><xref rid=\"ref-38\" ref-type=\"bibr\">38</xref></sup>.</p><p>\n<bold>Step 2. Structured prioritisation exercise to finalise attributes</bold>: Given the large number of possible attributes that may be relevant in the research, it is important to narrow the focus of the DCE to ensure that the included attributes and levels are feasible and meaningful to the wider research question and to safeguard the face validity of the DCE. A structured prioritisation exercise (SPE) will determine the relative importance of the attributes and factors that emerge from the qualitative work. These will be ranked in order of priority for inclusion in the DCE and to ascertain the levels required for each attribute. Considerations for attribute inclusion will comprise of issues relating to sample size calculation, ecological validity and ensuring adequate information is provided in the DCE. A panel made up of researchers, GPs, a health economist, parents and disability advocates will use the evidence from the SPE to decide on final design of the DCE.</p><p>\n<bold>Step 3. Pilot study</bold>: An experimental design will be used to generate the DCE choice cards that will allow for combinations of attributes and levels to be presented to participants in manageable subsets, presented in either table format or as vignettes. The result will be a series of hypothetical scenarios that each present combinations of attributes and levels to each participant. These will be piloted to test ease of use and to determine the length of time the DCE takes to complete, as well as establishing the plausibility to ensure clinical validity.</p><p>\n<bold>Step 4. DCE and analysis</bold>: Purposive sampling will be utilised when recruiting participants and sample size will be determined during the design of the DCE as it is contingent on the number of factors that emerge during the exploratory phase\n<sup><xref rid=\"ref-36\" ref-type=\"bibr\">36</xref></sup>. Recruitment of GPs and parents will target participants to ensure representation based on factors that emerge during the research, e.g., to ensure a geographic spread amongst respondents. The DCE will record relevant participant information such as age, years of experience, training, socio-economic status etc.</p><p>Random utility models (including conditional logit models and mixed logit models) will be estimated to establish which factors affect decision making and preferences and the characteristics (e.g., age, level of experience, training, socioeconomic variables) of those making the decisions\n<sup><xref rid=\"ref-18\" ref-type=\"bibr\">18</xref></sup>.</p></sec><sec><title>Work package 3. A consensus conference to generate evidence-based strategies for improving access to healthcare for children with ID</title><p>The final work package will employ a multi-stakeholder concept mapping design within a consensus conference to identify strategies for improving access to healthcare for children with intellectual disabilities based on the results from the two previous work packages and as a means to lessen the gap between policy and practice. Evidence from work package 1 will establish the magnitude of health access disparities for children with intellectual disabilities, particularly relating to a risk of preventable hospitalisations, while the DCEs in work package 2 will explicate GP referral practices and parental preferences for unscheduled healthcare for their children. Work package 3 will build on these findings in order to address areas for improvement by discussing and generating strategies in partnership with patients, parents, healthcare professionals, disability advocates and policy makers. In order to identify workable strategies for improving access to healthcare, it is crucial to engage with stakeholders from all levels of the health system to ensure that the results are aligned with needs and priorities. Concept mapping\n<sup><xref rid=\"ref-41\" ref-type=\"bibr\">41</xref></sup> can provide a structured format for key stakeholders, with varying degrees of expertise, and support equal contribution to the development of strategies for improving access to care and discussing the feasibility of these strategies. This methodology has been successfully implemented to address complex issues in primary care where the delivery of primary care was comprehensively explored by stakeholders to provide a practice index for GP integration\n<sup><xref rid=\"ref-42\" ref-type=\"bibr\">42</xref></sup>. Moreover, a modified version of this approach was used to generate priorities and strategies for improving access to maternity services among women with disabilities who have experienced domestic abuse\n<sup><xref rid=\"ref-43\" ref-type=\"bibr\">43</xref></sup>.</p><p>Concept mapping is a methodology that seeks to map ideas or concepts generated by a diverse group of stakeholders through the integration of activities such as brainstorming and unstructured sorting with statistical analyses to map the ideas generated\n<sup><xref rid=\"ref-41\" ref-type=\"bibr\">41</xref></sup>. The result is a visual map that represents the composite thinking of the group and can provide a framework for planning and programme development that incorporates complex elements perceived to be both important and feasible to stakeholders. A modified version of this approach will be adopted in this work package in the following steps:</p><p>1.\n<bold>Sampling and recruitment.</bold> Purposive sampling will be employed to ensure adequate representation across gender, disability-type and level of ID (i.e., mild/moderate to severe/profound). Up to 30 participants will be invited to participate and will include people with disabilities, parents of children with disabilities, disability advocates, policy makers, health and social care professionals (e.g., GPs, paediatricians, nursing staff, social care workers) and researchers and academics. Invitations will be sent out to GP surgeries, disability services, and children’s hospitals.</p><p>2.\n<bold>Procedure</bold>. The findings from work packages 1 and 2 will be presented to the group. Participants will then engage in a brainstorming session to derive strategies to address the issues, challenges and areas for improvement that emerge from the research in the previous work packages. Participants will then be asked to individually rank the strategies in terms of priority on a 5 point Likert scale ranging from 1 (Very High) to 5 (Not a priority) and in terms of feasibility on a 5 point Likert scale ranging from 1 (Very Feasible) to 5 (Impossible). This step is key in the production of data for concept mapping. The participants will then discuss the means through which the results of the can be used as a framework to enhance policy or practice in order to improve access to healthcare for children with ID.</p><p>3.\n<bold>Analysis</bold>. Findings will be synthesised and mapped to support the development of a set of recommendations for policy and practice to improve access to healthcare for children with ID. Hierarchical cluster-analysis will be used to rank and identify priority areas from the perspective of the different stakeholder groups. A bivariate plot of the two sets of ratings (priority and feasibly) will produce a ‘go zone’ graph to map the stated importance and feasibility of the strategies. The plot is divided into quadrants based on the average priority (\n<italic>x</italic> axis) and feasibility (\n<italic>y</italic> axis) scores where the top right quadrant will represent the strategies rates as being of highest priority and the most feasible. These ‘go-zones’ will identify the potential courses of action which are highly useful for planning purposes as they provide a detailed outline of the strategies that key stakeholders collectively view as important and feasible.</p><p>This procedure will enable the generation of a set of recommendations for policy and practice. Embedding stakeholder involvement throughout this process will provide a valuable mechanism to support meaningful and feasible impact of the research findings.</p></sec><sec><title>Ethical considerations</title><p>The proposed research has been granted full ethical approval by the University College Dublin Research Ethics Committee (Reference: LS-19-64-Nicholson). All participants will provide written consent on their own behalf for their participation in the study.</p></sec><sec><title>Public and patient involvement (PPI)</title><p>The project will adopt a disability-centred approach whereby patients, parents and people with intellectual disabilities will have input in the design, analysis and dissemination of the research throughout the project. At the outset of the project, a panel of PPI members comprising of parent and patient representatives from the disability community as well as disability advocates will be recruited to the project. This panel will contribute to the governance of the research and will be integral members of each work package sub-committee where they can advise and contribute to the design and analysis of the research. Members of this panel will also facilitate the consensus conference in work package 3.</p></sec></sec><sec><title>Study status</title><p>Work package 1: Early stages of analysis.</p><p>Work package 2: A systematic review to inform the qualitative inquiry was recently completed.</p><p>Work package 3: Not yet started.</p></sec><sec sec-type=\"discussion\"><title>Discussion</title><p>The overarching aim of the research is to establish the extent of health inequities for children with intellectual disabilities in Ireland compared to children without intellectual disabilities, with a focus on their utilisation of primary care and rates of hospitalisation, to gain a better understanding of what influences utilisation and avoidable hospitalisations in this population. Applying a systems-approach, whereby the various components of the health systems in which the health inequities and the intricacies of the relationships between these will be considered, can provide evidence towards understating health inequities for this population and seek to identify interventions that can address them\n<sup><xref rid=\"ref-15\" ref-type=\"bibr\">15</xref>,\n<xref rid=\"ref-44\" ref-type=\"bibr\">44</xref></sup>. Drawing on existing evidence, while also being cognizant of the challenges that may arise, the proposed research will seek to build on previous work by drawing on multiple data sources to examine disparities in healthcare utilization while critically, identifying key areas for improvements in consultation with stakeholder groups.</p><p>While describing the health inequities experienced by people with intellectual disabilities is an important element for study, research must endeavour to establish the causal factors behind inequities, such as increased hospitalisations\n<sup><xref rid=\"ref-3\" ref-type=\"bibr\">3</xref></sup>, and examine whether they can be considered avoidable and unjust\n<sup><xref rid=\"ref-1\" ref-type=\"bibr\">1</xref></sup> and critically, amenable to improvements in the quality of care\n<sup><xref rid=\"ref-14\" ref-type=\"bibr\">14</xref></sup>. The inclusion of the DCE methodology can provide explanatory models that have important applications within policy making in health and, critically, can point to modifiable factors that contribute to any disparities in referral patterns experienced by this population. GPs are required to make highly complex decisions within health systems of scarce resourcing and thus, establishing the trade-offs they dictate, their behaviour is important for planning purposes\n<sup><xref rid=\"ref-37\" ref-type=\"bibr\">37</xref></sup>. Moreover, preferences for healthcare beyond outcomes alone are important for planning quality care\n<sup><xref rid=\"ref-18\" ref-type=\"bibr\">18</xref></sup> and eliciting parents’ stated preferences for first contact care for children with intellectual disabilities will be key to identifying opportunities for interventions that can support meaningful change.</p><p>In keeping with the disability-centred approach of the project, ongoing and continuous knowledge exchange activities will be adopted to support an accessible and wide-reaching dissemination plan. Drawing on an evidence-based model for knowledge transfer of health research\n<sup><xref rid=\"ref-45\" ref-type=\"bibr\">45</xref></sup>, dissemination will strategically target stakeholders with materials designed specifically for their needs and through avenues chosen to maximise their reach. Four key components will be considered when disseminating the research: messages, stakeholders, processes and contexts. For instance, workshops will be held with healthcare professionals to outline the results from the studies and receive feedback. A key output from the project will be a set of recommendations developed in collaboration with stakeholders that will be aimed at policymakers. Alongside traditional peer-reviewed publications, accessible materials, such as infographics, will be created to communicate the results from each work package to the disability community. These will outline the results in clear and accessible formats using lay terminology and will be designed with the PPI panel members and developed by a graphic designer to ensure their suitability for dissemination to the general population.</p><sec><title>Limitations</title><p>Potential inconsistencies in the reporting of intellectual disabilities within the Irish health system may be a limitation for the proposed research, as it may hinder our ability to accurately identify patients with ID from the data administrative systems. For instance, people with mild ID may be significantly underrepresented in the data systems as their care needs will not be considered as great as people with severe and profound intellectual disabilities. Understanding the extent of reporting of intellectual disabilities will be beneficial in and of itself, in order to raise awareness of the differences in health presentations for this population, which may not be widely known among those involved with practice and planning\n<sup><xref rid=\"ref-5\" ref-type=\"bibr\">5</xref></sup>. Monitoring trends and identifying any health disparities for this population is crucial for the development of appropriate interventions that will facilitate good health for people with intellectual disabilities\n<sup><xref rid=\"ref-46\" ref-type=\"bibr\">46</xref></sup>. The recent ratification of the UNCRPD requires the government to use research and statistical data to develop policies that give effect to the Convention\n<sup><xref rid=\"ref-19\" ref-type=\"bibr\">19</xref></sup> and the availability of appropriate data is key for policy and planning purposes.</p></sec></sec><sec sec-type=\"conclusions\"><title>Conclusion</title><p>The proposed programme of research will apply a systems lens to the issue of health inequities for children with intellectual disabilities, specifically in relation to unscheduled healthcare. Across three work packages, the research will establish the extent of inequities for this population in relation to utilisation of unscheduled health services, elicit parents’ preferences for their children’s healthcare, as well as model the factors that influence GP decision-making. 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pub-id-type=\"doi\">10.1177/2377960819861854</pub-id>\n</mixed-citation></ref><ref id=\"ref-46\"><label>46</label><mixed-citation publication-type=\"journal\">\n<person-group person-group-type=\"author\"><name><surname>Lauer</surname><given-names>E</given-names></name><name><surname>McCallion</surname><given-names>P</given-names></name></person-group>:\n<article-title>Mortality of People with Intellectual and Developmental Disabilities from Select US State Disability Service Systems and Medical Claims Data.</article-title>\n<source><italic toggle=\"yes\">J Appl Res Intellect Disabil.</italic></source>\n<year>2015</year>;<volume>28</volume>(<issue>5</issue>):<fpage>394</fpage>–<lpage>405</lpage>.\n<pub-id pub-id-type=\"doi\">10.1111/jar.12191</pub-id>\n<pub-id pub-id-type=\"pmid\">25994364</pub-id></mixed-citation></ref></ref-list></back><sub-article id=\"report27672\" article-type=\"peer-review\"><front-stub><article-id pub-id-type=\"doi\">10.21956/hrbopenres.14203.r27672</article-id><title-group><article-title>Reviewer response for version 2</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>O'Leary</surname><given-names>Lisa</given-names></name><xref ref-type=\"aff\" rid=\"r27672a1\">1</xref><role>Referee</role><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0002-7131-2920</contrib-id></contrib><aff id=\"r27672a1\">\n<label>1</label>Edinburgh Napier University, Edinburgh, UK</aff></contrib-group><author-notes><fn fn-type=\"COI-statement\"><p>\n<bold>Competing interests: </bold>No competing interests were disclosed.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><permissions><copyright-statement>Copyright: © 2020 O'Leary L</copyright-statement><copyright-year>2020</copyright-year><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><related-article related-article-type=\"peer-reviewed-article\" id=\"d38e2912\" ext-link-type=\"doi\" xlink:href=\"10.12688/hrbopenres.12973.2\">Version 2</related-article><custom-meta-group><custom-meta><meta-name>recommendation</meta-name><meta-value>approve</meta-value></custom-meta></custom-meta-group></front-stub><body><p>Thank for addressing all my previous recommendations, I have no further comments to make.</p><p>Is the study design appropriate for the research question?</p><p>Yes</p><p>Is the rationale for, and objectives of, the study clearly described?</p><p>Yes</p><p>Are sufficient details of the methods provided to allow replication by others?</p><p>Partly</p><p>Are the datasets clearly presented in a useable and accessible format?</p><p>Not applicable</p><p>Reviewer Expertise:</p><p>Intellectual disabilities, mixed methods, systematic reviews, complex interventions, mortality patterns in people with intellectual disabilities, respiratory health.</p><p>I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.</p></body></sub-article><sub-article id=\"report27298\" article-type=\"peer-review\"><front-stub><article-id pub-id-type=\"doi\">10.21956/hrbopenres.14058.r27298</article-id><title-group><article-title>Reviewer response for version 1</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Harrison</surname><given-names>Reema</given-names></name><xref ref-type=\"aff\" rid=\"r27298a1\">1</xref><role>Referee</role></contrib><aff id=\"r27298a1\">\n<label>1</label>School of Public Health and Community Medicine, University of New South Wales, Sydney, Australia</aff></contrib-group><author-notes><fn fn-type=\"COI-statement\"><p>\n<bold>Competing interests: </bold>No competing interests were disclosed.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>7</day><month>5</month><year>2020</year></pub-date><permissions><copyright-statement>Copyright: © 2020 Harrison R</copyright-statement><copyright-year>2020</copyright-year><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><related-article related-article-type=\"peer-reviewed-article\" id=\"d38e2990\" ext-link-type=\"doi\" xlink:href=\"10.12688/hrbopenres.12973.1\">Version 1</related-article><custom-meta-group><custom-meta><meta-name>recommendation</meta-name><meta-value>approve</meta-value></custom-meta></custom-meta-group></front-stub><body><p>This protocol presents the proposed study of the utilisation of primary and emergency care amongst children with intellectual disability to understand the influences on this population through a healthcare equity lens.</p><p> The project addresses a significant and under-researched area that is of critical importance to health system equity, sustainability, value and quality. The program of work outlined integrates a range of relevant methods to generate the data required to address the proposed questions. The data quality relating to the identification of children with and without intellectual disability is unclear and given the lack of high-quality data identified elsewhere, it may be worth considering the implications of this for the proposed work.</p><p> Areas for further consideration:</p><p> It is notable that the terminology is a little inconsistent regarding intellectual disability, children and disability and would benefit from clarification. This may be in part due to reliance on literature relating to adult populations or disability more generally to form the development of the study but warrants review across the piece for clarity in some areas.</p><p> Whilst a mixed-methods approach is identified, there is insufficient detail as to the process of data synthesis and how these studies collectively address the research questions. Currently, the work packages appear to flow one after the other but it is not clear as to how there will be a higher level synthesis of the knowledge. Overall, whilst the methods are well-described, there is insufficient detail in some of the areas to enable replication based on the protocol alone.</p><p>Is the study design appropriate for the research question?</p><p>Yes</p><p>Is the rationale for, and objectives of, the study clearly described?</p><p>Yes</p><p>Are sufficient details of the methods provided to allow replication by others?</p><p>Partly</p><p>Are the datasets clearly presented in a useable and accessible format?</p><p>Not applicable</p><p>Reviewer Expertise:</p><p>Health services research; healthcare quality; patient safety.</p><p>I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.</p></body><sub-article id=\"comment3155-27298\" article-type=\"response\"><front-stub><contrib-group><contrib contrib-type=\"author\"><name><surname>Nicholson</surname><given-names>Emma</given-names></name><aff>University College Dublin, Ireland</aff></contrib></contrib-group><author-notes><fn fn-type=\"COI-statement\"><p>\n<bold>Competing interests: </bold>No competing interests were disclosed.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>3</day><month>7</month><year>2020</year></pub-date></front-stub><body><p>This protocol presents the proposed study of the utilisation of primary and emergency care amongst children with intellectual disability to understand the influences on this population through a healthcare equity lens.</p><p>The project addresses a significant and under-researched area that is of critical importance to health system equity, sustainability, value and quality. The program of work outlined integrates a range of relevant methods to generate the data required to address the proposed questions. The data quality relating to the identification of children with and without intellectual disability is unclear and given the lack of high-quality data identified elsewhere, it may be worth considering the implications of this for the proposed work.\n<list list-type=\"bullet\"><list-item><p>The Introduction and Discussion have been amended to address potential international impact</p></list-item></list>\n<bold>Areas for further consideration:</bold>\n</p><p>It is notable that the terminology is a little inconsistent regarding intellectual disability, children and disability and would benefit from clarification. This may be in part due to reliance on literature relating to adult populations or disability more generally to form the development of the study but warrants review across the piece for clarity in some areas.\n<list list-type=\"bullet\"><list-item><p>This has been addressed and intellectual disability has been used throughout</p></list-item></list> Whilst a mixed-methods approach is identified, there is insufficient detail as to the process of data synthesis and how these studies collectively address the research questions. Currently, the work packages appear to flow one after the other but it is not clear as to how there will be a higher level synthesis of the knowledge. Overall, whilst the methods are well-described, there is insufficient detail in some of the areas to enable replication based on the protocol alone.\n<list list-type=\"bullet\"><list-item><p>Given there is no plan to specifically triangulate data, the term ‘multi-method’ has been used in place of mixed-methods. A figure has also been included to outline the project (see Figure 1). Greater detail has been provided in the method sections.</p></list-item></list>\n</p></body></sub-article></sub-article><sub-article id=\"report27143\" article-type=\"peer-review\"><front-stub><article-id pub-id-type=\"doi\">10.21956/hrbopenres.14058.r27143</article-id><title-group><article-title>Reviewer response for version 1</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>O'Leary</surname><given-names>Lisa</given-names></name><xref ref-type=\"aff\" rid=\"r27143a1\">1</xref><role>Referee</role><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0002-7131-2920</contrib-id></contrib><aff id=\"r27143a1\">\n<label>1</label>Edinburgh Napier University, Edinburgh, UK</aff></contrib-group><author-notes><fn fn-type=\"COI-statement\"><p>\n<bold>Competing interests: </bold>No competing interests were disclosed.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>12</day><month>2</month><year>2020</year></pub-date><permissions><copyright-statement>Copyright: © 2020 O'Leary L</copyright-statement><copyright-year>2020</copyright-year><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><related-article related-article-type=\"peer-reviewed-article\" id=\"d38e3128\" ext-link-type=\"doi\" xlink:href=\"10.12688/hrbopenres.12973.1\">Version 1</related-article><custom-meta-group><custom-meta><meta-name>recommendation</meta-name><meta-value>approve-with-reservations</meta-value></custom-meta></custom-meta-group></front-stub><body><p>This is an important protocol for a potentially valuable study which has the potential to address health inequities experienced by children with ID in Ireland. I recommend this for indexing (subject to a few minor changes which I have specified in the summary below).</p><p> A clear rationale has been provided for this study and the objectives of the study have been clearly described. The authors have clearly identified the evidence base for poorer health outcomes in children with ID compared to the general population (with specific reference to higher mortality rates and higher hospital admissions amongst children with ID compared to equivalent general population). They have made reference to studies undertaken both within an Irish and international context, in order to demonstrate this evidence.</p><p> The authors have made reference to how inequitable access to health care may contribute to these poorer health outcomes in children with ID. They have also identified a gap in the evidence within an Irish context, wherein there is currently a lack of information related to accessing healthcare services for children with ID. They have referred to UNCRPD principles and gaps in research studies in order to emphasise the rationale for specifically investigating the complexity of factors that lead to health disparities in children with ID. The authors have emphasised the relevance of this within an Irish context (given the ratification of the UNCRPD). However, it would also have been helpful, if the authors could identify how the findings from this prospective study could have international impact on children with ID. This could increase the wider potential impact of the study. </p><p> The mixed methods three work package design appears appropriate for the research question\n<bold>. </bold>It would have been helpful if a figure was included in order to illustrate how each phase follows on and is integrated to the next, in terms of answering the research question. It would also be helpful to know what specific type of mixed method design is proposed? Do the authors propose to triangulate the results from each of the work packages (phases) of the study in any way?</p><p> The authors have identified how they will establish the extent of health inequities between children with and without LD in relation to access to healthcare in the first phase of the study (work package 1). The authors have identified how they will match the cases. They have reported how they will represent the demographic profile of the participants. They have identified the intended statistical tests for drawing comparisons with the general population.</p><p> However, I would recommend the following amendment for work package 1 phase, in order to ensure the data analysis plan in described in detail and allows replication by others. More information is required on the following in relation to work package 1. How is the proposed approach similar to the method used in Canada? Were there any adaptions that were made for the current study? How many children with ID are in this database? What specific age groups will the children be subdivided into for the purpose of analysis? What is the gender breakdown of the children in this database? How will missing data be treated? How many matched children are in this database?</p><p> The authors have identified how they will use a discrete choice experiment to examine the decision making and referral process of GPs and identify parental preference for unscheduled care for treatment in phase 2 (work package 2). They have specified the four steps of this experiment. In the first attribute development step, they have identified that a systematic review and qualitative inquiry will be undertaken in order to identify the attributes that influence decision making. It would have been helpful if more detail was provided in relation to how the findings from the review will be explored further in the qualitative inquiry. They have specified how the second structured prioritisation exercise to finalise attributes will be undertaken. They have identified that the discrete choice experiment cards will subsequently be piloted in the third phase (specifically looking at length of time/ease of use of completion). It would have been helpful if some information about planned sample of participants in phase 3 could have been included (intended number of participants, age, gender breakdown etc.). The sampling, recruitment and analysis plan for step 4 is clearly identified.</p><p> The authors have identified how the concept mapping approach in package 3 builds on findings from step 1 and 2. This is clearly reported and it is identified that the purpose of this is to elicit stakeholders’ recommendations for policy and practice. </p><p> Additional comments: the authors used the interchangeable terms of intellectual disability (ID) and disability throughout the transcript. It is suggested that if the focus of this study is just on children with an ID as opposed to children with any type of ‘disability’, then ID should be used instead of disability.</p><p>Is the study design appropriate for the research question?</p><p>Yes</p><p>Is the rationale for, and objectives of, the study clearly described?</p><p>Yes</p><p>Are sufficient details of the methods provided to allow replication by others?</p><p>Partly</p><p>Are the datasets clearly presented in a useable and accessible format?</p><p>Not applicable</p><p>Reviewer Expertise:</p><p>Intellectual disabilities, mixed methods, systematic reviews, complex interventions, mortality patterns in people with intellectual disabilities, respiratory health.</p><p>I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.</p></body><sub-article id=\"comment3154-27143\" article-type=\"response\"><front-stub><contrib-group><contrib contrib-type=\"author\"><name><surname>Nicholson</surname><given-names>Emma</given-names></name><aff>University College Dublin, Ireland</aff></contrib></contrib-group><author-notes><fn fn-type=\"COI-statement\"><p>\n<bold>Competing interests: </bold>No competing interests were disclosed.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>3</day><month>7</month><year>2020</year></pub-date></front-stub><body><p>This is an important protocol for a potentially valuable study which has the potential to address health inequities experienced by children with ID in Ireland. I recommend this for indexing (subject to a few minor changes which I have specified in the summary below).</p><p> A clear rationale has been provided for this study and the objectives of the study have been clearly described. The authors have clearly identified the evidence base for poorer health outcomes in children with ID compared to the general population (with specific reference to higher mortality rates and higher hospital admissions amongst children with ID compared to equivalent general population). They have made reference to studies undertaken both within an Irish and international context, in order to demonstrate this evidence.</p><p>The authors have made reference to how inequitable access to health care may contribute to these poorer health outcomes in children with ID. They have also identified a gap in the evidence within an Irish context, wherein there is currently a lack of information related to accessing healthcare services for children with ID. They have referred to UNCRPD principles and gaps in research studies in order to emphasise the rationale for specifically investigating the complexity of factors that lead to health disparities in children with ID. The authors have emphasised the relevance of this within an Irish context (given the ratification of the UNCRPD). However, it would also have been helpful, if the authors could identify how the findings from this prospective study could have international impact on children with ID. This could increase the wider potential impact of the study.\n<list list-type=\"bullet\"><list-item><p>The Introduction and Discussion have been amended to address potential international impact</p></list-item></list> The mixed methods three work package design appears appropriate for the research question. It would have been helpful if a figure was included in order to illustrate how each phase follows on and is integrated to the next, in terms of answering the research question.\n<list list-type=\"bullet\"><list-item><p>A figure has been included to illustrate the project (see Figure 1)</p></list-item></list> It would also be helpful to know what specific type of mixed method design is proposed? The term mixed methods reflects the variety of methods that will be used. Given there is no aim to triangulate\n<list list-type=\"bullet\"><list-item><p>Multi-method is a more appropriate term to describe the project and this has been amended throughout</p></list-item></list>  Do the authors propose to triangulate the results from each of the work packages (phases) of the study in any way?\n<list list-type=\"bullet\"><list-item><p>No, the data will not be triangulated</p></list-item></list> The authors have identified how they will establish the extent of health inequities between children with and without LD in relation to access to healthcare in the first phase of the study (work package 1). The authors have identified how they will match the cases. They have reported how they will represent the demographic profile of the participants. They have identified the intended statistical tests for drawing comparisons with the general population.</p><p>However, I would recommend the following amendment for work package 1 phase, in order to ensure the data analysis plan in described in detail and allows replication by others. More information is required on the following in relation to work package</p><p>1.         How is the proposed approach similar to the method used in Canada?\n<list list-type=\"bullet\"><list-item><p>The reference to this study has been removed</p></list-item></list> 2.         Were there any adaptions that were made for the current study?\n<list list-type=\"bullet\"><list-item><p>See above</p></list-item></list> 3.         How many children with ID are in this database?\n<list list-type=\"bullet\"><list-item><p>It is unclear at this stage how many children with ID are in the databases but it has</p></list-item></list> 4.         What specific age groups will the children be subdivided into for the purpose of analysis?\n<list list-type=\"bullet\"><list-item><p>There is no plan to subdivide the children into age groups.</p></list-item></list> 5.         What is the gender breakdown of the children in this database?\n<list list-type=\"bullet\"><list-item><p>Unknown at this stage, but it is expected that there will be more males than females in keeping with data from the CSO.</p></list-item></list> 6.         How will missing data be treated?\n<list list-type=\"bullet\"><list-item><p>This has been addressed in the section ‘Data Analysis Plan’.</p></list-item></list> 7.         How many matched children are in this database?\n<list list-type=\"bullet\"><list-item><p>Sample sizes have been provided where possible.</p></list-item></list> The authors have identified how they will use a discrete choice experiment to examine the decision making and referral process of GPs and identify parental preference for unscheduled care for treatment in phase 2 (work package 2). They have specified the four steps of this experiment. In the first attribute development step, they have identified that a systematic review and qualitative inquiry will be undertaken in order to identify the attributes that influence decision making. It would have been helpful if more detail was provided in relation to how the findings from the review will be explored further in the qualitative inquiry. They have specified how the second structured prioritisation exercise to finalise attributes will be undertaken. They have identified that the discrete choice experiment cards will subsequently be piloted in the third phase (specifically looking at length of time/ease of use of completion). It would have been helpful if some information about planned sample of participants in phase 3 could have been included (intended number of participants, age, gender breakdown etc.). The sampling, recruitment and analysis plan for step 4 is clearly identified. The authors have identified how the concept mapping approach in package 3 builds on findings from step 1 and 2. This is clearly reported and it is identified that the purpose of this is to elicit stakeholders’ recommendations for policy and practice.</p><p>Additional comments: the authors used the interchangeable terms of intellectual disability (ID) and disability throughout the transcript. It is suggested that if the focus of this study is just on children with an ID as opposed to children with any type of ‘disability’, then ID should be used instead of disability.\n<list list-type=\"bullet\"><list-item><p>The acronym ID has been removed and replaced with intellectual disabilities. Any reference to disability has also been removed and replaced with intellectual disabilities</p></list-item></list>\n</p></body></sub-article></sub-article></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">F1000Res</journal-id><journal-id journal-id-type=\"iso-abbrev\">F1000Res</journal-id><journal-id journal-id-type=\"pmc\">F1000Research</journal-id><journal-title-group><journal-title>F1000Research</journal-title></journal-title-group><issn pub-type=\"epub\">2046-1402</issn><publisher><publisher-name>F1000 Research Limited</publisher-name><publisher-loc>London, UK</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32850123</article-id><article-id pub-id-type=\"pmc\">PMC7431973</article-id><article-id pub-id-type=\"doi\">10.12688/f1000research.25181.2</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Research Article</subject></subj-group><subj-group><subject>Articles</subject></subj-group></article-categories><title-group><article-title>An assessment of the extent to which the contents of PROSPERO records meet the systematic review protocol reporting items in PRISMA-P</article-title><fn-group content-type=\"pub-status\"><fn><p>[version 2; peer review: 2 approved]</p></fn></fn-group></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Booth</surname><given-names>Alison</given-names></name><role content-type=\"http://credit.casrai.org/\">Conceptualization</role><role content-type=\"http://credit.casrai.org/\">Data Curation</role><role content-type=\"http://credit.casrai.org/\">Formal Analysis</role><role content-type=\"http://credit.casrai.org/\">Investigation</role><role content-type=\"http://credit.casrai.org/\">Methodology</role><role content-type=\"http://credit.casrai.org/\">Project Administration</role><role content-type=\"http://credit.casrai.org/\">Validation</role><role content-type=\"http://credit.casrai.org/\">Visualization</role><role content-type=\"http://credit.casrai.org/\">Writing – Original Draft Preparation</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0001-7138-6295</contrib-id><xref ref-type=\"corresp\" rid=\"c1\">a</xref><xref ref-type=\"aff\" rid=\"a1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Mitchell</surname><given-names>Alex S.</given-names></name><role content-type=\"http://credit.casrai.org/\">Formal Analysis</role><role content-type=\"http://credit.casrai.org/\">Investigation</role><role content-type=\"http://credit.casrai.org/\">Methodology</role><role content-type=\"http://credit.casrai.org/\">Software</role><role content-type=\"http://credit.casrai.org/\">Validation</role><role content-type=\"http://credit.casrai.org/\">Visualization</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0001-9311-2092</contrib-id><xref ref-type=\"aff\" rid=\"a1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Mott</surname><given-names>Andrew</given-names></name><role content-type=\"http://credit.casrai.org/\">Data Curation</role><role content-type=\"http://credit.casrai.org/\">Formal Analysis</role><role content-type=\"http://credit.casrai.org/\">Investigation</role><role content-type=\"http://credit.casrai.org/\">Methodology</role><role content-type=\"http://credit.casrai.org/\">Software</role><role content-type=\"http://credit.casrai.org/\">Validation</role><role content-type=\"http://credit.casrai.org/\">Visualization</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0001-7844-9033</contrib-id><xref ref-type=\"aff\" rid=\"a1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>James</surname><given-names>Sophie</given-names></name><role content-type=\"http://credit.casrai.org/\">Formal Analysis</role><role content-type=\"http://credit.casrai.org/\">Investigation</role><role content-type=\"http://credit.casrai.org/\">Methodology</role><role content-type=\"http://credit.casrai.org/\">Visualization</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0003-1878-2779</contrib-id><xref ref-type=\"aff\" rid=\"a1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Cockayne</surname><given-names>Sarah</given-names></name><role content-type=\"http://credit.casrai.org/\">Investigation</role><role content-type=\"http://credit.casrai.org/\">Methodology</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0002-1288-5497</contrib-id><xref ref-type=\"aff\" rid=\"a1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Gascoyne</surname><given-names>Samantha</given-names></name><role content-type=\"http://credit.casrai.org/\">Investigation</role><role content-type=\"http://credit.casrai.org/\">Methodology</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><xref ref-type=\"aff\" rid=\"a1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>McDaid</surname><given-names>Catriona</given-names></name><role content-type=\"http://credit.casrai.org/\">Methodology</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0002-3751-7260</contrib-id><xref ref-type=\"aff\" rid=\"a1\">1</xref></contrib><aff id=\"a1\">\n<label>1</label>Department of Health Sciences, York Trials Unit, University of York, York, YO10 5DD, UK</aff></contrib-group><author-notes><corresp id=\"c1\"><label>a</label><email xlink:href=\"mailto:alison.booth@york.ac.uk\">alison.booth@york.ac.uk</email></corresp><fn fn-type=\"COI-statement\"><p>\n<bold>Competing interests: </bold>AB led on the development of PROSPERO and is a group author of the PRISMA-P reporting guidelines papers. AB has not been involved in the management of PROSPERO since 2015. SC, SJ, SG, ASM, AM and CM declare they have no conflicts of interest.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>10</day><month>9</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>9</volume><elocation-id>773</elocation-id><history><date date-type=\"accepted\"><day>2</day><month>9</month><year>2020</year></date></history><permissions><copyright-statement>Copyright: © 2020 Booth A et al.</copyright-statement><copyright-year>2020</copyright-year><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri content-type=\"pdf\" xlink:href=\"f1000research-9-29320.pdf\"/><abstract><p>\n<bold>Background:</bold> PROSPERO is an international prospective register for systematic review protocols. Many of the registrations are the only available source of information about planned methods. This study investigated the extent to which records in PROSPERO contained the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P).</p><p>\n<bold>Methods</bold>: A random sample of 439 single entry PROSPERO records of reviews of health interventions registered in 2018 was identified. Using a piloted list of 19 PRISMA-P items, divided into 63 elements, two researchers independently assessed the registration records. Where the information was present or not applicable to the review, a score of 1 was assigned. Overall scores were calculated and comparisons made by stage of review at registration, whether or not a meta-analysis was planned and whether or not funding/sponsorship was reported.</p><p>\n<bold>Results</bold>: Some key methodological details, such as eligibility criteria, were relatively frequently reported, but much of the information recommended in PRISMA-P was not stated in PROSPERO registrations. Considering the 19 items, the mean score was 4.8 (SD 1.8; median 4; range 2-11) and across all the assessed records only 25% (2081/8227) of the items were scored as reported. Considering the 63 elements, the mean score was 33.4 (SD 5.8; median 33; range 18-47) and overall, 53% (14,469/27,279) of the elements were assessed as reported. Reporting was more frequent for items required in PROSPERO than optional items. The planned comparisons showed no meaningful differences between groups.</p><p>\n<bold>Conclusions</bold>: PROSPERO provides reviewers with the opportunity to be transparent in their planned methods and demonstrate efforts to reduce bias. However, where the PROSPERO record is the only available source of\n<italic>a priori</italic> reporting, there is a significant shortfall in the items reported, compared to those recommended. This presents challenges in interpretation for those wishing to assess the validity of the final review.</p></abstract><kwd-group kwd-group-type=\"author\"><kwd>Systematic review</kwd><kwd>protocol</kwd><kwd>reporting</kwd><kwd>registration</kwd></kwd-group><funding-group><funding-statement>The author(s) declared that no grants were involved in supporting this work.</funding-statement></funding-group></article-meta><notes notes-type=\"version-changes\"><sec sec-type=\"version-changes\"><label>Revised</label><title>Amendments from Version 1</title><p>In the discussion we have addressed the differences between a systematic review protocol and registration of key details, and explained the use of PRISMA-P guidelines, even though they do not align with PROSPERO fields. We have also added the use of a scoring system as a potential limitation of the study. Other minor issues raised in peer review have also been addressed.</p></sec></notes></front><body><sec sec-type=\"intro\"><title>Introduction</title><p>Detailing the planned methods for conducting a systematic review in advance of commencing the review is essential in order to minimise a range of potential biases\n<sup><xref rid=\"ref-1\" ref-type=\"bibr\">1</xref>,\n<xref rid=\"ref-2\" ref-type=\"bibr\">2</xref></sup>. The plan, set out in a protocol, should ideally be made available in the public domain to facilitate transparency\n<sup><xref rid=\"ref-3\" ref-type=\"bibr\">3</xref>,\n<xref rid=\"ref-4\" ref-type=\"bibr\">4</xref></sup>. In addition, registration of key protocol details is encouraged as best practice in reporting guidelines\n<sup><xref rid=\"ref-5\" ref-type=\"bibr\">5</xref>,\n<xref rid=\"ref-6\" ref-type=\"bibr\">6</xref></sup> by publishers like the British Medical Journal (BMJ), Public Library of Science (PLoS), and BioMed Central (BMC), and is mandated in their instructions to authors by journals such as BMC Systematic Reviews, BMJ, BMJ Open, PLoS One, and National Institute for Health Research (NIHR) journals.</p><p>There are a number of options for putting systematic review protocols into the public domain, such as publication in open access journals like BMC Systematic Reviews and uploading to open data repositories like the Open Science Framework (OSF) (\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://osf.io/registries/discover?q=protocols\">https://osf.io/registries/discover?q=protocols</ext-link>). PROSPERO (\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.crd.york.ac.uk/prospero/\">https://www.crd.york.ac.uk/prospero/</ext-link>) is a facility for registering key methodological details in advance of carrying out a review. Registration on PROSPERO requires completion of an internationally agreed minimum dataset for a systematic review protocol\n<sup><xref rid=\"ref-7\" ref-type=\"bibr\">7</xref>,\n<xref rid=\"ref-8\" ref-type=\"bibr\">8</xref></sup>. Registrants also have the option of uploading their protocol or providing a hyperlink to it.</p><p>PROSPERO remains the only free, open access registry of systematic review protocols, making it a single searchable source of the protocols of on-going and completed reviews. Uptake of registration has increased exponentially and by the end of 2019 there were over 60,000 registrations in PROSPERO. There is evidence that considerably more systematic reviews are registered in PROSPERO than have peer-reviewed protocols published. In 2016, 1058 records were accepted by PROSPERO; in the same time period, only 404 published systematic review protocols were identified\n<sup><xref rid=\"ref-3\" ref-type=\"bibr\">3</xref></sup>. Another study reported identifying 20,814 non-Cochrane systematic review protocols from web scraping PROSPERO and bibliographic database searches. Of these, 924 were only published in journals, 807 were published in journals and registered in PROSPERO and 19,890 were only available as a record in PROSPERO\n<sup><xref rid=\"ref-9\" ref-type=\"bibr\">9</xref></sup>. There is further evidence from Ge\n<italic>et al</italic>. (2018) that of the non-Cochrane reviews registered in PROSPERO, only 3% or 4% have a published protocol\n<sup><xref rid=\"ref-9\" ref-type=\"bibr\">9</xref>,\n<xref rid=\"ref-10\" ref-type=\"bibr\">10</xref></sup>. This means that for a large number of reviews a PROSPERO record is likely to be the only source providing details of the planned methods.</p><p>Published protocols and registration records aim to provide transparency in the review process by allowing public access to the key pre-specified elements for the conduct of a review. One of the stated aims of PROSPERO is to facilitate comparison between planned review methods and reported results\n<sup><xref rid=\"ref-8\" ref-type=\"bibr\">8</xref></sup>. Such a comparison enables peer reviewers and other readers of the final review to assess for themselves the potential for bias in the findings. There is also a steadily growing body of research using PROSPERO records to assess the risk of biases in final review reports\n<sup><xref rid=\"ref-10\" ref-type=\"bibr\">10</xref>–\n<xref rid=\"ref-15\" ref-type=\"bibr\">15</xref></sup>. Given this reliance on the information provided in PROSPERO records, it is important to understand the level of detail provided in records. The focus of this study was on the stated aim of PROSPERO to reduce the opportunity for bias by enabling comparison of the completed review with what was planned in the protocol\n<sup><xref rid=\"ref-8\" ref-type=\"bibr\">8</xref></sup>.</p><p>The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Protocols (PRISMA-P) were developed through expert consensus using internationally compiled datasets such as PROSPERO and SPIRIT\n<sup><xref rid=\"ref-4\" ref-type=\"bibr\">4</xref>,\n<xref rid=\"ref-6\" ref-type=\"bibr\">6</xref></sup>.</p><p>Key methodological aspects of a protocol are mandated for registration in PROSPERO; other items, mainly administrative fields, are optional\n<sup><xref rid=\"ref-7\" ref-type=\"bibr\">7</xref>,\n<xref rid=\"ref-8\" ref-type=\"bibr\">8</xref></sup>. Submissions for registration are not subject to any form of peer review or critical appraisal, they are simply checked for sense but not methodological rigor. Therefore, there is the possibility that PROSPERO records do not provide all the necessary information identified by the PRISMA-P guidelines to enable comparison with the completed systematic review. The registration record may be the only place where\n<italic>a priori</italic> methods are available for users, in particular peer reviewers, to check for potential issues such as selection, outcome reporting and publication biases. This study investigated the extent to which records in PROSPERO, where no protocol or other information was available, comply with each of the items for reporting of protocols set out in the PRISMA-P guidelines.</p></sec><sec sec-type=\"methods\"><title>Methods</title><p>A random sample of PROSPERO registration records were assessed against the systematic review protocol reporting criteria set out in the PRISMA-P 2015 checklist\n<sup><xref rid=\"ref-4\" ref-type=\"bibr\">4</xref></sup>. Key methods are provided here with further details available in the protocol for this study, which was prepared and made publicly available on the OSF, 17 March 2020 (\n<italic>Extended data</italic>\n<sup><xref rid=\"ref-16\" ref-type=\"bibr\">16</xref></sup>).</p><sec><title>Study sample of PROSPERO records</title><p>A dataset of non-Cochrane PROSPERO records was provided by Metaxis, the software managers of PROSPERO. Records of reviews defined by the record holder as a health intervention registered on or between 1 January 2018 and 31 December 2018, were identified.</p><p>Cochrane reviews, reviews of animal studies, non-intervention reviews as identified in PROSPERO, i.e. Diagnostic accuracy, Prognostic factors, Prevention, Epidemiological reviews relevant to health and social care, Public health, Service delivery in health and social care, Methodological reviews, reviews of reviews, and synthesis of qualitative studies, were all excluded as PROSPERO and PRISMA-P were developed for reviews of interventions. Only records with no evidence from the registration record of other protocol related information, for example in a published protocol or other links in the PROSPERO record, were included and we restricted the data set to those records with a single registry entry.</p><p>Records from the calendar year 2018 were used to allow time for dissemination and adoption of the PRISMA-P guidelines published in 2015. A sample of 20% of these records was randomly selected using simple random sampling for assessment against the PRISMA-P reporting criteria.</p></sec><sec><title>Assessment tool and scoring</title><p>The PRISMA-P checklist recommends 17 numbered items, with nine subdivisions, totalling 26 items be reported in a systematic review protocol\n<sup><xref rid=\"ref-4\" ref-type=\"bibr\">4</xref></sup>. Seven of the 26 items were excluded from the assessment as they would always or never meet registration requirements in PROSPERO. For example, registration is implicit for a record accepted in PROSPERO, and there is no field for author contributions or sponsor role so these would never be reported. The study assessment tool, developed specifically for this study as a Google Form, therefore contained 19 of the PRISMA-P items. Where the PRISMA-P description for an item specified more than one piece of information, the individual elements were listed as subsets of the items\n<sup><xref rid=\"ref-4\" ref-type=\"bibr\">4</xref>,\n<xref rid=\"ref-6\" ref-type=\"bibr\">6</xref></sup>. For example, item 14. Risk of bias in individual studies, says: “Describe anticipated methods for assessing risk of bias of individual studies, including whether this will be done at the outcome or study level, or both; state how this information will be used in data synthesis.” Scoring for this item was for each of the following separate elements: No risk of bias assessment planned and justification provided; Risk of bias tools named for all study types included; Outcome or study level or both; Domains/outcomes for risk of bias assessment stated; Risk of bias assessment process described; How risk of bias findings will be used in synthesis. Applying this approach to the 19 items resulted in a list containing 63 elements to be reported.</p><p>Where an item was reported or not applicable, a score of 1 was assigned. Where the information was not reported this scored 0. The maximum possible overall score for the PRISMA-P listed items was 19 per record. Scores for the breakdown of individual elements within the items was also reported, the maximum possible score was 63 per record.</p></sec><sec><title>Assessment procedure</title><p>The researchers undertaking the assessments (AB, ASM, AM, SJ, SC, SG) familiarised themselves with both PRISMA-P papers\n<sup><xref rid=\"ref-4\" ref-type=\"bibr\">4</xref>,\n<xref rid=\"ref-6\" ref-type=\"bibr\">6</xref></sup>. All had previously received training in systematic review methods and/or authored at least one systematic review. The draft assessment form and accompanying guidance notes were revised and finalised during a training session and piloted with the aim of achieving greater than 90% agreement.</p><p>Two researchers independently compared the information provided in each PROSPERO record with the relevant items in the study assessment tool. Options for decisions were: Reported (information provided as per PRISMA-P requirements); Not reported (some or all information not provided); and, Not applicable (where an item was not relevant to an individual record, e.g. a meta-analysis was not planned).</p><p>Records were randomly assigned to assessors by first creating a list of the sampled record unique identification numbers and dividing the list into 14 blocks of approximately equal size, with each block being assigned a colour. A copy of this list together with the block configuration was then placed alongside the original list. Seven sub-lists were then created by randomly selecting a block from the first list and a block from the second list, such that blocks of the same colour were not in the same sub-list, and each colour appeared in two sub-lists. Each sub-list was then randomly assigned to an assessor.</p><p>It was not feasible to blind the researchers to the authors of registrations in PROSPERO. None of the assessors were authors of included registrations. On completion of the pilot assessments and the full set of records, disagreements were resolved through discussion or recourse to a third researcher.</p><p>The assessment form and the guidance notes are available on the OSF (\n<italic>Extended data</italic>\n<sup><xref rid=\"ref-16\" ref-type=\"bibr\">16</xref></sup>).</p></sec><sec><title>Analysis</title><p>The primary outcome for this study was the compliance of PROSPERO registration records to PRISMA-P reporting items. This was measured by the total mean score allocated by the two independent assessors to each of the 19 items assessed (maximum possible score 19) for each record and by the total mean score for the individual elements within items (maximum possible score 63). Overall scores for the assessed dataset, scores by the 19 PRISMA-P items and by the 63 elements were the planned outcome measures.</p><p>For the eligible 2018 records that were assessed and those not assessed, demographic data for month of registration, funding/sponsor, planned meta-analysis, number of authors, stage of review at registration, topic and country of review were to be reported. Comparisons to identify any association between records registered before or after screening started; whether a meta-analysis was planned or not; and whether a review was funded/sponsored or not and completeness of reporting of items were planned.</p></sec><sec><title>Deviations from protocol</title><p>During piloting of the assessment form, it became clear that it would not be possible to assess records for PRISMA-P item 5a Sources and 5b Sponsor. This would have required separating sources of financial support from sponsorship or any other form of support as reported in the single PROSPERO field, which was not possible. This item was therefore removed from the assessment form. Instead, a series of regular expression patterns was compared to the list of eligible records to identify those where the record contained any indication of funding/ sponsorship/support or indicated there was none. These data were used in the presentation of demographics and subgroup comparison.</p></sec></sec><sec sec-type=\"results\"><title>Results</title><p>The PROSPERO dataset contained 5,313 records for reviews of health interventions first accepted in 2018 (excluding Cochrane and reviews of animal studies). Applying the other study inclusion/exclusion criteria resulted in 2,194 eligible registration records. The randomly selected sample of 20% for assessment included 439 records. During assessment, six records were excluded, for not meeting the inclusion criteria (4), being a duplicate (1) or no longer available on PROSPERO (1). Assessments were therefore carried out on 433 PROSPERO records. A flow chart of record selection is shown in\n<xref ref-type=\"fig\" rid=\"f1\">Figure 1</xref>.</p><fig fig-type=\"figure\" id=\"f1\" orientation=\"portrait\" position=\"anchor\"><label>Figure 1. </label><caption><title>Flow chart of record sample identification.</title></caption><graphic xlink:href=\"f1000research-9-29320-g0000\"/></fig><p>Agreement following initial piloting of the assessment form was 87%; after further discussions and revision of the assessment guidance notes and form a second pilot achieved 92% agreement. For all the records assessed, agreement between researchers was 90%, all differences were resolved through discussion or referral to a third researcher.</p><p>Demographic details of the sample of PROSPERO records selected for assessment and those not assessed are provided in\n<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>. The number of authors listed ranged between one and 17, with the exception of a single record, included in the assessed sample, where 47 authors were listed. The eligible sample for 2018 included records from 67 different countries: 20 records listed two countries and 15 listed between three and nine countries involved in the review. There were no substantial differences between the data sets in the month of registration; whether any details of funding and/or sponsorship were provided; whether a meta-analysis was planned or not; the number of authors listed per record; stage of review at registration; topic of review or country involved in undertaking the review.</p><table-wrap id=\"T1\" orientation=\"portrait\" position=\"anchor\"><label>Table 1. </label><caption><title>Demographic details of non-sample set and sample set of the eligible 2018 PROSPERO records.</title></caption><table frame=\"hsides\" rules=\"groups\" content-type=\"article-table\"><thead><tr><th align=\"center\" colspan=\"2\" valign=\"top\" rowspan=\"1\">Demographic</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Records for assessment\n<break/>(n = 439)</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Records not assessed\n<break/>(n = 1755)</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"12\" valign=\"middle\" colspan=\"1\">\n<bold>Month of registration n (%)</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>January</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">45 (10)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">168 (10)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>February</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">32 (7)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">141 (8)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>March</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">25 (6)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">100 (6)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>April</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">35 (8)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">122 (7)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>May</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">16 (4)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">110 (6)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>June</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">36 (8)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">151 (8)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>July</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">54 (12)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">188 (11)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>August</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">56(12)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">200 (11)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>September</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">31 (7)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">151 (9)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>October</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">37 (8)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">138 (8)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>November</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">37 (8)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">160 (9)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>December</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">35 (8)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">126 (7)</td></tr><tr><td align=\"left\" colspan=\"2\" valign=\"top\" rowspan=\"1\">\n<bold>Funding/support indicated n (%)</bold>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">386 (88)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1572 (90)</td></tr><tr><td align=\"left\" colspan=\"2\" valign=\"top\" rowspan=\"1\">\n<bold>Meta-analysis planned n (%)</bold>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">253 (58)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1064 (61)</td></tr><tr><td align=\"left\" colspan=\"2\" valign=\"top\" rowspan=\"1\">\n<bold>Number of listed authors (mean, range)</bold>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.1 (0 – 47\n<xref ref-type=\"other\" rid=\"TFN1\">*</xref>)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.9 (0 – 17)</td></tr><tr><td align=\"left\" rowspan=\"9\" valign=\"middle\" colspan=\"1\">\n<bold>Stage of review\n<xref ref-type=\"other\" rid=\"TFN2\">**</xref></bold>\n<break/>\n<bold>n (%)</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Not Started</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">96 (22)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">385 (22)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Searches Start</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">65(15)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">283 (16)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Searches Complete</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">12 (3)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">57 (3)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Pilot Selection Start</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">56 (13)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">252 (14)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Pilot Selection Complete</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">16 (4)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">50 (3)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Screening Start</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">80 (19)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">285 (16)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Screening Complete</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">13 (3)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">56 (3)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Extraction Start</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">93 (21)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">376 (21)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Extraction complete</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2 (0)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">8 (1)</td></tr><tr><td align=\"left\" rowspan=\"40\" valign=\"middle\" colspan=\"1\">\n<bold>Topic of review\n<xref ref-type=\"other\" rid=\"TFN3\">***</xref></bold>\n<break/>\n<bold>n (%)</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Alcohol/substance misuse/</italic>\n<break/>\n<italic>abuse</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">12 (3)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">28 (2)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Blood and immune system</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">13 (3)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">90 (5)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Cancer</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">42 (10)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">182 (10)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Cardiovascular</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">61 (14)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">220 (13)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Care of the elderly</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">16 (4)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">72 (4)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Child health</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">31 (7)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">139 (8)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Complementary therapies</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">43 (10)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">178 (10)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Crime and justice</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0 (0)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2 (0)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Dental</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">30 (7)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">138 (8)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Digestive system</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">34 8)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">127 (7)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Ear, nose and throat</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">7 (2)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">27 (2)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Education</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">10 (2)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">23 (1)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Endocrine and metabolic</italic>\n<break/>\n<italic>disorders</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">35 (8)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">144 (8)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Eye disorders</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3 (1)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">16 (1)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>General interest</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5 (1)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">29 (2)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Genetics</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3 (1)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5 (0)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Health inequalities/health</italic>\n<break/>\n<italic>equity</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3 (1)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">8 (1)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Infections and infestations</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">22 (5)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">97 (6)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>International development</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0 (0)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2 (0)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Mental health and</italic>\n<break/>\n<italic>behavioural conditions</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">51 (12)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">129 (7)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Musculoskeletal</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">70 (16)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">253 (14)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Neurological</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">44 (10)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">208 (12)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Nursing</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">11 (3)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">45 (3)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Obstetrics and gynaecology</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">23 (5)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">101 (6)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Oral health</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">21 (5)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">100 (6)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Palliative</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4 (1)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">16 (1)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Perioperative care</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">14 (3)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">81 (5)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Physiotherapy</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">36 (8)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">129 (7)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Pregnancy and childbirth</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">13 (3)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">60 (3)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Public Health</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0 (0)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0 (0)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Rehabilitation</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">43 (10)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">173 (10)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Respiratory disorders</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">16 (4)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">87 (5)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Service delivery</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0 (0)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0 (0)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Skin disorders</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">12 (3)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">40 (2)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Social care</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0 (0)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2 (0)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Surgery</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">49 (11)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">209 (12)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Tropical medicine</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0 (0)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0 (0)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Urological</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">20 (5)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">71 (4)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Wounds, injuries and</italic>\n<break/>\n<italic>accidents</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">11 (3)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">70 (4)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Violence and abuse</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3 (1)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">10 (1)</td></tr><tr><td align=\"left\" rowspan=\"11\" valign=\"top\" colspan=\"1\">\n<bold>Country of review\n<xref ref-type=\"other\" rid=\"TFN3\">***</xref></bold>\n<break/>\n<bold>n (%)</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Australia</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">33 (8)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">143 (8)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Brazil</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">53 (12)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">224 (13)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Canada</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">38 (9)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">121 (7)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>China</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">100 (23)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">414 (24)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>England</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">46 (10)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">163 (9)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Germany</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">13 (3)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">40 (2)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Italy</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">14 (3)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">62 (4)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Netherlands</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">13 (3)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">51 (3)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Spain</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">13 (3)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">39 (2)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>USA</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">48 (11)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">160 (9)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>57 other countries</italic>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">127 (29)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">562 (32)</td></tr></tbody></table><table-wrap-foot><fn><p id=\"TFN1\">\n<italic>* the record with 47 authors was a single outlier: range excluding this record was 0–15</italic>\n</p><p id=\"TFN2\">\n<italic>** details for three records were not available on PROSPERO</italic>\n</p><p id=\"TFN3\">\n<italic>*** all items reported by authors included; therefore totals are more than the number of records</italic>\n</p></fn></table-wrap-foot></table-wrap><p>None of the PROSPERO records assessed against the eligibility criteria reported on all elements in each of the items recommended for a systematic review protocol in the PRISMA-P guidelines. The mean total score for individual PROSPERO records, where 1 point was gained for each of the 19 items in the PRISMA-P checklist, was 4.8, the standard deviation 1.8, the median 4, and range 2 to 11. Considering all items across all the assessed records, only 25% (2081/8227) of the items were scored as reported.</p><p>The mean total score for individual PROSPERO records where 1 point was gained for each of the 63 elements of the PRISMA-P reporting guidelines was 33.4, the standard deviation 5.8, the median 33 and the range 18–47. Overall, 53% (14,469/27,279) of the elements were considered as reported.</p><sec><title>Scoring for 19 PRISMA-P items</title><p>The highest scoring item was PRISMA-P 1b which requires the protocol to be identified as to whether it is an update of a review; the high score was the result of this being a not-applicable item for 423 (98%) of the 433 records (\n<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>). Eligibility criteria (study design, setting, population, intervention, comparator, outcomes) was the next highest scoring item with 386 (89%) reporting all of these elements. Selection process (214, 49%), describing the criteria under which study data will be quantitatively synthesized (200, 46%), and describing the type of summary planned if quantitative synthesis is not appropriate (227, 52%) were the next highest scoring of the 19 items assessed.</p><table-wrap id=\"T2\" orientation=\"portrait\" position=\"anchor\"><label>Table 2. </label><caption><title>Assessment scores by item and breakdown for 433 PROSPERO records.</title></caption><table frame=\"hsides\" rules=\"groups\" content-type=\"article-table\"><thead><tr><th align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PRISMA-P reporting item</th><th align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Reported\n<break/>or not\n<break/>applicable n (%)</th><th align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not\n<break/>reported n (%)</th><th align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Breakdown of items</th><th align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Reported n (%)</th><th align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not\n<break/>reported n (%)</th><th align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not\n<break/>applicable n (%)</th></tr></thead><tbody><tr><td align=\"left\" colspan=\"7\" valign=\"top\" rowspan=\"1\">Section 1 Administrative information</td></tr><tr><td align=\"left\" rowspan=\"2\" style=\"background-color:#E7EAEF\" valign=\"top\" colspan=\"1\">1a. Identification in the title:\n<break/>Identify the report as a protocol of\n<break/>a systematic review</td><td align=\"right\" rowspan=\"2\" style=\"background-color:#E7EAEF\" valign=\"middle\" colspan=\"1\">22 (5)</td><td align=\"right\" rowspan=\"2\" style=\"background-color:#E7EAEF\" valign=\"middle\" colspan=\"1\">411 (95)</td><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Identify the report as a\n<break/>protocol</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">22 (5)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">411 (95)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Identify the report as a\n<break/>systematic review</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">342 (79)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">91 (21)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1b. Update: If the protocol is\n<break/>for an update of a previous\n<break/>systematic review</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">424 (98)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">9 (2)</td><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\"> Identify the report as an\n<break/>update</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1 (0)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">9 (2)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">423 (98)</td></tr><tr><td align=\"left\" colspan=\"7\" valign=\"top\" rowspan=\"1\">Section 2 Introduction</td></tr><tr><td align=\"left\" rowspan=\"2\" style=\"background-color:#E7EAEF\" valign=\"top\" colspan=\"1\">6. Rationale: Describe the\n<break/>rationale for the review in the\n<break/>context of what is already known</td><td align=\"right\" rowspan=\"2\" style=\"background-color:#E7EAEF\" valign=\"middle\" colspan=\"1\">38 (9)</td><td align=\"right\" rowspan=\"2\" style=\"background-color:#E7EAEF\" valign=\"middle\" colspan=\"1\">395 (91)</td><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Rationale described</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">44 (10)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">389 (90)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Context provided\n<xref ref-type=\"other\" rid=\"TFN4\">*</xref>\n</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">108 (25)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">325 (75)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td align=\"left\" rowspan=\"4\" style=\"background-color:#E7EAEF\" valign=\"top\" colspan=\"1\">7. Objectives: Provide an explicit\n<break/>statement of the question(s) the\n<break/>review will address with reference\n<break/>to participants, interventions,\n<break/>comparators, and outcomes\n<break/>(PICO)\n<xref ref-type=\"other\" rid=\"TFN4\">*</xref>\n</td><td align=\"right\" rowspan=\"4\" style=\"background-color:#E7EAEF\" valign=\"middle\" colspan=\"1\">134 (31)</td><td align=\"right\" rowspan=\"4\" style=\"background-color:#E7EAEF\" valign=\"middle\" colspan=\"1\">299 (69)</td><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Population</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">397 (92)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">36 (8)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Intervention</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">416 (96)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">17 (4)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Comparator</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">142 (33)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">264 (61)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">27 (6)</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Outcomes</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">237 (55)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">196 (45)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td align=\"left\" colspan=\"7\" style=\"background-color:#FFFFFF\" valign=\"top\" rowspan=\"1\">Section 3 Methods</td></tr><tr><td align=\"left\" rowspan=\"6\" style=\"background-color:#E7EAEF\" valign=\"top\" colspan=\"1\">8. Eligibility criteria: Specify the\n<break/>study characteristics (e.g., PICO,\n<break/>study design, setting, time frame)\n<break/>and report characteristics (e.g.,\n<break/>years considered, language,\n<break/>publication status) to be used as\n<break/>criteria for eligibility for the review\n<xref ref-type=\"other\" rid=\"TFN4\">*</xref>\n</td><td align=\"right\" rowspan=\"6\" style=\"background-color:#E7EAEF\" valign=\"middle\" colspan=\"1\">386 (89)</td><td align=\"right\" rowspan=\"6\" style=\"background-color:#E7EAEF\" valign=\"middle\" colspan=\"1\">47 (11)</td><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Study design specified\n<xref ref-type=\"other\" rid=\"TFN4\">*</xref>\n</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">427 (99)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">6 (1)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Setting (condition or\n<break/>domain) specified\n<xref ref-type=\"other\" rid=\"TFN4\">*</xref>\n</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">410 (95)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">23 (5)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Population\n<xref ref-type=\"other\" rid=\"TFN4\">*</xref>\n</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">429 (99)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4 (1)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Intervention\n<xref ref-type=\"other\" rid=\"TFN4\">*</xref>\n</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">428 (99)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5 (1)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Comparator\n<xref ref-type=\"other\" rid=\"TFN4\">*</xref>\n</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">392 (91)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">14 (3)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">27 (6)</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Outcome(s)\n<xref ref-type=\"other\" rid=\"TFN4\">*</xref>\n</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">424 (98)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">9 (2)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td align=\"left\" rowspan=\"6\" style=\"background-color:#E7EAEF\" valign=\"top\" colspan=\"1\">9. Information sources: Describe\n<break/>all intended information sources\n<break/>(e.g., electronic databases,\n<break/>contact with study authors, trial\n<break/>registers, or other grey literature\n<break/>sources) with planned dates of\n<break/>coverage\n<xref ref-type=\"other\" rid=\"TFN4\">*</xref>\n</td><td align=\"right\" rowspan=\"6\" style=\"background-color:#E7EAEF\" valign=\"middle\" colspan=\"1\">2 (1)</td><td align=\"right\" rowspan=\"6\" style=\"background-color:#E7EAEF\" valign=\"middle\" colspan=\"1\">431 (99)</td><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Electronic database(s)\n<break/>named</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">431 (99)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2 (1)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Grey literature sources</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">100 (23)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">333 (77)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Study registries</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">289 (67)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">144 (33)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Contact with study authors\n<break/>planned or statement that\n<break/>contact not planned</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">27 (6)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">406 (94)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Other: e.g. hand searching\n<break/>reference lists of included\n<break/>studies</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">152 (35)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">281 (65)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Planned search dates </td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">238 (55)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">195 (45)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td align=\"left\" rowspan=\"3\" style=\"background-color:#E7EAEF\" valign=\"top\" colspan=\"1\">10. Search strategy: Present draft\n<break/>of search strategy to be used for\n<break/>at least one electronic database,\n<break/>including planned limits, such that\n<break/>it could be repeated</td><td align=\"right\" rowspan=\"3\" style=\"background-color:#E7EAEF\" valign=\"middle\" colspan=\"1\">75 (17)</td><td align=\"right\" rowspan=\"3\" style=\"background-color:#E7EAEF\" valign=\"middle\" colspan=\"1\">358 (83)</td><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Draft search strategy\n<break/>provided</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">91 (21)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">342 (79)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Search terms given alone</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">100 (23)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">242 (56)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">91 (21)</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Approach to limits/\n<break/>restrictions reported\n<break/>e.g. language or dates/\n<break/>statement of no limits\n<xref ref-type=\"other\" rid=\"TFN4\">*</xref>\n</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">332 (77)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">101 (23)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td align=\"left\" rowspan=\"2\" style=\"background-color:#E7EAEF\" valign=\"top\" colspan=\"1\">11a. Data management: Describe\n<break/>the mechanism(s) that will be\n<break/>used to manage records and data\n<break/>throughout the review</td><td align=\"right\" rowspan=\"2\" style=\"background-color:#E7EAEF\" valign=\"middle\" colspan=\"1\">17 (4)</td><td align=\"right\" rowspan=\"2\" style=\"background-color:#E7EAEF\" valign=\"middle\" colspan=\"1\">416 (96)</td><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Software named/type\n<break/>indicated\n<xref ref-type=\"other\" rid=\"TFN4\">*</xref>\n</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">56 (13)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">377 (87)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">De-duplication planned</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">42 (9)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">391 (91)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td align=\"left\" rowspan=\"2\" style=\"background-color:#E7EAEF\" valign=\"top\" colspan=\"1\">11b. Selection process: State\n<break/>the process that will be used\n<break/>for selecting studies (e.g., two\n<break/>independent reviewers) through\n<break/>each phase of the review (i.e.,\n<break/>screening, eligibility, and inclusion\n<break/>in meta-analysis)</td><td align=\"right\" rowspan=\"2\" style=\"background-color:#E7EAEF\" valign=\"middle\" colspan=\"1\">214 (49)</td><td align=\"right\" rowspan=\"2\" style=\"background-color:#E7EAEF\" valign=\"middle\" colspan=\"1\">219 (51)</td><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Initial screening process\n<break/>described\n<xref ref-type=\"other\" rid=\"TFN4\">*</xref>\n</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">232 (54)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">201 (46)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Full paper screening\n<break/>process described\n<xref ref-type=\"other\" rid=\"TFN4\">*</xref>\n</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">219 (51)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">214 (49)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td align=\"left\" rowspan=\"3\" style=\"background-color:#E7EAEF\" valign=\"top\" colspan=\"1\">11c. Data collection process:\n<break/>Describe planned method of\n<break/>extracting data from reports\n<break/>(e.g., piloting forms, done\n<break/>independently, in duplicate),\n<break/>any processes for obtaining\n<break/>and confirming data from\n<break/>investigators\n<xref ref-type=\"other\" rid=\"TFN4\">*</xref>\n</td><td align=\"right\" rowspan=\"3\" style=\"background-color:#E7EAEF\" valign=\"middle\" colspan=\"1\">50 (12)</td><td align=\"right\" rowspan=\"3\" style=\"background-color:#E7EAEF\" valign=\"middle\" colspan=\"1\">383 (88)</td><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Data extraction form</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">169 (39)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">264 (61)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Data extraction process\n<break/>described</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">258 (60)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">175 (40)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Obtain missing data</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">76 (18)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">357 (82)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td align=\"left\" rowspan=\"3\" style=\"background-color:#E7EAEF\" valign=\"top\" colspan=\"1\">12. Data items: List and define all\n<break/>variables for which data will be\n<break/>sought (e.g., PICO items, funding\n<break/>sources), any pre-planned data\n<break/>assumptions and simplifications</td><td align=\"right\" rowspan=\"3\" style=\"background-color:#E7EAEF\" valign=\"middle\" colspan=\"1\">6 (1)</td><td align=\"right\" rowspan=\"3\" style=\"background-color:#E7EAEF\" valign=\"middle\" colspan=\"1\">427 (99)</td><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">List of data for extraction\n<xref ref-type=\"other\" rid=\"TFN4\">*</xref>\n</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">219 (51)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">214 (49)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Variables defined\n<xref ref-type=\"other\" rid=\"TFN4\">*</xref>\n</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">29 (7)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">404 (93)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Any data assumptions\n<break/>reported</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">17 (4)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">416 (96)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td align=\"left\" rowspan=\"5\" style=\"background-color:#E7EAEF\" valign=\"top\" colspan=\"1\">13. Outcomes and prioritisation:\n<break/>List and define all outcomes\n<break/>for which data will be sought,\n<break/>including prioritisation of main\n<break/>and additional outcomes, with\n<break/>rationale</td><td align=\"right\" rowspan=\"5\" style=\"background-color:#E7EAEF\" valign=\"middle\" colspan=\"1\">3 (1)</td><td align=\"right\" rowspan=\"5\" style=\"background-color:#E7EAEF\" valign=\"middle\" colspan=\"1\">430 (99)</td><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Primary/main outcome(s)\n<xref ref-type=\"other\" rid=\"TFN4\">*</xref>\n<break/>specified as such</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">418 (97)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">15 (3)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Primary/main outcome(s)\n<break/>measure specified\n<xref ref-type=\"other\" rid=\"TFN4\">*</xref>\n</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">235 (54)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">198 (46)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Additional outcomes\n<break/>specified/ state None\n<xref ref-type=\"other\" rid=\"TFN4\">*</xref>\n</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">430 (99)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3 (1)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Additional outcomes:\n<break/>measures specified\n<xref ref-type=\"other\" rid=\"TFN4\">*</xref>\n</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">131 (30)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">180 (42)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">122 (28)</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Rationale for choice of\n<break/>outcome(s)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">8 (2)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">425 (98)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td align=\"left\" rowspan=\"6\" style=\"background-color:#E7EAEF\" valign=\"top\" colspan=\"1\">14. Risk of bias in individual\n<break/>studies: Describe anticipated\n<break/>methods for assessing risk of bias\n<break/>of individual studies, including\n<break/>whether this will be done at the\n<break/>outcome or study level, or both;\n<break/>state how this information will be\n<break/>used in data synthesis\n<xref ref-type=\"other\" rid=\"TFN4\">*</xref>\n</td><td align=\"right\" rowspan=\"6\" style=\"background-color:#E7EAEF\" valign=\"middle\" colspan=\"1\">41 (9)</td><td align=\"right\" rowspan=\"6\" style=\"background-color:#E7EAEF\" valign=\"middle\" colspan=\"1\">392 (91)</td><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">No risk of bias assessment\n<break/>planned, and justification\n<break/>provided</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4 (1)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3 (1)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">426 (98)</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Risk of bias tools named\n<break/>for all study types included</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">362 (84)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">67 (16)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4 (1)</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Outcome or study level\n<break/>or both</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">310 (71)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">119 (28)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4 (1)</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Domains/outcomes for risk\n<break/>of bias assessment stated</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">342 (79)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">87 (20)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4 (1)</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Risk of bias assessment\n<break/>process described</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">296 (68)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">133 (31)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4 (1)</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">How risk of bias findings\n<break/>will be used in the\n<break/>synthesis</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">64 (15)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">365 (84)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4 (1)</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">15a. Synthesis: Describe criteria\n<break/>under which study data will be\n<break/>quantitatively synthesized</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">200 (46)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">233 (54)</td><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Criteria for doing a\n<break/>quantitative synthesis/\n<break/>meta-analysis described\n<xref ref-type=\"other\" rid=\"TFN4\">*</xref>\n</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">131 (30)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">233 (54)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">69 (16)</td></tr><tr><td align=\"left\" rowspan=\"7\" style=\"background-color:#E7EAEF\" valign=\"top\" colspan=\"1\">15b. If data are appropriate for\n<break/>quantitative synthesis, describe\n<break/>planned summary measures,\n<break/>methods of handling data, and\n<break/>methods of combining data from\n<break/>studies, including any planned\n<break/>exploration of consistency (e.g.,\n<break/>I\n<sup>2</sup>, Kendall’s tau)</td><td align=\"right\" rowspan=\"7\" style=\"background-color:#E7EAEF\" valign=\"middle\" colspan=\"1\">70 (16)</td><td align=\"right\" rowspan=\"7\" style=\"background-color:#E7EAEF\" valign=\"middle\" colspan=\"1\">363 (84)</td><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Summary measures\n<xref ref-type=\"other\" rid=\"TFN4\">*</xref>\n</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">202 (46)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">163 (38)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">68 (16)</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Statistical method\n<xref ref-type=\"other\" rid=\"TFN4\">*</xref>\n</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">89 (20)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">276 (64)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">68 (16)</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Use of fixed or random\n<break/>effects or both\n<xref ref-type=\"other\" rid=\"TFN4\">*</xref>\n</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">194 (44)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">171 (40)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">68 (16)</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Data handling: conversion\n<break/>to same format</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">106 (24)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">259 (60)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">68 (16)</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Data handling: missing\n<break/>data</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">14 (3)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">351 (81)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">68 (16)</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Combining data/\n<break/>exploration of consistency</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">179 (41)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">186 (43)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">68 (16)</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Name of software to be\n<break/>used for meta-analysis</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">204 (47)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">161 (37)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">68 (16)</td></tr><tr><td align=\"left\" rowspan=\"3\" style=\"background-color:#E7EAEF\" valign=\"top\" colspan=\"1\">15c. Describe any proposed\n<break/>additional analyses (e.g.,\n<break/>sensitivity or subgroup analyses,\n<break/>meta-regression)</td><td align=\"right\" rowspan=\"3\" style=\"background-color:#E7EAEF\" valign=\"middle\" colspan=\"1\">84 (19)</td><td align=\"right\" rowspan=\"3\" style=\"background-color:#E7EAEF\" valign=\"middle\" colspan=\"1\">349 (81)</td><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Subgroup analyses\n<break/>planned: co-variants\n<break/>named\n<xref ref-type=\"other\" rid=\"TFN4\">*</xref>\n</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">344 (79)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">21 (5)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">68 (16)</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Methods for subgroup\n<break/>analyses reported</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">25 (6)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">280 (65)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">128 (29)</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sensitivity analyses\n<break/>planned</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">85 (19)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">280 (65)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">68 (16)</td></tr><tr><td align=\"left\" rowspan=\"3\" style=\"background-color:#E7EAEF\" valign=\"top\" colspan=\"1\">15d. If quantitative synthesis is not\n<break/>appropriate, describe the type of\n<break/>summary planned\n<xref ref-type=\"other\" rid=\"TFN4\">*</xref>\n</td><td align=\"right\" rowspan=\"3\" style=\"background-color:#E7EAEF\" valign=\"middle\" colspan=\"1\">227 (52)</td><td align=\"right\" rowspan=\"3\" style=\"background-color:#E7EAEF\" valign=\"middle\" colspan=\"1\">206 (48)</td><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Descriptive, narrative,\n<break/>or qualitative synthesis\n<break/>planned</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">194 (45)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">55 (12)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">184 (43)</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Descriptive, narrative\n<break/>or qualitative synthesis\n<break/>methods described</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">49 (11)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">200 (46)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">184 (43)</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Other analyses planned</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3 (1)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">11 (3)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">419 (96)</td></tr><tr><td align=\"left\" rowspan=\"2\" style=\"background-color:#E7EAEF\" valign=\"top\" colspan=\"1\">16. Meta-bias(es): Specify any\n<break/>planned assessment of meta-\n<break/>bias(es) (e.g., publication bias\n<break/>across studies, selective reporting\n<break/>within studies)</td><td align=\"right\" rowspan=\"2\" style=\"background-color:#E7EAEF\" valign=\"middle\" colspan=\"1\">72 (17)</td><td align=\"right\" rowspan=\"2\" style=\"background-color:#E7EAEF\" valign=\"middle\" colspan=\"1\">361 (83)</td><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Publication bias to be\n<break/>assessed</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">94 (21)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">271 (63)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">68 (16)</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Outcome reporting bias to\n<break/>be assessed</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4 (1)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">361 (83)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">68 (16)</td></tr><tr><td align=\"left\" rowspan=\"2\" style=\"background-color:#E7EAEF\" valign=\"top\" colspan=\"1\">17. Confidence in cumulative\n<break/>evidence: Describe how the\n<break/>strength of the body of evidence\n<break/>will be assessed (e.g., GRADE)</td><td align=\"right\" rowspan=\"2\" style=\"background-color:#E7EAEF\" valign=\"middle\" colspan=\"1\">37 (9)</td><td align=\"right\" rowspan=\"2\" style=\"background-color:#E7EAEF\" valign=\"middle\" colspan=\"1\">396 (91)</td><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Overall assessment of\n<break/>included studies planned</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">40 (9)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">393 (91)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">/</td></tr><tr><td align=\"left\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Methods specified</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">38 (9)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">395 (91)</td><td align=\"right\" style=\"background-color:#E7EAEF\" valign=\"top\" rowspan=\"1\" colspan=\"1\">/</td></tr></tbody></table><table-wrap-foot><fn><p id=\"TFN4\">\n<italic>* Item/element required in PROSPERO **Item/element identified in PROSPERO but as optional</italic>\n</p></fn></table-wrap-foot></table-wrap><p>The scores by PRISMA-P item and by breakdown of items are presented in\n<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>. The full dataset with assessment outcomes and scores for individual records, and the subgroup analyses scoring are available on the OSF (\n<italic>Underlying data</italic>\n<sup><xref rid=\"ref-16\" ref-type=\"bibr\">16</xref></sup>).</p></sec><sec><title>Scoring for 63 elements of the PRISMA-P items</title><p>The score for some of the 19 items was reduced as a result of just one or two of the constituent elements being omitted from reports while others were relatively regularly identified.</p><p>Although overall the review question (item 7) was not found to contain all the expected elements, most did specify the elements of population (397, 92%) and the intervention (416, 96%) and just over half included the outcomes (237, 55%). The comparator was less frequently included (142, 33%); this may have been because of the intention of the review but where this was clear, the item was scored as not applicable (6%).</p><p>Information sources (item 9) was scored as completed in only two records (1%) overall; however, for the individual elements 431 (99%) did name the electronic databases to be searched, 289 (67%) said whether they planned to search study registries, and 238 (55%) indicated search dates. In item 10, provision of a draft search strategy (91, 21%) or search terms (100, 23%) was poor; but restrictions such as to English language papers were reported in 332 (77%).</p><p>Reporting of item 13, outcomes, scored badly overall (3, 1%) as, although the outcomes were included in most records (Primary 418, 97%; Secondary 430, 99%) only 8 (2%) were assessed as having provided a rationale for their choice of outcomes. Similarly, in item 14, the absence of information on how the risk of bias would be used in the synthesis, detracted from the high rate of inclusion of risk of bias tools and use. Reporting of the details for a quantitative synthesis, item 15b, had one element with a very low score (handling missing data, 14, 3%), the other six elements scored between 89 (20%) and 204 (47%).</p><p>In three items, the overall score reflected the general picture from the included elements. In item 6, rationale, both the reason for undertaking the review and the context were infrequently identified. PRIMSA-P items 16, meta-bias(es) and 17, confidence in cumulative evidence, were rarely reported. Only context is classified as optional information in PROSPERO, the remainder of these elements are not explicitly requested.</p><p>There appears to be a trend towards higher frequency of reporting of elements that are mandatory in PROSPERO, for example, in the eligibility criteria (item 8) and risk of bias (item 14). The trend is also seen in item 13, the required specification of primary and secondary outcomes, both frequently reported, but with a drop in specifying measures, which was optional.</p></sec><sec><title>Subgroup comparisons</title><p>The subgroup comparisons, which were all pre-defined, investigated the stage of review at registration; whether or not information was reported on source of funding, sponsorship or support and where none was indicated; and whether or not the relevant box in the registration form had been ticked to indicate a meta-analysis was planned.</p><p>There were no differences in total scores for the 19 PRISMA-P items or the 63 elements, between those records registered before screening against eligibility criteria had started and those records registered after screening had commenced. This held true for the mean, standard deviation, median and range of scores.</p><p>A 6% difference was seen in the total score achieved for the meta-analysis (23%) vs no meta-analysis (29%) groups in the assessment of the 19 PRISMA-P items. The difference was reduced to 2% when considering the breakdown of 63 elements within the reported items (52% vs 54%). At both item and element level, the group of records with no planned meta-analysis scored slightly higher, but with a higher standard deviation from the mean and wider range of scores achieved.</p><p>Across all results for both the 19 items and 63 elements, the group with funding, sponsorship or support, scored slightly higher than those not receiving funding, sponsorship or support.</p><p>The results of the subgroups investigated are presented in\n<xref rid=\"T3\" ref-type=\"table\">Table 3</xref>. The subgroup scores by individual PRISMA-P reporting item are available on the OSF (\n<italic>Underlying data</italic>\n<sup><xref rid=\"ref-16\" ref-type=\"bibr\">16</xref></sup>).</p><table-wrap id=\"T3\" orientation=\"portrait\" position=\"anchor\"><label>Table 3. </label><caption><title>Subgroup comparisons.</title></caption><table frame=\"hsides\" rules=\"groups\" content-type=\"article-table\"><thead><tr><th align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Subgroup</th><th align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Variable</th><th align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No. of\n<break/>records</th><th align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Total\n<break/>possible\n<break/>score</th><th align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Total score\n<break/>achieved\n<break/>N (%)</th><th align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mean\n<break/>score\n<break/>(SD)</th><th align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Median\n<break/>score</th><th align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Range of\n<break/>scores</th></tr><tr><th align=\"center\" colspan=\"8\" valign=\"middle\" rowspan=\"1\">For 63 PRISMA-P reporting elements</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"2\" valign=\"top\" colspan=\"1\">\n<bold>Stage of review at</bold>\n<break/>\n<bold>registration</bold>\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Before\n<break/>screening\n<break/>started</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">245</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4655</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1181 (25)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.8 (1.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2–11</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">After screening\n<break/>started</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">188</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3572</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">900 (25)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.8 (1.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2–10</td></tr><tr><td align=\"left\" rowspan=\"2\" valign=\"top\" colspan=\"1\">\n<bold>Meta-analysis planned</bold>\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M-A</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">250</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4750</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1088 (23)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.4 (1.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2–9</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No M-A</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">183</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3477</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">993 (29)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.4 (2.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2–11</td></tr><tr><td align=\"left\" rowspan=\"2\" valign=\"top\" colspan=\"1\">\n<bold>Funded / Sponsored /</bold>\n<break/>\n<bold>Supported</bold>\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Funded etc.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">381</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7239</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1841 (25)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.8 (1.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2–11</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not funded etc.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">52</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">988</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">240 (24)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.6 (1.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2–8</td></tr><tr><th align=\"center\" colspan=\"8\" valign=\"middle\" rowspan=\"1\">For 63 PRISMA-P reporting elements</th></tr><tr><td align=\"left\" rowspan=\"2\" valign=\"top\" colspan=\"1\">\n<bold>Stage of review at</bold>\n<break/>\n<bold>registration</bold>\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Before\n<break/>screening\n<break/>started</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">245</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15435</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8214 (53)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33.5 (5.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18–47</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">After screening\n<break/>started</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">188</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11844</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6255 (53)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33.3 (5.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21–47</td></tr><tr><td align=\"left\" rowspan=\"2\" valign=\"top\" colspan=\"1\">\n<bold>Meta-analysis</bold>\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M-A</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">250</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15750</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8244 (52)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33.0 (5.2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21–45</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No M-A</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">183</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11529</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6225 (54)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34.0 (6.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18–47</td></tr><tr><td align=\"left\" rowspan=\"2\" valign=\"top\" colspan=\"1\">\n<bold>Funded / Sponsored /</bold>\n<break/>\n<bold>Supported</bold>\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Funded etc.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">381</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24003</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12804 (53)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33.6 (5.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18–47</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not funded etc.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">52</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3276</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1665 (51)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32.0 (5.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22–46</td></tr></tbody></table></table-wrap><p>We present the scores by the 19 PRISMA-P items and by the breakdown of 63 elements for the ten countries and topics with the highest number of assessed records, and for number of authors listed in\n<xref rid=\"T4\" ref-type=\"table\">Table 4</xref>. None of these factors appear to have a marked influence on the number of PRISMA-P items or elements reported in PROSPERO records.</p><table-wrap id=\"T4\" orientation=\"portrait\" position=\"anchor\"><label>Table 4. </label><caption><title>Overall scores by country, number of authors and topic of review.</title></caption><table frame=\"hsides\" rules=\"groups\" content-type=\"article-table\"><thead><tr><th align=\"center\" rowspan=\"2\" valign=\"top\" colspan=\"1\"/><th align=\"center\" rowspan=\"2\" valign=\"top\" colspan=\"1\">No of\n<break/>records</th><th align=\"center\" colspan=\"4\" valign=\"top\" rowspan=\"1\">For the 19 PRISMA-P items assessed</th><th align=\"center\" colspan=\"4\" valign=\"top\" rowspan=\"1\">For the 63 elements assessed</th></tr><tr><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Overall score\n<break/>(% of possible\n<break/>score)</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Mean\n<break/>score\n<break/>(SD)</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Median\n<break/>score</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Range of\n<break/>scores</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Overall\n<break/>score (%\n<break/>of possible\n<break/>score)</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Mean\n<break/>score\n<break/>(SD)</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Median\n<break/>score</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Range of\n<break/>scores</th></tr></thead><tbody><tr><th align=\"left\" colspan=\"10\" valign=\"top\" rowspan=\"1\">Country (10 with most assessed records)</th></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Australia</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">33</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">179 (28)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.4 (2.1)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2–11</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1115 (54) </td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">33.8 (6.2)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">32</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">21–47</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Brazil</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">53</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">272 (27)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.1 (1.9)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2–9</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1826 (55) </td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">34.5 (6.0)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">18–46</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Canada </td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">37\n<xref ref-type=\"other\" rid=\"TFN5\">*</xref>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">197 (28)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.3 (2.1)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2–9</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1301 (56) </td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">35.2 (6.7)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">21–45</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">China</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">101</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">418 (22)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.1 (1.3)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2–10</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3385 (54)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">33.5 (4.5)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">34</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">23–45</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">England</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">46</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">259 (29)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.6 (2.2)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2–10</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1620 (55)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">35.2 (6.9)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">35.5</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">22–47</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Germany</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">11\n<xref ref-type=\"other\" rid=\"TFN5\">*</xref>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">59 (28)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.4 (2.3)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3–10</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">380 (55)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">34.5 (6.2)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">33</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">26–47</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Italy</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">71 (27)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.7 (1.8)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3–9</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">499 (57) </td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">33.3 (6.2)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">32</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">24–47</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Netherlands</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">68 (28)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.2 (2.1)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2–9</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">439 (53) </td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">33.8 (7.0)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">33</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">23–47</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Spain</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">64 (26)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.9 (1.8)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2–7</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">426 (52) </td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">32.8 (5.6)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">33</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">22–42</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">USA</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">48</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">242 (27)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.0 (2.2)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2–10</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1526 (51) </td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">31.8 (6.4)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">31</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">21–47</td></tr><tr><th align=\"left\" colspan=\"10\" valign=\"top\" rowspan=\"1\">Number of authors</th></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0–3</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">202</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">956 (25) </td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.7 (1.8)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2–10</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">6648 (52)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">32.9 (5.9)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">32</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">18–47</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4–6</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">179</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">867 (25) </td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.8 (1.9)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2–11</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">6008 (53)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">33.6 (5.7)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">34</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">21–47</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">7+</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">52</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">258 (27) </td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.0 (1.9)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2–9</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1813 (56)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">34.9 (5.9)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">34</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">21–47</td></tr><tr><th align=\"left\" colspan=\"10\" valign=\"top\" rowspan=\"1\">Topic of review (10 with most assessed records)</th></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Cancer</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">42</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">184 (23)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.4 (1.8)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2–10</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1326 (50) </td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">31.6 (5.6)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">31</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">21–47</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Cardiovascular</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">58\n<xref ref-type=\"other\" rid=\"TFN5\">*</xref>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">278 (25)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.8 (1.8)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2–10</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1952 (53) </td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">33.7 (5.5)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">33</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">21–46</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Complementary\n<break/>therapies</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">43</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">211 (26)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.9 (1.8)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2–9</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1511 (56) </td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">35.1 (6.0)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">36</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">22–44</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Endocrine\n<break/>and metabolic\n<break/>disorders</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">34\n<xref ref-type=\"other\" rid=\"TFN5\">*</xref>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">175 (27)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.1 (2.1)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2–10</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1204 (56) </td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">35.4 (6.1)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">36</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">21–47</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Mental health\n<break/>and behavioural\n<break/>conditions</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">51</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">266 (27)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.2 (2.0)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2–10</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1762 (55) </td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">34.5 (5.7)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">33</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">21–44</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Musculoskeletal</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">70</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">335 (25) </td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.8 (2.0)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2–11</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2295 (52) </td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">32.8 (6.2)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">32</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">18–47</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Neurological</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">42\n<xref ref-type=\"other\" rid=\"TFN5\">*</xref>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">221 (28)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.3 (1.9)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2–11</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1443 (55) </td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">34.4 (6.1)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">33.5</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">23–47</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Physiotherapy</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">36</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">174 (25)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.8 (1.8)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2–8</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1194 (53) </td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">33.2 (5.8)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">32.5</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">18–43</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Rehabilitation</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">42\n<xref ref-type=\"other\" rid=\"TFN5\">*</xref>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">201 (25) </td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.8 (2.1)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2–11</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1393 (53) </td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">33.2 (5.7)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">32.5</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">23–47</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Surgery</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">49</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">251 (27)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.1 (1.8)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2–10</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1644 (53)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">33.6 (5.2)</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">33</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">23–47</td></tr></tbody></table><table-wrap-foot><fn><p id=\"TFN5\">\n<italic>*numbers differ from\n<xref rid=\"T1\" ref-type=\"table\">Table 1</xref> because of the record(s) excluded at assessment</italic>\n</p></fn></table-wrap-foot></table-wrap></sec></sec><sec sec-type=\"discussion\"><title>Discussion</title><p>Publication and registration of a systematic review protocol provides transparency in the review process, allowing readers to see the efforts made to minimise biases and where biases may still have influenced the final review findings. There is empirical evidence that few of the protocol registrations in PROSPERO have a corresponding published report\n<sup><xref rid=\"ref-9\" ref-type=\"bibr\">9</xref></sup>. Where there is no protocol, the registration provides the only public record of what was originally planned. This study set out to establish to what extent PROSPERO registrations of systematic review protocols of healthcare interventions reported on items in the PRISMA-P reporting guidelines.</p><p>Using a random sample of 433 PROSPERO records from 2018, two researchers independently assessed the frequency of reporting of 19 PRISMA-P items, with 63 individual elements. The results show that while some key methodological details are relatively frequently reported, much of the information recommended in PRISMA-P is missing. Reporting was unsurprisingly more frequent for items that are mandatory in PROSPERO than those that are optional. Comparisons by stage of review at registration, whether meta-analysis was planned and whether funding or sponsorship was reported showed no meaningful differences between groups. The slight difference between groups with a planned meta-analysis or none may be because in PRISMA-P more details are specified for the reporting of a meta-analysis than for a descriptive, narrative or qualitative analysis.</p><p>The review protocol is a detailed record of the planned methods developed through an iterative process\n<sup><xref rid=\"ref-5\" ref-type=\"bibr\">5</xref></sup>. Once finalised or close to finalising, the key methodological details should be registered in PROSPERO\n<sup><xref rid=\"ref-8\" ref-type=\"bibr\">8</xref></sup>. These are two separate but inter-related activities. PROSPERO was launched in 2011, a time when there were few opportunities to publish protocols, however, registration is not meant to be a substitute for preparation of a protocol. PROSPERO and PRISMA-P 2015 requirements are not aligned as they serve different purposes. However, a stated aim of registration is to facilitate comparison of what was planned with what is reported. Even if limited information were registered, we would expect the mandatory fields in PROSPERO to be fully completed. This was not the case, particularly for details related to outcome measures, assessment of risk of bias and quantitative analysis methods. It would not be reasonable to expect that PROSPERO records meet all the PRISMA-P recommended items, given the differences in purpose between a protocol and registration, but it is important to understand what information is available where registration is the only public source.</p><p>Eligibility criteria and type of analysis planned were most frequently reported and are all separate required fields in PROSPERO. However, study selection process, which is optional, was also a higher frequency reported item. This may be explained by considering that some elements of items, such as eligibility criteria, study selection and risk of bias have what might be considered a standard, recognisable format that facilitates reporting. Other items need a more nuanced approach underpinned by a clear understanding of systematic review methods, and therefore may be associated with being less frequently reported due to a lack of confidence or experience with these aspects of review methods. For example, how risk of bias will be used in the synthesis, data handling in a meta-analysis, meta-biases and confidence in cumulative evidence, all had low scores. Part of the problem may be the uncertainty of what the searches will find when designing a systematic review but needing to know so the design is appropriate. For example, the intention may be to perform a meta-analysis, this may not be possible once the studies for inclusion have been identified. While, both PROSPERO and PRISMA-P acknowledge that protocols are iterative documents and may need to be amended, changes should be documented, justified and the stage of review at the time of the amendment made clear. Therefore, it is better to record alternative options for activities such as how data will be analysed and the conditions for selection of option when finalising the protocol.</p><p>Differences in frequency of reporting may also reflect where researchers considered items to be less or more important than others. For example, naming the software used for data management may not be seen as crucial, whereas the eligibility criteria and approach to synthesis are.</p><p>There are strengths and limitations to this study. The assessed sample of 433 records was representative of all the eligible 2018 non-Cochrane intervention reviews registered in PROSPERO. As a result, the findings may reasonably be generalised to other registrations of healthcare interventions, but not necessarily other types of registered reviews excluded from our sample.</p><p>PRISMA-P is a reporting guideline and not a rating scale, so judgements about whether sufficient information had been provided for some items carried a degree of subjectivity. The assessment guide and form developed for the study aimed to maximise objectivity but in accordance with PRISMA-P did not weight importance of items. Although two researchers independently carried out the assessments, achieving an overall agreement rate of 90%, subjectivity was minimised but not eliminated.</p><p>PROSPERO was developed in 2011 to record key protocol details and does not necessarily accord with everything subsequently recommended in the 2015 PRISMA-P reporting guidelines. Some registration items are mandatory and others optional. However, this study looked at records that had no other protocol output and arguably should therefore have provided PRISMA-P level detail. The evidence that protocol details are only available in PROSPERO for around 96% of non-Cochrane reviews makes the infrequency of reporting of items a concern\n<sup><xref rid=\"ref-9\" ref-type=\"bibr\">9</xref>,\n<xref rid=\"ref-10\" ref-type=\"bibr\">10</xref></sup>. Based on the findings of other studies, promoting improved reporting of protocol details may help increase the quality of systematic reviews\n<sup><xref rid=\"ref-17\" ref-type=\"bibr\">17</xref>,\n<xref rid=\"ref-18\" ref-type=\"bibr\">18</xref></sup>.</p><p>Protocols are iterative documents and even after a review has started there may be legitimate reasons for amendments. Such changes should and can be reported in a registration record, with their justification and timing. Just over two thirds of PROSPERO records have more than one version (\n<xref ref-type=\"fig\" rid=\"f1\">Figure 1</xref>). While focussing on single entry records to be certain that any changes were not made after completion of the review this may have excluded records where more complete information was added to the record over time at key points in the review process.</p><p>This study simply looked at whether items were reported and not at the level of detail or suitability/appropriateness of the planned methods. Use of a scoring system giving equal weight to all items and elements as PRISMA-P does, is a limitation of this study because PROSPERO identifies information as either mandatory or optional. However, the scoring used in this study only relates to the presence or absence of information, and we have indicated the mandatory/optional fields in\n<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>. The option of ‘partially reported’ could have been used at assessment but was avoided to minimise subjectivity. The focus was on simply establishing whether items were reported or not. The assessors focussed on whether the information was reported or could reasonably be inferred from what was reported. Assessing the quality of planned methods in protocol registrations needs to be the subject of further research.</p><p>This study shows that there is work to be done to promote the complete reporting of items recommended in the guidelines for systematic review protocols when the registration in PROSPERO is the only place they can be accessed. This is in line with other research that has identified issues with the quality of reporting, publication and outcome reporting biases in systematic review protocols in general\n<sup><xref rid=\"ref-3\" ref-type=\"bibr\">3</xref>,\n<xref rid=\"ref-9\" ref-type=\"bibr\">9</xref>,\n<xref rid=\"ref-11\" ref-type=\"bibr\">11</xref>,\n<xref rid=\"ref-13\" ref-type=\"bibr\">13</xref>,\n<xref rid=\"ref-19\" ref-type=\"bibr\">19</xref>,\n<xref rid=\"ref-20\" ref-type=\"bibr\">20</xref></sup>. As proposed in the PRISMA-P statement paper, actions and potential benefits to encourage adherence to PRISMA-P will take a joint effort on the part of a host of stakeholders, including reviewers, registries, and journal editors\n<sup><xref rid=\"ref-5\" ref-type=\"bibr\">5</xref>,\n<xref rid=\"ref-21\" ref-type=\"bibr\">21</xref></sup>.</p></sec><sec sec-type=\"conclusions\"><title>Conclusions</title><p>PROSPERO provides reviewers with the opportunity to be transparent in their planned methods and demonstrate efforts to reduce bias. However, where the PROSPERO record is the only available source of\n<italic>a priori</italic> reporting, there is a significant shortfall in the items reported, compared to those recommended in PRISMA-P. This presents peer reviewers and others wishing to assess the validity of the final review with challenges in interpretation. PROSPERO records are not peer reviewed or assessed for methodological quality, it is the responsibility of those registering their review to complete the registration form fully or provide access to a complete protocol. There are several areas requiring particular attention when completing the registration form. These include explaining the rationale for undertaking the review in the context of what is known; providing information sources beyond a list of databases to be searched; and reporting reproducible process methods for data management, study selection and risk of bias assessment. In addition, defining variables for data extraction, how specified outcomes will be measured, and the planned analyses, with criteria for undertaking a quantitative synthesis should all be included in detail.</p><p>This study only looked at whether recommended items were reported or not in PROSPERO records. Further research is needed to assess the quality of the planned methods in systematic review protocol registrations.</p></sec><sec sec-type=\"data-availability\"><title>Data availability</title><sec><title>Underlying data</title><p>Open Science Framework: PROSPERO and PRISMA-P,\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://doi.org/10.17605/OSF.IO/7PW4G\">https://doi.org/10.17605/OSF.IO/7PW4G</ext-link>\n<sup><xref rid=\"ref-16\" ref-type=\"bibr\">16</xref></sup>.</p></sec><sec><title>Extended data</title><p>Open Science Framework: PROSPERO and PRISMA-P,\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://doi.org/10.17605/OSF.IO/7PW4G\">https://doi.org/10.17605/OSF.IO/7PW4G</ext-link>\n<sup><xref rid=\"ref-16\" ref-type=\"bibr\">16</xref></sup>.</p><p>This project contains the following underlying data:\n<list list-type=\"simple\"><list-item><label>- </label><p>Study protocol</p></list-item><list-item><label>- </label><p>Items, scoring options and guidance/rules for assessment of PROSPERO records compared to PRISMA-P reporting requirements</p></list-item></list>\n</p><p>Data are available under the terms of the\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://creativecommons.org/licenses/by/4.0/legalcode\">Creative Commons Attribution 4.0 International license</ext-link> (CC-BY 4.0).</p></sec></sec></body><back><ack><title>Acknowledgements</title><p>The authors would like to thank Fiona Rose for her assistance with the assessment of records.</p></ack><ref-list><ref id=\"ref-1\"><label>1</label><mixed-citation publication-type=\"journal\">\n<collab>Centre for Reviews and Dissemination</collab>:\n<article-title>Systematic reviews: CRD’s guidance for undertaking reviews in health care</article-title>. 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contrib-type=\"author\"><name><surname>Sun</surname><given-names>Xin</given-names></name><xref ref-type=\"aff\" rid=\"r68890a1\">1</xref><role>Referee</role><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0002-6554-7088</contrib-id></contrib><aff id=\"r68890a1\">\n<label>1</label>Chinese Evidence-Based Medicine Center and Cochrane China Center, West China Hospital, Sichuan University, Chengdu, China</aff></contrib-group><author-notes><fn fn-type=\"COI-statement\"><p>\n<bold>Competing interests: </bold>No competing interests were disclosed.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><permissions><copyright-statement>Copyright: © 2020 Sun X</copyright-statement><copyright-year>2020</copyright-year><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><related-article related-article-type=\"peer-reviewed-article\" id=\"d38e4552\" ext-link-type=\"doi\" xlink:href=\"10.12688/f1000research.25181.1\">Version 1</related-article><custom-meta-group><custom-meta><meta-name>recommendation</meta-name><meta-value>approve</meta-value></custom-meta></custom-meta-group></front-stub><body><p>This study conducted a methodological survey to assess the extent to which the contents of PROSPERO records meet the systematic review protocol reporting items in PRISMA-P. This paper addresses an important research question, and the findings may have implications for the reporting of systematic review protocols. However, there are a few issues for authors to consider:\n<list list-type=\"order\"><list-item><p>One aim of PRISMA-P is to aid authors in transitioning their systematic review protocols prepared in accordance with PRISMA-P into full text, while the authors used records from PROSPERO (i.e., not full-text) to assess the compliance to PRISMA-P reporting items, which may be a limitation that should be discussed in this paper.</p></list-item><list-item><p>In the methods part, it could be desirable that the authors could clearly report how the 17 numbered items of PRISMA-P were broken down into 63 elements.</p></list-item><list-item><p>The author should clearly report whether the subgroup analyses reported in table 3 were pre-planned.</p></list-item><list-item><p>The use of a scoring scheme for PRISMA-P and the 63 elements may not be optimal, given the potential difference in item importance, which should be added to the discussion part as a limitation.</p></list-item><list-item><p>In table 2, values in parentheses are percentages, which should be indicated in the table.</p></list-item></list>\n</p><p>Is the work clearly and accurately presented and does it cite the current literature?</p><p>Yes</p><p>If applicable, is the statistical analysis and its interpretation appropriate?</p><p>Partly</p><p>Are all the source data underlying the results available to ensure full reproducibility?</p><p>Yes</p><p>Is the study design appropriate and is the work technically sound?</p><p>Yes</p><p>Are the conclusions drawn adequately supported by the results?</p><p>Yes</p><p>Are sufficient details of methods and analysis provided to allow replication by others?</p><p>Yes</p><p>Reviewer Expertise:</p><p>Clinical Epidemiology</p><p>I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.</p></body><sub-article id=\"comment5883-68890\" article-type=\"response\"><front-stub><contrib-group><contrib contrib-type=\"author\"><name><surname>Booth</surname><given-names>Alison</given-names></name><aff>York Trials Unit, Department of Health Sciences, University of York, Heslington, York, UK</aff></contrib></contrib-group><author-notes><fn fn-type=\"COI-statement\"><p>\n<bold>Competing interests: </bold>No competing interests were disclosed.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>1</day><month>9</month><year>2020</year></pub-date></front-stub><body><p>We thank you for your peer review and give our responses as follows:</p><p>\n<italic>1. One aim of PRISMA-P is to aid authors in transitioning their systematic review protocols prepared in accordance with PRISMA-P into full text, while the authors used records from PROSPERO (i.e., not full-text) to assess the compliance to PRISMA-P reporting items, which may be a limitation that should be discussed in this paper.</italic>\n</p><p>Thank you for raising this point. We agree and have addressed this point in the addition of the following to the discussion:</p><p>PROSPERO and PRISMA-P 2015 requirements are not aligned as they serve different purposes. However, a stated aim of registration is to facilitate comparison of what was planned with what is reported. Even if limited information were registered, we would expect the mandatory fields in PROSPERO to be fully completed. This was not the case, particularly for details related to outcome measures, assessment of risk of bias and quantitative analysis methods. It would not be reasonable to expect that PROSPERO records meet all the PRISMA-P recommended items, given the differences in purpose between a protocol and registration, but it is important to understand what information is available where registration is the only public source.</p><p>\n<italic>2. In the methods part, it could be desirable that the authors could clearly report how the 17 numbered items of PRISMA-P were broken down into 63 elements.</italic>\n</p><p>We have added an example from the study protocol to illustrate the description of how the elements were derived from the 19 PRISMA-P items, as follows:</p><p>Where the PRISMA-P description for an item specified more than one piece of information, the individual elements were listed as subsets of the items. For example, item 14. Risk of bias in individual studies, says: “Describe anticipated methods for assessing risk of bias of individual studies, including whether this will be done at the outcome or study level, or both; state how this information will be used in data synthesis.” Scoring for this item will be for each of the following separate elements: No risk of bias assessment planned and justification provided; Risk of bias tools named for all study types included; Outcome or study level or both; Domains/outcomes for risk of bias assessment stated; Risk of bias assessment process described; How risk of bias findings will be used in synthesis. Applying this approach to the 19 items resulted in a list containing 63 elements to be reported.</p><p>\n<italic>3. The author should clearly report whether the subgroup analyses reported in table 3 were pre-planned.</italic>\n</p><p>We can confirm they were all included in the study protocol, available at \n<underline><ext-link ext-link-type=\"uri\" xlink:href=\"https://doi.org/10.17605/OSF.IO/7PW4G\">https://doi.org/10.17605/OSF.IO/7PW4G</ext-link>.</underline> We have added ‘which were all pre-defined’ to the section on Subgroup comparisons in the manuscript.</p><p>\n<italic>4.The use of a scoring scheme for PRISMA-P and the 63 elements may not be optimal, given the potential difference in item importance, which should be added to the discussion part as a limitation.</italic>\n</p><p>This is an important point thank you, which we have incorporated into the discussion as follows:</p><p>This study simply looked at whether items were reported and not at the level of detail or suitability/appropriateness of the planned methods. Use of a scoring system particularly as all items and elements carried the same weight is a limitation of this study. The scoring does not accord with the PROSPERO dataset which identifies information as either mandatory or optional. For this reason we have indicated the mandatory/optional fields in Table 2. The scoring only relates to the presence or absence of information. The option of ‘partially reported’ could have been used at assessment but was avoided to minimise subjectivity. The focus was on simply establishing whether items were reported or not. The assessors focussed on whether the information was reported or could reasonably be inferred from what was reported. Assessing the quality of planned methods in protocol registrations needs to be the subject of further research.</p><p>We have also amended how the mandatory/optional fields are indicated in Table 2 so the difference is clearer. </p><p>\n<italic>5. In table 2, values in parentheses are percentages, which should be indicated in the table.</italic>\n</p><p>Apologies for this omission, this has been corrected in the revised version.</p></body></sub-article></sub-article><sub-article id=\"report68072\" article-type=\"peer-review\"><front-stub><article-id pub-id-type=\"doi\">10.5256/f1000research.27788.r68072</article-id><title-group><article-title>Reviewer response for version 1</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Pieper</surname><given-names>Dawid</given-names></name><xref ref-type=\"aff\" rid=\"r68072a1\">1</xref><role>Referee</role><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0002-0715-5182</contrib-id></contrib><aff id=\"r68072a1\">\n<label>1</label>Faculty of Health, Department of Medicine, University Witten/Herdecke, Cologne, Germany</aff></contrib-group><author-notes><fn fn-type=\"COI-statement\"><p>\n<bold>Competing interests: </bold>No competing interests were disclosed.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>31</day><month>7</month><year>2020</year></pub-date><permissions><copyright-statement>Copyright: © 2020 Pieper D</copyright-statement><copyright-year>2020</copyright-year><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><related-article related-article-type=\"peer-reviewed-article\" id=\"d38e4734\" ext-link-type=\"doi\" xlink:href=\"10.12688/f1000research.25181.1\">Version 1</related-article><custom-meta-group><custom-meta><meta-name>recommendation</meta-name><meta-value>approve</meta-value></custom-meta></custom-meta-group></front-stub><body><p>This article is an analysis of how PROSPERO records adhere to the PRISMA-P guideline. The Analysis is based on a random sample of 439 PROSPERO records published in 2018. The authors conclude that reporting in PROSPERO should be improved given the fact that the PROSPERO record is often the only available source of a priori reporting.</p><p> The manuscript is methodologically sound and well written. What I think can be improved is the discussion. I wonder what is the implication of this study. Do the authors want to make the point that PROSPERO records should follow PRISMA-P? To the best of my knowledge PRISMA-P is even not mentioned in the PROSPERO guidance. If this would be the intention then wy not allign PROSPERO with the PRISMA-P items. I admit that PRISMA-P has been primarily designed for SRs of healthcare interventions, but most items are General and would be applicable to other review types as well. I do not want to make the point that this is a great idea, but it is somehow a logical question resulting from your manuscript and this should be mentioned in the discussion.</p><p> Registries and protocols should be seen as different entities, and thus I think that a perfect result of all PROSPERO records meeting all PRISMA-P items cannot be what we aiming for. If this would be the case, this would probably dilute the difference between a PROSPERO record and a protocol.</p><p>Is the work clearly and accurately presented and does it cite the current literature?</p><p>Yes</p><p>If applicable, is the statistical analysis and its interpretation appropriate?</p><p>Not applicable</p><p>Are all the source data underlying the results available to ensure full reproducibility?</p><p>Yes</p><p>Is the study design appropriate and is the work technically sound?</p><p>Yes</p><p>Are the conclusions drawn adequately supported by the results?</p><p>Partly</p><p>Are sufficient details of methods and analysis provided to allow replication by others?</p><p>Yes</p><p>Reviewer Expertise:</p><p>Research methods, clinical epidemiology</p><p>I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.</p></body><sub-article id=\"comment5882-68072\" article-type=\"response\"><front-stub><contrib-group><contrib contrib-type=\"author\"><name><surname>Booth</surname><given-names>Alison</given-names></name><aff>York Trials Unit, Department of Health Sciences, University of York, Heslington, York, UK</aff></contrib></contrib-group><author-notes><fn fn-type=\"COI-statement\"><p>\n<bold>Competing interests: </bold>No competing interests were disclosed.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>1</day><month>9</month><year>2020</year></pub-date></front-stub><body><p>We thank you for your peer comments and agree this is an important point. We have added the following paragraph to the discussion section:</p><p>The review protocol is a detailed record of the planned methods developed through an iterative process. Once finalised or close to finalising, the key methodological details should be registered in PROSPERO. These are two separate but inter-related activities. PROSPERO was launched in 2011, a time when there were few opportunities to publish protocols, however, registration is not meant to be a substitute for preparation of a protocol. PROSPERO and PRISMA-P 2015 requirements are not aligned as they serve different purposes. However, a stated aim of registration is to facilitate comparison of what was planned with what is reported. Even if limited information were registered, we would expect the mandatory fields in PROSPERO to be fully completed. This was not the case, particularly for details related to outcome measures, assessment of risk of bias and quantitative analysis methods. It would not be reasonable to expect that PROSPERO records meet all the PRISMA-P recommended items, given the differences in purpose between a protocol and registration, but it is important to understand what information is available where registration is the only public source.</p></body></sub-article></sub-article></article>\n"
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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"methods-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Wellcome Open Res</journal-id><journal-id journal-id-type=\"iso-abbrev\">Wellcome Open Res</journal-id><journal-id journal-id-type=\"pmc\">Wellcome Open Res</journal-id><journal-title-group><journal-title>Wellcome Open Research</journal-title></journal-title-group><issn pub-type=\"epub\">2398-502X</issn><publisher><publisher-name>F1000 Research Limited</publisher-name><publisher-loc>London, UK</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32832700</article-id><article-id pub-id-type=\"pmc\">PMC7431975</article-id><article-id pub-id-type=\"doi\">10.12688/wellcomeopenres.15845.2</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Method Article</subject></subj-group><subj-group><subject>Articles</subject></subj-group></article-categories><title-group><article-title>Silent myelin-weighted magnetic resonance imaging</article-title><fn-group content-type=\"pub-status\"><fn><p>[version 2; peer review: 3 approved, 1 approved with reservations]</p></fn></fn-group></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Wood</surname><given-names>Tobias C.</given-names></name><role content-type=\"http://credit.casrai.org/\">Conceptualization</role><role content-type=\"http://credit.casrai.org/\">Data Curation</role><role content-type=\"http://credit.casrai.org/\">Formal Analysis</role><role content-type=\"http://credit.casrai.org/\">Investigation</role><role content-type=\"http://credit.casrai.org/\">Methodology</role><role content-type=\"http://credit.casrai.org/\">Project Administration</role><role content-type=\"http://credit.casrai.org/\">Resources</role><role content-type=\"http://credit.casrai.org/\">Software</role><role content-type=\"http://credit.casrai.org/\">Visualization</role><role content-type=\"http://credit.casrai.org/\">Writing – Original Draft Preparation</role><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0001-7640-5520</contrib-id><xref ref-type=\"corresp\" rid=\"c1\">a</xref><xref ref-type=\"aff\" rid=\"a1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Damestani</surname><given-names>Nikou L.</given-names></name><role content-type=\"http://credit.casrai.org/\">Data Curation</role><role content-type=\"http://credit.casrai.org/\">Investigation</role><role content-type=\"http://credit.casrai.org/\">Project Administration</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><xref ref-type=\"aff\" rid=\"a1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Lawrence</surname><given-names>Andrew J.</given-names></name><role content-type=\"http://credit.casrai.org/\">Formal Analysis</role><role content-type=\"http://credit.casrai.org/\">Visualization</role><role content-type=\"http://credit.casrai.org/\">Writing – Original Draft Preparation</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><xref ref-type=\"aff\" rid=\"a2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Ljungberg</surname><given-names>Emil</given-names></name><role content-type=\"http://credit.casrai.org/\">Methodology</role><role content-type=\"http://credit.casrai.org/\">Resources</role><role content-type=\"http://credit.casrai.org/\">Software</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0003-1456-7967</contrib-id><xref ref-type=\"aff\" rid=\"a1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Barker</surname><given-names>Gareth J.</given-names></name><role content-type=\"http://credit.casrai.org/\">Methodology</role><role content-type=\"http://credit.casrai.org/\">Resources</role><role content-type=\"http://credit.casrai.org/\">Software</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0002-5214-7421</contrib-id><xref ref-type=\"aff\" rid=\"a1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Solana</surname><given-names>Ana Beatriz</given-names></name><role content-type=\"http://credit.casrai.org/\">Methodology</role><role content-type=\"http://credit.casrai.org/\">Resources</role><role content-type=\"http://credit.casrai.org/\">Software</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><xref ref-type=\"aff\" rid=\"a3\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Wiesinger</surname><given-names>Florian</given-names></name><role content-type=\"http://credit.casrai.org/\">Methodology</role><role content-type=\"http://credit.casrai.org/\">Resources</role><role content-type=\"http://credit.casrai.org/\">Software</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><xref ref-type=\"aff\" rid=\"a1\">1</xref><xref ref-type=\"aff\" rid=\"a3\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Williams</surname><given-names>Steven C.R.</given-names></name><role content-type=\"http://credit.casrai.org/\">Conceptualization</role><role content-type=\"http://credit.casrai.org/\">Funding Acquisition</role><role content-type=\"http://credit.casrai.org/\">Project Administration</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0003-4299-1941</contrib-id><xref ref-type=\"aff\" rid=\"a1\">1</xref></contrib><aff id=\"a1\">\n<label>1</label>Department of Neuroimaging, King's College London, London, UK</aff><aff id=\"a2\">\n<label>2</label>Department of Psychological Medicine, King's College London, London, UK</aff><aff id=\"a3\">\n<label>3</label>ASL Europe, GE Healthcare, Munich, Germany</aff></contrib-group><author-notes><corresp id=\"c1\"><label>a</label><email xlink:href=\"mailto:tobias.wood@kcl.ac.uk\">tobias.wood@kcl.ac.uk</email></corresp><fn fn-type=\"COI-statement\"><p>\n<bold>Competing interests: </bold>ABS and FW receive salaries from GE Healthcare. GJB received honoraria for teaching from GE Healthcare.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>13</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>5</volume><elocation-id>74</elocation-id><history><date date-type=\"accepted\"><day>6</day><month>8</month><year>2020</year></date></history><permissions><copyright-statement>Copyright: © 2020 Wood TC et al.</copyright-statement><copyright-year>2020</copyright-year><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri content-type=\"pdf\" xlink:href=\"wellcomeopenres-5-17734.pdf\"/><abstract><p>\n<bold>Background:</bold> Inhomogeneous Magnetization Transfer (ihMT) is an emerging, uniquely myelin-specific magnetic resonance imaging (MRI) contrast. Current ihMT acquisitions utilise fast Gradient Echo sequences which are among the most acoustically noisy MRI sequences, reducing patient comfort during acquisition. We sought to address this by modifying a near silent MRI sequence to include ihMT contrast.</p><p>\n<bold>Methods: </bold>A Magnetization Transfer preparation module was incorporated into a radial Zero Echo-Time sequence. Repeatability of the ihMT ratio and inverse ihMT ratio were assessed in a cohort of healthy subjects. We also investigated how head orientation affects ihMT across subjects, as a previous study in a single subject suggests this as a potential confound.</p><p>\n<bold>Results:</bold> We demonstrated that ihMT ratios comparable to existing, acoustically loud, implementations could be obtained with the silent sequence. We observed a small but significant effect of head orientation on inverse ihMTR.</p><p>\n<bold>Conclusions: </bold>Silent ihMT imaging is a comparable alternative to conventional, noisy, alternatives. For all future ihMT studies we recommend careful positioning of the subject within the scanner.</p></abstract><kwd-group kwd-group-type=\"author\"><kwd>ihMT</kwd><kwd>Silent MRI</kwd><kwd>Myelin</kwd><kwd>ZTE</kwd><kwd>RUFIS</kwd></kwd-group><funding-group><award-group id=\"fund-1\"><funding-source>GE Healthcare</funding-source></award-group><award-group id=\"fund-2\"><funding-source>Wellcome Trust</funding-source><award-id>203148</award-id></award-group><award-group id=\"fund-3\"><funding-source>NIHR Biomedical Research Centre</funding-source></award-group><funding-statement>TCW received funding from the Wellcome/EPSRC Centre for Medical Engineering (Award Number WT 203148/Z/16/Z). ND, AJL, EL & SCRW thank the NIHR Maudsley Biomedical Research Centre at South London Maudsley Foundation Trust and King's College London for their support and funding this study. EL also thanks GE Healthcare for joint funding of his PhD studentship. </funding-statement><funding-statement><italic>The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</italic></funding-statement></funding-group></article-meta><notes notes-type=\"version-changes\"><sec sec-type=\"version-changes\"><label>Revised</label><title>Amendments from Version 1</title><p>The manuscript has been updated in response to the reviewer’s helpful and insightful comments. The most important changes are that the figures have been redesigned and the emphasis on the head-orientation study reduced. The MR images have been updated to use a consistent set of slices, Figures 3 & 4 have been merged into a single figure, and the average within-subject CoV has been added. Figure 1 (the number of spokes) and Figure 6 (colour scheme) have been updated for clarity. We hope that these new figures are clearer and more intuitive than the previous figures. The language used to refer to the head orientation study has been clarified to refer to results as “highly statistically significant” rather than “strong”. A reviewer provided a plausible explanation for the negative values of ihMTR in CSF, namely the use of Fermi pulses in the preparation module, and this limitation has been discussed. A table with the mean ihMTR and inverse ihMTR values has been added. The discussion has been expanded to better set the context of the paper within existing literature, with better comparisons between our results and previous papers. We think the resulting paper is much improved and thank the reviewers again for their valued input.</p></sec></notes></front><body><sec sec-type=\"intro\"><title>Introduction</title><p>Myelin is a critical part of a healthy nervous system and hence visualising it\n<italic>in vivo</italic> is of great use to clinicians. Fortuitously, myelin displays multiple physical properties that give rise to contrast in Magnetic Resonance (MR) images. Tissue containing myelinated axons has lower longitudinal and transverse relaxation times\n<sup><xref rid=\"ref-1\" ref-type=\"bibr\">1</xref>,\n<xref rid=\"ref-2\" ref-type=\"bibr\">2</xref></sup>, lower susceptibility\n<sup><xref rid=\"ref-3\" ref-type=\"bibr\">3</xref></sup>, increased Magnetization Transfer (MT) effects\n<sup><xref rid=\"ref-4\" ref-type=\"bibr\">4</xref></sup>, and reduced diffusion\n<sup><xref rid=\"ref-5\" ref-type=\"bibr\">5</xref></sup>, compared to non-myelinated tissue. However, while all of these MR parameters are sensitive to myelination, they are not specific, as other biological processes demonstrate the same effects\n<sup><xref rid=\"ref-6\" ref-type=\"bibr\">6</xref></sup>. An additional MR-relevant property of myelin is that it is semi-crystalline in nature, being formed of closely packed proteins and lipids\n<sup><xref rid=\"ref-7\" ref-type=\"bibr\">7</xref></sup>. This regular structure can maintain dipolar order, and recent work has shown that this can be exploited to produce inhomogeneous Magnetization Transfer (ihMT) contrast\n<sup><xref rid=\"ref-8\" ref-type=\"bibr\">8</xref>–\n<xref rid=\"ref-10\" ref-type=\"bibr\">10</xref></sup>. Although other substances and tissues such as muscle can exhibit ihMT\n<sup><xref rid=\"ref-11\" ref-type=\"bibr\">11</xref>,\n<xref rid=\"ref-12\" ref-type=\"bibr\">12</xref></sup>, it is possible to tune the acquisition parameters specifically to the properties of myelin\n<sup><xref rid=\"ref-13\" ref-type=\"bibr\">13</xref></sup>. This, along with the fact that there are no other candidate substances that can exhibit ihMT within the central nervous system, suggests that ihMT has the potential to produce genuinely myelin-specific contrast\n<sup><xref rid=\"ref-8\" ref-type=\"bibr\">8</xref></sup>.</p><p>Previous work has shown that within a single subject the ihMT effect exhibits a dependence on the orientation of axons to the magnetic field\n<sup><xref rid=\"ref-10\" ref-type=\"bibr\">10</xref>,\n<xref rid=\"ref-14\" ref-type=\"bibr\">14</xref>,\n<xref rid=\"ref-15\" ref-type=\"bibr\">15</xref></sup>. This is attributable to the preferential alignment of myelin sheaths with the axons, leading to a non-uniform distribution of orientations of the lipids and proteins in the sheath\n<sup><xref rid=\"ref-16\" ref-type=\"bibr\">16</xref>,\n<xref rid=\"ref-17\" ref-type=\"bibr\">17</xref></sup>. As the orientation of WM to the magnetic field will also depend on the orientation of the head, this positioning of the patient within the scanner may influence ihMT metrics. To our knowledge, this effect of bulk orientation across subjects has not been directly investigated.</p><p>Recent ihMT imaging methods utilise fast gradient-echo sequences to acquire full-brain images in a reasonable time frame\n<sup><xref rid=\"ref-18\" ref-type=\"bibr\">18</xref>,\n<xref rid=\"ref-19\" ref-type=\"bibr\">19</xref></sup>. Such sequences are among the loudest MR sequences as they utilise a short Repetition Time (TR) and require rapid switching of high amplitude field gradients\n<sup><xref rid=\"ref-20\" ref-type=\"bibr\">20</xref></sup>. Acoustic noise is a leading cause of discomfort for subjects during an MR examination\n<sup><xref rid=\"ref-21\" ref-type=\"bibr\">21</xref>,\n<xref rid=\"ref-22\" ref-type=\"bibr\">22</xref></sup>, and is of particular concern in paediatric and fetal MRI\n<sup><xref rid=\"ref-23\" ref-type=\"bibr\">23</xref>–\n<xref rid=\"ref-26\" ref-type=\"bibr\">26</xref></sup>.</p><p>It is possible to make 3D gradient echo acquisitions almost silent by swapping from the standard Cartesian to a radial zero echo-time (ZTE) acquisition scheme\n<sup><xref rid=\"ref-27\" ref-type=\"bibr\">27</xref>–\n<xref rid=\"ref-29\" ref-type=\"bibr\">29</xref></sup>, but due to the fixed (near zero) echo-time and RF amplifier limits it can be difficult to achieve strong tissue contrasts in such sequences\n<sup><xref rid=\"ref-30\" ref-type=\"bibr\">30</xref>,\n<xref rid=\"ref-31\" ref-type=\"bibr\">31</xref></sup>. In the current work we incorporate an MT preparation module into a radial ZTE sequence without compromising the acoustic noise level and show that myelin-weighted contrast can be achieved at the expense of only a small increase in scan time. The primary aim was to measure the repeatability of semi-quantitative ihMT and inverse ihMT ratios\n<sup><xref rid=\"ref-14\" ref-type=\"bibr\">14</xref></sup>. As a secondary aim we hence investigated the effect of head orientation across subjects on the ihMT effect.</p></sec><sec sec-type=\"methods\"><title>Methods</title><sec><title>MR sequence</title><p>The Rotating Ultra-Fast Imaging Sequence (RUFIS) was originally introduced to image flowing liquids\n<sup><xref rid=\"ref-27\" ref-type=\"bibr\">27</xref></sup>. It is essentially a gradient echo sequence, where each TR consists of a single RF pulse followed by a readout. The principal difference, illustrated in\n<xref ref-type=\"fig\" rid=\"f1\">Figure 1</xref>, is that the readout gradient is held constant during the TR, including during the excitation pulse, and hence each readout consists of a ‘spoke’ that starts in the center of k-space and moves towards one edge. This is in contrast to a standard Cartesian sequence where the imaging gradients acquire a line from one side of k-space to the other.</p><fig fig-type=\"figure\" id=\"f1\" orientation=\"portrait\" position=\"anchor\"><label>Figure 1. </label><caption><p>\n<bold>A</bold> - Sequence diagram for a RUFIS segment. The gradients are first ramped to a constant amplitude, and short hard RF pulses are used to avoid slab profile effects from the constant gradient magnitude. For clarity, only four spokes are illustrated in the segment, our acquisition had 32.\n<bold>B</bold> - The MT preparation block consists of multiple saturation pulses. To generate the ihMT effect the sign of the saturation frequency is alternated, for standard MT preparation all pulses would have the same sign.\n<bold>C</bold> - The overall sequence consists of RUFIS segments separated by MT preparation blocks. At the start of each image a saturation module nulls all signal, and dummy segments where no data is acquired are played until the steady-state magnetization is reached.</p></caption><graphic xlink:href=\"wellcomeopenres-5-17734-g0000\"/></fig><p>The innovations introduced in order to be robust to flow effects have numerous serendipitous effects, chiefly massively reduced acoustic noise levels compared to conventional MR imaging schemes\n<sup><xref rid=\"ref-29\" ref-type=\"bibr\">29</xref></sup>. As shown in\n<xref ref-type=\"fig\" rid=\"f1\">Figure 1A</xref>, the imaging gradients have constant magnitude and only their orientation is varied during the sequence in order to appropriately sample k-space. At the end of each TR the gradient direction is changed by a small step on each gradient channel. As there are no rapid or large gradient changes, which are the major source of acoustic noise, RUFIS acquisitions are extremely quiet.</p><p>Because Radio Frequency (RF) excitation occurs in the presence of the imaging gradients in RUFIS, high bandwidth excitation pulses are required to mitigate slab profile effects. In practice, very short hard pulses (on the order of 10µs) are required, which limits the range of available flip-angles to a few degrees due to RF amplifier and transmit coil limitations\n<sup><xref rid=\"ref-30\" ref-type=\"bibr\">30</xref></sup>. This restricts a naïve RUFIS implementation to Proton-Density (PD) weighting\n<sup><xref rid=\"ref-31\" ref-type=\"bibr\">31</xref></sup>. To circumvent this limitation, the sequence can be segmented, with preparation pulses played in between segments; this approach has previously been utilised for T2\n<sup><xref rid=\"ref-32\" ref-type=\"bibr\">32</xref></sup> and diffusion prepared RUFIS imaging\n<sup><xref rid=\"ref-33\" ref-type=\"bibr\">33</xref></sup>.</p><p>MT is a common technique for generating contrast in tissues that have high fractions of non-aqueous hydrogen protons\n<sup><xref rid=\"ref-4\" ref-type=\"bibr\">4</xref>,\n<xref rid=\"ref-34\" ref-type=\"bibr\">34</xref></sup>. Such tissues include White Matter (WM) in the brain, and so MT has seen wide application in WM diseases\n<sup><xref rid=\"ref-35\" ref-type=\"bibr\">35</xref>,\n<xref rid=\"ref-36\" ref-type=\"bibr\">36</xref></sup>. The most common acquisition method is a Cartesian gradient echo sequence with an off-resonance saturation pulse added to every TR\n<sup><xref rid=\"ref-37\" ref-type=\"bibr\">37</xref></sup>. It is not feasible to play a saturation pulse in every TR within RUFIS due to the constant presence of the imaging gradient. If a saturation pulse was played while this gradient was present, the effective frequency offset of the pulse would differ across the field of view. Instead, we added a train of saturation pulses, shown in\n<xref ref-type=\"fig\" rid=\"f1\">Figure 1B</xref> as a preparation module before each segment.</p><p>The approach of prepared segmented MT imaging has already been demonstrated with a Cartesian readout\n<sup><xref rid=\"ref-38\" ref-type=\"bibr\">38</xref></sup>, and can be tuned to produce an increased sensitivity to myelin by varying the width and spacing of the saturation pulses\n<sup><xref rid=\"ref-12\" ref-type=\"bibr\">12</xref></sup>. Cartesian readouts can choose their\n<italic>k</italic>-space view order to preferentially weight the center of\n<italic>k−</italic>space to the MT effect. Because the center of\n<italic>k</italic>-space is sampled in every repetition in RUFIS, such view re-ordering is not possible. T1 recovery during a segment is hence a significant issue in RUFIS, as it will dilute any weighting from a preparation module. We therefore used a short segment length to minimise T1 recovery. This results in playing the preparation pulses more frequently, which lengthens scan time. However, MT preparation requires very little dead time compared to either T1 or T2 preparation, and so the overall increase in scan time is minimal.</p><p>To minimise any contamination from transient signals at the start of scanning or when switching between different MT preparation schemes, we adopted two complementary measures. The first was the addition of a saturation module played once at the start of each volume. This consisted of a single adiabatic 90 degree pulse and spoiler to effectively null all longitudinal magnetization. Following this module, 48 dummy segments were played, where the no data was acquired, to allow the signal in brain parenchyma to approach a steady-state. The 48 dummy segments lasted for 3.3 seconds, which is longer than the approximate T1 of parenchyma (around 1s) at 3T. The full sequence schematic is shown in\n<xref ref-type=\"fig\" rid=\"f1\">Figure 1C</xref>.</p></sec><sec><title>Imaging study</title><p>We recruited 6 male and 6 female subjects (age range 25 to 54 years) through local advertisement within our research establishment (King’s College London). Subjects gave written informed consent in accordance with ethics approved by the King’s College London REC (approval number 04/Q0706/72), and standard MRI exclusion criteria were applied. Each subject had two imaging sessions, spaced approximately a week apart, in a 3 Tesla scanner using a 12-channel head coil (Discovery MR750, GE Healthcare). Each session consisted of two ihMT scans, for a total of four ihMT scans per subject. In addition to the ihMT data, to provide an anatomical reference, in the first imaging session a standard T1-weighted image was acquired using the ADNI-GO protocol\n<sup><xref rid=\"ref-39\" ref-type=\"bibr\">39</xref></sup>. No special instructions were given to subjects as to their head positions in order to acquire a representative sample of orientations with respect to the main magnetic field.</p><p>The ihMT scans consisted of five images (see below). All volumes were acquired with the following parameters: 22cm field of view, 1.5mm isotropic voxel size, readout bandwidth ±25kHz, TR 1.764ms, spokes-per-segment 32. We used a 2° hard pulse for excitation. This was lengthened from the manufacturer default of 8µs to 24µs to lower the B1 amplitude and hence minimise any saturation of the bound pool from the excitation pulses. A train of 10 Fermi saturation pulses was played between each segment with pulse-width of 500µs and a 500µs gap in between. The pulses had a root-mean-square B1 of 8.75µT and an offset frequency of 7kHz. This corresponds to a root-mean-square B1 of 6.2µT over the course of the preparation module. The total RUFIS segment time and preparation time (including ramps and switching time) were 68.6ms and 10.8ms respectively. We did not add an explicit spoiling gradient after the pulse train, instead relying on the initial gradient ramp of the acquisition segment to spoil spuriously generated transverse magnetization from the MT pulses.</p><p>To isolate the ihMT contrast images acquired under both single-sided and dual-sided irradiation are required. The five volumes were acquired with the following scheme saturation scheme: +/-, -/+, none, +, -, where + or - refers to the sign of the saturation offset frequency. We refer to the volumes with only positive or negative saturation frequency as MT-weighted and those with dual-sided saturation as enhanced MT (eMT) weighted. Scan time per ihMT volume was 65 seconds, and 5 minutes 41 seconds for all five volumes. The acoustic noise was measured with an MR-compatible microphone (Casella CEL-63X, IDEAL Industries) located in the scanner bore.</p><p>Previous work has shown that ihMT ratio (ihMTR) is sensitive to confounds from B1 (RF transmit inhomogeneity) and T1-weighting, but this can be potentially mitigated through the use of an inverse ihMTR\n<sup><xref rid=\"ref-14\" ref-type=\"bibr\">14</xref></sup>, and such inverse metrics have also been used to compensate for T1 effects in Chemical Exchange Saturation Transfer experiments\n<sup><xref rid=\"ref-40\" ref-type=\"bibr\">40</xref></sup>. To calculate this we additionally acquired a volume with T1-weighting. Because it is difficult to achieve high flip-angles with the short block pulses in RUFIS\n<sup><xref rid=\"ref-30\" ref-type=\"bibr\">30</xref></sup>, we opted to replace the ihMT preparation train with a single on-resonance 10ms 25° pulse. The on-resonance preparation pulse generates a large amount of unwanted transverse magnetization compared to the off-resonance MT pulses, and so we added a 10 cycles-per-voxel spoiler gradient which increased the preparation module time to 11.3ms. The spoiler gradient ramp time was lengthened to reduce acoustic noise to the level of the rest of the RUFIS sequence.</p><p>The RUFIS images were reconstructed using the manufacturer’s proprietary Orchestra toolbox (GE Healthcare). The reconstruction consisted of nearest-neighbour gridding on a twice-oversampled grid\n<sup><xref rid=\"ref-41\" ref-type=\"bibr\">41</xref></sup>,\n<italic>k</italic>-space center filling\n<sup><xref rid=\"ref-42\" ref-type=\"bibr\">42</xref>,\n<xref rid=\"ref-43\" ref-type=\"bibr\">43</xref></sup>, and Total Generalised Variation (TGV) regularization\n<sup><xref rid=\"ref-33\" ref-type=\"bibr\">33</xref>,\n<xref rid=\"ref-44\" ref-type=\"bibr\">44</xref></sup>. The TGV regularisation parameter was set to λ = 0.01 with a maximum of 64 iterations.</p></sec><sec><title>Analysis</title><p>We first motion corrected the MT-weighted images with mcFLIRT\n<sup><xref rid=\"ref-45\" ref-type=\"bibr\">45</xref></sup>. The MTR, eMTR, ihMTR, inverse ihMTR and MT-asymmetry were calculated using an open-source C++ program added to QUIT\n<sup><xref rid=\"ref-46\" ref-type=\"bibr\">46</xref></sup>. MT-asymmetry is a measure of whether the absorption rate differs for positive or negative irradiation frequencies\n<sup><xref rid=\"ref-47\" ref-type=\"bibr\">47</xref></sup>. The parameters were defined to match\n<sup><xref rid=\"ref-14\" ref-type=\"bibr\">14</xref></sup>:\n<disp-formula id=\"e1\"><mml:math id=\"math1\"><mml:mrow><mml:mtext>MTR</mml:mtext><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>S</mml:mi><mml:mo>+</mml:mo></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>S</mml:mi><mml:mo>−</mml:mo></mml:msub></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:msub><mml:mi>S</mml:mi><mml:mn>0</mml:mn></mml:msub></mml:mrow></mml:mfrac></mml:mrow><mml:mspace width=\"14.5em\"/><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:math></disp-formula>\n<disp-formula id=\"e2\"><mml:math id=\"math2\"><mml:mrow><mml:mtext>eMTR</mml:mtext><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>S</mml:mi><mml:mrow><mml:mo>+</mml:mo><mml:mo>/</mml:mo><mml:mo>−</mml:mo></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>S</mml:mi><mml:mrow><mml:mo>−</mml:mo><mml:mo>/</mml:mo><mml:mo>+</mml:mo></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:msub><mml:mi>S</mml:mi><mml:mn>0</mml:mn></mml:msub></mml:mrow></mml:mfrac></mml:mrow><mml:mspace width=\"11.5em\"/><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>2</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:math></disp-formula>\n<disp-formula id=\"e3\"><mml:math id=\"math3\"><mml:mrow><mml:msub><mml:mrow><mml:mtext>MT</mml:mtext></mml:mrow><mml:mrow><mml:mtext>asym</mml:mtext></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>S</mml:mi><mml:mo>+</mml:mo></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mi>S</mml:mi><mml:mo>−</mml:mo></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mi>S</mml:mi><mml:mn>0</mml:mn></mml:msub></mml:mrow></mml:mfrac></mml:mrow><mml:mspace width=\"15em\"/><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>3</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:math></disp-formula>\n<disp-formula id=\"e4\"><mml:math id=\"math4\"><mml:mrow><mml:mtext>ihMTR</mml:mtext><mml:mo>=</mml:mo><mml:mn>2</mml:mn><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mrow><mml:mtext>eMTR</mml:mtext><mml:mo>−</mml:mo><mml:mtext>MTR</mml:mtext></mml:mrow><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow><mml:mspace width=\"12em\"/><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>4</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:math></disp-formula>\n<disp-formula id=\"e5\"><mml:math id=\"math5\"><mml:mrow><mml:msub><mml:mrow><mml:mtext>ihMTR</mml:mtext></mml:mrow><mml:mrow><mml:mtext>inv</mml:mtext></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mn>2</mml:mn><mml:msub><mml:mi>S</mml:mi><mml:mrow><mml:mi>T</mml:mi><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:msub><mml:mi>S</mml:mi><mml:mrow><mml:mo>+</mml:mo><mml:mo>/</mml:mo><mml:mo>−</mml:mo></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:mo>+</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:msub><mml:mi>S</mml:mi><mml:mrow><mml:mo>−</mml:mo><mml:mo>/</mml:mo><mml:mo>+</mml:mo></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:mo>−</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:msub><mml:mi>S</mml:mi><mml:mo>+</mml:mo></mml:msub></mml:mrow></mml:mfrac><mml:mo>−</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:msub><mml:mi>S</mml:mi><mml:mo>−</mml:mo></mml:msub></mml:mrow></mml:mfrac></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mspace width=\"4em\"/><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>5</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:math></disp-formula> where\n<italic>S</italic>\n<sub>+</sub>,\n<italic>S</italic>\n<sub>−</sub>,\n<italic>S</italic>\n<sub>+/−</sub> and\n<italic>S</italic>\n<sub>−/+</sub> refer to the signal from the saturation schemes defined above, and\n<italic>S</italic>\n<sub><italic>T</italic>1</sub> is the signal from the T1-prepared image.</p><p>We calculated an affine transform from the eMTR image to each subject’s standard T1-weighted image using ANTs\n<sup><xref rid=\"ref-48\" ref-type=\"bibr\">48</xref></sup>. The eMTR image was selected because the contrast is broadly similar to the T1-weighted. We then constructed a study template from all subject’s T1-weighted images, and non-linearly registered the resulting image to the MNI atlas\n<sup><xref rid=\"ref-49\" ref-type=\"bibr\">49</xref>,\n<xref rid=\"ref-50\" ref-type=\"bibr\">50</xref></sup>. Analysis then proceeded in two complementary directions: first, for illustrative purposes, we resampled each subject’s MT metrics in MNI space, and second, for a quantitative region of interest (ROI) analysis we resampled the JHU WM atlas into the native space of each scanning session. To minimise the number of resampling operations all transforms between the MNI space and the MT-weighted native space were concatenated before application. Ten bilateral white matter tract ROIs were selected\n<sup><xref rid=\"ref-51\" ref-type=\"bibr\">51</xref></sup>.</p><p>Mean average value within the ROI was calculated for each ROI at each of the 4 scans (2 repeats at 2 sessions) for MTR, eMTR, ihMTR and inverse ihMTR. Intra-class correlation coefficients (ICC) were calculated over the 4 measurements using the regularised mixed-effects method of\n<xref rid=\"ref-52\" ref-type=\"bibr\">52</xref>. Specifically, two-way random ICC(2,1) values and 95% confidence intervals (bootstrap percentile method; 1000 resamples) were extracted from a random effects model with random effects of subject and scan. Regularisation of variance components was achieved via a weakly informative gamma prior (shape parameter 2, rate parameter 0.5)\n<sup><xref rid=\"ref-52\" ref-type=\"bibr\">52</xref></sup>. Calculations were performed in R version 3.6.2 using the\n<italic>blme</italic> package version 1.0.4\n<sup><xref rid=\"ref-53\" ref-type=\"bibr\">53</xref></sup>. ICC values were classified as poor if they were less than 0.5, moderate if between 0.5 and 0.75, good between 0.75 and 0.9, and excellent above 0.9\n<sup><xref rid=\"ref-54\" ref-type=\"bibr\">54</xref></sup>.</p><p>Finally, we investigated how head orientation affects ihMTR. As a proxy for how each subject’s head was aligned with the main magnetic field, we calculated the angle between the Z-direction (head-foot axis) of the MT scan space and the MNI atlas. This was found by first concatenating the three affine transforms (MT- to T1-weighted, T1-weighted to study template, and study template to atlas), applying the concatenated transform to the vector\n<italic>Z</italic> = (0, 0, 1) and then calculating the dot product between the result and\n<italic>Z</italic>.</p><p>We only examined the effect of orientation on ihMTRinv, as this is expected to be robust against B1- and T1-effects\n<sup><xref rid=\"ref-14\" ref-type=\"bibr\">14</xref></sup>, which may also have a spatial or angular dependence\n<sup><xref rid=\"ref-55\" ref-type=\"bibr\">55</xref></sup>. To probe the impact of head orientation in the presence of potential between-participant differences, we calculated linear mixed effects models using\n<italic>blme</italic> as above. These models included mean ihMTRinv values from all 10 ROIs, main effects and interactions of head angle & ROI and both a random and fixed effect for each participant, with subject allowed to interact with ROI. Of prime interest was the average linear effect of head angle within-subject and whether this varied over the ROIs. Statistical significance was tested with ANOVA adjusted by the Kenwood-Roger procedure, with p<0.05 considered significant.</p></sec></sec><sec sec-type=\"results\"><title>Results</title><sec><title>Images</title><p>\n<xref ref-type=\"fig\" rid=\"f2\">Figure 2</xref> shows the acquired raw images and calculated MT ratios from a single subject. The acoustic noise was measured as 72 dB, compared to a 69 dB background level, which is similar to our previous work where comparable Cartesian sequences were approximately 30 dB louder\n<sup><xref rid=\"ref-30\" ref-type=\"bibr\">30</xref></sup>.</p><fig fig-type=\"figure\" id=\"f2\" orientation=\"portrait\" position=\"anchor\"><label>Figure 2. </label><caption><title>Example raw weighted images (PD, T1, MT and eMT), ratio images (MTR, eMTR, ihMTR and ihMTRinv) and within-subject CoV for one subject.</title></caption><graphic xlink:href=\"wellcomeopenres-5-17734-g0001\"/></fig><p>The inverse ihMTR shows a subtly improved contrast between white and grey matter compared to ihMTR, particularly in the cerebellum and putamen (note the different color scale).</p><p>The eMT-weighted image in particular demonstrates good grey matter (GM)/WM contrast, while the MTR image exhibits little GM/WM contrast. The ihMTR, which is the difference between eMTR and MTR (with a scaling factor of two), hence also shows very good GM/WM contrast. It is very close to zero outside the brain, in contrast to both MTR and eMTR which are high in tissues outside the brain. The inverse ihMTR exhibits improved GM/WM contrast compared to ihMTR in the deep GM structures, such as the putamen, the cerebellum and reduced B1 inhomogeneity effects throughout WM.</p><p>\n<xref ref-type=\"fig\" rid=\"f3\">Figure 3</xref> shows the mean, between-subject and average within-subject Coefficient of Variation (CoV) for ihMTR and inverse ihMTR in MNI space, while\n<xref rid=\"T1\" ref-type=\"table\">Table 1</xref> summarises the average ihMTR and inverse ihMTR for the ROIs we examined. The ihMTR and inverse ihMTR values were, respectively, about 12% and 15% in WM, with values in tracts oriented parallel to the main magnetic field slightly higher as expected\n<sup><xref rid=\"ref-15\" ref-type=\"bibr\">15</xref></sup>. Values were lower in GM, and we observed a small negative ihMTR in cerebral spinal fluid (CSF) and the eyeballs.</p><fig fig-type=\"figure\" id=\"f3\" orientation=\"portrait\" position=\"anchor\"><label>Figure 3. </label><caption><title>Top - ihMTR and ihMTRinv values averaged across all scans in MNI space.</title><p>An asymmetric color scale has been used to highlight the small negative of ihMTR in cerebral spinal fluid. Heightened values can be observed in WM tracts oritented parallel to B0. Middle – Between-subject CoV calculated across all scans and subjects. Bottom – Average within-subject CoV. The within-subject CoV is lower than the between-subject in the cortex.</p></caption><graphic xlink:href=\"wellcomeopenres-5-17734-g0002\"/></fig><table-wrap id=\"T1\" orientation=\"portrait\" position=\"anchor\"><label>Table 1. </label><caption><title>The mean and standard deviation of ihMTR and inverse ihMTR in the 10 selected ROIs, calculated across all subjects.</title></caption><table frame=\"hsides\" rules=\"groups\" content-type=\"article-table\"><thead><tr><th align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">ROI</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">ihMTR\n<break/>Mean\n<break/>(%)</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">ihMTR SD\n<break/>(%)</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Inverse ihMTR\n<break/>Mean (%)</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Inverse ihMTR SD\n<break/>(%)</th></tr></thead><tbody><tr><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>Genu of CC</bold>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">10.81</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.16</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">15.20</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.38</td></tr><tr><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>Body of CC</bold>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">11.79</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.17</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">14.70</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.44</td></tr><tr><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>Splenium of CC</bold>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">12.52</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.18</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">15.78</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.52</td></tr><tr><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>Corticospinal</bold>\n<break/>\n<bold>tract</bold>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">15.18</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.08</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">18.52</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.58</td></tr><tr><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>Cerebral</bold>\n<break/>\n<bold>peduncle</bold>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">13.89</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.17</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">15.14</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.48</td></tr><tr><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>Internal Capsule</bold>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">12.67</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.22</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">15.47</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.53</td></tr><tr><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>Corona Radiata</bold>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">12.67</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.16</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">16.54</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.40</td></tr><tr><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>Thalamic</bold>\n<break/>\n<bold>Radiation</bold>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">12.17</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.19</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">15.67</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.48</td></tr><tr><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>Cingl. Cingulate</bold>\n<break/>\n<bold>Gyrus</bold>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">10.82</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.15</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">13.30</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.40</td></tr><tr><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>Cingl.</bold>\n<break/>\n<bold>Hippocampus</bold>\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">10.49</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.41</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">10.49</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.76</td></tr></tbody></table></table-wrap><p>The between-subject CoV was approximately 10% in WM, but approaches 50% in GM and reached over 100% in CSF. The average within-subject CoV was lower, at around 8% in WM and 30% in GM. The average value of inverse ihMTR is higher, at approximately 20%. The contrast in parenchyma is broadly similar to ihMTR, but there are subtle differences in deep GM, frontal WM and the brain stem and values in CSF are close to zero instead of negative. Both the between-subject and average within-subject CoV in WM is slightly higher than for ihMTR.</p><p>\n<xref ref-type=\"fig\" rid=\"f4\">Figure 4</xref> shows the mean MTR, eMTR and MT-asymmetry. The MTR image shows only limited contrast between WM and GM despite the high levels of saturation power, while the eMTR image shows the expected improved contrast. In contrast to ihMTR, non-zero MTR and eMTR can be observed in tissues outside the brain. We found a small consistently positive value of MT asymmetry in cerebral WM, which was increased in cerebellar WM and the major ascending arteries. MT asymmetry was negative in CSF and the eyeballs.</p><fig fig-type=\"figure\" id=\"f4\" orientation=\"portrait\" position=\"anchor\"><label>Figure 4. </label><caption><title>Mean MTR, eMTR and MT-Asymmetry in atlas space.</title><p>Elevated levels of MT asymmetry can be observed in the ascending arteries and cerebellum.</p></caption><graphic xlink:href=\"wellcomeopenres-5-17734-g0003\"/></fig></sec><sec><title>Reliability</title><p>\n<xref ref-type=\"fig\" rid=\"f5\">Figure 5</xref> shows the obtained ICC values in the atlas ROIs. ICC values were moderate or good for all measures, except for the cerebral peduncles in both ihMTR and inverse ihMTR and corticospinal tract in ihMTR only, where the ICC values were poor. ICC values were slightly higher for inverse ihMTR compared to ihMTR. The cerebral peduncle and corticospinal tract ROIs commonly gave results atypical of the remaining ROIs.\n<xref ref-type=\"fig\" rid=\"f6\">Figure 6</xref> shows the mean values of ihMTR and inverse ihMTR for all ROIs across all four scans. Most ROIs show good reliability and repeatability\n<sup><xref rid=\"ref-56\" ref-type=\"bibr\">56</xref></sup>, but there are several obvious outliers, for instance subject D in the corticospinal tract for inverse ihMTR, and subjects D and E for ihMTR in the cingulum hippocampus.</p><fig fig-type=\"figure\" id=\"f5\" orientation=\"portrait\" position=\"anchor\"><label>Figure 5. </label><caption><p>\n<bold>A</bold> - Intra-class correlation coefficient (ICC) distribution (Tukey Boxplot).\n<bold>B</bold> - ICC (solid line) and 95% bootstrap confidence intervals (dashed lines and shaded area), displaying a profile over the regions of interest (ROIs).</p></caption><graphic xlink:href=\"wellcomeopenres-5-17734-g0004\"/></fig><fig fig-type=\"figure\" id=\"f6\" orientation=\"portrait\" position=\"anchor\"><label>Figure 6. </label><caption><title>Subject-level variability in ihMTR and inverse ihMTR for the 10 atlas regions of interest.</title></caption><graphic xlink:href=\"wellcomeopenres-5-17734-g0005\"/></fig></sec><sec><title>Head orientation</title><p>The observed median head orientation angle was 7° (lower quartile 3.2°, upper quartile 10.3°). Subject D showed particularly elevated values around 25° (excluding subject D the maximum observed angle was 12°). Closer examination revealed that subject D had a fairly small head and in both sessions was scanned with their head tilted back within the coil (data not shown). Our analysis revealed a significant main effect of head angle (\n<italic>F</italic> = 27.7,\n<italic>p</italic> = 2.50\n<italic>×</italic> 10\n<sup>−7</sup>) on ihMTRinv, such that with increased rotation angle inverse ihMTR values were lower, illustrated in\n<xref ref-type=\"fig\" rid=\"f7\">Figure 7</xref>. There was no significant interaction between angle and ROI (\n<italic>F</italic> = 1.47,\n<italic>p</italic> = 0.15) indicating effects were relatively homogeneous over the ROIs. Because subject D (angle = 25°) could be interpreted as an outlier, we repeated the analysis with subject D excluded and results were comparable (main effect of angle:\n<italic>F</italic> = 37.24,\n<italic>p</italic> = 3.00\n<italic>×</italic> 10\n<sup>−9</sup>; angle\n<italic>×</italic>ROI interaction:\n<italic>F</italic> = 1.32,\n<italic>p</italic> = 0.23).</p><fig fig-type=\"figure\" id=\"f7\" orientation=\"portrait\" position=\"anchor\"><label>Figure 7. </label><caption><title>Estimated effects of participant head angle× region of interest (ROI) for inverse ihMTR.</title><p>As the angle of the head increases, the value of inverse ihMTR tends to decrease.</p></caption><graphic xlink:href=\"wellcomeopenres-5-17734-g0006\"/></fig></sec></sec><sec sec-type=\"discussion\"><title>Discussion</title><p>We have demonstrated full-brain 3D myelin-weighted ihMT images acquired with a silent and fast imaging sequence. The MT preparation module increases scan-time by a minimal amount and does not compromise the silent nature of RUFIS acquisition. ihMT has shown potential for assessing myelination in multiple sclerosis\n<sup><xref rid=\"ref-57\" ref-type=\"bibr\">57</xref>,\n<xref rid=\"ref-58\" ref-type=\"bibr\">58</xref></sup>, and the use of a silent sequence will extend this potential to noise intolerant patient cohorts, for instance non-sedated infants.</p><p>The single-sided saturation MT-weighted and MTR images showed fairly flat contrast between WM and GM. This is to be expected, as the 7 kHz frequency offset chosen is optimal for the ihMT effect, whereas a smaller offset, for example 2 kHz, would likely generate larger MT contrast. Although the eMTR image exhibits good WM/GM contrast, it also shows significant signal in other tissues outside the brain, such as muscle, cartilage and blood.</p><p>Combining the eMT and MT-weighted images into an ihMT ratio increases the specificity of the sequence to myelin, as evidenced by an ihMTR close to zero outside the brain. Our ihMTR values, at around 12% in WM, are similar to previous literature using a comparable preparation module with a Cartesian readout\n<sup><xref rid=\"ref-13\" ref-type=\"bibr\">13</xref>,\n<xref rid=\"ref-38\" ref-type=\"bibr\">38</xref></sup>, but lower than recent papers using a low-duty cycle preparation module\n<sup><xref rid=\"ref-13\" ref-type=\"bibr\">13</xref>,\n<xref rid=\"ref-18\" ref-type=\"bibr\">18</xref></sup>. Our protocol was adapted from that presented in\n<xref rid=\"ref-38\" ref-type=\"bibr\">38</xref>, which had comparable levels of power deposition during the preparation module, and the principal difference is our acquisition module acquires a larger number (32) of center-out readout spokes with a low flip-angle instead of a small number of Cartesian readouts with a higher flip-angle.</p><p>To mitigate against T1 recovery during the readout segment, which repeatedly samples the center of k-space, we minimised the number of acquired spokes to 32. The resulting segment time of less than 70 ms is much shorter than typical T1 times in parenchyma (approximately 1 s at 3T). Increasing the number of spokes per segment would lead to a reduction in scan-time, at a cost of increased T1 recovery and potentially reduced ihMTR. </p><p>A particular drawback of radial ZTE sequences compared to Cartesian is constrained SNR. We observed some residual T1-weighted contrast in the PD-weighted reference image. Reducing the excitation flip-angle below 2° to further reduce the T1-weighting would incur a linear reduction in SNR (from the small flip-angle approximation sin\n<italic>α</italic> ≈\n<italic>α</italic>), which would likely yield unacceptable image quality. We used Total Generalized Variation regularization in our reconstruction to primarily to improve image quality, whereas for Cartesian sequences such methods are generally used with parallel imaging to speed up acquisitions\n<sup><xref rid=\"ref-18\" ref-type=\"bibr\">18</xref>,\n<xref rid=\"ref-38\" ref-type=\"bibr\">38</xref>,\n<xref rid=\"ref-44\" ref-type=\"bibr\">44</xref></sup>. Although non-cartesian parallel imaging methods exist\n<sup><xref rid=\"ref-59\" ref-type=\"bibr\">59</xref></sup>, to our knowledge none has been specifically tailored to 3D radial acquisitions. Despite this limitation, we acquired 1.5 mm isotropic maps in 6 minutes, which is competitive with a recent cartesian ihMT acquisition with an MP-RAGE type readout which acquired 2.4 mm isotropic maps in the same time\n<sup><xref rid=\"ref-18\" ref-type=\"bibr\">18</xref></sup>.</p><p>We observed a small negative ihMTR in CSF and the vitreous humour of the eyeball. The likely cause of this is a small, unwanted, difference in direct saturation effects between our single-sided and dual-sided irradiation preparation modules. Changing the shape of the preparation pulses to one with better controlled sidebands, for example Hann or Gaussian, would likely remove this effect\n<sup><xref rid=\"ref-38\" ref-type=\"bibr\">38</xref></sup>. Use of the inverse ihMTR appeared to mitigate T1 and B1+ effects and led to improved contrast between WM and GM, and more consistent contrast within WM, for instance the internal capsule and genu of the corpus callosum have different ihMTR values but similar inverse ihMTR values. To fully determine whether the inverse ihMTR reduced B1+ contributions would require the acquisition of additional B1+ maps, which was beyond the scope of the current work.</p><p>We observed a large MT asymmetry in the carotid arteries and an elevated value in the cerebellum compared to the cerebrum. Blood is known to exhibit an MT effect, due to a high concentration of protein\n<sup><xref rid=\"ref-60\" ref-type=\"bibr\">60</xref>–\n<xref rid=\"ref-62\" ref-type=\"bibr\">62</xref></sup>, and this is also known to be asymmetric\n<sup><xref rid=\"ref-63\" ref-type=\"bibr\">63</xref></sup>. PET and Dynamic Contrast Enhanced MRI measurements indicate that the cerebellum has increased vascularity and cerebral blood volume compared to the cerebrum\n<sup><xref rid=\"ref-64\" ref-type=\"bibr\">64</xref>,\n<xref rid=\"ref-65\" ref-type=\"bibr\">65</xref></sup>. Hence this result appears to be consistent with previous literature.</p><p>The CoV maps in\n<xref ref-type=\"fig\" rid=\"f3\">Figure 3</xref> showed high values in cortical GM, indicating poor reliability of ihMTR metrics in the cortex. This is partly to be expected due to the very small absolute values of ihMTR in both GM. However, the average within-subject CoV was lower than the between-subject CoV, indicating that partial volume effects and registration quality affected the between-subject figure. These issues are not unique to ihMTR but affect all quantitative MRI measures\n<sup><xref rid=\"ref-66\" ref-type=\"bibr\">66</xref>,\n<xref rid=\"ref-67\" ref-type=\"bibr\">67</xref></sup>.</p><p>The repeatability of both the ihMTR and inverse ihMTR, as defined by the ICC scores, fell on the boundary of the moderate (0.5-0.75) and good (0.75-0.9) categories with the exception of the cerebral peduncles and corticospinal tract which had notably worse scores. The ICC scores of inverse ihMTR were slightly superior to ihMTR. Our ICC values are lower than a repeatability study of a steady-state Cartesian ihMT study\n<sup><xref rid=\"ref-51\" ref-type=\"bibr\">51</xref></sup>. However, the values are not directly comparable as that study used a 2.4×2.4×3.2 mm voxel size compared to our 1.5 mm isotropic voxel size, with a similar overall scan time. Our lower ICC values can hence at least be partly attributed to the smaller voxel size and correspondingly lower SNR.</p><p>As shown in\n<xref ref-type=\"fig\" rid=\"f6\">Figure 6</xref> most subjects had consistent measures across all four scans but others, notably subject A, showed high variability across sessions. This variation can at least in part be attributed to the orientation of the head with the main magnetic field. We found that the observed ihMTR decreased in all ROIs as the observed rotation angle of the head increased. Our method for quantifying the rotation angle is imperfect, as it cannot distinguish positive and negative rotations and the choice of the MNI template as “zero” is arbitrary. We also did not control for the average angle of each ROI, variations in tract orientation within an ROI, the effect of hemisphere, or the effect of inter-volume motion during the ihMT scan. Despite these limitations, we showed a small but highly statistically significant effect of angle on inverse ihMTR, and so conclude that head orientation is a potential confound in ihMT studies. Recent work has incorporated prospective motion correction into a cartesian ihMT sequence\n<sup><xref rid=\"ref-18\" ref-type=\"bibr\">18</xref></sup>, and such approaches could be of benefit in this radial implementation to minimise both inter- and intra-volume motion artefacts in problematic patient cohorts (e.g. infants).</p></sec><sec sec-type=\"conclusions\"><title>Conclusion</title><p>We have demonstrated that MT-weighting can generate significant additional GM/WM contrast in silent ZTE images with minimal extension of scan time. We have shown that the derived semi-quantitative MT ratios have good repeatability, and that the inverse ihMTR has advantages over the ihMTR. However, the ihMT effect depends on the orientation of the subject’s head within the bore, and hence we recommend that careful attention is paid to participant’s positioning in future work.</p></sec><sec sec-type=\"data-availability\"><title>Data availability</title><sec><title>Underlying data</title><p>Figshare: Silent Myelin-Weighted MR Imaging,\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://doi.org/10.6084/m9.figshare.12090645\">https://doi.org/10.6084/m9.figshare.12090645</ext-link>\n<sup><xref rid=\"ref-68\" ref-type=\"bibr\">68</xref></sup>.</p><p>This project contains the following data:</p><list list-type=\"bullet\"><list-item><p>Atlas images in MNI space format</p></list-item><list-item><p>ROI summary statistics in Comma Separated Value format</p></list-item></list><p>Data are available under the terms of the\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://creativecommons.org/licenses/by/4.0/legalcode\">Creative Commons Attribution 4.0 International 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It is demonstrated that MT impose additional GM/WM contrast onto the proton density-weighted ZTE images. From these, metrics of inhomogeneous MT (ihMT) are derived, which have been shown to correlate to myelin. Obviously, the presented methodology is especially suited to study myelination in children.  </p><p> The sequence implementation is thoroughly motivated. A careful evaluation of 12 healthy adults was conducted to define sequence-specific semi-quantitative values for MT and ihMT.  Repeatability and susceptibility to angulation is described both for individuals and on a group level. The presented documentation may be regarded exemplary for implementation of ihMT into the context of an imaging sequence.</p><p>Is the rationale for developing the new method (or application) clearly explained?</p><p>Yes</p><p>Is the description of the method technically sound?</p><p>Yes</p><p>Are the conclusions about the method and its performance adequately supported by the findings presented in the article?</p><p>Yes</p><p>If any results are presented, are all the source data underlying the results available to ensure full reproducibility?</p><p>Yes</p><p>Are sufficient details provided to allow replication of the method development and its use by others?</p><p>Yes</p><p>Reviewer Expertise:</p><p>Quantitative MRI, especially MT.</p><p>I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.</p></body></sub-article><sub-article id=\"report39965\" article-type=\"peer-review\"><front-stub><article-id pub-id-type=\"doi\">10.21956/wellcomeopenres.17734.r39965</article-id><title-group><article-title>Reviewer response for version 2</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Dortch</surname><given-names>Richard</given-names></name><xref ref-type=\"aff\" rid=\"r39965a1\">1</xref><role>Referee</role><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0002-9978-2203</contrib-id></contrib><aff id=\"r39965a1\">\n<label>1</label>Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ, USA</aff></contrib-group><author-notes><fn fn-type=\"COI-statement\"><p>\n<bold>Competing interests: </bold>No competing interests were disclosed.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>20</day><month>8</month><year>2020</year></pub-date><permissions><copyright-statement>Copyright: © 2020 Dortch R</copyright-statement><copyright-year>2020</copyright-year><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><related-article related-article-type=\"peer-reviewed-article\" id=\"d38e5045\" ext-link-type=\"doi\" xlink:href=\"10.12688/wellcomeopenres.15845.2\">Version 2</related-article><custom-meta-group><custom-meta><meta-name>recommendation</meta-name><meta-value>approve</meta-value></custom-meta></custom-meta-group></front-stub><body><p>The authors have addressed all of my critiques. I have no new comments.</p><p>Is the rationale for developing the new method (or application) clearly explained?</p><p>Partly</p><p>Is the description of the method technically sound?</p><p>Yes</p><p>Are the conclusions about the method and its performance adequately supported by the findings presented in the article?</p><p>Partly</p><p>If any results are presented, are all the source data underlying the results available to ensure full reproducibility?</p><p>Yes</p><p>Are sufficient details provided to allow replication of the method development and its use by others?</p><p>Yes</p><p>Reviewer Expertise:</p><p>Imaging science, quantitative MRI, myelin imaging.</p><p>I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.</p></body></sub-article><sub-article id=\"report39967\" article-type=\"peer-review\"><front-stub><article-id pub-id-type=\"doi\">10.21956/wellcomeopenres.17734.r39967</article-id><title-group><article-title>Reviewer response for version 2</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Dean</surname><given-names>Douglas</given-names></name><xref ref-type=\"aff\" rid=\"r39967a1\">1</xref><xref ref-type=\"aff\" rid=\"r39967a2\">2</xref><xref ref-type=\"aff\" rid=\"r39967a3\">3</xref><role>Referee</role><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0002-7057-1285</contrib-id></contrib><aff id=\"r39967a1\">\n<label>1</label>Department of Pediatrics, University of Wisconsin-Madison, Madison, WI, USA</aff><aff id=\"r39967a2\">\n<label>2</label>Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA</aff><aff id=\"r39967a3\">\n<label>3</label>Waisman Center, University of Wisconsin-Madison, Madison, WI, USA</aff></contrib-group><author-notes><fn fn-type=\"COI-statement\"><p>\n<bold>Competing interests: </bold>No competing interests were disclosed.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><permissions><copyright-statement>Copyright: © 2020 Dean D</copyright-statement><copyright-year>2020</copyright-year><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><related-article related-article-type=\"peer-reviewed-article\" id=\"d38e5143\" ext-link-type=\"doi\" xlink:href=\"10.12688/wellcomeopenres.15845.2\">Version 2</related-article><custom-meta-group><custom-meta><meta-name>recommendation</meta-name><meta-value>approve</meta-value></custom-meta></custom-meta-group></front-stub><body><p>The authors have addressed my previous comments and the manuscript is much improved. I have no further comments.</p><p>Is the rationale for developing the new method (or application) clearly explained?</p><p>Yes</p><p>Is the description of the method technically sound?</p><p>Partly</p><p>Are the conclusions about the method and its performance adequately supported by the findings presented in the article?</p><p>Yes</p><p>If any results are presented, are all the source data underlying the results available to ensure full reproducibility?</p><p>Yes</p><p>Are sufficient details provided to allow replication of the method development and its use by others?</p><p>Partly</p><p>Reviewer Expertise:</p><p>quantitative Magnetic Resonance imaging, white matter imaging.</p><p>I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.</p></body></sub-article><sub-article id=\"report38686\" article-type=\"peer-review\"><front-stub><article-id pub-id-type=\"doi\">10.21956/wellcomeopenres.17381.r38686</article-id><title-group><article-title>Reviewer response for version 1</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Helms</surname><given-names>Gunther</given-names></name><xref ref-type=\"aff\" rid=\"r38686a1\">1</xref><role>Referee</role><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0002-1371-5123</contrib-id></contrib><aff id=\"r38686a1\">\n<label>1</label>Medical Radiation Physics, Department of Clinical Sciences Lund, Lund University, Lund, Sweden</aff></contrib-group><author-notes><fn fn-type=\"COI-statement\"><p>\n<bold>Competing interests: </bold>No competing interests were disclosed.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>4</day><month>6</month><year>2020</year></pub-date><permissions><copyright-statement>Copyright: © 2020 Helms G</copyright-statement><copyright-year>2020</copyright-year><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><related-article related-article-type=\"peer-reviewed-article\" id=\"d38e5227\" ext-link-type=\"doi\" xlink:href=\"10.12688/wellcomeopenres.15845.1\">Version 1</related-article><custom-meta-group><custom-meta><meta-name>recommendation</meta-name><meta-value>approve-with-reservations</meta-value></custom-meta></custom-meta-group></front-stub><body><p>An MT-module of 8 off-resonance Fermi pulses was integrated into a 3D silent zero TE radial scan (RUFIS) to derive different metrics for magnetization transfer (MT) and inhomogeneous MT (ihMT). Since is MT is tissue specific, it is demonstrated that MT-weighting can be used to generate additional GM/WM contrast in silent ZTE images, which per se show poor tissue contrast due to low flip angle.</p><p> The paper is well motivated, carefully conducted, and presented clearly.</p><p> The reviewer has three points of concern:\n<list list-type=\"order\"><list-item><p>Since MT is not specific for myelin and the higher specificity of ihMT for myelin is not fully established yet, a more conservative descriptive title is recommended, e.g. Inhomogeneous MT (ihMT) in silent ZTE MRI.</p></list-item><list-item><p>Via the saturation of the bound pool/dipolar reservoir, the imposed saturation by MT will depend on spatial B\n<sub>1</sub>\n<sup>+</sup> inhomogenieties as acknowledged in the paper. Since the 3D images/maps are presented only by sagittal views, it is impossible for the reader to appreciate the residual spatial inhomogeity of the metrics. Transversal images and maps of (ih)MT metrics should also be presented. Since the paper specifically address the influence of orientation relative to B0, it would be good to compare this effect in size to the influence of the local deviation from the nominal B\n<sub>1</sub>\n<sup>+</sup> (varying between 80% and 120% across the brain at 3T). Variation in B\n<sub>1</sub>\n<sup>+</sup> can be mimicked by manipulating the transmit gain. </p></list-item><list-item><p>To mitigate these spatial effects, the authors calculate an inverse ihMT metric using a reference ZTE image, where T1-weighting has been imposed in the preparation module. However, this metric is not identical to the one in Ref 20, which is based on a simplified signal equation that approximates a sequence where single MT pulses and SPGR readouts are interleaved (Helms\n<italic> et al</italic>. 2008\n<sup><xref rid=\"rep-ref-38686-1\" ref-type=\"bibr\">1</xref></sup>). To complicate matters, Ref 20 did not show theoretically that their metric actually reduces B\n<sub>1</sub>\n<sup>+</sup>. Qualitative arguments become clear only after lengthy reconstruction, as the inverse MTR (reference of the same flip angle) is proportional to the MTsat. Since the framework of Helms\n<italic>et al</italic>. is not applicable to interleaved ZTE , the authors should mention the pragmatic nature of borrowing this approach. NB that comparing non-selective partial saturation by 1-cos (B\n<sub>1</sub>\n<sup>+</sup>*25°) = approx 0.95*B\n<sub>1</sub>\n<sup>+2</sup> to tissue-type-dependent repetitive absorption (roughly proportional to B\n<sub>1</sub>\n<sup>+</sup>\n<sup><sub>2</sub></sup>) seems reasonable well motivated to the reviewer, but will like obscure T1 relaxation effects even more than MTR-like metrics using S0.</p></list-item></list> Minor points:\n<list list-type=\"order\"><list-item><p>Intro: For the uninitiated reader, the “technical limitations” precluding strong ZTE contrast may be mentioned already in the introduction.</p></list-item><list-item><p>Methods: Please provide the measurement time required for one 3D ZTE volume (I calculated about 1:45 mins) and the total of 5 weightings.</p></list-item><list-item><p>Methods: Ref 20 used -/+ 5kHz offset. Please motivate the use of -/+ 7kHz. (My guess is shorter MT pulses.)</p></list-item><list-item><p>Methods: It would be helpful for the uninitiated reader to motivate the definition (sign) of MT\n<sub>asym </sub>Hua, Jones, Blakely\n<italic>et al</italic>. 2007\n<sup><xref rid=\"rep-ref-38686-2\" ref-type=\"bibr\">2</xref></sup> is the earliest report of asymmetric MT in brain.</p></list-item><list-item><p>The CV map in second right, lower row panels in Figures 3 and 4 do not seem to match the maps in the top row. CV maps 3 and 4 do, so please replace 5.</p></list-item><list-item><p>The dependence on surrogate head angle in Fig. 8 is largest in cortico-spinal tract (parallel to B0) and smallest in corpus collosum ROIs (always perpendicular to B0). It would be helpful to state this finding in the flow text.</p></list-item></list>\n</p><p>Is the rationale for developing the new method (or application) clearly explained?</p><p>Yes</p><p>Is the description of the method technically sound?</p><p>Yes</p><p>Are the conclusions about the method and its performance adequately supported by the findings presented in the article?</p><p>Yes</p><p>If any results are presented, are all the source data underlying the results available to ensure full reproducibility?</p><p>Yes</p><p>Are sufficient details provided to allow replication of the method development and its use by others?</p><p>Yes</p><p>Reviewer Expertise:</p><p>Quantitative MRI, especially MT.</p><p>I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.</p></body><back><ref-list><title>References</title><ref id=\"rep-ref-38686-1\"><label>1</label><mixed-citation publication-type=\"journal\">\n:\n<article-title>High-resolution maps of magnetization transfer with inherent correction for RF inhomogeneity and T1 relaxation obtained from 3D FLASH MRI.</article-title>\n<source><italic toggle=\"yes\">Magn Reson Med</italic></source>.<year>2008</year>;<volume>60</volume>(<issue>6</issue>) :\n<elocation-id>10.1002/mrm.21732</elocation-id>\n<fpage>1396</fpage>-<lpage>407</lpage>\n<pub-id pub-id-type=\"doi\">10.1002/mrm.21732</pub-id>\n<pub-id pub-id-type=\"pmid\">19025906</pub-id></mixed-citation></ref><ref id=\"rep-ref-38686-2\"><label>2</label><mixed-citation publication-type=\"journal\">\n:\n<article-title>Quantitative description of the asymmetry in magnetization transfer effects around the water resonance in the human brain.</article-title>\n<source><italic toggle=\"yes\">Magn Reson Med</italic></source>.<year>2007</year>;<volume>58</volume>(<issue>4</issue>) :\n<elocation-id>10.1002/mrm.21387</elocation-id>\n<fpage>786</fpage>-<lpage>93</lpage>\n<pub-id pub-id-type=\"doi\">10.1002/mrm.21387</pub-id>\n<pub-id pub-id-type=\"pmid\">17899597</pub-id></mixed-citation></ref></ref-list></back><sub-article id=\"comment4319-38686\" article-type=\"response\"><front-stub><contrib-group><contrib contrib-type=\"author\"><name><surname>Wood</surname><given-names>Tobias C.</given-names></name><aff>King's College London, UK</aff></contrib></contrib-group><author-notes><fn fn-type=\"COI-statement\"><p>\n<bold>Competing interests: </bold>No competing interests were disclosed.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>13</day><month>8</month><year>2020</year></pub-date></front-stub><body><p>We thank the reviewer for their time and insight. There were in total five reviewers, with many helpful suggestions, and hence there have been many edits to the paper. Responses to this particular review follow below.</p><p>1. We agree that MT is not specific for myelin, and while there is increasing evidence that ihMT can be made specific to myelin (e.g. Duhamel et al 2019), we opted for caution in the title “Silent myelin-\n<italic>weighted</italic> magnetic resonance imaging” (emphasis added) because myelin is likely to be the dominant cause of contrast. We think that this is justified in comparison to T1-weighted and T2-weighted imaging, where T1 and T2 are the dominant but not sole contrast mechanisms. Hence we opt to keep the title unchanged.</p><p>2. We have changed the 3 sagittal views of ihMTR and ihMTRinv to sagittal, axial & coronal views. In our opinion, visually, these show good spatial homogeneity. In our opinion assessing the deviation due to B1+ would be best assessed with the acquisition of a separate B1+ map, which due to time constraints could not be included in this study.</p><p>3. The suggested reference has been added. Additional references describing an asymmetric MT effect in blood have been added and the discussion amended accordingly.</p><p>Minor Points</p><p>1. The fixed echo-time and RF power limitations have been explicitly added to the introduction.</p><p>2. The volume measurement time is present in the text “Scan time per volume was 65 seconds”, we have added the total acquisition time for 5 volumes.</p><p>3. A range of offsets for the ihMT pulses have been used in the literature. Reference 14, Mchinda et al 2018, indicated that an offset frequency higher than 5 kHz produced a larger ihMT effect, in fact finally opting for 8 kHz. In early sequence testing 7 kHz was found to produce a sufficient ihMT effect.</p><p>4. The suggested reference has been added. In addition we have updated the discussion about the asymmetry effect in light of finding an additional reference on the topic (Zhou et al 2005).</p><p>5. We do not completely understand this comment. In figure 5, the bottom row is the MT-asymmetry, not a CoV map.</p><p>6. The lowest effect is present in the cingulum hippocampus, not the corpus callosum. The colour scheme for these two ROIs was quite similar, it has been amended.</p></body></sub-article></sub-article><sub-article id=\"report38685\" article-type=\"peer-review\"><front-stub><article-id pub-id-type=\"doi\">10.21956/wellcomeopenres.17381.r38685</article-id><title-group><article-title>Reviewer response for version 1</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Dean</surname><given-names>Douglas</given-names></name><xref ref-type=\"aff\" rid=\"r38685a1\">1</xref><xref ref-type=\"aff\" rid=\"r38685a2\">2</xref><xref ref-type=\"aff\" rid=\"r38685a3\">3</xref><role>Referee</role><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0002-7057-1285</contrib-id></contrib><aff id=\"r38685a1\">\n<label>1</label>Department of Pediatrics, University of Wisconsin-Madison, Madison, WI, USA</aff><aff id=\"r38685a2\">\n<label>2</label>Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA</aff><aff id=\"r38685a3\">\n<label>3</label>Waisman Center, University of Wisconsin-Madison, Madison, WI, USA</aff></contrib-group><author-notes><fn fn-type=\"COI-statement\"><p>\n<bold>Competing interests: </bold>No competing interests were disclosed.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>29</day><month>5</month><year>2020</year></pub-date><permissions><copyright-statement>Copyright: © 2020 Dean D</copyright-statement><copyright-year>2020</copyright-year><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><related-article related-article-type=\"peer-reviewed-article\" id=\"d38e5562\" ext-link-type=\"doi\" xlink:href=\"10.12688/wellcomeopenres.15845.1\">Version 1</related-article><custom-meta-group><custom-meta><meta-name>recommendation</meta-name><meta-value>approve-with-reservations</meta-value></custom-meta></custom-meta-group></front-stub><body><p>Overview: This study describes the development of a novel, silent inhomogeneous magnetization transfer (ihMT) technique for imaging myelin in a clinically feasible time. Specifically, the authors evaluate the reproducibility of ihMT derived metrics from a cohort of healthy subjects. The authors additionally explored the influence of head orientation on ihMT measures, as recent study has suggested this to possibly affect these measures. A total of 4 scans were acquired from each participant (2 sessions, 2 ihMT scans per session) and the authors examined ihMT metrics across the whole brain as well as from regions of interests (ROIs) from an existing brain atlas. Intra-class correlations were used to assess reliability. The authors demonstrate comparable and good reliability of ihMT metrics from the novel, silent sequence, while coefficent of variation of ihMT and inverse ihMT demonstrate high consistency of the measures, particularly in white matter. An association with head orientation was also observed. Overall, the manuscript is well written, the study is well designed, and appropriate methodologies were used. The results demonstrate the proposed silent ihMT method could provide a reliable alternative to conventional ihMT strategies. However, I do have several points of concern that I believe could be addressed through a minor revision: </p><p> Comments:\n<list list-type=\"order\"><list-item><p>Prior to computing the ihMT metrics, source images are motion corrected. Given the finding that the ihMT metrics are dependent on head orientation, I’m curious about how intra-scan motion (i.e. orientation changes between source images) may affect the measurements as well? Was there motion between source images during the initial motion correction step? This may be particularly relevant for the populations of interest (e.g. infants/young children/elderly) where motion may be more likely.</p></list-item><list-item><p>A study specific template was constructed from the (standard) T1-weighted images, with this study template subsequently registered to the MNI atlas. Which methods/software were used to create the study specific template? Was the study specific template registered to MNI using linear or nonlinear registration methods? </p></list-item><list-item><p>For resampling the MT metrics to the MNI space, was this performed in a single interpolation step? Or were transformations applied in multiple steps?</p></list-item><list-item><p>The coefficient of variations of Figures 3 and 4 in white matter look good, however, the values in gray matter, particularly the cortex, seem very high. The authors mention this is expected given small ihMTR in gray matter and that registration quality may also impact these values. This seems to suggest that gray matter ihMT values may be unreliable and that analyses with ihMT metrics should be restricted to white matter. This may be particularly relevant given recent interest in examining cortical microstructure measures. Also, were coefficient of variations calculated by combining all subject data together or were within subject coefficient of variations computed (from the 4 scans) and then averaged? If data were combined together, how do the within subject coefficient of variations look?</p></list-item><list-item><p>When reporting the ihMTR and inverse ihMTR values, it would be informative to provide a table of means and standard deviations either from broad tissue types (e.g. gray matter, white matter, whole brain) and/or from the white matter regions that were examined. This would allow a reader to see these values more readily and make them more accessible.</p></list-item><list-item><p>Minor Comment: What was the timing between scan sessions? It appears that measures and reliability were consistent within-session and across-sessions. Is this accurate?</p></list-item><list-item><p>Minor Comment: Page 5 – I’d consider a new paragraph with the sentence: “Finally, we investigated how head orientation affects ihMTR.”</p></list-item><list-item><p>Minor Comment: Figure 2: The red-yellow color scale of the calculated MT metrics makes it difficult to compare the contrast of these metrics with the weighted images, as described in the results. </p></list-item><list-item><p>Minor Comment: For statistical analyses, was a p-value of p<0.05 considered statistically significant? Were corrections for multiple comparisons (with multiple ROIs being tested) taken into account?</p></list-item><list-item><p>Minor comment: Page 6, first paragraph: I believe “slight” should be “slightly”.</p></list-item></list>\n</p><p>Is the rationale for developing the new method (or application) clearly explained?</p><p>Yes</p><p>Is the description of the method technically sound?</p><p>Partly</p><p>Are the conclusions about the method and its performance adequately supported by the findings presented in the article?</p><p>Yes</p><p>If any results are presented, are all the source data underlying the results available to ensure full reproducibility?</p><p>Yes</p><p>Are sufficient details provided to allow replication of the method development and its use by others?</p><p>Partly</p><p>Reviewer Expertise:</p><p>quantitative Magnetic Resonance imaging, white matter imaging.</p><p>I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.</p></body><sub-article id=\"comment4318-38685\" article-type=\"response\"><front-stub><contrib-group><contrib contrib-type=\"author\"><name><surname>Wood</surname><given-names>Tobias C.</given-names></name><aff>King's College London, UK</aff></contrib></contrib-group><author-notes><fn fn-type=\"COI-statement\"><p>\n<bold>Competing interests: </bold>No competing interests were disclosed.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>13</day><month>8</month><year>2020</year></pub-date></front-stub><body><p>We thank the reviewer for their time and insight. There were in total five reviewers, with many helpful suggestions, and hence there have been many edits to the paper. Responses to this particular review follow below.</p><p>1. We did not attempt to quantify how intra- & inter-volume motion affected the ihMT measurements and have now noted this in the discussion section. The amount of inter-volume motion was minimal in this study, likely because the subjects were well behaved adults. Intra-volume motion in 3D radial images generally manifests as blurring, due to the repeated acquisition and implicit averaging of the center of k-space. We have added a sentence and reference to the discussion section on this topic. </p><p>2. The study specific template was created using the antsMultivariateTemplateConstruction2 script, which is the method associated with the given citation. Non-linear registration was used to align this with the MNI template, this has now been noted in the text.</p><p>3. Transforms were concatenated before being applied in a single step. This has now been stated explicitly in the text.</p><p>4. Thank you for the suggestion to look at the average within-subject CoV. Figure 2 and the new figure 3 have been expanded to include this. The within-subject CoV is smaller, particularly in the cortex, than the between-subject CoV. This indicates that cortical registration failures are increasing the between-subject CoV in the cortex. The discussion has been amended in light of this.</p><p>5. A table of the average ihMTR and inverse ihMTR values in our selected ROIs has been added.</p><p>6. The spacing between sessions (approximately 1 week) has been added to the methods section.</p><p>7. The suggested change has been made.</p><p>8. In Figure 2 we now have used a greyscale colormap for all images except the CoV maps to aid comparability. We have kept the two-way colormap for figure 3 due to the negative CSF value issue.</p><p>9. There is only one statistical hypothesis in the paper – does ihMTRinv vary with head orientation? As this is a single F-test across all the acquired ROI data, it does not require multiple comparisons correction. p<0.05 was considered significant, and this has now been stated explicitly in the methods. The test was repeated twice, once with a potential outlier excluded, and the final p-values were several orders of magnitude below the significance threshold. The ICC values do not require MCC.</p><p>10. The typo has been corrected.</p></body></sub-article></sub-article><sub-article id=\"report38619\" article-type=\"peer-review\"><front-stub><article-id pub-id-type=\"doi\">10.21956/wellcomeopenres.17381.r38619</article-id><title-group><article-title>Reviewer response for version 1</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Girard</surname><given-names>Olivier</given-names></name><xref ref-type=\"aff\" rid=\"r38619a1\">1</xref><role>Referee</role><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0002-5296-638X</contrib-id></contrib><contrib contrib-type=\"author\"><name><surname>Soustelle</surname><given-names>Lucas</given-names></name><xref ref-type=\"aff\" rid=\"r38619a2\">2</xref><xref ref-type=\"aff\" rid=\"r38619a3\">3</xref><role>Co-referee</role><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0002-4688-3262</contrib-id></contrib><aff id=\"r38619a1\">\n<label>1</label>CNRS, CRMBM UMR 7339, Aix-Marseille University, Marseille, France</aff><aff id=\"r38619a2\">\n<label>2</label>Aix-Marseille Univ, CNRS, CRMBM UMR 7339, Marseille, France</aff><aff id=\"r38619a3\">\n<label>3</label>SATT Sud-Est, Marseille, France</aff></contrib-group><author-notes><fn fn-type=\"COI-statement\"><p>\n<bold>Competing interests: </bold>No competing interests were disclosed.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>27</day><month>5</month><year>2020</year></pub-date><permissions><copyright-statement>Copyright: © 2020 Girard O and Soustelle L</copyright-statement><copyright-year>2020</copyright-year><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><related-article related-article-type=\"peer-reviewed-article\" id=\"d38e5752\" ext-link-type=\"doi\" xlink:href=\"10.12688/wellcomeopenres.15845.1\">Version 1</related-article><custom-meta-group><custom-meta><meta-name>recommendation</meta-name><meta-value>approve-with-reservations</meta-value></custom-meta></custom-meta-group></front-stub><body><p>This paper describes an original implementation of the inhomogeneous magnetization transfer (ihMT) MRI technique within a Zero Echo Time (ZTE) pulse sequence allowing for strong reduction of acoustic noise during MRI acquisition. This allows generating myelin sensitive ihMT images with a silent MRI sequence to scan sensitive patient. The sequence is designed for neuroimaging and offers a whole brain coverage with a 1.5mm isotropic voxel size in a relatively short scan time of about 5 to 6 minutes. Whereas 1/ whole brain ihMT sequences based on cartesian gradient echo readout and 2/ ZTE imaging sequences, such as RUFIS used here, have been previously described in the literature, the combination of an ihMT module within a silent ZTE sequence is original and should make ihMT contrast available to patient population that are specifically sensitive to loud noise such as young children and elderly, which is important for brain maturation study for instance.</p><p> The sequence is demonstrated on a small cohort of healthy volunteers (N=12) and the study includes a repeatability assessment, with and without volunteer repositioning. This allows assessing the robustness of the technique as well as the confounds associated with the head orientation within the scanner bore.</p><p> The paper is well written and the presented data are rather convincing, however there are several concerns that need to be addressed in our opinion.\n<list list-type=\"bullet\"><list-item><p>General concerns:</p></list-item></list> 1/ About the systematic error observed in CSF</p><p> The negative ihMTR signal observed in CSF is likely a systematic error, which I believe is not related to MT effect in CSF. This raises concern about the sequence implementation which should be checked. First of all I doubt there is a highly significant MT effect in CSF (ref 46, does not seem appropriate to document this, but there are problems with the references in the current version of the paper), and I believe that “MT effects” observed in CSF are in major part due to direct saturation effects, especially when using routine clinical sequences for conventional MT experiments which use relatively low frequency offset to perform the saturation (which is not the case here). Besides, although it is correct that most previous ihMT studies have not focused on CSF, some have included CSF in the analysis (e.g. Girard et al.MRM 2017\n<sup><xref rid=\"rep-ref-38619-1\" ref-type=\"bibr\">1</xref></sup>), and the observed CSF ihMT signal was basically close to zero within measurement errors. Second, there is no theoretical reason why the ihMT signal would be negative; this would mean that the dual offset saturation would be less efficient than the single offset, which contradicts Provotorov theory.</p><p> However, there is a plausible explanation to the negative signal observed in CSF related to differential direct saturation effects obtained with single and dual offset MT experiments. When using a train of pulses the frequency response of the RF saturation not only depends on the carrier frequency of the MT pulses and on the timings of the pulse train, but also on the phase of each pulse, which means that the on-resonance (or close to resonance) component of the MT excitation will not be identical for single and dual offset MT experiment, and also for negative and positive offset single MT experiment. Together with the specific B0 shimming conditions this could explain unexpected ihMT and MT asymmetry signal in CSF and blood.</p><p> Direct saturation effects occurring with ihMT sequences may be mitigated with the use of a gradient spoiler after each burst of MT pulses, but in the proposed implementation the author do not use such a spoiler gradient and it is not clear whether readout gradients are sufficient to prevent direct saturation effects.</p><p> In order to check this hypothesis, and also as a general check of the sequence implementation, we encourage the author to perform a very single in vitro experiment: reproduce their ihMT acquisition on a non-MT sensitive doped aqueous sample (ideally with a size comparable to the human head, and a T2 value close to the free pool in vivo T2) using the same parameters as for the in vivo experiment, and check the direct saturation effects of each individual MT experiment (negative, positive, and dual offset). Ideally, author could also add in the field of view a MT responsive sample (like agar gel) and ihMT responsive sample (like commercial hair conditioner that are made of lamellar liquid crystals). Basically, there should be no MT signal, nor MTA, nor ihMT signal for the aqueous sample.</p><p> All this being said, it is agreed that inflow effects could also bias MT and ihMT metrics and that other causes may explain the observe systematic error, but we believe that performing the suggested in vitro experiment will help identifying the cause of the systematic error.</p><p> 2/ On the head orientation study</p><p> The scope of the head orientation study is limited, and the conclusion seems somewhat overstated. First of all the topic is not introduced with enough details and the study design only allows to extract very limited information on that topic: the only message from the paper is that the head orientation may bias ihMT metric and that one should be careful when positioning the patient. Whereas we agree that the head orientation has an effect on the ihMT signal and that one should be careful with patient positioning, the authors’ conclusions are too subjective as they conclude that the effect is “strong” without providing any quantitative comparison with other source of errors. We invite the author to expand a little more the introductory part of the study, and to be more objective in their conclusion. The abstract should be rephrased accordingly as well.</p><p> 3/ Comparison with existing literature</p><p> The comparison with ihMT metrics from previous literature is limited. The authors should expand the discussion on that topic. This is particularly important here since a new readout is proposed to probe the ihMT contrast. Although major differences in ihMTR are not expected with different readouts or weightings from our experience, providing that the sequence is run in “steady state” and that the RF power associated with readout pulses is low, some effects such as the k-space view ordering and associated point spread function may bias the measured signal. The author should discuss the specifics of the RUFIS readout (effects of radial UTE-like vs. standard cartesian GRE/SE signal acquisition) and how these affect the measured signal, ideally by providing simulations on the effect of the RUFIS readout module on ihMTR as functions of the segments composition (TR/number of shots/flip angle).\n<list list-type=\"bullet\"><list-item><p>Specific comments: </p></list-item></list> Introduction: The introductory part of the angular dependency of ihMT should be expanded. The paper provides very limited explanations on the origin of this effect. This has been partly documented previously (Girard\n<italic>et al</italic>. (2017)\n<sup><xref rid=\"rep-ref-38619-2\" ref-type=\"bibr\">2</xref></sup>) on the basis of the non isotropic lineshape of white matter introduced by Pampel et al. NIMG 2015\n<sup><xref rid=\"rep-ref-38619-3\" ref-type=\"bibr\">3</xref></sup>.</p><p> Method/MR sequence: Author should provide more details about the “transient suppression” method that they used (fig. 1C), and provide argument or simple simulations so as to determine why “48 dummy segments” are sufficient to generate a steady-state.</p><p> Method/Imaging Study: “ […] we found the gradient ramp was insufficient to remove residual transverse magnetization and so we added a 10 cycles-per-voxel spoiler gradient which increased the preparation module time to 11.3ms. The spoiler gradient ramp time was lengthened to reduce acoustic […]” Did the author consider adding the same spoiler gradient for the ihMT prep? (see general comment above)</p><p> Method/analysis: “As a proxy for how each subject’s head was aligned with the main magnetic field, we calculated the angle between the Z-direction (head-foot axis) of the MT scan space and the MNI atlas.“ This is an interesting approximation. However, in my opinion the main issue is that it misses the assessment of individual tracts orientation, which is necessary to address the angular dependency of ihMT.</p><p> Method/Analysis related to the head orientation study: Some part of this section should appear in introduction as they are not specific to the method: “Previous work has shown that the inhomogeneous MT effect partially depends on the orientation of myelin with the main magnetic field 10,20,21,as assessed by a voxel-wise comparison to diffusion data. To our knowledge, the effect of bulk orientation across subjects has not been directly investigated.”</p><p> In addition, Ref 20 and 21 do indeed show orientation dependency of ihMT as evaluated from diffusion data ; however Ref 10, explains how the residual dipolar coupling vary with the orientation, but does not specifically addresses the angular dependency of ihMT.</p><p> Method/analysis: “We only examined the effect of orientation on ihMTRinv, as this is expected to be robust against B1- and T1-effects 20, which may also have a spatial or angular dependence 43.” Unfortunately, the B1 sensitivity of the high flip angle reference inverse ihMT ratio (ihMTRinv) is still under investigation and may not be as robust as initially thought. In addition, the sequence may have different sensitivity to B1 depending on the sequence parameters (see Mchinda et al. MRM 2018\n<sup><xref rid=\"rep-ref-38619-4\" ref-type=\"bibr\">4</xref></sup>). In the presented study the ihMTRinv images from fig 2 show a strong hyperintense area located in the middle of the brain which looks somewhat colocalized with typical B1+ patterns observed @ 3T, as for the corresponding ihMTR images. This raises concern regarding the choice of ihMTRinv instead of ihMTR for the analysis. In addition, the residual dependency of ihMTRinv to B1 + shall be further discussed.</p><p> Discussion: “The single-sided saturation MT-weighted and MTR images showed fairly flat contrast between WM and GM. We attribute this to T1 relaxation effects during the segment, and residual T1-weighted contrast in the PD-weighted reference image. […] Switching from single-sided to dual-sided saturation increases contrast between WM and GM …“</p><p> It is surprising the authors do not mention dipolar order to explain the lack of contrast on the single-sided MT-weighted MTR images. Indeed, these effects act against saturation of the macromolecular pool, hence reducing MTR signal and contrast on single sided MT experiments. This is the key difference between single and dual sided MT experiments. Of interest, the MT experiments run in this study are not comparable to usual MT since relatively high frequency offsets and low duty cycle were used here. These condition favor dipolar order and hence are an important determinant of GM/WM contrast.</p><p> Discussion: “… Our ihMTR values, at around 15% in WM, are broadly in line with previous literature using a Cartesian sequence “ Previous literature has shown that ihMT depends on many sequence parameters. This should be contextualized here. Also it is hard to compare with previous literature without providing the rms B1 calculated over the sequence repetition time.\n<list list-type=\"bullet\"><list-item><p>Minor points: </p></list-item></list> Abstract:</p><p> “Silent ihMT imaging is a comparable alternative to conventional, noisy, alternatives.” Comparable should be removed.</p><p> Introduction:</p><p> “This, along with the fact that there are no other candidate substances that can exhibit ihMT within the nervous system, suggests that ihMT has the potential to produce genuinely myelin-specific contrast”. Please specify “central nervous system”.</p><p> p3: reference to citation 11 about ihMT is likely inappropriate.</p><p> Methods:</p><p> MR sequence: “… high bandwidth excitation pulses are required to avoid introducing a slab profile” and in caption of Fig 1A. Please use “mitigate” instead of “avoid”.</p><p> MR sequence: “… increased ihMT effect in myelin ” Please use WM instead of myelin, this is a shortcut toward considering that ihMT is a purely myelin-specific effect.</p><p> Imaging study: typo: plural “6 females, 6 males”.</p><p> Imaging study, first paragraph: we suggest to make explicit here the reason why the ihMT scans were repeated twice (repeatability) and repeated again in another session (head orientation study).</p><p> Imaging study: “We used a 2°, 24μs (low B1 amplitude) hard pulse for excitation to minimize any saturation of the bound pool from the excitation pulses”: the sentence sounds misleading since shorter RF pulses lead to stronger B1RMS for identical flip angle, please rephrase.</p><p> Imaging study: was the choice of Fermi pulse motivated by any specific criteria? Since there are free parameters in the Fermi window function, the authors should provide more information on the pulse shape, such as bandwidth and power integral (or B1peak and B1rms is preferred).</p><p> Imaging study: please provide the root-mean-square B1 of MT pulses calculated over the segment TR (ihMT prep + readout) to ease comparison of presented results with existing literature.</p><p> Imaging study: we suggest the author describe first that ihMT rely on comparing single and dual frequency MT experiment, before describing the five volumes to be acquired. Currently the ihMT method description may be difficult to follow for non-expert ihMT users.</p><p> Analysis: reference to citation 36 about the QUIT toolbox is likely inappropriate. Please check all references.</p><p> Analysis: “Previous work has shown that the inhomogenous MT effect partially depends on the orientation of myelin with the main magnetic field”. typo: “inhomogeneous”.</p><p> Results:</p><p> Images: No acoustic noise measurement was reported for the T1w-RUFIS acquisition.</p><p> p6 typo error: “The CoV in WM was below 10% in WM”. WM is repeated twice.</p><p> “the very small absolute values of ihMTR in both GM and CSF, …”..It is misleading to pool GM and CSF together here since ihMTR is usually low in GM (but may become high using dedicated ihMT implementation using low duty cycle RF irradiation and no short-T1D filtering) but still give rise to detectable signal, whereas it is null or artefactual in CSF. Please rephrase.</p><p> Head Orientation: “There was no significant interaction between angle and ROI (F = 1.47, p = 0.15) indicating effects were relatively homogeneous over the tracts.” This observation is surprising since each tract has a single orientation with respect to B0 and the orientation may differ between the right and left hemisphere; hence one expects different effects. Please discuss this point further. In addition, authors should provide more insight into the distribution of the head orientation within the tested population, e.g. by indicating the mean and standard deviation.</p><p>  Discussion:</p><p> “Although blood is known to exhibit an MT effect, due to a high concentration of protein47, to our knowledge this is the first evidence that the effect in blood is significantly asymmetric. Although we do not discount the possibility that this is caused by inflow of unsaturated blood, it is difficult to see how this could produce a differential effect between the positive and negative offset frequencies, … “. I would be cautious about this interpretation since direct saturation and inflow effects may bias MT asymmetry. Again direct saturation effects are a potential concern with the presented sequence implementation (see general comment above), and may explain the observed signal. We encourage the author to perform in-vitro experiments on blood sample (if possible) to support this statement or to rephrase it.</p><p> “there are no apparent drawbacks to switching from ihMTR to inverse ihMTR for future studies.” I agree there is no apparent drawback in terms of scan time, as describe here, but the advantages of inverse ihMTR in terms of robustness to B1 still need to be further supported by experiment. Consider rephrasing.</p><p> “ihMTR have good, but not excellent repeatability…“. Authors have to describe repeatability in a more quantitative way.</p><p> “This variation can at least in part be attributed to the orientation of the head with the main magnetic field. “ and “Despite these shortcomings, we showed a strong effect of angle on inverse ihMTR”. We invite the author to discuss more other sources of bias (residual B1 dependency, residual motion..) and to discuss the effect of the head orientation in a more quantitative way (e.g. compare the intra- vs -inter-session bias), rather than stating that the effect is “strong”.</p><p> All figures from figure 3 appear too late in the pdf file; also figures 4 to 8 are all placed together.</p><p>Is the rationale for developing the new method (or application) clearly explained?</p><p>Yes</p><p>Is the description of the method technically sound?</p><p>Partly</p><p>Are the conclusions about the method and its performance adequately supported by the findings presented in the article?</p><p>Partly</p><p>If any results are presented, are all the source data underlying the results available to ensure full reproducibility?</p><p>Yes</p><p>Are sufficient details provided to allow replication of the method development and its use by others?</p><p>Partly</p><p>Reviewer Expertise:</p><p>OMG is an expert of MR physics and inhomogeneous manetization transfer (ihMT) MRI. LS is a expert of MR physics and ultra short echo time MRI.</p><p>We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above.</p></body><back><ref-list><title>References</title><ref id=\"rep-ref-38619-1\"><label>1</label><mixed-citation publication-type=\"journal\">\n:\n<article-title>Magnetization transfer from inhomogeneously broadened lines (ihMT): Improved imaging strategy for spinal cord applications.</article-title>\n<source><italic toggle=\"yes\">Magn Reson Med</italic></source>.<volume>77</volume>(<issue>2</issue>) :\n<elocation-id>10.1002/mrm.26134</elocation-id>\n<fpage>581</fpage>-<lpage>591</lpage>\n<pub-id pub-id-type=\"doi\">10.1002/mrm.26134</pub-id>\n<pub-id pub-id-type=\"pmid\">26959278</pub-id></mixed-citation></ref><ref id=\"rep-ref-38619-2\"><label>2</label><mixed-citation publication-type=\"journal\">\n:\n<article-title>472 - Anisotropy of inhomogeneous Magnetization Transfer (ihMT) in White Matter</article-title>.<year>2017</year>;<volume>ISMRM 25th Annual Meeting</volume>:</mixed-citation></ref><ref id=\"rep-ref-38619-3\"><label>3</label><mixed-citation publication-type=\"journal\">\n:\n<article-title>Orientation dependence of magnetization transfer parameters in human white matter.</article-title>\n<source><italic toggle=\"yes\">Neuroimage</italic></source>.<year>2015</year>;<volume>114</volume>:\n<elocation-id>10.1016/j.neuroimage.2015.03.068</elocation-id>\n<fpage>136</fpage>-<lpage>46</lpage>\n<pub-id pub-id-type=\"doi\">10.1016/j.neuroimage.2015.03.068</pub-id>\n<pub-id pub-id-type=\"pmid\">25862261</pub-id></mixed-citation></ref><ref id=\"rep-ref-38619-4\"><label>4</label><mixed-citation publication-type=\"journal\">\n:\n<article-title>Whole brain inhomogeneous magnetization transfer (ihMT) imaging: Sensitivity enhancement within a steady-state gradient echo sequence.</article-title>\n<source><italic toggle=\"yes\">Magn Reson Med</italic></source>.<volume>79</volume>(<issue>5</issue>) :\n<elocation-id>10.1002/mrm.26907</elocation-id>\n<fpage>2607</fpage>-<lpage>2619</lpage>\n<pub-id pub-id-type=\"doi\">10.1002/mrm.26907</pub-id>\n<pub-id pub-id-type=\"pmid\">28940355</pub-id></mixed-citation></ref></ref-list></back><sub-article id=\"comment4317-38619\" article-type=\"response\"><front-stub><contrib-group><contrib contrib-type=\"author\"><name><surname>Wood</surname><given-names>Tobias C.</given-names></name><aff>King's College London, UK</aff></contrib></contrib-group><author-notes><fn fn-type=\"COI-statement\"><p>\n<bold>Competing interests: </bold>No competing interests were disclosed.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>13</day><month>8</month><year>2020</year></pub-date></front-stub><body><p>We thank the reviewers for their time and insight. There were in total five reviewers, with many helpful suggestions, and hence there have been many edits to the paper. Responses to this particular review follow below.</p><p>\n<italic>1. About the systematic error observed in CSF </italic>\n</p><p>We thank the reviewers for their suggestion that the negative effect in CSF may be caused by differential direct saturation effects from the single- and dual-sided irradiation. We agree that this is the most likely cause and the discussion has been amended as such. We agree that the suggested phantom experiment could clarify the cause, however due to the ongoing situation with Covid-19 we have not been able to acquire such data. As discussed further below, the issue can likely be mitigated in future studies by using a different RF pulse envelope, e.g. Hann or Gaussian.</p><p>\n<italic>2. On the head orientation study</italic>\n</p><p>We have reduced the emphasis on the head orientation study. The only message we intended to convey was that attention should be paid when positioning the subject. The word “strong” has been replaced with “highly statistically significant”, as this is a more accurate description of our result – which was a small effect but with a very small p-value. The abstract, introduction and discussion have been rephrased accordingly.</p><p>\n<italic>3. Comparison with existing literature </italic>\n</p><p>The introduction and discussion have been expanded to better explain the differences between a standard Cartesian gradient-echo sequence and the 3D radial sequence presented here. In response to another reviewer a table has been added with mean ihMTR and inverse ihMTR values from the ROIs which will make comparison with other papers easier. The achieved ICC values have been set in context against a Cartesian ihMT study that also used ICC scores.</p><p>We will address simulations of the RUFIS sequence in future work. Efficiently accounting for the combined action of the large number of readout pulses is an essential and general issue for Magnetization Prepared RUFIS and not specific to ihMT, and hence we consider it beyond the scope of this specific work. As the reviewers note it is essential that the readout pulses must have low RF power, and this was a key step in achieving good ihMT contrast (discussed further below).</p><p>\n<italic>Specific comments:</italic>\n</p><p>\n<italic>Introduction: The introductory part of the angular dependency of ihMT should be expanded</italic>. </p><p>Thank you for the suggested references which have been added to the introduction</p><p>\n<italic>Method/MR sequence: Author should provide more details about the “transient suppression” method that they used (fig. 1C), and provide argument or simple simulations so as to determine why “48 dummy segments” are sufficient to generate a steady-state. </italic>\n</p><p>The description of the transient suppression has been expanded in the methods section and a justification for the 48 dummy segments provided (that 48 segments corresponds to 3.3 seconds of acquisition time which is much longer than T1 in brain parenchyma).</p><p>\n<italic>Method/Imaging Study: Did the author consider adding the same spoiler gradient for the ihMT prep? (see general comment above)</italic>\n</p><p>Originally the sequence included the same spoiler gradient for the MT-prep. However, a short spoiler generated considerable acoustic noise while a long (quiet) spoiler added appreciable dead-time to the sequence. We hence investigated the possibility of removing the explicit spoiler gradient and relying on the gradient ramp at the start of the acquisition segment, which at 5 ms long, has a sizeable gradient area. In our experience, insufficient spoiling manifests as wave/zipper type artefacts visible across the whole image, and as we did not observe such artefacts in our images, we concluded that spoiling from the ramp was adequate.</p><p>\n<italic>Method/analysis: “As a proxy for how each subject’s head was aligned with the main magnetic field, we calculated the angle between the Z-direction (head-foot axis) of the MT scan space and the MNI atlas.“ This is an interesting approximation. However, in my opinion the main issue is that it misses the assessment of individual tracts orientation, which is necessary to address the angular dependency of ihMT. </italic>\n</p><p>We agree that in order to fully model the angular dependency of ihMT the individual tract orientation should be known. It was not possible within the time budget of the protocol to acquire the diffusion data that would have provided this information. However, the fact that we found a significant interaction of head orientation implies that there is a measurable impact of head orientation on ihMT. As noted above, we have revised the wording to more correctly state that the effect was small but significant, and we hope that this is acceptable. </p><p>\n<italic>Method/Analysis related to the head orientation study: Some part of this section should appear in introduction as they are not specific to the method:</italic>\n</p><p>The specified sentence has been moved to the introduction.</p><p>\n<italic>Unfortunately, the B1 sensitivity of the high flip angle reference inverse ihMT ratio (ihMTRinv) is still under investigation and may not be as robust as initially thought. In addition, the sequence may have different sensitivity to B1 depending on the sequence parameters (see Mchinda et al. MRM 20184). In the presented study the ihMTRinv images from fig 2 show a strong hyperintense area located in the middle of the brain which looks somewhat colocalized with typical B1+ patterns observed @ 3T, as for the corresponding ihMTR images. This raises concern regarding the choice of ihMTRinv instead of ihMTR for the analysis. In addition, the residual dependency of ihMTRinv to B1 + shall be further discussed. </italic>\n</p><p>The use of the inverse ihMTR metric was a best-efforts attempt to use the most recent recommended methods from the literature. Similar inverse metrics have been used previously in CEST to correct in particular for T1 relaxation, we have added a reference to a relevant paper in the methods section. Our results (higher ICC values) indicate that the inverse ihMTR is certainly no worse, and likely more robust than the ihMTR. Figure 2 has been revised to show axial and coronal sections, we hope that this makes it clear that the hyperintense area in the middle of what was the 3\n<sup>rd</sup> slice is an ascending WM tract. We have reviewed Mchinda et al 2018 and can find no discussion of the inverse ihMTR within. A full characterisation of the B1+ variation would have required acquiring B1+ maps which was beyond the scope of this study. This has been listed as a limitation of the study in the discussion.</p><p>\n<italic>Discussion: …Of interest, the MT experiments run in this study are not comparable to usual MT since relatively high frequency offsets and low duty cycle were used here…</italic>\n</p><p>We thank the reviewers for pointing this out, it was also raised in another review. The discussion has been amended to make it clear that the offset frequency is optimal for ihMTR and not MTR.</p><p>\n<italic>Discussion: Previous literature has shown that ihMT depends on many sequence parameters. This should be contextualized here. Also it is hard to compare with previous literature without providing the rms B1 calculated over the sequence repetition time. </italic>\n</p><p>We have now contextualised our parameters with reference to Mchinda et al. in the discussion. The rms B1 over the preparation module has been added (discussed further below).</p><p>\n<italic>Minor points: Abstract: Comparable should be removed. </italic>\n</p><p>Removed as suggested.</p><p>\n<italic>Introduction: Please specify “central nervous system”. </italic>\n</p><p>Added as suggested.</p><p>\n<italic>p3: reference to citation 11 about ihMT is likely inappropriate.</italic>\n</p><p>The citation was in the incorrect place. Thank you for spotting</p><p>.</p><p>\n<italic>Methods: Please use “mitigate” instead of “avoid”. </italic>\n</p><p>Changed as suggested.</p><p>\n<italic>MR sequence: “... increased ihMT effect in myelin ” Please use WM instead of myelin, this is a shortcut toward considering that ihMT is a purely myelin-specific effect. </italic>\n</p><p>We are unsure what the specific issue is here. We have amended the sentence to read “increased sensitivity to myelin”, and to reference the paper “Validating the sensitivity of inhomogeneous magnetization transfer (ihMT) MRI to myelin with fluorescence microscopy”, Duhamel et al 2019</p><p>\n<italic>Imaging study: typo: plural “6 females, 6 males”.</italic>\n</p><p>In this context female and male are not nouns but adjectives referring to the implied plural noun “subjects”. We have amended the sentence to make this explicit.</p><p>\n<italic>Imaging study, first paragraph: we suggest to make explicit here the reason why the ihMT scans were repeated twice (repeatability) and repeated again in another session (head orientation study).</italic>\n</p><p>This is an incorrect interpretation. All four measurements (two scans x two sessions) were used for the repeatability study, and then also included in the head orientation study.</p><p>\n<italic>Imaging study: “We used a 2°, 24μs (low B1 amplitude) hard pulse for excitation to minimize any saturation of the bound pool from the excitation pulses”: the sentence sounds misleading since shorter RF pulses lead to stronger B1RMS for identical flip angle, please rephrase. </italic>\n</p><p>The 24 μs pulse was lengthened from the manufacturer default of 8 μs, in order to reduce the B1 amplitude and consequent on-resonance saturation of the MT pool. This has been made explicit in the text.</p><p>\n<italic>Imaging study: was the choice of Fermi pulse motivated by any specific criteria? Since there are free parameters in the Fermi window function, the authors should provide more information on the pulse shape, such as bandwidth and power integral (or B1peak and B1rms is preferred).</italic>\n</p><p>Fermi pulses are used by our scanner manufacturer in some sequences and there was no specific criteria beyond the desire to maximise the achievable B1rms within a given pulse-width. The B1rms of the pulse (8.75 uT) is stated in the methods section (Imaging Study). Given the single/dual-sided irradiation issue identified by the reviewers above, switching to an envelope with lower sidebands, e.g. Hann or Gauss, is preferable for future studies, and this has been noted in the discussion.</p><p>\n<italic>Imaging study: please provide the root-mean-square B1 of MT pulses calculated over the segment TR (ihMT prep + readout) to ease comparison of presented results with existing literature. </italic>\n</p><p>Previous literature has used a wide variety of power metrics. In particular Mchinda et al used the rms B1 over the preparation module, not the segment TR. As we have used Mchinda et al as a reference point for several other features of our sequence, we have added the relevant figure (6.2uT) to the methods section.</p><p>\n<italic>Imaging study: we suggest the author describe first that ihMT rely on comparing single and dual frequency MT experiment, before describing the five volumes to be acquired. Currently the ihMT method description may be difficult to follow for non-expert ihMT users. </italic>\n</p><p>The suggested sentence has been added to the methods section.</p><p>\n<italic>Analysis: reference to citation 36 about the QUIT toolbox is likely inappropriate. Please check all references. </italic>\n</p><p>We wrote a new tool specifically for calculating MT ratios and added it to QUIT. This has been clarified in the text. The documentation for this tool is available here: https://quit.readthedocs.io/en/latest/Docs/MT.html#qi-mtr</p><p>\n<italic>Analysis: “Previous work has shown that the inhomogenous MT effect partially depends on the orientation of myelin with the main magnetic field”. typo: “inhomogeneous”. </italic>\n</p><p>Thank you for spotting the typo, in the course of responding to other comments the word has been removed.</p><p>\n<italic>Results:</italic>\n</p><p>\n<italic>Images: No acoustic noise measurement was reported for the T1w-RUFIS acquisition. p6 typo error: “The CoV in WM was below 10% in WM”. WM is repeated twice. </italic>\n</p><p>The T1w acquisition used the same readout module with the same acoustic characteristics as the MT module. The typo has been corrected.</p><p>\n<italic>“the very small absolute values of ihMTR in both GM and CSF, ...”..It is misleading to pool GM and CSF together here since ihMTR is usually low in GM (but may become high using dedicated ihMT implementation using low duty cycle RF irradiation and no short-T1D filtering) but still give rise to detectable signal, whereas it is null or artefactual in CSF. Please rephrase. </italic>\n</p><p>The mention of CSF has been removed.</p><p>\n<italic>Head Orientation: “There was no significant interaction between angle and ROI (F = 1.47, p = 0.15) indicating effects were relatively homogeneous over the tracts.” This observation is surprising since each tract has a single orientation with respect to B0 and the orientation may differ between the right and left hemisphere; hence one expects different effects. Please discuss this point further. In addition, authors should provide more insight into the distribution of the head orientation within the tested population, e.g. by indicating the mean and standard deviation. </italic>\n</p><p>“Tract” has been replaced by “ROI”, this is subtle but important point as ROIs may contain multiple tracts. We have added a note that we did not separate ROIs by hemisphere as a limitation in the discussion. The median head angle was stated in the results section, the lower and upper quartiles have been added.</p><p>\n<italic>Discussion: I would be cautious about this interpretation since direct saturation and inflow effects may bias MT asymmetry. Again direct saturation effects are a potential concern with the presented sequence implementation (see general comment above), and may explain the observed signal. We encourage the author to perform in-vitro experiments on blood sample (if possible) to support this statement or to rephrase it. </italic>\n</p><p>We have found additional references detailing the MT effect in blood and describing an asymmetric profile. These have been added to the discussion and we no longer claim to be the first to observe this effect.</p><p>\n<italic>“there are no apparent drawbacks to switching from ihMTR to inverse ihMTR for future studies.”</italic> \n<italic>I agree there is no apparent drawback in terms of scan time, as describe here, but the advantages of inverse ihMTR in terms of robustness to B1 still need to be further supported by experiment. Consider rephrasing. </italic>\n</p><p>This sentence has been removed while responding to other comments.</p><p>\n<italic>“ihMTR have good, but not excellent repeatability...“. Authors have to describe repeatability in a more quantitative way. </italic>\n</p><p>“Good” and “Excellent” refer to a standardised ICC classification score (ICC value 0.75-0.9 good, > 0.9 excellent). This has been clarified and the relevant citation added.</p><p>\n<italic>We invite the author to discuss more other sources of bias (residual B1 dependency, residual motion..) and to discuss the effect of the head orientation in a more quantitative way (e.g. compare the intra- vs -inter-session bias), rather than stating that the effect is “strong”. </italic>\n</p><p>As noted above, we have revised our wording to “small but highly significant”. Strong was used in reference to the small p-value, we hope the new wording is clearer.</p><p>\n<italic>All figures from figure 3 appear too late in the pdf file; also figures 4 to 8 are all placed together. </italic>\n</p><p>Final figure placement was determined by the journal and beyond the control of the authors.</p></body></sub-article></sub-article><sub-article id=\"report38493\" article-type=\"peer-review\"><front-stub><article-id pub-id-type=\"doi\">10.21956/wellcomeopenres.17381.r38493</article-id><title-group><article-title>Reviewer response for version 1</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Dortch</surname><given-names>Richard</given-names></name><xref ref-type=\"aff\" rid=\"r38493a1\">1</xref><role>Referee</role><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0002-9978-2203</contrib-id></contrib><aff id=\"r38493a1\">\n<label>1</label>Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ, USA</aff></contrib-group><author-notes><fn fn-type=\"COI-statement\"><p>\n<bold>Competing interests: </bold>No competing interests were disclosed.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>14</day><month>5</month><year>2020</year></pub-date><permissions><copyright-statement>Copyright: © 2020 Dortch R</copyright-statement><copyright-year>2020</copyright-year><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><related-article related-article-type=\"peer-reviewed-article\" id=\"d38e6416\" ext-link-type=\"doi\" xlink:href=\"10.12688/wellcomeopenres.15845.1\">Version 1</related-article><custom-meta-group><custom-meta><meta-name>recommendation</meta-name><meta-value>approve-with-reservations</meta-value></custom-meta></custom-meta-group></front-stub><body><p>This well-written manuscript seeks to develop and evaluate a silent myelin-specific MRI sequence for applications in infants and the elderly, where loud imaging sequences can be problematic. Recent work has demonstrated that so-called inhomogeneous MT (ihMT), which arises primarily from dipolar order effects in myelin lipids, may be a more specific assay of myelin content than other MRI measures (e.g., T\n<sub>2</sub> relaxation, diffusion, conventional magnetization transfer). As a result, there is significant interest in developing clinically feasible ihMT sequences for applications in neurodegenerative diseases, development, and aging. Overall, the study was well designed (e.g., strong repeatability and ROI analyses) and the results were compelling. However, there are several minor-to-moderate flaws, particularly in the motivation (e.g., the need for silent ihMT sequences) and methods (e.g., the influence of head orientation on ihMT), that slightly reduced my enthusiasm and lead me to recommend a minor revision.\n<list list-type=\"order\"><list-item><p>The case made for silent MT sequences is not particularly compelling. The authors mention that these are “among the loudest” sequences because they use fast gradient-echo readouts to obtain whole-brain data in clinically feasible scan times. However, these sequences are usually SAR-limited with fairly reasonable TRs (typically between 25-50 ms) that are acquired at lower resolutions to ensure adequate SNR. Together, this results in a sequence with reduced acoustic noise compared to most rapid, high-resolution gradient echo sequences as well as other quantitative approaches that use EPI (e.g., diffusion). (moderate)</p></list-item><list-item><p>Furthermore, the benefits of using a silent myelin sequence may not outweigh the drawbacks. For example, the proposed method requires very low flip angles (2 degrees), which results in a significant SNR penalty relative to standard ihMT sequences. In addition, the RUFIS readout results in a small increase in scan time. Given than SNR is already relatively low for ihMT indices, the proposed method may be suboptimal in many clinical scenarios. (moderate)</p></list-item><list-item><p>The study was not designed to specifically measure the effect of head orientation on ihMT. Subjects were scanned four times (across two sessions), but head orientation was not directly controlled or measured across these scans. Instead a mixed effects model was used and head orientation was inferred from the images (rather than the orientation of individual tracts being measured using DTI for example). Furthermore, the confounding influences of T\n<sub>1</sub> and B\n<sub>1</sub> were not measured. The authors attempt to overcome this by using inverse ihMT, which is less sensitive to these confounding influences. However, the inverse ihMT maps in Figures 2 and 4 show some shading artifacts that may be related to uncorrected B\n<sub>1 </sub>variations. (moderate)</p></list-item><list-item><p>Results from the silent sequence were not compared to conventional (i.e., “loud”) sequences, both in terms of acoustic noise and MT parameters. (moderate)</p></list-item><list-item><p>Figure 1: the “stair-stepping” of gradient amplitudes is difficult to see in panel A. (minor)</p></list-item><list-item><p>The same offset (7 kHZ) and RMS power was for the saturation pulses in ihMT and MTR acquisitions. For MTR, saturation pulses are typically applied at 1-2 kHZ off-resonance relative to water to maximize MT contrast; therefore, the authors are comparing a sequence that may be optimized for ihMT to a suboptimal MTR sequence. (minor)</p></list-item><list-item><p>It was not clear why spoiling was required for the T\n<sub>1</sub>-weighted scan but not the MT scans. (minor)</p></list-item></list>\n</p><p>Is the rationale for developing the new method (or application) clearly explained?</p><p>Partly</p><p>Is the description of the method technically sound?</p><p>Yes</p><p>Are the conclusions about the method and its performance adequately supported by the findings presented in the article?</p><p>Partly</p><p>If any results are presented, are all the source data underlying the results available to ensure full reproducibility?</p><p>Yes</p><p>Are sufficient details provided to allow replication of the method development and its use by others?</p><p>Yes</p><p>Reviewer Expertise:</p><p>Imaging science, quantitative MRI, myelin imaging.</p><p>I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.</p></body><sub-article id=\"comment4316-38493\" article-type=\"response\"><front-stub><contrib-group><contrib contrib-type=\"author\"><name><surname>Wood</surname><given-names>Tobias C.</given-names></name><aff>King's College London, UK</aff></contrib></contrib-group><author-notes><fn fn-type=\"COI-statement\"><p>\n<bold>Competing interests: </bold>No competing interests were disclosed.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>13</day><month>8</month><year>2020</year></pub-date></front-stub><body><p>We thank the reviewer for their time and insight. There were in total five reviewers, with many helpful suggestions, and hence there have been many edits to the paper. Responses to this particular review follow below.</p><p>1. We concede that the acoustic noise from any scan will depend on the precise sequence settings. However, we note that recent ihMT work has used both an MP-RAGE style acquisition, with an imaging TR of 4.3ms and also SSFP with a TR of only 5ms. The introduction has been amended to explicitly reference these papers.</p><p>2. We agree that radial sequences are SNR constrained relative to cartesian sequences, this has now been explicitly stated in the discussion. Although the 3D radial readout does imply a time penalty relative to cartesian, we note that our overall scan time is competitive with recent cartesian ihMT papers. This has been added to the discussion.</p><p>3. We agree that it would have been preferable to acquire explicit T1 & B1 maps for comparison, but total protocol time prevented that in this study. In our opinion the ihMTRinv maps display more even contrast than the ihMTR maps, we hope that the revised figures with axial and coronal sections make this clearer. </p><p>4. We did not have a conventional cartesian ihMT implementation available when this study was conducted. However, as there are multiple such implementations in the literature, it is possible to broadly compare image quality and achieved ihMTR values. We have added a table of ihMTR values to make this comparison easier. We concede that it is not possible to compare acoustic noise levels, because it is not standard in the MR literature to record and report the acoustic noise of a sequence. In previous work (reference 22) we did directly compare noise levels between a radial ZTE and cartesian implementation of Variable Flip-Angle T1 mapping, which in our opinion would be similar to the noise levels in this work and found a 30 dB reduction in noise level.</p><p>5. Figure 1 has been updated with a reduced number of spokes to emphasise the stepped gradients. We hope this is clearer.</p><p>6. We thank you for pointing out that the frequency offset is not ideal for generating single-sided MT contrast. With hindsight, this is obvious. The discussion has been amended to reflect this.</p><p>7. Because the MT pulses are applied off-resonance they should not significantly interact with the free water pool and hence generate a minimal amount of transverse magnetization that would require spoiling – in addition, the initial gradient ramp of the acquisition segment does provide some spoiling in a quasi-random direction. In contrast the T1-preparation pulse does excite a large amount of transverse magnetization (because it is applied on-resonance), which must be sufficiently spoiled before the start of the acquisition segment. We have added additional sentences to the methods to make this clearer.</p></body></sub-article></sub-article></article>\n"
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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.3 20210610//EN\" \"JATS-archivearticle1-3-mathml3.dtd\">\n<article xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" dtd-version=\"1.3\" xml:lang=\"en\" article-type=\"research-article\"><?properties open_access?><?properties manuscript?><processing-meta base-tagset=\"archiving\" mathml-version=\"3.0\" table-model=\"xhtml\" tagset-family=\"jats\"><restricted-by>pmc</restricted-by></processing-meta><front><journal-meta><journal-id journal-id-type=\"nlm-journal-id\">1256300</journal-id><journal-id journal-id-type=\"pubmed-jr-id\">22705</journal-id><journal-id journal-id-type=\"nlm-ta\">J Phys D Appl Phys</journal-id><journal-id journal-id-type=\"iso-abbrev\">J Phys D Appl Phys</journal-id><journal-title-group><journal-title>Journal of physics D: Applied physics</journal-title></journal-title-group><issn pub-type=\"ppub\">0022-3727</issn></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32831402</article-id><article-id pub-id-type=\"pmc\">PMC7431976</article-id><article-id pub-id-type=\"doi\">10.1088/1361-6463/ab83bb</article-id><article-id pub-id-type=\"manuscript\">NISTPA1616338</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Analysing quantized resistance behaviour in graphene Corbino <italic toggle=\"yes\">p-n</italic> junction devices</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Liu</surname><given-names>Chieh-I</given-names></name><xref rid=\"A1\" ref-type=\"aff\">1</xref><xref rid=\"A2\" ref-type=\"aff\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Scaletta</surname><given-names>Dominick S.</given-names></name><xref rid=\"A3\" ref-type=\"aff\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Patel</surname><given-names>Dinesh K.</given-names></name><xref rid=\"A1\" ref-type=\"aff\">1</xref><xref rid=\"A4\" ref-type=\"aff\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Kruskopf</surname><given-names>Mattias</given-names></name><xref rid=\"A1\" ref-type=\"aff\">1</xref><xref rid=\"A5\" ref-type=\"aff\">5</xref><xref rid=\"A6\" ref-type=\"aff\">6</xref></contrib><contrib contrib-type=\"author\"><name><surname>Levy</surname><given-names>Antonio</given-names></name><xref rid=\"A1\" ref-type=\"aff\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Hill</surname><given-names>Heather M.</given-names></name><xref rid=\"A1\" ref-type=\"aff\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Rigosi</surname><given-names>Albert F.</given-names></name><xref rid=\"A1\" ref-type=\"aff\">1</xref></contrib></contrib-group><aff id=\"A1\"><label>1</label>Physical Measurement Laboratory, National Institute of Standards and Technology (NIST), Gaithersburg, MD 20899, United States</aff><aff id=\"A2\"><label>2</label>Department of Chemistry and Biochemistry, University of Maryland, College Park, MD 20742, United States</aff><aff id=\"A3\"><label>3</label>Department of Physics, Mount San Jacinto College, Menifee, CA 92584, United States</aff><aff id=\"A4\"><label>4</label>Department of Physics, National Taiwan University, Taipei 10617, Taiwan</aff><aff id=\"A5\"><label>5</label>Joint Quantum Institute, University of Maryland, College Park, MD 20742, United States</aff><aff id=\"A6\"><label>6</label>Electricity Division, Physikalisch-Technische Bundesanstalt, Braunschweig 38116, Germany</aff><author-notes><corresp id=\"CR1\">\n<email>afr1(at)nist.gov</email>\n</corresp></author-notes><pub-date pub-type=\"nihms-submitted\"><day>1</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>01</day><month>1</month><year>2021</year></pub-date><volume>53</volume><issue>27</issue><elocation-id>10.1088/1361-6463/ab83bb</elocation-id><permissions><license><ali:license_ref xmlns:ali=\"http://www.niso.org/schemas/ali/1.0/\" specific-use=\"textmining\" content-type=\"ccbylicense\">https://creativecommons.org/licenses/by/3.0/</ali:license_ref><license-p>As the Version of Record of this article is going to be / has been published on a gold open access basis under a CC BY 3.0 licence, this Accepted Manuscript is available for reuse under a CC BY 3.0 licence immediately.</license-p><license-p>Everyone is permitted to use all or part of the original content in this article, provided that they adhere to all the terms of the licence <ext-link ext-link-type=\"uri\" xlink:href=\"https://creativecommons.org/licenses/by/3.0/\">https://creativecommons.org/licenses/by/3.0</ext-link></license-p><license-p>Although reasonable endeavours have been taken to obtain all necessary permissions from third parties to include their copyrighted content within this article, their full citation and copyright line may not be present in this Accepted Manuscript version. Before using any content from this article, please refer to the Version of Record on IOPscience once published for full citation and copyright details, as permissions may be required. All third party content is fully copyright protected and is not published on a gold open access basis under a CC BY licence, unless that is specifically stated in the figure caption in the Version of Record.</license-p></license></permissions><abstract id=\"ABS1\"><p id=\"P1\">Just a few of the promising applications of graphene Corbino <italic toggle=\"yes\">pn</italic>J devices include two-dimensional Dirac fermion microscopes, custom programmable quantized resistors, and mesoscopic valley filters. In some cases, device scalability is crucial, as seen in fields like resistance metrology, where graphene devices are required to accommodate currents of the order 100 μA to be compatible with existing infrastructure. However, fabrication of these devices still poses many difficulties. In this work, unusual quantized resistances are observed in epitaxial graphene Corbino <italic toggle=\"yes\">p-n</italic> junction devices held at the <italic toggle=\"yes\">ν</italic> = 2 plateau (<italic toggle=\"yes\">R</italic><sub>H</sub> ≈ 12906 Ω) and agree with numerical simulations performed with the LTspice circuit simulator. The formulae describing experimental and simulated data are empirically derived for generalized placement of up to three current terminals and accurately reflects observed partial edge channel cancellation. These results support the use of ultraviolet lithography as a way to scale up graphene-based devices with suitably narrow junctions that could be applied in a variety of subfields.</p></abstract><kwd-group><kwd>quantum Hall effect</kwd><kwd>Corbino geometry</kwd><kwd>graphene <italic toggle=\"yes\">p-n</italic> junctions</kwd></kwd-group></article-meta></front><body><sec id=\"S1\"><label>1.</label><title>Introduction</title><p id=\"P2\">Graphene and all devices fabricated from it have been studied extensively since its discovery [<xref rid=\"R1\" ref-type=\"bibr\">1</xref>-<xref rid=\"R4\" ref-type=\"bibr\">4</xref>]. Under strong magnetic flux densities leading to filled Landau levels, graphene exhibits fixed resistances that take the form <inline-formula><mml:math id=\"M5\" display=\"inline\"><mml:mrow><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:mn>2</mml:mn><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>2</mml:mn><mml:mi>n</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mfrac><mml:msub><mml:mi>R</mml:mi><mml:mi mathvariant=\"bold\">K</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>, where <inline-formula><mml:math id=\"M6\" display=\"inline\"><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mi mathvariant=\"bold\">K</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mi>h</mml:mi><mml:mrow><mml:msup><mml:mi>e</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:mrow></mml:mfrac></mml:mrow></mml:math></inline-formula> and is labelled as the von Klitzing constant, <italic toggle=\"yes\">n</italic> is an integer, <italic toggle=\"yes\">h</italic> is the Planck constant, and <italic toggle=\"yes\">e</italic> is the elementary charge. Conventional <italic toggle=\"yes\">p-n</italic> junction (<italic toggle=\"yes\">pn</italic>J) Hall devices may also exhibit a variety of ratios of the von Klitzing constant while in the quantum Hall regime [<xref rid=\"R5\" ref-type=\"bibr\">5</xref>-<xref rid=\"R18\" ref-type=\"bibr\">18</xref>]. Furthermore, similar phenomena have been observed in devices with a Corbino geometry [<xref rid=\"R19\" ref-type=\"bibr\">19</xref>-<xref rid=\"R25\" ref-type=\"bibr\">25</xref>]. When coupled with the commercial necessity of scaling graphene devices, applications involving millimeter-scale fabrication have the potential to provide solutions in a number of fields, notably those that focus on problems in photodetection [<xref rid=\"R26\" ref-type=\"bibr\">26</xref>-<xref rid=\"R30\" ref-type=\"bibr\">30</xref>], quantum Hall metrology [<xref rid=\"R31\" ref-type=\"bibr\">31</xref>-<xref rid=\"R41\" ref-type=\"bibr\">41</xref>], and electron optics [<xref rid=\"R42\" ref-type=\"bibr\">42</xref>-<xref rid=\"R45\" ref-type=\"bibr\">45</xref>].</p><p id=\"P3\">The first question that may come to mind regards how such devices could be applied specifically to various problems. Applications of these Corbino <italic toggle=\"yes\">pn</italic>J devices include the possible construction of more sophisticated two-dimensional Dirac fermion microscopes that rely on large-scale junction interfaces [<xref rid=\"R46\" ref-type=\"bibr\">46</xref>], custom programmable quantized resistors [<xref rid=\"R47\" ref-type=\"bibr\">47</xref>], and mesoscopic valley filters [<xref rid=\"R21\" ref-type=\"bibr\">21</xref>]. The scalability is crucial for some of these applications. For instance, in resistance metrology, graphene devices are required to accommodate currents of the order 10 μA and above (modern-day usage may even exceed 100 μA) in order to ensure compatibility with existing infrastructure [<xref rid=\"R31\" ref-type=\"bibr\">31</xref>, <xref rid=\"R37\" ref-type=\"bibr\">37</xref>, <xref rid=\"R40\" ref-type=\"bibr\">40</xref>].</p><p id=\"P4\">Two difficult steps in successfully fabricating millimeter-scale <italic toggle=\"yes\">pn</italic>J devices include the following: (1) uniformly doping large-area regions on epitaxial graphene (EG) such that it may exhibit both <italic toggle=\"yes\">p</italic>-type and <italic toggle=\"yes\">n</italic>-type behavior and (2) ensuring adequate junction narrowness to enable Landauer-Büttiker edge channel propagation and equilibration [<xref rid=\"R5\" ref-type=\"bibr\">5</xref>-<xref rid=\"R9\" ref-type=\"bibr\">9</xref>, <xref rid=\"R48\" ref-type=\"bibr\">48</xref>-<xref rid=\"R53\" ref-type=\"bibr\">53</xref>]. For the first case, common nanodevice fabrication practices such as using a top-gate are unable to be used due to an increasing probability of current leakage through the gate with lateral size. Furthermore, such typical practices are time-consuming when scaled up beyond the micron level. Comparisons on other fabrication techniques are provided in the <xref rid=\"SD1\" ref-type=\"supplementary-material\">Supplementary Material</xref>.</p><p id=\"P5\">Other further specific applications of interest to those exploring quantum Hall transport may include the utilization of <italic toggle=\"yes\">pn</italic>J devices for accessing different quantized resistances or the repurposing of Corbino geometries for quantum Hall devices. In the latter case, not much has been reported regarding how a periodic boundary condition affects measured quantized resistances.</p><p id=\"P6\">Recent studies show that the parameter space for quantized resistances opens up signficantly when using several terminals as sources or drains [<xref rid=\"R54\" ref-type=\"bibr\">54</xref>-<xref rid=\"R57\" ref-type=\"bibr\">57</xref>]. In only one of those cases, Corbino <italic toggle=\"yes\">pn</italic>J devices were used, but mostly as a proof of principle for a more complex quantum dartboard device [<xref rid=\"R57\" ref-type=\"bibr\">57</xref>]. The empirical understanding of how these values are obtained is still lacking.</p><p id=\"P7\">This work reports details on the millimeter-scale fabrication of EG Corbino <italic toggle=\"yes\">pn</italic>J devices and subsequent measurements of those devices in the quantum Hall regime to understand how periodic boundary conditions on edge channel currents affect quantized resistances. The data were compared with LTspice current simulations [<xref rid=\"R58\" ref-type=\"bibr\">58</xref>-<xref rid=\"R59\" ref-type=\"bibr\">59</xref>], and both were then used as the basis for deriving empirical formulae for the generalized case of using two or three current terminals of either polarity with any arbitrary configuration.</p><p id=\"P8\">Overall, these experiments further validate two endeavors: (1) fabrication of scalable of <italic toggle=\"yes\">pn</italic>J devices and their versatility in circuits (2) flexibility in device fabrication by transforming devices with Corbino geometries into ones that permit the flow of edge channel currents between the outer and inner edges [<xref rid=\"R21\" ref-type=\"bibr\">21</xref>, <xref rid=\"R52\" ref-type=\"bibr\">52</xref>].</p></sec><sec id=\"S2\"><label>2.</label><title>Experimental and Numerical Methods</title><sec id=\"S3\"><label>2.1</label><title>Graphene growth and device fabrication</title><p id=\"P9\">EG was grown on a 2.7 cm by 2.7 cm SiC square that was diced from a 4<italic toggle=\"yes\">H</italic>-SiC(0001) wafer (CREE) [see Notes]. The procedures for cleaning and treating the wafer before the growth are detailed in other works [<xref rid=\"R32\" ref-type=\"bibr\">32</xref>, <xref rid=\"R35\" ref-type=\"bibr\">35</xref>, <xref rid=\"R54\" ref-type=\"bibr\">54</xref>]. One crucial element to obtaining high-quality growth with limited SiC step formation was the AZ5214E solution, a polymer which has been shown to assist in homogenous sublimation [<xref rid=\"R60\" ref-type=\"bibr\">60</xref>]. The growth was performed at 1900 °C in an argon environment using a resistive-element furnace from Materials Research Furnaces Inc. [see Notes] with graphite-lining and heating and cooling rates of about 1.5 °C/s.</p><p id=\"P10\">Samples were inspected after growth with confocal laser scanning and optical microscopy to verify monolayer homogeneity [<xref rid=\"R61\" ref-type=\"bibr\">61</xref>]. For fabrication processes, it was important to protect the EG from photoresists and organic contamination, and this was achieved by depositing Pd and Au layers [<xref rid=\"R32\" ref-type=\"bibr\">32</xref>, <xref rid=\"R35\" ref-type=\"bibr\">35</xref>]. For improved cryogenic contact resistances, EG was contacted with pads composed of NbTiN, a superconducting alloy with a T<sub>c</sub> of about 12 K at 9 T [<xref rid=\"R34\" ref-type=\"bibr\">34</xref>, <xref rid=\"R41\" ref-type=\"bibr\">41</xref>]. All EG Corbino <italic toggle=\"yes\">pn</italic>J devices underwent functionalization treatment with Cr(CO)<sub>6</sub>, which sublimates in a furnace and decomposes into Cr(CO)<sub>3</sub> and bonds itself to the EG surface [<xref rid=\"R62\" ref-type=\"bibr\">62</xref>-<xref rid=\"R65\" ref-type=\"bibr\">65</xref>]. This treatment both provides uniformity along the millimeter-scale devices and reduces the electron density to a low value of the order 10<sup>10</sup> cm<sup>−2</sup>, thus enabling a greater control of the latter by annealing [<xref rid=\"R66\" ref-type=\"bibr\">66</xref>].</p><p id=\"P11\">For both the control and experimental devices, intended <italic toggle=\"yes\">n</italic>-type regions were protected by S1813 photoresist. Keeping control devices aside, ultraviolet photolithography was then used to remove S1813 from regions intended for <italic toggle=\"yes\">p</italic>-type adjustment. PMMA/MMA was deposited as a mediation layer for ZEP520A, a polymer with photoactive properties. The latter enables graphene to become <italic toggle=\"yes\">p</italic>-type (near 4 × 10<sup>11</sup> cm<sup>−2</sup>) upon exposure to an external ultraviolet lamp (254 nm) – see <xref rid=\"SD1\" ref-type=\"supplementary-material\">Supplementary Material</xref> [<xref rid=\"R54\" ref-type=\"bibr\">54</xref>, <xref rid=\"R67\" ref-type=\"bibr\">67</xref>]. Regions still protected by S1813 did not undergo significant electron density shifting but still required an annealing process of approximately 25 min (at 350 K) to shift the electron density to about 10<sup>11</sup> cm<sup>−2</sup>.</p><p id=\"P12\">To verify that the devices are properly adjusted to the desired electron density, two types of measurements were required. For the control device in <xref rid=\"F1\" ref-type=\"fig\">Fig. 1(a)</xref>, a simple Hall measurement was performed after annealing using the green dots as the current terminals and the blue triangles as the voltage terminals. An example result is shown in <xref rid=\"F1\" ref-type=\"fig\">Fig. 1(b)</xref>, where the electron density has been successfully shifted from low values neighboring the Dirac point to around 10<sup>11</sup> cm<sup>−2</sup>. This electron density is sufficient to see the quantized plateau at ν = 2, which, for the case of using epitaxial graphene, exhibits a stable plateau for a large range of magnetic flux densities. This stability, labelled as a pinning of the ν = 2 Landau level state and characterized by edge channels of opposite chirality, has been attributed to field-dependent charge transfer between the SiC surface and the graphene layer [<xref rid=\"R33\" ref-type=\"bibr\">33</xref>].</p><p id=\"P13\">The second measurement is explained in more detail in the <xref rid=\"SD1\" ref-type=\"supplementary-material\">Supplementary Material</xref>. In essence, a traditional Hall bar with a <italic toggle=\"yes\">pn</italic>J was fabricated using identical steps. Simple Hall data in the intended <italic toggle=\"yes\">p</italic>-type region was collected to show the electron (or hole, in this case) density after the exposure to the ultraviolet lamp. The annealing does shift <italic toggle=\"yes\">p</italic>-type regions slightly closer to the Dirac point, but the density remains well within the order 10<sup>11</sup> cm<sup>−2</sup>. Additional data from monitoring the carrier density during the photochemical gating process are also shown in the <xref rid=\"SD1\" ref-type=\"supplementary-material\">Supplementary Material</xref>.</p><p id=\"P14\">Though these two measurements are direct ways of obtaining the electron density, an indirect way of validating device functionality is to assess the agreement between two- and three-terminal simulations and corresponding experimental data. These analyses are part of the core of this work and will be presented in the next section.</p></sec><sec id=\"S4\"><label>2.2</label><title>Definitions for empirical framework</title><p id=\"P15\">Before continuing, one major assumption of the more specific framework below is that all regions are quantized at the ν = 2 plateau. That said, this framework may be reformulated to accurately reflect the conditions of any quantum Hall <italic toggle=\"yes\">pn</italic>J system, including conditions whereby some regions exhibit other plateaus such as the ν = 6 plateau. Now, to thoroughly investigate the large parameter space of quantized resistances subject to periodic boundary conditions, multiple current terminals must be used. One of the goals of this work is to develop an empirical framework for calculating the effective quantized resistance of the circuit shown in <xref rid=\"F2\" ref-type=\"fig\">Fig. 2</xref>. Definitions for that framework include: (1) <italic toggle=\"yes\">N</italic>, the total number of terminals, (2) <italic toggle=\"yes\">q</italic><sub><italic toggle=\"yes\">N</italic>−1</sub> and (3) <inline-formula><mml:math id=\"M7\" display=\"inline\"><mml:mrow><mml:msubsup><mml:mi>q</mml:mi><mml:mrow><mml:mi>N</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>L</mml:mi></mml:msubsup></mml:mrow></mml:math></inline-formula> are the <italic toggle=\"yes\">coefficients of effective resistance</italic> (CER) for the cases with (Corbino device) and without (traditional Hall bar device) periodic boundary conditions, respectively, (4) <italic toggle=\"yes\">n<sub>j</sub></italic>, where <italic toggle=\"yes\">j</italic> can be either 1 or 2 and is used to label the number of junctions between two terminals, (5) <italic toggle=\"yes\">M</italic>, the number of distinct regions in the Corbino <italic toggle=\"yes\">pn</italic>J device (must be an even, positive integer), and (6) <italic toggle=\"yes\">n</italic><sub><italic toggle=\"yes\">x</italic></sub>, where <italic toggle=\"yes\">n</italic><sub><italic toggle=\"yes\">x</italic></sub> = <italic toggle=\"yes\">M</italic> − <italic toggle=\"yes\">n</italic><sub>1</sub> for two-terminal circuits and <italic toggle=\"yes\">n</italic><sub><italic toggle=\"yes\">x</italic></sub> = <italic toggle=\"yes\">M</italic> − <italic toggle=\"yes\">n</italic><sub>1</sub> − <italic toggle=\"yes\">n</italic><sub>2</sub> for three-terminal circuits.</p><p id=\"P16\">For greater clarity, refer to the schematics in <xref rid=\"F2\" ref-type=\"fig\">Fig. 2 (a)</xref> and <xref rid=\"F2\" ref-type=\"fig\">(b)</xref>, which represent the device in <xref rid=\"F1\" ref-type=\"fig\">Fig. 1 (c)</xref> and are topologically identical (the actual schematic for LTspice simulations is accurately reflected by (b)). The experimental device has <italic toggle=\"yes\">M</italic> = 16. The <italic toggle=\"yes\">pn</italic>J circuit contains a total of 3 terminals (<italic toggle=\"yes\">N</italic> = 3), with the voltage always being measured between points <italic toggle=\"yes\">A</italic> and <italic toggle=\"yes\">B</italic> (green squares). This measurement yields a quantized resistance of the form <italic toggle=\"yes\">R</italic><sub>AB</sub> = <italic toggle=\"yes\">q</italic><sub><italic toggle=\"yes\">N</italic>−1</sub><italic toggle=\"yes\">R</italic><sub>H</sub>, where <italic toggle=\"yes\">R</italic><sub>H</sub> is the Hall resistance at the <italic toggle=\"yes\">ν</italic> = 2 plateau (<italic toggle=\"yes\">R</italic><sub>H</sub> ≈ 12906 Ω). The CER (<italic toggle=\"yes\">q</italic><sub><italic toggle=\"yes\">N</italic>−1</sub>) can be represented as a either an integer or a fraction.</p><p id=\"P17\">This work focused on varying the locations of the two (<italic toggle=\"yes\">N</italic> = 2) or three (<italic toggle=\"yes\">N</italic> = 3) current terminals, arbitrary in both position along the Corbino device and placement within the outer or inner circumference. The next step was to determine the best way of identifying <italic toggle=\"yes\">n</italic><sub>1</sub> (and <italic toggle=\"yes\">n</italic><sub>2</sub> for the <italic toggle=\"yes\">N</italic> = 3 case). These determinations and corresponding simulations will be shown and discussed in the <xref rid=\"S6\" ref-type=\"sec\">results</xref> section.</p></sec><sec id=\"S5\"><label>2.3</label><title>LTspice simulations</title><p id=\"P18\">The electronic circuit simulator LTspice was used for predicting the electrical behavior of the graphene Corbino <italic toggle=\"yes\">pn</italic>J devices. The circuit comprised interconnected <italic toggle=\"yes\">p</italic>-type and <italic toggle=\"yes\">n</italic>-type quantized regions that were modeled either as ideal clockwise (CW) or counterclockwise (CCW) <italic toggle=\"yes\">k</italic>-terminal quantum Hall effect elements. The terminal voltages and currents, represented as <italic toggle=\"yes\">e<sub>m</sub></italic> and <italic toggle=\"yes\">j<sub>m</sub></italic>, are related by <italic toggle=\"yes\">R<sub>H</sub>j<sub>m</sub></italic> = <italic toggle=\"yes\">e<sub>m</sub></italic> − <italic toggle=\"yes\">e</italic><sub><italic toggle=\"yes\">m</italic>−1</sub> (<italic toggle=\"yes\">m</italic> = 1, … , <italic toggle=\"yes\">k</italic>) for CW elements and <italic toggle=\"yes\">R<sub>H</sub>j<sub>m</sub> e<sub>m</sub></italic> − <italic toggle=\"yes\">e</italic><sub><italic toggle=\"yes\">m</italic>+1</sub> for CCW elements. The circuit’s behavior at <italic toggle=\"yes\">A</italic> and <italic toggle=\"yes\">B</italic> (<xref rid=\"F2\" ref-type=\"fig\">Fig. 2 (b)</xref>) could only be modeled for one polarity of magnetic flux density per simulation. For a positive <italic toggle=\"yes\">B</italic>-field, an <italic toggle=\"yes\">n</italic>-doped (<italic toggle=\"yes\">p</italic>-doped) graphene device was modeled by a CW (CCW) element, whereas, when <italic toggle=\"yes\">B</italic> is negative, a CWW (CW) element was used.</p></sec></sec><sec id=\"S6\"><label>3.</label><title>Results</title><sec id=\"S7\"><label>3.1</label><title>Interpreting simulation trends (N = 2)</title><p id=\"P19\">Simulations were first carried out for the <italic toggle=\"yes\">N</italic> = 2 case (which, by default, is one positive and one negative current terminal). By keeping the positive terminal (source) fixed on an arbitrary terminal on the outer circumference of the device, and by moving the negative terminal (drain) along both the outer and inner circumference, the resulting CERs (labelled <italic toggle=\"yes\">q</italic><sub>1</sub>) were simulated as a function of junction number <italic toggle=\"yes\">n</italic><sub>1</sub> between the two terminals, for several devices containing different numbers of total regions <italic toggle=\"yes\">M</italic>. These results are summarized in <xref rid=\"F3\" ref-type=\"fig\">Fig. 3 (a)</xref>.</p><p id=\"P20\">In the case where a positive terminal is held on the outer circumference of the device and a negative terminal is moved along the outer circumference, a parabolic trend appears to form having an intuitive symmetry like the device itself. However, alternating behavior was observed along this parabolic trace. Similarly, when the negative terminal is instead simulated along the inner circumference, a parabolic trend is also seen with alternating behavior. The combination of both, seen in <xref rid=\"F3\" ref-type=\"fig\">Fig. 3 (a)</xref>, suggests that two parabolic trends actually exist, with one of them taking on slightly lower values than the other.</p><p id=\"P21\">There are two consistent physical pictures that arise from the periodic boundary conditions, and these may provide insight into how to interpret the observed alternating behavior. Consider the cases shown in <xref rid=\"F3\" ref-type=\"fig\">Fig. 3 (b)</xref>. With the condition that current flows only if it eventually terminates on a positive terminal, then in one case, current is allowed to flow along the edges unimpeded by any other flow. Let us label this as a <italic toggle=\"yes\">harmonized</italic> configuration. The second case involves current flow that impedes itself in several regions of the device. There are special cases (within the <italic toggle=\"yes\">N</italic> = 3 configuration) where this impeding leads to outright cancellation, enabling the device to emulate a traditional Hall bar with several <italic toggle=\"yes\">pn</italic>Js. All instances of currents appearing to self-impede in this picture may be labelled as <italic toggle=\"yes\">discordant</italic>.</p><p id=\"P22\">Separating configurations as harmonized or discordant allows the data in <xref rid=\"F3\" ref-type=\"fig\">Fig. 3 (a)</xref> to be fit to a parabola exactly. In doing so, one may parameterize the problem for arbitrary devices and terminal placements. For this analysis, since <italic toggle=\"yes\">n</italic><sub>1</sub> is symmetric, one may choose <italic toggle=\"yes\">n</italic><sub>1</sub> to be the smaller spacing between the two terminals, leaving the larger one to be <italic toggle=\"yes\">n</italic><sub><italic toggle=\"yes\">x</italic></sub> = <italic toggle=\"yes\">M</italic> − <italic toggle=\"yes\">n</italic><sub>1</sub>. In the limit where <italic toggle=\"yes\">n</italic><sub><italic toggle=\"yes\">x</italic></sub> → ∞, the periodic boundary condition is effectively lifted, giving us a CER of <inline-formula><mml:math id=\"M8\" display=\"inline\"><mml:mrow><mml:msubsup><mml:mi>q</mml:mi><mml:mn>1</mml:mn><mml:mi>L</mml:mi></mml:msubsup></mml:mrow></mml:math></inline-formula>, which may be calculated for the traditional Hall bar case [<xref rid=\"R56\" ref-type=\"bibr\">56</xref>]. By simulating the CERs (<italic toggle=\"yes\">q</italic><sub>1</sub>) as a function of <italic toggle=\"yes\">n</italic><sub><italic toggle=\"yes\">x</italic></sub> (see <xref rid=\"F3\" ref-type=\"fig\">Fig. 3 (c)</xref>), a logistic function known as the Hill-Langmuir equation may be used to fit the curves exactly:\n<disp-formula id=\"FD1\">\n<label>(1)</label>\n<mml:math id=\"M1\" display=\"block\"><mml:mrow><mml:msub><mml:mi>q</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>n</mml:mi><mml:mi>x</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mi>B</mml:mi><mml:mo>+</mml:mo><mml:mfrac><mml:mrow><mml:mi>A</mml:mi><mml:mo>−</mml:mo><mml:mi>B</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>+</mml:mo><mml:msup><mml:mrow><mml:mo stretchy=\"true\">(</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mi>x</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mi>x</mml:mi><mml:mn>0</mml:mn></mml:msub></mml:mrow></mml:mfrac><mml:mo stretchy=\"true\">)</mml:mo></mml:mrow><mml:mi>p</mml:mi></mml:msup></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>B</mml:mi><mml:msub><mml:mi>n</mml:mi><mml:mi>x</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:mi>A</mml:mi><mml:msub><mml:mi>x</mml:mi><mml:mn>0</mml:mn></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mi>x</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>x</mml:mi><mml:mn>0</mml:mn></mml:msub></mml:mrow></mml:mfrac></mml:mrow></mml:math>\n</disp-formula></p><p id=\"P23\">The parameters in <xref rid=\"FD1\" ref-type=\"disp-formula\">Eq. (1)</xref> can be interpreted as meaningful quantities (with <italic toggle=\"yes\">p</italic> = 1). With the limiting case described earlier, <inline-formula><mml:math id=\"M9\" display=\"inline\"><mml:mrow><mml:mi>B</mml:mi><mml:mo>=</mml:mo><mml:msubsup><mml:mi>q</mml:mi><mml:mn>1</mml:mn><mml:mi>L</mml:mi></mml:msubsup></mml:mrow></mml:math></inline-formula>, and as <italic toggle=\"yes\">n</italic><sub><italic toggle=\"yes\">x</italic></sub> → 0, <inline-formula><mml:math id=\"M10\" display=\"inline\"><mml:mrow><mml:msub><mml:mi>q</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>=</mml:mo><mml:mi>A</mml:mi><mml:mo>≡</mml:mo><mml:msubsup><mml:mi>q</mml:mi><mml:mn>1</mml:mn><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>0</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup></mml:mrow></mml:math></inline-formula>. For all <italic toggle=\"yes\">N</italic> = 2 configurations, <italic toggle=\"yes\">x</italic><sub>0</sub> = <italic toggle=\"yes\">n</italic><sub>1</sub>. Furthermore, with the relation <inline-formula><mml:math id=\"M11\" display=\"inline\"><mml:mrow><mml:msubsup><mml:mi>q</mml:mi><mml:mn>1</mml:mn><mml:mi>L</mml:mi></mml:msubsup><mml:mo>=</mml:mo><mml:msub><mml:mi>n</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:math></inline-formula> [<xref rid=\"R56\" ref-type=\"bibr\">56</xref>], a function of <italic toggle=\"yes\">n</italic><sub>1</sub> can be expressed:\n<disp-formula id=\"FD2\">\n<label>(2)</label>\n<mml:math id=\"M2\" display=\"block\"><mml:mrow><mml:msub><mml:mi>q</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>n</mml:mi><mml:mi>x</mml:mi></mml:msub><mml:mo>→</mml:mo><mml:msub><mml:mi>n</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>n</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:mn>1</mml:mn><mml:mo stretchy=\"false\">)</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>M</mml:mi><mml:mo>−</mml:mo><mml:msub><mml:mi>n</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>+</mml:mo><mml:msubsup><mml:mi>q</mml:mi><mml:mn>1</mml:mn><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>0</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:msub><mml:mi>n</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow><mml:mi>M</mml:mi></mml:mfrac></mml:mrow></mml:math>\n</disp-formula></p><p id=\"P24\">In <xref rid=\"FD2\" ref-type=\"disp-formula\">Eq. (2)</xref>, <inline-formula><mml:math id=\"M12\" display=\"inline\"><mml:mrow><mml:msubsup><mml:mi>q</mml:mi><mml:mn>1</mml:mn><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>0</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup></mml:mrow></mml:math></inline-formula> can be interpreted as the initial condition for a fixed <italic toggle=\"yes\">n</italic><sub>1</sub> (and <italic toggle=\"yes\">n</italic><sub><italic toggle=\"yes\">x</italic></sub> = 0). It takes on a single value for all harmonized and discordant (within <italic toggle=\"yes\">N</italic> = 2) – either <inline-formula><mml:math id=\"M13\" display=\"inline\"><mml:mfrac><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>n</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:mn>1</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:mfrac></mml:math></inline-formula> or <inline-formula><mml:math id=\"M14\" display=\"inline\"><mml:mrow><mml:mfrac><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:math></inline-formula>, respectively. This distinction contributes to the observed separation of the two similar parabolas seen in <xref rid=\"F3\" ref-type=\"fig\">Fig. 3 (a)</xref> and expressed exactly in <xref rid=\"FD2\" ref-type=\"disp-formula\">Eq. (2)</xref>.</p></sec><sec id=\"S8\"><label>3.2</label><title>Comparing experimental data to corresponding simulations (N = 2)</title><p id=\"P25\">To assess the validity of <xref rid=\"FD2\" ref-type=\"disp-formula\">Eq. (2)</xref>, measurements were performed at a temperature of 1.6 K, with a current of 1 μA, on the device shown in <xref rid=\"F1\" ref-type=\"fig\">Fig. 1 (c)</xref> (<italic toggle=\"yes\">M</italic> = 16). The <xref rid=\"SD1\" ref-type=\"supplementary-material\">Supplementary Material</xref> also includes information about the mobility of the devices, which range from 3000 cm<sup>2</sup>V<sup>−1</sup>s<sup>−1</sup> and 5000 cm<sup>2</sup>V<sup>−1</sup>s<sup>−1</sup> for both region types. Recall that regarding edge channel dynamics in a bipolar graphene <italic toggle=\"yes\">pn</italic>J, the quantized states exhibited by the ν = 2 plateau circulate in opposite directions and merge to form a parallel edge channel at the junction. These channels, as mentioned in Ref. [<xref rid=\"R50\" ref-type=\"bibr\">50</xref>], supply particles at the junction from both reservoirs. After particles jointly propagate along the interface and to the device boundary, they return to their respective regions. Resistance quantization was explained by mode-mixing at the junction, with the idea that regardless of reservoir, all incoming charges had the same probability of crossing the junction [<xref rid=\"R50\" ref-type=\"bibr\">50</xref>]. For information regarding quantum shot noise and Fano factor calculations, please see the <xref rid=\"SD1\" ref-type=\"supplementary-material\">Supplementary Material</xref>. Overall, these dynamics manifest themselves as a quantized resistance across the junction and can be treated as a circuit element in LTspice.</p><p id=\"P26\">In <xref rid=\"F4\" ref-type=\"fig\">Fig. 4 (a)</xref>, two example measurements taken between ± 9 T are shown in black and red for the harmonized and discordant case of <italic toggle=\"yes\">n</italic><sub>1</sub> = 7, respectively. For Case 1 (black line), a thin gray line is used to mark the simulated CER of 5, and a shaded gold region marks the 1σ uncertainty of the experimental average, as calculated by the whole range excluding −5 T to 5 T. For Case 2 (red line), a dark red line is used to mark the simulated CER of <inline-formula><mml:math id=\"M15\" display=\"inline\"><mml:mfrac><mml:mn>79</mml:mn><mml:mn>16</mml:mn></mml:mfrac></mml:math></inline-formula>, with a corresponding experimental uncertainty range shaded in green. The simulated values fall within the error of the experimentally-obtained values.</p><p id=\"P27\">The CERs were calculated with <xref rid=\"FD2\" ref-type=\"disp-formula\">Eq. (2)</xref> for the <italic toggle=\"yes\">M</italic> = 16 device and are shown in <xref rid=\"F4\" ref-type=\"fig\">Fig. 4 (b)</xref> and <xref rid=\"F4\" ref-type=\"fig\">(c)</xref>. The calculations agreed exactly with the simulations, as expected. Both the calculations and simulations are represented by a red ‘X’ and were compared with experimental data, represented by blue points, for both harmonized and discordant cases. The error bars are shown in light blue, with many falling within the size of the experimental data points. The same gray and red lines from <xref rid=\"F4\" ref-type=\"fig\">Fig. 4 (a)</xref> are shown, along with a box surrounding the relevant data points. These markers enhance the clarity of the difference between the harmonized and discordant cases. The agreement between the experiment and calculated CERs supports the validity of <xref rid=\"FD2\" ref-type=\"disp-formula\">Eq. (2)</xref> for all <italic toggle=\"yes\">N</italic> = 2 configurations.</p></sec><sec id=\"S9\"><label>3.3</label><title>Interpreting simulation trends (N = 3)</title><p id=\"P28\">Simulations were next carried out for the <italic toggle=\"yes\">N</italic> = 3 case (two terminals of a single polarity and one terminal of opposite polarity). The CERs (now labelled <italic toggle=\"yes\">q</italic><sub>2</sub>) of numerous arbitrary configurations were again simulated as a function of junction number <italic toggle=\"yes\">n</italic><sub><italic toggle=\"yes\">x</italic></sub> = <italic toggle=\"yes\">M</italic> − <italic toggle=\"yes\">n</italic><sub>1</sub> − <italic toggle=\"yes\">n</italic><sub>2</sub>, where <italic toggle=\"yes\">n</italic><sub><italic toggle=\"yes\">x</italic></sub> is defined between the two like-polar terminals. The other two numbers <italic toggle=\"yes\">n</italic><sub>1</sub> and <italic toggle=\"yes\">n</italic><sub>2</sub> describe the junction number between the two opposite-polarity pairs, with <italic toggle=\"yes\">n</italic><sub>1</sub> being the smaller number to be consistent with the traditional Hall bar case [<xref rid=\"R56\" ref-type=\"bibr\">56</xref>].</p><p id=\"P29\">Two example simulation sets are shown in <xref rid=\"F5\" ref-type=\"fig\">Fig. 5 (a)</xref>, with both sets having <italic toggle=\"yes\">n</italic><sub>1</sub> = 1 and <italic toggle=\"yes\">n</italic><sub>2</sub> = 3. The number of regions <italic toggle=\"yes\">M</italic> was modulated, allowing one to model <italic toggle=\"yes\">q</italic><sub>2</sub>(<italic toggle=\"yes\">n</italic><sub><italic toggle=\"yes\">x</italic></sub>). Both the harmonized and discordant cases were modeled exactly to the Hill-Langmuir equation, and the limiting case of <italic toggle=\"yes\">n</italic><sub><italic toggle=\"yes\">x</italic></sub> → ∞ revealed again that <inline-formula><mml:math id=\"M16\" display=\"inline\"><mml:mrow><mml:msub><mml:mi>q</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>→</mml:mo><mml:msubsup><mml:mi>q</mml:mi><mml:mn>2</mml:mn><mml:mi>L</mml:mi></mml:msubsup></mml:mrow></mml:math></inline-formula>, which can be calculated [<xref rid=\"R56\" ref-type=\"bibr\">56</xref>]. In the case of <xref rid=\"F5\" ref-type=\"fig\">Fig. 5 (a)</xref>, <inline-formula><mml:math id=\"M17\" display=\"inline\"><mml:mrow><mml:msubsup><mml:mi>q</mml:mi><mml:mn>2</mml:mn><mml:mi>L</mml:mi></mml:msubsup><mml:mo>=</mml:mo><mml:mfrac><mml:mn>8</mml:mn><mml:mn>5</mml:mn></mml:mfrac></mml:mrow></mml:math></inline-formula>, and this value is marked by a dashed line. Additionally, <inline-formula><mml:math id=\"M18\" display=\"inline\"><mml:mrow><mml:msubsup><mml:mi>q</mml:mi><mml:mn>2</mml:mn><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>0</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup></mml:mrow></mml:math></inline-formula> is marked for both cases. The two values at <italic toggle=\"yes\">n</italic><sub><italic toggle=\"yes\">x</italic></sub> = 12 are simulated values with corresponding experimental data shown in the first cases of <xref rid=\"F5\" ref-type=\"fig\">Fig. 5 (b)</xref> and <xref rid=\"F5\" ref-type=\"fig\">(c)</xref>.</p><p id=\"P30\">By rewriting <xref rid=\"FD1\" ref-type=\"disp-formula\">Eq. (1)</xref> and <xref rid=\"FD2\" ref-type=\"disp-formula\">(2)</xref>, one may more clearly see the iterative nature of the formula that will describe all <italic toggle=\"yes\">N</italic> =3 cases. Recall that for all <italic toggle=\"yes\">N</italic> = 2 cases:\n<disp-formula id=\"FD3\">\n<label>(3)</label>\n<mml:math id=\"M3\" display=\"block\"><mml:mrow><mml:msub><mml:mi>q</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>n</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msubsup><mml:mi>q</mml:mi><mml:mn>1</mml:mn><mml:mi>L</mml:mi></mml:msubsup><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>M</mml:mi><mml:mo>−</mml:mo><mml:msub><mml:mi>n</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>+</mml:mo><mml:msubsup><mml:mi>q</mml:mi><mml:mn>1</mml:mn><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>0</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:msub><mml:mi>n</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>M</mml:mi><mml:mo>−</mml:mo><mml:msub><mml:mi>n</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>+</mml:mo><mml:msub><mml:mi>n</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:mfrac></mml:mrow></mml:math>\n</disp-formula></p><p id=\"P31\">Here, the only term that changes for harmonized or discordant cases is <inline-formula><mml:math id=\"M19\" display=\"inline\"><mml:mrow><mml:msubsup><mml:mi>q</mml:mi><mml:mn>1</mml:mn><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>0</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup></mml:mrow></mml:math></inline-formula>. For all cases in <italic toggle=\"yes\">N</italic> = 3, the parameter <inline-formula><mml:math id=\"M20\" display=\"inline\"><mml:mrow><mml:msub><mml:mi>x</mml:mi><mml:mn>0</mml:mn></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>n</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>n</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:mfrac></mml:mrow></mml:math></inline-formula>, and the general CER formula becomes:\n<disp-formula id=\"FD4\">\n<label>(4)</label>\n<mml:math id=\"M4\" display=\"block\"><mml:mrow><mml:msub><mml:mi>q</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>n</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>n</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msubsup><mml:mi>q</mml:mi><mml:mn>2</mml:mn><mml:mi>L</mml:mi></mml:msubsup><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>M</mml:mi><mml:mo>−</mml:mo><mml:msub><mml:mi>n</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mi>n</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>+</mml:mo><mml:msubsup><mml:mi>q</mml:mi><mml:mn>2</mml:mn><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>0</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup><mml:msub><mml:mi>x</mml:mi><mml:mn>0</mml:mn></mml:msub></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>M</mml:mi><mml:mo>−</mml:mo><mml:msub><mml:mi>n</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mi>n</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>+</mml:mo><mml:msub><mml:mi>x</mml:mi><mml:mn>0</mml:mn></mml:msub></mml:mrow></mml:mfrac></mml:mrow></mml:math>\n</disp-formula></p><p id=\"P32\">And again, the difference between harmonized and discordant cases is embedded in the term <inline-formula><mml:math id=\"M21\" display=\"inline\"><mml:mrow><mml:msubsup><mml:mi>q</mml:mi><mml:mn>2</mml:mn><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>0</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msubsup></mml:mrow></mml:math></inline-formula>, which takes on the values <inline-formula><mml:math id=\"M22\" display=\"inline\"><mml:mfrac><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>n</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:mn>1</mml:mn><mml:mo stretchy=\"false\">)</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>n</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:mn>1</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>n</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:mfrac></mml:math></inline-formula> or <inline-formula><mml:math id=\"M23\" display=\"inline\"><mml:mfrac><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>n</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>n</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:msub><mml:mi>n</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>n</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:mfrac></mml:math></inline-formula>, respectively (see <xref rid=\"SD1\" ref-type=\"supplementary-material\">Supplementary Material</xref> for more details on how these values were determined).</p></sec><sec id=\"S10\"><label>3.4</label><title>Comparing experimental data to corresponding simulations (N = 3)</title><p id=\"P33\">To verify <xref rid=\"FD4\" ref-type=\"disp-formula\">Eq. (4)</xref>, data were collected from several <italic toggle=\"yes\">N</italic> = 3 cases. Six example harmonized and discordant cases are shown in <xref rid=\"F5\" ref-type=\"fig\">Fig. 5 (b)</xref> and <xref rid=\"F5\" ref-type=\"fig\">(c)</xref>, respectively. Each experimental data point (light blue triangle) very nearly overlays with its corresponding simulation (red ‘X’), and the simulations match the calculations exactly. Additionally, each point is accompanied by an illustration of each configuration. The error bars, in a darker shade of blue, indicate 1σ uncertainty and have a similar size as the experimental data points in most cases. The exact CERs for all presented experimental data are listed in the <xref rid=\"SD1\" ref-type=\"supplementary-material\">Supplementary Material</xref>. The agreement within uncertainty with simulations demonstrates promise that these large-scale devices can be fabricated with excellent functionality.</p></sec></sec><sec id=\"S11\"><label>4.</label><title>Conclusion</title><p id=\"P34\">This work reports the successful fabrication of millimeter-scale graphene Corbino <italic toggle=\"yes\">pn</italic>J devices and correpsonding measurements of such devices in the quantum Hall regime to understand how the edge channel currents resulting from being in the ν = 2 plateau, manifesting as quantized effective circuit resistances, are affected by periodic boundary conditions. Experimental data were compared with results from LTspice current simulations. Furthermore, empirical formulae were derived for the case of using two or three current terminals of arbitrary configuration. Overall, these experiments have validated that these scalable <italic toggle=\"yes\">pn</italic>J devices are versatile in how they are implemented in circuits and that using Corbino geometries to permit edge channel current flow between the outer and inner edges offers another adjustable parameter for quantum electrical circuits.</p></sec><sec sec-type=\"supplementary-material\" id=\"SM1\"><title>Supplementary Material</title><supplementary-material id=\"SD1\" position=\"float\" content-type=\"local-data\"><label>Rigosi_JPD_SM</label><media xlink:href=\"NIHMS1616338-supplement-Rigosi_JPD_SM.docx\" id=\"d64e1877\" position=\"anchor\"/></supplementary-material></sec></body><back><ack id=\"S12\"><title>Acknowledgements and Notes</title><p id=\"P35\">The work of DKP at NIST was made possible by C-T Liang of National Taiwan University. 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Green dots and blue triangles are shown for an example configuration (in this case, a two-terminal measurement).</p></caption><graphic xlink:href=\"nihms-1616338-f0001\" position=\"float\"/></fig><fig position=\"float\" id=\"F2\"><label>Fig. 2.</label><caption><p id=\"P38\">(a) Schematic of the graphene Corbino <italic toggle=\"yes\">pn</italic>J device from <xref rid=\"F1\" ref-type=\"fig\">Fig. 1 (c)</xref> is shown as part of a circuit intended to exhibit many quantized resistances. In this case, two positive current terminals were used (with each the outer and inner ring hosting one terminal) and one negative terminal was used (outer ring). (b) A topologically identical schematic of the device is shown and accurately reflects the configuration of the quantum Hall elements (<italic toggle=\"yes\">n</italic>-type and <italic toggle=\"yes\">p</italic>-type regions) in the LTspice simulation.</p></caption><graphic xlink:href=\"nihms-1616338-f0002\" position=\"float\"/></fig><fig position=\"float\" id=\"F3\"><label>Fig. 3.</label><caption><p id=\"P39\">(a) Data representing the simulated CERs of the two-terminal measurements for Corbino <italic toggle=\"yes\">pn</italic>J devices of varying number of distinct regions, <italic toggle=\"yes\">M</italic>. (b) The illustrations shown here exemplify a physical interpretation for why an alternating behavior is observed in the simulations whereby the negative terminal is moved along the outer or inner circumference. (c) The two configurations in (b) are simulated for varying <italic toggle=\"yes\">n<sub>x</sub></italic>, with the results providing insight into how one may express a general formula to calculate the CER of an arbitrary <italic toggle=\"yes\">N</italic> = 2 case.</p></caption><graphic xlink:href=\"nihms-1616338-f0003\" position=\"float\"/></fig><fig position=\"float\" id=\"F4\"><label>Fig. 4.</label><caption><p id=\"P40\">(a) Magnetoresistance measurements were performed for a variety of <italic toggle=\"yes\">N</italic> = 2 configurations on the device shown in <xref rid=\"F1\" ref-type=\"fig\">Fig. 1 (c)</xref>. Two example magnetic flux density sweeps are shown in black and red for the harmonized and discordant case of <italic toggle=\"yes\">n</italic><sub>1</sub> = 7, respectively. The thin gray and dark red lines are the simulated quantized values, and the shaded gold and green regions are the 1σ uncertainty regions of the respective experimental values. (b) The CERs were simulated (red X) and compared with experimental data (blue points) in harmonized cases as a function of <italic toggle=\"yes\">n</italic><sub>1</sub>. (c) CERs were simulated and compared with experimental data in discordant cases as a function of <italic toggle=\"yes\">n</italic><sub>1</sub>. Error bars (same 1σ uncertainty as exemplified in (a)) are shown in light blue and fall within the size of the blue points in most cases.</p></caption><graphic xlink:href=\"nihms-1616338-f0004\" position=\"float\"/></fig><fig position=\"float\" id=\"F5\"><label>Fig. 5.</label><caption><p id=\"P41\">(a) Simulations for the two shown configurations were performed while varying <italic toggle=\"yes\">n</italic><sub><italic toggle=\"yes\">x</italic></sub>. (b) Experimental data for a variety of harmonized and (c) discordant cases are compared with their simulated counterparts (and verified again with <xref rid=\"FD4\" ref-type=\"disp-formula\">Eq. (4)</xref>). The exact configuration is depicted for each case, and error bars indicate 1σ uncertainty and are of similar size to the light blue triangles (experimental data points) in most cases.</p></caption><graphic xlink:href=\"nihms-1616338-f0005\" position=\"float\"/></fig></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"methods-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Wellcome Open Res</journal-id><journal-id journal-id-type=\"iso-abbrev\">Wellcome Open Res</journal-id><journal-id journal-id-type=\"pmc\">Wellcome Open Res</journal-id><journal-title-group><journal-title>Wellcome Open Research</journal-title></journal-title-group><issn pub-type=\"epub\">2398-502X</issn><publisher><publisher-name>F1000 Research Limited</publisher-name><publisher-loc>London, UK</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32832701</article-id><article-id pub-id-type=\"pmc\">PMC7431977</article-id><article-id pub-id-type=\"doi\">10.12688/wellcomeopenres.15881.2</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Method Article</subject></subj-group><subj-group><subject>Articles</subject></subj-group></article-categories><title-group><article-title>Dynamic causal modelling of COVID-19</article-title><fn-group content-type=\"pub-status\"><fn><p>[version 2; peer review: 2 approved]</p></fn></fn-group></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Friston</surname><given-names>Karl J.</given-names></name><role content-type=\"http://credit.casrai.org/\">Conceptualization</role><role content-type=\"http://credit.casrai.org/\">Data Curation</role><role content-type=\"http://credit.casrai.org/\">Formal Analysis</role><role content-type=\"http://credit.casrai.org/\">Funding Acquisition</role><role content-type=\"http://credit.casrai.org/\">Investigation</role><role content-type=\"http://credit.casrai.org/\">Methodology</role><role content-type=\"http://credit.casrai.org/\">Project Administration</role><role content-type=\"http://credit.casrai.org/\">Resources</role><role content-type=\"http://credit.casrai.org/\">Software</role><role content-type=\"http://credit.casrai.org/\">Validation</role><role content-type=\"http://credit.casrai.org/\">Visualization</role><role content-type=\"http://credit.casrai.org/\">Writing – Original Draft Preparation</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0001-7984-8909</contrib-id><xref ref-type=\"aff\" rid=\"a1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Parr</surname><given-names>Thomas</given-names></name><role content-type=\"http://credit.casrai.org/\">Methodology</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0001-5108-5743</contrib-id><xref ref-type=\"aff\" rid=\"a1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Zeidman</surname><given-names>Peter</given-names></name><role content-type=\"http://credit.casrai.org/\">Methodology</role><role content-type=\"http://credit.casrai.org/\">Software</role><role content-type=\"http://credit.casrai.org/\">Validation</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><xref ref-type=\"aff\" rid=\"a1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Razi</surname><given-names>Adeel</given-names></name><role content-type=\"http://credit.casrai.org/\">Methodology</role><role content-type=\"http://credit.casrai.org/\">Software</role><role content-type=\"http://credit.casrai.org/\">Validation</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0002-0779-9439</contrib-id><xref ref-type=\"corresp\" rid=\"c1\">a</xref><xref ref-type=\"aff\" rid=\"a1\">1</xref><xref ref-type=\"aff\" rid=\"a2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Flandin</surname><given-names>Guillaume</given-names></name><role content-type=\"http://credit.casrai.org/\">Methodology</role><role content-type=\"http://credit.casrai.org/\">Software</role><role content-type=\"http://credit.casrai.org/\">Validation</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><xref ref-type=\"aff\" rid=\"a1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Daunizeau</surname><given-names>Jean</given-names></name><role content-type=\"http://credit.casrai.org/\">Methodology</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><xref ref-type=\"aff\" rid=\"a3\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Hulme</surname><given-names>Ollie J.</given-names></name><role content-type=\"http://credit.casrai.org/\">Methodology</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><xref ref-type=\"aff\" rid=\"a4\">4</xref><xref ref-type=\"aff\" rid=\"a5\">5</xref></contrib><contrib contrib-type=\"author\"><name><surname>Billig</surname><given-names>Alexander J.</given-names></name><role content-type=\"http://credit.casrai.org/\">Methodology</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0002-4531-8616</contrib-id><xref ref-type=\"aff\" rid=\"a6\">6</xref></contrib><contrib contrib-type=\"author\"><name><surname>Litvak</surname><given-names>Vladimir</given-names></name><role content-type=\"http://credit.casrai.org/\">Methodology</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><xref ref-type=\"aff\" rid=\"a1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Moran</surname><given-names>Rosalyn J.</given-names></name><role content-type=\"http://credit.casrai.org/\">Methodology</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0002-2736-2621</contrib-id><xref ref-type=\"aff\" rid=\"a7\">7</xref></contrib><contrib contrib-type=\"author\"><name><surname>Price</surname><given-names>Cathy J.</given-names></name><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><xref ref-type=\"aff\" rid=\"a1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Lambert</surname><given-names>Christian</given-names></name><role content-type=\"http://credit.casrai.org/\">Methodology</role><role content-type=\"http://credit.casrai.org/\">Writing – Review & Editing</role><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0001-8281-4611</contrib-id><xref ref-type=\"aff\" rid=\"a1\">1</xref></contrib><aff id=\"a1\">\n<label>1</label>Wellcome Centre for Human Neuroimaging, University College London, London, WC1N 3BG, UK</aff><aff id=\"a2\">\n<label>2</label>Turner Institute for Brain and Mental Health & Monash Biomedical Imaging, Monash University, Clayton, VIC, 3800, Australia</aff><aff id=\"a3\">\n<label>3</label>Institut du Cerveau et de la Moelle épinière, INSERM UMRS, Paris, 1127, France</aff><aff id=\"a4\">\n<label>4</label>Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark</aff><aff id=\"a5\">\n<label>5</label>London Mathematical Laboratory, Hammersmith, London, UK</aff><aff id=\"a6\">\n<label>6</label>Ear Institute, University College London, London, UK</aff><aff id=\"a7\">\n<label>7</label>Centre for Neuroimaging Science, Department of Neuroimaging, IoPPN, King's College London, London, UK</aff></contrib-group><author-notes><corresp id=\"c1\"><label>a</label><email xlink:href=\"mailto:adeel.razi@monash.edu\">adeel.razi@monash.edu</email></corresp><fn fn-type=\"COI-statement\"><p>No competing interests were disclosed.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>7</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>7</day><month>8</month><year>2020</year></pub-date><!-- PMC Release delay is 0 months and 0 days and was based on the <pub-date pub-type=\"epub\"/>. --><volume>5</volume><elocation-id>89</elocation-id><history><date date-type=\"accepted\"><day>30</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright: © 2020 Friston KJ et al.</copyright-statement><copyright-year>2020</copyright-year><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri content-type=\"pdf\" xlink:href=\"wellcomeopenres-5-17779.pdf\"/><abstract><p>This technical report describes a dynamic causal model of the spread of coronavirus through a population. The model is based upon ensemble or population dynamics that generate outcomes, like new cases and deaths over time. The purpose of this model is to quantify the uncertainty that attends predictions of relevant outcomes. By assuming suitable conditional dependencies, one can model the effects of interventions (e.g., social distancing) and differences among populations (e.g., herd immunity) to predict what might happen in different circumstances. Technically, this model leverages state-of-the-art variational (Bayesian) model inversion and comparison procedures, originally developed to characterise the responses of neuronal ensembles to perturbations. Here, this modelling is applied to epidemiological populations—to illustrate the kind of inferences that are supported and how the model\n<italic>per se</italic> can be optimised given timeseries data. Although the purpose of this paper is to describe a modelling protocol, the results illustrate some interesting perspectives on the current pandemic; for example, the nonlinear effects of herd immunity that speak to a self-organised mitigation process.</p></abstract><kwd-group kwd-group-type=\"author\"><kwd>coronavirus</kwd><kwd>epidemiology</kwd><kwd>compartmental models</kwd><kwd>dynamic causal modelling</kwd><kwd>variational</kwd><kwd>Bayesian</kwd></kwd-group><funding-group><award-group id=\"fund-1\" xlink:href=\"http://dx.doi.org/10.13039/501100000265\"><funding-source>Medical Research Council</funding-source><award-id>MR/R006504/1</award-id></award-group><award-group id=\"fund-2\"><funding-source>Wellcome Trust</funding-source><award-id>203147</award-id><award-id>088130</award-id><award-id>091681</award-id></award-group><award-group id=\"fund-3\" xlink:href=\"http://dx.doi.org/10.13039/501100000923\"><funding-source>Australian Research Council</funding-source><award-id>DE170100128;DP200100757</award-id></award-group><funding-statement>This work was supported by Wellcome through core funding to the Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology [203147]; and a Principle Research Fellowship grant to KJF [088130]. AJB is supported by a Wellcome Trust grant [091681].\nAR is funded by the Australian Research Council [Refs: DE170100128 and DP200100757]. CL is supported by an MRC Clinician Scientist award [MR/R006504/1].</funding-statement><funding-statement><italic>The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</italic></funding-statement></funding-group></article-meta><notes notes-type=\"version-changes\"><sec sec-type=\"version-changes\"><label>Revised</label><title>Amendments from Version 1</title><p>There are two main changes that we have in this new version.   1. Regarding reviewers' request for providing specific predictive validity, we thought it would be disingenuous to change the predictions in light of subsequent outcomes—or the procedures that were applied in subsequent reports. However, we have now added an extensive ‘Posthoc evaluation of model predictions’ section in the revised draft that addresses the predictions in light of current data. This section implicitly addresses the specific points about predictions in the reviewers’ comments. 2. We have also attempted to make the demarcation between a procedural and predictive contribution clearer throughout the text by including several new footnotes (numbered 11, 13, 16, 24, 31 and 33).</p></sec></notes></front><body><sec sec-type=\"intro\"><title>Introduction</title><p>The purpose of this paper is to show how dynamic causal modelling can be used to make predictions—and test hypotheses—about the ongoing coronavirus pandemic (\n<xref rid=\"ref-16\" ref-type=\"bibr\">Huang\n<italic>et al</italic>., 2020</xref>;\n<xref rid=\"ref-45\" ref-type=\"bibr\">Wu\n<italic>et al</italic>., 2020</xref>;\n<xref rid=\"ref-48\" ref-type=\"bibr\">Zhu\n<italic>et al</italic>., 2020</xref>). It should be read as a technical report\n<sup><xref ref-type=\"other\" rid=\"FN1\">1</xref></sup>, written for people who want to understand what this kind of modelling has to offer (or just build an intuition about modelling pandemics). It contains a sufficient level of technical detail to implement the model using MATLAB (or its open source version Octave), while explaining things heuristically for non-technical readers. The examples in this report are used to showcase the procedures and subsequent inferences that can be drawn. Having said this, there are some quantitative results that will be of general interest. These results are entirely conditional upon the model used.</p><p>Dynamic causal modelling (DCM) refers to the characterisation of coupled dynamical systems in terms of how observable data are generated by unobserved (i.e., latent or hidden) causes (\n<xref rid=\"ref-12\" ref-type=\"bibr\">Friston\n<italic>et al</italic>., 2003</xref>;\n<xref rid=\"ref-28\" ref-type=\"bibr\">Moran\n<italic>et al</italic>., 2013</xref>). Dynamic causal modelling subsumes state estimation and system identification under one Bayesian procedure, to provide probability densities over unknown latent states (i.e., state estimation) and model parameters (i.e., system identification), respectively. Its focus is on estimating the uncertainty about these estimates to quantify the evidence for competing models, and the confidence in various predictions. In this sense, DCM combines data assimilation and uncertainty quantification within the same optimisation process. Specifically, the posterior densities (i.e., Bayesian beliefs) over states and parameters—and the precision of random fluctuations—are optimised by maximising a variational bound on the model’s marginal likelihood, also known as\n<italic>model evidence</italic>. This bound is known as variational free energy or the evidence lower bound (ELBO) in machine learning (\n<xref rid=\"ref-8\" ref-type=\"bibr\">Friston\n<italic>et al</italic>., 2007</xref>;\n<xref rid=\"ref-14\" ref-type=\"bibr\">Hinton & Zemel, 1993</xref>;\n<xref rid=\"ref-23\" ref-type=\"bibr\">MacKay, 1995</xref>;\n<xref rid=\"ref-43\" ref-type=\"bibr\">Winn & Bishop, 2005</xref>).</p><p>Intuitively, this means one is trying to optimise probabilistic beliefs—about the unknown quantities generating some data—such that the (marginal) likelihood of those data is as large as possible. The marginal likelihood\n<sup><xref ref-type=\"other\" rid=\"FN2\">2</xref></sup> or model evidence can always be expressed as\n<italic>accuracy</italic> minus\n<italic>complexity</italic>. This means that the best models provide an accurate account of some data as\n<italic>simply as possible</italic>. Therefore, the model with the highest evidence is not necessarily a description of the process generating data: rather, it is the simplest description that provides an accurate account of those data. In short, it is ‘as if’ the data were generated by this kind of model. Importantly, models with the highest evidence will generalise to new data and preclude overfitting, or overconfident predictions about outcomes that have yet to be measured. In light of this, it is imperative to select the parameters or models that maximise model evidence or variational free energy (as opposed to goodness of fit or accuracy). However, this requires the estimation of the uncertainty about model parameters and states, which is necessary to evaluate the (marginal) likelihood of the data at hand. This is why estimating uncertainty is crucial. Being able to score a model—in terms of its evidence—means that one can compare different models of the same data. This is known as\n<italic>Bayesian model comparison</italic> and plays an important role when testing different models or hypotheses about how the data are caused. We will see examples of this later. This aspect of dynamic causal modelling means that one does not have to commit to a particular form (i.e., parameterisation) of a model. Rather, one can explore a repertoire of plausible models and let the data decide which is the most apt.</p><p>Dynamic causal models are\n<italic>generative models</italic> that generate consequences (i.e., data) from causes (i.e., hidden states and parameters). The form of these models can vary depending upon the kind of system at hand. Here, we use a ubiquitous form of model; namely, a mean field approximation to loosely coupled ensembles or populations. In the neurosciences, this kind of model is applied to populations of neurons that respond to experimental stimulation (\n<xref rid=\"ref-25\" ref-type=\"bibr\">Marreiros\n<italic>et al</italic>., 2009</xref>;\n<xref rid=\"ref-28\" ref-type=\"bibr\">Moran\n<italic>et al</italic>., 2013</xref>). Here, we use the same mathematical approach to model a population of individuals and their response to an epidemic. The key idea behind these (mean field) models is that the constituents of the ensemble are exchangeable; in the sense that sampling people from the population at random will give the same average as following one person over a long period of time. Under this assumption\n<sup><xref ref-type=\"other\" rid=\"FN3\">3</xref></sup>, one can then work out, analytically, how the probability distribution over various states of people evolve over time, e.g., whether someone was infected or not. This involves parameterising the probability that people will transition from one state to another. By assuming the population is large, one can work out the likelihood of observing a certain number of people who were infected, given the probabilistic state of the population at that point in time. In turn, one can work out the probability of a sequence or timeseries of new cases. This is the kind of generative model used here, where the latent states were chosen to generate the data that are—or could be—used to track a pandemic.\n<xref ref-type=\"fig\" rid=\"f1\">Figure 1</xref> provides an overview of this model. In terms of epidemiological models, this can be regarded as an extended SEIR (susceptible, exposed, infected and recovered) compartmental model (\n<xref rid=\"ref-19\" ref-type=\"bibr\">Kermack\n<italic>et al</italic>., 1997</xref>). Please see (\n<xref rid=\"ref-20\" ref-type=\"bibr\">Kucharski\n<italic>et al</italic>., 2020</xref>) for an application of this kind of model to COVID-19\n<sup><xref ref-type=\"other\" rid=\"FN4\">4</xref></sup>.</p><fig fig-type=\"figure\" id=\"f1\" orientation=\"portrait\" position=\"anchor\"><label>Figure 1. </label><caption><title>generative model.</title><p>This figure is a schematic description of the generative model used in subsequent analyses. In brief, this compartmental model generates timeseries data based on a mean field approximation to ensemble or population dynamics. The implicit probability distributions are over four latent factors, each with four levels or states. These factors are sufficient to generate measurable outcomes; for example, the number of new cases or the proportion of people infected. The first factor is the location of an individual, who can be at\n<italic>home</italic>, at\n<italic>work</italic>, in a critical care unit (\n<italic>CCU</italic>) or in the\n<italic>morgue</italic>. The second factor is infection status; namely,\n<italic>susceptible</italic> to infection,\n<italic>infected</italic>,\n<italic>infectious</italic> or\n<italic>immune</italic>. This model assumes that there is a progression from a state of susceptibility to immunity, through a period of (pre-contagious) infection to an infectious (contagious) status. The third factor is clinical status; namely,\n<italic>asymptomatic</italic>,\n<italic>symptomatic</italic>,\n<italic>acute respiratory distress syndrome (ARDS)</italic> or\n<italic>deceased</italic>. Again, there is an assumed progression from asymptomatic to ARDS, where people with ARDS can either recover to an asymptomatic state or not. Finally, the fourth factor represents diagnostic or testing status. An individual can be\n<italic>untested</italic> or\n<italic>waiting</italic> for the results of a test that can either be\n<italic>positive</italic> or\n<italic>negative</italic>. With this setup, one can be in one of four places, with any infectious status, expressing symptoms or not, and having test results or not. Note that—in this construction—it is possible to be infected and yet be asymptomatic. However, the marginal distributions are not independent, by virtue of the dynamics that describe the transition among states within each factor. Crucially, the transitions within any factor depend upon the marginal distribution of other factors. For example, the probability of becoming infected, given that one is susceptible to infection, depends upon whether one is at home or at work. Similarly, the probability of developing symptoms depends upon whether one is infected or not. The probability of testing negative depends upon whether one is susceptible (or immune) to infection, and so on. Finally, to complete the circular dependency, the probability of leaving home to go to work depends upon the number of infected people in the population, mediated by social distancing. The curvilinear arrows denote a conditioning of transition probabilities on the marginal distributions over other factors. These conditional dependencies constitute the mean field approximation and enable the dynamics to be solved or integrated over time. At any point in time, the probability of being in any combination of the four states determines what would be observed at the population level. For example, the occupancy of the deceased level of the clinical factor determines the current number of people who have recorded deaths. Similarly, the occupancy of the positive level of the testing factor determines the expected number of positive cases reported. From these expectations, the expected number of new cases per day can be generated. A more detailed description of the generative model—in terms of transition probabilities—can be found in in the main text.</p></caption><graphic xlink:href=\"wellcomeopenres-5-17779-g0000\"/></fig><p>There are number of advantages to using a model of this sort. First, it means that one can include every variable that ‘matters’, such that one is not just modelling the spread of an infection but an ensemble response in terms of behaviour (e.g., social distancing). This means that one can test hypotheses about the contribution of various responses that are installed in the model—or what would happen under a different kind of response. A second advantage of having a generative model is that one can evaluate its evidence in relation to alternative models, and therefore optimise the structure of the model itself. For example, does social distancing behaviour depend upon the number of people who are infected? Or, does it depend on how many people have tested positive for COVID-19? (this question is addressed below). A third advantage is more practical, in terms of data analysis: because we are dealing with ensemble dynamics, there is no need to create multiple realisations or random samples to estimate uncertainty. This is because the latent states are not the states of an individual but the sufficient statistics of a probability distribution over individual states. In other words, we replace random fluctuations in hidden states with hidden states that parameterise random fluctuations. The practical consequence of this is that one can fit these models quickly and efficiently—and perform model comparisons over thousands of models. A fourth advantage is that, given a set of transition probabilities, the ensemble dynamics are specified completely. This has the simple but important consequence that the only unknowns in the model are the parameters of these transition probabilities. Crucially, in this model, these do not change with time. This means that we can convert what would have been a very complicated, nonlinear state space model for data assimilation into a nonlinear mapping from some unknown (probability transition) parameters to a sequence of observations. We can therefore make precise predictions about the long-term future, under particular circumstances. This follows because the only uncertainty about outcomes inherits from the uncertainty about the parameters, which do not change with time. These points may sound subtle; however, the worked examples below have been chosen to illustrate these properties.</p><p>This technical report comprises four sections. The first details the generative model, with a focus on the conditional dependencies that underwrite the ensemble dynamics generating outcomes. The outcomes in question here pertain to a regional outbreak. This can be regarded as a generative model for the first wave of an epidemic in a large city or metropolis. This section considers variational model inversion and comparison, under hierarchical models. In other words, it considers the distinction between (first level) models of an outbreak in one country and (second level) models of differences among countries, in terms of model parameters. The second section briefly surveys the results of second level (between-country) modelling, looking at those aspects of the model that are conserved over countries (i.e., random effects) and those which are not (i.e., fixed effects). The third section then moves on to the dynamics and predictions for a single country; here, the United Kingdom. It considers the likely outcomes over the next few weeks and how confident one can be about these outcomes, given data from all countries to date. This section drills down on the parameters that matter in terms of affecting death rates. It presents a sensitivity analysis that establishes the contribution of parameters or causes in the model to eventual outcomes. It concludes by looking at the effects of social distancing and herd immunity. The final section concludes with a consideration of predictive validity by comparing predicted and actual outcomes.</p></sec><sec><title>The generative model</title><p>This section describes the generative model summarised schematically in\n<xref ref-type=\"fig\" rid=\"f1\">Figure 1</xref>, while the data used to invert or fit this model are summarised in\n<xref ref-type=\"fig\" rid=\"f2\">Figure 2</xref>. These data comprise global (worldwide) timeseries from countries and regions from the initial reports of positive cases in China to the current day\n<sup><xref ref-type=\"other\" rid=\"FN5\">5</xref></sup>.</p><fig fig-type=\"figure\" id=\"f2\" orientation=\"portrait\" position=\"anchor\"><label>Figure 2. </label><caption><title>timeseries data.</title><p>This figure provides a brief overview of the timeseries used for subsequent modelling, with a focus on the early trajectories of mortality. The upper left panel shows the distribution, over countries, of the number of days after the onset of an outbreak—defined as 8 days before more than one case was reported. At the time of writing (4\n<sup>th</sup> April 2020), a substantial number of countries witnessed an outbreak lasting for more than 60 days. The upper right panel plots the total number of deaths against the durations in the left panel. Those countries whose outbreak started earlier have greater cumulative deaths. The middle left panel plots the new deaths reported (per day) over a 48-day period following the onset of an outbreak. The colours of the lines denote different countries. These countries are listed in the lower left panel, which plots the cumulative death rate. China is clearly the first country to be severely affected, with remaining countries evincing an accumulation of deaths some 30 days after China. The middle right panel is a logarithmic plot of the total deaths against population size in the initial (48-day) period. Interestingly, there is little correlation between the total number of deaths and population size. However, there is a stronger correlation between the total number of cases reported (within the first 48 days) and the cumulative deaths as shown in lower right panel. In this period, Germany has the greatest ratio of total cases to deaths. Countries were included if their outbreak had lasted for more than 48 days and more than 16 deaths had been reported. The timeseries were smoothed with a Gaussian kernel (full width half maximum of two days) to account for erratic reporting (e.g., recording deaths over the weekend).</p></caption><graphic xlink:href=\"wellcomeopenres-5-17779-g0001\"/></fig><p>The generative model is a mean field model of ensemble dynamics. In other words, it is a state space model where the states correspond to the sufficient statistics (i.e., parameters) of a probability distribution over the states of an ensemble or population—here, a population of people who are in mutual contact at some point in their daily lives. This kind of model is used routinely to model populations of neurons, where the ensemble dynamics are cast as density dynamics, under Gaussian assumptions about the probability densities; e.g., (\n<xref rid=\"ref-25\" ref-type=\"bibr\">Marreiros\n<italic>et al</italic>., 2009</xref>). In other words, a model of how the mean and covariance of a population affects itself and the means and covariances of other populations. Here, we will focus on a single population and, crucially, use a discrete state space model. This means that we will be dealing with the sufficient statistics (i.e. expectations) of the probability of being in a particular state at any one time. This renders the model a compartmental model (\n<xref rid=\"ref-19\" ref-type=\"bibr\">Kermack\n<italic>et al</italic>., 1997</xref>), where each state corresponds to a compartment. These latent states evolve according to transition probabilities that embody the causal influences and conditional dependencies that lend an epidemic its characteristic form. Our objective is to identify the right conditional dependencies—and form posterior beliefs about the model parameters that mediate these dependencies. Having done this, we can then simulate an entire trajectory into the distant future, even if we are only given data about the beginning of an outbreak\n<sup><xref ref-type=\"other\" rid=\"FN6\">6</xref></sup>.</p><p>The model considers four different sorts of states (i.e., factors) that provide a description of any individual—sampled at random—that is sufficient to generate the data at hand. In brief, these factors were chosen to be as conditionally independent as possible to ensure an efficient estimation of the model parameters\n<sup><xref ref-type=\"other\" rid=\"FN7\">7</xref></sup>. The four factors were an individual’s\n<italic>location</italic>,\n<italic>infection</italic> status,\n<italic>clinical</italic> status and\n<italic>diagnostic</italic> status. In other words, we considered that any member of the population can be characterised in terms of where they were, whether they were infected, infectious or immune, whether they were showing mild and severe or fatal symptoms, and whether they had been tested with an ensuing positive or negative result. Each of these factors had four levels. For example, the location factor was divided into\n<italic>home</italic>,\n<italic>work</italic>,\n<italic>critical care unit</italic>, and the\n<italic>morgue</italic>. These states should not be taken too literally. For example,\n<italic>home</italic> stands in for anywhere that has a limited risk of exposure to, or contact with, an infected person (e.g., in the domestic home, in a non-critical hospital bed, in a care home,\n<italic>etc</italic>).\n<italic>Work</italic> stands in for anywhere that has a larger risk of exposure to—or contact with—an infected person and therefore covers non-work activities, such as going to the supermarket or participating in team sports. Similarly, designating someone as severely ill with acute respiratory distress syndrome (ARDS) is meant to cover any life-threatening conditions that would invite admission to intensive care.</p><p>Having established the state space, we can now turn to the causal aspect of the dynamic causal model. The causal structure of these models depends upon the dynamics or transitions from one state or another. It is at this point that a mean field approximation can be used. Mean field approximations are used widely in physics to approximate a full (joint) probability density with the product of a series of marginal densities (\n<xref rid=\"ref-3\" ref-type=\"bibr\">Bressloff & Newby, 2013</xref>;\n<xref rid=\"ref-25\" ref-type=\"bibr\">Marreiros\n<italic>et al</italic>., 2009</xref>;\n<xref rid=\"ref-35\" ref-type=\"bibr\">Schumacher\n<italic>et al</italic>., 2015</xref>;\n<xref rid=\"ref-46\" ref-type=\"bibr\">Zhang\n<italic>et al</italic>., 2019</xref>). In this case, the factorisation is fairly subtle: we will factorise the transition probabilities, such that the probability of moving among states—within each factor—depends upon the marginal distribution of other factors (with one exception). For example, the probability of developing\n<italic>symptoms</italic> when\n<italic>asymptomatic</italic> depends on, and only on, the probability that I am\n<italic>infected</italic>. In what follows, we will step through the conditional probabilities for each factor to show how the model is put together (and could be changed).</p><sec><title>Transition probabilities and priors</title><p>The first factor has four levels,\n<italic>home</italic>,\n<italic>work</italic>,\n<italic>CCU</italic> and the\n<italic>morgue</italic>. People can leave home but will always return (with unit probability) over a day. The probability of leaving home has a (prior) baseline rate of one third but is nuanced by any social distancing imperatives. These imperatives are predicated on the proportion of the population that is currently infected, such that the social distancing parameter (an exponent) determines the probability of leaving home\n<sup><xref ref-type=\"other\" rid=\"FN8\">8</xref></sup>. For example, social distancing is modelled as the propensity to leave home and expose oneself to interpersonal contacts. This can be modelled with the following transition probability:</p><p>\n<disp-formula id=\"e1.1\"><mml:math id=\"math1\"><mml:mrow><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:msub><mml:mi>c</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mi>w</mml:mi><mml:mi>o</mml:mi><mml:mi>r</mml:mi><mml:mi>k</mml:mi><mml:mo stretchy=\"false\">|</mml:mo><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:msub><mml:mi>c</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mtext mathvariant=\"italic\">home</mml:mtext><mml:mo>,</mml:mo><mml:mi>c</mml:mi><mml:mi>l</mml:mi><mml:mi>i</mml:mi><mml:msub><mml:mi>n</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mi>a</mml:mi><mml:mi>s</mml:mi><mml:mi>y</mml:mi><mml:mi>m</mml:mi><mml:mi>p</mml:mi><mml:mi>t</mml:mi><mml:mi>o</mml:mi><mml:mi>m</mml:mi><mml:mi>a</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:msub><mml:mi>c</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>u</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:msup><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msubsup><mml:mi>p</mml:mi><mml:mrow><mml:mtext mathvariant=\"italic\">infected</mml:mtext></mml:mrow><mml:mrow><mml:mtext mathvariant=\"italic\">inf</mml:mtext></mml:mrow></mml:msubsup><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>s</mml:mi><mml:mi>d</mml:mi><mml:mi>e</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msup><mml:mspace width=\"14em\"/><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1.1</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:math></disp-formula>\n</p><p>This means that the probability of leaving home, given I have no symptoms, is the probability I would have gone out normally, multiplied by a decreasing function of the proportion of people in the population who are infected. Formally, this proportion is the marginal probability of being infected, where the marginal probability of a factor is an average over the remaining factors. The marginal probability\n<italic>p\n<sup>l</sup></italic> of the\n<italic>location</italic> factor is as follows:</p><p>\n<disp-formula id=\"e1.2\"><mml:math id=\"math2\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mspace width=\"11em\"/><mml:msub><mml:mi>p</mml:mi><mml:mrow><mml:mi>ℓ</mml:mi><mml:mi>i</mml:mi><mml:mi>j</mml:mi><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:mi>c</mml:mi><mml:mo>=</mml:mo><mml:mi>ℓ</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>f</mml:mi><mml:mo>=</mml:mo><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>c</mml:mi><mml:mi>l</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mi>j</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi><mml:mi>t</mml:mi><mml:mo>=</mml:mo><mml:mi>k</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mspace width=\"1em\"/><mml:msup><mml:mi>p</mml:mi><mml:mi>ℓ</mml:mi></mml:msup><mml:mo>=</mml:mo><mml:mstyle displaystyle=\"true\"><mml:msub><mml:mo>∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mrow><mml:msub><mml:mi>p</mml:mi><mml:mrow><mml:mi>ℓ</mml:mi><mml:mi>i</mml:mi><mml:mi>j</mml:mi><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mstyle></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mstyle displaystyle=\"true\"><mml:msub><mml:mo>∑</mml:mo><mml:mi>u</mml:mi></mml:msub><mml:mrow><mml:msubsup><mml:mi>p</mml:mi><mml:mi>u</mml:mi><mml:mi>ℓ</mml:mi></mml:msubsup></mml:mrow></mml:mstyle><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mspace width=\"5em\"/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mspace width=\"8em\"/><mml:msup><mml:mi>p</mml:mi><mml:mrow><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:mi>c</mml:mi></mml:mrow></mml:msup><mml:mo>=</mml:mo><mml:mo stretchy=\"false\">[</mml:mo><mml:msubsup><mml:mi>p</mml:mi><mml:mrow><mml:mi>h</mml:mi><mml:mi>o</mml:mi><mml:mi>m</mml:mi><mml:mi>e</mml:mi></mml:mrow><mml:mrow><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:mi>c</mml:mi></mml:mrow></mml:msubsup><mml:mo>,</mml:mo><mml:msubsup><mml:mi>p</mml:mi><mml:mrow><mml:mi>w</mml:mi><mml:mi>o</mml:mi><mml:mi>r</mml:mi><mml:mi>k</mml:mi></mml:mrow><mml:mrow><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:mi>c</mml:mi></mml:mrow></mml:msubsup><mml:mo>,</mml:mo><mml:msubsup><mml:mi>p</mml:mi><mml:mrow><mml:mi>C</mml:mi><mml:mi>C</mml:mi><mml:mi>U</mml:mi></mml:mrow><mml:mrow><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:mi>c</mml:mi></mml:mrow></mml:msubsup><mml:mo>,</mml:mo><mml:msubsup><mml:mi>p</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mi>o</mml:mi><mml:mi>r</mml:mi><mml:mi>g</mml:mi><mml:mi>u</mml:mi><mml:mi>e</mml:mi></mml:mrow><mml:mrow><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:mi>c</mml:mi></mml:mrow></mml:msubsup><mml:mo stretchy=\"false\">]</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mspace width=\"14em\"/><mml:msup><mml:mi>p</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>f</mml:mi></mml:mrow></mml:msup><mml:mo>=</mml:mo><mml:mo stretchy=\"false\">[</mml:mo><mml:msubsup><mml:mi>p</mml:mi><mml:mrow><mml:mi>s</mml:mi><mml:mi>u</mml:mi><mml:mi>s</mml:mi><mml:mi>c</mml:mi><mml:mi>e</mml:mi><mml:mi>p</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>b</mml:mi><mml:mi>l</mml:mi><mml:mi>e</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>f</mml:mi></mml:mrow></mml:msubsup><mml:mo>,</mml:mo><mml:msubsup><mml:mi>p</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>f</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>f</mml:mi></mml:mrow></mml:msubsup><mml:mo>,</mml:mo><mml:msubsup><mml:mi>p</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>f</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>o</mml:mi><mml:mi>u</mml:mi><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>f</mml:mi></mml:mrow></mml:msubsup><mml:mo>,</mml:mo><mml:msubsup><mml:mi>p</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>m</mml:mi><mml:mi>m</mml:mi><mml:mi>u</mml:mi><mml:mi>n</mml:mi><mml:mi>e</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>f</mml:mi></mml:mrow></mml:msubsup><mml:mo stretchy=\"false\">]</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mspace width=\"15em\"/><mml:msup><mml:mi>p</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>l</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msup><mml:mo>=</mml:mo><mml:mo stretchy=\"false\">[</mml:mo><mml:msubsup><mml:mi>p</mml:mi><mml:mrow><mml:mi>a</mml:mi><mml:mi>s</mml:mi><mml:mi>y</mml:mi><mml:mi>m</mml:mi><mml:mi>p</mml:mi><mml:mi>t</mml:mi><mml:mi>o</mml:mi><mml:mi>m</mml:mi><mml:mi>a</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>c</mml:mi></mml:mrow><mml:mrow><mml:mi>c</mml:mi><mml:mi>l</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msubsup><mml:mo>,</mml:mo><mml:msubsup><mml:mi>p</mml:mi><mml:mrow><mml:mi>s</mml:mi><mml:mi>y</mml:mi><mml:mi>m</mml:mi><mml:mi>p</mml:mi><mml:mi>t</mml:mi><mml:mi>o</mml:mi><mml:mi>m</mml:mi><mml:mi>a</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>c</mml:mi></mml:mrow><mml:mrow><mml:mi>c</mml:mi><mml:mi>l</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msubsup><mml:mo>,</mml:mo><mml:msubsup><mml:mi>p</mml:mi><mml:mrow><mml:mi>A</mml:mi><mml:mi>D</mml:mi><mml:mi>R</mml:mi><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mi>c</mml:mi><mml:mi>l</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msubsup><mml:mo>,</mml:mo><mml:msubsup><mml:mi>p</mml:mi><mml:mrow><mml:mi>d</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>e</mml:mi><mml:mi>a</mml:mi><mml:mi>s</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi></mml:mrow><mml:mrow><mml:mi>c</mml:mi><mml:mi>l</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msubsup><mml:mo stretchy=\"false\">]</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mspace width=\"12em\"/><mml:msup><mml:mi>p</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msup><mml:mo>=</mml:mo><mml:mo stretchy=\"false\">[</mml:mo><mml:msubsup><mml:mi>p</mml:mi><mml:mrow><mml:mi>u</mml:mi><mml:mi>n</mml:mi><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi></mml:mrow><mml:mrow><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msubsup><mml:mo>,</mml:mo><mml:msubsup><mml:mi>p</mml:mi><mml:mrow><mml:mi>w</mml:mi><mml:mi>a</mml:mi><mml:mi>i</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>g</mml:mi></mml:mrow><mml:mrow><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msubsup><mml:mo>,</mml:mo><mml:msubsup><mml:mi>p</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>o</mml:mi><mml:mi>s</mml:mi><mml:mi>i</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>v</mml:mi><mml:mi>e</mml:mi></mml:mrow><mml:mrow><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msubsup><mml:mo>,</mml:mo><mml:msubsup><mml:mi>p</mml:mi><mml:mrow><mml:mi>n</mml:mi><mml:mi>e</mml:mi><mml:mi>g</mml:mi><mml:mi>a</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>v</mml:mi><mml:mi>e</mml:mi></mml:mrow><mml:mrow><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msubsup><mml:mo stretchy=\"false\">]</mml:mo></mml:mtd></mml:mtr></mml:mtable><mml:mspace width=\"12em\"/><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1.2</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:math></disp-formula>\n</p><p>Where the final four equalities define each factor or state in the model. The parameters in this social distancing model are the probability of leaving home every day (\n<italic>θ\n<sub>out</sub></italic>) and the social distancing exponent (\n<italic>θ\n<sub>sde</sub></italic>).</p><p>The only other two places one can be are in a\n<italic>CCU</italic> or the\n<italic>morgue</italic>. The probability of moving to critical care depends upon bed (i.e., hospital) availability, which is modelled as a sigmoid function of the occupancy of this state (i.e., the probability that a\n<italic>CCU</italic> bed is occupied) and a bed capacity parameter (a threshold). If one has severe symptoms, then one stays in the\n<italic>CCU</italic>. Finally, the probability of moving to the morgue depends on, and only on, being\n<italic>deceased</italic>. Note that all these dependencies are different states of the\n<italic>clinical</italic> factor (see below). This means we can write the transition probabilities among the\n<italic>location</italic> factor for each level of the\n<italic>clinical</italic> factor as follows (with a slight abuse of notation):</p><p>\n<disp-formula id=\"e1.3\"><mml:math id=\"math3\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mspace width=\"21em\"/><mml:mi>P</mml:mi><mml:mo>=</mml:mo><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>u</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:msup><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msubsup><mml:mi>p</mml:mi><mml:mrow><mml:mtext mathvariant=\"italic\">infected</mml:mtext></mml:mrow><mml:mrow><mml:mtext mathvariant=\"italic\">inf</mml:mtext></mml:mrow></mml:msubsup><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>s</mml:mi><mml:mi>d</mml:mi><mml:mi>e</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msup></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mspace width=\"17em\"/><mml:mi>Q</mml:mi><mml:mo>=</mml:mo><mml:mi>σ</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>p</mml:mi><mml:mrow><mml:mi>C</mml:mi><mml:mi>C</mml:mi><mml:mi>U</mml:mi></mml:mrow><mml:mrow><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:mi>c</mml:mi></mml:mrow></mml:msubsup><mml:mo>,</mml:mo><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>a</mml:mi><mml:mi>p</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mspace width=\"3em\"/><mml:mi>P</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:msub><mml:mi>c</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>|</mml:mo><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:msub><mml:mi>c</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:mi>c</mml:mi><mml:mi>l</mml:mi><mml:mi>i</mml:mi><mml:msub><mml:mi>n</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mi>a</mml:mi><mml:mi>s</mml:mi><mml:mi>y</mml:mi><mml:mi>m</mml:mi><mml:mi>p</mml:mi><mml:mi>t</mml:mi><mml:mi>o</mml:mi><mml:mi>m</mml:mi><mml:mi>a</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>c</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mi>P</mml:mi></mml:mrow></mml:mtd><mml:mtd><mml:mn>1</mml:mn></mml:mtd><mml:mtd><mml:mn>1</mml:mn></mml:mtd><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mi>P</mml:mi></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mn>1</mml:mn></mml:mtd></mml:mtr></mml:mtable></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mspace width=\"2em\"/><mml:mi>P</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:msub><mml:mi>c</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>|</mml:mo><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:msub><mml:mi>c</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:mi>c</mml:mi><mml:mi>l</mml:mi><mml:mi>i</mml:mi><mml:msub><mml:mi>n</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mi>s</mml:mi><mml:mi>y</mml:mi><mml:mi>m</mml:mi><mml:mi>p</mml:mi><mml:mi>t</mml:mi><mml:mi>o</mml:mi><mml:mi>m</mml:mi><mml:mi>a</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>c</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mn>1</mml:mn></mml:mtd><mml:mtd><mml:mn>1</mml:mn></mml:mtd><mml:mtd><mml:mn>1</mml:mn></mml:mtd><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mn>1</mml:mn></mml:mtd></mml:mtr></mml:mtable></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mspace width=\"9em\"/><mml:mi>P</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:msub><mml:mi>c</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>|</mml:mo><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:msub><mml:mi>c</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:mi>c</mml:mi><mml:mi>l</mml:mi><mml:mi>i</mml:mi><mml:msub><mml:mi>n</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mi>A</mml:mi><mml:mi>R</mml:mi><mml:mi>D</mml:mi><mml:mi>S</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mi>Q</mml:mi></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mi>Q</mml:mi></mml:mrow></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mi>Q</mml:mi></mml:mtd><mml:mtd><mml:mi>Q</mml:mi></mml:mtd><mml:mtd><mml:mn>1</mml:mn></mml:mtd><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mn>1</mml:mn></mml:mtd></mml:mtr></mml:mtable></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mspace width=\"4em\"/><mml:mi>P</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:msub><mml:mi>c</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>|</mml:mo><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:msub><mml:mi>c</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:mi>c</mml:mi><mml:mi>l</mml:mi><mml:mi>i</mml:mi><mml:msub><mml:mi>n</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mi>d</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>e</mml:mi><mml:mi>a</mml:mi><mml:mi>s</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mn>1</mml:mn></mml:mtd><mml:mtd><mml:mn>1</mml:mn></mml:mtd><mml:mtd><mml:mn>1</mml:mn></mml:mtd><mml:mtd><mml:mn>1</mml:mn></mml:mtd></mml:mtr></mml:mtable></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mtd></mml:mtr></mml:mtable><mml:mspace width=\"17em\"/><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1.3</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:math></disp-formula>\n</p><p>Here, the columns and rows of each transition probability matrix are ordered: home, work, CCU, morgue. The column indicates the current location and the row indicates the next location. Parameter\n<italic>θ\n<sub>cap</sub></italic> is bed capacity threshold and\n<inline-formula><mml:math id=\"M4\"><mml:mrow><mml:mi>σ</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>s</mml:mi><mml:mo>,</mml:mo><mml:mi>θ</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:msup><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1</mml:mn><mml:mo>+</mml:mo><mml:msup><mml:mtext>e</mml:mtext><mml:mrow><mml:mn>4</mml:mn><mml:mo stretchy=\"false\">(</mml:mo><mml:mfrac><mml:mi>s</mml:mi><mml:mi>θ</mml:mi></mml:mfrac><mml:mo>−</mml:mo><mml:mn>1</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msup><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula> is a decreasing sigmoid function. In brief, these transition probabilities mean that I will go out when\n<italic>asymptomatic</italic>, unless social distancing is in play. However, when I have symptoms I will stay at home, unless I am hospitalised with acute respiratory distress. I remain in critical care unless I recover and go home or die and move to the morgue, where I stay. Technically, the\n<italic>morgue</italic> is an absorbing state.</p><p>In a similar way, we can express the probability of moving between different states of infection (i.e.,\n<italic>susceptible</italic>,\n<italic>infected</italic>,\n<italic>infectious</italic> and\n<italic>immune</italic>) as follows:</p><p>\n<disp-formula id=\"e1.4\"><mml:math id=\"math5\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mspace width=\"17em\"/><mml:mi>P</mml:mi><mml:mo>=</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mi>r</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msub><mml:mo>⋅</mml:mo><mml:msubsup><mml:mi>p</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>f</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>o</mml:mi><mml:mi>u</mml:mi><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>f</mml:mi></mml:mrow></mml:msubsup><mml:mo stretchy=\"false\">)</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mspace width=\"11em\"/><mml:mi>P</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:msub><mml:mi>f</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>|</mml:mo><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:msub><mml:mi>f</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:msub><mml:mi>c</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mi>h</mml:mi><mml:mi>o</mml:mi><mml:mi>m</mml:mi><mml:mi>e</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mrow><mml:msup><mml:mi>P</mml:mi><mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>R</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msup></mml:mrow></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msup><mml:mi>P</mml:mi><mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>R</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msup></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>f</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>f</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mtd><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mtd><mml:mtd><mml:mn>1</mml:mn></mml:mtd></mml:mtr></mml:mtable></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mspace width=\"11em\"/><mml:mi>P</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:msub><mml:mi>f</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>|</mml:mo><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:msub><mml:mi>f</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:msub><mml:mi>c</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mi>w</mml:mi><mml:mi>o</mml:mi><mml:mi>r</mml:mi><mml:mi>k</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mrow><mml:msup><mml:mi>P</mml:mi><mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>R</mml:mi><mml:mi>o</mml:mi><mml:mi>u</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msup></mml:mrow></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msup><mml:mi>P</mml:mi><mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>R</mml:mi><mml:mi>o</mml:mi><mml:mi>u</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msup></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>f</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>f</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mtd><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mtd><mml:mtd><mml:mn>1</mml:mn></mml:mtd></mml:mtr></mml:mtable></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mspace width=\"8em\"/><mml:mi>P</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:msub><mml:mi>f</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>|</mml:mo><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:msub><mml:mi>f</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:msub><mml:mi>c</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mi>C</mml:mi><mml:mi>C</mml:mi><mml:mi>U</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mn>1</mml:mn></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>f</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>f</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mtd><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mtd><mml:mtd><mml:mn>1</mml:mn></mml:mtd></mml:mtr></mml:mtable></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mi>P</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:msub><mml:mi>f</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>|</mml:mo><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:msub><mml:mi>f</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:msub><mml:mi>c</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mi>m</mml:mi><mml:mi>o</mml:mi><mml:mi>r</mml:mi><mml:mi>g</mml:mi><mml:mi>u</mml:mi><mml:mi>e</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mn>1</mml:mn></mml:mtd><mml:mtd><mml:mn>1</mml:mn></mml:mtd><mml:mtd><mml:mn>1</mml:mn></mml:mtd><mml:mtd><mml:mn>1</mml:mn></mml:mtd></mml:mtr></mml:mtable></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mtd></mml:mtr></mml:mtable><mml:mspace width=\"10em\"/><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1.4</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:math></disp-formula>\n</p><p>These transition probabilities mean that when susceptible, the probability of becoming infected depends upon the number of social contacts—which depends upon the proportion of time spent at home. This dependency is parameterised in terms of a transition probability per contact (\n<italic>θ\n<sub>trn</sub></italic>) and the expected number of contacts at home (\n<italic>θ\n<sub>Rin</sub></italic>) and work (\n<italic>θ\n<sub>Rou</sub></italic>)\n<sup><xref ref-type=\"other\" rid=\"FN9\">9</xref></sup>. Once infected, one remains in this state for a period of time that is parameterised by a transition rate (\n<italic>θ\n<sub>inf</sub></italic>). This parameterisation illustrates a generic property of transition probabilities; namely, an interpretation in terms of rate constants and, implicitly, time constants. The rate parameter\n<italic>θ</italic> is related to the rate constant\n<italic>κ</italic> and time constant\n<italic>τ</italic> according to:</p><p>\n<disp-formula id=\"e1.5\"><mml:math id=\"math6\"><mml:mrow><mml:mi>θ</mml:mi><mml:mo>=</mml:mo><mml:mtext>exp</mml:mtext><mml:mo stretchy=\"false\">(</mml:mo><mml:mo>−</mml:mo><mml:mi>κ</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mtext>exp</mml:mtext><mml:mo stretchy=\"false\">(</mml:mo><mml:mo>−</mml:mo><mml:mstyle scriptlevel=\"1\"><mml:mfrac><mml:mn>1</mml:mn><mml:mi>τ</mml:mi></mml:mfrac></mml:mstyle><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>≤</mml:mo><mml:mn>1</mml:mn><mml:mo>:</mml:mo><mml:mo>∀</mml:mo><mml:mi>τ</mml:mi><mml:mo>></mml:mo><mml:mn>0</mml:mn><mml:mspace width=\"33em\"/><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1.5</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:math></disp-formula>\n</p><p>In other words, the probability of staying in any one state is determined by the characteristic length of time that state is occupied. This means that the rate parameter above can be specified,\n<italic>a priori</italic>, in terms of the number of days we expect people to be infected, before becoming infectious. Similarly, we can parameterise the transition from being infectious to being immune in terms of a typical period of being contagious, assuming that immunity is enduring and precludes reinfection\n<sup><xref ref-type=\"other\" rid=\"FN10\">10</xref></sup>. Note that in the model, everybody in the morgue is treated as having acquired immunity. The transitions among clinical states depend upon both the infection status and location as follows:</p><p>\n<disp-formula id=\"e1.6\"><mml:math id=\"math7\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mspace width=\"2em\"/><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>f</mml:mi><mml:mi>a</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mrow><mml:mo>{</mml:mo><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>f</mml:mi><mml:mi>a</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:mi>c</mml:mi><mml:mo>=</mml:mo><mml:mi>C</mml:mi><mml:mi>C</mml:mi><mml:mi>U</mml:mi></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>s</mml:mi><mml:mi>u</mml:mi><mml:mi>r</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:mi>c</mml:mi><mml:mo>≠</mml:mo><mml:mi>C</mml:mi><mml:mi>C</mml:mi><mml:mi>U</mml:mi></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mi>P</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>c</mml:mi><mml:mi>l</mml:mi><mml:mi>i</mml:mi><mml:msub><mml:mi>n</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>|</mml:mo><mml:mi>c</mml:mi><mml:mi>l</mml:mi><mml:mi>i</mml:mi><mml:msub><mml:mi>n</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:msub><mml:mi>f</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>∈</mml:mo><mml:mo>{</mml:mo><mml:mi>s</mml:mi><mml:mi>u</mml:mi><mml:mi>s</mml:mi><mml:mi>c</mml:mi><mml:mi>e</mml:mi><mml:mi>p</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>b</mml:mi><mml:mi>l</mml:mi><mml:mi>e</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi><mml:mi>m</mml:mi><mml:mi>m</mml:mi><mml:mi>u</mml:mi><mml:mi>n</mml:mi><mml:mi>e</mml:mi><mml:mo>}</mml:mo></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mn>1</mml:mn></mml:mtd><mml:mtd><mml:mn>1</mml:mn></mml:mtd><mml:mtd><mml:mn>1</mml:mn></mml:mtd><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mn>1</mml:mn></mml:mtd></mml:mtr></mml:mtable></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mi>P</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>c</mml:mi><mml:mi>l</mml:mi><mml:mi>i</mml:mi><mml:msub><mml:mi>n</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>|</mml:mo><mml:mi>c</mml:mi><mml:mi>l</mml:mi><mml:mi>i</mml:mi><mml:msub><mml:mi>n</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:msub><mml:mi>f</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>∈</mml:mo><mml:mo>{</mml:mo><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>f</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>f</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>o</mml:mi><mml:mi>u</mml:mi><mml:mi>s</mml:mi><mml:mo>}</mml:mo></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>d</mml:mi><mml:mi>e</mml:mi><mml:mi>v</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>s</mml:mi><mml:mi>y</mml:mi><mml:mi>m</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>s</mml:mi><mml:mi>e</mml:mi><mml:mi>v</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>r</mml:mi><mml:mi>d</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>f</mml:mi><mml:mi>a</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mtd><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>d</mml:mi><mml:mi>e</mml:mi><mml:mi>v</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>s</mml:mi><mml:mi>y</mml:mi><mml:mi>m</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>s</mml:mi><mml:mi>y</mml:mi><mml:mi>m</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>s</mml:mi><mml:mi>e</mml:mi><mml:mi>v</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>r</mml:mi><mml:mi>d</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mtd><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>r</mml:mi><mml:mi>d</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>f</mml:mi><mml:mi>a</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mtd><mml:mtd><mml:mn>1</mml:mn></mml:mtd></mml:mtr></mml:mtable></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mtd></mml:mtr></mml:mtable><mml:mspace width=\"1em\"/><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1.6</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:math></disp-formula>\n</p><p>The transitions among clinical states (i.e.,\n<italic>asymptomatic</italic>,\n<italic>symptomatic</italic>,\n<italic>ARDS</italic> and\n<italic>deceased</italic>) are relatively straightforward: if I am not infected (i.e.,\n<italic>susceptible</italic> or\n<italic>immune</italic>) I will move to the asymptomatic state, unless I am dead. However, if I am infected (i.e.,\n<italic>infected</italic> or\n<italic>infectious</italic>), I will develop symptoms with a particular probability (\n<italic>θ\n<sub>dev</sub></italic>). Once I have developed symptoms, I will remain symptomatic and either recover to an asymptomatic state or develop acute respiratory distress with a particular probability (\n<italic>θ\n<sub>sev</sub></italic>). The parameterisation of these transitions depends upon the typical length of time that I remain symptomatic (\n<italic>θ\n<sub>sym</sub></italic>); similarly, when in acute respiratory distress (\n<italic>θ\n<sub>rds</sub></italic>). However, I may die following ARDS, with a probability that depends upon whether I am in a CCU, or elsewhere. This is the exception (mentioned above) to the conditional dependencies on marginal densities. Here, the probability of dying (\n<italic>θ\n<sub>fat</sub></italic>) depends on being infected and my location: I am more likely to die of ARDS, if I am not in CCU, where\n<italic>θ\n<sub>sur</sub></italic> is the probability of surviving at home. The implication here is that the transition probabilities depend upon two marginal densities, as opposed to one for all the other factors: see the first equality in (\n<xref ref-type=\"other\" rid=\"e1.6\">1.6</xref>). Please refer to\n<xref rid=\"T1\" ref-type=\"table\">Table 1</xref> for details of the model parameters.</p><p>Finally, we turn to diagnostic testing status (i.e.,\n<italic>untested</italic>,\n<italic>waiting</italic> or\n<italic>positive</italic> versus\n<italic>negative</italic>)\n<sup><xref ref-type=\"other\" rid=\"FN11\">11</xref></sup>. The transition probabilities here are parameterised in terms of test availability (\n<italic>θ\n<sub>tft</sub></italic>,\n<italic>θ\n<sub>sen</sub></italic>). and the probability that I would have been tested anyway, which is relatively smaller, if I am asymptomatic (\n<italic>θ\n<sub>tes</sub></italic>). Test availability is a decreasing sigmoid function of the number of people who are waiting (with a delay\n<italic>θ\n<sub>del</sub></italic>) for their results. I can only move from being\n<italic>untested</italic> to\n<italic>waiting</italic>. After this, I can only go into\n<italic>positive</italic> or\n<italic>negative</italic> test states, depending upon whether I have the virus (i.e.,\n<italic>infected</italic> or\n<italic>infectious</italic>) or not\n<sup><xref ref-type=\"other\" rid=\"FN12\">12</xref></sup>.</p><p>\n<disp-formula id=\"e1.7\"><mml:math id=\"math8\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mspace width=\"18em\"/><mml:mi>P</mml:mi><mml:mo>=</mml:mo><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>s</mml:mi><mml:mi>e</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msub><mml:mi>σ</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>p</mml:mi><mml:mrow><mml:mi>w</mml:mi><mml:mi>a</mml:mi><mml:mi>i</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>g</mml:mi></mml:mrow><mml:mrow><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msubsup><mml:mo>,</mml:mo><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mi>f</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mspace width=\"1.5em\"/><mml:mi>P</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi><mml:msub><mml:mi>t</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>|</mml:mo><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi><mml:msub><mml:mi>t</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:msub><mml:mi>f</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>∈</mml:mo><mml:mo>{</mml:mo><mml:mi>s</mml:mi><mml:mi>u</mml:mi><mml:mi>s</mml:mi><mml:mi>c</mml:mi><mml:mi>e</mml:mi><mml:mi>p</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>b</mml:mi><mml:mi>l</mml:mi><mml:mi>e</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi><mml:mi>m</mml:mi><mml:mi>m</mml:mi><mml:mi>u</mml:mi><mml:mi>n</mml:mi><mml:mi>e</mml:mi><mml:mo>}</mml:mo></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:msub><mml:mi>P</mml:mi></mml:mrow></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:msub><mml:mi>P</mml:mi></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>d</mml:mi><mml:mi>e</mml:mi><mml:mi>l</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mn>1</mml:mn></mml:mtd><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>d</mml:mi><mml:mi>e</mml:mi><mml:mi>l</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mn>1</mml:mn></mml:mtd></mml:mtr></mml:mtable></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mi>P</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi><mml:msub><mml:mi>t</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>|</mml:mo><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi><mml:msub><mml:mi>t</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:msub><mml:mi>f</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>∈</mml:mo><mml:mo>{</mml:mo><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>f</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>f</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>o</mml:mi><mml:mi>u</mml:mi><mml:mi>s</mml:mi><mml:mo>}</mml:mo></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mi>P</mml:mi></mml:mrow></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mi>P</mml:mi></mml:mtd><mml:mtd><mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>d</mml:mi><mml:mi>e</mml:mi><mml:mi>l</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>d</mml:mi><mml:mi>e</mml:mi><mml:mi>l</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mtd><mml:mtd><mml:mn>1</mml:mn></mml:mtd><mml:mtd><mml:mrow/></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mrow/></mml:mtd><mml:mtd><mml:mn>1</mml:mn></mml:mtd></mml:mtr></mml:mtable></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mtd></mml:mtr></mml:mtable><mml:mspace width=\"13em\"/><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1.7</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:math></disp-formula>\n</p><p>We can now assemble these transition probabilities into a probability transition matrix, and iterate from the first day to some time horizon, to generate a sequence of probability distributions over the joint space of all factors:</p><p>\n<disp-formula id=\"e1.8\"><mml:math id=\"math9\"><mml:mrow><mml:msub><mml:mi>p</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mi>T</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>θ</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>p</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:msub><mml:mi>p</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mspace width=\"42em\"/><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1.8</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:math></disp-formula>\n</p><p>Notice that this is a completely deterministic state space model, because all the randomness is contained in the probabilities. Notice also that the transition probability matrix\n<italic>T</italic> is both state\n<italic>and time</italic> dependent, because the transition probabilities above depend on marginal probabilities. Technically, (\n<xref ref-type=\"other\" rid=\"e1.8\">1.8</xref>) is known as a\n<italic>master equation</italic> (\n<xref rid=\"ref-36\" ref-type=\"bibr\">Seifert, 2012</xref>;\n<xref rid=\"ref-39\" ref-type=\"bibr\">Vespignani & Zapperi, 1998</xref>;\n<xref rid=\"ref-42\" ref-type=\"bibr\">Wang, 2009</xref>) and forms the basis of the dynamic part of the dynamic causal model.</p><p>This model of transmission supports an\n<italic>effective reproduction number or rate, R</italic>, which summarises how many people I am likely to infect, if I am infected\n<sup><xref ref-type=\"other\" rid=\"FN13\">13</xref></sup>. This depends upon the probability that any contact will cause an infection, the probability that the contact is susceptible to infection and number of people I contact:</p><p>\n<disp-formula id=\"e1.9\"><mml:math id=\"math10\"><mml:mrow><mml:mi>R</mml:mi><mml:mo>=</mml:mo><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mi>r</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msub><mml:mo>⋅</mml:mo><mml:msubsup><mml:mi>p</mml:mi><mml:mrow><mml:mi>s</mml:mi><mml:mi>u</mml:mi><mml:mi>s</mml:mi><mml:mi>c</mml:mi><mml:mi>e</mml:mi><mml:mi>p</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>b</mml:mi><mml:mi>l</mml:mi><mml:mi>e</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>f</mml:mi></mml:mrow></mml:msubsup><mml:mo>⋅</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>p</mml:mi><mml:mrow><mml:mi>h</mml:mi><mml:mi>o</mml:mi><mml:mi>m</mml:mi><mml:mi>e</mml:mi></mml:mrow><mml:mrow><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:mi>c</mml:mi><mml:mo>|</mml:mo><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>f</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>o</mml:mi><mml:mi>u</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:msubsup><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>R</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:msubsup><mml:mi>p</mml:mi><mml:mrow><mml:mi>w</mml:mi><mml:mi>o</mml:mi><mml:mi>r</mml:mi><mml:mi>k</mml:mi></mml:mrow><mml:mrow><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:mi>c</mml:mi><mml:mo>|</mml:mo><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>f</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>o</mml:mi><mml:mi>u</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:msubsup><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>R</mml:mi><mml:mi>o</mml:mi><mml:mi>u</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>⋅</mml:mo><mml:msub><mml:mi>τ</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msub><mml:mspace width=\"20em\"/><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1.9</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:math></disp-formula>\n</p><p>In this approximation, the number of contacts I make is a weighted average of the number of people I could infect at home and the number of people I meet outside, per day, times the number of days I am contagious. The effective reproduction rate is not a biological rate constant. However, it is a useful epidemiological summary statistic that indicates how quickly the disease spreads through a population. When less than one, the infection will decay to an endemic equilibrium. We will use this measure later to understand the role of herd immunity.</p><p>This completes the specification of the generative model of latent states. A list of the parameters and their prior means (and variances) is provided in\n<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>. Notice that all of the parameters are scale parameters, i.e., they are rates or probabilities that cannot be negative. To enforce these positivity constraints, one applies a log transform to the parameters during model inversion or fitting. This has the advantage of being able to simplify the numerics using Gaussian assumptions about the prior density (via a lognormal assumption). In other words, although the scale parameters are implemented as probabilities or rates, they are estimated as log parameters, denoted by\n<italic>ϑ</italic> = ln\n<italic>θ</italic>. Note that prior variances are specified for log parameters. For example, a variance of 1/64 corresponds to a prior confidence interval of ~25% and can be considered weakly informative.</p><table-wrap id=\"T1\" orientation=\"portrait\" position=\"anchor\"><label>Table 1. </label><caption><title>Parameters of the COVID-19 model and priors,\n<italic>N</italic>(\n<italic>η</italic>,\n<italic>C</italic>) (NB: prior means are for scale parameters\n<italic>θ</italic> = exp (\n<italic>ϑ</italic>) ).</title></caption><table frame=\"hsides\" rules=\"groups\" content-type=\"article-table\"><thead><tr><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Number</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Parameter</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Mean</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Variance</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Description</th></tr></thead><tbody><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>1</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>θ\n<sub>n</sub></italic>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1/4</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Number of initial cases</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>2</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>θ\n<sub>N</sub></italic>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1/16</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Effective population size (millions)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>3</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>θ\n<sub>m</sub></italic>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">10\n<sup>-6</sup>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Herd immunity (proportion)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>Location</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>4</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>θ\n<sub>out</sub></italic>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1/3</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1/64</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Pr(\n<italic>work</italic> |\n<italic>home</italic>): probability of going out</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>5</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>θ\n<sub>sde</sub></italic>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">32</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1/64</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Social distancing exponent</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>6</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>θ\n<sub>cap</sub></italic>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">128/100000</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1/64</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">CCU capacity threshold (per capita)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>Infection</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>7</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>θ\n<sub>Rin</sub></italic>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1/64</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Effective number of contacts: home</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>8</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>θ\n<sub>Rou</sub></italic>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">48</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1/64</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Effective number of contacts: work</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>9</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>θ\n<sub>trn</sub></italic>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1/4</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1/64</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Pr(\n<italic>contagion</italic> |\n<italic>contact</italic>)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>10</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<inline-formula><mml:math id=\"M11\"><mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mtext mathvariant=\"italic\">inf</mml:mtext></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mtext>exp</mml:mtext><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mo>−</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:msub><mml:mi>τ</mml:mi><mml:mrow><mml:mtext mathvariant=\"italic\">inf</mml:mtext></mml:mrow></mml:msub></mml:mrow></mml:mfrac></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>τ\n<sub>inf</sub></italic> = 5</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1/64</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Infected (pre-contagious) period (days)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>11</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<inline-formula><mml:math id=\"M12\"><mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mtext mathvariant=\"italic\">con</mml:mtext></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mtext>exp</mml:mtext><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mo>−</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:msub><mml:mi>τ</mml:mi><mml:mrow><mml:mtext mathvariant=\"italic\">con</mml:mtext></mml:mrow></mml:msub></mml:mrow></mml:mfrac></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>τ\n<sub>con</sub></italic> = 3</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1/64</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Infectious (contagious) period (days)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>Clinical</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>12</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>θ\n<sub>dev</sub></italic>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1/3</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1/64</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Pr(\n<italic>symptoms</italic> |\n<italic>infected</italic>)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>13</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>θ\n<sub>sev</sub></italic>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1/100</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1/64</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Pr(\n<italic>ARDS</italic> |\n<italic>symptomatic</italic>)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>14</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<inline-formula><mml:math id=\"M13\"><mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mtext mathvariant=\"italic\">sym</mml:mtext></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mtext>exp</mml:mtext><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mo>−</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:msub><mml:mi>τ</mml:mi><mml:mrow><mml:mtext mathvariant=\"italic\">sym</mml:mtext></mml:mrow></mml:msub></mml:mrow></mml:mfrac></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>τ\n<sub>sym</sub></italic> = 5</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1/64</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">symptomatic period (days)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>15</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<inline-formula><mml:math id=\"M14\"><mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mtext mathvariant=\"italic\">rds</mml:mtext></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mtext>exp</mml:mtext><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mo>−</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:msub><mml:mi>τ</mml:mi><mml:mrow><mml:mtext mathvariant=\"italic\">rds</mml:mtext></mml:mrow></mml:msub></mml:mrow></mml:mfrac></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>τ\n<sub>rds</sub></italic> = 12</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1/64</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">acute RDS period (days)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>16</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>θ\n<sub>fat</sub></italic>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1/3</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1/64</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Pr(\n<italic>fatality</italic> |\n<italic>CCU</italic>)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>17</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>θ\n<sub>sur</sub></italic>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1/16</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1/64</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Pr(\n<italic>survival</italic> |\n<italic>home</italic>)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>Testing</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>18</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>θ\n<sub>tft</sub></italic>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">500/100000</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1/64</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Threshold: testing capacity (per capita)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>19</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>θ\n<sub>sen</sub></italic>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1/100</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1/64</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Rate: testing capacity (%)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>20</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<inline-formula><mml:math id=\"M15\"><mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mtext mathvariant=\"italic\">del</mml:mtext></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mtext>exp</mml:mtext><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mo>−</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:msub><mml:mi>τ</mml:mi><mml:mrow><mml:mtext mathvariant=\"italic\">del</mml:mtext></mml:mrow></mml:msub></mml:mrow></mml:mfrac></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>τ\n<sub>del</sub></italic> = 2</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1/64</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Delay: testing capacity (days)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>21</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>θ\n<sub>tes</sub></italic>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1/8</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1/64</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Relative Pr(\n<italic>tested</italic> |\n<italic>uninfected</italic>)</td></tr></tbody></table></table-wrap><p>\n<bold>Sources</bold> (\n<xref rid=\"ref-16\" ref-type=\"bibr\">Huang\n<italic>et al</italic>., 2020</xref>;\n<xref rid=\"ref-27\" ref-type=\"bibr\">Mizumoto & Chowell, 2020</xref>;\n<xref rid=\"ref-33\" ref-type=\"bibr\">Russell\n<italic>et al</italic>., 2020</xref>;\n<xref rid=\"ref-38\" ref-type=\"bibr\">Verity\n<italic>et al</italic>., 2020</xref>;\n<xref rid=\"ref-41\" ref-type=\"bibr\">Wang\n<italic>et al</italic>., 2020a</xref>) and:</p><list list-type=\"bullet\"><list-item><p>\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.statista.com/chart/21105/number-of-critical-care-beds-per-100000-inhabitants/\">https://www.statista.com/chart/21105/number-of-critical-care-beds-per-100000-inhabitants/</ext-link>\n</p></list-item><list-item><p>\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.gov.uk/guidance/coronavirus-covid-19-information-for-the-public\">https://www.gov.uk/guidance/coronavirus-covid-19-information-for-the-public</ext-link>\n</p></list-item><list-item><p>\n<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/covid-19/\">http://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/covid-19/</ext-link>\n</p></list-item></list><p>These prior expectations should be read as the effective rates and time constants as they manifest in a real-world setting. For example, a three-day period of contagion is shorter than the period that someone might be infectious (\n<xref rid=\"ref-44\" ref-type=\"bibr\">Wölfel\n<italic>et al</italic>., 2020</xref>)\n<sup><xref ref-type=\"other\" rid=\"FN14\">14</xref></sup>, on the (prior) assumption that they will self-isolate, when they realise they could be contagious.</p></sec><sec><title>Initial conditions and population size</title><p>Further parameters are required to generate data, such as the size of the population and the number of people who are initially infected (\n<italic>θ\n<sub>N</sub></italic>,\n<italic>θ\n<sub>n</sub></italic>)\n<sup><xref ref-type=\"other\" rid=\"FN15\">15</xref></sup>, which parameterise the initial state of the population (where ⊗ denotes a Kronecker tensor product):</p><p>\n<disp-formula id=\"e1.10\"><mml:math id=\"math16\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:msubsup><mml:mi>p</mml:mi><mml:mn>0</mml:mn><mml:mrow><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:mi>c</mml:mi></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mo stretchy=\"false\">[</mml:mo><mml:mstyle scriptlevel=\"1\"><mml:mfrac><mml:mn>3</mml:mn><mml:mn>4</mml:mn></mml:mfrac></mml:mstyle><mml:mo>,</mml:mo><mml:mstyle scriptlevel=\"1\"><mml:mfrac><mml:mn>1</mml:mn><mml:mn>4</mml:mn></mml:mfrac></mml:mstyle><mml:mo>,</mml:mo><mml:mn>0</mml:mn><mml:mo>,</mml:mo><mml:mn>0</mml:mn><mml:mo stretchy=\"false\">]</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mspace width=\"3.5em\"/><mml:msubsup><mml:mi>p</mml:mi><mml:mn>0</mml:mn><mml:mrow><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>f</mml:mi></mml:mrow></mml:msubsup><mml:mo>∝</mml:mo><mml:mo stretchy=\"false\">[</mml:mo><mml:msup><mml:mn>10</mml:mn><mml:mn>6</mml:mn></mml:msup><mml:mo>⋅</mml:mo><mml:msub><mml:mi>θ</mml:mi><mml:mi>N</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>θ</mml:mi><mml:mi>n</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:mn>0</mml:mn><mml:mo>,</mml:mo><mml:mn>0</mml:mn><mml:mo stretchy=\"false\">]</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:msubsup><mml:mi>p</mml:mi><mml:mn>0</mml:mn><mml:mrow><mml:mi>c</mml:mi><mml:mi>l</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mo stretchy=\"false\">[</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mn>0</mml:mn><mml:mo>,</mml:mo><mml:mn>0</mml:mn><mml:mo>,</mml:mo><mml:mn>0</mml:mn><mml:mo stretchy=\"false\">]</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:msubsup><mml:mi>p</mml:mi><mml:mn>0</mml:mn><mml:mrow><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mo stretchy=\"false\">[</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mn>0</mml:mn><mml:mo>,</mml:mo><mml:mn>0</mml:mn><mml:mo>,</mml:mo><mml:mn>0</mml:mn><mml:mo stretchy=\"false\">]</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mspace width=\"8.5em\"/><mml:msub><mml:mi>p</mml:mi><mml:mn>0</mml:mn></mml:msub><mml:mo>=</mml:mo><mml:msubsup><mml:mi>p</mml:mi><mml:mn>0</mml:mn><mml:mrow><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:mi>c</mml:mi></mml:mrow></mml:msubsup><mml:mo>⊗</mml:mo><mml:msubsup><mml:mi>p</mml:mi><mml:mn>0</mml:mn><mml:mrow><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>f</mml:mi></mml:mrow></mml:msubsup><mml:mo>⊗</mml:mo><mml:msubsup><mml:mi>p</mml:mi><mml:mn>0</mml:mn><mml:mrow><mml:mi>c</mml:mi><mml:mi>l</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msubsup><mml:mo>⊗</mml:mo><mml:msubsup><mml:mi>p</mml:mi><mml:mn>0</mml:mn><mml:mrow><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msubsup></mml:mtd></mml:mtr></mml:mtable><mml:mspace width=\"27.5em\"/><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1.10</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:math></disp-formula>\n</p><p>These parameters are unknown quantities that have to be estimated from the data; however, we still have to specify their prior densities. This begs the question: what kind of population are we trying to model? There are several choices here; ranging from detailed grid models of the sort used in weather forecasting (\n<xref rid=\"ref-29\" ref-type=\"bibr\">Palmer & Zanna, 2013</xref>) and epidemiologic models (\n<xref rid=\"ref-7\" ref-type=\"bibr\">Ferguson\n<italic>et al</italic>., 2006</xref>). One could use models based upon partial differential equations; i.e., (Markov random) field models (\n<xref rid=\"ref-5\" ref-type=\"bibr\">Deco\n<italic>et al</italic>., 2008</xref>). In this technical report, we will choose a simpler option that treats a pandemic as a set of linked point processes that can be modelled as rare events. In other words, we will focus on modelling a single outbreak in a region or city and treat the response of the ‘next city’ as a discrete process\n<italic>post hoc</italic>. This simplifies the generative model; in the sense we only have to worry about the ensemble dynamics of the population that comprises one city\n<sup>.</sup> A complimentary perspective on this choice is that we are trying to model the first wave of an epidemic as it plays out in the first city to be affected. Any second wave can then be treated as the first wave of another city or region.</p></sec><sec><title>Effective Population</title><p>Under the initial conditions, the population size can be set, a priori, to 1,000,000; noting that a small city comprises (by definition) a hundred thousand people, while a large city can exceed 10 million. This population parameter is a prior that is updated based on the available data, providing an estimate of the “effective population” size. Effective population is defined here as the proportion of the total population who are susceptible to infection, and therefore participate in the outbreak. The assumption that the effective population size reflects the total population of a country is a hypothesis that we will test later\n<sup><xref ref-type=\"other\" rid=\"FN16\">16</xref></sup>. For clarity, we are not implying that the remainder of the population classed as “not susceptible” are immune or resistant to COVID-19, rather there exists a sub-population who do not take part in the current outbreak for any of a variety of reasons that may include being shielded or geographically isolated from infected cases. Furthermore, as the effective population (and other parameters) are estimated directly from the data, they will therefore reflect the source of the information. At the time of writing, in the UK this was dominated by the London outbreak. Finally, as all parameters pertain to the effective population, proportions (or probabilities)—such as population immunity—require appropriate scaling to be expressed as a percentage of the total (census) population.</p></sec><sec><title>The likelihood or observation model</title><p>The outcomes considered in\n<xref ref-type=\"fig\" rid=\"f2\">Figure 2</xref> are new cases (of positive tests and deaths) per day. These can be generated by multiplying the appropriate probability by the (effective) population size. The appropriate probabilities here are just the expected occupancy of positive test and deceased states, respectively. Because we are dealing with large populations, the likelihood of any observed daily count has a binomial distribution that can be approximated by a Gaussian density\n<sup><xref ref-type=\"other\" rid=\"FN17\">17</xref></sup>.</p><p>\n<disp-formula id=\"e1.11\"><mml:math id=\"math17\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mspace width=\"7.3em\"/><mml:msub><mml:mi>o</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>~</mml:mo><mml:mi>B</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>π</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>≈</mml:mo><mml:mi>N</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>n</mml:mi><mml:msub><mml:mi>π</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:mi>n</mml:mi><mml:msub><mml:mi>π</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msub><mml:mi>π</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>⇒</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:msub><mml:mi>O</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:msqrt><mml:mrow><mml:msub><mml:mi>o</mml:mi><mml:mi>t</mml:mi></mml:msub></mml:mrow></mml:msqrt><mml:mo>~</mml:mo><mml:mi>N</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msqrt><mml:mrow><mml:mi>n</mml:mi><mml:msub><mml:mi>π</mml:mi><mml:mi>t</mml:mi></mml:msub></mml:mrow></mml:msqrt><mml:mo>,</mml:mo><mml:mi>I</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mtd></mml:mtr></mml:mtable><mml:mspace width=\"26em\"/><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1.11</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:math></disp-formula>\n</p><p>Here, outcomes are counts of rare events with a small probability\n<italic>π</italic> « 1 of occurring in a large population of size\n<italic>n</italic> » 1. For example, the likelihood of observing a timeseries of daily deaths can be expressed as a function of the model parameters as follows:</p><p>\n<disp-formula id=\"e1.12\"><mml:math id=\"math18\"><mml:mtable columnalign=\"left\"><mml:mtr><mml:mtd><mml:mi>P</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>O</mml:mi><mml:mo>|</mml:mo><mml:mi>ϑ</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mi>P</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mi>O</mml:mi><mml:mn>0</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:mo>…</mml:mo><mml:mo>,</mml:mo><mml:msub><mml:mi>O</mml:mi><mml:mi>T</mml:mi></mml:msub><mml:mo>|</mml:mo><mml:mi>ϑ</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mstyle displaystyle=\"true\"><mml:msubsup><mml:mo>∏</mml:mo><mml:mn>0</mml:mn><mml:mi>T</mml:mi></mml:msubsup><mml:mrow><mml:mi>N</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msqrt><mml:mrow><mml:mi>n</mml:mi><mml:msub><mml:mi>π</mml:mi><mml:mi>t</mml:mi></mml:msub></mml:mrow></mml:msqrt><mml:mo>,</mml:mo><mml:mi>I</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mstyle></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mspace width=\"1.7em\"/><mml:mi>n</mml:mi><mml:msub><mml:mi>π</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mn>6</mml:mn></mml:msup><mml:mo>⋅</mml:mo><mml:msub><mml:mi>θ</mml:mi><mml:mi>N</mml:mi></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msubsup><mml:mi>p</mml:mi><mml:mrow><mml:mi>d</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>e</mml:mi><mml:mi>a</mml:mi><mml:mi>s</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi></mml:mrow><mml:mrow><mml:mi>c</mml:mi><mml:mi>l</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msubsup><mml:mo>−</mml:mo><mml:msubsup><mml:mi>p</mml:mi><mml:mrow><mml:mi>d</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>e</mml:mi><mml:mi>a</mml:mi><mml:mi>s</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>c</mml:mi><mml:mi>l</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msubsup></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mspace width=\"1.2em\"/><mml:msub><mml:mi>p</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mi>T</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>ϑ</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>p</mml:mi><mml:mi>t</mml:mi></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:msub><mml:mi>p</mml:mi><mml:mi>t</mml:mi></mml:msub></mml:mtd></mml:mtr></mml:mtable><mml:mspace width=\"27em\"/><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1.12</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:math></disp-formula>\n</p><p>The advantage of this limiting (large population) case is that a (variance stabilising) square root transform of the data counts renders their variance unity. With the priors and likelihood model in place, we now have a full joint probability over causes (parameters) and consequences (outcomes). This is the generative model\n<italic>P</italic>(\n<italic>O</italic>,\n<italic>ϑ</italic>) =\n<italic>P</italic>(\n<italic>O</italic> |\n<italic>ϑ</italic>)\n<italic>P</italic>(\n<italic>ϑ</italic>). One can now use standard variational techniques (\n<xref rid=\"ref-8\" ref-type=\"bibr\">Friston\n<italic>et al</italic>., 2007</xref>) to estimate the posterior over model parameters and evaluate a variational bound on the model evidence or marginal likelihood. Mathematically, this is expressed as follows:</p><p>\n<disp-formula id=\"e1.13\"><mml:math id=\"math19\"><mml:mtable columnalign=\"left\"><mml:mtr><mml:mtd><mml:mi>P</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mi>ϑ</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mi>N</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>η</mml:mi><mml:mo>,</mml:mo><mml:mi>C</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mi>Q</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mi>ϑ</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mi>N</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>μ</mml:mi><mml:mo>,</mml:mo><mml:mi>Σ</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mi>Q</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mi>ϑ</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:msub><mml:mtext>arg max</mml:mtext><mml:mi>Q</mml:mi></mml:msub><mml:mi>F</mml:mi><mml:mo stretchy=\"false\">[</mml:mo><mml:mi>Q</mml:mi><mml:mo>,</mml:mo><mml:mi>O</mml:mi><mml:mo stretchy=\"false\">]</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mspace width=\"1.5em\"/><mml:mi>F</mml:mi><mml:mo>=</mml:mo><mml:mover><mml:mover><mml:mrow><mml:msub><mml:mi>E</mml:mi><mml:mi>Q</mml:mi></mml:msub><mml:mo stretchy=\"false\">[</mml:mo><mml:mtext>ln</mml:mtext><mml:mspace width=\"0.2em\"/><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>O</mml:mi><mml:mo>|</mml:mo><mml:mi>ϑ</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo stretchy=\"false\">]</mml:mo></mml:mrow><mml:mo stretchy=\"true\">︷</mml:mo></mml:mover><mml:mrow><mml:mi>a</mml:mi><mml:mi>c</mml:mi><mml:mi>c</mml:mi><mml:mi>u</mml:mi><mml:mi>r</mml:mi><mml:mi>a</mml:mi><mml:mi>c</mml:mi><mml:mi>y</mml:mi></mml:mrow></mml:mover><mml:mo>−</mml:mo><mml:mover><mml:mover><mml:mrow><mml:msub><mml:mi>D</mml:mi><mml:mrow><mml:mi>K</mml:mi><mml:mi>L</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">[</mml:mo><mml:mi>Q</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>ϑ</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>∥</mml:mo><mml:mi>P</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mi>ϑ</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mo stretchy=\"false\">]</mml:mo></mml:mrow><mml:mo stretchy=\"true\">︷</mml:mo></mml:mover><mml:mrow><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:mi>m</mml:mi><mml:mi>p</mml:mi><mml:mi>l</mml:mi><mml:mi>e</mml:mi><mml:mi>x</mml:mi><mml:mi>i</mml:mi><mml:mi>t</mml:mi><mml:mi>y</mml:mi></mml:mrow></mml:mover></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mspace width=\"2.1em\"/><mml:mo>=</mml:mo><mml:msub><mml:mi>E</mml:mi><mml:mi>Q</mml:mi></mml:msub><mml:mo stretchy=\"false\">[</mml:mo><mml:mtext>ln</mml:mtext><mml:mspace width=\"0.2em\"/><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>ϑ</mml:mi><mml:mo>|</mml:mo><mml:mi>O</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>+</mml:mo><mml:mtext>ln</mml:mtext><mml:mspace width=\"0.2em\"/><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>O</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>−</mml:mo><mml:mtext>ln</mml:mtext><mml:mspace width=\"0.2em\"/><mml:mi>Q</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>ϑ</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo stretchy=\"false\">]</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mspace width=\"2.1em\"/><mml:mo>=</mml:mo><mml:munder><mml:munder><mml:mrow><mml:mtext>ln</mml:mtext><mml:mspace width=\"0.2em\"/><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>O</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo stretchy=\"true\">︸</mml:mo></mml:munder><mml:mrow><mml:mi>e</mml:mi><mml:mi>v</mml:mi><mml:mi>i</mml:mi><mml:mi>d</mml:mi><mml:mi>e</mml:mi><mml:mi>n</mml:mi><mml:mi>c</mml:mi><mml:mi>e</mml:mi></mml:mrow></mml:munder><mml:mo>−</mml:mo><mml:munder><mml:munder><mml:mrow><mml:msub><mml:mi>D</mml:mi><mml:mrow><mml:mi>K</mml:mi><mml:mi>L</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">[</mml:mo><mml:mi>Q</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>ϑ</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>∥</mml:mo><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>ϑ</mml:mi><mml:mo>|</mml:mo><mml:mi>O</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo stretchy=\"false\">]</mml:mo></mml:mrow><mml:mo stretchy=\"true\">︸</mml:mo></mml:munder><mml:mrow><mml:mi>b</mml:mi><mml:mi>o</mml:mi><mml:mi>u</mml:mi><mml:mi>n</mml:mi><mml:mi>d</mml:mi></mml:mrow></mml:munder><mml:mo>≤</mml:mo><mml:munder><mml:munder><mml:mrow><mml:mtext>ln</mml:mtext><mml:mspace width=\"0.2em\"/><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>O</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo stretchy=\"true\">︸</mml:mo></mml:munder><mml:mrow><mml:mi>e</mml:mi><mml:mi>v</mml:mi><mml:mi>i</mml:mi><mml:mi>d</mml:mi><mml:mi>e</mml:mi><mml:mi>n</mml:mi><mml:mi>c</mml:mi><mml:mi>e</mml:mi></mml:mrow></mml:munder></mml:mtd></mml:mtr></mml:mtable><mml:mspace width=\"27em\"/><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1.13</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:math></disp-formula>\n</p><p>These expressions show that maximising the variational free energy\n<italic>F</italic> with respect to an approximate posterior\n<italic>Q</italic>(\n<italic>ϑ</italic>) renders the Kullback-Leibler (KL) divergence between the true and approximate posterior as small as possible. At the same time, the free energy becomes a lower bound on the log evidence. The free energy can then be used to compare different models, where any differences correspond to a log Bayes factor or odds ratio (\n<xref rid=\"ref-17\" ref-type=\"bibr\">Kass & Raftery, 1995</xref>;\n<xref rid=\"ref-43\" ref-type=\"bibr\">Winn & Bishop, 2005</xref>).</p></sec><sec><title>Bayesian model comparison</title><p>One may be asking why we have chosen this particular state space and this parameterisation? Are there alternative model structures or parameterisations that would be more fit for purpose? The answer is that there will always be a better model, where ‘better’ is a model that has more evidence. This means that the model has to be optimised in relation to empirical data. This process is known as\n<italic>Bayesian model comparison</italic> based upon model evidence (\n<xref rid=\"ref-43\" ref-type=\"bibr\">Winn & Bishop, 2005</xref>). For example, in the above model we assumed that social distancing increases as a function of the proportion of the population who are infected (\n<xref ref-type=\"other\" rid=\"e1.1\">1.1</xref>). This stands in for a multifactorial influence on social behaviour that may be mediated in many ways. For example, government advice, personal choices, availability of transport, media reports of ‘panic buying’ and so on. So, what licenses us to model the causes of social distancing in terms of a probability that any member of the population is infected? The answer rests upon Bayesian model comparison. When inverting the model using data from countries with more than 16 deaths (see\n<xref ref-type=\"fig\" rid=\"f2\">Figure 2</xref>), we obtained a log evidence (i.e., variational free energy) of -15701 natural units (nats). When replacing the cause of social distancing with the probability of encountering someone with symptoms—or the number of people testing positive—the model evidence fell substantially to -15969 and -15909 nats, respectively. In other words, there was overwhelming evidence in favour of infection rates as a primary drive for social distancing, over and above alternative models. We will return to the use of Bayesian model comparison later, when asking what factors determine differences between each country’s response to the pandemic.</p></sec><sec><title>Summary</title><p>\n<xref rid=\"T1\" ref-type=\"table\">Table 1</xref> lists all the model parameters; henceforth,\n<italic>DCM parameters</italic>. In total, there are 21 DCM parameters. This may seem like a large number to estimate from the limited amount of data available (see\n<xref ref-type=\"fig\" rid=\"f2\">Figure 2</xref>). The degree to which a parameter is informed by the data depends upon how changes in the parameter are expressed in data space. For example, increasing the effective population size will uniformly elevate the expected cases per day. Conversely, decreasing the number of initially infected people will delay the curve by shifting it in time. In short, a parameter can be identified if it has a relatively unique expression in the data. This speaks to an important point, the information in the data is not just in the total count—it is in the shape or form of the transient\n<sup><xref ref-type=\"other\" rid=\"FN18\">18</xref></sup>.</p><p>On this view, there are many degrees of freedom in a timeseries that can be leveraged to identify a highly parameterised model. The issue of whether the model is over parameterised or under parameterised is exactly the issue resolved by Bayesian model comparison; namely, the removal of redundant parameters to suppress model complexity and ensure generalisation: see (\n<xref ref-type=\"other\" rid=\"e1.13\">1.13</xref>)\n<sup><xref ref-type=\"other\" rid=\"FN19\">19</xref></sup>. One therefore requires the best measures of model evidence. This is the primary motivation for using variational Bayes; here, variational Laplace (\n<xref rid=\"ref-8\" ref-type=\"bibr\">Friston\n<italic>et al</italic>., 2007</xref>). The variational free energy, in most circumstances, provides a better approximation than alternatives such as the widely used Akaike information criteria and the widely used Bayesian information criteria (\n<xref rid=\"ref-30\" ref-type=\"bibr\">Penny, 2012</xref>).</p><p>One special aspect of the model above is that it has absorbing states. For example, whenever one enters the morgue, becomes immune, dies or has a definitive test result, one stays in that state: see\n<xref ref-type=\"fig\" rid=\"f1\">Figure 1</xref>. This is important, because it means the long-term behaviour of the model has a fixed point. In other words, we know what the final outcomes will be. These outcomes are known as endemic equilibria. This means that the only uncertainty is about the trajectory from the present point in time to the distant future. We will see later that—when quantified in terms of Bayesian credible intervals—this uncertainty starts to decrease as we go into the distant future. This should be contrasted with alternative models that do not parameterise the influences that generate outcomes and therefore call upon exogenous inputs (e.g., statutory changes in policy or changes in people’s behaviour). If these interventions are unknown, they will accumulate uncertainty over time. By design, we elude this problem by including everything that matters within the model and parameterising strategic responses (like social distancing) as an integral part of the transition probabilities.</p><p>We have made the simplifying assumption that every country reporting new cases is, effectively, reporting the first wave of an affected region or city. Clearly, some countries could suffer simultaneous outbreaks in multiple cities. This is accommodated by an effective population size that could be greater than the prior expectation of 1 million. This is an example of finding a simple model that best predicts outcomes—that may not be a veridical reflection of how those outcomes were actually generated. In other words, we will assume that each country behaves\n<italic>as if</italic> it has a single large city of at-risk denizens. In the next section, we look at the parameter estimates that obtain by pooling information from all countries, with a focus on between country differences, before turning to the epidemiology of a single country (the United Kingdom).</p><p>Hitherto, we have focused on a generative model for a single city. However, in a pandemic, many cities will be affected. This calls for a hierarchical generative model that considers the response of each city at the first level and a global response at the second. This is an important consideration because it means, from a Bayesian perspective, knowing what happens elsewhere places constraints (i.e., Bayesian shrinkage priors) on estimates of what is happening in a particular city. Clearly, this rests upon the extent to which certain model parameters are conserved from one city to another—and which are idiosyncratic or unique. This is a problem of hierarchical Bayesian modelling or parametric empirical Bayes (\n<xref rid=\"ref-13\" ref-type=\"bibr\">Friston\n<italic>et al</italic>., 2016</xref>;\n<xref rid=\"ref-18\" ref-type=\"bibr\">Kass & Steffey, 1989</xref>). In the illustrative examples below, we will adopt a second level model in which key (log) parameters are sampled from a Gaussian distribution with a global (worldwide) mean and variance. From the perspective of the generative model, this means that to generate a pandemic, one first samples city-specific parameters from a global distribution, adds a random effect, and uses the ensuing parameters to generate a timeseries for each city.</p></sec></sec><sec><title>Parametric empirical Bayes and hierarchical models</title><p>This section considers the modelling of country-specific parameters, under a simple (general linear) model of between-country effects. This (second level) model requires us to specify which parameters are shared in a meaningful way between countries and which are unique to each country. Technically, this can be cast as the difference between\n<italic>random</italic> and\n<italic>fixed effects</italic>. Designating a particular parameter as a random effect means that this parameter was generated by sampling from a countrywide distribution, while a fixed effect is unique to each country. Under a general linear model, the distribution for random effects is Gaussian. In other words, to generate the parameter for a particular country, we take the global expectation and add a random Gaussian variate, whose variance has to be estimated under suitable hyperpriors. Furthermore, one has to specify systematic differences between countries in terms of independent variables; for example, does the latitude of a country have any systematic effect on the size of the at-risk population? The general linear model used here comprises a constant (i.e., the expectation or mean of each parameter over countries), the (logarithms of) total population size, and a series of independent variables based upon a discrete sine transform of latitude and longitude. The latter variables stand in for any systematic and geopolitical differences among countries that vary smoothly with their location. Notice that the total population size may or may not provide useful constraints on the effective size of the population at the first level. Under this hierarchical model, a bigger country may have a transport and communication infrastructure that could reduce the effective (at risk) population size. A hint that this may be the case is implicit in\n<xref ref-type=\"fig\" rid=\"f2\">Figure 2</xref>, where there is no apparent relationship between the early incidence of deaths and total population size.</p><p>In the examples below, we treated the number of initial cases and the parameters pertaining to testing as fixed effects and all remaining parameters as random effects. The number of initial infected people determines the time at which a particular country evinces its outbreak. Although this clearly depends upon geography and other factors, there is no\n<italic>a priori</italic> reason to assume a random variation about an average onset time. Similarly, we assume that each country’s capacity for testing was a fixed effect; thereby accommodating non-systematic testing or reporting strategies\n<sup><xref ref-type=\"other\" rid=\"FN20\">20</xref></sup>. Note that in this kind of modelling, outcomes such as new cases can only be interpreted in relation to the probability of being tested and the availability of tests\n<sup><xref ref-type=\"other\" rid=\"FN21\">21</xref></sup>.</p><p>With this model in place, we can now use standard procedures for parametric empirical Bayesian modelling (\n<xref rid=\"ref-13\" ref-type=\"bibr\">Friston\n<italic>et al</italic>., 2016</xref>;\n<xref rid=\"ref-18\" ref-type=\"bibr\">Kass & Steffey, 1989</xref>) to estimate the second level parameters that couple between-country independent variables to country-specific parameters of the DCM. However, there are a large number of these parameters—that may or may not contribute to model evidence. In other words, we need some way of removing redundant parameters based upon Bayesian model comparison. This calls upon another standard procedure called\n<italic>Bayesian model reduction</italic> (\n<xref rid=\"ref-9\" ref-type=\"bibr\">Friston\n<italic>et al</italic>., 2018</xref>;\n<xref rid=\"ref-13\" ref-type=\"bibr\">Friston\n<italic>et al</italic>., 2016</xref>). In brief, Bayesian model reduction allows one to evaluate the evidence for a model that one would have obtained if the model had been reduced by removing one or more parameters. The key aspect of Bayesian model reduction is that this evidence can be evaluated using the posteriors and priors of a parent model that includes all possible parameters. There are clearly an enormous number of combinations of parameters that one could consider. Fortunately, these can be scored quickly and efficiently using Bayesian model reduction, by making use of Savage-Dickey density ratios (\n<xref rid=\"ref-11\" ref-type=\"bibr\">Friston & Penny, 2011</xref>;\n<xref rid=\"ref-34\" ref-type=\"bibr\">Savage, 1954</xref>). Because Bayesian model reduction scores the effect of changing the precision of priors—on model evidence—it can be regarded as an automatic Bayesian sensitivity analysis, also known as robust Bayesian analysis (\n<xref rid=\"ref-1\" ref-type=\"bibr\">Berger, 2011</xref>).</p><p>\n<xref ref-type=\"fig\" rid=\"f3\">Figure 3</xref> shows the results of this analysis. The upper panels show the posterior probability of 256 models that had the greatest evidence (shown as a log posterior in the upper left panel). Each of these models corresponds to a particular combination of parameters that have been ‘switched off’, by shrinking their prior variance to zero. By averaging the posterior estimates in proportion to the evidence for each model, —known as\n<italic>Bayesian model averaging</italic> (\n<xref rid=\"ref-15\" ref-type=\"bibr\">Hoeting\n<italic>et al</italic>., 1999</xref>)—we can eliminate redundant parameters and thereby provide a simpler explanation for differences among countries. This is illustrated in the lower panels, which show the posterior densities before (left) and after (right) Bayesian model reduction. These estimates are shown in terms of their expectation or\n<italic>maximum a posteriori</italic> (MAP) value (as blue bars), with 90% Bayesian credible intervals (as pink bars).</p><p>The first 21 parameters are the global expectations of the DCM parameters. The remaining parameters are the coefficients that link various independent variables at the second level to the parameters of the transition probabilities at the first. Note that a substantial number of second level parameters have been removed; however, many are retained. This suggests that there are systematic variations over countries in certain random effects at the country level.\n<xref ref-type=\"fig\" rid=\"f4\">Figure 4</xref> provides an example based upon the largest effect mediated by the independent variables. In this analysis, latitude (i.e., distance from the South Pole) appears to reduce the effective size of an at-risk population. In other words, countries in the northern hemisphere have a smaller effective population size, relative to countries in the southern hemisphere. Clearly, there may be many reasons for this; for example, systematic differences in temperature or demographics.</p><fig fig-type=\"figure\" id=\"f3\" orientation=\"portrait\" position=\"anchor\"><label>Figure 3. </label><caption><title>Bayesian model reduction.</title><p>This figure reports the results of Bayesian model reduction. In this instance, the models compared are at the second or between-country level. In other words, the models compared contained all combinations of (second level) parameters (a parameter is removed by setting its prior variance to zero). If the model evidence increases—in virtue of reducing model complexity—then this parameter is redundant. The upper panels show the relative evidence of the most likely 256 models, in terms of log evidence (left panel) and the corresponding posterior probability (right panel). Redundant parameters are illustrated in the lower panels by comparing the posterior expectations before and after the Bayesian model reduction. The blue bars correspond to posterior expectations, while the pink bars denote 90% Bayesian credible intervals. The key thing to take from this analysis is that a large number of second level parameters have been eliminated. These second level parameters encode the effects of population size and geographical location, on each of the parameters of the generative model. The next figure illustrates the nonredundant effects that can be inferred with almost 100% posterior confidence.</p></caption><graphic xlink:href=\"wellcomeopenres-5-17779-g0002\"/></fig><fig fig-type=\"figure\" id=\"f4\" orientation=\"portrait\" position=\"anchor\"><label>Figure 4. </label><caption><title>between country effects.</title><p>This figure shows the relationship between parameters of the generative model and the explanatory variables in a general linear model (GLM) of between country effects. The left panel shows a regression of country-specific DCM parameters on the independent variable that had the greatest absolute value; namely, the contribution of an explanatory variable to a model parameter. Here, the effective size of the population appears to depend upon the latitude of a country. The right panel shows the absolute values of the GLM parameters in matrix form, showing that the effective size of the population was most predictable (the largest values are in white), though not necessarily predictable by total population size. The red circle highlights the parameter mediating the relationship illustrated in the left panel.</p></caption><graphic xlink:href=\"wellcomeopenres-5-17779-g0003\"/></fig><p>\n<xref ref-type=\"fig\" rid=\"f5\">Figure 5</xref> shows the Bayesian parameter averages (\n<xref rid=\"ref-22\" ref-type=\"bibr\">Litvak\n<italic>et al</italic>., 2015</xref>) of the DCM parameters over countries. The posterior density (blue bars and pink lines) are supplemented with the prior expectations (red bars) for comparison. The upper panel shows the MAP estimates of log parameters, while the lower panel shows the same results in terms of scale parameters. The key thing to take from this analysis is the tight credible intervals on the parameters, when averaging in this way. According to this analysis, the number of effective contacts at home is about three people, while this increases by an order of magnitude to about 30 people when leaving home. The symptomatic and acute respiratory distress periods have been estimated here at about five and 13 days respectively, with a delay in testing of about two days. These are the values that provide the simplest explanation for the global data at hand—and are in line with empirical estimates\n<sup><xref ref-type=\"other\" rid=\"FN22\">22</xref></sup>.</p><fig fig-type=\"figure\" id=\"f5\" orientation=\"portrait\" position=\"anchor\"><label>Figure 5. </label><caption><title>Bayesian parameter averages.</title><p>This figure reports the Bayesian parameter averages over countries following a hierarchical or parametric empirical Bayesian analysis that tests for—and applies shrinkage priors to—posterior parameter estimates for each country. The upper panel shows the parameters as estimated in log space, while the lower panel shows the same results for the corresponding scale (nonnegative) parameters. The blue bars report posterior expectations, while the thinner red bars in the upper panel are prior expectations. The pink bars denote 90% Bayesian credible intervals. One can interpret these parameters as the average value for any given parameter of the generative model, to which a random (country-specific) effect is added to generate the ensemble dynamics for each country. In turn, these ensemble distributions determine the likelihood of various outcome measures under large number (i.e., Gaussian) assumptions.</p></caption><graphic xlink:href=\"wellcomeopenres-5-17779-g0004\"/></fig><p>\n<xref ref-type=\"fig\" rid=\"f6\">Figure 6</xref> shows the country-specific parameter estimates for 12 of the 21 DCM parameters. These posterior densities were evaluated under the empirical priors from the parametric empirical Bayesian analysis above. As one might expect—in virtue of the second level effects that survived Bayesian model reduction—there are some substantial differences between countries in certain parameters. For example, the effective population size in the United States of America is substantially greater than elsewhere at about 25 million (the population in New York state is about 19.4 million). The effective population size in the UK (dominated by cases in London) is estimated to be about 2.5 million (London has a population of about 8.96 million)\n<sup><xref ref-type=\"other\" rid=\"FN23\">23</xref></sup>. Social distancing seems to be effective and sensitive to infection rates in France but much less so in Canada. The efficacy of social distancing in terms of the difference between the number of contacts at home and work is notably attenuated in the United Kingdom—that has the greatest number of home contacts and the least number of work contacts. Other notable differences are the increased probability of fatality in critical care evident in China. This is despite the effective population size being only about 2.5 million. Again, these assertions are not about actual states of affairs. These are the best explanations for the data under the simplest model of how those data were caused\n<sup><xref ref-type=\"other\" rid=\"FN24\">24</xref></sup>.</p><fig fig-type=\"figure\" id=\"f6\" orientation=\"portrait\" position=\"anchor\"><label>Figure 6. </label><caption><title>differences among countries.</title><p>This figure reports the differences among countries in terms of selected parameters of the generative model, ranging from the effective population size, through to the probability of testing its denizens. The blue bars represent the posterior expectations, while the pink bars are 90% Bayesian credible intervals. Notice that these intervals are not symmetrical about the mean because we are reporting scale parameters—as opposed to log parameters. For each parameter, the countries showing the smallest and largest values are labelled. The red asterisk denotes the country considered in the next section (the United Kingdom). The next figure illustrates the projections, in terms of new deaths and cases, based upon these parameter estimates. The order of the countries is listed in\n<xref ref-type=\"fig\" rid=\"f2\">Figure 2</xref>.</p></caption><graphic xlink:href=\"wellcomeopenres-5-17779-g0005\"/></fig></sec><sec><title>Summary</title><p>This level of modelling is important because it enables the data or information from one country to inform estimates of the first level (DCM) parameters that underwrite the epidemic in another country\n<sup><xref ref-type=\"other\" rid=\"FN25\">25</xref></sup>. This is another expression of the importance of having a hierarchical generative model for making sense of the data. Here, the generative model has latent causes that span different countries, thereby enabling the fusion of multimodal data from multiple countries (e.g., new test or death rates). Two natural questions now arise. Are there any systematic differences between countries in the parameters that shape epidemiological dynamics—and what do these dynamics or trajectories look like?</p><p>This concludes our brief treatment of between country effects, in which we have considered the potentially important role of Bayesian model reduction in identifying systematic variations in the evolution of an epidemic from country to country. The next section turns to the use of hierarchically informed estimates of DCM parameters to characterise an outbreak in a single country.</p></sec><sec><title>Dynamic causal modelling of a single country</title><p>This section drills down on the likely course of the epidemic in the UK, based upon the posterior density over DCM parameters afforded by the hierarchical (parametric empirical) Bayesian analysis of the previous section (listed in\n<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>).\n<xref ref-type=\"fig\" rid=\"f7\">Figure 7</xref> shows the expected trajectory of death rates, new cases, and occupancy of CCU beds over a six-month (180 day) period. These (posterior predictive) densities are shown in terms of an expected trajectory and 90% credible intervals (blue line and shaded areas, respectively). The black dots represent empirical data (available at the time of writing). Notice that the generative model can produce outcomes that may or may not be measured. Here, the estimates are based upon the new cases and deaths in\n<xref ref-type=\"fig\" rid=\"f2\">Figure 2</xref>.</p><table-wrap id=\"T2\" orientation=\"portrait\" position=\"anchor\"><label>Table 2. </label><caption><title>Posterior estimates of key DCM parameters (for the United Kingdom), with 90% credible range.</title></caption><table frame=\"hsides\" rules=\"groups\" content-type=\"article-table\"><thead><tr><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">parameter</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Mean</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Units</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Upper</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Lower</th></tr></thead><tbody><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>initial cases</bold>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.33</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.19</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.57</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>size of population</bold>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.49</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">M</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.99</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.11</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>Pr(work | home)</bold>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">%</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">23</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>social distancing</bold>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.60</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.159</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.2</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>contacts: home</bold>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">7.01</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.49</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">8.95</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>contacts: work</bold>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">16.02</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">12.12</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">21.19</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>Pr(contagion | contact)</bold>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">%</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">25</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>infected period</bold>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">6.44</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Days</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.87</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">8.51</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>contagious period</bold>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.93</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Days</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.15</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.99</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>Pr(symptoms | infected)</bold>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">47</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">%</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">34</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">65</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>Pr(ARDS | symptoms)</bold>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.70</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">%</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.31</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.20</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>symptomatic period</bold>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.45</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Days</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.55</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.68</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>acute RDS period</bold>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">10.89</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Days</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">8.07</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">14.70</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>Pr(fatality | CCU)</bold>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">48</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">%</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">39</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">60</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>Pr(survival | home)</bold>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">6.36</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">%</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.56</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">8.87</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>test delay</bold>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.02</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Days</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.668</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.45</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>Pr(tested | uninfected)</bold>\n</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">12.52</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">%</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"right\" valign=\"top\" rowspan=\"1\" colspan=\"1\">15</td></tr></tbody></table></table-wrap><p>The panels on the left show that our confidence about the causes of new cases is relatively high during the period for which we have data and then becomes uncertain in the future. This reflects the fact that the data are informing those parameters that shaped the initial transient, whereas other parameters responsible for the late peak and subsequent trajectory are less informed. Notice that the uncertainty about cumulative deaths itself accumulates. On this analysis, we can be 90% confident that in five weeks, between 13,000 and 22,000 people may have died. Relative to the total population, the proportion of people dying is very small; however, the cumulative death rates in absolute numbers are substantial, in relation to seasonal influenza (indicated with broken red lines). Although cumulative death rates are small, they are concentrated within a short period of time, with near-identical CCU needs—with the risk of over-whelming available capacity (not to mention downstream effects from blocking other hospital admissions to prioritise the pandemic).</p><fig fig-type=\"figure\" id=\"f7\" orientation=\"portrait\" position=\"anchor\"><label>Figure 7. </label><caption><title>projected outcomes.</title><p>This figure reports predicted\n<sup><xref ref-type=\"other\" rid=\"FN27\">27</xref></sup> new deaths and cases (and CCU occupancy) for an exemplar country; here, the United Kingdom. The panels on the left show the predicted outcomes as a function of weeks. The blue lines correspond to the expected trajectory, while the shaded areas are 90% Bayesian credible intervals. The black dots represent empirical data, upon which the parameter estimates are based. The lower right panel shows the parameter estimates for the country in question. As in previous figures, the prior expectations are shown as pink bars over the posterior expectations (and credible intervals). The upper right panel illustrates the equivalent expectations in terms of cumulative deaths. The dotted red lines indicate the number of people who died from seasonal influenza in recent years\n<sup><xref ref-type=\"other\" rid=\"FN28\">28</xref></sup>. The key point to take from this figure is the quantification of uncertainty inherent in the credible intervals. In other words, uncertainty about the parameters propagates through to uncertainty in predicted outcomes. This uncertainty changes over time because of the nonlinear relationship between model parameters and ensemble dynamics. By model design, one can be certain about the final states; however, uncertainty about cumulative death rates itself accumulates. The mapping from parameters, through ensemble dynamics to outcomes is mediated by latent or hidden states. The trajectory of these states is illustrated in the next figure.</p></caption><graphic xlink:href=\"wellcomeopenres-5-17779-g0006\"/></fig><p>The underlying latent causes of these trajectories are shown in\n<xref ref-type=\"fig\" rid=\"f8\">Figure 8</xref>. The upper panels reproduce the expected trajectories of the previous figure, while the lower panels show the underlying latent states in terms of expected rates or probabilities. For example, the social distancing measures are expressed in terms of an increasing probability of being at home, given the accumulation of infected cases in the population. During the peak expression of death rates, the proportion of people who are immune (herd immunity) increases to about 30% and then asymptotes at about 90%. This period is associated with a marked increase in the probability of developing symptoms (peaking at about 11 weeks, after the first reported cases). Interestingly, under these projections, the number of people expected to be in critical care should not exceed capacity: at its peak, the upper bound of the 90% credible interval for CCU occupancy is approximately 4200, this is within the current CCU capacity of London (corresponding to the projected capacity of the temporary Nightingale Hospital\n<sup><xref ref-type=\"other\" rid=\"FN26\">26</xref></sup> in London, UK).</p><fig fig-type=\"figure\" id=\"f8\" orientation=\"portrait\" position=\"anchor\"><label>Figure 8. </label><caption><title>latent causes of observed consequences.</title><p>The upper panels reproduce the expected trajectories of the previous figure, for an example country (here the United Kingdom). The expected death rate is shown in blue, new cases in red, predicted recovery rate in orange and CCU occupancy in yellow. The black dots correspond to empirical data. The lower four panels show the evolution of latent (ensemble) dynamics, in terms of the expected probability of being in various states. The first (location) panel shows that after about 5 to 6 weeks, there is sufficient evidence for the onset of an episode to induce social distancing, such that the probability of being found at work falls, over a couple of weeks to negligible levels. At this time, the number of infected people increases (to about 32%) with a concomitant probability of being infectious a few days later. During this time, the probability of becoming immune increases monotonically and saturates at about 20 weeks. Clinically, the probability of becoming symptomatic rises to about 30%, with a small probability of developing acute respiratory distress and, possibly death (these probabilities are very small and cannot be seen in this graph). In terms of testing, there is a progressive increase in the number of people tested, with a concomitant decrease in those untested or waiting for their results. Interestingly, initially the number of negative tests increases monotonically, while the proportion of positive tests starts to catch up during the peak of the episode. Under these parameters, the entire episode lasts for about 10 weeks, or less than three months. The broken red line in the upper left panel shows the typical number of CCU beds available to a well-resourced city, prior to the outbreak.</p></caption><graphic xlink:href=\"wellcomeopenres-5-17779-g0007\"/></fig><p>It is natural to ask which DCM parameters contributed the most to the trajectories in\n<xref ref-type=\"fig\" rid=\"f8\">Figure 8</xref>. This is addressed using a\n<italic>sensitivity analysis</italic>. Intuitively, this involves changing a particular parameter and seeing how much it affects the outcomes of interest.\n<xref ref-type=\"fig\" rid=\"f9\">Figure 9</xref> reports a sensitivity analysis of the parameters in terms of their direct contribution to cumulative deaths (upper panel) and how they interact (lower panel). These are effectively the gradient and Hessian matrix (respectively) of predicted cumulative deaths. The bars in the upper panel pointing to the left indicate parameters that decrease total deaths. These include social distancing and bed availability, which are—to some extent—under our control. Other factors that improve fatality rates include the symptomatic and acute respiratory distress periods and the probability of surviving outside critical care. These, at the present time, are not so amenable to intervention. Note that initial immunity has no effect in this analysis because we clamped the initial values to zero with very precise priors. We will relax this later. First, we look at the effect of social distancing by simulating the ensemble dynamics under increasing levels of the social distancing exponent (i.e., the sensitivity of our social distancing and self-isolation behaviour to the prevalence of the virus in the community).</p><fig fig-type=\"figure\" id=\"f9\" orientation=\"portrait\" position=\"anchor\"><label>Figure 9. </label><caption><title>sensitivity analysis.</title><p>These panels show the change in outcome measures—here cumulative deaths—with respect to model parameters (upper panel: first order derivatives. lower panel: second order derivatives). The bar charts in the upper panel are the derivatives of outcomes with respect to each of the parameters. Positive values (on the right) exacerbate new cases when increased, while negative values (on the left) decrease new cases. As one might expect, increasing social distancing, bed availability and the probability of survival outside critical care, tend to decrease death rate. Interestingly, increasing both the period of symptoms and ARDS decreases overall death rate, because (in this compartmental model) keeping someone alive for longer in a CCU reduces fatality rates (as long as capacity is not exceeded). The lower panel shows the second order derivatives. These reflect the effect of one parameter on the effect of another parameter on total deaths. For example, the effects of bed availability and fatality in CCU are positive, meaning that the beneficial (negative) effects of increasing bed availability—on total deaths—decrease with fatality rates.</p></caption><graphic xlink:href=\"wellcomeopenres-5-17779-g0008\"/></fig><p>It may be surprising to see that social distancing has such a small effect on total deaths (see upper panel in\n<xref ref-type=\"fig\" rid=\"f9\">Figure 9</xref>). However, the contribution of social distancing is in the context of how the epidemic elicits other responses; for example, increases in critical care capacity. Quantitatively speaking, increasing social distancing only delays the expression of morbidity in the population: it does not, in and of itself, decrease the cumulative cost (although it buys time to develop capacity, treatments, and primary interventions). This is especially the case if there is no effective limit on critical care capacity, because everybody who needs a bed can be accommodated. This speaks to the interaction between different causes or parameters in generating outcomes. In the particular case of the UK, the results in\n<xref ref-type=\"fig\" rid=\"f4\">Figure 4</xref> suggest that although social distancing is in play, self-isolation appears limited. This is because the number of contacts at home is relatively high (at over five); thereby attenuating the effect of social distancing. In other words, slowing the spread of the virus depends upon reducing the number of contacts by social distancing. However, this will only work if there is a notable difference between the number of contacts at home and work. One can illustrate this by simulating the effects of social distancing, when it makes a difference.</p><p>\n<xref ref-type=\"fig\" rid=\"f10\">Figure 10</xref> reproduces the results in\n<xref ref-type=\"fig\" rid=\"f8\">Figure 8</xref> but for 16 different levels of the social distancing parameter, while using the posterior expectation for contacts at home (of about four) from the Bayesian parameter average. Social distancing is expressed in terms of the probability of being found at home or work (see the panel labelled\n<italic>location</italic>). As we increase social distancing the probability and duration of being at home during the outbreak increases. This flattens the curve of death rates per day from about 600 to a peak of about 400. This is the basis of the mitigation (‘curve flattening’) strategies that have been adopted worldwide. The effect of this strategy is to reduce cumulative deaths and prevent finite resources being overwhelmed. In this example, from about 17,000 to 14,000, potentially saving about 3000 people. This is roughly four times the number of people who die in the equivalent period due to road traffic accidents. Interestingly, these (posterior predictive) projections suggest that social distancing can lead to an endgame in which not everybody has to be immune (see the middle panel labelled\n<italic>infection</italic>). We now look at herd immunity using the same analysis.</p><fig fig-type=\"figure\" id=\"f10\" orientation=\"portrait\" position=\"anchor\"><label>Figure 10. </label><caption><title>the effects of social distancing.</title><p>This figure uses the same format as\n<xref ref-type=\"fig\" rid=\"f9\">Figure 9</xref>. However, here trajectories are reproduced under different levels of social distancing; from zero through to four (in 16 steps). This parameter is the exponent applied to the probability of not being infected. In other words, it scores the sensitivity of social distancing to the prevalence of the virus in the population. In this example (based upon posterior expectations for the United Kingdom and Bayesian parameter averages over countries), death rates (per day) decrease progressively with social distancing. The cumulative death rate is shown as a function of social distancing in the upper right panel. The vertical line corresponds to the posterior expectation of the social distancing exponent for this country. These results suggest that social distancing relieves pressure on critical care capacities and ameliorates cumulative deaths by about 3000 people. Note that these projections are based upon an effective social distancing policy at home, with about four contacts. In the next figure, we repeat this analysis but looking at the effect of herd immunity.</p></caption><graphic xlink:href=\"wellcomeopenres-5-17779-g0009\"/></fig><p>\n<xref ref-type=\"fig\" rid=\"f11\">Figure 11</xref> reproduces the results in\n<xref ref-type=\"fig\" rid=\"f10\">Figure 10</xref> using the United Kingdom posterior estimates – but varying the initial (herd) immunity over 16 levels from, effectively, 0 to 100%. The effects of herd immunity are marked, with cumulative deaths ranging from about 18,000 with no immunity to very small numbers with a herd immunity of about 70%. The broken red lines in the upper right panel are the number of people dying from seasonal influenza (as in\n<xref ref-type=\"fig\" rid=\"f7\">Figure 7</xref>). These projections suggest that there is a critical level of herd immunity that will effectively avert an epidemic; in virtue of reducing infection rates, such that the spread of the virus decays exponentially. If we now return to\n<xref ref-type=\"fig\" rid=\"f8\">Figure 8</xref>, it can be seen that the critical level of herd immunity will, on the basis of these projections, be reached 2 to 3 weeks after the peak in death rates. At this point—according to the model—social distancing starts to decline as revealed by an increase in the probability of being at work. We will put some dates on this trajectory by expressing it as a narrative in the conclusion.</p><fig fig-type=\"figure\" id=\"f11\" orientation=\"portrait\" position=\"anchor\"><label>Figure 11. </label><caption><title>herd immunity.</title><p>This figure reproduces the format of the previous figure. However, here, we increased the initial proportion of the at-risk population who were initially immune. Increasing the initial immunity dramatically decreases death rates with a fall in the cumulative deaths from several thousand to negligible levels with an initial herd immunity of about 70%. The dashed lines in the upper panel shows the equivalent deaths over the same time period due to seasonal flu (based upon 2014/2014 and 2018/2019 figures). The lower deaths due to seasonal flu would require an initial herd immunity of about 60%. Note that predictions—like the percentage of herd immunity—pertain to the effective population. For example, if 80% of the effective (2.5 million) population are seropositive, one would expect 22% of the census (8.9 million) population of London to have seroconverted by early May.</p></caption><graphic xlink:href=\"wellcomeopenres-5-17779-g0010\"/></fig><p>From a modelling perspective, the influence of initial herd immunity is important because it could form the basis of modelling the spread of the virus from city to another—and back again. In other words, more sophisticated generative models can be envisaged, in which an infected person from one city is transported to another city with a small probability or rate. Reciprocal exchange between cities, (and ensuing ‘second waves’) will then depend sensitively on the respective herd immunities in different regions. Anecdotally, other major pandemics, without social isolation strategies, have almost invariably been followed by a second peak that is as high (e.g., the 2009 H1N1 pandemic), or higher, than the first. Under the current model, this would be handled in terms of a second region being infected by the first city and so on; like a chain of dominos or the spread of a bushfire (\n<xref rid=\"ref-32\" ref-type=\"bibr\">Rhodes & Anderson, 1998</xref>;\n<xref rid=\"ref-47\" ref-type=\"bibr\">Zhang & Tang, 2016</xref>). Crucially, the effect of the second city (i.e., wave) on the first will be sensitive to the herd immunity established by the first wave. In this sense, it is interesting to know how initial levels of immunity shape a regional outbreak, under idealised assumptions.</p><p>\n<xref ref-type=\"fig\" rid=\"f12\">Figure 12</xref> illustrates the interaction between immunity and viral spread as characterised by the effective reproduction rate,\n<italic>R</italic> (a.k.a. number or ratio); see (\n<xref ref-type=\"other\" rid=\"e1.9\">1.9</xref>). This figure plots the predicted death rates for the United Kingdom and the accompanying fluctuations in\n<italic>R</italic> and herd immunity, where both are treated as outcomes of the generative model. The key thing to observe is that with low levels of immunity,\n<italic>R</italic> is fairly high at around 2.5 (current estimates of the basic reproduction ratio\n<sup><xref ref-type=\"other\" rid=\"FN29\">29</xref></sup>\n<italic>R</italic>\n<sub>0</sub>, in the literature, range from 1.4 to 3.9). As soon as social distancing comes into play,\n<italic>R</italic> falls dramatically to almost 0. However, when social distancing is relaxed some weeks later,\n<italic>R</italic> remains low due to the partial acquisition of herd immunity, during the peak of the epidemic. Note that herd immunity in this setting pertains to, and only to, the effective or at-risk population: 80% herd immunity a few months from onset would otherwise be overly optimistic, compared to other\n<italic>de novo</italic> pandemics; e.g., (\n<xref rid=\"ref-6\" ref-type=\"bibr\">Donaldson\n<italic>et al</italic>., 2009</xref>). On the other hand, an occult herd immunity (i.e. not accompanied by symptoms) is consistent with undocumented infection and rapid dissemination (\n<xref rid=\"ref-21\" ref-type=\"bibr\">Li\n<italic>et al</italic>., 2020</xref>). Note that this way of characterising the spread of a virus depends upon many variables (in this model, two factors and three parameters). And can vary from country to country. Repeating the above analysis for China gives a much higher initial or basic reproduction rate, which is consistent with empirical reports (\n<xref rid=\"ref-37\" ref-type=\"bibr\">Sanche\n<italic>et al</italic>., 2020</xref>).</p><fig fig-type=\"figure\" id=\"f12\" orientation=\"portrait\" position=\"anchor\"><label>Figure 12. </label><caption><title>effective reproduction ratio.</title><p>This figure plots the predicted death rates for the United Kingdom from\n<xref ref-type=\"fig\" rid=\"f6\">Figure 6</xref> and the concomitant fluctuations in the effective reproduction rate (\n<italic>R</italic>) and herd immunity. The blue lines represent the posterior expectations while the shaded areas correspond to 90% credible intervals.</p></caption><graphic xlink:href=\"wellcomeopenres-5-17779-g0011\"/></fig><p>This concludes our characterisation of projections for what is likely to happen and what could happen under different scenarios for a particular country. In the final section, we revisit the confidence with which these posterior predictive projections can be made.</p></sec><sec><title>Predictive validity</title><p>Variational approaches—of the sort described in this technical report—use all the data at hand to furnish statistically efficient estimates of model parameters and evidence. This contrasts with alternative approaches based on cross-validation. In the cross-validation schemes, model evidence is approximated by cross-validation accuracy. In other words, the evidence for a model is scored by the log likelihood that some withheld or test data can be explained by the model. Although model comparison based upon a variational evidence bound renders cross-validation unnecessary, one can apply the same procedures to demonstrate predictive validity.\n<xref ref-type=\"fig\" rid=\"f13\">Figure 13</xref> illustrates this by fitting partial timeseries from one country (Italy) using the empirical priors afforded by the parametric empirical Bayesian analysis. These partial data comprise the early phase of new cases. If the model has predictive validity, the ensuing posterior predictive density should contain the data that was withheld during estimation.\n<xref ref-type=\"fig\" rid=\"f13\">Figure 13</xref> presents an example of forward prediction over a 10-day period that contains the peak death rate. In this example, the withheld data are largely within the 90% credible intervals, speaking to the predictive validity of the generative model. There are two caveats here: first, similar analyses using very early timeseries from Italy failed to predict the peak, because of insufficient (initial) constraints in the data. Second, the credible intervals probably suffer from the well-known overconfidence problem in variational Bayes, and the implicit mean field approximation (\n<xref rid=\"ref-24\" ref-type=\"bibr\">MacKay, 2003</xref>)\n<sup><xref ref-type=\"other\" rid=\"FN30\">30</xref></sup>.</p><fig fig-type=\"figure\" id=\"f13\" orientation=\"portrait\" position=\"anchor\"><label>Figure 13. </label><caption><title>predictive validity.</title><p>This figure uses the same format as\n<xref ref-type=\"fig\" rid=\"f7\">Figure 7</xref>; however, here, the posterior estimates are based upon partial data, from early in the timeseries for an exemplar country (Italy). These estimates were obtained under (parametric) empirical Bayesian priors. The red dots show outcomes that were not used to estimate the expected trajectories (and credible intervals). This example illustrates the predictive validity of the estimates for a 10-day period following the last datapoint, which capture the rise to the peak of new cases.</p></caption><graphic xlink:href=\"wellcomeopenres-5-17779-g0012\"/></fig></sec><sec sec-type=\"conclusions\"><title>Conclusions</title><p>We have rehearsed variational procedures for the inversion of a generative model of a viral epidemic—and have extended this model using hierarchical Bayesian inference (parametric empirical Bayes) to deal with the differential responses of each country, in the context of a worldwide pandemic. The utility of such modelling is self-evident: one can predict, with a quantified degree of confidence, what may happen in the near future. For example, under the posterior beliefs based upon data at the time of writing, one can sketch a narrative for what people in London may experience over the forthcoming weeks. This narrative\n<sup><xref ref-type=\"other\" rid=\"FN31\">31</xref></sup> would be something like the following:</p><p>\n<italic>“Based on current data, reports of new cases in London are expected to peak on April 5, followed by a peak in death rates around April 10 (Good Friday). At this time, critical care unit occupancy should peak, approaching—but not exceeding—capacity, based on current predictions and resource availability. At the peak of death rates, the proportion of people infected (in London) is expected to be about 32%, which should then be surpassed by the proportion of people who are immune at this time. Improvements should be seen by May 8, shortly after the May bank holiday, when social distancing will be relaxed. At this time herd immunity should have risen to about 80%, about 12% of London's population will have been tested. Just under half of those tested will be positive. By June 12, death rates should have fallen to low levels with over 90% of people being immune and social distancing will no longer be a feature of daily life.”</italic>\n</p><p>Clearly, this narrative is entirely conditioned on the generative model used to make these predictions (e.g., the assumption of lasting immunity, which may or may not be true). The narrative is offered in a deliberately definitive fashion to illustrate the effect of resolving uncertainty about what will happen. It has been argued that many deleterious effects of the pandemic are mediated by uncertainty. This is probably true at both a psychological level—in terms of stress and anxiety (\n<xref rid=\"ref-4\" ref-type=\"bibr\">Davidson, 1999</xref>;\n<xref rid=\"ref-26\" ref-type=\"bibr\">McEwen, 2000</xref>;\n<xref rid=\"ref-31\" ref-type=\"bibr\">Peters\n<italic>et al</italic>., 2017</xref>)—and at an economic level in terms of ‘loss of confidence’ and ‘uncertainty about markets’. Put simply, the harmful effects of the coronavirus pandemic are not just what will happen but the effects of the uncertainty about what will happen. This is a key motivation behind procedures that quantify uncertainty, above and beyond being able to evaluate the evidence for different hypotheses about what will happen.</p><p>One aspect of this is reflected in rhetoric such as “there is no clear exit strategy”. It is reassuring to note that, if one subscribes to the above model, there is a clear exit strategy inherent in the self-organised mitigation\n<sup><xref ref-type=\"other\" rid=\"FN32\">32</xref></sup> afforded by herd immunity. For example, within a week of the peak death rate, there should be sufficient herd immunity to preclude any resurgence of infections in, say, London. The term ‘self-organised’ is used carefully here. This is because we are part of this process, through the effect of social distancing on our location, contact with infected people and subsequent dissemination of COVID-19. In other words, this formulation does not preclude strategic (e.g., nonpharmacological) interventions; rather, it embraces them as part of the self-organising ensemble dynamics\n<sup><xref ref-type=\"other\" rid=\"FN33\">33</xref></sup>.</p><sec><title>Outstanding issues</title><p>This technical report describes an initial implementation of the DCM framework to provide a generative model of a viral epidemic, and to demonstrate the potential utility of such modelling. Clearly there are a number of ways this model could be refined. Our hope in making it open source is that it will allow others to identify issues, contribute to improvements—and help facilitate objective comparisons with other models—using Bayesian model comparison.</p><p>There remain a number of outstanding issues:</p><p>The generative model—at both the first and second level—needs to be explored more thoroughly. At the first level, this may entail the addition of other factors; for example, splitting the population into age groups or different classes of clinical vulnerability. Procedurally, this should be fairly simple, by specifying the DCM parameters for each age group (or cohort) separately and precluding transitions between age groups (or cohorts). One could also consider the fine graining of states within each factor. For example, making a more careful distinction between being in and not in critical care (e.g., being in self-isolation, being in a hospital, community care home, rural or urban location and so on). At the between city or country level, the parameters of the general linear model could be easily extended to include a host of demographic and geographic independent variables. Finally, it would be fairly straightforward to use increasingly fine-grained outcomes, using regional timeseries, as opposed to country timeseries (these data are currently available from:\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/CSSEGISandData/COVID-19\">https://github.com/CSSEGISandData/COVID-19</ext-link>).</p><p>Another plausible extension to the hierarchical model is to include previous outbreaks of MERS and SARS (Middle East and Severe Acute Respiratory Syndrome, respectively) in the model. This would entail supplementing the timeseries with historical (i.e., legacy) data and replicating the general linear model for each type of virus. In effect, this would place empirical priors or constraints on any parameter that shares characteristics with MERS-CoV and SARS-CoV.</p><p>In terms of the model parameters—as opposed to model structure—more precise knowledge about the underlying causes of an epidemic will afford more precise posteriors. In other words, more information about the DCM parameters can be installed through adjusting the prior expectations and variances. The utility of these adjustments would then be assessed in terms of model evidence. This may be particularly relevant as reliable data about bed occupancy, proportion of people recovered,\n<italic>etc</italic> becomes available.</p><p>A key aspect of the generative model used in this technical report is that it precludes any exogenous interventions of a strategic sort. In other words, the things that matter are built into the model and estimated as latent causes. However, prior knowledge about fluctuating factors, such as closing schools or limiting international air flights, could be entered by conditioning the DCM parameters on exogenous inputs. This would explicitly install intervention policies into the model. Again, these conditions would only be licensed by an increase in model evidence (i.e., through comparing the evidence for models with and without some structured intervention). This may be especially important when it comes to modelling future interventions, for example, a ‘sawtooth’ social distancing protocol. A simple example of this kind of extension would be including a time dependent increase in the capacity for testing: at present, constraints on testing rates are assumed to be constant.</p><p>A complementary approach would be to explore models in which social distancing depends upon variables that can be measured or inferred reliably (e.g., the rate of increase of people testing positive) and optimise the parameters of the ensuing model to minimise cumulative deaths. In principle, this should provide an operational equation that could be regarded as an adaptive (social distancing) policy, which accommodates as much as can be inferred about the epidemiology as possible.</p><p>A key outstanding issue is the modelling of how one region (or city) affects another—and how the outbreak spreads from region to region. This may be an important aspect of these kinds of models; especially when it comes to modelling second waves as ‘echoes’ of infection, which are reflected back to the original epicentre. As noted above, the ability of these echoes to engender a second wave may be sensitively dependent on the herd immunity established during the first episode. Herd immunity is therefore an important (currently latent or unobserved) state. This speaks to the importance of antibody testing in furnishing empirical constraints on herd immunity. In turn, this motivates antibody testing,\n<italic>even if the specificity and sensitivity of available tests are low</italic>. Sensitivity and specificity are not only part of generative models, they can be estimated along with the other model parameters. In this setting, the role of antibody testing would be to provide data for population modelling and strategic advice—not to establish whether any particular person is immune or not (e.g., to allow them to go back to work).</p><p>Finally, it would be useful to assess the construct validity of the variational scheme adopted in dynamic causal modelling, in relation to schemes that do not make mean field approximations. These schemes usually rely upon some form of sampling (e.g., Markov Chain Monte Carlo sampling) and cross-validation. Cross-validation accuracy can be regarded as a useful but computationally expensive proxy for model evidence and is the usual way that modellers perform automatic Bayesian computation. Given the prevalence of these sampling based (non-variational) schemes, it would be encouraging if both approaches converged on roughly the same predictions. The aim of this technical report is to place variational schemes on the table, so that construct validation becomes a possibility in the short-term future.</p></sec></sec><sec sec-type=\"methods\"><title>Methods</title><sec><title>Software note</title><p>The figures in this technical report can be reproduced using annotated (MATLAB) code that is available as part of the free and open source academic software SPM (\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.fil.ion.ucl.ac.uk/spm/\">https://www.fil.ion.ucl.ac.uk/spm/</ext-link>), released under the terms of the GNU GPL v2+ licence. This software package has a relatively high degree of validation; being used for the past 25 years by over 5000 scientists in the neurosciences. The routines are called by a demonstration script that can be invoked by typing >> DEM_COVID at the MATLAB prompt. For this technical report, we used\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.mathworks.com/products/matlab.html\">MATLAB</ext-link> R2019b and SPM12 r7814 (archived at\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://doi.org/10.6084/m9.figshare.12174006.v1\">https://doi.org/10.6084/m9.figshare.12174006.v1</ext-link> (\n<xref rid=\"ref-10\" ref-type=\"bibr\">Friston\n<italic>et al</italic>., 2020</xref>)). The code is also compatible with\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.gnu.org/software/octave/\">GNU Octave</ext-link> 5.2. Details about future developments of the software will be available from\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.fil.ion.ucl.ac.uk/spm/covid-19/\">https://www.fil.ion.ucl.ac.uk/spm/covid-19/</ext-link>.</p></sec></sec><sec><title>Posthoc evaluation of model predictions</title><p>This epilogue was written three months after the report was submitted, providing an opportunity to revisit some of the predictions in light of actual outcomes. Although the predictions in this report were used to illustrate the nature of the predictions supported by models that included social distancing, they can be used to assess the predictive validity of the DCM.</p><p>Subsequently, the DCM was optimized using Bayesian model comparison. A crucial addition was the inclusion of heterogeneity in the response of the population to viral infection. However, even the simple DCM above accommodated sufficient heterogeneity—in terms of the distinction between an effective and total (census) population—to provide some accurate predictions.</p><p>In brief, the shape and timing of the epidemic in London was predicted to within a few days. Conversely, the number of fatalities and positive test results were overestimated by a factor of about 3. In what follows, we list the accurate and inaccurate predictions. We assume that the census population of London was 8.96 million\n<sup><xref ref-type=\"other\" rid=\"FN34\">34</xref></sup>.\n<italic>London's population</italic> is taken to be the effective population estimated to be 2.49 million (see\n<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>) and social distancing is read as lockdown (i.e., the probability of leaving home).</p><sec><title>Accurate predictions</title><list list-type=\"bullet\"><list-item><p>\n<italic>“Based on current data, reports of new cases in London are expected to peak on April 5”</italic>\n</p></list-item></list><p>Daily confirmed cases of coronavirus in London (and the UK) peaked on April 5\n<sup><xref ref-type=\"other\" rid=\"FN35\">35</xref></sup>.</p><list list-type=\"bullet\"><list-item><p>\n<italic>“A peak in death rates around April 10 (Good Friday).”</italic>\n</p></list-item></list><p>Peak death rates in London reached 249 per day on April 9\n<sup><xref ref-type=\"other\" rid=\"FN36\">36</xref></sup>\n</p><list list-type=\"bullet\"><list-item><p>\n<italic>“At this time</italic> [April 10],\n<italic>critical care unit occupancy should peak, approaching—but not exceeding—capacity”</italic>\n</p></list-item></list><p>During the Easter weekend of 11–12 April, the NHS Nightingale Hospital London had only 19 patients. Existing London hospitals had sufficient capacity after increasing their combined intensive care capacity from 770 beds to 1,555. As of April 24, only 41 patients had been treated at the Nightingale hospital\n<sup><xref ref-type=\"other\" rid=\"FN37\">37</xref></sup>.</p><list list-type=\"bullet\"><list-item><p> “\n<italic>At the peak of death rates</italic> [April 10]\n<italic>, the proportion of people infected (in London) is expected to be about 32%</italic>”</p></list-item></list><p>This prediction corresponds to 8.9% = 32% x 2.49/8.98 of the census population of London, which coincides with the consensus estimates at that time. “Professor Chris Whitty admits he thinks at least 10% of the capital has been infected” (published on 24-April-2020)\n<sup><xref ref-type=\"other\" rid=\"FN38\">38</xref></sup>.</p><list list-type=\"bullet\"><list-item><p> “\n<italic>Improvements should be seen by May 8, shortly after the May bank holiday, when social distancing will be relaxed.</italic>”</p></list-item></list><p>On May 8, the first Black Lives Matter demonstrations started in London. This was followed by the\n<bold>first governmental relaxation of lockdown</bold> on May 10: “So, work from home if you can, but you should go to work if you can’t work from home.” (Prime Minister's address to the nation: 10-May-2020)\n<sup><xref ref-type=\"other\" rid=\"FN39\">39</xref></sup>\n</p><list list-type=\"bullet\"><list-item><p> “\n<italic>At this time</italic> [May 8]\n<italic> herd immunity should have risen to about 80%”</italic>\n</p></list-item></list><p>Population immunity in the effective population corresponds to 80% x 2.49 / 8.9 = 22% seroprevalence in the census population, which had risen to 17.5% in the previous week: “After making adjustments for the accuracy of the assay and the age and gender distribution of the population, the overall adjusted prevalence in London increased from 1.5% in week 13 to 12.3% in weeks 15 to 16 and 17.5% in week 18” (week ending May 3, 2020)\n<sup><xref ref-type=\"other\" rid=\"FN40\">40</xref></sup>.</p><list list-type=\"bullet\"><list-item><p> “\n<italic>By June 12, death rates should have fallen to low levels with over 90% of people being immune</italic>”</p></list-item></list><p>Weekly reported deaths in London hospitals for the week ending June 11 fell to 22 (with positive tests))\n<sup><xref ref-type=\"other\" rid=\"FN41\">41</xref></sup>. Seroprevalence for this period was not reported.</p><list list-type=\"bullet\"><list-item><p> “\n<italic>By June 12, social distancing</italic> [lockdown]\n<italic>will no longer be a feature of daily life.</italic>”</p></list-item></list><p>The\n<bold>second governmental relaxation of lockdown</bold> was announced on June 10 and June 23, with an initial reopening of shops, and an easing of the two-metre social distancing rule:</p><p>“[A]s the Business Secretary confirmed yesterday, we can now allow all shops to reopen from Monday.” (Prime Minister's statement that the coronavirus press conference: 10-June-2020)\n<sup><xref ref-type=\"other\" rid=\"FN42\">42</xref></sup>\n</p><p>“Thanks to our progress, we can now go further and safely ease the lockdown in England. At every stage, caution will remain our watchword, and each step will be conditional and reversible. Mr Speaker, given the significant fall in the prevalence of the virus, we can change the two-metre social distancing rule, from 4th July.” (Prime Minister's statement to the House: 23-June-2020)\n<sup><xref ref-type=\"other\" rid=\"FN43\">43</xref></sup>\n</p></sec><sec><title>Inaccurate predictions</title><list list-type=\"bullet\"><list-item><p> “\n<italic>About 12% of London's population will have been tested (May 8). Just under half of those tested will be positive</italic>.”</p></list-item></list><p>This was an overestimate: 12% of the effective population corresponds to 143,424 = 12% x .48 x 2.49 positive tests. At the time of writing (17-July-2020), only 34,397 people have tested positive in London\n<sup><xref ref-type=\"other\" rid=\"FN44\">44</xref></sup>—a quarter of the predicted number.</p><list list-type=\"bullet\"><list-item><p>\n<italic>From\n<xref ref-type=\"fig\" rid=\"f8\">Figure 8</xref>: Peak daily death rate 807 (710-950) with cumulative deaths of 17,500 (14,000-21,000)</italic>\n</p></list-item></list><p>These were overestimates; daily deaths in London peaked at 249 on April 9 with cumulative deaths at the time of writing (17-July-2020) of 6,106\n<sup><xref ref-type=\"other\" rid=\"FN45\">45</xref></sup>. This represents consistent overestimates by factors of 3.2 and 2.8, respectively. This may reflect the fact that the data used in the report included regions in the United Kingdom outside London.</p></sec></sec><sec><title>Software availability</title><p>Software is available from:\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.fil.ion.ucl.ac.uk/spm/covid-19/\">https://www.fil.ion.ucl.ac.uk/spm/covid-19/</ext-link>\n</p><p>Archived source code at time of publication:\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://doi.org/10.6084/m9.figshare.12174006.v1\">https://doi.org/10.6084/m9.figshare.12174006.v1</ext-link> (\n<xref rid=\"ref-10\" ref-type=\"bibr\">Friston\n<italic>et al</italic>., 2020</xref>).</p><p>License:\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.gnu.org/licenses/old-licenses/gpl-2.0.html\">GLP 2.0+</ext-link>\n</p></sec><sec sec-type=\"data-availability\"><title>Data availability</title><sec><title>Source data</title><p>The data used in this technical report are available for academic research purposes from the 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE, hosted on GitHub at\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/CSSEGISandData/COVID-19\">https://github.com/CSSEGISandData/COVID-19</ext-link>.</p><p>The data used in the manuscript correspond to commit `f7c2384622806d5297d16c314a7bc0b9cde24937`</p><p>\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/CSSEGISandData/COVID-19/tree/f7c2384622806d5297d16c314a7bc0b9cde24937/csse_covid_19_data/csse_covid_19_time_series\">https://github.com/CSSEGISandData/COVID-19/tree/f7c2384622806d5297d16c314a7bc0b9cde24937/csse_covid_19_data/csse_covid_19_time_series</ext-link>\n</p><p>and should be copied in this directory before running the `DEM_COVID` script.</p><p>```</p><p>url = '\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/f7c2384622806d5297d16c314a7bc0b9cde24937/csse_covid_19_data/csse_covid_19_time_series/\">https://raw.githubusercontent.com/CSSEGISandData/COVID-19/f7c2384622806d5297d16c314a7bc0b9cde24937/csse_covid_19_data/csse_covid_19_time_series/</ext-link>';</p><p>urlwrite([url,'time_series_covid19_confirmed_global.csv'],'time_series_covid19_confirmed_global.csv');</p><p>urlwrite([url,'time_series_covid19_deaths_global.csv'],'time_series_covid19_deaths_global.csv');</p><p>urlwrite([url,'time_series_covid19_recovered_global.csv'],'time_series_covid19_recovered_global.csv');</p><p>```</p></sec></sec></body><back><fn-group><fn id=\"FN1\"><label>1</label><p>Prepared as a proof of concept for submission to the SPI-M (\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.gov.uk/government/groups/scientific-pandemic-influenza-subgroup-on-modelling\">https://www.gov.uk/government/groups/scientific-pandemic-influenza-subgroup-on-modelling</ext-link>) and the RAMP (Rapid Assistance in Modelling the Pandemic) initiative (\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://royalsociety.org/topics-policy/Health-and-wellbeing/ramp/\">https://royalsociety.org/topics-policy/Health-and-wellbeing/ramp/</ext-link>).</p></fn><fn id=\"FN2\"><label>2</label><p>The marginal likelihood is the likelihood having marginalised (i.e., averaged) over unknown quantities like states and parameters: i.e., the probability of having observed some data under a particular model.</p></fn><fn id=\"FN3\"><label>3</label><p>Technically, this property reflects ergodicity that is a consequence of a weakly mixing system:\n<xref rid=\"ref-2\" ref-type=\"bibr\">Birkhoff, 1931</xref>. Proof of the ergodic theorem. Proc Natl Acad Sci USA 17, 656–660. Having said this, this model aims to make ensemble level predictions. Because ergodicity may not necessarily hold in reality, the ensemble level projections should not be interpreted as predictions for individual experiences of the epidemic</p></fn><fn id=\"FN4\"><label>4</label><p>Conventional (e.g., SEIR) compartmental models in epidemiology usually consider a single attribute of the population (e.g., infection status), such that the distribution over all states sums to one. In contrast, the DCM used in this work considers multiple attributes, where both the joint and marginal distributions (over each factor) sum to one.</p></fn><fn id=\"FN5\"><label>5</label><p>These data are available from:\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/CSSEGISandData/COVID-19\">https://github.com/CSSEGISandData/COVID-19</ext-link>.</p></fn><fn id=\"FN6\"><label>6</label><p>Note that part of the uncertainty about latent states inherits from uncertainty about how outcomes are generated; for example, uncertainty about whether reported death rates are a true reflection of actual death rates.</p></fn><fn id=\"FN7\"><label>7</label><p>This involves examining the eigenvectors of the posterior correlation matrix, to preclude marked posterior correlations.</p></fn><fn id=\"FN8\"><label>8</label><p>We will license this assumption using Bayesian model comparison later.</p></fn><fn id=\"FN9\"><label>9</label><p> Here,\n<inline-formula><mml:math id=\"M20\"><mml:mrow><mml:mi>P</mml:mi><mml:mo>=</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msub><mml:mi>θ</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mi>r</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msub><mml:mo>⋅</mml:mo><mml:msubsup><mml:mi>p</mml:mi><mml:mrow><mml:mtext mathvariant=\"italic\">infectious</mml:mtext></mml:mrow><mml:mrow><mml:mtext mathvariant=\"italic\">inf</mml:mtext></mml:mrow></mml:msubsup><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:math></inline-formula> can be interpreted as a probability of eluding infection with each interpersonal contact, such that the probability of remaining uninfected after\n<italic>θ\n<sub>R</sub></italic> contacts is given by\n<italic>P\n<sup>θ\n<sub>R</sub></sup></italic>. Note, that there is no distinction between people at home and at work; both are equally likely to be infectious.</p></fn><fn id=\"FN10\"><label>10</label><p>Although cases of double positive diagnoses have been reported, these may reflect false test results rather than re-infection\n<italic>per se</italic>. Having said this, it would be straightforward to include a transition from immunity to susceptible, with a suitably small transition rate to model the decay of immunity, or viral mutation. Similarly, one can incorporate transition from asymptomatic to symptomatic, when immune, to model partial resistance.</p></fn><fn id=\"FN11\"><label>11</label><p>It is revealing to note that the number of model parameters pertaining to PCR testing matches the number of parameters mediating the epidemiology per se. This reflects the fact that the generative model has to consider every aspect of how data are generated. In order to leverage the information in new positive tests, it is necessary to think carefully about all the parameters that contribute to these data; for example, the probability of being tested and the selection bias towards testing people who are more likely to be infected. Crucially, this bias has to be estimated during model inversion and could vary substantially from country to country. Although not implemented in this report, subsequent distinctions between Pillar 1 and 2 test data would be a nice example of different selection biases. This speaks to the importance of modelling Pillar 1 and 2 as distinct data modalities. From a technical perspective, equipping standard epidemiological models with an ‘observation model’ can be regarded as building a complete dynamic causal model. The key thing to bear in mind here is that the parameters of so-called observation models have to be treated in exactly the same way as epidemiological parameters, because they could show conditional dependencies. In dynamic causal modelling, all unknown parameters are treated in a uniform way to maximise (a free energy bound on) marginal likelihood.</p></fn><fn id=\"FN12\"><label>12</label><p>Notice that this model is configured for new cases that are reported based on buccal swabs (i.e., am I currently infected?), not tests for antibody or immunological status. A different model would be required for forthcoming tests of immunity (i.e., have I been infected?). Furthermore, one might consider the sensitivity and specificity of any test by including sensitivity and specificity in (\n<xref ref-type=\"other\" rid=\"e1.7\">1.7</xref>). For example, 1 in 3 tests may be false negatives; especially, when avoiding bronchoalveolar lavage to minimise risk to clinicians:\n<xref rid=\"ref-41\" ref-type=\"bibr\">Wang\n<italic>et al.</italic>, 2020b</xref>. Detection of SARS-CoV-2 in Different Types of Clinical Specimens. JAMA.</p></fn><fn id=\"FN13\"><label>13</label><p>Added in revision: the reproduction ratio in this report was based upon an approximation to the expected number of people that I might infect, if I was infectious. In subsequent reports, the reproduction ratio was brought into line with more formal definitions, based on the geometric rate of increase in the prevalence of infection and the period of contagion. A minimum reproduction ratio (R) of nearly zero in this report corresponds to about 0.7 in subsequent (and other) reports.</p></fn><fn id=\"FN14\"><label>14</label><p>Shedding of COVID-19 viral RNA from sputum can outlast the end of symptoms. Seroconversion occurs after 6–12 days but is not necessarily followed by a rapid decline of viral load.</p></fn><fn id=\"FN15\"><label>15</label><p>\n<xref rid=\"T1\" ref-type=\"table\">Table 1</xref> also includes a parameter for the proportion of people who are initially immune, which we will call on later.</p></fn><fn id=\"FN16\"><label>16</label><p>16 This technical report considered an outbreak in a single region or city. A country-wide (pandemic) model of viral spread would require multiple regional models to be coupled together, and is the focus of subsequent papers (K. J. Friston, T. Parr, P. Zeidman, A. Razi, G. Flandin, J. Daunizeau, O. J. Hulme, A. J. Billig, V. Litvak, C. J. Price, R. J. Moran, C. Lambert, (2020) ‘‘Second waves, social distancing, and the spread of COVID-19 across America’’, [version 1; peer review: 1 approved with reservations].\n<italic>Wellcome Open Res</italic> 2020, 5:103.\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://wellcomeopenresearch.org/articles/5-103)\">https://wellcomeopenresearch.org/articles/5-103)</ext-link>.</p></fn><fn id=\"FN17\"><label>17</label><p>This likelihood model can be finessed using a negative binomial distribution: MRC Centre for Global Infectious Disease Analysis: Report 13 (\n<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/covid-19/\">http://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/covid-19/</ext-link>. However, a binomial is sufficient for our purposes.</p></fn><fn id=\"FN18\"><label>18</label><p>A transient here refers to a transient perturbation to a system, characterising a response that evolves over time.</p></fn><fn id=\"FN19\"><label>19</label><p>Intuitively, this can be likened to a bat inverting its generative model of the world using the transients created by echo location. The shape of the transient contains an enormous amount of information, provided the bat has a good model of how echoes are generated. Exactly the same principle applies here: if one can find the right model, one can go beyond the immediate information in the data to make some precise inferences—based upon prior beliefs that constitute the generative model. This kind of abductive inference speaks to the importance of having a good forward or generative model—and the ability to select the best model based upon model evidence.</p></fn><fn id=\"FN20\"><label>20</label><p>This reflects concerns that data from different countries may not have been acquired or reported using the same criteria.</p></fn><fn id=\"FN21\"><label>21</label><p>These are two of several factors that conspire to produce the actual outcomes. The purpose of having a generative model is that reports of new cases may provide useful constraints on the latent causes of death rates. This means it is imperative to model the latent causes that interact in generating one sort of outcome, so that it can inform the causes of another. This fusion of different sorts of outcomes is the\n<italic>raison d’être</italic> for a generative model, when inferring their common underlying causes.</p></fn><fn id=\"FN22\"><label>22</label><p>MRC Centre for Global Infectious Disease Analysis: Report 13,\n<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/covid-19/\">http://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/covid-19/</ext-link>. Seth Flaxman, Swapnil Mishra, Axel Gandy et al. Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in 11 European countries. Imperial College London (2020).</p></fn><fn id=\"FN23\"><label>23</label><p>\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://en.wikipedia.org/wiki/Greater_London\">https://en.wikipedia.org/wiki/Greater_London</ext-link>\n</p></fn><fn id=\"FN24\"><label>24</label><p>However, there does appear to be some predictive validity to these that are addressed in an epilogue. Note rather than dissect the predictive validity of each parameter and country, which is widely recognised as a challenging problem (Moghadas, S.M., Shoukat, A., Fitzpatrick, M.C., Wells, C.R., Sah, P., Pandey, A., Sachs, J.D., Wang, Z., Meyers, L.A., Singer, B.H., Galvani, A.P., 2020. Projecting hospital utilization during the COVID-19 outbreaks in the United States. Proc Natl Acad Sci U S A 117, 9122-9126.), we have provided some representative examples. A comprehensive analysis of this type would be beyond the scope of this report. It is also important to note that predictions based upon rate parameters and probabilities are a reflection of prior assumptions about these parameters, whereas predictions based upon the hidden states speak to the predictive validity of the DCM model structure (see below).</p></fn><fn id=\"FN25\"><label>25</label><p>Or, indeed, a previous pandemic, such as the 2009 H1H1 pandemic. We will return to this in the conclusion.</p></fn><fn id=\"FN26\"><label>26</label><p>Public Health England estimates that on average 17,000 people have died from the flu in England annually between 2014/15 and 2018/19. However, yearly deaths vary widely, from a high of 28,330 in 2014/15 to a low of 1,692 in 2018/19 (broken red lines in\n<xref ref-type=\"fig\" rid=\"f7\">Figure 7</xref>).</p></fn><fn id=\"FN27\"><label>27</label><p>Note, only 2800 beds are ventilator/ITU beds.</p></fn><fn id=\"FN28\"><label>28</label><p>We will use predictions—as opposed to projections—when appropriate, to emphasise the point that the generative model is not a timeseries model, in the sense that the unknown quantities (DCM parameters) do not change with time. This means the there is uncertainty about predictions in the future\n<italic>and the past</italic>, given uncertainty about the parameters (see\n<xref ref-type=\"fig\" rid=\"f7\">Figure 7</xref>). This should be contrasted with the notion of forecasting or projection; however, predictions in the future, in this setting, can be construed as projections.</p></fn><fn id=\"FN29\"><label>29</label><p>The basic reproduction ratio is a constant that scores the spread of a contagion in a susceptible population. This corresponds to the effective reproduction ratio at the beginning of the outbreak, when everybody is susceptible. See\n<xref ref-type=\"fig\" rid=\"f12\">Figure 12</xref>\n</p></fn><fn id=\"FN30\"><label>30</label><p>Note further that the credible intervals can include negative values. This is an artefact of the way in which the intervals are computed: here, we used a first-order Taylor expansion to propagate uncertainty about the parameters through to uncertainty about the outcomes. However, because this generative model is non-linear in the parameters, high-order terms are necessarily neglected.</p></fn><fn id=\"FN31\"><label>31</label><p>This narrative is not offered as a prediction – but as an example of the kind of predictions afforded by dynamic causal modelling. An aspect of these predictions is that they include systemic factors beyond the epidemiology per se. The best example of this is the above predictions about social distancing, which could be read as ‘lockdown’; namely the probability that I will leave home. This highlights a key distinction between dynamic causal models and standard quantitative epidemiological modelling that treats things like ‘lockdown’ as interventions that are supplied to the model. In contrast, interventions such as social distancing and testing are modelled as an integral part of the process – and are estimated on the basis of the data at hand. One consequence of this is that one can make predictions about when ‘interventions’ – or their suspension – will occur in the future.</p></fn><fn id=\"FN32\"><label>32</label><p>This technical report does not distinguish between\n<italic>mitigation</italic> and\n<italic>suppression</italic>: in the generative model under consideration, both go hand-in-hand.</p></fn><fn id=\"FN33\"><label>33</label><p>To reiterate, the purpose of this technical report was to introduce the variational procedures entailed by dynamic causal modelling in the setting of quantitative, epidemiological modelling. Since this report was submitted, several papers have used procedures described in this report to address specific questions; for example, the impact of lockdown cycles, the effect of population fluxes among regional outbreaks, the efficacy of testing and tracing, and the impact of heterogeneous susceptibility and transmission. Crucially, in line with a key message of this foundational paper, each successive application of the dynamic causal modelling leveraged Bayesian model comparison to update the model as new data became available.</p></fn><fn id=\"FN34\"><label>34</label><p>\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://en.wikipedia.org/wiki/Greater_London\">https://en.wikipedia.org/wiki/Greater_London</ext-link>\n</p></fn><fn id=\"FN35\"><label>35</label><p>\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.bbc.co.uk/news/uk-51768274\">https://www.bbc.co.uk/news/uk-51768274</ext-link>\n</p></fn><fn id=\"FN36\"><label>36</label><p>\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://en.wikipedia.org/wiki/COVID-19_pandemic_data\">https://en.wikipedia.org/wiki/COVID-19_pandemic_data</ext-link>\n</p></fn><fn id=\"FN37\"><label>37</label><p>\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://en.wikipedia.org/wiki/NHS_Nightingale_Hospital_London#cite_note-HSJ-19\">https://en.wikipedia.org/wiki/NHS_Nightingale_Hospital_London#cite_note-HSJ-19</ext-link>.</p></fn><fn id=\"FN38\"><label>38</label><p>\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.dailymail.co.uk/news/article-8250371\">https://www.dailymail.co.uk/news/article-8250371</ext-link>\n</p></fn><fn id=\"FN39\"><label>39</label><p>\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.gov.uk/government/speeches/pm-address-to-the-nation-on-coronavirus-10-may-2020\">https://www.gov.uk/government/speeches/pm-address-to-the-nation-on-coronavirus-10-may-2020</ext-link>\n</p></fn><fn id=\"FN40\"><label>40</label><p>\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.gov.uk/government/publications/national-covid-19-surveillance-reports/sero-surveillance-of-covid-19\">https://www.gov.uk/government/publications/national-covid-19-surveillance-reports/sero-surveillance-of-covid-19</ext-link>\n</p></fn><fn id=\"FN41\"><label>41</label><p>\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.london.gov.uk/coronavirus/coronavirus-numbers-london\">https://www.london.gov.uk/coronavirus/coronavirus-numbers-london</ext-link>\n</p></fn><fn id=\"FN42\"><label>42</label><p>\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.gov.uk/government/speeches/pm-statement-at-the-coronavirus-press-conference-10-june-2020\">https://www.gov.uk/government/speeches/pm-statement-at-the-coronavirus-press-conference-10-june-2020</ext-link>\n</p></fn><fn id=\"FN43\"><label>43</label><p>\n<ext-link ext-link-type=\"uri\" 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contrib-type=\"author\"><name><surname>Hellewell</surname><given-names>Joel</given-names></name><xref ref-type=\"aff\" rid=\"r39871a1\">1</xref><role>Referee</role><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0003-2683-0849</contrib-id></contrib><aff id=\"r39871a1\">\n<label>1</label>Centre for the Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK</aff></contrib-group><author-notes><fn fn-type=\"COI-statement\"><p>\n<bold>Competing interests: </bold>No competing interests were disclosed.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><permissions><copyright-statement>Copyright: © 2020 Hellewell J</copyright-statement><copyright-year>2020</copyright-year><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><related-article related-article-type=\"peer-reviewed-article\" id=\"d38e9879\" ext-link-type=\"doi\" xlink:href=\"10.12688/wellcomeopenres.15881.2\">Version 2</related-article><custom-meta-group><custom-meta><meta-name>recommendation</meta-name><meta-value>approve</meta-value></custom-meta></custom-meta-group></front-stub><body><p>I'm very happy with the changes made in the revised version of the paper.</p><p> I personally still struggle with the idea of being able to infer the size of the effective population and what this means conceptually. However, I think the effective population parameter is now adequately explained in the manuscript and is more something for me to think about over the long term rather than something that should prevent indexing.</p><p> I think the authors engaged with my feedback and have responded to what I said, either by expanding the manuscript or showing how such work has been undertaken elsewhere.</p><p>Is the rationale for developing the new method (or application) clearly explained?</p><p>Yes</p><p>Is the description of the method technically sound?</p><p>Yes</p><p>Are the conclusions about the method and its performance adequately supported by the findings presented in the article?</p><p>No</p><p>If any results are presented, are all the source data underlying the results available to ensure full reproducibility?</p><p>Yes</p><p>Are sufficient details provided to allow replication of the method development and its use by others?</p><p>Yes</p><p>Reviewer Expertise:</p><p>Infectious disease modelling, infectious disease epidemiology</p><p>I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.</p></body></sub-article><sub-article id=\"report38865\" article-type=\"peer-review\"><front-stub><article-id pub-id-type=\"doi\">10.21956/wellcomeopenres.17418.r38865</article-id><title-group><article-title>Reviewer response for version 1</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Chowell</surname><given-names>Gerardo</given-names></name><xref ref-type=\"aff\" rid=\"r38865a1\">1</xref><role>Referee</role></contrib><aff id=\"r38865a1\">\n<label>1</label>Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA</aff></contrib-group><author-notes><fn fn-type=\"COI-statement\"><p>\n<bold>Competing interests: </bold>No competing interests were disclosed.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>6</day><month>7</month><year>2020</year></pub-date><permissions><copyright-statement>Copyright: © 2020 Chowell G</copyright-statement><copyright-year>2020</copyright-year><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><related-article related-article-type=\"peer-reviewed-article\" id=\"d38e9969\" ext-link-type=\"doi\" xlink:href=\"10.12688/wellcomeopenres.15881.1\">Version 1</related-article><custom-meta-group><custom-meta><meta-name>recommendation</meta-name><meta-value>approve</meta-value></custom-meta></custom-meta-group></front-stub><body><p>This technical report presents a dynamical causal model of the transmission dynamics of COVID-19. I believe this paper is one of very few (if any) that follow this type of approach which makes it interesting and an important contribution to the literature even after dozens of modeling papers on the topic have been published or are in the process of publication.</p><p> The paper is well described and the results are interesting and present a new approach for assessing the role of multiple factors on the spread of COVID-19. However, the epidemic has advanced significantly, and it would be good to see how the results and perspectives are shaped by more recent data. Authors should consider updating the paper with the most recent data available, and discuss how their analysis/conclusions are shaped by integrating additional data.</p><p>Is the rationale for developing the new method (or application) clearly explained?</p><p>Yes</p><p>Is the description of the method technically sound?</p><p>Yes</p><p>Are the conclusions about the method and its performance adequately supported by the findings presented in the article?</p><p>Yes</p><p>If any results are presented, are all the source data underlying the results available to ensure full reproducibility?</p><p>Yes</p><p>Are sufficient details provided to allow replication of the method development and its use by others?</p><p>Yes</p><p>Reviewer Expertise:</p><p>Mathematical epidemiology.</p><p>I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.</p></body><sub-article id=\"comment4298-38865\" article-type=\"response\"><front-stub><contrib-group><contrib contrib-type=\"author\"><name><surname>Razi</surname><given-names>Adeel</given-names></name><aff>Monash University, Australia</aff></contrib></contrib-group><author-notes><fn fn-type=\"COI-statement\"><p>\n<bold>Competing interests: </bold>No competing interests were disclosed.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>31</day><month>7</month><year>2020</year></pub-date></front-stub><body><p>We would like to thank you Dr. Chowell, for reviewing our manuscript and finding our approach interesting. We have now revised the paper, based on your and another reviewer’s feedback, which is now available.</p><p>We have tried to revise the paper to preserve its original content (by limiting changes to the main text to clarify and unpack things). We have used new footnotes no. 11, 13, 16, 24, 31, and 33 and a new section “Posthoc evaluation of model predictions” to address issues that have arisen since submission (for example, the validity of predictions in light of actual outcomes).</p><p>We hope this revised version is helpful in further clarifying our new approach.</p></body></sub-article></sub-article><sub-article id=\"report38986\" article-type=\"peer-review\"><front-stub><article-id pub-id-type=\"doi\">10.21956/wellcomeopenres.17418.r38986</article-id><title-group><article-title>Reviewer response for version 1</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Hellewell</surname><given-names>Joel</given-names></name><xref ref-type=\"aff\" rid=\"r38986a1\">1</xref><role>Referee</role><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0003-2683-0849</contrib-id></contrib><aff id=\"r38986a1\">\n<label>1</label>Centre for the Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK</aff></contrib-group><author-notes><fn fn-type=\"COI-statement\"><p>\n<bold>Competing interests: </bold>No competing interests were disclosed.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>18</day><month>6</month><year>2020</year></pub-date><permissions><copyright-statement>Copyright: © 2020 Hellewell J</copyright-statement><copyright-year>2020</copyright-year><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><related-article related-article-type=\"peer-reviewed-article\" id=\"d38e10089\" ext-link-type=\"doi\" xlink:href=\"10.12688/wellcomeopenres.15881.1\">Version 1</related-article><custom-meta-group><custom-meta><meta-name>recommendation</meta-name><meta-value>reject</meta-value></custom-meta></custom-meta-group></front-stub><body><p>This is an interesting and expansive modelling paper from a group of scientists that do not primarily focus on modelling infectious diseases, I think contributions to epidemiology from other fields should always be welcomed and this is no exception. The techniques employed in this paper are less of a different type of model and more of an entirely different modelling framework. As such, I see part of my job in this review as trying to bridge the gaps between the language and techniques of dynamic causal modelling and infectious disease modelling. Hopefully in doing so I am able to present any criticism in a way that both the authors and other infectious disease modellers are able to follow and understand.</p><p> The dynamic causal model developed in this paper can be understood roughly as a stochastic compartmental SEIR model that has 1) a “generative” model that describes movement between unobserved states over time (infection, recovery etc) and 2) an “observational model” that describes the likelihood for the parameter values in the generative model given the observed data (in this case daily deaths and positive tests). The generative model has four components: location, which determines where you are and the contacts you make; infection, which is akin to the susceptible - exposed - infectious - recovered model used commonly; clinical, which determines the clinical presentation should you become infected; testing, which links your current infection status to the result of a swab test. You can be at various states within any of these four components at once, for example I could be an asymptomatic, infectious person at work that has not been tested. How I move between these states is governed by a matrix of probabilities that can be non-linear in time and as a response to feedback from other parameters within the model (for example my probability of observing lockdown can grow as more people die during the outbreak).</p><p> I think ultimately the generative model is comparable to a complicated SEIR model and the next step in the mind of an infectious disease modeller is to use the likelihood from the observational model in a fitting method such as MCMC to generate samples from the posterior distribution of the generative model parameters. Instead, dynamic causal modelling has a developed body of theory that allows for approximation of the analytical solution to the posterior of the model parameters that maximises the model evidence (marginal likelihood). This allows for immediate comparison of different generative model structures on the same data through selecting the model with the optimal log model evidence, which is also referred to as “variational free energy” (a similar process to the commonly used Akaike or Bayesian information criterion). This was refreshing to me as it can sometimes be difficult to obtain AIC/BIC after fitting your model depending on how you have fit it, such as in the probabilistic modelling language Stan where you sometimes need to calculate the leave-one-out information criterion (LOO-IC) yourself.</p><p> Another interesting methodological addition from the dynamic causal modelling framework is fitting the model to data from several different countries and then assigning model parameters as fixed or random effects, using a generalised linear model to estimate the between-country effects of certain covariates. In the manuscript the authors show the results of this process, finding a relationship between the latitude of a country and the effective population size of the outbreak inferred by the model. While, as the authors acknowledge, latitude here is very likely a proxy for other socio-economic variables, this approach could potentially yield interesting results using a wider selection of between-country effects or as a heuristic device to try and understand what factors are driving the model fit in each country. This is complemented with a technique called “Bayesian model reduction”, which efficiently prunes redundant parameters out of the model to simultaneously achieve model parsimony and perform a sensitivity analysis of sorts since it involves fixing the prior of each parameter and looking at the difference in model fit. To me, the framework of dynamic causal modelling seems to make available several tools that should be of interest to infectious disease modellers. It is not the case that infectious disease modellers don’t already try to reduce models or compare them between countries, but what is attractive about the dynamic causal modelling approach is the coherency of the framework and the availability of software to perform the methods for models in general (although I think most infectious disease modellers would prefer to use R rather than MATLAB). At the very least, the methods employed in the dynamic causal modelling framework could be adapted to work with the more familiar combined compartmental model and MCMC approach. The methods in the dynamic causal modelling framework are heavily used and accepted in the field of neuroscience, so I don’t think it’s my job in this review to scrutinise them in particular outside of understanding them to the point where I can understand how the model in this particular paper was fitted. </p><p> With the general modelling approach summarised I can move on to the specifics of the structure of the generative and observation models:</p><p>\n<underline><bold>Predictive validity</bold></underline>\n</p><p> In a similar way to AIC/BIC, I think I am correct in thinking that model selection using variational free energy only provides a relative score of model fit and not an objective score. Choosing the best model out of a suite of models does not guarantee that this best model fits well, for this we need to turn to predictive validity and this is where I think the model laid out in the paper is at its weakest. Below is the best-fitting model’s prediction for London in full:</p><p>\n<italic>“Based on current data, reports of new cases in London are expected to peak on April 5, followed by a peak in death rates around April 10 (Good Friday). At this time, critical care unit occupancy should peak, approaching—but not exceeding—capacity, based on current predictions and resource availability. At the peak of death rates, the proportion of people infected (in London) is expected to be about 32%, which should then be surpassed by the proportion of people who are immune at this time. Improvements should be seen by May 8, shortly after the May bank holiday, when social distancing will be relaxed. At this time herd immunity should have risen to about 80%, about 12% of London’s population will have been tested. Just under half of those tested will be positive. By June 12, death rates should have fallen to low levels with over 90% of people being immune and social distancing will no longer be a feature of daily life.”</italic>\n</p><p> It’s quite hard to tell if we are meant to interpret this as an example of what sort of narrative could be derived from the results of the model, or whether this is a genuine model prediction. If it is the latter, then I would expect to see mention of when the prediction was made, as well as plots showing the prediction (shown in Figures 12 and 6) against the data which is now available. The authors do this for their predictions for Italy (Figure 13) but not London. I am writing this review on June 10th and at the time of writing the number of deaths on the 9th June was 286. Without numbers given for the prediction it’s hard to know if this counts as “low levels” or not, the 8th June was the beginning of week 24 and the corresponding prediction of daily deaths in Figure 12 is near zero. </p><p>\n<underline><bold>Effective population size</bold></underline>\n</p><p> Perhaps more concerning than the prediction for deaths is the prediction for immunity. In the paper I find it quite difficult to tell what exactly is being spoken about when it comes to immunity. The model fits a parameter called “effective population” (θN) that I think could do with some further explanation, it seems to be the case that immunity is presented as the number of infections inferred by the model divided by the effective population. When the model was fitted to UK data it inferred an effective population size of ~2.5 million people. It’s quite hard to tell but from Figure 8, looking at the cumulative cases inferred by the model and the proportion of the population entering the immune category, it seems like the model has predicted that nearly all of the 2.5 million people in the effective population are now immune. </p><p> Here is what the authors say about the effective population parameter:</p><p>\n<italic>“In this technical report, we will choose a simpler option that treats a pandemic as a set of linked point processes that can be modelled as rare events. In other words, we will focus on modelling a single outbreak in a region or city and treat the response of the ‘next city’ as a discrete process post hoc. This simplifies the generative model; in the sense we only have to worry about the ensemble dynamics of the population that comprises one city. A complimentary perspective on this choice is that we are trying to model the first wave of an epidemic as it plays out in the first city to be affected. Any second wave can then be treated as the first wave of another city or region .Under this choice, the population size can be set, a priori, to 1,000,000; noting that a small city comprises (by definition)a hundred thousand people, while a large city can exceed 10 million. </italic>\n</p><p>\n<italic>Note that this is a prior expectation, the effective population size is estimated from the data: the assumption that the effective population size reflects the total population of a country is a hypothesis (that we will test later).”</italic>\n</p><p> It is true that you can use a model with a population size under 67 million, look at the dynamics of the outbreak from the model output, and infer things about the potential effectiveness of social distancing, eventual likelihood of herd immunity, and so on, that would be true in a larger population. However, you would not fit a model to death data for all of the UK using a population parameter that is smaller than the population of the UK. I think the model output as shown in the manuscript is a best guess at the outbreak dynamics if the number of deaths and cases observed in a place with a population of 67 million people were instead observed in a place with a population of 2.5 million. As a result of fitting to death rates for a population 30 times bigger than the one in your model, you would expect to find that almost everyone is infected. </p><p> Since the writing of this manuscript, serological studies have started to emerge which estimate the percentage of the population that have been infected (which would correspond to the immune compartment in the model) . On the 24th May the ONS estimated that around 7% of the UK have antibodies for COVID-19, rising to 17% in London. Even acknowledging that serology studies are not perfect and that the ones performed so far have been quite small scale, this is really quite a different picture than the 90% population immunity presented by the model output.</p><p> The picture is similar in\n<ext-link ext-link-type=\"uri\" xlink:href=\"http://docs.google.com/spreadsheets/d/17Tf1Ln9VuE5ovpnhLRBJH-33L5KRaiB3NhvaiF3hWC0/htmlview#gid=0\">serological studies across the world</ext-link>, even in healthcare workers in hard-hit cities like Barcelona that would have faced constant exposure to infection.</p><p> What is the result of fitting the model to UK deaths and reported cases with a fixed, actual value for the effective population? Or at least using the UK population as the prior value? I think either a) the model output should be more clearly presented as an example or b) you should acknowledge that the model output gives predictions that seem very different from the emerging evidence</p><p>\n<underline><bold>CCU fatality</bold></underline>\n</p><p> The fitted probability that a person dies given that they are in the CCU (θfat) for China and Italy is very high (nearly 100% and well over 50%). How well does this compare to actual observed mortality rates in CCUs?</p><p> For example, this paper\n<sup><xref rid=\"rep-ref-38986-1\" ref-type=\"bibr\">1</xref></sup>, found 26% mortality in ICUs in Lombardy, Italy in early March.</p><p>\n<underline><bold>Observation model</bold></underline>\n</p><p> The UK data collated by the John Hopkins COVID-19 data repository that the authors use fetches data from\n<ext-link ext-link-type=\"uri\" xlink:href=\"http://coronavirus.data.gov.uk/#category=nations&map=rate\">here</ext-link>.</p><p> The observation model could be improved by including a delay between the actual occurrence of death and its eventual reporting in the official statistics, sometimes it can take a couple of days for deaths to appear in the government figures. I think this could interfere with the model fit as it tries to align deaths and reported cases (which it currently reasons have both happened on that day).</p><p>\n<underline><bold>Reporting structure</bold></underline>\n</p><p> It is also important to consider the structure of the surveillance system when trying to fit to reported cases. In the UK for a good while tests were only undertaken on hospital admissions that were severe enough to warrant being admitted overnight (or at least that is what the official policy was). Other countries like South Korea had drive-through test centres. This is going to cause a huge discrepancy in how you should interpret changes in reported cases.</p><p>\n<underline><bold>Between-country parameter value variation</bold></underline>\n</p><p> It is strange that there is so much variation in some of the parameters between countries. For example, the contagious period is around 1 day in China but around 3-5 days in France? What is the biological reasoning behind this? Arguably there could be some genetic variation in the virus between countries but could that cause such a significant difference? Is there any empirical evidence that supports differences in how long your are contagious between countries?</p><p> The same goes for the numbers of contacts at home or contacts at work. People in the United Kingdom are estimated to have around 7 contacts at home, but the average size of UK households is just\n<ext-link ext-link-type=\"uri\" xlink:href=\"http://population.un.org/Household/index.html#/countries/826\">2.3</ext-link>. It would be good to link the output of these variables to any empirical data that is available to show that they are meaningful and do actually correspond to whatever data might be available. One of the countries with the lowest effective contacts in the household (~ 1.5) has a higher average household size than the UK of\n<ext-link ext-link-type=\"uri\" xlink:href=\"http://population.un.org/Household/index.html#/countries/36\">2.5</ext-link>.</p><p> The variable for the probability of infection given contact (θtrn) is fairly stable apart from China and Australia where it is relatively large and small, respectively. Do the authors have any thoughts why this might be the case? </p><p>\n<underline><bold>Age structure</bold></underline>\n</p><p> The model does not include any kind of age structure. Age has a large effect on the fatality of infection and should therefore be accounted for. Countries with an older population would likely see a higher fatality rate. Age could also influence the amount and types of contacts that people make, with more intergenerational contacts happening within the home and more intragenerational contacts happening at work or school. </p><p>\n<underline><bold>Summary</bold></underline>\n</p><p> The model described in this paper is an interesting and important first step at putting together a model of infectious disease dynamics within the framework of dynamic causal modelling. However, when the particular model here is fit to data I don’t think it displays that it has captured the dynamics of the outbreak well wherever it is able to be compared to separately collected bits of data such as seroprevalence or CCU mortality. </p><p> I think what has happened in the model fitting process for the most part is that the variation introduced into the time series of deaths and reported cases due to differing surveillance and reporting structures, differing testing regimes, differing outbreak responses, and differing population demographics between countries have been accounted for within the generative model through between-country variation in parameters such as the effective population size, numbers of contacts at work (for example, do most people in China really have between 100 and 150 effective contacts at work?), CCU fatality, contagious period length, and others. The unfortunate reality is that with a flexible enough model (in terms of numbers of parameters) it is always possible to produce a fit that very closely matches the reported case and death data observed so far. The real test for this model is whether the estimated parameter values that can be compared to other sources of data match what we observe empirically and I think it is fairly obvious that this has not happened. </p><p> Sadly I don’t think that I can recommend this paper for indexing as it currently stands because I don’t think it is clear what it is trying to do. I think the easiest way of resolving this problem is for the authors to ask themselves the question “Do I think the model predictions made for the UK in this paper are plausible or are they examples of predictions that can be made from the model?”. If the predictions are examples then this paper is an introduction to disease modelling using dynamic causal modelling and the predictions should be more clearly labelled as examples. The paper could then be further improved by showing how methods such as the between-country parameter comparisons using the hierarchical GLM correspond to the types of questions that disease modellers want to answer. Alternatively, if the authors do think that the predictions made in this paper are accurate, then they need to be far more stringent comparing their predictions with data that has become available since they are made and have questions to answer regarding the gap between the 90% immunity in London that they predict and the 17% that has been estimated by the ONS. That London may have already reached herd immunity has huge implications for future intervention policies, the most significant being that there is no danger of a second wave. If we behave as if there is 90% immunity (completely end social distancing etc.) but we are in fact well below herd immunity, then we will have likely caused the second wave through our own actions.</p><p>\n<bold><underline>Recommendations in brief:</underline></bold>\n<list list-type=\"bullet\"><list-item><p>Compare the prediction made in the manuscript to observed deaths and cases that have happened since.</p></list-item><list-item><p>Compare the model output used to make the prediction to seroprevalence surveys and evaluate the predictive validity of the model on this.</p></list-item><list-item><p>Explain what is meant by effective population size and show what happens when predictions are generated using an effective population size equal to the population of the United Kingdom.</p></list-item><list-item><p>Consider improvements to the observational model that account for reporting delays in death data (and potentially in confirmed cases too, since in the UK they are likely people that were tested due to hospital admission and therefore there is a period of time between onset and hospital admission).</p></list-item><list-item><p>Try to compare the posterior estimates of other model parameters (household contacts etc) to existing data. The usual approach would be to fix the parameter values in the model using existing data and then fitting the unknown parameters. This model fits a lot of parameters including some for which there is existing data. If the model fits parameter values that differ from the existing data, this needs to be explained by the authors.</p></list-item></list>\n</p><p>Is the rationale for developing the new method (or application) clearly explained?</p><p>Yes</p><p>Is the description of the method technically sound?</p><p>Yes</p><p>Are the conclusions about the method and its performance adequately supported by the findings presented in the article?</p><p>No</p><p>If any results are presented, are all the source data underlying the results available to ensure full reproducibility?</p><p>Yes</p><p>Are sufficient details provided to allow replication of the method development and its use by others?</p><p>Yes</p><p>Reviewer Expertise:</p><p>Infectious disease modelling, infectious disease epidemiology</p><p>I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.</p></body><back><ref-list><title>References</title><ref id=\"rep-ref-38986-1\"><label>1</label><mixed-citation publication-type=\"journal\">\n:\n<article-title>Baseline Characteristics and Outcomes of 1591 Patients Infected With SARS-CoV-2 Admitted to ICUs of the Lombardy Region, Italy</article-title>.\n<source><italic toggle=\"yes\">JAMA</italic></source>.<year>2020</year>;<volume>323</volume>(<issue>16</issue>) :\n<elocation-id>10.1001/jama.2020.5394</elocation-id>\n<pub-id pub-id-type=\"doi\">10.1001/jama.2020.5394</pub-id>\n</mixed-citation></ref></ref-list></back><sub-article id=\"comment4282-38986\" article-type=\"response\"><front-stub><contrib-group><contrib contrib-type=\"author\"><name><surname>Razi</surname><given-names>Adeel</given-names></name><aff>Monash University, Australia</aff></contrib></contrib-group><author-notes><fn fn-type=\"COI-statement\"><p>\n<bold>Competing interests: </bold>No competing interests were disclosed.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>23</day><month>7</month><year>2020</year></pub-date></front-stub><body><p>Dear Dr. Hellewell,</p><p>We would like to thank you for the considerable time and effort you have spent reviewing our manuscript. Your thoroughness and attention to detail, in what must be very busy and challenging times, has been very much appreciated. We were particularly impressed with the summary of the technical aspects of this work, which are useful and informed descriptions in their own right.</p><p>We have tried to revise the paper to preserve its original content (by limiting changes to the main text to clarify and unpack things). We have used footnotes and a new 'to address issues that have arisen since submission (for example, the validity of predictions in light of actual outcomes).</p><p>Below are the replies to the comments, that for clarity we have grouped into key themes. We hope these revisions are what you had in mind:</p><p>\n<bold><underline>A) Predictive validity:</underline></bold>\n</p><p>The primary purpose of this paper was to serve as a technical report, introducing a methodology that could be, and was, used to answer specific questions about epidemiological parameters and epidemiological model structure. To clarify this, we have emphasised that the narrative at the end of the paper is an example of the kind of predictions that can be made, rather than a definitive prediction\n<italic>per se </italic>(footnote 31):</p><p>\n<italic>“This narrative is not offered as a prediction – but as an example of the kind of predictions afforded by dynamic causal modelling. An aspect of these predictions is that they include systemic factors beyond the epidemiology per se. The best example of this is the above predictions about social distancing, which could be read as ‘lockdown’; namely the probability that I will leave home. This highlights a key distinction between dynamic causal models and standard quantitative epidemiological modelling that treats things like ‘lockdown’ as interventions that are supplied to the model. In contrast, interventions such as social distancing and testing are modelled as an integral part of the process – and are estimated on the basis of the data at hand. One consequence of this is that one can make predictions about when ‘interventions’ – or their suspension – will occur in the future.”</italic>\n</p><p>Regarding specific predictive validity, we thought it would be disingenuous to change the predictions in light of subsequent outcomes—or the procedures that were applied in subsequent reports. However, we have now added an extensive ‘Posthoc evaluation of model predictions’ section in the revised version that addresses the predictions in light of current data. This section implicitly addresses the specific points about predictions in the reviewers’ comments. We have also attempted to make the demarcation between a procedural and predictive contribution clearer throughout the text by including footnotes like the following (footnote 33):</p><p>\n<italic>“To reiterate, the purpose of this technical report was to introduce the variational procedures entailed by dynamic causal modelling in the setting of quantitative, epidemiological modelling. Since this report was submitted, several papers have used procedures described in this report to address specific questions; for example, the impact of lockdown cycles, the effect of population fluxes among regional outbreaks, the efficacy of testing and tracing, and the impact of heterogeneous susceptibility and transmission. Crucially, in line with a key message of this foundational paper, each successive application of the dynamic causal modelling leveraged Bayesian model comparison to update the model as new data became available.”</italic>\n</p><p>We also take the opportunity to future-proof retrospective evaluations of the reproduction ratio with the following footnote 13:</p><p>\n<italic>“Added in revision: the reproduction ratio in this report was based upon an approximation to the expected number of people that I might infect, if I was infectious. In subsequent reports, the reproduction ratio was brought into line with more formal definitions, based on the geometric rate of increase in the prevalence of infection and the period of contagion. A minimum reproduction ratio (R) of nearly zero in this report corresponds to about 0.7 in subsequent (and other) reports.”</italic>\n</p><p>In addition to these, we have also incorporated a number of additional changes outlined below. </p><p>\n<bold><underline>B) Effective population </underline></bold>\n</p><p>It is clear that the “\n<italic>effective population</italic>” terminology, particularly in respect to immunity, represents a common source of confusion. To rectify this, we have made a number of changes throughout the paper. First, we have amended the “Initial Conditions and population size” section, splitting it and introducing a new subsection as follows: </p><p>\n<bold><italic>“Effective Population</italic></bold>\n<italic>:</italic>\n</p><p>\n<italic>Under the initial conditions, the population size can be set, a priori, to 1,000,000; noting that a small city comprises (by definition) a hundred thousand people, while a large city can exceed 10 million. This population parameter is a prior that is updated based on the available data, providing an estimate of the “effective population” size. Effective population is defined here as the proportion of the total population who are susceptible to infection, and therefore participate in the outbreak. The assumption that the effective population size reflects the total population of a country is a hypothesis that we will test later [footnote 16]. For clarity, we are not implying that the remainder of the population classed as “not susceptible” are immune or resistant to COVID-19, rather there exists a sub-population who do not take part in the current outbreak for any of a variety of reasons that may include being shielded or geographically isolated from infected cases. Furthermore, as the effective population (and other parameters) are estimated directly from the data, they will therefore reflect the source of the information. At the time of writing, in the UK this was dominated by the London outbreak. Finally, as all parameters pertain to the effective population, proportions (or probabilities)—such as population immunity—require appropriate scaling to be expressed as a percentage of the total (census) population.”</italic>\n</p><p>\n<italic>[Footnote 16]:This technical report considered an outbreak in a single region or city. A country-wide (pandemic) model of viral spread would require multiple regional models to be coupled together, and is the focus of subsequent papers (K. J. Friston, T. Parr, P. Zeidman, A. Razi, G. Flandin, J. Daunizeau, O. J. Hulme, A. J. Billig, V. Litvak, C. J. Price, R. J. Moran, C. Lambert, (2020) ‘‘Second waves, social distancing, and the spread of COVID-19 across America’’, arXiv: :2004.13017).</italic>\n</p><p>We have also annotated the legend to figure 11, and made the following change to immunity predictions, to clarify this further:</p><p>\n<italic>“Note that predictions—like the percentage of herd immunity—pertain to the effective population. For example, if 80% of the effective (2.5 million) population are seropositive, one would expect 22% of the census (8.9 million) population of London to have seroconverted by early May.”</italic>\n</p><p>\n<bold><underline>C) Improvements to model:</underline></bold> </p><p>We appreciate the number of suggestions to help refine or improve this model further. As surmised in the “Predictive Validity” section of your review, this report provides an initial technical description for the kind of analyses that could be used via the presented methodology. In a sense, it represents a proof of concept for this type of modelling, and we acknowledge there are many directions and improvements that could be made such as regional specific models (e.g.\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.fil.ion.ucl.ac.uk/spm/covid-19/dashboard/local/\">https://www.fil.ion.ucl.ac.uk/spm/covid-19/dashboard/local/</ext-link>), or integrating demographic data for known risk factors (e.g. age, ethnicity, BMI etc.,). To reflect this, we have amended the conclusion as follows:</p><p>\n<italic>“This technical report describes an initial implementation of the DCM framework to provide a generative model of a viral epidemic, and to demonstrate the potential utility of such modelling. Clearly there are a number of ways this model could be refined. Our hope in making it open source is that it will allow others to identify issues, contribute to improvements—and help facilitate objective comparisons with other models—using Bayesian model comparison.  </italic>\n</p><p>\n<italic>There remain a number of outstanding issues:</italic>\n<italic>”</italic>\n</p><p>Additionally, in a separate piece of work [1] we have also formally compared an ODE-based SEIR model to the DCM presented here. Here the SEIR was developed originally by Moghadas et al. [2] to assess CCU projections due to COVID-19 in the US. The SEIR model comprised 12 states including asymptomatic and subclinical infected states, self-isolation, and separate states of hospitalization [2]. We optimised parameters for both the SEIR and DCM using identical variational processes to those presented here. Taking data from seven European countries including the UK, we found that the approximate model evidence or Free Energy provided very strong support for the DCM as compared to the SEIR model, suggesting that marginal state occupancy was important when accounting for those data. In particular Log Bayes Factors of >100 was evidenced for all seven datasets. This comparative analysis is currently under review.</p><p>\n<bold><underline>D) Observation model</underline></bold>\n</p><p>We thank the reviewer for highlighting this. We are aware that delays in reporting deaths and reporting of statistics over weekends do represent potential confounds to the observed time series data. In this work, we perform smoothing of time series by several days to deal with these delays in reporting. Delays in reporting PCR testing were modelled explicitly in terms of a ‘waiting for a test’ state because entry into this state depends upon testing capacity. Conversely, a simple delay in reporting a death can be accommodated by an increase in effective dwell time in critical care. One could consider a DCM that modelled the delay in reporting deaths explicitly—and then use Bayesian model comparison to compare models with and without delays. We did not do this; however, the conditional dependencies between an additional delay parameter and the existing parameters would probably reduce the marginal likelihood (i.e., Bayesian model evidence) of an extended DCM.</p><p>\n<bold><underline>E) </underline></bold>\n<bold><underline>Reporting structure</underline></bold>\n</p><p>We agree that differences in testing and reporting strategies will impact the data. In the model presented, the testing rate parameter accounts for some of these differences. We have added the following footnote 11 to emphasise the importance of this part of the model.</p><p>\n<italic>“It is revealing to note that the number of model parameters pertaining to PCR testing matches the number of parameters mediating the epidemiology per se. This reflects the fact that the generative model has to consider every aspect of how data are generated. In order to leverage the information in new positive tests, it is necessary to think carefully about all the parameters that contribute to these data; for example, the probability of being tested and the selection bias towards testing people who are more likely to be infected. Crucially, this bias has to be estimated during model inversion and could vary substantially from country to country. Although not implemented in this report, subsequent distinctions between Pillar 1 and 2 test data would be a nice example of different selection biases. This speaks to the importance of modelling Pillar 1 and 2 as distinct data modalities. From a technical perspective, equipping standard epidemiological models with an ‘observation model’ can be regarded as building a complete dynamic causal model. The key thing to bear in mind here is that the parameters of so-called observation models have to be treated in exactly the same way as epidemiological parameters, because they could show conditional dependencies. In dynamic causal modelling, all unknown parameters are treated in a uniform way to maximise (a free energy bound on) marginal likelihood.”</italic>\n</p><p>\n<bold><underline>F) CCU fatality/Between-country parameter value variation</underline></bold>\n</p><p>First a disclaimer is that these assertions (for example Fig. 6, showing differences among countries) are not about actual states of affairs. These are the best explanations for the data available at the time, under the simplest model of how those data were caused. However, there does appear to be some degree of predictive validity; for example, the predicted CCU mortality rate in the UK in April (at the time of writing of the paper) of about 48%, was close to data published on the 4th April by the Intensive Care National Audit and Research Centre (critical care mortality = 50.1% [3]). Regarding the Italian data from Lombardy, whilst the mortality rate was lower (26%), the data was acquired earlier on in the pandemic (February 20 to March 18) before the peak in cases. Rather than dissect the predictive validity of each parameter and country, which is widely recognised as an extremely challenging problem [4], we would reiterate that this paper is intended as a technical report for DCM, and provides examples of the types of questions that could be addressed using this method. To clarify these points, we have modified the following in the “Parametric empirical Bayes and hierarchical modelling section”:</p><p>\n<italic>“Again, these assertions are not about actual states of affairs. These are the best explanations for the data available at the time, under the simplest model of how those data were caused [footnote 24]. </italic>\n</p><p>\n<italic>[Footnote 24]: However, there does appear to be some predictive validity to these that are addressed in a '</italic>Posthoc evaluation of model predictions' section\n<italic>. Note rather than dissect the predictive validity of each parameter and country, which is widely recognised as a challenging problem (2), we have provided some representative examples. A comprehensive analysis of this type would be beyond the scope of this report. It is also important to note that predictions based upon rate parameters and probabilities are a reflection of prior assumptions about these parameters, whereas predictions based upon the hidden states speak to the predictive validity of the DCM model structure (see below).”</italic>\n</p><p>We have also updated the London population (previously mentioned as 7 million) which is 8.96 million (mid-2019 estimate) [5].  </p><p>\n<bold>References</bold>\n</p><p>[1] Moran RJ, Fagerholm ED, Cullen M, Daunizeau J, Richardson MP, Williams S, et al. Estimating required'lockdown'cycles before immunity to SARS-CoV-2: model-based analyses of susceptible population sizes,'S0', in seven European countries, including the UK and Ireland [version 1; peer review: awaiting peer review]. Wellcome Open Research. 2020;5.</p><p>[2] Moghadas SM, Shoukat A, Fitzpatrick MC, Wells CR, Sah P, Pandey A, et al. Projecting hospital utilization during the COVID-19 outbreaks in the United States. Proceedings of the National Academy of Sciences. 2020.</p><p>[3] \n<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.icnarc.org/DataServices/Attachments/Download/76a7364b-4b76-ea11-9124-00505601089b\">www.icnarc.org/DataServices/Attachments/Download/76a7364b-4b76-ea11-9124-00505601089b</ext-link>\n</p><p>[4] Phua J, Weng L, Ling L, Egi M, Lim CM, Divatia JV, Shrestha BR, Arabi YM, Ng J, Gomersall CD, Nishimura M. Intensive care management of coronavirus disease 2019 (COVID-19): challenges and recommendations. The Lancet Respiratory Medicine. 2020 Apr 6.</p><p>[5]\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://en.wikipedia.org/wiki/Greater_London\">https://en.wikipedia.org/wiki/Greater_London</ext-link>\n</p><p>******************************************************************************************************************** </p><p>Posthoc evaluation of model predictions</p><p>This section was written three months after the report was submitted, providing an opportunity to revisit some of the predictions in light of actual outcomes. Although the predictions in this report were used to illustrate the nature of the predictions supported by models that included social distancing, they can be used to assess the predictive validity of the DCM.</p><p>Subsequently, the DCM was optimized using Bayesian model comparison. A crucial addition was the inclusion of heterogeneity in the response of the population to viral infection. However, even the simple DCM above accommodated sufficient heterogeneity—in terms of the distinction between an effective and total (census) population—to provide some accurate predictions.</p><p>In brief, the shape and timing of the epidemic in London was predicted to within a few days. Conversely, the number of fatalities and positive test results were overestimated by a factor of about 3. In what follows, we list the accurate and inaccurate predictions. We assume that the census population of London was 8.96 million [1].\n<italic>London's population</italic> is taken to be the effective population estimated to be 2.49 million (see Table 2) and\n<italic>social distancing</italic> is read as lockdown (i.e., the probability of leaving home).</p><p>\n<bold>Accurate predictions</bold>\n</p><p> \n<list list-type=\"bullet\"><list-item><p>“\n<italic>Based on current data, reports of new cases in London are expected to peak on April 5</italic>”</p></list-item></list> Daily confirmed cases of coronavirus in the UK peaked on April 5 [2]</p><p> \n<list list-type=\"bullet\"><list-item><p>“\n<italic>A peak in death rates around April 10 (Good Friday).</italic>”</p></list-item></list> Peak death rates in London reached 249 per day on April 9 [3]</p><p> \n<list list-type=\"bullet\"><list-item><p>“\n<italic>At this time </italic>[April 10]\n<italic>, critical care unit occupancy should peak, approaching—but not exceeding—capacity</italic>”</p></list-item></list> During the Easter weekend of 11–12 April, the NHS Nightingale Hospital London had only 19 patients. Existing London hospitals had sufficient capacity after increasing their combined intensive care capacity from 770 beds to 1,555. As of April 24, only 41 patients had been treated at the Nightingale hospital [4].</p><p> \n<list list-type=\"bullet\"><list-item><p>“\n<italic>At the peak of death rates </italic>[April 10]\n<italic>, the proportion of people infected (in London) is expected to be about 32%</italic>”</p></list-item></list> This prediction corresponds to 8.9% = 32% x 2.49/8.96 of the census population of London, which coincides with the consensus estimates at that time. “Professor Chris Whitty admits he thinks at least 10% of the capital has been infected” (published on 24-April-2020) [5].</p><p> \n<list list-type=\"bullet\"><list-item><p>“\n<italic>Improvements should be seen by May 8, shortly after the May bank holiday, when social distancing will be relaxed.</italic>”</p></list-item></list> On May 8, the first Black Lives Matter demonstrations started in London. This was followed by the\n<bold>first governmental relaxation of lockdown</bold> on May 10: “So, work from home if you can, but you should go to work if you can’t work from home.” (Prime Minister's address to the nation: 10-May-2020) [6]</p><p> \n<list list-type=\"bullet\"><list-item><p>“\n<italic>At this time </italic>[May 8]\n<italic> herd immunity should have risen to about 80%”</italic>\n</p></list-item></list> Population immunity in the effective population corresponds to 80% x 2.49 / 8.9 = 22% seroprevalence in the census population, which had risen to 17.5% in the previous week: “After making adjustments for the accuracy of the assay and the age and gender distribution of the population, the overall adjusted prevalence in London increased from 1.5% in week 13 to 12.3% in weeks 15 to 16 and 17.5% in week 18” (week ending May 3, 2020) [7].</p><p> \n<list list-type=\"bullet\"><list-item><p>“\n<italic>By June 12, death rates should have fallen to low levels with over 90% of people being immune</italic>”</p></list-item></list> Weekly reported deaths in London hospitals for the week ending June 11 fell to 22 (with positive tests)[8]. Seroprevalence for this period was not reported.</p><p> \n<list list-type=\"bullet\"><list-item><p>“\n<italic>By June 12, social distancing </italic>[lockdown]\n<italic>will no longer be a feature of daily life.</italic>”</p></list-item></list> The\n<bold>second governmental relaxation of lockdown</bold> was announced on June 10 and June 23, with an initial reopening of shops, and an easing of the two-metre social distancing rule:</p><p>“[A]s the Business Secretary confirmed yesterday, we can now allow all shops to reopen from Monday.” (Prime Minister's statement that the coronavirus press conference: 10-June-2020) [9]</p><p>“Thanks to our progress, we can now go further and safely ease the lockdown in England.  At every stage, caution will remain our watchword, and each step will be conditional and reversible. Mr Speaker, given the significant fall in the prevalence of the virus, we can change the two-metre social distancing rule, from 4th July.” (Prime Minister's statement to the House: 23-June-2020) [10]</p><p>\n<bold>Inaccurate predictions</bold>\n</p><p> \n<list list-type=\"bullet\"><list-item><p>“\n<italic>About 12% of London's population will have been tested (May 8). Just under half of those tested will be positive</italic>.”</p></list-item></list> This was an overestimate: 12% of the effective population corresponds to 143,424 = 12% x .48 x 2.49 positive tests. At the time of writing (17-July-2020), only 34,397 people have tested positive in London [11]—a quarter of the predicted number.</p><p> \n<list list-type=\"bullet\"><list-item><p>\n<italic>From Figure 8: Peak daily death rate 807 (710-950) with cumulative deaths of 17,500 (14,000-21,000)</italic>\n</p></list-item></list> These were overestimates; daily deaths in London peaked at 249 on April 9 with cumulative deaths at the time of writing (17-July-2020) of 6,106 [12]. This represents consistent overestimates by factors of 3.2 and 2.8, respectively. This may reflect the fact that the data used in the report included regions in the United Kingdom outside London.</p><p>[1]\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://en.wikipedia.org/wiki/Greater_London\">https://en.wikipedia.org/wiki/Greater_London</ext-link>\n</p><p>[2]\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.bbc.co.uk/news/uk-51768274\">https://www.bbc.co.uk/news/uk-51768274</ext-link>\n</p><p>[3]\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://en.wikipedia.org/wiki/COVID-19_pandemic_data\">https://en.wikipedia.org/wiki/COVID-19_pandemic_data</ext-link>\n</p><p>[4]\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://en.wikipedia.org/wiki/NHS_Nightingale_Hospital_London#cite_note-HSJ-19\">https://en.wikipedia.org/wiki/NHS_Nightingale_Hospital_London#cite_note-HSJ-19</ext-link>.</p><p>[5]\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.dailymail.co.uk/news/article-8250371\">https://www.dailymail.co.uk/news/article-8250371</ext-link>\n</p><p>[6]\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.gov.uk/government/speeches/pm-address-to-the-nation-on-coronavirus-10-may-2020\">https://www.gov.uk/government/speeches/pm-address-to-the-nation-on-coronavirus-10-may-2020</ext-link>\n</p><p>[7]\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.gov.uk/government/publications/national-covid-19-surveillance-reports/sero-surveillance-of-covid-19\">https://www.gov.uk/government/publications/national-covid-19-surveillance-reports/sero-surveillance-of-covid-19</ext-link>\n</p><p>[8]\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.london.gov.uk/coronavirus/coronavirus-numbers-london\">https://www.london.gov.uk/coronavirus/coronavirus-numbers-london</ext-link>\n</p><p>[9]\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.gov.uk/government/speeches/pm-statement-at-the-coronavirus-press-conference-10-june-2020\">https://www.gov.uk/government/speeches/pm-statement-at-the-coronavirus-press-conference-10-june-2020</ext-link>\n</p><p>[10]\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.gov.uk/government/speeches/prime-ministers-statement-to-the-house-on-covid-19-23-june-2020\">https://www.gov.uk/government/speeches/prime-ministers-statement-to-the-house-on-covid-19-23-june-2020</ext-link>\n</p><p>[11]\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://coronavirus.data.gov.uk/\">https://coronavirus.data.gov.uk/</ext-link>\n</p><p>[12]\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://en.wikipedia.org/wiki/COVID-19_pandemic_data\">https://en.wikipedia.org/wiki/COVID-19_pandemic_data</ext-link>\n</p></body></sub-article></sub-article></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"case-report\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Cureus</journal-id><journal-id journal-id-type=\"iso-abbrev\">Cureus</journal-id><journal-id journal-id-type=\"issn\">2168-8184</journal-id><journal-title-group><journal-title>Cureus</journal-title></journal-title-group><issn pub-type=\"epub\">2168-8184</issn><publisher><publisher-name>Cureus</publisher-name><publisher-loc>Palo Alto (CA)</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32821602</article-id><article-id pub-id-type=\"pmc\">PMC7431979</article-id><article-id pub-id-type=\"doi\">10.7759/cureus.9256</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Neurology</subject></subj-group><subj-group><subject>Neurosurgery</subject></subj-group></article-categories><title-group><article-title>False Localizing Trigeminal V2 Sensory Loss in Vestibular Schwannoma</article-title></title-group><contrib-group><contrib contrib-type=\"editor\"><name><surname>Muacevic</surname><given-names>Alexander</given-names></name></contrib><contrib contrib-type=\"editor\"><name><surname>Adler</surname><given-names>John R</given-names></name></contrib></contrib-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Feria</surname><given-names>Alejandro L</given-names></name><xref ref-type=\"aff\" rid=\"aff-1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Akinduro</surname><given-names>Oluwaseun O</given-names></name><xref ref-type=\"aff\" rid=\"aff-2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Rahmathulla</surname><given-names>Gazanfar</given-names></name><xref ref-type=\"aff\" rid=\"aff-3\">3</xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>Tavanaiepour</surname><given-names>Daryoush</given-names></name><xref ref-type=\"aff\" rid=\"aff-4\">4</xref></contrib></contrib-group><aff id=\"aff-1\">\n<label>1</label>\nInternal Medicine, University of Kentucky, Bowling Green, USA </aff><aff id=\"aff-2\">\n<label>2</label>\nNeurological Surgery, Mayo Clinic Florida, Jacksonville, USA </aff><aff id=\"aff-3\">\n<label>3</label>\nNeurological Surgery, University of Florida College of Medicine, Jacksonville, USA </aff><aff id=\"aff-4\">\n<label>4</label>\nNeurological Surgery, University of Florida Health, Jacksonville, USA </aff><author-notes><corresp id=\"cor1\">\nDaryoush Tavanaiepour <email>daryoush.tavanaiepour@jax.ufl.edu</email></corresp></author-notes><pub-date date-type=\"pub\" publication-format=\"electronic\"><day>18</day><month>7</month><year>2020</year></pub-date><pub-date date-type=\"collection\" publication-format=\"electronic\"><month>7</month><year>2020</year></pub-date><volume>12</volume><issue>7</issue><elocation-id>e9256</elocation-id><history><date date-type=\"received\"><day>26</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>18</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020, Feria et al.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Feria et al.</copyright-holder><license license-type=\"open-access\" xlink:href=\"http://creativecommons.org/licenses/by/3.0/\"><license-p>This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</license-p></license></permissions><self-uri xlink:href=\"https://www.cureus.com/articles/34454-false-localizing-trigeminal-v2-sensory-loss-in-vestibular-schwannoma\">This article is available from https://www.cureus.com/articles/34454-false-localizing-trigeminal-v2-sensory-loss-in-vestibular-schwannoma</self-uri><abstract><p>False localizing signs involving cranial nerves are rare, even more so when involving the trigeminal nerve. Here we present the first case of trigeminal V2 sensory loss as a false localizing sign. The sensory dysfunction was caused by a large contralateral cystic vestibular schwannoma and subsequently improved after tumor resection. The clinical and radiographic features are described, and proposed mechanisms for this false localizing sign are discussed.</p></abstract><kwd-group kwd-group-type=\"author\"><kwd>false localizing sign</kwd><kwd>vestibular schwannoma</kwd><kwd>trigeminal sensory loss</kwd></kwd-group></article-meta><notes><p content-type=\"disclaimer\">The content published in Cureus is the result of clinical experience and/or research by independent individuals or organizations. Cureus is not responsible for the scientific accuracy or reliability of data or conclusions published herein. All content published within Cureus is intended only for educational, research and reference purposes. Additionally, articles published within Cureus should not be deemed a suitable substitute for the advice of a qualified health care professional. Do not disregard or avoid professional medical advice due to content published within Cureus.</p></notes></front><body><sec sec-type=\"intro\"><title>Introduction</title><p>False localizing signs are neurological deficits, which deviate from what would be predicted using traditional neuroanatomical pathway and localization paradigms [<xref rid=\"REF1\" ref-type=\"bibr\">1</xref>]. False localizing signs of cranial nerves, particularly the trigeminal nerve, are uncommon [<xref rid=\"REF2\" ref-type=\"bibr\">2</xref>]. Although there have been five previously reported cases of lesions causing contralateral trigeminal nerve sensory dysfunction, this is the first report of a false localizing V2 trigeminal nerve sensory loss, and the first false localizing trigeminal sensory loss in the setting of a large Koos grade 4 cystic vestibular schwannoma (VS) [<xref rid=\"REF3\" ref-type=\"bibr\">3</xref>-<xref rid=\"REF7\" ref-type=\"bibr\">7</xref>].</p></sec><sec sec-type=\"cases\"><title>Case presentation</title><p>A 32-year-old woman with no significant past medical history presented with one month of right facial sensory loss, tinnitus, progressive imbalance, and two years of worsening hearing in the left ear. Her symptoms began with tingling on the right side of her tongue and upper lip, which progressed to her right cheek with the sensation of a swollen tongue. This continued to the point that that she would bite her right cheek when eating, and was unaware until her mouth bled. She had difficulty swallowing and felt that items frequently got stuck in her throat. She denied any nausea or vomiting, but endorsed progressively worsening headaches over the prior month.</p><p>On physical examination, she had loss of fine touch and pin-prick sensation in the right V2 distribution, House-Brackmann (HB) grade II facial dysfunction on the left, diminished left-sided hearing, bilateral horizontal nystagmus with fast phase to the left, right deviating uvula, loss of gag on the left, and left deviating tongue [<xref rid=\"REF8\" ref-type=\"bibr\">8</xref>]. The remainder of her neurological exam was unremarkable. MRI revealed a large cystic left cerebellopontine angle tumor, measuring 4.7 × 4.8 × 4.9 cm with heterogeneous contrast enhancement and expansion of the porus acousticus consistent with a large Koos grade 4 VS (Figure <xref ref-type=\"fig\" rid=\"FIG1\">1</xref>) [<xref rid=\"REF9\" ref-type=\"bibr\">9</xref>].</p><fig fig-type=\"figure\" id=\"FIG1\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><title>Preoperative MRI brain of large left-sided vestibular schwannoma in a patient who presented with false localizing right-sided isolated V2 sensory loss.</title><p>(A) Axial T1-contrasted MRI showing a left-sided cystic heterogeneously enhancing Koos grade 4 vestibular schwannoma (green arrow). (B) Sagittal T1 MRI shows 1.7 cm tonsillar herniation (red arrow). (C) Coronal T1 contrast-enhanced MRI redemonstrates large cystic vestibular schwannoma with significant rightward brainstem displacement (green arrow). (D) Axial T2-weighted MRI shows enlarged lateral ventricles with transependymal flow (blue arrow).</p></caption><graphic xlink:href=\"cureus-0012-00000009256-i01\"/></fig><p>There was significant mass effect with severe compression of the medulla, pons, cerebellum, and fourth ventricle, herniation of cerebellar tonsils 1.7 cm below foramen magnum, and resultant obstructive hydrocephalus with T2 transependymal flow.</p><p>One day after presentation, the patient underwent placement of a right frontal ventriculoperitoneal shunt (VPS) for treatment of her hydrocephalus. Following cerebrospinal fluid (CSF) diversion, her headaches had completely resolved and there was a marked improvement in her tongue and uvula deviation. Her facial nerve function remained HB II, and the remainder of her exam was unchanged. Soon after VPS placement, she subsequently underwent a retrosigmoid craniectomy with intraoperative neuromonitoring for tumor resection. To reduce risk of debilitating facial nerve palsy, a small portion of the tumor capsule was left adherent to facial nerve at the porus acousticus (Figure <xref ref-type=\"fig\" rid=\"FIG2\">2</xref>) [<xref rid=\"REF10\" ref-type=\"bibr\">10</xref>]. The tumor was confirmed to be a schwannoma.</p><fig fig-type=\"figure\" id=\"FIG2\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><title>Postoperative MRI after retrosigmoid craniectomy for left-sided vestibular schwannoma resection which resulted in near total improvement of right V2 sensory dysfunction.</title><p>(A) Axial T1-contrasted MRI shows resection of vestibular schwannoma with residual tumor capsule left adherent to facial nerve at the porus acousticus and improvement of rightward brainstem deviation (green arrow). (B) Axial T2 MRI shows resolution of hydrocephalus.</p></caption><graphic xlink:href=\"cureus-0012-00000009256-i02\"/></fig><p>Postoperatively, the patient had resolution of her facial nerve palsy, now HB I, and had improvement in her contralateral sensory loss to near normal with improved fine touch and pin-prick discrimination. There was further improvement of uvula and tongue deviation as well as her swallowing function (Figure <xref ref-type=\"fig\" rid=\"FIG3\">3</xref>).</p><fig fig-type=\"figure\" id=\"FIG3\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><title>Still images from pre- and postoperative barium swallow studies.</title><p>(A) Preoperative swallow study showing significant aspiration of thin and thick liquids (arrow). The patient was placed on honey thick liquid diet. (B) Postoperative swallow study after resection of vestibular schwannoma demonstrates resolution of aspiration with mild pharyngeal weakness remaining (arrow). The patient's diet was advanced to mechanical soft with thin liquids.</p></caption><graphic xlink:href=\"cureus-0012-00000009256-i03\"/></fig><p>The remainder of her neurological exam was unchanged. She did remarkably well after surgery and is scheduled to undergo stereotactic radiosurgery for the residual tumor.</p></sec><sec sec-type=\"discussion\"><title>Discussion</title><p>This is a rare case of contralateral V2 trigeminal sensory loss as a false localizing sign in VS. False localizing signs are classically described in the setting of a supratentorial mass causing ipsilateral hemiparesis (Kernohan’s notch phenomena) believed to be the result of brainstem herniation and displacement with resultant contralateral compression of the cerebral peduncle against the dural folds of the tentorium [<xref rid=\"REF11\" ref-type=\"bibr\">11</xref>]. First described in 1904 by James Collier and subsequently expanded upon by M. Michael Gassel, there have since been multiple reports and series that describe false localizing signs involving lesions of the cerebrum, brainstem, and spinal cord [<xref rid=\"REF1\" ref-type=\"bibr\">1</xref>,<xref rid=\"REF2\" ref-type=\"bibr\">2</xref>].</p><p>False localizing signs involving the brainstem and cranial nerves are rare and when present they classically affect the sixth or third cranial nerves. When affecting the trigeminal nerve, false localizing signs may present as motor loss, trigeminal neuralgia, or sensory loss [<xref rid=\"REF12\" ref-type=\"bibr\">12</xref>-<xref rid=\"REF14\" ref-type=\"bibr\">14</xref>]. A review of the literature has identified five published cases of contralateral trigeminal sensory loss as a false localizing sign with the presented case representing the sixth reported case (Table <xref rid=\"TAB1\" ref-type=\"table\">1</xref>) [<xref rid=\"REF3\" ref-type=\"bibr\">3</xref>-<xref rid=\"REF7\" ref-type=\"bibr\">7</xref>].</p><table-wrap id=\"TAB1\" orientation=\"portrait\" position=\"float\"><label>Table 1</label><caption><title>Previously reported cases of trigeminal sensory loss as a false localizing sign.</title></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Case</td><td rowspan=\"1\" colspan=\"1\">Symptoms</td><td rowspan=\"1\" colspan=\"1\">Tumor Location</td><td rowspan=\"1\" colspan=\"1\">Pathology</td><td rowspan=\"1\" colspan=\"1\">Treatment</td><td rowspan=\"1\" colspan=\"1\">Symptom Resolution</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Ehni 1950 [<xref rid=\"REF3\" ref-type=\"bibr\">3</xref>]</td><td rowspan=\"1\" colspan=\"1\">Approximately six-month history of headache and tinnitus followed by overnight onset of complete sensory loss in left V1 and V2, and moderate loss in V3 distribution. Complete paralysis of left masseter, temporalis, and pterygoid. Diminished left corneal reflex.</td><td rowspan=\"1\" colspan=\"1\">Supratentorial. Right pterional origin</td><td rowspan=\"1\" colspan=\"1\">Meningioma</td><td rowspan=\"1\" colspan=\"1\">Surgical resection</td><td rowspan=\"1\" colspan=\"1\">Complete return of sensation in all trigeminal divisions. Corneal reflex normal. No improvement in trigeminal innervated muscle paralysis.</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Turnbull 1974 [<xref rid=\"REF4\" ref-type=\"bibr\">4</xref>]</td><td rowspan=\"1\" colspan=\"1\">Nine months of headache. One week of neck pain, vomiting, slurred speech, and unsteadiness. One day of right facial weakness. Absent right corneal reflex. Right V1 sensory impairment. Right seventh nerve palsy. Partial right-sided deafness. Right ninth and tenth nerve palsies. Bilateral 11th nerve palsy. Mild right hemiparesis with mild ataxia.</td><td rowspan=\"1\" colspan=\"1\">Infratentorial. Large left cerebellar hemisphere cystic tumor</td><td rowspan=\"1\" colspan=\"1\">Cerebellar hemangioblastoma</td><td rowspan=\"1\" colspan=\"1\">Surgical resection</td><td rowspan=\"1\" colspan=\"1\">Immediate postoperative improvement of dysarthria, right facial weakness, loss of palatal sensation and right hemiparesis but absent right corneal reflex, palatal weakness, and ataxic gait persisted. Six months later, the patient showed no abnormal neurological signs.</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Maurice-Williams 1975 [<xref rid=\"REF5\" ref-type=\"bibr\">5</xref>]</td><td rowspan=\"1\" colspan=\"1\">Two years of morning vomiting with occasional occipital pain. Five months of unsteady gait, right face and palate numbness, transient diplopia, blurred vision, and dysphagia. Loss of pin-prick and light touch on right V1, V2, and V3. Absent right corneal reflex. Right 9th and 10th nerve dysfunction. Right leg cerebellar ataxia</td><td rowspan=\"1\" colspan=\"1\">Infratentorial. Left inferior leaf of lateral tentorium just above most outer part of petrous temporal bone</td><td rowspan=\"1\" colspan=\"1\">Meningioma</td><td rowspan=\"1\" colspan=\"1\">Surgical resection</td><td rowspan=\"1\" colspan=\"1\">At six weeks postop, all symptoms resolved except persistent reduction of the right corneal reflex and slight dulling of right oropharyngeal sensation.</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Koenig et al. 1984 [<xref rid=\"REF6\" ref-type=\"bibr\">6</xref>]</td><td rowspan=\"1\" colspan=\"1\">Left ear tinnitus of two years with left-sided deafness of eight months. Four months of progressive loss of right facial sensation. Decreased right corneal reflex. Decreased sensation, hypesthesia, hypoalgesia of right V1, V2, and V3 (motor normal). Orbicularis oculi reflex study showed absence of early and late response on the right of right-sided supraorbital nerve stimulation.</td><td rowspan=\"1\" colspan=\"1\">Infratentorial. Left cerebellopontine angle</td><td rowspan=\"1\" colspan=\"1\">Vestibular schwannoma</td><td rowspan=\"1\" colspan=\"1\">Surgical resection</td><td rowspan=\"1\" colspan=\"1\">One week postop, studies showed normal early and late responses from right orbicularis oculi muscle on stimulation of right supraorbital nerve. Other symptoms not reported.</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Ro et al. 1995 [<xref rid=\"REF7\" ref-type=\"bibr\">7</xref>]</td><td rowspan=\"1\" colspan=\"1\">Six years of progressive right facial weakness, three years of right facial numbness and diplopia, one year of unsteadiness and dysphagia. Right trigeminal sensory disturbance in V1 and V2. Depressed corneal reflexes R>L. Right sixth and seventh nerve palsies. Left hearing impairment. Weakness of the left soft palate, generalized hyperreflexia, and a left extensor plantar reflex. Masseter strength was normal.</td><td rowspan=\"1\" colspan=\"1\">Infratentorial. Left cerebellopontine angle</td><td rowspan=\"1\" colspan=\"1\">Vestibular schwannoma</td><td rowspan=\"1\" colspan=\"1\">Surgical resection</td><td rowspan=\"1\" colspan=\"1\">Three days postop showed improved diplopia, right facial hypoalgesia, and swallowing dysfunction. Right facial palsy persisted.</td></tr></tbody></table></table-wrap><p>This case is unique in the following respects: it is the first to describe a false localizing trigeminal sensory loss isolated to the V2 distribution, and the first in the setting of a large cystic Koos grade 4 VS.</p><p>There have been multiple proposed mechanisms for contralateral trigeminal nerve dysfunction, including sheer and torsional forces applied to nerve rootlets, foramen, and other compressive zones as a result of mass effect and brainstem displacement, compressive forces from arachnoid bands, or vascular compression at the nerve root entry zone [<xref rid=\"REF14\" ref-type=\"bibr\">14</xref>-<xref rid=\"REF16\" ref-type=\"bibr\">16</xref>]. In the presented case, the patient had multiple ipsilateral cranial nerve palsies of the facial, vestibulocochlear, glossopharyngeal, vagal, and hypoglossal nerves, which were likely due to direct mass effect from the tumor. We propose two potential theories for the contralateral sensory loss being limited to the V2 distribution. First, many studies have shown that there is a somatotropic organization within the Gasserian ganglion, with ophthalmic fibers located anteromedial, mandibular located posterolateral, and maxillary fibers in the middle [<xref rid=\"REF17\" ref-type=\"bibr\">17</xref>-<xref rid=\"REF19\" ref-type=\"bibr\">19</xref>]. It is possible that the organization of these fibers persists, and slightly varies within the trigeminal nerve and nuclei. This would leave potential for preferential compression of fibers in a particular distribution. Second, it is possible that there was some element of compressive, torsional, or sheer force applied to the nerve as it enters foramen rotundum (Figure <xref ref-type=\"fig\" rid=\"FIG4\">4</xref>) [<xref rid=\"REF20\" ref-type=\"bibr\">20</xref>].</p><fig fig-type=\"figure\" id=\"FIG4\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><title>Anatomic illustration of trigeminal nerve course and sites of proposed nerve disruption at foramen rotundum or trigeminal ganglion.</title><p>Anatomic illustration of normal right trigeminal nerve course with emphasis placed on proposed compressive sites resulting in selective contralateral V2 sensory loss in the presented case: 1. (red asterisk) V2 division (maxillary nerve) as it enters foramen rotundum and 2. (dashed circle) preferential compression of maxillary nerve fibers in the trigeminal (Gasserian) ganglion. Reprinted from ‘Human Anatomy: Including Structure and Development and Practical Considerations’ [<xref rid=\"REF20\" ref-type=\"bibr\">20</xref>].</p></caption><graphic xlink:href=\"cureus-0012-00000009256-i04\"/></fig><p>Of note, the right-sided V2 sensory loss improved after tumor debulking and resultant improvement of her brainstem displacement and not after VPS and treatment of her intracranial hypertension, which did provide some improvement to her ipsilateral glossopharyngeal and hypoglossal palsies. In this light, the cause of the sensory loss was more likely due to direct compressive forces from the tumor and significant brainstem displacement rather than elevated intracranial pressure.</p></sec><sec sec-type=\"conclusions\"><title>Conclusions</title><p>We have presented the sixth reported case of a false localizing contralateral trigeminal sensory loss and the first such case to be restricted to the V2 distribution. The recognition of false localizing findings in the neurological exam is important to improve diagnostic accuracy. The mechanisms causing false localizing signs remain poorly understood.</p></sec></body><back><fn-group content-type=\"competing-interests\"><fn fn-type=\"COI-statement\"><p>The authors have declared that no competing interests exist.</p></fn></fn-group><fn-group content-type=\"other\"><title>Human Ethics</title><fn fn-type=\"other\"><p>Consent was obtained by all participants in this study</p></fn></fn-group><ref-list><title>References</title><ref id=\"REF1\"><label>1</label><element-citation publication-type=\"journal\"><article-title>False localizing signs. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Cureus</journal-id><journal-id journal-id-type=\"iso-abbrev\">Cureus</journal-id><journal-id journal-id-type=\"issn\">2168-8184</journal-id><journal-title-group><journal-title>Cureus</journal-title></journal-title-group><issn pub-type=\"epub\">2168-8184</issn><publisher><publisher-name>Cureus</publisher-name><publisher-loc>Palo Alto (CA)</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32821604</article-id><article-id pub-id-type=\"pmc\">PMC7431980</article-id><article-id pub-id-type=\"doi\">10.7759/cureus.9258</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Emergency Medicine</subject></subj-group><subj-group><subject>Pediatrics</subject></subj-group><subj-group><subject>Trauma</subject></subj-group></article-categories><title-group><article-title>Morbidity Patterns of Non-Traffic Unintentional Injuries Among the Pediatric Age Group Attending the Emergency Department at King Abdul-Aziz Medical City, Riyadh, Saudi Arabia</article-title></title-group><contrib-group><contrib contrib-type=\"editor\"><name><surname>Muacevic</surname><given-names>Alexander</given-names></name></contrib><contrib contrib-type=\"editor\"><name><surname>Adler</surname><given-names>John R</given-names></name></contrib></contrib-group><contrib-group><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>AlAteeq</surname><given-names>Mohammed A</given-names></name><xref ref-type=\"aff\" rid=\"aff-1\">1</xref><xref ref-type=\"aff\" rid=\"aff-2\">2</xref><xref ref-type=\"aff\" rid=\"aff-3\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Alsulayhim</surname><given-names>Abdullah K</given-names></name><xref ref-type=\"aff\" rid=\"aff-2\">2</xref><xref ref-type=\"aff\" rid=\"aff-3\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>AlHargan</surname><given-names>Fahad</given-names></name><xref ref-type=\"aff\" rid=\"aff-2\">2</xref><xref ref-type=\"aff\" rid=\"aff-3\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>AlSamaani</surname><given-names>Ibrahim S</given-names></name><xref ref-type=\"aff\" rid=\"aff-2\">2</xref><xref ref-type=\"aff\" rid=\"aff-3\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Alyousef</surname><given-names>Mohammed</given-names></name><xref ref-type=\"aff\" rid=\"aff-2\">2</xref><xref ref-type=\"aff\" rid=\"aff-3\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>AlDossari</surname><given-names>Abdulrahman</given-names></name><xref ref-type=\"aff\" rid=\"aff-2\">2</xref><xref ref-type=\"aff\" rid=\"aff-3\">3</xref></contrib></contrib-group><aff id=\"aff-1\">\n<label>1</label>\nFamily Medicine, Ministry of National Guard - Health Affairs, Riyadh, SAU </aff><aff id=\"aff-2\">\n<label>2</label>\nFamily Medicine, King Abdullah International Medical Research Center, Riyadh, SAU </aff><aff id=\"aff-3\">\n<label>3</label>\nFamily Medicine, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, SAU </aff><author-notes><corresp id=\"cor1\">\nMohammed A. AlAteeq <email>malateeq@hotmail.com</email></corresp></author-notes><pub-date date-type=\"pub\" publication-format=\"electronic\"><day>18</day><month>7</month><year>2020</year></pub-date><pub-date date-type=\"collection\" publication-format=\"electronic\"><month>7</month><year>2020</year></pub-date><volume>12</volume><issue>7</issue><elocation-id>e9258</elocation-id><history><date date-type=\"received\"><day>16</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>18</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020, AlAteeq et al.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>AlAteeq et al.</copyright-holder><license license-type=\"open-access\" xlink:href=\"http://creativecommons.org/licenses/by/3.0/\"><license-p>This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</license-p></license></permissions><self-uri xlink:href=\"https://www.cureus.com/articles/35030-morbidity-patterns-of-non-traffic-unintentional-injuries-among-the-pediatric-age-group-attending-the-emergency-department-at-king-abdul-aziz-medical-city-riyadh-saudi-arabia\">This article is available from https://www.cureus.com/articles/35030-morbidity-patterns-of-non-traffic-unintentional-injuries-among-the-pediatric-age-group-attending-the-emergency-department-at-king-abdul-aziz-medical-city-riyadh-saudi-arabia</self-uri><abstract><p>Objectives</p><p>The aim of this study is to measure the magnitude and describe morbidity pattern, management, and outcome of non-traffic unintentional injuries among a pediatric age group at a tertiary hospital in Riyadh, Saudi Arabia.</p><p>Materials and Methods</p><p>This is a retrospective descriptive cross-sectional study conducted at Emergency Department (ED) of King Abdul-Aziz Medical City, Riyadh, Saudi Arabia, including all pediatric patients aged 0 to 14 years who had non-traffic unintentional injuries and admitted to the ED from January 1, 2016, to December 31, 2017. The number of children included for the specified period was 491 patients.</p><p>Results</p><p>A total of 491 patients were included over the study period; the majority were males (64%). The most common injury types were fractures, dislocations, and subluxations (47.3%) followed by penetration injuries (21%) and burn injuries (17.5%). The most involved body site was the upper limbs (45.2%) followed by head and neck (24.2%) and lower limbs (17.3%). Fall was the leading mechanism of injuries (47.7%) followed by hot liquids and chemical exposure (14.5%). Most of the cases resulted in no significant disabilities (40%), 21.6% resulted in short-term disability, 24.2% had long-term disability, and 12.8% had permanent disabilities. There were six cases (1.2%) of mortality.</p><p>Conclusions</p><p>Non-traffic unintentional pediatric injuries are common with significant morbidity and complications, and most of them are preventable. More efforts are needed to increase public awareness and to implement preventive measures at households and public places.</p></abstract><kwd-group kwd-group-type=\"author\"><kwd>pediatric</kwd><kwd>trauma</kwd><kwd>disability</kwd></kwd-group></article-meta><notes><p content-type=\"disclaimer\">The content published in Cureus is the result of clinical experience and/or research by independent individuals or organizations. Cureus is not responsible for the scientific accuracy or reliability of data or conclusions published herein. All content published within Cureus is intended only for educational, research and reference purposes. Additionally, articles published within Cureus should not be deemed a suitable substitute for the advice of a qualified health care professional. Do not disregard or avoid professional medical advice due to content published within Cureus.</p></notes></front><body><sec sec-type=\"intro\"><title>Introduction</title><p>Non-traffic unintentional pediatric injuries, which are defined as injuries that occur under accidental circumstances, excluding motor traffic accidents, are common causes of morbidity, disability, and even mortality [<xref rid=\"REF1\" ref-type=\"bibr\">1</xref>]. The injuries are often caused by falls, burns, ingestion of foreign bodies, and sport-related activities. Although injuries can happen to any child due to their natural behavior, there are some factors that might contribute to the occurrence of these injuries, such as lack of parental supervision, unsafe home environment, and lack of safety measures at home.</p><p>In a Canadian study, researchers investigated records from the Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP) in Kingston, Ontario, and reviewed the child injury patterns for all cases presented from 1999 to 2002. In that study, head injuries were most often observed among infants, whereas open wounds and fractures were more frequently represented in the 60- to 83-month age group. Head/facial and lower extremity fractures were common among infants, whereas upper extremity fractures were predominantly seen in the older age groups [<xref rid=\"REF2\" ref-type=\"bibr\">2</xref>].</p><p>In “Deaths: Final Data for 2002” report, which documents U.S. deaths and death rates according to demographic and medical characteristics, it was found that 23 infants die among every 100,000 due to unintentional injuries, with 90% of them occurring at home [<xref rid=\"REF3\" ref-type=\"bibr\">3</xref>].</p><p>In a retrospective study, analysis of data for infants ≤ 12 months of age from the American National Electronic Surveillance System-All Injury Program for 2001-2004 was performed, which revealed that falls were the major cause of these injuries and that the most common diagnoses were from contusions followed by laceration, foreign bodies, hematoma, fractures, and puncture injuries [<xref rid=\"REF4\" ref-type=\"bibr\">4</xref>].</p><p>Another study analyzed the non-accidental injuries data collected at school from the National Canadian Pediatric Trauma Registry. Among 1,558 injured children, the majority were male (10-14 years old), and the leading causes were sports activities affecting the extremities (41.3%) and head and neck (39.2%) [<xref rid=\"REF5\" ref-type=\"bibr\">5</xref>]. A study in the United States analyzed the snow-sports injuries of children attending the emergency department (ED) for 14 years from 1996 to 2010 and reported the total number of injuries as 78,538, of which 77% were intracranial injuries [<xref rid=\"REF6\" ref-type=\"bibr\">6</xref>].</p><p>In Saudi Arabia, a retrospective study collected data on burn injuries among the pediatric age group attending the ED at King Abdul-Aziz Medical City (KAMC), Riyadh, Saudi Arabia. The study found the annual rate of injuries to be 4.9/1,000 patients; children aged one to three years accounted for most of the burn cases (48.6%), with scaled burns being the leading cause of injuries, especially from hot water and drinks, and 35% of cases occurred at home [<xref rid=\"REF7\" ref-type=\"bibr\">7</xref>].</p><p>In another study in Saudi Arabia that reviewed 361 cases of hand fractures, 46% of cases were found among the 13- to 18-year-old group, and the most frequent place of injuries for the one- to four-year-old age group was at home (81.3%). The most common causes for the 1- to 8-year-old group were door slams, falls at home for 9- to 12-year-old group, and sports and falls at home for 13- to 18-year-old group [<xref rid=\"REF8\" ref-type=\"bibr\">8</xref>].</p><p>To our knowledge, few studies have been conducted, and little is known about this topic in Saudi Arabia. This study aims to estimate the magnitude and describe morbidities, management, and outcome of non-traffic unintentional injuries among the children attending the ED at KAMC.</p></sec><sec sec-type=\"materials|methods\"><title>Materials and methods</title><p>This is a descriptive cross-sectional study conducted in the pediatric ED at KAMC.</p><p>KAMC is a tertiary hospital and advanced trauma center that serves the population of the national guard and their dependents in addition to patient referrals from all over the Kingdom. The pediatric ED at KAMC has a total of 21 certified consultants and specialists, treating around 40,000 cases per year.</p><p>The population of the study comprised children between 0 and 14 years old, both genders, attending and admitted to the pediatric ED at KAMC with unintentional and non-traffic injuries. The upper limit of the pediatric age group at KAMC is 14 years.</p><p>Data were collected for the period from January 1, 2016, to December 31, 2017. All cases attending the pediatric ED and admitted during the study period due to unintentional and non-traffic injuries were included, with no randomization.</p><p>This is a chart review study. After obtaining permission, data were collected from the electronic medical records system (BestCare) at KAMC using a predesigned data collection sheet. The sheet includes three sections: the first section for age and gender, the second section for the injury description (place and activity during injury, type, mechanism, and body part involved), and the third section for the injury management and clinical outcome.</p><p>Data were entered and analyzed using Statistical Package for the Social Sciences (SPSS) Version 20, (IBM Corp., Armonk, NY, USA). Descriptive statistics were performed in the form of frequencies and percentages for categorical variables, whereas mean and standard deviation were used for the description of continuous variables. We used a chi-square test to assess differences between categorical variables. Means were compared using an independent Student’s t-test (analysis of variance when applicable). Statistical significance is set to less than 0.05.</p><p>Approval of the study was obtained from King Abdullah International Medical Research Center. All the information of the patient was kept confidential and used only for research purposes. The data were stored in password-protected computers, and only research members had access to it.</p></sec><sec sec-type=\"results\"><title>Results</title><p>The total number of children who attended and were admitted due to non-traffic unintentional injuries was 491. A total of 579 injuries were seen among them, with 540 having body sites affected. Of them, 56% were less than 5 years old, 28.3% were 5-10 years old, and 15.7% were more than 10 years old. Males made up the majority of patients (64%).</p><p>Table <xref rid=\"TAB1\" ref-type=\"table\">1</xref> represents the different types of injuries, body sites involved, type and place of activity during the injury, and mechanism of injury. Fractures, dislocations, and subluxations were the most common injuries found in all age groups (47.3%) followed by the burn in the 0-5 years age group (24.7%), and cut, laceration, and open wound in the 5-10 and >10-year-old groups (25.9% and 29.9%, respectively).</p><table-wrap id=\"TAB1\" orientation=\"portrait\" position=\"float\"><label>Table 1</label><caption><title>Type, site, activity, place, and mechanism of non-traffic unintentional injuries among children attending the emergency department, King Abdul-Aziz Medical City, 2017</title></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr style=\"background-color:#ccc\"><td colspan=\"2\" rowspan=\"1\"> </td><td rowspan=\"1\" colspan=\"1\">No</td><td rowspan=\"1\" colspan=\"1\">%</td></tr><tr><td rowspan=\"7\" colspan=\"1\">Type of injury</td><td rowspan=\"1\" colspan=\"1\">Fracture/dislocation/subluxation</td><td rowspan=\"1\" colspan=\"1\">232</td><td rowspan=\"1\" colspan=\"1\">47.3</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Cut/laceration/open wound</td><td rowspan=\"1\" colspan=\"1\">103</td><td rowspan=\"1\" colspan=\"1\">21.0</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Burn</td><td rowspan=\"1\" colspan=\"1\">86</td><td rowspan=\"1\" colspan=\"1\">17.5</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Brain injury/intracranial bleeding</td><td rowspan=\"1\" colspan=\"1\">63</td><td rowspan=\"1\" colspan=\"1\">12.8</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Near drowning</td><td rowspan=\"1\" colspan=\"1\">20</td><td rowspan=\"1\" colspan=\"1\">4.1</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Others</td><td rowspan=\"1\" colspan=\"1\">75</td><td rowspan=\"1\" colspan=\"1\">15.2</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Total</td><td rowspan=\"1\" colspan=\"1\">579</td><td rowspan=\"1\" colspan=\"1\">117.9</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"8\" colspan=\"1\">Site of injury</td><td rowspan=\"1\" colspan=\"1\">Upper limbs</td><td rowspan=\"1\" colspan=\"1\">222</td><td rowspan=\"1\" colspan=\"1\">45.2</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Head and neck</td><td rowspan=\"1\" colspan=\"1\">119</td><td rowspan=\"1\" colspan=\"1\">24.2</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Lower limbs</td><td rowspan=\"1\" colspan=\"1\">85</td><td rowspan=\"1\" colspan=\"1\">17.3</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Multiple or whole body</td><td rowspan=\"1\" colspan=\"1\">65</td><td rowspan=\"1\" colspan=\"1\">13.2</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Chest</td><td rowspan=\"1\" colspan=\"1\">17</td><td rowspan=\"1\" colspan=\"1\">3.5</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Abdomen</td><td rowspan=\"1\" colspan=\"1\">12</td><td rowspan=\"1\" colspan=\"1\">2.4</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Others</td><td rowspan=\"1\" colspan=\"1\">20</td><td rowspan=\"1\" colspan=\"1\">4.1</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Total</td><td rowspan=\"1\" colspan=\"1\">540</td><td rowspan=\"1\" colspan=\"1\">109.9</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"5\" colspan=\"1\">Activity at time of injury</td><td rowspan=\"1\" colspan=\"1\">Leisure</td><td rowspan=\"1\" colspan=\"1\">89</td><td rowspan=\"1\" colspan=\"1\">18.1</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Daily activities</td><td rowspan=\"1\" colspan=\"1\">111</td><td rowspan=\"1\" colspan=\"1\">22.6</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Others</td><td rowspan=\"1\" colspan=\"1\">5</td><td rowspan=\"1\" colspan=\"1\">1.0</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Unknown</td><td rowspan=\"1\" colspan=\"1\">286</td><td rowspan=\"1\" colspan=\"1\">58.2</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Total</td><td rowspan=\"1\" colspan=\"1\">491</td><td rowspan=\"1\" colspan=\"1\">100.0</td></tr><tr><td rowspan=\"6\" colspan=\"1\">Place of injury</td><td rowspan=\"1\" colspan=\"1\">Home</td><td rowspan=\"1\" colspan=\"1\">100</td><td rowspan=\"1\" colspan=\"1\">20.4</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Others</td><td rowspan=\"1\" colspan=\"1\">23</td><td rowspan=\"1\" colspan=\"1\">4.7</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Play area</td><td rowspan=\"1\" colspan=\"1\">19</td><td rowspan=\"1\" colspan=\"1\">3.9</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Chalet/resort</td><td rowspan=\"1\" colspan=\"1\">17</td><td rowspan=\"1\" colspan=\"1\">3.5</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Unknown</td><td rowspan=\"1\" colspan=\"1\">332</td><td rowspan=\"1\" colspan=\"1\">67.6</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Total</td><td rowspan=\"1\" colspan=\"1\">491</td><td rowspan=\"1\" colspan=\"1\">100.0</td></tr></tbody></table></table-wrap><p>Upper limbs were the most injured parts of the body (45.2%) followed by head and neck (24.2%) and lower limbs (17.3%). Fall was the leading cause of injuries in all age groups followed by exposure to hot liquids and chemicals in the zero- to five-year-old group, whereas sharp objects were the second most common mechanism of injury in children above five years old (Figure <xref ref-type=\"fig\" rid=\"FIG1\">1</xref>).</p><fig fig-type=\"figure\" id=\"FIG1\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><title>Age versus mechanism of non-traffic unintentional injuries among children attending the emergency department, King Abdul-Aziz Medical City, 2017</title></caption><graphic xlink:href=\"cureus-0012-00000009258-i01\"/></fig><p>In the sub-analysis of involved body site according to age, head and neck was the most common site of injury in patients from 0 to 10 years, whereas lower limbs were the most common in those above 10 years of age (Table <xref rid=\"TAB2\" ref-type=\"table\">2</xref>).</p><table-wrap id=\"TAB2\" orientation=\"portrait\" position=\"float\"><label>Table 2</label><caption><title>Commonest types of non-traffic unintentional injuries and body site involved among children attending the emergency department, King Abdul-Aziz Medical City, 2017</title></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr style=\"background-color:#ccc\"><td rowspan=\"2\" colspan=\"1\">Age group</td><td colspan=\"4\" rowspan=\"1\">Type of injuries</td><td colspan=\"3\" rowspan=\"1\">Body site involved</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Cut/laceration/open wound</td><td rowspan=\"1\" colspan=\"1\">Fracture/dislocation/subluxation</td><td rowspan=\"1\" colspan=\"1\">Burn</td><td rowspan=\"1\" colspan=\"1\">Brain injury/bleeding</td><td rowspan=\"1\" colspan=\"1\">Head and neck</td><td rowspan=\"1\" colspan=\"1\">Multiple or whole body</td><td rowspan=\"1\" colspan=\"1\">Lower limbs</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"2\" colspan=\"1\">Less than 5 years</td><td rowspan=\"1\" colspan=\"1\">44</td><td rowspan=\"1\" colspan=\"1\">112</td><td rowspan=\"1\" colspan=\"1\">68</td><td rowspan=\"1\" colspan=\"1\">48</td><td rowspan=\"1\" colspan=\"1\">80</td><td rowspan=\"1\" colspan=\"1\">47</td><td rowspan=\"1\" colspan=\"1\">36</td></tr><tr><td rowspan=\"1\" colspan=\"1\">16.0%</td><td rowspan=\"1\" colspan=\"1\">40.7%</td><td rowspan=\"1\" colspan=\"1\">24.7%</td><td rowspan=\"1\" colspan=\"1\">17.5%</td><td rowspan=\"1\" colspan=\"1\">29.1%</td><td rowspan=\"1\" colspan=\"1\">17.1%</td><td rowspan=\"1\" colspan=\"1\">13.1%</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"2\" colspan=\"1\">5 to 10 years</td><td rowspan=\"1\" colspan=\"1\">36</td><td rowspan=\"1\" colspan=\"1\">75</td><td rowspan=\"1\" colspan=\"1\">13</td><td rowspan=\"1\" colspan=\"1\">9</td><td rowspan=\"1\" colspan=\"1\">26</td><td rowspan=\"1\" colspan=\"1\">12</td><td rowspan=\"1\" colspan=\"1\">25</td></tr><tr><td rowspan=\"1\" colspan=\"1\">25.9%</td><td rowspan=\"1\" colspan=\"1\">54.0%</td><td rowspan=\"1\" colspan=\"1\">9.4%</td><td rowspan=\"1\" colspan=\"1\">6.5%</td><td rowspan=\"1\" colspan=\"1\">18.7%</td><td rowspan=\"1\" colspan=\"1\">8.6%</td><td rowspan=\"1\" colspan=\"1\">18.0%</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"2\" colspan=\"1\">Above 10 years</td><td rowspan=\"1\" colspan=\"1\">23</td><td rowspan=\"1\" colspan=\"1\">45</td><td rowspan=\"1\" colspan=\"1\">5</td><td rowspan=\"1\" colspan=\"1\">6</td><td rowspan=\"1\" colspan=\"1\">13</td><td rowspan=\"1\" colspan=\"1\">6</td><td rowspan=\"1\" colspan=\"1\">24</td></tr><tr><td rowspan=\"1\" colspan=\"1\">29.9%</td><td rowspan=\"1\" colspan=\"1\">58.4%</td><td rowspan=\"1\" colspan=\"1\">6.5%</td><td rowspan=\"1\" colspan=\"1\">7.8%</td><td rowspan=\"1\" colspan=\"1\">16.9%</td><td rowspan=\"1\" colspan=\"1\">7.8%</td><td rowspan=\"1\" colspan=\"1\">31.2%</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"2\" colspan=\"1\">Total</td><td rowspan=\"1\" colspan=\"1\">103</td><td rowspan=\"1\" colspan=\"1\">232</td><td rowspan=\"1\" colspan=\"1\">86</td><td rowspan=\"1\" colspan=\"1\">63</td><td rowspan=\"1\" colspan=\"1\">119</td><td rowspan=\"1\" colspan=\"1\">65</td><td rowspan=\"1\" colspan=\"1\">85</td></tr><tr><td rowspan=\"1\" colspan=\"1\">21.0%</td><td rowspan=\"1\" colspan=\"1\">47.3%</td><td rowspan=\"1\" colspan=\"1\">17.5%</td><td rowspan=\"1\" colspan=\"1\">12.8%</td><td rowspan=\"1\" colspan=\"1\">24.2%</td><td rowspan=\"1\" colspan=\"1\">13.2%</td><td rowspan=\"1\" colspan=\"1\">17.3%</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">p-Value</td><td rowspan=\"1\" colspan=\"1\">0.007</td><td rowspan=\"1\" colspan=\"1\">0.004</td><td rowspan=\"1\" colspan=\"1\">0.00</td><td rowspan=\"1\" colspan=\"1\">0.002</td><td rowspan=\"1\" colspan=\"1\">0.017</td><td rowspan=\"1\" colspan=\"1\">0.017</td><td rowspan=\"1\" colspan=\"1\">0.001</td></tr></tbody></table></table-wrap><p>In most cases, the type of activity during an injury was not reported. When reported, daily activity was the most common across all age groups and in the age group below five years (24.7%), whereas other age groups got injured mostly during leisure times (22.3% and 22.1%, respectively). In most cases, the place of injury was not reported. When reported, the home was the most commonplace of injury across all age groups (Tables <xref rid=\"TAB1\" ref-type=\"table\">1</xref>, <xref rid=\"TAB3\" ref-type=\"table\">3</xref>).</p><table-wrap id=\"TAB3\" orientation=\"portrait\" position=\"float\"><label>Table 3</label><caption><title>Age correlation with type and place of activity associated with non-traffic unintentional injuries among children attending the emergency department, King Abdul-Aziz Medical City, 2017</title></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr style=\"background-color:#ccc\"><td rowspan=\"2\" colspan=\"1\">\nAge group\n</td><td colspan=\"4\" rowspan=\"1\">\nType of activity\n</td><td colspan=\"5\" rowspan=\"1\">\nPlace of activity\n</td></tr><tr><td rowspan=\"1\" colspan=\"1\">\nLeisure\n</td><td rowspan=\"1\" colspan=\"1\">\nDaily activities\n</td><td rowspan=\"1\" colspan=\"1\">\nOthers\n</td><td rowspan=\"1\" colspan=\"1\">\nUnknown\n</td><td rowspan=\"1\" colspan=\"1\">\nHome\n</td><td rowspan=\"1\" colspan=\"1\">\nChalet/resort\n</td><td rowspan=\"1\" colspan=\"1\">\nPlay area\n</td><td rowspan=\"1\" colspan=\"1\">\nOthers\n</td><td rowspan=\"1\" colspan=\"1\">\nUnknown\n</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"2\" colspan=\"1\">\nLess than 5 years\n</td><td rowspan=\"1\" colspan=\"1\">\n41\n</td><td rowspan=\"1\" colspan=\"1\">\n68\n</td><td rowspan=\"1\" colspan=\"1\">\n0\n</td><td rowspan=\"1\" colspan=\"1\">\n166\n</td><td rowspan=\"1\" colspan=\"1\">\n69\n</td><td rowspan=\"1\" colspan=\"1\">\n11\n</td><td rowspan=\"1\" colspan=\"1\">\n5\n</td><td rowspan=\"1\" colspan=\"1\">\n6\n</td><td rowspan=\"1\" colspan=\"1\">\n184\n</td></tr><tr><td rowspan=\"1\" colspan=\"1\">\n14.9%\n</td><td rowspan=\"1\" colspan=\"1\">\n24.7%\n</td><td rowspan=\"1\" colspan=\"1\">\n0.0%\n</td><td rowspan=\"1\" colspan=\"1\">\n60.4%\n</td><td rowspan=\"1\" colspan=\"1\">\n25.1%\n</td><td rowspan=\"1\" colspan=\"1\">\n4.0%\n</td><td rowspan=\"1\" colspan=\"1\">\n1.8%\n</td><td rowspan=\"1\" colspan=\"1\">\n2.2%\n</td><td rowspan=\"1\" colspan=\"1\">\n66.9%\n</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"2\" colspan=\"1\">\n5 to 10 years\n</td><td rowspan=\"1\" colspan=\"1\">\n31\n</td><td rowspan=\"1\" colspan=\"1\">\n29\n</td><td rowspan=\"1\" colspan=\"1\">\n5\n</td><td rowspan=\"1\" colspan=\"1\">\n74\n</td><td rowspan=\"1\" colspan=\"1\">\n25\n</td><td rowspan=\"1\" colspan=\"1\">\n5\n</td><td rowspan=\"1\" colspan=\"1\">\n8\n</td><td rowspan=\"1\" colspan=\"1\">\n8\n</td><td rowspan=\"1\" colspan=\"1\">\n93\n</td></tr><tr><td rowspan=\"1\" colspan=\"1\">\n22.3%\n</td><td rowspan=\"1\" colspan=\"1\">\n20.9%\n</td><td rowspan=\"1\" colspan=\"1\">\n3.6%\n</td><td rowspan=\"1\" colspan=\"1\">\n53.2%\n</td><td rowspan=\"1\" colspan=\"1\">\n18.0%\n</td><td rowspan=\"1\" colspan=\"1\">\n3.6%\n</td><td rowspan=\"1\" colspan=\"1\">\n5.8%\n</td><td rowspan=\"1\" colspan=\"1\">\n5.8%\n</td><td rowspan=\"1\" colspan=\"1\">\n66.9%\n</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"2\" colspan=\"1\">\nAbove 10 years\n</td><td rowspan=\"1\" colspan=\"1\">\n17\n</td><td rowspan=\"1\" colspan=\"1\">\n14\n</td><td rowspan=\"1\" colspan=\"1\">\n0\n</td><td rowspan=\"1\" colspan=\"1\">\n46\n</td><td rowspan=\"1\" colspan=\"1\">\n6\n</td><td rowspan=\"1\" colspan=\"1\">\n1\n</td><td rowspan=\"1\" colspan=\"1\">\n6\n</td><td rowspan=\"1\" colspan=\"1\">\n9\n</td><td rowspan=\"1\" colspan=\"1\">\n55\n</td></tr><tr><td rowspan=\"1\" colspan=\"1\">\n22.1%\n</td><td rowspan=\"1\" colspan=\"1\">\n18.2%\n</td><td rowspan=\"1\" colspan=\"1\">\n0.0%\n</td><td rowspan=\"1\" colspan=\"1\">\n59.7%\n</td><td rowspan=\"1\" colspan=\"1\">\n7.8%\n</td><td rowspan=\"1\" colspan=\"1\">\n1.3%\n</td><td rowspan=\"1\" colspan=\"1\">\n7.8%\n</td><td rowspan=\"1\" colspan=\"1\">\n11.7%\n</td><td rowspan=\"1\" colspan=\"1\">\n71.4%\n</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"2\" colspan=\"1\">\nTotal\n</td><td rowspan=\"1\" colspan=\"1\">\n89\n</td><td rowspan=\"1\" colspan=\"1\">\n111\n</td><td rowspan=\"1\" colspan=\"1\">\n5\n</td><td rowspan=\"1\" colspan=\"1\">\n286\n</td><td rowspan=\"1\" colspan=\"1\">\n100\n</td><td rowspan=\"1\" colspan=\"1\">\n17\n</td><td rowspan=\"1\" colspan=\"1\">\n19\n</td><td rowspan=\"1\" colspan=\"1\">\n23\n</td><td rowspan=\"1\" colspan=\"1\">\n332\n</td></tr><tr><td rowspan=\"1\" colspan=\"1\">\n18.1%\n</td><td rowspan=\"1\" colspan=\"1\">\n22.6%\n</td><td rowspan=\"1\" colspan=\"1\">\n1.0%\n</td><td rowspan=\"1\" colspan=\"1\">\n58.2%\n</td><td rowspan=\"1\" colspan=\"1\">\n20.4%\n</td><td rowspan=\"1\" colspan=\"1\">\n3.5%\n</td><td rowspan=\"1\" colspan=\"1\">\n3.9%\n</td><td rowspan=\"1\" colspan=\"1\">\n4.7%\n</td><td rowspan=\"1\" colspan=\"1\">\n67.6%\n</td></tr></tbody></table></table-wrap><p>Most patients who attended the ED were admitted to wards, intensive care units (ICUs), or burn units (89.2%), whereas 10.6% were admitted for emergency surgery; one patient died on arrival to the ED (0.2%) (Table <xref rid=\"TAB4\" ref-type=\"table\">4</xref>). Six cases of death were reported; one died on arrival to the ED and five after admission.</p><table-wrap id=\"TAB4\" orientation=\"portrait\" position=\"float\"><label>Table 4</label><caption><title>Management and outcome of non-traffic unintentional injuries among children attending the ED, King Abdul-Aziz Medical City, 2017</title><p>ED, emergency department</p></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr style=\"background-color:#ccc\"><td colspan=\"2\" rowspan=\"1\">\n \n</td><td rowspan=\"1\" colspan=\"1\">\nNo\n</td><td rowspan=\"1\" colspan=\"1\">\n%\n</td></tr><tr><td rowspan=\"4\" colspan=\"1\">\nManagement\n</td><td rowspan=\"1\" colspan=\"1\">\nAdmitted to ward/burn unit/intensive care unit\n</td><td rowspan=\"1\" colspan=\"1\">\n438\n</td><td rowspan=\"1\" colspan=\"1\">\n89.2\n</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">\nAdmitted for emergency surgery\n</td><td rowspan=\"1\" colspan=\"1\">\n52\n</td><td rowspan=\"1\" colspan=\"1\">\n10.6\n</td></tr><tr><td rowspan=\"1\" colspan=\"1\">\nDied on arrival to ED\n</td><td rowspan=\"1\" colspan=\"1\">\n1\n</td><td rowspan=\"1\" colspan=\"1\">\n0.2\n</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">\nTotal\n</td><td rowspan=\"1\" colspan=\"1\">\n491\n</td><td rowspan=\"1\" colspan=\"1\">\n100.0\n</td></tr><tr><td rowspan=\"6\" colspan=\"1\">\nOutcome\n</td><td rowspan=\"1\" colspan=\"1\">\nNo significant disability\n</td><td rowspan=\"1\" colspan=\"1\">\n197\n</td><td rowspan=\"1\" colspan=\"1\">\n40.1\n</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">\nShort-term disability (<6 weeks)\n</td><td rowspan=\"1\" colspan=\"1\">\n106\n</td><td rowspan=\"1\" colspan=\"1\">\n21.6\n</td></tr><tr><td rowspan=\"1\" colspan=\"1\">\nLong-term disability (>6 weeks)\n</td><td rowspan=\"1\" colspan=\"1\">\n119\n</td><td rowspan=\"1\" colspan=\"1\">\n24.2\n</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">\nPermanent disability\n</td><td rowspan=\"1\" colspan=\"1\">\n63\n</td><td rowspan=\"1\" colspan=\"1\">\n12.8\n</td></tr><tr><td rowspan=\"1\" colspan=\"1\">\nDeath\n</td><td rowspan=\"1\" colspan=\"1\">\n6\n</td><td rowspan=\"1\" colspan=\"1\">\n1.2\n</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">\nTotal\n</td><td rowspan=\"1\" colspan=\"1\">\n491\n</td><td rowspan=\"1\" colspan=\"1\">\n100.0\n</td></tr></tbody></table></table-wrap><p>As a complication of injury, 40% of patients had no significant disability, 24.2% had long-term disability, 21.6% had short-term disability, 12.8% of injuries resulted in permanent disability, and 1.2% died because of injury (Table <xref rid=\"TAB4\" ref-type=\"table\">4</xref>).</p><p>The outcome of injuries according to different age groups is represented in Figure <xref ref-type=\"fig\" rid=\"FIG2\">2</xref>.</p><fig fig-type=\"figure\" id=\"FIG2\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><title>Age versus outcome of non-traffic unintentional injuries among children attending the emergency department, King Abdul-Aziz Medical City, 2017</title></caption><graphic xlink:href=\"cureus-0012-00000009258-i02\"/></fig><p>Tables <xref rid=\"TAB5\" ref-type=\"table\">5</xref> represents the management and outcome of the top four types of injuries.</p><table-wrap id=\"TAB5\" orientation=\"portrait\" position=\"float\"><label>Table 5</label><caption><title>Management and outcome of the top four types of non-traffic unintentional injuries among children attending ED, King Abdul-Aziz Medical City, 2017</title><p>ICU, intensive care unit; ED, emergency department</p></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr style=\"background-color:#ccc\"><td rowspan=\"2\" colspan=\"1\"> Type of injury</td><td colspan=\"3\" rowspan=\"1\">Type of management</td><td colspan=\"5\" rowspan=\"1\">Outcome of injury</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Admitted to ward/burn unit/ICU</td><td rowspan=\"1\" colspan=\"1\">Admitted for emergency surgery</td><td rowspan=\"1\" colspan=\"1\">Died in ED</td><td rowspan=\"1\" colspan=\"1\">No significant disability</td><td rowspan=\"1\" colspan=\"1\">Short-term disability (<6 weeks)</td><td rowspan=\"1\" colspan=\"1\">Long-term disability (>6 weeks)</td><td rowspan=\"1\" colspan=\"1\">Permanent disability</td><td rowspan=\"1\" colspan=\"1\">Death</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"2\" colspan=\"1\">Fracture, dislocation, subluxation  </td><td rowspan=\"1\" colspan=\"1\">199</td><td rowspan=\"1\" colspan=\"1\">33</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">62</td><td rowspan=\"1\" colspan=\"1\">64</td><td rowspan=\"1\" colspan=\"1\">102</td><td rowspan=\"1\" colspan=\"1\">4</td><td rowspan=\"1\" colspan=\"1\">0</td></tr><tr><td rowspan=\"1\" colspan=\"1\">85.7%</td><td rowspan=\"1\" colspan=\"1\">14.2%</td><td rowspan=\"1\" colspan=\"1\">0.0%</td><td rowspan=\"1\" colspan=\"1\">26.7%</td><td rowspan=\"1\" colspan=\"1\">27.6%</td><td rowspan=\"1\" colspan=\"1\">44.0%</td><td rowspan=\"1\" colspan=\"1\">1.7%</td><td rowspan=\"1\" colspan=\"1\">0.0%</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"2\" colspan=\"1\">Cut, laceration, open wound  </td><td rowspan=\"1\" colspan=\"1\">87</td><td rowspan=\"1\" colspan=\"1\">16</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">45</td><td rowspan=\"1\" colspan=\"1\">35</td><td rowspan=\"1\" colspan=\"1\">17</td><td rowspan=\"1\" colspan=\"1\">6</td><td rowspan=\"1\" colspan=\"1\">0</td></tr><tr><td rowspan=\"1\" colspan=\"1\">84.5%</td><td rowspan=\"1\" colspan=\"1\">15.5%</td><td rowspan=\"1\" colspan=\"1\">0.0%</td><td rowspan=\"1\" colspan=\"1\">43.7%</td><td rowspan=\"1\" colspan=\"1\">34.0%</td><td rowspan=\"1\" colspan=\"1\">16.5%</td><td rowspan=\"1\" colspan=\"1\">5.8%</td><td rowspan=\"1\" colspan=\"1\">0.0%</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"2\" colspan=\"1\">Burns  </td><td rowspan=\"1\" colspan=\"1\">86</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">31</td><td rowspan=\"1\" colspan=\"1\">2</td><td rowspan=\"1\" colspan=\"1\">0</td><td rowspan=\"1\" colspan=\"1\">52</td><td rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td rowspan=\"1\" colspan=\"1\">100%</td><td rowspan=\"1\" colspan=\"1\">0.0%</td><td rowspan=\"1\" colspan=\"1\">0.0%</td><td rowspan=\"1\" colspan=\"1\">36.0%</td><td rowspan=\"1\" colspan=\"1\">2.3%</td><td rowspan=\"1\" colspan=\"1\">0.0%</td><td rowspan=\"1\" colspan=\"1\">60.5%</td><td rowspan=\"1\" colspan=\"1\">1.2%</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"2\" colspan=\"1\">Brain injury/bleeding  </td><td rowspan=\"1\" colspan=\"1\">60</td><td rowspan=\"1\" colspan=\"1\">2%</td><td rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\">34</td><td rowspan=\"1\" colspan=\"1\">12</td><td rowspan=\"1\" colspan=\"1\">9</td><td rowspan=\"1\" colspan=\"1\">3</td><td rowspan=\"1\" colspan=\"1\">5</td></tr><tr><td rowspan=\"1\" colspan=\"1\">95.2%</td><td rowspan=\"1\" colspan=\"1\">3.2</td><td rowspan=\"1\" colspan=\"1\">1.6%</td><td rowspan=\"1\" colspan=\"1\">54%</td><td rowspan=\"1\" colspan=\"1\">19.0%</td><td rowspan=\"1\" colspan=\"1\">14.3%</td><td rowspan=\"1\" colspan=\"1\">4.8%</td><td rowspan=\"1\" colspan=\"1\">7.9%</td></tr></tbody></table></table-wrap></sec><sec sec-type=\"discussion\"><title>Discussion</title><p>Non-traffic unintentional injuries are common and considered a major cause of morbidity and mortality among children. This study aimed to describe the morbidity, management, and outcome of non-traffic unintentional injuries admitted to one of the largest pediatric ED in the region.</p><p>The reported rate of admission of children due to non-traffic unintentional injuries in this study was found to be 491 cases in two years, with almost five admissions every week. If this number is combined with the number of admissions due to traffic road accidents and child abuse and neglect, this represents a considerably high number that needs special attention.</p><p>Previous similar local studies addressed either specific types of injury, such as fractures or burns, or all types of injuries with no specification. For example, in a local study done by Alnasser et al., among the pediatric trauma cases attending the same center for the same age group, 50% of blunt trauma was attributable to road traffic accidents. Although not mentioned clearly, this number included all cases either admitted to a ward/ICU/emergency surgery or managed in ED and discharged home [<xref rid=\"REF9\" ref-type=\"bibr\">9</xref>].</p><p>In a study conducted by Alharthy et al. in the same hospital [<xref rid=\"REF7\" ref-type=\"bibr\">7</xref>], the rate of burn injuries was 4.9/1,000/year compared to this study, which reported an incidence rate of 2.15/1,000/year. The difference could be attributed to our study reported the admitted cases only.</p><p>Likewise, in another hospital in Riyadh, Saudi Arabia, the reported hand fracture cases for patients 18 years of age and below were almost 60 cases per year [<xref rid=\"REF8\" ref-type=\"bibr\">8</xref>]. In our study, fractures were not reported alone but within musculoskeletal injuries that include fractures, dislocations, or subluxations, and the number of cases was 232, thus being the most common type of injury in all age groups.</p><p>Internationally, and for comparison, 77,500 cases were reported in Iran from all over the country for children below the age of seven years, of which 99.7% reported as unintentional, and 56.6% of them were boys [<xref rid=\"REF10\" ref-type=\"bibr\">10</xref>]. The reported rate of unintentional injuries in Kampala, Uganda, in a tertiary hospital was 556 cases in five months, including injuries resulting from road traffic accidents [<xref rid=\"REF11\" ref-type=\"bibr\">11</xref>].</p><p>Most cases in this study were boys (64%), similar to what was reported in other studies [<xref rid=\"REF8\" ref-type=\"bibr\">8</xref>-<xref rid=\"REF10\" ref-type=\"bibr\">10</xref>]. This is expected since boys typically participate in more outdoor activities and are involved in more risky behaviors. Similar figures were reported by Alnasser et al., Esfanjani et al., and Alnasser et al., with boys constituting 68%, 56.6%, and 74.3% of trauma cases, respectively [<xref rid=\"REF9\" ref-type=\"bibr\">9</xref>-<xref rid=\"REF10\" ref-type=\"bibr\">10</xref>,<xref rid=\"REF12\" ref-type=\"bibr\">12</xref>].</p><p>More than half of the cases were less than five years old. This was also reported by Alharthy et al. [<xref rid=\"REF7\" ref-type=\"bibr\">7</xref>], where the majority of burn cases were in children less than three years old, compared to study from Iran which reported That 70% of cases were of children below five years old [<xref rid=\"REF10\" ref-type=\"bibr\">10</xref>]. This is a worrying issue since injuries in this age group of children most likely are a result of poor home safety or lack of adult supervision.</p><p>When reviewing the type of injuries in this study, one in four children aged five years or below had burn injury, which was the second most common type after musculoskeletal injuries, which was the most common across all age groups. Similar findings were found by Alharthy et al., who reported 48.6% of burn injuries occurred in children between one and three years of age [<xref rid=\"REF7\" ref-type=\"bibr\">7</xref>].</p><p>Data extracted from the Hospital-Based Trauma Registry at KAMC, and reported by Alghnam et al. showed that falls were the common type of non-traffic injuries and were more common among boys [<xref rid=\"REF13\" ref-type=\"bibr\">13</xref>]. Likewise, in an old local study, the most common type of injuries for children below the age of four years was fall [<xref rid=\"REF14\" ref-type=\"bibr\">14</xref>]. This is in agreement with our findings, where fall was the most common mechanism of injuries.</p><p>Comparing to other regional studies, one study was conducted in AlAin, United Arab Emirates (UAE), which reported that the most common types of injury among children less than five years of age were falls (14.1%), blunt trauma (4.4%), and burns (4.2%) [<xref rid=\"REF15\" ref-type=\"bibr\">15</xref>].</p><p>An interesting study was conducted in India to review domestic accidents, in which those authors performed 796 household surveys. Families reported the occurrence of accidents at home or immediate surroundings for children aged 14 years and below. This is similar to the findings of our study. The rate of domestic accidents was 2.7%, and the most commonly reported accident was fall, which was associated with younger age and female gender [<xref rid=\"REF16\" ref-type=\"bibr\">16</xref>]. This is different from findings of our study, in which injuries occurred more among boys. A study using a similar methodology was conducted in Bangladesh and showed that the most common type of injury was drowning and falls. Similar to our study findings, this study found injuries higher in boys and that homes were the most common site of injury [<xref rid=\"REF17\" ref-type=\"bibr\">17</xref>].</p><p>Considering the body part involved in the injury, upper limbs were the most common site of injury by fall for all ages (45%). With sub-analysis, 29.1% of injuries in children below five years of age involved the head and neck. This is again worrying since serious complications can be expected when the head and neck are injured. Alnasser et al. found that the involved body part was reported only for penetration type injury and that the most common body sites involved were the extremities followed by the face and orbit [<xref rid=\"REF9\" ref-type=\"bibr\">9</xref>].</p><p>For most of the cases, the type of activity during the injury and the place at which the injury occurred were not reported (58.2 and 67.6%, respectively). This could be either due to incomplete information gathering during the evaluation process or due to lack of documentation by health care staff. This needs to be noticed and corrected during the process of documentation of cases by ED staff since it is important for the management of cases and prevention of future injuries. In the reported cases of activity during the trauma, most of the children aged five years and above were engaged in leisure activities. This is expected since children in this age group mobilize more and are exposed to more blunt injuries compared to younger kids.</p><p>When the place of injury was reported, the home was the most commonplace in all age groups. Likewise, data form Alharthy et al. shows that most burn injuries (35%) are typically sustained at the home compared with 2.7% that occurred outside the home. [<xref rid=\"REF7\" ref-type=\"bibr\">7</xref>]. This implies a need for greater responsibility for parents and guardians in the prevention of childhood injuries. Home safety for children is not a well-covered issue neither by health care workers nor by other related parties in this community. More has to be done to raise public awareness and to implement trauma preventive measures at homes and public places.</p><p>It is reassuring that the vast majority of cases did not need emergency surgery, and this might reflect the low level of seriousness of injuries. Around 10% of cases were in need of emergency surgery, which was performed mainly for cases of musculoskeletal and open wound injuries. Unfortunately, none of the local studies reviewed reported the type of management performed for the reported injuries.</p><p>The majority of affected children in this study were under five years of age, and injuries occurred at home, which indicates the importance of practicing home safety for younger children; prevention efforts should be prioritized to ensure home safety for younger kids. Mack et al. suggested using the Health Impact Pyramid model to implement preventive measures for unintentional injuries [<xref rid=\"REF18\" ref-type=\"bibr\">18</xref>]. In this approach, injury prevention would be enhanced by “multilevel modifications of individual’s behavior, public policies, laws and enforcement, environment, consumer products, and engineering standards” [<xref rid=\"REF19\" ref-type=\"bibr\">19</xref>].</p><p>One of the limitations of this study is that it did not include the injury cases that were managed at the ED and discharged home. In addition, there was a lack of documentation for most of the cases for the place and type of activity when the injury occurred. The third limitation is the age range where the upper limit was set to be 14 years, whereas the upper limit internationally is 18 years.</p></sec><sec sec-type=\"conclusions\"><title>Conclusions</title><p>Non-traffic unintentional pediatric injuries are common with significant morbidity and complication, and most of them are preventable. More efforts are needed at family and community levels to increase public awareness and to implement prevention measures at households and public places.</p></sec></body><back><fn-group content-type=\"competing-interests\"><fn fn-type=\"COI-statement\"><p>The authors have declared that no competing interests exist.</p></fn></fn-group><fn-group content-type=\"other\"><title>Human Ethics</title><fn fn-type=\"other\"><p>Consent was obtained by all participants in this study. King Abdullah International Medical Research Center (KAIMRC) issued approval SP17/175/R. Approval of the study was obtained from King Abdullah International Medical Research Center (KAIMRC). All the information of the patient was kept confidential and used only for research purposes. The data were stored in password-protected computers, and only research members have access to it.</p></fn></fn-group><fn-group content-type=\"other\"><title>Animal Ethics</title><fn fn-type=\"other\"><p><bold>Animal subjects:</bold> All authors have confirmed that this study did not involve animal subjects or tissue.</p></fn></fn-group><ack><p>The research team would like to acknowledge Dr. Imad Yaseen for his valuable assistance in the statistical analysis and Dr. Afaf Moukaddem for her contributions and advice throughout the study.</p></ack><ref-list><title>References</title><ref id=\"REF1\"><label>1</label><element-citation publication-type=\"journal\"><article-title>Unintentional injuries in pediatrics</article-title><source>Pediatr Rev</source><person-group><name><surname>Karen</surname><given-names>Judy</given-names></name></person-group><fpage>431</fpage><lpage>439</lpage><volume>10</volume><year>2011</year></element-citation></ref><ref id=\"REF2\"><label>2</label><element-citation publication-type=\"journal\"><article-title>Understanding toddlers' in-home injuries: I. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Cureus</journal-id><journal-id journal-id-type=\"iso-abbrev\">Cureus</journal-id><journal-id journal-id-type=\"issn\">2168-8184</journal-id><journal-title-group><journal-title>Cureus</journal-title></journal-title-group><issn pub-type=\"epub\">2168-8184</issn><publisher><publisher-name>Cureus</publisher-name><publisher-loc>Palo Alto (CA)</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32821603</article-id><article-id pub-id-type=\"pmc\">PMC7431981</article-id><article-id pub-id-type=\"doi\">10.7759/cureus.9257</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Emergency Medicine</subject></subj-group><subj-group><subject>Medical Education</subject></subj-group><subj-group><subject>Quality Improvement</subject></subj-group></article-categories><title-group><article-title>Resuscitation Resident Impact in the Treatment of Sepsis</article-title></title-group><contrib-group><contrib contrib-type=\"editor\"><name><surname>Muacevic</surname><given-names>Alexander</given-names></name></contrib><contrib contrib-type=\"editor\"><name><surname>Adler</surname><given-names>John R</given-names></name></contrib></contrib-group><contrib-group><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>Burla</surname><given-names>Michael J</given-names></name><xref ref-type=\"aff\" rid=\"aff-1\">1</xref><xref ref-type=\"aff\" rid=\"aff-2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Shinthia</surname><given-names>Nashid</given-names></name><xref ref-type=\"aff\" rid=\"aff-3\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Boura</surname><given-names>Judith A</given-names></name><xref ref-type=\"aff\" rid=\"aff-4\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>QU</surname><given-names>Lihua</given-names></name><xref ref-type=\"aff\" rid=\"aff-5\">5</xref></contrib><contrib contrib-type=\"author\"><name><surname>Berger</surname><given-names>David A</given-names></name><xref ref-type=\"aff\" rid=\"aff-1\">1</xref></contrib></contrib-group><aff id=\"aff-1\">\n<label>1</label>\nEmergency Medicine, Beaumont Health System, Royal Oak, USA </aff><aff id=\"aff-2\">\n<label>2</label>\nEmergency Medicine, Southern Maine Health Care, Biddeford, USA </aff><aff id=\"aff-3\">\n<label>3</label>\nEmergency Medicine, Baylor University Medical Center, Houston, USA </aff><aff id=\"aff-4\">\n<label>4</label>\nResearch, Ascension Macomb-Oakland, Warren, USA </aff><aff id=\"aff-5\">\n<label>5</label>\nResearch, Beaumont Health System, Royal Oak, USA </aff><author-notes><corresp id=\"cor1\">\nMichael J. Burla <email>burlamic@gmail.com</email></corresp></author-notes><pub-date date-type=\"pub\" publication-format=\"electronic\"><day>18</day><month>7</month><year>2020</year></pub-date><pub-date date-type=\"collection\" publication-format=\"electronic\"><month>7</month><year>2020</year></pub-date><volume>12</volume><issue>7</issue><elocation-id>e9257</elocation-id><history><date date-type=\"received\"><day>28</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>17</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020, Burla et al.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Burla et al.</copyright-holder><license license-type=\"open-access\" xlink:href=\"http://creativecommons.org/licenses/by/3.0/\"><license-p>This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</license-p></license></permissions><self-uri xlink:href=\"https://www.cureus.com/articles/35711-resuscitation-resident-impact-in-the-treatment-of-sepsis\">This article is available from https://www.cureus.com/articles/35711-resuscitation-resident-impact-in-the-treatment-of-sepsis</self-uri><abstract><p>Background</p><p>The resuscitation of septic patients is a fundamental skill of emergency medicine (EM) training. We developed a required rotation designed to augment resident training in resuscitating critically ill patients in the emergency department (ED). The purpose of this study was to evaluate the successful completion of sepsis core measures alongside clinical outcomes between patients with a resuscitation resident (RR) involved in care versus patients without.</p><p>Methods</p><p>This retrospective study was conducted at a single site tertiary care Level 1 trauma center with an ED census of 130,000 visits annually. Data were collected from January 1, 2015, to December 31, 2016, using the electronic medical record (EMR) via an Epic query (Epic Systems Corp., Verona, WI). Patients admitted with severe sepsis or septic shock (Surviving Sepsis Campaign guidelines) were included and separated into two groups, one with RR involvement and one without. Emergency department length of stay, time to initial lactic acid draw, lactic acid value, time to bolus fluid initiation, time to antibiotic initiation, need for medical intensive care unit (ICU) admission, and 30-day mortality were compared between the two groups. Chi-square tests and Fisher’s exact tests were used to analyze the categorical variables. Two-sided t-tests and Wilcoxon rank-sum tests were used to examine continuous variables.</p><p>Results</p><p>Out of 4,746 patients admitted, 101 patients had an RR participate in their care. The median time to initial lactic acid draw was shorter (0.53 vs 1.05 hours; p < 0. 0001) and the lactic acid level was higher (2.5 vs 1.8 mmol/L; p < 0. 0001) with the presence of an RR. Resuscitation resident was correlated with a decrease in time to antibiotics and appropriate 30 cc/kg bolus, however, these were not statistically significant (p = 0.10 and p = 0.09 respectively). Resuscitation resident involvement was also associated with more medical ICU (45.5% vs 18.8%; p<0.0001) admissions and a higher 30-day mortality (14.9% vs 29.7%; p < 0. 0001). All other variables were not statistically significant.</p><p>Conclusion</p><p>Resuscitation residents demonstrate a statistically significant impact on lactic acid-related bundle compliance and help facilitate the care of higher acuity severe sepsis and septic shock patients.</p></abstract><kwd-group kwd-group-type=\"author\"><kwd>resuscitation</kwd><kwd>emergency department</kwd><kwd>sepsis</kwd><kwd>septic shock</kwd><kwd>residency</kwd></kwd-group></article-meta><notes><p content-type=\"disclaimer\">The content published in Cureus is the result of clinical experience and/or research by independent individuals or organizations. Cureus is not responsible for the scientific accuracy or reliability of data or conclusions published herein. All content published within Cureus is intended only for educational, research and reference purposes. Additionally, articles published within Cureus should not be deemed a suitable substitute for the advice of a qualified health care professional. Do not disregard or avoid professional medical advice due to content published within Cureus.</p></notes></front><body><sec sec-type=\"intro\"><title>Introduction</title><p>The resuscitation of severe sepsis and septic shock patients in the emergency department (ED) is an integral aspect of emergency medicine. Extensive strides have been made to improve outcomes in this patient population, beginning with the landmark description of early goal-directed therapy in 2004 [<xref rid=\"REF1\" ref-type=\"bibr\">1</xref>], to the subsequent studies of the Protocolized Care for Early Septic Shock (ProCESS) and Australasian Resuscitation in Sepsis Evaluation (ARISE) trials [<xref rid=\"REF2\" ref-type=\"bibr\">2</xref>-<xref rid=\"REF4\" ref-type=\"bibr\">4</xref>]. These subsequent trials demonstrated that protocol-based resuscitation did not improve patient outcomes. Given the high morbidity and mortality of sepsis and septic shock, the Surviving Sepsis Campaign was initiated globally to help improve the treatment of sepsis and septic shock [<xref rid=\"REF5\" ref-type=\"bibr\">5</xref>]. The campaign involves a six-prong approach, with sepsis core measures being one of the prongs [<xref rid=\"REF6\" ref-type=\"bibr\">6</xref>]. The initial core measures include obtaining blood cultures, lactic acid, starting board-spectrum antibiotics, and starting a 30 cc/kg fluid bolus when necessary, within the first three hours of a patient’s presentation.</p><p>Achieving the initial sepsis core-bundle within three hours can be challenging, and sepsis may not always be obvious based on the initial presentation. For this reason, a variety of approaches have been developed to maximize adherence to the sepsis core measures [<xref rid=\"REF7\" ref-type=\"bibr\">7</xref>-<xref rid=\"REF11\" ref-type=\"bibr\">11</xref>]. One method that could conceivably improve sepsis bundle achievement is having additional staff specifically designed to help with the resuscitation of patients that are critically ill. At our institution, we have developed a novel one-month rotation dedicated to resuscitative care in the ED [<xref rid=\"REF12\" ref-type=\"bibr\">12</xref>]. The purpose of this curriculum was to help supplement our residents' resuscitation education.</p><p>While this curriculum was developed for educational purposes, we hypothesize that having a resuscitation resident involved will improve patient care. Having an additional resident participating in the care of acutely ill populations, including patients with severe sepsis and septic shock, could lead to interventions and treatment plans initiated more efficiently. Given this potential, we performed a retrospective study assessing resuscitation resident impact on patient outcomes and the achievement of sepsis core measures on patients with severe sepsis and septic shock in the ED.</p></sec><sec sec-type=\"materials|methods\"><title>Materials and methods</title><p>Overview</p><p>This was a retrospective study, conducted at a single-center tertiary hospital, with an ED census of ~130,000 annually. Our institution’s admission rates have been approximately 30% overall and 17% in the intensive care unit (ICU).</p><p>Course description</p><p>As a required rotation within our standard post-graduate year 2 (PGY-2) curriculum, the resuscitation rotation is a one-month experience focused on the assessment and management of high-acuity patients. The location of this course is at our primary campus, a large level 1 trauma center, with a medical school affiliation. One to two residents are designated per month and are expected to be available in the department for a minimum of 40 hours a week, mostly during the highest volume hours (10 am - 6 pm). The target population that is focused on is the acutely ill, which includes patients with severe sepsis and septic shock. During this rotation, the resuscitation resident (RR) is expected to help facilitate the care of these patients, as well as augment resuscitative efforts. These residents do not take over as the primary caregiver but rather aid with procedures, monitoring, speaking with consultants, and disposition. The RR identifies which patients to become involved with via the guidance of the attending on shift, by screening patients in the high-acuity area of the department. The curriculum includes weekly didactics, required reading, article discussions, and pre/post-exam.</p><p>Design</p><p>Data collection was approved by our institution’s Institutional Review Board (IRB). Sepsis encounters were identified in the ED over a two-year time frame (January 2015 - December 2016) via a query of our facility’s electronic medical record (EMR, Epic by Epic Systems Corp., Verona, WI). These encounters were discovered utilizing sepsis codes from the International Classification of Diseases, 9th (995.91 - 995.92) and 10th (A41 - A41.9) revisions, Clinical Modification (ICD 9/10-CM). Resident encounters were independently recorded in a protected, web-based data bank, with individual login (New Innovations by New Innovations Inc., Uniontown, OH). These encounters were then compared to encounters of sepsis patients without RR involvement. The medical record numbers (MRN) of the patients were utilized to compare encounters. All Epic data were exacted by an experienced data user (LQ), and relevant clinical information was organized into a database. All data were managed on a password-protected platform (SharePoint by Microsoft Corp., Redmond, WA), with access granted only to key personnel.</p><p>Study population criteria</p><p>A total of 4,746 encounters were observed. Eligible encounters were included in enrollment if they were ≥ 18 years of age, met sepsis coding, and were admitted to the hospital. Pediatric and pregnant patients were excluded. As stated in the study design, patients were separated into two groups, one with RR involvement, and one without (Figure <xref ref-type=\"fig\" rid=\"FIG1\">1</xref>).</p><fig fig-type=\"figure\" id=\"FIG1\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><title>Flow diagram of sepsis encounters</title></caption><graphic xlink:href=\"cureus-0012-00000009257-i01\"/></fig><p>The basic demographics of age, sex, and body mass index (BMI) were compared between groups, as well as sepsis bundle adherence. Sepsis bundle parameters included time to initial lactic acid, second lactic acid order and time to order in the appropriate setting, time to antibiotics, and 30 cc/kg bolus and time to bolus as indicated. Lactic acid values, ICU admission, ED length of stay, and 30-day mortality were also compared between groups.</p><p>Outcome measures</p><p>The primary outcome of this study was to compare 30-day mortality between the patients who received RR care and those who did not. Secondary outcomes were to compare if sepsis bundle parameters were met between each group.</p><p>Data analysis</p><p>The analysis was completed by an experienced biostatistician (JB). Missing data remained missing and were not replaced with substitutions or interpolation. We compared several variables between patients who had RR involvement versus those that did not. Categorical variables were examined using Pearson’s Chi-square where appropriate (expected frequency >5); otherwise, Fisher’s exact tests were used. Age was examined using a two-sample t-test. The remaining continuous variables were examined using Wilcoxon rank tests. A p-value of <0.05 was considered statistically significant. SAS for Windows® version 9.3 (SAS Institute, Cary, NC) was used for all analyses.</p></sec><sec sec-type=\"results\"><title>Results</title><p>The demographics of patients and sepsis bundle variables who had RR involvement as compared to those that did not are illustrated in Table <xref rid=\"TAB1\" ref-type=\"table\">1</xref>.</p><table-wrap id=\"TAB1\" orientation=\"portrait\" position=\"float\"><label>Table 1</label><caption><title>Demographics of patients with RR involvement and without</title><p>RR: resuscitation resident; BMI: body mass index; ICU: intensive care unit; ED: emergency department; LOS: length of stay</p></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\"> </td><td rowspan=\"1\" colspan=\"1\">RR involved N=101</td><td rowspan=\"1\" colspan=\"1\">No-RR involved N=4,645</td><td rowspan=\"1\" colspan=\"1\">P-Value</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Age mean+/-SD (median) Min to max</td><td rowspan=\"1\" colspan=\"1\">67.6+/-16 (68) 18 to 94</td><td rowspan=\"1\" colspan=\"1\">65.5+/-19 (68) 18 to 106</td><td rowspan=\"1\" colspan=\"1\">0.27</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">BMI mean+/-SD (median) Min to max</td><td rowspan=\"1\" colspan=\"1\">N=101 29.9+/-7.9 (28.6) 16.6 to 52.1</td><td rowspan=\"1\" colspan=\"1\">N=4,225 28.9+/-8.5 (27.3) 11.0 to 105.7</td><td rowspan=\"1\" colspan=\"1\">0.14</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Males</td><td rowspan=\"1\" colspan=\"1\">50 (49.5%)</td><td rowspan=\"1\" colspan=\"1\">2,333 (50.2%)</td><td rowspan=\"1\" colspan=\"1\">0.89</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Time to lactic acid (hrs) Median (25<sup>th</sup>, 75<sup>th</sup>) Min to max</td><td rowspan=\"1\" colspan=\"1\">N=91 0.53 (0.20, 1.63) 0.03 to 1016</td><td rowspan=\"1\" colspan=\"1\">N=3,465 1.05 (0.45, 4.38) -21.3 to 1418</td><td rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Lactic acid values Median (25<sup>th</sup>, 75<sup>th</sup>) Min to max</td><td rowspan=\"1\" colspan=\"1\">N=91 2.5 (1.5, 4.6) 0.5 to 22.1</td><td rowspan=\"1\" colspan=\"1\">N=3,465 1.8 (1.2, 2.7) 0.4 to 22.9</td><td rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Time to 2<sup>nd</sup> lactic acid-hrs Median (25<sup>th</sup>, 75<sup>th</sup>) Min to max</td><td rowspan=\"1\" colspan=\"1\">N=67 7.7 (4.6, 19.2) 1.4 to 1025</td><td rowspan=\"1\" colspan=\"1\">N=1,883 16.0 (7.8, 52.6) 1.3 to 1501</td><td rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Time to 2<sup>nd</sup> lactic acid-hrs ≤ 3 hours</td><td rowspan=\"1\" colspan=\"1\">1/67 (1.5%)</td><td rowspan=\"1\" colspan=\"1\">37/1,883 (2.0%)</td><td rowspan=\"1\" colspan=\"1\">1.00</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Time to 2<sup>nd</sup> lactic acid-hrs ≤ 6 hours</td><td rowspan=\"1\" colspan=\"1\">27/67 (40.3%)</td><td rowspan=\"1\" colspan=\"1\">284/1,883 (15.1%)</td><td rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td rowspan=\"1\" colspan=\"1\">2<sup>nd</sup> lactic acid values Median (25<sup>th</sup>, 75<sup>th</sup>) Min to max</td><td rowspan=\"1\" colspan=\"1\">N=67 2.5 (1.6, 5.5) 0.7 to 12.3</td><td rowspan=\"1\" colspan=\"1\">N=1,884 1.8 (1.2, 2.9) 0.1 to 19.2</td><td rowspan=\"1\" colspan=\"1\">0.0002</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Time to antibiotics (hrs) Median (25<sup>th</sup>, 75<sup>th</sup>) Min to max</td><td rowspan=\"1\" colspan=\"1\">N=96 4.26 (2.58, 6.65) 0.70 to 373</td><td rowspan=\"1\" colspan=\"1\">N=4,396 4.60 (3.02, 8.93) 0.07 to 1042</td><td rowspan=\"1\" colspan=\"1\">0.10</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Time to bolus (hrs) Median (25<sup>th</sup>, 75<sup>th</sup>) Min to max</td><td rowspan=\"1\" colspan=\"1\">N=87 1.67 (0.85, 5.15) 0.17 to 310.3</td><td rowspan=\"1\" colspan=\"1\">N=3,601 1.93 (1.10, 3.77) 0.08 to 1279</td><td rowspan=\"1\" colspan=\"1\">0.18</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">30cc/kg bolus within 3 hours</td><td rowspan=\"1\" colspan=\"1\">9/60 (15.0%)</td><td rowspan=\"1\" colspan=\"1\">211/2,429 (8.7%)</td><td rowspan=\"1\" colspan=\"1\">0.09</td></tr><tr><td rowspan=\"1\" colspan=\"1\">ICU admission</td><td rowspan=\"1\" colspan=\"1\">46 (45.5%)</td><td rowspan=\"1\" colspan=\"1\">871 (18.8%)</td><td rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">ED LOS hr Median (25<sup>th</sup>, 75<sup>th</sup>) Min to max</td><td rowspan=\"1\" colspan=\"1\">6.9 (5.0, 8.3) 0.7 to 27.0</td><td rowspan=\"1\" colspan=\"1\">6.8 (5.0, 9.6) 0.02 to 48.5</td><td rowspan=\"1\" colspan=\"1\">0.45</td></tr><tr><td rowspan=\"1\" colspan=\"1\">30-day mortality</td><td rowspan=\"1\" colspan=\"1\">30 (29.7%)</td><td rowspan=\"1\" colspan=\"1\">690 (14.9%)</td><td rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Days to death Median (25<sup>th</sup>, 75<sup>th</sup>) Min to max</td><td rowspan=\"1\" colspan=\"1\">N=30 8.2 (2.5, 17.5) 0 to 20.5</td><td rowspan=\"1\" colspan=\"1\">N=690 9.0 (3.6, 16.3) 0 to 29.9</td><td rowspan=\"1\" colspan=\"1\">0.40</td></tr></tbody></table></table-wrap><p>A total of 4,746 patient encounters were observed during the time period. From these encounters, 101 (2.1%) of the patients had an RR involved. There was no evidence of any statistically significant differences between group baseline demographics, although comorbidities were not accounted for. Patients who had RR involvement did have a shorter time to initial lactic acid (0.53 hours vs 1.03 hours; p < 0.0001) and second lactic acid (7.7 hours vs 16.0 hours; p < 0.0001). In addition, RR involvement was associated with a higher percentage of patients achieving time to second lactic acid under the six-hour mark as compared to non-RR involvement (40.3% vs 15.1%; p < 0.0001). Encounters with RR involvement also experienced decreased time to antibiotics and more patients received 30 cc/kg fluid boluses when appropriate; however, these results were not statistically significant (p = 0.10 and p = 0.09, respectively).</p><p>Regarding the severity of illness, RR involvement was associated with higher lactic acid values, both with the first lactic acid (p < 0.0001) and second lactic acid (p = 0.0002), as well as higher ICU admission rates (45.5% vs 18.8%; p < 0.0001) and 30-day mortality (29.7% vs 14.9%; p <0.0001). For this reason, further analysis was done to assess the subset of ICU patients in both groups, as illustrated in Table <xref rid=\"TAB2\" ref-type=\"table\">2</xref>.</p><table-wrap id=\"TAB2\" orientation=\"portrait\" position=\"float\"><label>Table 2</label><caption><title>Demographics of ICU patients with RR involvement and without</title><p>ICU: intensive care unit; RR: resuscitation resident; ED: emergency department; LOS: length of stay; BMI: body mass index</p></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\"> </td><td rowspan=\"1\" colspan=\"1\">RR involved N=46</td><td rowspan=\"1\" colspan=\"1\">No RR involved N=871</td><td rowspan=\"1\" colspan=\"1\">P-Value</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Age mean+/-SD (median) Min to max</td><td rowspan=\"1\" colspan=\"1\">67+/-15 (66) 33 to 91</td><td rowspan=\"1\" colspan=\"1\">67+/-17 (68) 18 to 106</td><td rowspan=\"1\" colspan=\"1\">0.96</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">BMI mean+/-SD (median) Min to max</td><td rowspan=\"1\" colspan=\"1\">30.6+/-8.3 (29.3) 16.6 to 52.1</td><td rowspan=\"1\" colspan=\"1\">29.4+/-8.8 (27.7) 11.0 to 105.7</td><td rowspan=\"1\" colspan=\"1\">0.30</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Males</td><td rowspan=\"1\" colspan=\"1\">18 (39.1%)</td><td rowspan=\"1\" colspan=\"1\">467 (53.6%)</td><td rowspan=\"1\" colspan=\"1\">0.055</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Time to lactic acid (hrs) Median (25<sup>th</sup>, 75<sup>th</sup>) Min to max</td><td rowspan=\"1\" colspan=\"1\">N=41 0.4 (0.2, 2.0) 0.03 to 1016</td><td rowspan=\"1\" colspan=\"1\">N=823 1.0 (0.3, 7.5) -15 to 1418</td><td rowspan=\"1\" colspan=\"1\">0.008</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Lactic acid values Median (25<sup>th</sup>, 75<sup>th</sup>) Min to max</td><td rowspan=\"1\" colspan=\"1\">N=41 3.9 (1.7, 6.8) 0.8 to 22.1</td><td rowspan=\"1\" colspan=\"1\">N=823 2.2 (1.4, 3.5) 0.5 to 22.1</td><td rowspan=\"1\" colspan=\"1\">0.002</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Time to 2<sup>nd</sup> lactic acid-hrs Median (25<sup>th</sup>, 75<sup>th</sup>) Min to max</td><td rowspan=\"1\" colspan=\"1\">N=37 6.1 (4.5, 14.5) 1.4 to 1025</td><td rowspan=\"1\" colspan=\"1\">N=699 13.4 (7.1, 65) 1.7 to 981</td><td rowspan=\"1\" colspan=\"1\">0.0002</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Time to 2<sup>nd</sup> lactic acid-hrs ≤ 3 hours</td><td rowspan=\"1\" colspan=\"1\">1/37 (2.7%)</td><td rowspan=\"1\" colspan=\"1\">17/699 (2.4%)</td><td rowspan=\"1\" colspan=\"1\">0.61</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Time to 2<sup>nd</sup> lactic acid-hrs ≤ 6 hours</td><td rowspan=\"1\" colspan=\"1\">18/37 (48.7%)</td><td rowspan=\"1\" colspan=\"1\">111/699 (15.9%)</td><td rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td rowspan=\"1\" colspan=\"1\">2<sup>nd</sup> lactic acid values Median (25<sup>th</sup>, 75<sup>th</sup>) Min to max</td><td rowspan=\"1\" colspan=\"1\">N=37 3.1 (1.6, 5.9) 0.7 to 12.3</td><td rowspan=\"1\" colspan=\"1\">N=700 1.9 (1.3, 3.3) 0.1 to 19.2</td><td rowspan=\"1\" colspan=\"1\">0.006</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Time to antibiotics (hrs) median (25<sup>th</sup>, 75<sup>th</sup>) Min to max</td><td rowspan=\"1\" colspan=\"1\">N=44 4.7 (2.5, 7.8) 0.7 to 373</td><td rowspan=\"1\" colspan=\"1\">N=835 4.9 (2.7, 15.4) 0.5 to 1042</td><td rowspan=\"1\" colspan=\"1\">0.34</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Time to bolus (hrs) Median (25<sup>th</sup>, 75<sup>th</sup>) Min to max</td><td rowspan=\"1\" colspan=\"1\">N=42 1.3 (0.8, 5.4) 0.3 to 310</td><td rowspan=\"1\" colspan=\"1\">N=782 1.9 (0.9, 5.8) 0.1 to 661</td><td rowspan=\"1\" colspan=\"1\">0.35</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">30cc/kg bolus within 3 hours</td><td rowspan=\"1\" colspan=\"1\">7/29 (24.1%)</td><td rowspan=\"1\" colspan=\"1\">75/475 (15.8%)</td><td rowspan=\"1\" colspan=\"1\">0.30</td></tr><tr><td rowspan=\"1\" colspan=\"1\">ED LOS hr Median (25<sup>th</sup>, 75<sup>th</sup>) Min to max</td><td rowspan=\"1\" colspan=\"1\">7.1 (5.5, 9.1) 0.7 to 27.0</td><td rowspan=\"1\" colspan=\"1\">6.8 (5.0, 9.7) 0.4 to 36.2</td><td rowspan=\"1\" colspan=\"1\">0.58</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">30-day mortality</td><td rowspan=\"1\" colspan=\"1\">15 (32.6%)</td><td rowspan=\"1\" colspan=\"1\">301 (34.6%)</td><td rowspan=\"1\" colspan=\"1\">0.79</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Days to death Median (25<sup>th</sup>, 75<sup>th</sup>) Min to max</td><td rowspan=\"1\" colspan=\"1\">N=15 9.4 (1.4, 17.9) 0 to 20</td><td rowspan=\"1\" colspan=\"1\">N=301 10.6 (4.3, 16.9) 0 to 30</td><td rowspan=\"1\" colspan=\"1\">0.51</td></tr></tbody></table></table-wrap><p>This analysis of ICU only patients demonstrated no difference in basic demographics or 30-day mortality between patients with RR involvement and ones without. In addition, RR involvement was still associated with statistically significant shorter median time to first and second lactic acid (p = 0.008; p = 0.0002), as well as statistically significant higher lactic acid values (p = 0.002; p = 0.006). Lastly, RR involvement was associated with the second lactic acid being acquired ≤ 6 hours (p < 0.0001).</p></sec><sec sec-type=\"discussion\"><title>Discussion</title><p>Overall, our investigation demonstrated that RR involvement was associated with statistically significant improvement in the fulfillment of two aspects of sepsis bundle compliance. The first aspect was reduced time to initial lactic acid draw, and the second was achieving the second lactic acid draw under the six-hour mark. With respect to clinical outcomes and 30-day mortality, the initial analysis revealed that the RR arm did have a higher rate of 30-day mortality, though this effect was not observed when only including ICU admissions in each cohort. We suspect that the change in mortality is due to making the cohort's severity of illness more comparable, as the RR is typically only involved in the care of critically ill patients. In addition, patients who had RR involvement had a quicker time to appropriate fluid bolus and antibiotics, however, these findings were not statistically significant (p = 0.09 and p = 0.10, respectively). Given the small percentage of overall RR encounters, we suspect that more RR involvement could have potentially led to these findings being statistically significant.</p><p>As stated in the introduction, the resuscitation rotation is a novel curriculum designed to augment resuscitative training [<xref rid=\"REF12\" ref-type=\"bibr\">12</xref>]. Since this is a novel curriculum, there is no specific current literature that we have seen to compare our findings. However, there have been several studies assessing ways to achieve sepsis core measures and attempt to improve patient outcomes [<xref rid=\"REF13\" ref-type=\"bibr\">13</xref>]. Most of these studies focus on a sepsis alert protocol, either involving personnel or electronically driven. One model that has been studied is the implementation of a best practice alert (BPA) in the EMR to alert clinicians of possible sepsis patients. Studies have demonstrated a BPA leading to decreased time to antibiotics and other parameters [<xref rid=\"REF14\" ref-type=\"bibr\">14</xref>-<xref rid=\"REF15\" ref-type=\"bibr\">15</xref>], nevertheless, it did not lead to an improvement in mortality [<xref rid=\"REF14\" ref-type=\"bibr\">14</xref>-<xref rid=\"REF15\" ref-type=\"bibr\">15</xref>]. Personnel driven models, either with a sepsis response team or triage protocol, have also had varying degrees of success with decreasing time to sepsis bundle parameters [<xref rid=\"REF7\" ref-type=\"bibr\">7</xref>-<xref rid=\"REF11\" ref-type=\"bibr\">11</xref>], with only one demonstrating a possible mortality benefit [<xref rid=\"REF16\" ref-type=\"bibr\">16</xref>]. Overall, similar to our study, the vast majority of these interventions have led to some improvement in achieving different sepsis core measurements [<xref rid=\"REF7\" ref-type=\"bibr\">7</xref>-<xref rid=\"REF11\" ref-type=\"bibr\">11</xref>,<xref rid=\"REF13\" ref-type=\"bibr\">13</xref>-<xref rid=\"REF17\" ref-type=\"bibr\">17</xref>]. The difference in this approach is that the resuscitation rotation was designed to be educational in nature, where the above-mentioned interventions were all specifically designed to improve outcomes for sepsis patients. Other educational programs have been developed in the past to improve the proficiency of resuscitative training [<xref rid=\"REF18\" ref-type=\"bibr\">18</xref>-<xref rid=\"REF19\" ref-type=\"bibr\">19</xref>], but none appear to measure patient outcomes.</p><p>While this study is specific to assessing sepsis patient outcomes with RR involvement, emergency medicine physicians in our department were anecdotally noticing expedited care when the RR was working in general. For this reason, faculty at our institution decided that it might be of interest to evaluate patient-centered outcomes with RR involvement. This way, data can be collected not only to improve the residents' educational experience but also to assess how patient care can be improved. While the number of RR cases in this particular study are limited, we are continuing to develop ways to get RR involvement in patient care. In addition to examining the impact of a RR on septic patients, we are currently investigating the impact on other types of critical patients to include those suffering from cardiac arrest.</p><p>Improving resuscitative education and retention of training has proven to be a difficult task [<xref rid=\"REF20\" ref-type=\"bibr\">20</xref>]. The American Heart Association's recent scientific statement recognizes the concern of decay in resuscitative knowledge and skills over time [<xref rid=\"REF21\" ref-type=\"bibr\">21</xref>]. Moreover, it has also been suggested by the American Association of Medical Colleges that a gap exists between educational objectives and successful resuscitation practices in graduate medical education [<xref rid=\"REF20\" ref-type=\"bibr\">20</xref>]. These concerns are likely multifocal, however, one contributing factor could be the variation in resident experience [<xref rid=\"REF22\" ref-type=\"bibr\">22</xref>]. Since real-time resuscitations can be rare compared to other clinical scenarios, some residency programs have incorporated simulation or specific didactics to supplement resuscitative training. Simulation has been a great tool to provide educational opportunities for rare clinical scenarios, however, actual effectiveness may vary [<xref rid=\"REF23\" ref-type=\"bibr\">23</xref>]. Our rotation offers a combination of hands-on experience and didactics to standardize the resident experience as much as possible. The rotations didactics are weekly, focusing on sepsis resuscitation and other resuscitative education. In addition, there is a pre- and post-test to the rotation, implemented to discover and target the weaknesses of each individual resident. As stated in the introduction, dedicated rotations have been shown to improve competency in a given subject [<xref rid=\"REF24\" ref-type=\"bibr\">24</xref>]. Residents that have completed the rotation have reported an increase in resuscitative confidence, as well as a decrease in anxiety while preforming some resuscitative procedures [<xref rid=\"REF12\" ref-type=\"bibr\">12</xref>]. We believe that the resuscitation rotation curriculum has benefited our residents and provides a dedicated month of hands-on resuscitation experience that has the potential to improve patient care as well.</p><p>This was a retrospective, single-center study, with several limitations. First, it is hard to control for all confounding variables with retrospective data. Secondly, encounters, where RR was involved, were logged at the discretion of the resident. For this reason, it is possible some encounters had RR involved that were not logged by error. In addition, there was a significant imbalance between cohorts, where the majority of sepsis encounters did not have an RR involved. The small percentage of RR involvement could have led to a deficiency in data, resulting in time to fluid bolus and antibiotics not being statistically significant. We believe that this discrepancy is due to patients who met the ICD9/10 coding threshold for severe sepsis and septic shock, however, presented clinically as low acuity, for which the RR would not be involved in care. This is also supported by the fact that RR involvement was associated with higher initial lactic acid levels and higher mortality rates. It is also likely that many cases arrived outside the RR scheduled work hours. For this reason, a secondary analysis was done of ICU admission patients, which demonstrated no statistical difference in the mortality rate. It is difficult to speculate why RR involvement did not lead to a mortality benefit when controlling for ICU patients; however, there are a variety of factors and confounding variables during a patient's hospitalization that could not be accounted for. Finally, some data points were missing from the retrospective collection, which were not replaced in the analysis, thus potentially altering our findings.</p></sec><sec sec-type=\"conclusions\"><title>Conclusions</title><p>Septic patients with RR involvement demonstrated a statistically significant benefit to initial and repeat lactic acid draws. While other sepsis bundle criteria did not meet statistical significance, times to appropriate fluid bolus and antibiotics were shorter when a RR was involved in care. This curriculum was designed to augment residents’ resuscitation education; however, it does have the potential to improve patient care of the critically ill, including septic patients.</p></sec></body><back><fn-group content-type=\"competing-interests\"><fn fn-type=\"COI-statement\"><p>The authors have declared that no competing interests exist.</p></fn></fn-group><fn-group content-type=\"other\"><title>Human Ethics</title><fn fn-type=\"other\"><p>Consent was obtained by all participants in this study</p></fn></fn-group><fn-group content-type=\"other\"><title>Animal Ethics</title><fn fn-type=\"other\"><p><bold>Animal subjects:</bold> All authors have confirmed that this study did not involve animal subjects or tissue.</p></fn></fn-group><ack><p>We would like to thank the residents that have participated in the resuscitation rotation.</p></ack><ref-list><title>References</title><ref id=\"REF1\"><label>1</label><element-citation publication-type=\"journal\"><article-title>Early goal-directed therapy</article-title><source>Crit Care Med</source><person-group><name><surname>Rivers</surname><given-names>EP</given-names></name><name><surname>Nguyen</surname><given-names>HB</given-names></name><name><surname>Huang</surname><given-names>DT</given-names></name><name><surname>Donnino</surname><given-names>M</given-names></name></person-group><fpage>314</fpage><lpage>315</lpage><volume>32</volume><year>2004</year><pub-id pub-id-type=\"pmid\">14707615</pub-id></element-citation></ref><ref id=\"REF2\"><label>2</label><element-citation publication-type=\"journal\"><article-title>Severe sepsis and septic shock trials (ProCESS, ARISE, ProMISe). 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Genet</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Genet</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Genet.</journal-id><journal-title-group><journal-title>Frontiers in Genetics</journal-title></journal-title-group><issn pub-type=\"epub\">1664-8021</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849817</article-id><article-id pub-id-type=\"pmc\">PMC7431982</article-id><article-id pub-id-type=\"doi\">10.3389/fgene.2020.00824</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Genetics</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>Serum Calcium Levels and Parkinson’s Disease: A Mendelian Randomization Study</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Wang</surname><given-names>Yanchao</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"author-notes\" rid=\"fn002\"><sup>†</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1042050/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Gao</surname><given-names>Luyan</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><xref ref-type=\"author-notes\" rid=\"fn002\"><sup>†</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1041990/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Lang</surname><given-names>Wenjing</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/563533/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Li</surname><given-names>He</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/604631/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Cui</surname><given-names>Pan</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/563077/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Zhang</surname><given-names>Nan</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/413803/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Jiang</surname><given-names>Wei</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"corresp\" rid=\"c002\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/642640/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital</institution>, <addr-line>Tianjin</addr-line>, <country>China</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Department of Neurology, Affiliated Hospital of Chifeng University</institution>, <addr-line>Chifeng</addr-line>, <country>China</country></aff><aff id=\"aff3\"><sup>3</sup><institution>Department of Neurology, The Fourth Central Clinical College of Tianjin Medical University</institution>, <addr-line>Tianjin</addr-line>, <country>China</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Liangcai Zhang, Janssen Research and Development, United States</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Keshen Li, Jinan University, China; Liang Cheng, Harbin Medical University, China</p></fn><corresp id=\"c001\">*Correspondence: Nan Zhang, <email>nkzhangnan@yeah.net</email></corresp><corresp id=\"c002\">Wei Jiang, <email>jiangwei_med@163.com</email></corresp><fn fn-type=\"other\" id=\"fn002\"><p><sup>†</sup>These authors have contributed equally to this work</p></fn><fn fn-type=\"other\" id=\"fn004\"><p>This article was submitted to Statistical Genetics and Methodology, a section of the journal Frontiers in Genetics</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>824</elocation-id><history><date date-type=\"received\"><day>28</day><month>5</month><year>2019</year></date><date date-type=\"accepted\"><day>08</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Wang, Gao, Lang, Li, Cui, Zhang and Jiang.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Wang, Gao, Lang, Li, Cui, Zhang and Jiang</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><sec><title>Background</title><p>Though increasing epidemiological studies have evaluated the correlation between serum calcium contents and Parkinson’s disease (PD), the results are inconsistent. At present, whether there is a causal association between serum calcium content and PD remains undetermined.</p></sec><sec><title>Objective and Methods</title><p>This study was designed to explore the relationship between increased serum calcium contents and PD risk. In this present study, a Mendelian randomization trial was carried out using a large-scale serum calcium genome-wide association study (GWAS) dataset (<italic>N</italic> = 61,079, Europeans) and a large-scale PD GWAS dataset (<italic>N</italic> = 8,477, Europeans including 4,238 PD patients and 4,239 controls). Here, a total of four Mendelian randomization methods comprising weighted median, inverse-variance weighted meta-analysis (IVW), MR-Egger, and MR-PRESSO were used.</p></sec><sec><title>Results</title><p>Our data concluded that genetically higher serum calcium contents were not significantly related to PD.</p></sec><sec><title>Conclusion</title><p>In conclusion, we provided genetic evidence that there was no direct causal relationship between serum calcium contents and PD. Hence, calcium supplementation may not result in reduced PD risk.</p></sec></abstract><kwd-group><kwd>Parkinson’s disease</kwd><kwd>serum calcium</kwd><kwd>Mendelian randomization</kwd><kwd>pleiotropy analysis</kwd><kwd>power analysis</kwd></kwd-group><funding-group><award-group><funding-source id=\"cn001\">National Natural Science Foundation of China<named-content content-type=\"fundref-id\">10.13039/501100001809</named-content></funding-source><award-id rid=\"cn001\">81571600</award-id><award-id rid=\"cn001\">81322018</award-id><award-id rid=\"cn001\">81273287</award-id><award-id rid=\"cn001\">81100887</award-id></award-group></funding-group><counts><fig-count count=\"0\"/><table-count count=\"2\"/><equation-count count=\"1\"/><ref-count count=\"47\"/><page-count count=\"7\"/><word-count count=\"0\"/></counts></article-meta></front><body><sec id=\"S1\"><title>Introduction</title><p>Previous studies indicated that calcium is involved in many biological processes, and altered calcium homeostasis is widely regarded as a basis for cognitive deficits in normal subjects and certain neurodegenerative diseases (<xref rid=\"B19\" ref-type=\"bibr\">LaFerla, 2002</xref>; <xref rid=\"B20\" ref-type=\"bibr\">Larsson et al., 2017</xref>). Parkinson’s disease (PD) is the second most common neurodegenerative disorder, which affects 1–2% people older than 65 years (<xref rid=\"B15\" ref-type=\"bibr\">Gibrat et al., 2009</xref>; <xref rid=\"B40\" ref-type=\"bibr\">Saad et al., 2011</xref>; <xref rid=\"B22\" ref-type=\"bibr\">Liu et al., 2015a</xref>, <xref rid=\"B23\" ref-type=\"bibr\">b</xref>; <xref rid=\"B25\" ref-type=\"bibr\">Liu G. et al., 2016</xref>). Particularly, emerging epidemiological research has evaluated the correlation between serum calcium contents and PD, and calcium dysregulation has been found in PD (<xref rid=\"B1\" ref-type=\"bibr\">Abou-Raya et al., 2009</xref>; <xref rid=\"B30\" ref-type=\"bibr\">Meamar et al., 2013</xref>; <xref rid=\"B41\" ref-type=\"bibr\">Schapira, 2013</xref>; <xref rid=\"B27\" ref-type=\"bibr\">Liu J. et al., 2016</xref>). However, the results reported by these studies are often inconsistent.</p><p>In 2009, <xref rid=\"B1\" ref-type=\"bibr\">Abou-Raya et al. (2009)</xref> evaluated the relationship between bone changes and PD. In their study, the bone density and mineral metabolism in 82 PD patients and 68 age- and sex-matched controls were measured, of which the results showed that serum calcium and vitamin D levels were significantly decreased in PD patients compared with controls. Similarly, <xref rid=\"B30\" ref-type=\"bibr\">Meamar et al. (2013)</xref> compared serum calcium contents from 105 PD patients and 112 matched controls in 2013, and indicated that serum calcium contents were significantly decreased in PD patients relative to controls. In addition, <xref rid=\"B27\" ref-type=\"bibr\">Liu J. et al. (2016)</xref> also found that serum calcium contents were significantly decreased in 77 PD patients with dementia compared to 75 healthy control subjects in 2016. It should be noted that no significant difference in serum calcium content was observed between the PD group without dementia and the healthy control group in their study.</p><p>At present, it remains unclear whether there is a causal relationship between serum calcium content and PD. Here, we tried to find the causality between the serum calcium levels and PD, thereby providing effective therapies for PD. Recently, Mendelian randomization methods have been widely applied to assess causal relationships through genome-wide association study (GWAS) datasets (<xref rid=\"B32\" ref-type=\"bibr\">Mokry et al., 2015</xref>; <xref rid=\"B35\" ref-type=\"bibr\">Nelson et al., 2015</xref>; <xref rid=\"B20\" ref-type=\"bibr\">Larsson et al., 2017</xref>; <xref rid=\"B29\" ref-type=\"bibr\">Manousaki et al., 2017</xref>; <xref rid=\"B12\" ref-type=\"bibr\">Cheng et al., 2018</xref>, <xref rid=\"B11\" ref-type=\"bibr\">2019</xref>; <xref rid=\"B16\" ref-type=\"bibr\">Hu et al., 2018</xref>; <xref rid=\"B26\" ref-type=\"bibr\">Liu et al., 2018</xref>, <xref rid=\"B24\" ref-type=\"bibr\">2019</xref>; <xref rid=\"B42\" ref-type=\"bibr\">Sun et al., 2019</xref>; <xref rid=\"B46\" ref-type=\"bibr\">Zhuang et al., 2019a</xref>, <xref rid=\"B47\" ref-type=\"bibr\">b</xref>). Therefore, a Mendelian randomization study was carried out to explore the genetic relationship between serum calcium content and PD through the large-scale serum calcium and PD GWAS datasets in our study.</p></sec><sec sec-type=\"materials|methods\" id=\"S2\"><title>Materials and Methods</title><sec id=\"S2.SS1\"><title>Study Design</title><p>A previous study reported that human genetic variants are randomly allocated, which is the basis of the Mendelian randomization method (<xref rid=\"B14\" ref-type=\"bibr\">Emdin et al., 2017</xref>). These genetic variants are mostly independent from confounding variables and can be regarded as instrumental proxies to assess the causal relationship between serum calcium levels and its outcome. Mendelian randomization was conducted according to three primary assumptions as described in the previous reports (<xref rid=\"B14\" ref-type=\"bibr\">Emdin et al., 2017</xref>; <xref rid=\"B20\" ref-type=\"bibr\">Larsson et al., 2017</xref>). Assumption (1): the selected genetic variants are related to serum calcium levels; assumption (2): these genetic variants are not related to confounders; assumption (3): these genetic variants affect PD risk only via serum calcium levels. Meanwhile, assumptions (2 and 3) were together regarded as independence from pleiotropic effects. Our present work was performed using the publicly available large-scale GWAS dataset. Informed consent from all the participants was obtained in all the original research.</p></sec><sec id=\"S2.SS2\"><title>Serum Calcium GWAS Dataset</title><p>In this study, eight genetic variants influencing serum calcium content with genome-wide significance (<italic>P</italic> < 5.00E−08) were used as the instrumental proxies (<xref rid=\"B36\" ref-type=\"bibr\">O’Seaghdha et al., 2013</xref>). The dataset contained 39,400 subjects deriving from 17 population-based cohorts in the discovery stage and 21,679 subjects in the replication stage (<italic>N</italic> = 61,079, Europeans). All the genetic variants were distributed at various genes and were not in linkage disequilibrium as presented in <xref rid=\"T1\" ref-type=\"table\">Table 1</xref>. The measurement of serum calcium content was described in details in <xref ref-type=\"supplementary-material\" rid=\"FS1\">Additional File 1</xref>.</p><table-wrap id=\"T1\" position=\"float\"><label>TABLE 1</label><caption><p>Characteristics of 8 genetic variants in serum calcium and PD GWAS datasets.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SNP</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Chr</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Nearby genes</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">EA<sup>a</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">NEA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">EAF<sup>b</sup></td><td valign=\"top\" align=\"center\" colspan=\"3\" rowspan=\"1\">Serum calcium GWAS<hr/></td><td valign=\"top\" align=\"center\" colspan=\"3\" rowspan=\"1\">Discovery PD GWAS<hr/></td><td valign=\"top\" align=\"center\" colspan=\"3\" rowspan=\"1\">Validation PD GWAS<hr/></td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Beta (mg/dL)<sup>c</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">SE<sup>c</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>P-</italic>value<sup>c</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Beta<sup>d</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">SE<sup>d</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>P</italic>-value<sup>d</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Beta<sup>d</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">SE<sup>d</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>P</italic>-value<sup>d</sup></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">rs780094</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">GCKR</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">T</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">C</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.42</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.017</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.003</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.30E−10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.0037</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0332</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.911</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.0190</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0233</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.4156</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">rs1550532</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">DGKD</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">C</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">G</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.31</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.018</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.003</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.20E−11</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.0411</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0354</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.2458</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.0547</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0246</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0265</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">rs1801725</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">CASR</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">T</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">G</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.15</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.071</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.004</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.90E−86</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0425</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.046</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.3547</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0421</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0334</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.2052</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">rs10491003</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">GATA3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">T</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">C</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.09</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.027</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.005</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.80E−09</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.0427</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0566</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.4501</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.0564</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0399</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.1576</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">rs7336933</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">DGKH/KIAA0564</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">G</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.85</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.022</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.004</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.10E−10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.0352</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.046</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.4441</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0564</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0321</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0788</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">rs1570669</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">CYP24A1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">G</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.34</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.018</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.003</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.10E−12</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.0303</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0342</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.3757</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.0051</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0240</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.8318</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">rs7481584</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">CARS</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">G</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.018</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.003</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.20E−10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0429</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0365</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.24</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0018</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0251</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.9430</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">rs17711722</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">VKORC1L1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">T</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">C</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.47</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.021</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.003</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.80E−11</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0714</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0331</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.03075</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0266</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0228</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.2483</td></tr></tbody></table><table-wrap-foot><attrib><italic>PD, Parkinson’s disease; GWAS, genome-wide association studies; SNP, single-nucleotide polymorphism; Chr, chromosome; EA, Effect Allele; NEA, Non-Effect Allele; EAF, Effect Allele Frequency; SE, standard error. <sup>a</sup>Effect allele (Serum calcium raising allele). <sup>b</sup>Frequency of the effect allele (serum calcium raising allele) in the GWAS dataset of serum calcium (<xref rid=\"B36\" ref-type=\"bibr\">O’Seaghdha et al., 2013</xref>). <sup>c</sup>Beta (mg/dL) is mainly based on the effect allele (serum calcium raising allele) as a regression coefficient. Beta < 0 represents that the effector allele regulates the decrease in serum calcium levels and Beta > 0 represents that the effector allele regulates the increase in serum calcium levels. The summary statistics of Beta, SE and P-value are obtained from the serum calcium GWAS dataset (<xref rid=\"B36\" ref-type=\"bibr\">O’Seaghdha et al., 2013</xref>). <sup>d</sup>Beta is mainly based on the effect allele (serum calcium raising allele) as a regression coefficient. Beta < 0 represents that the effector allele regulates a decrease in PD risk and Beta > 0 represents that the effector allele regulates an increase in PD risk. Beta = ln(odd ratio), and represents the overall estimated effect size for the serum calcium raising allele. We obtained summary statistics of Beta, SE and P-value from the discovery and validation PD GWAS datasets of European descent (<xref rid=\"B37\" ref-type=\"bibr\">Pankratz et al., 2012</xref>; <xref rid=\"B28\" ref-type=\"bibr\">Liu et al., 2017</xref>).</italic></attrib></table-wrap-foot></table-wrap></sec><sec id=\"S2.SS3\"><title>PD GWAS Dataset</title><p>Discovery PD GWAS dataset was derived from a meta-analysis of five independent PD GWAS datasets (n = 8,477, Europeans including 4,238 PD patients and 4,239 controls) (<xref rid=\"B37\" ref-type=\"bibr\">Pankratz et al., 2012</xref>). The relationship between each dataset and PD susceptibility was tested using a logistic regression model, and the results of each dataset were performed for meta-analysis. Here, we used the meta-analysis results of about 2,525,704 SNPs. Validation of the PD GWAS dataset was derived from a case control-associated map obtained from family history of disease in the UK Biobank (<xref rid=\"B28\" ref-type=\"bibr\">Liu et al., 2017</xref>). In brief, this GWAS dataset consisted of 4,627 PD cases and 109,826 controls, all of which are of European ancestry. Here, we used the genome-wide analyzed results by proxy. Informed consent from all the participants was obtained in all the original research. Link for the serum calcium and PD GWAS datasets is <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ebi.ac.uk/gwas/\">https://www.ebi.ac.uk/gwas/</ext-link>.</p></sec><sec id=\"S2.SS4\"><title>Pleiotropy Analysis</title><p>For Mendelian randomization studies, a crucial problem is potential contradiction of assumptions (2, 3) via pleiotropic effects which occur when a genetic instrument is related to the outcome, independent of the exposure. In this work, a pleiotropy evaluation was conducted to confirm that the eight genetic variants did not affect PD risk via biological pathways outside serum calcium content, and three steps were performed to mitigate the risk of pleiotropy.</p><p>In 2016, a major review from the Lancet Neurology reported that PD risk factors included body mass index (BMI), diabetes, blood cholesterol, hypertension, alcohol, vitamins, fat and other micronutrients, and PD protective factors included smoking, coffee, tea and serum urate (<xref rid=\"B2\" ref-type=\"bibr\">Ascherio and Schwarzschild, 2016</xref>). At stage 1, we evaluated the potential pleiotropy using some known confounders including major lipids (total cholesterol and triglyceride, low density lipoprotein, high density lipoprotein, etc.), type 2 diabetes, hypertension, blood pressure, BMI, waist circumference, hip circumference and waist-to-hip ratio, smoking, alcohol consumption, serum urate and vitamin D levels. The significance threshold for the relationship between the eight variants and the above-mentioned confounders was defined to be a Bonferroni-corrected P < 0.05/8 = 0.00625.</p><p>Previous studies indicated that a statistical method, termed MR-Egger, could provide a reliable evaluation for the instrumental variable assumption, and check for the presence of potential pleiotropy (<xref rid=\"B13\" ref-type=\"bibr\">Dale et al., 2017</xref>; <xref rid=\"B43\" ref-type=\"bibr\">Tillmann et al., 2017</xref>). Therefore, at stage 2, the MR-Egger was used to assess potential pleiotropic relationships between the genetic variants and confounders.</p><p>At stage 3, a recently developed statistical method, Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) (<xref rid=\"B44\" ref-type=\"bibr\">Verbanck et al., 2018</xref>), was used to confirm the horizontal pleiotropic outliers.</p></sec><sec id=\"S2.SS5\"><title>Mendelian Randomization Analysis</title><p>In this study, Mendelian randomization analysis was conducted through four widely used methods, including MR-Egger, MR-PRESSO, inverse-variance weighted meta-analysis (IVW) and weighted median (<xref rid=\"B7\" ref-type=\"bibr\">Burgess et al., 2017</xref>; <xref rid=\"B44\" ref-type=\"bibr\">Verbanck et al., 2018</xref>), which are based on different assumptions and are useful for examining the robustness with each other. The details for the MR-Egger, IVW and weighted-median methods have been described in our previous study (<xref rid=\"B26\" ref-type=\"bibr\">Liu et al., 2018</xref>).</p><p>The IVW method assumed that all genetic variants conformed to the instrumental variable assumption, and the MR-Egger method assumed that these instrumental variables were not constant. A weaker hypothesis, the InSIDE assumption (Instrument Strength Independent of Direct Effect assumption), could still provide a consistent causal effect estimate (<xref rid=\"B4\" ref-type=\"bibr\">Bowden et al., 2015</xref>). In addition, the MR-PRESSO method was proven to require a basic assumption that more than half of the genetic variants were qualified instrumental variables and satisfied both the balanced pleiotropy and InSIDE assumption (<xref rid=\"B44\" ref-type=\"bibr\">Verbanck et al., 2018</xref>). Finally, the weighted-median method can estimate a consistent causal effect when more than half of the genetic variants are effective instrumental proxies (<xref rid=\"B5\" ref-type=\"bibr\">Bowden et al., 2016</xref>). The odds ratio (OR) and 95% CI of PD corresponded to per 0.5 mg/dL elevation [1 standard deviation (SD)] in serum calcium content. All analyses were performed through the R packages “MR-PRESSO” (<xref rid=\"B44\" ref-type=\"bibr\">Verbanck et al., 2018</xref>) or “Mendelian Randomization” (<xref rid=\"B45\" ref-type=\"bibr\">Yavorska and Burgess, 2017</xref>).</p><p>For the statistical analysis of the genetic association of serum calcium content with PD risk, P < 0.05 was considered as significant. In addition, a sensitivity analysis was conducted to evaluate the robustness of the causal estimates. Subsequently, each genetic variant was excluded one by one from the MR analysis via the leave-one-out permutation analysis, to assess the impact of each genetic variant on the causal estimates.</p></sec><sec id=\"S2.SS6\"><title>Power Analysis</title><p>Serum calcium variance (R<sup>2</sup>) proportion was assessed with the following formula:</p><disp-formula id=\"S2.Ex1\"><mml:math id=\"M1\"><mml:mrow><mml:msup><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>=</mml:mo><mml:mrow><mml:munderover><mml:mo largeop=\"true\" movablelimits=\"false\" symmetric=\"true\">∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>K</mml:mi></mml:munderover><mml:mfrac><mml:mrow><mml:mrow><mml:msubsup><mml:mi mathvariant=\"normal\">β</mml:mi><mml:mi>i</mml:mi><mml:mn>2</mml:mn></mml:msubsup><mml:mo>*</mml:mo><mml:mn>2</mml:mn><mml:mo>*</mml:mo><mml:mi>M</mml:mi></mml:mrow><mml:mo>⁢</mml:mo><mml:mi>A</mml:mi><mml:mo>⁢</mml:mo><mml:msub><mml:mi>F</mml:mi><mml:mrow><mml:mi>S</mml:mi><mml:mo>⁢</mml:mo><mml:mi>N</mml:mi><mml:mo>⁢</mml:mo><mml:msub><mml:mi>P</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:msub><mml:mo>⁢</mml:mo><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mrow><mml:mn>1</mml:mn><mml:mo>-</mml:mo><mml:mrow><mml:mi>M</mml:mi><mml:mo>⁢</mml:mo><mml:mi>A</mml:mi><mml:mo>⁢</mml:mo><mml:msub><mml:mi>F</mml:mi><mml:mrow><mml:mi>S</mml:mi><mml:mo>⁢</mml:mo><mml:mi>N</mml:mi><mml:mo>⁢</mml:mo><mml:msub><mml:mi>P</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:msub></mml:mrow></mml:mrow><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>v</mml:mi><mml:mo>⁢</mml:mo><mml:mi>a</mml:mi><mml:mo>⁢</mml:mo><mml:mi>r</mml:mi><mml:mo>⁢</mml:mo><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>X</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow></mml:mfrac></mml:mrow></mml:mrow></mml:math></disp-formula><p>Where β<sub><italic>i</italic></sub> is the effect size (beta coefficient) associated with the serum calcium content for <italic>SNP</italic><sub><italic>i</italic></sub>, <italic>MAF</italic><sub><italic>SNP_i</italic></sub>is the minor allele frequency for <italic>SNP</italic><sub><italic>i</italic></sub>, <italic>K</italic> represents genetic variant number, and<italic>v</italic><italic>a</italic><italic>r</italic>(<italic>X</italic>) represents serum calcium variance (<italic>v</italic><italic>a</italic><italic>r</italic>(<italic>X</italic>) = <italic>SD</italic><sup>2</sup>, and 1 <italic>SD</italic> = 0.5 mg/dL).</p><p>The instrumental variable strength (genetic variant related to serum calcium content) was assessed through the first-stage F-statistics. Generally, a threshold (<italic>F</italic> > 10) was used in case of bias in Mendelian randomization trials (<xref rid=\"B8\" ref-type=\"bibr\">Burgess and Thompson, 2011</xref>). Here, we calculated the F-statistics and statistical power using a web-based tool mRnd<sup><xref ref-type=\"fn\" rid=\"footnote1\">1</xref></sup> and a two-sided type-I error rate α of 0.05 (<xref rid=\"B6\" ref-type=\"bibr\">Brion et al., 2013</xref>).</p></sec></sec><sec id=\"S3\"><title>Results</title><sec id=\"S3.SS1\"><title>Association Between Serum Calcium Variants and PD</title><p>From the discovery and validation PD GWAS datasets, eight genetic variants associated with serum calcium levels were extracted for statistical analysis. As shown in <xref rid=\"T1\" ref-type=\"table\">Table 1</xref>, the results suggested that all these genetic variants were not significantly related to PD (<italic>P</italic> > 0.05/8 = 0.00625).</p></sec><sec id=\"S3.SS2\"><title>Pleiotropy Analysis</title><p>At stage 1, the results showed that rs780094 and rs1570669 variants were significantly related to some known confounders (<italic>P</italic> < 0.05/8 = 0.00625). More specifically, rs780094 was significantly related to low density lipoprotein (<italic>P</italic> = 1.02E−07), high density lipoprotein (<italic>P</italic> = 2.67E−03), total cholesterol (<italic>P</italic> = 5.28E−41), triglyceride (<italic>P</italic> = 7.08E−125), type II diabetes (<italic>P</italic> = 1.00E−05), alcohol consumption (<italic>P</italic> = 3.65E−09), hip circumference (<italic>P</italic> = 3.40E−05) serum urate (<italic>P</italic> = 6.52E−39) and waist-to-hip ratio adjusted for BMI (<italic>P</italic> = 1.80E−03); rs1570669 was significantly related to circulating vitamin D (<italic>P</italic> = 5.33E−06). Hence, we excluded rs780094 and rs1570669 variants to meet the assumptions of the Mendelian randomization design. More detailed datasets were provided in <xref ref-type=\"supplementary-material\" rid=\"FS2\">Additional File 2</xref>. At stage 2, using the remaining six genetic variants (excluding rs780094 and rs1570669 variants identified at stage 1), the MR-Egger intercept test demonstrated that no evidence of pleiotropy was detected in the PD discovery GWAS dataset, with intercept = 0.000 and <italic>P</italic> = 1.00, or in the PD validation GWAS dataset, intercept = −0.021 and <italic>P</italic> = 0.562. At stage 3, no horizontal pleiotropic outliers were identified in either the discovery or validation PD GWAS datasets by MR-PRESSO. Hence, our Mendelian randomization trial focused on the remaining six genetic variants.</p></sec><sec id=\"S3.SS3\"><title>Association Between Serum Calcium Content and PD Risk</title><p>For the PD discovery dataset, the IVW analysis showed that per SD increase in serum calcium content (0.5 mg/dL) was not causally associated with a decreased PD incidence (OR = 1.73, 95% CI: 0.47–6.37, <italic>P</italic> = 0.408) using the remaining 6 genetic variants. Likewise, the weighted-median estimate (OR = 1.72, 95% CI: 0.52–5.66, <italic>P</italic> = 0.374), MR-Egger estimate (OR = 1.73, 95% CI: 0.12–25.9, <italic>P</italic> = 0.691) and MR-PRESSO estimate (OR = 1.73, 95% CI: 0.47–6.37, <italic>P</italic> = 0.446) did not show a significant causal association of serum calcium content with PD risk (<italic>P</italic> < 0.05 was considered as significance). For the PD validation dataset, our study demonstrated a similar conclusion that neither the IVW estimate (OR = 1.39, 95% CI: 0.45–4.31, <italic>P</italic> = 0.564), weighted-median estimate (OR = 1.78, 95% CI: 0.76–4.15, <italic>P</italic> = 0.185), MR-Egger estimate (OR = 2.46, 95% CI: 0.25–23.9, <italic>P</italic> = 0.437) or MR-PRESSO estimate (OR = 1.39, 95% CI: 0.45–4.30, <italic>P</italic> = 0.589) demonstrated that the increased serum calcium content was significantly related to PD risk. The details are shown in <xref rid=\"T2\" ref-type=\"table\">Table 2</xref>. Furthermore, the leave-one-out permutation analysis indicated the direction and precision of causal association between enhanced serum calcium content and increased PD risk was largely unchanged among these methods. In addition, we provide the individual estimates from different methods inferring the causality between each of serum calcium genetic variants and PD in the PD discovery and validation datasets, respectively (<xref ref-type=\"supplementary-material\" rid=\"FS3\">Additional Files 3</xref>, <xref ref-type=\"supplementary-material\" rid=\"FS4\">4</xref>). The forest plot for MR estimates about the causal effect of genetically increased serum calcium levels on PD using IVW method is also added (<xref ref-type=\"supplementary-material\" rid=\"FS5\">Additional Files 5</xref>, <xref ref-type=\"supplementary-material\" rid=\"FS6\">6</xref>).</p><table-wrap id=\"T2\" position=\"float\"><label>TABLE 2</label><caption><p>Mendelian randomization analysis using four methods.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Dataset</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Method</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">OR</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">SE</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">95% CI</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>P-</italic>value</td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PD discovery dataset</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Weighted_median</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.72</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.61</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.52–5.66</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.374</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PD discovery dataset</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">IVW</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.73</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.66</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.47–6.37</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.408</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PD discovery dataset</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MR-Egger</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.73</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.38</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.12–25.9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.691</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PD discovery dataset</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MR-PRESSO</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.73</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.66</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.47–6.37</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.446</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PD validation dataset</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Weighted_median</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.78</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.43</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.76–4.15</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.185</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PD validation dataset</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">IVW</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.39</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.58</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.45–4.31</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.564</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PD validation dataset</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MR-Egger</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.46</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.25–23.9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.437</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PD validation dataset</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MR-PRESSO</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.39</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.58</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.45–4.30</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.589</td></tr></tbody></table><table-wrap-foot><attrib><italic>SE, standard error; OR > 0 and OR < 0 means that high serum calcium levels increase and reduce the risk of PD. OR, odds ratio; CI, confidence interval; IVW, Inverse-variance weighted meta-analysis. The association between serum calcium levels and PD was at the significance level P < 0.05.</italic></attrib></table-wrap-foot></table-wrap></sec><sec id=\"S3.SS4\"><title>Power Analysis</title><p>Here, all these six genetic variants could explain about 0.81% of the serum calcium variances (<italic>R</italic><sup>2</sup> = 0.81%). In both the PD discovery and validation GWAS datasets, the first-stage F-statistics for the instrument included six genetic variants with F more than 10, so there was no instrument bias. Our Mendelian randomization trial had 80% power to determine the impact size of moderate magnitudes with ORs > = 1.95 and 1.48 per SD (0.5 mg/dL) increases in serum calcium content for PD risk in the PD discovery and validation GWAS dataset, respectively.</p></sec></sec><sec id=\"S4\"><title>Discussion</title><p>Calcium (Ca<sup>2+</sup>) is a crucial second messenger that participates in a variety of cellular physiological processes. Particularly in neuronal cells, intracellular Ca<sup>2+</sup> signaling is under extremely precise control to ensure the smooth functioning of many electrophysiological activities such as neuronal excitability, neurotransmitter secretion and synaptic plasticity. As a result, dysregulation of Ca<sup>2+</sup> signaling is implicated in neurodegeneration disorders such as Alzheimer’s disease, PD and Huntington’s disease. Recently, the causality between genetically higher serum calcium and reduced AD incidence has been established (<xref rid=\"B18\" ref-type=\"bibr\">Jiang et al., 2018</xref>).</p><p>It has been well recognized that Ca<sup>2+</sup> signaling abnormalities play a crucial role in PD pathogenesis. The coordinated regulation of Ca<sup>2+</sup> fluxes is compromised in PD, which induces selective toxicity in the dopaminergic neurons in the substantia nigra. PD risk is significantly reduced in hypertensive patients treated with L-type Ca<sup>2+</sup> channel antagonists (<xref rid=\"B3\" ref-type=\"bibr\">Becker et al., 2008</xref>; <xref rid=\"B39\" ref-type=\"bibr\">Ritz et al., 2010</xref>; <xref rid=\"B38\" ref-type=\"bibr\">Pasternak et al., 2012</xref>). The voltage gated L-type Ca<sup>2+</sup> channel inhibitor isradipine that blocks Ca<sup>2+</sup> entry can protect SNc neurons in mouse models of PD (<xref rid=\"B17\" ref-type=\"bibr\">Ilijic et al., 2011</xref>). A phase III clinical trial of isradipine is in progress to evaluate if it can also benefit PD patients (<xref rid=\"B21\" ref-type=\"bibr\">Liss and Striessnig, 2019</xref>). However, the relationship between serum calcium and PD has not been well recognized by observational studies. Here, we investigated the genetic associations of serum calcium content with PD risk using the Mendelian randomization method, and the results were negative. Our results are comparable to the previous epidemiological reports, demonstrating that calcium supplementation was not associated with PD risk (<xref rid=\"B10\" ref-type=\"bibr\">Chen et al., 2007</xref>; <xref rid=\"B31\" ref-type=\"bibr\">Mischley et al., 2017</xref>). The relationship between altered serum calcium content and PD reported by a few observational studies might be mediated by some confounding factors such as daily physical activity. In conclusion, our results did not support the supplementation of calcium for preventing PD in the general population. Future clinical trials regarding this topic may fail to produce positive benefits.</p><p>Mendelian randomization research has many advantages. Firstly, the study can use the large-scale serum calcium and PD GWAS datasets to provide enough power, thereby detecting genetic associations of serum calcium content with PD. Secondly, these serum calcium and PD GWAS datasets were all of European descent, reducing population stratification effects. Thirdly, various independent genetic variants were used as instrument variants to decrease linkage disequilibrium effects. Fourthly, multiple methods were carried out for a comprehensive pleiotropy analysis to rule out two genetic variants related to potential confounding factors. Causal inference results should not depend on a single method. A previous study indicated that if the results were confirmed using a variety of methods, the causal findings might be more reliable, especially if these methods are based on different assumptions (<xref rid=\"B7\" ref-type=\"bibr\">Burgess et al., 2017</xref>). Therefore, we chose 4 different methods to test causal effects in this present study. The application of different methods under different situations may affect the results of the causal effect analysis. Specifically, when investigating whether there is a dose-response relation between the genetic instruments and their outcomes, the MR-Egger method is more effective than the IVW or weighted-median methods. However, when exploring the possible effects of outlying genetic variants on causality, the IVW estimates may be less severely affected by other factors compared to the MR-Egger estimate (<xref rid=\"B9\" ref-type=\"bibr\">Burgess and Thompson, 2017</xref>). In practical applications, it is recommended to use a series of sensitivity analysis methods. All these mentioned advantages could protect against violations of Mendelian randomization assumptions.</p><p>Meanwhile, this Mendelian randomization trial also has some limitations. Firstly, the scale of PD GWAS dataset used by us was small, with 8,477 individuals (<xref rid=\"B37\" ref-type=\"bibr\">Pankratz et al., 2012</xref>; <xref rid=\"B28\" ref-type=\"bibr\">Liu et al., 2017</xref>). In 2014, <xref rid=\"B34\" ref-type=\"bibr\">Nalls et al. (2014)</xref> conducted a large-scale GWAS meta-analysis containing 13708 PD cases and 95,282 controls from 15 independent GWAS datasets in European. However, this dataset was not publicly available, so our trial needs to be repeated when this dataset is available. Secondly, population stratification was not absolutely excluded due to its impacts on the assessment. Thirdly, the genetic relationship between serum calcium content and PD might be different due to the various ethnicities and ancestries, so the genetic relationship needs to be further assessed in other ethnicities and ancestries. However, other meta-analyses, such as meta-analysis based on the Bayesian method (<xref rid=\"B33\" ref-type=\"bibr\">Moreno et al., 2014</xref>), have also been used to test this kind of relationship. We will further conduct this meta-analysis in the next study to explore the correlation between serum calcium levels and PD and compared it with our Mendelian randomized analysis. Ultimately, there were some difficulties calculating the false positive rates of different methods and we indeed need to address these concerns. We will examine all the concerns mentioned above in future research.</p><p>In conclusion, our data indicated that elevated serum calcium levels were not causally related to PD risk. Hence, calcium supplementation might not contribute to reducing PD risk.</p></sec><sec sec-type=\"data-availability\" id=\"S5\"><title>Data Availability Statement</title><p>Publicly available datasets were analyzed in this study. This data can be found here: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ebi.ac.uk/gwas/\">https://www.ebi.ac.uk/gwas/</ext-link>.</p></sec><sec id=\"S6\"><title>Author Contributions</title><p>YW and LG designed the study, analyzed the data, and wrote the first draft of the manuscript. PC revised the manuscript. WL, HL, and PC collected the data and provided technological support. WJ conceived the study, formulated the research concept, and revised the manuscript. NZ provided suggestions for the study design, revision and proof of the manuscript. All authors contributed to the interpretation of the results and critical revision of the manuscript for important intellectual content and approved the final version of the manuscript.</p></sec><sec id=\"conf1\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> This work was supported by the National Natural Science Foundation of China (Nos. 81801197 and 81870954 to WJ) and the Natural Science Foundation of Tianjin (No. 19JCQNJC10500 to WJ).</p></fn></fn-group><ack><p>We thank Pankratz and colleagues for the PD GWAS summary results data. We also thank the Global Lipids Genetic Consortium (GLGC), International Consortium of Blood Pressure (ICBP) consortium, DIAbetes Genetics Replication and Meta-analysis (DIAGRAM) Consortium, Genetic Investigation of ANthropometric Traits (GIANT) consortium, Tobacco and Genetics Consortium (TGC), Alcohol Genome-Wide Association (AlcGen), and Cohorts for Heart and Aging Research in Genomic Epidemiology Plus (CHARGE +) Consortia, Global Urate Genetics Consortium (GUGC) and Ukbiobank for other GWAS datasets. We also thank Dr. Junwei Hao for the help in the study design and manuscript drafting.</p></ack><fn-group><fn id=\"footnote1\"><label>1</label><p><ext-link ext-link-type=\"uri\" xlink:href=\"https://cnsgenomics.shinyapps.io/mRnd/\">https://cnsgenomics.shinyapps.io/mRnd/</ext-link></p></fn></fn-group><sec id=\"S9\" sec-type=\"supplementary material\"><title>Supplementary Material</title><p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.frontiersin.org/articles/10.3389/fgene.2020.00824/full#supplementary-material\">https://www.frontiersin.org/articles/10.3389/fgene.2020.00824/full#supplementary-material</ext-link></p><supplementary-material content-type=\"local-data\" id=\"FS1\"><label>FILE S1</label><caption><p>Methods to measure serum calcium levels in the original study.</p></caption><media xlink:href=\"Table_1.doc\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"FS2\"><label>FILE S2</label><caption><p><italic>P-</italic>values for associations of 8 calcium-associated genetic variants with known PD risk factors.</p></caption><media xlink:href=\"Table_1.doc\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"FS3\"><label>FILE S3</label><caption><p>Individual causal estimates from each of serum calcium genetic variants and PD using different methods in Discovery PD GWAS dataset.</p></caption><media xlink:href=\"Table_1.doc\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"FS4\"><label>FILE S4</label><caption><p>Individual causal estimates from each of serum calcium genetic variants and PD using different methods in Validation PD GWAS dataset.</p></caption><media xlink:href=\"Table_1.doc\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"FS5\"><label>FILE S5</label><caption><p>Forest plot for MR estimates about the association between genetically increased serum calcium levels and PD in Discovery PD GWAS dataset.</p></caption><media xlink:href=\"Table_1.doc\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"FS6\"><label>FILE S6</label><caption><p>Forest plot for MR estimates about the association between genetically increased serum calcium levels and PD in Validation PD GWAS dataset.</p></caption><media xlink:href=\"Table_1.doc\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec><ref-list><title>References</title><ref id=\"B1\"><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Abou-Raya</surname><given-names>S.</given-names></name><name><surname>Helmii</surname><given-names>M.</given-names></name><name><surname>Abou-Raya</surname><given-names>A.</given-names></name></person-group> (<year>2009</year>). <article-title>Bone and mineral metabolism in older adults with Parkinson’s disease.</article-title>\n<source><italic>Age Ageing</italic></source>\n<volume>38</volume>\n<fpage>675</fpage>–<lpage>680</lpage>. <pub-id pub-id-type=\"doi\">10.1093/ageing/afp137</pub-id>\n<pub-id pub-id-type=\"pmid\">19684354</pub-id></mixed-citation></ref><ref id=\"B2\"><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Ascherio</surname><given-names>A.</given-names></name><name><surname>Schwarzschild</surname><given-names>M. 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] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Immunol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Immunol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Immunol.</journal-id><journal-title-group><journal-title>Frontiers in Immunology</journal-title></journal-title-group><issn pub-type=\"epub\">1664-3224</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849664</article-id><article-id pub-id-type=\"pmc\">PMC7431983</article-id><article-id pub-id-type=\"doi\">10.3389/fimmu.2020.02004</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Immunology</subject><subj-group><subject>Perspective</subject></subj-group></subj-group></article-categories><title-group><article-title>Serum Free Immunoglobulins Light Chains: A Common Feature of Common Variable Immunodeficiency?</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Guevara-Hoyer</surname><given-names>Kissy</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/657238/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Ochoa-Grullón</surname><given-names>Juliana</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/591743/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Fernández-Arquero</surname><given-names>Miguel</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Cárdenas</surname><given-names>Mariacruz</given-names></name><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Pérez de Diego</surname><given-names>Rebeca</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><xref ref-type=\"aff\" rid=\"aff5\"><sup>5</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/385562/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Sánchez-Ramón</surname><given-names>Silvia</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/3310/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Department of Immunology, IML and IdSSC, Hospital Clínico San Carlos</institution>, <addr-line>Madrid</addr-line>, <country>Spain</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Department of Immunology, Ophthalmology and ENT, School of Medicine, Complutense University of Madrid</institution>, <addr-line>Madrid</addr-line>, <country>Spain</country></aff><aff id=\"aff3\"><sup>3</sup><institution>Immunodeficiency Interdepartmental Group (GIID)</institution>, <addr-line>Madrid</addr-line>, <country>Spain</country></aff><aff id=\"aff4\"><sup>4</sup><institution>Clinical Analysis Department, Hospital Clínico San Carlos</institution>, <addr-line>Madrid</addr-line>, <country>Spain</country></aff><aff id=\"aff5\"><sup>5</sup><institution>Laboratory of Immunogenetics of Human Diseases, IdiPAZ Institute for Health Research</institution>, <addr-line>Madrid</addr-line>, <country>Spain</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Guzide Aksu, Ege University, Turkey</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Isabella Quinti, Sapienza University of Rome, Italy; Jennifer Heaney, University of Birmingham, United Kingdom</p></fn><corresp id=\"c001\">*Correspondence: Silvia Sánchez-Ramón, <email>ssramon@salud.madrid.org</email></corresp><fn fn-type=\"other\" id=\"fn004\"><p>This article was submitted to Primary Immunodeficiencies, a section of the journal Frontiers in Immunology</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>2004</elocation-id><history><date date-type=\"received\"><day>11</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>24</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Guevara-Hoyer, Ochoa-Grullón, Fernández-Arquero, Cárdenas, Pérez de Diego and Sánchez-Ramón.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Guevara-Hoyer, Ochoa-Grullón, Fernández-Arquero, Cárdenas, Pérez de Diego and Sánchez-Ramón</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>Serum free light chain (sFLC) is a recently proposed biomarker for CVID diagnosis. Most CVID patients present low or undetectable sFLC up to 10-fold lower compared to other primary antibody deficiencies. Given that κ and λ light chains are normally secreted in excess with respect to immunoglobulins, this finding points to an intrinsic defect of B cell differentiation in CVID. sFLC levels were prospectively evaluated in a cohort of 100 primary immunodeficiency (PID) patients and in 49 patients with secondary immunodeficiency to haematological malignancy (SID). CVID patients had significantly lower κ and/or λ values (mean: κ: 1.39 ± 1.7 mg/L and λ: 1.97 ± 2.24 mg/L) compared to “other PIDs” (κ: 13.97 ± 5.88 mg/L and λ: 12.92 ± 7.4 mg/L, respectively, <italic>p</italic> < 0.001 both), and SID (κ 20.9 ± 22.8 mg/L and λ 12.8 ± 8.7 mg/L, respectively, <italic>p</italic> < 0.001 both). The sum of kappa and lambda (sum κ + λ) in CVID patients (7.25 ± 7.90 mg/L) was significantly lower respect to other PIDs (26.44 ± 13.25 mg/L, <italic>p</italic> < 0.0001), and to SID patients (28.25 ± 26.24 mg/L, <italic>p</italic> = 0.0002). ROC analysis of the sum κ + λ disclosed an area under the curve (AUC) of 0.894 for CVID diagnosis (SD 0.031; 95% CI: 0.83–0.95, <italic>p</italic> < 0.0001), with optimal cut-off of 16.7 mg/L, giving the highest combination of sensitivity (92%), specificity (75%) and NPV (98%). The Relative Risk (RR) for patients presenting a sum κ + λ below 16.7 mg/L was 20.35-fold higher (95%, CI: 5.630–75.93) for CVID than below this threshold. A similar behavior of the sFLC in our CVID cohort with respect to previously published studies was observed. We propose a cut-off of sum κ + λ 16.7 with diagnostic application in CVID patients, and discuss potential specific defects converging in low or undetectable sFLC.</p></abstract><kwd-group><kwd>common variable immunodeficiency</kwd><kwd>serum-free immunoglobulins light chains</kwd><kwd>diagnostic tool</kwd><kwd>prognostic biomarkers</kwd><kwd>primary immunodeficiencies</kwd></kwd-group><counts><fig-count count=\"2\"/><table-count count=\"0\"/><equation-count count=\"0\"/><ref-count count=\"55\"/><page-count count=\"9\"/><word-count count=\"0\"/></counts></article-meta></front><body><sec id=\"S1\"><title>Background: Immunoglobulin, The Master Key of Many Locks</title><p>Given the high clinical variability and immunological heterogeneity in clinical manifestations of common variable immunodeficiency (CVID), several researchers have proposed combinations of clinical and immunological biomarkers in order to refine the diagnosis and to provide more personalized follow-up and treatment strategies that may improve the prognosis of the individual patient (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>–<xref rid=\"B4\" ref-type=\"bibr\">4</xref>). A recently proposed biomarker for CVID diagnosis is the quantification of serum free light chain (sFLC) (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>–<xref rid=\"B8\" ref-type=\"bibr\">8</xref>).</p><p>The key-shaped structure of immunoglobulins (Ig) as originally described by Ehrlich (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>), consists of four polypeptide chains, two pairwise identical copies of both heavy (H) and light (L) chains, the latter being named kappa (κ) or lambda (λ) chains (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>). This “key” opens up a wide range of processes associated with innate and adaptive immunity. Among these processes stand out the direct neutralization of an almost unlimited number of antigens and toxins, autoantibodies, modulation of fas death receptor, binding to lectins, modulation of the complement cascade, regulation of monocytes/macrophages, activation of NK cells, regulatory T cell expansion, suppression of T and B cell activation, suppression of cytokines, neuroregulatory effects and increased sensitivity of steroids (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>, <xref rid=\"B11\" ref-type=\"bibr\">11</xref>). However, functions related to this “master key” are not completely known. L chains are incorporated into Ig molecules during B-cell development. Initially, large pre-B cells express a pre-BCR that is assembled from antibody μ H chains and surrogate L chain (VPREB1 and IGLL1). At the next stage, in small pre-B cells, bona fide L chains (κ and λ) undergo recombination and when this results in a productively recombined L chain, it is expressed together with μHC forming a BCR on the surface of pre B-cells (<xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>) (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>). Production in excess of L chains occurs throughout B-cell development till plasma cells, where they bind to H chains, excess L chains enter the bloodstream as FLCs. Secretion of L chains would reflect B cell activation (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>, <xref rid=\"B14\" ref-type=\"bibr\">14</xref>).</p><fig id=\"F1\" position=\"float\"><label>FIGURE 1</label><caption><p>Potential hot-points in the development of sFLC synthesis, assembly and secretion. Scheme of the B cell maturation and differentiation, where an alteration in the rearrangement of the receptors of the pre-B cells, could condition the inadequate production of immunoglobulin light chains and, consequently, the defective expression of immunoglobulins. Modified from Winkler and Mårtensson (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>).</p></caption><graphic xlink:href=\"fimmu-11-02004-g001\"/></fig><p>In healthy individuals, small amounts of both free L κ and λ chains can be found (κ = 3.3–19.4 mg/l, λ = 5.7–26.3 mg/l), with a normal κ/λ ratio ranging between 0.26 and 1.65 depending on the technical assay (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>). These ranges were suggested using reference serum samples from 282 healthy donors between the ages of 21 and 90 years (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>), based on the polyclonal Freelite assay.</p><p>sFLC quantification may indicate the presence of B cell clonality and is widely used in clinical practice for the diagnosis of B-cell lymphoproliferative disorders (B-CLPD), in particular the progression of monoclonal gammopathy of undetermined significance (MGUS) to multiple myeloma (MM), as well as a marker of neuroinflammation, for instance, in multiple sclerosis (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>–<xref rid=\"B23\" ref-type=\"bibr\">23</xref>). Moreover, dysbalance in sFLC is used as a prognostic marker of various B-CLPD, such as chronic lymphocytic leukemia (CLL), B cell non-Hodgkin lymphomas (NHL), as well as for real-time monitoring of response to treatment and disease progression (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>, <xref rid=\"B17\" ref-type=\"bibr\">17</xref>, <xref rid=\"B24\" ref-type=\"bibr\">24</xref>–<xref rid=\"B27\" ref-type=\"bibr\">27</xref>). Due to the inherent immunological alterations in a relevant proportion of PID patients, such as polyclonal B cell proliferation, it is particularly challenging to make an early diagnosis of B malignancy in these patients. Interestingly, alterations of sFLC in PID patients (κ/λ ratio), especially in CVID, correlate with clonal processes (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>, <xref rid=\"B7\" ref-type=\"bibr\">7</xref>, <xref rid=\"B13\" ref-type=\"bibr\">13</xref>).</p><p>Here we sought to validate previous studies on the diagnostic and prognostic value of sFLC. Secondly, we suggest the sum κ + λ as a practical combined biomarker of CVID diagnosis and other potential applications for follow-up and prognosis. Finally, we present a hypothesis on the possible scenarios underlying very low sFLC in CVID and discuss potential experimental and clinical approximations.</p></sec><sec sec-type=\"materials|methods\" id=\"S2\"><title>Materials and Methods</title><p>sFLC levels were prospectively evaluated in a cohort of 100 primary immunodeficiency (PID) patients and in 49 patients diagnosed with a hematological malignancy referred to study of secondary immunodeficiency (SID) at the Clinical Immunology Dept., Hospital Clínico San Carlos of Madrid, Spain. All PID patients fulfilled the ESID registry diagnostic criteria (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>).</p><p>sFLC κ and λ chains were quantified by nephelometry (FREELITE, The Binding Site Group Ltd., Birmingham, United Kingdom), according to the manufacturer’s instructions, using a BNII nephelometer (Siemens Healthcare Diagnostics, Camberley, Surrey, United Kingdom).</p><p>SPSS statistics software (Chicago, IL, United States) was used for descriptive and statistical data analysis. Pearson’s correlation coefficient was used to assess the correlation between variables. <italic>p</italic> < 0.05 was considered statistically significant. Receiver operating characteristic curve (ROC curve) and contingency analysis were performed using GraphPad Prism version 8.3.0 for Windows, GraphPad Software, La Jolla, CA, United States<sup><xref ref-type=\"fn\" rid=\"footnote1\">1</xref></sup>.</p><p>Approval for the study was obtained from the hospital institutional Ethics Committee for PID and SID projects (19-284-E and 19/219-E), respectively. Written informed consent was obtained from all patients for inclusion in the study protocol.</p></sec><sec id=\"S3\"><title>Results</title><sec id=\"S3.SS1\"><title>sFLC Discriminates CVID From Other Primary Immunodeficiencies</title><p>We studied sFLC in 100 patients with different PIDs (selective IgA deficiency <italic>n</italic> = 38, unclassified antibody deficiency <italic>n</italic> = 27, CVID <italic>n</italic> = 26, Good syndrome <italic>n</italic> = 2, 22q11.2 deletion syndrome <italic>n</italic> = 2, complement system deficiency <italic>n</italic> = 2, X-linked hypogammaglobulinemia <italic>n</italic> = 1, hyper IgM syndrome <italic>n</italic> = 1, and Kabuki syndrome <italic>n</italic> = 1) as part as routine immunological work-up. CVID patients showed significantly lower κ and/or λ values in comparison to other PIDs (mean: κ: 1.39 ± 1.7 mg/L and λ: 1.97 ± 2.24 mg/L <italic>versus</italic> κ: 13.97 ± 5.88 mg/L and λ: 12.92 ± 7.4 mg/L, respectively, <italic>p</italic> < 0.001 both) (<xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref>). The sum of kappa and lambda (sum κ + λ) in CVID patients was 7.25 ± 7.90 mg/L versus 26.44 ± 13.25 mg/L respect to other PIDs (<italic>p</italic> < 0.0001).</p><fig id=\"F2\" position=\"float\"><label>FIGURE 2</label><caption><p><bold>(A)</bold> Distribution of lambda and kappa chains among CVID, PID and SID patients (<italic>N</italic> = 100). <bold>(B)</bold> Distribution of sFLC concentration (sum κ + λ) in CVID <italic>versus</italic> other PIDs and SID.</p></caption><graphic xlink:href=\"fimmu-11-02004-g002\"/></fig><p>When we analyzed the four previously described patterns of sFLCs in CVID (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>–<xref rid=\"B8\" ref-type=\"bibr\">8</xref>, <xref rid=\"B29\" ref-type=\"bibr\">29</xref>): 53.84% (14/26) disclosed the κ<sup>–</sup>λ<sup>–</sup> pattern; 30.76% (8/26) the κ<sup>+</sup>λ<sup>+</sup> pattern; 7.69% (2/26) the κ<sup>–</sup>λ<sup>+</sup> pattern; and 7.69% (2/26) κ<sup>+</sup>λ<sup>–</sup> pattern.</p><p>We then tested sFLC values in pure commercial gammaglobulin preparations, detecting mean levels of 4.15 mg/L of κ and 1.59 mg/L of λ, sum κ + λ 5.74 mg/L. However, it was insignificant when gammaglobulin was diluted 1:10, as found in normal plasma.</p></sec><sec id=\"S3.SS2\"><title>sFLCs May Aid in the Diagnosis of Secondary Immunodeficiencies</title><p>Regarding the comparison of sFLC expression in SID and CVID patients, we evaluated 49 patients with hematological malignancy: CLL (<italic>n</italic> = 12), NHL (<italic>n</italic> = 22), MGUS (<italic>n</italic> = 12) and MM (<italic>n</italic> = 3). CVID patients showed significantly lower kappa and/or lambda values than SID (mean: κ 1.39 ± 1.7 mg/L versus κ 20.9 ± 22.8 mg/L, <italic>p</italic> < 0.001; and λ 1.97 ± 2.24 mg/L versus λ 12.8 ± 8.7 mg/L, respectively, <italic>p</italic> < 0.001). When comparing the κ/λ ratio in both cohorts, CVID κ/λ ratio was significantly lower than SID (0.94 versus 1.91, <italic>p</italic> < 0.005). The sum κ + λ in CVID was also significantly lower than SID patients (7.25 ± 7.90 mg/L versus 28.25 ± 26.24 mg/L, <italic>p</italic> = 0.0002).</p><p>In our SID cohort to B-CLPD, 7 patients with CLL and NHL that showed very low or undetectable sFLC at diagnosis of malignancy were highly suspicious of an underlying CVID based on history of infections since childhood (<italic>n</italic> = 2) or suspicious family history of PID (<italic>n</italic> = 2), which were not diagnosed at the time, which could only be confirmed if PID-predisposing gene defect were found (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>).</p></sec><sec id=\"S3.SS3\"><title>ROC Curves</title><p>The diagnostic performance of a CVID-pattern (i.e., diminished levels of free L κ and/or λ in respect to the reference range) was evaluated for the diagnosis of CVID with respect to other PID and SID, showing a sensitivity of 76.00%, specificity of 85.71%, positive predictive value (PPV) 52.78% and negative predictive value (NPV) of 94.4% for CVID diagnosis.</p><p>We then compared the diagnostic value of the sum of free L κ + λ levels in this setting. ROC analysis disclosed an area under the curve (AUC) of 0.894 (SD 0.031; 95% CI: 0.83–0.95, <italic>p</italic> < 0.0001), with optimal cut-off of 16.7 mg/L for the sum κ + λ giving the highest combination of sensitivity (92%) and specificity (75.6%), with NPV (97.8%). The Relative Risk (RR) found for CVID was of 20.35-fold (95% CI 5.630–75.93) for patients with sum κ + λ levels below 16.7 mg/L (<xref ref-type=\"supplementary-material\" rid=\"FS3\">Supplementary Figure S3</xref>). The frequency of patients below the recommended sum κ + λ cut-off of 16.7 mg/L was 92% of CVID patients; 10% of other PIDs; and 28% of SID (<italic>p</italic> < 0.0001, both).</p></sec><sec id=\"S3.SS4\"><title>Comparison of sFLC Values With Clinical Associations in CVID</title><p>We observed a significant correlation between κ<sup>–</sup>λ<sup>–</sup> pattern and CD27<sup>+</sup>IgD<sup>–</sup>IgM<sup>–</sup> switched-memory B cells (<italic>p</italic> < 0.05). We did not find statistical significance between specific sFLC patterns and age (<italic>p</italic> = 0.20), sex (<italic>p</italic> = 0.30), clinical onset (<italic>p</italic> = 0.15), CD21<sup><italic>low</italic></sup> B cells (<italic>p</italic> = 0.76) or Ig levels at diagnosis of CVID (<italic>p</italic> = 0.94). We then compared sFLC patterns (κ<sup>–</sup>λ<sup>+</sup>, κ<sup>+</sup>λ<sup>–</sup>, κ<sup>–</sup>λ<sup>–</sup>, κ<sup>+</sup>λ<sup>+</sup>) with clinical associations in CVID patients. We found that κ<sup>–</sup>λ<sup>–</sup> pattern was highly prevalent in CVID patients with enteropathy (66.66%, 4/6 patients, <italic>p</italic> = 0.01), splenomegaly (50.00%, 7/14 patients, <italic>p</italic> = 0.2) and bronchiectasis (68.75%, 11/16 patients, <italic>p</italic> = 0.1), compatible with the association described by other groups (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>, <xref rid=\"B7\" ref-type=\"bibr\">7</xref>, <xref rid=\"B29\" ref-type=\"bibr\">29</xref>).</p><p>Regarding clinical associations in CVID patients using the sum κ + λ below 16.7 versus above this cut-off, 28.57% (6/21 patients) presented enteropathy versus 0% (<italic>p</italic> = 0.1), 57.14% (12/21) splenomegaly versus 40% (<italic>p</italic> = 0.4) and 66.66% (14/21) bronchiectasis versus 40% (<italic>p</italic> = 0.2).</p></sec></sec><sec id=\"S4\"><title>Discussion</title><sec id=\"S4.SS1\"><title>sFLC as a Diagnostic Tool in Primary Immunodeficiencies</title><p>Compatible with previous studies, a similar distribution of normal sFLC levels among all PID was observed, except for CVIDs, with significantly lower κ and/or λ values than other PIDs (<italic>p</italic> < 0.001). To better define the most optimal cutoff level for sFLC in our population, we used the sum κ + λ of 16.7 through standard statistical ROC analyses for discriminating CVID from other PID and SID, AUC of 0.894 (<italic>p</italic> < 0.0001, for both). The sum κ + λ provided a high sensitivity, specificity and NPV for CVID diagnosis.</p><p>Several groups have pioneered a greater understanding on the role of sFLCs in the clinical scenario of PIDs (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>–<xref rid=\"B8\" ref-type=\"bibr\">8</xref>, <xref rid=\"B29\" ref-type=\"bibr\">29</xref>). While many primary antibody deficiency (PAD) share a common profile of hypogammaglobulinemia, it has not been fully elucidated why only CVID presents a characteristic profile of low or undetectable sFLC (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>–<xref rid=\"B8\" ref-type=\"bibr\">8</xref>, <xref rid=\"B29\" ref-type=\"bibr\">29</xref>). Unsworth et al. described very low sFLCs in 18 out of 20 PID cases, CVID being the commonest diagnosis (16/20), followed by X-linked agammaglobulinemia (XLA, 2/20). Hyper-IgM syndrome (HIGM, 1/20) and non-HIGM with raised polyclonal IgM (1/20) showed normal sFLC values. The common denominator for all 20 patients was antibody immunodeficiency and associated increased frequency of bacterial infections (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>). Scarpa et al. have recently described significant variations in sFLCs values in a wide cohort of PID. CVID patients showed decreased or undetectable values of sFLCs compared to normal values in patients with unclassified antibody defects (<italic>p</italic> < 0.0001) (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>). Their findings were similar to those described in other studies with different PIDs cohorts (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>–<xref rid=\"B8\" ref-type=\"bibr\">8</xref>, <xref rid=\"B29\" ref-type=\"bibr\">29</xref>, <xref rid=\"B31\" ref-type=\"bibr\">31</xref>, <xref rid=\"B32\" ref-type=\"bibr\">32</xref>). Regarding XLA, there are contradictory results in the different cohorts described, ranging from low to normal sFLC, which may depend on the genetic defect (<xref rid=\"B33\" ref-type=\"bibr\">33</xref>).</p><p>In the present study, we used nephelometry with the same test, reagents, and platforms described by Scarpa et al. and Unsworth et al. in PIDs cohorts (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>, <xref rid=\"B8\" ref-type=\"bibr\">8</xref>), which reduces the variability among results. The technique used has shown the highest sensitivity for sFLC, with detection up to 0.1 mg/L for both κ and λ as low reference levels (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>). However, it could be interesting to design comparative studies between the different techniques currently available for measuring sFLC in the PID setting.</p><p>The marked decrease in sFLCs could reflect a profound damage, both quantitatively and functional of the BCR during lymphocyte differentiation in CVID. Moreover, a dysfunctional BCR might be at the origin of a lymphoproliferative or self-reactive status in this patients’ population (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>). In general, the production of both L chains increases during infections or inflammatory states, with higher absolute concentrations but without change in κ/λ ratio (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>, <xref rid=\"B6\" ref-type=\"bibr\">6</xref>, <xref rid=\"B8\" ref-type=\"bibr\">8</xref>). Infection and inflammation are very common and define the clinical phenotypes “cytopenia,” “polyclonal lymphocytic infiltration,” “unexplained enteropathy,” and “no disease-related complications (only infections)” of CVID patients (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>). We could infer that CVID patients with chronic inflammatory phenotype would present with high levels in a κ<sup>+</sup>λ<sup>+</sup> pattern. Scarpa et al. analyzed the association of sFLCs patterns (κ<sup>–</sup>λ<sup>+</sup>, κ<sup>+</sup>λ<sup>–</sup>, κ<sup>–</sup>λ<sup>–</sup>, κ<sup>+</sup>λ<sup>+</sup>) and CVID clinical phenotypes. Counterintuitively, infectious and inflammatory phenotypes were more frequently observed in CVID patients with low or absent levels of sFLC (κ<sup>–</sup>λ<sup>–</sup>) (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>, <xref rid=\"B7\" ref-type=\"bibr\">7</xref>, <xref rid=\"B29\" ref-type=\"bibr\">29</xref>). Likewise, we found that enteropathy, splenomegaly and bronchiectasis were more prevalent in the κ<sup>–</sup>λ<sup>–</sup> pattern in our cohort, although the non-significance in splenomegaly and bronchiectasis could be due to the small sample size (<xref ref-type=\"supplementary-material\" rid=\"FS1\">Supplementary Figure S1</xref>). Our findings did not demonstrate statistical significance between specific sFLC patterns and age, sex, clinical onset or Ig levels at diagnosis of CVID. In the study of Compagno et al., κ<sup>–</sup>λ<sup>+</sup> pattern was the most represented in CVID (21 out of 46 patients, 46%) with higher risk of mortality derived from autoimmune cytopenias, lymphoproliferation and enteropathy (12/21 patients, 57%), followed by κ<sup>–</sup>λ<sup>–</sup> pattern (15/46 patients, 33%) with a trend to present splenomegaly (6/15 patients, 40%) and malignancy (5/15 patients, 33%) (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>). Scarpa et al. hypothesized that low sFLC levels may be an epiphenomenon of a higher degree of impairment in B cell differentiation, with reduced B cell class-switch affecting immunoglobulins’ production (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>). Altogether, these findings support that diminished sFLC values observed in CVID are associated with this pathology and can be used as an accessory diagnostic tool to support CVID diagnosis. However, the clinical significance of these patterns is still under study and needs further validation.</p></sec><sec id=\"S4.SS2\"><title>Comparison of sFLC Patterns With Other CVID Biomarkers</title><p>There is no clear correlation between sFLC with all serum Ig levels at diagnosis in the different PIDs groups studied (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>, <xref rid=\"B8\" ref-type=\"bibr\">8</xref>, <xref rid=\"B29\" ref-type=\"bibr\">29</xref>). Scarpa et al. described a direct association of IgA and IgM with serum κ and λ chain concentrations in CVID but not in control groups, while no association between sFLC and serum IgG neither in CVID or control groups (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>). Unsworth et al. did not find correlation between IgG and IgA values with sFLC concentrations in their PID cohort (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>). Hanitsch et al. described significantly lower IgG levels in κ<sup>–</sup>λ<sup>–</sup> CVID, although IgA and IgM levels were not different (<xref rid=\"B29\" ref-type=\"bibr\">29</xref>).</p><p>There are controversial results regarding sFLC with B cell phenotype in CVID patients. We observed a significant correlation between κ<sup>–</sup>λ<sup>–</sup> pattern and class-switch CD27<sup>+</sup>IgD<sup>–</sup>IgM<sup>–</sup> memory B cells (<italic>p</italic> < 0.05), without association between CD21<sup><italic>low</italic></sup> B cells and sFLC patterns. In contrast, Compagno et al. described a significant decrease in numbers of switched memory, marginal zone, CD21<sup><italic>low</italic></sup> B cells in the κ-λ- pattern, and a marked decrease of the subsets linked to B-cell activation and Ig production, while no correlation with transitional B cells (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>). IN contrast, Scarpa et al. showed the highest frequency of CD21<sup><italic>low</italic></sup> B cells in κ-λ- group (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>). The clinical association derived from these results warrants further study.</p><p>Most of our patients were on Ig replacement therapy (IgRT), and serum testing at CVID diagnosis and pre-infusion. Commercial gammaglobulin preparations of pooled normal IgG did contain detectable κ and λ sFLCs, similar to previously published data (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>), thus it seems unlikely that they may affect the results, since all patients had normal renal function and the half-life of sFLC in the circulation is 2–6 h (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>). IgG infusions are typically repeated every 3–4 weeks so that pre-infusion concentrations measured are likely to only contain sFLC produced by the patient’s immune system. Likewise, the multi-time measurement of sFLC in order to determine intra-individual variability showed no difference in our patients (data not shown).</p></sec><sec id=\"S4.SS3\"><title>sFLC and the Dilema Between Primary and Secondary Immunodeficiencies</title><p>There is a cancer-immune paradox in PID described by some authors (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>). The type of malignancy seems to be highly dependent on the specific PID, the age of the patient, and chronic infectious stimuli or dysbiosis involving complex pathogenic mechanisms (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>). Cancer is 1.4 to 5-fold higher in registry-based PID studies respect to general population (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>, <xref rid=\"B37\" ref-type=\"bibr\">37</xref>), from which 70% corresponds to lymphoid malignancy (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>, <xref rid=\"B39\" ref-type=\"bibr\">39</xref>). Individuals with CVID are at 5 to 10-fold higher risk of developing hematological malignancies (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>, <xref rid=\"B37\" ref-type=\"bibr\">37</xref>, <xref rid=\"B40\" ref-type=\"bibr\">40</xref>), while unexpectedly lower incidence on most common cancers than general population has been described (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>, <xref rid=\"B42\" ref-type=\"bibr\">42</xref>). Cancer is a leading cause of mortality in PID, and thus early diagnosis and treatment of malignancy is a priority (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>, <xref rid=\"B43\" ref-type=\"bibr\">43</xref>). When comparing the κ/λ ratio in both cohorts, SID showed significantly higher κ/λ ratio (<italic>p</italic> < 0.005), as expected (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>, <xref rid=\"B29\" ref-type=\"bibr\">29</xref>, <xref rid=\"B43\" ref-type=\"bibr\">43</xref>). In CVID patients, the κ/λ ratio is usually normal. In CVID, a B-CLPD can be the first and only clinical manifestation and thus the diagnosis of PID versus SID represents a difficult clinical dilemma. Low κ and/or λ values at hematological malignancy diagnosis might be pointing an underlying CVID. There are no data on the “potential PID patients” in the whole pool of patients with B-CLPD, which may justify to investigate an underlying PID as cancer predisposing factor (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>, <xref rid=\"B40\" ref-type=\"bibr\">40</xref>, <xref rid=\"B41\" ref-type=\"bibr\">41</xref>, <xref rid=\"B44\" ref-type=\"bibr\">44</xref>). Also, κ/λ ratio could be important in the follow-up of CVID patients, and hence an altered κ/λ ratio or a sudden increase in sFLC values may be an indicator for further investigation (blood smear, LDH, serum β2-microglobulin, PET-TAC, etc.) that allows appropriate and timely strategies. We consider that the κ/λ ratio behaves like a more reliable marker than the isolated determination of the sFLC when comparing both cohorts.</p></sec><sec id=\"S4.SS4\"><title>The Key to CVID: Distinctive Light Chain Defect</title><p>The extremely low sFLC in CVID might be explained by different reasons: (i) the lowest the plasma cells numbers, the lowest secretion of sFLC; (ii) increased elimination of sFLC; (iii) altered rearrangement, assembly or secretion of LC during B cell ontogeny; (iv) an intrinsic defect of plasma cells secretion of light chains. To address precisely the first argument, we should quantify plasma cells in the bone marrow, which is not feasible. Indirect measures of total peripheral B cells, class-switched B cells or total serum IgG did not explain sFLC (<xref ref-type=\"supplementary-material\" rid=\"FS2\">Supplementary Figure S2</xref>). We discarded the second argument, since none of the patients had renal or other protein loss. The two last hypothesis imply that the low levels of sFLC in CVID patients may reflect an intrinsic alteration affecting normal production, assembly or secretion of L chains into Ig molecules, which points either to specific defects in plasma cell differentiation (<xref rid=\"B45\" ref-type=\"bibr\">45</xref>), or stretches the way back to a critical early event during B cell differentiation. Ig genes are first rearranged in early B cell development through the V(D)J recombination in the liver and then bone marrow and then further modified upon antigenic encounter through the somatic hypermutation (SHM) process in germinal centers of lymphoid nodes. We hypothesize that different underlying mechanisms might correlate with different sFLC patterns, which we discuss below according to clinical and immunological observations and experimental published data:</p><list list-type=\"simple\"><list-item><label>1.</label><p>Early B cell defect: Particularly in cases where sFLC are undetectable, L chains should be more profoundly affected than H chains, for which a revision of genetic and molecular processes in the generation of Ig diversity is required. L chains of Ig are encoded in different multigene families from H chains and in different chromosomes: κ in chromosome 2p11.2, λ in chromosome 22q11.2 and H in chromosome 14q32.2, Several potential genetic variations might occur during this process (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>, <xref rid=\"B31\" ref-type=\"bibr\">31</xref>), affecting rearrangement of H and L chains in CVID (germline polymorphisms, allelic variants, insertion, deletion, etc.) (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>, <xref rid=\"B46\" ref-type=\"bibr\">46</xref>). At pre-B cells stage, first phase (antigen-independent) rearrangement of the H chain occurs, which reacts with light chain-like molecule called surrogate L (SL) chains by allelic exclusion (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>, <xref rid=\"B46\" ref-type=\"bibr\">46</xref>, <xref rid=\"B47\" ref-type=\"bibr\">47</xref>). During the cell division cycles, the composition of the pre-B cell receptors (BCR) in the daughter cells engender successful production of a complete κ or λ light chain and further allows the expression of IgM on B cell surface (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>).</p></list-item></list><p>The pre-BCR is a heterodimer composed of a H chain covalently associated with a surrogate light chains (SL) chain, a temporary common light chain composed by two non-covalently associated proteins, namely lambda-5 (λ5) and V-preB, which together have structural homology with conventional L chains (<xref rid=\"B48\" ref-type=\"bibr\">48</xref>). VDJ recombination of the H chain (pre-B cell) precedes pairing with SL chains, proliferation of large pre-B cells and subsequently L chain rearrangement (<xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref>). At this stage (at pre-BI to pre-BII or at pre-BII cell to immature B cells for pre-BCR), any transcriptional error in the SL chains, involving the region of the interchain bond during pre-B cells, would affect the correct linkage of SL to H chains and results in lack of LC. Conley reported the first patient with autosomal recessive mutation in the λ5 gene causing severe B cell deficiency and agammaglobulinaemia (<xref rid=\"B33\" ref-type=\"bibr\">33</xref>). The mutant λ5 resulted in impaired protein folding and secretion of Ig. It is conceivable that alteration of the players of this highly coordinated process at the pre-BCR stage may result in complete lack of secretion of L chains and undetectable sFLC. Isolated lack of single L chains occurs lately during this sequence. Complete absence of κ chain with normal Ig concentrations has been reported in a patient due to a heterozygous point mutation (1288 GG) that generated an amino acid substitution from Cys to Gly in the protein sequencing, causing an abnormal folding of the polypeptidic constant region of κ chain (<xref rid=\"B31\" ref-type=\"bibr\">31</xref>). In this patient, lack of κ chains determined a reduced antibody repertoire despite normal Ig concentrations, associated to recurrent bacterial infections. A correct class switch of H chains requires that the functional CH genes, located at one end of the rearranged H chain, are activated (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>, <xref rid=\"B47\" ref-type=\"bibr\">47</xref>). This synergy of mechanisms might explain in part why, in CVID patients, the decrease in sFLC is frequently associated with the inability to generate CD27<sup>+</sup>IgD<sup>–</sup>IgM<sup>–</sup> switched memory B cells. Additionally, an aberrant recombination of the L chain gene repertoire might favor autoreactive phenomena (<xref rid=\"B49\" ref-type=\"bibr\">49</xref>), while altered DNA repair controlling this recombination process would entail an increased pool of aberrant protein involves in B-cell oncogenesis (<xref rid=\"B50\" ref-type=\"bibr\">50</xref>, <xref rid=\"B51\" ref-type=\"bibr\">51</xref>).</p><list list-type=\"simple\"><list-item><label>2.</label><p>Plasma cell defect: acquired or genetic functional B cell defects have been described after TLR, CD40 and BCR-mediated NFκB signaling pathways that account for altered memory B cell phenotype in CVID patients and low Ig (<xref rid=\"B52\" ref-type=\"bibr\">52</xref>, <xref rid=\"B53\" ref-type=\"bibr\">53</xref>). Deep sequencing of IgH locus demonstrated restriction of characteristic patterns of IgHV and IgHJ usage depending on the B cell stimulus (<xref rid=\"B54\" ref-type=\"bibr\">54</xref>). Specific defects in the terminal plasma cell differentiation were shown in a subgroup of CVID patients at germinal center responses, with diverse mechanisms converging into the block of final step of plasma cell differentiation (<xref rid=\"B45\" ref-type=\"bibr\">45</xref>). In addition, gene lesions in MSH5 has been related to a few CVID patients, with defective S region junctions between LC and HC that would take place after antigen encounter (<xref rid=\"B55\" ref-type=\"bibr\">55</xref>). Altogether, these diverse mechanisms result in reduced and modulated class-switched B cells and Ig, which may relate to L chains’ restriction and low levels of sFLC secretion (<xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>).</p></list-item></list></sec><sec id=\"S4.SS5\"><title>Concluding Remarks and Further Perspectives</title><p>Our data validates previous studies emphasizing the relevance of sFLC quantification in the diagnosis and follow-up of CVID patients. sFLC behaves as a promising biomarker in the differential diagnosis of CVID with other PID and SID, and κ/λ ratio as a prognostic biomarker associated with specific clinical phenotypes. A cutoff level κ + λ < 16.7 mg/L supports CVID diagnosis. Moreover, κ/λ ratio alteration or a sudden increase in sFLC values may alert lymphoid malignancy and prompt appropriate and timely diagnostic work-out and therapy, a major concern in this patients’ population that impacts the survival. Reference values and cut-off points must be validated for each technique and then compare the different available immunoassays to come up with a reference range for each assay in different populations.</p><p>We hypothesize that decreased levels of sFLC in CVID patients may reflect an intrinsic early defect at a critical common step of B cell differentiation in the bone marrow affecting SL or L chain assembly or secretion that would affect memory B cell phenotype. Work is ongoing to check the hypothesis rooting this phenomenon by discarding gene defects during early B cell ontology, or intrinsic alterations in the terminal plasma cell differentiation, by <italic>in vitro</italic> differentiation of plasma cells from CVID patients after stimulation with subsequent determination of sFLC production and secretion. Altogether, we provide new evidence that this biologic phenomenon of low κ and λ provides a common feature of CVID, and leaves entirely open the question of whether would it be necessary to revisit the classification of CVID according to it.</p></sec></sec><sec sec-type=\"data-availability\" id=\"S5\"><title>Data Availability Statement</title><p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.</p></sec><sec id=\"S6\"><title>Ethics Statement</title><p>The studies involving human participants were reviewed and approved by the ethical committee of the San Carlos Clinical Hospital, Madrid, Spain. The patients/participants provided their written informed consent to participate in this study.</p></sec><sec id=\"S7\"><title>Author Contributions</title><p>KG-H and SS-R designed the study, integrity and analysis of data, and writing of the manuscript. RP, JO-G, and MF-A contributed to the analysis of data. KG-H contributed to the design of the images and figures. MC contributed to the measurement of the serum-free light chain and the analysis of data. All authors reviewed and approved the final version of the manuscript.</p></sec><sec id=\"conf1\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><ack><p>We sincerely thank all our patients. We are grateful to Silvia Largacha, Luisa Campos, Nuno Barbosa, and Yolanda R. Carrasco for valuable discussions. Figures were created by Marina Lorne.</p></ack><fn-group><fn id=\"footnote1\"><label>1</label><p><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.graphpad.com\">www.graphpad.com</ext-link></p></fn></fn-group><sec id=\"S9\" sec-type=\"supplementary material\"><title>Supplementary Material</title><p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.frontiersin.org/articles/10.3389/fimmu.2020.02004/full#supplementary-material\">https://www.frontiersin.org/articles/10.3389/fimmu.2020.02004/full#supplementary-material</ext-link></p><supplementary-material content-type=\"local-data\" id=\"FS1\"><label>FIGURE S1</label><caption><p>Bar chart comparing sFLCs patterns (κ<sup>–</sup>λ<sup>+</sup>, κ<sup>+</sup>λ<sup>–</sup>, κ<sup>–</sup>λ<sup>–</sup>, κ<sup>+</sup>λ<sup>+</sup>) with infectious and inflammatory phenotypes (Enteropathy, splenomegaly, bronchiectasis and cytopenia’s).</p></caption><media xlink:href=\"Image_1.TIFF\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"FS2\"><label>FIGURE S2</label><caption><p>Scatter plots comparing Kappa light chain concentration against: <bold>(A)</bold> Serum IgG at diagnosis; <bold>(B)</bold> Class-switched Memory B cells (CD19 + IgD-IgM-CD27 +); <bold>(C)</bold> B cells CD19%.</p></caption><media xlink:href=\"Image_2.TIF\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"FS3\"><label>FIGURE S3</label><caption><p>Receiver operating characteristic (ROC) curves for CVID diagnosis with the sum κ + λ testing. The area under the curve (AUC) for CVID was 0.894 (<italic>P</italic> = 0.001). 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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Genet</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Genet</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Genet.</journal-id><journal-title-group><journal-title>Frontiers in Genetics</journal-title></journal-title-group><issn pub-type=\"epub\">1664-8021</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849841</article-id><article-id pub-id-type=\"pmc\">PMC7431984</article-id><article-id pub-id-type=\"doi\">10.3389/fgene.2020.00877</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Genetics</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>Muscle Transcriptome Analysis Reveals Potential Candidate Genes and Pathways Affecting Intramuscular Fat Content in Pigs</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Zhao</surname><given-names>Xueyan</given-names></name><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1048617/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Hu</surname><given-names>Hongmei</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Lin</surname><given-names>Haichao</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Wang</surname><given-names>Cheng</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Wang</surname><given-names>Yanping</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Wang</surname><given-names>Jiying</given-names></name><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/866923/overview\"/></contrib></contrib-group><aff><institution>Shandong Provincial Key Laboratory of Animal Disease Control and Breeding, Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences</institution>, <addr-line>Jinan</addr-line>, <country>China</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Peter Dovc, University of Ljubljana, Slovenia</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Roberta Davoli, Università di Bologna, Italy; Romi Pena i Subirà, Universitat de Lleida, Spain</p></fn><corresp id=\"c001\">*Correspondence: Jiying Wang, <email>jnwangjiying@163.com</email></corresp><fn fn-type=\"other\" id=\"fn004\"><p>This article was submitted to Livestock Genomics, a section of the journal Frontiers in Genetics</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>877</elocation-id><history><date date-type=\"received\"><day>05</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>17</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Zhao, Hu, Lin, Wang, Wang and Wang.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Zhao, Hu, Lin, Wang, Wang and Wang</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>Intramuscular fat (IMF) content plays an essential role in meat quality. For identifying potential candidate genes and pathways regulating IMF content, the IMF content and the <italic>longissimus dorsi</italic> transcriptomes of 28 purebred Duroc pigs were measured. As a result, the transcriptome analysis of four high- and four low-IMF individuals revealed a total of 309 differentially expressed genes (DEGs) using edgeR and DESeq2 (<italic>p</italic> < 0.05, |log<sub>2</sub>(fold change)| ≥ 1). Functional enrichment analysis of the DEGs revealed 19 hub genes significantly enriched in the Gene Ontology (GO) terms and pathways (<italic>q</italic> < 0.05) related to lipid metabolism and fat cell differentiation. The weighted gene coexpression network analysis (WGCNA) of the 28 pigs identified the most relevant module with 43 hub genes. The combined results of DEGs, WGCNA, and protein–protein interactions revealed <italic>ADIPOQ</italic>, <italic>PPARG</italic>, <italic>LIPE</italic>, <italic>CIDEC</italic>, <italic>PLIN1</italic>, <italic>CIDEA</italic>, and <italic>FABP4</italic> to be potential candidate genes affecting IMF. Furthermore, the regulation of lipolysis in adipocytes and the peroxisome proliferator-activated receptor (PPAR) signaling pathway were significantly enriched for both the DEGs and genes in the most relevant module. Some DEGs and pathways detected in our study play essential roles and are potential candidate genes and pathways that affect IMF content in pigs. This study provides crucial information for understanding the molecular mechanism of IMF content and would be helpful in improving pork quality.</p></abstract><kwd-group><kwd>pork quality</kwd><kwd>IMF content</kwd><kwd>RNA sequencing</kwd><kwd>differentially expressed genes</kwd><kwd>WGCNA</kwd></kwd-group><funding-group><award-group><funding-source id=\"cn001\">Natural Science Foundation of Shandong Province<named-content content-type=\"fundref-id\">10.13039/501100007129</named-content></funding-source></award-group><award-group><funding-source id=\"cn002\">National Natural Science Foundation of China<named-content content-type=\"fundref-id\">10.13039/501100001809</named-content></funding-source></award-group></funding-group><counts><fig-count count=\"5\"/><table-count count=\"3\"/><equation-count count=\"0\"/><ref-count count=\"69\"/><page-count count=\"13\"/><word-count count=\"0\"/></counts></article-meta></front><body><sec id=\"S1\"><title>Introduction</title><p>Meat quality is the main economic trait in pig production and can be evaluated by multiple indicators, such as intramuscular fat (IMF) content, pH, water holding capacity, color, and tenderness. Of these, IMF content is arguably the most important and is closely correlated to other meat quality traits, such as flavor, juiciness, and tenderness (<xref rid=\"B25\" ref-type=\"bibr\">Klont et al., 1998</xref>). High levels of IMF content or marbling have a positive influence on the eating quality of pork (<xref rid=\"B49\" ref-type=\"bibr\">Van Laack et al., 2001</xref>). However, for the past century, thinner backfat was considered as an important parameter in pig breeding. Although this selection led to higher muscularity and growth, IMF content, juiciness, and tenderness of the meat decreased (<xref rid=\"B19\" ref-type=\"bibr\">Hernández-Sánchez et al., 2013</xref>). It is extremely difficult to improve IMF content by traditional breeding methods, even as consumers have become more discerning about meat quality. Therefore, it is worthwhile to understand the molecular basis of IMF content and carry out molecular breeding for satisfying consumer preferences.</p><p>IMF content is a complex trait, which is regulated by many genes. Over the past two decades, using low-density microsatellite markers, quantitative trait loci (QTL) linkage analyses have been performed to determine the QTL underlying IMF content in pigs (<xref rid=\"B16\" ref-type=\"bibr\">Grindflek et al., 2001</xref>; <xref rid=\"B39\" ref-type=\"bibr\">Ma et al., 2009</xref>). However, due to the low density of markers, these QTL represent large chromosomal regions, and it is difficult for further fine mapping to identify causative genes (<xref rid=\"B42\" ref-type=\"bibr\">Pearson and Manolio, 2008</xref>). The emergence of high-throughput genotyping platforms, such as single nucleotide polymorphism (SNP) arrays, makes it possible to find genetic variants and QTL associated with traits of interest in a narrower region. Genome-wide association studies (GWAS), using pig-specific high-density SNP chips, have made substantial progress in identifying genetic factors associated with or underlying IMF content (<xref rid=\"B45\" ref-type=\"bibr\">Roger et al., 2016</xref>; <xref rid=\"B52\" ref-type=\"bibr\">Wang et al., 2020</xref>). To date, 709 QTL for IMF content have been deposited in the pigQTLdb (Release 41, Apr 26, 2020)<sup><xref ref-type=\"fn\" rid=\"footnote1\">1</xref></sup>. However, when compared with other traits, the progress of dissecting the genetic basis for IMF content is still limited due to its complexity.</p><p>RNA sequencing (RNA-seq), a method based on next-generation sequencing (NGS), offers opportunities to provide unprecedented details about the transcriptional landscape of a certain organism. Compared with real-time PCR and microarrays, RNA-seq, with low background noise and high dynamic ranges of gene expression level quantification, integrates advanced molecular biology techniques and bioinformatics (<xref rid=\"B56\" ref-type=\"bibr\">Wang et al., 2009</xref>). Therefore, it is a powerful method for studying complex quantitative traits controlled by many interacting genes. Several studies on transcriptome profiling of porcine <italic>longissimus dorsi</italic> (LD) have been performed to investigate genes or pathways influencing IMF content in pigs. However, most of these studies have been carried out using pigs of different breeds (<xref rid=\"B33\" ref-type=\"bibr\">Li et al., 2016</xref>, <xref rid=\"B32\" ref-type=\"bibr\">2020</xref>; <xref rid=\"B57\" ref-type=\"bibr\">Xu J. et al., 2018</xref>), with few studies on individuals of the same breed (<xref rid=\"B35\" ref-type=\"bibr\">Lim et al., 2017</xref>; <xref rid=\"B41\" ref-type=\"bibr\">Muñoz et al., 2018</xref>) with distinct IMF content to identify consistent candidate genes. Hence, based on the RNA-seq, further studies using multiple analysis methods should be conducted to unravel genes and the pathways that regulate IMF contents.</p><p>Comparing the gene expression profile across phenotypes of interest to identify differentially expressed genes (DEGs) is the most commonly used approach in RNA-seq. Complementary to it, weighted gene coexpression network analysis (WGCNA) method, developed by <xref rid=\"B27\" ref-type=\"bibr\">Langfelder and Horvath (2008)</xref> in the form of an R package, identifies gene sets that work cooperatively in related pathways and contribute to resulting phenotypes. To date, WGCNA has been widely used in the investigation of complex diseases and traits in humans and mice based on RNA-seq (<xref rid=\"B11\" ref-type=\"bibr\">Darlington et al., 2013</xref>; <xref rid=\"B31\" ref-type=\"bibr\">Li et al., 2015</xref>; <xref rid=\"B23\" ref-type=\"bibr\">Jin et al., 2019</xref>; <xref rid=\"B48\" ref-type=\"bibr\">Shi et al., 2020</xref>). In pigs, WGCNA was also employed to analyze a number of complex traits, such as feed efficiency (<xref rid=\"B58\" ref-type=\"bibr\">Xu Y. et al., 2018</xref>; <xref rid=\"B2\" ref-type=\"bibr\">Banerjee et al., 2020</xref>; <xref rid=\"B5\" ref-type=\"bibr\">Carmelo et al., 2020</xref>), heat tolerance (<xref rid=\"B18\" ref-type=\"bibr\">He et al., 2020</xref>), residual feed intake (<xref rid=\"B36\" ref-type=\"bibr\">Liu H. et al., 2016</xref>), and obesity (<xref rid=\"B26\" ref-type=\"bibr\">Kogelman et al., 2014</xref>). However, so far, only one study was found that have used WGCNA to analyze IMF content in Italian Large White pigs (<xref rid=\"B66\" ref-type=\"bibr\">Zappaterra et al., 2020</xref>).</p><p>The Duroc pig population is extensively used as the terminal male parent in the breeding of Duroc × Landrace × Yorkshire (DLY) commercial pigs due to its high meat quality and large muscle mass. Previous studies have shown that the Duroc breed has substantially higher levels of IMF content than other commercial breeds (<xref rid=\"B7\" ref-type=\"bibr\">Cisneros et al., 2010</xref>). Moreover, in this study, we found that IMF content of Duroc pigs also varied significantly between individuals (from 1.17 to 4.23%). Thus, individuals with extreme IMF content in the Duroc population are good specimens for transcriptomics study of IMF content. Hence, in the present study, we performed RNA sequencing of LD muscles from 28 Duroc pigs with variant IMF content, analyzed the transcriptome differences between the groups with extremely high- and low-IMF content, conducted WGCNA of all individuals, and determined key DEGs and biological pathways affecting IMF content. This study provides valuable information for understanding the molecular basis underlying IMF content in pigs.</p></sec><sec sec-type=\"materials|methods\" id=\"S2\"><title>Materials and Methods</title><sec id=\"S2.SS1\"><title>Animals and Sample Collection</title><p>In this study, we selected 28 Duroc pigs (13 male and 15 female) of similar ages with an average body weight of 108.29 ± 6.00 kg (mean ± standard deviation) (<xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary Table S1</xref>). These pigs were reared together in the same breeding farm in the Shandong province of China. The individuals were derived from different sires and dams, and thus, no sibling and half-sibling relationships existed among them. These pigs were fed on diets formulated according to their age and were provided free access to water under the same environment. They were slaughtered in one batch following stunning by electric shock, which is a common abattoir practice. All the experimental procedures were approved by the Institutional Animal Care and Use Committee of Institute of Animal Husbandry and Veterinary Medicine, Shandong Academy of Agricultural Sciences (approval code, IACC20060101, 1 January 2006). About 0.2 g of LD muscle from the last fourth and fifth thoracic vertebrae was collected for each pig, placed into a tube with RNAlater Stabilization Solution (Thermo Fisher Scientific, Waltham, MA, United States), and frozen at −80°C for RNA extraction.</p><p>For the determination of IMF content, about 200 g of LD muscle was also obtained from the last fourth and fifth thoracic vertebra of each pig. After removing the adipose and connective tissues, these muscle samples were oven dried to constant weight for removing moisture. After the samples were ground, IMF content was examined using the Soxhlet petroleum–ether method and expressed as the weight percentage of wet muscle tissue. Each sample was measured in triplicate to ensure its accuracy.</p></sec><sec id=\"S2.SS2\"><title>RNA Isolation, Library Construction, and Sequencing</title><p>Total RNA was isolated from 28 porcine LD samples using the TRIzol reagent (Invitrogen, Life Technologies). To evaluate the quality of RNA, RNA degradation and contamination were first monitored on 1% agarose gels. Then, the purity and concentration of RNA were examined with NanoPhotometer<sup>®</sup> spectrophotometer (IMPLEN, CA, United States) and Qubit<sup>®</sup> RNA Assay Kit in Qubit<sup>®</sup> 2.0 Fluorometer (Life Technologies, CA, United States), respectively. Finally, RNA integrity was confirmed using the RNA Nano 6000 Assay Kit of the Bioanalyzer 2100 (Agilent Technologies, CA, United States). RNA integrity numbers (RINs) of the samples were 8.0–8.6. A total amount of 3 μg RNA with RIN > 8.0 was used as input material for the RNA sample preparations. RNA libraries were constructed using the NEBNext<sup>®</sup> Ultra<sup>TM</sup> RNA Library Prep Kit for Illumina<sup>®</sup> (NEB, United States) following the manufacturer’s recommendations. The method used for library construction is based on the poly-T oligo-attached magnetic beads. Messenger RNA (mRNA), including coding RNA and a small group of long non-coding RNA (lncRNA) with ploy A tail, were isolated from the total RNA. Then, the library preparations were sequenced with 15 cycles on an Illumina Hiseq platform, and 150 bp paired-end reads were generated.</p></sec><sec id=\"S2.SS3\"><title>Quality Assessment of Sequencing Data and Mapping of mRNA-Seq Reads</title><p>To ensure the quality of bioinformatics analysis, raw reads were first filtered to obtain clean reads. We filtered raw reads in the FASTQ format by removing reads according to the following criteria: containing adapter sequences, ambiguous base content > 0.1%, and 50% of bases whose Q<sub>phred</sub> quality score ≤ 20. Clean reads were then mapped to the <italic>Sus scrofa</italic> reference genome (Sscrofa11.1) and the annotation database Ensembl Genes 95 using HISAT2 software (<xref rid=\"B8\" ref-type=\"bibr\">Daehwan et al., 2015</xref>). FeatureCounts tool of subread package was used for calculating the number of reads mapped to each gene for estimating gene expression levels (<xref rid=\"B60\" ref-type=\"bibr\">Yang et al., 2014</xref>). Gene expression levels were normalized using the expected number of fragments per kilobase of exon per million fragments (FPKM).</p></sec><sec id=\"S2.SS4\"><title>Differentially Expressed Genes’ Detection and Functional Enrichment Analysis</title><p>Based on the phenotypic data of IMF and sex from 28 Duroc pigs, samples from four high- (two male and two female) and four low-IMF content pigs (two male and two female) were chosen for transcriptome difference analysis. The differential expression analysis was carried out using the R package DESeq2 (<xref rid=\"B38\" ref-type=\"bibr\">Love et al., 2014</xref>) and edgeR (<xref rid=\"B44\" ref-type=\"bibr\">Robinson et al., 2010</xref>), and adjusted <italic>p</italic>-values (<italic>q</italic>-value) were calculated using Benjamini and Hochberg’s (BH) approach for controlling the false discovery rate. Compared to edgeR, DESeq2 allowing more general data gives an advantage selection of differential genes expression during the dynamic range of data (<xref rid=\"B51\" ref-type=\"bibr\">Varet et al., 2016</xref>). However, Alshehri and Alkharouf found that EdgeR had a large number of unique differential expression genes that were not shared with other tools including DESeq2 (<xref rid=\"B1\" ref-type=\"bibr\">Alshehri and Alkharouf, 2018</xref>). Hence, in the present study, overlapped DEGs were detected by edgeR and DESeq2.</p><p>Overlapped DEGs, which were detected by DESeq2 and edgeR methods in terms of |log<sub>2</sub>(fold change)| ≥ 1 and <italic>p</italic> < 0.05, were subjected to GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis using R package clusterProfiler (<xref rid=\"B62\" ref-type=\"bibr\">Yu et al., 2012</xref>). The GO terms and pathways with <italic>q</italic> value (adjusted <italic>p</italic>-value by BH method) < 0.05 were considered to be significantly enriched ones. Genes involved in the significantly enriched GO terms and pathways related to IMF content were treated as hub genes, and more attention was paid to these in downstream WGCNA (<xref rid=\"B27\" ref-type=\"bibr\">Langfelder and Horvath, 2008</xref>).</p></sec><sec id=\"S2.SS5\"><title>Weighted Gene Coexpression Network Analysis</title><p>To construct a coexpression network, we further applied WGCNA using FPKM values obtained from the mRNA-Seq of 28 Duroc pigs. The WGCNA, a comprehensive collection of R functions, was used for performing various aspects of weighted correlation network analysis (<xref rid=\"B27\" ref-type=\"bibr\">Langfelder and Horvath, 2008</xref>). Genes with FPKM values > 1 in more than 14 individuals were selected for a coexpression network setting, resulting in 10,676 genes. Based on these genes, hierarchical cluster analysis of all samples was carried out by group average method to detect outliers with a cutoff at a height of 2,000. Outliers are defined as the samples that appear to deviate markedly from other samples. After filtering the outliers, the other samples were employed for establishing an unsigned coexpression network.</p><p>Specifically, adjacency matrix <italic>a</italic> was first calculated by formula <italic>a</italic><sub><italic>ij</italic></sub> = |<italic>s</italic><sub><italic>ij</italic></sub>|<italic><sup>β</sup></italic>, where s<italic><sub><italic>ij</italic></sub></italic> is the absolute value of the correlation coefficient between gene <italic>i</italic> and gene <italic>j</italic>, and β is a soft-power threshold. In the present study, the power of 14 based on the scale-free topology criterion was used, resulting in a scale-free topology index (<italic>R</italic><sup>2</sup>) of 0.85. Next, to make networks less sensitive to spurious connections or to connection missing due to random noises, the topological overlap matrix (TOM) (<xref rid=\"B67\" ref-type=\"bibr\">Zhang and Horvath, 2005</xref>), representing the overlap in shared neighbors, was introduced to identify modules of highly coexpressed genes based on the adjacency matrix. The dissimilarity TOM, which was calculated by 1 minus the TOM, was used as input for the dendrogram. By hierarchical clustering and dynamic tree cutting (<xref rid=\"B28\" ref-type=\"bibr\">Langfelder et al., 2007</xref>), genes clustered into distinct modules were assigned to a color. The hybrid dynamic tree cutting method was used to cut branches using a minimum module size of 30, which is the default and commonly used value (<xref rid=\"B40\" ref-type=\"bibr\">Maertens et al., 2018</xref>). Furthermore, module eigengene (ME) (<xref rid=\"B69\" ref-type=\"bibr\">Zhao et al., 2010</xref>), representing the module expressions of each module, was calculated by the first principal component of the expression matrix. The ME can be considered as a weighted average expression profile. To identify biologically significant modules and to select potential critical modules for downstream analysis, the WGCNA approach defines module–trait relationships (MTRs) (<xref rid=\"B26\" ref-type=\"bibr\">Kogelman et al., 2014</xref>) and gene significance (GS) of each module (<xref rid=\"B37\" ref-type=\"bibr\">Liu J. et al., 2016</xref>). The Pearson’s correlations between the ME and the IMF content values and between the expression profiles and the IMF content values were calculated for estimating the MTRs and GS, respectively. Module GS (MS) was the mean value of GS of the module genes. According to the selection criteria for critical modules reported in previous studies, modules with MTRs > 0.30 and MS > 0.25 were considered as candidate modules for the following functional enrichment analysis (<xref rid=\"B20\" ref-type=\"bibr\">Howard et al., 2017</xref>; <xref rid=\"B61\" ref-type=\"bibr\">Yang et al., 2018</xref>).</p><p>Functional annotation using the R package clusterProfiler was conducted on the genes in each candidate module. The GO terms and pathways with <italic>q</italic> < 0.05 (adjusted <italic>p</italic>-value by BH method) were considered as significant. Among the candidate modules, modules were considered as critical ones when the module genes were involved in IMF-related GO terms and pathways. Meanwhile, to further identify biologically significant gene in each critical module, module membership (MM) (<xref rid=\"B20\" ref-type=\"bibr\">Howard et al., 2017</xref>) and connectivity of genes were also detected; MMs, the correlation coefficient between the gene expression and ME for module membership, were calculated to measure the gene-module membership. Intramodular connectivity of one gene, which was the sum of the adjacency matrix α between that gene and all the other genes in the module, was also calculated. Genes with GS > 0.3, MM > 0.85, and an intramodular connectivity > 5 were considered hub genes.</p></sec></sec><sec id=\"S3\"><title>Results and Discussion</title><sec id=\"S3.SS1\"><title>Summary of Sequencing Data and Sample Selection for Comparative Gene Expression Analysis</title><p>In this study, we measured IMF content of 28 Duroc pigs selected from a breeding farm. IMF content varied significantly among individuals, ranging from 1.17 to 4.23% with an average of 2.36% (<xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary Table S1</xref>). The LD muscles of these 28 pigs were sequenced using a paired-end mRNA-seq approach. In total, 52.43–86.93 million raw reads per sample were generated. After filtering 2.31% of raw reads, an average of 62.56 million clean reads were used for the following analyses (<xref ref-type=\"supplementary-material\" rid=\"TS2\">Supplementary Table S2</xref>). Of these, 94.36% of clean reads were successfully mapped to the <italic>S. scrofa</italic> (Sscrofa11.1) genome assembly, with 90.34% being uniquely mapped (<xref ref-type=\"supplementary-material\" rid=\"TS2\">Supplementary Table S2</xref>). In addition, out of the reads mapped to the reference genome, 91.58 and 3.85% were located in the exon and intron regions, respectively, and the remaining, a small group of lncRNA with ploy A tail, were in the intergenic regions (<xref ref-type=\"supplementary-material\" rid=\"TS2\">Supplementary Table S2</xref>).</p><p>Based on the sex and IMF content of these 28 Duroc pigs, four pigs with extremely high- (D8, D15, D19, and D21) and four with low-IMF content (D13, D17, D18, and D26) were chosen as the two divergent groups for comparative gene expression analysis. The mean IMF contents of the two groups were 3.70 and 1.47%, respectively. According to the alignment result of RNA sequencing data with the <italic>S. scrofa</italic> genome, a total of 26,918 coding genes (including 1,038 novel genes) were expressed in eight individuals with extreme IMF content. Taking into account genes with FPKM value > 1 in more than four pigs, we found 18,411 and 18,950 coding genes in the high- and low-IMF groups, respectively.</p></sec><sec id=\"S3.SS2\"><title>Differentially Expressed Genes Between Low- and High-IMF Groups</title><p>DESeq2 and edgeR were very popular and easy-to-use packages for the differential expression analysis of RNA-seq data (<xref rid=\"B51\" ref-type=\"bibr\">Varet et al., 2016</xref>). In the present study, both methods were employed to identify DEGs between the two groups. Using the DESeq2 method in terms of |log<sub>2</sub>(fold change)| ≥ 1, we identified 316, 130, and 10 DEGs between the high- and low-IMF groups at <italic>p</italic>-value cutoffs of 0.05 and 0.01 and a <italic>q-</italic>value cutoff of 0.05, respectively. It suggested that the transcript differences related to IMF content between the selected pigs were small. These results were similar to those from a previous study, in which 96 and 28 DEGs related to lipid profiles were detected in term of fold-change ≥ 1.5 at cutoffs of <italic>p</italic> ≤ 0.01 and <italic>q</italic> ≤ 0.05 in Duroc pigs, respectively (<xref rid=\"B4\" ref-type=\"bibr\">Cardoso et al., 2017</xref>). On the other hand, using the edgeR packages, we detected 457, 172, and 12 DEGs between the two groups at <italic>p</italic>-value cutoffs of 0.05 and 0.01 and a <italic>q</italic> value cutoff of 0.05, respectively. A total of 309 overlapped genes detected in terms of |log<sub>2</sub>(fold change)| ≥ 1 and <italic>p</italic> < 0.05 were considered as DEGs for the following functional enrichment analyses (<xref ref-type=\"supplementary-material\" rid=\"TS3\">Supplementary Table S3</xref>). Of these, 240 and 69 genes were up- and downregulated in the high-IMF group, respectively. <xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref> exhibits the heatmap of these DEGs, from which it can be seen that the expression patterns of DEGs are consistent within groups and different between groups.</p><fig id=\"F1\" position=\"float\"><label>FIGURE 1</label><caption><p>Heat map of differentially expressed genes (DEGs) between the high- and low-intramuscular fat (IMF) groups. The DEGs were detected by DEseq2 and edgeR with |log2(fold change)| ≥ 1 and <italic>p</italic> < 0.05.</p></caption><graphic xlink:href=\"fgene-11-00877-g001\"/></fig><p>Moreover, using DESeq2 and edgeR, only seven overlapped genes, including <italic>SPP1</italic>, <italic>KCNN1</italic>, <italic>ENSSSCG00000034371</italic>, <italic>LEP</italic>, <italic>CIDEC</italic>, <italic>SFRP1</italic>, and <italic>ENSSSCG00000040849</italic>, remained significant after correction for multiple testing (<italic>q</italic> ≤ 0.05 and |log<sub>2</sub>(fold change)| ≥ 1, <xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). Among these genes, <italic>LEP</italic> gene encodes a protein, leptin, which is secreted by white adipocytes into the circulation and exerts physiological action through the leptin receptor (<italic>LTPR</italic>), which is also associated with IMF content (<xref rid=\"B45\" ref-type=\"bibr\">Roger et al., 2016</xref>; <xref rid=\"B55\" ref-type=\"bibr\">Wang et al., 2019</xref>). Consistent with our result, <xref rid=\"B14\" ref-type=\"bibr\">Gao et al. (2004)</xref> found that expression of <italic>LEP</italic> is significantly different in the intramuscular fat tissue between Erhualian and Large white pigs. <italic>CIDEC</italic> encodes an adipocyte lipid drop protein, which negatively regulates lipolysis and promotes triglyceride accumulation (<xref rid=\"B43\" ref-type=\"bibr\">Puri et al., 2007</xref>).</p><table-wrap id=\"T1\" position=\"float\"><label>TABLE 1</label><caption><p>Description of the top seven differentially expressed genes (DEGs) detected between high- and low-intramuscular fat (IMF) groups with |log2(fold change)| ≥ 1 and <italic>q</italic> < 0.05.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ID</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Gene name</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">log<sub>2</sub>(fold change)</td><td valign=\"top\" align=\"center\" colspan=\"2\" rowspan=\"1\">DEseq2<hr/></td><td valign=\"top\" align=\"center\" colspan=\"2\" rowspan=\"1\">edgeR<hr/></td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-value</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>q-</italic>value</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>p-</italic>value</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>q-</italic>value</td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ENSSSCG00000009216</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>SPP1</italic></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.44</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.01E−12</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.11E−09</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.02E−08</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.69E−05</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ENSSSCG00000013892</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>KCNN1</italic></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–1.73</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.37E−12</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.14E−08</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.44E−07</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.84E−04</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ENSSSCG00000034371</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.73</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.67E−10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.82E−07</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.14E−09</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.08E−05</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ENSSSCG00000040464</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>LEP</italic></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.16E−05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.12E−02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.34E−05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.94E−02</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ENSSSCG00000011557</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>CIDEC</italic></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.83</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.16E−05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.12E−02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.65E−06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.06E−03</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ENSSSCG00000025822</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>SFRP1</italic></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.37</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.24E−05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.92E−02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.18E−06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.22E−03</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ENSSSCG00000040849</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–1.42</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.48E−05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.24E−02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.36E−05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.72E−02</td></tr></tbody></table></table-wrap></sec><sec id=\"S3.SS3\"><title>Gene Ontology and Pathway Enrichment Analyses of DEGs</title><p>To gain an insight into the function of DEGs detected in terms of |log2(fold change)| ≥ 1 and <italic>p</italic> < 0.05, we carried out GO term enrichment analysis for up- and downregulated genes, respectively. Consequentially, 58 significantly enriched GO terms (<italic>q</italic> < 0.05, <xref ref-type=\"supplementary-material\" rid=\"TS4\">Supplementary Table S4</xref>) were found for the upregulated genes and none for the downregulated genes. Among the 58 enriched GO terms, most belonged to the biological process (BP) category, and only two terms, lipid particle and natural killer cell lectin-like receptor binding, belonged to the molecular function (MF) and cellular component (CC) category, respectively (<xref ref-type=\"fig\" rid=\"F2\">Figure 2A</xref>). Importantly, 27 of the 58 GO terms identified were closely associated with lipid metabolism and fat cell differentiation, such as lipid catabolic process, low-density lipoprotein receptor particle metabolic process, white fat cell differentiation, and positive regulation of cholesterol efflux (<xref ref-type=\"fig\" rid=\"F2\">Figure 2B</xref>). Lipid particle was the most significant GO term (<italic>q</italic> = 1.56E−05). Moreover, other significant GO terms related to some physiological and biological events, such as response to cytokine, response to tumor necrosis factor, alcohol metabolic, and detoxification, were also enriched (<xref ref-type=\"fig\" rid=\"F2\">Figure 2A</xref>). In addition, we conducted KEGG pathway analysis for the DEGs. Five pathways were significantly enriched for the upregulated DEGs (<italic>q</italic> < 0.05, <xref rid=\"T2\" ref-type=\"table\">Table 2</xref>), whereas none were enriched for the downregulated genes. The <italic>q</italic> values of the enriched pathways of DEGs showed a low level of significance. It may be one feature of muscle tissue. Similar results could be found in the previous studies (<xref rid=\"B4\" ref-type=\"bibr\">Cardoso et al., 2017</xref>; <xref rid=\"B35\" ref-type=\"bibr\">Lim et al., 2017</xref>). Of the five significant pathways, two pathways, the regulation of lipolysis in adipocytes (<italic>q</italic> = 5.05E−03) and the peroxisome proliferator-activated receptor (PPAR) signaling pathway (<italic>q</italic> = 5.05E−03), were IMF related.</p><fig id=\"F2\" position=\"float\"><label>FIGURE 2</label><caption><p>Gene Ontology (GO) enrichment analysis of differentially expressed genes (DEGs). <bold>(A)</bold> Significantly enriched GO terms of the DEGs detected by DEseq2 and edgeR with |log2(fold change)| ≥ 1 and <italic>p</italic> < 0.05. The figure is composed of three parts, biological processes, cellular components, and molecular functions, which are divided by blue horizontal lines. The significance level of enrichment was set at corrected <italic>p</italic>-value (<italic>q-</italic>value) < 0.05. <bold>(B)</bold> Significantly enriched GO terms related to lipid metabolism and fat cell differentiation. Blue dots represent enriched GO terms. Genes involved in GO terms are represented by V shape. The colors of genes are decided by the <italic>p</italic>-values examined by DEseq2.</p></caption><graphic xlink:href=\"fgene-11-00877-g002\"/></fig><table-wrap id=\"T2\" position=\"float\"><label>TABLE 2</label><caption><p>Significantly enriched pathways of differentially expressed genes (DEGs) between low- and high-intramuscular fat (IMF) groups.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Description</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Gene representation</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>p-</italic>value</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>q</italic>-value</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Gene name</td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Regulation of lipolysis in adipocytes</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5/47</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.94E−05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.05E−03</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>PLIN1</italic>, <italic>LIPE</italic>, <italic>PTGER3</italic>, <italic>ADRB1</italic>, <italic>FABP4</italic></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Retinol metabolism</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4/47</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.20E−04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.05E−03</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>CYP2A19</italic>, <italic>SDR16C5</italic>, <italic>RETSAT</italic>, <italic>AOX1</italic></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PPAR signaling pathway</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5/47</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.34E−04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.05E−03</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>PLIN1</italic>, <italic>ACAA1</italic>, <italic>PPARG</italic>, <italic>ADIPOQ</italic>, <italic>FABP4</italic></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Vitamin B6 metabolism</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2/47</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.77E−03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.14E−02</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>PSAT1</italic>, <italic>AOX1</italic></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">cAMP signaling pathway</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6/47</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.83E−03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.14E−02</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>LIPE</italic>, <italic>PTGER3</italic>, <italic>HCAR1</italic>, <italic>ADRB1</italic>, <italic>PPP1R1B</italic></td></tr></tbody></table></table-wrap><p>The DEGs involved in GO terms and pathways related to lipid metabolism and fat cell differentiation were further analyzed. A total of 18 DEGs, as illustrated in <xref ref-type=\"fig\" rid=\"F2\">Figure 2B</xref>, were found to be involved in the 27 significantly enriched GO terms related to IMF content. Moreover, of the 18 genes, <italic>APOE</italic>, <italic>ADIPOQ</italic>, <italic>PPARG</italic>, <italic>CEBPA</italic>, <italic>LIPE</italic>, <italic>MT3</italic>, and <italic>PNPLA3</italic> were involved in more than five GO terms (<xref ref-type=\"fig\" rid=\"F2\">Figure 2B</xref>). Two IMF-related pathways covered eight genes, <italic>PLIN1</italic>, <italic>LIPE</italic>, <italic>PTGER3</italic>, <italic>ADRB1</italic>, <italic>FABP4</italic>, <italic>ACAA1</italic>, <italic>PPARG</italic>, and <italic>ADIPOQ</italic>, which were relevant to lipid metabolism and fat cell differentiation. It is noticeable that, except for <italic>PTGER3</italic>, all the others overlapped with the 18 upregulated DEGs covered by enriched GO terms (<xref ref-type=\"fig\" rid=\"F2\">Figure 2B</xref>). Therefore, functional enrichment analysis of DEGs detected 19 genes that were involved in GO terms and pathways related to lipid metabolism and fat cell differentiation. These DEGs would be regarded as key genes and used in the following detection of candidate genes.</p></sec><sec id=\"S3.SS4\"><title>Coexpressed Gene Modules Associated With IMF Content</title><p>The WGCNA relies on the assumption that strongly correlated expression levels of gene sets indicate that the genes work cooperatively in related pathways and contribute to the resulting phenotype. Based on this assumption, we further carried out WGCNA for all the expressed genes. By sample clustering with all genes, D22, deviating from other samples and exceeding the cutoff height, was considered to be an outlier (<xref ref-type=\"supplementary-material\" rid=\"FS1\">Supplementary Figure S1</xref>). Therefore, in WGCNA, we constructed a coexpression network with the other 27 individuals and ultimately identified 22 modules (<xref ref-type=\"fig\" rid=\"F3\">Figure 3A</xref>). Critical modules associated with IMF content were first selected based on two criteria: MTRs > 0.3 and MS > 0.25. The results of the MTRs showed that five modules, including cyan, turquoise, gray, midnight blue, and magenta, were moderately correlated with IMF content (correlation coefficients ranging from 0.30 to 0.36, correlation <italic>p</italic> < 0.1, <xref ref-type=\"fig\" rid=\"F3\">Figure 3B</xref>). The MS of the five gene modules was also detected for understanding the relationship between expression profiles and the phenotypic values based on the GS of each module genes. Among these modules, there were two modules, midnight blue (88 genes, MS = 0.27) and magenta modules (208 genes, MS = 0.28) whose MS reached 0.25 (<xref ref-type=\"fig\" rid=\"F3\">Figure 3B</xref>). The GS of midnight blue and magenta module genes varied from −0.07 to 0.50 and from −0.28 to 0.65 with 0.11 and 0.13 of standard deviation, respectively. Consequently, based on the combined results of MTRs and MS, midnight blue and magenta modules were selected for the downstream functional enrichment analysis. The detail information about genes in the two modules is presented in <xref ref-type=\"supplementary-material\" rid=\"TS5\">Supplementary Tables S5</xref>, <xref ref-type=\"supplementary-material\" rid=\"TS6\">S6</xref>.</p><fig id=\"F3\" position=\"float\"><label>FIGURE 3</label><caption><p>Coexpressed gene modules detected by weighted gene coexpression network analysis (WGCNA). <bold>(A)</bold> Cluster dendrogram showing the coexpression modules defined by WGCNA and labeled by colors. <bold>(B)</bold> Module–trait relationships and gene significances of each module.</p></caption><graphic xlink:href=\"fgene-11-00877-g003\"/></fig><p>Further functional enrichment analyses were conducted on the genes in each module to understand the biological function of the midnight blue and magenta modules. The significant GO terms and pathways (<italic>q</italic> < 0.05) are presented in <xref ref-type=\"supplementary-material\" rid=\"TS7\">Supplementary Tables S7</xref>, <xref ref-type=\"supplementary-material\" rid=\"TS8\">S8</xref>. It can be seen that genes in the magenta module were significantly enriched in GO terms and pathways related to lipid metabolism (<italic>q</italic> < 0.05, <xref ref-type=\"supplementary-material\" rid=\"TS7\">Supplementary Table S7</xref>). Among these GO terms, 32 were IMF related, such as fat cell differentiation, fatty acid metabolic process, and lipid storage. <xref ref-type=\"fig\" rid=\"F4\">Figure 4A</xref> illustrates the top 5 BP terms and all CC and MF terms of 65 GO terms identified with the magenta module genes. We found that there were 25 GO terms that overlapped between the GO terms identified by DEGs and magenta module genes (<xref ref-type=\"fig\" rid=\"F4\">Figure 4B</xref> and <xref ref-type=\"supplementary-material\" rid=\"TS9\">Supplementary Table S9</xref>). In addition, 19 of them were IMF related, such as lipid metabolism, lipoprotein metabolism, cholesterol metabolism, sterol metabolism, and fat cell differentiation (<xref ref-type=\"fig\" rid=\"F4\">Figure 4B</xref> and <xref rid=\"T3\" ref-type=\"table\">Table 3</xref>). This suggested that the results of the functional enrichment analysis of DEGs and magenta module genes could corroborate each other.</p><fig id=\"F4\" position=\"float\"><label>FIGURE 4</label><caption><p>Coexpressed genes in magenta module. <bold>(A)</bold> The top 5 biological processes (BP) terms, all cellular components (CC), and molecular functions (MF) of Gene Ontology (GO) terms identified with magenta module genes. <bold>(B)</bold> Venn diagram of GO terms identified with magenta module genes and differentially expressed genes (DEGs). <bold>(C)</bold> Pie chart of all significant pathways (<italic>q</italic> < 0.05) in the magenta module. Each sector of the pie chart is proportional to the log<sub>10</sub> (-<italic>q</italic> value) of each pathway it represents. <bold>(D)</bold> Association between the module membership (MM) and gene significance (GS) in the magenta module. <bold>(E)</bold> Association between the intramodular connectivity and module membership (MM) in the magenta module.</p></caption><graphic xlink:href=\"fgene-11-00877-g004\"/></fig><table-wrap id=\"T3\" position=\"float\"><label>TABLE 3</label><caption><p>Intramuscular fat (IMF)-related Gene Ontology (GO) terms overlapped between significantly enriched GO terms of differentially expressed genes (DEGs) and those of magenta module genes.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" colspan=\"3\" rowspan=\"1\">GO terms of DEGs<hr/></td><td valign=\"top\" align=\"center\" colspan=\"3\" rowspan=\"1\">GO terms of magenta module genes<hr/></td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Gene representation</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>p-</italic>value</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>q-</italic>value</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Gene representation</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>p-</italic>value</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>q-</italic>value</td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Fatty acid metabolic process</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7/71</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.78E−04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.02E−02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14/166</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.52E−07</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.05E−04</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">White fat cell differentiation</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3/71</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.38E−05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.64E−02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4/166</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.68E−06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.91E−03</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Brown fat cell differentiation</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4/71</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.47E−05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.64E−02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6/166</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.00E−06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.91E−03</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Positive regulation of lipid localization</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4/71</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.69E−04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.08E−02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7/166</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.86E−06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.91E−03</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Lipid catabolic process</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8/71</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.86E−06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.35E−02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12/166</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.79E−06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.91E−03</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Regulation of lipid localization</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4/71</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.23E−03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.66E−02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8/166</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.79E−05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.61E−03</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Sterol metabolic process</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4/71</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.29E−04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.89E−02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7/166</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.44E−05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.96E−03</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Fat cell differentiation</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7/71</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.44E−05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.64E−02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10/166</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.45E−05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.78E−03</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Positive regulation of sterol transport</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3/71</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.53E−04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.92E−02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4/166</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.31E−05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.05E−03</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Positive regulation of cholesterol transport</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3/71</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.53E−04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.92E−02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4/166</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.31E−05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.05E−03</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Positive regulation of lipid transport</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3/71</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.26E−03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.66E−02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5/166</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.18E−04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.42E−02</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Regulation of fat cell differentiation</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5/71</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.05E−04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.08E−02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7/166</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.32E−04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.46E−02</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Positive regulation of fat cell differentiation</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4/71</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.39E−05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.68E−02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5/166</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.50E−04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.52E−02</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Cholesterol metabolic process</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4/71</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.69E−04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.69E−02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6/166</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.71E−04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.56E−02</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Positive regulation of cholesterol efflux</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3/71</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.13E−05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.64E−02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3/166</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.02E−04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.05E−02</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Steroid metabolic process</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5/71</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.79E−04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.02E−02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8/166</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.47E−04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.55E−02</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Regulation of sterol transport</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3/71</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.68E−04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.70E−02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4/166</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.18E−04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.81E−02</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Regulation of cholesterol transport</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3/71</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.68E−04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.70E−02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4/166</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.18E−04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.81E−02</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Lipid particle</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6/67</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.65E−07</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.56E−05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12/170</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.05E−13</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.19E−11</td></tr></tbody></table></table-wrap><p>Six significantly enriched pathways were discovered in the KEGG analysis for the genes of the magenta module (<italic>q</italic> < 0.05, <xref ref-type=\"fig\" rid=\"F4\">Figure 4C</xref>). It is worth noting that regulation of lipolysis in adipocytes and PPAR signaling pathway, which were identified in the KEGG analysis of the DEGs, were also identified. According to the KEGG database<sup><xref ref-type=\"fn\" rid=\"footnote2\">2</xref></sup>, regulation of lipolysis in adipocytes is a pathway regulating triacylglycerol to release fatty acids and glycerol for other organs as energy substrates. Fat deposition is a dynamic process between lipid synthesis and degradation. Some studies have demonstrated that IMF content in LD is determined by a balance between fat accumulation and degradation (<xref rid=\"B21\" ref-type=\"bibr\">Jeong et al., 2012</xref>). It is implied that more lipid storage may lead to the increase in lipolysis (<xref rid=\"B47\" ref-type=\"bibr\">Serr et al., 2013</xref>). Consequently, in this study, we found that the regulation of lipolysis in adipocytes pathway was enriched in the high-IMF group compared with that in the low-IMF group. The PPAR signaling pathway is a canonical pathway involved in lipid metabolism. Expression patterns of many candidate genes in this pathway were correlated with meat quality traits in the LD muscles of pigs (<xref rid=\"B54\" ref-type=\"bibr\">Wang et al., 2016</xref>). Furthermore, previous studies showed that the PPAR signaling pathway was a significant enriched pathway for DEGs of subcutaneous and intramuscular stromal vascular cells during adipogenic differentiation (<xref rid=\"B22\" ref-type=\"bibr\">Jiang et al., 2013</xref>) and for DEGs detected by transcriptome analysis of LD muscles between Laiwu and Yorkshire pigs (<xref rid=\"B6\" ref-type=\"bibr\">Chen et al., 2017</xref>). This has provided new evidence that DEG analysis and WGCNA could corroborate each other.</p><p>In addition, for the magenta module, the correlation between MM and GS (correlation = 0.34, <italic>p</italic> = 5.4e−07), and the MM and connectivity (correlation = 0.98, <italic>p</italic> = 1e−145) both reached either moderate or strong levels (<xref ref-type=\"fig\" rid=\"F4\">Figures 4D,E</xref>). These suggest that the magenta module is a critical module, and some of the genes in it could be candidate genes affecting the IMF content. Totally, there were 200 genes in the module. As expected, the expression of these genes varied with individuals (<xref ref-type=\"fig\" rid=\"F5\">Figure 5A</xref>), and 39 genes out of them were overlapped with DEGs detected in terms of |log2(fold change)| ≥ 1 and <italic>p</italic> < 0.05.</p><fig id=\"F5\" position=\"float\"><label>FIGURE 5</label><caption><p>Hub genes in magenta module. <bold>(A)</bold> Heatmap of the magenta module genes. <bold>(B)</bold> Network visualization of the coexpression of 43 hub genes in magenta module. Hexagon represents 16 differentially expressed genes (DEGs) of the hub genes; orange hexagon represents nine DEGs in magenta module overlapping with 19 hub genes detected by functional enrichment analysis of DEGs. <bold>(C)</bold> Protein–protein interaction network of nine hub genes. ACAA1 without protein–protein interactions with the others is not displayed in the network.</p></caption><graphic xlink:href=\"fgene-11-00877-g005\"/></fig></sec><sec id=\"S3.SS5\"><title>Detection of Candidate Genes That Affect IMF Content</title><p>As the magenta module resulting from the WGCNA showed a strong association with IMF, we investigated the candidate genes that affected IMF content in this module. First, hub genes of magenta module were identified. As hub genes for each module, their expression should first correlate significantly to module eigengene (MM > 0.85), which would suggest that the gene was a member of the magenta module. In addition, the expression of these genes should either moderately or strongly correlate to IMF value (GS > 0.3) and have more connectivity with other coexpression genes (intramodular connectivity > 5). Under these criteria, we selected 43 hub genes (<xref ref-type=\"supplementary-material\" rid=\"TS10\">Supplementary Table S10</xref>), 16 of which were DEGs, which were detected in terms of |log2(fold change)| ≥ 1 and <italic>p</italic> < 0.05 (<xref ref-type=\"fig\" rid=\"F5\">Figure 5B</xref>). When comparing these 16 genes with the 19 key genes detected by the functional enrichment analysis of DEGs, we found that nine genes overlapped, viz. <italic>ADIPOQ</italic>, <italic>PPARG</italic>, <italic>LIPE</italic>, <italic>CIDEC</italic>, <italic>PLIN1</italic>, <italic>CIDEA</italic>, <italic>ACAA1</italic>, <italic>ADIRF</italic>, and <italic>FABP4</italic>.</p><p>Then, to evaluate the interactive relationships of the nine overlapped genes, we conducted a protein–protein interaction (PPI) network analysis using the STRING version 11.0<sup><xref ref-type=\"fn\" rid=\"footnote3\">3</xref></sup> online software. PPI networks were visualized and analyzed using Cytoscape 3.6.1 software. Except for <italic>ACAA1</italic> and <italic>ADIRF</italic>, the other seven genes, i.e., <italic>ADIPOQ</italic>, <italic>PPARG</italic>, <italic>LIPE</italic>, <italic>CIDEC</italic>, <italic>PLIN1</italic>, <italic>CIDEA</italic>, and <italic>FABP4</italic>, exhibited protein–protein interactions with each other (<xref ref-type=\"fig\" rid=\"F5\">Figure 5C</xref>).</p><p>IMF content is determined by the number (hyperplasia) and size (hypertrophia) of adipocytes within the muscle (<xref rid=\"B68\" ref-type=\"bibr\">Zhao and Gao, 2009</xref>). The differentiation of intramuscular preadipocytes into intramuscular adipocytes starts during the embryonic growth and continues immediately after birth, then slow down during the growth of the animal (<xref rid=\"B46\" ref-type=\"bibr\">Sepe et al., 2011</xref>). The hypertrophy process is determined by the ratio between lipogenesis and lipolysis in mature adipocytes. Some previous studies have proved these seven genes as relevant candidate genes because of their important roles in these biological processes related to IMF content. Adiponectin, encoded by the <italic>ADIPOQ</italic> genes, is a protein hormone secreted by adipocytes involved in the regulation and inhibition of lipogenesis and the stimulation of fatty acid oxidation (<xref rid=\"B63\" ref-type=\"bibr\">Yuchang et al., 2005</xref>). Expression of <italic>ADIPOQ</italic> was higher in Lantang, a high-IMF pig breed, compared to that in Landrace, a low-IMF pig breed (<xref rid=\"B24\" ref-type=\"bibr\">Kaifan et al., 2013</xref>). Hormone-sensitive lipase gene (<italic>LIPE</italic>) is one of the most important factors in controlling lipolysis and fat accumulation in animals (<xref rid=\"B17\" ref-type=\"bibr\">Guenter et al., 2003</xref>). <italic>LIPE</italic> were found to be significantly higher expressed in pigs with low-IMF content (<xref rid=\"B65\" ref-type=\"bibr\">Zappaterra et al., 2016</xref>), its polymorphisms associated with pig IMF content (<xref rid=\"B59\" ref-type=\"bibr\">Xue et al., 2015</xref>), and, in the candidate regions under positive selection of Laiwu pigs, one Chinese indigenous pig breed with extremely high proportion of IMF content (<xref rid=\"B6\" ref-type=\"bibr\">Chen et al., 2017</xref>). <italic>CIDEA</italic> and <italic>CIDEC</italic> are two members of the novel CIDE family of apoptosis-inducing factors. The expression of <italic>CIDEA</italic>, an adipose-specific gene, was associated with the terminal differentiation of fat cells (<xref rid=\"B10\" ref-type=\"bibr\">Danesch et al., 1992</xref>). It was discovered that both <italic>CIDEA</italic> and <italic>CIDEC</italic> were highly expressed in adipose tissues, and their expression levels were significantly higher in obese pigs than in their lean counterparts (<xref rid=\"B34\" ref-type=\"bibr\">Li et al., 2009</xref>). Notably, <italic>CIDEC</italic> was one of the top 7 DEGs distinguishing at |log2(fold change)| value ≥ 1 and <italic>q</italic> < 0.05 (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>).</p><p>The genes, <italic>PPARG</italic>, <italic>PLIN1</italic>, and <italic>FABP4</italic>, all belong to the significantly enriched PPAR signaling pathway, and <italic>PPARG</italic> was the core regulator gene of this pathway. <italic>PPARG</italic> regulates lipid metabolism and glucose homeostasis and promotes adipocyte differentiation and fat deposition (<xref rid=\"B50\" ref-type=\"bibr\">Vanden Heuvel and Peters, 2010</xref>). Previous studies have found that the polymorphisms of <italic>PPARG</italic> could significantly affect gene expression and intramuscular fat deposition in pigs (<xref rid=\"B53\" ref-type=\"bibr\">Wang et al., 2013</xref>). In addition, <italic>PPARG</italic> could enhance <italic>PLIN1</italic> expression by DNA demethylation on PPAR-response elements of <italic>PLIN1</italic> gene promoter upon differentiation (<xref rid=\"B12\" ref-type=\"bibr\">Fujiki et al., 2013</xref>). PLIN1 protein plays a key role in the regulation of the extra-myocellular lipid storage in pigs, which mainly determines the IMF content (<xref rid=\"B13\" ref-type=\"bibr\">Gandolfi et al., 2011</xref>). <italic>PLIN1</italic> was detected as a key gene affecting porcine IMF based on transcriptome and knockdown analysis (<xref rid=\"B30\" ref-type=\"bibr\">Li et al., 2018</xref>). Furthermore, once activated, the PPARG complex can recruit other transcription factors and activate the adipogenic gene transcription by the PPAR responsive elements including <italic>FABP4</italic> (<xref rid=\"B64\" ref-type=\"bibr\">Yutaka et al., 2008</xref>). As an adipokine, <italic>FABP4</italic> is secreted from adipocytes and induced during adipocyte differentiation. In pigs, <italic>FABP4</italic> levels were associated with the number of adipocytes and IMF content (<xref rid=\"B9\" ref-type=\"bibr\">Damon et al., 2006</xref>). <xref rid=\"B15\" ref-type=\"bibr\">Gerbens et al. (1998)</xref> found that a microsatellite sequence in the first intron of porcine <italic>FABP4</italic> is associated with IMF content in Duroc population, and approximately 1% IMF was observed between certain genotype classes. In bovine, polymorphisms in <italic>FABP4</italic> associated with IMF content were also found (<xref rid=\"B3\" ref-type=\"bibr\">Bartoň et al., 2016</xref>). Furthermore, differing from adipogenic differentiation of muscle stem cells, a novel mechanism for fatty infiltration, which might be regulated by inhibition of <italic>FABP4</italic>, was found in mouse (<xref rid=\"B29\" ref-type=\"bibr\">Lee et al., 2017</xref>).</p><p>To sum up, according to the results of the DEG analysis, WGCNA, and the relevant literature, the seven genes discussed above, <italic>ADIPOQ</italic>, <italic>PPARG</italic>, <italic>LIPE</italic>, <italic>CIDEC</italic>, <italic>PLIN1</italic>, <italic>CIDEA</italic>, and <italic>FABP4</italic>, could be potential candidate genes affecting IMF content in pigs.</p></sec></sec><sec id=\"S4\"><title>Conclusion</title><p>In this study, we performed a high throughput RNA sequencing to evaluate the transcriptome profiles differences of eight Duroc pigs with extreme IMF content. A total of 309 DEGs were screened using the DESeq2 and edgeR packages. The GO terms and pathway enrichment analysis of DEGs and WGCNA of 28 Duroc pigs revealed some potential candidate genes, such as <italic>ADIPOQ</italic>, <italic>PPARG</italic>, <italic>LIPE</italic>, <italic>CIDEC</italic>, <italic>PLIN1</italic>, <italic>CIDEA</italic>, and <italic>FABP4</italic>, and pathways, such as regulation of lipolysis in adipocytes and PPAR signaling pathway. These potential candidate genes and pathways play an essential role in affecting the IMF content of pigs, and further studies should be carried out to unravel their specific mechanism on IMF content.</p></sec><sec sec-type=\"data-availability\" id=\"S5\"><title>Data Availability Statement</title><p>All datasets generated for this study are included in the article/<xref ref-type=\"supplementary-material\" rid=\"FS1\">Supplementary Material</xref>. The raw data of RNA sequencing have been deposited in the National Center for Biotechnology Information Sequence Read Archive with accession no. <ext-link ext-link-type=\"DDBJ/EMBL/GenBank\" xlink:href=\"PRJNA527944\">PRJNA527944</ext-link> (available online: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/PRJNA527944\">https://www.ncbi.nlm.nih.gov/sra/PRJNA527944</ext-link>).</p></sec><sec id=\"S6\"><title>Ethics Statement</title><p>The animal study was reviewed and approved by the Institutional Animal Care and Use Committee of Institute of Animal Husbandry and Veterinary Medicine, Shandong Academy of Agricultural Sciences (approval code, IACC20060101, 1 January 2006).</p></sec><sec id=\"S7\"><title>Author Contributions</title><p>JW conceived and designed the experiments and explained the data. XZ analyzed the main content of the data with the help of HH and HL. YW collected the samples and examined the phenotype with the help of CW and HL. XZ wrote the manuscript with the help of JW. All authors contributed to the article and approved the submitted version.</p></sec><sec id=\"conf1\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> This study was supported by the Natural Science Foundations of Shandong Province of China (ZR2017MC043), Shandong Swine Industry Technology System Innovation (SDAIT-08-03), Agricultural Scientific and Technological Innovation Project of Shandong Academy of Agricultural Sciences (CXGC2017B02), and the National Natural Science Foundation of China (31372293). The funding agency provided financial support for the research but was not involved in the design of the study nor collection, analysis, and interpretation of data or in writing the manuscript.</p></fn></fn-group><ack><p>We wish to thank the Jianghai Pig Breeding Co., Ltd. and the Breeding Swine Quality Supervision and Testing Center, Ministry of Agriculture (Wuhan) for their cooperation in this study. Meanwhile, we would like to thank <italic>Editage</italic> (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.editage.cn\">www.editage.cn</ext-link>) for the English language editing. Furthermore, this manuscript has been released as a preprint at <italic>Research Square</italic> (<xref rid=\"B69\" ref-type=\"bibr\">Zhao et al., 2010</xref>).</p></ack><fn-group><fn id=\"footnote1\"><label>1</label><p><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.animalgenome.org/cgi-bin/QTLdb/SS/index\">https://www.animalgenome.org/cgi-bin/QTLdb/SS/index</ext-link></p></fn><fn id=\"footnote2\"><label>2</label><p><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.genome.jp/dbget-bin/www_bget?map04923\">https://www.genome.jp/dbget-bin/www_bget?map04923</ext-link></p></fn><fn id=\"footnote3\"><label>3</label><p><ext-link ext-link-type=\"uri\" xlink:href=\"https://string-db.org\">https://string-db.org</ext-link></p></fn></fn-group><sec id=\"S10\" sec-type=\"supplementary material\"><title>Supplementary Material</title><p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.frontiersin.org/articles/10.3389/fgene.2020.00877/full#supplementary-material\">https://www.frontiersin.org/articles/10.3389/fgene.2020.00877/full#supplementary-material</ext-link></p><supplementary-material content-type=\"local-data\" id=\"FS1\"><label>FIGURE S1</label><caption><p>Sample clustering with all expressed genes to detect outliers.</p></caption><media xlink:href=\"Image_1.TIF\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"TS1\"><label>TABLE S1</label><caption><p>Phenotypic information of 28 Duroc pigs.</p></caption><media xlink:href=\"Data_Sheet_1.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"TS2\"><label>TABLE S2</label><caption><p>Summary of sequencing data of 28 Duroc pigs.</p></caption><media xlink:href=\"Data_Sheet_1.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"TS3\"><label>TABLE S3</label><caption><p>DEGs detected by DESeq2 and edgeR between high- and low-IMF groups (|log2(foldchange)| ≥ 1, <italic>p</italic> < 0.05).</p></caption><media xlink:href=\"Data_Sheet_1.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"TS4\"><label>TABLE S4</label><caption><p>Significantly enriched GO terms (<italic>q</italic> < 0.05) of the DEGs detected between high- and low-IMF groups.</p></caption><media xlink:href=\"Data_Sheet_1.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"TS5\"><label>TABLE S5</label><caption><p>Genes in the magenta module.</p></caption><media xlink:href=\"Data_Sheet_1.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"TS6\"><label>TABLE S6</label><caption><p>Genes in the midnight blue module.</p></caption><media xlink:href=\"Data_Sheet_1.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"TS7\"><label>TABLE S7</label><caption><p>Significantly enriched GO terms and pathways (<italic>q</italic> < 0.05) of the magenta module genes.</p></caption><media xlink:href=\"Data_Sheet_1.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"TS8\"><label>TABLE S8</label><caption><p>Significantly enriched GO terms and pathways (<italic>q</italic> < 0.05) of genes in the midnight blue module.</p></caption><media xlink:href=\"Data_Sheet_1.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"TS9\"><label>TABLE S9</label><caption><p>Overlapped GO terms identified by DEGs and magenta module genes.</p></caption><media xlink:href=\"Data_Sheet_1.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"TS10\"><label>TABLE S10</label><caption><p>Description of the 43 hub genes in the magenta module.</p></caption><media xlink:href=\"Data_Sheet_1.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec><ref-list><title>References</title><ref id=\"B1\"><mixed-citation publication-type=\"book\"><person-group 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Cureus</journal-id><journal-id journal-id-type=\"iso-abbrev\">Cureus</journal-id><journal-id journal-id-type=\"issn\">2168-8184</journal-id><journal-title-group><journal-title>Cureus</journal-title></journal-title-group><issn pub-type=\"epub\">2168-8184</issn><publisher><publisher-name>Cureus</publisher-name><publisher-loc>Palo Alto (CA)</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32821636</article-id><article-id pub-id-type=\"pmc\">PMC7431985</article-id><article-id pub-id-type=\"doi\">10.7759/cureus.9792</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Internal Medicine</subject></subj-group></article-categories><title-group><article-title>Cardiac Troponin Elevation and Outcome in Subarachnoid Hemorrhage</article-title></title-group><contrib-group><contrib contrib-type=\"editor\"><name><surname>Muacevic</surname><given-names>Alexander</given-names></name></contrib><contrib contrib-type=\"editor\"><name><surname>Adler</surname><given-names>John R</given-names></name></contrib></contrib-group><contrib-group><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>Zahid</surname><given-names>Tehrim</given-names></name><xref ref-type=\"aff\" rid=\"aff-1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Eskander</surname><given-names>Noha</given-names></name><xref ref-type=\"aff\" rid=\"aff-2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Emamy</surname><given-names>Mina</given-names></name><xref ref-type=\"aff\" rid=\"aff-3\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Ryad</surname><given-names>Robert</given-names></name><xref ref-type=\"aff\" rid=\"aff-4\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Jahan</surname><given-names>Nusrat</given-names></name><xref ref-type=\"aff\" rid=\"aff-5\">5</xref></contrib></contrib-group><aff id=\"aff-1\">\n<label>1</label>\nInternal Medicine, California Institute of Behavioral Neurosciences and Psychology, Fairfield, USA </aff><aff id=\"aff-2\">\n<label>2</label>\nPsychiatry, California Institute of Behavioral Neurosciences and Psychology, Fairfield, USA </aff><aff id=\"aff-3\">\n<label>3</label>\nDepartment of Research, California Institute of Behavioral Neurosciences and Psychology, Fairfield, USA </aff><aff id=\"aff-4\">\n<label>4</label>\nInternal Medicine, California Institute of Behavioural Neurosciences and Psychology, Brentwood, USA </aff><aff id=\"aff-5\">\n<label>5</label>\nInternal Medicine, California Institute of Behavioural Neurosciences and Psychology, Fairfield, USA </aff><author-notes><corresp id=\"cor1\">\nTehrim Zahid <email>tehrimzahid9@gmail.com</email></corresp></author-notes><pub-date date-type=\"pub\" publication-format=\"electronic\"><day>16</day><month>8</month><year>2020</year></pub-date><pub-date date-type=\"collection\" publication-format=\"electronic\"><month>8</month><year>2020</year></pub-date><volume>12</volume><issue>8</issue><elocation-id>e9792</elocation-id><history><date date-type=\"received\"><day>20</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>16</day><month>8</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020, Zahid et al.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Zahid et al.</copyright-holder><license license-type=\"open-access\" xlink:href=\"http://creativecommons.org/licenses/by/3.0/\"><license-p>This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</license-p></license></permissions><self-uri xlink:href=\"https://www.cureus.com/articles/38037-cardiac-troponin-elevation-and-outcome-in-subarachnoid-hemorrhage\">This article is available from https://www.cureus.com/articles/38037-cardiac-troponin-elevation-and-outcome-in-subarachnoid-hemorrhage</self-uri><abstract><p>Many cardiopulmonary complications occur after aneurysmal subarachnoid hemorrhage. This is due to sympathetic nervous system activation which results in release of norepinephrine from myocardial sympathetic nerves. Cardiac troponin I is a marker for diagnosis of cardiac injury. Elevated levels of troponin in these patients are associated with worse clinical outcomes. PubMed was searched for literature using regular and Medical Subject Heading (MeSH) keywords for data collection. Papers published in English language involving human subjects within the last 20 years focusing on cardiac troponin elevation following subarachnoid hemorrhage were included. Systemic complications that occur after subarachnoid hemorrhage worsen the clinical outcome of patients and have negative effects on the mortality and morbidity of these patients. Cardiac troponin I elevation is significantly associated with the severity of the stroke, poor neurological status, longer ICU stay, and death. Cardiac troponin I should be measured in patients presented with acute stroke. Hemodynamic monitoring and appropriate supportive care can improve clinical outcomes.</p></abstract><kwd-group kwd-group-type=\"author\"><kwd>troponin</kwd><kwd>subarachnoid hemorrhage</kwd></kwd-group></article-meta><notes><p content-type=\"disclaimer\">The content published in Cureus is the result of clinical experience and/or research by independent individuals or organizations. Cureus is not responsible for the scientific accuracy or reliability of data or conclusions published herein. All content published within Cureus is intended only for educational, research and reference purposes. Additionally, articles published within Cureus should not be deemed a suitable substitute for the advice of a qualified health care professional. Do not disregard or avoid professional medical advice due to content published within Cureus.</p></notes></front><body><sec sec-type=\"intro\"><title>Introduction and background</title><p>Subarachnoid hemorrhage (SAH) is followed by systemic complications like cardiac dysfunction which is due to sympathetic nervous system activation and affects the overall outcome of the patients [<xref rid=\"REF1\" ref-type=\"bibr\">1</xref>,<xref rid=\"REF2\" ref-type=\"bibr\">2</xref>]. Previously, necrosis of myocardial cells and release of cardiac enzymes were thought to be due to coronary artery disease but now a lot of evidence has proved that it is due to the release of norepinephrine from myocardial sympathetic nerves [<xref rid=\"REF3\" ref-type=\"bibr\">3</xref>]. Cardiac troponin is a diagnostic biomarker for cardiac damage and it increases in patients of subarachnoid hemorrhage [<xref rid=\"REF4\" ref-type=\"bibr\">4</xref>]. Troponin levels are considered as normal if less than 0.04ng/ml and high if more than 0.04ng/ml. Troponin levels should be measured after the admission of patients with acute stroke as higher levels are associated with stroke severity and high mortality [<xref rid=\"REF5\" ref-type=\"bibr\">5</xref>]. There is an increased risk of left ventricular systolic dysfunction, greater neurological damage and a longer stay in the intensive care unit if measured cardiac troponin levels are very high. Perhaps, there is a risk of diastolic dysfunction and pulmonary congestion if cardiac troponin is increased mildly. There is supporting data that subarachnoid hemorrhage has been associated with worse clinical presentation and outcome in patients with an acute cardiac injury which is diagnosed by cardiac troponin I elevation [<xref rid=\"REF6\" ref-type=\"bibr\">6</xref>]. Cerebral perfusion both focal and global decreases after cardiac dysfunction in patients with aneurysmal subarachnoid hemorrhage. This can be attributed to cardiac dysfunction directly or due to the release of catecholamines in these patients [<xref rid=\"REF7\" ref-type=\"bibr\">7</xref>]. There is increased intracranial pressure at the time of aneurysm rupture which results in brainstem ischemia, followed by sudden activation in the sympathetic nervous system and increased release of catecholamine [<xref rid=\"REF8\" ref-type=\"bibr\">8</xref>].</p><p>Cardiopulmonary complications occur after aneurysmal subarachnoid hemorrhage which worsens clinical outcome and have a negative impact on morbidity and mortality of patients. The cardiac injury which results in troponin elevation and cardiac arrhythmia are seen in these patients. Therefore, cardiac monitoring and interventions play an important role in patients of aneurysmal subarachnoid hemorrhage as [<xref rid=\"REF9\" ref-type=\"bibr\">9</xref>]. There are electrocardiographic abnormalities, wall motion abnormalities on echocardiogram and elevation of creatine kinase MB isozyme or cardiac troponin I in patients of subarachnoid hemorrhage. Left ventricular dysfunction in patients with SAH is indicated by sensitive biomarker elevation i.e. cardiac troponin I. This cardiac dysfunction is reversible so during clinical management of these patients hemodynamic monitoring should be strict until the cardiac function returns to normal [<xref rid=\"REF10\" ref-type=\"bibr\">10</xref>].</p><p>This study aims to find the relationship between cardiac enzymes elevation, clinical presentation, and outcome in patients with aneurysmal subarachnoid hemorrhage.</p></sec><sec sec-type=\"review\"><title>Review</title><p>Method</p><p>Data was collected manually on PubMed using Medical Subject Heading (MeSH) keywords. Table <xref rid=\"TAB1\" ref-type=\"table\">1</xref> shows MeSH keywords for the literature search.</p><table-wrap id=\"TAB1\" orientation=\"portrait\" position=\"float\"><label>Table 1</label><caption><title>Medical Subject Heading (MeSH) keywords for the literature search</title></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">MeSH KEYWORDS</td><td rowspan=\"1\" colspan=\"1\">DATABASE</td><td rowspan=\"1\" colspan=\"1\"> RESULTS</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Troponin subheading aneurysmal subarachnoid hemorrhage</td><td rowspan=\"1\" colspan=\"1\">PubMed</td><td rowspan=\"1\" colspan=\"1\">112</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Subarachnoid hemorrhage subheading troponin</td><td rowspan=\"1\" colspan=\"1\">PubMed</td><td rowspan=\"1\" colspan=\"1\">76</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Subarachnoid hemorrhage subheading cardiac enzymes</td><td rowspan=\"1\" colspan=\"1\">PubMed</td><td rowspan=\"1\" colspan=\"1\">71</td></tr></tbody></table></table-wrap><p>Then studies were selected after applying the following inclusion/exclusion criteria:</p><p>Inclusion criteria:</p><p>1. Papers published within the last 20 years</p><p>2. Human studies</p><p>3. Papers published in the English language only</p><p>4. All study types</p><p>Exclusion criteria:</p><p>1. Animal studies</p><p>2. Non-English language literature</p><p>Results</p><p>Table <xref rid=\"TAB2\" ref-type=\"table\">2</xref> shows the total number of articles selected after applying the inclusion/exclusion criteria in following order using MeSH keywords.</p><p> </p><table-wrap id=\"TAB2\" orientation=\"portrait\" position=\"float\"><label>Table 2</label><caption><title>Total number of articles selected after applying the inclusion/exclusion criteria in the following order using Medical Subject Heading (MeSH) keywords.</title></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">MeSH keyword subarachnoid hemorrhage (subheading – troponin)</td><td rowspan=\"1\" colspan=\"1\"> </td></tr><tr><td rowspan=\"1\" colspan=\"1\">Total articles</td><td rowspan=\"1\" colspan=\"1\">76</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Articles selected  </td><td rowspan=\"1\" colspan=\"1\">  66</td></tr><tr><td rowspan=\"1\" colspan=\"1\">MeSH keyword subarachnoid hemorrhage (subheading – cardiac Enzymes)</td><td rowspan=\"1\" colspan=\"1\"> </td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Total articles</td><td rowspan=\"1\" colspan=\"1\">71</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Articles selected</td><td rowspan=\"1\" colspan=\"1\">22</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">MeSH keyword troponin (subheading – aneurysmal subarachnoid hemorrhage)</td><td rowspan=\"1\" colspan=\"1\"> </td></tr><tr><td rowspan=\"1\" colspan=\"1\">Total articles</td><td rowspan=\"1\" colspan=\"1\">112</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Articles selected</td><td rowspan=\"1\" colspan=\"1\">100  </td></tr></tbody></table></table-wrap><p>We obtained 259 articles using the MeSH keywords search. Ultimately, a total of 188 articles were selected regarding inclusion/exclusion criteria as shown in Table <xref rid=\"TAB2\" ref-type=\"table\">2</xref>. Out of the 188 articles, 65 articles were removed as they did not specify the disease of interest (those which did not show the effect of troponin elevation on the outcome of a patient). </p><p>Finally, 35 publications in PubMed were reviewed. Full articles were available for eight publications and 27 publications contained solely abstracts. All records reviewed including eight full articles and 27 abstracts were freely available for review. All studies selected have been in the English language published within the last 20 years.</p><p>- Twenty-eight observational studies [<xref rid=\"REF2\" ref-type=\"bibr\">2</xref>,<xref rid=\"REF4\" ref-type=\"bibr\">4</xref>-<xref rid=\"REF8\" ref-type=\"bibr\">8</xref>,<xref rid=\"REF10\" ref-type=\"bibr\">10</xref>-<xref rid=\"REF31\" ref-type=\"bibr\">31</xref>].</p><p>- Four review articles [<xref rid=\"REF1\" ref-type=\"bibr\">1</xref>,<xref rid=\"REF9\" ref-type=\"bibr\">9</xref>,<xref rid=\"REF32\" ref-type=\"bibr\">32</xref>,<xref rid=\"REF33\" ref-type=\"bibr\">33</xref>].</p><p>- Two case reports [<xref rid=\"REF34\" ref-type=\"bibr\">34</xref>,<xref rid=\"REF35\" ref-type=\"bibr\">35</xref>].</p><p>- One clinical trial [<xref rid=\"REF3\" ref-type=\"bibr\">3</xref>].</p><p>The maximum number of subjects in the observational study was 1,655 and the minimum was 30. The total number of all the subjects in 27 observational studies were 6140. The clinical trial had 223 subjects.</p><p>Discussion</p><p>In this review, we analyzed the outcome after cardiac troponin I elevation in the setting of aneurysmal subarachnoid hemorrhage. Our study shows that cardiac troponin elevation after aneurysmal subarachnoid hemorrhage is associated with many systemic complications, poor outcomes and increased mortality.</p><p>The present literature review aimed to provide a complete review of the evidence available from various studies to determine cardiac injury evidenced by elevation of cardiac troponin I and sequelae of many cardiopulmonary complications after aneurysmal subarachnoid hemorrhage. It should be noted that many of our studies were analyzed in our literature review. In Table <xref rid=\"TAB3\" ref-type=\"table\">3</xref>, we summarize seven studies. Overall five observational studies and the two review articles explained that cardiac troponin I elevation after subarachnoid hemorrhage is associated with poor outcome and high mortality. However, two other studies summarized in Table <xref rid=\"TAB4\" ref-type=\"table\">4</xref> showed that cardiac abnormalities are common after aneurysmal SAH but are not associated with increased mortality. The rest of the studies reviewed in the discussion also showed that cardiac troponin is related to poor outcome after subarachnoid hemorrhage.</p><table-wrap id=\"TAB3\" orientation=\"portrait\" position=\"float\"><label>Table 3</label><caption><title>Summary of seven studies showing elevated troponin following subarachnoid hemorrhage (SAH) is associated with high mortality.</title></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Author/ Publication year</td><td rowspan=\"1\" colspan=\"1\">Study Design</td><td rowspan=\"1\" colspan=\"1\">Sample size</td><td rowspan=\"1\" colspan=\"1\">Main points</td><td rowspan=\"1\" colspan=\"1\"> </td></tr><tr><td rowspan=\"1\" colspan=\"1\"> Ayham M Alkhachroum et al. [<xref rid=\"REF5\" ref-type=\"bibr\">5</xref>], 2019</td><td rowspan=\"1\" colspan=\"1\">Observational Study</td><td rowspan=\"1\" colspan=\"1\">1655</td><td rowspan=\"1\" colspan=\"1\">TYPE II Myocardial infarction is common in patients with ischemic strokes, intracerebral hemorrhage, and subarachnoid hemorrhage.  It is diagnosed by elevated troponin levels. In this study, mortality was 14.7% in ischemic stroke patients, 31.3% in intracerebral hemorrhage patients, and 43.8% in subarachnoid hemorrhage patients with higher troponin levels.</td><td rowspan=\"1\" colspan=\"1\"> </td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Maciej Tomasz Wybraniec et al. [<xref rid=\"REF33\" ref-type=\"bibr\">33</xref>], 2014</td><td rowspan=\"1\" colspan=\"1\">Review Article</td><td rowspan=\"1\" colspan=\"1\">N/A</td><td rowspan=\"1\" colspan=\"1\">  After subarachnoid hemorrhage, there is a release of troponin and cardiac dysfunction with ECG changes. Neurocardiogenic injury should be differentiated from acute myocardial infarction. Cardiac dysfunction is associated with increased mortality.</td><td rowspan=\"1\" colspan=\"1\"> </td></tr><tr><td rowspan=\"1\" colspan=\"1\">Nicolas Bruder et al. [<xref rid=\"REF9\" ref-type=\"bibr\">9</xref>], 2011</td><td rowspan=\"1\" colspan=\"1\">Review Article</td><td rowspan=\"1\" colspan=\"1\">N/A</td><td rowspan=\"1\" colspan=\"1\">In one literature review, the cardiac injury which is diagnosed by an elevation in troponin levels has been noted in about one-third of patients and arrhythmias also occur in about one-third of patients after aneurysmal subarachnoid hemorrhage. Cardiopulmonary complications worsen clinical outcome and negatively affect morbidity and mortality.</td><td rowspan=\"1\" colspan=\"1\"> </td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Preeti Ramappa et al. [<xref rid=\"REF4\" ref-type=\"bibr\">4</xref>], 2008</td><td rowspan=\"1\" colspan=\"1\">Observational study</td><td rowspan=\"1\" colspan=\"1\">83</td><td rowspan=\"1\" colspan=\"1\">Subarachnoid hemorrhage patients with elevated cardiac troponin I levels have worse neurological status at admission in-hospital mortality.    </td><td rowspan=\"1\" colspan=\"1\"> </td></tr><tr><td rowspan=\"1\" colspan=\"1\">Rasham Sandhu et al. [<xref rid=\"REF14\" ref-type=\"bibr\">14</xref>], 2008</td><td rowspan=\"1\" colspan=\"1\">Observational study</td><td rowspan=\"1\" colspan=\"1\">378</td><td rowspan=\"1\" colspan=\"1\">In patients with ischemic stroke, in-hospital mortality is 65% with increased troponin I compared with 4% with normal troponin I. In patients with intracerebral hemorrhage, in-hospital mortality 64% with increased troponin I compared with 28% with normal troponin I. In patients with subarachnoid hemorrhage, in-hospital mortality occurred in 40% with increased troponin I compared 11% with normal troponin I.  </td><td rowspan=\"1\" colspan=\"1\"> </td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Sirisha Yarlagadda et al. [<xref rid=\"REF17\" ref-type=\"bibr\">17</xref>], 2006</td><td rowspan=\"1\" colspan=\"1\">Observational study</td><td rowspan=\"1\" colspan=\"1\">300</td><td rowspan=\"1\" colspan=\"1\">Cardiovascular complications after SAH are predictors of patient mortality. In one study, we have 300 patients. BNP level was greater than 600 pg/mL and troponin I was greater than 0.3 mg/L and was associated with death.      </td><td rowspan=\"1\" colspan=\"1\"> </td></tr><tr><td rowspan=\"1\" colspan=\"1\">Andrew M Naidech et al. [<xref rid=\"REF11\" ref-type=\"bibr\">11</xref>], 2005</td><td rowspan=\"1\" colspan=\"1\">Observational study</td><td rowspan=\"1\" colspan=\"1\">253</td><td rowspan=\"1\" colspan=\"1\"> Peak troponin levels were linked to increased risk of echocardiographic left ventricular dysfunction, pulmonary edema, hypotension, and delayed cerebral ischemia from vasospasm. Peak troponin levels were predictors of death or  disability at discharge but this association was no longer significant at 3 months. Cardiac troponin elevation is associated with an increased risk of cardiopulmonary complications, delayed cerebral ischemia, and death or poor functional outcome at discharge.  </td><td rowspan=\"1\" colspan=\"1\"> </td></tr></tbody></table></table-wrap><table-wrap id=\"TAB4\" orientation=\"portrait\" position=\"float\"><label>Table 4</label><caption><title>Summary of tables showing elevated troponin following subarachnoid hemorrhage (SAH) is not associated with mortality.</title></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">AUTHOR/ Publication year</td><td rowspan=\"1\" colspan=\"1\">Study Design</td><td rowspan=\"1\" colspan=\"1\">Sample Size</td><td rowspan=\"1\" colspan=\"1\">Main points</td></tr><tr><td rowspan=\"1\" colspan=\"1\"> Klaudia Urbaniak et al. [<xref rid=\"REF16\" ref-type=\"bibr\">16</xref>], 2007</td><td rowspan=\"1\" colspan=\"1\">Observational study</td><td rowspan=\"1\" colspan=\"1\">266</td><td rowspan=\"1\" colspan=\"1\">Cardiac abnormalities after SAH do not increase mortality. However, they adversely affect discharge disposition and prolong hospital LOS. In this study, 50% (n = 133) demonstrated cardiac abnormalities as indicated by abnormal EKG, ECHO, or troponin I. There was no difference in mortality between the cardiac abnormality and control groups (P = .33). However, there was increased morbidity in the cardiac abnormality group as demonstrated by worse discharge disposition, in addition to increased length of hospital stay (22.6 vs 17.1 days, P < .01).</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Claire E Sommargren et al. [<xref rid=\"REF29\" ref-type=\"bibr\">29</xref>], 2002</td><td rowspan=\"1\" colspan=\"1\">Observational study</td><td rowspan=\"1\" colspan=\"1\">100</td><td rowspan=\"1\" colspan=\"1\">Electrocardiographic abnormalities like ventricular repolarization has been reported in subarachnoid hemorrhage. In a study of 100 patients with SAH, repolarization abnormalities occurred in 41% of patients, prolonged QTc interval >460 ms in 16%, ST segment elevation in 9%, ST depression in 3%, T wave inversion in 7%, and U wave >or=100 micro V in 15%. Serum cardiac troponin I was elevated in 21%, and was significantly associated with QTc interval >460 ms (P<.001 prolonged=\"\" qtc=\"\" interval=\"\" after=\"\" sah=\"\" is=\"\" related=\"\" to=\"\" myocardial=\"\" injury=\"\" but=\"\" unrelated=\"\" mortality.=\"\"/>\n</td></tr></tbody></table></table-wrap><p>Cardiac Biomarkers</p><p>Myocardial necrosis is a neurally mediated process due to the release of norepinephrine from myocardial sympathetic nerves after aneurysmal subarachnoid hemorrhage and results in the release of cardiac enzymes [<xref rid=\"REF1\" ref-type=\"bibr\">1</xref>]. Cardiac troponin I elevation is used to diagnose neurocardiac injury. In addition to that, Brain natriuretic peptide (BNP) is also secreted in response to injury, inflammation and cardiac myocyte stretch. BNP is significantly associated with cardiac troponin I. For every 1 unit increase in log BNP, troponin increased by 0.05ng/ml (P=.001). For every 1 unit increase in log BNP, patients are 3.16 times more likely to have a poor mRS (modified Rankin Scale) at discharge (P=.021) and 5.40 times more likely at three months (P<.0001). Therefore, both troponin and BNP predict worse outcomes after SAH [<xref rid=\"REF2\" ref-type=\"bibr\">2</xref>]. Ischemia-modified albumin (IMA), tumor necrosis factor α (TNF-α), and MPO levels were higher by mean values of 11.6%, 9.5%, and 2.9%, respectively, in SAH patients compared with the control group. White blood cells (WBCs), creatine kinase-MB (CK-MB), and troponin values were significantly higher in patients with SAH compared with healthy control (P<.001, P<.01, and P<.05, respectively). WBCs and troponin levels in the circulation correlates with patients’ clinical severity (r=0.598, P=.001 and r=0.461, P=.012, respectively). IMA has poor diagnostic value than creatine kinase-MB and cardiac troponin I in predicting cardiac injury in SAH patients [<xref rid=\"REF3\" ref-type=\"bibr\">3</xref>].</p><p>Pathophysiology</p><p>Following SAH, catecholamine-induced neurocardiogenic injury leads to ischemic ECG changes, elevation in cardiac-specific enzymes, neurogenic pulmonary edema, neurogenic stunned myocardium, segmental wall motion abnormalities, stress cardiomyopathy, left ventricular systolic dysfunction and myocardial dysfunction. There are normal myocardial perfusion and abnormal sympathetic innervation in SAH patients with left ventricular (LV) systolic dysfunction. This is due to the excessive release of norepinephrine from myocardial sympathetic nerves, which damage myocytes and nerve terminals [<xref rid=\"REF4\" ref-type=\"bibr\">4</xref>-<xref rid=\"REF7\" ref-type=\"bibr\">7</xref>]. Cardiac injury detected by increased troponin after SAH is associated with persistent electrocardiographic and echocardiographic abnormalities like ST-segment elevation, T wave inversion, QTc prolongation, ventricular arrhythmias, regional wall motion abnormalities and decreased ejection fraction [<xref rid=\"REF6\" ref-type=\"bibr\">6</xref>,<xref rid=\"REF8\" ref-type=\"bibr\">8</xref>,<xref rid=\"REF9\" ref-type=\"bibr\">9</xref>].</p><p>Cardiac Complications</p><p>SAH-induced myocardial dysfunction is associated with high blood pressure, increased heart rate, and ECG changes. Patients with echocardiographic abnormalities like LV diastolic dysfunction and LV systolic dysfunction had two-fold greater plasma BNP, and 40-fold greater troponin. In less severe forms of SAH, there is only left ventricular dysfunction. But in severe forms of SAH, there is excessive release of catecholamines which affect both systolic and diastolic left ventricular function. There is normal atrium volume which suggests an acute alteration in function in isolated left ventricular dysfunction. However, there is global cardiac dysfunction on echocardiography in severe myocardial injury associated with higher levels of troponin I and BNP in severe SAH [<xref rid=\"REF10\" ref-type=\"bibr\">10</xref>]. Cardiac injury is noted in one-third of patients and arrhythmias in one-third of patients after SAH which negatively affects morbidity and mortality. Cardiac dysfunction defined by myocardial wall motion abnormalities or positive troponin after SAH is also linked to decreased focal or global cerebral perfusion. This relation explains delayed cerebral ischemia after SAH. Therefore, (wall motion abnormalities) WMAs are risk factors for poor clinical outcome after SAH [<xref rid=\"REF11\" ref-type=\"bibr\">11</xref>,<xref rid=\"REF12\" ref-type=\"bibr\">12</xref>]. High-grade SAH, elevated troponin, history of prior stimulant drug use and tachycardia are independent predictors of LV regional wall motion abnormalities after SAH. Patients with WMAs have poor Glasgow Coma scale (GCS), poor functional recovery, and increased hospital stay [<xref rid=\"REF13\" ref-type=\"bibr\">13</xref>-<xref rid=\"REF15\" ref-type=\"bibr\">15</xref>].</p><p>Electrocardiographic (ECG) and Echocardiographic Findings</p><p>In another case-control study, peak troponin levels were used to differentiate LV dysfunction (stunned myocardium (SM) associated with SAH) from myocardial infarction in the control group. ECG changes did not match echocardiographic changes in SAH patients. Therefore, troponin values less than 2.8ng/ml in patients with ejection fractions (EFs) less than 40% are consistent with SM and CK-MB trend does not differ between SM and myocardial infarction (MI) but that echocardiograms inconsistent with EKGs indicate SM [<xref rid=\"REF16\" ref-type=\"bibr\">16</xref>]. Diastolic dysfunction also explains the development of pulmonary edema in SAH patients. This is also linked to the history of hypertension and older age [<xref rid=\"REF17\" ref-type=\"bibr\">17</xref>].</p><p>Systemic Complications</p><p>Cardiac troponin I elevation is also associated with pulmonary congestion, acute lung injury, cardiovascular morbidity, delayed cerebral ischemia (DCI), longer ICU stay, poor functional outcome at discharge, and mortality [<xref rid=\"REF18\" ref-type=\"bibr\">18</xref>-<xref rid=\"REF20\" ref-type=\"bibr\">20</xref>]. Neurogenic stunned myocardium (NSM) which is the triad of transient LV dysfunction, ECG changes, and elevated troponin resembles myocardial infarction but has good prognosis if managed early and appropriately [<xref rid=\"REF21\" ref-type=\"bibr\">21</xref>]. Aggressive hemodynamic monitoring should be done until cardiac functions return to normal [<xref rid=\"REF22\" ref-type=\"bibr\">22</xref>]. Serial troponin measurement, continuous ECG surveillance, for patients with poor clinical presentation and neurological examination can result in early detection of SAH induced myocardial infarction which will alert staff to observe patients for clinical manifestations of subarachnoid hemorrhage-induced myocardial infarction (SAHMI) and provide supportive care and treatment which in turn improve clinical outcome and reduced mortality.</p><p>Further studies are warranted to explore more consequences of troponin elevation, early detection methods, monitoring protocols, and treatment strategies to decrease mortality and morbidity.</p><p>Limitations</p><p>The current literature review has some limitations. It includes studies conducted within the last twenty-year period. This results in variations in the care of patients and changes in assays of cardiac biomarkers. There was no age-specific or gender-specific analysis performed. There are many unexplored factors that can be tested in further studies.</p></sec><sec sec-type=\"conclusions\"><title>Conclusions</title><p>After analysis of data, we found out that myocardial injury indicated by an elevation in cardiac troponin I occurs after aneurysmal subarachnoid hemorrhage. This is due to sympathetic nervous system activation and release of norepinephrine from myocardial sympathetic nerves after aneurysmal subarachnoid hemorrhage rather than coronary artery disease which was once thought to be the cause. Many systemic complications occur after subarachnoid hemorrhage like myocardial dysfunction, neurogenic pulmonary edema, neurogenic stunned myocardium, segmental wall motion abnormalities, stress cardiomyopathy, and delayed cerebral ischemia. These cardiopulmonary complications have been linked to worsened clinical outcomes in these patients and negatively affect morbidity and mortality. Cardiac troponin I elevation directly correlates with the severity of a stroke, poor neurological status, longer ICU stay, and death. It should be considered in patients who present with acute stroke. Hemodynamic monitoring and appropriate supportive care can improve clinical outcomes. Further research is needed to investigate and identify more quick and effective ways to monitor patients for neurocardiogenic injury in the acute phase of hemorrhage.</p></sec></body><back><fn-group content-type=\"competing-interests\"><fn fn-type=\"COI-statement\"><p>The authors have declared that no competing interests exist.</p></fn></fn-group><ref-list><title>References</title><ref id=\"REF1\"><label>1</label><element-citation publication-type=\"journal\"><article-title>Systemic complications following aneurysmal subarachnoid hemorrhage</article-title><source>Curr Neurol Neurosci Rep</source><person-group><name><surname>Garg</surname><given-names>R</given-names></name><name><surname>Bar</surname><given-names>B</given-names></name></person-group><fpage>7</fpage><volume>17</volume><year>2017</year><pub-id pub-id-type=\"pmid\">28168536</pub-id></element-citation></ref><ref id=\"REF2\"><label>2</label><element-citation publication-type=\"journal\"><article-title>Troponin elevation in 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Immunol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Immunol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Immunol.</journal-id><journal-title-group><journal-title>Frontiers in Immunology</journal-title></journal-title-group><issn pub-type=\"epub\">1664-3224</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849665</article-id><article-id pub-id-type=\"pmc\">PMC7431986</article-id><article-id pub-id-type=\"doi\">10.3389/fimmu.2020.02007</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Immunology</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>Escape of TLR5 Recognition by <italic>Leptospira</italic> spp.: A Rationale for Atypical Endoflagella</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Holzapfel</surname><given-names>Marion</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><xref ref-type=\"author-notes\" rid=\"fn002\"><sup>†</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/619317/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Bonhomme</surname><given-names>Delphine</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>†</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1048861/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Cagliero</surname><given-names>Julie</given-names></name><xref ref-type=\"aff\" rid=\"aff5\"><sup>5</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1026977/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Vernel-Pauillac</surname><given-names>Frédérique</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Fanton d’Andon</surname><given-names>Martine</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Bortolussi</surname><given-names>Sophia</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Fiette</surname><given-names>Laurence</given-names></name><xref ref-type=\"aff\" rid=\"aff6\"><sup>6</sup></xref><xref ref-type=\"author-notes\" rid=\"fn003\"><sup>‡</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Goarant</surname><given-names>Cyrille</given-names></name><xref ref-type=\"aff\" rid=\"aff7\"><sup>7</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/391903/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Wunder</surname><given-names>Elsio A.</given-names><suffix>Jr.</suffix></name><xref ref-type=\"aff\" rid=\"aff8\"><sup>8</sup></xref><xref ref-type=\"aff\" rid=\"aff9\"><sup>9</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/354380/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Picardeau</surname><given-names>Mathieu</given-names></name><xref ref-type=\"aff\" rid=\"aff10\"><sup>10</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/391997/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Ko</surname><given-names>Albert I.</given-names></name><xref ref-type=\"aff\" rid=\"aff8\"><sup>8</sup></xref><xref ref-type=\"aff\" rid=\"aff9\"><sup>9</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/529365/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Werling</surname><given-names>Dirk</given-names></name><xref ref-type=\"aff\" rid=\"aff11\"><sup>11</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/94000/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Matsui</surname><given-names>Mariko</given-names></name><xref ref-type=\"aff\" rid=\"aff5\"><sup>5</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/402238/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Boneca</surname><given-names>Ivo G.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/26784/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Werts</surname><given-names>Catherine</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/319442/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Institut Pasteur, Unité Biologie et Génétique de la Paroi Bactérienne</institution>, <addr-line>Paris</addr-line>, <country>France</country></aff><aff id=\"aff2\"><sup>2</sup><institution>CNRS, UMR 2001 Microbiologie Intégrative et Moléculaire</institution>, <addr-line>Paris</addr-line>, <country>France</country></aff><aff id=\"aff3\"><sup>3</sup><institution>Institut National de la Santé et de la Recherche Médicale</institution>, <addr-line>Equipe Avenir, Paris</addr-line>, <country>France</country></aff><aff id=\"aff4\"><sup>4</sup><institution>Sorbonne Paris Cité, Université de Paris</institution>, <addr-line>Paris</addr-line>, <country>France</country></aff><aff id=\"aff5\"><sup>5</sup><institution>Institut Pasteur de Nouvelle Calédonie, Immunity and Inflammation Group, Institut Pasteur International Network</institution>, <addr-line>Noumea</addr-line>, <country>France</country></aff><aff id=\"aff6\"><sup>6</sup><institution>Unité Histopathologie Humaine et Modèles Animaux, Institut Pasteur</institution>, <addr-line>Paris</addr-line>, <country>France</country></aff><aff id=\"aff7\"><sup>7</sup><institution>Leptospirosis Research and Expertise Unit, Institut Pasteur International Network, Institut Pasteur de Nouvelle Calédonie</institution>, <addr-line>Noumea</addr-line>, <country>France</country></aff><aff id=\"aff8\"><sup>8</sup><institution>Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Brazilian Ministry of Health</institution>, <addr-line>Salvador</addr-line>, <country>Brazil</country></aff><aff id=\"aff9\"><sup>9</sup><institution>Department of Epidemiology of Microbial Diseases, Yale School of Public Health</institution>, <addr-line>New Haven, CT</addr-line>, <country>United States</country></aff><aff id=\"aff10\"><sup>10</sup><institution>Unité Biologie des Spirochètes, Institut Pasteur</institution>, <addr-line>Paris</addr-line>, <country>France</country></aff><aff id=\"aff11\"><sup>11</sup><institution>Department of Pathobiology and Population Sciences, Royal Veterinary College</institution>, <addr-line>Hatfield</addr-line>, <country>United Kingdom</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Melissa Jo Caimano, University of Connecticut Health Center, United States</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: John J. Bromfield, University of Florida, United States; Ellie Jordan Putz, National Animal Disease Center (USDA ARS), United States; Juan Anguita, Center for Cooperative Research in Biosciences, Spain</p></fn><corresp id=\"c001\">*Correspondence: Catherine Werts, <email>cwerts@pasteur.fr</email></corresp><fn fn-type=\"other\" id=\"fn002\"><p><sup>†</sup>These authors share first authorship</p></fn><fn fn-type=\"other\" id=\"fn003\"><p><sup>‡</sup>Present address: Laurence Fiette, IMMR, Paris, France</p></fn><fn fn-type=\"other\" id=\"fn004\"><p>This article was submitted to Microbial Immunology, a section of the journal Frontiers in Immunology</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>2007</elocation-id><history><date date-type=\"received\"><day>20</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>24</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Holzapfel, Bonhomme, Cagliero, Vernel-Pauillac, Fanton d’Andon, Bortolussi, Fiette, Goarant, Wunder, Picardeau, Ko, Werling, Matsui, Boneca and Werts.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Holzapfel, Bonhomme, Cagliero, Vernel-Pauillac, Fanton d’Andon, Bortolussi, Fiette, Goarant, Wunder, Picardeau, Ko, Werling, Matsui, Boneca and Werts</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p><italic>Leptospira (L.) interrogans</italic> are invasive bacteria responsible for leptospirosis, a worldwide zoonosis. They possess two periplasmic endoflagellae that allow their motility. <italic>L. interrogans</italic> are stealth pathogens that escape the innate immune recognition of the NOD-like receptors NOD1/2, and the human Toll-like receptor (TLR)4, which senses peptidoglycan and lipopolysaccharide (LPS), respectively. TLR5 is another receptor of bacterial cell wall components, recognizing flagellin subunits. To study the contribution of TLR5 in the host defense against leptospires, we infected WT and TLR5 deficient mice with pathogenic <italic>L. interrogans</italic> and tracked the infection by <italic>in vivo</italic> live imaging of bioluminescent bacteria or by qPCR. We did not identify any protective or inflammatory role of murine TLR5 for controlling pathogenic <italic>Leptospira</italic>. Likewise, subsequent <italic>in vitro</italic> experiments showed that infections with different live strains of <italic>L. interrogans</italic> and <italic>L. biflexa</italic> did not trigger TLR5 signaling. However, unexpectedly, heat-killed bacteria stimulated human and bovine TLR5, but did not, or barely induced stimulation via murine TLR5. Abolition of TLR5 recognition required extensive boiling time of the bacteria or proteinase K treatment, showing an unusual high stability of the leptospiral flagellins. Interestingly, after using antimicrobial peptides to destabilize live leptospires, we detected TLR5 activity, suggesting that TLR5 could participate in the fight against leptospires in humans or cattle. Using different <italic>Leptospira</italic> strains with mutations in the flagellin proteins, we further showed that neither FlaA nor Fcp participated in the recognition by TLR5, suggesting a role for the FlaB. FlaB have structural homology to <italic>Salmonella</italic> FliC, and possess conserved residues important for TLR5 activation, as shown by <italic>in silico</italic> analyses. Accordingly, we found that leptospires regulate the expression of FlaB mRNA according to the growth phase <italic>in vitro</italic>, and that infection with <italic>L. interrogans</italic> in hamsters and in mice downregulated the expression of the FlaB, but not the FlaA subunits. Altogether, in contrast to different bacteria that modify their flagellin sequences to escape TLR5 recognition, our study suggests that the peculiar central localization and stability of the FlaB monomers in the periplasmic endoflagellae, associated with the downregulation of FlaB subunits in hosts, constitute an efficient strategy of leptospires to escape the TLR5 recognition and the induced immune response.</p></abstract><kwd-group><kwd>Leptospira</kwd><kwd>toll-like receptor</kwd><kwd>innate immunity</kwd><kwd>Flagelin genes</kwd><kwd>TLR5</kwd><kwd>mouse model</kwd></kwd-group><counts><fig-count count=\"8\"/><table-count count=\"1\"/><equation-count count=\"0\"/><ref-count count=\"60\"/><page-count count=\"20\"/><word-count count=\"0\"/></counts></article-meta></front><body><sec id=\"S1\"><title>Introduction</title><p>Leptospires are spirochetal bacteria responsible for leptospirosis, a neglected reemerging zoonosis (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>). Among the <italic>Leptospira</italic> genus, which includes more than 60 species and 300 different serovars, <italic>Leptospira (L.) interrogans</italic> gathers the most pathogenic strains (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>). Rodents and other animals can carry leptospires asymptomatically in the lumen of proximal renal tubules, excrete the bacteria in their urine and contaminate the environment. Vertebrates get infected through skin or mucosa. In most cases, humans show either no symptoms or suffer from a flu-like mild disease, but may also show acute severe, potentially fatal, leptospirosis. Antibiotic treatments are efficient only if administered at the onset of symptoms. The high number of leptospiral serovars and strains complicates the diagnosis and impairs vaccine strategies.</p><p><italic>Leptospira</italic> are motile bacteria able to swim very fast in viscous environments. They possess two endoflagella, one inserted at each pole of the bacteria, which do not protrude outside of the bacteria but are localized and rotate within the periplasmic space. As seen in other spirochetes, the leptospiral genomes exhibit an atypically high number of structural flagellar genes, including four FlaB subunits with homology to FliC, the unique flagellin monomer forming the filament of <italic>Salmonella</italic> spp. The structure of the leptospiral filament and the roles of the different flagellar proteins and additional specific components of leptospires, such as the Fcp proteins (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>–<xref rid=\"B7\" ref-type=\"bibr\">7</xref>), have been recently elucidated by high-resolution cryo-electron microscopy coupled to model building and crystallography analyses (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>). These studies revealed that the leptospiral filament has an atypical flattened helical shape, and that the four FlaB subunits constitute the core of the flagellum, surrounded by two FlaA and two Fcp subunits forming a sheath (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>).</p><p>The innate defense of the host relies, amongst other mechanisms, on activation of the complement system and on the recognition of microbe-associated molecular patterns (MAMPs) by immune receptors, such as the pattern recognition receptors (PRRs) families of Toll-like Receptor (TLR) and NOD-like receptor (NLR). After ligand recognition, TLRs and NLRs induce immune inflammatory responses that trigger cellular recruitment, ultimately leading to the destruction of microbes by phagocytes (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>).</p><p>FliC, the prototypical bacterial flagellin, forms a hairpin-like structure with 4 connected domains designated D0, D1, D2, and D3, with both C and N termini associated in the D0 domain (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>). The D2 and D3 domains are highly variable and support the antigenic diversity. FliC is recognized by different PRRs. TLR5 is expressed at the surface of cells and recognizes monomers of flagellin through the D1 domain, whereas in the cytosol FliC is recognized through the D0 domain by the NAIP inflammasome, which associates with the IPAF/NLRC4, a NOD-like receptor (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>, <xref rid=\"B12\" ref-type=\"bibr\">12</xref>). TLR5 is an essential innate immune receptor expressed in the kidney and, along with TLR4, important to control <italic>Enterobacteria</italic> (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>). Moreover it is one of the very few TLRs able to recognize a protein agonist, conferring potent adjuvant properties, and helping adaptive immune responses (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>).</p><p>We previously showed that <italic>Leptospira</italic> infection triggers the NLRP3 inflammasome, using the ASC adaptor. The results using ASCko mice reproduced the results obtained with the NLRP3ko mice and suggest that the contribution of other inflammasome receptors, such as the NAIP/NLRC4 would be minimal (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>). We also showed that <italic>L. interrogans</italic> escapes recognition by human TLR4 (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>) as well as murine and human NOD1 and NOD2 (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>). In this work, we investigated whether leptospiral flagellins are either recognized by or also escape recognition by TLR5. Our results suggest that live pathogenic leptospires largely escape recognition by human and murine TLR5, although their FlaB subunits are able to signal through human TLR5. This suggests that the periplasmic localization of the flagella and the concealing of FlaB in the core of the filament contribute to avoiding the TLR5 recognition pathway.</p></sec><sec sec-type=\"materials|methods\" id=\"S2\"><title>Materials and Methods</title><sec id=\"S2.SS1\"><title>Leptospiral Strains and Culture Conditions</title><p>Pathogenic <italic>L. interrogans</italic> serovar Icterohaemorrhagiae strain Verdun, <italic>L. interrogans</italic> serovar Copenhageni strain Fiocruz L1-130, <italic>L. interrogans</italic> serovar Manilae strain L495, and the saprophytic <italic>L. biflexa</italic> serovar Patoc strain Patoc I have been provided by the collection of the National Reference Center for Leptospirosis of the Institut Pasteur in Paris. The L495 derivative bioluminescent strain MFLum1 (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>), the clinical isolate Fiocruz LV2756 and its non-mobile <italic>fcpA</italic> mutant (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>), the <italic>L. interrogans</italic> Manilae <italic>flaA2</italic> mutant, as well as the <italic>flaB4</italic> mutant of <italic>L. biflexa</italic> Patoc have all been previously described (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>, <xref rid=\"B19\" ref-type=\"bibr\">19</xref>). The <italic>L. biflexa fcpA</italic> and <italic>L. interrogans</italic> Manilae <italic>flaB1</italic> mutants have been generated for this study by random mutagenesis (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>).</p><p>Bacteria were grown in Ellinghausen-McCullough-Johnson-Harris (EMJH) medium (Bio-Rad) at 30°C without agitation and weekly passaged, counted using a Petroff-Hauser chamber and seeded at 5 × 10<sup>6</sup> bacteria per mL (bact/mL). Bacteria in mid-log exponential phase (around 10<sup>8</sup> bact/mL), and bacteria in stationary phase (around 1 to 5 × 10<sup>9</sup> bact/mL) were harvested from 3–6-day old cultures and 10–14-day old cultures, respectively. Unless otherwise specified, experiments were performed with 1-week old cultures. The <italic>L. biflexa</italic> Patoc Patoc I strain was passaged twice a week by a 1/250 dilution and seeded at around 5 × 10<sup>6</sup> bact/mL. For experiments conducted in New Caledonia, virulent <italic>L. interrogans</italic> serovar Icterohaemorrhagiae strain Verdun was cultured in EMJH medium at 30°C under aerobic conditions as previously described (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>). For <italic>in vitro flaB</italic> gene expression assays, cultures of each <italic>Leptospira</italic> strain were seeded in triplicate at 5 × 10<sup>6</sup> (Day 0). On Day 3 (exponential growth phase) and Day 14 (stationary growth phase), 5 × 10<sup>8</sup> bacteria from each culture were harvested and centrifuged at 3,250 × <italic>g</italic> for 25 min, EMJH was discarded and bacteria were resuspended in 500 μL of RNAlater Buffer (Qiagen) for RNA stabilization, kept at room temperature for 2 h before conservation at −20°C until RNA extraction.</p></sec><sec id=\"S2.SS2\"><title><italic>In vivo</italic> Infection Experiments Using Leptospires</title><p>Male and female C57BL/6J mice (7- to 10-week old) were used in this study and were obtained from Janvier Labs (Le Genest, France). TLR5 deficient mice (TLR5KO) in a C57BL/6J background were bred at the Institut Pasteur Paris animal facility and were previously described (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>). Outbred OF1 mice (<italic>Mus musculus</italic>) and golden Syrian hamsters (<italic>Mesocricetus auratus</italic>), initially obtained from Charles River Laboratories, were bred in the animal facility in Institut Pasteur in New Caledonia.</p><p>Infections of C57BL/6J mice with <italic>L. interrogans</italic> strains were conducted as described (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>). Just before infection, bacteria were centrifuged at room temperature for 25 min at 3,250 × <italic>g</italic>, resuspended in endotoxin-free PBS. Infection in mice were done via the intraperitoneal route (IP) with sublethal doses (10<sup>7</sup> bacteria in 200 μL of PBS) of pathogenic <italic>L. interrogans</italic>. Live imaging (IVIS) using the bioluminescent MFLum1 derivative of Manilae L595 has been described earlier (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>) and recently reviewed (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>). Animals were bled at the facial vein sinus (around 50–100 μl of blood, recovered in tubes coated with 20 μl of EDTA 100 mM). A drop of urine was retrieved upon first handling of mice. Animals were killed by cervical dislocation and organs frozen in liquid nitrogen before storage at −80°C or fixed in formaldehyde for histopathology.</p><p>Virulence of <italic>L. interrogans</italic> Icterohaemorrhagiae strain Verdun was maintained by cyclic passages in golden Syrian hamsters after intraperitoneal (IP) injection of the LD<sub>100</sub> at 2 × 10<sup>8</sup> leptospires before re-isolation from blood by cardiac puncture at 4.5 days post-infection, after euthanasia with CO<sub>2</sub>.</p><p>For <italic>in vivo</italic> study of <italic>flaA</italic> and <italic>flaB</italic> gene expression, 6–8-week old healthy animals (<italic>n</italic> ≥ 5 individuals per condition) were infected and experiments were carried out as previously described (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>). Briefly, OF1 mice and hamsters were IP injected with 2 × 10<sup>8</sup> virulent <italic>L. interrogans</italic> Icterohaemorrhagiae strain Verdun in 500–800 μL of EMJH medium, as recently reviewed (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>). After euthanasia with CO<sub>2</sub>, whole blood was rapidly collected by cardiac puncture at 24 h p.i. and conserved in PAXgene blood RNA tubes (PreAnalytiX, Qiagen) for 2 h at room temperature to allow stabilization of total RNA before storage at −20°C until RNA extraction.</p></sec><sec id=\"S2.SS3\"><title>Ethics Statement</title><p>Animal manipulations were conducted according to the guidelines of Animal Care following the EU Directive 2010/63 EU. All protocols were reviewed and approved (#2013-0034, and #HA-0036) by the Institut Pasteur ethic committee (CETEA #89) (Paris, France), the competent authority, for compliance with the French and European regulations on Animal Welfare and with Public Health Service recommendations.</p></sec><sec id=\"S2.SS4\"><title>Histology and Immunohistochemistry</title><p>Transversal sections of kidneys were collected and fixed in formaldehyde 4% for at least 48 h at room temperature, embedded in paraffin, and 5 μm thick sections were stained with Hematoxylin-Eosin. Immunohistochemistry was performed on dewaxed sections as described (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>). A rabbit polyclonal serum against the LipL21 (kindly provided by David Haake) was used (1/1000<sup>e</sup>). A Periodic Acid-Schiff (PAS) staining was also associated to the Lip21 immunolabeling to visualize the membranes and brush borders typical of proximal tubules.</p></sec><sec id=\"S2.SS5\"><title>qPCR Quantification of Leptospiral DNA in Blood, Urine and Organs</title><p>The leptospiral load in blood, urine and organs was determined by quantitative real-time PCR (qPCR), as described (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>). Total DNA from blood and urine (around 50 μL) was extracted using a Maxwell 16 automat with the Maxwell blood DNA and cell LEV DNA purification kits (Promega), respectively. DNA was extracted with the QIAamp DNA kit (Qiagen) from organs mechanically disrupted for 3 min at 4°C with metal beads using an automat (Labomodern). Primers and probe designed in the <italic>lpxA</italic> gene of <italic>L. interrogans</italic> strain Fiocruz L1-130 (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>) were used to specifically detect pathogenic <italic>Leptospira sp.</italic> (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>), using the <italic>nidogen</italic> gene for normalization in kidneys. qPCR reactions were run on a Step one Plus real-time PCR apparatus using the absolute quantification program (Applied Biosystems), with the following conditions for FAM-TAMRA probes: 50°C for 2 min, 95°C for 10 min, followed by 40 cycles with denaturation at 95°C for 15 s and annealing temperature 60°C for 1 min.</p></sec><sec id=\"S2.SS6\"><title>Reverse and Real-Time Transcription PCR for Cytokine Gene Expression in Kidney</title><p>Total RNA was extracted from kidneys using the RNeasy mini kit (Qiagen) and RT-qPCR were performed as described (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>). The sequences of primers and probes for IL10, RANTES, and IFNγ have already been described (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>, <xref rid=\"B15\" ref-type=\"bibr\">15</xref>). Data were analyzed according to the method of relative gene expression using the comparative cycle threshold (<italic>Ct</italic>) method also referred to as the 2<sup>(–ΔΔCt)</sup> method. PCR data were reported as the relative increase in mRNA transcripts versus that found in kidneys from naive WT mice, corrected by the respective levels of Hypoxanthine phosphoribosyltransferase (HPRT) mRNA used as an internal standard.</p></sec><sec id=\"S2.SS7\"><title>Total RNA Extraction and cDNA Synthesis for Leptospiral <italic>fla</italic> Genes</title><p>Total RNA from blood was extracted using a PAXgene blood RNA system from PreAnalytiX (Qiagen). Total RNA from virulent <italic>Leptospira</italic> (4 × 10<sup>8</sup> bacteria) cultured <italic>in vitro</italic> at 30 and at 37°C in EMJH medium was also extracted using a High Pure RNA Isolation kit (Roche Applied Science) following the manufacturer’s recommendations. Total RNA samples were treated with DNase (Turbo DNA-Free kit; Ambion, Applied Biosystems) for elimination of residual genomic DNA. Before storage at −80°C, purified RNA was quantified by measurement of the optical density at 260 nm (OD<sub>260</sub>) using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific), and the quality of nucleic acids was verified by measurement of the OD<sub>260</sub>/OD<sub>280</sub> ratio. Then, 1 μg of total RNA was reverse transcribed using a Transcriptor First Strand cDNA synthesis kit (Roche Applied Science) and the provided random hexamer primers for the mix preparation, on a GeneAmp PCR system 9700 instrument (Applied Biosystems) with the following program: 10 min at 25°C; 30 min at 55°C; and 5 min at 85°C. The cDNA synthesized was conserved at −20°C until quantitative PCR (qPCR) assays.</p></sec><sec id=\"S2.SS8\"><title>Quantitative PCR and FlaA and FlaB Expression Analysis</title><p>After cDNA synthesis, qPCR assays were performed using primers purchased from Eurogentec (Seraing, Belgium; <xref rid=\"T1\" ref-type=\"table\">Table 1</xref>) and specific for the gene coding for the <italic>flaA</italic> and <italic>flaB</italic> subunit genes. Primers were designed using LightCycler Probe Design Software (version 2.0; Roche Applied Science) or the free online Primer3 software (version 0.4.0) using available sequences retrieved from GenBank (NCBI). Amplifications were carried out on a LightCycler 480 II instrument using LightCycler 480 software (v. 1.5.0) and a LightCycler 480 SYBR green I master kit (Roche Applied Science) according to the provided instructions. The amplification program was as follow: a first hot start (95°C for 10 min) and 50 cycles of an activation step at 95°C for 5 s, an annealing step at 62°C for 5 s, and an elongation step at 72°C for 8 s. Each sample was run in duplicate. A single acquisition of fluorescence for calculation of the <italic>Ct</italic> was processed during the elongation step. The specificity of amplification was verified by size visualization of the PCR product (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>) after electrophoresis on a 1.8% agarose gel (Sigma-Aldrich) in 1% TBE (Tris-borate-EDTA) for 30–45 min at 120 V and by analysis of the melting curves of the PCR products (melting temperatures, <italic>T</italic><sub>m</sub>, in <xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). All <italic>Ct</italic> values were analyzed using the qbase<sup>PLUS</sup> software (Biogazelle, Belgium).</p><table-wrap id=\"T1\" position=\"float\"><label>TABLE 1</label><caption><p>Details and sequence of primers used for qPCR assays.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Gene name</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Locus tag<sup>a</sup></bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Sequence (5′-3′)<sup>b</sup></bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>T</italic><sub>m</sub> (°C)<sup>c</sup></bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Size (pb)<sup>d</sup></bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>flaA1</italic></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">LIC10788</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(F) AGCAAGCGTATCAAGCGA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">81.1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">151</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(R) GCATTCTCTCCTGGATAAGTG</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>flaA2</italic></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">LIC10787</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(F) CGTCAGAGGATTTGATAGAGTG</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">80.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">210</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(R) CCAGGAATTGTAGCGGT</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>flaB1</italic></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">LIC11890</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(F) GCTGACGGTTCTCTCCTGAC</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">80.1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">280</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(R) ACGTTAGCCTGAGCAAGCAT</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>flaB2</italic></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">LIC11889</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(F) AGCGAGACAACTTCTTCCGCCATA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">78.4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">150</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(R) ATGAAGCAGAGAGCGGATATGGGA</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>flaB3</italic></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">LIC11532</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(F) GCAAGCGCAAACGCTATGAT</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">79</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">180</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(R) ATCCCTCACACGGCTTTCTG</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>flaB4</italic></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">LIC11531</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(F) ACTCCTTACCGGGGCTTTTG</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">78.8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">200</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(R) TCACAGAGTTTGCCTTGCCA</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>lipL21</italic></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">LIC10011</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(F) TGGTGAAGCTACTGCATCT</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">80.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">164</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(R) CACCTGGAAATTTTGCG</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>lipL36</italic></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">LIC13060</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(F) GGTTCAAATTGCGCTGTAG</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">80.8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">188</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(R) GCATAAACGGTTTTTCCGAG</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>lipL41</italic><sup>e</sup></td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(F) TTTACCAGTTGCCATAGAAGCGGC</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">77.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">150</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(R) GGAAATCTGATTGGAGCCGAAGCA</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr></tbody></table><table-wrap-foot><attrib><italic><sup><italic>a</italic></sup>Locus tag of corresponding gene sequence from <italic>L. interrogans</italic> serovar Copenhageni strain Fiocruz L1-130 referenced in GenBank (NCBI) under accession number <ext-link ext-link-type=\"DDBJ/EMBL/GenBank\" xlink:href=\"NC_005823.1\">NC_005823.1</ext-link> and used for primer design. <sup><italic>b</italic></sup>(F) and (R), forward and reverse primer sequences, respectively. <sup><italic>c</italic></sup>Tm, PCR product melting temperature. <sup><italic>d</italic></sup>PCR product size (in base pairs). <sup><italic>e</italic></sup>As described by Carrillo-Casas et al. (<xref rid=\"B60\" ref-type=\"bibr\">60</xref>).</italic></attrib></table-wrap-foot></table-wrap><p>For <italic>in vivo</italic> infections, the level of expression of each target gene was normalized to the levels of <italic>lipL21</italic>, <italic>lipL36</italic>, and <italic>lipL41</italic> gene, previously validated as reference genes in our conditions (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>). The relative normalized expression ratio was then calculated as the ratio of the <italic>in vivo</italic> to the <italic>in vitro</italic> expression level of bacteria cultured at 30°C. For the <italic>in vitro</italic> bacterial cultures, level of expression of <italic>flaB</italic> genes was normalized to the level of <italic>lipL41</italic> housekeeping gene (Normalized relative quantities).</p></sec><sec id=\"S2.SS9\"><title>Generation of Bone Marrow-Derived Macrophages (BMM)</title><p>Bone marrow cells (BMC) were obtained as recently described (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>). Briefly, mice were euthanized and femurs dissected, cleaned, and the femur heads were cut off. BMC were flushed out using a 21G needle to inject culture medium through the bones. BMC were centrifuged (300 × <italic>g</italic>, 5 min) and treated with Red Blood Cell Lysis Buffer (Sigma-Aldrich) for 10 min, followed by PBS washing. BMC were counted, and 5 × 10<sup>6</sup> cells seeded in 100 cm<sup>2</sup> cell culture dishes in 12 mL RPMI supplemented with 10% fetal calf serum (Lonza), 1X non-essential amino acids (NEEA, Gibco), 1 mM sodium pyruvate (complete medium) supplemented with 1X Antibiotic/Anti-mycotic solution (Gibco) and 10% L929 cell supernatant to provide a source of M-CSF1. Cells were incubated for 7-day at 37°C with 5% CO<sub>2</sub>. At day 3, 5 mL of the same medium was added. At day 7, the medium was removed, and 3 mL of cell dissociation buffer (Gibco) was added to harvest the bone marrow macrophages (BMMs). BMMs were collected by scrapping, centrifuged, enumerated and seeded in 96-well plates at a density of 2 × 10<sup>5</sup> cells per well in complete medium without antibiotics. BMMs were rested for 2–4 h and stimulated for 24 h with different leptospiral strains, live or heat-killed for 30 min at 100°C, at a MOI of 1:100, or 1:50 or with 100 ng/mL of controls [Standard Flagellin from <italic>Salmonella typhimurium</italic> (FLA) and LPS <italic>E. coli</italic> ultra-purified (both from InvivoGen)]. The keratinocyte-derived (KC/CXCL1) was measured in cell supernatants 24 h post-stimulation, by ELISA using Duo-Set kits (R&D Systems), according to the supplier’s instructions.</p></sec><sec id=\"S2.SS10\"><title>TLR5/NF-κB Assay in Human Epithelial Cell Line HEK-Blue-KD-TLR5</title><p>Human embryonic kidney (HEK)-Blue-KD-TLR5 cells (Invivogen) are HEK293 cells knocked-down (KD) for TLR5. In these HEK-BLUE cells, the activation of NF-κB drives the expression of the secreated alkaline phosphatase (SEAP) enzyme that induces a color shift from pink to blue of the chromogenic substrate in the HEK-Blue Detection Media (Invivogen). These cells were cultured in complete DMEM medium composed of DMEM GlutaMax (Gibco) with 1 mM sodium pyruvate (Gibco), 1X NEEA (Gibco) and 10% V/V heat-inactivated fetal calf serum (Hi FCS, Gibco). On day 1, cells were detached by 1 min incubation in cell dissociation buffer (Gibco) followed by gentle flush with medium. Cells were then seeded in 22.1 cm<sup>2</sup> cell culture dishes (TPP) at less than 30% confluence and incubated overnight at 37°C, 5% CO<sub>2</sub>. Cell transfections were performed on day 2, whilst the cells remained under 60% confluence and with a total amount of 3 μg of DNA per dish. For each dish, between 100 ng to 1 μg of pUNO1-humanTLR5, pUNO1-murineTLR5 (Invivogen), pcDNA3.1-bovine TLR5 (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>) or the corresponding empty vector was used, complemented up to 3 μg with pcDNA3.1. The transfection reagent 1X FuGENE HD (Promega) in serum free OptiMEM (Gibco) was incubated during 25 min with the DNA followed by transfection of the cells according to the manufacturer’s instruction. On day 3, transfected HEK-Blue-KD-TLR5 cells were stimulated in 96-wells plates. Briefly, 20 μL Flagellin from <italic>Salmonella typhimurium</italic> as a control (Standard FLA-ST (Invivogen) or leptospires resuspended in PBS, at a MOI between 1:50 to 1:200 were added in empty wells. Transfected HEK-Blue-KD-TLR5 cells were then gently flushed in PBS and resuspended in HEK-Blue Detection Media (Invivogen) at 2.8 × 10<sup>5</sup> cells/mL. 180 μL of cell suspension, corresponding to 50 000 cells, were then added on top of the agonists in each well and plates were incubated for 24h at 37°C, 5% CO<sub>2</sub>. In each well, the activation of NF-κB through TLR5 was assessed by absorbance measurements at 630 nm. All heat treatments were performed under agitation at 300 rpm and in PBS on the diluted leptospires preparations right before addition in the wells. Proteinase K treatments of leptospires (from <italic>Tritirachium album</italic>, Qiagen) were performed under agitation at 300 rpm in PBS for 2 h at 37°C, to avoid killing the leptospires. Such treatment was followed by heat inactivation of the enzyme and bacteria at 100°C for 30 min. The non-inactivated fraction and mock treatment without leptospires were also tested on HEK-Blue-KD-TLR5 cells. Leptospires in PBS were also treated with antimicrobial peptides: LL-37 (InvivoGen) and Bmap28 (Protegenix) at different concentrations (0–250 μg/mL) for 2 h.</p></sec><sec id=\"S2.SS11\"><title>Alamar Blue Viability Assay for Leptospires</title><p>Survival of leptospires upon treatments with antimicrobial peptides was assessed by Alamar Blue viability assay (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>). After 2 h incubation with LL-37 or Bmap28 in PBS, 2.5 × 10<sup>6</sup> leptospires in 100 μL in 96-well plates were mixed with 80 μL of EMJH and 20 μL of 10× Alamar Blue dye. Plates were incubated in a humid container at 30°C for 72 h. Viability of leptospires was visible by the color shift from blue to pink upon resazurin reduction to resorufin by live bacteria. Heat-killed (30 min, 100°C) leptospires were used as control for loss of viability.</p></sec><sec id=\"S2.SS12\"><title><italic>In silico</italic> Analyses of the Flagellin Protein Sequences</title><p>All the <italic>in silico</italic> analyses were performed using either Uniprot or GeneBank available sequences. All corresponding accession numbers are mentioned in the figure legends. Amino acid sequence homology percentage (identity) was obtained using BLAST. Alignments of the sequences were performed with MEGA X (<xref rid=\"B29\" ref-type=\"bibr\">29</xref>) and using the Clustal method. Structural predictions based on amino acid sequences were obtained using the Phyre2 (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>) and figures colored and modified with Chimera (<xref rid=\"B31\" ref-type=\"bibr\">31</xref>).</p></sec><sec id=\"S2.SS13\"><title>Statistical Analyses</title><p>Statistical analyses were performed using the Student <italic>t-</italic>test, with asterisks corresponding to the following <italic>p</italic> values: <sup>∗</sup><italic>p</italic> < 0.05; <sup>∗∗</sup><italic>p</italic> < 0.01; <sup>∗∗∗</sup><italic>p</italic> < 0.001.</p></sec></sec><sec id=\"S3\"><title>Results</title><sec id=\"S3.SS1\"><title>TLR5 Deficiency Does Not Modify the Course of Acute Leptospirosis in Mice</title><p>To study the potential involvement of the TLR5 receptor in the host defense against leptospires, we used a murine model of leptospirosis and compared the susceptibility of C57BL6/J (WT) mice <italic>versus tlr5</italic> knock-out (TLR5ko) mice after intraperitoneal infection with a sublethal dose of 10<sup>7</sup><italic>L. interrogans</italic> [serovar Manilae strain L495 MFlum1 (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>)]. We previously showed in this C57BL6/J mouse model that leptospires disseminate and grow in blood until day 3 and from day 4 post-infection (p.i) progressively disappear from the blood circulation and organs (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>). At day 7 p.i, pathogenic leptospires are exclusively found in urine, and in small numbers in kidney, where they progressively grow to establish a stable and lifelong renal colonization from 1-month p.i on. At 15 days p.i, leptospires are easily detected in kidneys either by qPCR or by IVIS imaging (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>, <xref rid=\"B32\" ref-type=\"bibr\">32</xref>). Here, leptospiral loads were measured by qPCR in blood and urine (<xref ref-type=\"fig\" rid=\"F1\">Figure 1A</xref>) and organs (<xref ref-type=\"fig\" rid=\"F1\">Figure 1B</xref>) in the first week p.i corresponding to the acute phase of infection. As expected, we found leptospires in blood, liver, spleen, lungs and kidney at day 3 p.i (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>) (<xref ref-type=\"fig\" rid=\"F1\">Figures 1A,B</xref>), and no difference of leptospiral loads could be observed between WT and TLR5ko mice in blood and organs. At day 7 p.i, leptospires were detected in urines but not in blood (<xref ref-type=\"fig\" rid=\"F1\">Figure 1A</xref>), similar as previously observed (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>). However, at day 7 p.i, we measured more leptospires in the urine of TLR5ko mice than in WT mice. Nevertheless, at day 7 p.i, we did not observe any difference in bacterial loads in kidneys between WT and TLR5ko mice (<xref ref-type=\"fig\" rid=\"F1\">Figure 1C</xref>). In addition, mRNA expression of pro-(IFNγ), anti-(IL-10) inflammatory cytokines and RANTES chemokine measured by RT-qPCR at day 7 p.i in the kidneys did not differ between WT and TLR5ko mice (<xref ref-type=\"fig\" rid=\"F1\">Figure 1D</xref>). Altogether these results suggest that the presence of TLR5 does not play a major role in the murine defense at the acute phase of experimental leptospirosis.</p><fig id=\"F1\" position=\"float\"><label>FIGURE 1</label><caption><p>No difference in leptospiral loads and inflammatory mediators between WT and TLR5ko mice during acute phase of leptospirosis. <bold>(A,B)</bold> Bacterial loads determined by qPCR of leptospiral DNA at several days post intraperitoneal infection of 7-week old female C57BL/6J (WT) mice and TLR5ko mice with 10<sup>7</sup>\n<italic>L. interrogans</italic> Manilae L495 derivative strain MFLum1; <bold>(A)</bold> in blood (red) and urine (yellow) in WT (<italic>n</italic> = 4, hatched bars) and TLR5ko mice (<italic>n</italic> = 4, empty bars) and <bold>(B)</bold> in liver (Li), spleen (Sp), lungs (Lu) and kidneys (Ki) from WT (<italic>n</italic> = 4, black bars) and TLR5ko (<italic>n</italic> = 4, blue bars) at day 3 post-infection (p.i). Data are expressed as mean (±SD) of all mice. Statistically significant differences between genotypes (Student <italic>t</italic>-test) are indicated. <bold>(C)</bold> Bacterial loads in kidneys determined by qPCR 7 days p.i of WT mice (<italic>n</italic> = 5, black dots) and TLR5ko mice (<italic>n</italic> = 7, blue dots). <bold>(D)</bold> Inflammation measured in kidney by mRNA expression of cytokines (RANTES, IL10, IFNγ) measured by RT-qPCR at 7 days p.i in WT mice (<italic>n</italic> = 5, black dots) and TLR5ko mice (<italic>n</italic> = 7, blue dots). Individual mice are represented as dots and lines correspond to mean (±SD) of all mice. No statistical difference was found between WT and TLR5ko mice. *<italic>p</italic> < 0.05.</p></caption><graphic xlink:href=\"fimmu-11-02007-g001\"/></fig></sec><sec id=\"S3.SS2\"><title>TLR5 Deficiency Does Not Impact Renal Colonization</title><p>Next, we established whether the presence of TLR5 is a necessary pre-requisite for the establishment of colonization or the localization of leptospires in the kidneys (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>, <xref rid=\"B32\" ref-type=\"bibr\">32</xref>). In contrast to day 7 p.i, we found equivalent loads of leptospires in urine from both genotypes 15 days p.i (<xref ref-type=\"fig\" rid=\"F2\">Figure 2A</xref>). Next, we used the bioluminescent property of <italic>L. interrogans</italic> Manilae strain MFLum1 to quantify and visualize leptospires by IVIS imaging in the kidneys of WT versus TLR5ko mice 15 days p.i. The levels and shape of emitted light, reflecting live bacteria (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>), were equivalent between WT and TLR5ko infected mice (<xref ref-type=\"fig\" rid=\"F2\">Figure 2B</xref>). In addition, we infected mice with 10<sup>7</sup><italic>L. interrogans</italic> Copenhageni Fiocruz strain L1-130, another serovar of pathogenic <italic>L. interrogans</italic>, and also found by qPCR 15 days p.i equivalent leptospiral loads in kidneys of WT and TLR5ko mice (<xref ref-type=\"fig\" rid=\"F2\">Figure 2C</xref>). Using immunohistochemistry, we further investigated the presence and localization of Manilae L495 leptospires in kidneys of WT and TLR5ko mice, as well as the <italic>Leptospira</italic>-induced nephritis 15 days p.i (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>). Minimal inflammatory cellular infiltrates were noted in the cortex of both WT and TLR5ko infected mice (<xref ref-type=\"fig\" rid=\"F2\">Figure 2C a–f</xref>), whereas no inflammation was observed in the naive WT control. Labeling of leptospires with an anti-LipL21 antibody (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>) revealed <italic>Leptospira</italic>-infected tubules in the renal cortex, as already described (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>) (<xref ref-type=\"fig\" rid=\"F2\">Figure 2C g–i</xref>). In histological sections of the kidney stained with Periodic Acid-Schiff (PAS), we found only leptospires in some proximal tubules in both WT and TLR5ko mice associated with the PAS positive microvilli of the brush border at the luminal surface of the tubular epithelium (<xref ref-type=\"fig\" rid=\"F2\">Figure 2C j,k</xref>), as previously described in rats (<xref rid=\"B33\" ref-type=\"bibr\">33</xref>). Altogether, the quantitative assessment by IVIS and qPCR, in combination with the qualitative results obtained by immunohistochemistry suggest that TLR5 does not play a major role in host protection during the acute or chronic phase of a <italic>L. interrogans</italic> infection in mice.</p><fig id=\"F2\" position=\"float\"><label>FIGURE 2</label><caption><p>No difference in kidney colonization in WT and TLR5ko mice 15 days post-infection. <bold>(A)</bold> Bacterial loads in urine quantified by qPCR (left panel), and quantification and images of live imaging (IVIS) (right panel) 15 days post-infection (p.i) of 7-week old female WT mice (<italic>n</italic> = 7, black) and TLR5ko mice (<italic>n</italic> = 8, blue) with 10<sup>7</sup>\n<italic>L. interrogans</italic> Manilae bioluminescent derivative strain MFLum1. Imaging was performed in dorsal position, 24 h post shaving, on anesthetized mice and after luciferin administration. The background level of light after luciferin administration was measured on a control TLR5ko mouse injected with PBS at the time of infection (dotted line). The average radiance in individual mice gated on the whole body is shown and represented as dots; lines correspond to the mean (±SD) of all mice. No statistical difference was found between WT and TLR5ko mice. <bold>(B)</bold> Bacterial loads in kidneys determined by qPCR of leptospiral DNA 14 days p.i of WT mice (<italic>n</italic> = 6, back dots) and TLR5ko (<italic>n</italic> = 6, blue dots) mice with 10<sup>7</sup><italic>L. interrogans</italic> Copenhageni Fiocruz L1-130. Individual mice are shown and represented as dots; lines correspond to mean (±SD) of all mice. No statistical difference was found between WT and TLR5ko mice. <bold>(C)</bold> Histological sections and immunolabeling of the kidneys of naive TLR5ko, infected WT and TLR5ko mice 15 days p.i with 10<sup>7</sup>\n<italic>L. interrogans</italic> Manilae strain MFLum1. <bold>(a–c)</bold> Kidney, Hematoxylin-Eosin stain, Original magnification ×2, Scale bar: 500 μm. Cortex (Co), Medulla (Me), Papilla (Pa), Capsule (Ca). <bold>(d–f)</bold> Kidney cortex, Hematoxylin-Eosin stain, Original magnification ×10, Scale bar: 100 μm. The asterisks indicate the focal inflammatory infiltrates. <bold>(g–i)</bold> Anti-LipL21 labeling of leptospires in renal tubules, Original magnification ×10, Scale bar: 100 μm. <bold>(j,k)</bold> Double labeling LipL21/Periodic Acid-Schiff (PAS) to stain the PAS positive brush borders present in proximal tubules only. Original magnification ×40, Scale bar: 25 μm.</p></caption><graphic xlink:href=\"fimmu-11-02007-g002\"/></fig></sec><sec id=\"S3.SS3\"><title>Live Pathogenic Leptospires Do Not Signal Through Murine and Human TLR5 <italic>in vitro</italic></title><p>To further investigate the role of TLR5 in recognition of <italic>Leptospira</italic>, bone marrow derived macrophages (BMMs) from WT and TLR5ko mice were infected with 3 different live serovars of <italic>L. interrogans.</italic> The production of the murine chemokine KC (CXCL1) was measured by ELISA 24 h p.i in the cellular supernatants. This chemokine was chosen as it was recently shown to be fully dependent on the MyD88-dependent signaling pathway (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>), with MyD88 being the first main adaptor involved in TLR5-induced signaling (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>). We therefore considered changes in KC secretion to be a good readout for analyzing TLR5 contribution to the leptospiral-induced signaling. We did not find any difference between both genotypes (<xref ref-type=\"fig\" rid=\"F3\">Figure 3A</xref>), which correlated with the <italic>in vivo</italic> experiments and indeed strongly supports the observation that live leptospires do not induce signaling through murine TLR5.</p><fig id=\"F3\" position=\"float\"><label>FIGURE 3</label><caption><p>Heat-killed, but not live leptospires, signal through hTLR5. <bold>(A,C)</bold> KC production measured by ELISA in the supernatants of BMMs from WT (black bars) and TLR5ko (blue bars) mice 24 h post-infection with MOI 50 of either <bold>(A)</bold> live or <bold>(C)</bold> heat-killed (30 min, 100°C) different serovars of virulent <italic>L. interrogans</italic> (Manilae strain L495, Copenhageni strain Fiocruz L1-130, Icterohaemorrhagiae strain Verdun). LPS from <italic>E. coli</italic> (100 ng/mL) and unpurified Fla from <italic>Salmonella typhimurium</italic> (500 ng/mL) were used as controls. Data are expressed as mean (±SD) of technical replicates (<italic>n</italic> = 5) on pooled BMMs preparations from mice (<italic>n</italic> = 3) and are representative of at least three independent experiments. Statistically significant differences between genotypes (Student <italic>t</italic>-test) are indicated. <bold>(B,D)</bold> NF-κB reporter assay in HEK-Blue-Knock Down (KD)-TLR5 cells transfected with the mouse TLR5 (light blue bars), human TLR5 (blue bars), or empty plasmid (empty bars) and stimulated for 24 h with MOI 200 of either <bold>(B)</bold> live or <bold>(D)</bold> heat-killed (30 min, 100°C) different serovars of virulent <italic>L. interrogans</italic>. Unpurified Fla from <italic>Salmonella typhimurium</italic> (100 ng/mL) was used as control. Data are expressed as the mean (±SD) of technical replicates (<italic>n</italic> = 3) and are representative of at least three independent experiments. Statistically significant differences (Student <italic>t</italic>-test) are indicated. *<italic>p</italic> < 0.05; ***<italic>p</italic> < 0.001.</p></caption><graphic xlink:href=\"fimmu-11-02007-g003\"/></fig><p>Next, we assessed whether the lack of signaling through or escape of recognition by murine TLR5 is a species-specific phenomenon. Indeed, we previously highlighted PRR species-specificities of leptospiral MAMPs recognition, such as murine TLR4 receptor that only partially recognizes the leptospiral LPS (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>), whereas human TLR4 does not. Conversely, human, but not the murine NOD1 is able to sense leptospiral muropeptides (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>, <xref rid=\"B36\" ref-type=\"bibr\">36</xref>). We therefore used the human HEK-blue reporter cells, with a NF-κB promoter driving SEAP expression, which can be measured using a colorimetric test. We transfected HEK-Blue-KD-TLR5 cells, which are Knocked-Down for TLR5, with either human TLR5, murine TLR5, or an empty control vector. No signal corresponding to murine or human TLR5-mediated NF-κB activation was obtained upon infection with different live <italic>L. interrogans</italic> serovars at MOI 10 and 100 (data not shown) or even at an MOI of 200 (<xref ref-type=\"fig\" rid=\"F3\">Figure 3B</xref>), which suggested that leptospires also evade human TLR5 recognition or at least do not signal through this TLR.</p></sec><sec id=\"S3.SS4\"><title>Heat-Killed Leptospires Signal Through Human TLR5, but Only the Heat-Killed Fiocruz Strain Signals Through Murine TLR5</title><p>The specificity of TLR5 activation and resulting signaling is usually assessed by denaturing a potential ligand through heat-inactivation. In the present study, we observed that inactivation of strains Manilae L495 and Icterohaemorrhagiae Verdun at 100°C for 30 min induced equivalent levels of KC production in WT and TLR5ko murine BMMs (<xref ref-type=\"fig\" rid=\"F3\">Figure 3C</xref>), which was consistent with the results obtained using live bacteria (<xref ref-type=\"fig\" rid=\"F3\">Figure 3A</xref>). In contrast, the heat-killed Copenhageni Fiocruz strain L1-130 induced less KC secretion in TLR5ko compared to WT BMMs (<xref ref-type=\"fig\" rid=\"F3\">Figure 3C</xref>), suggesting that an agonist present in the inactivated Fiocruz strain could be recognized by murine TLR5. Unexpectedly, heat-killed leptospires from all serovars strongly activated HEK-Blue-KD-TLR5 transfected with human TLR5 (<xref ref-type=\"fig\" rid=\"F3\">Figure 3D</xref>). Further, despite the fact that both strains, Manilae L495 and Icterohaemorrhagiae Verdun, did not stimulate murine TLR5, a slight activation signal was observed with the Copenhageni Fiocruz L130 strain, which was consistent with the chemokine result in BMMs (<xref ref-type=\"fig\" rid=\"F3\">Figure 3C</xref>). The experiment was performed in parallel with an empty plasmid, showing that these results were indeed specific to TLR5 activation, and did not depend on a different NF-κB activation pathway (<xref ref-type=\"fig\" rid=\"F3\">Figure 3D</xref>). Altogether these unexpected results suggested that only heat-killed leptospires can signal through human TLR5, but not or only barely through murine TLR5, providing a new example of species-specificity of PRR recognition of leptospiral MAMPs.</p></sec><sec id=\"S3.SS5\"><title>A Heat-Resistant Protein From Heat-Killed Leptospires Signals Through TLR5</title><p>To our knowledge, our results showing TLR5 activation using heat-inactivated leptospires have never been described before. Thus, we first ensured that the signal observed was indeed attributed to proteins of leptospires interacting with TLR5. Since only the stimulation with heat-killed bacteria resulted in TLR5 signaling, we anticipated that a proteinase K treatment would destroy the protein involved in the signaling. Therefore, we treated live and heat-killed Fiocruz L1-130 leptospires with proteinase K, followed or not by heating at 100°C for 30 min to inactivate the enzyme. We stimulated TLR5 transfected HEK-Blue-KD-TLR5 with these preparations and a mock control without bacteria. Although the proteinase K treatment had no effect on live bacteria, it decreased the signal on heat-killed bacteria (<xref ref-type=\"fig\" rid=\"F4\">Figure 4A</xref>). In contrast to live bacteria treated with proteinase K and subsequently heated, which resulted in a strong TLR5 activation, TLR5 signaling was not restored in heat-killed bacteria treated with proteinase K, suggesting that the proteinase K had digested all TLR5 agonists (<xref ref-type=\"fig\" rid=\"F4\">Figure 4A</xref>). This experiment confirmed the protein nature of the agonist present in heat-killed leptospires, which was not affected by proteinase K digestion in live bacteria. We hypothesize that in live leptospires, the periplasmic location of the endoflagella could protect the flagellin subunits from proteinase K digestion, thus potentially explaining why live bacteria do not signal through TLR5 and are not affected by the enzyme (<xref ref-type=\"fig\" rid=\"F4\">Figure 4B</xref>).</p><fig id=\"F4\" position=\"float\"><label>FIGURE 4</label><caption><p>A very stable protein from leptospires signal through TLR5. <bold>(A)</bold> NF-κB reporter assay in HEK-Blue-KD-TLR5 cells transfected with the human TLR5 (blue bars), or empty plasmid (empty bars) and stimulated with MOI 100 of either live or heat-killed (30 min, 100°C) <italic>L. interrogans</italic> Copenhageni strain Fiocruz L1-130 treated or not with Proteinase K (protK) followed or not by heat inactivation at 99°C for 30 min (inact or non-inact). Unpurified Fla from <italic>Salmonella typhimurium</italic> (100 ng/mL) was used as control. Data are expressed as the mean (±SD) of technical replicates (<italic>n</italic> = 3) and are representative of at least three independent experiments. Statistically significant differences (Student <italic>t</italic>-test) are indicated. <bold>(B)</bold> Chronogram of proteinase K experiments. <bold>(C)</bold> Picture of NF-κB reporter assay in HEK-Blue-KD-TLR5 cells transfected with the human TLR5 or empty plasmid and stimulated with MOI 100 of live <italic>L. interrogans</italic> Copenhageni strain Fiocruz L1-130 incubated at various temperatures during 30 min, 3, or 8 h. Unpurified Fla from <italic>Salmonella typhimurium</italic> (500 ng/mL) was used as control. Picture show technical duplicate for each condition and is representative of at least three independent experiments. <bold>(D)</bold> NF-κB reporter assay in HEK-Blue-KD-TLR5 cells transfected with the human TLR5 (blue bars) or empty plasmid (empty bars) and stimulated with MOI 100 of live <italic>L. interrogans</italic> Copenhageni strain Fiocruz L1-130 or Manilae L495 incubated at various temperatures during 3 h. Unpurified Fla from <italic>Salmonella typhimurium</italic> (500 ng/mL) was used as control. Data are expressed as the mean of technical replicates (<italic>n</italic> = 2, shown as dots) and are representative of at least three independent experiments for Fiocruz L1-130 and two independent experiments for L495. ***<italic>p</italic> < 0.001.</p></caption><graphic xlink:href=\"fimmu-11-02007-g004\"/></fig><p>Next, we investigated the unusual thermostability of the TLR5 agonist, by incubating live Fiocruz L1-130 leptospires at different temperatures (from 30°C and up to 99°C) and for different durations (from 30 min and up to 8 h) (<xref ref-type=\"fig\" rid=\"F4\">Figure 4C</xref>) before stimulation of HEK-Blue-KD-TLR5 transfected with human TLR5. Interestingly, after 8 h incubation at 30°C (the optimal temperature for leptospiral growth <italic>in vitro</italic>) or at 37°C (the host temperature), we did not observe any TLR5-dependent signaling. Of note, at 56°C, the usual temperature to inactivate leptospires (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>, <xref rid=\"B18\" ref-type=\"bibr\">18</xref>) whilst keeping the leptospiral shape integrity, a signal started after 3 h of incubation, but even 8 h were not enough to get a full TLR5 signaling. At 70°C, the temperature classically used to depolymerize the <italic>Salmonella</italic>’s flagellum filament (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>), a 30 min incubation was sufficient to stably activate TLR5 for 8 h. The signal observed with leptospires incubated for 30 min at 85°C disappeared after 8 h, whereas the positive signal observed after heating the bacteria at 99°C for 30 min disappeared after 3 h of heating (<xref ref-type=\"fig\" rid=\"F4\">Figure 4C</xref>). We then tested in parallel the Manilae L495 and Copenhageni Fiocruz L1-130 strains, after heating at 30, 70, and 99°C for 3 h, and obtained similar results for both 2 serovars (<xref ref-type=\"fig\" rid=\"F4\">Figure 4D</xref>). These results confirmed the protein nature of the TLR5 agonist of leptospires, since the activation can be extinguished by heating the bacteria for an extended time at high temperature.</p></sec><sec id=\"S3.SS6\"><title>Antimicrobial Peptides Destabilize Live Leptospires and Unmask a TLR5 Signal</title><p>Since we revealed the potential for TLR5 recognition of leptospires by heating at high non-physiological temperatures, we wondered whether leptospires could signal through TLR5 after being destabilized or killed with antibiotics or antimicrobial peptides. Antibiotic treatments of the leptospires (at MIC concentrations) including gentamicin, azithromycin, daptomycin and penicillin G, the latter being known to target the cell wall, didn’t induce any TLR5 signal (<xref ref-type=\"supplementary-material\" rid=\"FS1\">Supplementary Figure 1A</xref>). Next, we tested the effect of two different antimicrobial peptides (AMP), LL37 and Bmap28. Cathelicidin LL-37 is an AMP which has been shown to be active against leptospires (<xref rid=\"B37\" ref-type=\"bibr\">37</xref>), and its presence was recently associated with a better outcome in human patients with leptospirosis (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>). Furthermore, LL-37 has also been shown to prevent death in young hamsters experimentally infected with the Fiocruz L1-130 strain (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>). The second AMP, bovine Bmap28 has been described to be 50–100 times more efficient in killing leptospires compared to LL-37, but this depended on the serotypes (<xref rid=\"B37\" ref-type=\"bibr\">37</xref>). Therefore, we first tested the ability of both AMP to kill strains Manilae L495 and Copenhageni Fiocruz L1-130. Using an Alamar blue viability assay, we observed that both strains were killed after 2 h of incubation with 25 μg/mL of either LL-37 or Bmap28 (<xref ref-type=\"fig\" rid=\"F5\">Figure 5A</xref>). Next, we assessed whether leptospires treated with the different doses of AMP were recognized by human TLR5. Interestingly, both, live <italic>L. interrogans</italic> Manilae L495 and Copenhageni Fiocruz L1-130 bacteria pre-treated with either 25 or 250 μg/mL of LL-37 or Bmap28 induced a significant and dose-dependent signal in human TLR5 (<xref ref-type=\"fig\" rid=\"F5\">Figure 5B</xref>). These results suggest that antimicrobial peptides could participate <italic>in vivo</italic> in the exposure of flagellins and subsequently recognition by and signaling through TLR5.</p><fig id=\"F5\" position=\"float\"><label>FIGURE 5</label><caption><p>Human and bovine antimicrobial peptides unmask the leptospiral ability to signal through human and bovine TLR5 receptors. <bold>(A)</bold> Alamar blue viability assay of leptospires (Manilae L495 or Copenhageni Fiocruz L1-130) incubated with increasing concentration (0–250 μg/mL) of antimicrobial peptides LL-37 or Bmap28 for 2 h. Heat-killed (30 min, 100°C) leptospires are used as controls for loss of viability. Picture show technical triplicate for each condition and is representative of two independent experiments. <bold>(B)</bold> NF-κB reporter assay in HEK-Blue-KD-TLR5 cells transfected with the human TLR5 (blue bars), or empty plasmid (empty bars) and stimulated with MOI 100 of <italic>L. interrogans</italic> Manilae strain L495 or Copenhageni strain Fiocruz L1-130 treated with human peptide LL-37 or bovine peptide Bmap28 at various concentration (0–250 μg/mL) for 2 h before stimulation. <bold>(C)</bold> NF-κB reporter assay in HEK-Blue-KD-TLR5 cells transfected with the human TLR5 (blue bars), bovine TLR5 (dark blue bars), mouse TLR5 (light blue bars) or empty plasmid (empty bars) and stimulated with MOI 100 of either live or heat-killed (30 min, 100°C) <italic>L. interrogans</italic> Manilae strain L495 or Copenhageni strain Fiocruz L1-130. <bold>(D)</bold> NF-κB reporter assay in HEK-Blue-KD-TLR5 cells transfected with the bovine TLR5 (dark blue bars), mouse TLR5 (light blue bars) or empty plasmid (empty bars) and stimulated with MOI 100 of <italic>L. interrogans</italic> Copenhageni Fiocruz L1-130 treated with human peptide LL-37 or bovine peptide Bmap28 at various concentration (0–250 μg/mL) for 2 h before stimulation. <bold>(B–D)</bold> Unpurified Fla from <italic>Salmonella typhimurium</italic> (500 ng/mL) was used as control. Data are expressed as the mean (±SD) of technical replicates (<italic>n</italic> = 3), and are representative of at least three independent experiments for panels <bold>(A)</bold> and <bold>(C)</bold>. Statistically significant differences (Student <italic>t</italic>-test) are indicated. *<italic>p</italic> < 0.05; **<italic>p</italic> < 0.01; ***<italic>p</italic> < 0.001.</p></caption><graphic xlink:href=\"fimmu-11-02007-g005\"/></fig><p>As one of the AMP used was of bovine origin (bovine Bmap28) and pre-treatment of leptospires with Bmap28 had a clear effect on human TLR5 signaling, we tested whether bovine TLR5 could indeed recognize leptospires. In accordance with published data (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>), bovine TLR5 reacted only weakly to the positive control, Salmonella derived FliC (<xref ref-type=\"fig\" rid=\"F5\">Figure 5C</xref>). In contrast, we found that it recognized heat-killed Manilae L495 and Copenhageni Fiocruz L1-130 (<xref ref-type=\"fig\" rid=\"F5\">Figure 5C</xref>). Interestingly, the treatment of live Fiocruz L1-130 (<xref ref-type=\"fig\" rid=\"F5\">Figure 5D</xref>) and live L495 (<xref ref-type=\"supplementary-material\" rid=\"FS1\">Supplementary Figure 1B</xref>) with both AMP (LL37 and Bmap28) resulted in a dose-dependent bovine TLR5 signaling response. However, both treatments, even at high concentration, did not result in a significantly increased signaling response when leptospires were incubated on cells expressing murine TLR5 (<xref ref-type=\"fig\" rid=\"F5\">Figure 5D</xref> and <xref ref-type=\"supplementary-material\" rid=\"FS1\">Supplementary Figure 1B</xref>). These data suggest that, <italic>in vivo</italic>, degraded leptospires could be recognized by and signal through human and bovine TLR5, and confirm that mouse TLR5 does not recognize leptospires (<xref ref-type=\"fig\" rid=\"F5\">Figure 5D</xref>).</p></sec><sec id=\"S3.SS7\"><title><italic>In silico</italic> Analyses of Potential TLR5 Binding of Leptospiral FlaBs</title><p>Two leptospiral <italic>flaA</italic> (<italic>flaA1</italic> and <italic>flaA2</italic>) genes and four <italic>flaB</italic> genes (<italic>flaB1</italic> to <italic>flaB4</italic>) have been annotated in the <italic>L. interrogans</italic> genomes according to their similarity with the <italic>Salmonella</italic> flagellin (FliC), the two families sharing respectively around 25 and 38% identity at the protein level with FliC (<xref ref-type=\"fig\" rid=\"F6\">Figure 6A</xref>). Structural studies recently showed that the FlaB subunits constitute the core of the flagellum, and the other subunits constitute an asymmetric outer sheath, with FlaB interacting with FlaA on the concave site and with FcpA on the other side of the curvature. FcpA and FcpB associate in a lattice forming the convex part of the endoflagellum (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>) (<xref ref-type=\"supplementary-material\" rid=\"FS2\">Supplementary Figure 2A</xref>). Using the BLAST-P software, we found that the different FlaB subunits from the <italic>L. interrogans</italic> Fiocruz strain share 57–72% of identity and most probably result from gene duplication events (<xref ref-type=\"supplementary-material\" rid=\"FS3\">Supplementary Figure 3A</xref>). Similar results were obtained with the saprophytic <italic>L. biflexa</italic> Patoc strain (<xref ref-type=\"supplementary-material\" rid=\"FS3\">Supplementary Figure 3A</xref>). We then used the Phyre2 software to model the FlaBs structures according to their primary amino acid sequences, using the FliC protein sequence as a base. FliC folds in four regions D0 to D3, forming an inverted L shape (<xref ref-type=\"fig\" rid=\"F6\">Figure 6B</xref>), with both N-term and C-term in the D0 domain. Region D1, in the inner face of the monomer, is involved in the interaction of FliC with the leucine–rich repeat (LRR) domains of TLR5 via 3 binding sites (<xref ref-type=\"fig\" rid=\"F6\">Figures 6A,B</xref> and <xref ref-type=\"supplementary-material\" rid=\"FS2\">Supplementary Figure 2B</xref>) (<xref rid=\"B39\" ref-type=\"bibr\">39</xref>, <xref rid=\"B40\" ref-type=\"bibr\">40</xref>). There is also a region in the C-term part of the D0 domain that is not directly involved in the binding to TLR5 but important for the stabilization of the TLR5 dimers upon binding to FliC (<xref ref-type=\"fig\" rid=\"F6\">Figures 6A,B</xref> and <xref ref-type=\"supplementary-material\" rid=\"FS2\">Supplementary Figure 2B</xref>) (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>). Phyre 2 predictions showed that all FlaB subunits from <italic>L. interrogans</italic> and <italic>L. biflexa</italic> harbor orthologs of the D0 and D1 domains of FliC, while missing the D2 and D3 domains (<xref ref-type=\"fig\" rid=\"F6\">Figure 6C</xref>, and data not shown). We also checked whether FlaA1 or FlaA2 could have a structure mimicking the D2-D3 domains of FliC, but leptospiral FlaA1 and FlaA2 looked globular, mainly presenting ß sheets and do not resemble the missing domains (<xref ref-type=\"fig\" rid=\"F6\">Figure 6C</xref>). Interestingly, we found that the FlaB possessed the 3 conserved sequences important for TLR5 binding in the D1 domain (<xref ref-type=\"fig\" rid=\"F6\">Figures 6A–D</xref>). Then, we compared the different pathogenic <italic>L. interrogans</italic> and the saprophytic <italic>L. biflexa</italic> Patoc I strain and found that the four FlaB, although distinct from each other (<xref ref-type=\"supplementary-material\" rid=\"FS3\">Supplementary Figure 3A</xref>), were highly conserved in the consensus regions of the TLR5 binding domains in D1 (99–100% identity among the different pathogenic serovars, the Patoc FlaB being less conserved) (<xref ref-type=\"fig\" rid=\"F6\">Figure 6D</xref>). We also found in FlaB the consensus in the D0 domain involved in the flagellin/TLR5 complex stabilization (<xref ref-type=\"fig\" rid=\"F6\">Figures 6A,C</xref>). We compared the leptospiral FlaB sequences in these 3 consensus binding TLR5 regions with other spirochetes, <italic>Borrelia burgdorferi</italic> and <italic>Treponema</italic> spp., the latter known to signal via TLR5 when FlaB are expressed as recombinant proteins (<xref rid=\"B42\" ref-type=\"bibr\">42</xref>) and also with bacteria known to dodge the TLR5 response such as <italic>Helicobacter pylori</italic> (<xref rid=\"B43\" ref-type=\"bibr\">43</xref>) and <italic>Bartonella bacilliformis</italic> (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>), presenting variations in those consensus sequences of their flagellins (<xref ref-type=\"supplementary-material\" rid=\"FS4\">Supplementary Figure 4A</xref>). In addition, we also found this FlaB region to be 100% conserved in a panel of major species of <italic>Leptospira</italic> circulating all over the world, including potential human pathogens, such as <italic>L. borgpeterseni, L. kirschneri</italic>, <italic>L. noguchii</italic>, <italic>L. weilii</italic>, <italic>L. santarosai</italic>, as well as <italic>L. licerasiae</italic>, belonging to another clade of species of lower virulence (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>) (<xref ref-type=\"supplementary-material\" rid=\"FS4\">Supplementary Figure 4B</xref>). These alignments show that the TLR5 binding site region is highly conserved in all leptospiral FlaBs. Therefore each of the four FlaB subunit could potentially signal through TLR5, since leptospiral FlaBs share the 2 first consensus with TLR5 activating bacteria and the different residue observed in the consensus number 3 is also present in TLR5-activating <italic>Treponema</italic> flagellins (<xref rid=\"B44\" ref-type=\"bibr\">44</xref>).</p><fig id=\"F6\" position=\"float\"><label>FIGURE 6</label><caption><p>Comparison of leptospiral Flagellins and FliC structures in relation with TLR5. <bold>(A)</bold> Amino acid sequence homology average percentage between <italic>Salmonella typhimurium</italic> FliC (P06179) and <italic>Leptospira interrogans</italic> strain Fiocruz FlaBs (LIC11531, LIC11890, LIC11889 and LIC 11542) and FlaAs (LIC10788 and LIC10787) and primary structures of the flagellin proteins with TLR5 binding consensus. <bold>(B)</bold>\n<italic>In silico</italic> (Phyre2 and Chimera softwares) prediction of <italic>Salmonella typhimurium</italic> FliC (P06179) structure with the four described domains and with positions of the TLR5 binding consensus: 1 (red), 2 (yellow) and 3 (light blue) and stabilization region (light green) highlighted. <bold>(C)</bold>\n<italic>In silico</italic> (Phyre2 and Chimera softwares) prediction of <italic>Leptospira interrogans</italic> strain Fiocruz FlaB1 (LIC11531) with the positions of the TLR5 binding consensus and stabilization region highlighted, FlaA1 (LIC10788), FlaA2 (LIC10787), FcpA (Q72MM7) and FcpB (Q72RA0). <bold>(D)</bold> Clustal (MEGA software) alignment of the amino acid sequences for the TLR5 binding consensus regions of: <italic>Salmonella enterica</italic> FliC (GeneBank QDQ31983.1), <italic>L. biflexa</italic> (strain Patoc) FlaB1 (LEPBIa1589), FlaB2 (LEPBIa2133), FlaB3 (LEPBIa2132), FlaB4 (LEPBIa1872), <italic>L. interrogans</italic> (strain Fiocruz) FlaB1 (LIC11531), FlaB2 (LIC18890), FlaB3 (LIC11889), FlaB4 (LIC11532), <italic>L. interrogans</italic> (strain Manilae) FlaB1 (LMANv2_590023), FlaB2 (LMANv2_260016), FlaB3 (LMANv2_260015), FlaB4 (LMANv2_590024) and <italic>L. interrogans</italic> (strain Verdun) FlaB1 (AKWP_v1_110067), FlaB2 (AKWP_v1_110429), FlaB3 (AKWP_v1_110428) and FlaB4 (AKWP_v1_110068).</p></caption><graphic xlink:href=\"fimmu-11-02007-g006\"/></fig></sec><sec id=\"S3.SS8\"><title>FlaB, Not FlaA Nor Fcp, Induce TLR5 Signaling</title><p>To confirm the putative role of the FlaB subunits in inducing TLR5 signaling, we used different available mutants deficient in either FlaA, FlaB or Fcp subunits to stimulate HEK-blue reporter cells transfected with human TLR5. Of note, both <italic>flaAs</italic> and <italic>f</italic> genes are in operons, and the <italic>flaA2</italic> mutant lacks both FlaA1 and FlaA2 subunits (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>). Likewise, the <italic>fcpA</italic> mutant lacks both FcpA and FcpB subunits (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>, <xref rid=\"B6\" ref-type=\"bibr\">6</xref>). Our results showed that the TLR5 signaling induced with the heat-killed <italic>fcpA</italic> mutants in Fiocruz LV2756 was equivalent to the activation observed with parental strains (<xref ref-type=\"fig\" rid=\"F7\">Figure 7A</xref>). Moreover, we confirmed this result by using a <italic>fcpA</italic> mutant of the saprophytic <italic>L. biflexa</italic> Patoc Patoc I strain (<xref ref-type=\"fig\" rid=\"F7\">Figure 7A</xref>). Likewise, TLR5 signaling was not changed comparing heat-killed WT Manilae L495 and the <italic>flaA2</italic> mutant (<xref ref-type=\"fig\" rid=\"F7\">Figure 7B</xref>). However, the heat-killed <italic>flaB1</italic> mutant induced a lower activation than its parental counterpart (<xref ref-type=\"fig\" rid=\"F7\">Figure 7C</xref>). We also observed a decrease of the TLR5 response with the Patoc I <italic>flaB4</italic> mutant (<xref ref-type=\"fig\" rid=\"F7\">Figure 7C</xref>). These results suggest that the FlaB subunits, but not the FlaA or Fcp, are involved in the TLR5 signaling, and are in line with sequence comparison data.</p><fig id=\"F7\" position=\"float\"><label>FIGURE 7</label><caption><p>FlaB subunits, not FlaAs nor Fcps, contribute to the signaling. <bold>(A)</bold> NF-κB reporter assay in HEK-Blue-KD-TLR5 cells transfected with the human TLR5 (blue bars), or empty plasmid (empty bars) and stimulated with MOI 100 of either live or heat-killed (30 min, 100°C) <italic>L. interrogans</italic> Copenhageni Fiocruz LV2756 WT or ΔFcpA, Patoc Patoc I WT or ΔFcpA. Unpurified Fla from <italic>Salmonella typhimurium</italic> (500 ng/mL) was used as control. Data are expressed as the mean (±SD) of technical replicates (<italic>n</italic> = 3) and are representative of at least three independent experiments. <bold>(B)</bold> NF-κB reporter assay in HEK-Blue-KD-TLR5 cells transfected with the human TLR5 (blue bars), or empty plasmid (empty bars) and stimulated with MOI 100 of either live or heat-killed (30 min, 100°C) <italic>L. interrogans</italic> Manilae L495 WT or ΔFlaA2. Unpurified Fla from <italic>Salmonella typhimurium</italic> (500 ng/mL) was used as control. Data are expressed as the mean of technical duplicates (<italic>n</italic> = 2, shown as dots). <bold>(C)</bold> NF-κB reporter assay in HEK-Blue-KD-TLR5 cells transfected with the human TLR5 (blue bars), or empty plasmid (empty bars) and stimulated with MOI 100 of either live or heat-killed (30 min, 100°C) <italic>L. interrogans</italic> Manilae L495 WT or ΔFlaB1 and Patoc Patoc I WT or ΔFlaB4. Data are expressed as the mean (±SD) of technical replicates (<italic>n</italic> = 3) and are representative of at least three independent experiments. Statistically significant differences (Student <italic>t</italic>-test) are indicated. **<italic>p</italic> < 0.01; ***<italic>p</italic> < 0.001.</p></caption><graphic xlink:href=\"fimmu-11-02007-g007\"/></fig></sec><sec id=\"S3.SS9\"><title>FlaB mRNA Are Upregulated in Stationary Phase</title><p>Proteomic and high throughput mass spectrometry performed with the Fiocruz L1-130 strain grown in EMJH have shown that all four <italic>flaBs</italic> genes were expressed and part of the leptospiral flagellum (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>). To test whether leptospires could differently regulate the FlaBs expression, cultures of leptospires were harvested after 3–6 days, or after 10–14 days of culture corresponding to exponential growth or stationary phase, respectively. mRNA was purified and RT-qPCR performed with specific primers of the four leptospiral <italic>flaB</italic> genes. The results suggested that the mRNA expression of the different FlaB subunits might vary during bacterial growth <italic>in vitro</italic> (<xref ref-type=\"fig\" rid=\"F8\">Figure 8A</xref>). Indeed, the gene expressions of all FlaB subunits of the Copenhageni Fiocruz L1-130 strain were upregulated at the stationary phase compared to the exponential phase (<xref ref-type=\"fig\" rid=\"F8\">Figure 8A</xref>). In contrast, in the Icterohaemorrhagiae Verdun strain, only the expression of FlaB3 was upregulated at the stationary phase, whereas both, FlaB2 and FlaB3 were upregulated at the stationary phase in the Manilae L495 strain (<xref ref-type=\"fig\" rid=\"F8\">Figure 8A</xref>). Of note, and different from other strains, the Manilae L495 <italic>flaB1</italic> mRNA was undetectable at the stationary phase, and barely expressed at the exponential phase (<xref ref-type=\"fig\" rid=\"F8\">Figure 8A</xref>). Since in prokaryotes the process from transcription to translation is very rapid, these results of <italic>flaB</italic> mRNA expression together with TLR5 sensing suggest an unanticipated upregulation of the FlaB subunits at the stationary phase or conversely a downregulation at the exponential phase that could potentially influence the TLR5 sensing.</p><fig id=\"F8\" position=\"float\"><label>FIGURE 8</label><caption><p>FlaBs mRNA are upregulated in stationary phase and downregulated <italic>in vivo</italic>. <bold>(A)</bold>\n<italic>In vitro</italic> FlaBs mRNA expression in <italic>L. interrogans</italic> Copenhageni Fiocruz L1-130, Icterohaemorrhagiae Verdun and Manilae L495 at the exponential (E) and stationary (S) phase. Data of RT-qPCR are expressed as the relative mRNA quantities normalized to the expression of the <italic>lipl41</italic> mRNA. Technical replicates are represented as dots and lines correspond to mean (±SD) of replicates (3 < <italic>n</italic> < 9). Statistically significant differences (Student <italic>t</italic>-test) are indicated. <bold>(B)</bold>\n<italic>In vivo</italic> FlaAs and <bold>(C)</bold> FlaBs mRNA expression in blood of infected mice (<italic>n</italic> = 5, light blue) and hamsters (<italic>n</italic> = 5, dark blue), 24 h post intraperitoneal infection with 2 × 10<sup>8</sup> virulent <italic>L. interrogans</italic> Icterohaemorrhagiae strain Verdun, compared with mRNA expression in culture in EMJH at 30°C. Data of RT-qPCR are expressed as the ratio of mRNA quantities relatives to the EMJH control. Individual animals are represented as dots and lines correspond to mean (±SD) of all animals. Statistically significant differences (Student <italic>t</italic>-test) are indicated.</p></caption><graphic xlink:href=\"fimmu-11-02007-g008\"/></fig></sec><sec id=\"S3.SS10\"><title><italic>In vivo</italic> Infection of Rodent Models Leads to Downregulation of <italic>flaB</italic> mRNA</title><p>To further investigate whether FlaB regulation could be relevant or play a role <italic>in vivo</italic>, mice and hamsters were infected with the virulent Icterohaemorrhagiae Verdun strain, as described previously (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>). Blood was sampled to purify total mRNAs 24 h p.i, when leptospires start their exponential growth in blood of mice (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>). In parallel, <italic>in vitro</italic> cultures were performed in EMJH either at 37°C, the host temperature or at 30°C, the usual leptospiral growth culture conditions. First, the expressions of <italic>flaA1</italic> and <italic>flaA2</italic> were not different at 30°C and 37°C (<xref ref-type=\"supplementary-material\" rid=\"FS5\">Supplementary Figure 5</xref>), nor between the <italic>in vitro</italic> conditions and <italic>in vivo</italic> conditions, either in mice or hamsters (<xref ref-type=\"fig\" rid=\"F8\">Figure 8B</xref>). However, the FlaB expressions were strikingly different, with a weaker expression of the FlaB subunits in the hosts compared to the <italic>in vitro</italic> cultures at 30°C (<xref ref-type=\"fig\" rid=\"F8\">Figure 8C</xref>) or 37°C (<xref ref-type=\"supplementary-material\" rid=\"FS5\">Supplementary Figure 5</xref>). These data strongly suggest that 24 h p.i, compared to <italic>in vitro</italic> cultures, leptospires downregulate the expression of their FlaBs subunits in animal blood, which as a consequence could participate in the TLR5 avoidance.</p></sec></sec><sec id=\"S4\"><title>Discussion</title><p>In the present study, we showed that live leptospires largely evade induction of signaling through TLR5 and even may escape recognition by TLR5. However, TLR5 agonists were unexpectedly released after boiling for 30 min, and we further showed that these have an unusual thermoresistance. We determined that the TLR5 activity relied, as expected, on the FlaB subunits, known to form the core of the flagella. This subunit also shares some structural features and consensus domains of TLR5 binding with the FliC flagellin subunit of <italic>Salmonella</italic>. Our results also highlight the species specificity of the TLR5 recognition of the leptospiral FlaBs, and potentially differences among serovars. Indeed, we evidenced that human and bovine TLR5 recognized heat-killed leptospires, although the mouse TLR5 did not sense the Icterohaemorrhagiae Verdun and Manilae L495 strains, but recognized the Copenhageni Fiocruz L1-130 strain, although scantily. We showed that antimicrobial peptides were active against live bacteria and allowed for their signaling through human and bovine TLR5, but not through mouse TLR5. Finally, we showed that leptospires downregulated the FlaBs gene expression in blood from both resistant mice and susceptible hamsters, suggesting a mechanism of immune evasion.</p><p>Our results of the <italic>in vivo</italic> analyses performed in blood and in organs suggest that TLR5 does not play a central role in the control of leptospires, neither during the acute nor during the chronic phase of infection in a murine model. Whereas, we found a higher number of leptospires in the urine of TLR5ko mice 7 days p.i, we did not find such differences in either urine or kidneys of mice infected with the two different strains, Manilae L495 and Copenhageni Fiocruz L1-130 at 15 days p.i. This paradox is difficult to explain. Although the number of leptospires was normalized by the volume of urine, one parameter that we did not control was the flux of emitted urine that could have biased our result. Further studies with more animals would be required to check that the urine metabolism is not altered in TLR5ko, leading to an apparent higher excretion compared to WT mice.</p><p>The absence of TLR5 response in the mouse model was surprising because (i) it was shown that neutralizing TLR5 antibodies decreased the cytokine response of whole human blood upon infection with <italic>L. interrogans</italic> (<xref rid=\"B45\" ref-type=\"bibr\">45</xref>), (ii) we showed here that antimicrobial peptides could degrade live leptospires and induce human and bovine TLR5 recognition, and (iii) we previously demonstrated that leptospires were killed and cleared from blood during the first days following infection in mice (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>), suggesting the release of free flagellin subunits that could have stimulated the TLR5 response. Hence, our study highlights a species-specificity of the TLR5 recognition since murine TLR5, unlike human and bovine TLR5, was unable to detect the Manilae L495 and Icterohaemorrhagiae Verdun strains. This was unexpected since mouse TLR5 is usually more flexible and able to accommodate more different agonist structures compared to human TLR5 (<xref rid=\"B46\" ref-type=\"bibr\">46</xref>), similar to what is seen for human and mouse TLR4 (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>). However, the heat-killed Copenhageni Fiocruz L1-130 strain was recognized by mouse TLR5, although to a lesser extent than compared to human TLR5. The weak response seen in mouse TLR5 activation is consistent with our previous study showing equivalent levels of IL1ß release in BMMs from WT and TLR5ko mice infected with live Fiocruz L1-130 strain, although stimulation with heat-killed leptospires triggered less IL1ß in TLR5ko BMMs (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>). Interestingly, we previously showed by microdissection of the mouse kidney that TLR5 is expressed in renal tubules, mostly in the distal tubules and in the collecting duct cells while almost not expressed in the proximal tubules (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>). However, although Copenhageni Fiocruz L1-130 is the only strain recognized by mouse TLR5 upon destabilization by heating (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>, <xref rid=\"B47\" ref-type=\"bibr\">47</xref>), we did not find more leptospires in kidneys of TLR5ko mice compared to WT mice. This result is also in agreement with the fact that treatment with antimicrobial peptides on live leptospires did not unmask a mouse TLR5 signal. Therefore, we may hypothesize that the localization of leptospires in proximal tubules, apart from being the first place to be reached by leptospires and potentially providing rich surroundings for nutrients, could also constitute a favorable environment to avoid the innate TLR5 response in other animals.</p><p>Our results also highlighted an important feature of bovine immune response toward leptospires, since we showed that bovine TLR5 recognizes heated and <italic>Leptospira</italic> treated with antimicrobial peptides.</p><p>Since antimicrobial peptides affect live leptospires allowing for TLR5 recognition and signaling, and because bovine antimicrobial peptides are potent to kill leptospires (<xref rid=\"B37\" ref-type=\"bibr\">37</xref>), our results suggest that the bovine TLR5 response may be important to fight leptospires in cattle. Interestingly, bovine TLR5 has been described to present bacterial species-specificity of flagellin recognition (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>, <xref rid=\"B48\" ref-type=\"bibr\">48</xref>). In our study, the magnitude of the <italic>Leptospira</italic>-induced bovine TLR5 signaling was intermediate between the weak response observed with murine TLR5 and the response seen with human TLR5 (<xref ref-type=\"fig\" rid=\"F5\">Figure 5C</xref>). However, rather than reflect real differences between bovine and human TLR5, this lower response may actually result from the heterologous expression system of bovine TLR5 in the human HEK cell system, that has been shown to impact the responsiveness (<xref rid=\"B48\" ref-type=\"bibr\">48</xref>). Together, we speculate that these observed differences in TLR5 sensing between animals and also between the three strains of <italic>L. interrogans</italic> tested, could, at least partly, be responsible for shaping the preferential species-specificity adaptation of <italic>Leptospira</italic> serovars to their hosts (<xref rid=\"B49\" ref-type=\"bibr\">49</xref>).</p><p>Our results showing the lack of TLR5 signaling by live bacteria despite the involvement of the FlaB subunits in the TLR5 recognition could have been anticipated considering the peculiarities of the leptospiral endoflagella. Indeed, the recent published structure of the filament of the leptospiral flagella showed that the FlaBs form the core and are wrapped inside a lattice composed of both FlaAs, FcpA and FcpB subunits (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>) therefore hiding the FlaB monomers. The localization of the flagella inside the periplasm adds another additional layer of protection from the host innate immune system. In addition, in <italic>Enterobacteriaceae</italic>, a unique FliC monomer polymerizes to form 11 protofilaments that together assemble to constitute one flagellum filament. The consensus sites for TLR5 recognition in the flagellin FliC are localized at stacking sites between the flagellin monomers and therefore are not accessible when the filament is formed. Hence, when polymerized, the interaction domain of FliC with TLR5 is masked, therefore whole flagella do not signal through TLR5, which occurs only when FliC is monomeric (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>, <xref rid=\"B50\" ref-type=\"bibr\">50</xref>, <xref rid=\"B51\" ref-type=\"bibr\">51</xref>). Intact purified periplasmic flagella from <italic>Treponema denticola</italic> were not able to activate TLR5 as well (<xref rid=\"B52\" ref-type=\"bibr\">52</xref>). Interestingly, our data suggest that, similar to <italic>Enterobacteriaceae</italic> (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>), the leptospiral flagellum depolymerizes at 70°C, which would allow for the release of monomers recognized by the human TLR5. However, in contrast with the <italic>Salmonella</italic> FliC, which is inactivated after 15 min at 100°C, leptospiral flagellins in the context of whole flagella appear to be highly resistant to heating.</p><p>Interestingly, we also showed a very high stability of the leptospiral filaments and FlaB proteins that resist heating to 100°C for 30 min and 85°C for 3 h. This unusual thermoresistance of the leptospiral flagella is reminiscent of the hydrophobic and very highly glycosylated pili of hyperthermophilic <italic>Archaea</italic> (<xref rid=\"B53\" ref-type=\"bibr\">53</xref>). Glycosylations also occur in bacteria. Although we do not know whether the <italic>Treponema</italic> FlaBs are particularly stable, it has recently been shown that the FlaBs of <italic>Treponema denticola</italic> were glycosylated with an unusual novel glycan (<xref rid=\"B54\" ref-type=\"bibr\">54</xref>). Mass spectrometry analysis of these glycopeptides revealed FlaBs glycosylation by O-linkage at multiple sites near the D1 domain, in the very conserved region that interacts with TLR5 (encompassing the end of consensus 2) (<xref ref-type=\"supplementary-material\" rid=\"FS6\">Supplementary Figure 6A</xref>) (<xref rid=\"B54\" ref-type=\"bibr\">54</xref>). Interestingly, we found that these atypical glycosylations target sequences in <italic>Treponema</italic>, notably the two motifs “VEV<bold>S</bold>QL” and “DRIA<bold>S</bold>” are almost 100% conserved in the FlaB1, FlaB2 and FlaB3 of pathogenic and saprophytic <italic>Leptospira</italic> (<xref ref-type=\"supplementary-material\" rid=\"FS6\">Supplementary Figures 6B,C</xref>) (<xref rid=\"B54\" ref-type=\"bibr\">54</xref>). In addition, this consensus was also 100% conserved in leptospiral FlaB1 from other major species involved in leptospirosis in animals and humans (<xref ref-type=\"supplementary-material\" rid=\"FS6\">Supplementary Figure 6D</xref>). Interestingly, the two serine residues were substituted in the <italic>L. interrogans</italic> FlaB4 and FlaB from <italic>Borrelia burgdorferi</italic> (<xref ref-type=\"supplementary-material\" rid=\"FS6\">Supplementary Figure 6C</xref>), which might suggest a lack of glycosylation of the leptospiral FlaB4 subunit and <italic>B. burgdorferi</italic> FlaB. The authors hypothesized that in <italic>Treponema</italic> spp. these peculiar glycosylations could impair the TLR5 signaling of <italic>Treponema</italic>. Our study suggests, if these post-translational modifications exist in leptospires, that they would not impair the TLR5 recognition at least in human and bovine TLR5. Rather we may speculate that they could participate in the thermoresistance of the filament structure.</p><p>In other spirochetes, the filament structure differs from the leptospiral one since in <italic>Treponema</italic> and <italic>Borrelia</italic> spp. the FcpA subunits are absent, and beside, in <italic>Borrelia</italic> only one copy of FlaA and FlaB compose the filament (<xref rid=\"B55\" ref-type=\"bibr\">55</xref>, <xref rid=\"B56\" ref-type=\"bibr\">56</xref>). The stability of the leptospiral filament is most probably due to the particular association and spatial arrangement of the different FlaBs and to their recently described asymmetric interactions with FlaAs or with Fcps (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>). Whether the four FlaBs are randomly dispersed along the filament or would have specific structural functions remains to be studied. However, our results were obtained in the context of the whole bacteria. It would have been interesting to test individual leptospiral FlaB subunits to understand whether the high stability results from intrinsic properties of the individual FlaBs. However, our attempts to express recombinant FlaB monomers have failed. We cannot exclude a caveat in our cloning strategy but this failure was quite surprising considering that <italic>T. denticola</italic> and <italic>T. pallidum</italic> FlaB were expressed as stable recombinant proteins that were able to signal through TLR5 in THP1 monocytes or in human keratocytes, respectively (<xref rid=\"B42\" ref-type=\"bibr\">42</xref>). One hypothesis could be that the FlaBs that encompass a different shape than FliC would need to be stabilized by polymerization into the complex filament structure.</p><p>The respective role of the leptospiral FlaB1, FlaB2, FlaB3 and FlaB4 proteins remains unknown. The Phyre 2 models suggest that the four FlaBs structures are identical, which explains why the precise roles of the different FlaBs in the core could not be addressed in a recent structural study (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>). The only information available about differences in the four subunits comes from a proteomic study (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>) that finds all four FlaB subunits in Fiocruz L1-130 strain cultured in EMJH at 30°C, suggesting that all subunits were present in the filaments with different relative abundance of FlaB subunits. Each bacterium contained 12,000 copies of FlaB1, 2,000 copies of FlaB2, 300 copies of FlaB3 and 3,500 copies of FlaB4 (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>). We tested the expression of each of the four FlaBs mRNA in EMJH cultures and found that all the subunits were expressed in the Icterohaemorrhagiae Verdun and Copenhageni Fiocruz L1-130 strains. However, the relative mRNA levels of the different FlaB subunits did not match the data obtained in the proteomic study, since for example the relative mRNA quantities of <italic>flaB3</italic> seems to be higher than <italic>flaB4</italic> at the stationary phase. Of note the <italic>flaB3</italic> mRNA expression was upregulated at the stationary phase in the 3 serovars of <italic>L. interrogans</italic>, for which we have no explanation. Furthermore, in Manilae L495, we observed a strikingly weak expression of FlaB1 compared to the other <italic>L. interrogans</italic> tested, potentially suggesting a serovar-specific regulation of FlaB subunits. Of note, the <italic>flaB1</italic> expression was upregulated at the stationary phase in Fiocruz L1-130, which could potentially explain the striking difference between the Manilae L495 strain that was not recognized by the mouse TLR5 whereas the Copenhageni L1-130 strain exhibited a better recognition, despite the fact that all their FlaBs are almost identical and 100% conserved in the TLR5 consensus binding domains. In addition, the absence of one FlaB subunit in the <italic>flaB4</italic> mutant of <italic>L. biflexa</italic> Patoc I, which has been shown to impair the filament formation (<xref rid=\"B19\" ref-type=\"bibr\">19</xref>), also impairs the TLR5 signaling. A decreased TLR5 signaling was also observed with the Manilae <italic>flaB1</italic> mutant although the impact of this mutant on filament formation has not yet been studied. However, in both cases the TLR5 signal was not abolished, suggesting that despite the lack of observed motility and filaments, some other FlaB subunits were still expressed and able to signal through TLR5, in agreement with the <italic>in silico</italic> analyses suggesting that all FlaB subunits can in theory signal through TLR5.</p><p>The fact that we found a striking down-regulation of FlaBs, not of FlaAs, in blood of mice and hamsters 24 h p.i with the Icterohaemorrhagiae Verdun strain, suggests that a regulation of the FlaBs expression could favor an escape from the TLR5 immune surveillance upon infection. However, it remains to be demonstrated that the global down-regulation of FlaBs expression that we observed <italic>in vitro</italic> at the exponential phase correlates indeed with a decrease in TLR5 recognition. In animal blood, the downregulation of the FlaB expression could make sense to avoid the TLR5 response. It would have been interesting to check the expression of the FlaBs in <italic>Leptospira</italic> colonizing the kidney of animals. However, if amenable in the blood of animals, the purification of leptospires mRNA in kidneys is still challenging. The only example of published renal transcriptome dualseq analysis of <italic>L. interrogans</italic> (Fiocruz L1-130) infection in mice could only detect 29 leptospiral genes (<xref rid=\"B57\" ref-type=\"bibr\">57</xref>), among them <italic>lipL32</italic>, encoding the major lipoprotein and interestingly, one flagellin gene, <italic>flaB4</italic> (LIC11531), suggesting that the mRNA levels of FlaB4 were quite high, and potentially higher than the other FlaBs mRNA. As a whole, these results suggest a complex regulation of the leptospiral FlaB subunits that deserves further investigation. Interestingly, it was shown in another spirochete <italic>Brachyspira hyodysenteriae</italic> that the flagellin genes are transcribed by different transcription factors, with sigma 28 regulating the <italic>flaB1</italic> and <italic>flaB2</italic> genes, whereas the <italic>flaA</italic> and <italic>flaB3</italic> genes are controlled by sigma 70. The authors suggest that the relative ratio of the flagellin proteins could play a role in the stiffness of the flagellar filament and consequently that this regulation may play a role in motility (<xref rid=\"B58\" ref-type=\"bibr\">58</xref>). The regulation of FlaBs in leptospires that harbor an even more complex flagellar filament is an interesting question that remains to be studied. Our findings of <italic>in vivo</italic> downregulation of the FlaBs deserve further studies potentially linking it to regulation of <italic>Leptospira</italic> motility <italic>in vivo</italic>.</p><p>Interestingly, the leptospiral FlaBs share with the flagellin of <italic>Bacillus</italic> spp., that is also able to signal via TLR5, a similar structure made of the D0 and D1 domains of FliC and lacking the D2 and D3 domains (<xref rid=\"B59\" ref-type=\"bibr\">59</xref>). Of note, the D2 and D3 domains of FliC are highly variable and responsible for the strong antigenicity of flagellins in <italic>Enterobacteriaceae</italic> (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>). Flagellin is known to be a potent vaccine adjuvant, however, the antigenicity of the D2 and D3 domains can be a problem when booster immunizations are done. To circumvent this issue, several strategies have been recently proposed. The first consisted in using a FliC devoid of the D2 and D3 domains (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>), and the second to use the <italic>Bacillus</italic> flagellin as an expression platform (<xref rid=\"B59\" ref-type=\"bibr\">59</xref>). Likewise, we may speculate in the case of <italic>Leptospira</italic> spp. that upon <italic>in vivo</italic> killing and exposure of FlaB subunits, the lack of D2 and D3 domains could be advantageous to limit the antibody response. Hence, the peculiar structure of FlaBs could also participate in the adaptive immune evasion.</p><p>In conclusion, we showed here that pathogenic <italic>Leptospira</italic> largely escape recognition by TLR5. Other bacteria such as <italic>Helicobacter pylori</italic> have been shown to escape the TLR5 response through modification of the amino residues in the D0 or D1 regions of flagellin subunits (<xref rid=\"B43\" ref-type=\"bibr\">43</xref>), but leptospires seem to differ in avoiding TLR5 recognition. Indeed, our data demonstrate that the endoflagella play a role in the escape from TLR5 surveillance, which has never been shown before and might hold true for other spirochetes. We also evidenced regulatory mechanisms of <italic>flaB</italic> genes expression that may also play a role in this immune evasion and have important consequences since TLR5 ligation has a potent adjuvant role in immunity.</p></sec><sec sec-type=\"data-availability\" id=\"S5\"><title>Data Availability Statement</title><p>All datasets presented in this study are included in the article/<xref ref-type=\"supplementary-material\" rid=\"FS1\">Supplementary Material</xref>.</p></sec><sec id=\"S6\"><title>Ethics Statement</title><p>The animal study was reviewed and approved by (#2013-0034 and #HA-0036) the Institut Pasteur ethic committee (CETEA #89) (Paris, France), the competent authority, for compliance with the French and European (EU directive 2010/63) regulations on Animal Welfare and with Public Health Service recommendations.</p></sec><sec id=\"S7\"><title>Author Contributions</title><p>CW: conception, project administration, supervision, and writing original draft. MH, DB, JC, MM, and CW: visualization. MH, DB, JC, FV-P, SB, MF, MM, CG, and CW: investigation and data analysis. MF and FV-P: validation. LF, FV-P, and MM: methodology. DB and SB: sequences alignment. MP, EW, AK, and DW: resources. CG, EW, IB, and CW: funding acquisition. DW and IB: English editing. All authors contributed to the writing, review and editing of the manuscript, and approved the submitted version.</p></sec><sec id=\"conf1\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> This study received funding from the French Government’s Investissement d’Avenir program, Laboratoire d’Excellence “Integrative Biology of Emerging Infectious Diseases” (grant n° ANR-10-LABX-62-IBEID) to IB. DB received funding from the Ecole Doctorale Frontières de l’Innovation en Recherche et Education (FIRE), Program Bettencourt. JC was supported by a Calmette and Yersin fellowship from Institut Pasteur International network and EW by a NIH fund (R01AI121207).</p></fn></fn-group><ack><p>We thank Brigitte David-Watine for critical reading of the manuscript. We are grateful to Marie-Estelle Soupe-Gilbert for her participation in the design of primers used for the quantification of flagellar subunits gene expression.</p></ack><sec id=\"S10\" sec-type=\"supplementary material\"><title>Supplementary Material</title><p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.frontiersin.org/articles/10.3389/fimmu.2020.02007/full#supplementary-material\">https://www.frontiersin.org/articles/10.3389/fimmu.2020.02007/full#supplementary-material</ext-link></p><supplementary-material content-type=\"local-data\" id=\"FS1\"><label>FIGURE S1</label><caption><p>Not antibiotics but antimicrobial peptides reveal the L495 ability to activate TLR5. <bold>(A)</bold> NF-κB reporter assay in HEK-Blue-KD-TLR5 cells transfected with the human TLR5 (blue bars), or empty plasmid (empty bars) and stimulated with MOI 100 of <italic>L. interrogans</italic> Manilae strain L495 treated with gentamicin (50 μg/mL), penicillin G (10 μg/mL), azithromycin (10 μg/mL) or daptomycin (1 μg/mL) for 4 h before stimulation. Data are expressed as the mean (±SD) of technical replicates (<italic>n</italic> = 3). <bold>(B)</bold> NF-κB reporter assay in HEK-Blue-KD-TLR5 cells transfected with the bovine TLR5 (dark blue bars), mouse TLR5 (light blue bars) or empty plasmid (empty bars) and stimulated with MOI 100 of <italic>L. interrogans</italic> Manilae strain L495 treated with human peptide LL-37 or bovine peptide Bmap28 at various concentration (0–250 μg/mL) for 2 h before stimulation. Unpurified Fla from <italic>Salmonella typhimurium</italic> (500 ng/mL) was used as control. Data are expressed as the mean (±SD) of technical replicates (<italic>n</italic> = 3) and are representative of at least three independent experiments. Statistically significant differences (Student <italic>t</italic>-test) are indicated.</p></caption><media xlink:href=\"Data_Sheet_1.PDF\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"FS2\"><label>FIGURE S2</label><caption><p>Schematics of leptospiral filament and FliC association with TLR5. Schematic representations of <bold>(A)</bold> the leptospiral flagellin structure adapted from Gibson et al. (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>) and <bold>(B)</bold> the interaction of FliC subunits with TLR5 inducing dimerization of the receptors adapted from Yoon et al. (<xref rid=\"B40\" ref-type=\"bibr\">40</xref>).</p></caption><media xlink:href=\"Data_Sheet_1.PDF\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"FS3\"><label>FIGURE S3</label><caption><p>BLAST analyses between flagellin subunits and species. <bold>(A–C)</bold> Amino acid sequence homology percentage between (A) <italic>Leptospira interrogans</italic> strain Fiocruz FlaBs (LIC11531, LIC18890, LIC11889, LIC11532) and FlaAs (LIC10788, LIC10787), <bold>(B)</bold>\n<italic>Leptospira interrogans</italic> strain Fiocruz FlaBs or <italic>Leptospira biflexa</italic> strain Patoc FlaBs (LEPBIa1589, LEPBIa2133, LEPBIa2132, LEPBIa1872), <bold>(C)</bold>\n<italic>Leptospira interrogans</italic> FlaB1 of all serotypes (Fiocruz LIC11531, Manilae LMANv2_590023, Verdun AKWP_v1_110067, Patoc LEPBIa1589), FlaB2 of all serotypes (Fiocruz LIC11890, Manilae LMANv2_260016, Verdun AKWP_v1_110429, Patoc LEPBIa2133), FlaB3 of all serotypes (Fiocruz LIC11889, Manilae LMANv2_260015, Verdun AKWP_v1_110428, Patoc LEPBIa2132) and FlaB4 of all serotypes (Fiocruz LIC11532, Manilae LMANv2_590024, Verdun AKWP_v1_110068, Patoc LEPBIa1872).</p></caption><media xlink:href=\"Data_Sheet_1.PDF\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"FS4\"><label>FIGURE S4</label><caption><p>TLR5 binding and consensus sites in different species. Clustal (MEGA software) alignment of the amino acid sequences for the TLR5 binding consensus regions of: <italic>Leptospira interrogans</italic> strain Fiocruz FlaB1 (LIC11531), <italic>Leptospira biflexa</italic> strain Patoc FlaB1 (LEPBIa1589), <italic>Borrelia burgdorferi</italic> (GeneBank CAA45011.1), <italic>Treponema</italic> ssp. (GeneBank AIW88993.1), <italic>Bacillus subtilis</italic> strain W23 (GeneBank ADM39502.1), <italic>Salmonella enterica</italic> subsp. <italic>enterica</italic> serovar Typhimurium (GeneBank QDQ31983.1), <italic>Escherichia coli</italic> strain 0157:H7 (KKF82802.1), <italic>Helicobacter pylori</italic> strain J99 (GeneBank AKE81874.1) and <italic>Bartonnela bacilliformis</italic> (GeneBank AAA22899.1).</p></caption><media xlink:href=\"Data_Sheet_1.PDF\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"FS5\"><label>FIGURE S5</label><caption><p>Temperature of culture medium does not alter FlaAs and FlaBs regulation. FlaAs and FlaBs mRNA expression in culture in EMJH at 30 or 37°C. Data of RT-qPCR are expressed as the ratio of mRNA quantities relatives to the EMJH 30°C control. Technical replicates are represented as dots and lines correspond to mean (±SD) of all replicates.</p></caption><media xlink:href=\"Data_Sheet_1.PDF\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"FS6\"><label>FIGURE S6</label><caption><p>Glycosylation sites on FlaBs. <bold>(A,B)</bold>\n<italic>In silico</italic> (Phyre2 and Chimera softwares) prediction of <bold>(A)</bold>\n<italic>Treponema pallidum</italic> strain Nichols FlaB1 (P21990) and <bold>(B)</bold>\n<italic>Leptospira interrogans</italic> strain Fiocruz FlaB1 (LIC11531) with TLR5 binding consensus 2 (yellow) and potential glycosylation positions (pink) highlighted. <bold>(C)</bold> Clustal (MEGA software) alignment of the amino acid sequences for region with potential glycosylations of: <italic>Treponema denticola</italic> FlaB (GeneBank WP_010697276.1), <italic>Borrelia burgdorferi</italic> (GeneBank CAA45011.1), <italic>L. biflexa</italic> (strain Patoc) FlaB1 (LEPBIa1589), FlaB2 (LEPBIa2133), FlaB3 (LEPBIa2132), FlaB4 (LEPBIa1872), <italic>L. interrogans</italic> (strain Fiocruz) FlaB1 (LIC11531), FlaB2 (LIC18890), FlaB3 (LIC11889), FlaB4 (LIC11532), <italic>L. interrogans</italic> (strain Manilae) FlaB1 (LMANv2_590023), FlaB2 (LMANv2_260016), FlaB3 (LMANv2_260015), FlaB4 (LMANv2_590024) and <italic>L. interrogans</italic> (strain Verdun) FlaB1 (AKWP_v1_110067), FlaB2 (AKWP_v1_110429), FlaB3 (AKWP_v1_110428) and FlaB4 (AKWP_v1_110068).</p></caption><media xlink:href=\"Data_Sheet_1.PDF\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec><ref-list><title>References</title><ref id=\"B1\"><label>1.</label><mixed-citation publication-type=\"journal\"><person-group 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"case-report\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Cureus</journal-id><journal-id journal-id-type=\"iso-abbrev\">Cureus</journal-id><journal-id journal-id-type=\"issn\">2168-8184</journal-id><journal-title-group><journal-title>Cureus</journal-title></journal-title-group><issn pub-type=\"epub\">2168-8184</issn><publisher><publisher-name>Cureus</publisher-name><publisher-loc>Palo Alto (CA)</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32821605</article-id><article-id pub-id-type=\"pmc\">PMC7431987</article-id><article-id pub-id-type=\"doi\">10.7759/cureus.9259</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Urology</subject></subj-group><subj-group><subject>Oncology</subject></subj-group><subj-group><subject>Public Health</subject></subj-group></article-categories><title-group><article-title>Renal Cell Carcinoma as an Incidental Finding in Firefighters: A Case Series</article-title></title-group><contrib-group><contrib contrib-type=\"editor\"><name><surname>Muacevic</surname><given-names>Alexander</given-names></name></contrib><contrib contrib-type=\"editor\"><name><surname>Adler</surname><given-names>John R</given-names></name></contrib></contrib-group><contrib-group><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>Geiger</surname><given-names>Kyle W</given-names></name><xref ref-type=\"aff\" rid=\"aff-1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Wright</surname><given-names>Tanner J</given-names></name><xref ref-type=\"aff\" rid=\"aff-1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Deters</surname><given-names>Levi</given-names></name><xref ref-type=\"aff\" rid=\"aff-2\">2</xref></contrib></contrib-group><aff id=\"aff-1\">\n<label>1</label>\nMedicine, Elson S. Floyd College of Medicine, Washington State University, Spokane, USA </aff><aff id=\"aff-2\">\n<label>2</label>\nUrology, Spokane Urology, Spokane, USA </aff><author-notes><corresp id=\"cor1\">\nKyle W. Geiger <email>kyle.geiger@wsu.edu</email></corresp></author-notes><pub-date date-type=\"pub\" publication-format=\"electronic\"><day>18</day><month>7</month><year>2020</year></pub-date><pub-date date-type=\"collection\" publication-format=\"electronic\"><month>7</month><year>2020</year></pub-date><volume>12</volume><issue>7</issue><elocation-id>e9259</elocation-id><history><date date-type=\"received\"><day>26</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>18</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020, Geiger et al.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Geiger et al.</copyright-holder><license license-type=\"open-access\" xlink:href=\"http://creativecommons.org/licenses/by/3.0/\"><license-p>This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</license-p></license></permissions><self-uri xlink:href=\"https://www.cureus.com/articles/36210-renal-cell-carcinoma-as-an-incidental-finding-in-firefighters-a-case-series\">This article is available from https://www.cureus.com/articles/36210-renal-cell-carcinoma-as-an-incidental-finding-in-firefighters-a-case-series</self-uri><abstract><p>The link between cancer, including cancers of the kidney, and occupational exposure in firefighters has been well established. Renal cell carcinoma has a tendency to present incidentally on imaging rather than with the classic symptoms of flank pain and hematuria. In this case series, we identify four firefighter patients, all of whom initially presented with a kidney tumor as an incidental finding. We examine the absence of other risk factors in these patients along with current screening guidelines. This report aims to detail how these tumors present incidentally as well as evaluate the current screening guidelines in an effort to build awareness within this population. Patient demographics, risk factors, length of firefighting career, final pathology, and postoperative recurrence were evaluated. Four males underwent successful partial or total nephrectomy. All who have had follow-up have been tumor free with renal function intact. None are dialysis dependent. The role of routine renal imaging of this population is explored.</p></abstract><kwd-group kwd-group-type=\"author\"><kwd>renal cell carcinoma</kwd><kwd>firefighters</kwd><kwd>occupational medicine</kwd></kwd-group></article-meta><notes><p content-type=\"disclaimer\">The content published in Cureus is the result of clinical experience and/or research by independent individuals or organizations. Cureus is not responsible for the scientific accuracy or reliability of data or conclusions published herein. All content published within Cureus is intended only for educational, research and reference purposes. Additionally, articles published within Cureus should not be deemed a suitable substitute for the advice of a qualified health care professional. Do not disregard or avoid professional medical advice due to content published within Cureus.</p></notes></front><body><sec sec-type=\"intro\"><title>Introduction</title><p>In 2019, there were an estimated 73,820 cases and 14,770 deaths from cancers of the kidney and renal pelvis [<xref rid=\"REF1\" ref-type=\"bibr\">1</xref>]. Of these cancers, renal cell carcinoma (RCC) is the most common. RCC typically presents in the sixth to eighth decade of life. One study cites a median age of 64 years with just 4.4% of patients presenting in the age range of 20 to 40 years [<xref rid=\"REF2\" ref-type=\"bibr\">2</xref>]. It is established that firefighters are at increased risk for a number of types of cancer including cancers of the kidney [<xref rid=\"REF3\" ref-type=\"bibr\">3</xref>-<xref rid=\"REF5\" ref-type=\"bibr\">5</xref>]. This link is highlighted by occupational exposure to toxic substances [<xref rid=\"REF3\" ref-type=\"bibr\">3</xref>]. Other documented risk factors include genetic predisposition/hereditary disorders, obesity, smoking, and various nephrotoxic industrial chemicals [<xref rid=\"REF6\" ref-type=\"bibr\">6</xref>]. Routine screening may be indicated for populations with exposure and identifiable risk factors [<xref rid=\"REF7\" ref-type=\"bibr\">7</xref>]. In this study, we examine a series of RCC among young firefighters in the Pacific Northwest who had a diagnosis of RCC after a tumor was found on imaging as an incidental finding. We examine the absence of other risk factors in these patients along with current screening guidelines. This report aims to describe how these tumors present incidentally as well as evaluate the current screening guidelines in an effort to build awareness within this population. </p></sec><sec sec-type=\"cases\"><title>Case presentation</title><p>Materials and methods</p><p>A retrospective analysis was completed through our clinic’s electronic healthcare record. We searched for patients who were diagnosed with RCC with a history of a firefighting career. Records from 2014 to 2019 were analyzed. Four total patients were identified with a median age of 36.5 years at detection of tumor (mean 40.75, range 31-58). Demographics, risk factors, and imaging were gathered and imported into a Microsoft Excel file. A Qualtrics™ online survey tool was developed and used to gather information about length of firefighting career. This information is presented in Table <xref rid=\"TAB1\" ref-type=\"table\">1</xref>.</p><table-wrap id=\"TAB1\" orientation=\"portrait\" position=\"float\"><label>Table 1</label><caption><title>Demographics and Risk Factors </title><p>BMI, body mass index</p></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Case</td><td rowspan=\"1\" colspan=\"1\">Gender</td><td rowspan=\"1\" colspan=\"1\">Age</td><td rowspan=\"1\" colspan=\"1\">Length of fire career (years)</td><td rowspan=\"1\" colspan=\"1\">Smoking history</td><td rowspan=\"1\" colspan=\"1\">BMI</td></tr><tr><td rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\">Male</td><td rowspan=\"1\" colspan=\"1\">38</td><td rowspan=\"1\" colspan=\"1\">18</td><td rowspan=\"1\" colspan=\"1\">None</td><td rowspan=\"1\" colspan=\"1\">28.1</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">2</td><td rowspan=\"1\" colspan=\"1\">Male</td><td rowspan=\"1\" colspan=\"1\">59</td><td rowspan=\"1\" colspan=\"1\">40</td><td rowspan=\"1\" colspan=\"1\">None</td><td rowspan=\"1\" colspan=\"1\">30.8</td></tr><tr><td rowspan=\"1\" colspan=\"1\">3</td><td rowspan=\"1\" colspan=\"1\">Male</td><td rowspan=\"1\" colspan=\"1\">35</td><td rowspan=\"1\" colspan=\"1\">19</td><td rowspan=\"1\" colspan=\"1\">None</td><td rowspan=\"1\" colspan=\"1\">30.4</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">4</td><td rowspan=\"1\" colspan=\"1\">Male</td><td rowspan=\"1\" colspan=\"1\">31</td><td rowspan=\"1\" colspan=\"1\">7.5</td><td rowspan=\"1\" colspan=\"1\">None</td><td rowspan=\"1\" colspan=\"1\">28.5</td></tr></tbody></table></table-wrap><p>Case presentations</p><p>Case 1</p><p>A 38-year-old male with a history of chronic back pain underwent lumbar imaging. A 4-cm solid enhancing right lower pole renal mass was found incidentally (Figure <xref ref-type=\"fig\" rid=\"FIG1\">1</xref>). He was a professional firefighter for 18 years. His grandmother had kidney cancer at age 75 years. He denied gross hematuria and weight loss. The patient underwent robotic-assisted right partial nephrectomy. Pathology results showed T1a clear cell carcinoma with negative margins.</p><fig fig-type=\"figure\" id=\"FIG1\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><title>MRI showing image of right lower pole renal mass (red arrow)</title></caption><graphic xlink:href=\"cureus-0012-00000009259-i01\"/></fig><p>Case 2</p><p>A 58-year-old male with a history of papillary mucinous neuroendocrine tumor of the pancreas was found to have a renal mass on imaging for pancreatic disease. He was a professional firefighter for over 40 years. He had no family history of renal cancer. He denied gross hematuria and weight loss. Imaging showed an approximately 4-cm cystic lesion with septations and some enhancing components (Figure <xref ref-type=\"fig\" rid=\"FIG2\">2</xref>). The patient underwent a robotic-assisted right partial nephrectomy. Pathology results showed T1b clear cell carcinoma with negative margins. CT at four-month follow-up showed no suspicions lesions. </p><fig fig-type=\"figure\" id=\"FIG2\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><title>CT of the abdomen showing right renal mass (red arrow)</title></caption><graphic xlink:href=\"cureus-0012-00000009259-i02\"/></fig><p>Case 3</p><p>A 35-year-old male who initially presented with calcium oxalate renal stones had a follow-up ultrasound that showed a 2.4-cm hyperechoic area in the left kidney. The patient then underwent MRI and was found to have a 3-cm mass. He had no family history of kidney cancer. He was a professional firefighter for 19 years. The patient underwent an open left partial nephrectomy. Pathology results showed T1a clear cell carcinoma with negative margins. CT at four-month follow-up showed no suspicions lesions. </p><p>Case 4</p><p>A 31-year-old male admitted from emergency department for gastroenteritis underwent CT with contrast showing a 3-cm enhancing left renal mass (Figure <xref ref-type=\"fig\" rid=\"FIG3\">3</xref>). He had no family history of kidney cancer. He denied gross hematuria and weight loss. He was a firefighter for seven and a half years. The patient underwent a laparoscopic left radical nephrectomy. Pathology results showed T1a clear cell carcinoma with negative margins. CT showed no suspicious lesions at seven months.</p><fig fig-type=\"figure\" id=\"FIG3\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><title>CT showing left renal mass (red arrow)</title></caption><graphic xlink:href=\"cureus-0012-00000009259-i03\"/></fig></sec><sec sec-type=\"discussion\"><title>Discussion</title><p>This small series shows examples of RCC in firefighters within our community. We detail the diagnosis of RCC via incidental findings and subsequent treatment in this population. We present four patients, without other obvious risk factors and/or clinical symptoms, who were diagnosed with RCC after imaging for reasons other than routine screening (back pain, nephrolithiasis, etc.). </p><p>The literature has established that firefighters are at an increased risk for a variety of cancers including those of the kidney [<xref rid=\"REF3\" ref-type=\"bibr\">3</xref>-<xref rid=\"REF5\" ref-type=\"bibr\">5</xref>]. The mechanism for this increased risk is beyond the scope of our review, but is suspected to be due to exposure to carcinogens and possible genetic changes as a result [<xref rid=\"REF8\" ref-type=\"bibr\">8</xref>]. RCC is one of the deadliest urological cancers. The Surveillance, Epidemiology, and End Results (SEER) database found an incidence-based mortality rate at 5.3% [<xref rid=\"REF9\" ref-type=\"bibr\">9</xref>]. Recent studies within the firefighter population have recommended improving preventative measures [<xref rid=\"REF10\" ref-type=\"bibr\">10</xref>]. Current screening methods and protocols for firefighters in our community are set by the National Fire Protection Association (NFPA) 1582: Standard on Comprehensive Occupational Medical Program for Fire Departments [<xref rid=\"REF11\" ref-type=\"bibr\">11</xref>]. The NFPA recommends a yearly blood draw that screens for blood urea nitrogen (BUN), creatinine, and electrolyte abnormalities. They also recommend annual urinalysis for microscopic hematuria. However, renal function as measured by BUN, creatinine, and electrolyte abnormalities is not an established screening method for RCC. Other studies have found urinary dipstick is an inadequate test for RCC due to low sensitivity and specificity [<xref rid=\"REF12\" ref-type=\"bibr\">12</xref>]. Renal ultrasound is an attractive option as a screening tool for RCC. Ultrasound is non-invasive, inexpensive, and widely used in the urological setting. A recent review of screening methods reports that ultrasound enables the detection of 85%-100% tumors of >3 cm in size, and 67%-82% of tumors 2-3 cm in size [<xref rid=\"REF7\" ref-type=\"bibr\">7</xref>]. The same study reports ultrasound as less sensitive and specific compared to CT; however, we suggest this is outweighed by cost, convenience, and lack of radiation exposure.</p><p>All of our cases found renal masses incidentally, demonstrating a possible role for routine imaging. Other studies have found RCC is diagnosed incidentally over 50% of the time [<xref rid=\"REF12\" ref-type=\"bibr\">12</xref>,<xref rid=\"REF13\" ref-type=\"bibr\">13</xref>]. In addition, routine screening may be indicated for populations with exposure and identifiable risk factors such as firefighters [<xref rid=\"REF7\" ref-type=\"bibr\">7</xref>]. Evaluation of routine screening methods for kidney cancer may be supported in this population. We suggest a larger scale study examining the cost-benefit of ultrasound screening for RCC in our firefighter population.</p><p>Our study does not examine epidemiological data due to a limited sample size. This case series only provides an example of these patients in one small clinic in the Pacific Northwest. Other studies are needed to examine this relationship in the broader Pacific Northwest and United States.</p></sec><sec sec-type=\"conclusions\"><title>Conclusions</title><p>Four of our firefighter patients were diagnosed with RCC via incidental findings on imaging. These cases highlight the possible need for additional screening in firefighters. There are limited studies that examine the role of routine renal imaging in this at risk population. We suggest a larger scale study examining the cost-benefit of ultrasound screening for RCC in the firefighter population.</p></sec></body><back><fn-group content-type=\"competing-interests\"><fn fn-type=\"COI-statement\"><p>The authors have declared that no competing interests exist.</p></fn></fn-group><fn-group content-type=\"other\"><title>Human Ethics</title><fn fn-type=\"other\"><p>Consent was obtained by all participants in this study. Washington State University issued approval IRB #17866-001. This project was deemed \"exemption status\" by the Washington State University IRB in November 2019.</p></fn></fn-group><ref-list><title>References</title><ref id=\"REF1\"><label>1</label><element-citation publication-type=\"website\"><article-title>SEER. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"case-report\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Cureus</journal-id><journal-id journal-id-type=\"iso-abbrev\">Cureus</journal-id><journal-id journal-id-type=\"issn\">2168-8184</journal-id><journal-title-group><journal-title>Cureus</journal-title></journal-title-group><issn pub-type=\"epub\">2168-8184</issn><publisher><publisher-name>Cureus</publisher-name><publisher-loc>Palo Alto (CA)</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32821606</article-id><article-id pub-id-type=\"pmc\">PMC7431988</article-id><article-id pub-id-type=\"doi\">10.7759/cureus.9260</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Cardiac/Thoracic/Vascular Surgery</subject></subj-group><subj-group><subject>Cardiology</subject></subj-group></article-categories><title-group><article-title>Acute Myocardial Ischemia Secondary to Embolization of Left Atrial Myxoma to Coronary Artery</article-title></title-group><contrib-group><contrib contrib-type=\"editor\"><name><surname>Muacevic</surname><given-names>Alexander</given-names></name></contrib><contrib contrib-type=\"editor\"><name><surname>Adler</surname><given-names>John R</given-names></name></contrib></contrib-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Al-Musawi</surname><given-names>Mohammed</given-names></name><xref ref-type=\"aff\" rid=\"aff-1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Rubay</surname><given-names>David</given-names></name><xref ref-type=\"aff\" rid=\"aff-2\">2</xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>Ohanisian</surname><given-names>Levonti</given-names></name><xref ref-type=\"aff\" rid=\"aff-3\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Yi</surname><given-names>Slee</given-names></name><xref ref-type=\"aff\" rid=\"aff-2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>AlOmaishi</surname><given-names>Suhad</given-names></name><xref ref-type=\"aff\" rid=\"aff-4\">4</xref></contrib></contrib-group><aff id=\"aff-1\">\n<label>1</label>\nSurgery, Anschutz Medical Campus, University of Colorado, Aurora, USA </aff><aff id=\"aff-2\">\n<label>2</label>\nSurgery, Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, USA </aff><aff id=\"aff-3\">\n<label>3</label>\nOrthopaedic Surgery, Morsani College of Medicine, University of South Florida, Tampa, USA </aff><aff id=\"aff-4\">\n<label>4</label>\nInternal Medicine, Life Alliance Organ Recovery Agency, University of Miami, Miami, USA </aff><author-notes><corresp id=\"cor1\">\nLevonti Ohanisian <email>levonti.ohanisian@gmail.com</email></corresp></author-notes><pub-date date-type=\"pub\" publication-format=\"electronic\"><day>18</day><month>7</month><year>2020</year></pub-date><pub-date date-type=\"collection\" publication-format=\"electronic\"><month>7</month><year>2020</year></pub-date><volume>12</volume><issue>7</issue><elocation-id>e9260</elocation-id><history><date date-type=\"received\"><day>9</day><month>3</month><year>2020</year></date><date date-type=\"accepted\"><day>3</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020, Al-Musawi et al.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Al-Musawi et al.</copyright-holder><license license-type=\"open-access\" xlink:href=\"http://creativecommons.org/licenses/by/3.0/\"><license-p>This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</license-p></license></permissions><self-uri xlink:href=\"https://www.cureus.com/articles/29078-acute-myocardial-ischemia-secondary-to-embolization-of-left-atrial-myxoma-to-coronary-artery\">This article is available from https://www.cureus.com/articles/29078-acute-myocardial-ischemia-secondary-to-embolization-of-left-atrial-myxoma-to-coronary-artery</self-uri><abstract><p>Cardiac myxoma is the most common primary cardiac tumor. Rarely it can present with systemic or coronary embolization due to fragmentation of the tumor mass. We present a case of a young male who presented with an acute myocardial ischemia secondary to embolization of a left atrial myxoma originating from the left atrium. The patient underwent successful emergency surgical management of both the myxoma and the occlusion of the coronary artery. In this scenario, the surgery is the only effective treatment. The case also highlights the significance of performing emergency echocardiography in the setting of acute myocardial ischemia to look for possible associated pathology which can inform management plan.</p></abstract><kwd-group kwd-group-type=\"author\"><kwd>myxoma</kwd><kwd>st elevation</kwd><kwd>myocardial infarction</kwd><kwd>dca</kwd></kwd-group></article-meta><notes><p content-type=\"disclaimer\">The content published in Cureus is the result of clinical experience and/or research by independent individuals or organizations. Cureus is not responsible for the scientific accuracy or reliability of data or conclusions published herein. All content published within Cureus is intended only for educational, research and reference purposes. Additionally, articles published within Cureus should not be deemed a suitable substitute for the advice of a qualified health care professional. Do not disregard or avoid professional medical advice due to content published within Cureus.</p></notes></front><body><sec sec-type=\"intro\"><title>Introduction</title><p>Acute myocardial infarction (AMI) is caused by non-atherosclerotic lesions in 1-12% subset of cases [<xref rid=\"REF1\" ref-type=\"bibr\">1</xref>]. One such cause includes embolization [<xref rid=\"REF1\" ref-type=\"bibr\">1</xref>]. Sources of embolization include a mural thrombus, a septic embolus secondary to infective endocarditis, valve calcification or from a left atrial myxoma [<xref rid=\"REF2\" ref-type=\"bibr\">2</xref>-<xref rid=\"REF4\" ref-type=\"bibr\">4</xref>]. Although rare, embolization in the coronary artery is a possibility which cardiologists and cardiac surgeons should be aware of [<xref rid=\"REF5\" ref-type=\"bibr\">5</xref>-<xref rid=\"REF7\" ref-type=\"bibr\">7</xref>]. In one case series that spanned over 10 years they reported five cases of coronary embolization, including one case secondary to a left atrial myxoma embolus [<xref rid=\"REF1\" ref-type=\"bibr\">1</xref>]. In cases of an embolus secondary to a thrombus, patients often undergo a combination of interventions including systemic anticoagulation, coronary wiring and conventional aspiration thrombectomy devices [<xref rid=\"REF8\" ref-type=\"bibr\">8</xref>-<xref rid=\"REF10\" ref-type=\"bibr\">10</xref>]. Cases of acute coronary syndrome secondary to septic embolus from intracardiac infected valves have been managed with combined aspiration thrombectomy and coronary stenting [<xref rid=\"REF11\" ref-type=\"bibr\">11</xref>, <xref rid=\"REF12\" ref-type=\"bibr\">12</xref>]. Having intracardiac pathology as the source of the embolization such as a left atrial myxoma or infected left-sided valve is a valid justification for surgical intervention of direct surgical thrombectomy with valve replacement [<xref rid=\"REF2\" ref-type=\"bibr\">2</xref>, <xref rid=\"REF13\" ref-type=\"bibr\">13</xref>]. A review of the literature demonstrates no guideline or standard management for coronary artery thromboembolism. We intend to add to the literature with our case presentation and treatment method in order to increase the cases available in the literature and hopefully improve standard management of similar cases in the future [<xref rid=\"REF2\" ref-type=\"bibr\">2</xref>, <xref rid=\"REF14\" ref-type=\"bibr\">14</xref>].</p></sec><sec sec-type=\"cases\"><title>Case presentation</title><p>The patient is a 25-year-old male who was referred from cardiology with a diagnosis of acute ST-elevation myocardial infarction (STEMI), altered consciousness and acute renal impairment. From the onset of symptoms, time to angiography was two hours. Angiography demonstrated a left anterior descending artery (LAD) filling defect (Figure <xref ref-type=\"fig\" rid=\"FIG1\">1</xref>), and an echocardiogram performed showed a mobile left atrial mass (Figure <xref ref-type=\"fig\" rid=\"FIG2\">2</xref>) attached to the interatrial septum.</p><fig fig-type=\"figure\" id=\"FIG1\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><title>Filling defect in LAD</title><p>LAD: Left anterior descending artery</p></caption><graphic xlink:href=\"cureus-0012-00000009260-i01\"/></fig><fig fig-type=\"figure\" id=\"FIG2\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><title>Mobile mass in left atrium</title></caption><graphic xlink:href=\"cureus-0012-00000009260-i02\"/></fig><p>ECG showed ST elevation in the anterior chest leads with a positive troponin. Due to the multisystem nature of presentation, along with evidence of a mobile mass in the left atrium, it was suspected that the mass was the primary lesion responsible for showering small pieces throughout the vascular tree. The patient was taken to the operating room with plans to remove the mass from the left atrium and place a graft to the LAD to overcome acute ischemia and avoid MI. Using aortocaval cannulation and cardioplegic arrest on aortic cross clamp and moderate hypothermia, the left mammary artery (LIMA) was harvested. The left atrium was opened and a large 3 cm x 3.5 cm myxoma was excised and the left ventricle irrigated with normal saline to remove any missed detached particle. The LIMA was anastomosed to the LAD and routine rewarming and deairing of aortic root was completed. The heart began to beat spontaneously after removing the cross clamp. The patient was admitted to the cardiac intensive care unit (CICU) after surgery for 48 hours. Consciousness and renal function began recovering gradually. The mass was sent for histopathologic examination and returned with the diagnosis of myxoma. The patient spent another seven days in the ward and was discharged home in good health.</p></sec><sec sec-type=\"discussion\"><title>Discussion</title><p>Acute myocardial ischemia can be caused by non-atherosclerotic lesions in 1-12% subset of cases, one of those rare causes is embolization [<xref rid=\"REF1\" ref-type=\"bibr\">1</xref>]. Sources of embolization include a mural thrombus, a septic embolus secondary to infective endocarditis, valve calcification or from a left atrial myxoma [<xref rid=\"REF2\" ref-type=\"bibr\">2</xref>-<xref rid=\"REF4\" ref-type=\"bibr\">4</xref>]. Although rare, embolization in the coronary artery is a possibility which cardiologists and cardiac surgeons should be aware of [<xref rid=\"REF5\" ref-type=\"bibr\">5</xref>-<xref rid=\"REF7\" ref-type=\"bibr\">7</xref>]. In one case series that spanned over 10 years they reported five cases of coronary embolization, including one case secondary to a left atrial myxoma embolus [<xref rid=\"REF1\" ref-type=\"bibr\">1</xref>]. In cases of an embolus secondary to a thrombus, patients can undergo a combination of interventions including systemic anti-coagulation, coronary wiring and conventional aspiration thrombectomy devices [<xref rid=\"REF8\" ref-type=\"bibr\">8</xref>-<xref rid=\"REF10\" ref-type=\"bibr\">10</xref>]. Cases of acute coronary syndrome secondary to septic embolus from intracardiac infected valves have been managed with combined aspiration thrombectomy and coronary stenting [<xref rid=\"REF11\" ref-type=\"bibr\">11</xref>, <xref rid=\"REF12\" ref-type=\"bibr\">12</xref>]. Having intracardiac pathology as the source of the embolization such as a left atrial myxoma or infected left-sided valve is a valid justification for surgical intervention of direct surgical thrombectomy with valve replacement [<xref rid=\"REF2\" ref-type=\"bibr\">2</xref>, <xref rid=\"REF13\" ref-type=\"bibr\">13</xref>]. A review of the literature demonstrates no guideline or standard management for coronary artery thromboembolism. We intend to add to the literature with our case presentation and treatment method in order to increase the cases available in the literature and hopefully improve standard management of similar cases in the future [<xref rid=\"REF2\" ref-type=\"bibr\">2</xref>, <xref rid=\"REF14\" ref-type=\"bibr\">14</xref>].</p></sec><sec sec-type=\"conclusions\"><title>Conclusions</title><p>Acute embolic STEMI is a reported complication of left atrial myxoma. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">JAMA Netw Open</journal-id><journal-id journal-id-type=\"iso-abbrev\">JAMA Netw Open</journal-id><journal-id journal-id-type=\"pmc\">JAMA Netw Open</journal-id><journal-title-group><journal-title>JAMA Network Open</journal-title></journal-title-group><issn pub-type=\"epub\">2574-3805</issn><publisher><publisher-name>American Medical Association</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32804211</article-id><article-id pub-id-type=\"pmc\">PMC7431989</article-id><article-id pub-id-type=\"doi\">10.1001/jamanetworkopen.2020.11620</article-id><article-id pub-id-type=\"publisher-id\">zoi200447</article-id><article-categories><subj-group subj-group-type=\"category\" specific-use=\"electronic\"><subject>Research</subject></subj-group><subj-group subj-group-type=\"heading\"><subject>Original Investigation</subject></subj-group><subj-group subj-group-type=\"online-only\"><subject>Online Only</subject></subj-group><subj-group subj-group-type=\"subject-area\"><subject>Environmental Health</subject></subj-group></article-categories><title-group><article-title>Association Between Bisphenol A Exposure and Risk of All-Cause and Cause-Specific Mortality in US Adults</article-title><alt-title alt-title-type=\"headline\">Bisphenol A Exposure and All-Cause and Cause-Specific Mortality Risk in US Adults</alt-title><alt-title alt-title-type=\"running-head\">Bisphenol A Exposure and All-Cause and Cause-Specific Mortality Risk in US Adults</alt-title></title-group><contrib-group><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>Bao</surname><given-names>Wei</given-names></name><degrees>MD</degrees><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"zoi200447aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Liu</surname><given-names>Buyun</given-names></name><degrees>MD</degrees><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"zoi200447aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Rong</surname><given-names>Shuang</given-names></name><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"zoi200447aff2\"><sup>2</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Dai</surname><given-names>Susie Y.</given-names></name><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"zoi200447aff3\"><sup>3</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Trasande</surname><given-names>Leonardo</given-names></name><degrees>MD</degrees><degrees>MPP</degrees><xref ref-type=\"aff\" rid=\"zoi200447aff4\"><sup>4</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Lehmler</surname><given-names>Hans-Joachim</given-names></name><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"zoi200447aff5\"><sup>5</sup></xref></contrib></contrib-group><aff id=\"zoi200447aff1\"><label>1</label>Department of Epidemiology, College of Public Health, University of Iowa, Iowa City</aff><aff id=\"zoi200447aff2\"><label>2</label>Department of Nutrition and Food Hygiene, School of Public Health, Medical College, Wuhan University of Science and Technology Wuhan, Hubei, China</aff><aff id=\"zoi200447aff3\"><label>3</label>State Hygienic Laboratory, University of Iowa, Iowa City</aff><aff id=\"zoi200447aff4\"><label>4</label>Department of Pediatrics, New York University School of Medicine, New York</aff><aff id=\"zoi200447aff5\"><label>5</label>Department of Occupational and Environmental Health, College of Public Health, University of Iowa, Iowa City</aff><author-notes><title>Article Information</title><p><bold>Accepted for Publication:</bold> May 15, 2020.</p><p content-type=\"published-online\"><bold>Published:</bold> August 17, 2020. doi:<uri content-type=\"doi\">10.1001/jamanetworkopen.2020.11620</uri></p><p content-type=\"open-access-note\"><bold>Open Access:</bold> This is an open access article distributed under the terms of the <ext-link ext-link-type=\"uri\" xlink:href=\"https://jamanetwork.com/journals/jamanetworkopen/pages/instructions-for-authors#SecOpenAccess\">CC-BY License</ext-link>. © 2020 Bao W et al. <italic>JAMA Network Open</italic>.</p><corresp id=\"zoi200447cor1\"><bold>Corresponding Author:</bold> Wei Bao, MD, PhD, Department of Epidemiology, College of Public Health, University of Iowa, 145 N Riverside Dr, Room S431 CPHB, Iowa City, IA 52242 (<email xlink:href=\"wei-bao@uiowa.edu\">wei-bao@uiowa.edu</email>).</corresp><p content-type=\"author-contributions\"><bold>Author Contributions:</bold> Dr Bao had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.</p><p><italic>Concept and design:</italic> Bao, Trasande.</p><p><italic>Acquisition, analysis, or interpretation of data:</italic> Bao, Liu, Rong, Dai, Lehmler.</p><p><italic>Drafting of the manuscript:</italic> Bao, Trasande, Lehmler.</p><p><italic>Critical revision of the manuscript for important intellectual content:</italic> Bao, Liu, Rong, Dai, Lehmler.</p><p><italic>Statistical analysis:</italic> Liu.</p><p><italic>Obtained funding:</italic> Bao.</p><p><italic>Administrative, technical, or material support:</italic> Bao, Rong, Lehmler.</p><p><italic>Supervision:</italic> Bao, Trasande.</p><p content-type=\"COI-statement\"><bold>Conflict of Interest Disclosures:</bold> Dr Lehmler reported receiving grants from the National Institutes of Health during the conduct of the study. No other disclosures were reported.</p><p content-type=\"funding-statement\"><bold>Funding/Support:</bold> This work was supported by grant P30 ES005605 from the <funding-source rid=\"sp1\">National Institute of Environmental Health Sciences </funding-source>through the University of Iowa Environmental Health Sciences Research Center.</p><p><bold>Role of the Funder/Sponsor:</bold> The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.</p><p><bold>Additional Contributions:</bold> We thank the participants and staff of the National Health and Nutrition Examination Survey and the National Center for Environmental Health for their valuable contributions.</p></author-notes><pub-date pub-type=\"epub\" iso-8601-date=\"2020-08-17T10:00\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><!-- PMC Release delay is 0 months and\n\t\t\t\t\t\t0 days and was based on the <pub-date\n\t\t\t\t\t\tpub-type=\"epub\"/>. --><volume>3</volume><issue>8</issue><elocation-id>e2011620</elocation-id><history><date date-type=\"received\"><day>6</day><month>12</month><year>2019</year></date><date date-type=\"accepted\"><day>15</day><month>5</month><year>2020</year></date></history><permissions><copyright-statement>Copyright 2020 Bao W et al. <italic>JAMA Network Open</italic>.</copyright-statement><license license-type=\"open-access\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open access article distributed under the terms of the CC-BY License.</license-p></license></permissions><self-uri content-type=\"pdf-version\" xlink:href=\"jamanetwopen-3-e2011620.pdf\">jamanetwopen-3-e2011620.pdf</self-uri><self-uri content-type=\"silverchair\" xlink:href=\"https://jamanetwork.com/journals/jamanetworkopen/fullarticle/10.1001/jamanetworkopen.2020.11620\"/><abstract abstract-type=\"key-points\"><title>Key Points</title><sec id=\"ab-zoi200447-1\"><title>Question</title><p>Is exposure to bisphenol A, a ubiquitous chemical of concern, associated with long-term risk of mortality?</p></sec><sec id=\"ab-zoi200447-2\"><title>Findings</title><p>In a cohort study of 3883 adults in the United States, participants with higher urinary bisphenol A levels were at higher risk for death during approximately 10 years of observation. The adjusted hazard ratio comparing the highest vs lowest tertile of urinary bisphenol A levels was 49% higher for all-cause mortality and was 46% higher, albeit not statistically significant, for cardiovascular disease mortality.</p></sec><sec id=\"ab-zoi200447-3\"><title>Meaning</title><p>The findings in this study suggest that a higher level of bisphenol A exposure is associated with an increased risk of long-term all-cause mortality.</p></sec></abstract><abstract><sec id=\"ab-zoi200447-4\"><title>Importance</title><p>Bisphenol A (BPA) is a major public health concern because of its high-volume industrial production, ubiquitous exposure to humans, and potential toxic effects on multiple organs and systems in humans. However, prospective studies regarding the association of BPA exposure with long-term health outcomes are sparse.</p></sec><sec id=\"ab-zoi200447-5\"><title>Objective</title><p>To examine the association of BPA exposure with all-cause mortality and cause-specific mortality among adults in the United States.</p></sec><sec id=\"ab-zoi200447-6\"><title>Design, Setting, and Participants</title><p>This nationally representative cohort study included 3883 adults aged 20 years or older who participated in the US National Health and Nutrition Examination Survey 2003-2008 and provided urine samples for BPA level measurements. Participants were linked to mortality data from survey date through December 31, 2015. Data analyses were conducted in July 2019.</p></sec><sec id=\"ab-zoi200447-7\"><title>Exposures</title><p>Urinary BPA levels were quantified using online solid-phase extraction coupled to high-performance liquid chromatography–isotope dilution tandem mass spectrometry.</p></sec><sec id=\"ab-zoi200447-8\"><title>Main Outcomes and Measures</title><p>Mortality from all causes, cardiovascular disease, and cancer.</p></sec><sec id=\"ab-zoi200447-9\"><title>Results</title><p>This cohort study included 3883 adults aged 20 years or older (weighted mean [SE] age, 43.6 [0.3] years; 2032 women [weighted, 51.4%]). During 36 514 person-years of follow-up (median, 9.6 years; maximum, 13.1 years), 344 deaths occurred, including 71 deaths from cardiovascular disease and 75 deaths from cancer. Participants with higher urinary BPA levels were at higher risk for death. After adjustment for age, sex, race/ethnicity, socioeconomic status, dietary and lifestyle factors, body mass index, and urinary creatinine levels, the hazard ratio comparing the highest vs lowest tertile of urinary BPA levels was 1.49 (95% CI, 1.01-2.19) for all-cause mortality, 1.46 (95% CI, 0.67-3.15) for cardiovascular disease mortality, and 0.98 (95% CI, 0.40-2.39) for cancer mortality.</p></sec><sec id=\"ab-zoi200447-10\"><title>Conclusions and Relevance</title><p>In this nationally representative cohort of US adults, higher BPA exposure was significantly associated with an increased risk of all-cause mortality. Further studies are needed to replicate these findings in other populations and determine the underlying mechanisms.</p></sec></abstract><abstract abstract-type=\"teaser\"><p>This nationally representative cohort study assesses whether exposure to the ubiquitous chemical bisphenol A is associated with long-term risk of mortality from cardiovascular disease, cancer, or all causes among US adults.</p></abstract><funding-group><award-group><funding-source id=\"sp1\">National Institute of Environmental Health Sciences </funding-source></award-group></funding-group></article-meta></front><body><sec id=\"H1-1-ZOI200447\"><title>Introduction</title><p>Widespread exposure to bisphenol A (BPA) has emerged as a major public health concern.<sup><xref rid=\"zoi200447r1\" ref-type=\"bibr\">1</xref>,<xref rid=\"zoi200447r2\" ref-type=\"bibr\">2</xref></sup> Bisphenol A is a high-volume industrial chemical produced primarily for the manufacturing of polycarbonate plastics and epoxy resins. Polycarbonate plastics based on BPA are used in many consumer products, such as plastic bottles, sports equipment, compact discs, some medical devices, and dental sealants and composites. Epoxy resins that contain BPA are used to line water pipes, coat the inside of food and beverage cans, and make thermal paper such as that used in sales receipts.<sup><xref rid=\"zoi200447r3\" ref-type=\"bibr\">3</xref>,<xref rid=\"zoi200447r4\" ref-type=\"bibr\">4</xref></sup> As a result, BPA exposure to humans is ubiquitous from a variety of sources ranging from consumer products, food, and water to dust.<sup><xref rid=\"zoi200447r5\" ref-type=\"bibr\">5</xref></sup> National biomonitoring data in the United States show that BPA is detectable in more than 90% of urine samples in the general population.<sup><xref rid=\"zoi200447r6\" ref-type=\"bibr\">6</xref>,<xref rid=\"zoi200447r7\" ref-type=\"bibr\">7</xref></sup> Currently in the United States, 12 states and Washington, DC have restrictions in place against BPA. In Europe, the European Chemical Agency has added BPA to the Candidate List of substances of very high concern.</p><p>Evidence from animal studies has shown that BPA has endocrine-disrupting effects.<sup><xref rid=\"zoi200447r8\" ref-type=\"bibr\">8</xref>,<xref rid=\"zoi200447r9\" ref-type=\"bibr\">9</xref></sup> Exposure to BPA can disrupt endocrine function and metabolism, inducing the development of obesity and metabolic disorders.<sup><xref rid=\"zoi200447r10\" ref-type=\"bibr\">10</xref>,<xref rid=\"zoi200447r11\" ref-type=\"bibr\">11</xref></sup> Exposure to BPA can also induce cardiac arrhythmias, accelerate atherosclerosis, decrease atrial contraction rate and force, and lead to cardiac remodeling in animal models.<sup><xref rid=\"zoi200447r12\" ref-type=\"bibr\">12</xref>,<xref rid=\"zoi200447r13\" ref-type=\"bibr\">13</xref>,<xref rid=\"zoi200447r14\" ref-type=\"bibr\">14</xref>,<xref rid=\"zoi200447r15\" ref-type=\"bibr\">15</xref>,<xref rid=\"zoi200447r16\" ref-type=\"bibr\">16</xref>,<xref rid=\"zoi200447r17\" ref-type=\"bibr\">17</xref></sup> Moreover, previous epidemiologic studies have shown that BPA exposure is associated with an increased risk of obesity,<sup><xref rid=\"zoi200447r18\" ref-type=\"bibr\">18</xref>,<xref rid=\"zoi200447r19\" ref-type=\"bibr\">19</xref>,<xref rid=\"zoi200447r20\" ref-type=\"bibr\">20</xref>,<xref rid=\"zoi200447r21\" ref-type=\"bibr\">21</xref></sup> diabetes,<sup><xref rid=\"zoi200447r22\" ref-type=\"bibr\">22</xref>,<xref rid=\"zoi200447r23\" ref-type=\"bibr\">23</xref>,<xref rid=\"zoi200447r24\" ref-type=\"bibr\">24</xref></sup> hypertension,<sup><xref rid=\"zoi200447r25\" ref-type=\"bibr\">25</xref></sup> and cardiovascular disease (CVD).<sup><xref rid=\"zoi200447r22\" ref-type=\"bibr\">22</xref>,<xref rid=\"zoi200447r26\" ref-type=\"bibr\">26</xref></sup> However, most of the previous epidemiologic studies are cross-sectional, and prospective cohort studies examining the association of BPA exposure with long-term health outcomes are sparse. Although growing evidence suggests that BPA has potentially toxic effects on a variety of organs and systems in humans, the association between BPA exposure and risk of mortality remains unknown. In the present study, we used data from a nationally representative cohort to examine the association of BPA exposure with all-cause and cause-specific mortality in US adults.</p></sec><sec id=\"H1-2-ZOI200447\"><title>Methods</title><sec id=\"H2-1-ZOI200447\"><title>Study Population</title><p>The National Health and Nutrition Examination Survey (NHANES) is a nationally representative health survey program of the civilian noninstitutionalized resident population in the United States. It is administered by the National Center for Health Statistics (NCHS) at the Centers for Disease Control and Prevention (CDC). The uniqueness of the NHANES program is that it not only collects questionnaire data through in-person interviews but also performs health examinations in the Mobile Examination Center and collects specimens for laboratory tests. The NHANES protocol has been approved by the NCHS Ethics Review Board. Written informed consent was obtained in NHANES from all participants. All participants received a cash payment for their time and effort and were compensated for transportation and baby or elder care during their participation.</p><p>For the present analysis, we included adults aged 20 years or older who participated in NHANES during the period from 2003 to 2008 and had available data on BPA measurements. We linked all participants to mortality data through 2015, which enabled approximately 10 years of observation for mortality outcomes. Individuals with CVD or cancer at baseline were excluded. The data analysis was performed in July 2019. The present study followed the Strengthening the Reporting of Observational Studies in Epidemiology (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.equator-network.org/reporting-guidelines/strobe/\">STROBE</ext-link>) reporting guideline for cohort studies.</p></sec><sec id=\"H2-2-ZOI200447\"><title>Assessment of BPA Exposure</title><p>Spot urine samples are collected in the NHANES program from participants aged 6 years or older. The BPA levels in urine samples were measured in one-third of randomly selected NHANES participants using online solid-phase extraction coupled to high-performance liquid chromatography–isotope dilution tandem mass spectrometry at the Division of Laboratory Sciences, National Center for Environmental Health, CDC. The lower limit of detection (LLOD) for BPA was 0.36 μg/L for the 2003 to 2004 samples and 0.40 μg/L for the 2005 to 2008 samples. For BPA levels below the LLOD (<7% of the samples provided by this study population), the NHANES staff assigned a value of the LLOD divided by the square root of 2. A detailed description of the methods of BPA measurement in NHANES was published previously.<sup><xref rid=\"zoi200447r6\" ref-type=\"bibr\">6</xref></sup></p></sec><sec id=\"H2-3-ZOI200447\"><title>Ascertainment of Mortality Outcomes</title><p>We used the NHANES Public-Use Linked Mortality File through December 31, 2015, which was linked by the NCHS to the National Death Index with a probabilistic matching algorithm to determine mortality status.<sup><xref rid=\"zoi200447r27\" ref-type=\"bibr\">27</xref></sup> The National Death Index is an NCHS centralized database of all deaths in the United States. Data about the underlying cause of death were used for case definition according to the <italic>International Statistical Classification of Diseases, Tenth Revision</italic>.<sup><xref rid=\"zoi200447r28\" ref-type=\"bibr\">28</xref></sup> Accordingly, the NCHS classified cardiovascular mortality as death from heart disease (codes I00-I09, I11, I13, and I20-I51) or cerebrovascular disease (codes I60-I69) and cancer mortality as death from malignant neoplasms (codes C00-C97). This approach has been previously validated by the CDC and used in many CDC reports.<sup><xref rid=\"zoi200447r29\" ref-type=\"bibr\">29</xref>,<xref rid=\"zoi200447r30\" ref-type=\"bibr\">30</xref>,<xref rid=\"zoi200447r31\" ref-type=\"bibr\">31</xref></sup></p></sec><sec id=\"H2-4-ZOI200447\"><title>Assessment of Covariates</title><p>Information on age, sex, race/ethnicity, educational level, family income, smoking status, alcohol drinking, physical activity, and dietary intake was collected using questionnaires. According to the 1997 US federal Office of Management and Budget standards, race/ethnicity was categorized into Hispanic (including Mexican and non-Mexican Hispanic), non-Hispanic White, non-Hispanic Black, and other. Family income was categorized as the ratio of family income to the federal poverty level (<1.0, 1.0-1.9, 2.0-3.9, and ≥4.0). A higher income to poverty ratio indicates a better family income status. Self-reported educational status was grouped as lower than high school, high school, and college or higher. In accordance with the NCHS classifications, individuals who smoked less than 100 cigarettes in their lifetime were defined as never smokers; those who had smoked more than 100 cigarettes but did not smoke at the time of survey were considered former smokers; and those who had smoked more than 100 cigarettes in their lifetime and smoked cigarettes at the time of survey were considered current smokers. Alcohol intake was categorized as none (0 g/d), moderate drinking (0.1 to 27.9 g/d for men and 0.1 to 13.9 g/d for women), and heavy drinking (≥28 g/d for men and ≥14 g/d for women). For physical activity, participants were asked an array of questions related to daily activities in the questionnaire, from which metabolic equivalent of task (MET) minutes per week was calculated. There have been some changes in physical activity questionnaires in NHANES since the 2007 to 2008 questionnaire. Therefore, physical activity for each participant was categorized according to standards appropriate for each cycle as follows: (1) below, less than 600 MET min/wk or 150 min/wk of moderate-intensity exercise; (2) meet, 600 to 1200 MET min/wk or 150 to 300 min/wk of moderate-intensity exercise; or (3) exceed, at least 1200 MET min/wk or 300 min/wk of moderate-intensity exercise. Dietary information was collected by 24-hour dietary recall interviews, from which total energy intake was calculated using the US Department of Agriculture Automated Multiple-Pass Method. We used the Healthy Eating Index-2010 (HEI-2010) to indicate the overall quality of diet (HEI-2010 scores range from 0 to 100, with 100 being the best-quality diet).<sup><xref rid=\"zoi200447r32\" ref-type=\"bibr\">32</xref></sup> Body weight and height were measured by trained health technicians following the NHANES Anthropometry Procedures Manual. Body mass index (BMI) was calculated as the weight in kilograms divided by the height in meters squared. Urinary creatinine level was measured using the Jaffé rate reaction, in which creatinine reacts with picrate in an alkaline solution to form a red creatinine-picrate complex.</p></sec><sec id=\"H2-5-ZOI200447\"><title>Statistical Analysis</title><p>The NHANES program uses a complex, multistage probability sampling design to represent a national, civilian, noninstitutionalized population in the United States. Therefore, sample weights, strata, and primary sampling units were applied following the NHANES Analytic Guidelines<sup><xref rid=\"zoi200447r33\" ref-type=\"bibr\">33</xref></sup> to account for the unequal probability of selection, oversampling of certain subpopulations, and nonresponse adjustment.</p><p>Mean values and proportions of baseline characteristics were compared using linear regression for continuous variables and logistic regression for categorical variables. We used Cox proportional hazards regression models to estimate hazard ratios (HRs) and 95% CIs for the associations between BPA exposure and risk of mortality. Follow-up time for each person was calculated as the difference between the NHANES examination date and the last known date alive or censored from the linked mortality file. In the fully adjusted model, we adjusted for age, sex, race/ethnicity, educational level, family income level, smoking status, alcohol intake, physical activity, total energy intake, overall diet quality indicated by HEI-2010 score, and BMI. To account for urine dilution, urinary creatinine levels were adjusted for in all the analysis models in this study, as previously recommended.<sup><xref rid=\"zoi200447r34\" ref-type=\"bibr\">34</xref></sup> Furthermore, we performed stratified analyses and interaction analyses to examine whether the association differed by age, sex, race/ethnicity, diet quality, physical activity, and obesity status. In addition, we conducted a sensitivity analysis using the E-value method<sup><xref rid=\"zoi200447r35\" ref-type=\"bibr\">35</xref>,<xref rid=\"zoi200447r36\" ref-type=\"bibr\">36</xref></sup> to test whether and how our results were robust to potential unmeasured confounding. All statistical analyses were conducted using the survey modules of SAS software, version 9.4 (SAS Institute Inc). A 2-sided <italic>P</italic> < .05 was considered statistically significant.</p></sec></sec><sec id=\"H1-3-ZOI200447\"><title>Results</title><p>We included 3883 adults aged 20 years or older (weighted mean [SE] age, 43.6 [0.3] years; 2032 women [weighted, 51.4%]) in this study. During 36 514 person-years of follow-up (median follow-up, 9.6 years; maximum follow-up, 13.1 years), 344 deaths occurred, including 71 deaths from CVD and 75 deaths from cancer. Participants with higher urinary BPA levels were more likely to be younger, male, and non-Hispanic Black and have lower educational level, lower family income, lower physical activity, higher total energy intake, poorer dietary quality, and higher BMI (<xref rid=\"zoi200447t1\" ref-type=\"table\">Table 1</xref>).</p><table-wrap id=\"zoi200447t1\" orientation=\"portrait\" position=\"float\"><label>Table 1. </label><caption><title>Characteristics of the Study Population, According to Tertiles of Urinary BPA Levels</title></caption><table frame=\"hsides\" rules=\"groups\"><col width=\"39.3%\" span=\"1\"/><col width=\"17.02%\" span=\"1\"/><col width=\"17.02%\" span=\"1\"/><col width=\"15.69%\" span=\"1\"/><col width=\"10.97%\" span=\"1\"/><thead><tr><th rowspan=\"2\" valign=\"top\" align=\"left\" scope=\"col\" colspan=\"1\">Characteristic</th><th colspan=\"3\" valign=\"top\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">Tertile of urinary BPA level, mean (SE)<xref ref-type=\"table-fn\" rid=\"zoi200447t1n1\"><sup>a</sup></xref></th><th rowspan=\"2\" valign=\"top\" align=\"left\" scope=\"col\" colspan=\"1\"><italic>P</italic> value</th></tr><tr><th valign=\"top\" colspan=\"1\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">1</th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">2</th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">3</th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">No. of participants</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1295</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1301</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1287</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Age, y</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">46.8 (0.5)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">43.6 (0.5)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">40.4 (0.4)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><.001</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Sex, %</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td rowspan=\"3\" valign=\"middle\" align=\"left\" colspan=\"1\"><.001</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Male</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">42.5 (1.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">51.1 (2.1)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">52.1 (1.4)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Female</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">57.5 (1.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">48.9 (2.1)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">47.9 (1.4)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Race/ethnicity, %</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td rowspan=\"5\" valign=\"middle\" align=\"left\" colspan=\"1\"><.001</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Hispanic</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">12.0 (1.3)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">14.3 (1.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">13.9 (1.5)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Non-Hispanic White</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">72.8 (2.4)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">68.3 (2.5)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">64.6 (2.4)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Non-Hispanic Black</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">7.0 (1.1)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">11.4 (1.4)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">17.4 (1.9)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Other</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">8.1 (1.4)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">6.1 (0.9)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.1 (0.8)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Educational level, %</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td rowspan=\"4\" valign=\"middle\" align=\"left\" colspan=\"1\">.04</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> <High school</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">15.9 (1.1)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">17.7 (01.5)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">18.8 (1.2)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> High school</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">23.4 (1.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">24.5 (1.7)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">28.0 (1.7)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> College or higher</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">60.7 (2.1)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">57.8 (2.1)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">53.2 (1.8)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Family income to poverty ratio, %</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td rowspan=\"6\" valign=\"middle\" align=\"left\" colspan=\"1\"><.001</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> <1.0</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">9.0 (0.7)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">9.7 (1.0)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">14.1 (1.4)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 1.0-1.9</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">17.0 (1.4)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">19.9 (1.3)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">20.3 (1.3)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 2.0-3.9</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">25.7 (1.3)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">28.2 (1.4)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">30.7 (1.6)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> ≥4.0</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">43.7 (2.1)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">36.6 (2.0)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">29.7 (2.2)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Missing</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.6 (0.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5.7 (0.8)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5.3 (0.8)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Smoker, %</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td rowspan=\"4\" valign=\"middle\" align=\"left\" colspan=\"1\">.02</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Never</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">52.8 (1.5)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">55.1 (1.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">51.2 (1.9)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Ever</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">23.6 (1.4)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">22.3 (1.1)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">20.2 (1.2)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Current</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">23.6 (1.3)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">22.6 (1.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">28.5 (1.6)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Alcohol drinking, %</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td rowspan=\"5\" valign=\"middle\" align=\"left\" colspan=\"1\">.39</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> None</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">66.1 (1.7)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">67.3 (1.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">66.1 (1.9)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Moderate</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">8.0 (0.8)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">10.6 (1.0)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">8.8 (1.1)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Heavy</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">21.6 (1.5)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">18.1 (1.2)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">20.9 (1.5)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Missing</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.3 (0.7)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.1 (0.8)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.2 (0.8)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Physical activity category, %<xref ref-type=\"table-fn\" rid=\"zoi200447t1n2\"><sup>b</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td rowspan=\"4\" valign=\"middle\" align=\"left\" colspan=\"1\">.02</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Below</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">32.1 (1.4)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">38.4 (1.5)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">37.9 (1.6)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Meet</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">16.6 (1.4)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">14.1 (1.2)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">13.2 (0.9)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Exceed</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">51.2 (1.8)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">47.5 (1.8)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">48.9 (1.8)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Total energy intake, kcal/d</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2200.8 (34.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2287.0 (35.8)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2355.2 (0.5)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.001</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">HEI-2010 score</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">50.8 (0.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">48.4 (0.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">45.7 (0.5)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><.001</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">BMI category, %</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> <25</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">41.1 (1.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">29.7 (1.4)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">33.2 (1.8)</td><td rowspan=\"4\" valign=\"middle\" align=\"left\" colspan=\"1\"><.001</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 25-29.9</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">31.0 (1.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">33.1 (1.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">29.5 (1.7)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> ≥30</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">26.6 (1.2)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">36.2 (1.8)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">36.4 (1.9)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Missing</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.3 (0.4)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.1 (0.3)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.9 (0.4)</td></tr></tbody></table><table-wrap-foot><p>Abbreviations: BMI, body mass index (calculated as weight in kilograms divided by height in meters squared); BPA, bisphenol A; HEI-2010, Healthy Eating Index 2010; MET, metabolic equivalent of task.</p><fn id=\"zoi200447t1n1\"><label><sup>a</sup></label><p>Values are weighted mean (SE) for continuous variables and weighted percentages (SE) for categorical variables, except the number of participants.</p></fn><fn id=\"zoi200447t1n2\"><label><sup>b</sup></label><p>Physical activity for each participant was categorized as follows: (1) below, less than 600 MET min/wk per week or 150 min/wk of moderate-intensity exercise; (2) meet, 600 to 1200 MET min/wk or 150 to 300 min/wk of moderate-intensity exercise; or (3) exceed, at least 1200 MET min/wk or 300 min/wk of moderate-intensity exercise.</p></fn></table-wrap-foot></table-wrap><p>Participants with higher urinary BPA levels were at higher risk of death during the follow-up (<xref rid=\"zoi200447t2\" ref-type=\"table\">Table 2</xref>). After adjustment for age, sex, race/ethnicity, and urinary creatinine levels, participants with the highest tertile of urinary BPA levels had a 51% higher risk of all-cause mortality (HR, 1.51; 95% CI, 1.07-2.13) compared with those with the lowest tertile of urinary BPA levels. The association was not appreciably changed after further adjustment for other covariates. In the fully adjusted model including demographic characteristics, socioeconomic status, dietary and lifestyle factors, BMI, and urinary creatinine levels, the HR for all-cause mortality among participants with the highest tertile of urinary BPA levels compared with those with the lowest tertile was 1.49 (95% CI, 1.01-2.19). Similar results were observed for CVD mortality (HR, 1.46; 95% CI, 0.67-3.15), although this association was not statistically significant. Exposure to BPA was not associated with cancer mortality (HR, 0.98; 95% CI, 0.40-2.39). Stratified analyses showed that the observed associations of BPA exposure with mortality did not significantly differ by age, sex, race/ethnicity, diet quality, physical activity, or obesity status (<xref rid=\"zoi200447t3\" ref-type=\"table\">Table 3</xref>; eTable in the <xref ref-type=\"supplementary-material\" rid=\"note-ZOI200447-1-s\">Supplement</xref>). In the sensitivity analysis using the E-value to assess the potential of unmeasured confounding, the E-value was 2.34 for all-cause mortality for the point estimate and 1.11 for the lower confidence bound. The E-values for CVD mortality were 2.28 for the point estimate and 1.0 for the lower confidence bound; for cancer mortality, the E-value was 1.16 for the point estimate and 1.0 for the lower confidence bound.</p><table-wrap id=\"zoi200447t2\" orientation=\"portrait\" position=\"float\"><label>Table 2. </label><caption><title>Association of Urinary BPA Levels With All-Cause Mortality and With Cause-Specific Mortality</title></caption><table frame=\"hsides\" rules=\"groups\"><col width=\"33.52%\" span=\"1\"/><col width=\"20.7%\" span=\"1\"/><col width=\"22.89%\" span=\"1\"/><col width=\"22.89%\" span=\"1\"/><thead><tr><th rowspan=\"2\" valign=\"top\" align=\"left\" scope=\"col\" colspan=\"1\">Variable</th><th colspan=\"3\" valign=\"top\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">Tertile of urinary BPA level, hazard ratio (95% CI)</th></tr><tr><th valign=\"top\" colspan=\"1\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">1</th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">2</th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">3</th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Median BPA level, ng/mL</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.7</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5.7</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">All-cause mortality</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Deaths per person-years</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">133 per 11897</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">100 per 12268</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">111 per 12349</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Model 1<xref ref-type=\"table-fn\" rid=\"zoi200447t2n1\"><sup>a</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.06 (0.76-1.47)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.51 (1.07-2.13)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Model 2<xref ref-type=\"table-fn\" rid=\"zoi200447t2n2\"><sup>b</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.10 (0.78-1.55)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.49 (1.01-2.19)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">CVD mortality</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Deaths per person-years</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">32 per 11897</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">18 per 12268</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">21 per 12349</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Model 1<xref ref-type=\"table-fn\" rid=\"zoi200447t2n1\"><sup>a</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.13 (0.54-2.37)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.68 (0.82-3.44)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Model 2<xref ref-type=\"table-fn\" rid=\"zoi200447t2n2\"><sup>b</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.10 (0.53-2.31)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.46 (0.67-3.15)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Cancer mortality</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Deaths per person-years</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">31 per 11897</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">22 per 12268</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">22 per 12249</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Model 1<xref ref-type=\"table-fn\" rid=\"zoi200447t2n1\"><sup>a</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.13 (0.45-2.81)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.05 (0.44-2.47)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Model 2<xref ref-type=\"table-fn\" rid=\"zoi200447t2n2\"><sup>b</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.12 (0.48-2.63)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.98 (0.40-2.39)</td></tr></tbody></table><table-wrap-foot><p>Abbreviations: BPA, bisphenol A; CVD, cardiovascular disease.</p><fn id=\"zoi200447t2n1\"><label><sup>a</sup></label><p>Adjusted for age, sex, race/ethnicity, and urinary creatinine levels.</p></fn><fn id=\"zoi200447t2n2\"><label><sup>b</sup></label><p>Model 1 plus adjusted for educational level, family income status, smoking, alcohol drinking, physical activity, total energy intake, Healthy Eating Index 2010 score, and body mass index.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"zoi200447t3\" orientation=\"portrait\" position=\"float\"><label>Table 3. </label><caption><title>Stratified Analyses for the Association of Urinary BPA Levels With All-Cause Mortality<xref ref-type=\"table-fn\" rid=\"zoi200447t3n1\"><sup>a</sup></xref></title></caption><table frame=\"hsides\" rules=\"groups\"><col width=\"19.79%\" span=\"1\"/><col width=\"17.25%\" span=\"1\"/><col width=\"18.87%\" span=\"1\"/><col width=\"18.87%\" span=\"1\"/><col width=\"25.22%\" span=\"1\"/><thead><tr><th rowspan=\"2\" valign=\"top\" align=\"left\" scope=\"col\" colspan=\"1\">Variable</th><th colspan=\"3\" valign=\"top\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">Tertile of urinary BPA level, hazard ratio (95% CI)</th><th rowspan=\"2\" valign=\"top\" align=\"left\" scope=\"col\" colspan=\"1\"><italic>P</italic> value for interaction</th></tr><tr><th valign=\"top\" colspan=\"1\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">1</th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">2</th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">3</th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Age, y</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> <65</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.78 (0.46-1.31)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.12 (0.65-1.93)</td><td rowspan=\"2\" valign=\"middle\" align=\"left\" colspan=\"1\">.24</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> ≥65</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.04 (0.63-1.72)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.64 (0.94-2.88)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Sex</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Male</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.91 (0.57-1.45)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.35 (0.82-2.22)</td><td rowspan=\"2\" valign=\"middle\" align=\"left\" colspan=\"1\">.28</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Female</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.30 (0.81-2.09)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.62 (0.94-2.81)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Race/ethnicity</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> White</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.23 (0.77-1.98)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.90 (1.13-3.20)</td><td rowspan=\"2\" valign=\"middle\" align=\"left\" colspan=\"1\">.15</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Non-White</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.72 (0.43-1.22)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.87 (0.59-1.29)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Diet quality<xref ref-type=\"table-fn\" rid=\"zoi200447t3n2\"><sup>b</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Lower</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.33 (0.90-1.98)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.83 (1.11-3.02)</td><td rowspan=\"2\" valign=\"middle\" align=\"left\" colspan=\"1\">.24</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Higher</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.80 (0.45-1.43)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.15 (0.66-2.00)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Physical activity<xref ref-type=\"table-fn\" rid=\"zoi200447t3n3\"><sup>c</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Lower</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.12 (0.70-1.81)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.11 (0.71-1.75)</td><td rowspan=\"2\" valign=\"middle\" align=\"left\" colspan=\"1\">.90</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Higher</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.96 (0.54-1.70)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.78 (0.90-3.51)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Obesity</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> BMI <30</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.27 (0.84-1.91)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.90 (1.27-2.84)</td><td rowspan=\"2\" valign=\"middle\" align=\"left\" colspan=\"1\">.25</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> BMI ≥30</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.72 (0.35-1.46)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.98 (0.51-1.87)</td></tr></tbody></table><table-wrap-foot><p>Abbreviations: BMI, body mass index (calculated as weight in kilograms divided by height in meters squared); BPA, bisphenol A.</p><fn id=\"zoi200447t3n1\"><label><sup>a</sup></label><p>Adjusted for age, sex, race/ethnicity, urinary creatinine levels, educational level, family income status, smoking, alcohol drinking, physical activity, total energy intake, Healthy Eating Index 2010 score, and body mass index.</p></fn><fn id=\"zoi200447t3n2\"><label><sup>b</sup></label><p>Lower or higher diet quality was defined as a Healthy Eating Index 2010 score lower than the median score or the median score or above, respectively.</p></fn><fn id=\"zoi200447t3n3\"><label><sup>c</sup></label><p>Lower or higher physical activity level was defined as below or meeting the physical activity guidelines, respectively.</p></fn></table-wrap-foot></table-wrap></sec><sec id=\"H1-4-ZOI200447\"><title>Discussion</title><p>In a prospective cohort of a US nationally representative sample, we found that BPA exposure was significantly and positively associated with all-cause mortality in adults. The association remained significant after adjustment for demographic characteristics, socioeconomic status, dietary and lifestyle factors, BMI, and urinary creatinine levels. There was a statistically nonsignificant association between BPA exposure and CVD mortality and no association between BPA exposure and cancer mortality.</p><p>To our knowledge, this is the first study examining the association of BPA exposure with risk of mortality. Our findings are in line with previous epidemiologic studies showing a significant association of BPA exposure with cardiometabolic disorders, including diabetes, hypertension, and CVD.<sup><xref rid=\"zoi200447r37\" ref-type=\"bibr\">37</xref>,<xref rid=\"zoi200447r38\" ref-type=\"bibr\">38</xref>,<xref rid=\"zoi200447r39\" ref-type=\"bibr\">39</xref></sup> In addition, BPA exposure is also associated with atherosclerosis,<sup><xref rid=\"zoi200447r40\" ref-type=\"bibr\">40</xref>,<xref rid=\"zoi200447r41\" ref-type=\"bibr\">41</xref></sup> coronary artery stenosis,<sup><xref rid=\"zoi200447r42\" ref-type=\"bibr\">42</xref></sup> and reduction in heart rate variability in humans.<sup><xref rid=\"zoi200447r25\" ref-type=\"bibr\">25</xref></sup> The potential mechanisms underlying increased risk of mortality associated with BPA remain to be elucidated, which may include alteration in cardiac calcium handling, ion channel inhibition or activation, oxidative stress and inflammation, epigenetic modifications, and variations in transcriptome or proteome expression.<sup><xref rid=\"zoi200447r38\" ref-type=\"bibr\">38</xref>,<xref rid=\"zoi200447r39\" ref-type=\"bibr\">39</xref></sup></p><p>Our findings may have major public health implications. Exposure to BPA is ubiquitous among humans, affecting more than 90% of the general US population.<sup><xref rid=\"zoi200447r6\" ref-type=\"bibr\">6</xref>,<xref rid=\"zoi200447r7\" ref-type=\"bibr\">7</xref>,<xref rid=\"zoi200447r43\" ref-type=\"bibr\">43</xref></sup> Although BPA exposure has decreased over time in the United States,<sup><xref rid=\"zoi200447r44\" ref-type=\"bibr\">44</xref></sup> it was still detected in 95.7% of urine samples from participants in NHANES during the period from 2013 to 2014.<sup><xref rid=\"zoi200447r7\" ref-type=\"bibr\">7</xref></sup> Given the wide range of potentially toxic effects of BPA in humans, it is imperative and important to minimize human exposure to BPA. Substitution of BPA with other bisphenol analogues, such as bisphenol F and bisphenol S, is becoming popular<sup><xref rid=\"zoi200447r7\" ref-type=\"bibr\">7</xref>,<xref rid=\"zoi200447r45\" ref-type=\"bibr\">45</xref></sup>; however, the health effects of those emerging BPA substitutes remain largely unknown.<sup><xref rid=\"zoi200447r20\" ref-type=\"bibr\">20</xref>,<xref rid=\"zoi200447r46\" ref-type=\"bibr\">46</xref></sup> Evidence from animal and epidemiologic studies, although still limited, suggest that some BPA substitutes may have toxic effects similar to BPA.<sup><xref rid=\"zoi200447r45\" ref-type=\"bibr\">45</xref>,<xref rid=\"zoi200447r47\" ref-type=\"bibr\">47</xref>,<xref rid=\"zoi200447r48\" ref-type=\"bibr\">48</xref></sup></p><sec id=\"H2-6-ZOI200447\"><title>Strengths and Limitations</title><p>This study has several strengths. We used nationally representative data from NHANES, which enables us to generalize our findings to a broader population. In addition, the abundant data from NHANES, including comprehensive information on demographic and socioeconomic characteristics, anthropometric measures, and diet and lifestyle factors, provide the opportunity to adjust for a variety of potential confounding factors. There are some limitations in this study. First, spot urine samples were used to measure BPA concentrations in NHANES because it is challenging and less feasible to collect 24-hour urine samples in a large sample size, nationally representative cohort. Although within-person and between-person variability exists, previous evidence shows that urinary concentrations of BPA derived from a single spot-sampling approach may adequately reflect the average exposure of a population to BPA when urine samples are collected from a sufficiently large population with random meal ingestion and bladder emptying times.<sup><xref rid=\"zoi200447r49\" ref-type=\"bibr\">49</xref></sup> Second, the NHANES Linked Mortality File identified causes of death through linkage to the National Death Index, which is based on death certificates. This approach has been previously validated by the CDC and used in many CDC reports<sup><xref rid=\"zoi200447r29\" ref-type=\"bibr\">29</xref>,<xref rid=\"zoi200447r30\" ref-type=\"bibr\">30</xref>,<xref rid=\"zoi200447r31\" ref-type=\"bibr\">31</xref></sup> and other relevant literature. However, we could not rule out the possibility of errors in the classification of the cause of death. Third, although many potential confounders were adjusted for, there might still be residual confounding by unmeasured factors. However, the sensitivity analysis using E-values showed that the association between BPA and all-cause mortality could only be negated by an unmeasured cofounder that had associations with both BPA exposure and all-cause mortality with an HR of at least 2.34. This HR was higher than the HRs of the known confounders that were measured in this study (range, 1.02-1.97). Therefore, it is unlikely that an unmeasured confounder would be more substantially associated with all-cause mortality than the known risk factors evaluated in the present study by having an HR exceeding 2.34.</p></sec></sec><sec id=\"H1-5-ZOI200447\"><title>Conclusions</title><p>Our findings from a nationally representative cohort suggested that higher BPA exposure was significantly associated with an increased risk of all-cause mortality among US adults. The observed but statistically nonsignificant association between BPA exposure and CVD mortality warrants further investigation. 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] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">JAMA Netw Open</journal-id><journal-id journal-id-type=\"iso-abbrev\">JAMA Netw Open</journal-id><journal-id journal-id-type=\"pmc\">JAMA Netw Open</journal-id><journal-title-group><journal-title>JAMA Network Open</journal-title></journal-title-group><issn pub-type=\"epub\">2574-3805</issn><publisher><publisher-name>American Medical Association</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32804213</article-id><article-id pub-id-type=\"pmc\">PMC7431991</article-id><article-id pub-id-type=\"doi\">10.1001/jamanetworkopen.2020.13070</article-id><article-id pub-id-type=\"publisher-id\">zoi200494</article-id><article-categories><subj-group subj-group-type=\"category\" specific-use=\"electronic\"><subject>Research</subject></subj-group><subj-group subj-group-type=\"heading\"><subject>Original Investigation</subject></subj-group><subj-group subj-group-type=\"online-only\"><subject>Online Only</subject></subj-group><subj-group subj-group-type=\"subject-area\"><subject>Pediatrics</subject></subj-group></article-categories><title-group><article-title>Recommendations on Complementary Food Introduction Among Pediatric Practitioners</article-title><alt-title alt-title-type=\"headline\">Complementary Food Introduction Recommendations Among Pediatric Practitioners</alt-title><alt-title alt-title-type=\"running-head\">Complementary Food Introduction Recommendations Among Pediatric Practitioners</alt-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Samady</surname><given-names>Waheeda</given-names></name><degrees>MD</degrees><degrees>MSCI1</degrees><xref ref-type=\"aff\" rid=\"zoi200494aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"zoi200494aff3\"><sup>3</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Campbell</surname><given-names>Emily</given-names></name><degrees>MD</degrees><xref ref-type=\"aff\" rid=\"zoi200494aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"zoi200494aff3\"><sup>3</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Aktas</surname><given-names>Ozge Nur</given-names></name><degrees>MD</degrees><xref ref-type=\"aff\" rid=\"zoi200494aff4\"><sup>4</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Jiang</surname><given-names>Jialing</given-names></name><degrees>BA</degrees><xref ref-type=\"aff\" rid=\"zoi200494aff2\"><sup>2</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Bozen</surname><given-names>Alexandria</given-names></name><degrees>BS</degrees><xref ref-type=\"aff\" rid=\"zoi200494aff2\"><sup>2</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Fierstein</surname><given-names>Jamie L.</given-names></name><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"zoi200494aff2\"><sup>2</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Joyce</surname><given-names>Alanna Higgins</given-names></name><degrees>MD</degrees><degrees>MSE1</degrees><xref ref-type=\"aff\" rid=\"zoi200494aff3\"><sup>3</sup></xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>Gupta</surname><given-names>Ruchi S.</given-names></name><degrees>MD</degrees><degrees>MPH</degrees><xref ref-type=\"aff\" rid=\"zoi200494aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"zoi200494aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"zoi200494aff3\"><sup>3</sup></xref><xref ref-type=\"aff\" rid=\"zoi200494aff5\"><sup>5</sup></xref></contrib></contrib-group><aff id=\"zoi200494aff1\"><label>1</label>Ann and Robert H. Lurie Children’s Hospital, Chicago, Illinois</aff><aff id=\"zoi200494aff2\"><label>2</label>Center for Food Allergy & Asthma Research, Feinberg School of Medicine, Northwestern University, Chicago, Illinois</aff><aff id=\"zoi200494aff3\"><label>3</label>Department of Pediatrics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois</aff><aff id=\"zoi200494aff4\"><label>4</label>Department of Pediatrics, University of Illinois College of Medicine at Chicago, Chicago, Illinois</aff><aff id=\"zoi200494aff5\"><label>5</label>Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois</aff><author-notes><title>Article Information</title><p><bold>Accepted for Publication:</bold> May 29, 2020.</p><p content-type=\"published-online\"><bold>Published:</bold> August 17, 2020. doi:<uri content-type=\"doi\">10.1001/jamanetworkopen.2020.13070</uri></p><p content-type=\"open-access-note\"><bold>Open Access:</bold> This is an open access article distributed under the terms of the <ext-link ext-link-type=\"uri\" xlink:href=\"https://jamanetwork.com/journals/jamanetworkopen/pages/instructions-for-authors#SecOpenAccess\">CC-BY License</ext-link>. © 2020 Samady W et al. <italic>JAMA Network Open</italic>.</p><corresp id=\"zoi200494cor1\"><bold>Corresponding Author:</bold> Ruchi S. Gupta, MD, MPH, Center for Food Allergy & Asthma Research, Feinberg School of Medicine, Northwestern University, 750 N Lake Shore Dr, Ste 680, Chicago, IL 60611 (<email xlink:href=\"r-gupta@northwestern.edu\">r-gupta@northwestern.edu</email>).</corresp><p content-type=\"author-contributions\"><bold>Author Contributions:</bold> Drs Samady and Gupta had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.</p><p><italic>Concept and design:</italic> Campbell, Jiang, Bozen, Higgins Joyce, Gupta.</p><p><italic>Acquisition, analysis, or interpretation of data:</italic> Samady, Campbell, Aktas, Jiang, Bozen, Fierstein, Gupta.</p><p><italic>Drafting of the manuscript:</italic> Samady, Campbell, Aktas, Jiang, Bozen, Fierstein, Gupta.</p><p><italic>Critical revision of the manuscript for important intellectual content:</italic> All authors.</p><p><italic>Statistical analysis:</italic> Aktas, Fierstein.</p><p><italic>Obtained funding:</italic> Campbell, Gupta.</p><p><italic>Administrative, technical, or material support:</italic> Samady, Campbell, Jiang, Bozen, Fierstein, Higgins Joyce, Gupta.</p><p><italic>Supervision:</italic> Bozen, Gupta.</p><p content-type=\"COI-statement\"><bold>Conflict of Interest Disclosures:</bold> Dr Gupta reported receiving grants from the National Institutes of Health, Rho Inc, Stanford University’s Sean N. Parker Center for Allergy and Asthma Research, UnitedHealth Group, Thermo Fisher Scientific, Genentech, and the National Confectioners Association and personal fees from Before Brands, Kaléo Inc, Genentech, ICER, DOTS Technology, Food Allergy Research and Education, Aimmune Therapeutics, and DBV Technologies during the conduct of the study. No other disclosures were reported.</p><p content-type=\"funding-statement\"><bold>Funding/Support:</bold> This study was supported by a departmental grant from the Ann and Robert H. Lurie Children’s Hospital of Chicago (Dr Gupta, principal investigator).</p><p><bold>Role of the Funder/Sponsor:</bold> The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.</p></author-notes><pub-date pub-type=\"epub\" iso-8601-date=\"2020-08-17T10:00\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><!-- PMC Release delay is 0 months and\n\t\t\t\t\t\t0 days and was based on the <pub-date\n\t\t\t\t\t\tpub-type=\"epub\"/>. --><volume>3</volume><issue>8</issue><elocation-id>e2013070</elocation-id><history><date date-type=\"received\"><day>29</day><month>1</month><year>2020</year></date><date date-type=\"accepted\"><day>29</day><month>5</month><year>2020</year></date></history><permissions><copyright-statement>Copyright 2020 Samady W et al. <italic>JAMA Network Open</italic>.</copyright-statement><license license-type=\"open-access\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open access article distributed under the terms of the CC-BY License.</license-p></license></permissions><self-uri content-type=\"pdf-version\" xlink:href=\"jamanetwopen-3-e2013070.pdf\">jamanetwopen-3-e2013070.pdf</self-uri><self-uri content-type=\"silverchair\" xlink:href=\"https://jamanetwork.com/journals/jamanetworkopen/fullarticle/10.1001/jamanetworkopen.2020.13070\"/><abstract abstract-type=\"teaser\"><p>This survey study characterizes pediatric practitioner recommendations regarding complementary food introduction and waiting periods between the introduction of new foods.</p></abstract><abstract abstract-type=\"key-points\"><title>Key Points</title><sec id=\"ab-zoi200494-1\"><title>Question</title><p>Is it appropriate for pediatric practitioners to recommend waiting several days between the introduction of new foods considering the current emphasis on incorporating a wide variety of foods during infancy to prevent food allergy development?</p></sec><sec id=\"ab-zoi200494-2\"><title>Findings</title><p>In this survey study of 563 pediatric practitioners, 217 (39%) recommended waiting 3 days or longer before introducing new foods; however, for infants at risk for developing food allergy, 259 (66%) recommended waiting. Although 264 practitioners (47%) recommended that cereal be introduced first, 226 (40%) did not recommend any specific order during food introduction.</p></sec><sec id=\"ab-zoi200494-3\"><title>Meaning</title><p>This study found that there was variability among pediatric practitioners’ recommendations on infant diet, suggesting that a reevaluation of published guidelines may be warranted.</p></sec></abstract><abstract><sec id=\"ab-zoi200494-4\"><title>Importance</title><p>The American Academy of Pediatrics and the Centers for Disease Control and Prevention recommend waiting 3 to 5 days between the introduction of new complementary foods (solid foods introduced to infants <12 months of age), yet with advances in the understanding of infant food diversity, the guidance that pediatric practitioners are providing to parents is unclear.</p></sec><sec id=\"ab-zoi200494-5\"><title>Objective</title><p>To characterize pediatric practitioner recommendations regarding complementary food introduction and waiting periods between introducing new foods.</p></sec><sec id=\"ab-zoi200494-6\"><title>Design, Setting, and Participants</title><p>In this survey study, a 23-item electronic survey on complementary food introduction among infants was administered to pediatric health care professionals from February 1 to April 30, 2019. Responses were described among the total sample and compared among subgroups. Survey invitations were emailed to 2215 members of the Illinois Chapter of the American Academy of Pediatrics and the national American Academy of Pediatrics’ Council on Early Childhood. Participants were required to be primary medical practitioners, such as physicians, resident physicians, or nurse practitioners, providing pediatric care to infants 12 months or younger.</p></sec><sec id=\"ab-zoi200494-7\"><title>Main Outcomes and Measures</title><p>The main outcome measures were recommendations on age of complementary food introduction and waiting periods between the introduction of new foods. Categorical survey items were reported as numbers (percentages) and 95% CIs. Means (SDs) were used to describe continuous survey items.</p></sec><sec id=\"ab-zoi200494-8\"><title>Results</title><p>The survey was sent to 2215 practitioners and completed by 604 (response rate, 27.3%). Of these respondents, 41 were excluded because they did not provide care for infants or pediatric patients. The final analyses included responses from 563 surveys. Of these, 454 pediatricians (80.6%), 85 resident physicians (15.1%), and 20 nurse practitioners (3.6%) completed the survey. Only 217 practitioners (38.6%; 95% CI, 34.1%-44.6%) recommended waiting 3 days or longer between food introduction; 259 practitioners (66.3%; 95% CI, 61.4%-70.8%) recommended waiting that amount of time for infants at risk for food allergy development (<italic>P</italic> = .02). A total of 264 practitioners (46.9%; 95% CI, 42.8%-51.0%) recommended infant cereal as the first food, and 226 practitioners (40.1%; 95% CI, 36.1%-44.2%) did not recommend a specific order. A total of 268 practitioners (47.6%; 95% CI, 43.5%-51.7%) recommended food introduction at 6 months for exclusively breastfed (EBF) infants, and 193 (34.3%; 95% CI, 30.5%-38.3%) recommended food introduction at 6 months for non-EBF infants (<italic>P</italic> < .001); 179 practitioners (31.8%; 95% CI, 28.1%-35.8%) recommended food introduction at 4 months for EBF infants, and 239 practitioners (42.5%; 95% CI, 38.4%-46.6%) recommended food introduction at 4 months for non-EBF infants (<italic>P</italic> < .001). A need for additional training on complementary food introduction was reported by 310 practitioners (55.1%; 95% CI, 50.9%-59.1%).</p></sec><sec id=\"ab-zoi200494-9\"><title>Conclusions and Relevance</title><p>In this survey study, most pediatric practitioners did not counsel families to wait 3 days or longer between introducing foods unless infants were at risk for food allergy development. The findings suggest that the current recommendation limits infant food diversity and may delay early peanut introduction. Because the approach to food allergy prevention has changed, a reevaluation of published feeding guidelines may be necessary.</p></sec></abstract></article-meta></front><body><sec id=\"H1-1-ZOI200494\"><title>Introduction</title><p>Introduction of complementary foods (ie, solid foods introduced to infants between the ages of 4 and 11 months to complement breastfeeding and/or formula feeds) is a fundamental discussion every pediatrician has with families of young infants. The American Academy of Pediatrics (AAP) recommends introduction of solid foods between the ages of 4 and 6 months.<sup><xref rid=\"zoi200494r1\" ref-type=\"bibr\">1</xref></sup> The AAP and the Centers for Disease Control and Prevention (CDC) further recommend introducing 1 single-ingredient food at a time and observing the infant for 3 to 5 days between the introduction of each new food to monitor for allergic reactions.<sup><xref rid=\"zoi200494r1\" ref-type=\"bibr\">1</xref>,<xref rid=\"zoi200494r2\" ref-type=\"bibr\">2</xref></sup> Although it is important to monitor for adverse food reactions in infants, it is unclear why specifically a 3- to 5-day period is recommended.</p><p>The literature<sup><xref rid=\"zoi200494r3\" ref-type=\"bibr\">3</xref>,<xref rid=\"zoi200494r4\" ref-type=\"bibr\">4</xref>,<xref rid=\"zoi200494r5\" ref-type=\"bibr\">5</xref>,<xref rid=\"zoi200494r6\" ref-type=\"bibr\">6</xref>,<xref rid=\"zoi200494r7\" ref-type=\"bibr\">7</xref>,<xref rid=\"zoi200494r8\" ref-type=\"bibr\">8</xref>,<xref rid=\"zoi200494r9\" ref-type=\"bibr\">9</xref>,<xref rid=\"zoi200494r10\" ref-type=\"bibr\">10</xref></sup> suggests that a diverse diet in the first year of life and early introduction of certain allergenic foods is beneficial to an infant and is associated with a reduced risk of atopy. The landmark Learning Early About Peanut Allergy study<sup><xref rid=\"zoi200494r11\" ref-type=\"bibr\">11</xref></sup> has reinforced the importance of complementary food introduction in early infancy, finding an 86% relative risk reduction in peanut allergy development if peanut-containing foods were introduced between 4 and 11 months of age. Consequently, the National Institute of Allergy and Infectious Diseases (NIAID) published guidelines recommending peanut allergy risk assessment and early introduction of infant-safe peanut products between 4 and 6 months of age for high-risk infants.<sup><xref rid=\"zoi200494r12\" ref-type=\"bibr\">12</xref></sup> However, the current published feeding recommendations may hinder the rapid introduction of a diverse diet and negatively affect the timely coordination of early peanut introduction in infants.</p><p>Prior studies<sup><xref rid=\"zoi200494r13\" ref-type=\"bibr\">13</xref>,<xref rid=\"zoi200494r14\" ref-type=\"bibr\">14</xref>,<xref rid=\"zoi200494r15\" ref-type=\"bibr\">15</xref>,<xref rid=\"zoi200494r16\" ref-type=\"bibr\">16</xref>,<xref rid=\"zoi200494r17\" ref-type=\"bibr\">17</xref></sup> regarding infant feeding have focused on parental preference and practices on the timing of complementary food introduction. Several studies<sup><xref rid=\"zoi200494r14\" ref-type=\"bibr\">14</xref>,<xref rid=\"zoi200494r17\" ref-type=\"bibr\">17</xref></sup> report that parents value both family and peer opinions, as well as physician recommendations regarding solid food introduction. However, the manner in which pediatricians recommend introducing complementary food in daily practice has, to our knowledge, not yet been evaluated. The objective of this study was to describe current pediatric practitioners’ recommendations regarding complementary food introduction, focusing on food type, age of introduction, and waiting periods between the introduction of new foods.</p></sec><sec id=\"H1-2-ZOI200494\"><title>Methods</title><p>This survey study was performed from February 1 to April 30, 2019. An electronic survey on solid food introduction among infants was administered to members of the Illinois Chapter of the American Academy of Pediatrics (ICAAP) and the national AAP’s Council on Early Childhood (COEC). The study was reviewed and approved by the institutional review board of the Ann and Robert H. Lurie Children’s Hospital of Chicago and was determined to be exempt because it was not human subjects research. Consent was implied by survey completion, and all data were deidentified. This study followed the American Association for Public Opinion Research (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.aapor.org/Publications-Media/AAPOR-Journals/Standard-Definitions.aspx\">AAPOR</ext-link>) reporting guideline.</p><sec id=\"H2-1-ZOI200494\"><title>Survey Development</title><p>The survey was designed by a team of pediatricians and expert pediatric health service researchers. The instrument was pretested via interviews with pediatricians practicing in the Chicago area. A separate internal research team then iteratively reviewed and revised the instrument in accordance with the pediatricians’ feedback regarding the survey’s readability, layout, length, and appropriateness. The final survey instrument contained 25 items (eAppendix in the <xref ref-type=\"supplementary-material\" rid=\"note-ZOI200494-1-s\">Supplement</xref>) that evaluated recommendations regarding food introduction type, timing, and waiting periods; sources of guidance that pediatricians used to develop their recommendations; their experience with food allergies; and how their recommendations would change if food allergy risk factors were present.</p></sec><sec id=\"H2-2-ZOI200494\"><title>Survey Distribution</title><p>Surveys were electronically administered via email using REDCap (Research Electronic Data Capture) to members of the ICAAP (n = 1750) and COEC (n = 465). Participants were required to be nonretired primary medical practitioners, such as pediatric or family medicine physicians, resident physicians, or nurse practitioners, providing pediatric care to infants 12 months or younger. Information sheets that described the study’s objectives and participation requirements were shared online before participation in the survey. Participants were offered a $5 gift card for completing the survey. Emails were sent out twice, and all survey responses were anonymous.</p></sec><sec id=\"H2-3-ZOI200494\"><title>Measures</title><p>The primary variables of interest were pediatric practitioners’ current recommendations on complementary food type, age of introduction, and waiting periods (eAppendix in the <xref ref-type=\"supplementary-material\" rid=\"note-ZOI200494-1-s\">Supplement</xref>). Specifically, age of introduction was based on survey items that asked at approximately what age solid food was recommended for exclusively breastfed (EBF) infants and non-EBF infants. Response categories included ages of 4, 5, 6, and 7 months and other; these categories were chosen to coincide with the AAP recommendation to introduce foods when infants are developmentally ready (approximately 4-6 months of age). For EBF infants, the AAP recommends waiting until 6 months of age to introduce complementary foods.</p><p>In addition, respondents’ demographic information was collected; other variables of interest included modification of current recommendations according to infant food allergy risk, personal beliefs about food introduction guidelines on complementary food introduction, and assessment of the need for nutrition training among the medical community.</p></sec><sec id=\"H2-4-ZOI200494\"><title>Statistical Analysis</title><p>The primary objective of this study was to describe current recommendations regarding complementary food introduction among a sample of pediatric practitioners, with a focus on food type, timing, and waiting periods. Categorical survey items were reported as numbers (percentages) and 95% CIs. Means (SDs) were used to describe continuous survey items. With respect to survey items with missing data, numbers and/or means were calculated using the denominator of those who answered the particular survey item. One-sample tests were used to determine differences in proportions between survey item responses. Data from this survey were not weighted.</p><p>In addition to the primary analyses, we explored current recommendations preferred among sample subgroups. We used binary variables (yes or no) to assess whether practitioners had graduated medical school within 10 years, had an academic affiliation, and did or did not wait more than 2 days between introducing new foods. We used χ<sup>2</sup> tests to evaluate unadjusted associations between categorical survey items and binary indicators of each subgroup. A 2-sided <italic>P</italic> < .05 was considered to be statistically significant. The statistical analyses were performed using Stata SE, version 15.1 (StataCorp LLC).</p></sec></sec><sec id=\"H1-3-ZOI200494\"><title>Results</title><sec id=\"H2-5-ZOI200494\"><title>Characteristics of Survey Participants</title><p>The survey was sent to 2215 practitioners and completed by 604 (response rate, 27.3%). Of these respondents, 41 were excluded because they did not provide care for infants or pediatric patients. The final analyses included responses from 563 surveys. Respondents were mainly pediatricians (454 [80.6%; 95% CI, 77.2%-83.7%]), followed by resident physicians (85 [15.1%; 95% CI, 12.4%-18.3%]), nurse practitioners (20 [3.6%; 95% CI, 2.3%-5.4%]), and family medicine practitioners (4 [0.7% [95% CI, 0.3%-1.9%]). Participants were primarily from the Midwest (446 [81.7%; 95% CI, 78.2%-84.7%]), and 334 (61.3%; 95% CI, 57.1%-65.3%) reported that they had graduated medical school more than 10 years before (<xref rid=\"zoi200494t1\" ref-type=\"table\">Table 1</xref>). Practice type varied among participants, with substantial numbers providing care at academic institutions (167 [29.8%; 95% CI, 26.1%-33.7%]) and hospital-affiliated clinics (96 [17.1%; 95% CI, 14.2%-20.5%]). A total of 207 practitioners (37.0%; 95% CI, 32.9%-40.8%) reported that most of their patient population (>50%) was covered by Medicaid. Compared with a 2016 national report of AAP members (n = approximately 67 000),<sup><xref rid=\"zoi200494r18\" ref-type=\"bibr\">18</xref></sup> our surveyed population had a similar number of practitioners in the various practice types (AAP-reported private: 33.3% vs 213 [38.0%], AAP-reported hospital affiliated: 14.6% vs 96 [17.1%], and AAP-reported community health: 3.1% vs 48 [8.6%]), with the exception of our survey population having a higher number of practitioners in academic settings (AAP-reported 15.1% vs 167 [29.8%]) and a lower number in private solo practice (10.5% vs 18 [3.2%]). Hours spent per week working in pediatric care were similar between the surveyed population and AAP members (mean [SD], 36.6 [17.5] vs 32 hours).<sup><xref rid=\"zoi200494r18\" ref-type=\"bibr\">18</xref></sup></p><table-wrap id=\"zoi200494t1\" orientation=\"portrait\" position=\"float\"><label>Table 1. </label><caption><title>Demographic Data</title></caption><table frame=\"hsides\" rules=\"groups\"><col width=\"29.64%\" span=\"1\"/><col width=\"33.05%\" span=\"1\"/><col width=\"0.09%\" span=\"1\"/><col width=\"37.09%\" span=\"1\"/><col width=\"0.13%\" span=\"1\"/><thead><tr><th rowspan=\"2\" valign=\"middle\" align=\"left\" scope=\"col\" colspan=\"1\">Variable</th><th valign=\"middle\" colspan=\"4\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">Pediatric practitioners,<xref ref-type=\"table-fn\" rid=\"zoi200494t1n1\"><sup>a</sup></xref> No. (%) [95% CI]</th></tr><tr><th valign=\"middle\" colspan=\"2\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">All</th><th valign=\"middle\" colspan=\"2\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">Not following guideline to wait ≥3 d between new food introduction</th></tr></thead><tbody><tr><td valign=\"middle\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Practice type (n = 561/224)<xref ref-type=\"table-fn\" rid=\"zoi200494t1n2\"><sup>b</sup></xref></td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\"/><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\"/></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Private group</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">213 (38.0) [34.0-42.1]</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">78 (34.8) [28.9-41.3]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Academic</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">167 (29.8) [26.1-33.7]</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">73 (32.6) [26.7-39.0]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Hospital-affiliated clinic</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">96 (17.1) [14.2-20.5]</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">39 (17.4) [13.0-23.0]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Community health</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">48 (8.6) [6.5-11.2]</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">22 (9.8) [6.5-14.5]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Private solo</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">18 (3.2) [2.0-5.0]</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">3 (1.3) [0.4-4.0]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Managed care or HMO</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">9 (1.6) [0.8-3.1]</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">5 (2.2) [0.9-5.3]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Other</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">10 (1.8) (1.0-3.3)</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">4 (1.8) [0.6-4.7]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Medical specialty (n = 563/224)<xref ref-type=\"table-fn\" rid=\"zoi200494t1n2\"><sup>b</sup></xref></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\"/></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Pediatrics </td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">454 (80.6) [77.2-83.7]</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">188 (83.9) [78.1-87.9]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Resident physicians</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">85 (15.1) [12.4-18.3]</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">30 (13.4) [9.8-19.0]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Nurse practitioners</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">20 (3.6) [2.3-5.4]</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">4 (1.8) [0.6-4.7]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Family medicine</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">4 (0.7) [0.3-1.9]</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">2 (0.9) [0.2-3.5]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Year of graduation (n = 545/221)<xref ref-type=\"table-fn\" rid=\"zoi200494t1n2\"><sup>b</sup></xref></td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\"/><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\"/></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 2009-2019</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">211 (38.7) [34.7-42.9]</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">81 (36.7) [30.5-43.2]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Earlier than 2009</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">334 (61.3) [57.1-65.3]</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">140 (63.3) [56.8-69.5]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Medicaid patients, % (n = 560/223)<xref ref-type=\"table-fn\" rid=\"zoi200494t1n2\"><sup>b</sup></xref></td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\"/><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\"/></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 0-25</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">238 (42.5) [38.5-46.6]</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">91 (40.8) [34.5-47.4]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 26-50</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">87 (15.5) [12.8-18.8]</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">42 (18.8) [14.2-24.5]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 51-75</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">88 (15.7) [12.9-19.0]</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">33 (14.8) [10.7-20.1]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 76-100</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">119 (21.3) [18.1-24.8]</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">45 (20.2) [15.4-26.0]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Do not know</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">28 (5.0) [3.5-7.2]</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">12 (5.4) [3.1-9.3]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">WIC patients, % (n = 559/222)<xref ref-type=\"table-fn\" rid=\"zoi200494t1n2\"><sup>b</sup></xref></td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\"/><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\"/></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 0-25</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">251 (44.9) [40.8-49.1]</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">99 (44.6) [38.2-51.2]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 26-50</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">75 (13.4) [10.8-16.5]</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">32 (14.4) [10.4-19.7]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 51-75</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">78 (14.0) [11.3-17.1]</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">30 (13.5) [9.5-18.7]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 76-100</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">97 (17.4) [14.4-20.7]</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">37 (16.7) [12.3-22.2]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Do not know</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">58 (10.4) [8.1-13.2]</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">24 (10.8) [7.3-15.7]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Region (n = 546/224)<xref ref-type=\"table-fn\" rid=\"zoi200494t1n2\"><sup>b</sup></xref></td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">(n = 546)</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">(n = 217)</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Midwest</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">446 (81.7) [78.2-84.7]</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">173 (79.7) [73.8-84.6]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Northeast</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">46 (8.4) [6.4-11.1]</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">13 (6.0) [3.5-10.1]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> West</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">41 (7.5) [5.6-10.0]</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">25 (11.5) [7.9-16.5]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> South</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">13 (2.4) [1.4-4.1]</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">6 (2.8) [1.2-6.0]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Time spent on pediatric care, mean (SD), h/wk</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">36.6 (17.5)</td><td valign=\"middle\" colspan=\"2\" align=\"left\" rowspan=\"1\">35.5 (17.1)</td></tr></tbody></table><table-wrap-foot><p>Abbreviations: HMO, health maintenance organization; WIC, Special Supplemental Nutrition Program for Women, Infants, and Children.</p><fn id=\"zoi200494t1n1\"><label><sup>a</sup></label><p>Data are presented as number (percentage) [95% CI] unless otherwise indicated.</p></fn><fn id=\"zoi200494t1n2\"><label><sup>b</sup></label><p>Denominator for each proportion calculation is the number of nonmissing observations denoted in column sample size. n is sample size for all practitioners/sample size for those not following the guideline.</p></fn></table-wrap-foot></table-wrap></sec><sec id=\"H2-6-ZOI200494\"><title>Solid Food Recommendations</title><sec id=\"H3-1-ZOI200494\"><title>Food Type</title><p>A total of 264 pediatric practitioners (46.9%; 95% CI, 42.8%-51.0%) recommended infant cereal as the first food of introduction (<xref rid=\"zoi200494t2\" ref-type=\"table\">Table 2</xref>). However, 226 practitioners (40.1%; 95% CI, 36.1%-44.2%) reported no recommendation of a specific order. In regard to age at which food should be introduced in EBF and non-EBF infants, 268 practitioners (47.6%; 95% CI, 43.5%-51.7%) recommended 6 months for EBF infants and 193 practitioners (34.3%; 95% CI, 30.5%-38.3%) recommended 6 months for non-EBF infants (<italic>P</italic> < .001); 101 practitioners (17.9%; 95% CI, 15.0%-21.3%) recommended 5 months for EBF infants and 114 practitioners (20.2%; 95% CI, 17.1%-23.8%) recommended 5 months for non-EBF infants (<italic>P</italic> = .20); and 179 practitioners (31.8%; 95% CI, 28.1%-35.8%) recommended 4 months for EBF infants and 239 practitioners (42.5%; 95% CI, 38.4%-46.6%) recommended 4 months for non-EBF infants (<italic>P</italic> < .001).</p><table-wrap id=\"zoi200494t2\" orientation=\"portrait\" position=\"float\"><label>Table 2. </label><caption><title>Pediatric Solid Food Introduction Recommendations</title></caption><table frame=\"hsides\" rules=\"groups\"><col width=\"14.64%\" span=\"1\"/><col width=\"13.08%\" span=\"1\"/><col width=\"16.52%\" span=\"1\"/><col width=\"15.15%\" span=\"1\"/><col width=\"8.26%\" span=\"1\"/><col width=\"13.77%\" span=\"1\"/><col width=\"12.39%\" span=\"1\"/><col width=\"6.19%\" span=\"1\"/><thead><tr><th rowspan=\"2\" valign=\"middle\" align=\"left\" scope=\"col\" colspan=\"1\">Variable </th><th valign=\"middle\" colspan=\"4\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">Pediatric practitioners, No. (%) [95% CI]</th><th valign=\"middle\" colspan=\"3\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">Academic practice, No. (%) [95% CI]</th></tr><tr><th valign=\"middle\" colspan=\"1\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">All <break/>(N = 563)</th><th valign=\"middle\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Graduated <break/>>10 y ago<break/>(n = 334) </th><th valign=\"middle\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Graduated<break/>≤10 y ago<break/>(n = 211)</th><th valign=\"middle\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><italic>P</italic> value<xref ref-type=\"table-fn\" rid=\"zoi200494t2n1\"><sup>a</sup></xref></th><th valign=\"middle\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Yes (n = 339)</th><th valign=\"middle\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">No (n = 216)</th><th valign=\"middle\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><italic>P</italic> value<xref ref-type=\"table-fn\" rid=\"zoi200494t2n1\"><sup>a</sup></xref></th></tr></thead><tbody><tr><td valign=\"middle\" colspan=\"7\" align=\"left\" scope=\"col\" rowspan=\"1\">First food recommended for introduction</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> No recommendation</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">226 (40.1) [36.1-44.2]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">129 (38.6) [34.1-44.6]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">91 (43.1) [36.6-50.0]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">138 (40.7) [35.6-46.0]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">86 (39.8) [33.5-46.5]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Infant cereal</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">264 (46.9) [42.8-51.0]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">170 (50.9) [45.5-56.2]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">84 (39.8) [33.4-46.6]</td><td rowspan=\"5\" valign=\"middle\" align=\"left\" colspan=\"1\">.01</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">154 (45.4) [40.2-50.8]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">104 (48.2) [41.5-54.8]</td><td rowspan=\"5\" valign=\"middle\" align=\"left\" colspan=\"1\">.60</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Fruits</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">8 (1.4) [0.7-2.8]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">2 (0.6) [0.1-2.4]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">6 (2.8) [1.3-6.2]</td><td valign=\"middle\" colspan=\"1\" align=\"left\" rowspan=\"1\">6 (1.8) [0.8-3.9]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">2 (0.9) [0.2-3.7]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Vegetables</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">45 (8.0) [6.0-10.5]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">20 (6.0) [3.9-9.1]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">24 (11.4) [7.7-16.4]</td><td valign=\"middle\" colspan=\"1\" align=\"left\" rowspan=\"1\">28 (8.3) [5.8-11.7]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">17 (7.9) [4.9-12.3]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Meats</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">4 (0.7) [0.3-1.9]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">2 (0.6) [0.1-2.4]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">2 (1.0) [0.2-3.7]</td><td valign=\"middle\" colspan=\"1\" align=\"left\" rowspan=\"1\">4 (1.2) [0.4-3.1]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">0 (-)</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Other</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">16 (2.8) [1.0-3.3]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"> 11 (3.3) [1.8-5.9]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">4 (1.9) [0.1-4.9]</td><td valign=\"middle\" colspan=\"1\" align=\"left\" rowspan=\"1\">9 (2.7) [1.4-5.0]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">7 (3.2) [1.5-6.7]</td></tr><tr><td valign=\"middle\" colspan=\"7\" align=\"left\" scope=\"col\" rowspan=\"1\">Timing of introduction for exclusively breastfed infants, mo</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 4 </td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">179 (31.8) [28.1-35.8]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">98 (29.3) [24.7-34.5]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">74 (35.1) [28.9-41.8]</td><td rowspan=\"4\" valign=\"middle\" align=\"left\" colspan=\"1\">.20</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">106 (31.3) [26.5-36.4]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">70 (32.4) [26.5-38.9]</td><td rowspan=\"4\" valign=\"middle\" align=\"left\" colspan=\"1\">.80</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 5 </td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">101 (17.9) [15.0-21.3]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">66 (19.8) [15.8-24.4]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">33 (15.6) [11.3-21.2]</td><td valign=\"middle\" colspan=\"1\" align=\"left\" rowspan=\"1\">65 (19.2) [15.3-23.7]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">34 (15.7) [11.4-21.3]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 6 </td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">268 (47.6) [43.5-51.7]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">158 (47.3) [41.9-52.7]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">101 (47.9) [41.2-54.6]</td><td valign=\"middle\" colspan=\"1\" align=\"left\" rowspan=\"1\">159 (46.9) [41.6-52.2]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">106 (49.1) [42.4-55.8]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Other</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">15 (2.7) [1.6-4.4]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">12 (3.6) [2.0-6.2]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">3 (1.4) [0.5-4.3]</td><td valign=\"middle\" colspan=\"1\" align=\"left\" rowspan=\"1\">9 (2.7) [1.4-5.0]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">6 (2.8) [1.2-6.1]</td></tr><tr><td valign=\"middle\" colspan=\"8\" align=\"left\" scope=\"col\" rowspan=\"1\">Timing of introduction for nonexclusively breastfed infants, mo</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 4 </td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">239 (42.5) [38.4-46.6]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">133 (39.8) [34.7-45.2]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">95 (45.0) [38.4-51.8]</td><td rowspan=\"4\" valign=\"middle\" align=\"left\" colspan=\"1\">.20</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">142 (41.9) [36.7-47.2]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">93 (43.1) [36.6-49.7]</td><td rowspan=\"4\" valign=\"middle\" align=\"left\" colspan=\"1\">.60</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 5 </td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">114 (20.2) [17.1-23.8]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">72 (21.6) [17.5-26.3]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">39 (18.5) [13.8-24.3]</td><td valign=\"middle\" colspan=\"1\" align=\"left\" rowspan=\"1\">72 (21.2) [17.2-25.9]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">41 (18.9) [14.2-24.8]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 6 </td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">193 (34.3) [30.5-38.3]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">115 (34.4) [29.5-39.7]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">74 (35.1) [28.9-41.8]</td><td valign=\"middle\" colspan=\"1\" align=\"left\" rowspan=\"1\">117 (34.5) [29.6-39.7]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">73 (33.8) [27.8-40.4]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Other</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">17 (3.0) [1.9-4.8]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">14 (4.2) [2.5-7.0]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">3 (1.4) [0.5-4.3]</td><td valign=\"middle\" colspan=\"1\" align=\"left\" rowspan=\"1\">8 (2.4) [1.2-4.7]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">9 (4.2) [2.2-7.8]</td></tr><tr><td valign=\"middle\" colspan=\"8\" align=\"left\" scope=\"col\" rowspan=\"1\">Source of guidance</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Training</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">326 (57.9) [53.8-61.9]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">152 (45.5) [40.2-50.9]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">167 (79.2) [73.1-84.1]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><.001</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">206 (60.8) [55.4-65.8]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">114 (52.8) [46.1-59.4]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.06</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> AAP</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">279 (49.6) [45.4-53.7]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">169 (50.6) [45.2 - 55.9]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">101 (47.9) [41.2-54.6]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.50</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">161 (47.5) [42.2-52.8]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">114 (52.8) [46.1-59.4]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.20</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Professional experience</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">246 (43.7) [39.6-47.8]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">185 (55.4) [49.9-60.7]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">53 (25.1) [19.7-31.4]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><.001</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">136 (40.1) [35.0-45.4]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">108 (50.0) [43.3-56.7]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">.02</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Personal experience</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">199 (35.4) [31.5-39.4]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">140 (41.9) [36.7-47.3]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">57 (27.0) [21.4-33.4]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><.001</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">120 (35.4) [30.5-40.7]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">77 (35.6) [29.5-42.3]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">.90</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Colleagues</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">129 (22.9) [19.6-26.6]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">55 (16.5) [12.9-20.9]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">70 (33.2) [27.1-39.8]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><.001</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">89 (26.3) [21.8-31.2]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">39 (18.1) [13.5-23.8]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.03</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Medical meetings</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">85 (15.1) [12.4-18.3]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">13 (3.9) [2.3-6.6]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">4 (1.9) [0.7-4.9]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.10</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">8 (2.4) [1.2-4.7]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">9 (4.2) [2.2-7.8]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.20</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Cultural</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">63 (11.2) [8.8-14.0]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">38 (11.4) [8.4-15.3]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">23 (10.9) [7.3-15.9]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.90</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">44 (13.0) [9.8-17.0]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">18 (8.3) [5.3-12.9]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.09</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Professional organizations</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">17 (3.0) [1.9-4.8]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">51 (15.3) [11.8-19.6]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">30 (14.2) [10.0-19.6]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">.70</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">49 (14.5) [11.1-18.6]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">36 (16.7) [12.2-22.3]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">.50</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Other</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">15 (2.7) [1.6-4.4]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">12 (3.6) [2.0-6.2]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"> 3 (1.4) [0.4-4.3]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.10</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">7 (2.1) [1.0-4.3]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">8 (3.7) [1.9-7.3]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.20</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Need for training and education on solid food introduction</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">310 (55.1) [50.9-59.1]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">146 (72.0) [65.6-77.7]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">152 (43.7) [38.5-49.1]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><.001</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">197 (58.1) [52.8-63.2]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">107 (49.5) [42.9-56.2]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">.05</td></tr></tbody></table><table-wrap-foot><p>Abbreviation: AAP, American Academy of Pediatrics.</p><fn id=\"zoi200494t2n1\"><label><sup>a</sup></label><p><italic>P</italic> values are derived from χ<sup>2</sup> tests that evaluated associations between the categorical row variable and the binary column variable (ie, graduated within 10 years ago, yes or no; academic practice, yes or no).</p></fn></table-wrap-foot></table-wrap></sec><sec id=\"H3-2-ZOI200494\"><title>Time Between Introductions</title><p>With respect to time elapsed between food introduction, 217 participants (38.6%; 95% CI, 34.1%-44.6%) recommended waiting 3 or more days, whereas 112 (19.9%; 95% CI, 16.8%-23.4%) recommended waiting 2 days, and 154 (27.4%; 95% CI, 23.8%-31.2%) recommended waiting 1 day (<xref rid=\"zoi200494t3\" ref-type=\"table\">Table 3</xref>). A total of 56 practitioners (9.9%; 95% CI, 7.7%-12.7%) recommended introducing multiple new foods in 1 day; however, 340 (60.4%; 95% CI, 56.3%-64.3%) reported that they believed the introduction of multiple new foods (that are not common food allergens) together was safe (<xref rid=\"zoi200494t4\" ref-type=\"table\">Table 4</xref>). No significant associations were observed between recommending more than 2 days between food introduction and practitioner demographics, including patient Medicaid population.</p><table-wrap id=\"zoi200494t3\" orientation=\"portrait\" position=\"float\"><label>Table 3. </label><caption><title>Recommendations for Infants With and Without Food Allergy Risk Factors</title></caption><table frame=\"hsides\" rules=\"groups\"><col width=\"34.59%\" span=\"1\"/><col width=\"32.36%\" span=\"1\"/><col width=\"33.05%\" span=\"1\"/><thead><tr><th rowspan=\"2\" valign=\"middle\" align=\"left\" scope=\"col\" colspan=\"1\">Recommendation</th><th valign=\"middle\" colspan=\"2\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">Pediatric practitioners, No. (%) [95% CI]</th></tr><tr><th valign=\"middle\" colspan=\"1\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">General recommendations (n = 563)</th><th valign=\"middle\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Recommendation for infants with food allergy risk factors (n = 391)</th></tr></thead><tbody><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Introduce multiple foods in 1 d</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">56 (9.9) [7.7-12.7]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">5 (1.3) [0.5-3.0]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Introduce multiple foods in 1 meal</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">15 (2.7) [1.6-4.4]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 (0.3) [0-1.8]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Introduce 1 food a day</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">154 (27.4) [23.8-31.2]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">35 (9.0) [6.5-12.2]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Introduce 1 food, wait 2 d, <break/>introduce another</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">112 (19.9) [16.8-23.4]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">61 (15.6) [12.3-19.6]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Introduce 1 food, wait 3 d, introduce another</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">171 (30.4) [26.7-34.3]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">163 (41.7) [36.9-46.7]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Introduce 1 food, wait >3 d</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">46 (8.2) [6.2-10.7]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">96 (24.6) [20.5-29.1]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Other</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">9 (1.6) [0.8-3.0]</td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">30 (7.7) [5.4-10.8]</td></tr></tbody></table></table-wrap><table-wrap id=\"zoi200494t4\" orientation=\"portrait\" position=\"float\"><label>Table 4. </label><caption><title>Practitioner Beliefs Regarding Solid Food Introduction and Factors that Would Change Recommendations</title></caption><table frame=\"hsides\" rules=\"groups\"><col width=\"15.33%\" span=\"1\"/><col width=\"13.77%\" span=\"1\"/><col width=\"15.15%\" span=\"1\"/><col width=\"13.42%\" span=\"1\"/><col width=\"1.03%\" span=\"1\"/><col width=\"6.19%\" span=\"1\"/><col width=\"14.46%\" span=\"1\"/><col width=\"13.08%\" span=\"1\"/><col width=\"7.57%\" span=\"1\"/><thead><tr><th rowspan=\"2\" valign=\"middle\" align=\"left\" scope=\"col\" colspan=\"1\">Variable </th><th valign=\"middle\" colspan=\"3\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">Pediatric practitioners, No. (%) [95% CI]</th><th valign=\"middle\" colspan=\"5\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">Academic practice, No. (%) [95% CI]</th></tr><tr><th valign=\"middle\" colspan=\"1\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">All (N = 563)</th><th valign=\"middle\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Graduated <break/>>10 y ago<break/>(n = 334) </th><th valign=\"middle\" colspan=\"2\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">Graduated <break/>≤10 y ago<break/>(n = 211)</th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><italic>P</italic> value<xref ref-type=\"table-fn\" rid=\"zoi200494t4n1\"><sup>a</sup></xref></th><th valign=\"middle\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Yes (n = 339)</th><th valign=\"middle\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">No (n = 216)</th><th valign=\"middle\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><italic>P</italic> value<xref ref-type=\"table-fn\" rid=\"zoi200494t4n1\"><sup>a</sup></xref></th></tr></thead><tbody><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Think it is safe to introduce multiple foods (nontop allergens) together</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">340 (60.4) [56.3-64.3]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">208 (62.3) [56.9-67.3]</td><td valign=\"middle\" colspan=\"2\" align=\"center\" rowspan=\"1\">124 (58.8) [52.0-65.3]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">.40</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">212 (62.5) [57.2-67.5]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">123 (56.9) [50.2-63.4]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">.20</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Think waiting between basic food introduction (nontop allergens) is helpful for families</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">313 (55.6) [51.4-59.7]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">181 (54.2) [48.8-59.9]</td><td valign=\"middle\" colspan=\"2\" align=\"center\" rowspan=\"1\">121 (57.4) [50.5-63.9]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">.20</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">178 (52.5) [47.2-57.8]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">132 (61.1) [54.4-67.4]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">.10</td></tr><tr><td valign=\"middle\" colspan=\"9\" align=\"left\" scope=\"col\" rowspan=\"1\">Factors that would change recommendation </td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Older sibling with food allergy</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">387 (68.7) [64.8-72.4]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">226 (67.7) [62.4-72.5]</td><td valign=\"middle\" colspan=\"2\" align=\"center\" rowspan=\"1\">149 (70.6) [64.1-76.4]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">.50</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">237 (69.9) [64.8-74.6]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">146 (67.6) [61.0-73.5]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">.60</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Infant with moderate to severe eczema</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">374 (66.4) [62.4-70.2]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">227 (68.0) [62.7-72.8]</td><td valign=\"middle\" colspan=\"2\" align=\"center\" rowspan=\"1\">138 (65.4) [58.7-71.5]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">.50</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">236 (69.6) [64.5-74.3]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">136 (63.0) [56.3-69.2]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">.10</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Family history of food allergy</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">370 (65.7) [61.7-69.5]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">213 (63.8) [58.5-68.8]</td><td valign=\"middle\" colspan=\"2\" align=\"center\" rowspan=\"1\">146 (69.2) [62.6-75.1]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">.20</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">228 (67.3) [62.1-72.1]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">136 (63.0) [56.3-69.2]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">.30</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Family history of any allergies or asthma</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">165 (29.3) [25.6-33.2]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">89 (26.7) [22.2-31.7]</td><td valign=\"middle\" colspan=\"2\" align=\"center\" rowspan=\"1\">71 (33.7) [27.6-40.3]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">.08</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">105 (31.0) [26.3-36.1]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">58 (26.9) [21.3-33.2]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">.30</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Child has any eczema</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">86 (15.3) [12.5-18.5]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">50 (15.0) [11.5-19.2]</td><td valign=\"middle\" colspan=\"2\" align=\"center\" rowspan=\"1\">35 (16.6) [12.1-22.3]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">.60</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">50 (14.8) [11.3-19.0]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">34 (15.7) [11.4-21.3]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">.70</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Other</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">20 (3.6) [2.3-9.8]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">13 (3.9) [2.3-6.6]</td><td valign=\"middle\" colspan=\"2\" align=\"center\" rowspan=\"1\">5 (2.4) [1.0-5.6]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">.30</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">12 (3.5) [2.0-6.1]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">8 (3.7) [1.9-7.3]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">.90</td></tr><tr><td valign=\"middle\" colspan=\"9\" align=\"left\" scope=\"col\" rowspan=\"1\">Infants with food allergy in past year, %</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 0</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">113 (20.1) [17.0-23.6]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">55 (16.5) [12.9-20.9]</td><td valign=\"middle\" colspan=\"2\" align=\"center\" rowspan=\"1\">54 (25.6) [20.1-31.9]</td><td rowspan=\"7\" valign=\"middle\" align=\"center\" colspan=\"1\">.20</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">82 (24.2) [19.9-29.1]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">28 (13.0) [9.1-18.2]</td><td rowspan=\"7\" valign=\"middle\" align=\"center\" colspan=\"1\">.02</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> <5</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">314 (55.8) [51.6-59.8]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">194 (58.1) [52.7-63.3]</td><td valign=\"middle\" colspan=\"2\" align=\"center\" rowspan=\"1\">110 (52.1) [45.4-58.8]</td><td valign=\"middle\" colspan=\"1\" align=\"center\" rowspan=\"1\">170 (50.2) [44.8-55.5]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">141 (65.3) [58.7-71.4]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 5-10</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">110 (19.5) [16.4-23.0]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">67 (20.1) [16.0-24.7]</td><td valign=\"middle\" colspan=\"2\" align=\"center\" rowspan=\"1\">40 (19.0) [14.2-24.9]</td><td valign=\"middle\" colspan=\"1\" align=\"center\" rowspan=\"1\">72 (21.2) [17.2-25.9]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">36 (16.7) [12.2-22.3]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 11-20</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">20 (3.6) [2.3-5.4]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">15 (4.5) [2.7-7.3]</td><td valign=\"middle\" colspan=\"2\" align=\"center\" rowspan=\"1\">5 (2.4) [1.0-5.6]</td><td valign=\"middle\" colspan=\"1\" align=\"center\" rowspan=\"1\">11 (3.2) [1.8-5.8]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">9 (4.2) [2.2-7.8]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 21-40</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">2 (0.4) [0.1-1.4]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">1 (0.3) [0-2.1]</td><td valign=\"middle\" colspan=\"2\" align=\"center\" rowspan=\"1\">1 (0.5) [0.1-3.3]</td><td valign=\"middle\" colspan=\"1\" align=\"center\" rowspan=\"1\">0 (-)</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">2 (0.9) [0.2-3.7]</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> >40</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">1 (0.2) [0-1.3]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">1 (0.3) [0-2.1]</td><td valign=\"middle\" colspan=\"2\" align=\"center\" rowspan=\"1\">0 (-)</td><td valign=\"middle\" colspan=\"1\" align=\"center\" rowspan=\"1\">1 (0.3 [0.04-2.1]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">0 (-)</td></tr><tr><td valign=\"middle\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Other</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">3 (0.5) [0.2-1.6]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">1 (0.3) [0-2.2]</td><td valign=\"middle\" colspan=\"2\" align=\"center\" rowspan=\"1\">1 (0.5) [0.1-3.3]</td><td valign=\"middle\" colspan=\"1\" align=\"center\" rowspan=\"1\">3 (0.9) [0.3-2.7]</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">0 (-)</td></tr></tbody></table><table-wrap-foot><fn id=\"zoi200494t4n1\"><label><sup>a</sup></label><p><italic>P</italic> values are derived from χ<sup>2</sup> tests that evaluated associations between the categorical row variable and the binary column variable (ie, graduated within 10 years ago, yes or no; academic practice, yes or no).</p></fn></table-wrap-foot></table-wrap></sec></sec><sec id=\"H2-7-ZOI200494\"><title>Food Allergy Risk Factors and Solid Food Introduction</title><p>Overall, 391 practitioners (69.4%; 95% CI, 65.5%-73.1%) reported they would change their current recommendation because an infant had risk factors for food allergy development, including older siblings with food allergy (387 [68.7%; 95% CI, 64.8%-72.4%]), moderate to severe eczema (374 [66.4%; 95% CI, 62.4%-70.2%]), and family history of food allergy (370 [65.7%; 95% CI, 61.7%-69.5%]) (<xref rid=\"zoi200494t4\" ref-type=\"table\">Table 4</xref>). A total of 163 practitioners (41.7%; 95% CI, 36.9%-46.7%) recommended waiting 3 days between food introduction among infants with food allergy risk factors, whereas 96 practitioners (24.6%; 95% CI, 20.5%-29.1%) recommended waiting more than 3 days. A total of 35 practitioners (9.0%; 95% CI, 6.5%-12.2%) recommended waiting 1 day between the introduction of new foods for infants with food allergy risk factors. Practitioners more frequently recommended waiting 3 or more days in children with food allergy risk factors than they did in children without these risks (259 [66.3%] vs 217 [38.6%], <italic>P</italic> = .02). Food-related allergic reactions among infants during complementary food introduction occurred infrequently, with 314 practitioners (55.8%; 95% CI, 51.6%-59.8%) reporting they had occurred in less than 5% of infants and 110 (19.5%; 95% CI, 16.4%-23.0%) reporting they had occurred in 5% to 10% of infants.</p></sec><sec id=\"H2-8-ZOI200494\"><title>Need for Further Education</title><p>Additional training and education on complementary food introduction were identified as a need by 310 participants (55.1%; 95% CI, 50.9%-59.1%) overall and by 146 participants (72.0%; 95% CI, 65.6%-77.7%) who graduated medical school more than 10 years before the survey. Pediatric residency training (326 [57.9%; 95% CI, 53.8%-61.9%]), AAP guidelines (279 [49.6%; 95% CI, 45.4%-53.7%]), and professional experience (246 [43.7%; 95% CI, 39.6%-47.8%]) were the 3 most common reported sources of guidance in developing their complementary food recommendations (<xref rid=\"zoi200494t2\" ref-type=\"table\">Table 2</xref>).</p></sec></sec><sec id=\"H1-4-ZOI200494\"><title>Discussion</title><p>To our knowledge, this survey study is the first study to evaluate recommendations on complementary food introduction by pediatric practitioners. We found that most practitioners recommended waiting 2 days or less between the introduction of new foods, with only 2 of 5 reported following the AAP and CDC recommendation of waiting 3 to 5 days between the introduction of new foods. Most also believed that administering multiple nonallergenic foods was safe, although only 2.7% recommended it. Despite reporting that food-related reactions occurred infrequently among infants, most practitioners recommended waiting longer and complying with AAP recommendations for infants with food allergy risk factors, with 66.3% recommending 3 or more days between the introduction of new foods. Common sources of guidance for recommendations reported by practitioners were primarily clinical experience (both medical training and professional experience), and 55.1% reported a need for additional training in this area.</p><p>Previous studies<sup><xref rid=\"zoi200494r14\" ref-type=\"bibr\">14</xref>,<xref rid=\"zoi200494r17\" ref-type=\"bibr\">17</xref></sup> of parents regarding feeding practices identified pediatricians as trusted sources of information by parents during the solid food introduction phase; however, data are limited on how pediatric practitioners provide recommendations on complementary foods. Our study findings support those of previous studies<sup><xref rid=\"zoi200494r14\" ref-type=\"bibr\">14</xref>,<xref rid=\"zoi200494r16\" ref-type=\"bibr\">16</xref></sup> that evaluated parental feeding practices. For example, the Infant Feeding Practices Study II,<sup><xref rid=\"zoi200494r16\" ref-type=\"bibr\">16</xref></sup> which analyzed parental feeding practices in a large cohort of infants during the first year of life, found that at 4 months of age, 40% of infants had consumed infant cereal and 17% had consumed fruits and/or vegetables. This finding correlates with our study’s finding that 46.9% of pediatric practitioners recommended infant cereal as the first food and 42.5% recommended initiation of complementary foods at 4 months of age in non-EBF infants. This association between parental practices and physician recommendations suggests that anticipatory guidance with evidence-based practices is of value to parents and their infants.</p><p>Our study found that most practitioners diverged from published guidelines in their recommendation to wait 3 to 5 days between the introduction of each new food.<sup><xref rid=\"zoi200494r1\" ref-type=\"bibr\">1</xref>,<xref rid=\"zoi200494r2\" ref-type=\"bibr\">2</xref></sup> We found that many pediatric practitioners recommended waiting 2 days or less before starting a new food. Of note, although 60.4% of practitioners indicated that they believe administering multiple nonallergenic foods is safe, in clinical practice, only 1 in every 10 practitioners reported recommending multiple foods a day, and only 2.7% recommended giving multiple foods in 1 meal. The discrepancy between practitioners’ beliefs and clinical recommendations found in this study may represent the conflict between training and professional experiences and the current guidelines. This discrepancy, in addition to the scarcity of evidence supporting the current recommendations on how frequently new foods can be introduced to infants, reinforces the need to reevaluate the current recommendations.</p><p>Pediatric practitioners reported low rates of food-related allergic reactions in our study, a finding that comports with recent population-based estimates of food allergy burden among infants.<sup><xref rid=\"zoi200494r19\" ref-type=\"bibr\">19</xref></sup> Assessment of allergic reactions is an important consideration during solid food introduction. Per the American Academy of Allergy, Asthma, and Immunology, this cautionary waiting period is to evaluate for adverse reactions during introduction of a new food, with a goal of identifying foods that cause an allergic reaction.<sup><xref rid=\"zoi200494r20\" ref-type=\"bibr\">20</xref></sup> However, a 3-day waiting period does not match the clinical time course of most allergic reactions. IgE-mediated food reactions typically occur immediately or within 2 hours of ingestion. Non–IgE-mediated reactions, such as food protein–induced enterocolitis syndrome, can manifest as repetitive vomiting in the 1- to 4-hour period after ingestion of the suspect food, in the absence of classic IgE-mediated skin or respiratory symptoms, with cow’s milk and soy being the most common triggers.<sup><xref rid=\"zoi200494r21\" ref-type=\"bibr\">21</xref>,<xref rid=\"zoi200494r22\" ref-type=\"bibr\">22</xref>,<xref rid=\"zoi200494r23\" ref-type=\"bibr\">23</xref>,<xref rid=\"zoi200494r24\" ref-type=\"bibr\">24</xref></sup> Most other non–IgE-mediated food allergies follow a short-term or long-term timeline but can be managed by elimination of the most common suspected antigens, instead of introducing all foods with significant waiting periods. Furthermore, more than 90% of food allergies are caused by the top 8 allergens (peanut, tree nut, egg, milk, soy, wheat, fish, and shellfish) rather than cereal, fruits, vegetables, and meat, which were found in our study to be introduced first to infants.<sup><xref rid=\"zoi200494r19\" ref-type=\"bibr\">19</xref>,<xref rid=\"zoi200494r25\" ref-type=\"bibr\">25</xref></sup></p><p>Our study found that only 55.6% of practitioners believed that the recommendation to wait several days before introducing each new food was helpful to families. In practice, this recommendation may have a deleterious effect of limiting early infant food diversity, which has been reported to be associated with an increased risk of pediatric asthma and allergies.<sup><xref rid=\"zoi200494r3\" ref-type=\"bibr\">3</xref>,<xref rid=\"zoi200494r4\" ref-type=\"bibr\">4</xref>,<xref rid=\"zoi200494r6\" ref-type=\"bibr\">6</xref>,<xref rid=\"zoi200494r7\" ref-type=\"bibr\">7</xref>,<xref rid=\"zoi200494r8\" ref-type=\"bibr\">8</xref></sup> Introducing a more diverse diet in the first year of life has been shown to be associated with a reduced risk of atopic dermatitis, asthma, and food allergies up to 6 years of age.<sup><xref rid=\"zoi200494r3\" ref-type=\"bibr\">3</xref>,<xref rid=\"zoi200494r4\" ref-type=\"bibr\">4</xref>,<xref rid=\"zoi200494r8\" ref-type=\"bibr\">8</xref></sup> Specifically, late introduction of certain foods, including potatoes, oats, rye, wheat, meat, fish, and eggs, has been reported to be directly associated with sensitization to food allergens.<sup><xref rid=\"zoi200494r6\" ref-type=\"bibr\">6</xref></sup> Moreover, early introduction of grains, fish, and egg has been shown to be associated with decreased risks of asthma, allergic rhinitis, and atopic sensitization.<sup><xref rid=\"zoi200494r7\" ref-type=\"bibr\">7</xref></sup> Under the current guidelines, an infant is only exposed to 5 to 7 new foods a month, which can significantly limit infant food exposure. This diversity might be increased if a new food could be introduced daily and would support the current CDC general nutrition recommendation to increase food exposure in the first year and avoid picky eater syndrome.<sup><xref rid=\"zoi200494r26\" ref-type=\"bibr\">26</xref></sup></p><p>Reevaluation of the current waiting period should also include consideration of how to incorporate infant feeding of peanut-containing foods into an infant’s diet. A total of 259 surveyed practitioners recommended waiting longer for infants with food allergy risk factors, with 66.3% recommending 3 or more days between the introduction of new foods. In contrast, the NIAID currently recommends infants with atopic dermatitis be given peanut-containing foods between 4 and 6 months of age,<sup><xref rid=\"zoi200494r12\" ref-type=\"bibr\">12</xref></sup> with the intent of exposing them to peanut via their intestinal tract before sensitization via their skin.<sup><xref rid=\"zoi200494r27\" ref-type=\"bibr\">27</xref></sup> Of note, these guidelines specifically state that several new foods should be introduced before peanut. If infants are required to wait 3 to 5 days between the introduction of each new food, peanut introduction may be delayed past the recommended ages.</p><sec id=\"H2-9-ZOI200494\"><title>Limitations</title><p>This study has limitations. Our data are based on self-reported survey responses. Questions that target the source of guidance for complementary food introduction and the prevalence of food allergy seen in the practitioners’ clinical practice might have been subjected to response bias. To minimize response bias, survey participants’ answers were anonymous. Of note, our findings are more likely to be an underestimate than an overestimate of the lack of consistency with AAP recommendations among US pediatricians more broadly because participants were recruited via AAP LISTSERVS. Our survey had a response rate of 27.3%; however, because the survey was distributed to practicing practitioners via email, this response rate is consistent with a similar study.<sup><xref rid=\"zoi200494r28\" ref-type=\"bibr\">28</xref></sup> Although we were unable to compare responders and nonresponders of the specific AAP LISTSERVS in which the survey was distributed, we provide comparisons between responders and national AAP members and found many similarities in practice type and work hours. The survey population had a higher percentage of academic pediatricians than the overall national membership does, which should be noted when interpreting the results of this study. In addition, most responses were from Midwestern practitioners, so the results may not be entirely representative of practitioners nationally. Also, our study was conducted shortly after the release of the 2017 NIAID peanut prevention guidelines; although pediatric practitioners may have been aware of these new recommendations, it is possible that they did not affect the guidance practitioners provided patients on general food introduction. This guidance may change as these guidelines are more widely adopted.</p></sec></sec><sec id=\"H1-5-ZOI200494\"><title>Conclusions</title><p>In this survey study, most pediatric practitioners did not counsel families to wait 3 days or longer between introducing foods unless infants were at risk for food allergy development. The findings suggest that the current recommendation limits infant food diversity and may delay early peanut introduction. Because the approach to food allergy prevention has changed, a reevaluation of published feeding guidelines may be necessary. 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] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"correction\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">JAMA Netw Open</journal-id><journal-id journal-id-type=\"iso-abbrev\">JAMA Netw Open</journal-id><journal-id journal-id-type=\"pmc\">JAMA Netw Open</journal-id><journal-title-group><journal-title>JAMA Network Open</journal-title></journal-title-group><issn pub-type=\"epub\">2574-3805</issn><publisher><publisher-name>American Medical Association</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32804207</article-id><article-id pub-id-type=\"pmc\">PMC7431992</article-id><article-id pub-id-type=\"doi\">10.1001/jamanetworkopen.2020.18893</article-id><article-id pub-id-type=\"publisher-id\">zcx200048</article-id><article-categories><subj-group subj-group-type=\"category\" specific-use=\"electronic\"><subject>Other</subject></subj-group><subj-group subj-group-type=\"heading\"><subject>Correction</subject></subj-group><subj-group subj-group-type=\"online-only\"><subject>Online Only</subject></subj-group></article-categories><title-group><article-title>Data Processing and Patient Eligibility Indicator Errors in Study About Dose Tapering Among Patients Prescribed Long-term Opioid Therapy</article-title><alt-title alt-title-type=\"running-head\">Correction</alt-title></title-group><pub-date pub-type=\"epub\" iso-8601-date=\"2020-08-17T10:00\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><!-- PMC Release delay is 0 months and 0 days and was based on the <pub-date pub-type=\"epub\"/>. --><volume>3</volume><issue>8</issue><elocation-id>e2018893</elocation-id><permissions><copyright-statement>Copyright 2020. <italic>JAMA Network Open</italic>.</copyright-statement><license license-type=\"open-access\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open access article distributed under the terms of the CC-BY License.</license-p></license></permissions><self-uri content-type=\"pdf-version\" xlink:href=\"jamanetwopen-3-e2018893.pdf\">jamanetwopen-3-e2018893.pdf</self-uri><self-uri content-type=\"silverchair\" xlink:href=\"https://jamanetwork.com/journals/jamanetworkopen/fullarticle/10.1001/jamanetworkopen.2020.18893\"/><related-article related-article-type=\"corrected-article\" id=\"d38e64\" ext-link-type=\"doi\" xlink:href=\"10.1001/jamanetworkopen.2019.16271\" specific-use=\"electronic\"/></article-meta></front><body><p>In the Original Investigation, “Trends and Rapidity of Dose Tapering Among Patients Prescribed Long-term Opioid Therapy, 2008-2017,”<sup><xref rid=\"zcx200048r1\" ref-type=\"bibr\">1</xref></sup> that was published in <italic>JAMA Network Open</italic> on November 15, 2019, a data processing error occurred that resulted in an underestimation of the overall frequency of tapering and how rapidly patients are having their doses reduced. In addition, OptumLabs informed the authors that an indicator of patient eligibility for pharmacy benefits was incorrect for a small number of patients during the study period, which resulted in a corrected overall sample size of 99 874 participants (vs the previously reported 100 031 participants). The overall frequency of dose tapering in the original publication was underreported as 10.5% in 2008 and 22.4% in 2017; the corrected estimates are 12.7% in 2008 and 23.1% in 2017. The correct number of tapering events is 30 255 (vs 27 540), and the analysis includes 4129 tapering events in which the tapered dose was to 0 opioids (13.7% of all tapering events). Because of changes to the sample and the identification of more tapering events, the incidence rate ratios for patient-level variables associated with tapering reported in Table 2 have been corrected. The mean maximum rate of dose reduction is greater than previously estimated (34.0% per month vs 27.6% per month) and has greater skew toward very rapid dose reduction, with 26.5% of patients tapered at a maximum rate of 40% or greater per month. The data in Table 3 have also been corrected. In the corrected article, a higher maximum dose reduction rate was associated with an age of 18 to 34 years (vs older age groups), male sex, a high school education or less (vs more education), small town or rural residence (vs metropolitan, micropolitan, or unknown), a Charlson Comorbidity Index score of 3 or higher (vs 0), lower baseline doses (50-89 morphine milligram equivalents vs 150-299 or ≥300 morphine milligram equivalents), and a recent drug overdose. A new eTable 8 has been added to show the percentage of tapering episodes with dose reductions to 0 opioids. Years 2016 and 2017 were no longer statistically significantly associated with increased rate of dose reduction in corrected analyses. To address these errors, corrections have been made to the Abstract; Methods; Results; Discussion; Tables 1, 2, and 3; the Figure; and the Supplement. The article has been corrected online,<sup><xref rid=\"zcx200048r1\" ref-type=\"bibr\">1</xref></sup> and the authors have published an explanation as a Comment<sup><xref rid=\"zcx200048r2\" ref-type=\"bibr\">2</xref></sup> linked to the article.</p></body><back><ref-list id=\"REF-ZCX200048\"><title>Reference</title><ref id=\"zcx200048r1\"><label>1</label><mixed-citation publication-type=\"journal\"><person-group><name name-style=\"western\"><surname>Fenton</surname><given-names>JJ</given-names></name>, <name name-style=\"western\"><surname>Agnoli</surname><given-names>AL</given-names></name>, <name name-style=\"western\"><surname>Xing</surname><given-names>G</given-names></name>, <etal/></person-group>\n<article-title>Trends and rapidity of dose tapering among patients prescribed long-term opioid therapy, 2008-2017</article-title>. <source>JAMA Netw Open</source>. <year>2019</year>;<volume>2</volume>(<issue>11</issue>):<elocation-id>e1916271</elocation-id>. doi:<pub-id pub-id-type=\"doi\">10.1001/jamanetworkopen.2019.16271</pub-id><pub-id pub-id-type=\"pmid\">31730189</pub-id></mixed-citation></ref><ref id=\"zcx200048r2\"><label>2</label><mixed-citation publication-type=\"web\">Fenton JJ. Data processing and patient eligibility indicator errors. Accessed July 29, 2020. <ext-link ext-link-type=\"uri\" xlink:href=\"https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2755492\">https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2755492</ext-link></mixed-citation></ref></ref-list></back></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">JAMA Netw Open</journal-id><journal-id journal-id-type=\"iso-abbrev\">JAMA Netw Open</journal-id><journal-id journal-id-type=\"pmc\">JAMA Netw Open</journal-id><journal-title-group><journal-title>JAMA Network Open</journal-title></journal-title-group><issn pub-type=\"epub\">2574-3805</issn><publisher><publisher-name>American Medical Association</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32804212</article-id><article-id pub-id-type=\"pmc\">PMC7431993</article-id><article-id pub-id-type=\"doi\">10.1001/jamanetworkopen.2020.11799</article-id><article-id pub-id-type=\"publisher-id\">zoi200456</article-id><article-categories><subj-group subj-group-type=\"category\" specific-use=\"electronic\"><subject>Research</subject></subj-group><subj-group subj-group-type=\"heading\"><subject>Original Investigation</subject></subj-group><subj-group subj-group-type=\"online-only\"><subject>Online Only</subject></subj-group><subj-group subj-group-type=\"subject-area\"><subject>Psychiatry</subject></subj-group></article-categories><title-group><article-title>Effect of Clinician Training in the Modular Approach to Therapy for Children vs Usual Care on Clinical Outcomes and Use of Empirically Supported Treatments</article-title><subtitle>A Randomized Clinical Trial</subtitle><alt-title alt-title-type=\"headline\">Clinician Training in MATCH to Enhance Clinical Outcomes and the Use of Empirically Supported Treatments</alt-title><alt-title alt-title-type=\"running-head\">Clinician Training in MATCH to Enhance Clinical Outcomes and the Use of Empirically Supported Treatments</alt-title></title-group><contrib-group><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>Merry</surname><given-names>Sally N.</given-names></name><degrees>MD</degrees><xref ref-type=\"aff\" rid=\"zoi200456aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Hopkins</surname><given-names>Sarah</given-names></name><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"zoi200456aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Lucassen</surname><given-names>Mathijs F. G.</given-names></name><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"zoi200456aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"zoi200456aff2\"><sup>2</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Stasiak</surname><given-names>Karolina</given-names></name><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"zoi200456aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Weisz</surname><given-names>John R.</given-names></name><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"zoi200456aff3\"><sup>3</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Frampton</surname><given-names>Christopher M. A.</given-names></name><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"zoi200456aff4\"><sup>4</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Bearman</surname><given-names>Sarah Kate</given-names></name><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"zoi200456aff5\"><sup>5</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Ugueto</surname><given-names>Ana M.</given-names></name><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"zoi200456aff6\"><sup>6</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Herren</surname><given-names>Jennifer</given-names></name><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"zoi200456aff7\"><sup>7</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Cribb-Su’a</surname><given-names>Ainsleigh</given-names></name><degrees>DClinPsy</degrees><xref ref-type=\"aff\" rid=\"zoi200456aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Kingi-Uluave</surname><given-names>Denise</given-names></name><degrees>PGDDipClinPsych</degrees><xref ref-type=\"aff\" rid=\"zoi200456aff8\"><sup>8</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Loy</surname><given-names>Jik</given-names></name><degrees>MBChB</degrees><xref ref-type=\"aff\" rid=\"zoi200456aff9\"><sup>9</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Hartdegen</surname><given-names>Morgyn</given-names></name><degrees>BA(Hons)</degrees><xref ref-type=\"aff\" rid=\"zoi200456aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Crengle</surname><given-names>Sue</given-names></name><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"zoi200456aff10\"><sup>10</sup></xref></contrib></contrib-group><aff id=\"zoi200456aff1\"><label>1</label>Department of Psychological Medicine, School of Medicine, FMHS University of Auckland, Auckland, New Zealand</aff><aff id=\"zoi200456aff2\"><label>2</label>School of Health, Wellbeing and Social Care, The Open University, Milton Keynes, United Kingdom</aff><aff id=\"zoi200456aff3\"><label>3</label>Department of Psychology, Harvard University, Cambridge, Massachusetts</aff><aff id=\"zoi200456aff4\"><label>4</label>Department of Psychological Medicine, Christchurch School of Medicine, University of Otago, Christchurch, New Zealand</aff><aff id=\"zoi200456aff5\"><label>5</label>Department of Educational Psychology, The University of Texas at Austin, Austin</aff><aff id=\"zoi200456aff6\"><label>6</label>Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston</aff><aff id=\"zoi200456aff7\"><label>7</label>Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, Rhode Island</aff><aff id=\"zoi200456aff8\"><label>8</label>Le Va, Harakeke House, Manukau, Auckland, New Zealand</aff><aff id=\"zoi200456aff9\"><label>9</label>Infant, Child and Adolescent Mental Health Services, Waikato District Health Board, Hamilton, New Zealand</aff><aff id=\"zoi200456aff10\"><label>10</label>Department of Preventive and Social Medicine, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand</aff><author-notes><title>Article Information</title><p><bold>Accepted for Publication:</bold> May 18, 2020.</p><p content-type=\"published-online\"><bold>Published:</bold> August 17, 2020. doi:<uri content-type=\"doi\">10.1001/jamanetworkopen.2020.11799</uri></p><p content-type=\"open-access-note\"><bold>Open Access:</bold> This is an open access article distributed under the terms of the <ext-link ext-link-type=\"uri\" xlink:href=\"https://jamanetwork.com/journals/jamanetworkopen/pages/instructions-for-authors#SecOpenAccess\">CC-BY License</ext-link>. © 2020 Merry SN et al. <italic>JAMA Network Open</italic>.</p><corresp id=\"zoi200456cor1\"><bold>Corresponding Author:</bold> Sally N. Merry, MD, Department of Psychological Medicine, School of Medicine, FMHS University of Auckland, Level 12 Auckland City Hospital Support Bldg, Private Bag 92019, Auckland 1142, New Zealand (<email xlink:href=\"s.merry@auckland.ac.nz\">s.merry@auckland.ac.nz</email>).</corresp><p content-type=\"author-contributions\"><bold>Author Contributions:</bold> Drs Merry and Frampton had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.</p><p><italic>Concept and design:</italic> Merry, Lucassen, Stasiak, Weisz, Frampton, Bearman, Ugueto, Herren, Cribb-Su’a, Kingi-Uluave, Crengle.</p><p><italic>Acquisition, analysis, or interpretation of data:</italic> Merry, Hopkins, Lucassen, Weisz, Frampton, Ugueto, Herren, Cribb-Su’a, Loy, Hartdegen, Crengle.</p><p><italic>Drafting of the manuscript: </italic>Merry, Hopkins, Lucassen, Weisz, Bearman, Hartdegen, Crengle.</p><p><italic>Critical revision of the manuscript for important intellectual content:</italic> Merry, Hopkins, Lucassen, Stasiak, Weisz, Frampton, Bearman, Ugueto, Herren, Cribb-Su’a, Kingi-Uluave, Loy, Crengle.</p><p><italic>Statistical analysis:</italic> Frampton.</p><p><italic>Obtained funding:</italic> Merry, Lucassen, Stasiak, Crengle.</p><p><italic>Administrative, technical, or material support:</italic> Merry, Hopkins, Lucassen, Stasiak, Weisz, Bearman, Herren, Cribb-Su’a, Loy, Hartdegen.</p><p><italic>Supervision:</italic> Merry, Hopkins, Lucassen, Weisz, Bearman, Ugueto, Herren, Cribb-Su'a, Kingi-Uluave, Crengle.</p><p content-type=\"COI-statement\"><bold>Conflict of Interest Disclosures:</bold> Dr Weisz reported receiving royalties from sales of <italic>Modular Approach to Treatment of Children with Anxiety, Depression, or Conduct Problems</italic>. Dr Cribb-Su’a reported receiving personal fees from Uniservices outside the submitted work and reported being an employee at one of the organizations from which participants were drawn during the course of the trial (Counties Manukau District Health Board); her role was managerial and she had no direct involvement in the running of the trial at this site. No other disclosures were reported.</p><p content-type=\"funding-statement\"><bold>Funding/Support:</bold> The Health Research Council of New Zealand funded the study. Cure Kids and the Duke Family provide salary support for Drs Merry and Stasiak.</p><p><bold>Role of the Funder/Sponsor:</bold> The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.</p><p content-type=\"data-sharing-statement\"><bold>Data Sharing Statement:</bold> See <xref ref-type=\"supplementary-material\" rid=\"note-ZOI200456-1-s\">Supplement 3</xref>.</p><p><bold>Additional Contributions:</bold> We thank the young people and families who participated in the study, the study clinicians and the different teams that participated in the study, the District Health Boards that supported the study, and the Data Monitoring Committee for their oversight and advice.</p></author-notes><pub-date pub-type=\"epub\" iso-8601-date=\"2020-08-17T10:00\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><!-- PMC Release delay is 0 months and\n\t\t\t\t\t\t0 days and was based on the <pub-date\n\t\t\t\t\t\tpub-type=\"epub\"/>. --><volume>3</volume><issue>8</issue><elocation-id>e2011799</elocation-id><history><date date-type=\"received\"><day>4</day><month>3</month><year>2020</year></date><date date-type=\"accepted\"><day>18</day><month>5</month><year>2020</year></date></history><permissions><copyright-statement>Copyright 2020 Merry SN et al. <italic>JAMA Network Open</italic>.</copyright-statement><license license-type=\"open-access\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open access article distributed under the terms of the CC-BY License.</license-p></license></permissions><self-uri content-type=\"pdf-version\" xlink:href=\"jamanetwopen-3-e2011799.pdf\">jamanetwopen-3-e2011799.pdf</self-uri><self-uri content-type=\"silverchair\" xlink:href=\"https://jamanetwork.com/journals/jamanetworkopen/fullarticle/10.1001/jamanetworkopen.2020.11799\"/><abstract abstract-type=\"key-points\"><title>Key Points</title><sec id=\"ab-zoi200456-1\"><title>Question</title><p>Is training in the Modular Approach to Therapy for Children (MATCH) associated with more use of empirically supported treatments, better clinical outcomes, and better service efficiency than usual care?</p></sec><sec id=\"ab-zoi200456-2\"><title>Findings</title><p>This randomized clinical trial found that training clinicians in MATCH was associated with high levels of adherence to empirically supported treatments (80.0%) compared with usual care (57.0%), but it was not associated with improved clinical outcomes or efficiency.</p></sec><sec id=\"ab-zoi200456-3\"><title>Meaning</title><p>These findings suggest that training in MATCH increases clinicians’ use of empirically supported treatments but does not necessarily improve clinical outcomes.</p></sec></abstract><abstract abstract-type=\"teaser\"><p>This randomized clinical trial evaluates whether clinician training in the Modular Approach to Therapy for Children (MATCH) is associated with more use of empirically supported treatment and better clinical outcomes than usual care among adolescents receiving mental health care in New Zealand.</p></abstract><abstract><sec id=\"ab-zoi200456-4\"><title>Importance</title><p>The Modular Approach to Therapy for Children (MATCH) was developed to address the comorbidities common among clinically referred youth, with beneficial outcomes shown in 2 US randomized clinical trials, where it outperformed both usual clinical care and single disorder–specific treatments.</p></sec><sec id=\"ab-zoi200456-5\"><title>Objective</title><p>To determine whether MATCH training of clinicians would result in more use of empirically supported treatment (EST) and better clinical outcomes than usual care (UC) in the publicly funded, multidisciplinary context of New Zealand.</p></sec><sec id=\"ab-zoi200456-6\"><title>Design, Setting, and Participants</title><p>This multisite, single-blind, computer-randomized clinical effectiveness trial compared MATCH with UC in child and adolescent mental health services in 5 regions of New Zealand. Recruitment occurred from March 2014 to July 2015, and a 3-month follow-up assessment was completed by May 2016. Clinicians at participating child and adolescent mental health services were randomized (1:1) to undertake training in MATCH or to deliver UC, and young people with anxiety, depression, trauma-related symptoms, or disruptive behavior seeking treatment at child and adolescent mental health services were randomized (1:1) to receive MATCH or UC. Participants and research assistants were blind to allocation. Data analysis was performed from April 2016 to July 2017.</p></sec><sec id=\"ab-zoi200456-7\"><title>Interventions</title><p>MATCH comprises EST components for flexible management of common mental health problems. UC includes case management and psychological therapies. Both can include pharmacotherapy.</p></sec><sec id=\"ab-zoi200456-8\"><title>Main Outcomes and Measures</title><p>There were 3 primary outcomes: trajectory of change of clinical severity, as measured by weekly ratings on the Brief Problem Monitor (BPM); fidelity to EST content, as measured by audio recordings of therapy sessions coded using the <italic>Therapy Integrity in Evidence Based Interventions: Observational Coding System</italic>; and efficiency of service delivery, as measured by duration of therapy (days) and clinician time (minutes).</p></sec><sec id=\"ab-zoi200456-9\"><title>Results</title><p>The study included 65 clinicians (mean age, 38.7 years; range, 23.0-64.0 years; 54 female [83%]; MATCH, 32 clinicians; UC, 33 clinicians) and 206 young people (mean age, 11.2 years; range 7.0-14.0 years; 122 female [61%]; MATCH, 102 patients; UC, 104 patients). For the BPM total ratings for parents, there was a mean (SE) slope of –1.04 (0.14) (1-year change, −6.12) in the MATCH group vs –1.04 (0.10) (1-year change, −6.17) in the UC group (effect size, 0.00; 95% CI, −0.27 to 0.28; <italic>P</italic> = .96). For the BPM total for youths, the mean (SE) slope was –0.74 (0.15) (1-year change, −4.35) in the MATCH group vs –0.73 (0.10) (1-year change, −4.32) in the UC group (effect size, −0.02; 95% CI, −0.30 to 0.26; <italic>P</italic> = .97). Primary analyses (intention-to-treat) showed no difference in clinical outcomes or efficiency despite significantly higher fidelity to EST content in the MATCH group (58 coded sessions; mean [SD], 80.0% [20.0%]) than the UC group (51 coded sessions; mean [SD], 57.0% [32.0%]; <italic>F</italic><sub>(1,108)</sub> = 23.0; <italic>P</italic> < .001). With regard to efficiency of service delivery, there were no differences in total face-to-face clinician time between the MATCH group (mean [SD], 806 [527] minutes) and the UC group (mean [SD], 677 [539] minutes) or the overall duration of therapy between the MATCH group (mean [SD], 167 [107 days]) and the UC group (mean [SD], 159 [107] days).</p></sec><sec id=\"ab-zoi200456-10\"><title>Conclusions and Relevance</title><p>MATCH significantly increased adherence to EST practices but did not improve outcomes or efficiency. The nonsuperiority of MATCH may be attributable to high levels of EST use in UC in New Zealand.</p></sec><sec id=\"ab-zoi200456-11\"><title>Trial Registration</title><p>Australian New Zealand Clinical Trials Registry Identifier: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=365921\">ACTRN12614000297628</ext-link></p></sec></abstract></article-meta></front><body><sec id=\"H1-1-ZOI200456\"><title>Introduction</title><p>Mental health problems in children and adolescents are common and persistent.<sup><xref rid=\"zoi200456r1\" ref-type=\"bibr\">1</xref>,<xref rid=\"zoi200456r2\" ref-type=\"bibr\">2</xref></sup> There are effective therapies available<sup><xref rid=\"zoi200456r3\" ref-type=\"bibr\">3</xref></sup>; however, delivering these therapies in clinical practice has been challenging.<sup><xref rid=\"zoi200456r4\" ref-type=\"bibr\">4</xref></sup> This is partly because the evidence is primarily available for single disorders or a homogeneous cluster of problems,<sup><xref rid=\"zoi200456r5\" ref-type=\"bibr\">5</xref></sup> whereas clinicians are faced with comorbid presentations that may change in focus during therapy. Clinicians may adopt a pragmatic but eclectic approach, unintentionally eroding the impact of carefully designed best clinical practice. The Modular Approach to Therapy for Children with Anxiety, Depression, Trauma, or Conduct Problems (MATCH-ADTC or MATCH, for brevity) has been designed to address the issues of flexibility and clinical complexity after a brief but comprehensive training program and has been shown to be more effective and efficient than usual care (UC).<sup><xref rid=\"zoi200456r6\" ref-type=\"bibr\">6</xref>,<xref rid=\"zoi200456r7\" ref-type=\"bibr\">7</xref></sup></p><p>In New Zealand, preregistration courses for mental health professionals do not include in-depth training in psychological therapies for children and adolescents.<sup><xref rid=\"zoi200456r8\" ref-type=\"bibr\">8</xref></sup> For many working in child and adolescent mental health services (CAMHS), training in empirically supported treatments (ESTs), if it occurs, takes place after qualifying, either informally on the job or in courses that are time-consuming. Although there have been efforts to roll out training in ESTs in New Zealand, this is piecemeal so that having consistent delivery of ESTs is challenging.</p><p>We sought to determine whether training in MATCH could improve UC in New Zealand as it had been shown to do in studies in the US. Our primary hypotheses were that training CAMHS clinicians in MATCH, compared with UC, would increase the delivery of ESTs, improve clinical outcomes, and yield equal or better efficiency of service delivery.</p></sec><sec id=\"H1-2-ZOI200456\"><title>Methods</title><p>The study protocol has been published elsewhere<sup><xref rid=\"zoi200456r9\" ref-type=\"bibr\">9</xref></sup> and is available in <xref ref-type=\"supplementary-material\" rid=\"note-ZOI200456-1-s\">Supplement 1</xref>. This study was approved by New Zealand’s Health and Disability Ethics Committee and was overseen by a Data Monitoring Committee of the Health Research Council of New Zealand. Participants provided written informed consent. This study follows the Consolidated Standards of Reporting Trials (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.equator-network.org/reporting-guidelines/consort/\">CONSORT</ext-link>) reporting guideline.</p><sec id=\"H2-1-ZOI200456\"><title>Trial Design</title><p>We performed a multisite, single-blind, randomized clinical effectiveness trial comparing MATCH with UC in CAMHS in 5 District Health Boards in New Zealand. In New Zealand, 20 District Health Boards are responsible for funding or providing health services within their district or geographical region. Participating teams in the District Health Boards provided services in rural and urban settings and included 1 Kaupapa Māori and 2 Pacific teams. Kaupapa Māori teams are those in which the philosophical doctrine incorporates the knowledge, skills, attitudes, and values of Māori (ie, Indigenous) society. Pacific teams are those in which people originating from other Pacific Islands are seen in services designed to incorporate the knowledge, skills, attitudes, and values of Pasifika societies.</p><p>Data were collected at baseline, during treatment, and 3 months after the end of treatment. The recruitment period was March 2014 to July 2015, with follow-up completed in May 2016.</p></sec><sec id=\"H2-2-ZOI200456\"><title>Randomization, Blinding, and Allocation Concealment</title><p>Randomization was at 2 levels: first, clinicians were randomized in a 1:1 ratio stratified by service or team to undertake training in MATCH at the start or at the completion of the study; and second, young people and their families were randomized in a 1:1 ratio stratified by sex and ethnicity (Māori, Pacific, or an other ethnicity) to receive MATCH or UC. The major ethnic groups in New Zealand include Māori, the Indigenous people of New Zealand (14.9% of the population), New Zealand European people (74% of the population), Asian people (11.8% of the population), and non-Māori Pacific people (7.4% of the population) who have settled in New Zealand from the Pacific Islands such as Samoa. Inequities in the health and mental health for Māori and Pacific people have led to specific mental health services being set up in some regions to attempt to reduce the inequities. However, many Māori and Pacific young people are seen in the mainstream CAMHS.</p><p>Electronically generated randomization sequences ensured allocation concealment. Young people, their families, and the research assistants collecting data were blind to allocation.</p></sec><sec id=\"H2-3-ZOI200456\"><title>Participants</title><sec id=\"H3-1-ZOI200456\"><title>Inclusion and Exclusion Criteria for the Clinicians</title><p>Clinicians were eligible if they provided clinical treatment to young people and their families at participating CAMHS and provided written, informed consent. They were invited to take part in the study by their service manager.</p></sec><sec id=\"H3-2-ZOI200456\"><title>Inclusion and Exclusion Criteria for the Young People</title><p>English-speaking young people, aged 7 to 14 years, referred to CAMHS with a primary presenting problem that included anxiety, depression, trauma-related symptoms, or disruptive behavior were eligible for the study. Families were invited to take part in the study by the intake clinician and were included if parents provided written, informed consent and the young person assented. Youth were excluded if they were already being treated, the primary focus of treatment was for another disorder or problem, or a sibling had already been recruited into the study.</p></sec></sec><sec id=\"H2-4-ZOI200456\"><title>Setting</title><p>CAMHS provide mental health care for young people aged 0 to 19 years.<sup><xref rid=\"zoi200456r10\" ref-type=\"bibr\">10</xref></sup> CAMHS are organized into multidisciplinary teams of registered health practitioners, primarily nurses with training in mental health and social workers.<sup><xref rid=\"zoi200456r11\" ref-type=\"bibr\">11</xref></sup></p></sec><sec id=\"H2-5-ZOI200456\"><title>Interventions</title><sec id=\"H3-3-ZOI200456\"><title>MATCH</title><p>MATCH is a manualized program of 33 modules addressing 4 problem domains commonly encountered in clinical practice.<sup><xref rid=\"zoi200456r12\" ref-type=\"bibr\">12</xref></sup> MATCH combines written resources, a framework for choice of intervention, and guidance from an online system for monitoring progress and providing timely feedback to clinicians. It combines empirically supported elements of existing therapies within 1 protocol and accommodates comorbidity and changes in clinical presentation during therapy.<sup><xref rid=\"zoi200456r13\" ref-type=\"bibr\">13</xref></sup></p><p>Therapists randomized to deliver MATCH were provided with 5 days’ training and then 1-hour weekly Skype-based group consultation (mean [SD] group size, 4 [1.4] individuals; range, 2-6 individuals) provided by MATCH experts (A.M.U., S.K.B., and J.H.). At the start of treatment, the young person and their family collaboratively established the top problems to be addressed.<sup><xref rid=\"zoi200456r14\" ref-type=\"bibr\">14</xref></sup> Clinicians used these problems to tailor treatment. Pharmacotherapy was used as part of standard CAMHS practice, as in UC.</p></sec><sec id=\"H3-4-ZOI200456\"><title>Usual Care</title><p>Usual care includes case management, psychological therapies, and pharmacotherapy. UC is overseen in multidisciplinary team meetings, typically focused on brief reviews and care coordination.</p></sec></sec><sec id=\"H2-6-ZOI200456\"><title>Outcomes</title><p>Demographic information was provided by parents at enrollment in the study. Because of disparities in mental health outcomes for Māori and Pacific people in New Zealand, ethnicity data were collected.</p><p>Research assistants who were blinded to participant treatment group collected most clinical measures by telephone. Additional data were collected from clinicians after the participants had been discharged.</p><sec id=\"H3-5-ZOI200456\"><title>Primary Outcomes</title><p>In keeping with the primary hypotheses, there were 3 primary outcomes. First, the trajectory of change of clinical severity was assessed using the parent-rated Brief Problem Monitor (BPM)<sup><xref rid=\"zoi200456r15\" ref-type=\"bibr\">15</xref></sup> administered weekly. Second, the fidelity (ie, adherence and competence) with which therapists used EST content<sup><xref rid=\"zoi200456r12\" ref-type=\"bibr\">12</xref></sup> was measured from audio recordings of therapy sessions using the methods and coding system adapted from those used in the initial trial of MATCH.<sup><xref rid=\"zoi200456r16\" ref-type=\"bibr\">16</xref></sup> Therapist competence was rated as follows: 0, not at all; 1, superficial or incomplete; 2, adequate but not optimal; 3, thorough; and 4, expert. Ten percent of recorded therapy sessions (MATCH and UC) were assessed by the research coding team (blind to treatment group). Ten percent of this sample were independently double-coded and had acceptable interrater agreement (mean intraclass correlation coefficient [ICC] on adherence, 0.70; mean ICC on competence, 0.67). Because the results of our initial coding of therapy sessions of UC were markedly different from those of previous studies,<sup><xref rid=\"zoi200456r6\" ref-type=\"bibr\">6</xref>,<xref rid=\"zoi200456r7\" ref-type=\"bibr\">7</xref></sup> we conducted a second round of coding with a subset of 100 randomly selected sessions, coded by experienced independent coders from the US, with 20% of this sample independently assessed by a coder in New Zealand to check for systematic discrepancies between countries. The interrater agreement was acceptable across US and New Zealand coders for adherence (mean ICC, 0.74) and competence (mean ICC, 0.73). Third, the efficiency of service delivery (extracted from logs completed by clinicians) was assessed using duration of therapy (days), clinician time (minutes), and the number of therapy sessions attended and missed.</p></sec><sec id=\"H3-6-ZOI200456\"><title>Secondary Outcomes</title><p>Secondary outcomes included the youth-rated BPM administered weekly,<sup><xref rid=\"zoi200456r15\" ref-type=\"bibr\">15</xref></sup> the parent- and youth-rated Strengths and Difficulties Questionnaire administered monthly,<sup><xref rid=\"zoi200456r17\" ref-type=\"bibr\">17</xref></sup> the parent- and youth-rated Top Problems Assessment administered weekly,<sup><xref rid=\"zoi200456r14\" ref-type=\"bibr\">14</xref></sup> the Child Health Utility (a quality of life assessment administered at baseline, discharge, and follow-up),<sup><xref rid=\"zoi200456r18\" ref-type=\"bibr\">18</xref></sup> and the number and type of diagnoses assessed at baseline and discharge using the Development and Well-Being Assessment.<sup><xref rid=\"zoi200456r19\" ref-type=\"bibr\">19</xref></sup> Prescribed medications were recorded at baseline, discharge, and follow-up. Clinician satisfaction with therapy was assessed using the Therapist Satisfaction Index.<sup><xref rid=\"zoi200456r20\" ref-type=\"bibr\">20</xref></sup> A treatment satisfaction questionnaire for parents and youth was developed for this study.</p></sec></sec><sec id=\"H2-7-ZOI200456\"><title>Measures of Harm</title><p>Reports of serious adverse events<sup><xref rid=\"zoi200456r9\" ref-type=\"bibr\">9</xref></sup> were collected and reported to the Data Monitoring Committee. At the request of the Data Monitoring Committee, we developed a measure for moderate adverse events that were reported during the study and collected from the parents through specific enquiry at follow-up.</p></sec><sec id=\"H2-8-ZOI200456\"><title>Sample Size and Power</title><p>Details of the initial sample size and changes to it have been published elsewhere.<sup><xref rid=\"zoi200456r9\" ref-type=\"bibr\">9</xref></sup> From the results of previous studies,<sup><xref rid=\"zoi200456r7\" ref-type=\"bibr\">7</xref></sup> we estimated that 200 participants would be needed for 90% power to detect a significant difference in the change that was clinically important (ie, >2 units, with an effect size of approximately 0.45) on the parent BPM change (2-tailed α = .05).</p></sec><sec id=\"H2-9-ZOI200456\"><title>Statistical Analysis</title><p>The primary analyses used the intention-to-treat population. Because neither MATCH nor UC has a fixed duration, testing of the primary clinical hypothesis compared the trajectory of change across time on the parent-rated BPM (total score) as per the original studies.<sup><xref rid=\"zoi200456r6\" ref-type=\"bibr\">6</xref>,<xref rid=\"zoi200456r7\" ref-type=\"bibr\">7</xref></sup> A mixed-effects regression model was used, with outcome = <italic>a</italic><sub>0</sub>(intercept) + <italic>a</italic><sub>1</sub>(treatment group) + <italic>a</italic><sub>2</sub>(time) + <italic>a</italic><sub>3</sub>(treatment × time), with treatment and time (log<sub>e</sub> day) treated as fixed effects and the participant intercepts and slopes as random events. Sensitivity analyses were performed to test for the consistency of treatment response across medication use, clinician site, and previous evidence-based therapy training categories, by testing the appropriate interaction terms. Per-protocol analyses included participants who completed the study, received therapy as per their allocated treatment group, and had completed at least 4 therapy sessions. For the second and third primary hypotheses and all treatment satisfaction measures, between-group comparisons were made using univariable ANOVA. Trajectories of change for secondary clinical outcomes were compared for the Strengths and Difficulties Questionnaire and Top Problems Assessment as described previously in this article. Changes in the Child Health Utility were compared between groups using univariable ANOVA to assess changes between baseline, discharge, and at 3-month follow-up. Two-tailed <italic>P</italic> < .05 was considered to indicate statistical significance. Data analysis was performed using SPSS statistical software version 25 (IBM Corp) from April 2016 to July 2017.</p></sec></sec><sec id=\"H1-3-ZOI200456\"><title>Results</title><sec id=\"H2-10-ZOI200456\"><title>Group Characteristics</title><p>Sixty-five clinicians (mean age, 38.7 years; range, 23.0-64.0 years; 54 female [83%]; 29 New Zealand European [44.6%], 11 Māori [16.9%], 6 Pacific [9.2%], 2 Asian [3.1%], and 17 other 17 [26.2%], including 5 British, 4 American, and 3 South African) were recruited and randomized to receive MATCH training (32 clinicians) or to deliver UC (33 clinicians). They were balanced with regard to age, sex, and ethnicity.</p><p>Clinicians included 19 nurses trained in mental health (29.2%), 19 social workers (29.2%), 12 clinical psychologists (18.5%), 11 occupational therapists (16.9%), and 4 other clinicians (6.2%). They had been in their current CAMHS roles for a mean (SD) of 3.5 (3.3) years (range, <3 months to 15 years). Overall clinical practice experience ranged from less than 1 year to 40 years (mean [SD], 11.4 [9.4] years). A similar percentage between groups had previously received training in relevant ESTs, such as cognitive behavioral therapy or parent management training (MATCH, 12 clinicians [38%]; UC, 11 clinicians [33%]) and had at least 10 years postqualification practice (MATCH, 15 clinicians [47%]; UC, 15 clinicians [45%]).</p><p>Two hundred six young people (mean age, 11.2 years; range 7.0-14.0 years; 122 female [61%]) (<xref rid=\"zoi200456t1\" ref-type=\"table\">Table 1</xref>), were recruited and randomized, with 6 young people (5 from MATCH, 1 from UC) leaving the study before treatment began or any data were collected. Outcome data for at least 2 time points were available for the remaining 200 participants (97%), and 164 participants (80%) completed the 3-month follow-up assessment (<xref ref-type=\"fig\" rid=\"zoi200456f1\">Figure</xref>).</p><table-wrap id=\"zoi200456t1\" orientation=\"portrait\" position=\"float\"><label>Table 1. </label><caption><title>Baseline Characteristics by Treatment Group</title></caption><table frame=\"hsides\" rules=\"groups\"><col width=\"55.43%\" span=\"1\"/><col width=\"20.47%\" span=\"1\"/><col width=\"24.1%\" span=\"1\"/><thead><tr><th rowspan=\"2\" valign=\"top\" align=\"left\" scope=\"col\" colspan=\"1\">Characteristic</th><th colspan=\"2\" valign=\"top\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">Participants, No. (%)</th></tr><tr><th valign=\"top\" colspan=\"1\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">MATCH (n = 97)</th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Usual care (n = 103)</th></tr></thead><tbody><tr><td valign=\"top\" align=\"justify\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Age at study entry, mean (SD), y</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\">10.9 (2.3)</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\">11.3 (2.4)</td></tr><tr><td valign=\"top\" align=\"justify\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Sex</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"justify\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Female</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\">59 (60.8)</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\">63 (61.2)</td></tr><tr><td valign=\"top\" align=\"justify\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Male</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\">38 (39.2)</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\">40 (38.8)</td></tr><tr><td valign=\"top\" align=\"justify\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Ethnicity (total response)<xref ref-type=\"table-fn\" rid=\"zoi200456t1n1\"><sup>a</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"justify\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> New Zealand European or European</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\">84 (86.6)</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\">91 (88.3)</td></tr><tr><td valign=\"top\" align=\"justify\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Māori</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\">16 (16.5)</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\">21 (20.4)</td></tr><tr><td valign=\"top\" align=\"justify\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Pacific</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\">10 (10.3)</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\">9 (8.7)</td></tr><tr><td valign=\"top\" align=\"justify\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Asian</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\">3 (3.1)</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\">3 (2.9)</td></tr><tr><td valign=\"top\" align=\"justify\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Other<xref ref-type=\"table-fn\" rid=\"zoi200456t1n2\"><sup>b</sup></xref></td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\">3 (3.1)</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\">2 (1.9)</td></tr><tr><td valign=\"top\" align=\"justify\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Previous use of mental health services?</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"justify\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Yes</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\">30 (30.9)</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\">48 (46.6)</td></tr><tr><td valign=\"top\" align=\"justify\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> No</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\">64 (66.0)</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\">53 (51.5)</td></tr><tr><td valign=\"top\" align=\"justify\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Missing data</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\">3 (3.1)</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\">2 (1.9)</td></tr><tr><td valign=\"top\" align=\"justify\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Service type</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"justify\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Mainstream child and adolescent mental health services</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\">85 (87.6)</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\">90 (87.4)</td></tr><tr><td valign=\"top\" align=\"justify\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Kaupapa Māori Services</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\">6 (6.2)</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\">6 (5.8)</td></tr><tr><td valign=\"top\" align=\"justify\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Pasifika Services</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\">6 (6.2)</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\">7 (6.8)</td></tr><tr><td valign=\"top\" align=\"justify\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Baseline Brief Problem Monitor, mean (SD), total score (range 0-38)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"justify\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Parent</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\">17.9 (7.3)</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\">16.0 (8.0)</td></tr><tr><td valign=\"top\" align=\"justify\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Youth</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\">13.5 (6.3)</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\">13.8 (7.0)</td></tr><tr><td valign=\"top\" align=\"justify\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Baseline Strengths and Difficulties Questionnaire, mean (SD), total difficulties score (range 0-40)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"justify\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Parent</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\">18.9 (6.2)</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\">18.1 (7.0)</td></tr><tr><td valign=\"top\" align=\"justify\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Youth</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\">16.6 (6.7)</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\">16.9 (6.5)</td></tr></tbody></table><table-wrap-foot><p>Abbreviation: MATCH, Modular Approach to Therapy for Children.</p><fn id=\"zoi200456t1n1\"><label><sup>a</sup></label><p>Ethnicity is recorded as total response; therefore, more than 1 ethnicity may be reported.</p></fn><fn id=\"zoi200456t1n2\"><label><sup>b</sup></label><p>Responses for other ethnicity were 1 Chilean, 1 Iranian, 1 Jamaican, and 2 not specified.</p></fn></table-wrap-foot></table-wrap><fig id=\"zoi200456f1\" fig-type=\"figure\" orientation=\"portrait\" position=\"float\"><label>Figure. </label><caption><title>Flow Diagram of Youth Participants</title><p>MATCH indicates Modular Approach to Therapy for Children.</p></caption><graphic xlink:href=\"jamanetwopen-3-e2011799-g001\"/></fig><p>The 2 groups of youth participants (MATCH and UC) were balanced with regard to age, sex, and ethnicity (<xref rid=\"zoi200456t1\" ref-type=\"table\">Table 1</xref>). The ethnic make-up was similar to the general New Zealand population, apart from an underrepresentation of Asian people (<xref rid=\"zoi200456t1\" ref-type=\"table\">Table 1</xref> and <xref rid=\"zoi200456t2\" ref-type=\"table\">Table 2</xref>; eTable 1 in <xref ref-type=\"supplementary-material\" rid=\"note-ZOI200456-1-s\">Supplement 2</xref>).</p><table-wrap id=\"zoi200456t2\" orientation=\"portrait\" position=\"float\"><label>Table 2. </label><caption><title>Diagnoses by Category at Baseline by Treatment Group</title></caption><table frame=\"hsides\" rules=\"groups\"><col width=\"37.47%\" span=\"1\"/><col width=\"12.52%\" span=\"1\"/><col width=\"18.37%\" span=\"1\"/><col width=\"12.11%\" span=\"1\"/><col width=\"19.53%\" span=\"1\"/><thead><tr><th rowspan=\"2\" valign=\"top\" align=\"left\" scope=\"col\" colspan=\"1\">Diagnosis<xref ref-type=\"table-fn\" rid=\"zoi200456t2n1\"><sup>a</sup></xref></th><th colspan=\"2\" valign=\"top\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">MATCH</th><th colspan=\"2\" valign=\"top\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">Usual Care</th></tr><tr><th valign=\"top\" colspan=\"1\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">Valid, %</th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Participants, No. (%)</th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Valid, %</th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Participants, No. (%)</th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Depressive disorder</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">58</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">14 (24.1)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">73</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">13 (17.8)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Anxiety disorder or posttraumatic stress disorder<xref ref-type=\"table-fn\" rid=\"zoi200456t2n2\"><sup>b</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">61</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">30 (49.2)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">76</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">49 (64.4)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Disruptive behavior disorder<xref ref-type=\"table-fn\" rid=\"zoi200456t2n3\"><sup>c</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">51</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">19 (37.2)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">71</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">19 (26.7)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Other<xref ref-type=\"table-fn\" rid=\"zoi200456t2n4\"><sup>d</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">53</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">7 (13.2)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">74</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">12 (16.3)</td></tr></tbody></table><table-wrap-foot><p>Abbreviation: MATCH, Modular Approach to Therapy for Children.</p><fn id=\"zoi200456t2n1\"><label><sup>a</sup></label><p>Diagnoses were made with the Development and Well-Being Assessment. The presence of disorder was defined as less than 50% probability or 50% probability or more.</p></fn><fn id=\"zoi200456t2n2\"><label><sup>b</sup></label><p>Includes separation anxiety, specific phobia, social phobia, panic, agoraphobia, generalized anxiety, posttraumatic stress disorder, and obsessive-compulsive disorder.</p></fn><fn id=\"zoi200456t2n3\"><label><sup>c</sup></label><p>Includes oppositional or conduct disorders.</p></fn><fn id=\"zoi200456t2n4\"><label><sup>d</sup></label><p>Includes autism spectrum disorder, tics, self-harm, bipolar disorder, hyperactivity, and anorexia or bulimia.</p></fn></table-wrap-foot></table-wrap></sec><sec id=\"H2-11-ZOI200456\"><title>Clinical Outcomes</title><p>There were no significant differences between groups on the primary outcome measure, the trajectory of change for total difficulties on the BPM as reported by parents (mean [SE] slope, –1.04 [0.14] and 1-year change of −6.12 in the MATCH group vs –1.04 [0.10] and 1-year change of −6.17 in the UC group; effect size, 0.00; 95% CI, – 0.27 to – 0.28; <italic>P</italic> = .96) and as reported by youths (mean [SE] slope, –0.74 [0.15] and 1-year change of −4.35 in the MATCH group vs –0.73 [0.10] and 1-year change of −4.32 in the UC group; effect size, −0.02; 95% CI, −0.30 to 0.26; <italic>P</italic> = .97) or any other clinical outcome measure (<xref rid=\"zoi200456t3\" ref-type=\"table\">Table 3</xref>). Both groups improved significantly, with effects maintained to 3 months of follow-up (eTable 2 in <xref ref-type=\"supplementary-material\" rid=\"note-ZOI200456-1-s\">Supplement 2</xref>). Sensitivity analyses on the primary clinical outcome showed that the result was not affected by medication use at baseline (<italic>F</italic><sub>1,183</sub> = 0.248; <italic>P</italic> = .78), prior relevant EST accreditation of the clinician (<italic>F</italic><sub>1,183</sub> = 0.296; <italic>P</italic> = .14), or study site (<italic>F</italic><sub>1,184</sub> = 0.483; <italic>P</italic> = .94). There were also no significant differences in the per-protocol analysis on the primary clinical outcome (72 participants in the MATCH group vs 84 participants in the UC group).</p><table-wrap id=\"zoi200456t3\" orientation=\"portrait\" position=\"float\"><label>Table 3. </label><caption><title>Clinical Outcomes: Trajectories of Change by Treatment Group<xref ref-type=\"table-fn\" rid=\"zoi200456t3n1\"><sup>a</sup></xref></title></caption><table frame=\"hsides\" rules=\"groups\"><col width=\"19.42%\" span=\"1\"/><col width=\"14.05%\" span=\"1\"/><col width=\"11.87%\" span=\"1\"/><col width=\"13.16%\" span=\"1\"/><col width=\"12.03%\" span=\"1\"/><col width=\"20.47%\" span=\"1\"/><col width=\"9%\" span=\"1\"/><thead><tr><th rowspan=\"2\" valign=\"top\" align=\"left\" scope=\"col\" colspan=\"1\">Outcome</th><th colspan=\"2\" valign=\"top\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">MATCH (n = 97)</th><th colspan=\"2\" valign=\"top\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">Usual care (n = 103)</th><th rowspan=\"2\" valign=\"top\" align=\"left\" scope=\"col\" colspan=\"1\">Effect size (95% CI)</th><th rowspan=\"2\" valign=\"top\" align=\"left\" scope=\"col\" colspan=\"1\"><italic>P</italic> value</th></tr><tr><th valign=\"top\" colspan=\"1\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">Slope, mean (SE)</th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">1-Year change</th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Slope, mean (SE)</th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">1-Year change</th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">BPM, total</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Parent</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–1.04 (0.14)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–6.12</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–1.04 (0.10)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–6.17</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.00 (–0.27 to 0.28)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.96</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Youth</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–0.74 (0.15)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–4.35</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–0.73 (0.10)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–4.32</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–0.02 (–0.30 to 0.26)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.97</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">BPM, internalizing</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Parent</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–0.66 (0.09)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–3.87</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–0.63 (0.07)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–3.71</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.04 (–0.24 to 0.32)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.78</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Youth</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–0.46 (0.09)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–2.71</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–0.38 (0.06)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–2.23</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.13 (–0.15 to 0.41)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.37</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">BPM, externalizing</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Parent</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–0.38 (0.08)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–2.24</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–0.41 (0.06)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–2.42</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–0.05 (–0.33 to 0.23)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.72</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Youth</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–0.27 (0.08)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–1.62</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–0.36 (0.05)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–2.10</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.30</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">SDQ, total difficulties</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Parent</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–1.17 (0.15)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–6.92</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–1.17 (0.11)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–6.88</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.01 (–0.27 to 0.29)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.96</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Youth</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–0.89 (0.16)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–5.27</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–1.09 (0.11)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–6.46</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–0.17 (–0.45 to 0.11)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.22</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">SDQ, internalizing</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Parent</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–0.70 (0.11)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–4.11</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–0.73 (0.07)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–4.33</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–0.05 (–0.33 to 0.23)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.72</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Youth</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–0.50 (0.10)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–2.92</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–0.59 (0.07)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–3.48</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–0.14 (–0.42 to 0.14)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.33</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">SDQ, externalizing</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Parent</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–0.46 (0.08)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–2.70</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–0.42 (0.06)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–2.50</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.06 (–0.22 to 0.34)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.69</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Youth</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–0.38 (0.10)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–2.25</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–0.49 (0.07)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–2.90</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–0.16 (–0.44 to 0.12)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.26</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Top problems assessment</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Parent</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–0.85 (0.09)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–5.03</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–0.81 (0.06)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–4.77</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.07 (–0.21 to 0.35)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.61</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Youth</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–0.94 (0.10)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–5.56</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–0.82 (0.07)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–4.84</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.17 (–0.11 to 0.45)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.24</td></tr></tbody></table><table-wrap-foot><p>Abbreviations: BPM, Brief Problem Monitor; MATCH, Modular Approach to Therapy for Children; SDQ, Strengths and Difficulties Questionnaire.</p><fn id=\"zoi200456t3n1\"><label><sup>a</sup></label><p>The slope is the estimate of the change in scale score per log day, and the 1-year change is the estimate of the change in scale score 1 year after the initial assessment. The primary clinical outcome was the trajectory of change of parent BPM.</p></fn></table-wrap-foot></table-wrap><p>There were no significant differences between groups in the number of diagnoses from before to after treatment (eTable 3 in <xref ref-type=\"supplementary-material\" rid=\"note-ZOI200456-1-s\">Supplement 2</xref>). Prescription of medications for psychiatric conditions did not differ between treatment groups at baseline or during therapy (eTable 4 in <xref ref-type=\"supplementary-material\" rid=\"note-ZOI200456-1-s\">Supplement 2</xref>).</p></sec><sec id=\"H2-12-ZOI200456\"><title>Delivery of EST</title><p>EST content adherence was significantly higher in the MATCH group (mean [SD] level of adherence, 80.0% [20.0%] for 58 coded sessions) than in the UC group (mean [SD] level of adherence, 57.0% [32.0%] for 51 coded sessions) (<italic>F</italic><sub>1,108</sub> = 23.0; <italic>P</italic> < .001). Therapist competence ratings in delivery of EST content were also significantly higher in the MATCH group (mean [SD], 2.30 [0.57]) than in the UC group (mean [SD], 1.75 [0.50]) (<italic>F</italic><sub>1,108</sub> = 8.0; <italic>P</italic> = .001), with both adherence and competence corresponding most closely to an adequate but not optimal rating.<sup><xref rid=\"zoi200456r10\" ref-type=\"bibr\">10</xref></sup> The second round of coding showed almost identical findings, with greater adherence in the MATCH group (58 participants; mean [SD] level of adherence, 81.0% [22.1%]) than in the UC group (42 participants; mean [SD] level of adherence, 56.0% [30.2%]) (<italic>F</italic><sub>1,76</sub> = 10.7; <italic>P</italic> = .002) and greater therapist competence in the MATCH group (mean [SD] rating, 2.25 [0.62]) compared with the UC group (mean [SD] rating, 1.76 [0.64]) (<italic>F</italic><sub>1,79</sub> = 9.1; <italic>P</italic> = .003).</p></sec><sec id=\"H2-13-ZOI200456\"><title>Efficiency of Service Delivery</title><p>Participants receiving MATCH attended significantly more therapy sessions than did participants in the UC group (mean [SD], 13.4 [8.4] sessions vs 10.7 [7.7] sessions; <italic>F</italic><sub>1,190</sub> = 5.6; <italic>P</italic> = .02). However, there were no significant differences in total face-to-face clinician time (mean [SD], 806 [527] minutes in the MATCH group vs 677 [539] minutes in the UC group) or the overall duration of therapy (mean [SD], 167 [107] days in the MATCH group vs 159 [107] days in the UC group) (<xref rid=\"zoi200456t4\" ref-type=\"table\">Table 4</xref>).</p><table-wrap id=\"zoi200456t4\" orientation=\"portrait\" position=\"float\"><label>Table 4. </label><caption><title>Service Delivery Outcomes by Treatment Group</title></caption><table frame=\"hsides\" rules=\"groups\"><col width=\"28.6%\" span=\"1\"/><col width=\"11.13%\" span=\"1\"/><col width=\"12.68%\" span=\"1\"/><col width=\"11.13%\" span=\"1\"/><col width=\"12.68%\" span=\"1\"/><col width=\"13%\" span=\"1\"/><col width=\"10.78%\" span=\"1\"/><thead><tr><th rowspan=\"2\" valign=\"top\" align=\"left\" scope=\"col\" colspan=\"1\">Outcome</th><th colspan=\"2\" valign=\"top\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">MATCH (n = 97)</th><th colspan=\"2\" valign=\"top\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">Usual care (n = 103)</th><th rowspan=\"2\" valign=\"top\" align=\"left\" scope=\"col\" colspan=\"1\">Effect size</th><th rowspan=\"2\" valign=\"top\" align=\"left\" scope=\"col\" colspan=\"1\"><italic>P</italic> Value</th></tr><tr><th valign=\"top\" colspan=\"1\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">Valid, %</th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Mean (SD)</th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Valid, %</th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Mean (SD)</th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Clinician time, min</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">91</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">806 (527)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">99</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">677 (539)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.24</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.10</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Duration of contact, d</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">90</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">167 (107)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">100</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">159 (107)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.07</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.61</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Attended therapy sessions, No.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">92</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">13.4 (8.4)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">100</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">10.7 (7.7)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.34</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.02</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Missed therapy sessions, No.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">92</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.7 (3.0)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">100</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.2 (3.4)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.16</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.28</td></tr></tbody></table><table-wrap-foot><p>Abbreviation: MATCH, Modular Approach to Therapy for Children.</p></table-wrap-foot></table-wrap></sec><sec id=\"H2-14-ZOI200456\"><title>Satisfaction With Treatment</title><p>MATCH therapists were significantly more satisfied with the treatment they had provided (mean [SD] Therapist Satisfaction Index total scores, 4.0 [0.6] for MATCH delivered to 79 participants vs 3.7 [0.6] for UC delivered to 88 participants; <italic>F</italic><sub>1,162</sub> = 9.567; <italic>P</italic> = .002). Parent-rated and youth-rated total satisfaction scores (maximum of 32 and 20, respectively) were high and not significantly different between treatment groups (mean [SD] scores for parents, 25.7 [6.5] for MATCH vs 24.3 [7.3] for UC; <italic>F</italic><sub>1,170</sub> = 1.780; <italic>P</italic> = .19; mean [SD] scores for youth, 16.4 [3.7] for MATCH vs 15.5 [4.2] for UC; <italic>F</italic><sub>1,158</sub> = 2.076; <italic>P</italic> = .16).</p></sec><sec id=\"H2-15-ZOI200456\"><title>Harms</title><p>There were no significant group differences in terms of serious and moderate adverse events (eTable 5 in <xref ref-type=\"supplementary-material\" rid=\"note-ZOI200456-1-s\">Supplement 2</xref>). Initial treatment focus by group is shown in eTable 6 in <xref ref-type=\"supplementary-material\" rid=\"note-ZOI200456-1-s\">Supplement 2</xref>.</p></sec></sec><sec id=\"H1-4-ZOI200456\"><title>Discussion</title><p>Training in MATCH resulted in significantly improved delivery of ESTs by clinicians, and the trajectory of change in clinical outcomes resembled that found in other trials of MATCH.<sup><xref rid=\"zoi200456r6\" ref-type=\"bibr\">6</xref>,<xref rid=\"zoi200456r7\" ref-type=\"bibr\">7</xref></sup> However, the increased delivery of EST did not translate into improved clinical outcomes or efficiency. Young people in both treatment groups improved similarly at discharge and maintained this improvement at 3 months’ follow-up. Effects were not moderated by individual services, clinician training, medication use, or initial focus of treatment.</p><p>In 2 previous RCTs,<sup><xref rid=\"zoi200456r6\" ref-type=\"bibr\">6</xref>,<xref rid=\"zoi200456r7\" ref-type=\"bibr\">7</xref></sup> MATCH improved clinical outcomes and service efficiency, increased EST use compared with UC, and increased use of standard manual-based behavioral and cognitive behavioral therapy.<sup><xref rid=\"zoi200456r7\" ref-type=\"bibr\">7</xref></sup> It is notable that similar clinical change was achieved in our study despite fewer therapy sessions and shorter duration than the previous studies. Our study was adequately powered and had more participants per group (200 participants across 2 groups) than the other 2 studies, with 174 participants across 3 groups<sup><xref rid=\"zoi200456r7\" ref-type=\"bibr\">7</xref></sup> and 138 participants across 2 groups.<sup><xref rid=\"zoi200456r6\" ref-type=\"bibr\">6</xref></sup> The training and weekly consultation in all 3 studies was provided by experts from the developers’ team. Despite the use of group rather than individual consultation, the delivery of EST (80.0% adherence in the present study compared with 83% in a previous study<sup><xref rid=\"zoi200456r7\" ref-type=\"bibr\">7</xref></sup>) and the trajectory of clinical change in the MATCH group (−1.04 in the current study compared with −0.94 in a previous study<sup><xref rid=\"zoi200456r7\" ref-type=\"bibr\">7</xref></sup>) was as good as that achieved in the first trial of MATCH.<sup><xref rid=\"zoi200456r7\" ref-type=\"bibr\">7</xref></sup> The populations studied, clinical severity and context, and measures used in the 3 studies were very similar. The main difference between the studies lies in the extent of EST use in the UC groups, which was 7% to 8% in both previous studies<sup><xref rid=\"zoi200456r6\" ref-type=\"bibr\">6</xref>,<xref rid=\"zoi200456r7\" ref-type=\"bibr\">7</xref></sup> compared with 57.0% in this study. In addition, the trajectory of change in our UC group was equivalent to that in the MATCH group, in contrast to the flatter trajectory in UC in the other 2 studies.<sup><xref rid=\"zoi200456r6\" ref-type=\"bibr\">6</xref>,<xref rid=\"zoi200456r7\" ref-type=\"bibr\">7</xref></sup> An additional RCT comparing MATCH with UC has recently been published<sup><xref rid=\"zoi200456r21\" ref-type=\"bibr\">21</xref></sup> and found delivery rates of EST of 67% for MATCH clinicians and 27% for UC clinicians, but there were no differences between MATCH and UC for any clinical measure. Perhaps 57.0% adherence to ESTs results in clinical outcomes similar to those for 80.0% adherence, whereas 7% to 8%, as reported in the US,<sup><xref rid=\"zoi200456r6\" ref-type=\"bibr\">6</xref>,<xref rid=\"zoi200456r7\" ref-type=\"bibr\">7</xref></sup> does not. The most recent trial of MATCH<sup><xref rid=\"zoi200456r22\" ref-type=\"bibr\">22</xref></sup> also showed MATCH clinicians providing higher levels of EST than UC but no differences in clinical outcomes.</p><p>Training in MATCH is consistently associated with improved levels of EST delivered, whereas the variation in level of EST provided in UC across the 4 RCTs is marked. We considered the possibility of contamination between MATCH and UC, with UC clinicians learning MATCH techniques through team meetings that are the norm in New Zealand. However, case discussions in team meetings are not detailed, and measures to protect against contamination in the current study were identical to those in previous studies.<sup><xref rid=\"zoi200456r6\" ref-type=\"bibr\">6</xref>,<xref rid=\"zoi200456r7\" ref-type=\"bibr\">7</xref></sup> The intensive monitoring involved in this study that may have improved UC is also common to all studies, and so is unlikely to explain the differences. The efforts undertaken to upskill the workforce through training in individual ESTs over the last 2 decades may have been successful in improving UC in New Zealand.</p><sec id=\"H2-16-ZOI200456\"><title>Strengths and Limitations</title><p>Strengths of this study include the multisite clinical settings to maximize generalizability, measurement of adverse events and clinical changes, allocation concealment, and blinded assessments and analyses.<sup><xref rid=\"zoi200456r9\" ref-type=\"bibr\">9</xref></sup> Limitations include the underrepresentation of Asian young people, the single-blind nature of the study, and the low percentage of Development and Well-Being Assessment completion. The ICC ratings for the coding of ESTs were lower than we would have liked. There was no clear pattern of disagreement, although the sample of sessions used for coding fidelity, which was appropriate for assessing overall intercoder reliability of the system within New Zealand and between New Zealand and US coders, was not large enough to do more fine-grained analyses.</p></sec></sec><sec id=\"H1-5-ZOI200456\"><title>Conclusions</title><p>These findings suggest that a brief 5-day training in MATCH resulted in a significant increase in the delivery of ESTs. The lack of change in clinical outcome in our study despite a significant change in delivery of ESTs and in contrast to previous studies may be partly explained by the high level of EST delivered by UC clinicians. 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] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">JAMA Netw Open</journal-id><journal-id journal-id-type=\"iso-abbrev\">JAMA Netw Open</journal-id><journal-id journal-id-type=\"pmc\">JAMA Netw Open</journal-id><journal-title-group><journal-title>JAMA Network Open</journal-title></journal-title-group><issn pub-type=\"epub\">2574-3805</issn><publisher><publisher-name>American Medical Association</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32804217</article-id><article-id pub-id-type=\"pmc\">PMC7431994</article-id><article-id pub-id-type=\"doi\">10.1001/jamanetworkopen.2020.13605</article-id><article-id pub-id-type=\"publisher-id\">zoi200517</article-id><article-categories><subj-group subj-group-type=\"category\" specific-use=\"electronic\"><subject>Research</subject></subj-group><subj-group subj-group-type=\"heading\"><subject>Original Investigation</subject></subj-group><subj-group subj-group-type=\"online-only\"><subject>Online Only</subject></subj-group><subj-group subj-group-type=\"subject-area\"><subject>Substance Use and Addiction</subject></subj-group></article-categories><title-group><article-title>National Patterns in Prescription Opioid Use and Misuse Among Cancer Survivors in the United States</article-title><alt-title alt-title-type=\"headline\">National Patterns in Prescription Opioid Use and Misuse Among Adult Cancer Survivors</alt-title><alt-title alt-title-type=\"running-head\">National Patterns in Prescription Opioid Use and Misuse Among Adult Cancer Survivors</alt-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Jairam</surname><given-names>Vikram</given-names></name><degrees>MD</degrees><xref ref-type=\"aff\" rid=\"zoi200517aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Yang</surname><given-names>Daniel X.</given-names></name><degrees>MD</degrees><xref ref-type=\"aff\" rid=\"zoi200517aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Verma</surname><given-names>Vivek</given-names></name><degrees>MD</degrees><xref ref-type=\"aff\" rid=\"zoi200517aff2\"><sup>2</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Yu</surname><given-names>James B.</given-names></name><degrees>MD</degrees><degrees>MHS</degrees><xref ref-type=\"aff\" rid=\"zoi200517aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"zoi200517aff3\"><sup>3</sup></xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>Park</surname><given-names>Henry S.</given-names></name><degrees>MD</degrees><degrees>MPH</degrees><xref ref-type=\"aff\" rid=\"zoi200517aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"zoi200517aff3\"><sup>3</sup></xref></contrib></contrib-group><aff id=\"zoi200517aff1\"><label>1</label>Department of Therapeutic Radiology, Yale University School of Medicine, New Haven, Connecticut</aff><aff id=\"zoi200517aff2\"><label>2</label>Department of Radiation Oncology, Allegheny General Hospital, Pittsburgh, Pennsylvania</aff><aff id=\"zoi200517aff3\"><label>3</label>Cancer Outcomes, Public Policy, and Effectiveness Research Center, Yale School of Medicine, New Haven, Connecticut</aff><author-notes><title>Article Information</title><p><bold>Accepted for Publication:</bold> June 2, 2020.</p><p content-type=\"published-online\"><bold>Published:</bold> August 17, 2020. doi:<uri content-type=\"doi\">10.1001/jamanetworkopen.2020.13605</uri></p><p content-type=\"open-access-note\"><bold>Open Access:</bold> This is an open access article distributed under the terms of the <ext-link ext-link-type=\"uri\" xlink:href=\"https://jamanetwork.com/journals/jamanetworkopen/pages/instructions-for-authors#SecOpenAccess\">CC-BY License</ext-link>. © 2020 Jairam V et al. <italic>JAMA Network Open</italic>.</p><corresp id=\"zoi200517cor1\"><bold>Corresponding Author:</bold> Henry S. Park, MD, MPH, Department of Therapeutic Radiology, Yale University School of Medicine, 35 Park St, Lower Level, New Haven, CT 06520 (<email xlink:href=\"henry.park@yale.edu\">henry.park@yale.edu</email>).</corresp><p content-type=\"author-contributions\"><bold>Author Contributions:</bold> Dr Jairam had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.</p><p><italic>Concept and design:</italic> Jairam, Yang, Yu, Park.</p><p><italic>Acquisition, analysis, or interpretation of data: </italic>Jairam, Yang, Verma.</p><p><italic>Drafting of the manuscript:</italic> Jairam.</p><p><italic>Critical revision of the manuscript for important intellectual content:</italic> All authors.</p><p><italic>Statistical analysis:</italic> Jairam.</p><p><italic>Administrative, technical, or material support:</italic> Park.</p><p><italic>Supervision:</italic> Yu, Park.</p><p content-type=\"COI-statement\"><bold>Conflict of Interest Disclosures:</bold> Dr Yu reported receiving research funding from 21st Century Oncology and serving as a consultant for Augmentix outside the submitted work. Dr Park reported receiving honoraria from RadOncQuestions LLC outside the submitted work. No other disclosures were reported.</p></author-notes><pub-date pub-type=\"epub\" iso-8601-date=\"2020-08-17T10:00\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><!-- PMC Release delay is 0 months and\n\t\t\t\t\t\t0 days and was based on the <pub-date\n\t\t\t\t\t\tpub-type=\"epub\"/>. --><volume>3</volume><issue>8</issue><elocation-id>e2013605</elocation-id><history><date date-type=\"received\"><day>21</day><month>3</month><year>2020</year></date><date date-type=\"accepted\"><day>2</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>Copyright 2020 Jairam V et al. <italic>JAMA Network Open</italic>.</copyright-statement><license license-type=\"open-access\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open access article distributed under the terms of the CC-BY License.</license-p></license></permissions><self-uri content-type=\"pdf-version\" xlink:href=\"jamanetwopen-3-e2013605.pdf\">jamanetwopen-3-e2013605.pdf</self-uri><self-uri content-type=\"silverchair\" xlink:href=\"https://jamanetwork.com/journals/jamanetworkopen/fullarticle/10.1001/jamanetworkopen.2020.13605\"/><abstract abstract-type=\"key-points\"><title>Key Points</title><sec id=\"ab-zoi200517-1\"><title>Question</title><p>How do patterns in prescription opioid use and misuse among cancer survivors compare with those among populations without cancer?</p></sec><sec id=\"ab-zoi200517-2\"><title>Findings</title><p>In this cross-sectional study of 169 162 adult respondents, cancer survivors with an incidence of cancer or cancer treatment in the past year had a nearly 2-fold higher likelihood of prescription opioid use compared with respondents without cancer; prescription opioid use decreased among long-term cancer survivors but remained higher compared with the cohort without cancer. Prescription opioid misuse was similar among cancer survivors compared with respondents without cancer.</p></sec><sec id=\"ab-zoi200517-3\"><title>Meaning</title><p>In this study, cancer survivors reported higher rates of prescription opioid use compared with respondents without cancer, but this finding may not translate to increased opioid misuse at a given time.</p></sec></abstract><abstract abstract-type=\"teaser\"><p>This cross-sectional study of results from the National Survey on Drug Use and Health compares patterns of opioid use and misuse among cancer survivors with respondents without a history of cancer.</p></abstract><abstract><sec id=\"ab-zoi200517-4\"><title>Importance</title><p>Prescription opioids are frequently prescribed to treat cancer-related pain. However, limited information exists regarding rates of prescription opioid use and misuse in populations with cancer.</p></sec><sec id=\"ab-zoi200517-5\"><title>Objectives</title><p>To estimate the prevalence and likelihood of prescription opioid use and misuse in adult cancer survivors compared with respondents without cancer and to identify characteristics associated with prescription opioid use and misuse in adult cancer survivors.</p></sec><sec id=\"ab-zoi200517-6\"><title>Design, Setting, and Participants</title><p>This cross-sectional study is a retrospective, population-based study using data from 169 162 respondents to the National Survey on Drug Use and Health from January 2015 to December 2018. Survey data sets were queried for all respondents aged 18 years or older. Those with a reported history of cancer were termed <italic>cancer survivors</italic> and further divided into <italic>more recent</italic> (had cancer within 12 months of survey) and <italic>less recent</italic> (had cancer more than 12 months prior to survey) cohorts. Respondents with nonmelanoma skin cancer were excluded.</p></sec><sec id=\"ab-zoi200517-7\"><title>Main Outcomes and Measures</title><p>Prescription opioid use and misuse within the past 12 months.</p></sec><sec id=\"ab-zoi200517-8\"><title>Results</title><p>Among 169 162 respondents, 5139 (5.2%) were cancer survivors, with 1243 (1.2%) and 3896 (4.0%) reporting having more recent and less recent cancer histories, respectively. Higher rates of prescription opioid use were observed among more recent cancer survivors (54.3%; 95% CI, 50.2%-58.4%; odds ratio [OR], 1.86; 95% CI, 1.57-2.20; <italic>P</italic> < .001) and less recent cancer survivors (39.2%; 95% CI, 37.3%-41.2%; OR, 1.18; 95% CI, 1.08-1.28; <italic>P</italic> < .001) compared with respondents without cancer (30.5%, reference group). Rates of prescription opioid misuse were similar among more recent (3.5%; 95% CI, 2.4%-5.2%; OR, 1.27; 95% CI, 0.82-1.96; <italic>P</italic> = .36) and less recent (3.0%; 95% CI, 2.4%-3.6%; OR, 1.03; 95% CI, 0.83-1.28; <italic>P</italic> = .76) survivors compared with respondents without cancer (4.3%, reference group). Younger age (aged 18-34 years vs ≥65 years: OR, 7.06; 95% CI, 3.03-16.41; <italic>P</italic> < .001), alcohol use disorder (OR, 3.22; 95% CI, 1.45-7.14; <italic>P</italic> = .005), and nonopioid drug use disorder (OR, 14.76; 95% CI, 7.40-29.44; <italic>P</italic> < .001) were associated with prescription opioid misuse among cancer survivors.</p></sec><sec id=\"ab-zoi200517-9\"><title>Conclusions and Relevance</title><p>In this study, prescription opioid use was higher among more and less recent cancer survivors compared with the population without a history of cancer. Rates of prescription opioid misuse were low and similar among all 3 cohorts. These findings suggest that higher prescription opioid use among cancer survivors may not correspond to increased short-term or long-term misuse.</p></sec></abstract></article-meta></front><body><sec id=\"H1-1-ZOI200517\"><title>Introduction</title><p>Cancer-related pain is a common complication among cancer survivors and can negatively affect quality of life.<sup><xref rid=\"zoi200517r1\" ref-type=\"bibr\">1</xref></sup> As many as 60% of patients with cancer undergoing active treatment and 33% of cancer survivors may experience cancer-related pain.<sup><xref rid=\"zoi200517r1\" ref-type=\"bibr\">1</xref>,<xref rid=\"zoi200517r2\" ref-type=\"bibr\">2</xref>,<xref rid=\"zoi200517r3\" ref-type=\"bibr\">3</xref>,<xref rid=\"zoi200517r4\" ref-type=\"bibr\">4</xref></sup> Recent reports have also shown higher prescription rates for opioids among cancer survivors compared with the general population.<sup><xref rid=\"zoi200517r5\" ref-type=\"bibr\">5</xref>,<xref rid=\"zoi200517r6\" ref-type=\"bibr\">6</xref>,<xref rid=\"zoi200517r7\" ref-type=\"bibr\">7</xref>,<xref rid=\"zoi200517r8\" ref-type=\"bibr\">8</xref>,<xref rid=\"zoi200517r9\" ref-type=\"bibr\">9</xref></sup> As cancer treatments advance, survival rates will increase accordingly. The American Cancer Society estimates that there were more than 17 million US residents alive with cancer in 2019, with a projected increase to 22 million by 2030.<sup><xref rid=\"zoi200517r10\" ref-type=\"bibr\">10</xref></sup> Therefore, there is a growing need for health care professionals to optimize symptom management for cancer survivors.</p><p>Prescription opioids are often used for the treatment of cancer-related pain. While effective at treating moderate to severe cancer-related pain,<sup><xref rid=\"zoi200517r3\" ref-type=\"bibr\">3</xref></sup> opioids also have the potential to be highly addictive. Severe cases of opioid abuse may lead to overdose and death. This fact is underscored by the current nationwide opioid epidemic, in which more than 47 000 people died of opioid-related overdoses in 2017.<sup><xref rid=\"zoi200517r11\" ref-type=\"bibr\">11</xref></sup> Although some studies suggest cancer survivors may have a reduced risk of opioid-related death compared with their counterparts without cancer<sup><xref rid=\"zoi200517r12\" ref-type=\"bibr\">12</xref>,<xref rid=\"zoi200517r13\" ref-type=\"bibr\">13</xref></sup> and that they adhere to their prescribed opioid regimen,<sup><xref rid=\"zoi200517r14\" ref-type=\"bibr\">14</xref></sup> it is unclear how prescription opioid use compares between cancer survivors and patients without cancer. Moreover, reports have shown that a subset of cancer survivors may be at risk for prescription opioid misuse,<sup><xref rid=\"zoi200517r15\" ref-type=\"bibr\">15</xref>,<xref rid=\"zoi200517r16\" ref-type=\"bibr\">16</xref>,<xref rid=\"zoi200517r17\" ref-type=\"bibr\">17</xref></sup> although these studies have generally been conducted in specific populations or in single-institution settings. A national analysis of prescription opioid use and misuse among cancer survivors could serve to inform opioid-prescribing practices by health care professionals in oncology treatment fields.</p><p>This study provides a comprehensive analysis of prescription opioid use and misuse among adult cancer survivors. The aims of this study were to identify the prevalence of prescription opioid use and misuse among cancer survivors, identify factors associated with prescription opioid use and misuse, and compare rates of opioid use and misuse between cancer survivors and the population without cancer.</p></sec><sec id=\"H1-2-ZOI200517\"><title>Methods</title><sec id=\"H2-1-ZOI200517\"><title>Data Source and Population</title><p>The National Survey on Drug Use and Health (NSDUH), conducted by the Substance Abuse and Mental Health Data Archive, is a large national survey on the use of illicit drugs, alcohol, and tobacco as well as on mental health issues among US civilians aged 12 years or older.<sup><xref rid=\"zoi200517r18\" ref-type=\"bibr\">18</xref></sup> Each year, approximately 70 000 individuals are randomly selected across the US to participate, excluding unhoused persons not in shelters, military personnel on active duty, and institutionalized residents (ie, individuals in jails and hospitals). The NSDUH uses a stratified, multistage area probability design that provides estimates at both the state and national level using sample weights.<sup><xref rid=\"zoi200517r19\" ref-type=\"bibr\">19</xref></sup> The NSDUH underwent a partial redesign in 2015 with the implementation of new measures, which included the introduction of specific questions on cancer history and type of cancer that were absent from previous versions of the NSDUH. Therefore, survey data prior to 2015 were excluded from analysis. This study followed Strengthening the Reporting of Observational Studies in Epidemiology (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.equator-network.org/reporting-guidelines/strobe/\">STROBE</ext-link>) reporting guideline for cross-sectional studies.<sup><xref rid=\"zoi200517r20\" ref-type=\"bibr\">20</xref></sup> The Yale Human Investigations Committee granted this study an institutional review board exemption and requirements for informed consent were waived because the data was publicly available and deidentified.</p><p>The 2015 to 2018 NSDUH data sets were queried for all respondents aged 18 years or older. Respondents were asked whether they had ever been told they had cancer by a doctor or other health care professional. Participants responding yes were termed cancer survivors and were further divided by recency of cancer history into more recent (had cancer within 12 months of survey) and less recent (had cancer more than 12 months prior to survey) cancer history cohorts. For each respondent, comorbidity history was recorded based on whether the participant reported having certain health conditions. For each cancer survivor, the type of cancer was recorded. Respondents with nonmelanoma skin cancers were excluded because of their low likelihood of requiring opioids for cancer-related pain. A flow diagram depicting the inclusion and exclusion criteria is shown in <xref ref-type=\"fig\" rid=\"zoi200517f1\">Figure 1</xref>.</p><fig id=\"zoi200517f1\" fig-type=\"figure\" orientation=\"portrait\" position=\"float\"><label>Figure 1. </label><caption><title>Flow Diagram of Inclusion and Exclusion Criteria for Study</title></caption><graphic xlink:href=\"jamanetwopen-3-e2013605-g001\"/></fig><p><italic>Prescription opioid use</italic> was defined as the use of any prescription opioid in the last 12 months as directed by a doctor. <italic>Prescription opioid misuse</italic> was defined as the use of any prescription opioid in the last 12 months in any way not directed by a doctor. This category included respondents who were prescribed opioids by physicians but did not use them as instructed (eg, using greater doses, higher frequencies, or a longer duration than prescribed) as well as those who were not prescribed opioids but received prescription opioids from other sources (eg, friends or relatives). Prescription opioid misuse also included respondents who, within the past 12 months, met the criteria for prescription opioid abuse or dependence as defined by the American Psychiatric Association <italic>Diagnostic and Statistical Manual of Mental Disorders</italic> (Fourth edition).</p></sec><sec id=\"H2-2-ZOI200517\"><title>Statistical Analysis</title><p>Baseline sociodemographic and clinical characteristics were compared between cancer survivors and respondents without cancer using the Pearson χ<sup>2</sup> test. Weighted prevalence estimates of prescription opioid use and misuse were stratified by cancer type. Unadjusted and adjusted rates of prescription opioid use and misuse were compared between cancer survivors and participants without cancer using the Pearson χ<sup>2</sup> test and multivariable logistic regression models, respectively. A second set of multivariable models was created to identify factors associated with prescription opioid use and misuse among cancer survivors. Models were adjusted for age, sex, race, survey year, education level, insurance status, income, urban/rural status, employment status, marital status, self-reported health status, depression history, alcohol use disorder, and nonopioid drug use disorder. Evaluation of depression history, alcohol use disorder, or nonopioid drug use disorder were limited to the 12 months prior to the survey. Adjusted rates of prescription opioid use and misuse over time were also estimated, with estimated margins used to define prevalence. To account for multiple comparisons, a Bonferroni correction was applied, based on the 2 primary outcome variables (prescription opioid use and misuse), with an adjusted significance threshold for 2-sided <italic>P</italic> < .025 (ie, .05 / 2). Sample weighting was used in all models to account for the complex survey design of the NSDUH and to obtain nationally representative estimates. Weighted interview response rates ranged from 66% to 69% between 2015 and 2018. Data analysis was carried out using Stata MP version 16.0 (StataCorp LP).</p></sec></sec><sec id=\"H1-3-ZOI200517\"><title>Results</title><sec id=\"H2-3-ZOI200517\"><title>Baseline Characteristics</title><p>Among 169 162 respondents, 164 023 (94.8%) had no cancer history and 5139 (5.2%) reported having a cancer history. Participants with a cancer history included 1243 (24.2%) with a more recent cancer history and 3896 (75.8%) with a less recent cancer history. In the entire cohort, 51.8% (90 670) were women, 50.2% (64 746) were between 35 and 64 years old, and 64.0% (102 203) were White patients. The most common cancer types among the 5139 cancer survivors were breast (1118 respondents [24.9%]), prostate or testis (621 respondents [15.6%]), and melanoma (502 respondents [9.5%]). Respondents with a history of cancer had a lower prevalence of prescription opioid use (42.8%; 95% CI, 41.2%-44.5%) compared with those with liver cirrhosis (56.4%; 95% CI, 49.3%-63.1%), chronic obstructive pulmonary disease (52.6%; 95% CI, 50.6%-54.6%), or kidney disease (50.5%; 47.9%-53.1%) (eTable 1 in the <xref ref-type=\"supplementary-material\" rid=\"note-ZOI200517-1-s\">Supplement</xref>). The prevalence of prescription opioid misuse among cancer survivors was the second lowest (3.1%; 95% CI, 2.7%-3.6%) for all comorbidities except diabetes (2.9%; 95% CI, 2.6%-3.3%).</p><p>Baseline characteristics of respondents with and without a cancer history are shown in eTable 2 in the <xref ref-type=\"supplementary-material\" rid=\"note-ZOI200517-1-s\">Supplement</xref>. Compared with respondents without a history of cancer, respondents with a cancer history were more likely to be older (age ≥65 years: 53.2%; 95% CI, 51.7%-54.9% vs 17.7%; 95% CI, 17.3%-18.1%), to be women (60.2%; 95% CI, 58.2%-62.2% vs 51.3%; 95% CI, 51.0%-51.7%), to be White patients (81.2%; 95% CI, 79.8%-82.5% vs 63.0%; 95% CI, 62.4%-63.5%), to have public health insurance (63.8%; 95% CI, 62.1%-65.5% vs 34.5%; 95% CI, 34.1%-34.9%), and to be married (59.8%; 95% CI, 57.8%-61.8% vs 51.3%; 95% CI, 50.8%-51.8%) (<italic>P</italic> < .001 for all). Participants with a cancer history were less likely to report having alcohol use disorder (3.0%; 95% CI, 2.5%-3.6% vs 6.1%; 95% CI, 6.0%-6.3%) and nonopioid drug use disorder (1.4%; 95% CI, 1.1%-1.8% vs 2.7%; 95% CI, 2.6%-2.8%) compared with respondents without cancer (<italic>P</italic> < .001 for all). A comparison of characteristics between more recent and less recent cancer survivors is shown in eTable 3 in the <xref ref-type=\"supplementary-material\" rid=\"note-ZOI200517-1-s\">Supplement</xref>.</p></sec><sec id=\"H2-4-ZOI200517\"><title>Clinical and Sociodemographic Determinants of Prescription Opioid Use and Misuse</title><p>Prescription opioid use was higher among respondents with a more recent (54.3%; 95% CI, 50.2%-58.4%) and less recent (39.2%; 95% CI, 37.3%-41.2%) cancer history compared with respondents without cancer (30.5%; 95% CI, 30.2%-30.9%) (<italic>P</italic> < .001) (<xref rid=\"zoi200517t1\" ref-type=\"table\">Table 1</xref>). Prescription opioid misuse was lower among respondents with a less recent cancer history compared with participants without cancer (3.0%; 95% CI, 2.4%-3.6% vs 4.3%; 95% CI, 4.2%-4.3%; <italic>P</italic> < .001). On multivariable analysis, cancer survivors with a more recent (OR, 1.86; 95% CI, 1.57-2.20; <italic>P</italic> < .001) and less recent (OR, 1.18; 95% CI, 1.08-1.28; <italic>P</italic> < .001) cancer history had a higher odds of prescription opioid use compared with respondents without a history of cancer. However, neither more recent (OR, 1.27; 95% CI, 0.82-1.96; <italic>P</italic> = .36) nor less recent (OR 1.03; 95% CI, 0.83-1.28; <italic>P</italic> = .76) cancer history was associated with a significant difference in rates of prescription opioid misuse when compared with respondents without a history of cancer.</p><table-wrap id=\"zoi200517t1\" orientation=\"portrait\" position=\"float\"><label>Table 1. </label><caption><title>Unadjusted and Adjusted Analysis of Prescription Opioid Use or Misuse by Reported Cancer History</title></caption><table frame=\"hsides\" rules=\"groups\"><col width=\"10.28%\" span=\"1\"/><col width=\"12.71%\" span=\"1\"/><col width=\"8.94%\" span=\"1\"/><col width=\"14.2%\" span=\"1\"/><col width=\"9.02%\" span=\"1\"/><col width=\"12.69%\" span=\"1\"/><col width=\"8.94%\" span=\"1\"/><col width=\"14.2%\" span=\"1\"/><col width=\"9.02%\" span=\"1\"/><thead><tr><th rowspan=\"2\" valign=\"top\" align=\"left\" scope=\"col\" colspan=\"1\">Cancer history</th><th colspan=\"4\" valign=\"top\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">Prescription opioid use</th><th colspan=\"4\" valign=\"top\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">Prescription opioid misuse</th></tr><tr><th valign=\"top\" colspan=\"1\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">Weighted % (95% CI)</th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><italic>P</italic> value<xref ref-type=\"table-fn\" rid=\"zoi200517t1n1\"><sup>a</sup></xref></th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">OR (95% CI)</th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><italic>P</italic> value<xref ref-type=\"table-fn\" rid=\"zoi200517t1n2\"><sup>b</sup></xref></th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Weighted % (95% CI)</th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><italic>P</italic> value<xref ref-type=\"table-fn\" rid=\"zoi200517t1n1\"><sup>a</sup></xref></th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">OR (95% CI)</th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><italic>P</italic> value<xref ref-type=\"table-fn\" rid=\"zoi200517t1n2\"><sup>b</sup></xref></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">No cancer history</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">30.5 (30.2-30.9)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.3 (4.2-4.4)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NA</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">More recent cancer history</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">54.3 (50.2-58.4)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><.001</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.86 (1.57-2.20)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><.001</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.5 (2.4-5.2)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.339</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.27 (0.82-1.96)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.36</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Less recent cancer history</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">39.2 (37.3-41.2)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><.001</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.18 (1.08-1.28)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><.001</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.0 (2.4-3.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><.001</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.03 (0.83-1.28)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.76</td></tr></tbody></table><table-wrap-foot><p>Abbreviation: NA, not applicable; OR, odds ratio.</p><fn id=\"zoi200517t1n1\"><label><sup>a</sup></label><p><italic>P </italic>values calculated from <italic>χ<sup>2</sup></italic> comparisons of rates of prescription opioid use and misuse by reported cancer history.</p></fn><fn id=\"zoi200517t1n2\"><label><sup>b</sup></label><p>Multivariable models adjusted for age, sex, race, survey year, education level, insurance status, income, urban/rural status, employment status, marital status, self-reported health status, depression history, alcohol use disorder, and nonopioid drug use disorder.</p></fn></table-wrap-foot></table-wrap><p>Among cancer survivors, notable factors associated with prescription opioid use included younger age (ages 35-64 years vs ≥65 years: OR, 1.49; 95% CI, 1.15-1.93; <italic>P</italic> = .003), poorer health status (poor vs excellent: OR, 4.35; 95% CI, 3.00-6.32; <italic>P</italic> < .001), and experiencing a major depressive episode within the past year (OR, 1.52; 95% CI, 1.19-1.95; <italic>P</italic> = .001) (<xref rid=\"zoi200517t2\" ref-type=\"table\">Table 2</xref>). Nonopioid drug use disorder was associated with decreased likelihood of prescription opioid use (OR, 0.39; 95% CI, 0.21-0.74; <italic>P</italic> = .005). Factors associated with prescription opioid misuse included younger age (age 18-34 years vs ≥65 years: OR, 7.06; 95% CI, 3.03-16.41; <italic>P</italic> < .001), alcohol use disorder (OR, 3.22; 95% CI, 1.45-7.14; <italic>P</italic> = .005), and nonopioid drug use disorder (OR, 14.76; 95% CI, 7.40-29.44; <italic>P</italic> < .001).</p><table-wrap id=\"zoi200517t2\" orientation=\"portrait\" position=\"float\"><label>Table 2. </label><caption><title>Multivariable Analysis for Sociodemographic and Clinical Factors Associated With Prescription Opioid Use and Misuse Among Cancer Survivors</title></caption><table frame=\"hsides\" rules=\"groups\"><col width=\"39.31%\" span=\"1\"/><col width=\"18.78%\" span=\"1\"/><col width=\"10.37%\" span=\"1\"/><col width=\"21.17%\" span=\"1\"/><col width=\"10.37%\" span=\"1\"/><thead><tr><th rowspan=\"2\" valign=\"top\" align=\"left\" scope=\"col\" colspan=\"1\">Characteristic</th><th colspan=\"2\" valign=\"top\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">Prescription opioid use</th><th colspan=\"2\" valign=\"top\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">Prescription opioid misuse</th></tr><tr><th valign=\"top\" colspan=\"1\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">OR (95% CI)</th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><italic>P</italic> value</th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">OR (95% CI)</th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><italic>P</italic> value</th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Age, y</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> ≥65</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NA</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 35-64</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.49 (1.15-1.93)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.003</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.83 (1.79-8.19)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.001</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 18-34</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.40 (0.97-2.01)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.07</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">7.06 (3.03-16.41)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><.001</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Sex</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Women</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NA</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Men</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.96 (0.81-1.14)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.65</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.60 (0.88-2.91)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.12</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Race</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> White</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NA</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> African American</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.21 (0.90-1.62)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.21</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.31 (0.52-3.30)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.56</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Hispanic</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.68 (0.48-0.96)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.03</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.33 (0.66-2.67)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.41</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Other<xref ref-type=\"table-fn\" rid=\"zoi200517t2n1\"><sup>a</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.88 (0.64-1.21)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.42</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.04 (0.53-2.07)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.90</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Year</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 2015</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NA</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 2016</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.95 (0.80-1.14)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.59</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.97 (0.48-1.95)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.92</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 2017</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.95 (0.75-1.22)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.70</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.79 (0.45-1.38)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.40</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 2018</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.86 (0.70-1.05)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.14</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.09 (0.62-1.92)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.77</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Education level</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> <High school</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NA</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> High school graduate</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.15 (0.85-1.56)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.35</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.52 (0.26-1.06)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.07</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Some college or associates degree</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.53 (1.16-2.02)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.003</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.70 (0.33-1.51)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.36</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> College graduate</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.30 (0.92-1.84)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.14</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.50 (0.24-1.04)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.06</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Health insurance</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Other</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NA</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Private</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.01 (0.39-2.61)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.99</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.64 (0.06-7.05)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.71</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Public</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.35 (0.53-3.46)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.53</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.86 (0.08-9.57)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.90</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Unknown</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.70 (0.24-2.04)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.51</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.83 (0.08-8.84)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.88</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Income, $</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> <20 000</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NA</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 20 000-49 999</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.80 (0.61-1.03)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.09</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.95 (0.45-1.99)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.90</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 50 000-74 999</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.86 (0.66-1.11)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.23</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.35 (0.62-2.95)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.45</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> ≥75 000</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.77 (0.57-1.04)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.09</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.56 (0.75-3.25)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.22</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Setting</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Urban</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NA</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Rural</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.00 (0.82-1.22)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.99</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.11 (0.66-1.85)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.70</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Employment</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Full time</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NA</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Part time</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.05 (0.78-1.42)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.75</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.45 (0.23-0.91)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.03</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Unemployed</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.73 (0.43-1.26)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.26</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.03 (0.32-3.25)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.96</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Other</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.98 (0.77-1.25)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.88</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.98 (0.51-1.89)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.95</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Marital status</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Never married</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NA</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Married</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.03 (0.78-1.36)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.83</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.86 (0.47-1.58)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.63</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Widowed</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.93 (0.69-1.25)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.62</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.78 (0.17-3.56)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.74</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Divorced or separated</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.16 (0.85-1.58)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.34</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.88 (0.48-1.63)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.69</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Health status</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Excellent</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 [Reference]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NA</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Very good</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.40 (1.07-1.84)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.02</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.77 (0.32-1.87)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.56</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Good</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.90 (1.45-2.49)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><.001</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.04 (0.41-2.65)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.93</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Fair</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.42 (1.78-3.29)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><.001</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.23 (0.47-3.26)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.67</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Poor</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.35 (3.00-6.32)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><.001</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.36 (0.45-4.10)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.58</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Major depressive episode within year</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.52 (1.19-1.95)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.001</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.05 (0.89-4.72)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.09</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Missing data</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.68 (0.32-1.45)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.31</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.82 (0.26-2.60)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.73</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Alcohol use disorder</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.76 (0.49-1.17)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.21</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.22 (1.45-7.14)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.005</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Nonopioid drug use disorder</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.39 (0.21-0.74)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">.005</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">14.76 (7.40-29.44)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><.001</td></tr></tbody></table><table-wrap-foot><p>Abbreviation: NA, not applicable; OR, odds ratio.</p><fn id=\"zoi200517t2n1\"><label><sup>a</sup></label><p>Includes Native American and Alaskan Native, Native Hawaiian and Pacific Islander, Asian, and non-Hispanic mixed race.</p></fn></table-wrap-foot></table-wrap></sec><sec id=\"H2-5-ZOI200517\"><title>Prescription Opioid Use and Misuse by Cancer Type</title><p>Respondents with gallbladder, liver, or pancreatic cancer (72.2% [44 of 73 respondents]), larynx, windpipe, or lung cancer (53.5% [80 of 160 respondents]), and cervical cancer (48.4% [248 of 509 respondents]) reported the highest rates of prescription opioid use. Those with prostate or testis cancer (37.9% [231 of 621 respondents]) and uterine cancer (37.6% [103 of 240 respondents]) had the lowest rates of opioid use. Meanwhile, prescription opioid misuse was highest among respondents with esophagus or stomach cancer (10.1% [4 of 64 respondents]) and gallbladder, liver, or pancreatic cancer (7.3% [3 of 73 respondents]) and lowest among those with breast cancer (1.7% [26 of 1118 respondents]) (<xref ref-type=\"fig\" rid=\"zoi200517f2\">Figure 2</xref>).</p><fig id=\"zoi200517f2\" fig-type=\"figure\" orientation=\"portrait\" position=\"float\"><label>Figure 2. </label><caption><title>Weighted Prevalence Estimates of Prescription Opioid Use and Misuse by Cancer Type</title><p>Error bars indicate 95% CIs.</p></caption><graphic xlink:href=\"jamanetwopen-3-e2013605-g002\"/></fig></sec><sec id=\"H2-6-ZOI200517\"><title>Temporal Trends in Prescription Opioid Use and Misuse</title><p>Prescription opioid use declined 3.9%, from 32.7% (95% CI, 32.0%-33.4%) in 2015 to 28.8% (95% CI, 28.2%-29.5%) in 2018 (<italic>P</italic> < .001) among respondents without cancer (<xref ref-type=\"fig\" rid=\"zoi200517f3\">Figure 3</xref>). A trend toward decreasing opioid use from 57.3% (95% CI, 50.1%-63.8%) in 2015 to 46.9% (95% CI, 40.3%-53.8%) in 2018 (<italic>P</italic> = .03), representing a decline of 10.4%, was also observed among more recent cancer survivors. The prevalence of prescription opioid misuse declined by 1.1%, from 4.8% (95% CI, 4.5%-5.1%) in 2015 to 3.7% (95% CI, 3.5%-4.0%) in 2018 (<italic>P</italic> < .001) among respondents without cancer and was unchanged among more recent (2015: 3.5%; 95% CI, 1.8%-6.7% vs 2018: 4.3%; 95% CI, 2.5%-7.3%; <italic>P</italic> = .98) and less recent (2015: 3.1%, 95% CI, 2.1%-4.1% vs 2018: 3.2%, 95% CI, 2.0%-5.2%; <italic>P</italic> = .96) cancer survivors.</p><fig id=\"zoi200517f3\" fig-type=\"figure\" orientation=\"portrait\" position=\"float\"><label>Figure 3. </label><caption><title>Temporal Trends in Adjusted Prevalence of Prescription Opioid Use and Misuse by Reported Cancer History</title><p>Error bars indicate 95% CIs.</p></caption><graphic xlink:href=\"jamanetwopen-3-e2013605-g003\"/></fig></sec></sec><sec id=\"H1-4-ZOI200517\"><title>Discussion</title><p>In this novel examination of national patterns in prescription opioid behavior among cancer survivors, multiple findings emerged. First, prescription opioid use was higher among cancer survivors than among respondents without cancer, with more recent cancer survivors reporting a nearly 2-fold higher rate of use. Second, adjusted rates of prescription opioid misuse were no different among cancer survivors compared with those without a history of cancer. Third, notable factors associated with prescription opioid use or misuse included younger age, cancer type, having a major depressive episode, alcohol use disorder, and nonopioid drug use disorder. Fourth, rates of prescription opioid use and misuse varied by cancer type. Fifth, reported rates of prescription opioid use decreased over time among respondents without cancer but decreased more steeply among more recent cancer survivors.</p><p>Patterns in opioid use and misuse among cancer survivors compared with the population without cancer warrant further discussion. We found a nearly 2-fold increase in opioid use among patients with a more recent cancer history, who are likely to be more symptomatic from their disease or treatment-related side effects.<sup><xref rid=\"zoi200517r7\" ref-type=\"bibr\">7</xref></sup> In respondents with less recent cancer histories, opioid use decreased but remained nearly 20% higher compared with the population without cancer. Similar findings were observed by Salz et al,<sup><xref rid=\"zoi200517r6\" ref-type=\"bibr\">6</xref></sup> who found the highest rates of opioid use occurred in the first year after diagnosis of lung and colorectal cancer. However, by 6 years after diagnosis, opioid use decreased to similar levels as control participants without cancer. Our study also showed that adjusted rates of prescription opioid misuse were no different among cancer survivors than among patients without a history of cancer. This suggests that increased prescription opioid use among cancer survivors does not necessarily translate to a higher risk of misuse. Our findings dovetail with a 2020 report<sup><xref rid=\"zoi200517r21\" ref-type=\"bibr\">21</xref></sup> showing a 10-fold lower incidence of opioid-related deaths in cancer survivors compared with the general population. Moreover, we found that the prevalence of opioid misuse among cancer survivors in any given year was relatively low (ranging from 2.2% to 4.1%), consistent with the estimate of 2.9% reported in another study.<sup><xref rid=\"zoi200517r17\" ref-type=\"bibr\">17</xref></sup> Given recent concerns about inadequate opioid prescribing in cancer survivors,<sup><xref rid=\"zoi200517r22\" ref-type=\"bibr\">22</xref>,<xref rid=\"zoi200517r23\" ref-type=\"bibr\">23</xref></sup> our analysis could reassure both patients and professionals who have concerns about opioid misuse or addiction.</p><p>Our study found multiple sociodemographic and clinical factors associated with prescription opioid use and misuse among cancer survivors. Younger respondents had higher rates of opioid use and misuse, consistent with multiple studies in populations with and without cancer.<sup><xref rid=\"zoi200517r16\" ref-type=\"bibr\">16</xref>,<xref rid=\"zoi200517r24\" ref-type=\"bibr\">24</xref>,<xref rid=\"zoi200517r25\" ref-type=\"bibr\">25</xref></sup> This highlights the importance of accurately screening for and identifying aberrant opioid use in younger patients, who may experience long-term survivorship. Participants reporting a major depressive episode within the past year had a higher likelihood of opioid use. These patients may be more susceptible to experiencing cancer-related pain, which can be modulated by psychosocial factors.<sup><xref rid=\"zoi200517r26\" ref-type=\"bibr\">26</xref></sup> However, on adjusted analysis, respondents with major depressive episodes were not more likely to misuse opioids. This may be because of the overlap between cancer survivors with depression and those with alcohol or substance use disorders, with the latter 2 being stronger risk factors. Notably, nonopioid drug use disorder was associated with a decreased likelihood of prescription opioid use. This may be because patients are usually screened for substance use disorders prior to treatment with prescription opioids. Oncologists may be less likely to prescribe opioids to patients who screen positive. Finally, comorbid alcohol or nonopioid drug use disorder were among the strongest factors associated with opioid misuse. Our findings underscore the importance of performing a thorough social history to identify underlying mental health or substance use disorders in patients suspected of opioid misuse.</p><p>We observed a high rate of prescription opioid use and misuse among respondents with upper gastrointestinal (GI) cancers, including gallbladder, liver, or pancreatic cancer as well as esophagus or stomach cancer. Previous studies<sup><xref rid=\"zoi200517r5\" ref-type=\"bibr\">5</xref>,<xref rid=\"zoi200517r17\" ref-type=\"bibr\">17</xref></sup> have demonstrated elevated rates of opioid prescribing and abuse in survivors of GI cancer using registry data, although this is the first to use self-reported survey data, to our knowledge. There may be multiple reasons for this finding. First, more than half of patients with GI cancers report having chronic pain.<sup><xref rid=\"zoi200517r2\" ref-type=\"bibr\">2</xref></sup> Second, comorbid alcohol or tobacco use disorders are risk factors for developing GI cancers and for long-term opioid use.<sup><xref rid=\"zoi200517r27\" ref-type=\"bibr\">27</xref>,<xref rid=\"zoi200517r28\" ref-type=\"bibr\">28</xref></sup> Our findings also showed that respondents with cervical cancer had high rates of prescription opioid use and misuse. Patients with cervical cancer may be at higher risk of opioid misuse compared with those with other gynecologic malignant neoplasms, possibly because of a higher incidence of chronic pain.<sup><xref rid=\"zoi200517r29\" ref-type=\"bibr\">29</xref>,<xref rid=\"zoi200517r30\" ref-type=\"bibr\">30</xref>,<xref rid=\"zoi200517r31\" ref-type=\"bibr\">31</xref></sup> Similarly, patients with larynx or lung cancer may be at risk for long-term opioid use because of the incidence of advanced disease at diagnosis and need for trimodality therapy.<sup><xref rid=\"zoi200517r32\" ref-type=\"bibr\">32</xref></sup> Meanwhile, low rates of prescription opioid use and misuse were observed among respondents with prostate or testis, uterine, and breast cancer. This may be because of a combination of having more indolent histologies, more effective screening methods in prostate and breast cancer, and a high likelihood of requiring only single or bimodality therapy, thereby reducing long-term toxicity.</p><p>The time frame of this study coincided with the passage of legislation and guidelines intended to curb excess opioid prescribing among health care professionals. Such measures included mandatory-access prescription drug monitoring programs and opioid prescription guidelines for chronic pain issued by the US Centers for Disease Control and Prevention in March 2016.<sup><xref rid=\"zoi200517r33\" ref-type=\"bibr\">33</xref></sup> Indeed, our findings showed a numerically larger decline in prescription opioid use (−10.4%) among more recent cancer survivors who are likely to be receiving active treatment, compared with respondents without a history of cancer (−3.9%). This suggests that legislation and guidelines designed to target patients without cancer or protect cancer survivors may be inadvertently affecting cancer survivors. This is consistent with recent data showing that the largest decline in opioid prescriptions among oncology patients occurred in states with cancer-exempt monitoring programs.<sup><xref rid=\"zoi200517r34\" ref-type=\"bibr\">34</xref></sup> Meanwhile, rates of prescription opioid misuse declined by 1.1% among respondents without cancer but did not change appreciably among cancer survivors. The decrease in prescription opioid use by 10% among more recent cancer survivors in the absence of a decrease in misuse may indicate reduced opioid access for cancer survivors who stand to benefit, while ineffectively addressing the issue of opioid misuse. Despite some monitoring programs and the Centers of Disease Control and Prevention guidelines exempting patients with cancer, there is concern that legislation and prescribing recommendations are being inappropriately applied to patients with cancer.<sup><xref rid=\"zoi200517r23\" ref-type=\"bibr\">23</xref>,<xref rid=\"zoi200517r35\" ref-type=\"bibr\">35</xref></sup> One survey study conducted in 2018<sup><xref rid=\"zoi200517r36\" ref-type=\"bibr\">36</xref></sup> of cancer survivors found that 35% of respondents reported that their physician refused to give them an opioid prescription, and nearly half reported their physician told them their pain treatment options were limited by laws, guidelines, or insurance coverage. While caution against opioid misuse is certainly warranted, it should not come at the expense of effective pain management and improved quality of life for cancer survivors.</p><sec id=\"H2-7-ZOI200517\"><title>Limitations</title><p>There are multiple limitations to this study. First, respondents may have limited ability or be unwilling to accurately self-report opioid misuse, resulting in the risk of being underreported in the survey. However, the consistency in the reported rate of misuse between our study and others in the literature imparts a degree of confidence to our findings. Second, the survey lacks detailed information on cancer stage and treatment history, both of which have known associations with long-term opioid use. Third, opioid misuse as defined by the NSDUH comprises a heterogeneous category, so determining clinically meaningful misuse relies on input from the providing physician. Fourth, the NSDUH only allows for temporal dichotomization of cancer history either within a year or more than 1 year since survey administration. This in turn may group cancer survivors who are without evidence of disease for 1 year with those who have been in remission for many years in the less recent cancer history cohort. Nevertheless, to our knowledge, the NSDUH is the only existing data set with the capability to provide a nationally representative analysis of self-reported opioid use and misuse among patients with a cancer history.</p></sec></sec><sec id=\"H1-5-ZOI200517\"><title>Conclusions</title><p>In this study, cancer survivors reported higher rates of prescription opioid use and similar rates of prescription opioid misuse compared with respondents without cancer. These findings suggest that increased prescription opioid use among cancer survivors does not necessarily translate to a higher risk of misuse. It is imperative that opioid legislation and policies recognize that cancer survivors will have a higher rate of prescription opioid use and that restrictions on prescription opioid access for cancer survivors are incongruent with their opioid utilization patterns. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"correction\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">JAMA Netw Open</journal-id><journal-id journal-id-type=\"iso-abbrev\">JAMA Netw Open</journal-id><journal-id journal-id-type=\"pmc\">JAMA Netw Open</journal-id><journal-title-group><journal-title>JAMA Network Open</journal-title></journal-title-group><issn pub-type=\"epub\">2574-3805</issn><publisher><publisher-name>American Medical Association</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32804208</article-id><article-id pub-id-type=\"pmc\">PMC7431995</article-id><article-id pub-id-type=\"doi\">10.1001/jamanetworkopen.2020.19157</article-id><article-id pub-id-type=\"publisher-id\">zcx200049</article-id><article-categories><subj-group subj-group-type=\"category\" specific-use=\"electronic\"><subject>Other</subject></subj-group><subj-group subj-group-type=\"heading\"><subject>Correction</subject></subj-group><subj-group subj-group-type=\"online-only\"><subject>Online Only</subject></subj-group></article-categories><title-group><article-title>Error in Figure</article-title><alt-title alt-title-type=\"running-head\">Correction</alt-title></title-group><pub-date pub-type=\"epub\" iso-8601-date=\"2020-08-17T10:00\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><!-- PMC Release delay is 0 months and 0 days and was based on the <pub-date pub-type=\"epub\"/>. --><volume>3</volume><issue>8</issue><elocation-id>e2019157</elocation-id><permissions><copyright-statement>Copyright 2020. <italic>JAMA Network Open</italic>.</copyright-statement><license license-type=\"open-access\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open access article distributed under the terms of the CC-BY License.</license-p></license></permissions><self-uri content-type=\"pdf-version\" xlink:href=\"jamanetwopen-3-e2019157.pdf\">jamanetwopen-3-e2019157.pdf</self-uri><self-uri content-type=\"silverchair\" xlink:href=\"https://jamanetwork.com/journals/jamanetworkopen/fullarticle/10.1001/jamanetworkopen.2020.19157\"/><related-article related-article-type=\"corrected-article\" id=\"d38e64\" ext-link-type=\"doi\" xlink:href=\"10.1001/jamanetworkopen.2020.7932\" specific-use=\"electronic\"/></article-meta></front><body><p>In the Original Investigation titled “National Trends in the Prevalence of Chronic Kidney Disease Among Racial/Ethnic and Socioeconomic Status Groups, 1988-2016,”<sup><xref rid=\"zcx200049r1\" ref-type=\"bibr\">1</xref></sup> published on July 16, 2020, the key to panel B of the Figure was incorrectly labeled. The dark blue line indicates an educational level of less than high school (and should be labeled “<High school”), and the light blue line indicates an educational level of more than high school (and should be labeled “>High school”). This article has been corrected.</p></body><back><ref-list id=\"REF-ZCX200049\"><title>Reference</title><ref id=\"zcx200049r1\"><label>1</label><mixed-citation publication-type=\"journal\"><person-group><name name-style=\"western\"><surname>Vart</surname><given-names>P</given-names></name>, <name name-style=\"western\"><surname>Powe</surname><given-names>NR</given-names></name>, <name name-style=\"western\"><surname>McCulloch</surname><given-names>CE</given-names></name>, <etal/>; <collab>Centers for Disease Control and Prevention Chronic Kidney Disease Surveillance Team</collab></person-group>\n<article-title>National trends in the prevalence of chronic kidney disease among racial/ethnic and socioeconomic status groups, 1988-2016</article-title>. <source>JAMA Netw Open</source>. <year>2020</year>;<volume>3</volume>(<issue>7</issue>):<elocation-id>e207932</elocation-id>. doi:<pub-id pub-id-type=\"doi\">10.1001/jamanetworkopen.2020.7932</pub-id><pub-id pub-id-type=\"pmid\">32672828</pub-id></mixed-citation></ref></ref-list></back></article>\n"
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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">JAMA Netw Open</journal-id><journal-id journal-id-type=\"iso-abbrev\">JAMA Netw Open</journal-id><journal-id journal-id-type=\"pmc\">JAMA Netw Open</journal-id><journal-title-group><journal-title>JAMA Network Open</journal-title></journal-title-group><issn pub-type=\"epub\">2574-3805</issn><publisher><publisher-name>American Medical Association</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32804210</article-id><article-id pub-id-type=\"pmc\">PMC7431996</article-id><article-id pub-id-type=\"doi\">10.1001/jamanetworkopen.2020.17521</article-id><article-id pub-id-type=\"publisher-id\">zld200130</article-id><article-categories><subj-group subj-group-type=\"category\" specific-use=\"electronic\"><subject>Research</subject></subj-group><subj-group subj-group-type=\"heading\"><subject>Research Letter</subject></subj-group><subj-group subj-group-type=\"online-only\"><subject>Online Only</subject></subj-group><subj-group subj-group-type=\"subject-area\"><subject>Health Informatics</subject></subj-group></article-categories><title-group><article-title>Periodic Oscillations in Daily Reported Infections and Deaths for Coronavirus Disease 2019</article-title><alt-title alt-title-type=\"headline\">Periodic Oscillations in Daily Reported Infections and Deaths for Coronavirus Disease 2019</alt-title><alt-title alt-title-type=\"running-head\">Periodic Oscillations in Daily Reported Infections and Deaths for Coronavirus Disease 2019</alt-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Bukhari</surname><given-names>Qasim</given-names></name><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"zld200130aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Jameel</surname><given-names>Yusuf</given-names></name><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"zld200130aff2\"><sup>2</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Massaro</surname><given-names>Joseph M.</given-names></name><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"zld200130aff3\"><sup>3</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>D’Agostino</surname><given-names>Ralph B.</given-names><suffix>Sr</suffix></name><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"zld200130aff3\"><sup>3</sup></xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>Khan</surname><given-names>Sheraz</given-names></name><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"zld200130aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"zld200130aff4\"><sup>4</sup></xref><xref ref-type=\"aff\" rid=\"zld200130aff5\"><sup>5</sup></xref></contrib></contrib-group><aff id=\"zld200130aff1\"><label>1</label>McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge</aff><aff id=\"zld200130aff2\"><label>2</label>Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge</aff><aff id=\"zld200130aff3\"><label>3</label>Department of Mathematics and Statistics, Boston University, Boston, Massachusetts</aff><aff id=\"zld200130aff4\"><label>4</label>Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston</aff><aff id=\"zld200130aff5\"><label>5</label>Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Massachusetts Institute of Technology, Boston</aff><author-notes><title>Article Information</title><p><bold>Accepted for Publication:</bold> July 7, 2020.</p><p content-type=\"published-online\"><bold>Published:</bold> August 17, 2020. doi:<uri content-type=\"doi\">10.1001/jamanetworkopen.2020.17521</uri></p><p content-type=\"open-access-note\"><bold>Open Access:</bold> This is an open access article distributed under the terms of the <ext-link ext-link-type=\"uri\" xlink:href=\"https://jamanetwork.com/journals/jamanetworkopen/pages/instructions-for-authors#SecOpenAccess\">CC-BY License</ext-link>. © 2020 Bukhari Q et al. <italic>JAMA Network Open</italic>.</p><corresp id=\"zld200130cor1\"><bold>Corresponding Author:</bold> Sheraz Khan, PhD, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 149 13th St, CNY-2275, Boston, MA 02129 (<email xlink:href=\"sheraz@nmr.mgh.harvard.edu\">sheraz@nmr.mgh.harvard.edu</email>).</corresp><p content-type=\"author-contributions\"><bold>Author Contributions:</bold> Dr Khan had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.</p><p><italic>Concept and design:</italic> Bukhari, Khan.</p><p><italic>Acquisition, analysis, or interpretation of data:</italic> All authors.</p><p><italic>Drafting of the manuscript:</italic> Bukhari, Jameel, Khan.</p><p><italic>Critical revision of the manuscript for important intellectual content: </italic>All authors.</p><p><italic>Statistical analysis:</italic> All authors.</p><p><italic>Supervision:</italic> Khan.</p><p content-type=\"COI-statement\"><bold>Conflict of Interest Disclosures:</bold> None reported.</p></author-notes><pub-date pub-type=\"epub\" iso-8601-date=\"2020-08-17T10:00\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><!-- PMC Release delay is 0 months and\n\t\t\t\t\t\t0 days and was based on the <pub-date\n\t\t\t\t\t\tpub-type=\"epub\"/>. --><volume>3</volume><issue>8</issue><elocation-id>e2017521</elocation-id><history><date date-type=\"received\"><day>14</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>7</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright 2020 Bukhari Q et al. <italic>JAMA Network Open</italic>.</copyright-statement><license license-type=\"open-access\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open access article distributed under the terms of the CC-BY License.</license-p></license></permissions><self-uri content-type=\"pdf-version\" xlink:href=\"jamanetwopen-3-e2017521.pdf\">jamanetwopen-3-e2017521.pdf</self-uri><self-uri content-type=\"silverchair\" xlink:href=\"https://jamanetwork.com/journals/jamanetworkopen/fullarticle/10.1001/jamanetworkopen.2020.17521\"/><abstract abstract-type=\"teaser\"><p>This cross-sectional study investigates oscillatory patterns in daily reported infections and deaths for coronavirus disease 2019.</p></abstract></article-meta></front><body><sec id=\"H1-1-ZLD200130\"><title>Introduction</title><p>Severe acute respiratory syndrome coronavirus 2 has affected millions of people worldwide. The trend of coronavirus disease 2019 (COVID-19) cases is not similar across countries, with several countries experiencing a decrease in the daily reported cases and deaths, while several others are reporting a surge in the daily reported cases and deaths. Studies of prior epidemics<sup><xref rid=\"zld200130r1\" ref-type=\"bibr\">1</xref>,<xref rid=\"zld200130r2\" ref-type=\"bibr\">2</xref></sup> have suggested oscillatory patterns and cyclicity when analyzing long-term (ie, decades) epidemiological data. However, to our knowledge, high-frequency oscillations (ie, weekly) have not been reported during prior epidemics. In this cross-sectional study, we investigate oscillatory patterns in COVID-19 cases and deaths.</p></sec><sec id=\"H1-2-ZLD200130\"><title>Methods</title><p>This study was not submitted for institutional review board approval and informed consent was not sought because it uses publicly available data at the population level, in accordance with 45 CFR §46. The underlying methods in this analysis are described in detail in the eAppendix in the <xref ref-type=\"supplementary-material\" rid=\"note-ZLD200130-1-s\">Supplement</xref>. In brief, we obtained the daily new cases and deaths for COVID-19 between February 29 and July 2, 2020, from Worldometer,<sup><xref rid=\"zld200130r2\" ref-type=\"bibr\">2</xref></sup> applied a 3-day moving average to remove high-frequency fluctuations in the daily new cases and deaths, and then performed spectral analysis and calculated the phase-lag between daily reported cases and deaths. Data analysis was performed in July 2020 with Python statistical software version 3.7 (Python) and open-source signal-processing toolboxes, as described in the eAppendix in the <xref ref-type=\"supplementary-material\" rid=\"note-ZLD200130-1-s\">Supplement</xref>.</p></sec><sec id=\"H1-3-ZLD200130\"><title>Results</title><p>We identified oscillatory patterns in the daily reported new cases and deaths with a periodicity of approximately 1 week for the US, Germany, Canada, Italy, Brazil, and the United Kingdom (<xref ref-type=\"fig\" rid=\"zld200130f1\">Figure</xref>). The data from Germany and Italy show dampened oscillations (decreasing amplitude with time) for both newly reported infections and deaths, with a −92% change in peak-to-peak oscillatory pattern in the daily reported deaths in Germany between April and July 2020, which might indicate a substantial decay in the spread of the virus. However, the data from the US and Brazil show no sign of dampening, with a −43% change in peak-to-peak oscillatory pattern in the daily reported deaths in the US between April and July 2020, which suggests that the US and Brazil are still not at the decaying phase. The spectral density plot in panel B of the <xref ref-type=\"fig\" rid=\"zld200130f1\">Figure</xref> confirms an oscillatory pattern of 7 days. The rose plot in panel C of the <xref ref-type=\"fig\" rid=\"zld200130f1\">Figure</xref> shows the polar histogram of the phase angle difference between daily new cases and deaths and demonstrates a lag between daily new cases and deaths of 2 days for the US and 1 day for Germany. However, this lag is not due to the epidemiology of the disease but possibly is associated with bias in the surveillance system.</p><fig id=\"zld200130f1\" fig-type=\"figure\" orientation=\"portrait\" position=\"float\"><label>Figure. </label><caption><title>Oscillation Patterns for Coronavirus Disease 2019 Cases and Deaths in the US, Germany, Canada, Brazil, UK, and Italy</title><p>A, Three-day moving average time series for daily new cases and deaths. B, Spectral density calculated using the Welch method. C, Phase difference (lag) between new cases and deaths in the US and Germany.</p></caption><graphic xlink:href=\"jamanetwopen-3-e2017521-g001\"/></fig></sec><sec id=\"H1-4-ZLD200130\"><title>Discussion</title><p>It is possible that these periodic oscillations in daily reported cases are associated with testing bias, with higher rates of testing during certain days of a week. However, these periodic oscillations were also observed for positive test rates,<sup><xref rid=\"zld200130r2\" ref-type=\"bibr\">2</xref></sup> suggesting that other variables, such as epidemiological or social factors leading to higher transmission on certain days, might be associated with these oscillations. Interestingly, periodic oscillations in new cases have been observed in multiple models for the spread of infectious diseases. For instance, consistent seasonal oscillations have been reported for smallpox in Japan, India, and Sweden in data sets that span several decades.<sup><xref rid=\"zld200130r3\" ref-type=\"bibr\">3</xref>,<xref rid=\"zld200130r4\" ref-type=\"bibr\">4</xref></sup> Importantly, these oscillations arise naturally from the model instead of a periodic forcing term or other exogenous factors. Spatiotemporal oscillations have also been reported in large data sets for dengue hemorrhagic fever from Thailand, suggesting that immune interactions between the serotypes might play a role in the observed pattern.<sup><xref rid=\"zld200130r5\" ref-type=\"bibr\">5</xref></sup> These oscillations should be included in the estimation of the effective reproduction number (R<sub>t</sub>), similar to the way seasonality is accounted for in influenza. We urge the scientific community to conduct an in-depth exploration of the periodicity in COVID-19 cases and deaths, which might lead to improved COVID-19 predictions and understanding of the transmission of the disease.</p></sec></body><back><ref-list id=\"REF-ZLD200130\"><title>References</title><ref id=\"zld200130r1\"><label>1</label><mixed-citation publication-type=\"journal\"><person-group><name name-style=\"western\"><surname>Dong</surname><given-names>E</given-names></name>, <name name-style=\"western\"><surname>Du</surname><given-names>H</given-names></name>, <name name-style=\"western\"><surname>Gardner</surname><given-names>L</given-names></name></person-group>\n<article-title>An interactive web-based dashboard to track COVID-19 in real time</article-title>. <source>Lancet Infect Dis</source>. <year>2020</year>;<volume>20</volume>(<issue>5</issue>):<fpage>533</fpage>-<lpage>534</lpage>. doi:<pub-id pub-id-type=\"doi\">10.1016/S1473-3099(20)30120-1</pub-id><pub-id pub-id-type=\"pmid\">32087114</pub-id></mixed-citation></ref><ref id=\"zld200130r2\"><label>2</label><mixed-citation publication-type=\"web\"><person-group><collab>Worldometer</collab></person-group> COVID-19 coronavirus pandemic. Published <year>2020</year> Accessed July 2, 2020. <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.worldometers.info/coronavirus/\">https://www.worldometers.info/coronavirus/</ext-link></mixed-citation></ref><ref id=\"zld200130r3\"><label>3</label><mixed-citation publication-type=\"journal\"><person-group><name name-style=\"western\"><surname>Olsen</surname><given-names>LF</given-names></name>, <name name-style=\"western\"><surname>Truty</surname><given-names>GL</given-names></name>, <name name-style=\"western\"><surname>Schaffer</surname><given-names>WM</given-names></name></person-group>\n<article-title>Oscillations and chaos in epidemics: a nonlinear dynamic study of six childhood diseases in Copenhagen, Denmark</article-title>. <source>Theor Popul Biol</source>. <year>1988</year>;<volume>33</volume>(<issue>3</issue>):<fpage>344</fpage>-<lpage>370</lpage>. doi:<pub-id pub-id-type=\"doi\">10.1016/0040-5809(88)90019-6</pub-id><pub-id pub-id-type=\"pmid\">3266037</pub-id></mixed-citation></ref><ref id=\"zld200130r4\"><label>4</label><mixed-citation publication-type=\"journal\"><person-group><name name-style=\"western\"><surname>Greer</surname><given-names>M</given-names></name>, <name name-style=\"western\"><surname>Saha</surname><given-names>R</given-names></name>, <name name-style=\"western\"><surname>Gogliettino</surname><given-names>A</given-names></name>, <name name-style=\"western\"><surname>Yu</surname><given-names>C</given-names></name>, <name name-style=\"western\"><surname>Zollo-Venecek</surname><given-names>K</given-names></name></person-group>\n<article-title>Emergence of oscillations in a simple epidemic model with demographic data</article-title>. <source>R Soc Open Sci</source>. <year>2020</year>;<volume>7</volume>(<issue>1</issue>):<elocation-id>191187</elocation-id>. doi:<pub-id pub-id-type=\"doi\">10.1098/rsos.191187</pub-id><pub-id pub-id-type=\"pmid\">32218949</pub-id></mixed-citation></ref><ref id=\"zld200130r5\"><label>5</label><mixed-citation publication-type=\"journal\"><person-group><name name-style=\"western\"><surname>Cummings</surname><given-names>DA</given-names></name>, <name name-style=\"western\"><surname>Irizarry</surname><given-names>RA</given-names></name>, <name name-style=\"western\"><surname>Huang</surname><given-names>NE</given-names></name>, <etal/></person-group>\n<article-title>Travelling waves in the occurrence of dengue haemorrhagic fever in Thailand</article-title>. <source>Nature</source>. <year>2004</year>;<volume>427</volume>(<issue>6972</issue>):<fpage>344</fpage>-<lpage>347</lpage>. doi:<pub-id pub-id-type=\"doi\">10.1038/nature02225</pub-id><pub-id pub-id-type=\"pmid\">14737166</pub-id></mixed-citation></ref></ref-list><notes notes-type=\"supplementary-material\" id=\"note-ZLD200130-1\"><supplementary-material content-type=\"local-data\" id=\"note-ZLD200130-1-s\"><label>Supplement.</label><caption><p><bold>eAppendix.</bold> Supplementary Methods</p><p><bold>eReferences.</bold></p></caption><media xlink:href=\"jamanetwopen-3-e2017521-s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></notes></back></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">JAMA Netw Open</journal-id><journal-id journal-id-type=\"iso-abbrev\">JAMA Netw Open</journal-id><journal-id journal-id-type=\"pmc\">JAMA Netw Open</journal-id><journal-title-group><journal-title>JAMA Network Open</journal-title></journal-title-group><issn pub-type=\"epub\">2574-3805</issn><publisher><publisher-name>American Medical Association</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32804214</article-id><article-id pub-id-type=\"pmc\">PMC7431997</article-id><article-id pub-id-type=\"doi\">10.1001/jamanetworkopen.2020.13226</article-id><article-id pub-id-type=\"publisher-id\">zoi200500</article-id><article-categories><subj-group subj-group-type=\"category\" specific-use=\"electronic\"><subject>Research</subject></subj-group><subj-group subj-group-type=\"heading\"><subject>Original Investigation</subject></subj-group><subj-group subj-group-type=\"online-only\"><subject>Online Only</subject></subj-group><subj-group subj-group-type=\"subject-area\"><subject>Oncology</subject></subj-group></article-categories><title-group><article-title>Incidence Trends of Breast Cancer Molecular Subtypes by Age and Race/Ethnicity in the US From 2010 to 2016</article-title><alt-title alt-title-type=\"headline\">Incidence Trends of Breast Cancer Molecular Subtypes by Age and Race/Ethnicity in the US</alt-title><alt-title alt-title-type=\"running-head\">Incidence Trends of Breast Cancer Molecular Subtypes by Age and Race/Ethnicity in the US</alt-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Acheampong</surname><given-names>Teofilia</given-names></name><degrees>MPH</degrees><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"zoi200500aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Kehm</surname><given-names>Rebecca D.</given-names></name><degrees>MPH</degrees><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"zoi200500aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Terry</surname><given-names>Mary Beth</given-names></name><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"zoi200500aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"zoi200500aff2\"><sup>2</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Argov</surname><given-names>Erica Lee</given-names></name><degrees>MPH</degrees><xref ref-type=\"aff\" rid=\"zoi200500aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>Tehranifar</surname><given-names>Parisa</given-names></name><degrees>DrPH</degrees><xref ref-type=\"aff\" rid=\"zoi200500aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"zoi200500aff2\"><sup>2</sup></xref></contrib></contrib-group><aff id=\"zoi200500aff1\"><label>1</label>Mailman School of Public Health, Department of Epidemiology, Columbia University, New York, New York</aff><aff id=\"zoi200500aff2\"><label>2</label>Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, New York</aff><author-notes><title>Article Information</title><p><bold>Accepted for Publication:</bold> May 30, 2020.</p><p content-type=\"published-online\"><bold>Published:</bold> August 17, 2020. doi:<uri content-type=\"doi\">10.1001/jamanetworkopen.2020.13226</uri></p><p content-type=\"open-access-note\"><bold>Open Access:</bold> This is an open access article distributed under the terms of the <ext-link ext-link-type=\"uri\" xlink:href=\"https://jamanetwork.com/journals/jamanetworkopen/pages/instructions-for-authors#SecOpenAccess\">CC-BY License</ext-link>. © 2020 Acheampong T et al. <italic>JAMA Network Open</italic>.</p><corresp id=\"zoi200500cor1\"><bold>Corresponding Author:</bold> Parisa Tehranifar, DrPH, Mailman School of Public Health, Department of Epidemiology, Columbia University, New York, NY (<email xlink:href=\"pt140@cumc.columbia.edu\">pt140@cumc.columbia.edu</email>).</corresp><p content-type=\"author-contributions\"><bold>Author Contributions:</bold> Dr Acheampong had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.</p><p><italic>Concept and design:</italic> Acheampong, Kehm, Tehranifar.</p><p><italic>Acquisition, analysis, or interpretation of data:</italic> All authors.</p><p><italic>Drafting of the manuscript:</italic> Acheampong, Terry, Lee Argov.</p><p><italic>Critical revision of the manuscript for important intellectual content:</italic> Acheampong, Kehm, Terry, Tehranifar.</p><p><italic>Statistical analysis:</italic> All authors.</p><p><italic>Supervision:</italic> Tehranifar.</p><p content-type=\"COI-statement\"><bold>Conflict of Interest Disclosures:</bold> None reported.</p><p content-type=\"funding-statement\"><bold>Funding/Support:</bold> Drs Acheampong and Kehm were supported by Cancer Epidemiology Training grant No. T32-CA009529 from the National Institutes of Health, National Cancer Institute.</p><p><bold>Role of the Funder/Sponsor:</bold> The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.</p></author-notes><pub-date pub-type=\"epub\" iso-8601-date=\"2020-08-17T10:00\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><!-- PMC Release delay is 0 months and\n\t\t\t\t\t\t0 days and was based on the <pub-date\n\t\t\t\t\t\tpub-type=\"epub\"/>. --><volume>3</volume><issue>8</issue><elocation-id>e2013226</elocation-id><history><date date-type=\"received\"><day>13</day><month>3</month><year>2020</year></date><date date-type=\"accepted\"><day>30</day><month>5</month><year>2020</year></date></history><permissions><copyright-statement>Copyright 2020 Acheampong T et al. <italic>JAMA Network Open</italic>.</copyright-statement><license license-type=\"open-access\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open access article distributed under the terms of the CC-BY License.</license-p></license></permissions><self-uri content-type=\"pdf-version\" xlink:href=\"jamanetwopen-3-e2013226.pdf\">jamanetwopen-3-e2013226.pdf</self-uri><self-uri content-type=\"silverchair\" xlink:href=\"https://jamanetwork.com/journals/jamanetworkopen/fullarticle/10.1001/jamanetworkopen.2020.13226\"/><abstract abstract-type=\"key-points\"><title>Key Points</title><sec id=\"ab-zoi200500-1\"><title>Question</title><p>Are incidence rates of US breast cancer molecular subtypes changing across age and racial/ethnic groups?</p></sec><sec id=\"ab-zoi200500-2\"><title>Findings</title><p>In this cross-sectional study including 320 124 women diagnosed with breast cancer from 2010 to 2016, incidence rates of luminal A breast cancer increased in non-Hispanic White and Asian/Pacific Islander women aged 40 to 69 years, and in non-Hispanic black women aged 55 to 69 years, for luminal B breast cancer in non-Hispanic White and Hispanic women of all ages, and for ERBB2-enriched breast cancer in non-Hispanic White women aged 25 to 39 years. Incidence rates for triple-negative breast cancer decreased in non-Hispanic Black and non-Hispanic White women aged 40 to 69 years.</p></sec><sec id=\"ab-zoi200500-3\"><title>Meaning</title><p>These findings of differing breast cancer molecular subtype–specific trends may suggest changes in the prevalence of breast cancer risk factors by race/ethnicity and age.</p></sec></abstract><abstract abstract-type=\"teaser\"><p>This cross-sectional study examines the incidence of different molecular subtypes of breast cancers among different racial/ethnic and age groups.</p></abstract><abstract><sec id=\"ab-zoi200500-4\"><title>Importance</title><p>Breast cancer incidence trends by age and race/ethnicity have been documented; it is less clear whether incidence trends of breast cancer molecular subtypes, which differ in risk factors and prognosis, also vary by age and race/ethnicity.</p></sec><sec id=\"ab-zoi200500-5\"><title>Objective</title><p>To estimate annual percentage changes and trends in breast cancer molecular subtype–specific incidence rates by age at diagnosis and race/ethnicity in the US.</p></sec><sec id=\"ab-zoi200500-6\"><title>Design, Setting, and Participants</title><p>This population-based cross-sectional study included data from 18 cancer registries in the Surveillance, Epidemiology and End Results database, capturing 27.8% of the US population. Hispanic and non-Hispanic White, Black, and Asian/Pacific Islander women aged 25 to 84 years who were diagnosed with invasive breast cancer from 2010 to 2016 were included. Data were analyzed from September 2019 to February 2020.</p></sec><sec id=\"ab-zoi200500-7\"><title>Exposures</title><p>Age and racial/ethnic groups.</p></sec><sec id=\"ab-zoi200500-8\"><title>Main Outcomes and Measures</title><p>Annual percentage change (APC) and 95% CIs for age-standardized breast cancer incidence rates stratified by 15-year age groups at diagnosis and race/ethnicity.</p></sec><sec id=\"ab-zoi200500-9\"><title>Results</title><p>Of 320 124 women diagnosed with breast cancer from 2010 to 2016, 232 558 (72.6%) had luminal A, 35 869 (11.2%) had luminal B, 15 472 (4.8%) had ERBB2-enriched, and 36 225 (11.3%) had triple-negative breast cancer subtypes. Luminal A breast cancer incidence rates increased in non-Hispanic White (APC from 2010-2014, 2.3%; 95% CI, 0.3% to 4.2%) and non-Hispanic Asian/Pacific Islander (APC from 2010-2016, 2.5%; 95% CI, 0.6% to 4.5%) women aged 40 to 54 years, and in non-Hispanic Black women aged 55 to 69 years women (APC from 2010-2012, 4.9%; 95% CI, 4.0% to 5.7%). Luminal B breast cancer incidence rates increased in all age groups for non-Hispanic White women (age 25-39 years: APC, 4.3%; 95% CI, 1.5% to 7.%2; age 40-54 years: APC, 3.5%; 95% CI, 1.4% to 5.6%; age 55-69 years: APC, 3.3%; 95% CI, 1.6% to 5.0%; age 70-84 years: APC, 3.9%; 95% CI, 1.9% to 6.0%) and Hispanic women (age 25-39 years: APC, 8.4%; 95% CI, 5.8% to 11.2%; age 40-54 years: APC, 6.1%; 95% CI, 4.2% to 8.0%; age 55-69 years: APC, 5.1%; 95% CI, 1.5% to 8.8%; age 70-84 years: APC, 7.1%; 95% CI, 4.6% to 9.6%) and in non-Hispanic Asian/Pacific Islander women aged 55 to 69 years (APC, 6.1%; 95% CI, 3.2% to 9.0%). ERBB2-enriched breast cancer incidence rates increased in non-Hispanic White women aged 25 to 39 years (APC, 4.7%; 95% CI, 1.5% to 8.0%). Triple-negative breast cancer incidence rates decreased in non-Hispanic White women aged 40 to 54 years (APC, –2.3%; 95% CI, –3.8% to –0.7%) and 55 to 69 years (APC, –3.6%; 95% CI, –5.1% to –2.1%) and in non-Hispanic Black women aged 55 to 69 years (APC, –1.4%; 95% CI, –2.2% to –0.7%).</p></sec><sec id=\"ab-zoi200500-10\"><title>Conclusions and Relevance</title><p>The findings of this cross-sectional study suggest that between 2010 and 2016, luminal A and luminal B breast cancer incidence rates increased for many racial/ethnic and age groups, with the largest increases observed for luminal B breast cancer. ERBB2-enriched breast cancer incidence rates increased for young non-Hispanic White women, while triple-negative breast cancer incidence rates decreased for midlife non-Hispanic White and non-Hispanic Black women. These trends may suggest changes in breast cancer risk factor profiles across age and racial/ethnic groups.</p></sec></abstract></article-meta></front><body><sec id=\"H1-1-ZOI200500\"><title>Introduction</title><p>Population-based studies suggest a mostly stable trend in overall breast cancer incidence rates,<sup><xref rid=\"zoi200500r1\" ref-type=\"bibr\">1</xref>,<xref rid=\"zoi200500r2\" ref-type=\"bibr\">2</xref></sup> although there are documented differences in trends by age at diagnosis and racial/ethnic groups.<sup><xref rid=\"zoi200500r1\" ref-type=\"bibr\">1</xref>,<xref rid=\"zoi200500r3\" ref-type=\"bibr\">3</xref>,<xref rid=\"zoi200500r4\" ref-type=\"bibr\">4</xref>,<xref rid=\"zoi200500r5\" ref-type=\"bibr\">5</xref></sup> Moreover, studies have reported racial/ethnic differences in the incidence rates of breast cancer subtypes, defined according to hormone receptor status or ERBB2 (formerly HER2) receptor status, but these studies have mostly considered few broad molecular subtype, race/ethnicity, or age categories.<sup><xref rid=\"zoi200500r6\" ref-type=\"bibr\">6</xref>,<xref rid=\"zoi200500r7\" ref-type=\"bibr\">7</xref>,<xref rid=\"zoi200500r8\" ref-type=\"bibr\">8</xref></sup> A 2019 report<sup><xref rid=\"zoi200500r2\" ref-type=\"bibr\">2</xref></sup> of temporal changes in breast cancer incidence rates from 2004 through 2016, which used only breast cancer subtypes by hormone receptor status, found that incidence rates increased for hormone receptor–positive breast cancers (corresponding to both luminal A and B molecular breast cancer subtypes) for all races/ethnicities, although rates began to stabilized after 2011 for non-Hispanic Black and American Indian and Alaska Native women. In contrast, hormone receptor–negative tumors (corresponding to both ERBB2-enriched and triple-negative molecular breast cancer subtypes) decreased for women of all racial/ethnic groups by 1.5% to 2.6% per year. Furthermore, differences in trends for women by 2 broad age groups (<50 years vs ≥50 years), were similar, although not statistically significant for the hormone receptor–negative decline in young American Indian and Alaska Native or older Hispanic women.<sup><xref rid=\"zoi200500r2\" ref-type=\"bibr\">2</xref></sup> A previous cross-sectional study also examined breast cancer incidence rates by racial/ethnic and age group differences in 4 breast cancer molecular subtypes based on joint assessment of both hormone receptor status and ERBB2 status using 2010 cancer registry data.<sup><xref rid=\"zoi200500r6\" ref-type=\"bibr\">6</xref></sup> However, studies assessing trends in the incidence rates of breast cancer subtypes are limited, as ERBB2 status was only required to be collected by cancer registries starting in 2010.</p><p>The heterogeneity of breast cancer at the molecular level is etiologically and clinically meaningful, as it maps to distinct risk factors as well as to differences in treatment effectiveness and prognosis.<sup><xref rid=\"zoi200500r2\" ref-type=\"bibr\">2</xref>,<xref rid=\"zoi200500r9\" ref-type=\"bibr\">9</xref>,<xref rid=\"zoi200500r10\" ref-type=\"bibr\">10</xref>,<xref rid=\"zoi200500r11\" ref-type=\"bibr\">11</xref></sup> Furthermore, the disproportionate distribution of molecular subtypes by race/ethnicity may partially account for racial/ethnic disparities in breast cancer outcomes, particularly among younger women.<sup><xref rid=\"zoi200500r12\" ref-type=\"bibr\">12</xref>,<xref rid=\"zoi200500r13\" ref-type=\"bibr\">13</xref></sup> Therefore, breast cancer subtype-specific trends may reveal patterns with important implications for breast cancer etiology and reducing breast cancer health disparities.</p><p>We used data from US population-based Surveillance Epidemiology and End Results (SEER) cancer registries to examine trends in the incidence of breast cancer by age and racial/ethnic groups from 2010 to 2016 for 4 molecular subtypes, defined by joint expression of hormone receptor and ERBB2 status. This time period includes the most complete data on breast cancer molecular subtypes, including ERBB2 status, to our knowledge.</p></sec><sec id=\"H1-2-ZOI200500\"><title>Methods</title><sec id=\"H2-1-ZOI200500\"><title>Study Population</title><p>We obtained data from the SEER 18 cancer registry database (November 2018 submission, 2000-2016),<sup><xref rid=\"zoi200500r14\" ref-type=\"bibr\">14</xref></sup> representing 12 states and capturing 27.8% of the US population. We filed a data-use agreement with the SEER National Cancer Institute for access to the deidentified database; therefore, the Columbia University institutional review board deemed this study exempt from approval and informed consent, per institutional policy. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.equator-network.org/reporting-guidelines/strobe/\">STROBE</ext-link>) reporting guideline.</p><p>We identified women aged 25 to 84 years who had been diagnosed with a primary invasive breast cancer (<italic>International Classification of Diseases for Oncology, Third Edition</italic> [<italic>ICD-O-3</italic>]<sup><xref rid=\"zoi200500r15\" ref-type=\"bibr\">15</xref></sup>: C50.0 to C50.9) from 2010 to 2016. We excluded women with tumors that were of unknown molecular subtype or not microscopically confirmed. We focused on the largest racial/ethnic subgroups, excluding the American Indian and Native Alaskan group owing to having fewer than 5 cases by subtype and age group per year for Joinpoint analyses.</p><p>We categorized age group at diagnosis based on 15-year intervals (ie, ages 25-39, 40-54, 55-69, and 70-84 years) and categorized race/ethnicity into 4 mutually exclusive groups: non-Hispanic White, non-Hispanic Black, non-Hispanic Asian/Pacific Islander, and Hispanic ethnicity (97.1% of whom were Hispanic White). We used SEER’s breast subtype variable,<sup><xref rid=\"zoi200500r16\" ref-type=\"bibr\">16</xref></sup> which groups tumors into 4 breast cancer subtype categories based on joint expressions of hormone receptors capturing estrogen and progesterone receptors and with or without the ERBB2 marker: luminal A, with hormone receptor expression and without ERBB2 expression; luminal B, with hormone receptor and ERBB2 expressions; ERBB2-enriched, without hormone receptor expression and with ERBB2 expression; and triple negative, without hormone receptor or ERBB2 expressions.<sup><xref rid=\"zoi200500r6\" ref-type=\"bibr\">6</xref></sup></p></sec><sec id=\"H2-2-ZOI200500\"><title>Statistical Analysis</title><p>We calculated annual age-standardized breast cancer incidence rates per 100 000 women and SEs using SEER*Stat software version 8.3.6.<sup><xref rid=\"zoi200500r17\" ref-type=\"bibr\">17</xref></sup> We also calculated changes in trends using the National Cancer Institute’s Joinpoint software version 4.7.0.0.<sup><xref rid=\"zoi200500r18\" ref-type=\"bibr\">18</xref></sup> We fit weighted least-squares regression models, with a log-linear function, to estimate change in age-standardized annual incidence rates for each population subgroup, defined by age and racial/ethnic groups. We defined the independent variable as the year of diagnosis, and a maximum of 1 Joinpoint was automatically set based on the number of years. The selection of final models was based on a series of permutation tests and an overall α level of 0.05. We estimated the annual percentage change (APC) from the slope of the final model and corresponding 95% CIs. Statistically significant APCs were based on 2-sided tests using <italic>t</italic> distribution. Data were analyzed from September 2019 to February 2020.</p></sec></sec><sec id=\"H1-3-ZOI200500\"><title>Results</title><p>Among 348 586 women with breast cancer in the SEER database, we excluded 26 520 women with tumors of unknown molecular subtype and 82 women with tumors that were not microscopically confirmed. We excluded 1860 American Indian and Native Alaskan women owing to having fewer than 5 cases by subtype and age group per year. The final sample comprised 320 124 women (91.8% of original sample) diagnosed with incident breast cancer during 2010 to 2016. Most women were non-Hispanic White (216 092 women [67.5%]) and aged 55 to 69 years at diagnosis (132 986 women [41.5%]) (<xref rid=\"zoi200500t1\" ref-type=\"table\">Table 1</xref>). A total of 232 558 tumors (72.6%) were Luminal A, 36 225 tumors (11.3%) were triple negative, 35 869 tumors (11.2%) were luminal B, and 15 472 tumors (4.8%) were ERBB2-enriched. Missing data on molecular subtype were more common in Hispanic women overall and women aged 70 to 84 years in each race/ethnic group (eTable in the <xref ref-type=\"supplementary-material\" rid=\"note-ZOI200500-1-s\">Supplement</xref>).<sup><xref rid=\"zoi200500r5\" ref-type=\"bibr\">5</xref></sup></p><table-wrap id=\"zoi200500t1\" orientation=\"portrait\" position=\"float\"><label>Table 1. </label><caption><title>Number and Incidence Rates of Breast Cancer Per 100 000 Women Stratified by Race/Ethnicity, Age-Group and Molecular Subtype From 2010 to 2016</title></caption><table frame=\"hsides\" rules=\"groups\"><col width=\"12.39%\" span=\"1\"/><col width=\"10.68%\" span=\"1\"/><col width=\"11.27%\" span=\"1\"/><col width=\"10.62%\" span=\"1\"/><col width=\"11.15%\" span=\"1\"/><col width=\"10.55%\" span=\"1\"/><col width=\"11.01%\" span=\"1\"/><col width=\"11.57%\" span=\"1\"/><col width=\"10.76%\" span=\"1\"/><thead><tr><th rowspan=\"2\" valign=\"top\" align=\"left\" scope=\"col\" colspan=\"1\">Subtype</th><th colspan=\"2\" valign=\"top\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">Non-Hispanic White (n = 216 092)</th><th colspan=\"2\" valign=\"top\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">Non-Hispanic Black (n = 36 267)</th><th colspan=\"2\" valign=\"top\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">Non-Hispanic Asian/Pacific Islander (n = 29 309)</th><th colspan=\"2\" valign=\"top\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">Hispanic (n = 38 456)</th></tr><tr><th valign=\"top\" colspan=\"1\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">No. (%)</th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Rate<xref ref-type=\"table-fn\" rid=\"zoi200500t1n1\"><sup>a</sup></xref> (95% CI)</th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">No. (%)</th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Rate<xref ref-type=\"table-fn\" rid=\"zoi200500t1n1\"><sup>a</sup></xref> (95% CI)</th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">No. (%)</th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Rate<xref ref-type=\"table-fn\" rid=\"zoi200500t1n1\"><sup>a</sup></xref> (95% CI)</th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">No. (%)</th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Rate<xref ref-type=\"table-fn\" rid=\"zoi200500t1n1\"><sup>a</sup></xref> (95% CI)</th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Age 25-39 y</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Luminal A<xref ref-type=\"table-fn\" rid=\"zoi200500t1n2\"><sup>b</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4950 (54.5)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">17.9 (17.4-18.4)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1300 (48.3)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">17.8 (17.0-18.9)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1307 (59.9)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">17.6 (16.7-18.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1897 (51.9)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">12.6 (12.1-13.2)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Luminal B<xref ref-type=\"table-fn\" rid=\"zoi200500t1n3\"><sup>c</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1845 (20.3)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">6.6 (6.3-6.9)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">491 (18.2)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">6.7 (6.1-7.3)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">405 (18.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5.4 (4.9-6.0)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">648 (17.7)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.3 (4.0-4.6)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> ERBB2 enriched<xref ref-type=\"table-fn\" rid=\"zoi200500t1n4\"><sup>d</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">676 (7.4)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.4 (2.2-2.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">192 (7.1)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.6 (2.3-3.0)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">169 (7.7)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.3 (1.9-2.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">299 (8.2)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.0 (1.8-2.2)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Triple negative<xref ref-type=\"table-fn\" rid=\"zoi200500t1n5\"><sup>e</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1611 (17.7)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5.7 (5.4-6.0)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">711 (26.4)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">9.7 (9.0-10.5)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">301 (13.8)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.0 (3.6-4.5)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">814 (22.3)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5.4 (5.0-5.7)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Age 40-54 y</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Luminal A<xref ref-type=\"table-fn\" rid=\"zoi200500t1n2\"><sup>b</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">41 930 (71.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">113.3 (112.2-114.5)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">6783 (56.5)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">84.4 (82.4-86.5)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">7564 (69.5)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">102.7 (100.4-105)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">9855 (65.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">75.7 (74.2-77.2)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Luminal B<xref ref-type=\"table-fn\" rid=\"zoi200500t1n3\"><sup>c</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">7430 (12.7)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">20.4 (20.0-20.9)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1597 (13.3)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">19.9 (19.0-20.9)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1611 (14.8)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">21.8 (20.8-22.9)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2175 (14.5)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">16.7 (16.0-17.4)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> ERBB2 enriched<xref ref-type=\"table-fn\" rid=\"zoi200500t1n4\"><sup>d</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2826 (4.8)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">7.6 (7.3-7.9)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">842 (7.0)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">10.4 (9.7-11.2)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">809 (7.4)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">10.9 (10.1-11.7)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">965 (6.4)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">7.4 (7.0-7.9)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Triple negative<xref ref-type=\"table-fn\" rid=\"zoi200500t1n5\"><sup>e</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">6351 (10.8)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">17.5 (17.1-17.9)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2788 (23.2)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">34.6 (33.3-35.9)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">902 (8.3)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">12.3 (11.5-13.1)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2019 (13.4)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">15.5 (14.8-16.2)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Age 55-69 y</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Luminal A<xref ref-type=\"table-fn\" rid=\"zoi200500t1n2\"><sup>b</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">71 194 (76.7)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">207.9 (206.4-209.5)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">9260 (62.5)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">164.3 (160.9-167.7)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">8324 (72.7)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">157.1 (153.7-160.5)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">10 032 (72.3)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">147.0 (144.1-149.9)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Luminal B<xref ref-type=\"table-fn\" rid=\"zoi200500t1n3\"><sup>c</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">9242 (10.0)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">27.1 (26.5-27.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1720 (11.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">30.3 (28.8-31.7)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1357 (11.9)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">25.5 (24.2-26.9)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1626 (11.7)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">23.4 (22.2-24.5)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> ERBB2 enriched<xref ref-type=\"table-fn\" rid=\"zoi200500t1n4\"><sup>d</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3841 (4.1)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">11.3 (10.9-11.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">849 (5.7)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">14.9 (13.9-15.9)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">809 (7.1)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">15.2 (14.1-16.2)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">775 (5.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">11.1 (10.3-11.9)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Triple negative<xref ref-type=\"table-fn\" rid=\"zoi200500t1n5\"><sup>e</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">8562 (9.2)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">25.1 (24.5-25.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2997 (20.2)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">52.6 (50.8-54.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">955 (8.3)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">18 (16.9-19.2)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1443 (10.4)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">20.9 (19.8-22)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Age 70-84 y</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Luminal A<xref ref-type=\"table-fn\" rid=\"zoi200500t1n2\"><sup>b</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">45 021 (80.9)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">268.0 (265.6-270.5)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4748 (70.5)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">214.1 (208.0-220.3)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3764 (78.5)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">161.9 (156.7-167.1)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4629 (78.4)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">173.6 (168.6-178.7)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Luminal B<xref ref-type=\"table-fn\" rid=\"zoi200500t1n3\"><sup>c</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4258 (7.7)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">25.3 (24.6-26.1)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">564 (8.4)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">25.4 (23.4-27.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">394 (8.2)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">17 (15.3-18.7)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">506 (8.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">19 (17.4-20.7)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> ERBB2 enriched<xref ref-type=\"table-fn\" rid=\"zoi200500t1n4\"><sup>d</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1663 (3.0)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">9.9 (9.4-10.4)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">325 (4.8)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">14.7 (13.2-16.4)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">200 (4.2)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">8.6 (7.5-9.9)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">232 (3.9)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">8.7 (7.7-10.0)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Triple negative<xref ref-type=\"table-fn\" rid=\"zoi200500t1n5\"><sup>e</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4692 (8.4)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">27.8 (27.0-28.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1100 (16.3)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">49.6 (46.7-52.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">438 (9.1)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">18.8 (17.1-20.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">541 (9.2)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">20.2 (18.5-22)</td></tr></tbody></table><table-wrap-foot><fn id=\"zoi200500t1n1\"><label><sup>a</sup></label><p>Per 100 000 women and age-adjusted to the 2000 US Standard Population (5-year age groups).</p></fn><fn id=\"zoi200500t1n2\"><label><sup>b</sup></label><p>Indicates with hormone receptor expression and without ERBB2 expression.</p></fn><fn id=\"zoi200500t1n3\"><label><sup>c</sup></label><p>Indicates with hormone receptor and ERBB2 expression.</p></fn><fn id=\"zoi200500t1n4\"><label><sup>d</sup></label><p>Indicates without hormone receptor expression and with ERBB2 expression.</p></fn><fn id=\"zoi200500t1n5\"><label><sup>e</sup></label><p>Indicates without hormone receptor or ERBB2 expression.</p></fn></table-wrap-foot></table-wrap><p>Annual age-standardized breast cancer molecular subtype-specific incidence rates by age and racial/ethnic groups are displayed in <xref ref-type=\"fig\" rid=\"zoi200500f1\">Figure 1</xref> and <xref ref-type=\"fig\" rid=\"zoi200500f2\">Figure 2</xref>, and the APCs in incidence rates along with 95% CIs are provided in <xref rid=\"zoi200500t2\" ref-type=\"table\">Table 2</xref>. Luminal A breast cancer incidence rates increased for non-Hispanic White women aged 40 to 54 years old by 2.3% (95% CI, 0.3% to 4.2%) annually until 2014; the slope of the remaining trend line (ie, from 2014-2016) was not statistically significantly different from 0 in this group, indicating stable rates for that segment of time (<xref rid=\"zoi200500t2\" ref-type=\"table\">Table 2</xref>; <xref ref-type=\"fig\" rid=\"zoi200500f1\">Figure 1</xref>A). Incidence rates for luminal A breast cancer also increased for non-Hispanic Black women aged 55 to 69 years by 4.9% (95% CI, 4.0% to 5.7%) annually until 2012, with a subsequent decline from 2013 to 2016 (APC, –0.7%; 95% CI, –0.9% to –0.5%) (<xref rid=\"zoi200500t2\" ref-type=\"table\">Table 2</xref>, <xref ref-type=\"fig\" rid=\"zoi200500f1\">Figure 1</xref>A). There was also an increase in luminal A incidence rates for non-Hispanic Asian/Pacific Islander women aged 40 to 54 years from 2010 to 2016 (APC, 2.5%; 95% CI, 0.6% to 4.5%).</p><fig id=\"zoi200500f1\" fig-type=\"figure\" orientation=\"portrait\" position=\"float\"><label>Figure 1. </label><caption><title>Incidence of Luminal A and B Breast Cancer Per 100 000 Women Stratified by Race/Ethnicity and Age Group</title><p>Luminal A indicates hormone receptor–positive and ERBB2-negative; luminal B, hormone receptor–positive and ERBB2 positive.</p></caption><graphic xlink:href=\"jamanetwopen-3-e2013226-g001\"/></fig><fig id=\"zoi200500f2\" fig-type=\"figure\" orientation=\"portrait\" position=\"float\"><label>Figure 2. </label><caption><title>Incidence of ERBB2-Enriched and Triple-Negative Breast Cancer Per 100 000 Women Stratified by Race/Ethnicity and Age Group</title><p>ERBB2-enriched indicates hormone receptor–negative and ERBB2-positive; triple-negative, hormone receptor–negative and ERBB2-negative.</p></caption><graphic xlink:href=\"jamanetwopen-3-e2013226-g002\"/></fig><table-wrap id=\"zoi200500t2\" orientation=\"portrait\" position=\"float\"><label>Table 2. </label><caption><title>Annual Percent Change Age-Standardized Breast Cancer Incidence Rates per 100 000 Women Stratified by Race/Ethnicity, Age Group, and Molecular Subtype From 2010 to 2016</title></caption><table frame=\"hsides\" rules=\"groups\"><col width=\"24.36%\" span=\"1\"/><col width=\"16.81%\" span=\"1\"/><col width=\"16.65%\" span=\"1\"/><col width=\"26.14%\" span=\"1\"/><col width=\"16.04%\" span=\"1\"/><thead><tr><th rowspan=\"2\" valign=\"top\" align=\"left\" scope=\"col\" colspan=\"1\">Subtype</th><th colspan=\"4\" valign=\"top\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">Annual percent change (95% CI)</th></tr><tr><th valign=\"top\" colspan=\"1\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">Non-Hispanic White</th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Non-Hispanic Black</th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Non-Hispanic Asian/Pacific Islander</th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Hispanic</th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Age 25-39 y</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Luminal A<xref ref-type=\"table-fn\" rid=\"zoi200500t2n1\"><sup>a</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.6 (–2.4 to 3.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.1 (–2.8 to 5.1)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.0 (–2.9 to 5.1)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.1 (–1.1 to 5.3)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Luminal B<xref ref-type=\"table-fn\" rid=\"zoi200500t2n2\"><sup>b</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.3 (1.5 to 7.2)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–1.6 (–3.8 to 7.5)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.2 (–0.1 to 6.7)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">8.4 (5.8 to 11.2)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> ERBB2 enriched<xref ref-type=\"table-fn\" rid=\"zoi200500t2n3\"><sup>c</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.7 (1.5 to 8.0)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.1 (–4.2 to 6.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5.4 (–8.6 to 21.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.4 (–2.3 to 9.5)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Triple negative<xref ref-type=\"table-fn\" rid=\"zoi200500t2n4\"><sup>d</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.6 (–1.9 to 3.2)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.2 (–4.8 to 5.5)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–1.3 (–8.6 to 6.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.6 (–1.1 to 4.3)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Age 40-54 y</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Luminal A<xref ref-type=\"table-fn\" rid=\"zoi200500t2n1\"><sup>a</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.3 (0.3 to 4.2)<xref ref-type=\"table-fn\" rid=\"zoi200500t2n5\"><sup>e</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.1 (–0.8 to 3.0)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.5 (0.6 to 4.5)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–0.8 (–6.8 to 5.7)<xref ref-type=\"table-fn\" rid=\"zoi200500t2n5\"><sup>e</sup></xref></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Luminal A, includes data for 2010-2012<xref ref-type=\"table-fn\" rid=\"zoi200500t2n1\"><sup>a</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–0.7 (–6.7 to 5.8)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">10.5 (–11.4 to 37.7)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Luminal B<xref ref-type=\"table-fn\" rid=\"zoi200500t2n2\"><sup>b</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.5 (1.4 to 5.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.5 (–0.2 to 7.3)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.3 (–0.2 to 6.8)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">6.1 (4.2 to 8.0)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> ERBB2<xref ref-type=\"table-fn\" rid=\"zoi200500t2n3\"><sup>c</sup></xref> enriched</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.8 (–0.9 to 6.8)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.0 (–2.2 to 8.4)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–0.2 (–3.6 to 3.3)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.9 (–3.8 to 7.9)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Triple negative<xref ref-type=\"table-fn\" rid=\"zoi200500t2n4\"><sup>d</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–2.3 (–3.8 to –0.7)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–0.8 (–3.5 to 2.0)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–4.1 (–9.2 to 1.2)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–0.8 (–6.1 to 4.9)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Age 55-69 y</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Luminal A<xref ref-type=\"table-fn\" rid=\"zoi200500t2n1\"><sup>a</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.7 (0.2 to 1.2)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–0.7 (–0.9 to –0.5<xref ref-type=\"table-fn\" rid=\"zoi200500t2n5\"><sup>e</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.3 (0.0 to 2.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.2 (–0.8 to 3.3)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Luminal A, includes data for 2010-2012<xref ref-type=\"table-fn\" rid=\"zoi200500t2n1\"><sup>a</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.9 (4.0 to 5.7)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Luminal B<xref ref-type=\"table-fn\" rid=\"zoi200500t2n2\"><sup>b</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.3 (1.6 to 5.0)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.4 (–0.7 to 5.5)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">6.1 (3.2 to 9.0)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5.1 (1.5 to 8.8)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> ERBB2 enriched<xref ref-type=\"table-fn\" rid=\"zoi200500t2n3\"><sup>c</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.6 (–2.0 to 5.3)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.5 (–3.4 to 8.7)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–1.8 (–4.3 to 0.8)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.3 (–3.8 to 10.9)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Triple negative<xref ref-type=\"table-fn\" rid=\"zoi200500t2n4\"><sup>d</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–3.6 (–5.1 to –2.1)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–1.4 (–2.2 to –0.7)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.3 (–1.8 to 4.4)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–0.3 (–1.9 to 1.3)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Age 70-84 y</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Luminal A<xref ref-type=\"table-fn\" rid=\"zoi200500t2n1\"><sup>a</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.7 (–0.3 to 1.7)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.4 (–1.8 to 4.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.5 (–2.4 to 9.7)<xref ref-type=\"table-fn\" rid=\"zoi200500t2n5\"><sup>e</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.4 (–0.6 to 3.3)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Luminal A, includes data for 2010-2012<xref ref-type=\"table-fn\" rid=\"zoi200500t2n1\"><sup>a</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–4.2 (–18.9 to 13.2)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Luminal B<xref ref-type=\"table-fn\" rid=\"zoi200500t2n2\"><sup>b</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.9 (1.9 to 6.0)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.2 (–2.2 to 10.9)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–1.0 (–8.0 to 6.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">7.1 (4.6 to 9.6)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> ERBB2 enriched<xref ref-type=\"table-fn\" rid=\"zoi200500t2n3\"><sup>c</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.7 (–1.5 to 2.8)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.2 (–9.3 to 12.9)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.8 (–5.5 to 9.8)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.7 (–5.1 to 13.3)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Triple negative<xref ref-type=\"table-fn\" rid=\"zoi200500t2n4\"><sup>d</sup></xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">–1.8 (–3.9 to 0.5)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.9 (–1.8 to 3.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.8 (–5.2 to 9.2)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.5 (–8.5 to 14.8)</td></tr></tbody></table><table-wrap-foot><fn id=\"zoi200500t2n1\"><label><sup>a</sup></label><p>Indicates with hormone receptor expression and without ERBB2 expression.</p></fn><fn id=\"zoi200500t2n2\"><label><sup>b</sup></label><p>Indicates with hormone receptor and ERBB2 expression.</p></fn><fn id=\"zoi200500t2n3\"><label><sup>c</sup></label><p>Indicates without hormone receptor expression and with ERBB2 expression.</p></fn><fn id=\"zoi200500t2n4\"><label><sup>d</sup></label><p>Indicates without hormone receptor or ERBB2 expression.</p></fn><fn id=\"zoi200500t2n5\"><label><sup>e</sup></label><p>Includes data from 2010 to 2014.</p></fn></table-wrap-foot></table-wrap><p>Luminal B, ERBB2-enriched, and triple-negative breast cancer incidence rates had no statistically significant changes in the slope of the trends (ie, inflection point in the trend) during the time period; however, there were significant changes in the rates over the entire time period from 2010 to 2016. Luminal B breast cancer incidence rates statistically significantly increased in all age groups for non-Hispanic White women (age 25-39 years: APC, 4.3%; 95% CI, 1.5% to 7.2%; age 40-54 years: APC, 3.5%; 95% CI, 1.4% to 5.6%; age 55-69 years: APC, 3.3%; 95% CI, 1.6% to 5.0%; age 70-84 years: APC, 3.9%; 95% CI, 1.9% to 6.0%) and Hispanic women (age 25-39 years: APC, 8.4%; 95% CI, 5.8% to 11.2%; age 40-54 years: APC, 6.1%; 95% CI, 4.2% to 8.0%; age 55-69 years: APC, 5.1%; 95% CI, 1.5% to 8.8%; age 70-84 years: APC, 7.1%; 95% CI, 4.6% to 9.6%) (<xref rid=\"zoi200500t2\" ref-type=\"table\">Table 2</xref> and <xref ref-type=\"fig\" rid=\"zoi200500f1\">Figure 1</xref>B), with the largest increases for each of these racial/ethnic groups observed for women aged 25 to 39-years. There was also a 6.1% (95% CI, 3.2% to 9.0%) annual increase in luminal B breast cancer incidence rates for non-Hispanic Asian/Pacific Islander women aged 55 to 69 years (<xref rid=\"zoi200500t2\" ref-type=\"table\">Table 2</xref> and <xref ref-type=\"fig\" rid=\"zoi200500f1\">Figure 1</xref>B).</p><p>Incidence rates for ERBB2-enriched breast cancer increased by 4.7% (95% CI, 1.5% to 8.0%) annually for non-Hispanic White women aged 25 to 39 years (<xref rid=\"zoi200500t2\" ref-type=\"table\">Table 2</xref> and <xref ref-type=\"fig\" rid=\"zoi200500f2\">Figure 2</xref>A). There were no statistically significant changes in ERBB2-enriched incidence rates for any other racial/ethnic and age group combinations, but the CIs for some of the estimates were wide. Triple-negative breast cancer incidence rates decreased by 2.3% (95% CI, –3.8% to –0.7%) annually in non-Hispanic White women aged 40 to 54 years, by 3.6% (95% CI, –5.1% to –2.1%) annually in non-Hispanic White women aged 55 to 69 years, and by 1.4% (95% CI, –2.2% to –0.7%) annually in non-Hispanic Black women (<xref rid=\"zoi200500t2\" ref-type=\"table\">Table 2</xref> and <xref ref-type=\"fig\" rid=\"zoi200500f2\">Figure 2</xref>B).</p></sec><sec id=\"H1-4-ZOI200500\"><title>Discussion</title><p>This cross-sectional study found that between 2010 and 2016, hormone receptor–positive breast cancer subtypes (ie, luminal A and B) increased for many racial/ethnic and age groups, with the largest and most consistent increases observed for luminal B breast cancer in non-Hispanic White and Hispanic women. Luminal A breast cancer incidence rates increased for non-Hispanic White and non-Hispanic Asian/Pacific Islander women aged 40 to 69 years and for non-Hispanic Black women aged 55 to 69 years, although the incidence rates began to decline for non-Hispanic Black women by 2012. Trends for hormone receptor–negative breast cancer subtypes (ie, ERBB2-enriched and triple negative) were mixed; ERBB2-enriched breast cancer increased for non-Hispanic White women aged 25 to 30 years, while triple-negative breast cancer decreased for non-Hispanic White women aged 40 to 69 years, and non-Hispanic Black women aged 55 to 69 years.</p><p>Our results are consistent with another population-based study<sup><xref rid=\"zoi200500r2\" ref-type=\"bibr\">2</xref></sup> that found an increase in hormone receptor–positive breast cancer and a decline in hormone receptor–negative breast cancer, which occurred to varying degrees for all racial/ethnic groups among women younger than 50 years and those 50 years or older. By further considering ERBB2 status and smaller age groups, we found differing trends in hormone receptor–positive and hormone receptor–negative breast cancers based on positive ERBB2 expression, enabling us to identify additional racial/ethnic and age group variation in trends. Specifically, we found that the increases for hormone receptor–positive tumors were driven by increases in luminal A subtype in midlife non-Hispanic White and non-Hispanic Asian/Pacific Islander women and increases in luminal B subtype that spanned across all age groups for non-Hispanic White and Hispanic women. Similarly, considering the ERBB2-enriched subtype separately from the triple-negative subtype, we were able to confirm that declines in hormone receptor–negative breast cancers were restricted to the triple-negative subtype in midlife non-Hispanic White and non-Hispanic Black women and did not include the ERBB2-enriched subtype, for which an increase in young non-Hispanic White women was observed.</p><p>Reasons for age and racial/ethnic group variations in subtype-specific breast cancer incidence are not adequately known. Increasing evidence, including a 2018 pooled analysis of 9 prospective cohorts,<sup><xref rid=\"zoi200500r9\" ref-type=\"bibr\">9</xref></sup> suggests variations in the direction and magnitude of associations of known breast cancer risk factors (eg, parity and age at first birth, menarche, and menopause) with breast cancer subtypes, indicating heterogeneity in breast cancer origins; variations in the prevalence of breast cancer risk factors across race/ethnicity and birth cohorts have been documented.<sup><xref rid=\"zoi200500r2\" ref-type=\"bibr\">2</xref>,<xref rid=\"zoi200500r9\" ref-type=\"bibr\">9</xref>,<xref rid=\"zoi200500r19\" ref-type=\"bibr\">19</xref>,<xref rid=\"zoi200500r20\" ref-type=\"bibr\">20</xref>,<xref rid=\"zoi200500r21\" ref-type=\"bibr\">21</xref></sup> Thus, differences in the distribution of population-level breast cancer risk factors by race/ethnicity and birth cohorts may drive subtype-specific breast cancer incidence rates, and may partially reflect racial/ethnic and age incidence trends in breast cancer at the molecular level,<sup><xref rid=\"zoi200500r2\" ref-type=\"bibr\">2</xref>,<xref rid=\"zoi200500r21\" ref-type=\"bibr\">21</xref>,<xref rid=\"zoi200500r22\" ref-type=\"bibr\">22</xref></sup> although this requires further empirical confirmation. Moreover, changes in incidence trends for particular subtypes, such as ERBB2-positive breast cancers, that are consistent across all age groups or that are present for younger age groups who are not likely participating in breast cancer screening suggest a real change in risk for those subtypes rather than greater detection through increasing screening over time.</p><p>Strengths of this study include the large study population with a wide range of age groups. Additionally, this cross-sectional study included detailed and consistent assessment and reporting of data for characterizing molecular subtypes of breast cancer for the time period covered in this analysis.</p><sec id=\"H2-3-ZOI200500\"><title>Limitations</title><p>There are some limitations of this study. As data for ERBB2 status were not collected by cancer registries until 2010, we restricted this analysis to 2010 to 2016, the most recent years for which more complete data were available. The relatively short period of time did not allow for detection of longer-term changes in the incidence rates and allowed for identifying only 1 possible inflection point in the incidence trend in the Joinpoint analysis. The small sample of American Indian and Alaska Native women did not allow for including this population in Joinpoint analysis, and the small sample of women diagnosed with ERBB2-enriched breast cancer subtype resulted in imprecise estimates. Replication of our analysis with additional years as well as a larger sample of American Indian and Alaska Native women is warranted. Our analysis excluded approximately 7% of patients identified with invasive breast cancer in 2010 to 2016 owing to missing tumor marker status data. The distribution of missing tumor status data was similar across age and race/ethnicity groups, although a slightly larger number of Hispanic women overall and women aged 70 to 84 years in each race/ethnic group had unknown tumor marker data.<sup><xref rid=\"zoi200500r5\" ref-type=\"bibr\">5</xref></sup> As a result, we cannot rule out that the observed rates may be overestimated or underestimated for different groups.</p></sec></sec><sec id=\"H1-5-ZOI200500\"><title>Conclusions</title><p>The findings of this cross-sectional study suggest that overall breast cancer trends mask differences that may exist across molecular subtypes by age and racial/ethnic groups. In recent years, luminal A and luminal B breast cancer incidence has increased, whereas triple-negative breast cancer trends have gradually declined for certain age and racial/ethnic groups. 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] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">JAMA Netw Open</journal-id><journal-id journal-id-type=\"iso-abbrev\">JAMA Netw Open</journal-id><journal-id journal-id-type=\"pmc\">JAMA Netw Open</journal-id><journal-title-group><journal-title>JAMA Network Open</journal-title></journal-title-group><issn pub-type=\"epub\">2574-3805</issn><publisher><publisher-name>American Medical Association</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32804215</article-id><article-id pub-id-type=\"pmc\">PMC7431998</article-id><article-id pub-id-type=\"doi\">10.1001/jamanetworkopen.2020.13463</article-id><article-id pub-id-type=\"publisher-id\">zoi200511</article-id><article-categories><subj-group subj-group-type=\"category\" specific-use=\"electronic\"><subject>Research</subject></subj-group><subj-group subj-group-type=\"heading\"><subject>Original Investigation</subject></subj-group><subj-group subj-group-type=\"online-only\"><subject>Online Only</subject></subj-group><subj-group subj-group-type=\"subject-area\"><subject>Rheumatology</subject></subj-group></article-categories><title-group><article-title>Physical Activity Throughout Adolescence and Peak Hip Strength in Young Adults</article-title><alt-title alt-title-type=\"headline\">Physical Activity Throughout Adolescence and Peak Hip Strength in Young Adults</alt-title><alt-title alt-title-type=\"running-head\">Physical Activity Throughout Adolescence and Peak Hip Strength in Young Adults</alt-title></title-group><contrib-group><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>Elhakeem</surname><given-names>Ahmed</given-names></name><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"zoi200511aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"zoi200511aff2\"><sup>2</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Heron</surname><given-names>Jon</given-names></name><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"zoi200511aff2\"><sup>2</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Tobias</surname><given-names>Jon H.</given-names></name><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"zoi200511aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"zoi200511aff3\"><sup>3</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Lawlor</surname><given-names>Deborah A.</given-names></name><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"zoi200511aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"zoi200511aff2\"><sup>2</sup></xref></contrib></contrib-group><aff id=\"zoi200511aff1\"><label>1</label>MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom</aff><aff id=\"zoi200511aff2\"><label>2</label>Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom</aff><aff id=\"zoi200511aff3\"><label>3</label>Musculoskeletal Research Unit, Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom</aff><author-notes><title>Article Information</title><p><bold>Accepted for Publication:</bold> June 2, 2020.</p><p content-type=\"published-online\"><bold>Published:</bold> August 17, 2020. doi:<uri content-type=\"doi\">10.1001/jamanetworkopen.2020.13463</uri></p><p content-type=\"open-access-note\"><bold>Open Access:</bold> This is an open access article distributed under the terms of the <ext-link ext-link-type=\"uri\" xlink:href=\"https://jamanetwork.com/journals/jamanetworkopen/pages/instructions-for-authors#SecOpenAccess\">CC-BY License</ext-link>. © 2020 Elhakeem A et al. <italic>JAMA Network Open</italic>.</p><corresp id=\"zoi200511cor1\"><bold>Corresponding Author:</bold> Ahmed Elhakeem, PhD, MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, United Kingdom (<email xlink:href=\"a.elhakeem@bristol.ac.uk\">a.elhakeem@bristol.ac.uk</email>).</corresp><p content-type=\"author-contributions\"><bold>Author Contributions:</bold> Dr Elhakeem had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.</p><p><italic>Concept and design:</italic> Elhakeem, Lawlor.</p><p><italic>Acquisition, analysis, or interpretation of data:</italic> All authors.</p><p><italic>Drafting of the manuscript:</italic> Elhakeem, Lawlor.</p><p><italic>Critical revision of the manuscript for important intellectual content:</italic> Elhakeem, Heron, Tobias.</p><p><italic>Statistical analysis:</italic> Elhakeem.</p><p><italic>Obtained funding:</italic> Lawlor.</p><p><italic>Supervision:</italic> Heron, Lawlor.</p><p content-type=\"COI-statement\"><bold>Conflict of Interest Disclosures:</bold> Dr Lawlor reported grants from European Union's Horizon 2020 Research and Innovation Programme under grant agreement No 733206 (LifeCycle) during the conduct of the study. Dr Lawlor reported grants from national and international government and charity funders, Roche Diagnostics, and Medtronic Ltd for work unrelated to this publication. No other disclosures were reported.</p><p content-type=\"funding-statement\"><bold>Funding/Support:</bold> The study was supported by grant 217065/Z/19/Z from the UK Medical Research Council and the Wellcome Trust, grant 733206 (LifeCycle) from the European Union Horizon 2020 Research and Innovation Programme (Dr Elhakeem), grant MC_UU_00011/6 from the University of Bristol and the UK Medical Research Council (Drs Elhakeem and Lawlor), and grant NF-0616-10102 from the National Institute for Health Research (Dr Lawlor). A comprehensive list of grant funding is available on the Avon Longitudinal Study of Parents and Children website (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf\">http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf</ext-link>).</p><p><bold>Role of the Funder/Sponsor:</bold> The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.</p><p><bold>Additional Contributions:</bold> We thank all of the families who took part in this study, the midwives for their help in recruiting them, and the whole Avon Longitudinal Study of Parents and Children team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, and nurses.</p></author-notes><pub-date pub-type=\"epub\" iso-8601-date=\"2020-08-17T10:00\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><!-- PMC Release delay is 0 months and\n\t\t\t\t\t\t0 days and was based on the <pub-date\n\t\t\t\t\t\tpub-type=\"epub\"/>. --><volume>3</volume><issue>8</issue><elocation-id>e2013463</elocation-id><history><date date-type=\"received\"><day>3</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>2</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>Copyright 2020 Elhakeem A et al. <italic>JAMA Network Open</italic>.</copyright-statement><license license-type=\"open-access\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open access article distributed under the terms of the CC-BY License.</license-p></license></permissions><self-uri content-type=\"pdf-version\" xlink:href=\"jamanetwopen-3-e2013463.pdf\">jamanetwopen-3-e2013463.pdf</self-uri><self-uri content-type=\"silverchair\" xlink:href=\"https://jamanetwork.com/journals/jamanetworkopen/fullarticle/10.1001/jamanetworkopen.2020.13463\"/><related-article related-article-type=\"commentary\" id=\"d38e234\" ext-link-type=\"doi\" xlink:href=\"10.1001/jamanetworkopen.2020.13900\"/><abstract abstract-type=\"key-points\"><title>Key Points</title><sec id=\"ab-zoi200511-1\"><title>Question</title><p>Is the amount of time spent in moderate to vigorous–intensity and light-intensity physical activity throughout adolescence associated with a clinical marker of hip strength in young adult men and women?</p></sec><sec id=\"ab-zoi200511-2\"><title>Findings</title><p>In this cohort study of 2569 young people who received repeated accelerometer assessments beginning at age 12 years, more time spent in moderate to vigorous–intensity physical activity in adolescence was associated with greater hip bone mineral density at age 25 years, whereas more time spent in light-intensity physical activity was not associated with bone mineral density at age 25 years.</p></sec><sec id=\"ab-zoi200511-3\"><title>Meaning</title><p>The findings indicate that higher-intensity physical activity in early life may be important for maximizing peak adult hip strength and protecting against osteoporosis in later life.</p></sec></abstract><abstract><sec id=\"ab-zoi200511-4\"><title>Importance</title><p>Peak bone strength, which occurs in early adulthood, is an important marker of the future risk of osteoporosis. It is therefore important to identify modifiable early life factors that are associated with the attainment of peak hip strength.</p></sec><sec id=\"ab-zoi200511-5\"><title>Objective</title><p>To investigate the association of time spent in moderate to vigorous–intensity and light-intensity physical activity throughout adolescence with peak hip strength in adulthood.</p></sec><sec id=\"ab-zoi200511-6\"><title>Design, Setting, and Participants</title><p>The Avon Longitudinal Study of Parents and Children is a prospective birth cohort study that initially recruited all pregnant women residing within the catchment area of 3 health authorities in southwest England who had an expected delivery date between April 1, 1991, and December 31, 1992. In total, 15 454 eligible pregnant women were enrolled, and 15 589 infants were delivered. Of those, 14 901 infants were alive at age 1 year. The present analysis examined 2569 healthy offspring who had valid physical activity measurements obtained during a clinical assessment for at least 1 age (12, 14, 16, and/or 25 years), with up to 4 repeated accelerometer assessments performed (1 per age-associated clinical visit). Data were analyzed from June 2019 to June 2020.</p></sec><sec id=\"ab-zoi200511-7\"><title>Exposures</title><p>Trajectories of accelerometer-assessed time spent in moderate to vigorous–intensity and light-intensity physical activity at ages 12, 14, 16, and 25 years (measured in minutes per day) were identified using latent trajectory modeling. Moderate to vigorous–intensity and light-intensity physical activity were determined using established thresholds of acceleration counts per minute.</p></sec><sec id=\"ab-zoi200511-8\"><title>Main Outcomes and Measures</title><p>Femur neck bone mineral density (BMD; measured in g/cm<sup>2</sup>) at age 25 years assessed by dual-energy radiography absorptiometry scans of the hip.</p></sec><sec id=\"ab-zoi200511-9\"><title>Results</title><p>A total of 2569 participants (1588 female participants [62%]) were included in the analysis. Male participants spent more time in moderate to vigorous–intensity activity at each age and had greater adult femur neck BMD than female participants. For each sex, 3 moderate to vigorous–intensity trajectory subgroups and 3 light-intensity trajectory subgroups were identified. With regard to the moderate to vigorous–intensity trajectories, most male participants (85%) were in the low adolescent subgroup, with only 6% and 9% in the high early-adolescent and high mid-adolescent subgroups, respectively. Moderate to vigorous–intensity trajectories in female participants were divided into low adolescent-low adult (73%), low adolescent-high adult (8%), and high adolescent (19%) subgroups. Light-intensity physical activity trajectories were classified into low nonlinear, moderate decreasing, and high decreasing subgroups for both sexes. Femur neck BMD in male participants was greater in the high early-adolescent subgroup (0.38 g/cm<sup>2</sup>; 95% CI, 0.11-0.66 g/cm<sup>2</sup>) and the high mid-adolescent subgroup (0.33 g/cm<sup>2</sup>; 95% CI, 0.07-0.60 g/cm<sup>2</sup>) compared with the low adolescent (reference) subgroup. Femur neck BMD in female participants was greater in the high adolescent subgroup (0.28 g/cm<sup>2</sup>; 95% CI, 0.15-0.41 g/cm<sup>2</sup>) but not in the low adolescent-high adult subgroup (−0.12 g/cm<sup>2</sup>; 95% CI, −0.44 to 0.20 g/cm<sup>2</sup>) compared with the low adolescent-low adult (reference) subgroup. A sensitivity analysis using a negative-outcome control variable to explore unmeasured confounding supported these findings. The light-intensity trajectories were not associated with femur neck BMD; for example, differences in femur neck BMD between the high decreasing and low nonlinear subgroups were 0.16 g/cm<sup>2</sup> (95% CI, −0.08 to 0.40 g/cm<sup>2</sup>) in male participants and 0.20 g/cm<sup>2</sup> (95% CI, −0.05 to 0.44 g/cm<sup>2</sup>) in female participants.</p></sec><sec id=\"ab-zoi200511-10\"><title>Conclusions and Relevance</title><p>Supporting high-intensity physical activity throughout early life may help to maximize peak hip strength and prevent osteoporosis in later life. Replication of our findings in independent studies will be important.</p></sec></abstract><abstract abstract-type=\"teaser\"><p>This cohort study of healthy offspring from the Avon Longitudinal Study of Parents and Children examines the association of time spent in moderate to vigorous–intensity and light-intensity physical activity throughout adolescence with peak hip strength in adulthood.</p></abstract></article-meta></front><body><sec id=\"H1-1-ZOI200511\"><title>Introduction</title><p>Peak bone strength occurs in early adulthood<sup><xref rid=\"zoi200511r1\" ref-type=\"bibr\">1</xref>,<xref rid=\"zoi200511r2\" ref-type=\"bibr\">2</xref>,<xref rid=\"zoi200511r3\" ref-type=\"bibr\">3</xref>,<xref rid=\"zoi200511r4\" ref-type=\"bibr\">4</xref>,<xref rid=\"zoi200511r5\" ref-type=\"bibr\">5</xref></sup> and is considered an important marker of bone strength, osteoporosis risk, and fracture risk in later life.<sup><xref rid=\"zoi200511r4\" ref-type=\"bibr\">4</xref>,<xref rid=\"zoi200511r5\" ref-type=\"bibr\">5</xref>,<xref rid=\"zoi200511r6\" ref-type=\"bibr\">6</xref>,<xref rid=\"zoi200511r7\" ref-type=\"bibr\">7</xref>,<xref rid=\"zoi200511r8\" ref-type=\"bibr\">8</xref></sup> Hip fractures compose a large proportion of the disease burden of osteoporosis<sup><xref rid=\"zoi200511r9\" ref-type=\"bibr\">9</xref>,<xref rid=\"zoi200511r10\" ref-type=\"bibr\">10</xref>,<xref rid=\"zoi200511r11\" ref-type=\"bibr\">11</xref>,<xref rid=\"zoi200511r12\" ref-type=\"bibr\">12</xref></sup>; thus, it is important to identify modifiable early life factors that have consequences for the attainment of peak hip strength. Data suggest that higher-intensity physical activity is beneficial for bone strength.<sup><xref rid=\"zoi200511r13\" ref-type=\"bibr\">13</xref>,<xref rid=\"zoi200511r14\" ref-type=\"bibr\">14</xref>,<xref rid=\"zoi200511r15\" ref-type=\"bibr\">15</xref>,<xref rid=\"zoi200511r16\" ref-type=\"bibr\">16</xref>,<xref rid=\"zoi200511r17\" ref-type=\"bibr\">17</xref>,<xref rid=\"zoi200511r18\" ref-type=\"bibr\">18</xref>,<xref rid=\"zoi200511r19\" ref-type=\"bibr\">19</xref>,<xref rid=\"zoi200511r20\" ref-type=\"bibr\">20</xref>,<xref rid=\"zoi200511r21\" ref-type=\"bibr\">21</xref>,<xref rid=\"zoi200511r22\" ref-type=\"bibr\">22</xref>,<xref rid=\"zoi200511r23\" ref-type=\"bibr\">23</xref>,<xref rid=\"zoi200511r24\" ref-type=\"bibr\">24</xref>,<xref rid=\"zoi200511r25\" ref-type=\"bibr\">25</xref>,<xref rid=\"zoi200511r26\" ref-type=\"bibr\">26</xref></sup> Of the studies conducted among younger people, most were of young adolescents and examined activity at a single point or used self-reported data.<sup><xref rid=\"zoi200511r21\" ref-type=\"bibr\">21</xref>,<xref rid=\"zoi200511r22\" ref-type=\"bibr\">22</xref>,<xref rid=\"zoi200511r23\" ref-type=\"bibr\">23</xref>,<xref rid=\"zoi200511r24\" ref-type=\"bibr\">24</xref>,<xref rid=\"zoi200511r25\" ref-type=\"bibr\">25</xref>,<xref rid=\"zoi200511r26\" ref-type=\"bibr\">26</xref></sup> Studies examining physical activity at a single point do not address the role that different patterns of change in or maintenance of physical activity has in bone strength and may be biased by regression to the mean.<sup><xref rid=\"zoi200511r27\" ref-type=\"bibr\">27</xref>,<xref rid=\"zoi200511r28\" ref-type=\"bibr\">28</xref></sup> Self-reports are susceptible to errors and not well suited to capturing light-intensity activity.<sup><xref rid=\"zoi200511r29\" ref-type=\"bibr\">29</xref>,<xref rid=\"zoi200511r30\" ref-type=\"bibr\">30</xref></sup> In addition to the role of higher-intensity activities in bone strength, studies suggest that activities producing higher gravitational force may be needed to strengthen bones.<sup><xref rid=\"zoi200511r31\" ref-type=\"bibr\">31</xref>,<xref rid=\"zoi200511r32\" ref-type=\"bibr\">32</xref>,<xref rid=\"zoi200511r33\" ref-type=\"bibr\">33</xref></sup> However, to our knowledge, the association between accelerometer-assessed gravitational force during physical activity and peak hip strength has not been examined.</p><p>The aim of this study was to investigate the association between accelerometer-measured moderate to vigorous–intensity and light-intensity physical activity trajectories beginning at age 12 years and hip strength at age 25 years. We also explored the association of gravitational force during physical activity measured by custom-built accelerometers at age 18 years with hip strength at age 25 years.</p></sec><sec id=\"H1-2-ZOI200511\"><title>Methods</title><sec id=\"H2-1-ZOI200511\"><title>Study Population</title><p>The Avon Longitudinal Study of Parents and Children (ALSPAC) is a prospective birth cohort study that initially recruited all pregnant women residing within the catchment area of 3 health authorities in southwest England who had an expected delivery date between April 1, 1991, and December 31, 1992.<sup><xref rid=\"zoi200511r34\" ref-type=\"bibr\">34</xref>,<xref rid=\"zoi200511r35\" ref-type=\"bibr\">35</xref>,<xref rid=\"zoi200511r36\" ref-type=\"bibr\">36</xref></sup> In total, 15 454 eligible pregnant women (75% response rate) were enrolled in ALSPAC, and 15 589 infants were delivered. Of those, 14 901 infants were alive at age 1 year. Detailed information has been collected from index offspring and parents using questionnaires, data from linked health and social records, and clinical assessments up to the last completed contact in 2019. The present analysis examined 2569 healthy index offspring who had valid physical activity measurements obtained during a clinical assessment for at least 1 age (12, 14, 16, and/or 25 years), with up to 4 repeated accelerometer assessments performed (1 per age-associated clinical visit). Participants with missing covariate data (527 of 3096 individuals [18%] who were potentially eligible) were excluded. Details of all available data can be found at the ALSPAC study website,<sup><xref rid=\"zoi200511r37\" ref-type=\"bibr\">37</xref></sup> which includes a searchable data dictionary and variable search tool. A flowchart of participant selection for the present analysis is shown in <xref ref-type=\"fig\" rid=\"zoi200511f1\">Figure 1</xref>.</p><fig id=\"zoi200511f1\" fig-type=\"figure\" orientation=\"portrait\" position=\"float\"><label>Figure 1. </label><caption><title>Study Flowchart</title><p>ALSPAC indicates Avon Longitudinal Study of Parents and Children; LPA, light-intensity physical activity; and MVPA, moderate to vigorous–intensity physical activity.</p></caption><graphic xlink:href=\"jamanetwopen-3-e2013463-g001\"/></fig><p>Ethical approval was obtained from the ALSPAC Ethics and Law Committee and the local research ethics committees. Full details of ethics committee approvals can be found on the study website.<sup><xref rid=\"zoi200511r37\" ref-type=\"bibr\">37</xref></sup> Written informed consent was obtained from all participants. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.equator-network.org/reporting-guidelines/strobe/\">STROBE</ext-link>) reporting guideline for cohort studies.</p></sec><sec id=\"H2-2-ZOI200511\"><title>Physical Activity Intensity</title><p>All offspring who attended clinical assessments at ages 12, 14, 16, and 25 years were asked to wear an AM7164 accelerometer (Actigraph) for 7 days during waking hours and to remove the accelerometer only when showering, bathing, and performing water sports.<sup><xref rid=\"zoi200511r38\" ref-type=\"bibr\">38</xref>,<xref rid=\"zoi200511r39\" ref-type=\"bibr\">39</xref>,<xref rid=\"zoi200511r40\" ref-type=\"bibr\">40</xref>,<xref rid=\"zoi200511r41\" ref-type=\"bibr\">41</xref>,<xref rid=\"zoi200511r42\" ref-type=\"bibr\">42</xref></sup> These devices capture movement in terms of acceleration as a combined function of frequency and intensity. Data were processed using Kinesoft software, version 3.3.75 (Kinesoft), according to a predefined protocol described elsewhere.<sup><xref rid=\"zoi200511r41\" ref-type=\"bibr\">41</xref>,<xref rid=\"zoi200511r43\" ref-type=\"bibr\">43</xref></sup></p><p>The analysis was restricted to participants with 3 or more days of valid data (≥500 minutes per day, after excluding intervals of ≥60 minutes of 0 counts). Activity counts per minute thresholds validated in young people<sup><xref rid=\"zoi200511r44\" ref-type=\"bibr\">44</xref></sup> and adults<sup><xref rid=\"zoi200511r45\" ref-type=\"bibr\">45</xref></sup> were used to calculate the amount of time spent in moderate to vigorous–intensity and light-intensity physical activity throughout adolescence (ie, at ages 12, 14, and 16 years; for moderate to vigorous–intensity activity, >2296 counts per minute; for light-intensity activity, 100-2296 counts per minute) and in adulthood (ie, at age 25 years; for moderate to vigorous–intensity activity, >2020 counts per minute; for light-intensity activity, 100-2020 counts per minute).</p></sec><sec id=\"H2-3-ZOI200511\"><title>Physical Activity Gravitational Force</title><p>At the clinical assessment for age 18 years, a subgroup of participants (depending on device availability) was fitted with a custom-built accelerometer (Newtest; Newtest Oy), which was used to explore the association between gravitational force during physical activity and bone health. All participants in this subgroup had previously worn an Actigraph accelerometer during at least 1 clinical assessment for at least 1 age, (ie, age 12, 14, and/or 16 years).</p><p>The Newtest device recorded gravitational force from vertical accelerations within separate bands across the range of 0.3<italic>g</italic> to 9.9<italic>g</italic> above the conventional value of gravitational acceleration (ie, 1.0<italic>g</italic>, or approximately 9.8 m/s<sup>2</sup>). Participants were asked to wear the device for 7 consecutive days during waking hours, recharge it overnight, and remove it only for contact sports or for situations in which it might get wet. A valid recording was defined as 8 or more hours of recording per day for 2 or more days.<sup><xref rid=\"zoi200511r31\" ref-type=\"bibr\">31</xref></sup> For this study, gravitational force was expressed as counts across 4 bands (0.5<italic>g</italic> to ≤1.1<italic>g</italic>, >1.1<italic>g</italic> to ≤3.1<italic>g</italic>, >3.1<italic>g</italic> to ≤5.1<italic>g</italic>, and >5.1<italic>g</italic>). These bands represent gravitational force from movements, such as normal walking (0.5<italic>g </italic>to ≤1.1<italic>g</italic>) and jumping (>5.1<italic>g</italic>), as determined by previous studies.<sup><xref rid=\"zoi200511r46\" ref-type=\"bibr\">46</xref>,<xref rid=\"zoi200511r47\" ref-type=\"bibr\">47</xref>,<xref rid=\"zoi200511r48\" ref-type=\"bibr\">48</xref>,<xref rid=\"zoi200511r49\" ref-type=\"bibr\">49</xref></sup></p></sec><sec id=\"H2-4-ZOI200511\"><title>Adult Hip Strength Assessment</title><p>All participants were invited to receive dual-energy radiography absorptiometry scans of the hip as part of the clinical assessment at age 25 years. Scans were performed between June 2015 and October 2017 using the same scanner (GE Lunar Prodigy; GE Healthcare) for all participants. All scans were performed on the left hip and were repeated if correct alignment was not achieved. Scans were analyzed using the manufacturer’s standard scanning software and positioning protocols. A total of 50 scans with artifacts, positioning errors, incorrect neck or shaft angles, missing hip parts, or high room temperature (>27 °C) were excluded.</p><p>Total hip and femoral neck bone mineral density (BMD; measured in g/cm<sup>2</sup>) were generated from the scans.<sup><xref rid=\"zoi200511r50\" ref-type=\"bibr\">50</xref></sup> Bone mineral density is the criterion standard for diagnosing osteoporosis in clinical practice,<sup><xref rid=\"zoi200511r51\" ref-type=\"bibr\">51</xref></sup> but it only provides information on the quantity of bone tissue.<sup><xref rid=\"zoi200511r52\" ref-type=\"bibr\">52</xref></sup> Because bone strength is a function of both the quantity and quality of bone tissue,<sup><xref rid=\"zoi200511r52\" ref-type=\"bibr\">52</xref></sup> and bone geometry is associated with bone quality and strength,<sup><xref rid=\"zoi200511r52\" ref-type=\"bibr\">52</xref>,<xref rid=\"zoi200511r53\" ref-type=\"bibr\">53</xref>,<xref rid=\"zoi200511r54\" ref-type=\"bibr\">54</xref>,<xref rid=\"zoi200511r55\" ref-type=\"bibr\">55</xref>,<xref rid=\"zoi200511r56\" ref-type=\"bibr\">56</xref>,<xref rid=\"zoi200511r57\" ref-type=\"bibr\">57</xref></sup> we used the manufacturer's automated hip analysis software to derive 4 hip geometric parameters (minimum femur neck width [measured in mm], cross-sectional area [measured in mm<sup>2</sup>], section modulus [measured in mm<sup>3</sup>], and cross-sectional moment of inertia [measured in mm<sup><xref rid=\"zoi200511r4\" ref-type=\"bibr\">4</xref></sup>]) as additional outcomes.</p></sec><sec id=\"H2-5-ZOI200511\"><title>Confounding Variables</title><p>Childhood socioeconomic position, ethnicity, height, adiposity, and muscle mass were defined a priori as potential confounding variables based on the assumption that they were associated with both adolescent physical activity and adult hip strength.<sup><xref rid=\"zoi200511r58\" ref-type=\"bibr\">58</xref></sup> These factors were all assessed before the first Actigraph accelerometer assessment. Self-reported maternal socioeconomic position (highest educational level [≥college degree vs <college degree]) and maternal ethnicity (White with European ancestry vs other ethnicity) were obtained at recruitment (ie, during pregnancy).</p><p>Childhood height, adiposity, and muscle mass were measured during the clinical assessment at age 10 years by accredited field workers. Height was measured without shoes, with the head in the correct position, using a stadiometer (Harpenden; Holtain). Fat (adiposity) and lean (muscle) mass were obtained from whole-body dual-energy radiography absorptiometry scans. Height-adjusted indices were calculated by dividing mass in kilograms by height in meters<sup><xref rid=\"zoi200511r1\" ref-type=\"bibr\">1</xref>,<xref rid=\"zoi200511r2\" ref-type=\"bibr\">2</xref>,<xref rid=\"zoi200511r59\" ref-type=\"bibr\">59</xref></sup></p></sec><sec id=\"H2-6-ZOI200511\"><title>Statistical Analysis</title><p>We performed latent trajectory modeling<sup><xref rid=\"zoi200511r40\" ref-type=\"bibr\">40</xref>,<xref rid=\"zoi200511r60\" ref-type=\"bibr\">60</xref>,<xref rid=\"zoi200511r61\" ref-type=\"bibr\">61</xref>,<xref rid=\"zoi200511r62\" ref-type=\"bibr\">62</xref>,<xref rid=\"zoi200511r63\" ref-type=\"bibr\">63</xref></sup> using Mplus software, version 8 (Muthen & Muthen), to identify sex-specific trajectories of moderate to vigorous–intensity and light-intensity physical activity from age 12 to 25 years. These models aim to classify individuals into distinct subgroups that share similar trajectories over time, such that individuals within a group are more similar than individuals between groups. Modeling was conducted according to published guidelines<sup><xref rid=\"zoi200511r62\" ref-type=\"bibr\">62</xref>,<xref rid=\"zoi200511r63\" ref-type=\"bibr\">63</xref></sup> and is detailed in eMethods, eTable 1 to eTable 6, and eFigure 1 to eFigure 12 in the <xref ref-type=\"supplementary-material\" rid=\"note-ZOI200511-1-s\">Supplement</xref>.</p><p>A linear regression analysis was used to estimate the association of derived moderate to vigorous–intensity and light-intensity trajectory subgroups with hip strength markers at age 25 years. Linear regression models were also used to explore the associations between vertical acceleration counts within each gravitational force band at age 18 years and hip strength markers at age 25 years. Counts were log-transformed to minimize skew; estimates were reported as differences in outcomes per doubling in the number of force measurements. Both unadjusted and adjusted (for all confounding variables) models were fitted for each outcome. Data were analyzed from June 2019 to June 2020.</p></sec><sec id=\"H2-7-ZOI200511\"><title>Sensitivity Analysis</title><p>We assessed whether the associations of physical activity with hip strength were robust to uncontrolled confounding by performing a negative-outcome control analysis.<sup><xref rid=\"zoi200511r64\" ref-type=\"bibr\">64</xref>,<xref rid=\"zoi200511r65\" ref-type=\"bibr\">65</xref></sup> Detailed descriptions of the rationale for performing negative-outcome control analyses and our choice of negative-outcome control variable are available in eMethods, eTable 7, and eFigure 13 in the <xref ref-type=\"supplementary-material\" rid=\"note-ZOI200511-1-s\">Supplement</xref>.</p><p>In brief, an ideal negative-outcome control would share the same confounding variables (measured or unmeasured) as adult hip strength but would not plausibly be associated with adolescent physical activity.<sup><xref rid=\"zoi200511r64\" ref-type=\"bibr\">64</xref>,<xref rid=\"zoi200511r65\" ref-type=\"bibr\">65</xref></sup> For this study, adult leg length (calculated by subtracting seated height from standing height at age 25 years) was used as a negative-outcome control. Because leg length is sensitive to early-life environments,<sup><xref rid=\"zoi200511r66\" ref-type=\"bibr\">66</xref>,<xref rid=\"zoi200511r67\" ref-type=\"bibr\">67</xref>,<xref rid=\"zoi200511r68\" ref-type=\"bibr\">68</xref></sup> it likely shares similar early life factors with hip strength; however, an association between physical activity (intensity or gravitational force) across adolescence and adult leg length seemed unlikely. Therefore, any association with adult leg length would likely be owing to confounding and suggests the same may be true for the hip strength analyses.</p></sec></sec><sec id=\"H1-3-ZOI200511\"><title>Results</title><p>Among 2569 participants included in the analysis, 1588 individuals (62%) were female and 981 individuals (38%) were male. Among both sexes, the mean (SD) ages at the adolescent clinic visits were 11.7 (0.2) years at the assessment for age 12 years, 13.8 (0.2) years at the assessment for age 14 years, and 15.4 (0.3) years at the assessment for age 16 years. All of the participants had valid physical activity measurements that were obtained during a clinical assessment for at least 1 age (6140 moderate to vigorous–intensity and light-intensity activity measurements in total, with a median of 2 measurements [interquartile range, 1-3 measurements] per individual) and complete data on hip outcomes and confounding variables (<xref rid=\"zoi200511t1\" ref-type=\"table\">Table</xref>).</p><table-wrap id=\"zoi200511t1\" orientation=\"portrait\" position=\"float\"><label>Table. </label><caption><title>Characteristics of Participants Included in the Trajectory Analyses</title></caption><table frame=\"hsides\" rules=\"groups\"><col width=\"55.35%\" span=\"1\"/><col width=\"21.12%\" span=\"1\"/><col width=\"23.53%\" span=\"1\"/><thead><tr><th rowspan=\"2\" valign=\"top\" align=\"left\" scope=\"col\" colspan=\"1\">Characteristic</th><th colspan=\"2\" valign=\"top\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">Mean (SD)</th></tr><tr><th valign=\"top\" colspan=\"1\" align=\"left\" scope=\"colgroup\" rowspan=\"1\">Male participants (n = 981)</th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Female participants (n = 1588)</th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Age at Actigraph accelerometer assessment, y</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 12</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">11.7 (0.2)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">11.7 (0.2)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 14</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">13.8 (0.2)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">13.8 (0.2)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 16</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">15.4 (0.3)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">15.4 (0.3)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 25</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">24.5 (0.8)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">24.4 (0.8)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Moderate to vigorous–intensity physical activity min/d at each age (counts/min)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 12 y (>2295)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">65.1 (28.5)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">45.4 (19.8)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 14 y (>2295)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">58.9 (28.1)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">43.4 (22.3)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 16 y (>2295)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">54.9 (30.4)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">38.6 (21.4)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 25 y (>2020)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">54.2 (33.0)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">46.4 (27.1)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Light-intensity physical activity min/d at each age (counts/min)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 12 y (100-2295)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">366.3 (61.1)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">363.0 (59.4)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 14 y (100-2295)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">327.7 (63.3)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">308.1 (60.1)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 16 y (100-2295)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">285.9 (67.9)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">269.1 (62.5)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> 25 y (100-2020)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">148.6 (60.6)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">148.5 (53.5)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Hip strength markers at age 25 y</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\"> Bone mineral density, g/cm<sup>2</sup></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Total hip</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.13 (0.2)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.05 (0.1)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Femur neck</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.11 (0.2)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.04 (0.1)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Femur minimum neck width, mm</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">33.9 (2.7)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">28.6 (2.1)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Cross-sectional area, mm<sup>2</sup></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">186.9 (31.1)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">150.0 (21.6)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Section modulus, mm<sup>3</sup></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">920.4 (199.4)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">629.1 (117.3)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Cross-sectional moment of inertia, mm<sup><xref rid=\"zoi200511r4\" ref-type=\"bibr\">4</xref></sup></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">16 572 (4366)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">9412 (2285)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Early-life anthropometry and body composition at age 10 y</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Height, cm</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">140.1 (6.0)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">138.9 (6.3)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Fat mass index, kg/m<sup>1.2</sup></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.8 (3.0)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">6.2 (3.0)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Lean mass index, kg/m<sup>1.2</sup></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">17.0 (1.3)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">15.7 (1.4)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Ethnicity, No. (%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> White European</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">963 (98)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1554 (98)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> Other</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">18 (2)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">34 (2)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\">Maternal educational level, No. (%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> ≥College degree</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">233 (24)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">330 (21)</td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> <College degree</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">748 (76)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1258 (79)</td></tr></tbody></table></table-wrap><p>Male participants compared with female participants spent more time in moderate to vigorous activity at each age (eg, at age 12 years, the mean [SD] level of moderate to vigorous physical activity was 65.1 [28.5] minutes per day in male participants vs 45.4 [19.8] minutes per day in female participants) and had greater adult hip BMD (eg, mean [SD] total hip BMD was 1.13 [0.2] g/cm<sup>2</sup> in male participants vs 1.05 [0.1] g/cm<sup>2</sup> in female participants) and geometric parameters (eg, mean [SD] femur minimum neck width was 33.9 [2.7] mm in male participants vs 28.6 [2.1] mm in female participants) (<xref rid=\"zoi200511t1\" ref-type=\"table\">Table</xref>). Overall, between age 12 and 25 years, the levels of light-intensity physical activity decreased with age in both male participants (mean [SD], 366.3 [61.1] minutes per day at age 12 years vs 148.6 [60.6] minutes per day at age 25 years) and female participants (mean [SD], 363.0 [59.4] minutes per day at age 12 years vs 148.5 [53.5] minutes per day at age 25 years). The level of moderate to vigorous–intensity activity decreased in male participants throughout adolescence (mean [SD], 65.1 [28.5] minutes per day at age 12 years vs 54.9 [30.4] minutes per day at age 16 years), remained stable in female participants through early adolescence (mean [SD], 45.4 [19.8] minutes per day at age 12 years vs 43.4 [22.3] minutes per day at age 14 years), and increased in female participants at age 25 years (mean [SD], 38.6 [21.4] minutes per day at age 16 years vs 46.4 [27.1] minutes per day at age 25 years) (<xref rid=\"zoi200511t1\" ref-type=\"table\">Table</xref>; eFigure 14 in the <xref ref-type=\"supplementary-material\" rid=\"note-ZOI200511-1-s\">Supplement</xref>). Additional early-life characteristics of study participants are shown in the <xref rid=\"zoi200511t1\" ref-type=\"table\">Table</xref>.</p><sec id=\"H2-8-ZOI200511\"><title>Physical Activity Intensity Trajectories</title><p>We identified 3 activity trajectory subgroups in male and female participants for both moderate to vigorous–intensity and light-intensity physical activity. Among male participants, the 3 moderate to vigorous–intensity trajectory subgroups had notably different mean amounts of time spent in this activity at age 12 years (<xref ref-type=\"fig\" rid=\"zoi200511f2\">Figure 2</xref>A). The mean amount of time spent in moderate to vigorous activity decreased as age increased to 25 years in the group with the highest level of time spent in this activity at age 12 years (6%). The mean amount of time spent in moderate to vigorous activity increased throughout adolescence and decreased at age 25 years in the group with the second-highest level of time spent in this activity at age 12 years (9%). The group of male participants with the least amount of time spent in moderate to vigorous activity at age 12 years (85%) had a pattern of decreasing levels of time spent in this activity throughout adolescence and a small increase in the level of time spent in this activity at age 25 years. We named these 3 subgroups high early-adolescent moderate to vigorous–intensity physical activity, high mid-adolescent moderate to vigorous–intensity physical activity, and low adolescent moderate to vigorous–intensity physical activity.</p><fig id=\"zoi200511f2\" fig-type=\"figure\" orientation=\"portrait\" position=\"float\"><label>Figure 2. </label><caption><title>Association of Moderate to Vigorous–Intensity and Light-Intensity Physical Activity Trajectories With Hip Strength Markers in Male Participants</title><p>Estimates are adjusted for ethnicity, maternal education, childhood height, fat and lean mass indices, and age at hip scan. CSA indicates cross-sectional area; CSMI, cross-sectional moment of inertia; FNBMD, femur neck bone mineral density; LPA, light-intensity physical activity; MNW, minimum neck width; MVPA, moderate to vigorous–intensity physical activity; SM, section modulus; and THBMD, total hip bone mineral density. A, Estimated mean time spent in moderate to vigorous–intensity activity. Shaded areas surrounding mean trajectories represent 95% CIs. B, Estimated mean time spent in light-intensity physical activity. Shaded areas surrounding mean trajectories represent 95% CIs. C, Difference in hip strength markers at age 25 years for moderate to vigorous–intensity activity trajectory subgroup. The low adolescent subgroup was the reference group. Horizontal bars represent 95% CIs. D, Difference in hip strength markers at age 25 years for light-intensity activity trajectory subgroup. The low nonlinear subgroup was the reference group. Horizontal bars represent 95% CIs.</p></caption><graphic xlink:href=\"jamanetwopen-3-e2013463-g002\"/></fig><p>Among female participants, 1 trajectory subgroup had a notably higher mean amount of time spent in moderate to vigorous activity at age 12 years compared with the 2 other subgroups (<xref ref-type=\"fig\" rid=\"zoi200511f3\">Figure 3</xref>A). This subgroup (19%) maintained higher levels of time spent in moderate to vigorous–intensity activity throughout adolescence and had slightly decreased level of time spent in this activity at age 25 years. The other 2 trajectory subgroups had similarly low levels of time spent in moderate to vigorous activity at age 12 years and throughout adolescence. The smallest of these groups (8%) had the highest overall levels of time spent in moderate to vigorous activity at age 25 years, whereas the last subgroup (73%) had the lowest overall levels of time spent in moderate to vigorous activity at age 25 years. We named these subgroups high adolescent moderate to vigorous–intensity physical activity, low adolescent-high adult moderate to vigorous–intensity physical activity, and low adolescent-low adult moderate to vigorous–intensity physical activity.</p><fig id=\"zoi200511f3\" fig-type=\"figure\" orientation=\"portrait\" position=\"float\"><label>Figure 3. </label><caption><title>Association of Moderate to Vigorous–Intensity and Light-Intensity Physical Activity Trajectories With Hip Strength Markers in Female Participants</title><p>Estimates are adjusted for ethnicity, maternal education, childhood height, fat and lean mass indices, and age at hip scan. CSA indicates cross-sectional area; CSMI, cross-sectional moment of inertia; FNBMD, femur neck bone mineral density; LPA, light-intensity physical activity; MNW, minimum neck width; MVPA, moderate to vigorous–intensity physical activity; SM, section modulus; and THBMD, total hip bone mineral density. A, Estimated mean time spent in moderate to vigorous–intensity activity. Shaded areas surrounding mean trajectories represent 95% CIs. B, Estimated mean time spent in light-intensity physical activity. Shaded areas surrounding mean trajectories represent 95% CIs. C, Difference in hip strength markers at age 25 years for moderate to vigorous–intensity activity trajectory subgroup. The low adolescent-low adult subgroup was the reference group. Horizontal bars represent 95% CIs. D, Difference in hip strength markers at age 25 years for light-intensity activity trajectory subgroup. The low nonlinear subgroup was the reference group. Horizontal bars represent 95% CIs.</p></caption><graphic xlink:href=\"jamanetwopen-3-e2013463-g003\"/></fig><p>Among both male and female participants, the 3 similar light-intensity trajectory subgroups had notably different mean levels of time spent in light-intensity activity at age 12 years (<xref ref-type=\"fig\" rid=\"zoi200511f2\">Figure 2</xref>B and <xref ref-type=\"fig\" rid=\"zoi200511f3\">Figure 3</xref>B). The mean levels of time spent in light-intensity activity decreased monotonically with increasing age to 25 years in the 2 subgroups with the highest and second-highest levels of time spent in light-intensity activity at age 12 years such that, by age 25 years, the difference in time spent in light-intensity activity between these 2 groups was similar to that observed at age 12 years. Those spending the least time in LPA at age 12 showed a pattern of decreasing time spent through adolescence, after which mean time spent in LPA increased. We named these 3 subgroups high decreasing light-intensity physical activity, moderate decreasing light-intensity physical activity, and low nonlinear light-intensity physical activity. Most male participants were in the moderate decreasing subgroup (67%), with a similar proportion of the remaining male participants in the high decreasing and low nonlinear trajectory subgroups. In comparison with male participants, most female participants were in either the low nonlinear subgroup (51%) or the moderate decreasing (43%) subgroup.</p></sec><sec id=\"H2-9-ZOI200511\"><title>Physical Activity Intensity Trajectories and Adult Hip Strength</title><p>Among the moderate to vigorous–intensity trajectory subgroups, the mean adult hip BMD and geometric parameters in male participants were all notably higher in the high early-adolescent (eg, femur neck BMD, 0.38 g/cm<sup>2</sup> [95% CI, 0.11-0.66 g/cm<sup>2</sup>]; total hip BMD, 0.43 g/cm<sup>2</sup> [95% CI, 0.15-0.71 g/cm<sup>2</sup>]) and high mid-adolescent (eg, femur neck BMD, 0.33 g/cm<sup>2</sup> [95% CI, 0.07-0.60 g/cm<sup>2</sup>]; total hip BMD, 0.35 g/cm<sup>2</sup> [95% CI, 0.09-0.62 g/cm<sup>2</sup>]) subgroups compared with the low adolescent (reference) subgroup (<xref ref-type=\"fig\" rid=\"zoi200511f2\">Figure 2</xref>C). Estimates were similar for both of these moderate to vigorous–intensity activity groups. In female participants, adult hip BMD and geometric parameters were higher in the high adolescent subgroup (eg, femur neck BMD, 0.28 g/cm<sup>2</sup> [95% CI, 0.15 to 0.41 g/cm<sup>2</sup>]; total hip BMD, 0.25 g/cm<sup>2</sup> [95% CI, 0.12-038 g/cm<sup>2</sup>]) but not in the low adolescent-high adult subgroup (eg, femur neck BMD, −0.12 g/cm<sup>2</sup> [95% CI, −0.44 to 0.20 g/cm<sup>2</sup>]; total hip BMD, -0.25 g/cm<sup>2</sup> [95% CI, −0.57 to 0.07 g/cm<sup>2</sup>]) compared with the low adolescent-low adult (reference) subgroup (<xref ref-type=\"fig\" rid=\"zoi200511f3\">Figure 3</xref>C). There was no difference in adult hip strength markers between the low adolescent-high adult and low adolescent-low adult subgroups (eFigure 15 in the <xref ref-type=\"supplementary-material\" rid=\"note-ZOI200511-1-s\">Supplement</xref>).</p><p>An association between light-intensity activity trajectories and adult hip strength parameters was less consistently observed. In male participants, the mean adult hip BMD and geometric parameters in the high decreasing subgroup (eg, femur neck BMD, 0.16 g/cm<sup>2</sup> [95% CI, −0.08 to 0.40 g/cm<sup>2</sup>]; total hip BMD, 0.08 g/cm<sup>2</sup> [95% CI, −0.16 to 0.33 g/cm<sup>2</sup>]) and the moderate decreasing subgroup (eg, femur neck BMD, 0.12 g/cm<sup>2</sup> [95% CI, −0.06 to 0.30 g/cm<sup>2</sup>]; total hip BMD, 0.06 g/cm<sup>2</sup> [95% CI, −0.13 to 0.24 g/cm<sup>2</sup>]) were similar to those of the low nonlinear (reference) subgroup (<xref ref-type=\"fig\" rid=\"zoi200511f2\">Figure 2</xref>D). In female participants, the mean adult hip BMD and geometric parameters were higher in both the high decreasing subgroup (eg, femur neck BMD, 0.20 g/cm<sup>2</sup> [95% CI, −0.05 to 0.44 g/cm<sup>2</sup>]; total hip BMD, 0.14 g/cm<sup>2</sup> [95% CI, −0.11 to 0.39 g/cm<sup>2</sup>]) and the moderate decreasing subgroup (eg, femur neck BMD, 0.13 g/cm<sup>2</sup> [95% CI, 0.04-0.23 g/cm<sup>2</sup>]; total hip BMD, 0.12 g/cm<sup>2</sup> [95% CI, 0.02-0.22 g/cm<sup>2</sup>]) compared with the low nonlinear (reference) subgroup (<xref ref-type=\"fig\" rid=\"zoi200511f3\">Figure 3</xref>D). Results from unadjusted and adjusted models are presented in eTable 9 and eTable 10 in the <xref ref-type=\"supplementary-material\" rid=\"note-ZOI200511-1-s\">Supplement</xref>. There was no difference between light-intensity trajectory subgroups for adult leg length (negative-outcome control variable) (eFigure 15 in the <xref ref-type=\"supplementary-material\" rid=\"note-ZOI200511-1-s\">Supplement</xref>).</p></sec><sec id=\"H2-10-ZOI200511\"><title>Physical Activity Gravitational Force</title><p>A total of 478 participants (183 male participants and 295 female participants) with vertical gravitational force measurements recorded at age 18 years and complete data on adult hip outcomes and confounding variables were included in the analysis of this exposure (<xref ref-type=\"fig\" rid=\"zoi200511f1\">Figure 1</xref>; eTable 8 in the <xref ref-type=\"supplementary-material\" rid=\"note-ZOI200511-1-s\">Supplement</xref>). Most gravitational force measurements were low in magnitude; only 58 of 23 923 registered measurements (0.2%) were greater than 5.1g (<xref ref-type=\"fig\" rid=\"zoi200511f4\">Figure 4</xref>A, <xref ref-type=\"fig\" rid=\"zoi200511f4\">Figure 4</xref>B, <xref ref-type=\"fig\" rid=\"zoi200511f4\">Figure 4</xref>C, and <xref ref-type=\"fig\" rid=\"zoi200511f4\">Figure 4</xref>D).</p><fig id=\"zoi200511f4\" fig-type=\"figure\" orientation=\"portrait\" position=\"float\"><label>Figure 4. </label><caption><title>Association of Gravitational Force Measurements During Physical Activity With Hip Strength Markers</title><p>Estimates are adjusted for sex, ethnicity, maternal education, childhood height, fat and lean mass indices, and age at hip scan. CSA indicates cross-sectional area of hip; CSMI, cross-sectional moment of inertia of hip; FNBMD, femur neck hip bone mineral density; MNW, minimum femur neck width; SM, section modulus of hip; and THBMD, total hip bone mineral density. A, Distribution of vertical acceleration counts in 0.5<italic>g</italic> to ≤1.1<italic>g</italic> band at age 18 years. B, Distribution of vertical acceleration counts in >1.1<italic>g</italic> to ≤3.1<italic>g</italic> band at age 18 years. C, Distribution of vertical acceleration counts in >3.1<italic>g</italic> to ≤5.1<italic>g</italic> band at age 18 years. D, Distribution of vertical acceleration counts in >5.1<italic>g</italic> band at age 18 years. For panels A through D, middle lines indicate the median, ends of boxes indicate the upper and lower quartiles, and whiskers indicate the range. E, Difference in hip strength markers at age 25 years. Difference per doubling in number of gravitational force measurements recorded in each band. Horizontal bars indicate 95% CIs.</p></caption><graphic xlink:href=\"jamanetwopen-3-e2013463-g004\"/></fig><p>Despite their rarity, gravitational force measurements greater than 5.1<italic>g</italic> were positively associated with peak adult hip BMD and geometric parameters (<xref ref-type=\"fig\" rid=\"zoi200511f4\">Figure 4</xref>E). Positive associations with adult hip strength markers were observed for low gravitational force measurements (0.5<italic>g</italic> to ≤1.1<italic>g</italic>), whereas the association of medium gravitational force measurements (>1.1<italic>g</italic> to ≤3.1<italic>g</italic> and >3.1<italic>g</italic> to ≤5.1<italic>g</italic>) with hip strength markers was closer to the null. Numerical results from unadjusted and adjusted models are presented in eTable 11 in the <xref ref-type=\"supplementary-material\" rid=\"note-ZOI200511-1-s\">Supplement</xref>. Force measurements greater than 5.1<italic>g</italic> were not associated with adult leg length (negative-outcome control variable) (eFigure 15 in the <xref ref-type=\"supplementary-material\" rid=\"note-ZOI200511-1-s\">Supplement</xref>).</p></sec></sec><sec id=\"H1-4-ZOI200511\"><title>Discussion</title><p>We used repeated accelerometer assessments of participants beginning at age 12 years to identify trajectories of the amount of time spent in moderate to vigorous–intensity and light-intensity physical activity throughout adolescence, and we investigated their associations with hip strength markers at age 25 years. A greater amount of time spent in moderate to vigorous–intensity activity during adolescence was associated with substantial and favorable differences in hip BMD and geometric parameters, whereas these associations were not consistently observed for the amount of time spent in light-intensity activity during adolescence. Exploratory analyses using custom-built accelerometers worn by participants at age 18 years indicated that, despite being rare, exposure to high-magnitude gravitational force was positively associated with hip strength. Our negative-outcome control sensitivity analysis suggests these findings are unlikely to be fully explained by uncontrolled confounding.</p><p>Our finding of an association between hip BMD and geometric parameters and moderate to vigorous–intensity activity, but not light-intensity activity, expands on previous accelerometer-based cross-sectional studies reporting that time spent in moderate to vigorous activity was positively associated with hip BMD and geometry.<sup><xref rid=\"zoi200511r14\" ref-type=\"bibr\">14</xref>,<xref rid=\"zoi200511r24\" ref-type=\"bibr\">24</xref></sup> The findings also complement reported associations between consistent participation in organized sports from ages 5 to 17 years and greater leg BMD at age 20 years.<sup><xref rid=\"zoi200511r25\" ref-type=\"bibr\">25</xref></sup> Our results from female participants indicate that moderate to vigorous activity during adolescence is more important for adult hip strength than the participant’s current participation in moderate to vigorous activity, which is consistent with the hypothesis that adolescence is a sensitive period for bone development,<sup><xref rid=\"zoi200511r69\" ref-type=\"bibr\">69</xref></sup> particularly given data indicating that bone accrues rapidly during puberty.<sup><xref rid=\"zoi200511r1\" ref-type=\"bibr\">1</xref>,<xref rid=\"zoi200511r50\" ref-type=\"bibr\">50</xref>,<xref rid=\"zoi200511r70\" ref-type=\"bibr\">70</xref></sup> Furthermore, our findings from male participants indicate that both the early- and mid-adolescent moderate to vigorous–intensity subgroups were also associated with adult hip parameters, despite the early adolescent trajectory subgroup having a substantially greater decrease in moderate to vigorous activity. This finding suggests that moderate to vigorous–intensity physical activity may be more important in early adolescence than in later adolescence, which is consistent with data indicating that younger prepubertal skeletons are more responsive to mechanical loading from physical activity.<sup><xref rid=\"zoi200511r71\" ref-type=\"bibr\">71</xref></sup></p><p>The association found between high-magnitude gravitational force measurements in late adolescence and peak hip BMD and geometric parameters extends the previous cross-sectional results from ALSPAC<sup><xref rid=\"zoi200511r31\" ref-type=\"bibr\">31</xref></sup> and is consistent with self-reported data indicating that replacing low-impact activities with high-impact activities in childhood is associated with increased hip BMD.<sup><xref rid=\"zoi200511r23\" ref-type=\"bibr\">23</xref></sup> However, because our findings were derived from limited high-magnitude impact observations in a relatively small sample, the conclusions that can be drawn are limited. Nevertheless, when taken together, the results of our study suggest that moderate to vigorous–intensity physical activity (vs light-intensity activity) and higher gravitational force measurements (vs lower gravitational force measurements) throughout early life are associated with increases in bone mass during growth.<sup><xref rid=\"zoi200511r20\" ref-type=\"bibr\">20</xref>,<xref rid=\"zoi200511r21\" ref-type=\"bibr\">21</xref>,<xref rid=\"zoi200511r22\" ref-type=\"bibr\">22</xref></sup> These increases may be owing to direct osteogenic mechanisms and the indirect implications of high-intensity and high-impact activities for bone through the associated increases in lean mass.<sup><xref rid=\"zoi200511r71\" ref-type=\"bibr\">71</xref>,<xref rid=\"zoi200511r72\" ref-type=\"bibr\">72</xref>,<xref rid=\"zoi200511r73\" ref-type=\"bibr\">73</xref>,<xref rid=\"zoi200511r74\" ref-type=\"bibr\">74</xref></sup></p><sec id=\"H2-11-ZOI200511\"><title>Limitations</title><p>Participants with missing covariate data (18% of those potentially eligible) were excluded, which might have introduced bias if the excluded participants had systematically different hip measurements. Participants missing all accelerometry assessments were also excluded, and these participants had socioeconomic differences from the analytical sample, which might limit the generalizability of our findings. Participants with 1 or more measurement of moderate to vigorous–intensity or light-intensity activity were included in the latent trajectory models under the missing-at-random assumption, which cannot be fully tested. However, the probability of missing accelerometer data was associated with model confounders, which suggests that these data may be consistent with the missing-at-random assumption. Latent trajectory modeling is an important strength of the present study; however, these models can be data-specific, meaning that data from identified subgroups may not replicate in other cohorts, which limits their generalizability. Our sample mostly comprised White individuals of European ancestry, which might limit the study’s generalizability to individuals of other ethnicities. While these associations cannot be interpreted as causal, our negative-outcome control sensitivity analysis provides some indication that the findings are not fully explained by uncontrolled confounding.</p></sec></sec><sec id=\"H1-5-ZOI200511\"><title>Conclusions</title><p>This prospective cohort study indicated that a greater amount of time spent in moderate to vigorous–intensity physical activity from age 12 years and a greater exposure to higher-magnitude gravitational force at age 18 years were associated with greater hip strength at age 25 years. Our findings suggest that higher-intensity physical activity, along with potential bursts of higher-impact activity, throughout adolescence may be important for maximizing peak hip strength during early adulthood. 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With Varying Internal Model Structure</p><p><bold>eTable 3.</bold> Results of the Final 3-Class MVPA and LPA Latent Trajectory Models: Male Participants</p><p><bold>eTable 4.</bold> Results of the Final 3-Class MVPA and LPA Latent Trajectory Models: Female Participants</p><p><bold>eTable 5.</bold> Comparison Between Participants Included in Main Analysis With MVPA/LPA Accelerometer Assessments at All 4 Ages and Participants Included in Main Analysis With At Least 1 Missing MVPA/LPA Accelerometer Assessment</p><p><bold>eTable 6.</bold> Comparison Between Participants Included in Main Analysis and Participants Excluded From Main Analysis Due to Missing All 4 MVPA/LPA Accelerometer Assessments</p><p><bold>eTable 7.</bold> Association of Model Covariates With Adult Hip Strength Markers and Leg Length</p><p><bold>eTable 8.</bold> Characteristics of ALSPAC Participants Included in the Vertical Impact Analysis</p><p><bold>eTable 9.</bold> Associations of MVPA and LPA Trajectory With Hip Strength Markers: Male Participants</p><p><bold>eTable 10.</bold> Associations of MVPA and LPA Trajectory With Hip Strength Markers: Female Participants</p><p><bold>eTable 11.</bold> Association of Physical Activity Gravitational Impacts With Hip Strength Markers</p><p><bold>eFigure 1.</bold> Latent Trajectory Model Conceptualizations</p><p><bold>eFigure 2.</bold> MVPA Trajectories From Initial Latent Trajectory Models With Varying Number of Classes: Male Participants</p><p><bold>eFigure 3.</bold> LPA Trajectories From Initial Latent Trajectory Models With Varying Number of Classes: Male Participants</p><p><bold>eFigure 4.</bold> MVPA Trajectories From Initial Latent Trajectory Models With Varying Number of Classes: Female Participants</p><p><bold>eFigure 5.</bold> LPA Trajectories From Initial Latent Trajectory Models With Varying Number of Classes: Female Participants</p><p><bold>eFigure 6.</bold> MVPA Trajectories From 3-Class MVPA Latent Trajectory Models With Varying Internal Model Structure: Male Participants</p><p><bold>eFigure 7.</bold> LPA Trajectories From 3-Class LPA Latent Trajectory Models With Varying Internal Model Structure: Male Participants</p><p><bold>eFigure 8.</bold> MVPA Trajectories From 3-Class MVPA Latent Trajectory Models With Varying Internal Model Structure: Male Participants</p><p><bold>eFigure 9.</bold> MVPA Trajectories From 3-Class MVPA Latent Trajectory Models With Varying Internal Model Structure: Male Participants</p><p><bold>eFigure 10.</bold> Observed MVPA and LPA Individual Trajectories by Most Likely Class From the Final 3-Class MVPA and LPA Trajectory Models</p><p><bold>eFigure 11.</bold> Physical Activity Trajectories From the Final 3-Class MVPA and LPA Latent Trajectory Models When Derived in the Maximum Sample Size (Not Restricted to Those With Complete Data on Confounders and Hip Outcomes)</p><p><bold>eFigure 12.</bold> MVPA/LPA Missing Data Patterns and Proportions</p><p><bold>eFigure 13.</bold> Causal Diagram Depicting the Assumptions of a Negative-Outcome Control Study to Evaluate the Association of Physical Activity Throughout Adolescence on Peak Hip Strength in Young Adults</p><p><bold>eFigure 14.</bold> Scatter Plot of Observed MVPA and LPA by Age and Sex</p><p><bold>eFigure 15.</bold> Association of MVPA and LPA Trajectory and Physical Activity Gravitational Impacts With Adult Leg Length (the Negative-Outcome Control)</p><p><bold>eReferences.</bold></p></caption><media xlink:href=\"jamanetwopen-3-e2013463-s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></notes></back></article>\n"
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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751337</article-id><article-id pub-id-type=\"pmc\">PMC7431999</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155483</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05483</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Epidemics of HIV Infection among Heavy Drug Users of Depressants Only, Stimulants Only, and Both Depressants and Stimulants in Mainland China: A Series, Cross-Sectional Studies</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-2244-5388</contrib-id><name><surname>Zhang</surname><given-names>Bo</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05483\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05483\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Yan</surname><given-names>Xiang-Yu</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05483\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05483\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Li</surname><given-names>Yong-Jie</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05483\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05483\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Liu</surname><given-names>Zhi-Min</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05483\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Lu</surname><given-names>Zu-Hong</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05483\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Jia</surname><given-names>Zhong-Wei</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05483\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05483\">2</xref><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05483\">4</xref><xref ref-type=\"aff\" rid=\"af5-ijerph-17-05483\">5</xref><xref rid=\"c1-ijerph-17-05483\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05483\"><label>1</label>Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; <email>bibibabo@pku.edu.cn</email> (B.Z.); <email>yanxiangyu@bjmu.edu.cn</email> (X.-Y.Y.); <email>li.yongjie@outlook.com</email> (Y.-J.L.)</aff><aff id=\"af2-ijerph-17-05483\"><label>2</label>National Institute on Drug Dependence, Peking University, Beijing100191, China; <email>zhiminliu@bjmu.edu.cn</email></aff><aff id=\"af3-ijerph-17-05483\"><label>3</label>Biomedical Engineering, Southeast University, Nanjing 211189, China; <email>zhlu@seu.edu.cn</email></aff><aff id=\"af4-ijerph-17-05483\"><label>4</label>Center for Drug Abuse Control and Prevention, National Institute of Health Data Science, Peking University, Beijing 100191, China</aff><aff id=\"af5-ijerph-17-05483\"><label>5</label>Center for Intelligent Public Health, Institute for Artificial Intelligence, Peking University, Beijing 100191, China</aff><author-notes><corresp id=\"c1-ijerph-17-05483\"><label>*</label>Correspondence: <email>urchinjj@163.com</email>; Tel.: +86-010-82802457</corresp></author-notes><pub-date pub-type=\"epub\"><day>29</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5483</elocation-id><history><date date-type=\"received\"><day>19</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>21</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p><italic>Background:</italic> Heavy drug users was a global consensus high-risk population of HIV infection. However, the specific impact of drug on HIV infection has not yet been established. Depressants and stimulants were most widely used drugs in mainland China, and mix use of the two drugs was also serious. We assessed the HIV infection rate and trends in heavy drug users by analyzing data from the National Dynamic Management and Control Database for Drug Users (NDMCDDU). <italic>Methods:</italic> All heavy drug users with HIV test results in NDMCDDU from 2008 to 2016 were grouped into depressants only group (DOG), stimulants only group (SOG), and both depressants and stimulants group (DSG). We used joinpoint regression to examine trends of HIV infection rates. Multivariable logistic regression was used to examine factors related to HIV infection. <italic>Results:</italic> A total of 466,033 heavy drug users with 9522 cases of HIV infection were included in this analysis. HIV infection rate was estimated at 2.97% (95% CI 2.91–3.04%) of 265,774 users in DOG, 0.45% (95% CI 0.42–0.49%) of 140,895 users in SOG, and 1.65% (95% CI 1.55–1.76%) of 59,364 users in DSG. In DOG, a U-shaped curve of HIV infection rate decreased from 3.85% in 2008 to 2.19% in 2010 (annual percent change (APC) −12.9, 95% CI −19.3–−6.0, <italic>p</italic> < 0.05), then increased to 4.64% in 2016 (APC 8.3, 95% CI 6.1–10.4, <italic>p</italic> < 0.05) was observed. However, SOG and DSG showed consistent increases from 0.15% in 2008 to 0.54% in 2016 (APC 8.2, 95% CI 4.8–11.8, <italic>p</italic> < 0.05) and from 0.78% in 2008 to 2.72% in 2016 (APC 13.5, 95% CI 10.7–16.4, <italic>p</italic> < 0.05), respectively. HIV infection rate of DOG in the southwest region presented a U-shaped trend. All groups showed significant increases in HIV infection in east and central regions. <italic>Conclusions:</italic> The U-shaped curve for HIV infection rate among DOG users and consistent increases among SOG and DSG users implies drug abuse is still a critical focus of HIV infection in China. It is urgently needed to reassess the effectiveness of current strategies on HIV prevention and control among drug users.</p></abstract><kwd-group><kwd>HIV infection</kwd><kwd>epidemics</kwd><kwd>stimulants</kwd><kwd>depressants</kwd><kwd>injection drug use</kwd><kwd>China</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05483\"><title>1. Introduction</title><p>Drug users was a global consensus high-risk population of HIV infection [<xref rid=\"B1-ijerph-17-05483\" ref-type=\"bibr\">1</xref>]. Especially heavy drug users, who endured more severe health burden such as HIV and other blood-borne infectious diseases [<xref rid=\"B2-ijerph-17-05483\" ref-type=\"bibr\">2</xref>]. The studies of the “pure” heavy drug users (who uses one specific drug type exclusively) would provide information on the relationship between drug and HIV infection [<xref rid=\"B3-ijerph-17-05483\" ref-type=\"bibr\">3</xref>]. However, the specific impact of “pure” drug on HIV infection has not yet been established in China, because heavy drug use is usually accompanied by the use of multiple drugs [<xref rid=\"B4-ijerph-17-05483\" ref-type=\"bibr\">4</xref>].</p><p>In mainland China, depressants (opiates and synthetic opioids) and stimulants (amphetamines, methamphetamine, etc.) were major widely used drugs, which accounted for 93% of all drug users in 2019 [<xref rid=\"B5-ijerph-17-05483\" ref-type=\"bibr\">5</xref>]. Depressants, were traditionally, the most abused drugs. The majority of depressants users were young (30 years or younger), non-married (60%), and males (60–70%) with low education levels and without stable jobs [<xref rid=\"B6-ijerph-17-05483\" ref-type=\"bibr\">6</xref>]. In southwest regions, with a high prevalence of depressants use (Yunnan, Sichuan, Gansu, and Guangdong provinces), the rate of intravenous administration in depressants users was about 1/3 [<xref rid=\"B6-ijerph-17-05483\" ref-type=\"bibr\">6</xref>]. Intravenous administration suggested heavy drug use, and sharing contaminated syringes could make heavy drug users endure the greatest health risks, more than one in eight among them live with HIV [<xref rid=\"B1-ijerph-17-05483\" ref-type=\"bibr\">1</xref>]. Prior to 2005, persons who inject drugs (PWID), mainly depressants users, were predominantly responsible for new HIV/AIDS infections [<xref rid=\"B7-ijerph-17-05483\" ref-type=\"bibr\">7</xref>]. However, thanks to the expansion of harm reduction programs, such as methadone maintenance treatment (MMT) and Needle and Syringe Programs (NSP), the national HIV prevalence among PWID decreased slightly from 10.6% in 2002 to 9.1% in 2010 [<xref rid=\"B8-ijerph-17-05483\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05483\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijerph-17-05483\" ref-type=\"bibr\">10</xref>].</p><p>The number of stimulants users has exceeded depressants users since 2014, and they have become the most widely used drugs in mainland China [<xref rid=\"B11-ijerph-17-05483\" ref-type=\"bibr\">11</xref>]. The abuse of stimulants has spread throughout the country, with concentrations in southeastern regions and business-centered cities [<xref rid=\"B6-ijerph-17-05483\" ref-type=\"bibr\">6</xref>,<xref rid=\"B12-ijerph-17-05483\" ref-type=\"bibr\">12</xref>]. More and more company staffers, entertainers, and students are becoming stimulants users [<xref rid=\"B6-ijerph-17-05483\" ref-type=\"bibr\">6</xref>,<xref rid=\"B12-ijerph-17-05483\" ref-type=\"bibr\">12</xref>]. Different from heavy depressants users, Shu Su reported more heavy stimulants users were female, younger, single and higher monthly income [<xref rid=\"B13-ijerph-17-05483\" ref-type=\"bibr\">13</xref>]. Stimulants are powerful central nervous system drugs associated with high-risk sexual practices and chem-sex addiction [<xref rid=\"B14-ijerph-17-05483\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijerph-17-05483\" ref-type=\"bibr\">15</xref>]. Accordingly, unprotected sexual behaviors increase the risk of HIV transmission among stimulants users [<xref rid=\"B16-ijerph-17-05483\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijerph-17-05483\" ref-type=\"bibr\">17</xref>]. This suggested the influence of drugs on HIV transmission is no longer limited in injection drug user (IDU), drugs have potentially linked high risk populations of HIV infection [<xref rid=\"B16-ijerph-17-05483\" ref-type=\"bibr\">16</xref>,<xref rid=\"B18-ijerph-17-05483\" ref-type=\"bibr\">18</xref>]. Yanming Sun’s study found that stimulants abuse appeared to be correlated with commercial sexual behaviors and high HIV prevalence among female drug users [<xref rid=\"B16-ijerph-17-05483\" ref-type=\"bibr\">16</xref>]. Xu JJ reported that stimulants abuse was popular among Chinese men who have sex with men (MSM) and was associated with significant increased HIV infection risk [<xref rid=\"B18-ijerph-17-05483\" ref-type=\"bibr\">18</xref>].</p><p>In 2002, China issued “Standards for HIV Surveillance” to standardize the practice of HIV surveillance [<xref rid=\"B19-ijerph-17-05483\" ref-type=\"bibr\">19</xref>]. The approach utilized surveillance sites for drug users over the country and sampled 400 people twice each year from every surveillance site to estimate the prevalence, incidence and risk behaviors of HIV infection [<xref rid=\"B19-ijerph-17-05483\" ref-type=\"bibr\">19</xref>]. Similarly, global researches on nationwide HIV epidemic of drug users are also based on surveillance data. In 2003, Centers for Disease Control and Prevention (CDC) in U.S. created National HIV Behavioral Surveillance (NHBS) to conduct behavioral surveillance among persons at high risk for HIV infection in 23 cities [<xref rid=\"B20-ijerph-17-05483\" ref-type=\"bibr\">20</xref>]. However, researches on national HIV epidemic of drug users based surveillance data have some limitations. First, self-reported drug abuse behavior has potential report bias, especially underestimated the number of depressants users (heroin, etc.) who are more hard-to-reach as penalty is more severe [<xref rid=\"B21-ijerph-17-05483\" ref-type=\"bibr\">21</xref>]. Second, data such as drug types investigated through questionnaires of sentinel surveillance sites are not precise as expected because the answers will be greatly affected by the subjective consciousness of the respondents. Last, there are more than 660,000 villages (smallest administrative unit) in China, the number is far more than the number of surveillance sites for drug users, thus, the representativeness of sample data collected by surveillance sites is limited [<xref rid=\"B22-ijerph-17-05483\" ref-type=\"bibr\">22</xref>].</p><p>China has set up National Dynamic Management and Control Database for Drug Users (NDMCDDU) to register nationwide drug users [<xref rid=\"B23-ijerph-17-05483\" ref-type=\"bibr\">23</xref>]. In NDMCDDU, drug types were verified by urine tests, which were more accurate than data investigated by sentinel surveillance sites [<xref rid=\"B19-ijerph-17-05483\" ref-type=\"bibr\">19</xref>,<xref rid=\"B24-ijerph-17-05483\" ref-type=\"bibr\">24</xref>]. In addition, data in the NDMCDDU is collected by Government staff, which covered all the smallest administrative units and recorded all observed drug users in mainland China [<xref rid=\"B22-ijerph-17-05483\" ref-type=\"bibr\">22</xref>]. We presume that the database is representative and such a work of evaluate a real database is of great value and helpful for prevention and control on drug and infectious diseases in real public issues. It may give some policy implications for the Chinese Government and other countries in the world. Therefore, in this study, we aim to assess nationwide HIV infection trends and to examine socio-demographic and geographic characteristics of HIV infection among heavy drug users in mainland China from 2008 to 2016 by analyzing data in the NDMCDDU.</p></sec><sec id=\"sec2-ijerph-17-05483\"><title>2. Materials and Methods</title><sec id=\"sec2dot1-ijerph-17-05483\"><title>2.1. Definitions</title><p>The following terms are used in this study.</p><p>Depressants: drugs recorded in NDMCDDU includes heroin, opium, poppy capsule, morphine, pethidine hydrochloride, dihydroetorphine, buprenorphine, and tramadol.</p><p>Stimulants: drugs recorded in NDMCDDU includes ecstasy, methamphetamine and amphetamine.</p><p>Drug users: those who used prohibited depressants and stimulants for non-medical purposes and observed by the Government.</p><p>Heavy drug users: according to Chinese “Measures for Drug Addiction”, drug users who injected drugs or abused drugs multi-times for non-medical use were judged by professionals as heavy drug users. Heavy drug users shall be tested for HIV and recorded in NDMCDDU before entering detoxification centers for treatment [<xref rid=\"B25-ijerph-17-05483\" ref-type=\"bibr\">25</xref>].</p><p>Depressants only group (DOG): included drug users who had been recorded using depressants only.</p><p>Stimulants only group (SOG): included drug users who had been recorded using stimulants only.</p><p>Depressants and stimulants group (DSG): included drug users who had been recorded as using depressants and stimulants but not any other type drugs.</p><p>PWID: self-reported injection drug use and recorded in NDMCDDU.</p><p>HIV infection rate: calculated as the number of newly tested and observed HIV-positive drug users (numerator) divided by the number of newly tested drug users in the same year (denominator) recorded in NDMCDDU.</p></sec><sec id=\"sec2dot2-ijerph-17-05483\" sec-type=\"methods\"><title>2.2. Study Design and Procedures</title><p>NDMCDDU is a national registry database set up by the Chinese Government to register drug users, covering all administrative units in the 31 provinces of mainland China over the whole country. All drug users in NDMCDDU were those who have been found using illicit drugs for non-medical purposes and registered by the Government. As of 2016, NDMCDDU recorded about 4 million drug users historically observed in mainland China, which provided strong support for studies on nationwide drug users. Since 2008, the Government started to register HIV test results of heavy drug users in NDMCDDU [<xref rid=\"B24-ijerph-17-05483\" ref-type=\"bibr\">24</xref>]. Therefore, we assessed all data we have registered in NDMCDDU from 1 January 2008 to 30 June 2016. Socio-demographic characteristics (sex, age, ethnicity, education, and marital status), registered date, location, drug types, methadone treatment history, and HIV status of these drug users were recorded in NDMCDDU, and were extracted directly from the database. The drug types recorded in the database were verified by urine tests. Referring to the “Diagnostic criteria for HIV/AIDS”, the diagnosis of HIV infection was conducted by professional medical institutions through serological screening test and confirmation test [<xref rid=\"B26-ijerph-17-05483\" ref-type=\"bibr\">26</xref>]. For health concern, HIV-positive drug users who meet the Chinese national treatment criteria (WHO stage 3 or 4 disease or CD4 count of 350 cells per μL or less) are referred for treatment with standard three-drug therapy. In addition to these structured variables, injection drug use behavior was extracted from the text record document by the automatic keyword-based matching technique (details in <xref ref-type=\"app\" rid=\"app1-ijerph-17-05483\">Appendix A</xref>). To protect the privacy of drug users, the data used for analysis given by the Government were anonymized. Names and other individually identifiable information of these drug users were not included in the data, only the ID numbers were used as the unique identification code.</p><p>Drug users were classified into DOG, SOG, and DSG according to the drug types they used. Data for individuals with a history of using drugs other than depressants or stimulants were excluded. Primary outcomes were HIV infection trends of the three groups from 2008 to 2016. Secondary outcomes were sociodemographic and geographic characteristics associated with HIV infection.</p></sec><sec id=\"sec2dot3-ijerph-17-05483\"><title>2.3. Measures</title><p>Socio-demographic characteristics. Socio-demographic characteristics were registered in NDMCDDU by Government staff when drug users entered detoxification centers, including sex (male and female), ethnicity (Han and minority), education (primary school or no schooling, junior high school, and high school or above), marital status (divorced or widowed, married, and unmarried), year of HIV test and birth in database. The age was measured as interval years between year of birth and first HIV test in NDMCDDU, and we coded age as 4 levels (≤17, 18~24, 25~44, and ≥45 years).</p><p>Geographic characteristics. We categorized regions where drug users done HIV test as 7 areas based on “The Physical Geography of China”, including Northeast (Heilongjiang, Jilin, and Liaoning), North (Inner Mongolia, Shanxi, Hebei, Beijing, and Tianjin), East (Shandong, Jiangsu, Zhejiang, Shanghai, Fujian, Jiangxi, and Anhui), Central (Henan, Hubei, and Hunan), South (Guangxi, Guangdong, and Hainan), Southwest (Yunnan, Xizang, Sichuan, Chongqing, and Guizhou), and Northwest (Xinjiang, Gansu, Ningxia, Qinghai, and Shaanxi) [<xref rid=\"B27-ijerph-17-05483\" ref-type=\"bibr\">27</xref>].</p><p>Drug use related variables. We compared methadone treatment and HIV test year in order to determine if the drug user received methadone treatment or not before HIV test (yes or no). Injection drug use or not (yes or no) were extracted directly from the database.</p></sec><sec id=\"sec2dot4-ijerph-17-05483\"><title>2.4. Statistical Analysis</title><p>Socio-demographic characteristics and drug use related variables of the three groups were compared by chi-squared tests for categorical variables and one-way ANOVA for continuous variable (age). Logistic regression models were used to compare unadjusted and adjusted odds ratios (ORs and AORs, respectively) and 95% CIs of HIV infection rate among the three groups and by characteristics. Joinpoint regression was used to examine the changing trend of HIV infection rate among the three groups across the country and regions during the study period. Annual percent change (APC) for each line segment and the corresponding 95% confidence interval (95% CI) were estimated. The APC is tested to determine whether a difference exists from the null hypothesis of no change (0%). Each joinpoint informs a statistically significant change in trends (increase or decrease) and each of trends is described by an APC [<xref rid=\"B28-ijerph-17-05483\" ref-type=\"bibr\">28</xref>].</p><p>A two-sided <italic>p</italic> value of 0.05 or less was regarded as significant. Data were checked in PostgreSQL 9.3 (The PostgreSQL Global Development Group, open source database) and SAS version 9.4 (SAS Campus Drive, Cary, NC, USA). Statistical analyses were carried out using SPSS version 22.0 (IBM Corp., Armonk, NY, USA), SAS version 9.4 (SAS Campus Drive, Cary, NC, USA) and Joinpoint Regression Program 4.6.0 (U.S. National Cancer Institute, Bethesda, MD, USA). Geographic visualization was done with ArcGIS 10.0 (Esri Corp, Redlands, CA, USA).</p></sec><sec id=\"sec2dot5-ijerph-17-05483\"><title>2.5. Ethical Issues</title><p>The data we have was anonymized by the Government to protect the privacy of drug users, so this study focused on population-level analyses only and did not access any individually identifiable data. Thus, after the assessment by Institutional Review Board, ethical approval was not sought. At the time of registration, drug users were informed and agreed that their information would be registered in database for management and research.</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05483\"><title>3. Results</title><p>After excluding 1371 users who had used drugs other than depressants and stimulants, a total of 466,033 heavy drug users were tested for HIV between Jan, 2008 and Jun, 2016. Among which 265,774 (57.0%), 140,895 (30.2%), and 59,364 (12.7%) users were classified as DOG, SOG, and DSG respectively (<xref ref-type=\"fig\" rid=\"ijerph-17-05483-f001\">Figure 1</xref>, <xref ref-type=\"app\" rid=\"app2-ijerph-17-05483\">Appendix B</xref>). Drug users included in this study aged 34.3 (SD = 8.6) years. Most users were male (87.9%), Han (83.1%), had junior high school and below education (85.6%). Over half of these drug users were in East (25.3%) and South (25.8%). Compared with DOG and DSG, SOG users tended to be more female (16.7%, <italic>p</italic> < 0.05), younger (30.8 ± 8.3 years), junior school and above education (82.9%) and non-PWID (99.5%) (<xref rid=\"ijerph-17-05483-t001\" ref-type=\"table\">Table 1</xref>). The HIV infection rates of DOG, SOG and DSG users were 2.97% (95% CI 2.91–3.04%), 0.45% (95% CI 0.42–0.49%) and 1.65% (95% CI 1.55–1.76%) respectively (<xref rid=\"ijerph-17-05483-t002\" ref-type=\"table\">Table 2</xref>).</p><sec id=\"sec3dot1-ijerph-17-05483\"><title>3.1. Trends and Socio-Demographic Characteristics of HIV Infection</title><p>The HIV infection rate of DOG users presented a U-shaped trend which decreased from 3.85% in 2008 to 2.19% in 2010 (APC −12.9, 95% CI −19.3–−6.0, <italic>p</italic> < 0.05), then increased to 4.64% in 2016 (APC 8.3, 95% CI 6.1–10.4, <italic>p</italic> < 0.05). While the HIV infection rates of SOG and DSG users both maintained increased trends. In SOG, the infection rate increased from 0.15% in 2008 to 0.54% in 2016 (APC 8.2, 95% CI 4.8–11.8, <italic>p</italic> < 0.05). In DSG, the infection rate increased from 0.78% in 2008 to 2.72% in 2016 (APC 13.5, 95% CI 10.7–16.4, <italic>p</italic> < 0.05) (<xref rid=\"ijerph-17-05483-t002\" ref-type=\"table\">Table 2</xref>, <xref ref-type=\"fig\" rid=\"ijerph-17-05483-f002\">Figure 2</xref>).</p><p>DOG users were more likely to be HIV infected than SOG (AOR 2.07, 95% CI 1.88–2.28, <italic>p</italic> < 0.05) and DSG (AOR 1.28, 95% CI 1.19–1.37, <italic>p</italic> < 0.05) users. Almost, in all subgroups stratified by characteristics, DOG users had higher risk of HIV infection than SOG and DSG users. While there was no statistical difference of HIV infection rate among people in the Northwest or people who have high school or above education in three groups (<xref rid=\"ijerph-17-05483-t002\" ref-type=\"table\">Table 2</xref>).</p><p>Among DOG users, females were associated with higher odds ratio of HIV infection (AOR 1.17, 95% CI 1.08–1.26, <italic>p</italic> < 0.05), the same as among SOG (AOR 1.34, 95% CI 1.09–1.66, <italic>p</italic> < 0.05) and DSG (AOR 1.55, 95% CI 1.29–1.86, <italic>p</italic> < 0.05) users. In DOG, a higher proportion of people aged between 25 and 44 were HIV infected compared with adolescents aged under 17 (AOR 3.78, 95% CI 2.07–6.89, <italic>p</italic> < 0.05). While more HIV infections were among aged 45 years older in SOG (AOR 7.20, 95% CI 1.73–29.89, <italic>p</italic> < 0.05) and DSG (AOR 3.04, 95% CI 2.10–4.39, <italic>p</italic> < 0.05). Compared with Han, minorities in DOG (AOR 2.87, 95% CI 2.72–3.02, <italic>p</italic> < 0.05), SOG (AOR 1.68, 95% CI 1.29–2.20, <italic>p</italic> < 0.05) and DSG (AOR 1.87, 95% CI 1.56–2.25, <italic>p</italic> < 0.05) were associated with higher odds ratios for HIV infection. Lower education suggested more HIV infections, individuals who had primary school or below education were associated with higher odds ratio of HIV infection than individuals had high school or above education in DOG (AOR 1.89, 95% CI 1.70–2.09, <italic>p</italic> < 0.05), SOG (AOR 1.35, 95% CI 1.03–1.78, <italic>p</italic> < 0.05) and DSG (AOR 1.97, 95% CI 1.50–2.58, <italic>p</italic> < 0.05). Unmarried people had lower HIV infection rate among DOG (AOR 0.83, 95% CI 0.79–0.87, <italic>p</italic> < 0.05) and DSG (AOR 0.82, 95% CI 0.72–0.95, <italic>p</italic> < 0.05) users. PWID in DOG (AOR 4.96, 95% CI 4.58–5.37, <italic>p</italic> < 0.05), SOG (AOR 7.42, 95% CI 4.79–11.48, <italic>p</italic> < 0.05) and DSG (AOR 2.68, 95%CI 2.28–3.17, <italic>p</italic> < 0.05) endured a higher risk of HIV infection compared with non-PWID. DOG users who received MMT before the HIV test had a higher HIV infection rate compared to individuals who did not receive treatment (AOR 1.80, 95%CI 1.69–1.91, <italic>p</italic> < 0.05) (<xref rid=\"ijerph-17-05483-t003\" ref-type=\"table\">Table 3</xref>).</p></sec><sec id=\"sec3dot2-ijerph-17-05483\"><title>3.2. Geographic Trends in HIV Infection Rate</title><p>The HIV infection rate of DOG users in the Southwest was 5.93% (95% CI 5.74–6.13%), higher than other regions except for the Northeast (<xref rid=\"ijerph-17-05483-t002\" ref-type=\"table\">Table 2</xref> and <xref rid=\"ijerph-17-05483-t003\" ref-type=\"table\">Table 3</xref>). The rate in Southwest presented a U-shaped trend, decreased from 12.00% in 2008 to 4.01% in 2011 (APC −17.2, 95% CI −25.2–−8.3, <italic>p</italic> < 0.05), then increased to 7.38% in 2016 (APC 5.4, 95% CI 0.7–10.2, <italic>p</italic> < 0.05) (<xref rid=\"ijerph-17-05483-t003\" ref-type=\"table\">Table 3</xref>, <xref ref-type=\"fig\" rid=\"ijerph-17-05483-f003\">Figure 3</xref>). The HIV infection rate in four regions presented increased trends, which were South (APC 10.3, 95% CI 6.0–14.9, <italic>p</italic> < 0.05), North (APC 6.5, 95% CI 2.6–10.7, <italic>p</italic> < 0.05), East (APC 12.9, 95% CI 8.2–17.8, <italic>p</italic> < 0.05) and Central (APC 46.3, 95% CI 27.7–67.6, <italic>p</italic> < 0.05), respectively. While in the Northwest, the HIV infection rate maintained a decreased trend from 6.36% in 2008 to 2.39% in 2016 (APC −7.2, 95% CI −10.9–−3.3, <italic>p</italic> < 0.05) (<xref rid=\"ijerph-17-05483-t002\" ref-type=\"table\">Table 2</xref>, <xref ref-type=\"fig\" rid=\"ijerph-17-05483-f003\">Figure 3</xref>).</p><p>The HIV infection rate of SOG users in the Northwest (1.22% (95% CI 0.83–1.74%)) was highest. However, East and Central regions maintained increased trends. Increases from 0.08% in 2008 to 0.30% in 2016 (APC 8.1, 95% CI 3.7–12.8) were observed in the East, and from 0.15% in 2009 to 0.40% in 2016 (APC 16.1, 95% CI 6.8–26.3) were observed in Central, respectively (<xref rid=\"ijerph-17-05483-t002\" ref-type=\"table\">Table 2</xref>, <xref ref-type=\"fig\" rid=\"ijerph-17-05483-f003\">Figure 3</xref>).</p><p>Among DSG users, the HIV infection rate in the Southwest (4.24% (95%CI 3.78–4.73%)) was highest. In the South, the HIV infection rate increased from 1.36% in 2009 to 3.68% in 2016 (APC 11.2, 95% CI 7.5–15.0). Furthermore, increased trends were also observed in Central (APC 19.2, 95% CI 8.6–30.9) and East regions (APC 14.4, 95% CI 8.8–20.4) (<xref rid=\"ijerph-17-05483-t002\" ref-type=\"table\">Table 2</xref>, <xref ref-type=\"fig\" rid=\"ijerph-17-05483-f003\">Figure 3</xref>).</p></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05483\"><title>4. Discussion</title><p>In this study, we extracted HIV data from NDMCDDU between 2008 and 2016, which covered all registered heavy drug users in 31 provinces in mainland China. Our data showed that depressants and stimulants were most commonly used drugs in mainland China, it is meaningful to focus on the health issues of population used these two types of drugs (<xref rid=\"ijerph-17-05483-t001\" ref-type=\"table\">Table 1</xref>, <xref ref-type=\"app\" rid=\"app2-ijerph-17-05483\">Appendix B</xref>\n<xref rid=\"ijerph-17-05483-t0A1\" ref-type=\"table\">Table A1</xref>). We noted strikingly accelerated HIV infection rates among DOG, SOG and DSG users since 2010, although the HIV infection rate of DOG users decreased from 2008 to 2010. DOG users were associated with highest HIV infection rate than DSG and SOG users, this mainly attributed to the intravenous administration of drugs. A larger proportion (32.8%) of DOG users were PWID followed by 25.7% of DSG users, compared with 0.5% of SOG users, so unsafe injecting practices relating to the sharing of contaminated needles and syringes was the main cause of HIV infection [<xref rid=\"B7-ijerph-17-05483\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijerph-17-05483\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05483\" ref-type=\"bibr\">9</xref>]. In our study, the similarity between DOG and DSG users was reflected in the distribution of IDU of drugs and sociodemographic characteristics, and it is reported that depressants users have shifted to stimulants [<xref rid=\"B6-ijerph-17-05483\" ref-type=\"bibr\">6</xref>]. PWID are a key population at increasing risk of HIV infection around the world. In our study, the HIV infection rate of PWID among DOG users was 5.3%, lower than 17.8% of the global HIV infection rate among PWID, 28.5% in South-West Asia and 24.0% in Eastern and South-Eastern Europe [<xref rid=\"B29-ijerph-17-05483\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijerph-17-05483\" ref-type=\"bibr\">30</xref>]. In the Russian Federation, the prevalence of HIV among depressants users (especially in registered PWID) rose steadily over the period 2009–2014, from 13.2% to 19.9%, which was consistent with the increase trend of DOG and DSG in China after 2010 [<xref rid=\"B30-ijerph-17-05483\" ref-type=\"bibr\">30</xref>]. While in developed countries such as the USA, the HIV infection rate among this population had decreased from 12% in 2006 to 6% in 2018 [<xref rid=\"B31-ijerph-17-05483\" ref-type=\"bibr\">31</xref>]. In addition, increasing HIV epidemics were reported among stimulants users in different populations in the USA, Russia and other countries, and a similar trend is also observed in our study [<xref rid=\"B32-ijerph-17-05483\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijerph-17-05483\" ref-type=\"bibr\">33</xref>].</p><p>In China, prior to 2007, injection depressants use was predominantly responsible for new HIV/AIDS infection cases [<xref rid=\"B6-ijerph-17-05483\" ref-type=\"bibr\">6</xref>,<xref rid=\"B12-ijerph-17-05483\" ref-type=\"bibr\">12</xref>]. Therefore, China has scaled up harm reduction programs such as MMT and NSP to address this issue, which might lead to the decrease of HIV infection rate before 2010 in DOG [<xref rid=\"B6-ijerph-17-05483\" ref-type=\"bibr\">6</xref>,<xref rid=\"B12-ijerph-17-05483\" ref-type=\"bibr\">12</xref>]. However, the limitations of these programs’ implementation in recent years could be a potential reason of the rebound in HIV infection rate of DOG. Though MMT can reduce the risk of HIV transmission by reducing needle sharing and improving self-protection awareness, currently, there are still some problems affecting the effect of MMT treatment in China, such as low treatment coverage, serious discontinuation of treatment and short duration time of treatment [<xref rid=\"B34-ijerph-17-05483\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijerph-17-05483\" ref-type=\"bibr\">35</xref>]. Furthermore, in our study, drug users who received MMT were associated with a higher HIV infection rate than those who had not received MMT. This may be attributed to the fact that people receiving MMT were addicted to drugs, more heavily. Heavier addiction may increase the risk of HIV infection due to long-term use of drugs and high frequency of IDU [<xref rid=\"B2-ijerph-17-05483\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijerph-17-05483\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijerph-17-05483\" ref-type=\"bibr\">4</xref>]. These issues posed challenges to MMT’s effect of HIV control, recently, and the effect of the NSP is also elusive. High coverage of needle-syringe programs (246 needles and syringes per person who injects drugs per year) have been reported in China, but the coverage might be overestimated because those who had not yet attended the program were not included in the statistics [<xref rid=\"B36-ijerph-17-05483\" ref-type=\"bibr\">36</xref>]. Further, NSP has not been extensively evaluated to explore factors influencing acceptability and feasibility [<xref rid=\"B37-ijerph-17-05483\" ref-type=\"bibr\">37</xref>]. Lei Zhang et al. suggested that continued law enforcement and mandatory detoxification remain as major barriers to the necessary program scale-up and may even counteract the benefits of NSPs [<xref rid=\"B38-ijerph-17-05483\" ref-type=\"bibr\">38</xref>]. Our study observed the increasing trend of HIV infection rate among depressants only users after 2010, which implied that the promotion and effectiveness of nationwide harm reduction programs needs to be systematically evaluated.</p><p>In our study, a higher proportion of HIV infection among heavy drug users was associated with being female, older, racial minorities and undereducated. Females move faster than males towards drug addiction and have a greater vulnerability than males to HIV and other blood-borne infections [<xref rid=\"B6-ijerph-17-05483\" ref-type=\"bibr\">6</xref>]. Females also have less access to healthcare services to address drug-related health needs, and they have to face the combined stigma of their gender and their status as drug users, including discrimination, in accessing healthcare services [<xref rid=\"B6-ijerph-17-05483\" ref-type=\"bibr\">6</xref>]. In addition, high HIV infection rates among female drug users may be partly due to the phenomenon of “sex exchange for drugs”. Multiple sexual partners, low condom use, and prevalent sexually transmitted diseases (STDs) have been observed among female drug users in China [<xref rid=\"B16-ijerph-17-05483\" ref-type=\"bibr\">16</xref>]. According to the latest report on China’s drug situation, the proportion of young people among newly discovered drug users has decreased, but the proportion of drug users over 60 years old has increased by 3.5%, the similar trend has also been observed in Western countries [<xref rid=\"B5-ijerph-17-05483\" ref-type=\"bibr\">5</xref>]. Longer terms of using drugs among aging drug users and their tendency to have condom-less sex because of the less risk of pregnancy might increase their risk of HIV infection [<xref rid=\"B39-ijerph-17-05483\" ref-type=\"bibr\">39</xref>]. Racial minorities mainly settled in southwest China, which lags behind on life expectancy and per-capita GDP, faces disproportionately greater HIV vulnerabilities, due in part to high HIV prevalence caused by IDU, and reportedly greater practices of sexual concurrency and inconsistent condom use [<xref rid=\"B40-ijerph-17-05483\" ref-type=\"bibr\">40</xref>]. It has also been reported that undereducated (OR = 2.32, 95% CI 1.02–5.25) drug users contributed substantially to new HIV infections [<xref rid=\"B41-ijerph-17-05483\" ref-type=\"bibr\">41</xref>].</p><p>From a spatial perspective, most HIV cases among DOG and DSG users were still concentrated in South and Southwest regions initially affected by the epidemic, where situated between the two largest heroin producing regions in the world, the “Golden Crescent” and the “Golden Triangle”. There is a big gap between the western regions (Northwest and Southwest regions) and the eastern (North, East, Northeast and South regions) and central (Central region) regions in terms of the economic situation, income level, technological development level and industrial structure. By the end of 2019, the ratio of per-capita GDP of eastern region, dividing central region and western region was 1.65 and 1.71, respectively. In addition, western regions feature racial minorities inhabiting the districts. The differences in human geography and socio-economic level make people in western regions vulnerable to HIV infection and lead to the imbalance of drug and HIV epidemic in China [<xref rid=\"B42-ijerph-17-05483\" ref-type=\"bibr\">42</xref>]. Since 2005, western regions have been the focus of the “People’s War on Drugs” in China, and the war has achieved remarkable success, the proportion of heroin users decreased by 52.3% among new drug users between 2003 and 2010, and the HIV prevalence of PWID decreased from 10.6% in 2002 to 9.1% in 2010 [<xref rid=\"B9-ijerph-17-05483\" ref-type=\"bibr\">9</xref>,<xref rid=\"B43-ijerph-17-05483\" ref-type=\"bibr\">43</xref>]. But in our study, since 2010, HIV infection rate among DOG users in other regions almost all maintained increased trends, the rise of HIV infection rate requires constant vigilance. The significantly sharpest increased trends of HIV infection rate among stimulants users were observed in East and Central regions, and our study showed that the increased trends were consistent with substantial increases of stimulants users in East and Central regions, where contain population densities of >450 people per square kilometer and account for about 46% of China’s population [<xref rid=\"B44-ijerph-17-05483\" ref-type=\"bibr\">44</xref>]. This finding implies that HIV infection is easily spreading to the general population, which highlighted the new challenge of HIV epidemic.</p><p>Our study has several limitations. First, people tested for HIV are heavy drug users, therefore, there is a lack of information about drug users with mild addiction. Second, self-reported drug use patterns might contain report bias and cause the underestimation of the number of PWID. Last, this study was a secondary data analysis, data on sexual behavior were not available, and we were unable to adjust for this risk factor. Nevertheless, to our knowledge, this is the first estimate of HIV infection rate among the largest population of heavy drug users, nationwide. The findings of our study highlight implications of public health policy for HIV prevention and control of heavy drug users. First of all, given the expanding of HIV epidemic among heavy depressants users, the Government needs to scale up the coverage of NSP and MMT to prevent the development of drug addiction and reduce the risk of HIV transmission, especially in central and east regions. Further, adequate assessment system and methods are urgently needed to be constructed to evaluate the effectiveness of harm reduction programs. Second, drug use and HIV prevention, treatment and care should be tailored to the specific needs of vulnerable populations such as female, older and undereducated people. Another issue to note is that the global COVID-19 pandemic has plunged the world into an unprecedented crisis. Restrictions on movement and gatherings put in place to halt the spread of COVID-19, may lead to an overall decrease in consumption of recreational drugs. However, drug supply shortages lead to an increase in prices, and some users began seeking out cheaper synthetic substances, and thus the patterns of use shifted towards injecting drugs. Therefore, governments should not reduce drug-related budgets, and also consider especially, the delivery of drug treatment and care services. What is more, during the COVID-19 pandemic, attention should be paid to the provision of routine medical services and drug supply to HIV infected persons, to ensure continuity of HIV prevention and treatment services.</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05483\"><title>5. Conclusions</title><p>The U-shaped curve for HIV infection rate among DOG users and consistent increases among SOG and DSG users implies heavy drug use is a critical focus of HIV infection in China. Our results suggest that we should still focus on the continuous rise of HIV infection rate of heavy drug users. Drug use and HIV prevention, treatment and care, should be tailored to the specific needs of vulnerable populations such as females, older people, racial minorities, undereducated and non-married persons, who are those living with HIV and heavily using drugs. Although most HIV cases were still concentrated in western provinces, initially affected by the drug abuse and HIV epidemic, HIV infection rate among heavy drug users in Central and East regions all maintained increased trends. Therefore, we urgently need to reassess the effectiveness of current strategies on HIV prevention and control among heavy drug users and eliminate discrimination and unfair distribution of health resources regarding gender, social status and geographic inequality.</p></sec></body><back><ack><title>Acknowledgments</title><p>We thank Chun Hao, Pei Gao and Leizhen Wang for helpful comments and suggestions. The opinions expressed herein show the collective views of the coauthors and do not necessarily represent the official position of the Chinese Ministry of Public Security.</p></ack><notes><title>Author Contributions</title><p>Z.-W.J., Z.-H.L., and B.Z. designed the study. B.Z. and X.-Y.Y. cleaned the data. B.Z., Y.-J.L., and X.-Y.Y. analyzed the data. Z.-W.J., Z.-H.L., B.Z. and Z.-M.L. explained the results. Z.-W.J. and B.Z. wrote the initial draft of the manuscript. Z.-W.J. and Z.-H.L. revised the report from preliminary draft to submission. All authors read and approved the final manuscript.</p></notes><notes><title>Funding</title><p>This study was supported by the National Natural Science Foundation of China [grant number 91546203, 91846302], the Ministry of Public Security of the People’s Republic of China [0716-1541GA590508], the Ministry of Science and Technology of the People’s Republic of China [2020YFC0849200] and the Beijing Advanced Discipline Construction Project [BMU2019GJJXK005].</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><app-group><app id=\"app1-ijerph-17-05483\"><title>Appendix A</title><sec><title>Data Extraction</title><p>It is unrealistic to process more than 6 million text records only by manpower, so we designed an iterative data processing process. We randomly selected 10,000 text records of drug used facts to initial keywords sets of drugs used patterns such as “inject”, “heat inhale”, “snuffle” and “oral” artificially. Then identified drug used patterns of text records of all drug users. If a text record once contained “inject” or “needle” then first be identified as “IDU (injection drug user)”, after this step, if unidentified text records contained other keywords (“roast”, “chasing dragon”, “ironing”, “hookah”, “pot”, “smog”, “volatilization”, “smoke”, etc.) then the drug user would be identified as “non-IDU (non-injection drug use)”. Another 10,000 text records were selected randomly from final unidentified text records to be checked whether contained new keywords out of keywords set. New keywords were added to the keywords set if exist. The above work was repeated until no new keywords appeared.</p></sec></app><app id=\"app2-ijerph-17-05483\"><title>Appendix B</title><table-wrap id=\"ijerph-17-05483-t0A1\" orientation=\"portrait\" position=\"anchor\"><object-id pub-id-type=\"pii\">ijerph-17-05483-t0A1_Table A1</object-id><label>Table A1</label><caption><p>Characteristics of drug users who used drugs other than depressants and stimulants in China, 2008–2016.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Characteristics</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cannabis</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Methcathinone</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Caffeine</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Sodium benzoate</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Methaqualone</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Mix Used Multiple Drugs <italic>N</italic> (%)</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>N</italic> (%)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>N</italic> (%)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>N</italic> (%)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>N</italic> (%)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>N</italic> (%)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>N (</italic>%)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Total</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1371</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">578</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">213</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">54</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">493</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>HIV</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Positive</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23 (1.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19 (3.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (0.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2 (3.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (0.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (0.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2 (0.4)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Negative</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1348 (98.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">559 (96.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">213 (100.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">52 (96.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20 (100.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13 (100.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">491 (99.6)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Sex</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Male</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1325 (96.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">563 (97.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">211 (99.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">54 (100.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19 (95.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12 (92.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">466 (94.5)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Female</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">46 (3.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15 (2.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2 (0.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (0.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1 (5.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1 (7.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27 (5.5)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><bold>Age</bold> (<inline-formula><mml:math id=\"mm1\"><mml:mrow><mml:mrow><mml:mstyle mathvariant=\"bold\" mathsize=\"normal\"><mml:mi>I</mml:mi><mml:mi>Q</mml:mi><mml:mi>R</mml:mi></mml:mstyle></mml:mrow></mml:mrow></mml:math></inline-formula>)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32 (26–41)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30 (25−38)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">37 (31−43)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">42 (32−47)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">36 (29−42)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">37 (32−46)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31 (25−40)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Ethnicity</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Han</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">656 (47.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">36 (6.2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">175 (82.2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32 (59.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18 (90.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12 (92.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">383 (77.7)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Minority</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">552 (40.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">495 (85.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (0.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2 (3.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1 (5.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (0.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">54 (11.1)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Missing</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">163 (11.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47 (8.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38 (17.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20 (37.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1 (5.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1 (7.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">56 (11.4)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Education</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">High school or above</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">122 (8.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45 (7.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29 (13.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2 (3.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (0.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (0.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">46 (9.3)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Junior high school</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">712 (51.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">214 (37.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">147 (69.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30 (55.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10 (50.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6 (46.2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">305 (61.9)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Primary school or below</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">457 (33.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">270 (46.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">37 (17.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20 (37.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9 (45.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5 (38.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">116 (23.5)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Missing</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">80 (5.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">49 (8.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (0.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2 (3.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1 (5.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2 (15.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26 (5.3)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Marital status</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Married</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">439 (32.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">206 (35.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24 (11.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8 (14.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2 (10.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2 (15.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">197 (40.0)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Unmarried</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">811 (59.2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">305 (52.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">182 (85.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">42 (77.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16 (80.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9 (69.2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">257 (52.1)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Divorced or Widowed</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45 (3.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18 (3.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7 (3.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2 (3.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1 (5.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (0.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17 (3.4)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Missing</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">76 (5.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">49 (8.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (0.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2 (3.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1 (5.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2 (15.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22 (4.5)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Injection drug use</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">186 (13.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19 (3.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">69 (32.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2 (3.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2 (10.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (0.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94 (19.1)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">74 (5.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12 (2.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (0.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4 (7.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (0.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (0.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">58 (11.8)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not sure</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1111 (81.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">547 (94.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">144 (67.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">48 (88.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18 (90.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13 (100.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">341 (69.2)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Region</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Southwest</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34 (2.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4 (0.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (0.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3 (5.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (0.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (0.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27 (5.5)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Northwest</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">491 (35.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">457 (79.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (0.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1 (1.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4 (20.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (0.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29 (5.9)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">South</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">165 (12.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10 (1.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (0.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7 (13.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (0.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (0.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">148 (30.0)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Northeast</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29 (2.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8 (1.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (0.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (0.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (0.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (0.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21 (4.3)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Central</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">133 (9.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16 (2.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13 (6.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33 (61.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (0.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (0.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">71 (14.4)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">North</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">423 (30.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23 (4.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">200 (93.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3 (5.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16 (80.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13 (100.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">168 (34.1)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">East</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">96 (7.0)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">60 (10.4)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0 (0.0)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7 (13.0)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0 (0.0)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0 (0.0)</td><td 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DOG: depressants users. SOG: stimulants users. DSG: both depressants and stimulants users. * suggested <italic>p</italic> < 0.05.</p></caption><graphic xlink:href=\"ijerph-17-05483-g002\"/></fig><fig id=\"ijerph-17-05483-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Trends of HIV infection rate among depressants only, stimulants only and both depressants and stimulants users stratified by regions. DOG: depressants only users. SOG: stimulants only users. DSG: both depressants and stimulants users. 1st: the first half year from January to June. 2nd: the second half year from July to December. * suggested <italic>p</italic> < 0.05.</p></caption><graphic xlink:href=\"ijerph-17-05483-g003\"/></fig><table-wrap id=\"ijerph-17-05483-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05483-t001_Table 1</object-id><label>Table 1</label><caption><p>Characteristics of depressants only users, stimulants only users and both depressants and stimulants users in China, 2008–2016.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Characteristics</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">DOG</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SOG</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">DSG</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\"><italic>p</italic>-Value</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">N (%)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">N (%) </th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">N (%)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">N (%)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Total</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">466,033</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">265,774</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">140,895</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">59,364</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>HIV</bold>\n<sup>a,b,c</sup>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Positive</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9522 (2.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7906 (3.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">635 (0.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">981 (1.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Negative</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">456,511 (98.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">257,868 (97.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">140,260 (99.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">58,383 (98.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Sex</bold>\n<sup>a,b,c</sup>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Male</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">409792 (87.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">240,730 (90.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">117,297 (83.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">51,765 (87.2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Female</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">56241 (12.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25,044 (9.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23,598 (16.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7599 (12.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><bold>Age</bold><sup>a,b,c,d</sup> (<inline-formula><mml:math id=\"mm2\"><mml:mrow><mml:mrow><mml:mi>IQR</mml:mi></mml:mrow></mml:mrow></mml:math></inline-formula>)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34 (28–40)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35 (29–41) </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29 (25–36)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">36 (30–42)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Ethnicity</bold>\n<sup>a,b,c</sup>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Han</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">387,488 (83.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">215,647 (81.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">119,358 (84.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">52,483 (88.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Minority</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">46,698 (10.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35,014 (13.2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7485 (5.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4199 (7.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Missing</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31,847 (6.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15,113 (5.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14,052 (10.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2682 (4.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Education</bold>\n<sup>a,b,c</sup>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">High school or above</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">54,002 (11.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24,874 (9.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21,977 (15.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7151 (12.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Junior high school</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">287,312 (61.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">154,243 (58.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94,789 (67.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38,280 (64.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Primary school or below</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">111,886 (24.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">77,349 (29.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21,666 (15.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12,871 (21.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Missing</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12,833 (2.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9308 (3.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2463 (1.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1062 (1.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Marital status</bold>\n<sup>a,b,c</sup>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Married</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">201,101 (43.2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100,079 (37.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">76,786 (54.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24,236 (40.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Unmarried</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">217,183 (46.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">135,569 (51.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">52,955 (37.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28,659 (48.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Divorced or Widowed</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38,190 (8.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22,748 (8.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9592 (6.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5850 (9.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Missing</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9559 (2.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7378 (2.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1562 (1.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">619 (1.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Injection drug use</bold>\n<sup>a,b,c</sup>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">146,537 (31.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">59,374 (22.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">65,330 (46.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21,833 (36.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">103,117 (22.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">87,142 (32.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">726 (0.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15,249(25.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not sure</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">216,379 (46.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">119,258 (44.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">74,839 (53.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22,282 (37.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Methadone treatment</bold>\n<sup>a,b,c</sup>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">419,313 (90.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">229,374 (86.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">140,895 (100.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50,169 (84.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">46,720(10.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">36,400 (13.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0(0.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9195 (15.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Date of HIV test</bold>\n<sup>a,b,c</sup>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jan, 2008~Jun, 2008</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2266 (0.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2078 (0.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">116 (0.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">72 (0.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jul, 2008~Dec, 2008</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24,754 (5.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21,040 (7.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2045 (1.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1669 (2.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jan, 2009~Jun, 2009</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27,565 (5.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24,030 (9.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1877 (1.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1658 (2.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jul, 2009~Dec, 2009</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31,843 (6.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24,894 (9.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4416 (3.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2533 (4.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jan, 2010~Jun, 2010</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27,677 (6.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20,936 (7.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4150 (2.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2591 (4.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jul, 2010~Dec, 2010</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24,673 (5.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18,062 (6.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4176 (3.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2435 (4.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jan, 2011~Jun, 2011</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22,418 (4.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16,181 (6.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3846 (2.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2391 (4.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jul, 2011~Dec, 2011</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19,075 (4.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13,425 (5.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3388 (2.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2262 (3.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jan, 2012~Jun, 2012</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22,412 (4.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14,519 (5.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5091 (3.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2802 (4.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jul, 2012~Dec, 2012</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21,898 (4.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13,360 (5.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5595 (4.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2943 (5.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jan, 2013~Jun, 2013</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26,754 (5.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14,420 (5.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8207 (5.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4127 (7.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jul, 2013~Dec, 2013</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31,350 (6.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16,439 (6.2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10,473 (7.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4438 (7.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jan, 2014~Jun, 2014</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31,597 (6.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14,687 (5.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11,871 (8.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5048 (8.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jul, 2014~Dec, 2014</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40,380 (8.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14,769 (5.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19,063 (13.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6548 (11.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jan, 2015~Jun, 2015</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40,248 (8.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13,391 (5.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20,191 (14.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6666 (11.2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jul, 2015~Dec, 2015</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40,451 (8.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13,188 (5.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20,902 (14.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6361 (10.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jan, 2016~Jun, 2016</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30,672 (6.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10,364 (3.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15,488 (11.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4820 (8.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Region</bold>\n<sup>a,b,c</sup>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Southwest</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">79,040 (17.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60,507 (22.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11,452 (8.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7081 (11.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Northwest</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32,790 (7.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29,397 (11.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2047 (1.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1346 (2.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">South</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">120,146 (25.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">80,841 (30.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23,049 (16.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16,256 (27.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Northeast</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13,321 (2.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1703 (0.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10,685 (7.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">933 (1.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Central</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">64,177 (13.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32,475 (12.2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24,522 (17.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7180 (12.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">North</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38,622 (8.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21,309 (8.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12,712 (9.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4601 (7.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">East</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">117,878 (25.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39,491 (14.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">56,420 (40.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21,967 (37.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Missing</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">59 (0.0)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">51 (0.0)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8 (0.0)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0 (0.0)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr></tbody></table><table-wrap-foot><fn><p>DOG: depressants only users. SOG: stimulants only users. DSG: both depressants and stimulants users. <sup>a</sup> refers to a difference between DOG and SOG that was significant (<italic>p</italic> < 0.001). <sup>b</sup> refers to a difference between DOG and DSG that was significant (<italic>p</italic> < 0.001). <sup>c</sup> refers to a difference between SOG and DSG that was significant (<italic>p</italic> < 0.001). <sup>d</sup> One-way ANOVA was used to compare the age among the three groups. </p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05483-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05483-t002_Table 2</object-id><label>Table 2</label><caption><p>Comparison of HIV infection rates by characteristics among opioid-type only, synthetic-type only and both opioid-type and synthetic-type drug users.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Characteristics</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">DOG</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">SOG</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">DSG</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">AOR (95%CI)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">AOR (95%CI)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">AOR (95%CI)</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">HIV</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">N</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Rate (95%CI)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">HIV</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">N</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Rate (95%CI)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">HIV</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">N</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Rate (95%CI)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(DOG vs SOG)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(DSG vs SOG)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">(DOG vs DSG)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Total</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7906</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26,5774</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.97 (2.91–3.04)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">635</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">140,895</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.45 (0.42–0.49)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">981</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">59,364</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.65 (1.55–1.76)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.07 (1.88–2.28) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.95 (1.72–2.22) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.28 (1.19–1.37) ***</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Sex</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Male</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7078</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24,0730</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.94 (2.87–3.01)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">521</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">117,297</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.44 (0.41–0.48)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">832</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">51,765</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.61 (1.50–1.72)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.17 (1.95–2.40) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.91 (1.67–2.20) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.32 (1.22–1.42) ***</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Female</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">828</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25,044</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.31 (3.09–3.54)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">114</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23,598</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.48 (0.40–0.58)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">149</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7599</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.96 (1.67–2.29)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.60 (1.24–2.07) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.18 (1.59–2.99) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.08 (0.89–1.31)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Age</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">≤17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">911</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.21 (0.68–2.02)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1564</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.13 (0.04–0.36)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">137</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (-)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.79 (0.18–17.77)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18~24</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">405</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23,946</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.69 (1.53–1.86)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">137</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32,951</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.42 (0.35–0.49)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4636</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.84 (0.62–1.13)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.66 (1.28–2.15) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.35 (0.88–2.05)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.27 (0.90–1.81)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25~44</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6406</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20,1105</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.19 (3.11–3.26)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">435</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">95,836</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.45 (0.41–0.50)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">727</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">44,416</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.64 (1.52–1.76)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.24 (2.00–2.51) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.92 (1.66–2.23) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.34 (1.24–1.46) ***</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45~</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1082</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39,786</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.72 (2.56–2.88)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10,528</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.57 (0.44–0.72)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">215</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10,172</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.11 (1.85–2.41)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.59 (1.17–2.15) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.67 (1.91–3.74) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.00 (0.86–1.18)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Missing</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.69 (2.38–21.43)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.25 (1.51–23.06)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (-)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.25 (0.10–15.01) <sup>a</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Ethnicity</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Han</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5040</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">215,647</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.34 (2.27–2.40)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">467</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">119,358</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.39 (0.36–0.43)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">761</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">52,483</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.45 (1.35–1.55)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.72 (1.53–1.92) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.87 (1.62–2.16) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.18 (1.09–1.28) ***</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Minority</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2520</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35,014</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.20 (6.92–7.48)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">68</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7485</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.91 (0.72–1.14)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">158</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4199</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.76 (3.22–4.37)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.96 (3.04–5.15) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.13 (1.51–3.01) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.65 (1.39–1.97) ***</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Missing</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">346</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15,113</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.29 (2.06–2.54)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14,052</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.71 (0.59–0.86)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">62</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2682</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.31 (1.81–2.92)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.23 (1.67–2.97) <sup>***</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.35 (1.61–3.41) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.09 (0.81–1.45)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Education</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">High school or above</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">477</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24,874</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.92 (1.75–2.09)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">88</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21,977</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.40 (0.33–0.49)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">69</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7151</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.96 (0.76–1.21)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.27 (0.95–1.71)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.51 (1.00–2.28)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.09 (0.83–1.44)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Junior high school</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3847</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15,4243</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.49 (2.42–2.57)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">400</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94,789</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.42 (0.38–0.46)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">564</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38,280</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.47 (1.36–1.60)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.83 (1.61–2.07) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.98 (1.68–2.33) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.18 (1.07–1.29) **</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Primary school or below</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3261</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">77,349</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.22 (4.07–4.36)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">134</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21,666</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.62 (0.52–0.73)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">329</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12,871</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.56 (2.29–2.84)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.75 (2.28–3.32) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.06 (1.62–2.63) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.38 (1.22–1.55) ***</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Missing</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">321</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9308</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.45 (3.09–3.84)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2463</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.53 (0.31–0.85)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1062</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.79 (1.15–2.68)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.06 (1.66–5.67) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.70 (1.15–6.33) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.46 (0.88–2.44)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Marital status</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Married</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3325</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100,079</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.32 (3.21–3.44)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">348</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">76,786</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.45 (0.41–0.50)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">444</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24,236</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.83 (1.67–2.01)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.10 (1.84–2.41) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.04 (1.70–2.44) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.32 (1.18–1.46) ***</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Unmarried</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3571</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">135,569</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.63 (2.55–2.72)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">240</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">52,955</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.45 (0.40–0.51)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">415</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28,659</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.45 (1.32–1.59)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.96 (1.68–2.27) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.76 (1.45–2.14) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.28 (1.15–1.43) ***</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Divorced or Widowed</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">735</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22,748</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.23 (3.01–3.47)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9592</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.42 (0.31–0.56)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">116</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5850</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.98 (1.65–2.36)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.11 (1.46–3.06) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.40 (1.55–3.72) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.05 (0.85–1.31)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Missing</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">275</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7378</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.73 (3.31–4.18)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1562</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.45 (0.22–0.84)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">619</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.97 (0.45–1.89)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.40 (1.52–7.61) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.28 (0.65–8.04)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.33 (1.02–5.34) *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Injection drug use</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">759</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">59,374</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.28 (1.19–1.37)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">233</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">65,330</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.36 (0.31–0.40)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">228</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21,833</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.04 (0.92–1.18)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.86 (1.54–2.23) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.14 (1.74–2.62) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.95 (0.81–1.12)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4635</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">87,142</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.32 (5.17–5.47)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">726</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.31 (2.23–4.75)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">472</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15,249</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.10 (2.83–3.38)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.38 (0.90–2.09)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.81 (0.53–1.26)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.55 (1.40–1.72) ***</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not sure</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2512</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">119,258</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.11 (2.03–2.19)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">378</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">74,839</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.51 (0.46–0.56)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">281</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22,282</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.26 (1.12–1.41)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.39 (2.10–2.72) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.90 (1.59–2.26) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.23 (1.08–1.40) **</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Methadone treatment</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6257</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">229,374</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.73 (2.66–2.80)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">635</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">140,895</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.45 (0.41–0.48)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">759</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50,169</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.51 (1.41–1.62)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.18 (1.97–2.40) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.94 (1.70–2.21) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.26 (1.16–1.36) ***</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1649</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">36,400</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.53 (4.32–4.75)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">222</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9195</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.41 (2.12–2.74)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.33 (1.14–1.54) ***</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Date of HIV test</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jan, 2008~Jun, 2008</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">80</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2078</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.85 (3.10–4.74)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">116</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (-)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">72</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (-)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jul, 2008~Dec, 2008</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">673</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21,040</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.20 (2.97–3.44)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2045</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.15 (0.05–0.35)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1669</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.78 (0.46–1.26)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.08 (2.88–28.64) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.36 (1.40–20.56) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.07 (1.17–3.65) *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jan, 2009~ Jun, 2009</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">792</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24,030</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.30 (3.07–3.53)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1877</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.32 (0.15–0.62)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1658</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.60 (0.33–1.03)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.16 (1.37–7.30) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.27 (0.39–4.11)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.32 (1.22–4.40) *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jul, 2009~ Dec, 2009</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">626</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24,894</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.51 (2.33–2.72)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4416</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.20 (0.11–0.36)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2533</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.47 (0.27–0.78)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.83 (1.42–5.65) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.74 (0.60–5.12)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.97 (1.10–3.54) *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jan, 2010~ Jun, 2010</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">427</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20936</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.04 (1.86–2.24)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4150</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.31 (0.18–0.51)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2591</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.85 (0.56–1.24)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.47 (0.82–2.65)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.86 (0.81–4.25)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.14 (0.73–1.79)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jul, 2010~ Dec, 2010</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">395</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18,062</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.19 (1.98–2.41)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4176</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.29 (0.17–0.47)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2435</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.74 (0.47–1.12)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.09 (1.14–3.81) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.71 (0.27–1.85)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.64 (1.01–2.68) *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jan, 2011~ Jun, 2011</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">364</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16,181</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.25 (2.03–2.49)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3846</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.34 (0.20–0.55)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2391</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.84 (0.54–1.24)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.97 (1.60–5.53) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.92 (0.81–4.55)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.95 (1.22–3.12) **</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jul, 2011~ Dec, 2011</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">304</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13,425</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.26 (2.02–2.53)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3388</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.27 (0.14–0.47)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2262</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.02 (0.68–1.47)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.36 (1.16–4.79) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.05 (1.26–7.42) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.34 (0.86–2.10)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jan, 2012~ Jun, 2012</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">352</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14,519</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.42 (2.18–2.68)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5091</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.29 (0.18–0.46)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2802</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.86 (0.58–1.23)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.89 (1.65–5.05) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.49 (0.65–3.38)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.89 (1.22–2.91) **</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jul, 2012~ Dec, 2012</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">350</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13,360</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.62 (2.36–2.90)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5595</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.36 (0.23–0.53)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2943</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.99 (0.69–1.38)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.72 (1.03–2.87) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.12 (0.53–2.38)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.64 (1.01–2.43) *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jan, 2013~ Jun, 2013</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">372</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14,420</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.58 (2.33–2.85)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8207</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.30 (0.21–0.44)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4127</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.09 (0.82–1.43)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.00 (1.26–3.16) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.87 (1.04–3.38) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.46 (1.06–2.02) *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jul, 2013~ Dec, 2013</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">514</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16,439</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.13 (2.87–3.40)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10,473</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.37 (0.27–0.50)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">72</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4438</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.62 (1.29–2.02)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.43 (1.67–3.54) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.07 (1.25–3.43) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.32 (1.01–1.71) *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jan, 2014~ Jun, 2014</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">422</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14,687</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.87 (2.61–3.15)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">52</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11,871</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.44 (0.33–0.56)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">78</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5048</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.55 (1.24–1.91)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.57 (1.11–2.23) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.83 (1.19–2.79) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.11 (0.86–1.44)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jul, 2014~ Dec, 2014</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">606</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14,769</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.1 (3.79–4.44)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">98</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19,063</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.51 (0.42–0.62)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">129</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6548</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.97 (1.66–2.32)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.32 (1.77–3.04) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.73 (1.23–2.44) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.60 (1.30–1.95) ***</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jan, 2015~ Jun, 2015</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">587</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13,391</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.38 (4.04–4.75)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">82</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20,191</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.41 (0.33–0.50)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">148</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6666</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.22 (1.89–2.59)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.85 (2.13–3.81) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.28 (1.61–3.22) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.40 (1.15–1.70) **</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jul, 2015~ Dec, 2015</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">561</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13188</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.25 (3.92–4.61)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">155</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20,902</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.74 (0.63–0.86)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">207</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6361</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.25 (2.84–3.71)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.37 (1.87–3.00) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.56 (1.97–3.33) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.03 (0.87–1.22)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jan, 2016~ Jun, 2016</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">481</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10364</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.64 (4.24–5.06)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">84</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15,488</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.54 (0.44–0.66)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">131</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4820</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.72 (2.29–3.20)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.83 (2.11–3.78) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.73 (1.19–2.51) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.42 (1.16–1.75) **</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Region</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Southwest</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3589</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60,507</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.93 (5.74–6.13)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">114</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11,452</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.00 (0.83–1.19)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">300</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7081</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.24 (3.78–4.73)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.47 (2.03–3.01) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.52 (1.96–3.25) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.36 (1.20–1.54) ***</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Northwest</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">812</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29,397</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.76 (2.58–2.96)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2047</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.22 (0.83–1.74)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1346</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.15 (1.50–3.01)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.15 (0.75–1.77)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.00 (0.51–1.93)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.94 (0.63–1.40)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">South</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2142</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">80,841</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.65 (2.54–2.76)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">147</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23,049</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.64 (0.54–0.74)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">380</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16256</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.34 (2.11–2.58)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.12 (1.75–2.55) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.99 (1.58–2.50) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.23 (1.09–1.39) **</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Northeast</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">102</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1703</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.99 (4.94–7.21)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10,685</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.20 (0.13–0.29)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">933</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.18 (3.06–5.59)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.69 (3.21–18.38) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.32 (1.58–11.86) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.33 (0.88–2.00)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Central</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">464</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32,475</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.43 (1.3–1.56)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">155</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24,522</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.63 (0.54–0.74)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">122</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7180</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.70 (1.42–2.01)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.40 (2.66–4.34) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.60 (1.95–3.48) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.29 (1.04–1.61) *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">North</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">281</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21,309</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.32 (1.17–1.48)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">54</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12,712</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.42 (0.33–0.55)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">36</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4601</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.78 (0.57–1.06)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.10 (1.49–2.95) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.00 (0.57–1.75)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.59 (1.10–2.30) *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">East</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">516</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39,491</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.31 (1.2–1.42)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">119</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">56,420</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.21 (0.18–0.25)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">75</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21,967</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.34 (0.27–0.42)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.04 (2.33–3.96) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.10 (0.73–1.64)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.46 (1.90–3.19) ***</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Missing</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">51</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0(-)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0(-)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td></tr></tbody></table><table-wrap-foot><fn><p>DOG: depressants only users. SOG: stimulants only users. DSG: both depressants and stimulants users. AOR adjusted for sex, age, ethnicity, education, marital status, injection drug use, methadone treatment, date of HIV test and region. * suggested <italic>p</italic> < 0.05, ** suggested <italic>p</italic> < 0.01, *** suggested <italic>p</italic> < 0.001.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05483-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05483-t003_Table 3</object-id><label>Table 3</label><caption><p>Associated characteristics of HIV infection among depressants only, stimulants only and both depressants and stimulants users.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Characteristics</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">DOG</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">SOG</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">DSG</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">OR (95% CI)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">AOR (95% CI)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">OR (95% CI)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">AOR (95% CI)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">OR (95% CI)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">AOR (95% CI)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Sex</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Male</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Female</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.13 (1.05–1.21) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.17 (1.08–1.26) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.09 (0.89–1.33)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.34 (1.09–1.66) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.22 (1.03–1.46) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.55 (1.29–1.86) ***</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Age when first HIV test</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">≤17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18~24</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.41 (0.77–2.57)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.66 (0.90–3.04)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.26 (0.81–13.18)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.82 (0.94–15.47)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25~44</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.69 (1.48–4.88) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.78 (2.07–6.89) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.56 (0.89–14.30)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.90 (1.21–19.80) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.96 (1.42–2.71) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.18 (1.56–3.04) ***</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45~</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.29 (1.26–4.16) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.69 (2.01–6.76) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.48 (1.09–18.33) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.20 (1.73–29.89) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.55 (1.81–3.59) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.04 (2.10–4.39) ***</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Missing</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.82 (1.43–32.45) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.84 (0.99–23.58)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">52.07 (4.48–605.62) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30.63 (2.53–370.29) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Ethnicity</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Han</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Minority</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.24 (3.09–3.40) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.87 (2.72–3.02) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.33 (1.81–3.01) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.68 (1.29–2.20) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.66 (2.23–3.16) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.87 (1.56–2.25) ***</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Missing</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.98 (0.88–1.09)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.11 (0.99–1.24)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.83 (1.47–2.27) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.32 (1.05–1.65) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.61 (1.24–2.09) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.42 (1.08–1.86) *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Education</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">High school or above</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Junior high school</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.31 (1.19–1.44) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.34 (1.22–1.48) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.05 (0.84–1.33)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.05 (0.83–1.33)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.54 (1.19–1.97) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.41 (1.09–1.82) **</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Primary school or below</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.25 (2.04–2.48) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.89 (1.70–2.09) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.55 (1.18–2.03) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.35 (1.03–1.78) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.69 (2.07–3.50) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.97 (1.50–2.58) ***</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Missing</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.83 (1.58–2.11) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.75 (1.28–2.39) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.32 (0.74–2.37)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.07 (0.46–2.49)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.87 (1.12–3.12) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.01 (1.08–3.73) *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Marital status</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Married</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Unmarried</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.79 (0.75–0.83) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.83 (0.79–0.87) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.00 (0.85–1.18)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.98 (0.81–1.17)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.79 (0.68–0.90) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.82 (0.72–0.95) **</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Divorced or Widowed</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.97 (0.90–1.05)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.92 (0.84–1.00)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.92 (0.66–1.28)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.94 (0.67–1.33)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.08 (0.88–1.33)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.06 (0.85–1.32)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Missing</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.13 (0.99–1.28)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.22 (0.88–1.68)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.99 (0.47–2.09)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.94 (0.61–6.14)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.52 (0.23–1.18)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.82 (0.29–2.27)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Injection drug use</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.34 (4.02–4.69) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.96 (4.58–5.37) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.55 (6.23–14.64) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.42 (4.79–11.48) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.03 (2.58–3.55) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.68 (2.28–3.17) ***</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not sure</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.66 (1.53–1.80) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.53 (1.40–1.66) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.42 (1.20–1.67) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.16 (0.97–1.38)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.21 (1.02–1.44) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.96 (0.80–1.15)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Methadone treatment</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.69 (1.60–1.79) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.80 (1.69–1.91) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.61 (1.39–1.87) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.42 (1.21–1.67) ***</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Date of HIV test</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jan, 2008~Jun, 2008</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jul, 2008~Dec, 2008</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.83 (0.65–1.05)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.79 (0.62–1.01)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jan, 2009~Jun, 2009</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.85 (0.67–1.08)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.80 (0.63–1.02)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.18 (0.55–8.74)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.32 (0.58–9.30)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.77 (0.34–1.77)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.83 (0.36–1.91)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jul, 2009~Dec, 2009</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.64 (0.51–0.82) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.68 (0.53–0.87) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.39 (0.38–5.14)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.50 (0.40–5.55)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.61 (0.28–1.33)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.65 (0.29–1.45)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jan, 2010~Jun, 2010</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.52 (0.41–0.66) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.51 (0.40–0.66) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.14 (0.61–7.51)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.39 (0.68–8.45)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.09 (0.55–2.17)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.12 (0.55–2.25)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jul, 2010~Dec, 2010</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.56 (0.44–0.71) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.57 (0.44–0.73) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.96 (0.55–6.96)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.08 (0.58–7.45)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.95 (0.46–1.94)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.89 (0.43–1.85)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jan, 2011~Jun, 2011</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.58 (0.45–0.74) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.55 (0.43–0.71) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.31 (0.66–8.11)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.53 (0.71–9.04)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.08 (0.53–2.17)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.93 (0.46–1.90)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jul, 2011~Dec, 2011</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.58 (0.45–0.74) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.50 (0.39–0.65) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.81 (0.49–6.70)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.08 (0.55–7.80)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.31 (0.66–2.59)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.09 (0.54–2.19)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jan, 2012~Jun, 2012</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.62 (0.49–0.80) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.55 (0.43–0.71) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.01 (0.58–6.96)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.18 (0.62–7.66)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.10 (0.56–2.17)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.87 (0.43–1.74)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jul, 2012~Dec, 2012</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.67 (0.53–0.86) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.57 (0.44–0.74) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.44 (0.73–8.23)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.77 (0.81–9.50)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.27 (0.66–2.45)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.93 (0.47–1.82)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jan, 2013~Jun, 2013</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.66 (0.52–0.85) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.53 (0.41–0.68) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.08 (0.63–6.90)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.33 (0.69–7.86)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.40 (0.76–2.61)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.96 (0.51–1.82)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jul, 2013~Dec, 2013</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.81 (0.63–1.03)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.62 (0.48–0.79) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.54 (0.79–8.24)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.76 (0.84–9.08)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.10 (1.16–3.80) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.28 (0.70–2.37)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jan, 2014~Jun, 2014</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.74 (0.58–0.94) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.54 (0.42–0.69) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.00 (0.93–9.60)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.22 (0.99–10.51)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.00 (1.11–3.61) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.24 (0.68–2.28)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jul, 2014~Dec, 2014</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.07 (0.84–1.36)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.82 (0.64–1.05)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.52 (1.11–11.11) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.80 (1.18–12.21) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.56 (1.44–4.54) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.49 (0.82–2.69)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jan, 2015~Jun, 2015</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.15 (0.90–1.45)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.81 (0.63–1.04)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.78 (0.88–8.79)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.07 (0.95–9.89)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.89 (1.64–5.11) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.55 (0.86–2.80)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jul, 2015~Dec, 2015</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.11 (0.87–1.41)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.86 (0.67–0.11)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.09 (1.62–15.95) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.75 (1.49–15.20) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.29 (2.44–7.52) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.21 (1.24–3.97) **</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jan, 2016~Jun, 2016</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.22 (0.96–1.55)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.96 (0.75–1.24)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.71 (1.17–11.75) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.90 (1.21–12.58) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.56 (2.01–6.31) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.76 (0.97–3.17)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Region</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Southwest</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Northwest</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.45 (0.42–0.49) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.50 (0.46–0.54) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.23 (0.80–1.90)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.21 (0.78–1.88)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.50 (0.34–0.73) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.60 (0.40–0.88) *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">South</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.43 (0.41–0.46) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.43 (0.40–0.45) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.64 (0.50–0.82) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.70 (0.55–0.91) **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.54 (0.46–0.63) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.64 (0.54–0.75) ***</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Northeast</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.01 (0.83–1.24)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.83 (0.67–1.02)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.20 (0.12–0.31) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.19 (0.12–0.31) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.99 (0.70–1.39)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.90 (0.63–1.28)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Central</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.23 (0.21–0.25) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.20 (0.18–0.22) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.63 (0.50–0.81) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.63 (0.49–0.82) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.39 (0.32–0.48) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.44 (0.35–0.55) ***</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">North</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.21 (0.19–0.24) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.29 (0.25–0.32) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.42 (0.31–0.59) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.45 (0.32–0.62) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.18 (0.13–0.25) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.22 (0.15–0.31) ***</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">East</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.21 (0.19–0.23) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.20 (0.18–0.22) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.21 (0.16–0.27) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.25 (0.19–0.33) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.08 (0.06–0.10) ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.10 (0.08–0.13) ***</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Missing</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td></tr></tbody></table><table-wrap-foot><fn><p>DOG: depressants only users. SOG: stimulants only users. DSG: both depressants and stimulants users. AOR adjusted for sex, age, ethnicity, education, marital status, injection drug use, methadone treatment, date of HIV test and region. * suggested <italic>p</italic> < 0.05, ** suggested <italic>p</italic> < 0.01, *** suggested <italic>p</italic> < 0.001.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32722225</article-id><article-id pub-id-type=\"pmc\">PMC7432000</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155342</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05342</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>The Effect of Bariatric Surgery on Weight Loss and Metabolic Changes in Adults with Obesity</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-7752-0459</contrib-id><name><surname>Głuszek</surname><given-names>Stanisław</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05342\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05342\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-0356-8211</contrib-id><name><surname>Bociek</surname><given-names>Arkadiusz</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05342\">1</xref><xref rid=\"c1-ijerph-17-05342\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-3063-0380</contrib-id><name><surname>Suliga</surname><given-names>Edyta</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05342\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Matykiewicz</surname><given-names>Jarosław</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05342\">1</xref><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05342\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Kołomańska</surname><given-names>Magdalena</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05342\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05342\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Bryk</surname><given-names>Piotr</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05342\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05342\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Znamirowski</surname><given-names>Przemysław</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05342\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-9653-9841</contrib-id><name><surname>Nawacki</surname><given-names>Łukasz</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05342\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05342\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Głuszek-Osuch</surname><given-names>Martyna</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05342\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Wawrzycka</surname><given-names>Iwona</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05342\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05342\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-8114-1814</contrib-id><name><surname>Kozieł</surname><given-names>Dorota</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05342\">3</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05342\"><label>1</label>The Institute of Medical Sciences, Medical College, Jan Kochanowski University, 25-369 Kielce, Poland; <email>sgluszek@wp.pl</email> (S.G.); <email>jaroslaw.matykiewicz@ujk.edu.pl</email> (J.M.); <email>mkolomanska92@gmail.com</email> (M.K.); <email>piotr.bryk@ujk.edu.pl</email> (P.B.); <email>lukasz.nawacki@ujk.edu.pl</email> (Ł.N.); <email>iwona.wawrzycka@ujk.edu.pl</email> (I.W.)</aff><aff id=\"af2-ijerph-17-05342\"><label>2</label>Clinic of General, Oncological and Endocrinological Surgery, Provincial Hospital in Kielce, 25-736 Kielce, Poland; <email>znamirowski79@gmail.com</email></aff><aff id=\"af3-ijerph-17-05342\"><label>3</label>The Institute of Health Sciences, Medical College, Jan Kochanowski University, 25-369 Kielce, Poland; <email>edyta.suliga@ujk.edu.pl</email> (E.S.); <email>mgluszekosuch@ujk.edu.pl</email> (M.G.-O.); <email>dorota.koziel@ujk.edu.pl</email> (D.K.)</aff><aff id=\"af4-ijerph-17-05342\"><label>4</label>Clinic of Oncological Surgery of the Swiętokrzyskie Center of Oncology in Kielce, 25-734 Kielce, Poland</aff><author-notes><corresp id=\"c1-ijerph-17-05342\"><label>*</label>Correspondence: <email>arkadiusz33333@gmail.com</email>; Tel.: +48-41-349-69-11</corresp></author-notes><pub-date pub-type=\"epub\"><day>24</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5342</elocation-id><history><date date-type=\"received\"><day>19</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>21</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Methods of treating obesity, such as changes in lifestyle, physical activity, restrictive diets, and psychotherapy, are not sufficient. Currently, it is considered that in the case of patients who meet the eligibility criteria for surgery, the treatment of choice should be bariatric surgery. The aim of this study was to assess the weight loss and metabolic changes in a group of adults with obesity undergoing bariatric surgery. The study involved 163 patients whose body mass index (BMI) exceeded 40 or 35 kg/m<sup>2</sup>, concurrent with at least one metabolic sequelae. In 120 of the cases (74%), sleeve gastrectomy was used; in 35 (21%), gastric banding was used; and in 8 (5%), laparoscopic Roux-en-Y gastric bypass was used. Metabolic parameters such as total cholesterol, LDL-cholesterol (low-density lipoprotein cholesterol), HDL-cholesterol (high-density lipoprotein cholesterol), triglycerides, and glucose were measured preoperatively and postoperatively, as well as the creatinine, creatine kinase (CK-MB), and leptin activity. In patients undergoing bariatric surgery, a significant decrease in excess weight (<italic>p</italic> < 0.001) was observed at all the analyzed time points, compared to the pre-surgery value. Weight loss after surgery was associated with a significant improvement in glycemia (109.6 ± 48.0 vs. 86.6 ± 7.9 mg/dL >24 months after surgery; <italic>p</italic> = 0.003), triglycerides (156.9 ± 79.6 vs. 112.7 ± 44.3 mg/dL >24 months after surgery; <italic>p</italic> = 0.043) and leptin (197.50 ± 257.3 vs. 75.98 ± 117.7 pg/mL 12 months after surgery; <italic>p</italic> = 0.0116) concentration. The results of the research confirm the thesis on the effectiveness of bariatric surgery in reducing excess body weight and improving metabolic parameters in patients with extreme obesity.</p></abstract><kwd-group><kwd>sleeve gastrectomy (SG)</kwd><kwd>gastric banding (GB)</kwd><kwd>laparoscopic Roux-en-Y gastric bypass (LRYGB)</kwd><kwd>weight loss</kwd><kwd>metabolic parameters</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05342\"><title>1. Introduction</title><p>One of the biggest challenges for modern medicine is the treatment of eating disorders and metabolic disorders, in particular, obesity, as well as the prevention of their complications. In 2016, among the world’s population, 39% of adults (39% men, 40% women) were overweight (body mass index (BMI) of ≥25 kg/m<sup>2</sup>) and 13% were obese (11% men, 15% women) (BMI of ≥30 kg/m<sup>2</sup>). Additionally, in Poland 59% of adults (63% men, 55% women) were overweight and 25% were obese (24% men, 27% women). In Poland, among school students aged around 11, the incidence of overweight or obesity was 36% among boys and 23% among girls [<xref rid=\"B1-ijerph-17-05342\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijerph-17-05342\" ref-type=\"bibr\">2</xref>]. Despite the actions taken among both children and adults, a progressive increase in average body weight has been observed [<xref rid=\"B1-ijerph-17-05342\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijerph-17-05342\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijerph-17-05342\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijerph-17-05342\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijerph-17-05342\" ref-type=\"bibr\">5</xref>].</p><p>However, the biggest problem associated with obesity is not the excess of adipose tissue itself, but the metabolic disorders and complications resulting from the disease, including the increased risk of premature death [<xref rid=\"B4-ijerph-17-05342\" ref-type=\"bibr\">4</xref>,<xref rid=\"B6-ijerph-17-05342\" ref-type=\"bibr\">6</xref>]. Metabolic syndrome (MetS) is particularly related to excess weight. Although MetS may be present even in people with a high normal BMI (BMI in range of 23–25 kg/m<sup>2</sup>), especially those with low physical activity or unhealthy nutritional patterns [<xref rid=\"B7-ijerph-17-05342\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijerph-17-05342\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05342\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijerph-17-05342\" ref-type=\"bibr\">10</xref>], the highest probability of the occurrence of MetS and other complications is associated with obesity (BMI of ≥30 kg/m<sup>2</sup>); these include type 2 diabetes, hypertension, cardiovascular disease (with an increased risk of myocardial infarction and stroke), obstructive sleep apnea, lipid disorders, osteoarthritis, and some cancers (endometrial, breast, ovarian, prostate, esophageal, hepatic, gallbladder, kidney, and colon) [<xref rid=\"B1-ijerph-17-05342\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijerph-17-05342\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijerph-17-05342\" ref-type=\"bibr\">3</xref>].</p><p>The existing conservative methods of treatment, including changes in lifestyle, physical activity, restrictive diets, and psychotherapy, are not very effective, resulting in only up to 10% of the desired weight loss [<xref rid=\"B11-ijerph-17-05342\" ref-type=\"bibr\">11</xref>]. Maintaining body weight is a much more difficult problem, and reduces the effectiveness of these methods of therapy [<xref rid=\"B11-ijerph-17-05342\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05342\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijerph-17-05342\" ref-type=\"bibr\">13</xref>]. The pharmacological methods of treatment of obesity are lipostatins (orlistat); drugs restricting appetite (bupropion, naltrexone); and others, such as lorcaserin, phentermine-topiramate, or liraglutide. All of these medicines were effective in previous studies, with a loss of weight of about 5% after 52 weeks of therapy [<xref rid=\"B14-ijerph-17-05342\" ref-type=\"bibr\">14</xref>].</p><p>Taking the above into consideration, at the current level of medical knowledge bariatric surgery is the most effective treatment for obesity and is now considered to be the treatment of choice in patients who meet the eligibility criteria for surgery [<xref rid=\"B3-ijerph-17-05342\" ref-type=\"bibr\">3</xref>,<xref rid=\"B15-ijerph-17-05342\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijerph-17-05342\" ref-type=\"bibr\">16</xref>]. The main indications for surgery in adults are a BMI ≥ 40 kg/m<sup>2</sup> or a BMI ≥ 35 kg/m<sup>2</sup>, with the simultaneous occurrence of at least one disease caused by obesity. In children and adolescents, surgery should be considered with a BMI ≥ 40 kg/m<sup>2</sup> and one obesity-related disease, or with a BMI ≥ 35 kg/m<sup>2</sup> with severe comorbidities resulting from the excess weight [<xref rid=\"B3-ijerph-17-05342\" ref-type=\"bibr\">3</xref>].</p><p>Bariatric procedures include, among others, sleeve gastrectomy (SG); laparoscopic Roux-en-Y gastric bypass (LRYGB); gastric banding (GB); and the gastric balloon, which is mainly used in preparation for other bariatric operations [<xref rid=\"B17-ijerph-17-05342\" ref-type=\"bibr\">17</xref>].</p><p>However, there are still insufficient data to clearly indicate which type of surgery is most effective in a given group of patients. The results of research on postoperative metabolic effects, such as changes in the lipidogram, are also ambiguous. Spivak et al. [<xref rid=\"B18-ijerph-17-05342\" ref-type=\"bibr\">18</xref>] found that different types of bariatric surgery had different effects on dyslipidemia, regardless of weight loss. LRYGB was associated with the greatest reduction in the concentration of total cholesterol and Low-Density Lipoproteins (LDL-cholesterol) in plasma, while SG influenced the concentration of High-Density Lipoproteins (HDL-cholesterol) to the greatest extent. Szczuko et al. [<xref rid=\"B19-ijerph-17-05342\" ref-type=\"bibr\">19</xref>] did not observe statistically significant differences in glucose concentrations after the LRYGB and SG procedures. The highest differences were observed for triglycerides and all cholesterol fractions, which decreased after LRYGB but increased in the first months after SG surgery. Carswell et al. [<xref rid=\"B20-ijerph-17-05342\" ref-type=\"bibr\">20</xref>] observed that triglycerides decreased 3 months after surgery, but unlike other studies they found that HDL increased from 1 year after surgery.</p><p>The aim of the study was to assess the loss of weight and metabolic changes in a group of adults with obesity undergoing bariatric surgery.</p></sec><sec id=\"sec2-ijerph-17-05342\"><title>2. Materials and Methods</title><p>Initially, 184 patients eligible for bariatric surgery were enrolled—i.e., those whose BMI exceeded 40 kg/m<sup>2</sup> or 35 kg/m<sup>2</sup> with at least one obesity complication. In the study patients were included after the following surgeries: sleeve gastrectomy (SG), gastric banding (GB), and laparoscopic Roux-en-Y gastric bypass (LRYGB). The exclusion criteria included lack of consent for surgery; general health according to World Health Organization (WHO) performance status 2 (presence of disease symptoms, significantly limited activity, ability to perform daily activities, lack of ability to perform work, the need to stay in bed less than 50% of the day); above all, a negative opinion on the psychological preparation of the patient, stating the inability to change patient lifestyle after surgery, and being below 18 years of age or above 65. At the final stage, 163 patients qualified for the study.</p><p>In the patients included in the study, metabolic parameters such as total cholesterol, LDL, HDL, triglycerides, and glucose were measured preoperatively and postoperatively, as well as the muscle breakdown parameters creatinine and creatine kinase (CK-MB) in the preoperative period and in the early stages after the surgery (3–5 days), and leptin activity (double examination—before and about 12 months after the procedure). In addition, the concentration of lipids and glucose was assessed during follow-up visits at the following time points after surgery: 1 month, 3 months, 6 months, 12 months, 24 months, and after more than 24 months. After an extensive surgery, the patient may develop rhabdomyolysis and following acute kidney injury, thus creatinine kinase and creatinine measuring was performed [<xref rid=\"B21-ijerph-17-05342\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijerph-17-05342\" ref-type=\"bibr\">22</xref>]. Almost none of the patients suffered from diabetes before treatment. Serum fasting glucose level is the main parameter to diagnose conditions such as diabetes and impaired fasting glucose. According to some researches, the usefulness of HbA1c for the diagnosis of impaired fasting glucose and diabetes in patients aged <30 years remains to be determined due to discrepancies between the results of glucose- and HbA1c-based tests. This was the main reason to use the serum fasting glucose instead of HBa1c [<xref rid=\"B23-ijerph-17-05342\" ref-type=\"bibr\">23</xref>]. According to some studies, improvements in leptin and ghrelin levels following bariatric surgery appear to contribute to postoperative cognitive benefits. These gains may involve multiple mechanisms, such as reduced inflammation and improved glycemic control [<xref rid=\"B24-ijerph-17-05342\" ref-type=\"bibr\">24</xref>].</p><p>The BMI, ideal body weight, and excess weight were calculated based on the measured height and body weight. Ideal body weight (IBW) is the patient’s weight calculated using the following formula [<xref rid=\"B20-ijerph-17-05342\" ref-type=\"bibr\">20</xref>]:<list list-type=\"simple\"><list-item><p>Men: IBW = 50 kg + ((height − 150 cm) 0.7 kg/cm),</p></list-item><list-item><p>Women: IBW = 50 kg + ((height − 150 cm) 0.6 kg/cm).</p></list-item></list></p><p>The observed changes in BMI, excess weight, or metabolic parameters could not be compared by age and sex due to the limited probe size at each time point.</p><p>In the postoperative period, for the first two weeks (from the third day onward) the patients were nourished with a liquid diet complemented by liquids in a summary quantity of 1500 mL per day. Further, the limitation of calories was established at the level of 1200–1500 kcal per day. Then, it was gradually adjusted to the individual demand of each patient. The diet was complemented by supplementation with vitamins and minerals, especially B<sub>1</sub>, B<sub>6</sub>, D<sub>3</sub>, and iron. The intake of these supplements was recommended depending on the results of laboratory analyzes to achieve a laboratorial normal range and clinical balance.</p><p>For the statistical description of quantitative features, we used the arithmetic means, standard deviations, medians, quartiles, and ranges of values (minimum and maximum). The distributions of qualitative features were described by way of frequency and percentages. The frequencies were compared using the chi-square test or Fisher’s exact test. The normality of distributions was checked using the Shapiro Wilk test. For data compliance with the normal distribution, the Student’s <italic>t</italic>-test was used to compare distributions (for independent samples or for pair-related samples). In the absence of the normality of distributions, the Mann Whitney <italic>U</italic> test was used for the independent samples, and the Wilcoxon signed-rank test was used for two related samples. The correlations between pairs of quantitative variables were evaluated using the Spearman rank correlation coefficient. All the statistical tests performed were bilateral, and <italic>p</italic> < 0.05 was accepted as the statistical significance criterion. The STATISTICA program was used in the calculations (TIBCO Software Inc. (2017), Statistica (data analysis software system) version 13. <uri xlink:href=\"http://statistica.io\">http://statistica.io</uri>, Palo Alto, Santa Clara, CA, USA).</p><p>The study was approved by the Committee on Bioethics at the Faculty of Health Sciences, Jan Kochanowski University in Kielce, Poland, on 26 March 2018 (number of approval: 24/2018). All the participants gave written consent to participate in the study.</p></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05342\"><title>3. Results</title><sec id=\"sec3dot1-ijerph-17-05342\"><title>3.1. Characteristics of the Study Group</title><p>The analysis included patients who underwent surgery and had at least one check-up (BMI, biochemical tests) in the postoperative period. The study involved 163 patients, including 136 women (83%) and 27 men (17%), of whom 75 female (46%) and 14 male (9%) patients were under 40 years old and the remaining 61 women (37%) and 13 men (8%) were aged 40 or above. Forty-four subjects (27%) were free from comorbidities. Among the 119 patients (73%) in whom comorbidities were present, the most common were the following: hypertension (67 cases, 40%), diabetes (40%), obstructive sleep apnea (8%), and ischemic heart disease (5%). There was no anastomotic leak after SG or after LRYGB. In the early postoperative period, one case of gastrointestinal bleeding after LRYGB and one case of bleeding into the peritoneal cavity after SG were found, with a slight intensity (bleeding from the drain). The patients did not require surgical procedures, though the patient after LRYGB required the substitution of red blood cell concentrate.</p><p>The other parameters of the test group before bariatric surgery are presented in <xref rid=\"ijerph-17-05342-t001\" ref-type=\"table\">Table 1</xref>, taking into account the division into subgroups according to sex. It is shown that the men in the analyzed group were statistically significantly (<italic>p</italic> < 0.05) older; were taller; and had a higher initial weight, BMI, ideal body weight, and initial excess weight.</p><p>Among the patients who underwent surgery, in 120 (74%) SG was used; in 35 (21%), GB was used; and in 8 (5%), LRYGB was used. Almost one-third of the patients (51 observed subjects) were examined only on the day of the surgery and did not report for follow-up visits, so a BMI assessment in the follow-up period was not available in these cases. The total number of patients whose BMI was examined at least twice (including once before the procedure) was 112 (67%).</p></sec><sec id=\"sec3dot2-ijerph-17-05342\"><title>3.2. Analysis of Weight Loss</title><p>A significant decrease in excess weight in relation to the pre-treatment value was observed in all the analyzed time points (<italic>p</italic> < 0.001). The final average value of weight decrease (after over 24 months) was 30.3 kg. Details on the loss of excess weight are shown in <xref rid=\"ijerph-17-05342-t002\" ref-type=\"table\">Table 2</xref>. However, no significant differences were found in relation to sex or age (groups of ≤40 and >40 years were compared) (data not shown).</p><p>Significant differences were also observed by comparing time points 6 and 12 (6 and 12 months after surgery) and 24 and 12 (<italic>p</italic> < 0.001). Moreover, for the pairs of points 1 and 0 (1 month after surgery and before surgery), 6 and 0, 12 and 0, 24 and 0, >24 and 0, and 6 and 12, a positive correlation of weight loss was observed (<italic>p</italic> < 0.001, adjusted using the false discovery rate method).</p><p><xref ref-type=\"fig\" rid=\"ijerph-17-05342-f001\">Figure 1</xref> shows the mean and median values of the BMI changes relative to pre-operative BMI values. Comparing the time points 6 and 0, 12 and 6, and >24 and 12, it was found that the more time that had elapsed since the surgery, the greater the BMI loss was (<italic>p</italic> < 0.001 for each comparison)—i.e., the highest loss in BMI was recorded in the group with more than 24 months of observation time (average of 10.32 kg/m<sup>2</sup> BMI loss after over 24 months compared to 8.48 kg/m<sup>2</sup> after 12 months and 7.32 kg/m<sup>2</sup> after 6 months).</p><p>During follow-up, in 18 patients (11%) a ”rebound effect” was also observed, which is a re-increase in the patient’s weight during the follow-up visits. Most often, the “rebound effect” was observed 24 months after the surgery (21% of patients were examined at this time point). In 7% of the patients examined after 6 months and 13% examined after 12 months, a transient increase in weight was observed. However, in most of these patients (5% after 6 months and 9% after 12 months) their weight decreased again in following time points. The “rebound effect” was also observed in patients after 24 months (13%), but it is unknown whether their mass would stay increased or decrease because they did not undergo any further control examination.</p><p>The loss of excess weight depending on the type of surgery (SG vs. GB vs. LRYGB) is shown in <xref ref-type=\"fig\" rid=\"ijerph-17-05342-f002\">Figure 2</xref>. The analysis showed that the BMI loss did not differ significantly (<italic>p</italic> > 0.05) depending on the type of surgery, which might be due to the lack of power of the performed test caused by the probe size limitation.</p></sec><sec id=\"sec3dot3-ijerph-17-05342\"><title>3.3. Analysis of Metabolic Parameters</title><p>Before surgery glucose (<italic>n</italic> = 120), the triglycerides (<italic>n</italic> = 83) and total cholesterol (<italic>n</italic> = 75) were measured. The number of patients with measured metabolic parameters measured is presented in <xref rid=\"ijerph-17-05342-t003\" ref-type=\"table\">Table 3</xref>. Changes in the glucose levels showed a steady downward trend during the year after the surgery and showed statistical significance (<italic>p</italic> = 0.003) (<xref rid=\"ijerph-17-05342-t004\" ref-type=\"table\">Table 4</xref>). The mean glucose concentration before the procedure was 110.45 mg/dL, and that one year after the procedure was 89.88 mg/dL. The percentage of patients with normal fasting glycemia increased from 40% before surgery to 67% one year after surgery. The decrease in glucose concentration was not correlated with the loss of weight. An analysis of the changes in cholesterol levels showed significant fluctuations of this parameter within one year after the procedure; however, these changes did not correlate with weight loss. Due to the above, it was not possible to determine the trend of changes after surgery. The total percentage of patients with cholesterol levels of >190 mg/dL remained constant and amounted to 39%. The other parameters of the lipidogram (total cholesterol, LDL, HDL) also showed no significant changes during the follow-up period for individual patients. However, significant differences were found for mean triglyceride concentrations between the time points (before the surgery and after 1, 6, 12, or more months) (<italic>p</italic> = 0.043).</p><p>There were no significant changes in the serum creatinine before or after the procedure (<xref rid=\"ijerph-17-05342-t005\" ref-type=\"table\">Table 5</xref>). These values were within the reference range for over 90% of patients. However, the analysis showed a significant decrease in leptin concentration when comparing the concentrations before and 12 months after surgery (<italic>p</italic> = 0.0116).</p></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05342\"><title>4. Discussion</title><p>Obesity as a disease is associated with a significant increase in mortality and many health threats, including type 2 diabetes, hypertension, dyslipidemia, coronary heart disease, development of cancer, and osteoarticular disorders. The higher the body mass index (BMI), the higher the risk of morbidity and mortality [<xref rid=\"B25-ijerph-17-05342\" ref-type=\"bibr\">25</xref>]. Randomized studies have shown that weight loss through lifestyle or pharmacological treatment reduces morbidity by reducing risk factors for cardiovascular disease (CVD) [<xref rid=\"B26-ijerph-17-05342\" ref-type=\"bibr\">26</xref>], although its efficacy is less than that of surgical treatment. For people who are unable to reduce their body weight by means of behavioral therapy and pharmacological treatment, surgical treatment should be considered. However, even then appropriate behavioral therapy with adequate dietary recommendations and physical activity adapted to the patient’s ability is necessary [<xref rid=\"B27-ijerph-17-05342\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijerph-17-05342\" ref-type=\"bibr\">28</xref>].</p><p>The slightly lower loss of weight and BMI 3, 6, and 12 months after bariatric surgery in our examined group in comparison with the outcomes of other authors, might be result of, firstly, the lower initial BMI of our patient group (X ± SD) = 44.5 ± 6.8 kg/m<sup>2</sup>; Me = 43.4 kg/m<sup>2</sup> (40.2–46.3) vs. X = 45.91 kg/m<sup>2</sup> (min–max 41.40–50.11) [<xref rid=\"B29-ijerph-17-05342\" ref-type=\"bibr\">29</xref>] and Me = 51.6 kg/m<sup>2</sup> (35.9–72.0) [<xref rid=\"B30-ijerph-17-05342\" ref-type=\"bibr\">30</xref>]. Secondly, it might be a result of the fact, that in 21% of our patients, GB surgery was performed. The difference in loss of weight depending on the surgery method was statistically insignificant (most probably due to the limitation of size); however, the loss of weight observed after this type of surgery was noticeably lower. In the cited studies [<xref rid=\"B29-ijerph-17-05342\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijerph-17-05342\" ref-type=\"bibr\">30</xref>], all the patients underwent SG. From a long-term perspective (>24 months of follow-up), our results did not differ significantly from others. The average BMI loss after 24 months was 10.3 kg/m<sup>2</sup>, while the average percentage of excess weight loss was 50.5%. In a study of Kowalewski et al., the analogous loss of BMI was 12.1 kg/m<sup>2</sup>, while the average percentage excess weight loss was 51.1% [<xref rid=\"B30-ijerph-17-05342\" ref-type=\"bibr\">30</xref>].</p><p>The results of our own research confirm favorable metabolic changes in the course of bariatric treatment, a significant improvement in glycemia and triglycerides, and a tendency to normalize HDL cholesterol. In the 12 months after surgery, the participants of the follow up reached lower glucose concentrations (89.9 vs. 98.0 mg/dL), similar to how the concentrations of total cholesterol, LDL, and TG behaved, while a lower HDL (42.6 vs. 55.0 mg/dL) was observed compared to that of the patients in the study by Wojciak et al. [<xref rid=\"B29-ijerph-17-05342\" ref-type=\"bibr\">29</xref>]. Our patients reached a significant improvement in the concentration of HDL (61.9 mg/dL) a bit later, more than 12 months after surgery. The significant decrease in leptin concentration in the group of patients we studied is consistent with the observations of other authors, who indicated that rapid weight loss in morbidly subjects with obesity undergoing bariatric surgery leads to significant changes in the concentration of some adipokines and hormones that control the appetite and energy processes in peripheral blood [<xref rid=\"B31-ijerph-17-05342\" ref-type=\"bibr\">31</xref>]. In diabetic patients, weight loss following surgical treatment makes possible the withdrawal of antidiabetic agents or a reduction in doses. In studies by other authors [<xref rid=\"B32-ijerph-17-05342\" ref-type=\"bibr\">32</xref>], diabetes prevention programs significantly reduced the rate of progression from impaired glucose tolerance to diabetes over three years in participants randomly assigned to intensive lifestyle modification focusing on weight loss [<xref rid=\"B14-ijerph-17-05342\" ref-type=\"bibr\">14</xref>]. The effectiveness of intensive lifestyle modification in the prevention of diabetes persisted for 15 years, but over time it weakened [<xref rid=\"B32-ijerph-17-05342\" ref-type=\"bibr\">32</xref>]. Definitely improved diabetes control results were obtained after surgical treatment by performing laparoscopic Roux-en-Y gastric bypass (LRYGB), and to a lesser extent when using sleeve gastrectomy [<xref rid=\"B29-ijerph-17-05342\" ref-type=\"bibr\">29</xref>]. After bariatric treatment, there is a sustained reduction in risk factors for cardiovascular diseases (CVD) [<xref rid=\"B33-ijerph-17-05342\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijerph-17-05342\" ref-type=\"bibr\">34</xref>]. Similar observations were made in the individuals in the group we have studied. In some patients, the doses of antidiabetic agents have been reduced, and some individuals no longer have to take any antidiabetic agents. Such observations concern those patients in whom the effect of weight loss was significant (over 50% excess weight loss). In most cases, the lack of significant weight loss or the maintenance of intensive lifestyle modification weakens the “antidiabetic” effect and does not significantly improve other elements of quality of life, such as ailments related to the condition of the musculoskeletal system. The duration of diabetes and obesity is also important in the prevention of diabetes or in predicting the remission of diabetes after bariatric treatment. The earlier the bariatric treatment, the greater the chance of avoiding the development of diabetes or reducing the use of drugs (due to preserved endocrine capacity of the pancreas). Similar results were obtained in studies comparing the effects of laparoscopic Roux-en-Y gastric bypass (LRYGB) in adolescent and adult patients (BMI values of 54 kg/m<sup>2</sup> and 51 kg/m<sup>2</sup>). Diabetes remission was higher in adolescents (86%) compared to adults (53%), as was hypertension (68% vs. 41%). Within 5 years of surgery, 3 in 161 patients (1.9%) and 7/396 (1.8%) adults died [<xref rid=\"B35-ijerph-17-05342\" ref-type=\"bibr\">35</xref>].</p><p>A number of observational studies also confirmed a reduction in mortality along with weight loss [<xref rid=\"B28-ijerph-17-05342\" ref-type=\"bibr\">28</xref>,<xref rid=\"B31-ijerph-17-05342\" ref-type=\"bibr\">31</xref>]. Arteburn et al. [<xref rid=\"B36-ijerph-17-05342\" ref-type=\"bibr\">36</xref>] showed that among surgical patients (<italic>n</italic> = 2500 mean age, 52; mean BMI 47 kg/m<sup>2</sup>) and patients in a control group (<italic>n</italic> = 7462; average age, 53; average BMI 46 kg/m<sup>2</sup>), there were 263 deaths in the surgery group (mean follow-up period, 6.9 years) and 1277 deaths in the control group (mean follow-up period, 6.6 years). In the whole group of patients, complications within 30 days were found in 8.4%. For patients aged 50 to 54 years, 55 to 59 years, and ≥60 years, this risk increased significantly to 9.8%, 10.0%, and 10.2%, respectively. The indicators of specific surgical complications, such as an anastomotic leak, bleeding, and deep infections/abscesses, were significantly increased by 14% to 41% in patients aged 50 to 54 years with a slight additional, though insignificant, risk increase in older patients.</p><p>Patients with obesity should be treated in a particularly individual way in terms of the choice of procedure. An assessment of their general health, cardiovascular fitness, respiration, excretory system health (kidney function), and neurological status are key elements in deciding upon a treatment. The risk of complications exceeding the benefits of surgical treatment and the risk of excessive side effects of pharmacological treatment indicate the need to abandon such treatments. However, for a vast majority of patients with class I to class III obesity, after unsuccessful conservative treatments surgical treatment already has a documented value. In most cases, weight loss is associated with an improved quality of life (improvement of mobility, resolution of sleep apnea), a reduction in the components of metabolic syndrome and glycemic and lipidogram disorders, and a lower risk of cardiovascular illnesses and death.</p><p>A limitation of this study was the relatively small number of participants and the time of follow-up. Despite the still-rising percentage of people suffering from morbid obesity (BMI of ≥40 kg/m<sup>2</sup>) in Poland, compared to that in other countries, this percentage is still fairly small (only 1.3% of men and 1.8% of women) [<xref rid=\"B37-ijerph-17-05342\" ref-type=\"bibr\">37</xref>]. Another limitation was the relatively small number of patients in whom the LRYGB was performed, which made it difficult to compare the results of the surgery methods.</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05342\"><title>5. Conclusions</title><p>In patients undergoing bariatric surgery, a significant decrease in excess weight was observed at all analyzed points in time compared to the pre-operative values. The highest weight loss was noted more than 24 months after the procedure. Bariatric treatment was associated with a significant improvement in glycemia, triglycerides, and leptin.</p><p>The results of this study confirm the thesis on the effectiveness of bariatric surgery in reducing excess body weight and improving metabolic parameters in patients with extreme obesity. It is necessary to conduct further, long-term observations in order to identify the most effective yet safe methods of bariatric treatment.</p></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization, S.G., E.S., J.M., M.K., P.B., P.Z., Ł.N., M.G.-O., and D.K. Data curation, J.M., M.K., P.B., P.Z., Ł.N., and I.W. Formal analysis, Ł.N. and M.G.-O. Funding acquisition, S.G. and D.K. Investigation, S.G., J.M., M.K., P.B., P.Z., and Ł.N. Methodology, S.G., E.S., M.G.-O., and D.K. Software, A.B. Supervision, E.S., M.G.-O., and D.K. Visualization, A.B. and I.W. Writing—original draft, A.B. and I.W. Writing—review and editing, S.G., and E.S. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>The project was supported under the program of the Minister of Science and Higher Education under the name “Regional Initiative of Excellence” in 2019–2022, project number: 024/RID/2018/19, financing amount: 11.999.000,00PLN, in data analysis and linguistic correction of the text.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijerph-17-05342\"><label>1.</label><element-citation publication-type=\"web\"><person-group person-group-type=\"author\"><collab>World Health Organization</collab></person-group><article-title>Obesity and Overweight. 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Rev.</source><year>2020</year><volume>21</volume><fpage>e12986</fpage><pub-id pub-id-type=\"doi\">10.1111/obr.12986</pub-id><pub-id pub-id-type=\"pmid\">32017386</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijerph-17-05342-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Body mass index (BMI) changes at individual follow-up time points in relation to the presurgery BMI measurements.</p></caption><graphic xlink:href=\"ijerph-17-05342-g001\"/></fig><fig id=\"ijerph-17-05342-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Trend lines of excess weight loss (%) in relation to excess weight before surgery, depending on the type of procedure.</p></caption><graphic xlink:href=\"ijerph-17-05342-g002\"/></fig><table-wrap id=\"ijerph-17-05342-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05342-t001_Table 1</object-id><label>Table 1</label><caption><p>Mean values of the parameters of the study group before bariatric surgery.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Parameters</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Total (<italic>n</italic> = 163)</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Women (<italic>n</italic> = 136) X ± SD</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Men (<italic>n</italic> = 27) X ± SD</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\"><italic>p</italic>-Value</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">X ± SD</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Me (Q1–Q3)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Age</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39.6 ± 10.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40.0 (31.0–46.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38.4 ± 10.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">43.4 ± 11.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0071</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Height (cm)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">168.2 ± 8.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">167.0 (164–172)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">166.3 ± 8.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">175.4 ± 9.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Initial weight (kg)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">123.6 ± 21.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">120.0 (109–134)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">120.9 ± 19.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">148.1 ± 25.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Initial BMI (kg/m<sup>2</sup>)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">44.5 ± 6.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">43.4 (40.2–46.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">44.0 ± 9.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">48.1 ± 8.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.0003</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Initial ideal body weight (kg)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">61.3 ± 5.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60.2 (58.4–64.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">59.8 ± 5.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">67.8 ± 6.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Initial excess weight (kg)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">62.3 ± 18.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">59.8 (49.8–72.0)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">61.2 ± 18.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">80.15 ± 23.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr></tbody></table><table-wrap-foot><fn><p>X: arithmetic mean; SD: standard deviation; Me: median; Q1: first quartile; Q3: third quartile; BMI: body mass index.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05342-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05342-t002_Table 2</object-id><label>Table 2</label><caption><p>Excess weight loss (X%) compared to the pre-operative excess weight value (100%) at particular points in time during the follow up.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Time after Procedure (Months)</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">\n<italic>N</italic>\n</th><th colspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Excess Weight Loss</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">X ± SD (kg)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">X (%)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Me (kg)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Me (%)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">37</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.5 ± 16.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.3 ± 10.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26.8</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">42</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19.4 ± 13.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23.9 ± 11.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25.1 ± 13.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">44.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">42.8</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">>24</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">30.3 ± 19.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">50.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">31.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">56.5</td></tr></tbody></table><table-wrap-foot><fn><p>X: arithmetic mean; SD: standard deviation; Me: median.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05342-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05342-t003_Table 3</object-id><label>Table 3</label><caption><p>Number of patients with metabolic parameters measured (1, 6, 12, and >12 months after surgery).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Biochemical Parameters</th><th colspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Time (Months) After Surgery</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">>12</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Glucose</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Triglyceride</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Total cholesterol</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">LDL</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">HDL</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8</td></tr></tbody></table><table-wrap-foot><fn><p>LDL: low-density lipoprotein; HDL: high-density lipoprotein.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05342-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05342-t004_Table 4</object-id><label>Table 4</label><caption><p>Mean concentrations of biochemical parameters measured before the procedure and during the follow-up period (mg/dL).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Biochemical Parameters (mg/dL)</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Before Surgery <break/>(X ± SD)</th><th colspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Time (Months) After Surgery</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\"><italic>p</italic>-Value</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1 (X ± SD)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6 (X ± SD)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12 (X ± SD)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">>12 (X ± SD)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Glucose</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">109.6 ± 48.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">104 ± 8.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">91.3 ± 14.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">89.9 ± 18.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">86.6 ± 7.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.003</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Triglyceride</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">156.9 ± 79.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">137.7 ± 55.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">126.5 ± 34.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">111.6 ± 43.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">112.7 ± 44.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.043</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Total cholesterol</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">198.4 ± 47.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">188.7 ± 36.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">165.2 ± 82.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">186.9 ± 35.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">191.7 ± 42.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.180</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">LDL</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">122.7 ± 40.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">137 ± 32.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">112 ± 18.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">131 ± 36.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.261</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">HDL</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">41.2 ± 12.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">41.7 ± 7.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">42.6 ± 6.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">61.9 ± 23.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.084</td></tr></tbody></table><table-wrap-foot><fn><p>X: arithmetic mean; SD: standard deviation; LDL: low-density lipoprotein; HDL: high-density lipoprotein.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05342-t005\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05342-t005_Table 5</object-id><label>Table 5</label><caption><p>Mean concentrations of the biochemical parameters associated with surgery measured before surgery and 12 months after surgery.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Biochemical Parameters</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Concentration before Surgery</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Concentration 12 Months after Surgery</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\"><italic>p</italic>-Value</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">X ± SD</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Me</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">X ± SD</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Me</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Creatinine (mg/dL)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.87 ± 18.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.88</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.93 ± 13.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.86</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3912</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CK-MB (mg/dL)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.61 ± 15.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.97 ± 3.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6056</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CPK (mg/dL)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">156.1 ± 134.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">113.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">192.24 ± 112.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">155.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1240</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Leptin (pg/mL)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">197.50 ± 257.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">50.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">75.98 ± 117.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">39.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0116</td></tr></tbody></table><table-wrap-foot><fn><p>CK-MB: creatine kinase fraction of the heart; CPK: phosphocreatine kinase; X: arithmetic mean; SD: standard deviation; Me: median.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32707830</article-id><article-id pub-id-type=\"pmc\">PMC7432001</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155274</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05274</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Effect of Antenatal Exercises, Including Yoga, on the Course of Labor, Delivery and Pregnancy: A Retrospective Study</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Wadhwa</surname><given-names>Yogyata</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05274\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Alghadir</surname><given-names>Ahmad H.</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05274\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-0504-6863</contrib-id><name><surname>Iqbal</surname><given-names>Zaheen A.</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05274\">2</xref><xref rid=\"c1-ijerph-17-05274\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05274\"><label>1</label>Health on My Mind, Gurugram, Haryana 122002, India; <email>physio_yogyata@yahoo.co.in</email></aff><aff id=\"af2-ijerph-17-05274\"><label>2</label>Rehabilitation Research Chair, College of Applied Medical Sciences, King Saud University, Riyadh 11433, Saudi Arabia; <email>aha@ksu.edu.sa</email></aff><author-notes><corresp id=\"c1-ijerph-17-05274\"><label>*</label>Correspondence: <email>zaiqbal@ksu.edu.sa</email> or <email>z_iqbal001@yahoo.com</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>22</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5274</elocation-id><history><date date-type=\"received\"><day>08</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>16</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p><italic>Background</italic>: Delivering a child is a very stressful experience for women. Pregnancy and labor entail complex events that are unique to each individual female. The management of labor pain is often done using analgesics and anesthesia, which have been shown to have some side effects. More comprehensive data are needed to provide clinically significant evidence for clinicians to confidently prescribe exercises to patients. This study was done to evaluate the effect of antenatal exercises, including yoga, on the course of labor, delivery, and pregnancy outcomes. <italic>Methods</italic>: A retrospective study was conducted among 200 primiparous subjects (aged 20–40). A questionnaire was provided to the subjects to obtain their demographic and obstetrical information 6 weeks after delivery, and their hospital records were also assessed for further details. Based on the nature and details obtained for the antenatal exercises, subjects were divided into two groups: control and exercise. Outcome measures included the need for labor induction, self-perceived pain and perceived exertion during labor, duration and nature of the delivery, newborn infant weight, maternal weight gain, history of back pain, and post-partum recovery. The total maternal weight gain (in kilograms) was calculated from weight at 6 weeks after delivery minus the weight at 12–14 weeks of gestation. Back pain during pregnancy and self-perceived labor pain were measured using a visual analog scale (VAS). The overall perceived exertion during labor was measured using an adapted Borg scale for perceived effort. <italic>Results</italic>: The subjects who followed regular antenatal exercises, including yoga, had significantly lower rates of cesarean section, lower weight gain, higher newborn infant weight, lower pain and overall discomfort during labor, lower back pain throughout pregnancy, and earlier post-partum recovery compared to those who did no specific exercises or only walked during pregnancy. <italic>Conclusions</italic>: This retrospective study showed that regular antenatal exercises, including yoga, result in better outcomes related to the course of labor, delivery, and pregnancy. These results notably indicated that pregnant women should be active throughout pregnancy and follow a supervised exercise program that includes yoga unless contraindicated. We require further large-scale prospective studies and quasi-experimental trials to confirm the observed findings.</p></abstract><kwd-group><kwd>pregnancy</kwd><kwd>antenatal exercises</kwd><kwd>yoga</kwd><kwd>labor</kwd><kwd>pain</kwd><kwd>delivery</kwd><kwd>women’s health</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05274\"><title>1. Introduction</title><p>Delivering a child is a very stressful experience for women, especially for the first time [<xref rid=\"B1-ijerph-17-05274\" ref-type=\"bibr\">1</xref>]. Pregnancy and labor entail complex events that are unique to each individual female [<xref rid=\"B2-ijerph-17-05274\" ref-type=\"bibr\">2</xref>]. Preparation for delivery can be effective in decreasing adverse responses during labor [<xref rid=\"B1-ijerph-17-05274\" ref-type=\"bibr\">1</xref>,<xref rid=\"B3-ijerph-17-05274\" ref-type=\"bibr\">3</xref>]. A prolonged duration of labor and other complications, such as abnormal fetal position or heart rate, can result in a cesarean section [<xref rid=\"B4-ijerph-17-05274\" ref-type=\"bibr\">4</xref>]. The rate of cesarean deliveries has increased in recent times, although it varies with healthcare providers and the site of delivery [<xref rid=\"B5-ijerph-17-05274\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijerph-17-05274\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijerph-17-05274\" ref-type=\"bibr\">7</xref>]. A cesarean section is an invasive procedure with various risks, including infection, hemorrhage, thromboembolism, and potential morbidity for the mother and the infant [<xref rid=\"B8-ijerph-17-05274\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05274\" ref-type=\"bibr\">9</xref>]. It has also become the choice for women who are afraid of pain during the course of labor and delivery [<xref rid=\"B10-ijerph-17-05274\" ref-type=\"bibr\">10</xref>]. However, obstetricians should restrict the option of surgical delivery to complicated cases, where the risk for the mother, her infant, or both are high [<xref rid=\"B5-ijerph-17-05274\" ref-type=\"bibr\">5</xref>]. In addition to reducing complications, decreasing the rate of cesarean sections would also reduce the financial burden on healthcare [<xref rid=\"B11-ijerph-17-05274\" ref-type=\"bibr\">11</xref>].</p><p>Management of labor pain is often done using analgesics and anesthesia, which have been shown to provide relief to mothers [<xref rid=\"B12-ijerph-17-05274\" ref-type=\"bibr\">12</xref>]. The usage of analgesics and anesthesia at this stage can lead to negative effects for both the mother and the infant [<xref rid=\"B4-ijerph-17-05274\" ref-type=\"bibr\">4</xref>,<xref rid=\"B12-ijerph-17-05274\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijerph-17-05274\" ref-type=\"bibr\">13</xref>]. The use of non-pharmacological methods for pain reduction can be more effective physically, psychologically, and emotionally, as well as less damaging for the mother and fetus [<xref rid=\"B14-ijerph-17-05274\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijerph-17-05274\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijerph-17-05274\" ref-type=\"bibr\">16</xref>].</p><p>A few studies on the effects of exercises during pregnancy have reported mixed results related to preterm labor chances, intrauterine growth retardation (IUGR), pregnancy-induced hypertension, gestational diabetes, labor pain and duration, and the risk of cesarean section [<xref rid=\"B4-ijerph-17-05274\" ref-type=\"bibr\">4</xref>,<xref rid=\"B11-ijerph-17-05274\" ref-type=\"bibr\">11</xref>,<xref rid=\"B17-ijerph-17-05274\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijerph-17-05274\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijerph-17-05274\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijerph-17-05274\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijerph-17-05274\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijerph-17-05274\" ref-type=\"bibr\">22</xref>]. This could be due to various limitations, including a smaller sample size, the involvement of different forms of exercises and their protocols, and failure to comply with the exercises [<xref rid=\"B11-ijerph-17-05274\" ref-type=\"bibr\">11</xref>,<xref rid=\"B23-ijerph-17-05274\" ref-type=\"bibr\">23</xref>]. More comprehensive data are needed to provide clinically significant evidence for clinicians to confidently prescribe exercises to patients.</p><p>This study was performed to observe the effects of antenatal exercises, including yoga, on the course of labor, delivery, and pregnancy outcomes. We hypothesized that females who follow antenatal exercises, including yoga, for at least three months would have better outcomes in the course of labor and delivery, maternal weight gain and newborn infant weight, post-partum recovery, back pain during pregnancy, and pain and overall discomfort during labor.</p></sec><sec id=\"sec2-ijerph-17-05274\"><title>2. Materials and Methods</title><p>A retrospective study was conducted among 200 primiparous subjects (aged 20–40), who delivered their infant between 1 April 2018 and 30 April 2019. Subjects were excluded if they had a history of any serious illness (diabetes, hypertension, etc.) before pregnancy or any high-risk complication during pregnancy or delivery, such as fetal abnormalities and IUGR. Data for subjects who opted for a planned cesarean section were not included in the study. After considering these criteria, 158 subjects were included in this study. All subjects were informed about the aims and nature of the study and their written consent was obtained prior to data collection. This study was approved by the Ethics Research Committee in Institutional Review Board (Ref no. KSU/RRC/045/03).</p><p>A questionnaire was provided to the subjects to obtain their demographic and obstetrical information 6 weeks after delivery. This included age, education level, job details, body mass index (BMI), lifestyle before becoming pregnant, the nature and details of their antenatal exercises, need for labor induction, self-perceived labor pain and perceived exertion, duration and nature of delivery, newborn infant weight, maternal weight gain, history of back pain, and post-partum recovery (<xref rid=\"ijerph-17-05274-t001\" ref-type=\"table\">Table 1</xref>). Their hospital records were also assessed for further details.</p><p>Based on the information obtained, subjects were divided into two groups: control and exercise. Subjects who followed supervised antenatal exercises, including resistance, aerobic, yoga, pelvic floor, stretching, relaxation exercises, or a combination program with or without walking, for at least 3 months during pregnancy and for at least one (minimum half an hour) session per week were included in the exercise group. Subjects who did no specific exercises or only walked during pregnancy were included in the control group.</p><p>The total maternal weight gain (in kilograms) was calculated by weight at 6 weeks after delivery minus the weight at 12–14 weeks of gestation. Participants were requested to report their post-partum recovery based on the time after delivery that they returned to household tasks, including making the bed, sweeping/mopping/cleaning, grocery shopping, kitchen work without help, and working a job, if employed. The time to resume these activities was recorded as early (2–3 weeks) or delayed (>3 weeks).</p><p>Back pain during pregnancy and self-perceived labor pain were measured using the visual analog scale (VAS) [<xref rid=\"B24-ijerph-17-05274\" ref-type=\"bibr\">24</xref>]. Subjects had to rate their worst pain on a scale of 10, with 0 meaning no pain and 10 meaning the most severe pain possible. The overall perceived exertion during labor was measured using the adapted Borg scale for perceived effort [<xref rid=\"B25-ijerph-17-05274\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijerph-17-05274\" ref-type=\"bibr\">26</xref>]. This scale is graded from 6 to 20, starting with 6 (no feeling of exertion) and ending with 20 (very, very hard). A score of 11–14 indicates moderate activity (fairly light to somewhat hard), while vigorous activities (hard to very, very hard) are represented by 15 or higher.</p><sec><title>Data Analysis</title><p>Data were analyzed using GraphPad InStat 3.0 (GraphPad Software Inc.: San Diego, CA, USA). The mean ± standard deviation, frequency, and percentage were used to represent the data. Two-sample t-tests and Wilcoxon sum rank tests were used to compare continuous data between the control and exercise groups, while a chi-square test was used to compare the categorical data. The null hypothesis was rejected at <italic>p</italic> > 0.05. Graph-Pad Instat 3.0 (GraphPad Soft-ware: San Diego, CA, USA).</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05274\"><title>3. Results</title><sec id=\"sec3dot1-ijerph-17-05274\"><title>3.1. Demographic Data</title><p>Out of the 158 subjects who agreed to participate in the study, six were excluded due to incomplete data, leaving 76 participants in each group. The average age of subjects in the control and exercise groups was 25.80 and 26.10 years, respectively (<xref rid=\"ijerph-17-05274-t002\" ref-type=\"table\">Table 2</xref>). There were no statistically significant differences in age, height, BMI, or job details among the subjects between the two groups (<italic>p</italic> > 0.05). At least 33% and 32% of the subjects in the exercise and control groups, respectively, reported to follow an active lifestyle before becoming pregnant. In the exercise group, 39% of the subjects reported to have completed post-graduate education, while 17% reported senior secondary or lower. On the other hand, in the control group, 26% were postgraduates, while 26% reported senior secondary or lower (<italic>p</italic> < 0.05).</p></sec><sec id=\"sec3dot2-ijerph-17-05274\"><title>3.2. Nature and Duration of Exercises in the Control and Experimental Groups</title><p>Details about the exercises performed by the subjects in the control and experimental groups are provided in <xref rid=\"ijerph-17-05274-t003\" ref-type=\"table\">Table 3</xref>. At least 47% of the subjects in the control group reported to have walked for 6 months to full term, while 66% subjects in the exercise group reported to have followed supervised exercises, including or excluding yoga, for the same duration. The majority of the subjects in both the groups reported walking or exercising 1–3 times per week with sessions lasting for half an hour. In the exercise group, 8% of the subjects reported performing only exercises, 3% only yoga, 11% engaged in walking in addition to exercises, 69% engaged in walking in addition to yoga, and 11% engaged in walking, exercises, and yoga.</p></sec><sec id=\"sec3dot3-ijerph-17-05274\"><title>3.3. Obstetrical Data</title><p><italic>For the course of labor and delivery</italic> (<xref rid=\"ijerph-17-05274-t004\" ref-type=\"table\">Table 4</xref>): 22% of the subjects in the exercise group, compared to 49% in the control group, needed labor induction (<italic>p</italic> < 0.05). The cesarean section rate was 37% in the exercise group compared to 95% in the control group. In total, 63% of subjects in the exercise group delivered vaginally compared to 5% of the subjects in the control group (<italic>p</italic> < 0.05). The mean duration of delivery in the exercise group was 401.05 min, while that in the control group was 607.45 min (<italic>p</italic> < 0.05).</p><p><italic>Maternal weight gain and newborn infant weight</italic> (<xref rid=\"ijerph-17-05274-t004\" ref-type=\"table\">Table 4</xref>): The average weight gain from 12–14 weeks of gestation to 6 weeks after delivery was 11.5 kg in the exercise group, which was significantly lower than the 15.1 kg in the control group (<italic>p</italic> < 0.05). The mean newborn birth weight was significantly higher in the exercise group (3156.6 g) compared with the control (2905.5 g) group (<italic>p</italic> < 0.05).</p><p><italic>Post-partum recovery</italic> (<xref rid=\"ijerph-17-05274-t004\" ref-type=\"table\">Table 4</xref>): The time to resume household tasks and return to one’s job (if employed) was significantly shorter in the exercise group (86%) compared with the control (67%) group (<italic>p</italic> < 0.05).</p><p><italic>Back pain during pregnancy</italic> (<xref rid=\"ijerph-17-05274-t005\" ref-type=\"table\">Table 5</xref>): All the subjects included in this study reported feeling back pain at some point during their pregnancy. However, the mean score of their worst pain on the VAS was significantly lower in the exercise group (6.5 points) compared with the control (8.0 points) group (<italic>p</italic> < 0.05).</p><p><italic>Pain and overall discomfort during labor</italic> (<xref rid=\"ijerph-17-05274-t005\" ref-type=\"table\">Table 5</xref>): The results indicated that the labor pain score reported by the subjects in the exercise group (7.5 points) was significantly lower than that in the control group (9 points) (<italic>p</italic> < 0.05). However, there was no statistically significant difference in the overall discomfort perceived by the subjects during labor between the groups.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05274\"><title>4. Discussion</title><p>This retrospective study was performed to assess the effects of antenatal exercises, including yoga, on the course of labor, delivery, and pregnancy outcomes. The results showed that the subjects who followed antenatal exercises, including yoga, had a lower rate of cesarean section, lower weight gain, higher newborn infant weight, lower pain and overall discomfort during labor, lower back pain throughout pregnancy, and earlier post-partum recovery.</p><p>According to the American College of Obstetricians and Gynecologists (ACOG), pregnant women should exercise for at least 30 min on most days of the week [<xref rid=\"B5-ijerph-17-05274\" ref-type=\"bibr\">5</xref>,<xref rid=\"B27-ijerph-17-05274\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijerph-17-05274\" ref-type=\"bibr\">28</xref>]. However, this recommendation is not widely followed due to the various concerns that exercise at this stage may lead to maternal or fetal injury [<xref rid=\"B29-ijerph-17-05274\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijerph-17-05274\" ref-type=\"bibr\">30</xref>]. Only a small number of pregnant women (21% in Ireland, 20% in Spain, and 16% in the USA) have been reported to exercise according to the ACOG guidelines [<xref rid=\"B31-ijerph-17-05274\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijerph-17-05274\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijerph-17-05274\" ref-type=\"bibr\">33</xref>]. For most of these women, it was difficult to find correct advice, motivation, and family/community support to begin regular exercise [<xref rid=\"B34-ijerph-17-05274\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijerph-17-05274\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijerph-17-05274\" ref-type=\"bibr\">36</xref>]. Inconclusive evidence on the benefits and risks of exercise for the mother and her fetus is another reason [<xref rid=\"B22-ijerph-17-05274\" ref-type=\"bibr\">22</xref>].</p><p>The results of studies showing the effects of exercises on the rate of cesarean section seem to vary with the type of exercise and its dosage [<xref rid=\"B11-ijerph-17-05274\" ref-type=\"bibr\">11</xref>]. There was no effect from light resistance and toning exercises done for 3 days a week on the mode of delivery [<xref rid=\"B37-ijerph-17-05274\" ref-type=\"bibr\">37</xref>]. On the other hand, women who did 40 min of moderate intensity exercise regularly throughout their pregnancy had a significantly lower rate of cesarean section [<xref rid=\"B38-ijerph-17-05274\" ref-type=\"bibr\">38</xref>]. It has also been reported that inactive women are 3.7 times more likely to have a cesarean section than active women who engage in at least 30 min of moderate physical activity every day [<xref rid=\"B21-ijerph-17-05274\" ref-type=\"bibr\">21</xref>]. According to a Danish National Birth Cohort study, active women have a 40% lower risk for premature births than those who do no exercises at all [<xref rid=\"B39-ijerph-17-05274\" ref-type=\"bibr\">39</xref>]. Primiparous women have three times higher risk of having a cesarean section than multiparous women [<xref rid=\"B31-ijerph-17-05274\" ref-type=\"bibr\">31</xref>]. The rate of cesarean section has also been shown to depend on the healthcare facility where the delivery is conducted, as well as population characteristics including BMI [<xref rid=\"B11-ijerph-17-05274\" ref-type=\"bibr\">11</xref>].</p><p>In the current study, the cesarean section rate in the exercise group was 37% compared with 95% in the control group. Nine subjects reported last minute changes in the infant’s position from cephalic to breech, increased blood pressure, increased bilirubin, no dilatation, a large infant head, unbearable pain, intrahepatic cholestasis during pregnancy, meconium, and prolonged labor as the reasons to opt for cesarean section. Our results were consistent with other studies showing that exercise intervention for more than 50 h throughout the pregnancy can offer a greater reduction in risk for cesarean section [<xref rid=\"B11-ijerph-17-05274\" ref-type=\"bibr\">11</xref>,<xref rid=\"B40-ijerph-17-05274\" ref-type=\"bibr\">40</xref>,<xref rid=\"B41-ijerph-17-05274\" ref-type=\"bibr\">41</xref>,<xref rid=\"B42-ijerph-17-05274\" ref-type=\"bibr\">42</xref>,<xref rid=\"B43-ijerph-17-05274\" ref-type=\"bibr\">43</xref>]. In the exercise group, 65% of the subjects performed exercises throughout the pregnancy, 42% of the subjects did exercise 35 times per week, and for 24% of the subjects, exercise sessions lasted for at least half an hour to one hour.</p><p>At least 92% of the subjects in the exercise group reported to perform some form of yoga in addition to walking or other forms of exercises. In the last two to three decades, yoga has been extensively used as a technique to prevent and treat various diseases worldwide [<xref rid=\"B44-ijerph-17-05274\" ref-type=\"bibr\">44</xref>,<xref rid=\"B45-ijerph-17-05274\" ref-type=\"bibr\">45</xref>]. Yoga is a non-invasive and non-pharmacological method that has been shown to improve strength and flexibility [<xref rid=\"B46-ijerph-17-05274\" ref-type=\"bibr\">46</xref>]. The regular practice of yoga has been shown to be beneficial for both mind and body, for various reasons, including increased spinal flexibility, improved circulation of cerebrospinal fluid, and enhanced release of endorphins and serotonin [<xref rid=\"B47-ijerph-17-05274\" ref-type=\"bibr\">47</xref>,<xref rid=\"B48-ijerph-17-05274\" ref-type=\"bibr\">48</xref>]. It also has the capacity to raise the threshold of pain perception [<xref rid=\"B4-ijerph-17-05274\" ref-type=\"bibr\">4</xref>,<xref rid=\"B17-ijerph-17-05274\" ref-type=\"bibr\">17</xref>]. Yoga can strengthen and increase the flexibility of the perineal, vaginal, and urinary sphincter muscles and may thus facilitate labor and delivery through an increase in pelvic diameters [<xref rid=\"B49-ijerph-17-05274\" ref-type=\"bibr\">49</xref>]. It can help mothers tune their bodies to the consequences of labor by increasing muscle tone, energy, and relaxation [<xref rid=\"B45-ijerph-17-05274\" ref-type=\"bibr\">45</xref>,<xref rid=\"B50-ijerph-17-05274\" ref-type=\"bibr\">50</xref>]. It can also improve maternal posture and strengthen the muscles of the back, abdomen, and pelvis that are stressed during labor [<xref rid=\"B45-ijerph-17-05274\" ref-type=\"bibr\">45</xref>,<xref rid=\"B50-ijerph-17-05274\" ref-type=\"bibr\">50</xref>,<xref rid=\"B51-ijerph-17-05274\" ref-type=\"bibr\">51</xref>]. These could also be the reasons for the lower rates of labor induction, shorter durations of delivery, and lower self-perceived labor pain in the exercise group.</p><p>Although it has been argued that physical exercise can reduce the infant birthweight and thereby reduce the risk of cesarean delivery [<xref rid=\"B22-ijerph-17-05274\" ref-type=\"bibr\">22</xref>,<xref rid=\"B33-ijerph-17-05274\" ref-type=\"bibr\">33</xref>], our results showed that the newborn weight was significantly higher (approximately 250 g) in the exercise group. Other studies have also shown that performing exercises, including yoga, during pregnancy can significantly increase infant weight [<xref rid=\"B4-ijerph-17-05274\" ref-type=\"bibr\">4</xref>,<xref rid=\"B17-ijerph-17-05274\" ref-type=\"bibr\">17</xref>]. It has also been reported that exercise augments placental growth during the early and middle periods of pregnancy [<xref rid=\"B52-ijerph-17-05274\" ref-type=\"bibr\">52</xref>]. The exact phenomenon behind this finding remains unclear.</p><p>The overall maternal weight gain during pregnancy was significantly higher in the control (15.1 kg) group compared to the exercise (11.5 kg) group. As mentioned earlier, as the risk of cesarean delivery is positively correlated with BMI [<xref rid=\"B53-ijerph-17-05274\" ref-type=\"bibr\">53</xref>], the BMI could be another explanation for the lower rate of cesarean delivery in the exercise group. Furthermore, lower weight gain during pregnancy can be beneficial for women’s health in the long term. Post-partum recovery to perform daily activities was significantly faster among the subjects in the exercise group. This could be due to the lower rate of cesarean section, as well as the lower weight gain during pregnancy. Weight gain during pregnancy is normal and is usually due to increased fat and muscle mass, as well as water retention [<xref rid=\"B54-ijerph-17-05274\" ref-type=\"bibr\">54</xref>]. However, the inability to lose weight after delivery may make the recovery process slower and cause a delay in returning to a normal daily routine.</p><p>The nine-month period prevalence of back pain among pregnant women was reported to be as high as 49% [<xref rid=\"B55-ijerph-17-05274\" ref-type=\"bibr\">55</xref>]. This pain has been shown to prevent such women from performing their daily activities and being physically active [<xref rid=\"B56-ijerph-17-05274\" ref-type=\"bibr\">56</xref>]. It has also been related to back problems before pregnancy and various physical and psychological factors [<xref rid=\"B57-ijerph-17-05274\" ref-type=\"bibr\">57</xref>,<xref rid=\"B58-ijerph-17-05274\" ref-type=\"bibr\">58</xref>]. Although all the subjects reported suffering back pain at some point during their pregnancy, the overall self-perceived pain intensity was significantly lower in the exercise group. The various previous studies showing that exercises during pregnancy can help reduce the intensity of back pain [<xref rid=\"B59-ijerph-17-05274\" ref-type=\"bibr\">59</xref>,<xref rid=\"B60-ijerph-17-05274\" ref-type=\"bibr\">60</xref>] support our findings. Yoga improves bodily posture and strengthens the back and abdominal muscles [<xref rid=\"B50-ijerph-17-05274\" ref-type=\"bibr\">50</xref>], which can in turn decrease back pain [<xref rid=\"B46-ijerph-17-05274\" ref-type=\"bibr\">46</xref>]. Promoting health alongside other personal values may not only facilitate the introduction of healthy behaviors, but could also reduce several adverse pregnancy outcomes [<xref rid=\"B61-ijerph-17-05274\" ref-type=\"bibr\">61</xref>].</p><p>This study did not consider the effects of exercises on the need for an episiotomy or epidural anesthesia, perineal tears, stages of labor, or Apgar score. We recommend developing a prospective study design on a larger sample size that includes various outcome measures to assess the effects of antenatal exercises, including yoga, on various aspects of pregnancy.</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05274\"><title>5. Conclusions</title><p>The results of this retrospective study showed that regular antenatal exercises, including yoga, can help to lower the rate of cesarean section, decrease maternal weight gain, increase newborn infant weight, decrease pain and overall discomfort during labor, and lower back pain throughout pregnancy and aid in earlier post-partum recovery. These results indicate that pregnant women should be active throughout their pregnancy and follow a supervised exercise program, including yoga, unless contraindicated. We require a large-scale prospective studies and quasi-experimental trials to confirm the observed findings.</p></sec></body><back><ack><title>Acknowledgments</title><p>The authors are grateful to the Deanship of Scientific Research, King Saud University, for funding through the Vice Deanship of Scientific Research Chairs.</p></ack><notes><title>Author Contributions</title><p>Conceptualization, Y.W. and Z.A.I.; funding acquisition, A.H.A.; methodology, Y.W. and Z.A.I.; project administration, Y.W. and A.H.A.; supervision, A.H.A.; writing—original draft, Z.A.I. All authors have read and agreed to the published version of the manuscript. The datasets used in this study are available from the corresponding author on request.</p></notes><notes><title>Funding</title><p>This study was funded through the Vice Deanship of Scientific Research Chairs, King Saud Universities.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare that they have no competing interests.</p></notes><glossary><title>Abbreviations</title><array orientation=\"portrait\"><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">VAS</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Visual analog scale</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IUGR</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Intrauterine growth retardation</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">BMI</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Body mass index</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ACOG</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">American College of Obstetricians and Gynecologists</td></tr></tbody></array></glossary><ref-list><title>References</title><ref id=\"B1-ijerph-17-05274\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Lowe</surname><given-names>N.K.</given-names></name></person-group><article-title>Maternal confidence for labor: Development of the childbirth self-efficacy inventory</article-title><source>Res. 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rowspan=\"1\">Demographic information</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Age (years)</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Height (cm)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Weight at 12–14 weeks of gestation (kg)<break/>Weight at 6 weeks after delivery (kg)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">4</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Educational level</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Senior secondary or lower</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Diploma</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Graduation</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Postgraduation or higher</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">5</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Job details</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">House wife</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Working</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">6</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Lifestyle before becoming pregnant</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sedentary</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Active</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Very active</td></tr><tr><td colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Nature and duration of exercises</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">1</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Did you do any supervised exercises during pregnancy?</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Only walking</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">2</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Duration of exercises during pregnancy</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0–3 months </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3–6 months </td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6 months–full term</td></tr><tr><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">3</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Frequency of exercises </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">None</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1–3 times/week</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3–5 times/week</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">>5 times/week</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">4</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Duration of exercise session </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><Half an hour</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Half an hour–one hour</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">>One hour</td></tr><tr><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">5</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Nature of exercises</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">None</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Walking</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Supervised antenatal exercises (including resistance, aerobic, pelvic floor, stretching, and relaxation exercises)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yoga</td></tr><tr><td colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Obstetrical information</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">1</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Need for labor induction</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Duration of delivery (minutes)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">3</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Nature of delivery</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Normal vaginal</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cesarean section<break/>If so any specific reason?</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Newborn infant weight (grams)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total maternal weight gain (kg)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">6</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Post-partum recovery (time taken after delivery to return to household tasks like making bed, sweeping/mopping/cleaning, grocery shopping, kitchen work without help, and return to job if employed)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Early (2–3 weeks)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Delayed (>3 weeks)</td></tr><tr><td colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Self-perceived back pain</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">1</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Did you suffer back pain during pregnancy</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">If yes, what was your worst pain on VAS, 0–10?<break/>0 = no pain, 10 = maximum pain</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td colspan=\"3\" align=\"center\" valign=\"middle\" rowspan=\"1\">Self-perceived pain and perceived exertion during labor</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Self-perceived pain (VAS, 0–10)<break/>0 = no pain, 10 = maximum pain</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Overall perceived exertion during labor (adapted Borg scale, 6–20)<break/>6 = no feeling of exertion, 20 = very, very hard</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr></tbody></table><table-wrap-foot><fn><p>VAS, visual analog scale.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05274-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05274-t002_Table 2</object-id><label>Table 2</label><caption><p>Demographic information of the subjects in the control (<italic>n</italic> = 76) and experimental (<italic>n</italic> = 76) groups).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Variables</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Control Group </th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Exercise Group</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Age * (years)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25.80 ± 2.50</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">26.10 ± 1.98</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">>0.05</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Height * (cm)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">170.01 ± 2.36</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">169.80 ± 2.20</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">>0.05</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Body mass index * (kg/cm<sup>2</sup>)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">24.60 ± 2.60</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">23.90 ± 2.90</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">>0.05</td></tr><tr><td colspan=\"4\" align=\"center\" valign=\"middle\" rowspan=\"1\">Educational level#</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">Senior secondary or lower</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">20 (26.31)</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">13 (17.10)</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\"><0.05</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Diploma</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15 (19.73)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13 (17.10)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">>0.05</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Graduation</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21 (27.63)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20 (16.31)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">>0.05</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Postgraduation or higher</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20 (26.31)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">30 (39.47)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.05</td></tr><tr><td colspan=\"4\" align=\"center\" valign=\"middle\" rowspan=\"1\">Job details#</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">House wife</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">45 (59.21)</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">44 (57.89)</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">>0.05</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Working</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">31 (40.78)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">32 (42.10)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">>0.05</td></tr><tr><td colspan=\"4\" align=\"center\" valign=\"middle\" rowspan=\"1\">Lifestyle before becoming pregnant#</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">Sedentary</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">48 (63.15)</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">47 (61.84)</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">>0.05</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Active</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24 (31.57)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25 (32.89)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">>0.05</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Very active</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4 (5.26)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4 (5.26)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">>0.05</td></tr></tbody></table><table-wrap-foot><fn><p>* Mean ± SD, # number (%).</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05274-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05274-t003_Table 3</object-id><label>Table 3</label><caption><p>Nature and duration of exercises in the control (<italic>n</italic> = 76) and experimental (<italic>n</italic> = 76) groups.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Variables</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Control Group<break/>Number (%)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Exercise Group<break/>Number (%)</th></tr></thead><tbody><tr><td colspan=\"3\" align=\"center\" valign=\"middle\" rowspan=\"1\">Duration of exercises during pregnancy</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">0–3 months </td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">28 (36.84)</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">2 (2.63)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3–6 months </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12 (15.78)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24 (31.57)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6 months–full term</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">36 (47.36)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">50 (65.78)</td></tr><tr><td colspan=\"3\" align=\"center\" valign=\"middle\" rowspan=\"1\">Frequency of exercises</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">None</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">8 (10.52)</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">0 (00)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1–3 times/week</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">52 (68.42)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30 (39.47)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3–5 times/week</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8 (10.52)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32 (42.10)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">>5 times/week</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8 (10.52)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14 (18.42)</td></tr><tr><td colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\">Duration of exercise session</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\"><Half an hour</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">44 (54.89)</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">32 (42.10)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Half an hour–one hour</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20 (26.31)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26 (34.21)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">>One hour</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12 (15.78)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18 (23.68)</td></tr><tr><td colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\">Nature of exercises</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">None</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">20 (26.31)</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">0 (00)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Only walking</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">56 (73.68)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (00)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Only supervised antenatal exercises</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (00)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6 (7.89)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Only yoga</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (00)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2 (2.63)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Walking + yoga </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (00)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">52 (68.42)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Walking + supervised antenatal exercises</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (00)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8 (10.52)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yoga + supervised antenatal exercises</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (00)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (00)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Walking + supervised antenatal exercises + yoga</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0 (00)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8 (10.52)</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05274-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05274-t004_Table 4</object-id><label>Table 4</label><caption><p>Obstetrical information of the subjects in control (<italic>n</italic> = 76) and experimental (<italic>n</italic> = 76) groups.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Variables</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Control Group</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Exercise Group</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th></tr></thead><tbody><tr><td colspan=\"4\" align=\"center\" valign=\"middle\" rowspan=\"1\">Need for labor induction#</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">37 (48.66)</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">17 (22.36)</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\"><0.05</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39 (51.31)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">59 (77.63)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.05</td></tr><tr><td colspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\">Nature of delivery#</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">Normal vaginal</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">4 (5.26)</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">48 (63.15)</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\"><0.05</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cesarean section</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">72 (94.73)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28 (36.84)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.05</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">Duration of delivery * (minutes)</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">607.00 ± 45.03</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">401.50 ± 50.01</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\"><0.05</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">Maternal weight gain * (kg)</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">15.10 ± 1.60</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">11.50 ± 2.10</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\"><0.05</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">Newborn infant weight * (grams)</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">2905.50 ± 350.10</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">3156.60 ± 420.10</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\"><0.05</td></tr><tr><td colspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\">Post-partum recovery#</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">Early</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">51 (67.10)</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">65 (85.52)</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\"><0.05</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Delayed</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25 (32.89)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11 (14.47)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.05</td></tr></tbody></table><table-wrap-foot><fn><p>* Mean ± SD, # number (%).</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05274-t005\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05274-t005_Table 5</object-id><label>Table 5</label><caption><p>Score of back pain (VAS) during pregnancy, labor pain (VAS), and overall discomfort level (adapted Borg scale) during labor in the control (<italic>n</italic> = 75) and experimental (<italic>n</italic> = 75) groups.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Variables </th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Control Group<break/>Mean ± SD</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Exercise Group<break/>Mean ± SD</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Self-perceived back pain during pregnancy</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8.00 ± 1.00</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.50 ± 1.50</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.05</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Self-perceived labor pain</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9.00 ± 1.00</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.50 ± 1.50</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.05</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Overall discomfort level during labor</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17.00 ± 1.00</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16.00 ± 1.00</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">>0.05</td></tr></tbody></table><table-wrap-foot><fn><p>VAS, visual analog scale.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751625</article-id><article-id pub-id-type=\"pmc\">PMC7432002</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155437</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05437</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Review</subject></subj-group></article-categories><title-group><article-title>Innate Immune Response to Tick-Borne Pathogens: Cellular and Molecular Mechanisms Induced in the Hosts</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Torina</surname><given-names>Alessandra</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05437\">1</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05437\">†</xref></contrib><contrib contrib-type=\"author\"><name><surname>Villari</surname><given-names>Sara</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05437\">1</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05437\">†</xref></contrib><contrib contrib-type=\"author\"><name><surname>Blanda</surname><given-names>Valeria</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05437\">1</xref><xref rid=\"c1-ijms-21-05437\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Vullo</surname><given-names>Stefano</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05437\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>La Manna</surname><given-names>Marco Pio</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05437\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-0642-0431</contrib-id><name><surname>Shekarkar Azgomi</surname><given-names>Mojtaba</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05437\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Di Liberto</surname><given-names>Diana</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05437\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-7383-9649</contrib-id><name><surname>de la Fuente</surname><given-names>José</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05437\">3</xref><xref ref-type=\"aff\" rid=\"af4-ijms-21-05437\">4</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-3969-0155</contrib-id><name><surname>Sireci</surname><given-names>Guido</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05437\">2</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05437\"><label>1</label>Istituto Zooprofilattico Sperimentale della Sicilia, Via Gino Marinuzzi 3, 90100 Palermo, Italy; <email>alessandra.torina@izssicilia.it</email> (A.T.); <email>sara.villari@izssicilia.it</email> (S.V.); <email>stefano.vullo@izssicilia.it</email> (S.V.)</aff><aff id=\"af2-ijms-21-05437\"><label>2</label>Central Laboratory of Advanced Diagnostic and Biological Research (CLADIBIOR), BIND, University Hospital “Paolo Giaccone”, Università degli studi di Palermo, Via del Vespro 129, 90100 Palermo, Italy; <email>marcopio.lamanna@unipa.it</email> (M.P.L.M.); <email>mojtaba.shekarkarazgomi@unipa.it</email> (M.S.A.); <email>diana.diliberto@unipa.it</email> (D.D.L.); <email>guido.sireci@unipa.it</email> (G.S.)</aff><aff id=\"af3-ijms-21-05437\"><label>3</label>SaBio, Instituto de Investigación en Recursos Cinegéticos IREC-CSIC-UCLM-JCCM, Ronda de Toledo s/n, 13005 Ciudad Real, Spain; <email>JosedeJesus.Fuente@uclm.es</email></aff><aff id=\"af4-ijms-21-05437\"><label>4</label>Department of Veterinary Pathobiology, Center for Veterinary Health Sciences, Oklahoma State University, Stillwater, OK 74078, USA</aff><author-notes><corresp id=\"c1-ijms-21-05437\"><label>*</label>Correspondence: <email>valeria.blanda@izssicilia.it</email></corresp><fn id=\"fn1-ijms-21-05437\"><label>†</label><p>A.T. and S.V. (Sara Villari) equally contributed to the manuscript.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>30</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5437</elocation-id><history><date date-type=\"received\"><day>17</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>29</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Many pathogens are transmitted by tick bites, including <italic>Anaplasma</italic> spp., <italic>Ehrlichia</italic> spp., <italic>Rickettsia</italic> spp., <italic>Babesia</italic> and <italic>Theileria</italic> sensu stricto species. These pathogens cause infectious diseases both in animals and humans. Different types of immune effector mechanisms could be induced in hosts by these microorganisms, triggered either directly by pathogen-derived antigens or indirectly by molecules released by host cells binding to these antigens. The components of innate immunity, such as natural killer cells, complement proteins, macrophages, dendritic cells and tumor necrosis factor alpha, cause a rapid and intense protection for the acute phase of infectious diseases. Moreover, the onset of a pro-inflammatory state occurs upon the activation of the inflammasome, a protein scaffold with a key-role in host defense mechanism, regulating the action of caspase-1 and the maturation of interleukin-1β and IL-18 into bioactive molecules. During the infection caused by different microbial agents, very similar profiles of the human innate immune response are observed including secretion of IL-1α, IL-8, and IFN-α, and suppression of superoxide dismutase, IL-1Ra and IL-17A release. Innate immunity is activated immediately after the infection and inflammasome-mediated changes in the pro-inflammatory cytokines at systemic and intracellular levels can be detected as early as on days 2–5 after tick bite. The ongoing research field of “inflammasome biology” focuses on the interactions among molecules and cells of innate immune response that could be responsible for triggering a protective adaptive immunity. The knowledge of the innate immunity mechanisms, as well as the new targets of investigation arising by bioinformatics analysis, could lead to the development of new methods of emergency diagnosis and prevention of tick-borne infections.</p></abstract><kwd-group><kwd>inflammasome</kwd><kwd>innate immune response</kwd><kwd>tick borne pathogens</kwd><kwd>gene ontology analysis</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05437\"><title>1. Introduction</title><p>Tick-borne diseases are emerging infectious diseases caused by bacterial, viral and parasitic pathogens, including the bacteria <italic>Anaplasma</italic> spp., <italic>Ehrlichia</italic> spp. and <italic>Rickettsia</italic> spp., and the protozoa <italic>Babesia</italic> spp. and <italic>Theileria</italic> spp. [<xref rid=\"B1-ijms-21-05437\" ref-type=\"bibr\">1</xref>].</p><p><italic>Anaplasma</italic> species (order Rickettsiales, family Anaplasmataceae) are obligate intracellular pathogens, which survive in host cells avoiding the host immune response [<xref rid=\"B2-ijms-21-05437\" ref-type=\"bibr\">2</xref>]. The genus includes species affecting companion, domestic and wildlife animals. <italic>Anaplasma marginale</italic> and <italic>Anaplasma phagocytophilum</italic> are two relevant pathogenic species, with the latter also able to infect humans [<xref rid=\"B3-ijms-21-05437\" ref-type=\"bibr\">3</xref>]. Other species pathogenic towards animals are <italic>Anaplasma bovis</italic>, <italic>Anaplasma centrale</italic>, <italic>Anaplasma ovis</italic> and <italic>Anaplasma platys</italic> [<xref rid=\"B4-ijms-21-05437\" ref-type=\"bibr\">4</xref>].</p><p><italic>Ehrlichia</italic> species (order Rickettsiales, family Anaplasmataceae) are able to infect several vertebrate hosts [<xref rid=\"B5-ijms-21-05437\" ref-type=\"bibr\">5</xref>]. <italic>Ehrlichia</italic> genus includes six species: <italic>E. chaffeensis</italic>, <italic>E. ewingii</italic>, <italic>E. canis</italic>, <italic>E. muris</italic>, <italic>E. ruminantium</italic> and <italic>E.ovis</italic> that affect several vertebrate hosts [<xref rid=\"B6-ijms-21-05437\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijms-21-05437\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijms-21-05437\" ref-type=\"bibr\">8</xref>]. <italic>E. chaffeensis</italic> and <italic>E. ewingii</italic> are also important zoonotic agents [<xref rid=\"B9-ijms-21-05437\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijms-21-05437\" ref-type=\"bibr\">10</xref>]. <italic>Ehrlichia</italic> efficiently establishes an intracellular infection and avoids immune defenses in vertebrate and invertebrate hosts through complex molecular and cellular reprogramming strategies [<xref rid=\"B11-ijms-21-05437\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijms-21-05437\" ref-type=\"bibr\">12</xref>].</p><p><italic>Rickettsia</italic> genus (order Rickettsiales, family Rickettsiaceae) includes obligate intracellular bacteria causing increasingly emerging human febrile diseases, including Mediterranean Spotted Fever, Rocky Mountain Spotted Fever, Epidemic typhus, murine typhus, scrub typhus [<xref rid=\"B13-ijms-21-05437\" ref-type=\"bibr\">13</xref>]. Within the <italic>Rickettsia</italic> genus, Spotted Fever Group comprises two main pathogens: <italic>R. rickettsii</italic>, responsible for Rocky Mountain Spotted Fever, the most severe rickettsioses in the western hemisphere, and <italic>R. conorii</italic>, responsible for Mediterranean Spotted Fever, whose spreading and severity is increasing throughout Europe and Africa [<xref rid=\"B14-ijms-21-05437\" ref-type=\"bibr\">14</xref>]. The Typhus Group includes the pathogenic agents of epidemic typhus (<italic>R. prowazekii</italic>) and murine typhus (<italic>R. typhi</italic>), displaying severe outbreaks worldwide. After a tick bite, bacteria replication within the infection site can lead to a necrotic lesion (eschar) and, subsequently, endothelial cells are the first cellular targets for rickettsia infection, with vascular severe damages.</p><p><italic>Babesia</italic> sensu stricto (Order Piroplasmida, family Babesiidae) includes infecting agents of a wide range of domestic and wild animals [<xref rid=\"B15-ijms-21-05437\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijms-21-05437\" ref-type=\"bibr\">16</xref>]. It comprehends both the so-called large (<italic>B</italic>. <italic>caballi</italic>, <italic>B</italic>. <italic>bigemina</italic>, <italic>B</italic>. <italic>canis</italic>, <italic>B</italic>. <italic>rossi</italic>, <italic>B</italic>. <italic>vogeli</italic>) and small (<italic>B</italic>. <italic>gibsoni</italic>, <italic>B</italic>. <italic>bovis</italic>) <italic>Babesia</italic> species [<xref rid=\"B17-ijms-21-05437\" ref-type=\"bibr\">17</xref>]. The pathogens infect red blood cells where they reproduce asexually.</p><p><italic>Theileria</italic> sensu stricto species (order Piroplasmida, family Theileridae) are the aetiologic agents of a variety of diseases in domestic and wild ruminants [<xref rid=\"B16-ijms-21-05437\" ref-type=\"bibr\">16</xref>,<xref rid=\"B18-ijms-21-05437\" ref-type=\"bibr\">18</xref>]. They include all the <italic>Theileria</italic> species pathogens for cattle, i.e. <italic>T. annulata</italic>, <italic>T. parva</italic>, <italic>T. orientalis</italic> [<xref rid=\"B17-ijms-21-05437\" ref-type=\"bibr\">17</xref>]. After the tick bite, injected sporozoites infect leukocytes and multiply inside them by merogony. Mature schizonts develop into merozoites, which are released and invade erythrocytes, forming piroplasms [<xref rid=\"B19-ijms-21-05437\" ref-type=\"bibr\">19</xref>].</p><p>The interaction of tick-transmitted pathogens with the host immune system has been widely studied [<xref rid=\"B20-ijms-21-05437\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijms-21-05437\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05437\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05437\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05437\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijms-21-05437\" ref-type=\"bibr\">25</xref>].</p><p>In the present review, we discuss the main interactions of the above-mentioned pathogens with different effector mechanisms of the host innate immunity, with a particular attention to the activation of the inflammasome, the leading actor of innate immunity. The review also discusses new targets of investigation arising by bioinformatics analysis.</p></sec><sec id=\"sec2-ijms-21-05437\"><title>2. Inflammasomes as Key Multimolecular Mechanisms Reacting to Infections</title><p>The innate immune signalling structures, the so called innate immune sensors, include Toll-like receptors (TLRs), Nod-like receptors (NLR), absent in myeloma (AIM2), C-type lectin receptors, retinoid acid-inducible gene I-like receptors (RIG I-like) and cyclic GMP-AMP synthase (cGAS)/STING (stimulator of interferon genes) [<xref rid=\"B26-ijms-21-05437\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijms-21-05437\" ref-type=\"bibr\">27</xref>]. The best analyzed pathways are those activated from TLR and NLR receptors, which localize and respond to antigens either on membrane surface or inside the cells, respectively [<xref rid=\"B28-ijms-21-05437\" ref-type=\"bibr\">28</xref>]. TLRs are pattern recognition receptors, which sense a broad range of microbial ligands leading to expression of genes involved in inflammation and other immune responses [<xref rid=\"B29-ijms-21-05437\" ref-type=\"bibr\">29</xref>], while NLR proteins are cytosolic pathogen recognition receptors (PRRs) able to oligomerize into a large inflammasome. Inflammasome is a protein scaffold with a key-role in host defence mechanism regulating the action of caspase-1 (CASP1) and the maturation of interleukin-1β (IL-1β) and IL-18.</p><p>The inflammasome activation was shown to be induced during infections by Gram negative- (i.e., <italic>Escherichia coli</italic>), Gram positive- (i.e., <italic>Staphylococcus aureus</italic>), Gram variable-bacteria (i.e., <italic>Mycobacterium tuberculosis</italic>), DNA- (i.e., <italic>Adenovirus</italic>), RNA-virus (i.e., <italic>Hepatitis C virus</italic>), Fungi (i.e., <italic>Aspergillus Fumigatus</italic>) and Protozoa (<italic>Leishmania</italic> spp.) [<xref rid=\"B30-ijms-21-05437\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijms-21-05437\" ref-type=\"bibr\">31</xref>].</p><p>Upon activation, multimeric complexes assemble to function as activation platforms for the autoproteolysis of CASP1, a protease which cleaves pro- IL-1β and IL-18 into their mature forms [<xref rid=\"B32-ijms-21-05437\" ref-type=\"bibr\">32</xref>] (<xref ref-type=\"fig\" rid=\"ijms-21-05437-f001\">Figure 1</xref>).</p><p>The most well-established inflammasomes are NLRP1 (nucleotide-binding domain leucine-rich repeat-containing [NLR] family, pyrin domain [PYD]-containing 1), NLRP3 (NLR family, PYD-containing 3), NLRC4 (NLR family, caspase activation and recruitment domain [CARD]-containing 4), AIM2 (absent in melanoma 2), and pyrin. It was reported that other NLR family proteins, including NLRP6 and NLRP9b, may also form functional inflammasomes [<xref rid=\"B33-ijms-21-05437\" ref-type=\"bibr\">33</xref>].</p><p>In particular, TLR2 promotes NLRP3 inflammasome activation providing a priming signal (signal 1) necessary for activation of the inflammasome by a second potassium-depleting signal (signal 2) [<xref rid=\"B29-ijms-21-05437\" ref-type=\"bibr\">29</xref>].</p><p>Activation of inflammasomes causes a sequence of responses, including release of IL-1β andIL-18 and the induction of pyroptotic, or inflammatory, cell death through cleavage of gasdermin D (GSDMD) [<xref rid=\"B34-ijms-21-05437\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijms-21-05437\" ref-type=\"bibr\">35</xref>]. GSDMD is a pore-forming protein, which cleavage by the inflammatory caspases critically determines pyroptosis by releasing the cleaved gasdermin-N domain that bears intrinsic pyroptosis-inducing activity [<xref rid=\"B36-ijms-21-05437\" ref-type=\"bibr\">36</xref>]. In particular, active CASP1 enzymatically cleave GSDMD into two fragments (the N domain and C domain). The GSDMD-N domain can form pores on lipid membranes, drive K+ efflux and induce pyroptosis through cell membrane disruption [<xref rid=\"B37-ijms-21-05437\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijms-21-05437\" ref-type=\"bibr\">38</xref>].</p><p>Inflammasomes can interact directly with the inflammatory effector CASP1 through CARDs or by utilizing the adaptor apoptosis-associated speck-like (ASC) protein to mediate the interaction between PYD-containing sensors and CARD-containing CASP1.</p><p>Non-canonical inflammasome activation has two main effects: (1) the induction of pyroptosis and (2) the secretion of the pro-inflammatory cytokines IL-1β and IL-18 via the activation of the canonical NLRP3 [<xref rid=\"B39-ijms-21-05437\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijms-21-05437\" ref-type=\"bibr\">40</xref>,<xref rid=\"B41-ijms-21-05437\" ref-type=\"bibr\">41</xref>]. GSDMD is the effector molecule linking these two downstream processes [<xref rid=\"B34-ijms-21-05437\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijms-21-05437\" ref-type=\"bibr\">35</xref>]. Indeed, murine CASP11 (CASP4 and CASP5 in humans) oligomerizes upon binding with cytosolic LPS and becomes active. Active CASP4/5 cleaves GSDMD to drive pyroptosis and NLRP3 inflammasome-dependent cleavage of CASP1 [<xref rid=\"B42-ijms-21-05437\" ref-type=\"bibr\">42</xref>]. CASP4-mediated NLRP3 activation depends on potassium efflux [<xref rid=\"B43-ijms-21-05437\" ref-type=\"bibr\">43</xref>].</p><p>Some recent studies showed that LPS is not directly recognized by caspases but through a receptor called guanylate-binding protein 1 (GBP-1), which protects in a cell-autonomous manner against infection with bacteria, parasites and viruses and promotes the activation of human CASP4 upon transfection of LPS [<xref rid=\"B44-ijms-21-05437\" ref-type=\"bibr\">44</xref>]. Moreover, GBP1 acts as a cytosolic LPS sensor detecting and targeting the LPS-containing membranes of Gram-negative bacteria, where it assembles a platform that promotes CASP4 recruitment and activation [<xref rid=\"B45-ijms-21-05437\" ref-type=\"bibr\">45</xref>].</p><p>An oversimplification of the interactions between canonical and non canonical inflammasomes could happen as follows: the canonical inflammasomes, through the activation of CASP1, generate the mature forms of IL1β and IL18 while the non canonical inflammasomes, activated by LPS, through bioactivity of CASP4 contributes with IL1β and IL18 to inflammatory processes and pyroptosis.</p><p>There are probably aspects of inflammasome biology, which may be elucidated in the context of infections with uncommon pathogens. Ticks transmitted pathogens are deeply different from other, more commonly studied microbes, especially for their life strategy and induced pathology. For example, many of them induce a less severe disease compared to the pathogens commonly activating inflammasome. Some tick-borne bacteria do not display canonical PAMPs, such as lipopolysaccharides (LPS) [<xref rid=\"B46-ijms-21-05437\" ref-type=\"bibr\">46</xref>,<xref rid=\"B47-ijms-21-05437\" ref-type=\"bibr\">47</xref>,<xref rid=\"B48-ijms-21-05437\" ref-type=\"bibr\">48</xref>].</p></sec><sec id=\"sec3-ijms-21-05437\"><title>3. Innate Immune Response to Tick-Borne Pathogens as the First and, in Many Cases, Resolutive Mechanisms of Protection</title><p><italic>Anaplasma</italic> genus show no evidence of peptidoglycan or LPS [<xref rid=\"B49-ijms-21-05437\" ref-type=\"bibr\">49</xref>], molecules typically identified by the innate immune system through recognition receptors expressed by macrophages or neutrophils, in order to clear the pathogens. More precisely, peptidoglycan and LPS have been eliminated at the genomic level through reductive evolution, as they lost genes for their biosynthesis [<xref rid=\"B50-ijms-21-05437\" ref-type=\"bibr\">50</xref>]. Consequently, their absence could allow <italic>Anaplasma</italic> to infect the host without activating any response, thus persisting in its cells without triggering an effective innate immune response. Nevertheless, persistent infections cannot be identified in cases with low bacteremia.</p><p><italic>A. phagocytophilum</italic> binds to sialylated scaffold proteins on neutrophil and granulocyte surface, adhering to polymorphonuclear leukocytes (PMNs) via P-selectin glycoprotein ligand 1 (PSGL-1), which is used to engage P-selectin displayed on inflamed endothelium during rolling adhesion. After bacterial internalization, the endosome ceases to mature and <italic>A</italic>. <italic>phagocytophilum</italic> divides until cell lysis or discharge [<xref rid=\"B51-ijms-21-05437\" ref-type=\"bibr\">51</xref>]. Infection results in significant disruption or inhibition of normal neutrophil functions, including endothelial cell adhesion, motility, apoptosis, IFN-γ signaling pathways, respiratory burst and phagocytosis. In vivo responses are dominated by IFN-γ and IL-10, while they lack tumor necrosis factor α (TNFα) and IL-4. IFN-γ activates macrophages production (nitric oxide, reactive oxygen species, TNFα, phagocytosis) causing cytotoxic effects [<xref rid=\"B49-ijms-21-05437\" ref-type=\"bibr\">49</xref>]. As a result, infection in humans and in animal models leads to a macrophage activation syndrome (MAS) [<xref rid=\"B52-ijms-21-05437\" ref-type=\"bibr\">52</xref>] where a key role in severity of tissue damage is related to high serum levels of IL-10, IL-12, ferritin and especially IFN-γ [<xref rid=\"B20-ijms-21-05437\" ref-type=\"bibr\">20</xref>,<xref rid=\"B31-ijms-21-05437\" ref-type=\"bibr\">31</xref>,<xref rid=\"B49-ijms-21-05437\" ref-type=\"bibr\">49</xref>,<xref rid=\"B50-ijms-21-05437\" ref-type=\"bibr\">50</xref>], secreted by NK and NKT cells, but also by a component of adaptive immunity, CD8 T lymphocytes.</p><p>As for <italic>A. marginale,</italic> vascular endothelial cells could serve as reservoirs for the bacteria and pass them on to PMNs, which are recruited to the feeding lesion by pro-inflammatory cytokines, without any returning to the circulatory system. In fact, <italic>A. marginale</italic> could easily infect red blood cells in the microcapillary environment, where there is intimate contact between erythrocytes and endothelial cells. Thus, a reservoir of <italic>Anaplasma</italic> in endothelium may provide an opportunity for the ongoing, direct cell-to-cell infection of blood cells, and at the same time avoid host immune effectors such as antibodies and complement [<xref rid=\"B53-ijms-21-05437\" ref-type=\"bibr\">53</xref>].</p><p>Major surface proteins (MSPs) are crucial for the interactions between <italic>A. marginale</italic> and host cells, especially adhesins, which mediate binding and entry into host cells, are essential for survival. <italic>A. marginale</italic> mostly uses its invasins OmpA, Asp14, and AipA to infect cells [<xref rid=\"B54-ijms-21-05437\" ref-type=\"bibr\">54</xref>]. <italic>A. marginale</italic> passes into host cells by endocytosis and it exhibits a biphasic developmental cycle. After entrance, the infectious dense form evolves into the non-infectious reticulated form, thus originating ample rickettsiae colonies by binary fission. The non-infectious form then reconverts into the dense form, which is released from cells and survives extracellularly, thus invading naive host cells and thereby initiating new infections.</p><p>NK cells actively contribute to the host defence against <italic>Ehrlichia</italic> when primary infection occurs. Wildtype C57BL/6 mice, that already overcame primary infection with <italic>Ehrlichia muris</italic>, showed a better protection versus <italic>Ixodes ovatus Ehrlichia</italic> (IOE), a highly virulent <italic>Ehrlichia</italic> strain causing fatal disease in naïve mice. Habib and colleagues [<xref rid=\"B22-ijms-21-05437\" ref-type=\"bibr\">22</xref>] showed that this protective memory against IOE was abolished if NK cells were depleted in such <italic>E. muris</italic>-primed mice, leading to 80% of mice deceased to the infection, similar to naïve mice infected with the same dose of IOE. NK cell-depleted mice showed also a smaller number of <italic>Ehrlichia</italic>-specific IFN-γ-producing memory CD4<sup>+</sup> and CD8<sup>+</sup> T-cells and a low titre of <italic>Ehrlichia</italic>- specific antibodies, suggesting a role for NK even in the adaptive response activation. In the same study, recipient Rag2<sup>−</sup>/<sup>−</sup>Il2rg<sup>−</sup>/<sup>−</sup>mice, lacking T, B and NK cells, which were transferred with NK cells from <italic>E. muris</italic>-primed mice, showed a higher survival rate suggesting that NK cells from <italic>E. muris</italic>-primed mice may acquire memory-like phenotype.</p><p>As concerning <italic>Ehrlichia</italic> interaction with macrophages, Zhang and collaborators [<xref rid=\"B55-ijms-21-05437\" ref-type=\"bibr\">55</xref>] showed that <italic>E. chaffeensis</italic> is able to significantly modify gene expression in a THP1 human monocyte cell line. For example, <italic>E. chaffeensis</italic> is able to inhibit the transcription of cytokines as IL-12, IL-15, and IL-18 acting in the early innate immune response. These cytokines are involved in stimulating TH1 and NK cells to produce IFN-γ, which activates macrophages [<xref rid=\"B56-ijms-21-05437\" ref-type=\"bibr\">56</xref>]. This downregulation could be useful to allow <italic>Ehrlichia</italic> survival against macrophages. Other mechanisms induced by <italic>E. chaffeensis</italic> included the up-regulation of Nuclear factor-kB (NF-kB) and apoptosis inhibitors, cyclins and CDK, to promote host cell survival and inhibition of proteins involved in membrane trafficking. A different mechanism recently discovered involves an <italic>Ehrlichia</italic>-dependent induction of a strong pro-inflammatory response leading to the cleavage of CASP1 in bone marrow–derived dendritic cells (BMDCs) and bone marrow derived–macrophages (BMDMs), promoting IL-1b, IL-1a and type I interferon (IFN-I) production. IFN-I increases host susceptibility to fatal ehrlichiosis, favouring <italic>Ehrlichia</italic>-induced immunopathology and replication. Possible mechanisms include inflammasome activation, autophagy reduction, suppression of protective CD4<sup>+</sup> T cells and NKT-cell responses against the pathogen [<xref rid=\"B57-ijms-21-05437\" ref-type=\"bibr\">57</xref>].</p><p>IFN-γ is a relevant macrophage-activating cytokine with a role in inhibiting <italic>E. chaffeensis</italic> infection at early stages. This cytokine is able to decrease iron availability, necessary for intracellular bacteria survival [<xref rid=\"B11-ijms-21-05437\" ref-type=\"bibr\">11</xref>]. However, IFN-γ has an effective anti-ehrlichial role only in the early stages of the infection, while it is ineffective when the infection is established. This result is due to the <italic>Ehrlichia</italic>-induced expression of transferrin receptor on the host cell surface and disruption of the IFN-γ induced signalling that involves the Janus kinase (JAK) and the signal transducer and activator of transcription (STAT) pathways. In particular, in the inhibition of IFN-γ induced tyrosine phosphorylation of STAT1, JAK1, and JAK2 a role for Tandem repeat protein 47 (TRP47) was reported though the interaction with the Protein Tyrosine Phosphatase, Non-Receptor Type 2 (PTPN2) [<xref rid=\"B58-ijms-21-05437\" ref-type=\"bibr\">58</xref>]. Tandem repeat proteins are secreted serine/threonine-rich proteins showing antibody epitopes in the tandem repeat regions [<xref rid=\"B59-ijms-21-05437\" ref-type=\"bibr\">59</xref>,<xref rid=\"B60-ijms-21-05437\" ref-type=\"bibr\">60</xref>,<xref rid=\"B61-ijms-21-05437\" ref-type=\"bibr\">61</xref>]. TRPs interact with a diverse network of host proteins involved in many host cellular processes including cell signalling, transcriptional and translational regulation, post-translational modification, intracellular trafficking, cytoskeletal organization, and apoptosis [<xref rid=\"B11-ijms-21-05437\" ref-type=\"bibr\">11</xref>]. Through the interaction with several host proteins, TRPs contribute to the ehrlichial evasion from the host immune response and to the infection establishment [<xref rid=\"B62-ijms-21-05437\" ref-type=\"bibr\">62</xref>]. In particular, TRP47 is an ehrlichial effector protein that interacts with multiple host cell proteins essential for cellular entry and survival.</p><p>As mentioned above, the first event in <italic>Rickettsia</italic> infection is the entry and replication within ECs [<xref rid=\"B63-ijms-21-05437\" ref-type=\"bibr\">63</xref>], an infectious process including adhesion, endocytosis, endosome escape, replication in cytosol and release of bacteria (via focal lysis for SFG <italic>Rickettsia</italic>, via cell lysis for TG <italic>Rickettsia</italic> and via budding for <italic>O. tsustugamushi</italic>). <italic>Rickettsia</italic> in ECs leads to the production of a plethora of inflammatory mediators. As an example, ECs release CCL5/RANTES, CCL2/MCP-1 and CXCL8/IL-8, three chemokines responsible for chemo-attraction of leucocytes, especially monocytes and neutrophils [<xref rid=\"B64-ijms-21-05437\" ref-type=\"bibr\">64</xref>]. Furthermore, the upregulation of VCAM1, ICAM-1, αVβ3 integrin, E-selectin in ECs supports transmigration and adhesion of leucocytes. Regarding the recruitment of NK cells and T cells, ECs produce both CXCL10/IP10 and CXCL9/MIG capable of recruiting T lymphocytes and NK cells. Since CXCL9 and CXCL10 have been demonstrated to act via CXCR3 [<xref rid=\"B65-ijms-21-05437\" ref-type=\"bibr\">65</xref>], and CXCR3 has been suggested to be present in NKT cells, the further recruitment and action of NKT cells in local <italic>Rickettsia</italic>-related inflammatory event may be suggested [<xref rid=\"B66-ijms-21-05437\" ref-type=\"bibr\">66</xref>]. Following chemo-attraction of innate and adaptive immune cells, ECs are further responsible for a promotion of inflammation via the production and release of relevant cytokines, as TNFα, IL-1β and IL-6, leading to further triggering of both innate and adaptive immune cells activity. Of importance, further inflammatory mediators secreted by ECs include prostaglandins, leading to classical alteration of vascular permeability and edema following rickettsial infections [<xref rid=\"B67-ijms-21-05437\" ref-type=\"bibr\">67</xref>], an event potentiated by endothelial denudation of vessels, COX-2 production and nitric oxide (NO) production by damaged endothelium.</p><p>Regarding NK cells, they have been widely studied in the context of rickettsial infection. Undoubtedly, NK cells are closely involved in early innate immunity against <italic>Rickettsia</italic>, and different studies have discovered a peculiar role for NK cells in IFN-γ secretion, prior to the same release by late activation of T cells [<xref rid=\"B68-ijms-21-05437\" ref-type=\"bibr\">68</xref>]. IFN-γ is of clear relevance in early immune defense against <italic>Rickettsia</italic>, acting on ECs and macrophages for promotion of bacterial killing via nitrergic pathways, tryptophan depletion, pathogen growth inhibition and other mechanisms [<xref rid=\"B69-ijms-21-05437\" ref-type=\"bibr\">69</xref>,<xref rid=\"B70-ijms-21-05437\" ref-type=\"bibr\">70</xref>,<xref rid=\"B71-ijms-21-05437\" ref-type=\"bibr\">71</xref>]. IFN-γ has a wide range of effects including enhancement of NK cells activity, augmented expression of MHC-I and MHC-II on Antigen-Presenting Cells (APCs), activation of inducible NOS, promotion of phagocytic activity [<xref rid=\"B67-ijms-21-05437\" ref-type=\"bibr\">67</xref>]. Enhanced NK cells activity and their increased IFN-γ production have been demonstrated for SFG rickettsiae, especially in <italic>R. conorii</italic>, with a main role of NK-derive IFN-γ-mediated immunity compared to cytotoxicity [<xref rid=\"B72-ijms-21-05437\" ref-type=\"bibr\">72</xref>]. Regarding TG <italic>Rickettsia</italic>, although enhanced NK cell activity has also been reported during early rickettsia infection, the contribution of NK cells to a relevant production of IFN-γ has been questioned by the observation of unaltered IFN-γ release after NK cell depletion in mice, suggesting that other cell sources may sustain IFN-γ production (i.e. tissue macrophages) [<xref rid=\"B73-ijms-21-05437\" ref-type=\"bibr\">73</xref>]. In contrast, infection by <italic>O. tsustugamushi</italic> has been showed to be correlated to an increase in NK cells; both CD69<sup>+</sup> and CD69<sup>−</sup> NK cells were found in patient blood samples, but, importantly, NK CD69<sup>+</sup> cells produce a larger amount of IFN-γ than CD69<sup>−</sup> ones [<xref rid=\"B74-ijms-21-05437\" ref-type=\"bibr\">74</xref>]. In a study by Keller and colleagues [<xref rid=\"B75-ijms-21-05437\" ref-type=\"bibr\">75</xref>] an increase of serum IFN-γ in a murine experimental model has been revealed, and in vitro studies demonstrated the role of IFN-γ in bacterial killing via induction of iNOS in macrophages. In summary, the NK cells-IFN-γ pathway is of importance in rickettsial infection, although further studies in humans are needed, especially for TG rickettsiae.</p><p>NKT cells are an emerging group of immune cells bridging innate and adaptive immunity, displaying both killing and cytokine-producing properties [<xref rid=\"B76-ijms-21-05437\" ref-type=\"bibr\">76</xref>]. Although the presence of NKT-activating ligands as α-linked glycuronylceramide and glycosyldiacylglycerol have been demonstrated in <italic>Rickettsia</italic> [<xref rid=\"B77-ijms-21-05437\" ref-type=\"bibr\">77</xref>], the contribution of such subset of immune cells in rickettsial infection is still overlooked. In a study involving an experimental mouse model for <italic>R. conorii</italic> infection [<xref rid=\"B78-ijms-21-05437\" ref-type=\"bibr\">78</xref>], an increased number of NKT cells have been reported in the spleen of infected mice at 3 days post-infection. Of relevance, a significant increase of IL-17<sup>+</sup> NKT cells were reported compared to a not relevant percentage of IFN-γ<sup>+</sup> NKT cells. Thus, cytokine microenvironment in <italic>R. conorii</italic> infection may favour the development of IL-17<sup>+</sup> cells, possibly sustaining the onset of immune response, although the protective versus pathogenic role of such subset will require further in-depth investigations.</p><p>As previously discussed, apart from endothelial cells, rickettsiae may invade other target cells especially tissue macrophages (MΦ). <italic>R. akari</italic> has been reported to promote the MΦ production of IL-6, TNF-α and IL-1β, as well as to induce upregulation of nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kB) [<xref rid=\"B79-ijms-21-05437\" ref-type=\"bibr\">79</xref>]. Pathogen recognition receptors as TLR2 and TLR4 have been identified as signal transduction receptors for <italic>R. akari</italic>. In particular, TLR4 seems to be essential for <italic>Rickettsia</italic> recognition given that its absence in KO mice leads to fatal disease at non-lethal doses [<xref rid=\"B80-ijms-21-05437\" ref-type=\"bibr\">80</xref>]. Experimental studies showed that <italic>R. australis</italic> infection results in MΦ release of a plethora of cytokines including IL-1β, IL-6, TNF-α, IL-12p40, IL-18, which are reduced in MyD88 KO mice, lacking TLR-related MyD88 signaling pathway [<xref rid=\"B81-ijms-21-05437\" ref-type=\"bibr\">81</xref>]. Importantly, bactericidal activity by macrophages have been attributed both to cytokine release and to NO production. However, apart from different studies confirming the capability of MΦ to effectively mediate rickettsia clearance, a hypothesis on the exploitation of MΦ by some <italic>Rickettsia</italic> species for dissemination throughout the host tissues has been also proposed [<xref rid=\"B74-ijms-21-05437\" ref-type=\"bibr\">74</xref>]. In addition, MΦ activity has been addressed to contribute deeply to pathogenic overactivation of immune system, suggesting that immune homeostasis in rickettsial infection represents a major issue for the host organism [<xref rid=\"B82-ijms-21-05437\" ref-type=\"bibr\">82</xref>].</p><p>Regarding DC, in an interesting study by Fang et al. [<xref rid=\"B83-ijms-21-05437\" ref-type=\"bibr\">83</xref>], the differential role of bone-marrow derived DCs (BDMCs) have been revealed in mice resistant (B6) or susceptible (C3H) to <italic>R. conorii</italic> infection. Importantly, BMDC from B6 mice were more potent in internalizing and processing <italic>Rickettsia</italic>, leading to a proficient IL-12p40 production and to priming of CD4<sup>+</sup> T cells. Instead, BDMCs from C3H mice displayed a late CD4<sup>+</sup> T-cell activation with development of immunosuppressive Foxp3<sup>+</sup> T Regulatory cells (T Regs), whose activation may explain the major infection susceptibility of C3H mice compared to B6. Such observations confirmed the necessity of a subtle regulation of the immune system during rickettsial infection, whose disturbance may influence the shift from a protective immune response to a pathogenic immune response by overwhelming immunity [<xref rid=\"B84-ijms-21-05437\" ref-type=\"bibr\">84</xref>]. In addition, further studies on KO mice have showed that TLR4-mediated recognition of <italic>Rickettsia</italic> by DCs could represent a fundamental event in initiating and promoting the early immune response [<xref rid=\"B71-ijms-21-05437\" ref-type=\"bibr\">71</xref>], suggesting that the definition of the exact role of TLRs in rickettsial infection should be addressed to depict a complete picture on early immunity related to rickettsiae.</p><p>Antigens derived from microbes could promote immune response due to the activation of effector mechanisms of innate immune response such as complement, natural killer cells, phagocytosis, receptors for molecular profiles. In this section, different types of interactions between tick-transmitted microbes and innate immunity will be discussed. Innate immune mechanisms could promote a rapid type of protection that could neutralize tissue damages induced by pathogens during the acute phase of infectious diseases.</p><p>Recognition of microbial compounds by host immune system could be due to receptors for molecular profiles such as TLRs mainly produced on APCs. It was reported that a lipid fraction of <italic>B. bovis</italic>-infected erythrocytes is able to stimulate macrophage response in bovine [<xref rid=\"B85-ijms-21-05437\" ref-type=\"bibr\">85</xref>]. Moreover, a different phlogistic response was induced by the lipids of the attenuated vaccine strain of <italic>B. bovis</italic> R1A (LA) and the pathogenic <italic>B. bovis</italic> strain S2P (LV) in murine peritoneal macrophages. LA and particularly its fractions of phosphatidic acid and phosphatidylserine plus phosphatidylinositol (PS + PI), induced an activation of these cells with lipid body formation, cyclooxygenase-2, TNF-α and IL-6 secretion. However, the comparison from wild type and TLR2 deficient (TLR2KO) mice demonstrate that TLR2 mediate macrophage activation by the lipid fractions [<xref rid=\"B86-ijms-21-05437\" ref-type=\"bibr\">86</xref>].</p><p>Another branch of innate immunity is constituted by the complement proteins. <italic>B. gibsoni</italic> ribosomal phosphoriboprotein P0 (rBgP0) previously reported to be a cross-protective antigen against <italic>Babesia</italic> infection, was used to immunize C57BL/6 wild-type (WT) and C3-deficient mice. Following the immunization with rBgP0, WT mice induced a specifically strong humoral response consisting of mixed immunoglobulins IgG1 and IgG2 associated with high production of IFN-γ in the supernatant of splenocytes. While C3KO mice had significantly decreased total IgG, IgG1 and IgG2b responses, the secretions of IL-12 and IFN-γ tended to be lower than those in WT mice. Partial protection was only observed in rBgP0-immunized WT mice, but not in C3KO mice or controls. Indeed, rBgP0-immunized WT mice showed significant reductions in the initiation of parasitaemia correlated with delayed mortalities and considerable survival rates [<xref rid=\"B87-ijms-21-05437\" ref-type=\"bibr\">87</xref>].</p><p>An additional interesting field of study of innate immunity interaction with microorganism antigens is the characterization of DC and NK and their interactions. It was shown that bovine NK cells can respond to microbial-exposed splenic DC with both regulatory and effector activities. The results demonstrated the influence of accessory DC phenotype and maturation state on the ability to induce NK cell activity along with the importance of the microbial agent being processed. In addition, NK cell activation by DC is cell-to-cell contact-dependent. These data are supported by immunohistological evidence for DC/NK cell contact during the early response to <italic>B. bovis</italic> infection [<xref rid=\"B23-ijms-21-05437\" ref-type=\"bibr\">23</xref>]. Two subsets of DC were characterized in cattle. The phenotypic profile of each population was CD13<sup>+</sup> and CD14<sup>+</sup>, respectively. The two cell populations differed in their ability to produce nitric oxide and had a different pattern of cytokine mRNA when stimulated with <italic>Mycobacterium bovis</italic> BCG or <italic>Babesia bovis</italic> [<xref rid=\"B88-ijms-21-05437\" ref-type=\"bibr\">88</xref>].</p><p>A different lymphocyte subset involved in innate immunity is constituted by lymphocytes with TCR γδ. Regarding the distribution of lymphocytes in the spleen of <italic>Babesia</italic>-infected spleen, the role of these cells is still not clear, but bovine WC1<sup>−</sup> γδT cells express CD2 and CD8. This subset can produce IFN-γ in response to cytokine stimulation, and it is found in largest proportion into spleen and intestine. The observed accumulation of these cells in the red pulp of naive calves infected with <italic>B. bovis</italic> is consistent with their expected role in the transition from innate to acquired immunity [<xref rid=\"B89-ijms-21-05437\" ref-type=\"bibr\">89</xref>].</p><p>NK quick response could be useful to protect hosts in the acute phase of babesiosis. Innate immunity was reacting to antigens for 2 weeks after infection of calves with <italic>B. bovis</italic>. Each calf that survived from the acute disease responded by increasing systemic IFN-γ and type-1 cytokine, macrophage and splenic CD8<sup>+</sup> activation. The activated CD8<sup>+</sup> cells display a bioactivity of NK-like cells, and the expansion occurred with an increase in IL-15 mRNA in the spleen [<xref rid=\"B90-ijms-21-05437\" ref-type=\"bibr\">90</xref>]. The specific splenic cell types producing IFN-γ in response to infection and the cellular factors regulating the induction have not been fully determined. Splenic NKp46<sup>+</sup> cells were identified and purified. They consisted of CD3<sup>−</sup>, CD2<sup>+/−</sup>, and CD8<sup>+/−</sup> populations. NK cells responded to exogenous IL-12 and IL-18 with the production of IFN-γ. Functionally, IL-18 served as a potent co-stimulant with IL-12 for IFN-γ production [<xref rid=\"B24-ijms-21-05437\" ref-type=\"bibr\">24</xref>].</p><p>Moreover, a strong innate spleen-dependent immune response was observed in calves to infection with <italic>Babesia</italic> parasites and it was related to the protection of young calves from clinical consequences of the infection. For example, when a first <italic>B. bovis</italic> infection occurs in young cattle they usually have no clinical consequences and a long-lasting immunity is found. On the contrary, a severe illness is induced in older cattle [<xref rid=\"B91-ijms-21-05437\" ref-type=\"bibr\">91</xref>]. As concerning this mechanism, it was reported for <italic>B. bovis</italic> that in young calves the infection with virulent strains of the pathogen is followed by an early production of IL-12 and IFN-γ, occurring before the influence of IL-10 and associated to protection. In the spleen of adult cattle, in contrast, delayed and lesser levels of IL-12 and IFN-γ mRNA expression were observed and they decreased following IL-10 expression. A CD8<sup>+</sup> T-cell expansion was revealed in both calves and adults, while an antibody response was elicited only in calves [<xref rid=\"B92-ijms-21-05437\" ref-type=\"bibr\">92</xref>].</p><p>Innate immunity is involved in the initial response against <italic>T. annulata</italic> infection. NK cells and several plasma proteins, including the proteins of the complement system, mediate the early defence against the pathogen. In addition, the innate immune system enhances adaptive immune responses towards the infectious agent. <italic>T. annulata</italic>-infected cells stimulate polyclonal naïve T lymphocyte proliferation by mechanisms involving NF-kB, MAPK and PI3-K-PKB pathways, followed by the up-regulation of pro-inflammatory cytokines and MHC class II molecules [<xref rid=\"B93-ijms-21-05437\" ref-type=\"bibr\">93</xref>].</p><p>In particular, the pathway of NF-kB, one of the most important cellular factors involved in the regulation of the host innate antimicrobial response, is targeted directly by the schizont in both the transformant species <italic>T. parva</italic> and <italic>T. annulata</italic>. This factor is essential for protection against apoptosis and it contributes to maintaining a continuous proliferation, regulating the expression of anti-apoptotic proteins and downregulating tumor-suppressor genes. Its inhibition results in immediate apoptosis of <italic>Theileria</italic>-transformed cells [<xref rid=\"B94-ijms-21-05437\" ref-type=\"bibr\">94</xref>]. <italic>Theileria</italic> parasites induce the constitutive NF-kB activation by the recruitment of large aggregates of IkB kinase (IKK) complexes to the surface of the transforming schizont. IKK is the regulator of NF-kB pathways and acts phosphorylating cytoplasmic inhibitors of NF-kB that are, subsequently, polyubiquitinated and degradated, allowing NF-kB to translocate to the nucleus [<xref rid=\"B95-ijms-21-05437\" ref-type=\"bibr\">95</xref>].</p><p>Aberrantly activated T lymphocytes switch to a Th1 phenotype, producing large quantities of IFN-γ [<xref rid=\"B96-ijms-21-05437\" ref-type=\"bibr\">96</xref>], which, together with the excessive production of pro-inflammatory cytokines, is mainly responsible for parasite-induced pathology [<xref rid=\"B97-ijms-21-05437\" ref-type=\"bibr\">97</xref>]. In details, it was reported that <italic>T. parva</italic>-transformed cells produce exclusively IFN-γ, whereas <italic>T. annulata</italic>-infected cells express type I IFN, particularly IFN-β [<xref rid=\"B98-ijms-21-05437\" ref-type=\"bibr\">98</xref>]. Other proinflammatory cytokines expressed by <italic>T. annulata</italic>-infected cells included IL-1α, IL-1β, IL-6, TNFα [<xref rid=\"B99-ijms-21-05437\" ref-type=\"bibr\">99</xref>]. Following <italic>T. annulata</italic>-activation, macrophages elicit NO production, which can inhibit invasion and proliferation of the pathogen in host cells, even if, an excessive NO production plays a prominent role in the pathogenesis of the disease [<xref rid=\"B100-ijms-21-05437\" ref-type=\"bibr\">100</xref>].</p><p>Moreover, several studies have reported a role in host immune responses against <italic>T. annulata</italic> infections for TLRs, an important group of pattern recognition receptors, whose activation leads to the induction of IFNs and cytokines acting in host defence and pathogenesis [<xref rid=\"B101-ijms-21-05437\" ref-type=\"bibr\">101</xref>]. In particular, it was found that mRNA expression of TLR10 was significantly higher in <italic>T. annulata</italic>-infected PBMCs from the more resistant indigenous cattle (<italic>Bos indicus</italic>) with respect to crossbreds, suggesting a role for TLR10 in enhancing the host immune responses in <italic>T. annulata</italic> infections [<xref rid=\"B102-ijms-21-05437\" ref-type=\"bibr\">102</xref>].</p><p>Recently, the transcriptional profiles of genes involved in the TLR and NLR signalling pathways in response to <italic>T. annulata</italic> infection were investigated at different sampling times during the infection course. Authors found significant differences in the transcription levels of TLR1, TLR6, TLR10, NLRP1, and MyD88 genes and their downstream signalling molecules in samples collected from 72 h to 168 h following <italic>T. annulata</italic> infection, compared to pre-infection values. The serum concentrations of IL-6, IL-1β, and TNFα significantly increased at 96 h and 168 h postinfection. These results may contribute in determining the mechanisms of TLR and NLR signalling pathways in immune response against <italic>T. annulata</italic> [<xref rid=\"B103-ijms-21-05437\" ref-type=\"bibr\">103</xref>].</p></sec><sec id=\"sec4-ijms-21-05437\"><title>4. Gene Ontology Analysis of Interactions between Innate Immune Response and Tick-Borne Pathogens</title><p>Statistical overrepresentation analysis of enriched genes can reveal high significance in association with Gene Ontology (GO) terms for biological function, clearly reflecting a molecular signature of innate immune response to tick-borne pathogens, in particular the inflammasome activation. At least six NLRP Nucleotide-binding domain and Leucine rich Repeat-containing receptors (NLRP) with different activation stimuli functions, such as bacterial toxins able to active NLRP3 or lipopeptides activating stimuli on NLRP7, can trigger CASP1. Therefore, inflammasomes can be formed by several different NLR family sensors. As explained above, the tick-borne pathogen infections are not controlled by innate immunity, but a pro-inflammatory state occurs upon innate immune cells activation of the inflammasome via TLR2, leading to the mature forms of IL-1β and IL-18, cleaved by caspases, which consequently cause a macrophages activation. Based on protein-chemical interactions, the correlation between inflammasome and macrophages activation pathways is not direct, as they are mediated by apoptosis-related cysteine peptidase 1 (CASP1). Macrophage activation is controlled by IFN-γ signalling pathways and tick-borne pathogens infection leads to a MAS, caused by a significant increase of IFN-γ secretion induced by the transcription of cytokines like IL-12, IL-15, and IL-18 (<xref ref-type=\"fig\" rid=\"ijms-21-05437-f002\">Figure 2</xref>). The increasing of IFN-γ has a positive transcriptional regulation or activation effect on STAT1, which contributes to the transcriptional up-regulation of a number of genes involved in the inflammasome activation.</p><p>Furthermore, the TNFα and IL-4 play a critical role in this downregulation and in MAS. As can be seen in <xref ref-type=\"fig\" rid=\"ijms-21-05437-f001\">Figure 1</xref>, IL-4 has a negative transcriptional regulation action on toll-like receptor 2, while the expression of TNFα has inhibition action on IFN-γ; consequently, this balance may have effect on the inflammasome assembly. To better understand the role of this inhibition in human and animal infection models, we chose some biological terms to create an annotation data set to analyse the weak part of innate immunity, which cannot control the infection in its early onset. These Gene Ontology (GO) terms are: interferon-gamma secretion (GO:0072643), inflammasome complex (GO:0061702), transferrin receptor activity (GO:0004998), macrophage activation (GO:0042116), and toll-like receptor signaling pathway (GO:0002224). A total number of 2388 of annotations were recorded (<xref rid=\"ijms-21-05437-t001\" ref-type=\"table\">Table 1</xref>).</p><p>Based on this gene set, the GO analysis was run on PANTHER GO-slim Biological Process on False Discovery Rate (FDR), with <italic>p</italic> < 0.05. Then, the GO biological process, which is related to innate immunity, was chosen with a total number of 166 genes observed (<xref rid=\"ijms-21-05437-t002\" ref-type=\"table\">Table 2</xref>).</p><p>Another role in inducing the expression of Transferrin receptor and iron control is played by iron ion transport genes, with seven genes involved in our dataset (TCIRG1, TFR2, TFRC, ATP6V1G2, LTF, TF, SLC11A1). Moreover, in our gene interaction analysis, we showed that transferrin has a positive activation on IL-18.</p><p>As explained before, the increasing of IFN-γ can cause MAS, as NK cells display a pivotal role in early innate immunity against rickettsial infection by IFN-γ secretion via different pathways. We also chose some biological terms for creating an annotation dataset to study how NK cells and IFN-γ secretion lead to an early innate immune response against <italic>Rickettsia</italic> or other pathogens. These terms were: natural killer cell activation (GO:0030101), regulation of interferon-gamma secretion (GO:1902713), interferon-gamma secretion (GO:0072643), response to redox state (GO:0051775), tryptophan metabolic process (GO:0006568), nitric-oxide synthase activity (GO:0004517), regulation of interleukin-17 secretion (GO:1905076). A total of 202 annotations were recorded for these gene ontology terms (<xref rid=\"ijms-21-05437-t003\" ref-type=\"table\">Table 3</xref>).</p><p>Based on this gene set, GO analysis was run on PANTHER GO-slim Biological Process on FDR with <italic>p</italic> < 0.05. Then GO biological process was chosen with a total number of 202 genes observed. Our GO study and gene product interaction showed that at the level of high confidence interaction score (0.700), there is an significant interaction score between Myeloid Differentiation Primary Response Protein (MyD88) and the TLR and IL-1 receptor signalling pathway in the innate immune response (<xref ref-type=\"fig\" rid=\"ijms-21-05437-f001\">Figure 1</xref>). Furthermore, the interaction between these two biological functions (IFN-γ production/regulation of NK-mediated immunity) probably represent the hotlink between macrophages and NK cells innate immune response. Based on experimental and biochemical data, posttranslational modifications between MyD88 and JAK2 were identified.</p><p>In our dataset, there is a significant score for JAK2 receptor involved in various processes either in innate or adaptive immunity, consequently leading to IFN-γ production where there is a lack of IL-12A and IL-18 due to MAS, for example in response to <italic>A. phagocytophilum</italic> infection (<xref ref-type=\"fig\" rid=\"ijms-21-05437-f003\">Figure 3</xref>).</p><p>Another essential role of MyD88 and the JAK-STAT pathways is linked to IFN-α (IFNA1, IFNA4, IFNA5, IFNA6, IFNA7, IFNA8, IFNA10, IFNA14, IFNA16, IFNA17, IFNA20, IFNA21). These genes are linked with a biological function known as natural killer cell activation that is involved in immune response and positive regulation of IFN-γ due to NK activated by specific antigens or mitogens, displaying antiviral activity (GO:0002323, raw <italic>p</italic>-value = 7.23 × 10<sup>−23</sup>, FDR = 6.77 × 10<sup>−21</sup>; <xref ref-type=\"fig\" rid=\"ijms-21-05437-f004\">Figure 4</xref>).</p><p>Moreover, our gene interactions study showed that IL-12, STAT5A and STAT5B have a negative inhibition on IL-17 and positive activation on FAS (TNF receptor superfamily, member 6), which consequently leads to activation of CASP8, apoptosis-related cysteine peptidase (significant score: 0.567) (<xref ref-type=\"fig\" rid=\"ijms-21-05437-f005\">Figure 5</xref>).</p><p>As discussed above, one of the mechanisms affected by <italic>E. chaffeensis</italic> is the up-regulation of NF-kB1 and apoptosis inhibitors. Based on our dataset, the critical gene in this network is Nucleotide Binding Oligomerization Domain Containing 1 (NOD1), as there is evidence for post-translational modification and expression with inhibition effect on NF-kB1, although this interaction is not direct but mediated by receptor-interacting serine-threonine kinase 2 (RIPKI2). Even if our Gene Ontology computation is mainly analyzing human and mouse genome, it was found that the same models of inflammasome dysregulation and interactions with other molecules were detectable in different species [<xref rid=\"B104-ijms-21-05437\" ref-type=\"bibr\">104</xref>]. Clearly, deeper molecular analysis in ruminants, dogs and cats are needed to assess how inflammasomes-side effects could be relevant in cells from several species.</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijms-21-05437\"><title>5. Conclusions</title><p>Even though recent studies have shed light on the role of innate immunity during tick-borne infections, much remains unknown. Host immune response could interact with <italic>Babesia</italic> spp., <italic>Rickettsia</italic> spp., <italic>Anaplasma</italic> spp., <italic>Ehrlichia</italic> spp. and <italic>Theileria</italic> spp. using different cells and/or molecules responsible for the innate immune response onset. Effector mechanisms of innate immune responses, displayed by host in short time, are able to inhibit symptoms in the acute infection phases, but they do not contain the infection. Instead, this task is up to T and B cell responses, mounted by the immune system afterwards and able to induce a long-term protection against pathologies induced by tick-borne microbes.</p><p>Tick-transmitted microbes differ both physiologically and in their pathogenic potential from other well-characterized pathogens. This is likely the result of the exposition of TBPs to the evolutionary pressures of complex interactions among vertebrate hosts and vectors [<xref rid=\"B105-ijms-21-05437\" ref-type=\"bibr\">105</xref>,<xref rid=\"B106-ijms-21-05437\" ref-type=\"bibr\">106</xref>]. As previously proposed [<xref rid=\"B107-ijms-21-05437\" ref-type=\"bibr\">107</xref>], tick-host-pathogen molecular interactions evolved as conflict and cooperation with mutual beneficial effects for all parts. These interactions evidence coevolutionary mechanisms by which pathogens manipulate tick vector and animal host protective responses to facilitate the infection and transmission of the disease.</p><p>An example of this selective pressure may be the ability of <italic>A. phagocytophilum</italic> to persist in immune-competent hosts between seasons of tick activity. This ability is the result of a complex and coordinated interaction that have led the bacterium to reduce its genomes to only essential genes allowing for nearly infinite numbers of recombined antigens and macromolecular exchange with its host cell [<xref rid=\"B3-ijms-21-05437\" ref-type=\"bibr\">3</xref>,<xref rid=\"B108-ijms-21-05437\" ref-type=\"bibr\">108</xref>,<xref rid=\"B109-ijms-21-05437\" ref-type=\"bibr\">109</xref>]. Another finding supporting the close co-evolutionary relationship among microorganisms, ticks and animal hosts resulted by a recent geographically extensive phylogenetic study on groEL sequences of <italic>A. phagocytophilum,</italic> showing a considerable spread of some of its haplotypes and the affinity of some others towards well-defined groups of vertebrates, ticks and regions [<xref rid=\"B110-ijms-21-05437\" ref-type=\"bibr\">110</xref>].</p><p>Despite some tick-borne pathogens lack of PAMPs, they may elicit inflammasome activation by inducing a dysregulated state within the host cell by causing aberrant compartmentalization of molecules, proteins and/or lipids. Consequently, the downstream inflammasome signalling culminates in proinflammatory cytokine secretion [<xref rid=\"B111-ijms-21-05437\" ref-type=\"bibr\">111</xref>] and changes in their plasma levels can be detected as early as on days 2–5 after tick bite. The GO analysis underlined five key points that need to be deeply characterized to better understand the role of innate immunity in response to tick borne pathogens. These key points include IFN-γ secretion, inflammasome complex, transferrin receptor activity, macrophage activation and TLR signalling pathway. IFN-γ secretion, TLR signalling and macrophage activation in immune system of hosts infected by tick-borne pathogens were previously studied but a more profound analysis of these mechanisms needs to be performed for an optimal understanding of the role of these pathways in immunity against tick-borne pathogens. Future research projects need to better characterize the role of the transferrin receptor pathway in innate immune response to tick-borne pathogens.</p><p>We hypothesize that research in the fields of inflammasome as well as transferrin receptor pathway, will advance the discovery of mechanistic details of inflammasome activation and innate immunity, which may ultimately be related with disease progression and immunological resistance to tick-borne infections. The deep knowledge of the innate immunity mechanisms could lead to design new strategies of diagnosis and prevention of tick-borne diseases and allow the assessment of possible new immunotherapies for these infectious diseases.</p></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization and Supervision, G.S.; Writing—Review & Editing, A.T.; Writing—Original Draft Preparation, V.B. and S.V. (Sara Villari); Project Administration S.V. (Stefano Vullo); Resources and Data Curation, M.P.L.M. and D.D.L.; Software and Visualization M.S.A.; Supervision, J.d.l.F. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was funded by Italian Ministry of Health RC IZSSI 07/18 and RC IZSSI 08/19.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijms-21-05437\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Rizzoli</surname><given-names>A.</given-names></name><name><surname>Silaghi</surname><given-names>C.</given-names></name><name><surname>Obiegala</surname><given-names>A.</given-names></name><name><surname>Rudolf</surname><given-names>I.</given-names></name><name><surname>Hubálek</surname><given-names>Z.</given-names></name><name><surname>Földvári</surname><given-names>G.</given-names></name><name><surname>Plantard</surname><given-names>O.</given-names></name><name><surname>Vayssier-Taussat</surname><given-names>M.</given-names></name><name><surname>Bonnet</surname><given-names>S.</given-names></name><name><surname>Spitalská</surname><given-names>E.</given-names></name><etal/></person-group><article-title>Ixodes Ricinus and Its Transmitted Pathogens in Urban and Peri-Urban Areas in Europe: New Hazards and Relevance for Public Health</article-title><source>Front. 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Med.</source><year>2015</year><volume>21</volume><fpage>677</fpage><lpage>687</lpage><pub-id pub-id-type=\"doi\">10.1038/nm.3893</pub-id><pub-id pub-id-type=\"pmid\">26121197</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijms-21-05437-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Inflammasomes activation and production of IL1β and IL18: Surface-expressed TLRs, such as TLR1, 2, 5, and 6, bind TLR-dependent stimuli, for example bacterial PAMPs. Following this binding, TLRs, through the adaptor protein MyD88, activate the transcription factor NF-κB to induce the expression of inflammatory genes, such as IL1β and IL18, leading to the production of pro-IL1β and pro-IL18. On the other hand, different PAMPs or DAMPs activate the inflammasome through various NLRs, such as NLRP3. Inflammasome leads to active Caspase-1 that processes pro-IL1β/pro-IL18, leading to the active form of IL1β and IL18. Active cytokines leave the cell and act as pro-inflammatory molecules.</p></caption><graphic xlink:href=\"ijms-21-05437-g001\"/></fig><fig id=\"ijms-21-05437-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Interaction among IFN-γ, TLR2, CASP1, TNFα and IL-4 that may have effect on inflammasome assembly and activation during tick-borne pathogens infection.</p></caption><graphic xlink:href=\"ijms-21-05437-g002\"/></fig><fig id=\"ijms-21-05437-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Interaction among MyD88, Toll-like receptor 4, IL-1 receptor signalling pathway in the innate immune response to tick-borne pathogens that could positively control the IFN-γ secretion; meanwhile, there is lack of IL-18 and IL-12 due to MAS.</p></caption><graphic xlink:href=\"ijms-21-05437-g003\"/></fig><fig id=\"ijms-21-05437-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Interaction between MyD88 and JAK-STAT pathway, linked to IFN-α; this interaction is mediated by suppressors of cytokine signalling 3, SOCS family proteins which are part of a classical negative feedback system regulating cytokine signal transduction.</p></caption><graphic xlink:href=\"ijms-21-05437-g004\"/></fig><fig id=\"ijms-21-05437-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>Interaction between IL-1β and I-kB kinase/NF-kB signalling pathway, which have positive activation of Nuclear Factor Kappa B Subunit 1. NF-kB is a pleiotropic transcription factor present in almost all cell types. It is the endpoint of a series of signal transduction events initiated by a vast array of stimuli related to many biological processes such as inflammation, immunity, differentiation, cell growth, tumorigenesis and apoptosis. Positive activation of CASP8 (score: 0.900).</p></caption><graphic xlink:href=\"ijms-21-05437-g005\"/></fig><table-wrap id=\"ijms-21-05437-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05437-t001_Table 1</object-id><label>Table 1</label><caption><p>Total number of GO Annotations for THE study of innate immune responses and tick-borne pathogens, which might be playing a critical role in TNF alpha and IL-4 downregulation and Macrophage Activation Syndrome.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">GO ID</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">GO Terms</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Number of Genes Associated with GO Terms</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GO:0072643</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IFN-γ secretion</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">483</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GO:0061702</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">inflammasome complex</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">463</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GO:0004998</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">transferrin receptor activity</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">344</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GO:0042116</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">macrophage activation</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">897</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GO:0002224</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">toll-like receptor signalling pathway</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1371</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Total number in all GO terms </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2388</td></tr></tbody></table></table-wrap><table-wrap id=\"ijms-21-05437-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05437-t002_Table 2</object-id><label>Table 2</label><caption><p>Total number of Annotations for GO study of innate immune responses and tick-borne pathogens which might be playing a critical role of NK cell in IFN-γ secretion.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">GO ID</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">GO Terms</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Number of Gene Products Associated with GO Terms</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GO:0030101</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">natural killer cell activation</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">122</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GO:1902713</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">regulation of interferon-gamma secretion</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GO:0072643</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">interferon-gamma secretion</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">44</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GO:0051775</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Response to redox state</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GO:0006568</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">tryptophan metabolic process</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">52</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GO:0004517</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">nitric-oxide synthase activity</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">109</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GO:1905076</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">regulation of interleukin-17 secretion</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Total number in all GO terms</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">202</td></tr></tbody></table></table-wrap><table-wrap id=\"ijms-21-05437-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05437-t003_Table 3</object-id><label>Table 3</label><caption><p>GO analysis of 2388 annotations; the immune-related terms have been chosen based on FDR <italic>p</italic> < 0.05.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Biological Process</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No. of Genes </th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Genes Involved </th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Raw <italic>p</italic>-Value</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">FDR</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">regulation of toll-like receptor 3 signaling pathway</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">TIRAP, F2RL1, WDFY1, FLOT1, PELI1, CAV1, TNFAIP3, Ptpn22, UBQLN1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.14 × 10<sup>−05</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.16 × 10<sup>−04</sup></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">regulation of interferon-gamma secretion</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Nr1h4, HMHB1, Cd160, Cd2, ZC3H12A, Ptpn22, App, Il36rn, CD244, Rasgrp1, ABL1, Lgals9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.74 × 10<sup>−16</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.28 × 10<sup>−14</sup></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">response to triacyl bacterial lipopeptide</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Tlr1, TLR2, Cd14</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.14 × 10<sup>−05</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.15 × 10<sup>−04</sup></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">type I interferon production</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5×</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Trex1, myd88, Irf3, Irf7, TBK1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.21 × 10<sup>−06</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.83 × 10<sup>−05</sup></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MyD88-independent toll-like receptor signaling pathway</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">32</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cd300lf, CD14, TICAM2, PRKCE, BIRC2, TLR3, IKBKB, UBB, TANK, tlr6, Irf3, UBE2D2, CHUK, TRAF3, Irf7, UBC, Tnip3, UBE2D1, CASP8, UBA52, UBE2D3, IKBKE, IKBKG, TLR4, TBK1, RPS27A, FADD, TICAM1, LY96, RIPK1, RAB11FIP2, BIRC3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.93 × 10<sup>−42</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.69 × 10<sup>−40</sup></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">toll-like receptor 2 signaling pathway</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">TLR2, IRAK1, RIPK2, TNIP2, PIK3AP1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.60 × 10<sup>−07</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.73 × 10<sup>−06</sup></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">positive regulation of antigen processing and presentation of peptide antigen via MHC class II</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">TREM2, PYCARD</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.27 × 10<sup>−03</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.14 × 10<sup>−02</sup></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">regulation of MyD88-dependent toll-like receptor signaling pathway</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CD300LF, IRF7, CD300A, IRF1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.27 × 10<sup>−03</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.14 × 10<sup>−02</sup></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">macrophage apoptotic process</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">IRF3, CTSL</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.27 × 10<sup>−03</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.14 × 10<sup>−02</sup></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">regulation of interleukin-4 biosynthetic process</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cd86, IRF-4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.27 × 10<sup>−03</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.14 × 10<sup>−02</sup></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">macrophage activation involved in immune response</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">TREM2, TREX1, PRKCE, IL33, TYROBP, Syk, DYSF, GRN, SUCNR1, TICAM1, LBP, SBNO2, HAVCR2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.06 × 10<sup>−17</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.97 × 10<sup>−16</sup></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">regulation of interleukin-18 production</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">TLR9, TLR2, IL10, NLRP9, Cd84</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.91 × 10<sup>−07</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.86 × 10<sup>−06</sup></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">detection of diacyl bacterial lipopeptide</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">TLR6, TLR2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.27 × 10<sup>−03</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.12 × 10<sup>−02</sup></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">interferon-gamma secretion</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">TCIRG1, VTCN1, LILRB1, ISG15, TRIM27, BTN3A2, GATA3, F2RL1, BTN3A1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.27 × 10<sup>−12</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.46 × 10<sup>−11</sup></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">interleukin-10 secretion</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ISG15, F2RL1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.27 × 10<sup>−03</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.12 × 10<sup>−02</sup></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">regulation of interleukin-1 beta biosynthetic process</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">JAK2, AGER, IFNG, App, TYROBP, AZU1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.54 × 10<sup>−08</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.97 × 10<sup>−07</sup></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">interleukin-1 beta secretion</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">TLR6, AIM2, CD36, TLR4, NLRC4, F2RL1, TMEM106A</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.45 × 10<sup>−09</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.09 × 10<sup>−08</sup></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">positive regulation of interleukin-18 production</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">TLR9, TLR2, NLRP9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.06 × 10<sup>−04</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.19 × 10<sup>−03</sup></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">regulation of interleukin-12 production</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">27</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ARRB2, TLR9, AGER, FOXP1, TLR3, IRAK3, IFNG, CD36, LILRB1, TLR2, TRAF6, IL12B, SYK, RIPK2, HSPD1, THBS1, SCIMP, TLR4, RELA, IL10, cd40, HMGB1, IRF1, TLR8, Lgals9, SLAMF1, TIRAP</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.05 × 10<sup>−29</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.62 × 10<sup>−27</sup></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">positive regulation of granzyme B production</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PTPN22, CD244</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.10 × 10<sup>−03</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.75 × 10<sup>−02</sup></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">regulation of gamma-delta T cell differentiation</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SYK, PTPRC</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.13 × 10<sup>−03</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.45 × 10<sup>−02</sup></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">iron ion transport</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">TCIRG1, TFR2, TFRC, ATP6V1G2, LTF, TF, SLC11A1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.53 × 10<sup>−04</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.66 × 10<sup>−03</sup></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">regulation of inflammatory response to wounding</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">AGER, Grn</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.13 × 10<sup>−03</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.43 × 10<sup>−02</sup></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">positive regulation of type I interferon-mediated signaling pathway</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">IRF3, IRF7, WNT5A, IKBKE, TBK1, FADD</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.26 × 10<sup>−07</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.79 × 10<sup>−06</sup></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">regulation of epithelial cell apoptotic process</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ARRB2, JAK2, IL6, Il4, CD160, PDPK1, FGB, IL13, Tnf, TNFAIP3, FGA, THBS1, cd40, GATA3, TNIP2, FGG, ABL1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.20 × 10<sup>−14</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.88 × 10<sup>−12</sup></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">negative regulation of inflammatory response to antigenic stimulus</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">FCGR2B, IL12B, NLRP6, IL10</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9.48 × 10<sup>−05</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.07 × 10<sup>−03</sup></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">negative regulation of interferon-beta production</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PYCARD, LILRB1, CACTIN, PTPRS</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.20 × 10<sup>−04</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.33 × 10<sup>−03</sup></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">cellular response to interferon-beta</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">TREX1, TLR3, AIM2, IFNB, IRF1, ACOD1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.80 × 10<sup>−06</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.02 × 10<sup>−05</sup></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">interleukin-12-mediated signaling pathway</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">JAK2, IFNG, IL12B, MIF, IL10, IL12A</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.13 × 10<sup>−04</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.24 × 10<sup>−03</sup></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">interferon-gamma-mediated signaling pathway</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">JAK2, IFNGR1, IRF4, IFNG, IRF3, Ifngr2, b2m, IRF1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.02 × 10<sup>−06</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.30 × 10<sup>−05</sup></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">macrophage activation involved in immune response</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Csf2, SNCA, JAK2, AGER, TREM2, TREX1, ITGB2, PRKCE, FOXP1, TLR3, C1QA, TRPV1, MAPT, IFNGR1, JUN, IL33, ITGAM, IFNG, TLR6, CX3CL1, TLR2, TLR7, CRTC3, CLU, IL13, App, SLC7A2, CD93, TYROBP, Tnf, SYK, DYSF, EDN2, FPR2, SUCNR1, AIF1, JMJD6, GRN, NAMPT, TLR4, AZU1, TICAM1, Ifngr2, TLR1, LBP, TLR8, TMEM106A, SLC11A1, SBNO2, HAVCR2, C5AR1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.06 × 10<sup>−17</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.97 × 10<sup>−16</sup></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">regulation of macrophage cytokine production</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Irak3, CD36, LILRB1, WNT5A, CD74, TLR4, RTN4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.92 × 10<sup>−08</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.23 × 10<sup>−06</sup></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">macrophage chemotaxis</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CX3CL1, SFTPD, AZU1, EDN2, CCL3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.62 × 10<sup>−06</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8.87 × 10<sup>−05</sup></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">negative regulation of macrophage apoptotic process</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CLU, LDLR</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.72 × 10<sup>−03</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.13 × 10<sup>−02</sup></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total genes</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">166</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">immune system process</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">28</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">regulation of immune system process</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">18</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr></tbody></table></table-wrap></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32727096</article-id><article-id pub-id-type=\"pmc\">PMC7432003</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155334</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05334</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Communication</subject></subj-group></article-categories><title-group><article-title>Building on Surface-Active Ionic Liquids for the Rescuing of the Antimalarial Drug Chloroquine</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Silva</surname><given-names>Ana Teresa</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05334\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Lobo</surname><given-names>Lis</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05334\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-0515-9470</contrib-id><name><surname>Oliveira</surname><given-names>Isabel S.</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05334\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Gomes</surname><given-names>Joana</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05334\">1</xref><xref ref-type=\"aff\" rid=\"af3-ijms-21-05334\">3</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-9506-3781</contrib-id><name><surname>Teixeira</surname><given-names>Cátia</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05334\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-0313-0778</contrib-id><name><surname>Nogueira</surname><given-names>Fátima</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05334\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Marques</surname><given-names>Eduardo F.</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05334\">3</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-1761-117X</contrib-id><name><surname>Ferraz</surname><given-names>Ricardo</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05334\">1</xref><xref ref-type=\"aff\" rid=\"af4-ijms-21-05334\">4</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-6018-4724</contrib-id><name><surname>Gomes</surname><given-names>Paula</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05334\">1</xref><xref rid=\"c1-ijms-21-05334\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05334\"><label>1</label>LAQV-REQUIMTE, Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, P-4169-007 Porto, Portugal; <email>up201303026@gmail.com</email> (A.T.S.); <email>up201606401@fc.up.pt</email> (J.G.); <email>catia.teixeira@fc.up.pt</email> (C.T.); <email>ricardoferraz@eu.ipp.pt</email> (R.F.)</aff><aff id=\"af2-ijms-21-05334\"><label>2</label>Global Health and Tropical Medicine, Instituto de Higiene e Medicina Tropical, Universidade Nova de Lisboa, P-1349-008 Lisboa, Portugal; <email>lis.lobo@ihmt.unl.pt</email> (L.L.); <email>FNogueira@ihmt.unl.pt</email> (F.N.)</aff><aff id=\"af3-ijms-21-05334\"><label>3</label>CIQ-UP, Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, P-4169-007 Porto, Portugal; <email>isabelmscoliveira@gmail.com</email> (I.S.O.); <email>efmarque@fc.up.pt</email> (E.F.M.)</aff><aff id=\"af4-ijms-21-05334\"><label>4</label>Ciências Químicas e das Biomoléculas, Escola Superior de Saúde, Politécnico do Porto, P-4200-072 Porto, Portugal</aff><author-notes><corresp id=\"c1-ijms-21-05334\"><label>*</label>Correspondence: <email>pgomes@fc.up.pt</email>; Tel.: +351-2-2040-2563</corresp></author-notes><pub-date pub-type=\"epub\"><day>27</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5334</elocation-id><history><date date-type=\"received\"><day>10</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>24</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Ionic liquids derived from classical antimalarials are emerging as a new approach towards the cost-effective rescuing of those drugs. Herein, we disclose novel surface-active ionic liquids derived from chloroquine and natural fatty acids whose antimalarial activity in vitro was found to be superior to that of the parent drug. The most potent ionic liquid was the laurate salt of chloroquine, which presented IC<sub>50</sub> values of 4 and 110 nM against a chloroquine-sensitive and a chloroquine-resistant strain of <italic>Plasmodium falciparum</italic>, respectively, corresponding to an 11- and 6-fold increase in potency as compared to the reference chloroquine bisphosphate salt against the same strains. This unprecedented report opens new perspectives in both the fields of malaria chemotherapy and of surface-active ionic liquids derived from active pharmaceutical ingredients.</p></abstract><kwd-group><kwd>antimalarial</kwd><kwd>chloroquine</kwd><kwd>ionic liquid</kwd><kwd>malaria</kwd><kwd>rescuing</kwd><kwd>repurposing</kwd><kwd>surface active</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05334\"><title>1. Introduction</title><p>Ionic liquids (ILs) are gaining prominence as chemical entities of interest in medicinal chemistry, as well as pharmaceutical science and technology [<xref rid=\"B1-ijms-21-05334\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijms-21-05334\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijms-21-05334\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijms-21-05334\" ref-type=\"bibr\">4</xref>]. In fact, ILs can contribute to overcoming the undesirable features of conventional saline forms of active pharmaceutical ingredients (APIs), such as polymorphism, low solubility, and limited bioavailability [<xref rid=\"B5-ijms-21-05334\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijms-21-05334\" ref-type=\"bibr\">6</xref>]. As such, the development of ILs derived from APIs (API-ILs) is an appealing strategy towards the rescuing of drugs that are falling into disuse due to these and other detrimental traits. With this idea in mind, and following our previous promising findings on cinnamic acid conjugates of classical antimalarial drugs [<xref rid=\"B7-ijms-21-05334\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijms-21-05334\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijms-21-05334\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijms-21-05334\" ref-type=\"bibr\">10</xref>], we applied the API-IL concept onto such drugs, by disclosing room temperature ionic liquids (RTILs) derived from primaquine (PQ) and cinnamic acids as triple-stage antimalarial hits [<xref rid=\"B11-ijms-21-05334\" ref-type=\"bibr\">11</xref>]. The most remarkable property of these RTILs was their increased activity against blood-stage malaria parasites, on which PQ has a practically negligible action [<xref rid=\"B12-ijms-21-05334\" ref-type=\"bibr\">12</xref>]. This might be due to a more efficient interaction of RTILs with membranes of infected erythrocytes than with healthy ones, according to subsequent studies using model lipid membranes [<xref rid=\"B13-ijms-21-05334\" ref-type=\"bibr\">13</xref>]. In view of this, we hypothesized that the combination of a blood schizonticide like chloroquine (CQ) with amphiphilic natural fatty acids might deliver new organic salts (<xref ref-type=\"scheme\" rid=\"ijms-21-05334-sch001\">Scheme 1</xref>) in the form of RTILs with enhanced blood-stage activity, possibly suitable for oral administration, as recently reported for lipid-based formulations of the lumefantrine docusate IL [<xref rid=\"B14-ijms-21-05334\" ref-type=\"bibr\">14</xref>].</p></sec><sec sec-type=\"results\" id=\"sec2-ijms-21-05334\"><title>2. Results and Discussion</title><sec id=\"sec2dot1-ijms-21-05334\"><title>2.1. Chemical Synthesis and Thermal Stability</title><p>Commercially available CQ phosphate salt was first converted into the free amine form <bold>1a</bold>, which then reacted with fatty acids <bold>2a</bold>–<bold>e</bold> via an acid-base neutralization previously reported by us [<xref rid=\"B11-ijms-21-05334\" ref-type=\"bibr\">11</xref>], to afford <bold>3a</bold>–<bold>e</bold> (<xref ref-type=\"scheme\" rid=\"ijms-21-05334-sch001\">Scheme 1</xref>). In parallel, <bold>1b</bold>, the primary amine analogue of <bold>1a</bold>, was prepared as previously reported by us [<xref rid=\"B7-ijms-21-05334\" ref-type=\"bibr\">7</xref>] and conjugated with <bold>2a</bold>–<bold>e</bold> to produce amides <bold>4a</bold>–<bold>e</bold> for comparative assessment of in vitro antimalarial activity alongside salts <bold>3a</bold>–<bold>e</bold> (<xref ref-type=\"scheme\" rid=\"ijms-21-05334-sch001\">Scheme 1</xref>). The organic salts <bold>3a</bold>–<bold>e</bold> were obtained in nearly quantitative yields as colorless to pale yellow viscous liquids (i.e., <bold>3a</bold>–<bold>e</bold> are all RTILs), whereas amides <bold>4a</bold>–<bold>e</bold> were obtained as white to brownish-yellow solids in moderate to good yields (<xref rid=\"ijms-21-05334-t001\" ref-type=\"table\">Table 1</xref>). Spectroscopic data, supplied as <xref ref-type=\"app\" rid=\"app1-ijms-21-05334\">Supplementary Materials (SM)</xref>, were in agreement with the expected structures. Notably, although proton nuclear magnetic resonance (<sup>1</sup>H-NMR) data are not conclusive regarding protonation of the basic groups in CQ (as spectra were acquired from hexadeuterated dimethylsulfoxide, DMSO-d6, which is an H-bond acceptor), it was possible to confirm complete transfer of the carboxylic acid proton to the aminoquinoline. This is because this proton was observed in the <sup>1</sup>H-NMR spectra of solutions of the fatty acids <bold>2a</bold>–<bold>e</bold> in DMSO-d6, but not in the solutions of their respective CQ salts <bold>3a</bold>–<bold>e</bold> in the same solvent (<xref ref-type=\"app\" rid=\"app1-ijms-21-05334\">Figure S1 of the SM</xref>). This is a remarkable finding, as it has been established by Stoimenovski et al. that a Δp<italic>K</italic><sub>a</sub> >10 is required to assure complete proton transfer between the proton donor and a tertiary amine [<xref rid=\"B15-ijms-21-05334\" ref-type=\"bibr\">15</xref>].</p><p>All compounds were analyzed by simultaneous thermogravimetric analysis (STA), as given in detail in the <xref ref-type=\"app\" rid=\"app1-ijms-21-05334\">SM</xref>, to assess their thermal stability, an important issue for APIs typically employed in the treatment of diseases, such as malaria, that are endemic to tropical and sub-tropical countries. The temperatures at which thermal degradation events were observed by STA are provided in <xref rid=\"ijms-21-05334-t001\" ref-type=\"table\">Table 1</xref>, and the corresponding thermograms are provided in the <xref ref-type=\"app\" rid=\"app1-ijms-21-05334\">SM</xref>. Data show that all the RTILs <bold>3a</bold>–<bold>e</bold> are slightly less thermally stable than the commercial CQ phosphate salt, but still remain unaltered up to about 90 °C (<bold>3a</bold>) or higher temperatures (<bold>3b</bold>–<bold>e</bold>). As expected, covalent amide analogues <bold>4a</bold>–<bold>e</bold> displayed higher thermal stability, with only one thermal degradation event occurring at temperature values that generally increased with the size of the alcanoyl chain, in the 291.3–336.5 °C range. Interestingly, RTIL <bold>3a</bold>–<bold>c</bold> presented two thermal degradation events, whereas <bold>3d</bold> and <bold>3e</bold> showed only one degradation event in the temperature range of the study (50–500 °C). The possibility that two degradation events might be occurring equally in these two cases, but at very close temperature values, cannot be ruled out at this stage. Still, data from STA clearly show that <bold>3a</bold>–<bold>e</bold> do not behave as the mere sum of their parent compounds, otherwise thermal degradation events would have been observed at the same values recorded for CQ and the relevant fatty acids <bold>2a</bold>–<bold>e</bold>. A deeper study to establish the mechanisms of thermal degradation of RTILs <bold>3a</bold>–<bold>e</bold> is under way, to enable the full profiling of these novel compounds.</p></sec><sec id=\"sec2dot2-ijms-21-05334\"><title>2.2. Antimalarial Activity In Vitro</title><p>Compounds <bold>3a</bold>–<bold>e</bold> and <bold>4a</bold>–<bold>e</bold> were evaluated in vitro according to previously reported methods (cf. <xref ref-type=\"app\" rid=\"app1-ijms-21-05334\">SM</xref>) [<xref rid=\"B11-ijms-21-05334\" ref-type=\"bibr\">11</xref>], against a CQ-sensitive (3D7) and a CQ-resistant (Dd2) strain of <italic>Plasmodium falciparum</italic> (<italic>Pf</italic>). Interestingly, while all RTILs <bold>3a</bold>–<bold>e</bold> did not pose any significant solubility issues in the course of the in vitro assays, only amides <bold>4a</bold>–<bold>c</bold> were sufficiently soluble in the same conditions. Moreover, although accurate values for solubility (<italic>log S</italic>) are yet to be determined, RTILs <bold>3a</bold>–<bold>e</bold> were also slightly more soluble in water (albeit low) than their covalent analogues <bold>4a</bold>–<bold>e</bold>. More importantly, all RTILs displayed stronger activity than CQ, which is classically formulated as a phosphate salt, against both CQ-sensitive and CQ–resistant strains of <italic>Pf</italic>, the species responsible for the deadliest form of human malaria. The activities of <bold>3a</bold>–<bold>e</bold> were in the low- to mid-nanomolar ranges against the 3D7 and Dd2 strains, respectively, with <bold>3c</bold> (derived from dodecanoic, or lauric, acid <bold>2c</bold>) being the most potent of the set. This RTIL was nearly 20-fold more potent than its amide counterpart, <bold>4c</bold>, and over 10-fold more potent than the reference drug, against the 3D7 strain. Regarding activity against the Dd2 strain, <bold>3c</bold> was virtually comparable to <bold>4c</bold>, but nearly five-fold better than CQ phosphate. The in vitro activity of <bold>3c</bold> was further compared to those of the parent fatty acid, <bold>2c</bold>, and of an equimolar mixture of this acid with the commercial CQ phosphate. Although <bold>2c</bold> was completely devoid of antiplasmodial activity, its equimolar mixture with the reference drug was significantly less active than <bold>3c</bold> against both strains. These observations further reinforce that <bold>3a</bold>–<bold>e</bold> are chemical entities on their own, and not the simple mixtures of their parent compounds. These data also support that, as originally hypothesized, acid-base pairing of CQ with fatty acids delivers RTILs showing a clear gain over the parent drug concerning in vitro antimalarial activity.</p><p>One interesting observation from in vitro data above was that the laurate salt of CQ, <bold>3c</bold>, seemed to possess the optimal size for antiplasmodial activity, which is slightly decreased for compounds <bold>3</bold> with shorter and longer fatty acid chains. Considering the amphiphilicity of fatty acids and derivatives [<xref rid=\"B16-ijms-21-05334\" ref-type=\"bibr\">16</xref>], and the probable enhancement of this property when pairing the fatty carboxylates with an amphiphilic cation-like protonated CQ, we anticipated that RTILs <bold>3</bold> could act as surface-active ionic liquids (SAILs) [<xref rid=\"B17-ijms-21-05334\" ref-type=\"bibr\">17</xref>], and that such activity might be one factor contributing to their enhanced antiplasmodial potency as compared to the standard formulation of CQ. To test this hypothesis, we carried out surface tension studies as follows (cf. experimental details in the <xref ref-type=\"app\" rid=\"app1-ijms-21-05334\">SM</xref>, <xref ref-type=\"sec\" rid=\"sec3-ijms-21-05334\">Section 3</xref>).</p></sec><sec id=\"sec2dot3-ijms-21-05334\"><title>2.3. Surface Tension Studies</title><p>The best antimalarial RTIL, <bold>3c</bold>, along with one shorter (<bold>3b</bold>) and two larger (<bold>3f</bold> and <bold>3g</bold>, respectively; X=(CH<sub>2</sub>)<sub>12</sub> and X=(CH<sub>2</sub>)<sub>14</sub> in <xref ref-type=\"scheme\" rid=\"ijms-21-05334-sch001\">Scheme 1</xref>) analogues, were further studied regarding their surface activity properties, to obtain a finer scrutiny of the effect of alkyl chain size variation on surface activity. The two additional compounds, <bold>3f</bold>,<bold>g</bold>, were synthesized as above described for <bold>3a</bold>–<bold>e</bold> (cf. <xref ref-type=\"scheme\" rid=\"ijms-21-05334-sch001\">Scheme 1</xref> and <xref ref-type=\"app\" rid=\"app1-ijms-21-05334\">SM</xref>).</p><p>As mentioned before, compounds <bold>3</bold> display low solubility in water. However, a first indication of their surface activity came from the foaming observed for saturated aqueous solutions of <bold>3c</bold> (cf. <xref ref-type=\"app\" rid=\"app1-ijms-21-05334\">Figure S2 of the SM</xref>) and, particularly, from surface tension measurements at 25.0 °C, which yielded a value of 29.8 ± 0.3 mN·m<sup>−1</sup>, a considerable decrease from the surface tension of neat water (72.0 mN·m<sup>−1</sup>), hence indicative of strong adsorption of the compound at the air–solution interface. We also verified that the different SAILs <bold>3b</bold>, <bold>3c</bold>, <bold>3f</bold> and <bold>3g</bold> have improved solubility in aqueous solutions of a conventional cationic surfactant, cetyltrimethylammonium bromide (CTAB). For comparisons, we evaluated the effect of adding each SAIL at a fixed 0.10 molar fraction, defined as <italic>x</italic><sub>SAIL</sub> = <italic>n</italic><sub>SAIL</sub>/(<italic>n</italic><sub>SAIL</sub> + <italic>n</italic><sub>CTAB</sub>), on the critical micellar concentration (<italic>cmc</italic>) of the systems and on the surface tension at the <italic>cmc</italic> (<italic>g</italic><sub>cmc</sub>). The results are shown in <xref ref-type=\"fig\" rid=\"ijms-21-05334-f001\">Figure 1</xref>a and the obtained <italic>cmc</italic> and <italic>g</italic><sub>cmc</sub> values are presented in <xref rid=\"ijms-21-05334-t002\" ref-type=\"table\">Table 2</xref>.</p><p>It is clear that all SAILs form mixed micelles with CTAB and induce a marked decrease in <italic>cmc</italic> compared to neat CTAB, even at the low <italic>x</italic><sub>SAIL</sub> studied (equal to a SAIL/CTAB molecular ratio of 1:9). <xref rid=\"ijms-21-05334-t002\" ref-type=\"table\">Table 2</xref> also shows that there is seemingly a U-shaped variation of <italic>cmc</italic> with an increasing chain length of fatty acids, a somewhat unexpected trend that, on the other hand, demonstrates that surface activities observed are not solely due to the presence of the fatty carboxylate. Significantly, the laurate derivative, <bold>3c</bold>, is the SAIL that brings about the highest decrease in <italic>cmc</italic>, with the mixture’s <italic>cmc</italic> being ca. 15 times smaller than that of neat CTAB. The corresponding <italic>g</italic><sub>cmc</sub> for <bold>3c</bold> is also very low, 22 mN·m<sup>−1</sup>, consistent with a strong surface adsorption for this mixture. We further tested the effect of increasing the molar fraction of <bold>3c</bold> on the <italic>cmc</italic> of the mixture (cf. surface tension curves in <xref ref-type=\"app\" rid=\"app1-ijms-21-05334\">Figure S3 of the SM</xref>). <xref ref-type=\"fig\" rid=\"ijms-21-05334-f001\">Figure 1</xref>b shows that the <italic>cmc</italic> continually decreases with increasing <italic>x</italic><sub>3c</sub>, again confirming the high interfacial activity of <bold>3c</bold>.</p><p>Taken together, these findings not only demonstrate that the <bold>3</bold> RTILs used in the surface activity assays do behave as SAILs, but also suggest that surface activity may influence the antimalarial action of these compounds, as the compound with the most potent antimalarial activity, <bold>3c</bold>, was also the one with a most dramatic effect in lowering <italic>cmc</italic> and surface tension of CTAB, in comparison to the effects exhibited by its counterparts derived from shorter (<bold>3b</bold>) or longer (<bold>3f</bold>,<bold>g</bold>) fatty acids. A more in-depth interpretation of this observation requires further studies, namely by including the butyric (<bold>3a</bold>) and caprylic (<bold>3b</bold>) acid derivatives, among others, in the surface activity assays, in particular because <bold>3a</bold> was also quite active in vitro, despite the fact that butyrate salts are not usually associated with self-assembling properties. Still, the “counterion” to butyrate is, in <bold>3a</bold>, a protonated hydrophobic 4-aminoquinoline, and is hence quite different to counterions used in most common butyrate salts, e.g., sodium butyrate. Moreover, distinct concentration ranges are used in the in vitro assays vs. the surface activity ones, which might put into question the comparability of data from both types of study.</p></sec></sec><sec id=\"sec3-ijms-21-05334\"><title>3. Concluding Remarks</title><p>The observed parallelism between surface activity and antimalarial activity hardly seems coincidental, meaning that this activity, despite being due primarily to the bioactive cation, (i.e., protonated CQ), is also significantly influenced by the amphipathicity and surface activity conveyed by the fatty carboxylate. To the best of our knowledge, this is a first-time disclosure of CQ-derived SAILs whose antimalarial activity is (i) higher than that of CQ, (ii) modulated by the amphipathic anion used, and (iii) influenced by surface activity. Furthermore, the ability of CQ-derived SAILs to co-assemble into colloidal nanostructures (in this case, mixed micelles with CTAB), strongly suggests that these systems could potentially act both as drugs and as enhanced drug delivery systems. We are aware that this may be speculative at this stage, but ongoing studies will shed light onto this and other open questions. Further physico-chemical, biophysical, and biological, including in vivo, studies are under way, which will allow us to fully validate the reported CQ-derived SAILs as a new antimalarial chemotype. Although ILs derived from API have been thoroughly explored over recent years [<xref rid=\"B18-ijms-21-05334\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijms-21-05334\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijms-21-05334\" ref-type=\"bibr\">20</xref>], validation of drug-derived SAILs acting as therapeutic agents that are able to promote their own intracellular delivery will represent a noteworthy development in molecular pharmacology.</p></sec><sec id=\"sec4-ijms-21-05334\"><title>4. Materials and Methods</title><sec id=\"sec4dot1-ijms-21-05334\"><title>4.1. Chemical Synthesis</title><sec id=\"sec4dot1dot1-ijms-21-05334\"><title>4.1.1. Conversion of Chloroquine Phosphate into <bold>1a</bold></title><p>Commercial chloroquine phosphate was converted into its free base form <bold>1a</bold> as previously described by us for primaquine [<xref rid=\"B11-ijms-21-05334\" ref-type=\"bibr\">11</xref>]. Briefly, triethylamine (1.5 mL) was added to a suspension of chloroquine bisphosphate (1.06 g, 2.05 mmol) in dichloromethane (DCM), and the mixture was stirred for 30 min at room temperature (RT). The organic layer was washed with water (10 mL × 3), dried over anhydrous Na<sub>2</sub>SO<sub>4</sub>, and evaporated to dryness under reduced pressure, to afford <bold>1a</bold> (0.63 g, 1.97 mmol) in nearly quantitative yield (93%), and with correct <sup>1</sup>H-NMR spectral data, as given in the <xref ref-type=\"app\" rid=\"app1-ijms-21-05334\">SM</xref>.</p></sec><sec id=\"sec4dot1dot2-ijms-21-05334\"><title>4.1.2. Synthesis of Chloroquine Analogue <bold>1b</bold></title><p>The synthesis of <bold>1b</bold> was performed as previously described by us [<xref rid=\"B7-ijms-21-05334\" ref-type=\"bibr\">7</xref>]. Briefly, 1,4-diaminobutane (1.12 g, 12.7 mmol) and 4,7-dichloroquinoline (0.25 g, 1.27 mmol) were stirred at 100 °C for 3 h. After cooling to RT, the mixture was diluted with DCM (25 mL), and the solution was washed with 5% aqueous Na<sub>2</sub>CO<sub>3</sub> (25 mL × 3). The organic layer was separated, dried over anhydrous Na<sub>2</sub>SO<sub>4</sub>, filtered, and concentrated to afford <bold>1b</bold> (0.22, 0.87) without need for further purification. Spectroscopic data were in agreement with previous reports [<xref rid=\"B7-ijms-21-05334\" ref-type=\"bibr\">7</xref>].</p></sec><sec id=\"sec4dot1dot3-ijms-21-05334\"><title>4.1.3. Synthesis of Ionic Liquids <bold>3</bold></title><p>All compounds <bold>3</bold> were synthesized by exactly the same experimental procedure, using the amounts of reactants included in <xref ref-type=\"app\" rid=\"app1-ijms-21-05334\">Table S1 of the SM</xref>. Briefly, compound <bold>1a</bold> (1 molar equivalent, eq) was dissolved in methanol (10 mL). In parallel, the convenient fatty acid <bold>2a</bold>–<bold>g</bold> (1 eq) was dissolved in methanol (10 mL). The methanolic solution of <bold>1a</bold> was placed under magnetic stirring and the methanolic solution of the convenient fatty acid <bold>2</bold> was added dropwise. Upon addition of the acid, the reaction mixture was kept under stirring for 30 min, at RT. The solvent was removed by evaporation under reduced pressure in the rotary evaporator, and finally dried at high vacuum. The residue obtained was analyzed by <sup>1</sup>H-NMR and <sup>13</sup>C-NMR, allowing for verification of the identity of the desired salt, with an anion/cation stoichiometry of 1:1, according to the <sup>1</sup>H-NMR data given in the <xref ref-type=\"app\" rid=\"app1-ijms-21-05334\">SM</xref>.</p></sec><sec id=\"sec4dot1dot4-ijms-21-05334\"><title>4.1.4. Synthesis of Amides <bold>4</bold></title><p>All compounds <bold>4</bold> were synthesized by exactly the same experimental procedure, using the amounts of reactants included in <xref ref-type=\"app\" rid=\"app1-ijms-21-05334\">Table S2 of the SM</xref>. Briefly, compound <bold>1b</bold> (1 eq) was dissolved in either dimethylformamide DMF or DCM. In parallel, 1.1 eq of the convenient fatty acid <bold>2a</bold>–<bold>e</bold>, 1.1 eq of <italic>O</italic>-(benzotriazol-1-yl)-<italic>N,N,N′,N′</italic>-tetramethyluronium tetrafluoroborate (TBTU), and 2 eq of <italic>N,N</italic>-diisopropylethylamine (DIEA) were dissolved in DMF or DCM; this solution was placed under magnetic stirring for 10 min at RT, and the solution of <bold>1b</bold> was added dropwise. Stirring was prolonged for 24 h more, at RT in the dark. The mixture was diluted with DCM, and washed 3 times with 5% aqueous Na<sub>2</sub>CO<sub>3</sub>. The organic layer was separated, dried over anhydrous Na<sub>2</sub>SO<sub>4</sub>, and filtered. The solvent was removed under reduced pressure by rotatory evaporation, and the residue was purified by silica gel column chromatography. Chromatographically homogeneous fractions collected were pooled, and the solvent was evaporated under reduced pressure, to afford the final compound. Structural data obtained by <sup>1</sup>H-NMR, <sup>13</sup>C-NMR, and ESI-IT MS are given in the <xref ref-type=\"app\" rid=\"app1-ijms-21-05334\">SM</xref>.</p></sec></sec><sec id=\"sec4dot2-ijms-21-05334\"><title>4.2. Simultaneous Thermogravimetric Analysis</title><p>The thermal stability of the compounds was evaluated using STA equipment from Scancsi, model 7200RV, following the manufacturer’s instructions. The compounds were subjected to heating from room temperature to 500 °C at a speed of 5 °C/min, obtaining the thermograms provided in the <xref ref-type=\"app\" rid=\"app1-ijms-21-05334\">SM</xref>. For a better visualization of the degradative events, the derivatives of the thermogravimetric curves are also displayed in the thermograms.</p></sec><sec id=\"sec4dot3-ijms-21-05334\"><title>4.3. Surface Tension Measurements</title><p>A DCAT11 tensiometer from Dataphysics GmbH with a Wilhelmy plate was used, and all measurements were performed at 25.0 °C ± 0.5 °C, using a thermostated Julabo F20 circulating bath. The measurements for the <italic>cmc</italic> determination of the CTAB/SAIL solutions were performed by adding aliquots from a stock mixed CTAB/SAIL solution to the solution in the measuring vessel (starting initially with neat water). No dynamic surface tension effects were observed in any measurements.</p></sec><sec id=\"sec4dot4-ijms-21-05334\"><title>4.4. In Vitro Assays</title><p>Laboratory-adapted <italic>Pf</italic> 3D7 (chloroquine- and mefloquine-sensitive), Dd2 (chloroquine-resistant and mefloquine-resistant) were continuously cultured and sorbitol synchronized, as previously described [<xref rid=\"B21-ijms-21-05334\" ref-type=\"bibr\">21</xref>]. Staging and parasitemia were determined by light microscopy of Giemsa-stained thin blood smears. Anti-malarial activity was determined using the SYBR Green I assay, as previously described [<xref rid=\"B22-ijms-21-05334\" ref-type=\"bibr\">22</xref>]. Briefly, early ring stage parasites (> 80% of rings) were challenged with a 1:3 serial dilution in medium from a stock solution of each compound in DMSO, with final concentrations ranging from 10,000–0.169 nM. Fluorescence intensity was measured with a multi-mode microplate reader (Triad, Dynex Technologies), with excitation and emission wavelengths of 485 and 535 nm, respectively, and analyzed by nonlinear regression using GraphPad Prism to determine IC<sub>50</sub> values.</p></sec></sec></body><back><ack><title>Acknowledgments</title><p>Thanks are also due to the Portuguese NMR network (RNRMN) for supporting the Laboratory for Structural Elucidation (LAE) of the Materials Centre of the University of Porto (CEMUP).</p></ack><app-group><app id=\"app1-ijms-21-05334\"><title>Supplementary Materials</title><p>The following are available online at <uri xlink:href=\"https://www.mdpi.com/1422-0067/21/15/5334/s1\">https://www.mdpi.com/1422-0067/21/15/5334/s1</uri>, Table S1: amounts of reactants used for the synthesis of <bold>3a–g</bold>; spectral data and traces for compounds <bold>3a–g</bold>; Table S2: amounts of reactants used for the synthesis of <bold>4a–e</bold>; spectral data and traces for compounds <bold>4a–e</bold>; Figure S1: superimposed <sup>1</sup>H NMR spectra of octanoic acid <bold>2b</bold>, basic chloroquine <bold>1a</bold>, and their derived ionic liquid <bold>3b</bold>; thermograms for ionic liquids <bold>3</bold>; thermograms for amides <bold>4</bold>; Figure S2: appearance of a saturated solution of <bold>3c</bold> in water, displaying turbidity and foam formation; Figure S3: surface tension plots and cmc determination, at 25.0 °C, of aqueous CTAB/SAIL <bold>3c</bold> mixtures for increasing molar fraction of <bold>3c</bold>; Table S4: values for cmc and surface tension at the cmc (γcmc) for CTAB/SAIL 3c with increasing molar fraction of <bold>3c</bold>.</p><supplementary-material content-type=\"local-data\" id=\"ijms-21-05334-s001\"><media xlink:href=\"ijms-21-05334-s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>Conceptualization, F.N., E.F.M., P.G.; Investigation, A.T.S., J.G., I.S.O., L.L., C.T., R.F.; Writing—Original Draft, A.T.S., L.L., I.S.O., E.F.M., P.G.; Writing—Review & Editing, F.N., R.F., E.F.M., P.G.; Supervision, F.N., R.F., E.F.M., P.G. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was funded by Fundação para a Ciência e Tecnologia (FCT, Portugal), through grants UIDB/50006/2020 (to LAQV-REQUIMTE Research Unit), UIDB/00081/2020 (to CIQ-UP Research Unit), UID/Multi/04413/2013 (to GHTM Research Unit), and also for project grant PTDC/BTM-SAL/29786/2017. Thanks are due to FCT for doctoral grants to I.S.O. (SFRH/BD/108629/2015) and to A.T.S. (SFRH/BD/150649/2020.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><glossary><title>Abbreviations</title><array orientation=\"portrait\"><tbody><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">API</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Active pharmaceutical ingredient</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">CQ</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Chloroquine</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">CTAB</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Cetyltrimethylammonium bromide</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">DCM</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Dichloromethane</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">DIEA</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"><italic>N</italic>-ethyl-<italic>N</italic>,<italic>N</italic>-diisopropylamine</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">DMF</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Dimethylformamide</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">DMSO</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Dimethylsulfoxide</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">DMSO-d6</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Hexadeuterated dimethylsulfoxide</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">eq</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Molar equivalent</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">ESI-IT MS</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Electrospray ionization-ion trap mass spectrometry</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">IC<sub>50</sub></td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Half-maximal inhibitory concentration</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">IL</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Ionic liquid</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">MeOH</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Methanol</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">NMR</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Nuclear magnetic resonance</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Pf</italic>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>Plasmodium falciparum</italic>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">PQ</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Primaquine</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">RT</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Room temperature</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">RTIL</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Room temperature ionic liquid</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">SAIL</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Surface-active ionic liquid</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">SD</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Standard deviation</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">SM</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Supplementary materials</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">STA</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Simultaneous thermogravimetric analysis</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">TBTU</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"><italic>O</italic>-(benzotriazol-1-yl)-<italic>N,N,N′,N</italic>′-tetramethyluronium tetrafluoroborate</td></tr></tbody></array></glossary><ref-list><title>References</title><ref id=\"B1-ijms-21-05334\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Marrucho</surname><given-names>I.M.</given-names></name><name><surname>Branco</surname><given-names>L.C.</given-names></name><name><surname>Rebelo</surname><given-names>L.P.N.</given-names></name></person-group><article-title>Ionic liquids in pharmaceutical applications</article-title><source>Ann. 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Clin. Med. Microbiol.</source><year>2016</year><volume>2</volume><fpage>1010</fpage></element-citation></ref></ref-list><sec sec-type=\"display-objects\"><title>Figure, Scheme and Tables</title><fig id=\"ijms-21-05334-sch001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05334-sch001_Scheme 1</object-id><label>Scheme 1</label><caption><p>Routes towards organic salts <bold>3a</bold>–<bold>e</bold>, derived from chloroquine (CQ) (<bold>1a</bold>) and fatty acids <bold>2a</bold>–<bold>e</bold>, and their amide covalent counterparts <bold>4a</bold>–<bold>e</bold>, derived from CQ analogue <bold>1b</bold> and <bold>2a</bold>–<bold>e</bold>: (i) <bold>1a</bold> (1 molar equivalent, eq), <bold>2a</bold>–<bold>e</bold> (1 eq), methanol (MeOH), room temperature (RT), 30 min; (ii) <bold>2a</bold>–<bold>e</bold> (1 eq), <italic>O</italic>-(benzotriazol-1-yl)-<italic>N,N,N’,N’</italic>-tetramethyluronium tetrafluoroborate (TBTU; 1 eq), <italic>N</italic>-ethyl-<italic>N,N</italic>-diisopropylamine (DIEA, 2 eq), <italic>N,N</italic>-dimethylformamide (DMF), 0 °C, 10 min, then addition of <bold>1b</bold> (1 eq), RT, 24 h.</p></caption><graphic xlink:href=\"ijms-21-05334-sch001\"/></fig><fig id=\"ijms-21-05334-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Surface tension plots and <italic>cmc</italic> determination at 25 °C of aqueous surface-active ionic liquid (SAIL)/CTAB mixtures: (<bold>a</bold>) surface tension vs. the logarithm of total SAIL+CTAB concentration expressed in molality; the <italic>cmc</italic> are obtained from the intersection points of the linear fits in each system; (<bold>b</bold>) <italic>cmc</italic> vs. molar fraction of SAIL <bold>3c</bold> in mixtures of SAIL <bold>3c</bold>/CTAB, showing the marked effect of the SAIL in <italic>cmc</italic> reduction.</p></caption><graphic xlink:href=\"ijms-21-05334-g001\"/></fig><table-wrap id=\"ijms-21-05334-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05334-t001_Table 1</object-id><label>Table 1</label><caption><p>Synthesis yields, thermal degradation data, and in vitro activity against <italic>Plasmodium falciparum</italic> (<italic>Pf</italic>) 3D7 and Dd2 strains obtained for room temperature ionic liquids (RTILs) <bold>3a</bold>–<bold>e</bold> and for their covalent analogues <bold>4a</bold>–<bold>e</bold>; thermal degradation data also provided for CQ phosphate and <bold>2a</bold>–<bold>e</bold>; the in vitro activity data obtained for the commercial CQ phosphate salt, for <bold>2c</bold>, and for an equimolar mixture of these two compounds are also included.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Compound</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Synthesis Yield/%</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Temperature of Degradation Events Observed/°C</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Half-Maximal Inhibitory Concentration (IC<sub>50</sub>) ± SD/nM</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>Pf</italic> 3D7</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>Pf</italic> Dd2</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>3a</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">93.2; 236.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12 ± 5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">384 ± 142</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>3b</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">120.7; 220.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13 ± 2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">402 ± 190</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>3c</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">88</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">156.9; 228.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4 ± 1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">110 ± 36</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>3d</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">98</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">227.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12 ± 5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">235 ± 79</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>3e</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">197.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15 ± 4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">365 ± 126</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>4a</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">75</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">291.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">627 ± 142</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">588 ± 44</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>4b</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">66</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">314.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">51 ± 9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">109 ± 7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>4c</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">74</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">310.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">70 ± 7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">160 ± 14</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>4d</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">70</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">335.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">n.d. <sup>2</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">n.d. <sup>2</sup></td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>4e</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">48</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">336.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">n.d. <sup>2</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">n.d. <sup>2</sup></td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>2a</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">75.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">n.d.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">n.d.</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>2b</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">131.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">n.d.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">n.d.</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>2c</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">172.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">>10,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">>10,000</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>2d</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">218.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">n.d.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">n.d.</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>2e</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">211.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">n.d.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">n.d.</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>CQ <sup>1</sup></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">301.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45 ± 15</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">660 ± 11</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>CQ <sup>1</sup> + 2c</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">59 ± 16</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">415 ± 44</td></tr></tbody></table><table-wrap-foot><fn><p><sup>1</sup> chloroquine phosphate (standard formulation of this API); <sup>2</sup> insoluble; n.d., not determined.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijms-21-05334-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05334-t002_Table 2</object-id><label>Table 2</label><caption><p>Critical micellar concentration (<italic>cmc</italic>) and surface tension at the <italic>cmc</italic> (©<sub>cmc</sub>) for CTAB and different CTAB/SAIL solutions with a molar fraction of SAIL, x<sub>SAIL</sub>, equal to 0.10.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">System</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">cmc/mmol·kg<sup>−1</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">©<sub>cmc</sub>/mN·m<sup>−1</sup></th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CTAB</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.84</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CTAB + <bold>3b</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.57</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32.4</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CTAB + <bold>3c</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.057</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CTAB + <bold>3f</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.8</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CTAB + <bold>3g</bold></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.40</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">30.0</td></tr></tbody></table></table-wrap></sec></back></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751334</article-id><article-id pub-id-type=\"pmc\">PMC7432004</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155472</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05472</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Mobile Phone Addiction and Risk-Taking Behavior among Chinese Adolescents: A Moderated Mediation Model</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-2449-7969</contrib-id><name><surname>Dou</surname><given-names>Kai</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05472\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Wang</surname><given-names>Lin-Xin</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05472\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Li</surname><given-names>Jian-Bin</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05472\">2</xref><xref rid=\"c1-ijerph-17-05472\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Wang</surname><given-names>Guo-Dong</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05472\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Li</surname><given-names>Yan-Yu</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05472\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Huang</surname><given-names>Yi-Ting</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05472\">1</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05472\"><label>1</label>Department of Psychology and Research Center of Adolescent Psychology and Behavior, School of Education, Guangzhou University, Guangzhou 510006, China; <email>psydk@gzhu.edu.cn</email> (K.D.); <email>wlx_psy@e.gzhu.edu.cn</email> (L.-X.W.); <email>1708400055@e.gzhu.edu.cn</email> (G.-D.W.); <email>2111808175@e.gzhu.edu.cn</email> (Y.-Y.L.); <email>1666100038@e.gzhu.edu.cn</email> (Y.-T.H.)</aff><aff id=\"af2-ijerph-17-05472\"><label>2</label>Department of Early Childhood Education, The Education University of Hong Kong, Hong Kong, China</aff><author-notes><corresp id=\"c1-ijerph-17-05472\"><label>*</label>Correspondence: <email>lijianbin@eduhk.hk</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>29</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5472</elocation-id><history><date date-type=\"received\"><day>29</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>27</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p><italic>Objectives</italic>: The mobile phone (MP) is an indispensable digital device in adolescents’ daily lives in the contemporary era, but being addicted to MP can lead to more risk-taking behavior. However, little is known about the mediating and moderating mechanisms underlying this relation. To address the gaps in the literature, the present study examined the idea that MP addiction is associated with reduced self-control, which further associates with increased risk-taking behavior. In addition, this study also investigated the moderation effect of adolescent sex in the association between MP addiction and self-control. <italic>Methods</italic>: A three-wave longitudinal study, each wave spanning six months apart, was conducted in a sample of Chinese adolescents (final <italic>N</italic> = 333, 57.4% girls). <italic>Results</italic>: Results of the moderated mediation model suggest that after controlling for demographic variables and baseline levels of self-control and risk-taking behavior, MP addiction at T1 positively predicted increased risk-taking behavior at T3 through reduced self-control at T2 for girls but not for boys. <italic>Conclusions</italic>: Theoretically, these findings contribute to the understanding about the working processes in the association between MP addiction and risk-taking behavior in adolescents. Practically, the results implied that boosting self-control appeared as a promising way to reduce girls’ risk-taking behavior, particularly for those who are addicted to MPs.</p></abstract><kwd-group><kwd>mobile phone addiction</kwd><kwd>risk-taking behavior</kwd><kwd>self-control</kwd><kwd>sex</kwd><kwd>adolescents</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05472\"><title>1. Introduction</title><p>Mobile phones (MPs) are ubiquitous in the contemporary era. With the advancement of high-speed internet, MPs become more versatile and play a significant role in people’s daily lives. According to a recent report, China’s MP users reached 897 million, an increase of 4.2 million compared to 2018 [<xref rid=\"B1-ijerph-17-05472\" ref-type=\"bibr\">1</xref>]. Adolescents constitute a major proportion of MP users in China. Although the appropriate use of MPs (e.g., looking up useful information and maintaining positive social ties) can be beneficial to adolescents (e.g., feeling higher subjective well-being) [<xref rid=\"B2-ijerph-17-05472\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijerph-17-05472\" ref-type=\"bibr\">3</xref>], being addicted to a MP is associated with a wide array of undesirable outcomes in adolescents [<xref rid=\"B4-ijerph-17-05472\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijerph-17-05472\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijerph-17-05472\" ref-type=\"bibr\">6</xref>]. Among others, a salient undesirable consequence associated with MP addiction is risk-taking behavior [<xref rid=\"B7-ijerph-17-05472\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijerph-17-05472\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05472\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijerph-17-05472\" ref-type=\"bibr\">10</xref>]. MP addiction is a type of addiction to technology and it can be defined as the uncontrolled or excessive use of mobile phones, with an inability to control craving, feeling anxious, withdrawal, and productivity loss as symptoms [<xref rid=\"B11-ijerph-17-05472\" ref-type=\"bibr\">11</xref>]. Although the association between MP addiction and risk-taking behavior in adolescents has been well documented, scant research has examined how and for whom MP addiction is associated with risk-taking behavior. Examining these issues may shed light on the intervention and prevention of adolescents’ risk-taking behavior. In this study, we propose that MP addiction would be related to reduced self-control which further drives them to engage in more risk-taking behavior. In addition, we also explore whether sex would play a role as well, given the sex differences in self-control [<xref rid=\"B12-ijerph-17-05472\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijerph-17-05472\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijerph-17-05472\" ref-type=\"bibr\">14</xref>] and MP addiction [<xref rid=\"B15-ijerph-17-05472\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijerph-17-05472\" ref-type=\"bibr\">16</xref>]. We examined these issues in a sample of Chinese adolescents.</p><sec id=\"sec1dot1-ijerph-17-05472\"><title>1.1. Mobile Phone Addiction and Risk-Taking Behavior</title><p>Risk-taking behavior refers to the actions that may potentially result in adverse consequences [<xref rid=\"B17-ijerph-17-05472\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijerph-17-05472\" ref-type=\"bibr\">18</xref>]. Adolescents are more prone to engage in risk-taking behavior compared to their younger peers and adults [<xref rid=\"B19-ijerph-17-05472\" ref-type=\"bibr\">19</xref>]. According to the dual system model, the socioemotional system of adolescents develops rapidly but the cognitive control system develops relatively more slowly [<xref rid=\"B19-ijerph-17-05472\" ref-type=\"bibr\">19</xref>]. The rapid development of the socioemotional system enhances adolescents’ pursuit of rewarding stimuli, while the slow development of the cognitive control system limits the adolescents’ inhibition of risk-taking behavior [<xref rid=\"B20-ijerph-17-05472\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijerph-17-05472\" ref-type=\"bibr\">21</xref>]. The imbalance between the development of the socioemotional system and the development of the cognitive control system results in the increase in adolescents’ risk-taking behavior.</p><p>Compared to computers, the MP is more flexible, mobile and timely. Being addicted to a MP can be associated with risk-taking behavior in adolescents for two reasons [<xref rid=\"B21-ijerph-17-05472\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijerph-17-05472\" ref-type=\"bibr\">22</xref>]. First, individuals tend to seek rewarding incentives, novel stimuli and excitement through engaging in risk-taking behavior, common in adolescents [<xref rid=\"B19-ijerph-17-05472\" ref-type=\"bibr\">19</xref>]. The use of a MP is often accompanied with rewarding incentives, novel stimuli and excitement, and thus MP addiction may exacerbate the adolescents’ tendencies of engaging in risk-taking behavior [<xref rid=\"B23-ijerph-17-05472\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijerph-17-05472\" ref-type=\"bibr\">24</xref>]. Previous research has indicated that adolescents who frequently use MPs engage in more reckless driving [<xref rid=\"B25-ijerph-17-05472\" ref-type=\"bibr\">25</xref>], smoking and alcohol abuse [<xref rid=\"B26-ijerph-17-05472\" ref-type=\"bibr\">26</xref>] than those who are not addicted to MPs. Second, MP addiction may restrain adolescents’ cognitive control, which further contributes to their risk-taking behavior. In accordance with this perspective, a previous study has found that those who are addicted to MPs are reluctant to spend cognitive resources and are prone to adopt intuitive thinking in daily lives [<xref rid=\"B27-ijerph-17-05472\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijerph-17-05472\" ref-type=\"bibr\">28</xref>]. In the absence of cognitive control and analytical thinking, individuals may have more difficulties in restraining the tendencies of seeking novelty, rewarding incentives, and excitement through risk-taking behavior [<xref rid=\"B29-ijerph-17-05472\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijerph-17-05472\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijerph-17-05472\" ref-type=\"bibr\">31</xref>]. Taken together, we assume that MP addiction would be related to an increase in risk-taking behavior in adolescents.</p></sec><sec id=\"sec1dot2-ijerph-17-05472\"><title>1.2. The Mediation Effect of Self-Control</title><p>Self-control is defined as the ability that individuals make effort to overcome impulsion and automatic reaction, and to support the pursuit of long-term goals [<xref rid=\"B32-ijerph-17-05472\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijerph-17-05472\" ref-type=\"bibr\">33</xref>]. As a vital psychological function, self-control is associated with a number of positive outcomes, including less risk-taking behavior [<xref rid=\"B32-ijerph-17-05472\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijerph-17-05472\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijerph-17-05472\" ref-type=\"bibr\">34</xref>]. The general theory of crime postulates that individuals with low self-control are inclined to be short-sighted and impulsive, which is a core cause of young people’s delinquency [<xref rid=\"B35-ijerph-17-05472\" ref-type=\"bibr\">35</xref>]. Research has found that adolescents with low self-control are more likely to have excessive drinking [<xref rid=\"B36-ijerph-17-05472\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijerph-17-05472\" ref-type=\"bibr\">37</xref>], substance abuse [<xref rid=\"B38-ijerph-17-05472\" ref-type=\"bibr\">38</xref>], gambling [<xref rid=\"B39-ijerph-17-05472\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijerph-17-05472\" ref-type=\"bibr\">40</xref>] and other risk-taking behavior. In addition, the strength model of self-control posits that self-control resource depletion in the previous stage limits the availability of self-control for the next stage, which may increase the likelihood of the occurrence of risk-taking behavior [<xref rid=\"B41-ijerph-17-05472\" ref-type=\"bibr\">41</xref>,<xref rid=\"B42-ijerph-17-05472\" ref-type=\"bibr\">42</xref>].</p><p>Self-control may play a “bridge” role between MP addiction and adolescents’ risk-taking behavior. There could be different pathways from mobile phone addiction to low self-control. On the one hand, MP addiction may reduce cognitive control, distract attention, and make the cognitive control system “lazied” in adolescents, and thus they prefer intuitive cognitive processing [<xref rid=\"B27-ijerph-17-05472\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijerph-17-05472\" ref-type=\"bibr\">28</xref>,<xref rid=\"B43-ijerph-17-05472\" ref-type=\"bibr\">43</xref>]. For instance, in a sample of 1721 adolescents, Hong et al. (2020) found that MP addiction leads to cognitive failures. On the other hand, MPs may provide immediate stimulation and feedback that may activate the socioemotional system, rendering adolescents vulnerable to instant gratification and short-term rewards. Individual differences in low self-control and the temporary depletion of self-control resources due to MP addiction can render adolescents’ cognitive resources insufficient to override the tendencies of seeking novelty and excitement, which may be further associated with more risk-taking behavior. On these bases, we assume that MP addiction can indirectly affect adolescents’ risk-taking via reduced self-control.</p></sec><sec id=\"sec1dot3-ijerph-17-05472\"><title>1.3. The Moderation Effect of Sex</title><p>Previous research has revealed sex differences in the pattern of mobile phone use [<xref rid=\"B15-ijerph-17-05472\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijerph-17-05472\" ref-type=\"bibr\">16</xref>]. For instance, girls are more prone to use MPs for social networking, entertainment and shopping [<xref rid=\"B44-ijerph-17-05472\" ref-type=\"bibr\">44</xref>,<xref rid=\"B45-ijerph-17-05472\" ref-type=\"bibr\">45</xref>], while boys are more likely to use MPs for work and games [<xref rid=\"B15-ijerph-17-05472\" ref-type=\"bibr\">15</xref>]. In this sense, compared to boys, girls are considered to be more emotionally involved when using MPs, experience more emotional swifts, and have higher social motivation [<xref rid=\"B15-ijerph-17-05472\" ref-type=\"bibr\">15</xref>]. In addition, boys are prone to engage in more risk-taking behavior than girls during adolescence [<xref rid=\"B46-ijerph-17-05472\" ref-type=\"bibr\">46</xref>], because boys have higher sensation-seeking tendencies and lower impulsive control [<xref rid=\"B47-ijerph-17-05472\" ref-type=\"bibr\">47</xref>,<xref rid=\"B48-ijerph-17-05472\" ref-type=\"bibr\">48</xref>]. As a relatively malleable personal characteristic, self-control can be affected by environment. Compared to boys, girls are more likely to be susceptible to external factors (e.g., MP addiction) [<xref rid=\"B13-ijerph-17-05472\" ref-type=\"bibr\">13</xref>]. In line with this, MP addiction could suppress girls’ self-control rather than boys. Therefore, we assume that sex would moderate the effect of MP addiction on adolescents’ self-control.</p></sec><sec id=\"sec1dot4-ijerph-17-05472\"><title>1.4. The Present Study</title><p>Taken together, this three-wave longitudinal study, with each wave spanning six months apart, investigates the association between MP addiction and risk-taking behavior as well as the underlying mechanisms in a sample of Chinese adolescents. Specifically, we would examine the idea that MP addiction would be associated with increased risk-taking behavior through reduced self-control. Moreover, we would examine the moderating role of sex (see in <xref ref-type=\"fig\" rid=\"ijerph-17-05472-f001\">Figure 1</xref>). In sum, we hypothesized that: (1) MP addiction would be positively related with adolescent risk-taking behavior; (2) self-control would mediate the relation between MP addiction and risk-taking behavior; (3) sex would moderate the effect of MP addiction on adolescents’ self-control, with the negative effect of MP addiction on self-control being stronger for girls than boys; and (4) sex would moderate the mediation effect of self-control, with the mediation effect of self-control being more pronounced for girls than boys. Combining all these hypotheses results in a moderated mediation model (<xref ref-type=\"fig\" rid=\"ijerph-17-05472-f001\">Figure 1</xref>).</p></sec></sec><sec id=\"sec2-ijerph-17-05472\"><title>2. Method</title><sec id=\"sec2dot1-ijerph-17-05472\" sec-type=\"subjects\"><title>2.1. Participants and Procedures</title><p>The data were collected from a public middle school in a large city in southern China. All the procedures involving human participants were reviewed and approved by the research ethics committee in the School of Education at Guangzhou University (Protocol Number: GZHU2019018). Written consent forms from the parents and oral assent from the adolescents were obtained before data collection across the waves. At each wave, two trained research assistants hosted the survey and the participants completed the questionnaires during regular class hours in the classroom. All the participants received a small gift worthy of 15 RMB (approximately 2.5 US Dollars) after completing the questionnaires each time.</p><p>A total of 412 parents provided consent for their children’s participation. Finally, 399 10th graders (M = 15.37, SD = 0.52, 52.1% girls) participated in the first wave of data collection (Time 1, T1). Of the 399 adolescents, 353 (attrition rate = 11.53%) and 386 (attrition rate = 3.26%) participated in the assessments at Time 2 (T2) and Time 3 (T3), respectively. The time interval of the data collection between each wave was six months. Detailed demographic characteristics of the T1 sample are presented in <xref rid=\"ijerph-17-05472-t001\" ref-type=\"table\">Table 1</xref>.</p></sec><sec id=\"sec2dot2-ijerph-17-05472\"><title>2.2. Measures</title><sec id=\"sec2dot2dot1-ijerph-17-05472\"><title>2.2.1. Mobile Phone Addiction at T1</title><p>We used the Mobile Phone Addiction Index Scale (MPAI) [<xref rid=\"B49-ijerph-17-05472\" ref-type=\"bibr\">49</xref>] to measure the participants’ frequency of using MPs at T1. This scale consists of 17 items rated on a five-point scale (from 1 = never done to 5 = almost always). A mean score can be calculated by averaging all the items, with a higher score indicating more the frequent use of MP. Sample items are “Your friends and family complained about your use of the mobile phone” and “You feel lost without your mobile phone”. In the current study, the Cronbach’s alpha of this scale was 0.97.</p></sec><sec id=\"sec2dot2dot2-ijerph-17-05472\"><title>2.2.2. Self-Control at T1 and T2</title><p>We used the Chinese version of Tangney et al.’s (2004) Brief Self-Control Scale (BSCS) [<xref rid=\"B50-ijerph-17-05472\" ref-type=\"bibr\">50</xref>,<xref rid=\"B51-ijerph-17-05472\" ref-type=\"bibr\">51</xref>] to assess the participants’ self-control ability at T1 and T2. This scale consists of 13 items rated on a five-point scale (from 1 = not like me at all to 5 = like me very much). A higher mean score indicates a better self-control ability. The sample items are “I am good at resisting temptation” and “Sometimes I can’t stop myself from doing something, even if I know it is wrong”. In this research, the Cronbach’s alpha of this scale at T1 and T2 was 0.79 and 0.80, respectively.</p></sec><sec id=\"sec2dot2dot3-ijerph-17-05472\"><title>2.2.3. Risk-Taking Behavior at T1, T2, and T3</title><p>Risk-taking behavior was assessed by the 15-item Adolescent Risk-Taking Questionnaire (ARQ) [<xref rid=\"B17-ijerph-17-05472\" ref-type=\"bibr\">17</xref>] through T1 to T3. Adolescents reported the frequency of performing various risk-taking behavior (e.g., unprotected sex, driving/cycling after drinking) on a five-point Likert scale (from 0 = never done to 4 = done very often). A mean score was calculated by averaging all the items. This measure has been translated into Chinese and demonstrated to be valid and reliable in Chinese samples [<xref rid=\"B18-ijerph-17-05472\" ref-type=\"bibr\">18</xref>]. In this study, Cronbach’s alpha of this scale at T1, T2 and T3 was 0.96, 0.81 and 0.76, respectively.</p></sec><sec id=\"sec2dot2dot4-ijerph-17-05472\"><title>2.2.4. Covariates at T1</title><p>The child’s age, only child at home or not (0 = Yes, 1 = No), parents’ employment status (1 = freelance, 2 = par-time job, 3 = full-time job) and educational levels (1 = junior middle school and below, 2 = high school degree, 3 = college degree, 4 = bachelor’s degree, 5 = master’s degree or doctoral degree) were included as covariates since prior studies have found significant associations between these demographic variables with risk-taking behavior [<xref rid=\"B52-ijerph-17-05472\" ref-type=\"bibr\">52</xref>,<xref rid=\"B53-ijerph-17-05472\" ref-type=\"bibr\">53</xref>].</p></sec></sec><sec id=\"sec2dot3-ijerph-17-05472\"><title>2.3. Data Analyses</title><p>Initially, descriptive statistics and bivariate correlations were performed using SPSS 22.0 ((IBM, Armonk, NY, USA) to examine the centrality and association among the variables of interest. Second, structural equation modeling (SEM) was performed using Mplus 7.0 (Muthén & Muthén, Los Angeles, CA, USA) to test the hypothesized moderated mediation model. The missing data were handled with the full information maximum likelihood estimation (FIML) [<xref rid=\"B54-ijerph-17-05472\" ref-type=\"bibr\">54</xref>]. In this model, T1 MP addiction was the independent variable; T2 self-control was the mediator; T3 risk-taking behavior was the outcome; and sex was the moderator. In this model, we also controlled for the baseline levels of self-control at T1 and risk-taking behavior at T1 and T2, as well as the effect of covariates on the outcome (i.e., T3 risk-taking behavior). Given that the bootstrapping technique has several advantages over the traditional approaches in examining mediation models such as higher statistical power [<xref rid=\"B55-ijerph-17-05472\" ref-type=\"bibr\">55</xref>], we used bootstrapping (<italic>N</italic> = 5000) and its 95% confidence intervals to judge the significance of the mediation. As long as the 95% confidence interval excludes 0, significant mediation effect is tenable. The following indices were used to evaluate the overall model fit [<xref rid=\"B56-ijerph-17-05472\" ref-type=\"bibr\">56</xref>]: a nonsignificant chi-square statistics (<italic>χ</italic><sup>2</sup>), the comparative fit index (CFI), the root mean square error of approximation (RMSEA) [<xref rid=\"B57-ijerph-17-05472\" ref-type=\"bibr\">57</xref>] with its 90% confidence interval (CI), and the standardized root mean square residual (SRMR). However, given that the sample size of the current study is large and the <italic>χ</italic><sup>2</sup> statistic is sensitive to sample size, a significant <italic>χ</italic><sup>2</sup> statistic was expected.</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05472\"><title>3. Results</title><sec id=\"sec3dot1-ijerph-17-05472\"><title>3.1. Descriptive Statistics and Bivariate Correlation</title><p>Means, standard deviations, and bivariate associations are shown in <xref rid=\"ijerph-17-05472-t002\" ref-type=\"table\">Table 2</xref>. As can be seen in the table, MP addiction at T1 was negatively related to T1 and T2 self-control (<italic>r</italic> = −0.43 and −0.35, <italic>ps</italic> < 0.001), but positively associated with T1/T2/T3 risk-taking behavior (<italic>r</italic> = 0.22–0.37, <italic>ps</italic> < 0.001). Adolescent self-control and risk-taking behavior were also negatively correlated, within and across time points (<italic>r</italic> = −0.22–−0.30, <italic>ps</italic> < 0.001). According to Cohen’s (1992) standard [<xref rid=\"B58-ijerph-17-05472\" ref-type=\"bibr\">58</xref>], the effect sizes of these correlation coefficients were small-to-medium.</p></sec><sec id=\"sec3dot2-ijerph-17-05472\"><title>3.2. Examination of the Hypothesized Moderated Mediation Model</title><p>The hypothesized moderated mediation model was examined. The fit indices were <italic>χ</italic><sup>2</sup> = 36.91, <italic>df</italic> = 22, <italic>p</italic> < 0.05, RMSEA = 0.04 (90% CI = [0.015, 0.064]), CFI = 0.959, and SRMR = 0.039, indicating that the model was a good fit. This model accounted for the 20.5% variance of “risk-taking behavior”, and the corresponding effect size was medium to large (<italic>f</italic> = 0.26) [<xref rid=\"B58-ijerph-17-05472\" ref-type=\"bibr\">58</xref>].</p><p>As shown in <xref ref-type=\"fig\" rid=\"ijerph-17-05472-f002\">Figure 2</xref> and <xref rid=\"ijerph-17-05472-t003\" ref-type=\"table\">Table 3</xref>, T1 MP addiction was not directly related to T3 risk-taking behavior at the statistically significant level. Nevertheless, the T1 MP addiction was significantly related to T2 self-control (<italic>B</italic> = −0.08, <italic>SE</italic> = 0.03, <italic>p</italic> = 0.009). Moreover, T2 self-control was significantly related to T3 risk-taking behavior (<italic>B</italic> = −0.11, <italic>SE</italic> = 0.04, <italic>p</italic> = 0.003). More importantly, T2 self-control significantly linked the association between T1 MP addiction and T3 risk-taking behavior (<italic>B</italic> = 0.01, 95% CI = [0.002, 0.021]), but not the effect of baseline levels of self-control, risk-taking behavior, and covariates.</p><p>Regarding the moderation effect of sex, there is a significant interaction effect between MP addiction and adolescent sex on T2 self-control (<italic>B</italic> = −0.13, <italic>SE</italic> = 0.06, <italic>p</italic> = 0.02). As shown in <xref ref-type=\"fig\" rid=\"ijerph-17-05472-f003\">Figure 3</xref> and <xref rid=\"ijerph-17-05472-t004\" ref-type=\"table\">Table 4</xref>, the association between T1 MP addiction and T2 self-control was significant only for girls (<italic>B</italic> = −0.14, <italic>SE</italic> = 0.04, <italic>p</italic> < 0.001), but not for boys (<italic>B</italic> = −0.03, <italic>SE</italic> = 0.04, <italic>p</italic> = 0.56). Moreover, we found that the mediation of T2 self-control was significant for girls (<italic>B</italic> = 0.018, <italic>SE</italic> = 0.007, 95% CI = [0.006, 0.036]) but not for boys (<italic>B</italic> = 0.004, <italic>SE</italic> = 0.005, 95% CI = [−0.006, 0.016]).</p></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05472\"><title>4. Discussion</title><p>MP has become an inseparable part of adolescents’ life, but MP addiction can be related to various undesirable outcomes such as high-stake risk-taking behavior. To examine how and for whom MP addiction is related to risk-taking behaviors in adolescents, this three-wave longitudinal study examines the mediation role of self-control and the moderation role of sex in a sample of Chinese high school students. The results reveal that adolescents’ MP addiction is related to increased risk-taking behavior via self-control, but this mediation model appears as only significant for girls but not for boys.</p><sec id=\"sec4dot1-ijerph-17-05472\"><title>4.1. MP Addiction and Adolescents’ Risk-Taking Behavior</title><p>Prior studies have found that MP addiction is related to risk-driving behavior [<xref rid=\"B25-ijerph-17-05472\" ref-type=\"bibr\">25</xref>,<xref rid=\"B59-ijerph-17-05472\" ref-type=\"bibr\">59</xref>]. The current study adds to this line of literature. Supporting our first hypothesis, this study reveals that MP addiction is related to other forms of risk-taking behavior in addition to risk driving. More importantly, using a three-wave longitudinal study and controlling for the baseline levels of risk-taking behavior, our results indicate that MP addiction increases risk-taking behavior over time, although the effect is indirect rather than direct. MPs can be used for multiple content categories, such as gathering information, playing games, and maintaining social networking. Different content categories can be related to different consequences. For example, exposure to risk-taking photos that are posted on the internet may increase adolescents’ acceptance and propensity of engaging in risk-taking behavior [<xref rid=\"B60-ijerph-17-05472\" ref-type=\"bibr\">60</xref>,<xref rid=\"B61-ijerph-17-05472\" ref-type=\"bibr\">61</xref>]. However, the current study does not examine which content category is most related to risk-taking behavior. This could be a promising avenue for future research to explore.</p></sec><sec id=\"sec4dot2-ijerph-17-05472\"><title>4.2. The Mediation Effect of Self-Control</title><p>Confirming the second hypothesis, the results of the mediation model show that self-control mediates the effect of MP addiction on risk-taking behavior. The first part of the mediation process (i.e., mobile phone addiction → self-control) supports the flow theory. The flow theory suggests that immediate gratification and intrinsic rewards induced by mobile phones render individuals to lose themselves in electronic devices [<xref rid=\"B62-ijerph-17-05472\" ref-type=\"bibr\">62</xref>]. The perception of the presence of MPs is a temptation, even when people are not using it, because it distracts individuals’ attention and increases the difficulty for individuals to focus on a task [<xref rid=\"B9-ijerph-17-05472\" ref-type=\"bibr\">9</xref>,<xref rid=\"B63-ijerph-17-05472\" ref-type=\"bibr\">63</xref>]. MP addiction may lead adolescents to develop a bad habit of checking their mobile phone frequently in daily life, which may undermine adolescents’ self-control ability [<xref rid=\"B64-ijerph-17-05472\" ref-type=\"bibr\">64</xref>] and render them vulnerable to immediate rewards [<xref rid=\"B8-ijerph-17-05472\" ref-type=\"bibr\">8</xref>]. The second part of the mediation process (i.e., self-control → risk-taking behavior) is consistent with previous findings [<xref rid=\"B42-ijerph-17-05472\" ref-type=\"bibr\">42</xref>,<xref rid=\"B65-ijerph-17-05472\" ref-type=\"bibr\">65</xref>]. Self-control is a crucial psychological function associated with numerous life outcomes [<xref rid=\"B34-ijerph-17-05472\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijerph-17-05472\" ref-type=\"bibr\">35</xref>]. The mediation model suggests that MP addiction may increase risk-taking behavior by limiting one’s self-control ability. We call this a “restraining path”, such that MP addiction increases risk-taking behavior by restraining protective factors such as self-control. Although the current study does not provide a direct examination, it is worthwhile to note that according to the dual system model, there may be also a “promotive path” that may explain how MP addiction increases risk-taking behavior. For instance, MP addiction may increase adolescents’ sensitivity to incentives and rewarding stimuli [<xref rid=\"B10-ijerph-17-05472\" ref-type=\"bibr\">10</xref>,<xref rid=\"B21-ijerph-17-05472\" ref-type=\"bibr\">21</xref>], and thus adolescents may meet their desires by engaging in more risk-taking behavior. As this study does not examine this assumption directly, future research may examine other mediators (e.g., increased sensitivity) in the association between MP addiction and risk-taking behavior.</p></sec><sec id=\"sec4dot3-ijerph-17-05472\"><title>4.3. The Moderation of Sex</title><p>Confirming the third and the fourth hypotheses, our results reveal that the mediation of self-control in the association between MP addiction and risk-taking behavior is only significant for girls. This could be because girls’ self-control is more sensitive to environmental stimuli (e.g., cell phone) and more malleable [<xref rid=\"B13-ijerph-17-05472\" ref-type=\"bibr\">13</xref>] compared to boys, and thus MP addiction imposes more negative effect on self-control in girls than boys. As discussed above, the current study provides support to the “restraining path” and finds that this working mechanism only works among girls. Specifically, given that girls’ self-control is more malleable and matures earlier [<xref rid=\"B13-ijerph-17-05472\" ref-type=\"bibr\">13</xref>], girls’ self-control can sever as a protective factor of risk-taking behavior. However, MP addiction, as a risk factor, may restrain girls’ self-control, thus increasing the likelihood of risk-taking behaviors. In contrast, boys generally have higher sensation-seeking and impulsivity than girls during adolescence, which indicates that boys’ self-control cannot play its role well in prohibiting risk-taking behaviors [<xref rid=\"B46-ijerph-17-05472\" ref-type=\"bibr\">46</xref>]. In this case, the “restraining path” of self-control plays a little role in the association between MP addiction and the increase in risk-taking behaviors in boys. Thus, we suspect whether the “promotive path” works equally well in both sexes. Future research may examine this line of research to further deepen the working mechanisms underlying the “MP addiction–risk-taking behavior” link.</p></sec><sec id=\"sec4dot4-ijerph-17-05472\"><title>4.4. Implications</title><p>This study bears two implications for the prevention and intervention of adolescents’ risk-taking behavior. On the one hand, the results show that MP addiction may increase risk-taking behavior over time. This implies that addressing MP addiction may be a crucial way to reduce the occurrence of risk-taking behavior in adolescents. On the other hand, reduced self-control significantly mediates the association between MP addiction and risk-taking behavior in girls. This suggests that using evidence-based programs (e.g., mindfulness) to boost girls’ self-control may be useful in reducing girls’ risk-taking behavior.</p></sec><sec id=\"sec4dot5-ijerph-17-05472\"><title>4.5. Limitations</title><p>We must acknowledge that this study has several limitations. First, only self-reported data were collected and thus the associations could be inflated because of the common method bias. To enhance the internal validity of the results, future research may use multiple measurement modalities to triangulate each variable. In addition, as discussed above, this study does not reveal which content category of MP addiction is related to risk-taking behavior. Future research may deepen this issue to achieve a fuller understanding of the relationship between MP addiction and risk-taking behavior in adolescents. Finally, family relationship has been found to be associated with adolescents’ screen behaviors and risk behaviors [<xref rid=\"B11-ijerph-17-05472\" ref-type=\"bibr\">11</xref>]. Future research should take family relationship into further consideration.</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05472\"><title>5. Conclusions</title><p>Taken together, this study reveals that MP addiction is a risk factor for risk-taking behavior via reduced self-control in adolescent girls. These findings bear important implications for the prevention and intervention of adolescents’ risk-taking behavior and to the promotion of positive youth development.</p></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization, K.D. and J.-B.L.; data curation, K.D. and L.-X.W.; formal analysis, J.-B.L.; funding acquisition, K.D.; investigation, G.-D.W., Y.-Y.L. and Y.-T.H.; methodology, J.-B.L.; project administration, L.-X.W. and Y.-Y.L.; resources, G.-D.W.; software, J.-B.L.; supervision, J.-B.L.; validation, G.-D.W.; visualization, L.-X.W.; writing—original draft, K.D. and L.-X.W.; writing—review and editing, J.-B.L., Y.-Y.L. and Y.-T.H. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was funded by the National Natural Science Foundation of China (31800938), Natural Science Foundation of Guangdong Province (2018A030313406), the 13th Five-Year Plan of Philosophy and Social Science of Guangzhou (2020GZYB92) and the Key Research Items from Guangzhou University (YK2020025).</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijerph-17-05472\"><label>1.</label><element-citation publication-type=\"web\"><person-group person-group-type=\"author\"><collab>China Internet Network Information Center</collab></person-group><article-title>The 45th China Statistical Report on Internet Development</article-title><comment>Available online: <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.cnnic.net.cn/hlwfzyj/hlwxzbg/hlwtjbg/202004/t20200428_70974.htm\">http://www.cnnic.net.cn/hlwfzyj/hlwxzbg/hlwtjbg/202004/t20200428_70974.htm</ext-link></comment><date-in-citation content-type=\"access-date\" iso-8601-date=\"2020-05-27\">(accessed on 27 May 2020)</date-in-citation></element-citation></ref><ref id=\"B2-ijerph-17-05472\"><label>2.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Barczyk</surname><given-names>A.N.</given-names></name><name><surname>Thompson</surname><given-names>S.J.</given-names></name><name><surname>Rew</surname><given-names>L.</given-names></name></person-group><article-title>The impact of psychosocial factors on subjective well-being among homeless young adults</article-title><source>Health Soc. 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Youth Adolesc.</source><year>2015</year><volume>44</volume><fpage>1674</fpage><lpage>1687</lpage><pub-id pub-id-type=\"doi\">10.1007/s10964-015-0332-y</pub-id><pub-id pub-id-type=\"pmid\">26202153</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijerph-17-05472-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Conceptual moderated mediation model of the association between mobile phone addiction and risk-taking behavior; T1 = Time 1; T2 = Time 2; T3 = Time 3.</p></caption><graphic xlink:href=\"ijerph-17-05472-g001\"/></fig><fig id=\"ijerph-17-05472-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>The mediation model of self-control in the association between mobile phone addiction and risk-taking behavior. Note: unstandardized estimates are presented; <italic>χ</italic><sup>2</sup> = 36.91, <italic>df</italic> = 22, <italic>p</italic> < 0.05, root mean square error of approximation (RMSEA) = 0.041 with 90% CI [0.015, 0.064], comparative fit index (CFI) = 0.959, standardized root mean square residual (SRMR) = 0.039; * <italic>p</italic> < 0.05, ** <italic>p</italic> < 0.01, *** <italic>p</italic> < 0.001. Dashed line indicates a non-significant coefficient.</p></caption><graphic xlink:href=\"ijerph-17-05472-g002\"/></fig><fig id=\"ijerph-17-05472-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Moderation effect of adolescent sex on the link from T1 mobile phone addiction to T2 self-control.</p></caption><graphic xlink:href=\"ijerph-17-05472-g003\"/></fig><table-wrap id=\"ijerph-17-05472-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05472-t001_Table 1</object-id><label>Table 1</label><caption><p>Summary of the demographic variables at T1.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Variables</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>N</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">%</th></tr></thead><tbody><tr><td colspan=\"3\" align=\"left\" valign=\"middle\" rowspan=\"1\">Adolescent sex</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Boys</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">191</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47.9%</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Girls</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">208</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">52.1%</td></tr><tr><td colspan=\"3\" align=\"left\" valign=\"middle\" rowspan=\"1\">Only child at home</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">198</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">49.6%</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">201</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">50.4%</td></tr><tr><td colspan=\"3\" align=\"left\" valign=\"middle\" rowspan=\"1\">Father’s work status</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Unemployment</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.0%</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Part-time job</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.3%</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Full-time job</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">322</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">80.7%</td></tr><tr><td colspan=\"3\" align=\"left\" valign=\"middle\" rowspan=\"1\">Mother’s work status</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Unemployment</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">82</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.6%</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Part-time job</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">53</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.3%</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Full-time job</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">264</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">66.2%</td></tr><tr><td colspan=\"3\" align=\"left\" valign=\"middle\" rowspan=\"1\">Father’s highest educational level</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">High school or below</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">234</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">58.6%</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">College or undergraduate</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">152</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38.1%</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Master or above</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.3%</td></tr><tr><td colspan=\"3\" align=\"left\" valign=\"middle\" rowspan=\"1\">Mother’s highest educational level</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">High school or below</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">254</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">63.7%</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">College or undergraduate</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">140</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35.1%</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Master or above</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.3%</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">399</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100%</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05472-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05472-t002_Table 2</object-id><label>Table 2</label><caption><p>Descriptive statistics and bivariate correlations among the study variables and covariates.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Study Variables</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13</th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1. MP addiction (T1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">——</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2. Self-control (T1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.43 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3. Self-control (T2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.35 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.51 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.Risk-taking behavior (T1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.37 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.28 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.22 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.Risk-taking behavior (T2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.32 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.25 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.30 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.27 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.Risk-taking behavior (T3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.22 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.22 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.27 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.19 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.38 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7. Sex (0 = boys, 1 = girls)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.17 ***</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.06</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.01</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.24 ***</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.19 ***</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.13 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td colspan=\"14\" align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Covariates at T1</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8. Age</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.02</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.01</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.07</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.03</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.03</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.04</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.00</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9. Only child at home</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.16 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.03</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.02</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.11 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.04</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.20 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.01</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10. Father’s work status</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.02</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.07</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.01</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.01</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.06</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.04</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.09</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.04</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.03</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11. Mother’s work status</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.03</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.07</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.06</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.06</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.01</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.06</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.11 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.09</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.23 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12. Father’s education</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.13 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.11 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.14 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.01</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.02</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.00</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.10 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.18 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.07</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.00</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13. Mother’s education</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.03</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.19 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.08</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.03</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.02</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.09</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.17 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.02</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.11 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.56 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.16</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.19</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.41</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.28</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.27</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.52</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.37</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.50</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.73</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.46</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.53</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.38</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SD</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.99</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.55</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.54</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.73</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.37</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.33</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.50</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.52</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.50</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.60</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.81</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.02</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.95</td></tr></tbody></table><table-wrap-foot><fn><p>Note: <italic>N</italic> ranges from 353 to 399. MP addiction = mobile phone addiction; T1 = Time 1; T2 = Time 2; T3 = Time 1. * <italic>p</italic> < 0.05, ** <italic>p</italic> < 0.01, *** <italic>p</italic> < 0.001.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05472-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05472-t003_Table 3</object-id><label>Table 3</label><caption><p>Regression model for the moderation effect of adolescent sex.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">\n</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Self-Control (T2, <italic>R</italic><sup>2</sup> = 0.29)</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Risk-Taking Behavior (T2, <italic>R</italic><sup>2</sup> = 0.13)</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Risk-Taking Behavior (T3, <italic>R</italic><sup>2</sup> = 0.20)</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>B</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>SE</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>B</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>SE</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>B</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>SE</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th></tr></thead><tbody><tr><td colspan=\"10\" align=\"left\" valign=\"middle\" rowspan=\"1\">Covariates at T1</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Age</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.03</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.03</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.41</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Only child at home </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.03</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.02</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.19</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Father’s work status </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.01</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.02</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.56</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mother’s work status </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.01</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.03</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.92</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Father’s education </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.01</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.04</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.80</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mother’s education </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.01</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.02</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.77</td></tr><tr><td colspan=\"10\" align=\"left\" valign=\"middle\" rowspan=\"1\">Independent variable</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MP addiction (T1)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.03</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.05</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.48</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.10</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.02</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><0.001</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.02</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.02</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.37</td></tr><tr><td colspan=\"10\" align=\"left\" valign=\"middle\" rowspan=\"1\">Mediating variable</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Self-control (T1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.40</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.06</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold><0.001</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Self-control (T2)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.11</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.04</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.002</bold>\n</td></tr><tr><td colspan=\"10\" align=\"left\" valign=\"middle\" rowspan=\"1\">Moderating variable</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Sex</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.23</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.14</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.10</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td colspan=\"10\" align=\"left\" valign=\"middle\" rowspan=\"1\">Interaction term</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MP addiction (T1) × Sex</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.13</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.06</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.02</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr></tbody></table><table-wrap-foot><fn><p>Note: only child at home: 0 = yes, 1 = no; work status: 1 = freelance, 2 = par-time job, 3 = full-time job; education: 1 = junior middle school and below, 2 = high school degree, 3 = college degree, 4 = bachelor’s degree, 5 = master’s degree or doctoral degree; sex: 0 = boys, 1 = girls; MP addiction = mobile phone addiction; T1 = Time 1; T2 = Time 2; T3 = Time 3. Significant results are in bold.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05472-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05472-t004_Table 4</object-id><label>Table 4</label><caption><p>The mediation effect of self-control by sex.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Sex</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>B</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>S.E.</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">95% CI</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Boys</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.004</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.005</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[−0.006, 0.016]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Girls</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.018</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.007</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>[0.006, 0.036]</bold>\n</td></tr></tbody></table><table-wrap-foot><fn><p>Note: significant results are in bold.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32707826</article-id><article-id pub-id-type=\"pmc\">PMC7432005</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155273</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05273</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Applying Lean in Process Innovation in Healthcare: The Case of Hip Fracture</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Morales-Contreras</surname><given-names>Manuel Francisco</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05273\">1</xref><xref rid=\"c1-ijerph-17-05273\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Chana-Valero</surname><given-names>Pedro</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05273\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-6305-6122</contrib-id><name><surname>Suárez-Barraza</surname><given-names>Manuel F.</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05273\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Saldaña Díaz</surname><given-names>Andrés</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05273\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>García García</surname><given-names>Elena</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05273\">2</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05273\"><label>1</label>Department of Management, ICADE, Universidad Pontificia Comillas, ICADE, 28015 Madrid, Spain</aff><aff id=\"af2-ijerph-17-05273\"><label>2</label>Fundación San Juan de Dios, Centro de CC de la Salud San Rafael, Universidad Nebrija, 28036 Madrid, Spain; <email>pchana@nebrija.es</email> (P.C.-V.); <email>egarciga@nebrija.es</email> (E.G.G.)</aff><aff id=\"af3-ijerph-17-05273\"><label>3</label>International Business Department, School of Business and Economy, Universidad de las Américas Puebla (UDLAP), Puebla 72810, Mexico; <email>manuel.suarez@udlap.mx</email></aff><aff id=\"af4-ijerph-17-05273\"><label>4</label>Hospital San Juan de Dios León, 24010 León, Spain; <email>ansaldiaz@gmail.com</email></aff><author-notes><corresp id=\"c1-ijerph-17-05273\"><label>*</label>Correspondence: <email>mfcontreras@comillas.edu</email>; Tel.: +34-91-5422800 (ext. 2461)</corresp></author-notes><pub-date pub-type=\"epub\"><day>22</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5273</elocation-id><history><date date-type=\"received\"><day>12</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>20</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Academic literature and practitioners acknowledge that there is a need to improve efficiency and service quality in the healthcare industry. In Spain, osteoporotic fractures represent a great cost in socio-economic and morbi-mortality terms, hip fracture being the surgical pathology with the second highest consumption of resources. The research questions that govern this study concern the use of Lean principles to identify waste, and an evaluation of the application of an innovative approach in the hip fracture surgery process. A research design based on a case study and action research was developed. Findings relate to (i) the identification of the main types of waste or <italic>muda</italic> (being the most frequent delay, transportation, over-processing and defects); (ii) the analysis of existing processes based on a Lean approach (identifying opportunities for improvement as a reduction of the number of steps and participants, improving communication, automation, standardization, etc.); and (iii) the application of an innovative process based on the Lean approach and action research in the healthcare industry. This research provides insights for academia, practitioners, management, and society: waste identification and process redesign helps to continue the improvement of operations, increase efficiency, reduce costs, and enhance services, providing benefits to patients, families, hospital employees, and the healthcare system.</p></abstract><kwd-group><kwd>lean</kwd><kwd>process innovation</kwd><kwd>healthcare</kwd><kwd>quality improvement</kwd><kwd>hip fracture integrated care pathway</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05273\"><title>1. Introduction</title><p>Among the high-risk industries, healthcare is the most poorly managed of all and is very late in coming to recognize the importance of the system factors that underlie adverse events [<xref rid=\"B1-ijerph-17-05273\" ref-type=\"bibr\">1</xref>], making a healthcare system analysis and support for patients and staff an absolute priority [<xref rid=\"B2-ijerph-17-05273\" ref-type=\"bibr\">2</xref>]. Overtreatment, undertreatment, overdiagnosis, underdiagnosis, uncontrolled costs and budgets, and medical treatment errors have been reported in health systems across the developed world [<xref rid=\"B3-ijerph-17-05273\" ref-type=\"bibr\">3</xref>]. Medical error can be defined as a deviation from the process of care that may or may not cause harm to the patient [<xref rid=\"B4-ijerph-17-05273\" ref-type=\"bibr\">4</xref>]. In the USA, medical errors are the third cause of death after cancer and heart disease. In Spain, it is estimated that medical errors were involved in 25.9% of court verdicts, 98.5% of them resulted in compensation awards [<xref rid=\"B5-ijerph-17-05273\" ref-type=\"bibr\">5</xref>], leading to an increase in healthcare costs. But not only medical errors impact patient experience and lack of efficiency in healthcare; other examples are: delays, long waiting times, waiting lists for service delivery, lack or excess of capacity, lack or excess of inventory, patients going to the hospital several times until a service is provided, unsatisfied patients, etc. The management of a healthcare organization needs to be able to make decisions on the value delivered to patients and relatives, so patient value must be the key to making responsible decisions as a health system. Porter defines patient value as the patient-relevant medical outcome divided by cost [<xref rid=\"B3-ijerph-17-05273\" ref-type=\"bibr\">3</xref>].</p><p>Funding and efficiency in the health sector becomes a priority issue in the context of an ageing population [<xref rid=\"B6-ijerph-17-05273\" ref-type=\"bibr\">6</xref>]. To address the crisis, the NHS (National Health Service, UK) has turned to the use of different “quality improvement” methodologies, often discovered outside the health sector, Lean management systems being one of them [<xref rid=\"B7-ijerph-17-05273\" ref-type=\"bibr\">7</xref>].</p><p>The term Lean applied to production or management systems was used for the first time by Womack et al. [<xref rid=\"B8-ijerph-17-05273\" ref-type=\"bibr\">8</xref>] It was derived from the Toyota Production System, which was oriented to the continuous improvement of quality, productivity, and efficiency, as well as to the reduction of costs and delivery times within the Japanese automotive industry [<xref rid=\"B8-ijerph-17-05273\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05273\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijerph-17-05273\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijerph-17-05273\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05273\" ref-type=\"bibr\">12</xref>]. Lean is doing more with less and refers to a methodology that pursues the identification and elimination of waste (<italic>Muda</italic> in Japanese) [<xref rid=\"B8-ijerph-17-05273\" ref-type=\"bibr\">8</xref>]. Waste is defined as any activity that does not add any value from the customer perspective, thus reducing the efficiency of a process and increasing its costs [<xref rid=\"B9-ijerph-17-05273\" ref-type=\"bibr\">9</xref>,<xref rid=\"B11-ijerph-17-05273\" ref-type=\"bibr\">11</xref>]. Literature states that seven types of waste could be found in any process: defects, movements, process, inventory, overproduction, transportation, and time [<xref rid=\"B8-ijerph-17-05273\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05273\" ref-type=\"bibr\">9</xref>,<xref rid=\"B11-ijerph-17-05273\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05273\" ref-type=\"bibr\">12</xref>].</p><p>Although Lean management systems were originated within the manufacturing sector, there is a growing interest for its implementation in service companies [<xref rid=\"B13-ijerph-17-05273\" ref-type=\"bibr\">13</xref>]. There are many opportunities of finding waste in the service sector as the processes tend to be slow, and tend to include high values of work in process (or customers waiting), which has an impact on the costs, service quality, and customer satisfaction [<xref rid=\"B14-ijerph-17-05273\" ref-type=\"bibr\">14</xref>].</p><p>Lean practices, with the aim of improving medical care processes, can help to achieve more reliable healthcare systems by addressing the three steps for reducing system errors in healthcare, proposed by Makary et al., in 2016 [<xref rid=\"B15-ijerph-17-05273\" ref-type=\"bibr\">15</xref>]: making errors more visible (facilitate a culture of speaking up), responding to error (support clinician needs), and making errors less frequent (foster culture of safety). Lean has been implemented in different areas in healthcare as intensive care units, X-Ray, cardiology surgery, oncology, mental health units, and clinical laboratories [<xref rid=\"B7-ijerph-17-05273\" ref-type=\"bibr\">7</xref>,<xref rid=\"B16-ijerph-17-05273\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijerph-17-05273\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijerph-17-05273\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijerph-17-05273\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijerph-17-05273\" ref-type=\"bibr\">20</xref>]. A national survey in the United States found that 70% of hospitals used Lean healthcare or related methodologies to deal with both quality and cost issues [<xref rid=\"B21-ijerph-17-05273\" ref-type=\"bibr\">21</xref>]. Gonzalez-Aleu et al. in 2018 analysed the critical success factors when implementing continuous improvement projects in hospitals [<xref rid=\"B22-ijerph-17-05273\" ref-type=\"bibr\">22</xref>]. But there is not enough evidence to address its sustainability in broad healthcare adoption as well as its impact in quality healthcare [<xref rid=\"B7-ijerph-17-05273\" ref-type=\"bibr\">7</xref>] and achieve both effectiveness and efficiency improvements [<xref rid=\"B18-ijerph-17-05273\" ref-type=\"bibr\">18</xref>]. Thus, it remains a “challenge for academics and practitioners to evaluate Lean healthcare under a more critical perspective” [<xref rid=\"B17-ijerph-17-05273\" ref-type=\"bibr\">17</xref>].</p><p>Hip fracture is one of the leading pathologies in terms of resource consumption [<xref rid=\"B23-ijerph-17-05273\" ref-type=\"bibr\">23</xref>], with a progressive increase in the estimate of new cases per year [<xref rid=\"B24-ijerph-17-05273\" ref-type=\"bibr\">24</xref>] and a high-risk patient profile. Applying Lean to this scenario, with the aim of identifying barriers, sensitized with the patient’s needs and mapping the experience of the different actors involved, can improve quality of care and increase the efficiency of processes, helping managers and staff address more complex issues and deepen our understanding of how Lean works in healthcare [<xref rid=\"B25-ijerph-17-05273\" ref-type=\"bibr\">25</xref>].</p><p>The main research questions that govern the study are:<list list-type=\"simple\"><list-item><label>RQ1.</label><p>→ What types of waste or <italic>muda</italic> could be identified around a hip fracture surgery process?</p></list-item><list-item><label>RQ2.</label><p>→ How a process innovation methodology using Lean techniques is applied in healthcare, in particular in a hip fracture surgery processes?</p></list-item></list></p><p>With the aim of answering these research questions, we decided to conduct a methodology based on a case study and action research, by involving the research team in the process evaluation and redesign. The article is structured as follows: (i) an introduction; (ii) a literature review and theoretical framework; (iii) a description of the research methodology; (iv) the case study results; (v) discussion; and (vi) conclusion.</p></sec><sec id=\"sec2-ijerph-17-05273\"><title>2. Theoretical Framework</title><p>Hip fractures are potentially catastrophic (deadly) events with adverse outcomes including alteration in function, institutionalization, and death [<xref rid=\"B26-ijerph-17-05273\" ref-type=\"bibr\">26</xref>]. Mortality rates have increased in ageing patients who live alone and who have poor pre-fracture mental status and mobility [<xref rid=\"B26-ijerph-17-05273\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijerph-17-05273\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijerph-17-05273\" ref-type=\"bibr\">28</xref>], other associated factors being: white race, osteoporosis, previous hip fracture, level of physical functioning, medication use, and hormonal and dietary factors [<xref rid=\"B29-ijerph-17-05273\" ref-type=\"bibr\">29</xref>]. Osteoporosis fractures are the fourth leading cause of chronic disease morbidity. Over 2 million women and 750,000 men are estimated to suffer osteoporosis at the level of the femoral neck, with an expected prevalence of 24.2% in women between 70 and 80 years old. The cost to the Spanish healthcare system related to osteoporosis fractures amounted to 4.2 billion euros in 2017 and is expected to increase to 5.5 billion in 2030 [<xref rid=\"B30-ijerph-17-05273\" ref-type=\"bibr\">30</xref>].</p><p>More than 500,000 acute hip fractures will occur annually by 2040; each year at least 300,000 old people are hospitalized by hip fractures [<xref rid=\"B24-ijerph-17-05273\" ref-type=\"bibr\">24</xref>]. The number of geriatric hip fractures is expected to rise annually all over the world related to the elderly population ages, which will significantly increase care costs for healthcare systems, patients, relatives, and societies. The incidence of this complication for the elderly in the United States is expected to grow to half a million by 2040, with a worldwide incidence of geriatric hip fractures reaching up to 4.5 million by 2050 [<xref rid=\"B31-ijerph-17-05273\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijerph-17-05273\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijerph-17-05273\" ref-type=\"bibr\">33</xref>]. In Spain, hip fractures are the second highest obstetric and surgical condition in resource consumption after colorectal and anal cancer regarding the average costs of the Diagnosis Related Groups (DRG) [<xref rid=\"B23-ijerph-17-05273\" ref-type=\"bibr\">23</xref>], with an estimation of 263,351 new hip fractures in women and 84,213 in men between 2010 and 2020, with an existing tendency to increase in older age groups (>80 years) [<xref rid=\"B34-ijerph-17-05273\" ref-type=\"bibr\">34</xref>]. In the 2018 National Registry of Hip Fractures (RNFC) report, the overall profile, of the 11,431 patients included from the 72 participating hospitals, described a profile of patients with an average age greater than 87 years, usually living at home, with 76% being women [<xref rid=\"B35-ijerph-17-05273\" ref-type=\"bibr\">35</xref>].</p><p>Looking for efficiency in the process at hand, with a mortality rate within the first year at 20–33% (that may persist beyond 5 years) is a priority, as is relying on value-based healthcare models [<xref rid=\"B31-ijerph-17-05273\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijerph-17-05273\" ref-type=\"bibr\">32</xref>]. In the first three months after surgery, older adults have an eight times higher risks of dying of a hip fracture compared with people who have not suffered from it, continuing the risk of death still in the first ten years [<xref rid=\"B36-ijerph-17-05273\" ref-type=\"bibr\">36</xref>]. Related to functional outcomes, which directly impact the patient´s social dimension, it is important to describe functional outcomes such as the inability to walk independently (40% of hip fractures patients), the situation of vulnerability, needing assistance to perform daily tasks (60%) and patients who become totally dependent requiring a nursing home one year after fracture.</p><p>Social impact is relevant, according to a study published in the British Medical Journal, which detected 80% of women over 75 years who would prefer to die rather than experience the loss of independence and living in a nursing home after hip fracture discharge [<xref rid=\"B37-ijerph-17-05273\" ref-type=\"bibr\">37</xref>].</p><p>According to Bradeanu et al., health and social care for hip fractures in the elderly in one year amounts to two billion euros in the UK, where the annual incidence was 79,243 euros in 2019, expecting to increase to 104,000 cases by 2025. Dementia incidence will reach 75.6 million in 2030 and 135.46 in 2050 in the whole population, but doubles every five years among people aged 65 to 90. They also highlight the social impact that both pathologies together have on the caregivers, including depression, a higher risk of developing anxiety, and more hours per day spent helping patients. Furthermore, hip fractures are associated with the development of disability, depression, and cardiovascular diseases in the elderly, with additional cost for society [<xref rid=\"B36-ijerph-17-05273\" ref-type=\"bibr\">36</xref>].</p><p>Therefore, with an increasing incidence and associated poor clinical outcomes, it is relevant to evaluate the impact of hip fractures in the elderly not only on the healthcare system but also in terms of the social impact that is generated by patients as well as by their relatives, the society and the environment [<xref rid=\"B32-ijerph-17-05273\" ref-type=\"bibr\">32</xref>]. Living in changing times regarding healthcare, political agendas, budgetary limitations, and new leaderships contributes to demanding from professionals the provision of greater value with fewer resources, promoting a bigger impact on patients and other stakeholders [<xref rid=\"B38-ijerph-17-05273\" ref-type=\"bibr\">38</xref>].</p><p>Nowadays, patients are increasingly demanding immediate, high-quality, and individualized care to their specific needs. To meet these demands, strategies are needed to combine sustainable quality improvement with increased efficiency [<xref rid=\"B39-ijerph-17-05273\" ref-type=\"bibr\">39</xref>]. Improving and ensuring effectiveness of clinical procedures is necessary, but so is improving healthcare performance using appropriate indicators: what works, for whom, and in which circumstances? This must be addressed to not leave anyone behind [<xref rid=\"B40-ijerph-17-05273\" ref-type=\"bibr\">40</xref>,<xref rid=\"B41-ijerph-17-05273\" ref-type=\"bibr\">41</xref>].</p><p>In a progressive ageing population scenario, indicators that measure the social value of interventions are becoming key indicators to be analyzed in a process of quality continuous improvement. It is important to detect key performance indicators during the process analysis so that, once improvements have been implemented, they can be measured; because collecting data is not enough, they must be used to improve healthcare [<xref rid=\"B40-ijerph-17-05273\" ref-type=\"bibr\">40</xref>].</p><p>Despite being the focus on which to work, currently the most important indicators to measure the performance of the health care process are the length of hospital stay (LOS), i.e., the number of days comprised between the date of a patient admission and the discharge date [<xref rid=\"B42-ijerph-17-05273\" ref-type=\"bibr\">42</xref>], as well as the intra-hospital, 30-day, and year-round survival. Exploring Lean healthcare on patient flow, the main outcomes categorized related to the utilization of services and access to services [<xref rid=\"B21-ijerph-17-05273\" ref-type=\"bibr\">21</xref>,<xref rid=\"B43-ijerph-17-05273\" ref-type=\"bibr\">43</xref>] are the length of stay, mean waiting time to see a doctor, mean waiting time to get treatment, median time for daily treatment, median waiting time to see a professional (nurse preparation), and the median process time of being discharged. Therefore, LOS and shorter waiting times after Lean healthcare intervention were the most common measures related to process outcomes. Related to the hip fracture care pathway, usual measurements are time to surgery, duration of surgery, detection of complications, hospital cost, allogenic transfusion rate, thirty-day readmission, and in-hospital mortality [<xref rid=\"B44-ijerph-17-05273\" ref-type=\"bibr\">44</xref>,<xref rid=\"B45-ijerph-17-05273\" ref-type=\"bibr\">45</xref>].</p><p>The provision of quality health services depends on an adequate and efficient execution of each of the processes in which not only clinical activities exist, but also in which a series of non-clinical support activities are executed by different types of resources, which varies from one organization to another. These processes are highly complex and dynamic, and it is becoming increasingly common to design them ad hoc with a multi-disciplinary character but, seeking a balance in the improvement of processes between the generation of impact on patient´s quality of life on the one hand and the need to reduce costs, reduce waiting times, and to improve the productivity of each resource on the other, is not an easy task [<xref rid=\"B46-ijerph-17-05273\" ref-type=\"bibr\">46</xref>].</p><p>In 2012, the European Commission proposed a new cooperation framework for innovation in the field of active and healthy ageing, adopting the “Quadruple Helix” innovation model, which aims to generate shared value involving and benefiting civil society, private companies, academia, and the public sector, thus creating a powerful innovation ecosystem [<xref rid=\"B47-ijerph-17-05273\" ref-type=\"bibr\">47</xref>]. Designing strategies to implement effective solutions according to this model requires the use of specific tools that can trigger improvements in the management and handling of risks, including psychosocial risk management and, subsequently, better well-being [<xref rid=\"B48-ijerph-17-05273\" ref-type=\"bibr\">48</xref>]. A useful method is the “design thinking” or user-centred design, proposed by the Stanford University Institute of Design, which establishes five unavoidable phases in the process of designing a digital solution, so that it is finally adopted by the target audience: empathizing, defining, devising, prototyping, and evaluating [<xref rid=\"B47-ijerph-17-05273\" ref-type=\"bibr\">47</xref>].</p><p>A more sceptical and scientifically rigorous approach to the development, evaluation, and dissemination of quality improvement methodologies is required, combined with the demand of more robust evidence for the methods and approaches that they use, in those areas of knowledge where a mix of theoretical, empirical, and experimental evidence is used to enable guidance and planning for their application [<xref rid=\"B49-ijerph-17-05273\" ref-type=\"bibr\">49</xref>].</p><p>Strategies focused on the improvement of care quality, waiting times, resource consumption, etc., with the aim of assuring sustainable high-quality care, are needed. For instance, innovative methods such as design thinking combined with Lean. Lean has proved to be a method that has become, in the past decade, one of the most commonly used as a quality improvement approach in healthcare settings to improve delivery of care [<xref rid=\"B25-ijerph-17-05273\" ref-type=\"bibr\">25</xref>,<xref rid=\"B50-ijerph-17-05273\" ref-type=\"bibr\">50</xref>], guaranteeing the improvement of the effectiveness and efficiency of health care delivery, and providing an impetus for establishing the best practice within an organization [<xref rid=\"B42-ijerph-17-05273\" ref-type=\"bibr\">42</xref>].</p><p>The Lean methodology approach was developed in the car manufacturing industry, and was later adopted in the healthcare field with the aim of improving quality of care and the efficiency of processes [<xref rid=\"B25-ijerph-17-05273\" ref-type=\"bibr\">25</xref>,<xref rid=\"B39-ijerph-17-05273\" ref-type=\"bibr\">39</xref>,<xref rid=\"B51-ijerph-17-05273\" ref-type=\"bibr\">51</xref>]. The first authors to carry out research on Lean in health were Young et al. in 2004, and Spear in 2005 [<xref rid=\"B52-ijerph-17-05273\" ref-type=\"bibr\">52</xref>,<xref rid=\"B53-ijerph-17-05273\" ref-type=\"bibr\">53</xref>]. Both argue that carrying out Kaizen and Lean Thinking efforts in health systems can help eliminate errors, delays, inadequate processes, duplications, and all kinds of MUDA in the activities of health services. Another pioneering research work on the subject was that of Kollberg and Dahlgaard et al., in 2007, who emphasize in their article that continuous improvement techniques help to significantly improve the performance of processes and services in health systems (specifically in Sweden in this case) [<xref rid=\"B54-ijerph-17-05273\" ref-type=\"bibr\">54</xref>]. Drotz and Poksinska also confirm the benefits of implementing Lean and Kaizen in health organizations because it generates positive effects on the positions and roles of medical personnel, moving from a bureaucratic style to an approach of agile processes and teamwork [<xref rid=\"B20-ijerph-17-05273\" ref-type=\"bibr\">20</xref>]. On the other hand, authors such as Bortolotti et al. have found 14 specific factors that increase the ability of employees to solve problems when using Kaizen in health systems [<xref rid=\"B55-ijerph-17-05273\" ref-type=\"bibr\">55</xref>]. The clarity of goals, the degree of the difficulty of objectives, the autonomy of the work teams, and the support of the top managers are critical to the success of the application of Kaizen according to these researchers.</p><p>On the other hand, Ortíz-Barrios and Alfaro-Saiz (2020) carried out a literature review of the application of process improvement in emergency processes in health hospitals [<xref rid=\"B56-ijerph-17-05273\" ref-type=\"bibr\">56</xref>]. The selected papers were categorized considering the leading ED problems and publication year. Two hundred and three (203) papers distributed in 120 journals were found to meet the inclusion criteria. In Latin America, Brazilian authors such as Coehlo et al. (2015) present a case study of process improvement, in which the performance improvement of the total workspace was 75% and the reduction in waiting for patient care went from 2 h to 30 min [<xref rid=\"B57-ijerph-17-05273\" ref-type=\"bibr\">57</xref>]. Coelho et al. (2015) also point out that Lean and Kaizen’s efforts can eliminate at least three hours a day of overtime in hospitals in Brazil. Curatolo et al., in 2014, also performed a literature review indicating that a Lean approach with a high-methodological maturity level that includes the 11 characteristic activities of process innovation or Business Process Improvement has never been reported [<xref rid=\"B58-ijerph-17-05273\" ref-type=\"bibr\">58</xref>]. Considering this, the paper suggests a meta model for a high-methodological maturity-level Lean method based on the characteristic activities of Business Process Improvement. Finally, Meyer et al. present the successful application of Lean Six Sigma, a set of quality improvement (QI) tools, to streamline their processes and uncover the root causes of program inefficiencies. All this for a hospital that performs treatments for cancer patients by Tobacco [<xref rid=\"B59-ijerph-17-05273\" ref-type=\"bibr\">59</xref>].</p><p>Godley et al. affirmed that quality improvement studies improving timeliness in healthcare are essential for reducing delays in care and for improving quality [<xref rid=\"B60-ijerph-17-05273\" ref-type=\"bibr\">60</xref>]. In 2018, Woodnutt et al. carried out a systematic literature review on the Lean sustainable method in NHS hospitals, finding that waiting times were the most common area in which Lean practices could have an effect [<xref rid=\"B7-ijerph-17-05273\" ref-type=\"bibr\">7</xref>]. In the management of patients undergoing hip replacement surgery, recent studies applying Lean methodology in combination with other strategies improved quality and at the same time reduced costs, resource consumption, and waiting times [<xref rid=\"B42-ijerph-17-05273\" ref-type=\"bibr\">42</xref>,<xref rid=\"B44-ijerph-17-05273\" ref-type=\"bibr\">44</xref>]. Moreover, interventions aimed at improving pre-fracture function and post-fracture social support could increase health perception following hip fracture [<xref rid=\"B61-ijerph-17-05273\" ref-type=\"bibr\">61</xref>] and there is evidence that psychological and social factors, particularly social support, influence recovery and post-fracture quality of life [<xref rid=\"B62-ijerph-17-05273\" ref-type=\"bibr\">62</xref>].</p><p>Therefore, Lean methodology, with the aim of identifying fundamental areas of delay and inefficiency throughout the process, has not been fully implemented to hip fracture care individually or combined with other methodologies [<xref rid=\"B44-ijerph-17-05273\" ref-type=\"bibr\">44</xref>], such as design thinking. This would enable the mapping of the patient’s experience (journey map) during hospital admission and subsequent discharge from the perspective of what he/she sees, hears, and feels at each phase of the process [<xref rid=\"B63-ijerph-17-05273\" ref-type=\"bibr\">63</xref>].</p><p>Lean methodology is used to increase value in healthcare, but it is seen that the determination of value is variable. It is not easily quantified under evaluation of healthcare-related services, maybe because much of this value is not based on clinical outcomes but on social ones, which are difficult to describe, capture, and translate into a decision-maker´s language, usually financial [<xref rid=\"B38-ijerph-17-05273\" ref-type=\"bibr\">38</xref>]. There is no standardized way to capture the social value inherent in healthcare programs, but we must work to obtain not only the direct and indirect costs, but also to determine the impacts they are creating.</p><p>Lean and Kaizen are focused on improving processes towards an ideal state, with the focus always on adding value to the client (patients), identifying waste (tasks that do not benefit or add value), reducing costs, and improving the work of professionals [<xref rid=\"B7-ijerph-17-05273\" ref-type=\"bibr\">7</xref>,<xref rid=\"B48-ijerph-17-05273\" ref-type=\"bibr\">48</xref>]. The origins of business process innovation could be traced back to the seminal works of Harrington [<xref rid=\"B64-ijerph-17-05273\" ref-type=\"bibr\">64</xref>] and Davenport and Short [<xref rid=\"B65-ijerph-17-05273\" ref-type=\"bibr\">65</xref>]. Davenport and Short defined processes as “a set of logically-related tasks performed to achieve a defined business outcome”, and they stated that a company should redesign such processes when they prove to be inefficient or ineffective [<xref rid=\"B65-ijerph-17-05273\" ref-type=\"bibr\">65</xref>]. Their research proposed a methodology consisting on five steps: (i) Develop the business vision and process objectives; (ii) Identify the processes to be redesigned; (iii) Understand and measure the performance of existing processes; (iv) Identify IT levers; and (v) Design and build a process prototype and implement improvements (Davenport and Short, 1990). Harrington defined business process innovation as “a systematic methodology developed to help significant advances in the way its business processes operate”. His model is composed of five stages: (i) Organising for quality; (ii) Understand the process; (iii) Rationalise processes; (iv) Implement, measure, and monitor, and (v) Continuous Improvement [<xref rid=\"B64-ijerph-17-05273\" ref-type=\"bibr\">64</xref>]. The literature also presents different approaches for business process improvement (Hammer and Champy [<xref rid=\"B66-ijerph-17-05273\" ref-type=\"bibr\">66</xref>]; Elzinga et al., [<xref rid=\"B67-ijerph-17-05273\" ref-type=\"bibr\">67</xref>]; Lee and Chuah [<xref rid=\"B68-ijerph-17-05273\" ref-type=\"bibr\">68</xref>]; Gardner [<xref rid=\"B69-ijerph-17-05273\" ref-type=\"bibr\">69</xref>]; Alange and Steiber [<xref rid=\"B70-ijerph-17-05273\" ref-type=\"bibr\">70</xref>]; Page [<xref rid=\"B71-ijerph-17-05273\" ref-type=\"bibr\">71</xref>]; and [<xref rid=\"B72-ijerph-17-05273\" ref-type=\"bibr\">72</xref>], among others).</p><p>The next section describes the methodology of this research paper, including a justification for the selected business improvement process framework.</p></sec><sec sec-type=\"methods\" id=\"sec3-ijerph-17-05273\"><title>3. Methodology</title><p>The objective of this research is to develop and apply a process of innovation methodology based on Lean principles in the healthcare industry, in particular in hip fracture processes. In order to pursue the objective, a research design based on a case study and action research (AR) was developed.</p><p>A qualitative case study methodology is appropriate when there is an interest in knowing the “how” and “why” of a phenomenon and it is focused in contemporary events [<xref rid=\"B73-ijerph-17-05273\" ref-type=\"bibr\">73</xref>]. Theory built from cases is likely “to have important strengths like novelty, testability, and empirical validity, which arise from the intimate linkage with empirical evidence” [<xref rid=\"B74-ijerph-17-05273\" ref-type=\"bibr\">74</xref>], and it is also likely to be interesting, accurate and testable, as they use a wide range of data sources such as interviews, documentation, quantitative data, and direct observations [<xref rid=\"B75-ijerph-17-05273\" ref-type=\"bibr\">75</xref>].</p><p>Greenwood and Levin defined AR as “the research in which the validity and value of the research results are tested through collaborative insider-professional researcher knowledge generation and application processes in projects of social change that aim to increase fairness, wellness, and self-determination”. AR allows collaboration between professional researchers and community and organizational stakeholders in “defining the objectives, constructing the research questions, learning research skills, pooling knowledge and efforts, conducting the research, interpreting the results, and applying what is learned to produce positive social change” [<xref rid=\"B76-ijerph-17-05273\" ref-type=\"bibr\">76</xref>]. An AR methodology aims at simultaneously generating an action and building knowledge related to this action; thus, the results are both the action or intervention and the research itself [<xref rid=\"B77-ijerph-17-05273\" ref-type=\"bibr\">77</xref>,<xref rid=\"B78-ijerph-17-05273\" ref-type=\"bibr\">78</xref>]. Empirical AR is carried out, as the researchers document a current phenomenon, follow the process and share the results. It is also a participatory research, as the researchers maintain active and close contact with agents within the organization, as both are part of the research team [<xref rid=\"B77-ijerph-17-05273\" ref-type=\"bibr\">77</xref>]. AR is research in action, participative, concurrent with action and consisting of a sequence of events with a focus on problem solving [<xref rid=\"B78-ijerph-17-05273\" ref-type=\"bibr\">78</xref>].</p><p>Alfaro and Avella in 2013 proposed to conduct a preliminary stage in AR which consists of the identification of the problem or opportunity that the research team pretends to study; this should be done as a teamwork activity among researchers and practitioners [<xref rid=\"B77-ijerph-17-05273\" ref-type=\"bibr\">77</xref>]. Once the study topic has been identified, the six main steps in AR are data gathering, data feedback, data analysis, action planning, implementation, and evaluation [<xref rid=\"B78-ijerph-17-05273\" ref-type=\"bibr\">78</xref>].</p><p>A private general medical surgical hospital has been chosen as a case study. It is located in a +120,000 habitants city in the North of Spain, in a sparsely populated region. This study focuses on optimization and improvement in the design of hip fracture surgery processes in the above-mentioned hospital. The justification for the case study lies in the fact that the hospital is located in a region with a clear aging population and consequently there is a high prevalence of hip fracture cases. The hospital decided to start offering the service “hip fracture surgery” in 2019; thus, operating rooms were assigned, and professionals were hired for this purpose. Before 2019, this service was only provided in particular and punctual cases; since 2019 the demand forecast is 150 surgeries per year.</p><p>The case study hospital has a long trajectory of working according to high quality standards (as it is certified ISO 9001 and EFQM 500+). Moreover, it shows a strong commitment towards continuous improvement, as the new service “hip fracture surgery” implies high values of LOS of patients, as well as high resource consumption. This justifies the interest, both for the hospital and for the Public Health system, in studying, analysing, and proposing improvements in the process with the aim of improving the quality of the services provided, the efficiency of the operations, and the experience of the patients and their quality of life once they have left the hospital facilities.</p><p>In this research, AR is ensured by the collaboration of researchers (expert in Lean, process innovation, and healthcare) with hospital management and professionals (doctors, nurses, technicians), who jointly defined the research objectives, conducted the research, analysed and discussed the results, and planned for action implementation and the next steps.</p><p>The research team decided to use the framework based on process innovation by Suárez-Barraza et al. (2019) [<xref rid=\"B72-ijerph-17-05273\" ref-type=\"bibr\">72</xref>]. The main justification for this is that the selected model is based on Kaizen and Lean management systems [<xref rid=\"B8-ijerph-17-05273\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05273\" ref-type=\"bibr\">9</xref>] with a gradual and continuous improvement focus. On the other hand, other models are closer to engineering and are more appropriate for manufacturing operations, and are more oriented to breakthrough or radical innovation. Lean management systems originated in Toyota’s automotive factories, and they are used today in countless companies and organizations, both in manufacturing and services, having begun to be used in the healthcare sector in recent years [<xref rid=\"B16-ijerph-17-05273\" ref-type=\"bibr\">16</xref>,<xref rid=\"B19-ijerph-17-05273\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijerph-17-05273\" ref-type=\"bibr\">20</xref>]. Lean management systems seek to analyse production processes with the aim of identifying the activities that add value to the customer, and then minimizing or eliminating all the activities that do not add any value, called waste (<italic>muda</italic>, in Japanese).</p><p>The process innovation framework consists of the following stages [<xref rid=\"B72-ijerph-17-05273\" ref-type=\"bibr\">72</xref>], as shown in <xref ref-type=\"fig\" rid=\"ijerph-17-05273-f001\">Figure 1</xref>: (1) process selection and understanding the process, (2) mapping the process, (3) process measurement, (4) process analysis, (5) process redesign [<xref rid=\"B72-ijerph-17-05273\" ref-type=\"bibr\">72</xref>].</p><p>Data analysis and collection</p><p>We selected the 2019 and 2020 years for observation because we wanted to know the details of the complete process related to hip fracture. Data and statistics were collected during 2019 and 2020. The field work and analytical phase occurred from November 2019 to March 2020. We combined the statistical analysis of the hip fracture patient’s dataset attended over 2019 and 2020 with process innovation and Lean healthcare analysis, including user experience ones. In this case, the study combined administrative data, experts’ point of view, and an observational process review.</p></sec><sec id=\"sec4-ijerph-17-05273\"><title>4. Case Study: Hip Fracture Surgery Description</title><p>In this section we present a general description of the hospital case study, as well as a summary of descriptive statistics about the hip fracture surgery process in 2019 and 2020.</p><p>The special features that make the case study relevant are related to the aging of the population, the depopulation of this region, social isolation, and the lack of rural doctors and health care centers. The importance of the management of patient´s admission after hip fracture and discharge is undeniable. The case study hospital counts with over 300 beds, and provides medical services in close to 40 disciplines, which include traumatology, geriatrics, and cardiology, among others.</p><p>The hospital, immersed in a process of continuous quality improvement, analyzed the 2019 data and consequently took some actions oriented to improve them. Actions consisted of standardizing the medical-surgical process, consolidating an orthogeriatrics team, and carrying out an exhaustive follow-up. According to the data described below, a high impact was achieved, resulting in a reduction in average stays and mortality. The research described in this paper comes as the next step in this process of continuous improvement at the case study hospital, and consists of the analysis with Lean methodology with the aim of detecting potential points of improvement, within the real possibilities of the process at hand.</p><p>Hip fracture surgery at the hospital provides a service to 150 hip interventions annually as mentioned above in the case study justification in the Methodology section (<xref ref-type=\"sec\" rid=\"sec3-ijerph-17-05273\">Section 3</xref>).</p><p>A descriptive statistical analysis has been carried out to describe the population:</p><p><bold>Total patient population</bold>. We reviewed 148 clinical record histories in 2019, from 39 men and 109 women. In 2020, we reviewed 106 clinical records, from 26 men and 80 women.</p><p><bold>Age</bold>. Related to age, in 2019, the average age was 86.5 years, the minimum being 68.5 years and the maximum being 99.9 years. In 2020, the average was 85.5 years, the minimum being 66 years and the maximum being 101 years. By gender, in 2019 women/men average ages were 87/85 years, and in 2020 women/men average ages were 87/81 years.</p><p><bold>In-hospital mortality</bold>. In 2019, in-hospital mortality was about 6.77% of admissions or 8.78% of patients (13 patients). Discharge due to death occurs at different times, on average two weeks after admission, although half of the deaths occur before 9 days. Moreover, 30 days after discharge, the mortality rate is 1.35% (2 patients). In 2020, in-hospital mortality was reduced to 3.7%.</p><p><bold>Total hospitalization time.</bold> Total hospitalization time refers to the total process cycle time, from patient arrival to the hospital to the moment when the patient is discharged and leaves the hospital. In 2019, half of patients (median value for cycle times) were discharged within 11.9 days, the average being 15.9 days; 25% of patients were discharged at 16 days or more. In 2020, the LOS median value was 9 days, the average being 9.41 days (40.97% less than 2019 data).</p><p><bold>Presurgical time</bold><bold>.</bold> The average presurgical time (from patient admission to surgery) was approximately 4 days in 2019, half of patients underwent surgery in 3.16 days or less. During 2020, presurgical time was 3.15 days.</p><p><bold>Post-surgical average stay.</bold> The average length of stay after surgery in 2019 was 10.12 days. A length of stay after surgery of more than 20 days could be considered exceptional. On the other hand, during 2020, the average post-surgical time was reduced to 5.25 days, a reduction of approximately 50%.</p><p>A summary of the descriptive statistics is shown in <xref rid=\"ijerph-17-05273-t001\" ref-type=\"table\">Table 1</xref> below:</p></sec><sec id=\"sec5-ijerph-17-05273\"><title>5. Applying Lean in Process Innovation in Hip Fracture: Results</title><sec id=\"sec5dot1-ijerph-17-05273\"><title>5.1. Process Selection and Understanding the Process</title><p>The quality management system at the case study hospital has been built based on the analysis and understanding of the different needs and expectations of all the involved stakeholders (patients, society, suppliers, collaborators, finance clients) with the aim of providing them with the highest level of satisfaction. The system classifies internal processes in strategic processes, care processes, and support processes. <xref ref-type=\"app\" rid=\"app1-ijerph-17-05273\">Appendix A</xref>\n<xref ref-type=\"fig\" rid=\"ijerph-17-05273-f0A1\">Figure A1</xref> presents a detailed map of all of them.</p><p>Strategic processes refer to external relations of the hospital, to the management and planning, and to the continuous improvement.</p><p>Care processes refer to health attention, psychological attention, and social attention. Health attention consists of all the processes oriented to provide a health service to the patients, involving health operational steps (such as emergency, admissions, external medical consultations, hospitalization, surgery, rehabilitation), but also to diagnosis support (laboratory, image, other tests), clinical support (pharmacy, sterilization, blood reserves, nutrition, and dietetics) and care support (patient care, social work, volunteering, spiritual and religious care, bioethics, and patient safety). Psychological attention consists of all the processes oriented to provide a psychological rehabilitation service. Social attention consists of all the processes oriented to provide a service to homeless patients.</p><p>Support processes refer to information technologies, procurement and logistics, human resources, administration, and other processes.</p></sec><sec id=\"sec5dot2-ijerph-17-05273\"><title>5.2. Mapping the Process</title><p>The research team conducting this project is formed by the paper authors, two of them being experts in healthcare processes (in particular, in hip fracture) and two of them being experts in Lean management and innovation process methodologies. Collaboration with hospital management and hospital professionals (technicians, nurses, doctors) has been necessary in this research; in particular, the collaboration of a traumatologist doctor who is in charge of hip fracture surgery. Mapping the process refers to the documentation of the present situation and to the identification of flow.</p><p>Documenting the present situation must be done as it is in reality and not under ideal conditions. This has been conducted in two steps: first, the experts in hip fracture wrote the flow or sequence of activities from the first to the last step, i.e., from the first moment a patient enters the hospital until the moment the same patient leaves the facilities of the mentioned hospital. Second, a mixed team (expert in hip fracture and expert in Lean implementation) visited the hospital and followed the process from the first to the last activity, taking notes and pictures, asking questions to the different participants in the process activities, and walking the distances all along the path.</p><p>The results of these two steps are the design of a block diagram (which provides a general description of the sequence of the process) and, based on it, the detailed process mapping using flow diagrams. <xref ref-type=\"fig\" rid=\"ijerph-17-05273-f002\">Figure 2</xref> shows a block diagram, which is the first step in the process analysis. The flow diagram of the hip fracture process uses symbols according to the American National Standard Institute (ANSI). <xref ref-type=\"fig\" rid=\"ijerph-17-05273-f003\">Figure 3</xref> presents an excerpt of a flow diagram of the process. This first excerpt contains only 7 activities of a total of 236. The complete flow diagram can be found in <xref ref-type=\"app\" rid=\"app2-ijerph-17-05273\">Appendix B</xref>\n<xref ref-type=\"fig\" rid=\"ijerph-17-05273-f0A2\">Figure A2</xref>. The flow diagram was built on 23 pages that can be seen in <xref ref-type=\"app\" rid=\"app2-ijerph-17-05273\">Appendix B</xref>, showing that the hip care process map is a complex process in its current situation.</p></sec><sec id=\"sec5dot3-ijerph-17-05273\"><title>5.3. Process Measurements</title><p>Measuring the existing process implies identifying different indicators related to the process with the aim of quantifying them. Process redesign will propose some initiatives oriented to improve the values of the proposed metrics. The indicators that we consider are:<list list-type=\"simple\"><list-item><label>(i)</label><p>Number of activities in the process. The number of activities is a relevant metric, as it is a measure of the complexity of the process. Each activity in the process has been identified and numbered.</p><p>The total number of activities resulted in 236.</p></list-item><list-item><label>(ii)</label><p>Number of participants. The number of participants is also a relevant metric, as a high number shows a more complicated process, as the number of interactions between them is higher, so there is a higher opportunity or risk of miscommunication, misunderstanding, delays, and potential errors. We understand the “number of participants” as the number of different jobs or profiles of employees, but the real number of employees intervening in the process is much higher. For instance, “nurse” is considered one participant, but in reality, we can find different persons working as nurses involved in the different activities of the process (same for doctors, assistants, technicians, etc.)</p><p>The number of participants is 18 (*).</p><p>They are grouped in different areas (see detail in <xref rid=\"ijerph-17-05273-t002\" ref-type=\"table\">Table 2</xref>).</p></list-item><list-item><label>(iii)</label><p>Process cycle times. This refers to the total process time, from patient arrival at the hospital to the moment that the patient is discharged and leaves the hospital (also known as total hospitalization times or LOS). Total process cycle times in 2020 are reported above in the case study description, the average being 9.41 days (40.97% less than 2019 data), the median being 9 days, the standard deviation being 4.17, the minimum being 0 days, and the maximum value being 32 days.</p></list-item></list></p></sec><sec id=\"sec5dot4-ijerph-17-05273\"><title>5.4. Process Analysis</title><p>The process analysis is carried out in two steps:\n<list list-type=\"simple\"><list-item><label>(i)</label><p>First, the process is analyzed in detail, identifying the different types of activities within the process. The following <xref rid=\"ijerph-17-05273-t003\" ref-type=\"table\">Table 3</xref> provides this information:</p></list-item><list-item><label>(ii)</label><p>Second, the process is analyzed with the aim of identifying the different types of <italic>muda</italic> all along the different stages. According to the literature, the following types of <italic>muda</italic> can be found in a process:\n<list list-type=\"bullet\"><list-item><p>Defects: errors or defects when performing a task, producing a service or making a product.</p></list-item><list-item><p>Overprocessing: repeating tasks or activities during the process.</p></list-item><list-item><p>Overproduction: producing more than necessary.</p></list-item><list-item><p>Movement: unnecessary or inadequate movement of personnel to execute a task (related to ergonomics or efficiency of movements).</p></list-item><list-item><p>Transportation: carrying out or moving materials or patients from one location to a different location where a new task will be performed. This also refers to the transportation of employees.</p></list-item><list-item><p>Inventory: excess of materials or goods which are cumulated in case they are needed.</p></list-item><list-item><p>Delay: additional waiting time when the process stops more than usual.</p></list-item><list-item><p>Unused Talent: underused qualified workers (it could also be—but not in this case—a bad attitude from employees affecting the process results).</p></list-item></list></p></list-item></list></p><p>Our analysis shows that 60 opportunities for potential <italic>muda</italic> have been identified in 54 activities (i.e., 6 activities present 2 types of <italic>muda</italic>). <xref rid=\"ijerph-17-05273-t004\" ref-type=\"table\">Table 4</xref> shows a summary of the different types of <italic>Muda</italic>. <xref ref-type=\"app\" rid=\"app3-ijerph-17-05273\">Appendix C</xref>\n<xref rid=\"ijerph-17-05273-t0A1\" ref-type=\"table\">Table A1</xref> provides the details of all of them.</p></sec><sec id=\"sec5dot5-ijerph-17-05273\"><title>5.5. Process Redesign</title><p>AR is involved in the teamwork activities carried out between researchers and practitioners. Process measurements and analysis have been conducted by the research team (based on its experience in Lean, process innovation, and healthcare) with the collaboration of the hospital director, hospital employees (nurses, technicians), and mainly by the active support and involvement of a traumatologist doctor who is also in charge of hip fracture surgery at the hospital.</p><p>After process analysis has been conducted by the research team in collaboration with hospital professionals (management, doctors, nurses), process redesign will consist of the proposal and implementation of some initiatives oriented to enhance value creation from the patient point of view, i.e., reducing waste, increasing efficiency, improving patient experience, and improving the values of the process metrics. <xref rid=\"ijerph-17-05273-t005\" ref-type=\"table\">Table 5</xref> below summarizes a list of actions that have been proposed for further implementation, identifying the type of <italic>muda</italic> that could be impacted by them:</p><p>As a next step, beyond the scope of this research paper, the authors suggest that an implementation plan could be designed, scheduled, executed, and evaluated. A new multidisciplinary team (involving researchers and hospital professionals) could be formed with the aim of guaranteeing that expected results are confirmed.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec6-ijerph-17-05273\"><title>6. Discussion</title><p>The results of the care activity of the case study hospital are similar in average age to those found in the national hip fracture registry in Spain, with an average age of 87 years [<xref rid=\"B35-ijerph-17-05273\" ref-type=\"bibr\">35</xref>]. The average LOS of this registry is 10 days, slightly higher than the LOS of the analysed hospital.</p><p>The research questions that govern this study are (i) to try to identify the types of waste or <italic>muda</italic>, and (ii) to evaluate the application of a process innovation approach in healthcare, in particular in the hip fracture surgery process.</p><p>This research allowed us to identify 60 <italic>muda</italic> opportunities along the hip fracture surgery process, which is composed of 236 activities and where 18 participants interact. It is relevant to point out that 18 participants does not refer to 18 people, but to 18 job positions, each of them being performed by 1 or more people. For instance, one position is a nurse; if the patient stays at the hospital for 2 weeks, there will be different people working as nurses to take care of him (the nursing team is composed of more than 10 employees per shift, morning, evening, and night, 24 h a day).</p><p>The following different types of <italic>muda</italic> have been identified along the process: defects, overprocessing, overproduction, movement, transportation, inventory, delay, and unused talent. Some of them occur punctually, but others (we consider them as critical <italic>muda</italic>) are more repetitive all along the process; these are the ones that we are going to discuss. Although three types of <italic>muda</italic> represent 72% of the total (enough for critical <italic>muda</italic> discussion), we also decided to consider the fourth type (which only represents 10% of the total).</p><p>Delay is the most frequent waste in the hip fracture surgery process. Delay represents 33% of all cases, the most common being as follows: (i) the patient needs to wait because the clinical process requires it (for instance, the patient needs to be stabilized—pulse, temperature, blood pressure, etc.—before being transferred to the next stage); (ii) the patient needs to wait because of a lack of available resources (no available hospital porter for transfer) or because all resources are busy (for example, X-Ray is being provided to a different patient; (iii) delays produced because an employee has forgotten or neglected a task, or presents a bad attitude. Type (i) is normal; type (ii) needs to be minimized (better resource planning); type (iii) needs to be eradicated. Communication between employees patients is crucial in each case because, even if the delay cannot be avoided, the patient experience could be positive if there is a clear explanation and justification for it.</p><p>Transportation is the second type of waste. In general, transportation refers to transferring the patient from one location to a different location, and, although frequent, it is not very relevant, as distances within the hospital are not big. When referring to transportation of employees (doctors, nurses, porters, etc.) there is evidence of inefficiency (repeated distances are walked to supervise, take care, provide service etc., to patients). Spaghetti charts and cause-and-effect analyses could be used to study this type of <italic>muda</italic> in detail.</p><p>Overprocessing waste could happen when (i) some tasks need to be repeated; (ii) the process is not well- or properly defined; or (iii) lack of resources or when employees perform tasks that are not supposed to be carried out by them. Waste reduction could be achieved by different means such as automation (e.g., when initiating the hip fracture process the system automatically requests a bed, avoiding the need to phone or call a floor supervisor), process standardization with poka-yokes or checklists (e.g., to avoid repeating blood tests or X-Rays), better planning (e.g., to avoid repeating the pre-surgery preparation); or better maintenance planning (e.g., reliable maintenance plans avoid equipment and machinery failures).</p><p>Defect refers to waste when the results of an activity are not right, i.e., when an error is produced. Errors could be clinical, or cause by service, attention, etc. Causes of errors could be diverse, but if clinical errors are excluded, many are related to miscommunication (too many participants in the process), a lack of a standardized processes (every employee adopts his/her own criteria to execute an activity or to take a decision), or to an excessive workload (attention and concentration decrease). A leaner process with less participants, standardized processes (process defines, trained employees, checklists, and poka-yokes) and a planned workload will reduce waste opportunities.</p><p>An inventory of materials is necessary at many stages in the process: labels, paper and bracelets in admissions; and X-Ray plates, medicines, and medical goods in emergency areas, rooms, UCEs, and operating rooms. Every supervisor is responsible for inventory management (inventory levels, keeping control, placing the orders). No specific method (economic order quantity, fixed period ordering, first in first out, etc.), nor software is used, but a manual control is carried out according to his/her own criteria. Lack or excess of an inventory has been reported, an excess of it being more frequent due to the fact that healthcare deals with patient lives and risks should be minimized. There is a pharmacy inside the hospital, in case of any material need.</p><p>Movement waste in this process deals mainly with patient manipulation to be transferred from a stretcher to accommodate a mattress, the handling of X-Rays, electrode tests, blood test sampling, or movement inside the operating room. Process standardization according to different patient types (weight, volume, health condition) would help to minimize this <italic>muda</italic>.</p><p>Different types of <italic>muda</italic> according to the literature [<xref rid=\"B8-ijerph-17-05273\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05273\" ref-type=\"bibr\">9</xref>,<xref rid=\"B11-ijerph-17-05273\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05273\" ref-type=\"bibr\">12</xref>] have been identified in the process, being delay the most frequent. This agrees with Godley et al. [<xref rid=\"B60-ijerph-17-05273\" ref-type=\"bibr\">60</xref>] and Woodnutt [<xref rid=\"B7-ijerph-17-05273\" ref-type=\"bibr\">7</xref>], who identified delay and waiting times as the relevant types of waste that should be addressed with the aim of optimizing processes. The literature shows different examples of authors who have studied the different types of <italic>muda</italic> in the same way. In the educational sector, Doman indicates that with process innovation efforts, the graduate and graduate management processes can be improved [<xref rid=\"B79-ijerph-17-05273\" ref-type=\"bibr\">79</xref>]. Walters et al. identified specific areas of internal production waste including defects and waiting, and in the process of our investigation, identified a significant shift in process efficiency due to resource allocation [<xref rid=\"B80-ijerph-17-05273\" ref-type=\"bibr\">80</xref>] focusing on 1040 financial norms. In fact, Ann Douglas et al. similarly identified the 8 types of <italic>muda</italic> in Higher Education [<xref rid=\"B81-ijerph-17-05273\" ref-type=\"bibr\">81</xref>] and Suárez-Barraza et al. also identified three new types of <italic>muda</italic> in the 21st century such as unnecessary emails, excess work meetings, and technological distractors [<xref rid=\"B82-ijerph-17-05273\" ref-type=\"bibr\">82</xref>]; processes in hospitals cannot be free of these three new types of <italic>muda</italic> either.</p><p>Finally, Coelho et al. present evidence of process innovation in health processes, for example, they reduced the cancer outpatient chemotherapy process cycle time from 2 h to 30 min [<xref rid=\"B57-ijerph-17-05273\" ref-type=\"bibr\">57</xref>].</p><p>Close collaboration between clinical staff, hospital management, and researchers allowed the collection of precise data, as well as information sharing, which is very valuable for the process analysis and redesign. A new process design presents the following advantages, which are aligned with the literature [<xref rid=\"B31-ijerph-17-05273\" ref-type=\"bibr\">31</xref>,<xref rid=\"B42-ijerph-17-05273\" ref-type=\"bibr\">42</xref>,<xref rid=\"B44-ijerph-17-05273\" ref-type=\"bibr\">44</xref>,<xref rid=\"B60-ijerph-17-05273\" ref-type=\"bibr\">60</xref>]:</p><p>For patients, a reduction of LOS, potential errors, and, as a consequence, an improvement in patient satisfaction and experience. Quality improvement impacts directly on the quality of life of the patient, including both psychological and social aspects related to the social isolation in which the patient returns after discharge.</p><p>For hospital management, an increase in efficiency and better planning, thus a cost reduction, a capacity increase, and, as a consequence, a potential increase of activity.</p><p>For hospital employees, waste reduction (times, overprocessing, potential errors, etc.) and standardized processes will reduce the workload, stress and fatigue, increasing their satisfaction, thus, their motivation and commitment.</p><p>The above-mentioned findings justify the selection of this process innovation framework, as it has a clear focus on waste identification and Lean operations redesign [<xref rid=\"B72-ijerph-17-05273\" ref-type=\"bibr\">72</xref>]. The identification of changes in the analyzed process with the proposed methodology involves a second phase of analysis and reflection with the orthogeriatric team to go in depth into the findings, and to identify those that can really undergo an improvement process.</p></sec><sec sec-type=\"conclusions\" id=\"sec7-ijerph-17-05273\"><title>7. Conclusions</title><p>The research examines the identification of waste or <italic>muda</italic> in a hip fracture surgery process in healthcare. Eight types of <italic>muda</italic> have been identified, the most frequent being delay, transportation, overprocessing, and defects, and actions based on them for improvement have been proposed.</p><p>The application of a process innovation approach has also been examined in this research, the result being that, although innovative, this approach is appropriate for the healthcare sector, as it is appropriate in any other service industry. Applying the process innovation methodology represents an effort of the Kaizen philosophy in critical processes in the health sector. In fact, it allows crystallizing redesigns and changes to eliminate <italic>muda</italic> from the activities of health processes. In our case study, all the proposals will lower the <italic>muda</italic> percentage by at least 30% for all hip process activities in the current situation.</p><p>This research is innovative on the implementation of the technical approach, and its contributions on the implementation side can be summarized by a set of proposals that have been done including: process standardization, reduction of the number of participants in the process, techniques to improve communication, automation initiatives, training, implementation of inventory management techniques, the implementation of some tools (such as 5 s, checklists, or poka-yokes), including new performance indicators, as well as patient satisfaction measurement systems, among others.</p><p>This research is also innovative on the action-research process itself, as it brings the following contributions: (1) although not common, we succeeded in obtaining a formal collaboration among clinical personnel (doctors, nurses, etc.), service managers, and researchers to carry out a joint research project; (2) a research team (expert in Lean and process innovation, as well as in healthcare) entering the hospital and visiting the whole detailed process overcoming potential problems or communication barriers; (3) the advantages of documenting and recording observations and results (the power of direct observation in the <italic>gemba</italic>, the Japanese word for “place”); (4) fruitful discussions among specialists, experts, managers, and researchers towards sharing knowledge and ideas aiming at process improvement; (5) the adaptation of a process innovation approach within the healthcare sector, which is different from other service industries due to different needs, priorities, and vocabulary, as well as differences between private or public hospitals.</p><p>When the average stay data is adjusted to normal [<xref rid=\"B35-ijerph-17-05273\" ref-type=\"bibr\">35</xref>], it can be complex to introduce improvements that contribute to the sustainability of the service and increase the impact on the patient. Therefore, an exhaustive analysis of each one of the <italic>mudas</italic> found before their implantation is necessary.</p><p>This research also offers some practical implications for healthcare managers:<list list-type=\"simple\"><list-item><label>(i)</label><p>the identification of all different types of <italic>muda</italic> all along the hip fracture surgery process provides hospital managers with an opportunity for continuous improvement, by trying to eliminate or minimize them;</p></list-item><list-item><label>(ii)</label><p>some initiatives have been proposed to redesign the process, which allows the management to take action towards gaining efficiency and service quality, which in turn impacts on operating costs and patient satisfaction;</p></list-item><list-item><label>(iii)</label><p>the observation and the analysis have been carried out by researchers, but a basic training and a checklist (an audit tool) would help employees to carry out this assessment any time they need in the future.</p></list-item></list></p><p>Finally, some limitations of the research are: (i) the data cannot be generalized due to its qualitative nature; (ii) the findings refer to the specific context of hip fracture surgery in a case study in Spain; (iii) process redesign initiatives have been proposed for implementation, but have not been evaluated yet. Future studies could be carried out with the objective of evaluating the effectiveness of the implemented actions and their outcome, as well as by using data analysis techniques to better understand the variability of data dispersion. Other future areas for research could be initiated using several years of data history in the case study, and by using a larger sample of hospitals, as well as other geographical areas.</p></sec></body><back><ack><title>Acknowledgments</title><p>Hospital management and personnel that kindly participated and collaborated with the research team.</p></ack><notes><title>Author Contributions</title><p>All authors made a proportional contribution. Conceptualization, M.F.M.-C., E.G.G.; methodology, M.F.M.-C., M.F.S.-B.; validation, P.C.-V., M.F.S.-B.; formal analysis, M.F.M.-C., M.F.S.-B.; investigation, M.F.M.-C., P.C.-V., A.S.D., E.G.G.; resources, A.S.D.; data curation, M.F.M.-C., P.C.-V., A.S.D., E.G.G.; writing—original draft preparation, M.F.M.-C., P.C.-V., M.F.S.-B., E.G.G.; writing—review and editing, M.F.M.-C., P.C.-V., E.G.G.; visualization, M.F.M.-C., P.C.-V., E.G.G.; supervision, M.F.M.-C., E.G.G.; project administration, E.G.G. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research received no external funding.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><app-group><app id=\"app1-ijerph-17-05273\"><title>Appendix A</title><p>Main Process s at case study hospital.</p><fig id=\"ijerph-17-05273-f0A1\" orientation=\"portrait\" position=\"anchor\"><label>Figure A1</label><caption><p>Source: Hospital Quality Management System.</p></caption><graphic xlink:href=\"ijerph-17-05273-g0A1\"/></fig></app><app id=\"app2-ijerph-17-05273\"><title>Appendix B</title><fig id=\"ijerph-17-05273-f0A2\" orientation=\"portrait\" position=\"anchor\"><label>Figure A2</label><caption><p>Hip Fracture Process Flowcharts.</p></caption><graphic xlink:href=\"ijerph-17-05273-g0A2a\"/><graphic xlink:href=\"ijerph-17-05273-g0A2b\"/><graphic xlink:href=\"ijerph-17-05273-g0A2c\"/></fig></app><app id=\"app3-ijerph-17-05273\"><title>Appendix C</title><table-wrap id=\"ijerph-17-05273-t0A1\" orientation=\"portrait\" position=\"anchor\"><object-id pub-id-type=\"pii\">ijerph-17-05273-t0A1_Table A1</object-id><label>Table A1</label><caption><p>Analysis of different types of <italic>muda</italic>.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Muda #</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Process Activity #</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Direct Observation</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Type of Muda</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Ambulance arrival. Patient cumulation (sometimes up to 6 patients)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Delay</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Transportation of patients from ambulance to emergency box</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Transportation</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3, 4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MRI technician or nurse working as a hospital porter if required</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Unused Talent<break/>Overprocessing</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Handling patients from an ambulance stretcher to a hospital stretcher and placing mattress</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Movement</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6, 7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MRI technician working as a nurse if required to register patients</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Unused Talent<break/>Overprocessing</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Registration activities to be redone when IMDH systems fail</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Overprocessing</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Waiting time while phone is answered</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Delay</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Phone calls to be repeated until somebody answers</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Overprocessing</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Excessive or potential lack of inventory of paper, labels, or bracelets</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Inventory</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Waiting for a nurse check</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Delay</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">21</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Communication delay</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Delay</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Blood extraction preparation to be repeated in the case of difficulties</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Overprocessing</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">28</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Transportation of blood sample</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Transportation</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">32</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Waiting time if several patients are in the emergency area</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Delay</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">34</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Unnecessary doctor movement between box (patient) and desk (PC)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Movement</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">48</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Transportation of patients from emergency box to X-Ray</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Transportation</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">47</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Waiting times upon X-Ray availability</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Delay</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Handling of patients for X-Ray preparation</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Movement</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">21, 22</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">51</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">System error provoking delay (up to 5 min) and repeating operations</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Delay, Overprocessing</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">23</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">53</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Defective X-Ray</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Defect</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">24</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">53</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Repeated defective X-Ray</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Overprocessing</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">54</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Duplicity X-Ray in computer and in paper</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Overproduction</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">26</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">55</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Inventory of paper X-Ray plates</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Inventory</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">27</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">59</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Waiting times to take patient to room</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Delay</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">28</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">60</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Transportation of patients from X-Ray to room</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Transportation</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">29</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">65</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Error in understanding doctor’s orders</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Defect</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">30</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">79</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Deviations from original message given to patients</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Defect</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">31</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">70</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Communication delay</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Delay</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">32</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">86</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Communication delay</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Delay</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">33</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">96</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">If social issue, high risk of huge delays</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Delay</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">34, 35</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">85–100</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Many actors, potential failures and errors</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Defect, Overprocessing</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">36</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">101</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Daily visits to patients</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Transportation</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">37</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">114</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Waiting for a decision</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Delay</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">38</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">126</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Waiting for transportation to surgery area</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Delay</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">39</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">127</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">If surgery is delayed or postponed, patient preparation must be redone</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Overprocessing</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">40</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">129</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Transportation of patients to surgery level</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Transportation</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">41, 42</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">131</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Clothes changing</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Movement, Delay</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">43</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">133</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Transportation of patient to surgery transfer area</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Transportation</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">44</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">137</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Waiting for surgery</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Delay</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">45</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">135–143</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Movement and preparation before and during surgery</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Movement</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">46</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">143</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Potential errors during surgery</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Defects</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">47</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">147</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Doctor transportation for cleaning and talking to the patient family</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Transportation</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">48</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">153</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Transportation of patient to UCE</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Transportation</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">49</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">155</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Waiting time if busy area</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Delay</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">164</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Transportation of blood sample to Laboratory</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Transportation</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">51</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">178</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Homoderivative might not be necessary</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Overproduction</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">52</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">183</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Delay </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Delay</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">53</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">187</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Transportation of patient to X-Ray</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Transportation</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">54</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">190</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Delay </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Delay</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">55</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">193</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Transportation of patient to room</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Transportation</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">56</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">210</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Daily visits to patients</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Transportation</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">57</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">211</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Delay</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Delay</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">58</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">218–221</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">If social issue, high risk of huge delays</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Delay</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">59, 60</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">240</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Patient returns sooner than expected due to a recovery issue</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Defect; Overprocessing</td></tr></tbody></table></table-wrap></app></app-group><ref-list><title>References</title><ref id=\"B1-ijerph-17-05273\"><label>1.</label><element-citation publication-type=\"book\"><person-group 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Sci.</source><year>2016</year><volume>8</volume><fpage>377</fpage><lpage>394</lpage><pub-id pub-id-type=\"doi\">10.1108/IJQSS-04-2016-0028</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijerph-17-05273-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Process innovation framework.</p></caption><graphic xlink:href=\"ijerph-17-05273-g001\"/></fig><fig id=\"ijerph-17-05273-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Block diagram.</p></caption><graphic xlink:href=\"ijerph-17-05273-g002\"/></fig><fig id=\"ijerph-17-05273-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Flow diagram (partial) showing the existing process.</p></caption><graphic xlink:href=\"ijerph-17-05273-g003\"/></fig><table-wrap id=\"ijerph-17-05273-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05273-t001_Table 1</object-id><label>Table 1</label><caption><p>Summary of descriptive statistical analysis.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\">2019</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\">2020</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Average</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Std Dev</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Average</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Std Dev</th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total patient population</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total (Women/Men)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">148 (109/39)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">106 (80/26)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Age (years)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">86.55</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.34</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">85.48</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.77</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">In-hospital mortality (%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.77</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total hospitalization time (days)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.36</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9.41</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.17</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Presurgical time (days)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.98</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.24</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.15</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.55</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Post-surgical average stay (days)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.12</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.77</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.25</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.25</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05273-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05273-t002_Table 2</object-id><label>Table 2</label><caption><p>Detail of areas and participants in hip fracture surgery processes.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">General:</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Emergency Area:</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Surgery:</th></tr></thead><tbody><tr><td rowspan=\"3\" align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>Patient</p></list-item><list-item><p>Family</p></list-item><list-item><p>Traumatologist doctor (*)</p></list-item><list-item><p>Cardiologist doctor</p></list-item><list-item><p>Laboratory technician</p></list-item><list-item><p>X-Ray technician</p></list-item><list-item><p>Social assistant</p></list-item><list-item><p>Hospital porter</p></list-item></list>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>Emergency nurse</p></list-item><list-item><p>Emergency doctor</p></list-item></list>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>Surgery nurse</p></list-item><list-item><p>Surgery hospital porter</p></list-item></list>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Hospital floor level:</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>UCE (short stay unit):</bold>\n</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>Floor nurse</p></list-item><list-item><p>Floor nurse supervisor</p></list-item><list-item><p>Floor assistant</p></list-item><list-item><p>Geriatric doctor</p></list-item></list>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>UCE nurse</p></list-item><list-item><p>UCE supervisor</p></list-item></list>\n</td></tr></tbody></table><table-wrap-foot><fn><p>Note (*): It is important to acknowledge that the traumatologist doctor also acts as the surgeon.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05273-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05273-t003_Table 3</object-id><label>Table 3</label><caption><p>Analysis of hip fracture process activities.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Activity Type</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Flowchart Symbol</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Count</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Activity</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<inline-graphic xlink:href=\"ijerph-17-05273-i001.jpg\"/>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">151</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Delay</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<inline-graphic xlink:href=\"ijerph-17-05273-i002.jpg\"/>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Transport</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<inline-graphic xlink:href=\"ijerph-17-05273-i003.jpg\"/>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Decisions</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<inline-graphic xlink:href=\"ijerph-17-05273-i004.jpg\"/>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Inspection</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<inline-graphic xlink:href=\"ijerph-17-05273-i005.jpg\"/>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Internal document</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<inline-graphic xlink:href=\"ijerph-17-05273-i006.jpg\"/>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Electronic transport</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<inline-graphic xlink:href=\"ijerph-17-05273-i007.jpg\"/>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Type of Muda</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<inline-graphic xlink:href=\"ijerph-17-05273-i008.jpg\"/>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">60</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Process activities<break/>Muda</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">240<break/>60</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05273-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05273-t004_Table 4</object-id><label>Table 4</label><caption><p>Summary of the different types of Muda: (<bold>a</bold>) Data, (<bold>b</bold>) Pareto chart.</p></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Type of Muda</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Count</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>%</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Cumul %</bold>\n</td><td rowspan=\"10\" align=\"center\" valign=\"middle\" colspan=\"1\">\n<inline-graphic xlink:href=\"ijerph-17-05273-i009.jpg\"/>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Delay</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33%</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Transportation</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">55%</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Overprocessing</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">72%</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Defects</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">82%</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Movement</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90%</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Overproduction</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">93%</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Inventory</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">97%</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Unused Talent</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100%</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">60</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100%</td></tr><tr><td colspan=\"4\" align=\"center\" valign=\"middle\" rowspan=\"1\">(<bold>a</bold>)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(<bold>b</bold>)</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05273-t005\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05273-t005_Table 5</object-id><label>Table 5</label><caption><p>List of proposals.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Proposals</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>Muda</italic>\n</th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Establish a communication protocol with the Public Health system to ensure that every new patient brings a medical history file (in paper or electronic format), thus a more efficient approach will be adopted (some tests might not be necessary, avoiding duplicity and improving patient experience).</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Delay<break/>Transportation<break/>Overprocessing<break/>Movement<break/>Overproduction</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reduce the number of activities and the number of participants. <list list-type=\"simple\"><list-item><p>Ex. 1. Portable X-Ray equipment would help patients to be transferred from emergency area directly to floor, as X-Ray test could be carried out at that moment.</p><p>Ex. 2. Transfer of patient to operating area to be done in a way that only one hospital porter is required to dress/undress with safety protection equipment.</p></list-item></list></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">All 8 types of <italic>muda</italic></td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Avoid duplication of tasks (for instance, printing X-Ray in paper and saving it electronically).</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Delay<break/>Overproduction</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reduce distances by locating some areas in a different place. Ex. After surgery doctor speaking to patients in a meeting room.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Delay<break/>Transportation</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Review and maintenance of electronic equipment and IT systems with the aim of detecting potential causes for malfunctioning.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Delay<break/>Overprocessing</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Automate some steps: When starting the process, some actions could be automatically initiated as tests (blood test, X-Ray) and medical consultations (calls to geriatrics or cardiologist). </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Delay<break/>Overprocessing</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Implement a system to measure the quality of the delivered service (patient satisfaction, patient experience, etc.).</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Overprocessing<break/>Defects</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Implement poka-yokes in operating processes within the operating room: marking leg, surgery checklist to be displayed in operating area, screen or board with main steps identifying critical activities with symbols.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Overprocessing<break/>Defects<break/>Overproduction</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Implementation of 5s techniques in operating processes within the operating room.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">All 8 types of <italic>muda</italic></td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Clinical process standardization (for surgery, doctors and nurses).<break/>Administrative and data input process standardization.<break/>Training of personnel according to the abovementioned new standardized processes.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">All 8 types of <italic>muda</italic></td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Implementation of communication practices between doctors and nurses based on documentation of orders and instructions (it could be in the computer system or in patient´s logbooks) with the aim of minimizing errors due to misunderstandings or wrong interpretations.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Transportation<break/>Overprocessing<break/>Defects<break/>Overproduction</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Implementation of an adequate inventory management system (EOQ, FIFO, etc.).</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Inventory</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Implementation of tools to record and share best practices within the hospital and among different hospitals within the group.<break/>Further data analysis to understand the variability of data.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">All 8 types of <italic>muda</italic></td></tr></tbody></table></table-wrap></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751422</article-id><article-id pub-id-type=\"pmc\">PMC7432006</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155493</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05493</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Genome-Wide DNA Methylation Profiles in Community Members Exposed to the World Trade Center Disaster</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-7775-8085</contrib-id><name><surname>Arslan</surname><given-names>Alan A.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05493\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05493\">2</xref><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05493\">3</xref><xref rid=\"c1-ijerph-17-05493\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Tuminello</surname><given-names>Stephanie</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05493\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Yang</surname><given-names>Lei</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05493\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Zhang</surname><given-names>Yian</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05493\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Durmus</surname><given-names>Nedim</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05493\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Snuderl</surname><given-names>Matija</given-names></name><xref ref-type=\"aff\" rid=\"af5-ijerph-17-05493\">5</xref></contrib><contrib contrib-type=\"author\"><name><surname>Heguy</surname><given-names>Adriana</given-names></name><xref ref-type=\"aff\" rid=\"af5-ijerph-17-05493\">5</xref><xref ref-type=\"aff\" rid=\"af6-ijerph-17-05493\">6</xref></contrib><contrib contrib-type=\"author\"><name><surname>Zeleniuch-Jacquotte</surname><given-names>Anne</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05493\">2</xref><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05493\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Shao</surname><given-names>Yongzhao</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05493\">2</xref><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05493\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Reibman</surname><given-names>Joan</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05493\">4</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05493\"><label>1</label>Department of Obstetrics and Gynecology, New York University Langone Health, New York, NY 10016, USA</aff><aff id=\"af2-ijerph-17-05493\"><label>2</label>Department of Population Health, New York University Langone Health, New York, NY 10016, USA; <email>Stephanie.Tuminello@nyulangone.org</email> (S.T.); <email>ly888@nyu.edu</email> (L.Y.); <email>Yian.Zhang@nyulangone.org</email> (Y.Z.); <email>Anne.Jacquotte@nyulangone.org</email> (A.Z.-J.); <email>Yongzhao.Shao@nyulangone.org</email> (Y.S.)</aff><aff id=\"af3-ijerph-17-05493\"><label>3</label>NYU Perlmutter Comprehensive Cancer Center, New York, NY 10016, USA</aff><aff id=\"af4-ijerph-17-05493\"><label>4</label>Department of Medicine, New York University Langone Health, New York, NY 10016, USA; <email>Nedim.Durmus@nyulangone.org</email> (N.D.); <email>Joan.Reibman@nyulangone.org</email> (J.R.)</aff><aff id=\"af5-ijerph-17-05493\"><label>5</label>Department of Pathology, New York University Langone Health, New York, NY 10016, USA; <email>Matija.Snuderl@nyulangone.org</email> (M.S.); <email>Adriana.Heguy@nyulangone.org</email> (A.H.)</aff><aff id=\"af6-ijerph-17-05493\"><label>6</label>NYU Langone’s Genome Technology Center, New York, NY 10016, USA</aff><author-notes><corresp id=\"c1-ijerph-17-05493\"><label>*</label>Correspondence: <email>alan.arslan@nyumc.org</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>30</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5493</elocation-id><history><date date-type=\"received\"><day>30</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>25</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>The primary goal of this pilot study was to assess feasibility of studies among local community members to address the hypothesis that complex exposures to the World Trade Center (WTC) dust and fumes resulted in long-term epigenetic changes. We enrolled 18 WTC-exposed cancer-free women from the WTC Environmental Health Center (WTC EHC) who agreed to donate blood samples during their standard clinical visits. As a reference WTC unexposed group, we randomly selected 24 age-matched cancer-free women from an existing prospective cohort who donated blood samples before 11 September 2001. The global DNA methylation analyses were performed using Illumina Infinium MethylationEpic arrays. Statistical analyses were performed using R Bioconductor package. Functional genomic analyses were done by mapping the top 5000 differentially expressed CpG sites to the Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway database. Among cancer-free subjects, we observed substantial methylation differences between WTC-exposed and unexposed women. The top 15 differentially methylated gene probes included BCAS2, OSGIN1, BMI1, EEF1A2, SPTBN5, CHD8, CDCA7L, AIDA, DDN, SNORD45C, ZFAND6, ARHGEF7, UBXN8, USF1, and USP12. Several cancer-related pathways were enriched in the WTC-exposed subjects, including endocytosis, mitogen-activated protein kinase (MAPK), viral carcinogenesis, as well as Ras-associated protein-1 (Rap1) and mammalian target of rapamycin (mTOR) signaling. The study provides preliminary data on substantial differences in DNA methylation between WTC-exposed and unexposed populations that require validation in further studies.</p></abstract><kwd-group><kwd>environmental exposure</kwd><kwd>epigenome-wide association study</kwd><kwd>exposure assessment</kwd><kwd>methylation</kwd><kwd>pathway analysis</kwd><kwd>World Trade Center</kwd><kwd>9/11</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05493\"><title>1. Introduction</title><p>The collapse of the World Trade Center (WTC) towers on September 11th, 2001 was an unprecedented disaster, with rescue workers, local workers, residents, and commuters exposed to the dust and smoke from the buildings’ pulverization and subsequent fires [<xref rid=\"B1-ijerph-17-05493\" ref-type=\"bibr\">1</xref>]. WTC dust was comprised mostly of building materials such as cement, cellulose, and glass fibers, but also contained asbestos [<xref rid=\"B1-ijerph-17-05493\" ref-type=\"bibr\">1</xref>]. Incomplete combustion and unburnt jet fuel released polycyclic hydrocarbons and phthalates [<xref rid=\"B2-ijerph-17-05493\" ref-type=\"bibr\">2</xref>]. Also measured in the settled dust/smoke were polychlorinated chlorinated biphenols (PCBs), polybrominated diphenol ethers (PBDEs), dioxins, furans, and polycyclic aromatic hydrocarbons (PAHs), as well as heavy metals including arsenic, beryllium, cadmium, chromium, nickel, and other elements such as copper, lead, and mercury [<xref rid=\"B2-ijerph-17-05493\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijerph-17-05493\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijerph-17-05493\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijerph-17-05493\" ref-type=\"bibr\">5</xref>].</p><p>Environmental exposures, including metals, benzene, polycyclic aromatic hydrocarbons, and organic pollutants, can all induce epigenetic changes [<xref rid=\"B6-ijerph-17-05493\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijerph-17-05493\" ref-type=\"bibr\">7</xref>]. Epigenetic modification occurs when hereditable changes are made that alter gene expression without changing the underlying genetic sequence [<xref rid=\"B7-ijerph-17-05493\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijerph-17-05493\" ref-type=\"bibr\">8</xref>]. CG dinucleotides (CpGs) sites are platforms for DNA methylation, a significant epigenetic mechanism of the human genome [<xref rid=\"B8-ijerph-17-05493\" ref-type=\"bibr\">8</xref>]. Generally, CpG methylation induces gene silencing through inhibition of transcription factor binding to the promotor regions of genes, inhibition of RNA polymerase, and recruitment of transcriptional repressor complexes [<xref rid=\"B8-ijerph-17-05493\" ref-type=\"bibr\">8</xref>]. Aberrant DNA methylation has been implicated in multiple human diseases, including cancer [<xref rid=\"B7-ijerph-17-05493\" ref-type=\"bibr\">7</xref>,<xref rid=\"B9-ijerph-17-05493\" ref-type=\"bibr\">9</xref>].</p><p>Methylation marks can have clinical utility. For example, Morrow et al. recently showed that DNA taken from peripheral blood of smokers could be used to identify methylation sites predictive of mortality [<xref rid=\"B10-ijerph-17-05493\" ref-type=\"bibr\">10</xref>]. However, despite the complex assortment of WTC-related environmental exposures, and the poor health outcomes related to that exposure [<xref rid=\"B11-ijerph-17-05493\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05493\" ref-type=\"bibr\">12</xref>], the epigenetic consequences of the WTC disaster have been largely unexplored. Looking at the DNA methylation profile of WTC responders according to an exposure index, Kuan et al. has recently reported preliminary evidence of a methylation signature for cancer-related exposure in this population [<xref rid=\"B13-ijerph-17-05493\" ref-type=\"bibr\">13</xref>]. However, the epigenetic impact of WTC exposure in residents and local workers (also known as “survivors”) has never been examined.</p><p>Populations not involved in rescue and recovery activities (community members) remain understudied in terms of WTC exposures and related outcomes. These populations include those who were working in the WTC towers or in the many surrounding offices, stores, and restaurants (local workers) as well as residents of the surrounding buildings (residents). Over 36,000 local workers and over 57,000 residents south of Canal Street in lower Manhattan alone have been estimated to have had potential for dust and fume exposure [<xref rid=\"B14-ijerph-17-05493\" ref-type=\"bibr\">14</xref>]. Additional work-exposed populations include those involved in the cleanup of the surrounding area (cleanup workers). For some community members, exposure to the dust and smoke persisted for weeks or months after the event, with many cleanup workers not wearing the proper respiratory protection, and many residents returning to inadequately cleaned buildings [<xref rid=\"B2-ijerph-17-05493\" ref-type=\"bibr\">2</xref>].</p><p>We hypothesized that high-dose and prolonged WTC dust and fume exposures may have created persistent epigenetic changes in the community members exposed to the WTC disaster. We performed a pilot study to assess the feasibility of blood DNA collection among community members from the Bellevue Hospital WTC Environmental Health Center (WTC EHC) cohort, with the objective of comparing their genome-wide DNA methylation profile to a reference group of New York City residents from the NYU Women’s Health Study (NYUWHS) prospective cohort, who donated blood samples before 9 December 2001. As a secondary objective we aimed to characterize these changes, specifically in terms of gene expression pathways that may have been altered.</p></sec><sec sec-type=\"methods\" id=\"sec2-ijerph-17-05493\"><title>2. Methods</title><sec id=\"sec2dot1-ijerph-17-05493\" sec-type=\"subjects\"><title>2.1. Study Participants</title><sec id=\"sec2dot1dot1-ijerph-17-05493\"><title>2.1.1. WTC Environmental Health Center (WTC EHC)</title><p>As part of the World Trade Center Health Program (WTCHP), created by the Centers for Disease Control (CDC) and the National Institute of Safety and Occupational Health (NIOSH), the Bellevue Hospital WTC EHC is the only program that provides medical and mental health treatment for local community members and cleanup workers exposed to the WTC disaster [<xref rid=\"B15-ijerph-17-05493\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijerph-17-05493\" ref-type=\"bibr\">16</xref>]. The WTC EHC program started through joint efforts of the local community, organized labor, and the medical community [<xref rid=\"B15-ijerph-17-05493\" ref-type=\"bibr\">15</xref>]. This program became law (James Zadroga 9/11 Health and Compensation Act H.R. 847) in 2010 for implementation in 2011. Conditions which could be included in this program were deemed “certifiable illnesses” and included pulmonary, digestive, and mental health disorders. Cancer was added as a “certifiable” condition in 2012.</p><p>In contrast to the responder programs, the WTC EHC includes a substantial women population (~50%) and is ethnically diverse. Community members self-refer into this program and under law inclusion requires the presence of a “certifiable condition” linked to the WTC disaster [<xref rid=\"B16-ijerph-17-05493\" ref-type=\"bibr\">16</xref>]. As of 31 December 2019, 11,048 individuals have been enrolled in the WTC EHC program, with 2840 individuals diagnosed with cancer. Between March and September 2018, we enrolled and collected samples from 18 WTC-exposed cancer-free women from the WTC EHC.</p></sec><sec id=\"sec2dot1dot2-ijerph-17-05493\"><title>2.1.2. New York University Women’s Health Study (NYUWHS)</title><p>The reference group of subjects was selected from the New York City women who donated blood samples before 9 December 2001 as part of the NYUWHS prospective cohort. Between March 1985 and June 1991, 14,274 women between the ages of 35 and 65 years were enrolled as volunteers in the NYUWHS at the Guttman Breast Diagnostic Institute, a mammography screening center in New York City. Eligibility was restricted to women who had not used hormonal medications, or been pregnant or lactating, in the preceding 6 months. Subjects completed a self-administered baseline questionnaire that collected information on demographic, medical, reproductive, regular physical activity, and anthropometric variables, as well as recent medication use. Cohort participation required donation of venous blood, drawn using collection tubes with and without anticoagulant. With rare exceptions, blood collection took place between 9:30 AM and 1:00 PM. Subjects were not asked to fast but time of last meal was recorded. Exact time at venipuncture was also recorded. After drawing, tubes were kept covered at room temperature (25 °C) for 15 min, then at 4 °C for 60 min to allow clot retraction and then centrifuged. Supernatant serum was partitioned into 1 mL aliquots in capped plastic vials within 2 h after separation. Up to 12 labeled aliquots per blood sample were immediately banked at −80 °C. Beginning in 1988, two 1 mL aliquots of the cellular precipitate from centrifuging the blood were stored at −80 °C. Beginning in 1989, blood clots were also collected and stored at −40 °C as an additional source of DNA in sealable plastic-lined aluminum bags.</p><p>The NYUWHS provided de-identified cellular precipitate samples of 24 age-matched cancer-free women as a reference group for the current study.</p></sec></sec><sec id=\"sec2dot2-ijerph-17-05493\"><title>2.2. Blood Sample Collection, Nucleic Acid Isolation, and DNA Processing</title><p>After obtaining informed consent, blood samples from 18 cancer-free WTC EHC women were collected during their routine clinical visit to the WTC EHC and immediately centrifuged and processed to separate white blood cells (buffy coat) using standard protocol [<xref rid=\"B17-ijerph-17-05493\" ref-type=\"bibr\">17</xref>]. Reference samples from 24 age-matched cancer-free NYUWHS women were identified and retrieved from storage. Both WTC EHC and NYUWHS buffy coat samples were sent to the NYU Langone’s Biospecimen Research and Development (CBRD) laboratory for DNA extraction. DNA was recovered using the PicoPure DNA extraction kit (Thermo Fisher Scientific, Boston, MA, USA), which enables recovery of genomic DNA from formalin-fixed, paraffin-embedded tissue. DNA was then subjected to bead purification with the Sphere quality control kit (Thermo Fisher Scientific, Boston, MA, USA). DNA purity and quantity were assessed using NanoDrop spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA). The DNA was bisulfite converted using the EZ-96 DNA methylation kit (Zymo Research, Irvine, CA, USA). Extracted DNA was restored using the Infinium HD formalin-fixed, paraffin-embedded DNA restore kit (Illumina<sup>®</sup>, San Diego, CA, USA) prior to hybridization on the bead chips provided by the manufacturer (Illumina<sup>®</sup>).</p></sec><sec id=\"sec2dot3-ijerph-17-05493\"><title>2.3. Differential Methylation Analysis and Unsupervised Hierarchical Clustering</title><p>The Infinium Methylation EPIC array (Illumina<sup>®</sup>) was used to determine the DNA methylation status of 866,562 CpG sites, following the manufacturer’s instructions. All statistical analysis and modeling were performed using the open-source software <italic>R</italic>. Minfi <italic>R</italic> package was used to process and analyze the methylation data [<xref rid=\"B18-ijerph-17-05493\" ref-type=\"bibr\">18</xref>]. Using minfi package, the probes were quantile normalized and background adjusted. The resulting set of samples and probes was used for differentially methylated probes analysis. The differentially methylated probes Finder function in Minfi package was used to identify differentially methylated probes between WTC EHC (exposed) and NYUWHS (unexposed) samples. Probes with statistical significance using Benjamini–Hochberg false discovery rate <italic>q</italic> cutoff of <italic>q</italic> < 0.05 were considered most significant, and corresponding heatmap is shown. <italic>β</italic>-values for all 866,562 CpG sites tested were defined as the ratio of fluorescence intensity of the methylated probe over the overall intensity of probes. <italic>β</italic>  <  0.2 indicated hypomethylation (blue); <italic>β</italic>  >  0.8 indicated hypermethylation (red). All graphs and heatmaps were generated using the <italic>R</italic> package. Unsupervised hierarchical clustering was done with Euclidean measure for distance matrix and complete agglomeration method for clustering was used for unsupervised hierarchical clustering.</p></sec><sec id=\"sec2dot4-ijerph-17-05493\"><title>2.4. Functional Genomic Pathway Analysis</title><p>All probes included in the array were annotated using the HumanMethylation850 manifest provided by the manufacturer (MethylationEPIC_v-1-0_B4; Illumina, San Diego, CA, USA). Genomic information, including DNA sequence and coordinates of gene-coding regions, were obtained from the University of California Santa Cruz Genome Browser database [<xref rid=\"B19-ijerph-17-05493\" ref-type=\"bibr\">19</xref>]. All probes covering promoters and enhancers of coding genes were filtered in and considered for the enrichment pathway network analysis. This rationale was adopted to limit nonspecific enrichment pathway results that may occur when all coding and noncoding genes are included. Using the R package, we ran in parallel Cluster profiler and the DAVID Bioinformatics Resources interrogating 5867 genes in order to detect differentially methylated genes [<xref rid=\"B20-ijerph-17-05493\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijerph-17-05493\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijerph-17-05493\" ref-type=\"bibr\">22</xref>]. The enrichment test is based on the Fisher exact test, which indicates if the overlap between genes in a cluster and in a Gene Ontology term is higher than expected by chance. All sources of interaction evidence are calibrated against previous knowledge using the high-level functional groupings provided by the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway map [<xref rid=\"B23-ijerph-17-05493\" ref-type=\"bibr\">23</xref>].</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05493\"><title>3. Results</title><p>Basic descriptive characteristics of WTC-exposed (WTC EHC) and unexposed (NYUWHS) women are presented in <xref rid=\"ijerph-17-05493-t001\" ref-type=\"table\">Table 1</xref>. The comparison groups were comparable in terms of age at blood donation. Compared to the WTC EHC women, NYUWHS subjects had a higher proportion of Caucasian (44.4% and 79.2%, respectively) and lower proportion of Hispanic (44.4% vs. 12.5%, respectively) women, reflecting the ethnic distributions in respective study sites. Among the WTC EHC subjects, 12 subjects reported acute WTC dust cloud exposure on 11 September 2001 and six subjects reported exposure to the WTC re-suspended dust and fumes between 11 September 2001 and 31 December 2001, reflecting chronic exposure.</p><p>Focusing on the top 5000 differentially expressed probes between WTC-exposed and unexposed women, we observed that the majority of probes (4778 out of 5000; 95.6%) had higher mean methylation values among the WTC-exposed women compared to unexposed women. Only 222 (4.4%) out of the top 5000 differentially methylated probes had a mean methylation value lower in the WTC EHC compared to the NYUWHS participants.</p><p><xref ref-type=\"fig\" rid=\"ijerph-17-05493-f001\">Figure 1</xref> presents the heatmap based on the top 50 differentially methylated probe sets. Unsupervised hierarchical clustering focusing on the top 50 differentially methylated probes showed different patterns of methylation among WTC-exposed and unexposed women.</p><p><xref rid=\"ijerph-17-05493-t002\" ref-type=\"table\">Table 2</xref> lists the top 15 differentially methylated probe sets between WTC-exposed and unexposed women. All 15 CpG sites had higher mean methylation values in the WTC exposed community members compared to the unexposed NYUWHS group (<xref rid=\"ijerph-17-05493-t002\" ref-type=\"table\">Table 2</xref>). Eleven out of the top 15 differentially methylated sites had higher mean methylation values in both WTC EHC subgroups: acutely exposed to the WTC dust cloud on 11 September 2001 and chronically exposed to the WTC dust after 11 September 2001 compared to WTC unexposed subjects (<xref ref-type=\"fig\" rid=\"ijerph-17-05493-f002\">Figure 2</xref>). The top two differentially methylated CpG sites were cg19234509 and cg00187635, associated with genes BCAS2 and OSGIN1, respectively.</p><p>We have compared the methylation status of established tumor suppressor genes and oncogenes [<xref rid=\"B24-ijerph-17-05493\" ref-type=\"bibr\">24</xref>] between the WTC exposed and unexposed subjects. Sixteen out of 24 tumor suppressor genes had higher mean methylation values in the promoter region of the WTC exposed subjects compared to unexposed subjects (<xref rid=\"ijerph-17-05493-t003\" ref-type=\"table\">Table 3</xref>). Similarly, compared to the unexposed subjects, WTC exposed subjects had higher mean methylation values at the locations of known oncogenes (<xref ref-type=\"app\" rid=\"app1-ijerph-17-05493\">Supplementary Table S1</xref>).</p><p>Functional gene pathway enrichment analysis revealed that genes in the endocytosis, mitogen-activated protein kinase (MAPK) signaling, and viral carcinogenesis pathways were enriched in the WTC EHC community members compared to the NYUWHS controls (<xref ref-type=\"fig\" rid=\"ijerph-17-05493-f003\">Figure 3</xref>). For all of these biological pathways the ratio of observed differentially methylated genes out of the total number of genes in the corresponding KEGG pathway reference set is between 4% and 5%. Among the top 15 functional pathways up-regulated in the WTC-exposed women, MAPK, Ras-associated protein-1 (RAP1), mammalian target of rapamycin (mTOR), and the AMP-activated protein kinase (AMPK) signaling pathways and the phosphatidylinositol signaling system are all involved in signal transduction for environmental information processing [<xref rid=\"B25-ijerph-17-05493\" ref-type=\"bibr\">25</xref>]. RNA transport, protein processing in the endoplasmic reticulum, ubiquitin mediated proteolysis and base excision repair are all vital pathways for genetic information processing [<xref rid=\"B25-ijerph-17-05493\" ref-type=\"bibr\">25</xref>].</p></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05493\"><title>4. Discussion</title><p>The present study is one of the first to directly compare genome-wide DNA methylation profiles of WTC exposed (WTC EHC) vs. WTC unexposed (NYUWHS) individuals without cancer. This is also the first analysis to investigate the epigenetic profiles of the WTC-exposed community members who were free of cancer at the time of blood donation. The study demonstrates the feasibility of obtaining blood samples from the WTC-exposed community members, collected as part of routine clinical care, for comprehensive DNA methylation analysis. Given the size and patient diversity of the WTC EHC program, this provides an opportunity for the testing of genetic and epigenetic consequences of the WTC exposures among local community members.</p><p>Moreover, our preliminary findings could have important implications for more objective assessment of WTC exposures, and should be more thoroughly explored in future studies of local community and respondent populations. Unsupervised cluster analysis showed distinct patterns of methylation based on the WTC exposure status suggesting that methylation changes related to the WTC exposures are persistent and can be detected many years after the original exposure. The top two differentially methylated CpG sites cg00187635 (BCAS2) and cg19234509 (OSGIN1) have important biological implications. Breast cancer amplified sequence 2 (BCAS2) is located on chromosome 1 and is thought as having multiple roles in development of breast cancer, possibly through its interaction with estrogen receptor alpha [<xref rid=\"B26-ijerph-17-05493\" ref-type=\"bibr\">26</xref>], or as a negative regulator of the p53 tumor suppressor gene [<xref rid=\"B27-ijerph-17-05493\" ref-type=\"bibr\">27</xref>]. Oxidative stress induced growth inhibitor 1(OSGIN1) is a tumor suppressor gene linked to cellular stress and apoptosis, variants of which have been implicated in cancer development, specifically in hepatocellular carcinoma [<xref rid=\"B28-ijerph-17-05493\" ref-type=\"bibr\">28</xref>].</p><p>Among the rest of the top 15 differentially methylated probes, there were several other genes that may play a role in cancer development. BMI1 is a cancer stem cell marker [<xref rid=\"B29-ijerph-17-05493\" ref-type=\"bibr\">29</xref>] that has been implicated in acute myeloid leukemia [<xref rid=\"B30-ijerph-17-05493\" ref-type=\"bibr\">30</xref>], cervical cancer [<xref rid=\"B31-ijerph-17-05493\" ref-type=\"bibr\">31</xref>], and prostate cancer [<xref rid=\"B32-ijerph-17-05493\" ref-type=\"bibr\">32</xref>], among others, by acting as a chromatic remodeler of regulatory genes. EEF1A2 expression is elevated in poor prognosis of triple negative breast cancers [<xref rid=\"B33-ijerph-17-05493\" ref-type=\"bibr\">33</xref>]. CDCA7L is important for cell cycle regulation and is dysregulated in multiple cancers [<xref rid=\"B34-ijerph-17-05493\" ref-type=\"bibr\">34</xref>]. ARHGEF7 plays a role in cytoskeleton remodeling, and may regulate cancer cell motility [<xref rid=\"B35-ijerph-17-05493\" ref-type=\"bibr\">35</xref>], USF1 expression is related to cellular stress and senescence, immune response and carcinogenesis and has been shown to be associated with shorter life expectancy [<xref rid=\"B36-ijerph-17-05493\" ref-type=\"bibr\">36</xref>]. USP12 is a deubiquitinating enzyme that plays a critical role in TP53 tumor suppressor levels [<xref rid=\"B37-ijerph-17-05493\" ref-type=\"bibr\">37</xref>].</p><p>Functional genomic pathway analysis revealed in the WTC-exposed local community group upregulation of several cancer-related pathways, including the MAPK signaling pathway, viral carcinogenesis, Rap1 signaling pathway, cell cycle, ubiquitin-mediated proteolysis, AMPK signaling pathway, phosphatidylinositol signaling system, amino sugar and nucleotide sugar metabolism, and base excision repair. A recent analysis by Morrow et al. examined sites of DNA methylation predictive of mortality among smokers identified seven CpG predicate sites [<xref rid=\"B10-ijerph-17-05493\" ref-type=\"bibr\">10</xref>], two of which (corresponding to genes FAM178B and USP2) were found to have higher mean methylation values among WTC-exposed local community members in our study. FAM178B’s function is understudied, but it may play a role in the CD34+ hematopoietic stem lineage [<xref rid=\"B38-ijerph-17-05493\" ref-type=\"bibr\">38</xref>]. USP2 is a ubiquitin-specific protease found to be capable of promoting metastasis in triple negative breast cancer [<xref rid=\"B39-ijerph-17-05493\" ref-type=\"bibr\">39</xref>]. While all participants in our study were cancer-free, an increase in cancer incidence is well documented among WTC-exposed individuals. Three separate cohort studies have shown overall cancer rates of those exposed to WTC dust are 6–14% higher than expected [<xref rid=\"B11-ijerph-17-05493\" ref-type=\"bibr\">11</xref>,<xref rid=\"B40-ijerph-17-05493\" ref-type=\"bibr\">40</xref>,<xref rid=\"B41-ijerph-17-05493\" ref-type=\"bibr\">41</xref>].</p><p>We observed that the majority of probes had higher mean methylation values among the WTC-exposed women (including both groups: acutely exposed to the dust cloud on 11 September 2001 and chronically exposed to re-suspended WTC dust after 11 September 2001) compared to unexposed women in the current study. Hypermethylation of the promoter region is usually associated with gene silencing and is a key event in carcinogenesis [<xref rid=\"B42-ijerph-17-05493\" ref-type=\"bibr\">42</xref>]. There were several tumor suppressor genes implicated in cancer susceptibility, including both high penetrance genes (i.e., NF1, PTEN) [<xref rid=\"B43-ijerph-17-05493\" ref-type=\"bibr\">43</xref>,<xref rid=\"B44-ijerph-17-05493\" ref-type=\"bibr\">44</xref>,<xref rid=\"B45-ijerph-17-05493\" ref-type=\"bibr\">45</xref>] and lower penetrance genes (PALB2, ATM) [<xref rid=\"B46-ijerph-17-05493\" ref-type=\"bibr\">46</xref>,<xref rid=\"B47-ijerph-17-05493\" ref-type=\"bibr\">47</xref>,<xref rid=\"B48-ijerph-17-05493\" ref-type=\"bibr\">48</xref>], that were found to have higher mean methylation values in the promoter regions of WTC exposed compared to unexposed individuals. Our preliminary results suggest an epigenetic link between the WTC exposure and cancer, although this potential carcinogenic mechanism requires further validation. WTC-exposure related health effects are not, however, limited to cancer, and other potential epigenetic-mediated impacts also warrant consideration.</p><p>This work is preliminary feasibility study, and as such there are limitations that must be acknowledged. Our sample size was small and limited to women only, so we cannot comment here on the DNA methylation profiles of WTC-exposed male community members. There is also the possibility of selection bias as the WTC-exposed community members enrolled into our study were women who sought out regular WTC EHC clinical care instead of being randomly selected from the entire WTC EHC cohort. Moreover, blood was collected from local community members more than 15 years after the WTC disaster, and while DNA methylation changes are known to be persistent [<xref rid=\"B8-ijerph-17-05493\" ref-type=\"bibr\">8</xref>], it is unknown how long epigenetic changes that occurred as a consequence of WTC-exposure might last. DNA for methylation analysis was also collected from mixed white blood cells, and blood cell type may impact DNA methylation profiles [<xref rid=\"B49-ijerph-17-05493\" ref-type=\"bibr\">49</xref>]. Given the small sample size, it was challenging to perform subgroup analyses and take into account potential confounders. We are collecting detailed documentation of psychological exposures and relevant mental health symptoms (PTSD, anxiety, depression), lung function abnormalities, neuropathic symptoms and other comorbidities, as well as income information in the WTC EHC and NYUWHS cohorts and will account for these and other potential confounders in the future larger study of these cohorts. Lastly, we were unable to quantify the level of WTC exposure of the local community members. However, we saw an overlap of multiple differentially methylated genes that Kuan et al. found to be related to increased WTC exposure status, including NOTCH4, GRIN2A, IFNAR2, and MPP5, among others [<xref rid=\"B13-ijerph-17-05493\" ref-type=\"bibr\">13</xref>].</p><p>Our study had several strengths including the availability of the control group of randomly chosen individuals without WTC exposure with blood samples collected prior to 11 September 2001. We used the latest global methylation platform from Illumina to assess methylation status at more than 850,000 methylation sites. This was also the first study to look at DNA methylation in the WTC EHC local community members, and as such we included an ethnically diverse group of women with WTC exposures, a group that has traditionally been understudied in WTC-related research.</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05493\"><title>5. Conclusions</title><p>In conclusion, we demonstrated feasibility of performing comprehensive genetic and epigenetic studies in the WTC EHC, with preliminary data that WTC-exposure manifests as DNA methylation changes evident many years after the initial WTC exposure. Several of the differentially-methylated genes between the WTC-exposed and unexposed women were previously described to play a role in cancer. As those exposed to the WTC disaster continue to age and adverse health issues become increasingly more common, understanding the epigenetic mechanisms behind these conditions will become even more important, with implications for exposure assessment and health effects evaluation. These results, however, require further replication and validation by larger studies, including both local community members as well as first respondents.</p></sec></body><back><app-group><app id=\"app1-ijerph-17-05493\"><title>Supplementary Materials</title><p>The following are available online at <uri xlink:href=\"https://www.mdpi.com/1660-4601/17/15/5493/s1\">https://www.mdpi.com/1660-4601/17/15/5493/s1</uri>, Table S1: Methylation values of known oncogenes in the WTC-exposed (WTC EHC) cancer-free subjects compared to the unexposed cancer-free subjects (NYUWHS).</p><supplementary-material content-type=\"local-data\" id=\"ijerph-17-05493-s001\"><media xlink:href=\"ijerph-17-05493-s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>Conceptualization, A.A.A. and J.R.; Methodology, Y.S., L.Y., Y.Z, N.D., M.S., A.H., and A.Z.-J.; Software, L.Y. and Y.Z.; Validation, L.Y., Y.Z., and S.T.; Formal Analysis, L.Y. and Y.Z.; Investigation, A.A.A, S.T., and N.D.; Resources, M.S., A.H., and A.Z.-J.; Data Curation, L.Y., Y.Z., S.T., and N.D.; Writing—Original Draft Preparation, A.A.A; Writing—Review & Editing, A.A.A., S.T., N.D., Y.S., and J.R.; Visualization, L.Y. and Y.Z.; Supervision, Y.S., A.Z.-J., and J.R.; Project Administration, A.A.A. and J.R.; Funding Acquisition, A.A.A. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This work was funded through NYU Laura & Isaac Perlmutter Comprehensive Cancer Center Support Inter-Disciplinary Population Research Pilot Grant Program (P30CA016087).</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijerph-17-05493\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Lioy</surname><given-names>P.J.</given-names></name><name><surname>Weisel</surname><given-names>C.P.</given-names></name><name><surname>Millette</surname><given-names>J.R.</given-names></name><name><surname>Eisenreich</surname><given-names>S.</given-names></name><name><surname>Vallero</surname><given-names>D.</given-names></name><name><surname>Offenberg</surname><given-names>J.</given-names></name><name><surname>Buckley</surname><given-names>B.</given-names></name><name><surname>Turpin</surname><given-names>B.</given-names></name><name><surname>Zhong</surname><given-names>M.</given-names></name><name><surname>Cohen</surname><given-names>M.D.</given-names></name><etal/></person-group><article-title>Characterization of the dust/smoke aerosol that settled east of the World Trade Center (WTC) in lower Manhattan after the collapse of the WTC 11 September 2001</article-title><source>Environ. 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Sci.</source><year>2006</year><volume>1089</volume><fpage>119</fpage><lpage>126</lpage><pub-id pub-id-type=\"doi\">10.1196/annals.1386.024</pub-id><pub-id pub-id-type=\"pmid\">17261761</pub-id></element-citation></ref><ref id=\"B49-ijerph-17-05493\"><label>49.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Wu</surname><given-names>H.-C.</given-names></name><name><surname>Delgado-Cruzata</surname><given-names>L.</given-names></name><name><surname>Flom</surname><given-names>J.D.</given-names></name><name><surname>Kappil</surname><given-names>M.</given-names></name><name><surname>Ferris</surname><given-names>J.S.</given-names></name><name><surname>Liao</surname><given-names>Y.</given-names></name><name><surname>Santella</surname><given-names>R.M.</given-names></name><name><surname>Terry</surname><given-names>M.B.</given-names></name></person-group><article-title>Global methylation profiles in DNA from different blood cell types</article-title><source>Epigenetics</source><year>2011</year><volume>6</volume><fpage>76</fpage><lpage>85</lpage><pub-id pub-id-type=\"doi\">10.4161/epi.6.1.13391</pub-id><pub-id pub-id-type=\"pmid\">20890131</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijerph-17-05493-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Unsupervised hierarchial clustering heatmap for the 50 most significantly differentially methylated CpG sites between the WTC-exposed (WTC EHC) vs. unexposed (NYUWHS) cancer-free subjects.</p></caption><graphic xlink:href=\"ijerph-17-05493-g001\"/></fig><fig id=\"ijerph-17-05493-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Mean methylation values of the top 11 differentially methylated probes of the WTC exposed groups (no dust cloud and dust cloud exposure) compared to the unexposed group (NYUWHS).</p></caption><graphic xlink:href=\"ijerph-17-05493-g002\"/></fig><fig id=\"ijerph-17-05493-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Functional genomic pathways enriched in the WTC-exposed (WTC EHC) cancer-free subjects compared to the unexposed cancer-free subjects (NYUWHS). Summary of pathway network analysis highlights the relationship between probe sets enriched in the WTC-exposed women compared to unexposed women. <italic>Y</italic>-axis shows the probe sets with significant overlap with the reference probe sets/genes from the KEGG database. <italic>X</italic>-axis shows the ratio of the number of differentially expressed probe sets/genes to the total number of genes included in the particular pathway gene set from the reference KEGG pathway database. The dot sizes are proportional to the number of overlapping probe sets/genes. The dot colors show the p-value adjusted for false discovery rate.</p></caption><graphic xlink:href=\"ijerph-17-05493-g003\"/></fig><table-wrap id=\"ijerph-17-05493-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05493-t001_Table 1</object-id><label>Table 1</label><caption><p>Descriptive characteristics of the WTC-exposed (WTC EHC) and unexposed (NYUWHS) cancer-free subjects.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Characteristic</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">WTC EHC<break/><italic>n</italic> = 18</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NYUWHS<break/><italic>n</italic> = 24</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Age at blood donation, mean (SD)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">57.4 (8.4)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">56.0 (8.3)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Race/ethnicity, <italic>n</italic> (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Caucasian</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8 (44.4%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19 (79.2%)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hispanic</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8 (44.4%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3 (12.5%)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">African-American</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1 (5.6%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2 (8.3%)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Asian</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1 (5.6%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0 (0%)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Body mass index, <italic>n</italic> (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Normal weight (<25)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7 (39%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13 (54%)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Overweight (25–30)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6 (33%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6 (25%)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Obese (≥30)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5 (28%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5 (21%)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Smoking, (<italic>n</italic>%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Never</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12 (67%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12 (50%)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Former</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5 (28%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10 (42%)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Current</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1 (5%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2 (8%)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Location status, <italic>n</italic> (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Resident</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5 (27%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24 (100%)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Local worker</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11 (61%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Clean-up worker</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2 (11%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">WTC dust exposure, <italic>n</italic> (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Acute, on 9/11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12 (67%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Chronic, post 9/11</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6 (33%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05493-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05493-t002_Table 2</object-id><label>Table 2</label><caption><p>Top 15 differentially methylated probes of the WTC-exposed (WTC EHC) vs. unexposed (NYUWHS) cancer-free subjects.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Gene(s)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Probe ID</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">WTC EHC Methylation Value, Mean</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NYUWHS Methylation Value, Mean</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic> Value</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>q</italic> Value</th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">BCAS2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">cg19234509</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.107</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.072</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.29 × 10<sup>−17</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.09 × 10<sup>−11</sup></td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">OSGIN1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">cg00187635</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.224</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.145</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.67 × 10<sup>−16</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.09 × 10<sup>−11</sup></td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">BMI1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">cg10091662</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.141</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.089</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.76 × 10<sup>−16</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.09 × 10<sup>−11</sup></td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">EEF1A2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">cg01915951</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.096</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.060</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.06 × 10<sup>−16</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.09 × 10<sup>−11</sup></td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SPTBN5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">cg25507299</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.092</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.058</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.20 × 10<sup>−16</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.09 × 10<sup>−11</sup></td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CHD8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">cg19818298</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.201</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.137</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.49 × 10<sup>−16</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.09 × 10<sup>−11</sup></td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CDCA7L</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">cg22107210</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.134</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.088</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.63 × 10<sup>−16</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.09 × 10<sup>−11</sup></td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">AIDA (C1orf58)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">cg04844391</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.061</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.043</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.98 × 10<sup>−16</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.09 × 10<sup>−11</sup></td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">DDN</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">cg01163298</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.159</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.114</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.15 × 10<sup>−16</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.09 × 10<sup>−11</sup></td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SNORD45C (RABGGTB)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">cg11245233</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.130</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.090</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.53 × 10<sup>−16</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.10 × 10<sup>−11</sup></td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ZFAND6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">cg08562141</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.131</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.085</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.26 × 10<sup>−16</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.12 × 10<sup>−11</sup></td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ARHGEF7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">cg07959338</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.129</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.085</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.41 × 10<sup>−16</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.12 × 10<sup>−11</sup></td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">UBXN8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">cg08837587</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.135</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.093</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.73 × 10<sup>−16</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.12 × 10<sup>−11</sup></td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">USF1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">cg13382707</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.107</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.070</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.04 × 10<sup>−16</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.12 × 10<sup>−11</sup></td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">USP12</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">cg01517521</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.104</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.065</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.79 × 10<sup>−16</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.20 × 10<sup>−11</sup></td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05493-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05493-t003_Table 3</object-id><label>Table 3</label><caption><p>Methylation values of known tumor suppressor genes in the WTC-exposed (WTC EHC) cancer-free subjects compared to the unexposed cancer-free subjects (NYUWHS).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Gene</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Probe ID</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">WTC EHC Methylation Value, Mean</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NYUWHS Methylation Value, Mean</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Methylation Status *</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Regulatory Feature</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ATM</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">cg18457775</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.056</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.042</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Higher</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Promoter</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">BCL11B</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">cg03498048</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.114</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.191</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Lower</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Promoter</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CDK6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">cg12548629</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.084</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.057</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Higher</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Promoter</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CDKN2C</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">cg07013994</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.054</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.042</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Higher</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Promoter</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CLYD</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">cg06458795</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.051</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.039</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Higher</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Promoter</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IDH1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">cg18755114</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.103</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.071</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Higher</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Promoter</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">JAK2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">cg03693943</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.078</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.057</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Higher</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Promoter</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NOTCH1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">cg04271687</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.059</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.041</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Higher</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Promoter</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NF1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">cg25204988</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.052</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.041</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Higher</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Promoter</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NUP98</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">cg20457962<break/>cg01954337</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.154<break/>0.192</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.121<break/>0.144</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Higher<break/>Higher</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Promoter<break/>Promoter</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PALB2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">cg15645140<break/>cg07627390</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.052<break/>0.099</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.037<break/>0.067</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Higher<break/>Higher</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Promoter<break/>Promoter</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PTEN</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">cg18665732<break/>cg16687447</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.054<break/>0.053</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.038<break/>0.038</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Higher<break/>Higher</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Promoter<break/>Promoter</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SOCS1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">cg11973052</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.047</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.035</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Higher</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Promoter</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TCF3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">cg24680852</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.068</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.045</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Higher</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Promoter</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TSC1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">cg11295002</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.092</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.066</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Higher</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Promoter</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TSC2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">cg02263479</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.051</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.038</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Higher</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Promoter</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">WRN</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">cg03410815</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.115</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.075</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Higher</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Promoter</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CDH11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">cg09631415</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.505</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.601</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Lower</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NA</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MEN1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">cg15893070</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.203</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.160</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Higher</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NA</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">RUNX1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">cg01337293</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.096</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.069</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Higher</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NA</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SUFU</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">cg01357317</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.858</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.804</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Higher</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NA</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TNFAIP3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">cg14527802</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.055</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.042</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Higher</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NA</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SDHD</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">cg11542469</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.049</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.037</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Higher</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Non-Gene Associated</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MLH1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">cg25837710<break/>cg06590608</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.050<break/>0.039</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.036<break/>0.067</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Higher<break/>Lower</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Unclassified<break/>Unclassified</td></tr></tbody></table><table-wrap-foot><fn><p>* Methylation status refers to mean methylation value of the WTC exposed subjects relative to mean methylation value of unexposed subjects.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32707918</article-id><article-id pub-id-type=\"pmc\">PMC7432007</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155194</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05194</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Overexpression of <italic>ATG5</italic> Gene Makes Granulocyte-Like HL-60 Susceptible to Release Reactive Oxygen Species</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Mroczek</surname><given-names>Agnieszka</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05194\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Cieloch</surname><given-names>Adrianna</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05194\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-3766-5466</contrib-id><name><surname>Manda-Handzlik</surname><given-names>Aneta</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05194\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05194\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Kuźmicka</surname><given-names>Weronika</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05194\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05194\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Muchowicz</surname><given-names>Angelika</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05194\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Wachowska</surname><given-names>Małgorzata</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05194\">1</xref><xref rid=\"c1-ijms-21-05194\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05194\"><label>1</label>Department of Laboratory Medicine and Clinical Immunology of Developmental Age, Medical University of Warsaw, Zwirki i Wigury 63a Street, 02-091 Warsaw, Poland; <email>askrobot@wum.edu.pl</email> (A.M.); <email>adrianna.cieloch@gmail.com</email> (A.C.); <email>aneta.manda-handzlik@wum.edu.pl</email> (A.M.-H.); <email>weronika.kuzmicka@wum.edu.pl</email> (W.K.)</aff><aff id=\"af2-ijms-21-05194\"><label>2</label>Postgraduate School of Molecular Medicine, Medical University of Warsaw, Zwirki i Wigury 61 Street, 02-091 Warsaw, Poland</aff><aff id=\"af3-ijms-21-05194\"><label>3</label>Department of Immunology, Medical University of Warsaw, Jana Nielubowicza 5 Street, 02-097 Warsaw, Poland; <email>angelikamuchowicz@gmail.com</email></aff><author-notes><corresp id=\"c1-ijms-21-05194\"><label>*</label>Correspondence: <email>malgorzata.wachowska@wum.edu.pl</email>; Tel.: +22-317-95-03</corresp></author-notes><pub-date pub-type=\"epub\"><day>22</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5194</elocation-id><history><date date-type=\"received\"><day>31</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>20</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Neutrophils represent the first line of defense against pathogens using various strategies, such as phagocytosis, production of reactive oxygen species (ROS) and neutrophil extracellular traps (NETs) formation. Recently, an autophagy-independent role of autophagy related (<italic>ATG</italic>) gene 5 in immune cells, including neutrophils, was emphasized. Our aim was to investigate the role of ATG5 protein in neutrophils’ antimicrobial functions, proliferation and apoptosis. To this end, we used genetically modified human promyelocytic leukemia (HL-60) cells overexpressing ATG5, differentiated toward granulocyte-like cells with all-trans retinoic acid (ATRA) and dimethylformamide. The level of differentiation, phagocytosis, proliferation and apoptosis were determined by flow cytometry. ROS production and NETs release was assessed by fluorometry and fluorescent microscopy. <italic>ATG5</italic> gene expression was evaluated by real-time PCR, whereas the protein level of ATG5 and LC3-II was determined by Western blot. We did not observe the induction of autophagy in differentiated HL-60 cells overexpressing ATG5. The increased expression of ATG5 affects the differentiation of HL-60 cells with ATRA, ROS production and phagocytosis. However, we did not detect changes in NETs release. Moreover, ATG5 protects differentiated HL-60 cells from apoptosis but does not cause changes in proliferation rate.</p></abstract><kwd-group><kwd>ATG5</kwd><kwd>autophagy</kwd><kwd>neutrophils</kwd><kwd>neutrophils functions</kwd><kwd>promyelocytic leukemia cells (HL-60)</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05194\"><title>1. Introduction</title><p>In the last two decades, scientists have begun to pay attention not only to adaptive but also to innate immunity as a key player in the outcome of various diseases. It is well documented that the crucial components of the innate arm of immune response are neutrophils, the most abundant type of white blood cells in human blood. Neutrophils are directed by cytokines and other stimuli into infected tissues, where they eliminate invading microbes [<xref rid=\"B1-ijms-21-05194\" ref-type=\"bibr\">1</xref>]. The role of these cells was underestimated for a long time, though recent research uncovered that neutrophils are sophisticated immune cells. They are able to precisely control enzymes in the granules, release chemokines and interact with several constituents of the immune system, including the adaptive branch. Neutrophils eliminate microbes through a variety of mechanisms such as phagocytosis, the production of reactive oxygen species (ROS), degranulation and neutrophil extracellular trap formation (NETs) [<xref rid=\"B2-ijms-21-05194\" ref-type=\"bibr\">2</xref>].</p><p>Interestingly, studies of the last decade have identified autophagy as one of vital processes involved in neutrophil functions and bone marrow differentiation [<xref rid=\"B3-ijms-21-05194\" ref-type=\"bibr\">3</xref>]. However, there are also data pointing to the potent autophagy-independent roles of proteins involved in autophagy, such as autophagy-related (ATG) 5 protein in neutrophils [<xref rid=\"B4-ijms-21-05194\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijms-21-05194\" ref-type=\"bibr\">5</xref>]. ATG5 was initially described as a crucial element in a process of autophagy. This membrane-trafficking mechanism is responsible for the turnover of cytoplasmic constituents and also serve as a cell survival and cell death pathway [<xref rid=\"B6-ijms-21-05194\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijms-21-05194\" ref-type=\"bibr\">7</xref>]. The process of autophagy is mostly controlled by <italic>ATG</italic> genes. Among them, the <italic>ATG5</italic> is essential for the early stages of autophagosome formation and for many years it was suggested that ATG5 protein is specifically required for autophagy [<xref rid=\"B8-ijms-21-05194\" ref-type=\"bibr\">8</xref>]. However, a growing body of evidence shows that ATG5, as well as other components of the autophagy machinery, may be crucial elements in other processes [<xref rid=\"B9-ijms-21-05194\" ref-type=\"bibr\">9</xref>]. It was reported that ATG5 plays a critical role in multiple aspects of lymphocyte development and function. Murine CD4 and CD8 lymphocytes lacking ATG5 failed to undergo efficient proliferation after TCR stimulation [<xref rid=\"B10-ijms-21-05194\" ref-type=\"bibr\">10</xref>]. Intriguingly, ATG5 can eliminate pathogens by regulating cytokine secretion through crosstalk with various pathways. Moreover, there is direct evidence that ATG5 is involved in apoptosis. Calpain-cleaved ATG5 is further translocated to the mitochondria and triggers cytochrome c release together with caspase activation, without inducing the autophagy process [<xref rid=\"B11-ijms-21-05194\" ref-type=\"bibr\">11</xref>].</p><p>Neutrophils are short-lived cells and any genetic manipulations are impossible to introduce by currently available techniques. Therefore, in the present study we used a differentiated human promyelocytic leukemia (dHL-60) cell line, recently described and characterized by us, resembling neutrophils’ functions [<xref rid=\"B12-ijms-21-05194\" ref-type=\"bibr\">12</xref>]. Here, we developed the HL-60 cell line overexpressing <italic>ATG</italic>5, differentiated with all-trans retinoic acid (ATRA) or dimethylformamide (DMF), in order to examine the ATG5 role in neutrophils’ functions.</p></sec><sec sec-type=\"results\" id=\"sec2-ijms-21-05194\"><title>2. Results</title><sec id=\"sec2dot1-ijms-21-05194\"><title>2.1. ATG5 Overexpression in HL-60 Cells Does Not Result in the Upregulation of Autophagy</title><p>In order to investigate the role of autophagy/ATG5 in neutrophils functions, HL-60 cells were transduced with a lentiviral expression system. Vectors containing the human <italic>ATG5</italic> gene sequence—pLVX-IRES-Puro-<italic>ATG</italic>5 or control vector—pLVX-IRES-Puro-Ø were used. A stable cell line overexpressing ATG5 (HL-60-ATG5) and mock control cells, was obtained by a puromycin selection. The level of increased <italic>ATG5</italic> expression was measured at mRNA level by quantitative (q) PCR (<xref ref-type=\"fig\" rid=\"ijms-21-05194-f001\">Figure 1</xref>a) and at the protein level by Western blotting (<xref ref-type=\"fig\" rid=\"ijms-21-05194-f001\">Figure 1</xref>b). Our results confirm that the mRNA level of <italic>ATG5</italic> was increased in the HL-60-ATG5 cells, comparing with its mock control. Consequently, the protein level of ATG5 was strongly elevated in the HL-60-ATG5 cells, as we were able to detect the 33kDa monomer, which was not present in mock control cells. Subsequently, to determine the effect of increased ATG5 expression on the level of autophagy, mock control HL-60 cells and HL-60-ATG5 cells were incubated for 16 h with 50 μM chloroquine (CQ), an inhibitor of autophagosome–lysosome fusion. The conversion of LC3-I to LC3-II was assessed by Western blotting. In all cells incubated with CQ, we observed an increased conversion of the LC3-I protein to the LC3-II form when compared to untreated cells. However, we failed to observe stronger accumulation of LC3-II in HL-60 cells with increased ATG5 expression in comparison to mock HL-60 cells after incubation with CQ (<xref ref-type=\"fig\" rid=\"ijms-21-05194-f001\">Figure 1</xref>c) proving lack of autophagy upregulation in HL-60 cells overexpressing ATG5.</p></sec><sec id=\"sec2dot2-ijms-21-05194\"><title>2.2. Increased Expression of ATG5 Affects Differentiation of HL-60 Cells with ATRA</title><p>Mock control HL-60 and HL-60-ATG5 cells were cultured in the presence of ATRA or DMF for 5 days. Flow cytometry analysis revealed that both agents, ATRA and DMF, efficiently differentiated HL-60 cells toward granulocyte-like cells (% of CD11b cells: 94 ± 1.3% in DMF-dHL-60-ATG5 cells; 90 ± 1.4% in ATRA-dHL-60-ATG5 cells). However, mock control HL-60 cells differentiated with ATRA were characterized by significantly higher expression of CD11b than ATRA-dHL-60-ATG5 (<xref ref-type=\"fig\" rid=\"ijms-21-05194-f002\">Figure 2</xref>a). Importantly, we did not observe the presence of monocytic marker, CD14, on the surface of differentiated cell lines (<xref ref-type=\"fig\" rid=\"ijms-21-05194-f002\">Figure 2</xref>b), whereas CD15 expression was constant before and after differentiation (<xref ref-type=\"fig\" rid=\"ijms-21-05194-f002\">Figure 2</xref>c).</p></sec><sec id=\"sec2dot3-ijms-21-05194\"><title>2.3. Increased Expression of ATG5 in Granulocyte-Like Cells Does Not Affect Ability to Form NETs</title><p>To characterize neutrophils functions in dHL-60 cells overexpressing ATG5, we first determined their ability to release NETs. ATRA- and DMF-differentiated mock control and HL-60-ATG5 cells were stimulated for 3 h with phorbol 12-myristate 13-acetate (PMA) and calcium ionophore A23187 (CI). The measurement of extracellular DNA by fluorometry revealed that in ATRA-dHL-60 cells, PMA is a stronger NETs inducer than CI, however there was no statistically significant difference in the ability to form NETs between examined cell lines. In DMF-dHL-60 cells, the highest amount of DNA was released after CI stimulation and, similarly to ATRA-dHL-60, tested cell lines released comparable amounts of DNA (<xref ref-type=\"fig\" rid=\"ijms-21-05194-f003\">Figure 3</xref>c,d). In addition, the qualitative analysis of NETs release was carried out using fluorescent microscopy. This analysis of in vitro NETs release confirmed observations based on the quantitative method (<xref ref-type=\"fig\" rid=\"ijms-21-05194-f003\">Figure 3</xref>a,b).</p></sec><sec id=\"sec2dot4-ijms-21-05194\"><title>2.4. ROS Production Is Augmented in ATRA-dHL-60-ATG5 Cells</title><p>Next, as ATRA-dHL-60 cells reveal higher ability to generate ROS than HL-60 cells differentiated with polar planar compounds including DMF [<xref rid=\"B12-ijms-21-05194\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijms-21-05194\" ref-type=\"bibr\">13</xref>], we studied an oxidative burst in ATRA-dHL-60 cells overexpressing ATG5 and in mock control H-60 cells. To this end, we used dihydrorhodamine 123 (DHR) fluorescent probe, nonfluorescent ROS indicator which is oxidized to cationic rhodamine 123 exhibiting green fluorescence. Fluorometric analysis of oxidized DHR123 in ATRA dHL-60 cells lines indicated that ROS formation after PMA stimulation is significantly higher in cells overexpressing ATG5 than in mock control dHL-60 cells (<xref ref-type=\"fig\" rid=\"ijms-21-05194-f004\">Figure 4</xref>a).</p></sec><sec id=\"sec2dot5-ijms-21-05194\"><title>2.5. Phagocytosis Level Tends to Be Elevated in dHL-60 Cells Overexpressing ATG5</title><p>Furthermore, we investigated whether overexpression of the <italic>ATG5</italic> gene in dHL-60 cells may affect phagocytosis, another antimicrobial function, besides NET formation and oxidative burst. Interestingly, we observed that undifferentiated HL-60 cells with increased expression of the <italic>ATG5</italic> gene reveal statistically significantly higher ability to phagocyte <italic>E. coli</italic> particles when compared to mock control. In addition, similar observations were made for ATRA-differentiated and DMF-differentiated HL-60 cells (<xref ref-type=\"fig\" rid=\"ijms-21-05194-f004\">Figure 4</xref>b).</p></sec><sec id=\"sec2dot6-ijms-21-05194\"><title>2.6. Increased Expression of ATG5 Does Not Influence the Proliferative Potential of HL-60 Cells</title><p>The proliferative potential of transduced cell lines was compared using Violet Proliferation Dye (VPD450) and flow cytometry. Analysis was carried out on the 1st, 3rd and 5th day of the experiment. The proliferation rate of tested cells lines on the 3rd and 5th day of analysis was comparable, as the mean fluorescence values were similar for both HL-60-ATG5 and mock control HL-60 cells (<xref ref-type=\"fig\" rid=\"ijms-21-05194-f005\">Figure 5</xref>a). These results suggest that the increased expression of ATG5 does not affect the proliferation of HL-60 cells.</p></sec><sec id=\"sec2dot7-ijms-21-05194\"><title>2.7. HL-60-ATG5 Are More Resistant to Chemical Apoptosis Inducers</title><p>It was highlighted that ATG5 protein is also engaged in the regulation of apoptosis [<xref rid=\"B9-ijms-21-05194\" ref-type=\"bibr\">9</xref>] and may protect from H<sub>2</sub>O<sub>2</sub>-induced apoptosis [<xref rid=\"B14-ijms-21-05194\" ref-type=\"bibr\">14</xref>]. Therefore, we incubated HL-60 cells with two distinct commonly used apoptosis inducers (actinomycin or H<sub>2</sub>O<sub>2</sub>). Subsequently, phosphatidylserine (PS) plasma membrane externalization, one of early stages of apoptosis, was evaluated by annexin V/propidium iodide (PI) and analyzed by flow cytometry. Interestingly, the percentage of early and late apoptotic cells was lower in cells overexpressing ATG5 than in mock control in both actinomycin D-treated and H<sub>2</sub>O<sub>2</sub>-treated groups, whereas statistical significance was observed only for H<sub>2</sub>O<sub>2</sub> (<xref ref-type=\"fig\" rid=\"ijms-21-05194-f005\">Figure 5</xref>b,c). Our results suggest that ATG5 may play a protective role against cell death in HL-60 cell line.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec3-ijms-21-05194\"><title>3. Discussion</title><p>It is well documented that neutrophils play an invaluable role in immune host defense. Inherited or acquired neutropenia results in severe infections, underlining the key role of neutrophils in fighting the pathogens. Even though their role in the immunity is invaluable, we still know relatively little about their function when compared to other immune cells [<xref rid=\"B15-ijms-21-05194\" ref-type=\"bibr\">15</xref>].</p><p>The aim of this study was to evaluate the role of ATG5 in the antimicrobial functions of granulocyte-like cells such as NETs formation, ROS production and phagocytosis. To that end, we employed a model based on a genetic modification of granulocytic-like cells. We developed the HL-60 cell line overexpressing ATG5 and differentiated it with ATRA or DMF into granulocytic-like cells. Even though ATG5 level was increased both at the mRNA and at the protein level, we found that overexpression of ATG5 in HL-60 cells does not lead to induction of autophagy. Importantly, we observed significant differences in ROS production and phagocytosis level following the overexpression of ATG5, but not in the ability to release NETs, between dHL-60 cells overexpressing ATG5 and mock control cells. Moreover, we did not detect differences in the proliferation rate of HL-60-ATG5 when compared to mock control. However, we noticed that HL-60-ATG5 are more resistant to apoptosis inducers.</p><p>Autophagy is based on the formation of distinctive double-membrane vesicles, termed autophagosomes, and further lipidation of the LC3-I protein to its membrane-bound LC3-II form [<xref rid=\"B16-ijms-21-05194\" ref-type=\"bibr\">16</xref>]. As it was mentioned before, autophagy-related proteins take part in this process, among which ATG5 is indispensable for autophagic vesicles formation [<xref rid=\"B11-ijms-21-05194\" ref-type=\"bibr\">11</xref>]. It was reported that the overexpression of ATG5 in different cell lines resulted in the presence of 33kDa ATG5 monomer and led to the induction of autophagy, as measured by the lipidated form of LC3, i.e., LC3-II [<xref rid=\"B17-ijms-21-05194\" ref-type=\"bibr\">17</xref>]. Similarly, in our studies the overexpression of ATG5 in HL-60 cell line was detectable, since we observed increased ATG5 both at mRNA and protein level. Western blot analysis clearly shows an additional 33 kDa band for ATG5 monomer in HL-60-ATG5 cells. However, in contrast to previous studies [<xref rid=\"B18-ijms-21-05194\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijms-21-05194\" ref-type=\"bibr\">19</xref>] we were not able to detect enhanced accumulation of LC3 II in cells overexpressing ATG5. Such discrepancies may arise from cell line model used in our experimental settings, as the HL-60 cell line was suggested to be defective in the ability to induce autophagy [<xref rid=\"B20-ijms-21-05194\" ref-type=\"bibr\">20</xref>].</p><p>It was demonstrated several times that the ATRA-induced granulocyte differentiation of acute promyelocytic leukemia cell line NB4 and/or HL-60 cells upregulates autophagy. The pharmacologic inhibition of autophagy attenuates the ATRA-induced differentiation of NB4 and/or HL-60 cells [<xref rid=\"B21-ijms-21-05194\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05194\" ref-type=\"bibr\">22</xref>]. Moreover, it was suggested that the expression of autophagy-related genes such as <italic>BECN1</italic>, <italic>ATG1</italic> or <italic>ATG5</italic> is upregulated after ATRA treatment in NB4 and/or HL-60 cells [<xref rid=\"B23-ijms-21-05194\" ref-type=\"bibr\">23</xref>]. Therefore, it was interesting to evaluate whether increased availability of the ATG5 protein may influence the ability of ATRA and DMF to differentiate HL-60 into granulocytic-like cells. Flow cytometry analysis of surface marker CD11b revealed that both HL-60-ATG5 and mock control HL-60 cells can be effectively differentiated, by ATRA and DMF, into granulocyte-like cells. Unexpectedly, dHL-60-ATG5 cells revealed lower expression of CD11b than mock control only after ATRA treatment. Since ATG5 expression may be increased in ATRA differentiated cells [<xref rid=\"B23-ijms-21-05194\" ref-type=\"bibr\">23</xref>], we assume that the level of ATG5 protein in ATRA dHL-60-ATG5 cells is higher than in DMF-dHL-60-ATG5 cells. Therefore, we can speculate that increased ATG5 accumulation in ATRA-treated HL-60 cells may lead to the excessive protein degradation in these cells, but not in DMF-treated HL-60.</p><p>Interestingly, it was suggested that ATG5 itself may activate neutrophils as well as interact with MyD88 [<xref rid=\"B11-ijms-21-05194\" ref-type=\"bibr\">11</xref>]. MyD88 is a signalling adaptor molecule and an important player in activating nuclear factor (NF)-κB signalling and mitogen-activated protein kinase signalling cascades. Furthermore, it is responsible for the transcription of many genes associated with innate immunity [<xref rid=\"B11-ijms-21-05194\" ref-type=\"bibr\">11</xref>]. Therefore, we hypothesized that ATG5 overexpression may influence the antimicrobial function of granulocytes. To verify this hypothesis, we evaluated neutrophils functions, such as phagocytosis, and ROS production as well as NETs formation, in ATRA- and DMF-differentiated HL-60-ATG5 cells.</p><p>There are contrary data concerning ATG5 role in NETs formation. It was reported that downregulation of ATG5 impairs NETs formation [<xref rid=\"B24-ijms-21-05194\" ref-type=\"bibr\">24</xref>] whereas others showed that murine neutrophils lacking the <italic>Atg5</italic> gene and ATG5-dependent autophagy are able to form NETs [<xref rid=\"B25-ijms-21-05194\" ref-type=\"bibr\">25</xref>]. In our study, we were unable to detect differences in the ability to release NETs between dHL-60 overexpressing ATG5 and mock control cell line. There are data suggesting the relationship of ATG5 with ROS production, for example, Tal et al. showed that ATG5−/− cells accumulated ROS localized to the mitochondria [<xref rid=\"B26-ijms-21-05194\" ref-type=\"bibr\">26</xref>], whereas Pyo et al. described that transgenic mice overexpressing Atg5 revealed higher levels of antioxidants than their control littermates [<xref rid=\"B27-ijms-21-05194\" ref-type=\"bibr\">27</xref>]. In our study, the increased expression of ATG5 in ATRA dHL-60 led to increased basal level of ROS and further enhanced ROS production after PMA stimulation. These observations suggest that the aberration of ATG5 level influences ROS production. Moreover, it was demonstrated that silencing of <italic>ATG5</italic> by siRNA leads to a reduction in the neutrophils phagocytosis rate [<xref rid=\"B28-ijms-21-05194\" ref-type=\"bibr\">28</xref>]. Our results are in line with this report, as we observed increased phagocytosis of <italic>E. coli</italic> particles by differentiated (ATRA and DMF) HL-60 overexpressing ATG5 in comparison to mock control.</p><p>It was recently reported that the role of ATG5 is much broader than initially thought, as it occurred that, besides playing a vital role in autophagy, ATG5 also affects the cell proliferation rate and apoptosis. In studies done by Zheng et al., the increased expression of ATG5, achieved by viral transduction led to the increased proliferation of tested cell lines [<xref rid=\"B19-ijms-21-05194\" ref-type=\"bibr\">19</xref>]. Our results revealed that the enhanced expression of ATG5 did not affect the proliferation rate of HL-60 cells. This feature may be cell-line-dependent. Interestingly, it was described by Weng et al. that the overexpression of ATG5 protects cells from H<sub>2</sub>O<sub>2</sub>-induced apoptosis [<xref rid=\"B14-ijms-21-05194\" ref-type=\"bibr\">14</xref>]. Our results are in line with already existing data. In comparison to mock control, HL-60-ATG5 cells were less susceptible to PS membrane externalization, an early stage apoptosis marker, after treatment with H<sub>2</sub>O<sub>2</sub>.</p><p>Altogether, here we report that the induction of autophagy by the overexpression of <italic>ATG</italic> genes may not be achieved with all types of cell lines, since we did not observe such a phenomenon in HL-60 cell line, while previous reports showed such dependence. ATG5 was previously described as an immunomodulatory agent, independently from its autophagy-related role. Our data are in line with these observations, as we showed that an excess of ATG5 protein facilitates ROS production and influence phagocytosis. Moreover, an excess of ATG5 itself may protect HL-60 cells from apoptosis.</p></sec><sec id=\"sec4-ijms-21-05194\"><title>4. Materials and Methods</title><sec id=\"sec4dot1-ijms-21-05194\"><title>4.1. Reagents</title><p>Fetal bovine serum (FBS) was purchased from Biochrom (Berlin, Germany). Roswell Park Memorial Institute (RPMI) 1640 medium, HEPES, SYTOX Green, SYTOX Orange, <italic>E. coli</italic> (K-12 strain) BioParticles, dihydrorhodamine (DHR) 123 were obtained from Thermo Fisher Scientific (Waltham, MA, USA). Anti-CD11b- phycoerythrin (PE) (IM2581U) was from Beckmann Coulter (Brea, CA, USA). Anti-CD15-PE-Cy7 (560827) antibody and VPD 450 (562158) were purchased from BD Bioscience (San Jose, CA, USA) and anti-CD14-alexa fluor (AF) 750 (FAB3832S) from R&D System (Minneapolis, MN, Canada). Anti-neutrophil elastase (NE) (ab21595), anti-myloperoxidase (MPO) (ab11729) and secondary anti-rabbit horseradish peroxidase (HRP)-conjugated (ab97051) antibodies were obtained from Abcam (Cambridge, UK). Anti-LC3A/B (#4108), anti-ATG5 (#129945), secondary anti-rabbit (#7074) conjugated with HRP antibodies were purchased from Cell Signaling Technology (Beverly, MA, USA). Anti-glyceraldehyde 3-phosphate dehydrogenase (GAPDH) antibody (G9295), anti-β actin (ACTB) antibody (A3854), HL-60 cells (98070106), bovine serum albumin (BSA), calcium ionophore A23187 and all other reagents, unless otherwise stated, were purchased from Sigma Aldrich (St Louis, MO, USA).</p></sec><sec id=\"sec4dot2-ijms-21-05194\"><title>4.2. Cell Culture and Differentiation</title><p>Human embryonic kidney epithelial cells (HEK293T) and human promyelocytic leukemia cells HL-60 were cultured in Roswell Park Memorial Institute (RPMI) 1640 medium supplemented with 10% FBS and antibiotic-antimycotic solution at 37 °C, 5% CO<sub>2</sub>. HL-60 cells were differentiated with 1 µM ATRA or 70 mM DMF, as described previously [<xref rid=\"B12-ijms-21-05194\" ref-type=\"bibr\">12</xref>]. Level of HL-60 cells differentiation was evaluated by flow cytometry analysis of CD11b, CD14 and CD15 expression using a BD LSRFortessa Flow Cytometer and BD FACSDiva Software. The viability of differentiated cells was determined using a trypan blue exclusion assay. For all further studies, cells were suspended in protein-free RPMI 1640 medium without phenol red supplemented with 10 mM HEPES.</p></sec><sec id=\"sec4dot3-ijms-21-05194\"><title>4.3. Plasmid Construction</title><p><italic>ATG5</italic> sequence was multiplied by PCR using commercially available plasmid pEGFP-C1-hATG5 with the forward and reverse primers containing restriction sites for Not I and Bam H I, respectively (<xref rid=\"ijms-21-05194-t001\" ref-type=\"table\">Table 1</xref>). The PCR product was digested and cloned into the multicloning site of the puromycin-resistant mammalian expression vector pLVX-IRES-Puro. The sequence of the pLVX-IRES-puro-ATG5- construct was confirmed by DNA sequencing.</p></sec><sec id=\"sec4dot4-ijms-21-05194\"><title>4.4. Generation of HL-60 Cells Line Stably Overexpressing ATG5 Gene</title><p>HL-60 cells stably overexpressing human <italic>ATG</italic>5 were generated using a second-generation lentiviral system. HEK 293T cells were co-transfected with pLVX-IRES-Puro-ATG5 or pLVX-IRES-Puro, packaging psPAX2 and envelope pMD2.G vectors using GeneJuice transfection reagent (Calbiochem, San Diego, CA, USA), according to the manufacturer’s instruction. Forty-eight hours post-transfection, lentiviruses-containing medium from the HEK 293T cells was collected, filtered and HL-60 cells were infected in the presence of 8 µg/mL Polybrene. Stable cell line was obtained by puromycin (10 µg/mL) selection, and overexpression of ATG5 was confirmed by Western blotting.</p></sec><sec id=\"sec4dot5-ijms-21-05194\"><title>4.5. qPCR</title><p>2 × 10<sup>6</sup> HL-60-ATG5 cells and mock control cells were collected, and total RNA was isolated, using the Universal RNA Purification Kit (EURx, Gdansk, Poland). The concentration and purity of RNA was evaluated spectrophotometrically using a Nanodrop. Equal amounts of RNA (1 ug) were transcribed into complementary DNA (cDNA) using oligo(dT) primer and Avian Myeloblastosis Virus (AMV) reverse transcriptase (EURx, Gdansk, Poland). qPCR was carried out using gene-specific primers (<xref rid=\"ijms-21-05194-t002\" ref-type=\"table\">Table 2</xref>), cDNAs, and LightCycler 480 SYBRGreen I Master kit (Roche, Basel, Switzerland) according to the manufacturer’s recommendations using a LightCycler 480 II device (Roche). The results were normalized to two reference genes, actin β (<italic>ACTB</italic>) and hypoxanthine-guanine phosphoribosyltransferase (<italic>HPRT</italic>), and analyzed with the LightCycler<sup>®</sup> 480 Software.</p></sec><sec id=\"sec4dot6-ijms-21-05194\"><title>4.6. Western Blot</title><p>HL-60 cells were lysed in RIPA buffer supplemented with a protease inhibitor cocktail. Protein concentration was measured using Quick Start Bradford 1× Dye Reagent (Bio-Rad, Hercules, CA, USA). Lysates were sonicated, boiled for 5 min at 95 °C in 5× Laemmli buffer and equal amounts of protein were separated by sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to the nitrocellulose membrane. Membranes were blocked with 5% milk for 1 h at room temperature (RT) and incubated with primary antibodies, anti-LC3A/B (1:1000 in 1% BSA overnight at 4 °C), anti-ATG5 (1:1000 in 5% BSA overnight at 4 °C), and subsequently with secondary antibodies conjugated with HRP (#7074 at 1:2000, 1 h, RT). Anti-GAPDH or anti-ACTB antibodies conjugated with HRP (1:50,000 in 5% milk, 1 h incubation at RT) were used as loading controls.</p></sec><sec id=\"sec4dot7-ijms-21-05194\"><title>4.7. NETs Quantification</title><p>Differentiated HL-60 cells were seeded into 24-well plates (5 × 10<sup>4</sup> cells per well) and allowed to settle for 30 min at 37 °C, 5% CO<sub>2</sub>. Subsequently, cells were stimulated with 100 nM PMA or 4 µM CI and incubated for 3 h. Unstimulated cells were used as negative controls. Following incubation, 500 mIU of micrococcal nuclease was added to detach the DNA from the cell surface and the plate was incubated for 20 min at 37 °C, 5% CO<sub>2</sub>. The reaction was then stopped with 5 mM EDTA and the plates were centrifuged (10 min at 415 g). Subsequently, the supernatant was collected, and 100 nM SYTOX green fluorescent dye was added. Extracellular DNA release was measured fluorometrically using FLUOstar Omega plate reader (BMG Labtech, Ortenberg, Germany).</p></sec><sec id=\"sec4dot8-ijms-21-05194\"><title>4.8. NETs Visualization</title><p>To visualize NETs release in vitro, dHL-60 cells were seeded into 48-well plates (2.5 × 10<sup>4</sup> cells per well) and stimulated with 100 nM PMA or 4 µM CI. After 3 h, 100 nM Sytox Green was added and DNA of compromised cell membranes was visualized under a Leica DMi8 fluorescent microscope (Leica, Wetzlar, Germany) equipped with a 40× and a 10× magnification objectives.</p></sec><sec id=\"sec4dot9-ijms-21-05194\"><title>4.9. Oxidative Burst Measurement</title><p>Differentiated HL-60 cells were incubated with 4 µg/mL dihydrorodamine (DHR)123 for 30 min in 37 °C in the darkness, then washed and seeded into the wells of black 96-well plates (1 × 10<sup>5</sup> cells per well). Cells were allowed to settle and then stimulated with PMA. Analysis of fluorescence intensity of DHR123 was measured with FLUOstar Omega plate reader (BMG Labtech, Ortenberg, Germany) every 15 min for 2.5 h.</p></sec><sec id=\"sec4dot10-ijms-21-05194\"><title>4.10. Phagocytosis Assay</title><p>Differentiated HL-60 cells (5 × 10<sup>5</sup>) were incubated for 30 min (37 °C, 5% CO<sub>2</sub>) with 25 µg <italic>E. coli</italic> Bio Particles conjugated with fluorescein isothiocyanate (FITC). After incubation, trypan blue solution was added and samples were centrifuged, and subsequently washed twice by 5-min centrifugation at 250 g. The analysis was carried out using BD LSRFortessa flow cytometer and Diva Software (BD Biosciences, San Jose, CA, USA).</p></sec><sec id=\"sec4dot11-ijms-21-05194\"><title>4.11. Proliferation Assay</title><p>Proliferation potential of HL-60 cell lines was measured by flow cytometry and VPD450 staining. HL-60 cells (5 × 10<sup>6</sup> cells/mL) were suspended in phosphate-buffered saline (PBS) and stained with VPD450 (1 μM) for 15 min at 37 °C, 5% CO<sub>2</sub>. Subsequently, cells were washed with PBS and seeded in 6-well plates and allowed to grow for 5 consecutive days. The flow cytometry analysis of proliferating cells was carried out on the first, third and fifth day of the experiment using BD LSRFortessa flow cytometer and Diva Software.</p></sec><sec id=\"sec4dot12-ijms-21-05194\"><title>4.12. Phosphatidylserine Externalization</title><p>Phosphatidylserine plasma membrane externalization was detected with Annexin V-FITC Apoptosis detection Kit (eBioscience, San Diego, CA, USA). HL-60 cells were seeded in 12-well plates (2.5 × 10<sup>5</sup> cells per well) and incubated with actinomycin D (1 µg/mL) or H<sub>2</sub>O<sub>2</sub> (10 µM) for 16 h at 37 °C, 5% CO<sub>2</sub>. After incubation, cells were collected, washed with PBS, resuspended in Binding Buffer and stained with annexin V and PI. The percentage of apoptotic cells was analyzed using BD LSRFortessa flow cytometer and BD FACSDiva Software.</p></sec><sec id=\"sec4dot13-ijms-21-05194\"><title>4.13. Statistical Analysis</title><p>All data were analyzed using GraphPad Prism Software 8 (GraphPad Software, La Jolla, CA, USA). Differences between multiple groups were compared with a Kruskal–Wallis test; for data without normal distribution, or one-way ANOVA for normally distributed data, <italic>p</italic> ≤ 0.05 was considered as significant.</p></sec></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization—M.W.; methodology—A.M.-H., M.W., A.M. (Agnieszka Mroczek); Funding acquisition—M.W.; Formal acquisition—A.M.; writing original draft preparation—M.W. and A.M. (Agnieszka Mroczek), data analysis—A.M. (Agnieszka Mroczek); performing experiments (investigation)—A.M. (Agnieszka Mroczek), A.C., A.M.-H., W.K., A.M. (Angelika Muchowicz); writing—review and editing—A.M.-H., W.K., A.M. (Angelika Muchowicz); supervision—M.W. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This study was financially supported by the Foundation for Polish Science (POWROTY/2016-2/7; A.M.-H., A.M. (Agnieszka Mroczek), M.W.).</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijms-21-05194\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Kumar</surname><given-names>V.</given-names></name><name><surname>Sharma</surname><given-names>A.</given-names></name></person-group><article-title>Neutrophils: Cinderella of innate immune system</article-title><source>Int. 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USA</source><year>2009</year><volume>106</volume><fpage>2770</fpage><lpage>2775</lpage><pub-id pub-id-type=\"doi\">10.1073/pnas.0807694106</pub-id><pub-id pub-id-type=\"pmid\">19196953</pub-id></element-citation></ref><ref id=\"B27-ijms-21-05194\"><label>27.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Pyo</surname><given-names>J.O.</given-names></name><name><surname>Yoo</surname><given-names>S.M.</given-names></name><name><surname>Ahn</surname><given-names>H.H.</given-names></name><name><surname>Nah</surname><given-names>J.</given-names></name><name><surname>Hong</surname><given-names>S.H.</given-names></name><name><surname>Kam</surname><given-names>T.I.</given-names></name><name><surname>Jung</surname><given-names>S.</given-names></name><name><surname>Jung</surname><given-names>Y.K.</given-names></name></person-group><article-title>Overexpression of Atg5 in mice activates autophagy and extends lifespan</article-title><source>Nat. Commun.</source><year>2013</year><volume>4</volume><fpage>2300</fpage><pub-id pub-id-type=\"doi\">10.1038/ncomms3300</pub-id><pub-id pub-id-type=\"pmid\">23939249</pub-id></element-citation></ref><ref id=\"B28-ijms-21-05194\"><label>28.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Ullah</surname><given-names>I.</given-names></name><name><surname>Ritchie</surname><given-names>N.D.</given-names></name><name><surname>Evans</surname><given-names>T.J.</given-names></name></person-group><article-title>The interrelationship between phagocytosis, autophagy and formation of neutrophil extracellular traps following infection of human neutrophils by Streptococcus pneumoniae</article-title><source>Innate Immun.</source><year>2017</year><volume>23</volume><fpage>413</fpage><lpage>423</lpage><pub-id pub-id-type=\"doi\">10.1177/1753425917704299</pub-id><pub-id pub-id-type=\"pmid\">28399692</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijms-21-05194-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>ATG5 overexpression in HL-60 cells does not result in the upregulation of autophagy. (<bold>a</bold>) HL-60 cells stably transduced with vector containing human ATG5 sequence (pLVX-IRES-Puro-ATG5) and control vector (pLVX-Ires-Puro-Ø, mock control) were collected and total RNA was isolated and reverse transcribed into cDNA. qPCR with LightCycler 480 SYBRGreen I Master kit was performed to determine the mRNA level of <italic>ATG5</italic> vs reference genes (β-actin (<italic>ACTB</italic>) and hypoxanthine–guanine phosphoribosyltransferase (<italic>HPRT</italic>)) in stably transduced HL-60 cell lines. (<bold>b</bold>) ATG5 overexpression in stable HL-60-ATG5 and mock control cell lines was confirmed by Western blotting. The band at 53 kD corresponds to the ATG12-ATG5 conjugate and the 33 kDa band corresponds to the unbound ATG5 form. (<bold>c</bold>) The HL-60 cells were incubated with 50 µM CQ (chloroquine) for 16 h and the level of accumulated LC3-II protein was evaluated by western blotting. MW—molecular weight, GAPDH—glyceraldehyde 3-phosphate dehydrogenase—served as a loading control. (<bold>b</bold>,<bold>c</bold>) Representative results are shown for one out of four independent experiments.</p></caption><graphic xlink:href=\"ijms-21-05194-g001\"/></fig><fig id=\"ijms-21-05194-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Increased expression of ATG5 affects the all-trans retinoic acid (ATRA)-induced differentiation of HL-60 cells. HL-60-ATG5 and mock control cells were differentiated into granulocyte-like cells by treatment with 1 µM ATRA or 70 mM dimethylformamide for 5 days. (<bold>a</bold>–<bold>c</bold>) Cells were harvested, stained with appropriate antibodies and differentiation was assessed by evaluating surface expression level of CD11b, CD14 and CD15 by flow cytometry. The data are presented as the means + SEM, <italic>n</italic> = 5 where <italic>n</italic> is the number of replicate experiments. **** <italic>p</italic> ≤ 0.0001 versus mock control, Kruskal–Wallis test with post-hoc Dunn’s test.</p></caption><graphic xlink:href=\"ijms-21-05194-g002\"/></fig><fig id=\"ijms-21-05194-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Increased expression of ATG5 in granulocyte-like cells does not affect their ability to form neutrophil extracellular traps (NETs). HL-60-ATG5 and mock control HL-60 cells were differentiated for 5 days with 1 µM ATRA and 70 mM DMF and stimulated with 100 nM phorbol 12-myristate 13-acetate (PMA) or 4 µM calcium ionophore A23187 (CI) or left unstimulated for 3 h. Neutrophil extracellular traps (NETs) formation was assessed qualitatively: (<bold>a</bold>,<bold>b</bold>) by conventional fluorescent microscopy and quantitatively (<bold>c</bold>,<bold>d</bold>) by fluorometric measurement of DNA release. (<bold>a</bold>,<bold>b</bold>) After the stimulation, 100 nM Sytox Green was added to the wells and NETs were visualized under the microscope using fluorescent and transient light, at 10× magnification. Representative images of one out of six independent experiments are shown. Results are shown as means + SEM from 6 independent experiments and were analyzed by one-way ANOVA with post hoc Dunn’s test.</p></caption><graphic xlink:href=\"ijms-21-05194-g003\"/></fig><fig id=\"ijms-21-05194-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Increased expression of ATG5 leads to augmented reactive oxygen species (ROS) production and increased phagocytosis level in dHL-60-ATG5 cells. (<bold>a</bold>) The mock control HL-60 and HL-60-ATG5 cells after differentiation with 1 µM ATRA were loaded with dihydrorhodamine (DHR) 123 and left unstimulated or stimulated to release NETs with 100 nM PMA. Fluorescence was monitored every 15 min for 150 min post stimulation. (<bold>b</bold>) Mock control HL-60 and HL-60-ATG5 cells differentiated with 1 µM ATRA or 70 mM DMF were incubated with fluorescently labeled 25 µg <italic>E. coli</italic> bioparticles and after a 30-min incubation the percentage of phagocyting cells was measured with a flow cytometer (<bold>a</bold>,<bold>b</bold>) The means + SEM from 5 independent experiments or (<bold>b</bold>) 7 independent experiments are shown, (<bold>a</bold>) two-way ANOVA with Dunnett’s correction for multiple comparisons or (<bold>b</bold>) one-way ANOVA with post-hoc Newman-Keuls test. * <italic>p</italic> ≤ 0.05, **** <italic>p</italic> ≤ 0.0001</p></caption><graphic xlink:href=\"ijms-21-05194-g004\"/></fig><fig id=\"ijms-21-05194-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>HL-60-ATG5 cells do not reveal altered proliferative potential but are more resistant to chemically induced apoptosis. (<bold>a</bold>) Mock control and HL-60-ATG5 cells were stained with 1 µM VPD450 dye and incubated for 5 days. Proliferation rate was assessed by flow cytometry analysis of median fluorescence intensity (MFI) on the first, third and the fifth day of the experiment. The data are representative of one out of five independent experiments. (<bold>b</bold>–<bold>d</bold>) Mock control and HL-60-ATG5 cells were incubated in the presence of 10 µM H<sub>2</sub>O<sub>2</sub> or 1 µg/mL actinomycin D for 16 h, stained using fluorescein isothiocyanate (FITC)-conjugated annexin V and propidium iodide (PI) and analyzed by flow cytometry. (<bold>b</bold>) Representative dot plots. Percentage of early (<bold>c</bold>) and late (<bold>d</bold>) apoptotic cells. The data are presented as means + SEM of five independent experiments. ** <italic>p</italic> ≤ 0.01, two-way ANOVA with post-hoc Holm–Šídák’s test.</p></caption><graphic xlink:href=\"ijms-21-05194-g005\"/></fig><table-wrap id=\"ijms-21-05194-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05194-t001_Table 1</object-id><label>Table 1</label><caption><p>Sequence of primers used for cloning.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Primer Sequence (5′–3′)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><bold>For <italic>ATG5NotI</italic></bold>: GC<underline>GCGGCCGC</underline>GCCACCATGACAGA</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><bold>Rev <italic>ATG5BamHI</italic></bold>: GC<underline>GGATCC</underline>TCAATCTGTTGC</td></tr></tbody></table><table-wrap-foot><fn><p>Restriction enzymes’ cleavage sites are underlined, For—forward, Rev—reverse.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijms-21-05194-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05194-t002_Table 2</object-id><label>Table 2</label><caption><p>Sequences of primers used for qPCR.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Primer Sequences (5′–3′)</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Gene Name</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Forward</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reverse</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>ATG5</italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">GGACGAAACAGCTTCTGAAT</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">GATGGGATTGCAAAATGACA</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>ACTB</italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">AAATCTGGCACCACACCTTC</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">GGGGTGTTGAAGGTCTCAAA</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>HPRT</italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">GACCAGTCAACAGGGGACAT</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">AACACTTCGTGGGGTCCTTTTC</td></tr></tbody></table><table-wrap-foot><fn><p><italic>ATG5</italic>—autophagy-related (ATG) 5, <italic>ACTB</italic>—actin β, <italic>HPRT</italic>—hypoxanthine-guanine Phosphoribosyltransferase.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32756382</article-id><article-id pub-id-type=\"pmc\">PMC7432008</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155588</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05588</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Communication</subject></subj-group></article-categories><title-group><article-title>What Happened to People with Non-Communicable Diseases during COVID-19: Implications of H-EDRM Policies</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-8854-5093</contrib-id><name><surname>Chan</surname><given-names>Emily Ying Yang</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05588\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05588\">2</xref><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05588\">3</xref><xref rid=\"c1-ijerph-17-05588\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Kim</surname><given-names>Jean Hee</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05588\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Lo</surname><given-names>Eugene Siu Kai</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05588\">1</xref><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05588\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Huang</surname><given-names>Zhe</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05588\">1</xref><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05588\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Hung</surname><given-names>Heidi</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05588\">3</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-8706-7758</contrib-id><name><surname>Hung</surname><given-names>Kevin Kei Ching</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05588\">1</xref><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05588\">4</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-9983-6219</contrib-id><name><surname>Wong</surname><given-names>Eliza Lai Yi</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05588\">3</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-8267-9384</contrib-id><name><surname>Lee</surname><given-names>Eric Kam Pui</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05588\">3</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-7706-9370</contrib-id><name><surname>Wong</surname><given-names>Martin Chi Sang</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05588\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Wong</surname><given-names>Samuel Yeung Shan</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05588\">3</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05588\"><label>1</label>Collaborating Centre for Oxford University and CUHK for Disaster and Medical Humanitarian Response (CCOUC), The Chinese University of Hong Kong, Hong Kong, China; <email>Euglsk@cuhk.edu.hk</email> (E.S.K.L.); <email>huangzhe@cuhk.edu.hk</email> (Z.H.); <email>kevin.hung@cuhk.edu.hk</email> (K.K.C.H.)</aff><aff id=\"af2-ijerph-17-05588\"><label>2</label>Nuffield Department of Medicine, University of Oxford, Oxford OX37BN, UK</aff><aff id=\"af3-ijerph-17-05588\"><label>3</label>JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China; <email>JHKim@cuhk.edu.hk</email> (J.H.K.); <email>heidihung@link.cuhk.edu.hk</email> (H.H.); <email>lywong@cuhk.edu.hk</email> (E.L.Y.W.); <email>lkp032@cuhk.edu.hk</email> (E.K.P.L.); <email>wong_martin@cuhk.edu.hk</email> (M.C.S.W.); <email>yeungshanwong@cuhk.edu.hk</email> (S.Y.S.W.)</aff><aff id=\"af4-ijerph-17-05588\"><label>4</label>Accident & Emergency Medicine Academic Unit, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China</aff><author-notes><corresp id=\"c1-ijerph-17-05588\"><label>*</label>Correspondence: <email>emily.chan@cuhk.edu.hk</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>03</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5588</elocation-id><history><date date-type=\"received\"><day>18</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>27</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>People with existing non-communicable diseases (NCDs) are particularly vulnerable to health risks brought upon by emergencies and disasters, yet limited research has been conducted on disease management and the implications of Health-EDRM policies that address health vulnerabilities of people with NCDs during the COVID-19 pandemic. This paper reports the baseline findings of an anonymous, random, population-based, 6-month cohort study that aimed to examine the experiences of people with NCDs and their relevant self-care patterns during the COVID-19 pandemic. A total of 765 telephone interviews were completed from 22nd March to 1st April 2020 in Hong Kong, China. The dataset was representative of the population, with 18.4% of subjects reporting at least one NCD. Results showed that low household income and residence in government-subsidized housing were significant predictors for the subjects who experienced difficulty in managing during first 2 months of the pandemic (11% of the NCD patients). Of those on long-term NCD medication, 10% reported having less than one week’s supply of medication. Targeted services for vulnerable groups during a pandemic should be explored to support NCD self-care.</p></abstract><kwd-group><kwd>Health-EDRM</kwd><kwd>non-communicable disease</kwd><kwd>COVID-19</kwd><kwd>self-care</kwd><kwd>NCD management</kwd><kwd>home care</kwd><kwd>early phase of pandemic</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05588\"><title>1. Introduction</title><p>People with existing non-communicable diseases (NCDs) are particularly vulnerable to health risks brought upon by emergencies and disasters [<xref rid=\"B1-ijerph-17-05588\" ref-type=\"bibr\">1</xref>]. People who suffer from chronic diseases, such as cardiovascular disease, chronic lung disease, and diabetes, are more vulnerable to disruption and stress induced by disasters. A significant proportion of mortality in post-disaster phases results from the failure of health care services to cater to the needs of patients with chronic diseases [<xref rid=\"B2-ijerph-17-05588\" ref-type=\"bibr\">2</xref>].</p><p>The Health emergency and disaster risk management (Health-EDRM) framework emphasizes prevention and risk mitigation through hazard and vulnerability reduction, disaster preparedness, and response and recovery measures [<xref rid=\"B3-ijerph-17-05588\" ref-type=\"bibr\">3</xref>]. As the presence of NCDs are reported to be associated with worse outcomes of the COVID-19 disease, [<xref rid=\"B4-ijerph-17-05588\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijerph-17-05588\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijerph-17-05588\" ref-type=\"bibr\">6</xref>] strengthening NCD self-care and disease management during the pandemic could mitigate the health harm caused by COVID-19. In addition to the maintenance of healthy behaviours (e.g., regular exercise, personal hygiene), NCD patients should continue their regular medication and are recommended to stockpile at least one-month’s supply of medication during the pandemic [<xref rid=\"B7-ijerph-17-05588\" ref-type=\"bibr\">7</xref>]. Ironically, some of the infection control measures, such as lockdowns, and the reallocation of healthcare resources to handle COVID-19 cases, have posed challenges for maintaining care among NCD patients.</p><p>The impact of COVID-19 on NCD management has caused global concerns, and the European WHO Regional Office has begun devising recommended actions for people with NCDs during this pandemic [<xref rid=\"B8-ijerph-17-05588\" ref-type=\"bibr\">8</xref>]. However, research on the status and disease management of NCD patients in the context of COVID-19 remains very limited. This study examines the situation of people with NCDs, their disease management difficulties, and household supply of medication during the early phase of the pandemic. The most vulnerable NCD patient subgroups were identified and discussed.</p></sec><sec id=\"sec2-ijerph-17-05588\"><title>2. Materials and Methods</title><p>This is an anonymous, random, population-based, 6-month cohort study. This report highlights findings of the baseline data collection (22nd March to 1st April 2020). Participants were recruited through computerized random digit dialing (RDD). Stratified sampling was used to ensure that the dataset was representative of the Hong Kong general population in terms of age group, gender, and district of residence. Details of the methodology were reported in our previous study [<xref rid=\"B9-ijerph-17-05588\" ref-type=\"bibr\">9</xref>], which investigated the perception, attitude and preparation for the COVID-19 epidemic among the Hong Kong population. Data were collected in Hong Kong, a southern metropolis in China, and the health services delivery was in an urban setting.</p><p>The study population included those aged 18 years or above and residing in Hong Kong. Socio-demographic data (age, gender, household income, employment status, housing type) and details of NCD patients’ disease management situation (presence and types of chronic condition(s), healthcare services utilization, routine care requirements) were collected by a standardized questionnaire. Participants were also asked about their past medical history and whether their family members had chronic condition(s). An NCD was defined as a self-reported, existing, chronic condition through the questions “Do you suffer from any form of chronic disease?” and “Which type of the chronic disease(s) are you diagnosed”. Households reporting at least one member with an NCD were asked if they had at least one week’s supply of NCD medications at home during the COVID-19 pandemic. In addition, they were further asked whether the COVID-19 pandemic had caused difficulty to their usual NCD care and the nature of these difficulties [<xref rid=\"B9-ijerph-17-05588\" ref-type=\"bibr\">9</xref>].</p><p>Differences between participants with and without perceived difficulty in their usual NCD care during the COVID-19 pandemic were examined by Chi-square tests and Fisher’s exact tests (α = 0.05). Respondents gave verbal informed consent and the study was approved by the Survey and Behavioral Research Ethics Committee at The Chinese University of Hong Kong (SBRE-19-498).</p></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05588\"><title>3. Results</title><p>Our telephone survey reached 765 households, and the final response rate was 44.0% (765/1738). Our sample was comparable to the Hong Kong general population [<xref rid=\"B9-ijerph-17-05588\" ref-type=\"bibr\">9</xref>]. Of all the households interviewed, 31.5% reported the presence of at least one person in the household diagnosed with an NCD, and among them, 9.1% reported not having at least one week’s supply of NCD medications at the time of phone interview.</p><p>Of all the participants, 18.4% (<italic>n</italic> = 141) reported having at least one type of NCD, and approximately 5% (or 27% of these patients) reported more than one type of NCD. Approximately 44.7% of these NCD patients were aged 65 or above. The most commonly reported NCDs were hypertension (48.6%), diabetes (22.1%), cardiovascular diseases (16.4%), and hyperlipidemia (10.0%). Of NCD patients, nearly four-fifths (<italic>n</italic> = 110) had required medication(s) for their condition.</p><p>Around 11% of participants with NCDs reported difficulty in their routine NCD care, with the most common reasons being difficulty in getting to medical consultations/follow-up visits during the pandemic (62.5%) and difficulty in purchasing supplies, such as face masks and hand sanitizers, during this period (56.3%) (<xref ref-type=\"fig\" rid=\"ijerph-17-05588-f001\">Figure 1</xref>). Among participants who reported difficulties in NCD management, those with lower income and those living in government-subsidized housing were more likely to perceive difficulties in NCD management (<xref rid=\"ijerph-17-05588-t001\" ref-type=\"table\">Table 1</xref>), while no statistically significant differences were noted for other demographic variables. The results also revealed no statistically significant difference between participants with different types of NCD or between patients with one NCD versus multiple NCDs.</p></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05588\"><title>4. Discussion</title><p>In our study, we found that around one-fifth of the Hong Kong population reported to have NCDs. Among those NCD patients, lower household income and residing in government-subsidized housing were found to be significantly associated with difficulty in NCD management during the first two months of the pandemic. In addition, households with NCD patients were reasonably well-prepared in terms of medication stockpiling during the COVID pandemic in Hong Kong, with over 90% possessing at least a week’s supply of drugs. Moreover, all public outpatient clinics were open during COVID-19 with enhanced infection control measures, and allowed relatives/friends to obtain drugs for NCDs on the patients’ behalf. Nonetheless, nearly one in ten NCD patients were insufficiently prepared with their medication supply.</p><p>Previous studies indicated that social distancing and quarantine could result in poor management of NCD behavioral risk factors, including various unhealthy lifestyle habits [<xref rid=\"B10-ijerph-17-05588\" ref-type=\"bibr\">10</xref>]. In particular, reduced social interaction, uncertainty in economic situations, and changes in the activities of daily living could further worsen disease management among NCD patients [<xref rid=\"B8-ijerph-17-05588\" ref-type=\"bibr\">8</xref>]. While only about 11% of the NCD patients in this study reported perceived difficulties in managing their NCD during the pandemic, the results indicate the pandemic disrupted access to NCD clinical care, possibly due to services/traffic interruption and difficulties arising from rescheduling of routine check-ups. NCD patients of lower income and those living in government-subsidized housing were significantly more likely to perceive difficulty in NCD management during the pandemic, indicating that material resources may be major barriers to care. A possible reason may be that since the willingness to wear face masks to prevent infection transmission in Hong Kong is high (e.g., around 90% of Hong Kong residents wore mask during the A/H5N1 avian influenza period in 2007 and A/H1N1 influenza period in 2009 [<xref rid=\"B11-ijerph-17-05588\" ref-type=\"bibr\">11</xref>]), it is not surprising that the most commonly reported difficulties for NCD care are getting to medical consultations/follow-up visits and purchasing medical supplies, given the soaring price of face masks in the first few months of the pandemic [<xref rid=\"B12-ijerph-17-05588\" ref-type=\"bibr\">12</xref>]. Thus, NCD patients should thereby receive more targeted services to facilitate their NCD self-care during a pandemic. Further studies, in particular on telemedicine, can investigate interventions to minimize such NCD management interruptions [<xref rid=\"B13-ijerph-17-05588\" ref-type=\"bibr\">13</xref>].</p><p>Health-EDRM concerns the analysis and management of health risks through reduction in hazard, exposure and vulnerability in every phase of the disaster management cycle [<xref rid=\"B14-ijerph-17-05588\" ref-type=\"bibr\">14</xref>]. Resilience-building is a key concept for minimizing the health risks of older people and chronic disease patients, and could be built through empowerment initiatives to improve their health outcomes. Self-care by the population concerned should also be promoted. For chronic disease patients, they should have adequate knowledge on how to use their medication (e.g., type of insulin used, insulin self-injection kit with instructions). For people with multiple drug prescriptions, it would be important for them to identify the critical, life-maintaining ones, and the key contraindications of their regular medications. In the event that health facilities and medical supplies are interrupted, it is important for chronic disease patients to stockpile, preferably, a 10–14 days’ supply of medications [<xref rid=\"B15-ijerph-17-05588\" ref-type=\"bibr\">15</xref>]. Moreover, extensive effort is required to promote emergency preparedness among chronic disease patients, as a systematic review published in 2014 found that a considerable number of chronic disease patients lost their medication and medical aids during evacuation. Many did not bring prescriptions with them when evacuated, which made it difficult to fill in prescriptions, and that medication and prescription loss posed a significant burden on the medical relief teams [<xref rid=\"B16-ijerph-17-05588\" ref-type=\"bibr\">16</xref>]. Community partnership is crucial, and health care workers who are involved in disaster response and relief should be sensitive in choosing the most appropriate NCD health interventions (e.g., adverse drug interactions, unsuitable diets for people with diabetes [<xref rid=\"B17-ijerph-17-05588\" ref-type=\"bibr\">17</xref>]) to support patients during extreme events.</p><p>There were some limitations of this study. For the question of NCD medication stockpiling, since the interview could be answered by the patient’s family members, the accuracy could be undermined by recall bias. In addition, the sample size of the chronic disease patients was very small and did not permit multivariable analysis. Although the results provided some initial insight into NCD healthcare needs and service gaps in a region that was affected by the early phase of the COVID-19 pandemic, the representativeness of the NCD subsample to the general population of NCD patients is unknown. The results should therefore be taken with caution, and future studies need to capture a larger, representative sample of NCDs patients for examination. In order to capture a representative sample of NCD patients with high quality data, a future study should be conducted by randomly sampling NCD patients from patient lists (which provides investigators with documentation of the clinical diagnoses and prescribed medications). These patients can be followed periodically during the epidemic in order to track changes in their healthcare needs and service gaps in the early and later phases. The time effect of self-reported disease management patterns will be examined in the second phase of data collection. Future studies should also examine the impacts of large-scale pandemics and public health emergencies on long-term NCD management.</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05588\"><title>5. Conclusions</title><p>This study examines the disease management difficulties faced by NCD patients during the early phase (first 2 months) of the COVID-19 pandemic and identified the most vulnerable NCD patient subgroups in an urban context. Study findings indicated low household income and residence in government-subsidized housing were found to be significant predictors among the 11% who reported difficultly in managing during first 2 months of the pandemic. Of those on long-term NCD medication, 10% reported having less than one week’s supply of medication. Targeted services for vulnerable groups during a pandemic should be explored to facilitate resilience-building in Health-EDRM and to enable better self-care for people with NCDs.</p></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization, E.Y.Y.C., E.L.Y.W., S.Y.S.W., and K.K.C.H.; methodology, J.H.K., E.K.P.L., E.S.K.L. and Z.H.; validation, J.H.K., M.C.S.W., H.H.; formal analysis, E.S.K.L. and Z.H.; investigation, E.Y.Y.C.; resources, E.Y.Y.C.; data curation, E.S.K.L. and Z.H.; writing—original draft preparation, J.H.K. and H.H.; writing—review and editing, E.Y.Y.C., J.H.K., E.L.Y.W., E.K.P.L., M.C.S.W. and S.Y.S.W.; supervision, K.K.C.H. and J.H.K.; project administration, E.S.K.L. and Z.H.; funding acquisition, E.Y.Y.C. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was fully funded by CCOUC development fund.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijerph-17-05588\"><label>1.</label><element-citation publication-type=\"book\"><person-group person-group-type=\"author\"><name><surname>Chan</surname><given-names>E.Y.Y.</given-names></name></person-group><source>Public Health Humanitarian Responses to Natural Disasters</source><publisher-name>Routledge</publisher-name><publisher-loc>Abingdon, UK</publisher-loc><year>2017</year></element-citation></ref><ref id=\"B2-ijerph-17-05588\"><label>2.</label><element-citation publication-type=\"web\"><person-group person-group-type=\"author\"><collab>World Health Organization</collab></person-group><article-title>United Kingdom Health Protection Agency & Partners. 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thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Perceived No Difficulty (<italic>n</italic> = 125)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Perceived Difficulty (<italic>n</italic> = 16)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Gender</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.394</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Male</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">48.8%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">37.5%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Female</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">51.2%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">62.5%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Age <sup>a</sup></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.307</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18–24</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.2%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25–44</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.2%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18.8%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">45–64</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">45.6%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">31.3%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">65 or above</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">44.0%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">50.0%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Education attainment <sup>a</sup></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.087</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Primary level or below</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14.5%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18.8%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Secondary level</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">54.0%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">75.0%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Tertiary level</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">31.5%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.3%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Living alone <sup>a</sup></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.224</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Not living alone</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">85.6%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100.0%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Living alone</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14.4%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Employment group <sup>a</sup></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.289</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">White collar</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">24.2%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.3%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Blue collar</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14.5%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">31.3%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Housewives</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20.2%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25.0%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Students</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.6%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Unemployment or retired</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">39.5%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">37.5%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Housing type <sup>a</sup></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.012 *</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Public housing</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">32.0%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18.8%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Government subsidized housing</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.4%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">31.3%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Private housing</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">57.6%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">43.8%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Others</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.0%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.3%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Monthly h ousehold income (HK$) <sup>a,b</sup></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.018 *</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">< 8000</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">22.8%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12.5%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8000–19999</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25.4%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">62.5%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20000–39999</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20.2%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18.8%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">40000 or more</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">31.6%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.3%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr></tbody></table><table-wrap-foot><fn><p><sup>a</sup> Fisher’s exact test. <sup>b</sup> USD = 7.8 HKD. * <italic>p</italic> < 0.05.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32752186</article-id><article-id pub-id-type=\"pmc\">PMC7432009</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155515</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05515</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title><italic>Indoleamine 2,3-Dioxygenase 2</italic> Deficiency Exacerbates Imiquimod-Induced Psoriasis-Like Skin Inflammation</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Fujii</surname><given-names>Kento</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05515\">1</xref><xref rid=\"c1-ijms-21-05515\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Yamamoto</surname><given-names>Yasuko</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05515\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Mizutani</surname><given-names>Yoko</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05515\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Saito</surname><given-names>Kuniaki</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05515\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Seishima</surname><given-names>Mariko</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05515\">1</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05515\"><label>1</label>Department of Dermatology, Gifu University Graduate School of Medicine, 1-1 Yanagito, Gifu 501-1194, Japan; <email>yoko@gifu-u.ac.jp</email> (Y.M.); <email>seimarik@gifu-u.ac.jp</email> (M.S.)</aff><aff id=\"af2-ijms-21-05515\"><label>2</label>Department of Disease Control and Prevention, Fujita Health University Graduate School of Health Sciences, 1-98 Dengakugakubo, Kutsukake, Toyoake, Aichi 470-1192, Japan; <email>yama-y@fujita-hu.ac.jp</email> (Y.Y.); <email>saitok@fujita-hu.ac.jp</email> (K.S.)</aff><author-notes><corresp id=\"c1-ijms-21-05515\"><label>*</label>Correspondence: <email>w2111031@edu.gifu-u.ac.jp</email>; Tel.: +81-58-230-6394</corresp></author-notes><pub-date pub-type=\"epub\"><day>01</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5515</elocation-id><history><date date-type=\"received\"><day>16</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>31</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p><italic>Indoleamine 2,3-dioxygenase 1</italic> (<italic>IDO1</italic>) is an enzyme known to suppress immune responses, and several reports have showed that it is associated with psoriasis. <italic>IDO2</italic> is an isoform of <italic>IDO1</italic>, recently identified as a catalytic enzyme in the tryptophan-kynurenine pathway, which is expressed in dendritic cells and monocytes. The expression of <italic>IDO2</italic> in immune cells suggests that <italic>IDO2</italic> may contribute to immune functions. However, the role of <italic>IDO2</italic> in the pathogenesis of psoriasis remains unclear. In this study, to elucidate the role of <italic>IDO2</italic> in psoriasis, we assessed imiquimod (IMQ)-induced psoriasis-like dermatitis in <italic>IDO2</italic> knockout (KO) mice. Skin inflammation, evaluated by scoring erythema, scaling, and ear thickness, was significantly worse in the <italic>IDO2</italic> KO mice than in the wild-type (WT) mice. The mRNA expression levels of TNF-α, IL-23p19, and IL-17A, key cytokines involved in the development of psoriasis, were also increased in the <italic>IDO2</italic> KO mice. Furthermore, immunohistochemistry revealed that the number of Ki67-positive cells in the epidermis and CD4-, CD8-, and IL-17-positive lymphocytes infiltrating the dermis were significantly increased in the <italic>IDO2</italic> KO mice. These results suggest that <italic>IDO2</italic> might decrease IL-17 expression, thereby resulting in the suppression of skin inflammation in IMQ-induced psoriasis-like dermatitis.</p></abstract><kwd-group><kwd>psoriasis</kwd><kwd><italic>IDO2</italic></kwd><kwd><italic>IDO1</italic></kwd><kwd>tryptophan</kwd><kwd>kynurenine</kwd><kwd>IMQ</kwd><kwd>skin</kwd><kwd>inflammation</kwd><kwd>IL-17</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05515\"><title>1. Introduction</title><p>Psoriasis is a chronic inflammatory skin disease that affects 2%–3% of the population [<xref rid=\"B1-ijms-21-05515\" ref-type=\"bibr\">1</xref>]. This disease is characterized by the hyperproliferation of keratinocytes and parakeratosis, accompanied by skin tissue infiltration of T cells, monocytes, and neutrophils [<xref rid=\"B2-ijms-21-05515\" ref-type=\"bibr\">2</xref>]. The exact etiology of psoriasis remains unknown, but antigenic stimulation causes activation of innate immune cells, which results in the production of proinflammatory cytokines in the skin, leading to the differentiation of Th1 and Th17 cells [<xref rid=\"B2-ijms-21-05515\" ref-type=\"bibr\">2</xref>]. Several cytokines, including TNF-α, IL-23, IL-17A, and IL-22, are involved in the pathogenesis of the disease, and neutralizing therapies against TNF-α, IL-12/IL-23-p40, or IL-17A are very efficient for the treatment of patients [<xref rid=\"B3-ijms-21-05515\" ref-type=\"bibr\">3</xref>].</p><p>Topical treatment with imiquimod (IMQ) induces psoriasiform dermatitis in mice, which shows many features of human psoriasis, including the formation of micro-abscesses, hyperkeratosis, acanthosis, erythema, and scaling [<xref rid=\"B4-ijms-21-05515\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijms-21-05515\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijms-21-05515\" ref-type=\"bibr\">6</xref>]. IMQ is a ligand for toll-like receptor 7/8 [<xref rid=\"B7-ijms-21-05515\" ref-type=\"bibr\">7</xref>] and is used in humans for the topical treatment of genital and perianal warts caused by the human papillomavirus, superficial basal cell carcinoma, and actinic keratosis [<xref rid=\"B8-ijms-21-05515\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijms-21-05515\" ref-type=\"bibr\">9</xref>]. As a side effect, topical treatment with IMQ may cause psoriasis or exacerbate it in patients with well-controlled psoriasis [<xref rid=\"B10-ijms-21-05515\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijms-21-05515\" ref-type=\"bibr\">11</xref>]. The development of IMQ-induced dermatitis in mice is highly dependent on the IL-23/IL-17 axis [<xref rid=\"B6-ijms-21-05515\" ref-type=\"bibr\">6</xref>].</p><p><italic>Indoleamine 2,3-dioxygenase 1</italic> (<italic>IDO1</italic>) is an enzyme that metabolizes tryptophan to kynurenine in the first step of the kynurenine pathway [<xref rid=\"B12-ijms-21-05515\" ref-type=\"bibr\">12</xref>]. <italic>IDO1</italic> also acts as an immune modulator in several types of immune cells, including dendritic cells and macrophages [<xref rid=\"B13-ijms-21-05515\" ref-type=\"bibr\">13</xref>]. Tryptophan degradation and kynurenine accumulation by <italic>IDO1</italic> induce T cell apoptosis and regulatory T cell proliferation [<xref rid=\"B14-ijms-21-05515\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijms-21-05515\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijms-21-05515\" ref-type=\"bibr\">16</xref>]. In patients with psoriasis, immune cells including dendritic cells and CD4<sup>+</sup> T lymphocytes have a defect in upregulating <italic>IDO1</italic> in response to inflammation associated with the severity of the disease [<xref rid=\"B17-ijms-21-05515\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijms-21-05515\" ref-type=\"bibr\">18</xref>].</p><p><italic>Indoleamine 2,3-dioxygenase 2</italic> (<italic>IDO2</italic>) is an isoform of <italic>IDO1</italic> that was recently identified as a catalytic enzyme in the tryptophan-kynurenine pathway [<xref rid=\"B19-ijms-21-05515\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijms-21-05515\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijms-21-05515\" ref-type=\"bibr\">21</xref>]. The genomic structures of these two genes are well conserved, and they are present via duplication on the same chromosome in mammals [<xref rid=\"B19-ijms-21-05515\" ref-type=\"bibr\">19</xref>,<xref rid=\"B22-ijms-21-05515\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05515\" ref-type=\"bibr\">23</xref>]. A recent study showed that <italic>IDO1</italic> was expressed in the colon and epididymis, but <italic>IDO2</italic> was expressed mainly in the liver, kidney, and epididymis in mice [<xref rid=\"B19-ijms-21-05515\" ref-type=\"bibr\">19</xref>,<xref rid=\"B22-ijms-21-05515\" ref-type=\"bibr\">22</xref>,<xref rid=\"B24-ijms-21-05515\" ref-type=\"bibr\">24</xref>]. In addition, <italic>IDO2</italic> is expressed in dendritic cells and monocytes and regulates immune functions through autoantibodies and regulatory T cell production [<xref rid=\"B25-ijms-21-05515\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijms-21-05515\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijms-21-05515\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05515\" ref-type=\"bibr\">28</xref>]. However, the role of <italic>IDO2</italic> in the pathogenesis of psoriasis remains unclear.</p><p>In this study, to elucidate the role of <italic>IDO2</italic> in psoriasis, we assessed IMQ-induced psoriasis-like dermatitis in <italic>IDO2</italic> knockout (KO) mice. Based on our data, we propose that <italic>IDO2</italic> might decrease IL-17 expression, leading to the suppression of skin inflammation in IMQ-induced psoriasis-like dermatitis.</p></sec><sec sec-type=\"results\" id=\"sec2-ijms-21-05515\"><title>2. Results</title><sec id=\"sec2dot1-ijms-21-05515\"><title>2.1. Psoriasiform Lesions in the Imiquimod-Induced Mouse Model of Psoriasis Are Associated with IDO2 But Not with IDO1</title><p>To investigate the association of <italic>IDO1</italic> and <italic>IDO2</italic> with psoriasis, we immunohistologically evaluated the normal skin samples of healthy volunteers (<italic>n</italic> = 7), and the lesional and non-lesional skin samples of patients with psoriasis (<italic>n</italic> = 8), using antibodies against <italic>IDO1</italic> and <italic>IDO2</italic>. All skin samples, of both patients with psoriasis and healthy volunteers, were negative for <italic>IDO1</italic> in the epidermis (<xref ref-type=\"fig\" rid=\"ijms-21-05515-f001\">Figure 1</xref>a). Instead, all lesional and non-lesional skin samples of psoriatic patients were positive for <italic>IDO2</italic> in the epidermis. Moreover, while most of the normal skin samples of healthy volunteers were negative for <italic>IDO2</italic>, some showed weak positive signals in the epidermis. </p><p>We next immunohistologically evaluated skin samples from the ears of wild-type (WT) mice treated with vehicle or IMQ for 7 consecutive days. All ear skin samples from vehicle- or IMQ-treated WT mice were negative for <italic>IDO1</italic> in the epidermis (<xref ref-type=\"fig\" rid=\"ijms-21-05515-f001\">Figure 1</xref>b). Although most of the samples from vehicle-treated mice were also negative for <italic>IDO2</italic> in the epidermis, some showed weak positive staining. By contrast, all samples from IMQ-treated mice were positive for <italic>IDO2</italic> in the epidermis. Moreover, we determined the mRNA levels of <italic>IDO1</italic> and <italic>IDO2</italic> in ear skin samples from WT mice treated with vehicle or IMQ for 7 days using quantitative real-time PCR. The mRNA levels of <italic>IDO1</italic> were below the detection sensitivity in WT mice receiving either treatment for 7 days (<xref ref-type=\"fig\" rid=\"ijms-21-05515-f001\">Figure 1</xref>c). Instead, the mRNA levels of <italic>IDO2</italic> were higher in WT mice treated with IMQ than in those treated with vehicle. </p><p>We next compared the effect of IMQ on the skin of <italic>IDO1</italic> KO and WT mice. We applied IMQ cream or Vaseline once daily on both ears of the mice (8–10 weeks of age at the start of treatment) for 7 consecutive days. IMQ treatment induced a psoriasiform skin inflammation in both WT and <italic>IDO1</italic> KO mice. We determined individual scores for erythema, scaling, and ear thickness daily for 7 days. The scores for erythema and scaling did not differ between WT and <italic>IDO1</italic> KO mice treated with IMQ (<xref ref-type=\"fig\" rid=\"ijms-21-05515-f001\">Figure 1</xref>d). Although the ear thickness was higher in IMQ-treated <italic>IDO1</italic> KO mice than WT mice on days 2, 3, and 4 of treatment, there was no difference thereafter. In addition, microscopic observation showed that, after 7 days of treatment, the ear skin epidermis was significantly thicker in WT and <italic>IDO1</italic> KO mice treated with IMQ than in those receiving vehicle (<xref ref-type=\"fig\" rid=\"ijms-21-05515-f001\">Figure 1</xref>e). However, the epidermal thickness did not differ between WT and <italic>IDO1</italic> KO mice receiving IMQ treatment. Thus, these results suggest that psoriasiform lesions in the IMQ-induced mouse model of psoriasis are associated with <italic>IDO2</italic> but not with <italic>IDO1</italic>.</p></sec><sec id=\"sec2dot2-ijms-21-05515\"><title>2.2. IMQ-Induced Psoriasis-Like Inflammation Is Significantly Increased in IDO2 KO Mice</title><p>To elucidate whether IMQ-induced psoriasis-like inflammation is related to <italic>IDO2</italic>, we next assessed the effect of IMQ on the skin of <italic>IDO2</italic> KO and WT mice. We applied IMQ cream or Vaseline once daily on both ears of the mice (8–10 weeks of age at the start of treatment) for 7 consecutive days. IMQ treatment induced a psoriasiform skin inflammation in both WT and <italic>IDO2</italic> KO mice. However, the deterioration of psoriasis-like skin lesions on the ear after 7 days of IMQ treatment was more pronounced in <italic>IDO2</italic> KO than in WT mice (<xref ref-type=\"fig\" rid=\"ijms-21-05515-f002\">Figure 2</xref>a). Individual scores for erythema, scaling, and ear thickness were significantly increased in <italic>IDO2</italic> KO mice. In particular, all scores on days 5 and 7 after topical IMQ were significantly higher in <italic>IDO2</italic> KO than in WT mice (<xref ref-type=\"fig\" rid=\"ijms-21-05515-f002\">Figure 2</xref>b). Scores in vehicle-treated mice remained normal (<xref ref-type=\"fig\" rid=\"ijms-21-05515-f002\">Figure 2</xref>a,b). Microscopic evaluation of ear sections from WT and <italic>IDO2</italic> KO mice after 7 days of IMQ treatment revealed characteristic changes in psoriasiform lesions, including hyperkeratosis, parakeratosis, acanthosis, and micro-abscess formation, which were not observed in vehicle-treated mice. In addition, the IMQ-induced skin lesions in <italic>IDO2</italic> KO mice showed more characteristic changes than those in WT mice (<xref ref-type=\"fig\" rid=\"ijms-21-05515-f002\">Figure 2</xref>c). Epidermal thickness of the ear skin from WT and <italic>IDO2</italic> KO mice, microscopically measured after 7 days of IMQ treatment, was significantly increased. Moreover, the epidermis of the ears of <italic>IDO2</italic> KO mice treated with IMQ was significantly thicker than that of WT mice (<xref ref-type=\"fig\" rid=\"ijms-21-05515-f002\">Figure 2</xref>d), indicating that IMQ-induced psoriasis-like skin inflammation was significantly exacerbated in <italic>IDO2</italic> KO mice.</p></sec><sec id=\"sec2dot3-ijms-21-05515\"><title>2.3. The Deletion of IDO2 Affects the mRNA Expression of Key Cytokines Involved in the Pathogenesis of Psoriasis</title><p>We determined the mRNA expression levels of several inflammatory cytokines that are important in psoriasis (TNF-α, IL-23p19, IL-17A, IL-22, IFN-α, IL-1β, IL-6, and IL-10) and a chemokine that promotes the migration of neutrophils (CXCL2) using quantitative real-time PCR [<xref rid=\"B2-ijms-21-05515\" ref-type=\"bibr\">2</xref>,<xref rid=\"B29-ijms-21-05515\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijms-21-05515\" ref-type=\"bibr\">30</xref>]. Since the peak-time of activity is different for each cytokine, we measured the mRNA levels of cytokines in the ear skin of WT and <italic>IDO2</italic> KO mice before and 24 h, 48 h, and 7 days after IMQ treatment (<xref ref-type=\"fig\" rid=\"ijms-21-05515-f003\">Figure 3</xref>). Cytokine mRNA levels were not significantly different between WT and <italic>IDO2</italic> KO mice before and 24 h after IMQ treatment. However, 48 h and 7 days after IMQ treatment, the mRNA levels of most key cytokines involved in psoriasis development were higher in <italic>IDO2</italic> KO than in WT mice. Furthermore, the expression of TNF-α, IL-23p19, and IL-17A was significantly higher in <italic>IDO2</italic> KO than in WT mice on day 7. Thus, these results show that the deletion of <italic>IDO2</italic> affects the mRNA expression of key cytokines for the pathogenesis of psoriasis.</p></sec><sec id=\"sec2dot4-ijms-21-05515\"><title>2.4. Keratinocyte Proliferation and Inflammatory Cell Infiltration Are Significantly Induced in IMQ-Treated IDO2 KO Mice</title><p>We next examined the immunohistological differences between WT and <italic>IDO2</italic> KO mice treated with IMQ or vehicle after 7 days. Firstly, to assess keratinocyte proliferation in the ear skin of mice, we stained for Ki-67, a cell proliferation marker (<xref ref-type=\"fig\" rid=\"ijms-21-05515-f004\">Figure 4</xref>a,b). We counted the number of Ki-67-positive cells in the epidermis per high power field. Treatment with IMQ significantly increased the number of Ki-67-positive cells in WT and <italic>IDO2</italic> KO mice. In addition, the number of Ki-67-positive cells was significantly higher in IMQ-treated <italic>IDO2</italic> KO mice than WT mice. Secondly, to test T cell infiltration in the dermis, we investigated the expression of CD4 and CD8, which are the markers of helper and cytotoxic T cells, respectively (<xref ref-type=\"fig\" rid=\"ijms-21-05515-f004\">Figure 4</xref>a,b). We counted the number of CD4- or CD8-positive cells in the dermis per high power field. IMQ treatment significantly increased the number of CD4- or CD8-positive cells in WT and <italic>IDO2</italic> KO mice. The number was significantly higher in IMQ-treated <italic>IDO2</italic> KO than WT mice. Finally, because IL-17 signaling plays an important role in psoriasis [<xref rid=\"B6-ijms-21-05515\" ref-type=\"bibr\">6</xref>,<xref rid=\"B31-ijms-21-05515\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijms-21-05515\" ref-type=\"bibr\">32</xref>], we counted the number of IL-17-positive cells in the dermis per high power field (<xref ref-type=\"fig\" rid=\"ijms-21-05515-f004\">Figure 4</xref>a,b). Similarly, the number of IL-17-positive cells in WT and <italic>IDO2</italic> KO mice receiving IMQ treatment was significantly higher compared to mice receiving vehicle. Moreover, we found a significantly greater number of IL-17-positive cells in IMQ-treated <italic>IDO2</italic> KO mice than WT mice. Therefore, these results show that keratinocyte proliferation and infiltration of inflammatory cells were significantly induced in <italic>IDO2</italic> KO mice treated with IMQ, and IMQ-induced psoriasis-like dermatitis is induced in <italic>IDO2</italic> KO mice via IL-17-positive lymphocytes.</p></sec><sec id=\"sec2dot5-ijms-21-05515\"><title>2.5. Exacerbation of IMQ-Induced Psoriasis-Like Skin Inflammation in IDO2 KO Mice Is Associated with IDO2 Itself rather than Tryptophan Metabolites</title><p>To elucidate the mechanism by which <italic>IDO2</italic> deficiency exacerbates IMQ-induced psoriasis-like skin inflammation, we next measured the concentration of tryptophan metabolites, including tryptophan, kynurenine, kynurenic acid, anthranilic acid, and 3-hydroxy anthranilic acid (3-HAA), in the ears of WT and <italic>IDO2</italic> KO mice treated with vehicle or IMQ for 7 days, using high performance liquid chromatography (HPLC). Although it is generally considered that the concentration of kynurenine decreases upon deletion of <italic>IDO2</italic> gene, unexpectedly, it did not decrease in <italic>IDO2</italic> KO mice compared to WT mice. Moreover, tryptophan metabolites (kynurenine, kynurenic acid and anthranilic acid) were found in higher concentrations in IMQ-treated <italic>IDO2</italic> KO mice than WT mice (<xref ref-type=\"fig\" rid=\"ijms-21-05515-f005\">Figure 5</xref>). Kynurenine is known to actively suppress the immune system by binding to the aryl hydrocarbon receptor, thereby inducing regulatory T cells [<xref rid=\"B33-ijms-21-05515\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijms-21-05515\" ref-type=\"bibr\">34</xref>]. Kynurenic acid also suppresses the production of IL-17 and IL-23 in CD4<sup>+</sup> T cells and dendritic cells in vitro [<xref rid=\"B35-ijms-21-05515\" ref-type=\"bibr\">35</xref>]. Thus, the accumulation of kynurenine and kynurenic acid is associated with severe inflammation, resulting in an increase in the downstream metabolites of kynurenine, kynurenic acid, and anthranilic acid. These results show that exacerbation of IMQ-induced psoriasis-like skin inflammation in <italic>IDO2</italic> KO mice might be associated with <italic>IDO2</italic> itself rather than with tryptophan metabolites.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec3-ijms-21-05515\"><title>3. Discussion</title><p>Our results showed that <italic>IDO2</italic> deletion in mice exacerbated IMQ-induced psoriasis-like dermatitis. IL-17 signaling plays an important role in IMQ-induced dermatitis models as well as psoriasis [<xref rid=\"B6-ijms-21-05515\" ref-type=\"bibr\">6</xref>,<xref rid=\"B31-ijms-21-05515\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijms-21-05515\" ref-type=\"bibr\">32</xref>]. Interestingly, we found a remarkably higher mRNA level of IL-17A and more IL-17-positive cells infiltrating the dermis of <italic>IDO2</italic> KO mice treated with IMQ than in WT mice. This suggests that <italic>IDO2</italic> acts on the IL-17 signaling pathway and consequently suppresses IL-17 production.</p><p>The relationship between the pathophysiology of psoriasis and <italic>IDO1</italic> has previously been reported. Although <italic>IDO1</italic> has a protective effect in psoriasis by inducing regulatory T cells, immune cells from patients with psoriasis have a defect in upregulating <italic>IDO1</italic> in response to inflammation which is proportional to the disease severity [<xref rid=\"B17-ijms-21-05515\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijms-21-05515\" ref-type=\"bibr\">18</xref>]. However, our data show that the IMQ-induced mouse model of psoriasis was not associated with <italic>IDO1</italic>. One reason might be that the IMQ-induced mouse model does not accurately recapitulate human psoriasis because of the lack of chronicity or comorbidities of psoriasis [<xref rid=\"B36-ijms-21-05515\" ref-type=\"bibr\">36</xref>]. Instead, the role of <italic>IDO2</italic> in psoriasis had remained unclear until now. Our data show that <italic>IDO2</italic> might suppress IMQ-induced psoriasis-like skin inflammation. With respect to <italic>IDO1</italic> and skin, keratinocytes and fibroblasts can upregulate <italic>IDO1</italic> expression and activation in vitro in response to proinflammatory stimuli [<xref rid=\"B37-ijms-21-05515\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijms-21-05515\" ref-type=\"bibr\">38</xref>]. Nevertheless, myeloid dendritic cells are the major <italic>IDO1</italic>-expressing skin cells in vivo, and <italic>IDO1</italic> is not expressed in the epidermis [<xref rid=\"B39-ijms-21-05515\" ref-type=\"bibr\">39</xref>]. Our results also show that, in the epidermis of patients with psoriasis and WT mice treated with IMQ, <italic>IDO2</italic> was expressed but <italic>IDO1</italic> was not. In addition, while the deletion of IL-17RA in T cells, neutrophils, or macrophages has no impact on IMQ-induced dermatitis, only deletion of this receptor in keratinocytes reflects the effects of its systemic deletion, resulting in strongly reduced dermatitis development [<xref rid=\"B32-ijms-21-05515\" ref-type=\"bibr\">32</xref>]. Therefore, because <italic>IDO1</italic> is not expressed or weakly expressed in the epidermis, it might not affect IMQ-induced dermatitis. By contrast, because <italic>IDO2</italic> is strongly expressed in the epidermis, it might influence IMQ-induced dermatitis. In future experiments, it will be necessary to selectively remove cells such as keratinocytes, dendritic cells, and monocytes in order to confirm this hypothesis.</p><p>The mechanism by which <italic>IDO2</italic> controls IMQ-induced dermatitis remains unclear. In our previous report, <italic>IDO2</italic> was demonstrated to be important in the regulation of STAT3 signaling using a lipopolysaccharide-induced endotoxin shock model [<xref rid=\"B40-ijms-21-05515\" ref-type=\"bibr\">40</xref>]. STAT3 participates in signaling downstream of multiple cytokines implicated in psoriasis, such as IL-6, IL-10, IL-20, IL-22, and IL-23, and may have a role in mediating the innate immune response in psoriatic epidermis [<xref rid=\"B41-ijms-21-05515\" ref-type=\"bibr\">41</xref>]. STAT3 is also required for the differentiation of Th17 cells [<xref rid=\"B42-ijms-21-05515\" ref-type=\"bibr\">42</xref>]. Therefore, <italic>IDO2</italic> might modulate STAT3 signaling, leading to decreased IL-17 expression and suppressing IMQ-induced dermatitis.</p><p>To investigate whether the mechanism that regulates IMQ-induced dermatitis by <italic>IDO2</italic> is associated with tryptophan metabolites, we measured the concentration of tryptophan metabolites in the ears of treated mice using HPLC. Although the concentration of kynurenine is generally expected to decrease when <italic>IDO2</italic> is deleted, in this study, it was significantly higher in IMQ-treated <italic>IDO2</italic> KO mice than in WT mice. Previous reports have shown that <italic>IDO2</italic> has a low affinity for tryptophan in a cell-free assay, resulting in a lower catalytic efficiency compared with <italic>IDO1</italic> [<xref rid=\"B43-ijms-21-05515\" ref-type=\"bibr\">43</xref>,<xref rid=\"B44-ijms-21-05515\" ref-type=\"bibr\">44</xref>,<xref rid=\"B45-ijms-21-05515\" ref-type=\"bibr\">45</xref>]. Therefore, the mechanism by which <italic>IDO2</italic> regulates IMQ-induced dermatitis might not be associated with tryptophan metabolites, and our results on the metabolites might just reflect the consequences of inflammation caused by IMQ treatment.</p></sec><sec id=\"sec4-ijms-21-05515\"><title>4. Materials and Methods</title><sec id=\"sec4dot1-ijms-21-05515\"><title>4.1. Mice</title><p>All mice used in experiments were 8–10-week-old females. <italic>IDO2</italic> KO mice on a C57BL/6N background were obtained from the Knockout Mouse Project (KOMP, CA, USA). Mice that were homozygous null (<italic>IDO2</italic> KO) by targeted disruption of the <italic>IDO2</italic> gene were selected from the offspring of heterozygous-homozygous mating based on PCR of tail DNA for genotyping. <italic>IDO1</italic> KO mice on a C57BL/6 background were obtained from Jackson Laboratory (Bar Harbor, ME, USA). We purchased WT C57BL/6N mice from Charles River Laboratories (Yokohama, Japan). Mice were housed in the animal facilities of Fujita Health University Graduate School of Medicine under specific pathogen-free conditions, maintained at 25 °C on a standard 12-h light/dark cycle (lights on at 08:00) and with free access to food and water. The protocol for all animal experiments was approved by the Animal Experimentation Committee of Fujita Health University Graduate School of Medicine (AP19067, approved on 20 May 2019). Procedures involving mice and their care conformed to international guidelines, as described in the Principles of Laboratory Animal Care (National Institutes of Health publication 85–23, revised in 1985).</p></sec><sec id=\"sec4dot2-ijms-21-05515\"><title>4.2. IMQ-Induced Psoriasis-Like Inflammation Models</title><p>WT and IDO2 KO mice at 8–10 weeks of age received a daily topical dose of 62.5 mg commercially available IMQ cream (5%) (Mochida Pharmaceutical, Tokyo, Japan) on both ears for 7 consecutive days. Vaseline (Maruishi Pharmaceutical, Osaka, Japan) was used as a vehicle control. To evaluate the severity of inflammation of the ear skin, an objective scoring system was assessed based on the clinical psoriasis area and severity index (PASI), except for the affected area. The ear thickness was measured in millimeters, and erythema and scaling were scored independently on a scale from 0 to 4: 0, none; 1, slight; 2, moderate; 3, marked; 4, very marked. </p></sec><sec id=\"sec4dot3-ijms-21-05515\" sec-type=\"subjects\"><title>4.3. Patients</title><p>This study was approved by the ethical committee of Gifu University and was performed in strict adherence with the Declaration of Helsinki principles (2020-087, approved on 01 Jul 2020). Skin biopsy specimens were taken from healthy volunteers and patients with psoriasis that were diagnosed clinically and histopathologically in our department.</p></sec><sec id=\"sec4dot4-ijms-21-05515\"><title>4.4. RNA Extraction and Quantitative Real-Time PCR</title><p>Total RNA was extracted from ear tissue using Isogen Ⅱ (Nippon Gene, Tokyo, Japan) and reverse transcription-PCR was carried out using ReverTra Ace qPCR RT kits (Toyobo, Osaka, Japan). Quantitative real-time PCR was performed using SYBR Green (SsoAdvanced Universal SYBR Green SuperMix; Bio-Rad, Hercules, CA, USA) with an ABI PRISM-7900HT (Applied Biosystems, Foster City, CA, USA). The mRNA expression of target genes was normalized to Glyceraldehyde-3-phosphate dehydrogenase (GAPDH). The primer sequences were as follows: GAPDH, sense 5′-CAATGTGTCCGTCGTGGATCT-3′, antisense 5′-GTCCTCAGTCTAGCCCAAGATG-3′; IL-17A, sense 5′-AGCTGGACCACCACATGAATTC-3′, antisense 5′-AGCATCTTCTCGACCCTGAAAG-3′; IL-22, sense 5′-GGTGACGACCAGAACATCA-3′, antisense 5′-CAGCAGGTCCAGTTCCCCAAT-3′; IL-23p19, sense 5′-AATAATGTGCCCC GTATCCA-3′, antisense 5′-CATGGGGCTATCAGGGAGTA-3′; IFN-α, sense 5′-CCTGCTGGCTGTGAGGACAT-3′, antisense 5′-GGCTGTGTTTCTTCTTTCTCAGGTA-3′; IL-1β, sense 5′-TCCACCTCAATGGACAGAATATCA-3′, antisense 5′-GGTTCTCCTTGTACAAAGCTCATG-3′; IL-6, sense 5′-ATGGATGCTACCAAACTGGAT-3′, antisense 5′-TGAAGGACTCTGGCTTTGTCT-3′; IL-10, sense 5′-CTGGCTCAGCACTGCTATGC-3′, antisense 5′-CTGGCCCCTGCTGATCCT-3′; TNF-α, sense 5′-GCCAGCCGATGGGTTGT-3′, antisense 5′-GCAGCCTTGTCCCTTGAAGA-3′; CXCL2, sense 5′-TTGCCTTGACCCTGAAGCCCCC-3′, antisense 5′-GGCACATCAGGTACGATC CAGGC-3′; IDO1, sense 5′-AGTTGGGCCTGCCTCCTATTC-3′, antisense 5′-GAAGAAGCCCTTG TCGCAGTC-3′; IDO2, sense 5′-CATACCAGGCAATTGCTCCAC3′, antisense 5′-GCCTGGGCTA AAGAGCTCAATAC-3′.</p></sec><sec id=\"sec4dot5-ijms-21-05515\"><title>4.5. Histology and Immunohistochemistry</title><p>Paraffin-embedded specimens were prepared using standard methods. Sections were stained with hematoxylin and eosin (H & E) and immunohistochemical staining. The primary Abs were anti-IDO1 (ab106134; Abcam, Cambridge, UK), anti-IDO2 (ab214214; Abcam), anti-CD3 (ab5690; Abcam), anti-CD4 (ab183685; Abcam), anti-CD8 (ab203035; Abcam), anti-IL-17 (ab79056; Abcam), and anti-Ki-67 (NB500-170; Novus Biologicals, Centennial, USA). Histofine Simple Stain MAX PO (Nichirei Biosciences, Tokyo, Japan) was used for mouse samples and Dako Envision System-Labeled Polymer HRP (Dako, Tokyo, Japan) for human samples as secondary antibodies. </p></sec><sec id=\"sec4dot6-ijms-21-05515\"><title>4.6. Measurement of Tryptophan Metabolites</title><p>Tryptophan, kynurenine, kynurenic acid, anthranilic acid, and 3-HAA were measured by HPLC (SHIMAZU, Kyoto, Japan). Ear tissue was weighed and homogenized (1:5, <italic>w</italic>/<italic>v</italic>) in 10% perchloric acid. The mixture was centrifuged at 13,000 rpm for 15 min (4 °C). Thereafter, 50 μL of the resulting supernatant was injected into the HPLC instrument for quantification. Tryptophan, kynurenine, kynurenic acid, anthranilic acid, and 3-HAA were eluted from a reverse-phase column by an isocratic method (TSKgel ODS-100 mv 3 μm 4.6 mm [ID] × 15 cm [L]), using a mobile phase with 10 mmol/L sodium acetate and 1.7−2.0% acetonitrile (pH adjusted to 4.5 with acetic acid) at a flow rate of 0.8 mL/min. Tryptophan and kynurenine were measured by a photodiode array detector (SPD-M30A: SHIMAZU, Kyoto, Japan) at wavelengths of 280 nm and 365 nm, respectively. Kynurenic acid, anthranilic acid, and 3-HAA were measured by a fluorescence detector (RF-20Axs: SHIMAZU, Kyoto, Japan) at excitation/emission wavelengths of 320 nm and 420 nm for anthranilic acid and 3-HAA and of 334 nm and 380 nm for kynurenic acid, respectively.</p></sec><sec id=\"sec4dot7-ijms-21-05515\"><title>4.7. Statistical Analysis</title><p>All data are expressed as the mean ± SEM. Statistically significant differences between groups were determined using Student’s <italic>t</italic>-test, one-way ANOVA, or two-way ANOVA. A <italic>p</italic> value of less than 0.05 was considered statistically significant. GraphPad Prism software version 6 was used for all statistical analyses.</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec5-ijms-21-05515\"><title>5. Conclusions</title><p>Our results suggest that IDO2 might modulate the IL-17 signaling pathway, leading to decreased IL-17 expression and suppressing the skin inflammation in IMQ-induced psoriasis-like dermatitis. In addition, IDO2 may provide an effective means of treating psoriasis.</p></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization, K.S. and M.S.; Acquisition of data, K.F., Y.Y. and Y.M.; Data analysis and interpretation, K.F., Y.Y. and K.S.; writing—original draft preparation, K.F.; writing—review and editing, Y.Y., K.S. and M.S.; supervision, Y.Y.; project administration, K.S. and M.S. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research received no external funding.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><glossary><title>Abbreviations</title><array orientation=\"portrait\"><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Imiquimod</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IMQ</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Indoleamine 2,3-dioxygenase 1</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IDO1</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Indoleamine 2,3-dioxygenase 2</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IDO2</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3-hydroxy anthranilic acid</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3-HAA</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">High performance liquid chromatography</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HPLC</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Psoriasis area and severity index</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PASI</td></tr></tbody></array></glossary><ref-list><title>References</title><ref id=\"B1-ijms-21-05515\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Wagner</surname><given-names>E.F.</given-names></name><name><surname>Schonthaler</surname><given-names>H.B.</given-names></name><name><surname>Guinea-Viniegra</surname><given-names>J.</given-names></name><name><surname>Tschachler</surname><given-names>E.</given-names></name></person-group><article-title>Psoriasis: What we have learned from mouse models</article-title><source>Nat. 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(<bold>a</bold>) Immunohistochemical staining of the skin from healthy volunteers and patients with psoriasis using antibodies to <italic>IDO1</italic> and <italic>IDO2</italic>. Scale bar = 100 µm. (<bold>b</bold>) Immunohistochemical staining of the ear from wild-type (WT) mice treated with vehicle or imiquimod (IMQ) for 7 days using antibodies to <italic>IDO1</italic> and <italic>IDO2</italic>. Scale bar = 100 µm. (<bold>c</bold>) The mRNA levels of <italic>IDO1</italic> and <italic>IDO2</italic> in the ears of WT mice treated with vehicle or IMQ for 7 days, detected using quantitative real-time PCR. Data are presented as mean ± SEM. (Student’s <italic>t</italic>-test). Control group, <italic>n</italic> = 6; IMQ-treated group, <italic>n</italic> = 9. (<bold>d</bold>) Erythema, scaling, and thickness of the ear in WT and <italic>IDO1</italic> KO mice treated with vehicle or IMQ for 7 days were evaluated daily (0 = none, 1 = slight, 2 = moderate, 3 = marked, 4 = very marked). ** <italic>p</italic> < 0.01, *** <italic>p</italic> < 0.001 when comparing <italic>IDO1</italic> KO-IMQ with WT-IMQ (two-way ANOVA). Control groups, <italic>n</italic> = 3; IMQ-treated groups, <italic>n</italic> = 6. (<bold>e</bold>) Microscopic evaluation of epidermal hyperplasia of the ear in WT and <italic>IDO1</italic> KO mice treated with vehicle or IMQ for 7 days. Data are presented as mean ± SEM. *** <italic>p</italic> < 0.001 (one-way ANOVA). Control groups, <italic>n</italic> = 3; IMQ-treated groups, <italic>n</italic> = 6. ND: not-detected, WT: wild type, <italic>IDO</italic>: <italic>indoleamine 2,3-dioxygenase</italic>, Con: control, IMQ: imiquimod, KO: knockout.</p></caption><graphic xlink:href=\"ijms-21-05515-g001\"/></fig><fig id=\"ijms-21-05515-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>IMQ-induced psoriasis-like inflammation is significantly more severe in <italic>IDO2</italic> KO mice. (<bold>a</bold>) Macroscopic phenotypic representation of the left ear in WT and <italic>IDO2</italic> KO mice treated with vehicle or IMQ for 7 days. (<bold>b</bold>) Erythema, scaling, and thickness of the ear were evaluated daily (0 = none, 1 = slight, 2 = moderate, 3 = marked, 4 = very marked). Data are representative of three independent experiments. * <italic>p</italic> < 0.05, ** <italic>p</italic> < 0.01, *** <italic>p</italic> < 0.001 when comparing <italic>IDO2</italic> KO-IMQ to WT-IMQ (two-way ANOVA). Control groups, <italic>n</italic> = 4; IMQ-treated groups, <italic>n</italic> = 7. (<bold>c</bold>) Hematoxylin and eosin staining of the ear in WT and <italic>IDO2</italic> KO mice treated with vehicle or IMQ for 7 days. Scale bar = 200 µm. (<bold>d</bold>) Microscopic evaluation of epidermal hyperplasia after 7 days of treatment. Data are presented as mean ± SEM. *** <italic>p</italic> < 0.001 (one-way ANOVA). Control groups, <italic>n</italic> = 6–7; IMQ-treated groups, <italic>n</italic> = 9. WT: wild type, <italic>IDO</italic>: <italic>indoleamine 2,3-dioxygenase</italic>, Con: control, IMQ: imiquimod, KO: knockout.</p></caption><graphic xlink:href=\"ijms-21-05515-g002\"/></fig><fig id=\"ijms-21-05515-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>The deletion of <italic>IDO2</italic> affects the mRNA expression of key cytokines involved in the pathogenesis of psoriasis. Quantitative real-time PCR analysis of the ear before and 24 h, 48 h, and 7 days after IMQ treatment. Data are presented as mean ± SEM. * <italic>p</italic> < 0.05, ** <italic>p</italic> < 0.01 (Student’s <italic>t</italic>-test). <italic>n</italic> = 4–7. WT: wild type, <italic>IDO</italic>: <italic>indoleamine 2,3-dioxygenase</italic>, IMQ: imiquimod, KO: knockout.</p></caption><graphic xlink:href=\"ijms-21-05515-g003\"/></fig><fig id=\"ijms-21-05515-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Keratinocyte proliferation and inflammatory cell infiltration are significantly increased in <italic>IDO2</italic> KO mice treated with IMQ. (<bold>a</bold>) Immunohistochemical staining of the ear of WT and <italic>IDO2</italic> KO mice treated with vehicle or IMQ for 7 days using Ki-67, CD4, CD8, and IL-17 antibody. Ki-67 antibody, scale bar = 50 µm; CD4, CD8, and IL-17 antibody, scale bar = 100 µm. (<bold>b</bold>) The number of Ki67<sup>+</sup> cells in the epidermis and infiltrating CD4<sup>+</sup>, CD8<sup>+</sup>, and IL-17<sup>+</sup> lymphocytes in the dermis per high power field. Data are presented as mean ± SEM. * <italic>p</italic> < 0.05, ** <italic>p</italic> < 0.01, *** <italic>p</italic> < 0.001 (one-way ANOVA). Control groups, <italic>n</italic> = 6–7; IMQ-treated groups, <italic>n</italic> = 9. WT: wild type, <italic>IDO</italic>: <italic>indoleamine 2,3-dioxygenase</italic>, Con: control, IMQ: imiquimod, KO: knockout.</p></caption><graphic xlink:href=\"ijms-21-05515-g004\"/></fig><fig id=\"ijms-21-05515-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>The concentration of tryptophan metabolites is higher in IMQ-treated <italic>IDO2</italic> KO mice than WT mice. The concentration of tryptophan, kynurenine, kynurenic acid, anthranilic acid, and 3-HAA in the ears of WT and <italic>IDO2</italic> KO mice with vehicle or IMQ treatment for 7 days were measured by high performance liquid chromatography. Data are presented as mean ± SEM. * <italic>p</italic> < 0.05, *** <italic>p</italic> < 0.001 (one-way ANOVA). Control groups, <italic>n</italic> = 4; IMQ-treated groups, <italic>n</italic> = 7. 3-HAA: 3-hydroxy anthranilic acid, ND: not-detected, WT: wild type, <italic>IDO</italic>: <italic>indoleamine 2,3-dioxygenase</italic>, Con: control, IMQ: imiquimod, KO: knockout.</p></caption><graphic xlink:href=\"ijms-21-05515-g005\"/></fig></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32756387</article-id><article-id pub-id-type=\"pmc\">PMC7432010</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155548</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05548</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>The LuxI/LuxR-Type Quorum Sensing System Regulates Degradation of Polycyclic Aromatic Hydrocarbons via Two Mechanisms</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-4849-7055</contrib-id><name><surname>Yu</surname><given-names>Zhiliang</given-names></name><xref rid=\"c1-ijms-21-05548\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Hu</surname><given-names>Zeyu</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Xu</surname><given-names>Qimiao</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Zhang</surname><given-names>Mengting</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Yuan</surname><given-names>Nate</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Liu</surname><given-names>Jiongru</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Meng</surname><given-names>Qiu</given-names></name></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-1311-7606</contrib-id><name><surname>Yin</surname><given-names>Jianhua</given-names></name><xref rid=\"c1-ijms-21-05548\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05548\">College of Biotechnology and Bioengineering, Zhejiang University of Technology, Hangzhou 310014, China; <email>2111705001@zjut.edu.cn</email> (Z.H.); <email>18757109635@163.com</email> (Q.X.); <email>2111705006@zjut.edu.cn</email> (M.Z.); <email>yuannate@163.com</email> (N.Y.); <email>17816106774@163.com</email> (J.L.); <email>mengq@zjut.edu.cn</email> (Q.M.)</aff><author-notes><corresp id=\"c1-ijms-21-05548\"><label>*</label>Correspondence: <email>zlyu@zjut.edu.cn</email> (Z.Y.); <email>jianhuay@zjut.edu.cn</email> (J.Y.); Tel./Fax: +86-571-8832-0057 (Z.Y.)</corresp></author-notes><pub-date pub-type=\"epub\"><day>03</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5548</elocation-id><history><date date-type=\"received\"><day>03</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>31</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Members of the <italic>Sphingomonadales</italic> are renowned for their ability to degrade polycyclic aromatic hydrocarbons (PAHs). However, little is known about the regulatory mechanisms of the degradative pathway. Using cross-feeding bioassay, a functional LuxI/LuxR-type acyl-homoserine lactone (AHL)-mediated quorum sensing (QS) system was identified from <italic>Croceicoccus naphthovorans</italic> PQ-2, a member of the order <italic>Sphingomonadales</italic>. Inactivation of the QS system resulted in a significant decrease in PAHs degradation. The QS system positively controlled the expression of three PAH-degrading genes (<italic>ahdA1e</italic>, <italic>xylE</italic> and <italic>xylG</italic>) and a regulatory gene <italic>ardR</italic>, which are located on the large plasmid. Interestingly, the transcription levels of these three PAH-degrading genes were significantly down-regulated in the <italic>ardR</italic> mutant. In addition, bacterial cell surface hydrophobicity and cell morphology were altered in the QS-deficient mutant. Therefore, the QS system in strain PQ-2 positively regulates PAH degradation via two mechanisms: (i) by induction of PAH-degrading genes directly and/or indirectly; and (ii) by an increase of bacterial cell surface hydrophobicity. The findings of this study improve our understanding of how the QS system influences the degradation of PAHs, therefore facilitating the development of new strategies for the bioremediation of PAHs.</p></abstract><kwd-group><kwd>quorum sensing</kwd><kwd>polycyclic aromatic hydrocarbons</kwd><kwd>biodegradation</kwd><kwd>cell surface hydrophobicity</kwd><kwd><italic>Sphingomonadales</italic></kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05548\"><title>1. Introduction</title><p>Quorum sensing (QS) is a process of bacterial cell–cell communication that controls many important population-level behaviors, such as bioluminescence, biofilm formation, antibiotic resistance, and virulence factor production [<xref rid=\"B1-ijms-21-05548\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijms-21-05548\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijms-21-05548\" ref-type=\"bibr\">3</xref>]. Bacteria produce and release signal molecules whose concentration accumulates as bacterial population density increases. When the signal molecules pass a specific threshold, QS alters global patterns of gene expression. In general, Gram-negative bacteria communicate using acyl-homoserine lactones (AHLs) as signal molecules [<xref rid=\"B4-ijms-21-05548\" ref-type=\"bibr\">4</xref>]. AHLs are composed of a homoserine-lactone ring and a 4~18 carbon acyl chain that is occasionally modified by an oxo- or hydroxyl group at the 3-C position. The canonical AHL-mediated QS system was firstly discovered in <italic>Vibrio fischeri</italic> and then identified in many other Gram-negative bacteria. This system consists of two components, LuxI-type and LuxR-type proteins. The LuxI-type proteins are AHL synthases that catalyze the synthesis of AHLs, while LuxR-type proteins are transcription factors responsible for the perception of AHLs. LuxR-family proteins possess an N-terminal AHL-binding domain and a C-terminal DNA-binding domain. In the absence of AHLs, most LuxR proteins are unstable and fail to fold. However, the binding of AHLs to LuxR results in the stabilization and dimerization of LuxR. The LuxR-AHL complex binds to a conserved 20-bp palindrome termed “<italic>lux</italic> box” (5′-NNCT-N<sub>12</sub>-AGNN-3′) and then activates the expression of target genes [<xref rid=\"B4-ijms-21-05548\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijms-21-05548\" ref-type=\"bibr\">5</xref>].</p><p>Polycyclic aromatic hydrocarbons (PAHs) are a large class of hydrophobic organic compounds composed of two or more fused benzene rings arranged in various configurations, such as naphthalene, phenanthrene, anthracene, fluorine, and pyrene [<xref rid=\"B6-ijms-21-05548\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijms-21-05548\" ref-type=\"bibr\">7</xref>]. PAHs are widespread in the environment and most of them are persistent due to their poor aqueous solubility. Importantly, many PAHs are known to be toxic, mutagenic, and carcinogenic. Therefore, it is of great concern to develop efficient methods for removal of PAHs [<xref rid=\"B8-ijms-21-05548\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijms-21-05548\" ref-type=\"bibr\">9</xref>]. The conventional methods, which involve physical and chemical processes, have several drawbacks such as higher treatment cost and incomplete degradation of the pollutants. However, many of these drawbacks can be overcome by the use of bioremediation. It has been characterized that several microbial organisms can degrade PAHs via catabolism. It is especially interesting that the members of the order <italic>Sphingomonadales</italic> are regularly isolated from soils and marine sediments contaminated with PAHs. They are able to degrade various aromatic and/or xenobiotic compounds including PAHs [<xref rid=\"B10-ijms-21-05548\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijms-21-05548\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijms-21-05548\" ref-type=\"bibr\">12</xref>]. Notably, members of the order <italic>Sphingomonadales</italic> contain glycosphingolipids rather than lipopolysaccharides in their outer membrane. This change may increase bacterial cell surface hydrophobicity and thus the degradation efficiency of hydrophobic PAHs [<xref rid=\"B10-ijms-21-05548\" ref-type=\"bibr\">10</xref>].</p><p>Although the biochemical pathways for the degradation of PAHs have been widely studied for many years [<xref rid=\"B9-ijms-21-05548\" ref-type=\"bibr\">9</xref>,<xref rid=\"B12-ijms-21-05548\" ref-type=\"bibr\">12</xref>], little is known concerning the regulatory mechanism of the degradative pathway. Recently, a few studies have shown that the AHL-type QS system is involved in the regulation of PAHs degradation [<xref rid=\"B13-ijms-21-05548\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijms-21-05548\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijms-21-05548\" ref-type=\"bibr\">15</xref>]. The QS system promotes aromatics degradation in <italic>Pseudomonas aeruginosa</italic> CGMCC1.860 but negatively affects phenanthrene removal in <italic>Novosphingobium pentaromativorans</italic> US6-1 [<xref rid=\"B13-ijms-21-05548\" ref-type=\"bibr\">13</xref>,<xref rid=\"B15-ijms-21-05548\" ref-type=\"bibr\">15</xref>]. Therefore, the mechanism underlying QS regulation on PAHs degradation is diverse and may vary among different species of bacteria. </p><p>Several years ago, <italic>Croceicoccus naphthovorans</italic> PQ-2, a member of the family <italic>Erythrobacteraceae</italic> within the order <italic>Sphingomonadales</italic>, was isolated from marine biofilm [<xref rid=\"B16-ijms-21-05548\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijms-21-05548\" ref-type=\"bibr\">17</xref>]. The strain PQ-2 can degrade various PAHs and also produce AHLs [<xref rid=\"B16-ijms-21-05548\" ref-type=\"bibr\">16</xref>]. However, the role of the AHL-type QS system in the degradation of PAHs remains unknown. In this study, we characterized the LuxI/LuxR-type QS system in <italic>C. naphthovorans</italic> PQ-2 and explored the involvement of QS regulation in the degradation of PAHs. We found that the QS system in strain PQ-2 positively regulates not only the transcription of PAH-degradative genes but also bacterial cell surface hydrophobicity.</p></sec><sec sec-type=\"results\" id=\"sec2-ijms-21-05548\"><title>2. Results</title><sec id=\"sec2dot1-ijms-21-05548\"><title>2.1. Identification of an AHL-Type QS System in C. naphthovorans PQ-2</title><p><italic>C. naphthovorans</italic> PQ-2 can produce AHL molecules, but genes responsible for the AHL-type QS systemremain unidentified. According to the genome annotation information from NCBI, we found a pseudogene with a length of 515 bp (AB433_RS00085) that was annotated to encode an autoinducer synthase. This pseudogene contains only a partial coding region. In contrast, the full-length sequence of the gene (locus tag: Ga0111307_123190) can be obtained from the JGI database. This gene has a length of 657 bp and was designated <italic>luxI</italic>. Notably, a transposase gene (AB433_RS00065) and an integrase gene (AB433_RS00100) lie in the flanking sequence of the <italic>luxI</italic> gene.</p><p>To determine whether the gene product of <italic>luxI</italic> is indeed an AHL synthase, the <italic>luxI</italic> gene was expressed in the <italic>Escherichia coli</italic> BL21 (DE3) strain that cannot produce any AHLs [<xref rid=\"B18-ijms-21-05548\" ref-type=\"bibr\">18</xref>]. The <italic>Agrobacterium tumefaciens</italic> A136 was used as a biosensor strain to detect AHLs with medium and long acyl chains [<xref rid=\"B19-ijms-21-05548\" ref-type=\"bibr\">19</xref>]. As expected, the culture extract of <italic>E. coli</italic> harboring the empty plasmid did not affect the biosensor strain. However, the recombinant <italic>E. coli</italic> sample induced a blue coloration on the indicator plate, which results from the expression of <italic>β</italic>-galactosidase in <italic>A. tumefaciens</italic> A136 (<xref ref-type=\"fig\" rid=\"ijms-21-05548-f001\">Figure 1</xref>A). TLC analysis showed that the strain PQ-2 produces three AHLs, C6-HSL and C8-HSL with an oxo- or hydroxyl group at the 3-C position, and an unknown AHL that needs to be further investigated (<xref ref-type=\"fig\" rid=\"ijms-21-05548-f001\">Figure 1</xref>B). Similar results were also observed in the crude AHLs extract from the recombinant <italic>E. coli</italic> cells (<xref ref-type=\"fig\" rid=\"ijms-21-05548-f001\">Figure 1</xref>B).</p><p>To further confirm whether the <italic>luxI</italic> gene is active in <italic>C. naphthovorans</italic> PQ-2, an <italic>rpsL</italic>-based markerless gene deletion system for Sphingomonads was employed to construct the ∆<italic>luxI</italic> strain [<xref rid=\"B20-ijms-21-05548\" ref-type=\"bibr\">20</xref>]. Cross-feeding bioassay results showed that the ∆<italic>luxI</italic> strain failed to induce a blue coloration of <italic>A. tumefaciens</italic> A136, while expression of the <italic>luxI</italic> gene under its native promoter regained the ability to generate a blue coloration (<xref ref-type=\"fig\" rid=\"ijms-21-05548-f001\">Figure 1</xref>C). Therefore, the <italic>luxI</italic> gene is responsible for the synthesis of AHLs in <italic>C. naphthovorans</italic> PQ-2. </p><p>Given that the expression of <italic>luxI</italic> depends on a LuxR-type autoinducer-responsive regulator, the cognate gene for LuxR was identified from the PQ-2 genome. The locus tag Ga0111307_123182, which locates 6.3 kb upstream of the <italic>luxI</italic> gene, was predicted to encode a LuxR homolog. Accordingly, we renamed this gene <italic>luxR</italic>. Similar to the strain lacking <italic>luxI</italic>, the ∆<italic>luxR</italic> strain no longer induced a blue coloration of the biosensor strain, which could be restored by genetic complementation (<xref ref-type=\"fig\" rid=\"ijms-21-05548-f001\">Figure 1</xref>C). In addition, the transcription of <italic>luxI</italic> was significantly reduced in the ∆<italic>luxR</italic> strain (<xref ref-type=\"fig\" rid=\"ijms-21-05548-f001\">Figure 1</xref>D), while the transcription of <italic>luxR</italic> was down-regulated in the ∆<italic>luxI</italic> strain (<xref ref-type=\"fig\" rid=\"ijms-21-05548-f001\">Figure 1</xref>E). Both transcription levels were restored by complementation (<xref ref-type=\"fig\" rid=\"ijms-21-05548-f001\">Figure 1</xref>D,E). Collectively, we can conclude that <italic>C. naphthovorans</italic> PQ-2 possesses a functional AHL-type QS system, which is composed of an AHL synthase LuxI and an autoinducer-responsive regulator LuxR.</p></sec><sec id=\"sec2dot2-ijms-21-05548\"><title>2.2. The AHL-Type QS System Is Crucial for the Degradation of PAHs</title><p>To investigate the degradation of PAHs, <italic>C. naphthovorans</italic> PQ-2 was dropped onto minimal medium plates containing different PAHs (fluorene, phenanthrene, anthracene, fluoranthene, and pyrene) as the sole carbon and energy source. As shown by the results in <xref ref-type=\"fig\" rid=\"ijms-21-05548-f002\">Figure 2</xref>A, growth of PQ-2 was observed on all the plates tested, suggesting that this bacterium can use various PAHs as the sole carbon and energy source. Notably, PQ-2 had the highest degradation ability for phenanthrene and anthracene, which forms a halo around bacterial colonies. Phenanthrene was then used in subsequent studies. </p><p>To explore the involvement of the AHL-type QS system in the degradation of PAHs, growth of the ∆<italic>luxI</italic> and ∆<italic>luxR</italic> strains was measured in liquid culture media. Deletion of <italic>luxI</italic> and <italic>luxR</italic> per se did not affect bacterial growth in nutrient-rich P5Y3 medium (<xref ref-type=\"app\" rid=\"app1-ijms-21-05548\">Figure S1</xref>). However, in minimal medium with phenanthrene as the sole carbon and energy source, the colony-forming units (CFUs) of strains lacking either <italic>luxI</italic> or <italic>luxR</italic> were significantly reduced when compared to the wild type (<xref ref-type=\"fig\" rid=\"ijms-21-05548-f002\">Figure 2</xref>B). CFUs of the ∆<italic>luxI</italic> strain were restored by exogenous addition of AHLs, which were extracted from the wild type of PQ-2. On the contrary, the addition of AHLs was unable to restore the CFUs of the Δ<italic>luxR</italic> strain. The residual phenanthrene in the supernatants was analyzed by HPLC. Both ∆<italic>luxI</italic> and ∆<italic>luxR</italic> strains displayed an obvious reduction in phenanthrene degradation (<xref ref-type=\"fig\" rid=\"ijms-21-05548-f002\">Figure 2</xref>C,D). The degradation ability of the ∆<italic>luxI</italic> strain but not the Δ<italic>luxR</italic> strain was almost reversed to the wild type when supplemented with the AHLs extracted from the PQ-2 (<xref ref-type=\"fig\" rid=\"ijms-21-05548-f002\">Figure 2</xref>C). Moreover, the expression of either <italic>luxI</italic> or <italic>luxR</italic> in the corresponding mutants partially recovered the ability of phenanthrene degradation (<xref ref-type=\"fig\" rid=\"ijms-21-05548-f002\">Figure 2</xref>D). These results collectively indicate that the AHL-mediated QS system plays an important role in the degradation of phenanthrene in <italic>C. naphthovorans</italic> PQ-2.</p></sec><sec id=\"sec2dot3-ijms-21-05548\"><title>2.3. PQ-2 Degrades Phenanthrene through the Salicylic Acid Pathway</title><p>The common microbial pathway for PAHs degradation involves the formation of catechol, which will then be transformed into the tricarboxylic acid (TCA) cycle [<xref rid=\"B8-ijms-21-05548\" ref-type=\"bibr\">8</xref>]. To determine the degradation pathway of phenanthrene in <italic>C. naphthovorans</italic> PQ-2, the degradation products were analyzed by GC-MS (<xref ref-type=\"app\" rid=\"app1-ijms-21-05548\">Figure S2</xref>). After degradation by strain PQ-2 for 48 h, the samples were separated into neutral and acidic fractions. Phenanthrene was detected in the neutral fraction, which results from the incomplete degradation. By contrast, the acidic metabolites of phenanthrene contain salicylic acid and catechol, suggesting that the strain PQ-2 also transforms phenanthrene to the common intermediate catechol.</p></sec><sec id=\"sec2dot4-ijms-21-05548\"><title>2.4. The Large Plasmid Is Responsible for the Degradation of Phenanthrene</title><p>The complete genome of <italic>C. naphthovorans</italic> PQ-2 is composed of a chromosome (3.54 Mb) and two plasmids, P1 (0.19 Mb) and P2 (0.13 Mb). Sequence analysis demonstrated that the large plasmid P1 possesses a putative PAH-degrading cluster (from AB433_RS17995 to AB433_RS18220), which shares high identities with several aromatic compound degradation gene clusters in other <italic>Sphingomonas</italic> strains (<xref ref-type=\"fig\" rid=\"ijms-21-05548-f003\">Figure 3</xref>A). For example, the nucleotide sequence from AB433_RS18095 to AB433_RS18120 of PQ-2 has 95% identity with the <italic>phnIJKN</italic> cluster of <italic>Sphingomonas</italic> sp. 14DN-61, which has been confirmed to be involved in PAHs degradation [<xref rid=\"B21-ijms-21-05548\" ref-type=\"bibr\">21</xref>]. Similar to the aforementioned <italic>luxI</italic> gene, several transposase genes are located in the flanking sequence of the putative PAH-degrading cluster.</p><p>To explore whether the large plasmid P1 is involved in phenanthrene degradation, PQ-2 was treated with rifampicin to eliminate the plasmid. The strain lacking the P1 (ΔP1) was screened in the presence of 20 μg/mL rifampicin. Compared to the wild type, the ΔP1 strain was no longer able to degrade phenanthrene (<xref ref-type=\"fig\" rid=\"ijms-21-05548-f003\">Figure 3</xref>B), indicating that the plasmid P1 is required for phenanthrene degradation.</p></sec><sec id=\"sec2dot5-ijms-21-05548\"><title>2.5. The AHL-Type QS System Regulates the Expression of PAH-Degrading Genes</title><p>To investigate how the AHL-mediated QS system regulates phenanthrene degradation in <italic>C. naphthovorans</italic> PQ-2, we analyzed the transcription levels of genes within the PAH-degrading gene cluster in the Δ<italic>luxI</italic> strain. Based on gene annotation and the degradative pathway of phenanthrene in PQ-2, eight PAH-degrading genes within the cluster were chosen for qRT-PCR analysis, including <italic>ahdA2e</italic>, <italic>ahdA1e</italic>, <italic>bphC</italic>, <italic>xylG</italic>, <italic>xylE</italic>, <italic>xylX</italic>, <italic>xylY</italic> and <italic>ahdA2c</italic>. Among them, the transcription levels of three PAH-degrading genes (<italic>ahdA1e</italic>, <italic>xylE</italic> and <italic>xylG</italic>) were significantly down-regulated in the Δ<italic>luxI</italic> strain, which could be fully (<italic>ahdA1e</italic> and <italic>xylE</italic>) or partially (<italic>xylG</italic>) rescued when supplemented with exogenous AHLs extracted from PQ-2 broth (<xref ref-type=\"fig\" rid=\"ijms-21-05548-f004\">Figure 4</xref>A). The <italic>ahdA1e</italic> gene encodes the <italic>α</italic> subunit of the aromatic ring-hydroxylating dioxygenase, while the <italic>xylE</italic> and <italic>xylG</italic> genes encode a catechol 2, 3 dioxygenase (C23O) and a 2-hydroxymuconic semialdehyde dehydrogenase, respectively. Notably, the promoter region of all three PAH-degrading genes possess the conserved “<italic>lux</italic> box” element.</p><p>We also determined the expression of AB433_RS18080 gene (now designated as <italic>ardR</italic> for aromatic degradation regulator), which is predicted to encode a sigma-54-dependent Fis family transcriptional regulator. As shown by the results in <xref ref-type=\"fig\" rid=\"ijms-21-05548-f004\">Figure 4</xref>A, the expression of <italic>ardR</italic> was dramatically reduced in the absence of <italic>luxI</italic>, which could be partially restored when exogenous addition of the PQ-2 AHLs extract. These results indicate that ArdR may be involved in the regulation of PAHs degradation. To confirm, the <italic>ardR</italic> gene was deleted from the plasmid P1 and then the resultant mutant (Δ<italic>ardR</italic>) was subjected to phenanthrene degradation assay (<xref ref-type=\"fig\" rid=\"ijms-21-05548-f004\">Figure 4</xref>B). Deletion of the <italic>ardR</italic> gene led to a significant decrease in the degradation of phenanthrene, which could be relieved by the expression of the <italic>ardR</italic> gene <italic>in trans</italic>. The upstream sequence of <italic>ardR</italic> also contains a conserved “<italic>lux</italic> box” sequence. Interestingly, the transcription levels of these three PAH-degrading genes were sharply reduced in the Δ<italic>ardR</italic> strain when compared to the wild type (<xref ref-type=\"fig\" rid=\"ijms-21-05548-f004\">Figure 4</xref>C). Relative expression of <italic>ahdA1e</italic> but not <italic>xylE</italic> and <italic>xylG</italic> was partially restored in the complemented version of <italic>ardR</italic> (Δ<italic>ardR</italic><sup>C</sup>) <italic>in trans</italic>.</p></sec><sec id=\"sec2dot6-ijms-21-05548\"><title>2.6. The AHL-Type QS System Affects Bacterial Cell Surface Hydrophobicity</title><p>Bacterial cell surface hydrophobicity is crucial for the degradation of organic pollutants in the environment [<xref rid=\"B15-ijms-21-05548\" ref-type=\"bibr\">15</xref>,<xref rid=\"B22-ijms-21-05548\" ref-type=\"bibr\">22</xref>]. To determine whether the AHL-type QS system affects cell surface properties in <italic>C. naphthovorans</italic> PQ-2, microbial adherence to hydrocarbons (MATH) assay was performed to determine cell surface hydrophobicity (<xref ref-type=\"fig\" rid=\"ijms-21-05548-f005\">Figure 5</xref>A). Compared to the wild type, deletion of the <italic>luxI</italic> gene led to a ~2-fold decrease in the cell surface hydrophobicity, which could be recovered by expression of the <italic>luxI</italic> gene <italic>in trans</italic>. To further investigate this, we measured the content of glycosphingolipids by Enzyme-linked immunosorbent assay (ELISA), but no significant change was observed in all tested strains (<xref ref-type=\"app\" rid=\"app1-ijms-21-05548\">Figure S3</xref>). We next determined the content of hydrophobic proteins on the cell surface using the fluorescent probe bis-ANS (<xref ref-type=\"fig\" rid=\"ijms-21-05548-f005\">Figure 5</xref>B). The Δ<italic>luxI</italic> strain displayed a significant decrease in the bis-ANS fluorescence when compared to the wild type, while expression of the <italic>luxI</italic> gene restored the fluorescence of the complemented strain to the level of the wild type. Therefore, the Δ<italic>luxI</italic> strain has lower amount of hydrophobic proteins than the wild type on the cell surface.</p><p>Cell morphology was also observed by scanning electron microscopy (SEM) (<xref ref-type=\"fig\" rid=\"ijms-21-05548-f005\">Figure 5</xref>C). Results showed that the cell surface morphology of the Δ<italic>luxI</italic> strain was quite different from that of the wild type. The wild type strain exhibited a rough cell surface with many extracellular matrix components, whereas the Δ<italic>luxI</italic> strain had a smooth cell surface without extracellular matrix components.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec3-ijms-21-05548\"><title>3. Discussion</title><p>The biochemical pathways for the degradation of PAHs by microorganisms have been extensively studied in the last few decades [<xref rid=\"B9-ijms-21-05548\" ref-type=\"bibr\">9</xref>,<xref rid=\"B12-ijms-21-05548\" ref-type=\"bibr\">12</xref>]. In comparison, little is known about the regulatory mechanisms of the degradative pathways. Recently, the AHL-type QS system was reported to be involved in the regulation of PAHs degradation in some Gram-negative bacteria. The QS system positively controls the degradation of PAHs in <italic>P. aeruginosa</italic> strains through either biofilm formation or induction of key degradative genes [<xref rid=\"B13-ijms-21-05548\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijms-21-05548\" ref-type=\"bibr\">14</xref>,<xref rid=\"B23-ijms-21-05548\" ref-type=\"bibr\">23</xref>]. In this study, we characterized an AHL-type QS system from <italic>C. naphthovorans</italic> PQ-2, a member of the order <italic>Sphingomonadales.</italic> This QS system positively regulates PAHs degradation not only by the induction of PAH-degrading genes but also by an increase of cell surface hydrophobicity (<xref ref-type=\"fig\" rid=\"ijms-21-05548-f006\">Figure 6</xref>).</p><p>The <italic>Sphingomonadaceae</italic>, the largest family within the order <italic>Sphingomonadales</italic>, are renowned for their ability to degrade recalcitrant compounds and xenobiotics including PAHs [<xref rid=\"B10-ijms-21-05548\" ref-type=\"bibr\">10</xref>,<xref rid=\"B24-ijms-21-05548\" ref-type=\"bibr\">24</xref>]. <italic>C. naphthovorans</italic> PQ-2 belongs to the family <italic>Erythrobacteraceae</italic>, which is the second largest family within the order <italic>Sphingomonadales</italic> [<xref rid=\"B17-ijms-21-05548\" ref-type=\"bibr\">17</xref>]. Our results demonstrate that <italic>C. naphthovorans</italic> PQ-2 can degrade PAHs containing three and four rings, such as phenanthrene, anthracene, fluorene, fluoranthene and pyrene. Among them, PQ-2 has the highest degradation ability for three-ring molecules phenanthrene and anthracene. These two PAHs are known to be more susceptible to biodegradation, since they are more volatile and more soluble in water [<xref rid=\"B25-ijms-21-05548\" ref-type=\"bibr\">25</xref>]. It is unclear whether PQ-2 is capable of using PAHs with five or more than five rings.</p><p>The genes involved in PAHs degradation in the <italic>Sphingomonadaceae</italic> are very often located on large plasmids (megaplasmids) [<xref rid=\"B10-ijms-21-05548\" ref-type=\"bibr\">10</xref>,<xref rid=\"B23-ijms-21-05548\" ref-type=\"bibr\">23</xref>,<xref rid=\"B26-ijms-21-05548\" ref-type=\"bibr\">26</xref>]. Consistently, our results show that the large plasmid P1 in <italic>C. naphthovorans</italic> PQ-2 is responsible for the degradation of phenanthrene. This megaplasmid possesses a PAH-degrading gene cluster which is composed of 32 open reading frames. There are several transposase genes located in the flanking regions of the gene cluster, implying that this cluster may be transferred horizontally from other bacteria. The degradative pathway for phenanthrene in <italic>C. naphthovorans</italic> PQ-2 is also in agreement with that in the members of the family <italic>Sphingomonadaceae</italic> [<xref rid=\"B8-ijms-21-05548\" ref-type=\"bibr\">8</xref>,<xref rid=\"B10-ijms-21-05548\" ref-type=\"bibr\">10</xref>,<xref rid=\"B27-ijms-21-05548\" ref-type=\"bibr\">27</xref>]. Under aerobic conditions, the aromatic ring-hydroxylating dioxygenase (including the <italic>α</italic> subunit AhdA1e and <italic>β</italic> subunit AhdAle2) catalyzes the initial reaction in the degradative pathway, resulting in the modification of the aromatic ring. After several steps, <italic>C. naphthovorans</italic> PQ-2 degrades phenanthrene to the common intermediate catechol. Subsequently, the catechol 2, 3 dioxygenase XylE and 2-hydroxymuconic semialdehyde dehydrogenase XylG convert catechol to 2-hydroxymuconate, which will be further transformed to TCA cycle (<xref ref-type=\"fig\" rid=\"ijms-21-05548-f006\">Figure 6</xref>).</p><p>Although the research on regulation of the PAH-degrading pathways is limited, the existing studies demonstrated that the AHL-type QS system is involved in the regulation of PAHs degradation process in <italic>P. aeruginosa</italic> CGMCC1.860 [<xref rid=\"B13-ijms-21-05548\" ref-type=\"bibr\">13</xref>]. The AHL-type QS system in this bacterium directly and indirectly controls the expression of catechol 2,3-dioxygenase gene, resulting in the enhanced aromatics biodegradation [<xref rid=\"B13-ijms-21-05548\" ref-type=\"bibr\">13</xref>]. Similar results were observed in our studies on <italic>C. naphthovorans</italic> PQ-2. The AHL-type QS system in <italic>C. naphthovorans</italic> PQ-2 positively regulates the expression of three PAH-degrading genes (including <italic>ahdA1e</italic>, <italic>xylE</italic> and <italic>xylG</italic>), as well as the regulatory gene <italic>ardR</italic>. It is worth mention that the upstream regions of these genes contain a conserved “<italic>lux</italic> box” sequence (5′-NNCT-N<sub>12</sub>-AGNN-3′). Therefore, the LuxR-AHL complex may bind to the “<italic>lux</italic> box” site and then directly regulate the expression of these target genes, thus affecting phenanthrene degradation (<xref ref-type=\"fig\" rid=\"ijms-21-05548-f006\">Figure 6</xref>). </p><p>The transcription of the three PAH-degradative genes is also regulated by the <italic>ardR</italic> gene, which is located within the PAH-degrading gene cluster. The transcriptional regulators for PAHs degradation have been reported in several <italic>Pseudomonas</italic> and <italic>Novosphingobium</italic> species [<xref rid=\"B13-ijms-21-05548\" ref-type=\"bibr\">13</xref>,<xref rid=\"B28-ijms-21-05548\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijms-21-05548\" ref-type=\"bibr\">29</xref>]. It is also possible that the AHL-type QS system in <italic>C. naphthovorans</italic> PQ-2 indirectly modulates the expression of PAH-degrading genes, which is mediated by the transcriptional regulator ArdR (<xref ref-type=\"fig\" rid=\"ijms-21-05548-f006\">Figure 6</xref>). The expression of PAH-degrading genes may be regulated in a hierarchical manner. The AHL-mediated QS system is at the apex of the regulatory cascade, while the specific transcriptional regulator sets up the second cascade driving the expression of PAH-degrading genes.</p><p>In addition to up-regulation of the degradative enzymes, our results show that the AHL-type QS system in <italic>C. naphthovorans</italic> PQ-2 also positively controls bacterial cell surface hydrophobicity. Many studies have demonstrated that there is a positive correlation between cell surface hydrophobicity and PAHs degradation [<xref rid=\"B15-ijms-21-05548\" ref-type=\"bibr\">15</xref>,<xref rid=\"B22-ijms-21-05548\" ref-type=\"bibr\">22</xref>,<xref rid=\"B30-ijms-21-05548\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijms-21-05548\" ref-type=\"bibr\">31</xref>]. PAH-degrading bacteria normally have high cell surface hydrophobicity, which stimulates the direct attachment of bacterial cells to hydrophobic surface including PAHs and the partition of dissolved PAHs to the cell surface [<xref rid=\"B22-ijms-21-05548\" ref-type=\"bibr\">22</xref>]. As a result, PAHs enter into bacterial cell for biodegradation. Thus, the QS system of <italic>C. naphthovorans</italic> PQ-2 facilitates the attachment of bacterial cells to PAHs and then enhances the uptake of PAHs. Once PAHs enter into the cytoplasm of the bacterial cell, they will induce the expression of PAH-degrading genes and then the degradation of PAHs (<xref ref-type=\"fig\" rid=\"ijms-21-05548-f006\">Figure 6</xref>). Due to the presence of glycosphingolipids, the cell surface of sphingomonads is more hydrophobic than those of other bacteria [<xref rid=\"B10-ijms-21-05548\" ref-type=\"bibr\">10</xref>]. However, the enhanced cell surface hydrophobicity in <italic>C. naphthovorans</italic> PQ-2 is not derived from glycosphingolipids, but related to hydrophobic proteins on the cell surface. There are many factors that can affect cell surface hydrophobicity. For example, it has been reported that biosurfactants secreted by <italic>Bacillus subtilis</italic> and <italic>P. aeruginosa</italic> enhance bacterial cell surface hydrophobicity, resulting in higher uptake and use of pyrene [<xref rid=\"B22-ijms-21-05548\" ref-type=\"bibr\">22</xref>,<xref rid=\"B32-ijms-21-05548\" ref-type=\"bibr\">32</xref>]. The detailed mechanism in <italic>C. naphthovorans</italic> PQ-2 needs to be further investigated. </p><p>In contrast to our results, a recent study demonstrated that the AHL-type QS system negatively regulates phenanthrene removal in <italic>N. pentaromativorans</italic> US6-1, a member belongs to the family <italic>Sphingomonadaceae</italic> [<xref rid=\"B15-ijms-21-05548\" ref-type=\"bibr\">15</xref>]. Deletion of the AHL-type QS system in US6-1 increases phenanthrene removal efficiency. This phenomenon is also related to PAH-degrading genes, since the relative expression levels of 12 PAH-degrading genes are up-regulated in QS-deficient mutants. Interestingly, QS-deficient mutants have significantly higher cell surface hydrophobicity, which partially results from the increase of hydrophobic glycosphingolipids. Therefore, the two AHL-type QS systems play an opposite role in the regulation of PAHs degradation in <italic>N. pentaromativorans</italic> US6-1 and <italic>C. naphthovorans</italic> PQ-2. At present, the underlying mechanism remains unclear. It should be noted that <italic>N. pentaromativorans</italic> US6-1 was isolated from muddy sediment [<xref rid=\"B33-ijms-21-05548\" ref-type=\"bibr\">33</xref>], while <italic>C. naphthovorans</italic> PQ-2 was isolated from marine biofilm [<xref rid=\"B17-ijms-21-05548\" ref-type=\"bibr\">17</xref>]. Biofilm-based bioremediation is one of the most efficient approaches for the decontamination of pollutants [<xref rid=\"B14-ijms-21-05548\" ref-type=\"bibr\">14</xref>,<xref rid=\"B34-ijms-21-05548\" ref-type=\"bibr\">34</xref>]. Thus, <italic>C. naphthovorans</italic> PQ-2 has a great potential in bioremediation of PAHs.</p></sec><sec id=\"sec4-ijms-21-05548\"><title>4. Materials and Methods</title><sec id=\"sec4dot1-ijms-21-05548\"><title>4.1. Bacterial Strains, Plasmids, Primers and Culture Conditions</title><p>The bacterial strains and plasmids used in this study are listed in <xref rid=\"ijms-21-05548-t001\" ref-type=\"table\">Table 1</xref>. The primers used in this study are listed in <xref ref-type=\"app\" rid=\"app1-ijms-21-05548\">Table S1</xref>. <italic>E. coli</italic> and <italic>A. tumefaciens</italic> A136 (pCF218/pCF372) were grown in Luria-Bertani (LB) medium at 37 °C and 30 °C, respectively. For cross-feeding bioassay, <italic>C. naphthovorans</italic> PQ-2 was cultured in P5Y3 medium at 30 °C [<xref rid=\"B17-ijms-21-05548\" ref-type=\"bibr\">17</xref>]. For biodegradation assay, <italic>C</italic>. <italic>naphthovorans</italic> was cultivated at 30 °C in marine minimal medium supplemented with phenanthrene (200 mg/L) [<xref rid=\"B17-ijms-21-05548\" ref-type=\"bibr\">17</xref>]. When needed, the medium was supplemented with chemicals at the following concentrations: ampicillin (Amp), 100 μg/mL; gentamycin (Gm), 50 μg/mL; kanamycin (Km), 50 μg/mL; streptomycin (Sm), 100 μg/mL; 2,6-diaminopimelic acid (DAP), 0.3 mmol/L.</p></sec><sec id=\"sec4dot2-ijms-21-05548\"><title>4.2. Expression of AHL Synthase of Strain PQ-2 in E. coli</title><p>The full-length sequence of <italic>luxI</italic> encoding AHL synthase was obtained from the JGI database (locus tag: Ga0111307_123190). PCR amplification was performed with the primers containing restriction enzyme sites (<xref ref-type=\"app\" rid=\"app1-ijms-21-05548\">Table S1</xref>). The resulting PCR product was cloned into the expression vector pET-28b(+), and then the recombinant plasmid pET-28b(+)-<italic>luxI</italic> was transformed into <italic>E. coli</italic> BL21 (DE3). Finally, the recombinant vector was verified by sequencing.</p></sec><sec id=\"sec4dot3-ijms-21-05548\"><title>4.3. Extraction and Analysis of AHLs</title><p>The AHLs produced by <italic>E. coli</italic> and <italic>C. naphthovorans</italic> were extracted from culture supernatants as previously described [<xref rid=\"B36-ijms-21-05548\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijms-21-05548\" ref-type=\"bibr\">37</xref>]. 100 mL of overnight bacterial culture was centrifuged at 8000 rpm for 10 min. The supernatant was extracted three times with an equal volume of acidified ethyl acetate. The combined extract from the organic phase was evaporated on a rotatory evaporator at 50 °C. The dried residue was dissolved into 1 mL of acidified ethyl acetate and stored at −20 °C. </p><p>The profile of AHLs was determined using thin layer chromatography (TLC) [<xref rid=\"B36-ijms-21-05548\" ref-type=\"bibr\">36</xref>]. Briefly, the separation of AHL extracts was carried out on a TLC plate (TLC aluminum sheets, 20 cm × 20 cm, Silica gel 60 F254; Merck, Germany) using methanol–ultrapure water (60:40, <italic>v</italic>/<italic>v</italic>) as mobile phase. After chromatography and air-dry, the plate was covered by LB agar containing the reporter strain <italic>A. tumefaciens</italic> A136, which is capable of sensing medium-/long-chain AHLs [<xref rid=\"B19-ijms-21-05548\" ref-type=\"bibr\">19</xref>]. X-gal was then spread onto the LB agar. The profile of AHLs was analyzed after incubation for 24 h at 30 °C. All standard AHLs including C6-HSL, C8-HSL, C10-HSL, 3-OH-C6-HSL, 3-oxo-C6-HSL, 3-OH-C8-HSL, and 3-oxo-C8-HSL were purchased from Sigma (Bejing, China).</p></sec><sec id=\"sec4dot4-ijms-21-05548\"><title>4.4. Cross-Feeding Bioassay</title><p>The AHLs produced by bacteria were measured by cross-feeding bioassay as described elsewhere [<xref rid=\"B16-ijms-21-05548\" ref-type=\"bibr\">16</xref>,<xref rid=\"B36-ijms-21-05548\" ref-type=\"bibr\">36</xref>]. The tested bacteria and the biosensor <italic>A. tumefaciens</italic> A136 were streaked in parallel on LB-P5Y3 plate (containing equal amounts of LB and P5Y3) supplemented with X-gal (40 μg/mL). The cross-feeding plate was then cultivated at 30 °C for 12 h before color visualization. </p><p>For analysis of AHL extracts, 2 μL of each AHL extract was dropped onto a sterile filter paper on the indicator plate. The plate was incubated for 12 h at 30 °C and then photographed. C8-HSL and acidified ethyl acetate were used as positive control and negative control, respectively.</p></sec><sec id=\"sec4dot5-ijms-21-05548\"><title>4.5. Markerless Gene Deletion Mutagenesis and Complementation</title><p>The gene deletion mutant of <italic>C. naphthovorans</italic> PQ-2 was constructed by an <italic>rpsL</italic>-based markerless gene deletion system [<xref rid=\"B20-ijms-21-05548\" ref-type=\"bibr\">20</xref>] with a little modification. In brief, two fragments flanking the target gene were amplified by PCR with primers containing the restriction enzyme sites and then were joined by fusion PCR (<xref ref-type=\"app\" rid=\"app1-ijms-21-05548\">Table S1</xref>). The fusion fragment and the suicide plasmid pAK405 were ligated together by T4 DNA ligase and transformed into <italic>E. coli</italic> WM3064 (DAP auxotroph) [<xref rid=\"B35-ijms-21-05548\" ref-type=\"bibr\">35</xref>]. The resulting plasmid was transferred into <italic>C. naphthovorans</italic> PQ-2 via conjugation. Integration of the mutagenesis constructs into the chromosome was selected by resistance to kanamycin and verified by PCR. The correct transconjugant was grown in P5Y3 broth for three days and then plated onto the P5Y3 plate supplemented with streptomycin. Kanamycin-sensitive and streptomycin-resistant colonies were screened by PCR for deletion of the targeted gene. Finally, the deletion mutations were verified by sequencing.</p><p>The broad-host-range plasmid pBBR1MCS-5 was used for the genetic complementation of mutants [<xref rid=\"B36-ijms-21-05548\" ref-type=\"bibr\">36</xref>]. A fragment containing the gene of interest and its native promoter was amplified by PCR and then cloned into pBBR1MCS-5. The resulting recombinant plasmid was then transferred into the corresponding mutant via conjugation.</p></sec><sec id=\"sec4dot6-ijms-21-05548\"><title>4.6. RNA Isolation and Quantitative Real-Time PCR (qRT-PCR)</title><p>Total RNA from <italic>C. naphthovorans</italic> strains was isolated using the RNAiso Plus Kit (TaKaRa, Dalian, China) according to the manufacturer’s instructions. The qRT-PCR analysis was performed with a CFX Connect Real-Time PCR Detection System (BioRad, Hercules, CA, USA) as described previously [<xref rid=\"B36-ijms-21-05548\" ref-type=\"bibr\">36</xref>]. The cycle threshold (<italic>C<sub>T</sub></italic>) values for each gene of interest were normalized against the <italic>C<sub>T</sub></italic> values of the 16S rRNA gene. The relative expression level of each gene of interest was determined from three replicates in a single experiment.</p></sec><sec id=\"sec4dot7-ijms-21-05548\"><title>4.7. Analysis of Phenanthrene and Its Metabolites</title><p>For phenanthrene analysis, the sample was extracted with ethyl acetate and dried under vacuum. Then it was dissolved in acetonitrile, filtrated, and subjected to HPLC analysis [<xref rid=\"B38-ijms-21-05548\" ref-type=\"bibr\">38</xref>]. The column for the measurement was InertsilODS-3 C18 column (4.6 × 250 mm, 5 μm), and the mobile phase was methanol/water (90:10) at a flow rate of 1.0 mL/min using a Waters 2487 dual-wavelength detector. </p><p>The phenanthrene metabolites were analyzed by gas chromatography–mass spectrometry (GC-MS), as previously described [<xref rid=\"B39-ijms-21-05548\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijms-21-05548\" ref-type=\"bibr\">40</xref>]. For brief, phenanthrene was allowed to degrade for 48 h. The culture was centrifuged, and the supernatant was then acidified to pH 2.3 with HCl and extracted with ethyl acetate. The organic phase was extracted three times with an equal volume of NaOH (10 mmol/L). The remaining organic phase was dried over anhydrous sodium sulfated and concentrated to 5mLof ethyl acetate (neutral fraction). The aqueous phase was acidified to pH 2.3 with HCl and extracted with ethyl acetate (acidic fraction). The GC-MS analysis of neutral and acidic fractions was carried out with/without derivatization. </p></sec><sec id=\"sec4dot8-ijms-21-05548\"><title>4.8. Microbial Adhesion to Hydrocarbons (MATH) Test</title><p>The MATH test was carried out to determine bacterial cell surface hydrophobicity as described previously [<xref rid=\"B15-ijms-21-05548\" ref-type=\"bibr\">15</xref>,<xref rid=\"B41-ijms-21-05548\" ref-type=\"bibr\">41</xref>] with some modifications. After incubation, bacterial cultures were resuspended in phosphate-buffered saline (PBS) and the OD<sub>600</sub> values were measured (OD<sub>0</sub>). Then, 4 mL of xylene was added to 8 mL of the suspension. The mixtures were vortexed for 15 s, and allowed phase separation for 20 min at room temperature. Subsequently, the OD<sub>600</sub> of the aqueous phase of the suspension was measured (OD<sub>1</sub>). The cell surface hydrophobicity values were calculated according to the following equation:<disp-formula>Cell surface hydrophobicity (%) = [(OD<sub>0</sub> − OD<sub>1</sub>)/OD<sub>0</sub>] × 100%.<label>(1)</label></disp-formula></p></sec><sec id=\"sec4dot9-ijms-21-05548\"><title>4.9. Determination of Hydrophobic Proteins Using Fluorescent Dye</title><p>The fluorescent probe bis (8-anilinonaphthalene-1-sulfonate) (bis-ANS) was used to determine hydrophobic proteins [<xref rid=\"B42-ijms-21-05548\" ref-type=\"bibr\">42</xref>]. Bacterial cultures were resuspended in PBS (pH = 7.4) and the bacterial cell density was adjusted to 0.15 at OD<sub>600</sub>. 30 µL bis-ANS was added to 170 µL of these samples to achieve a final concentration of 5 µmol/L. The fluorescence intensity of bis-ANS was measured by the UV-Vis spectrophotometer (Molecular Devices SpectraMax M4, Sunnyvale, CA, USA). The excitation and emission wavelengths were 385 nm and 530 nm, respectively.</p></sec><sec id=\"sec4dot10-ijms-21-05548\"><title>4.10. Scanning Electron Microscopy (SEM)</title><p><italic>C. naphthovorans</italic> strains were cultivated in marine minimal medium supplemented with phenanthrene. 1 mL of bacterial culture was centrifuged at 4000 rpm for 5 min. The pellet was fixed with 4% (<italic>v</italic>/<italic>v</italic>) glutardialdehyde overnight at 4 °C, washed with PBS and dehydrated by passage through a graded ethanol series (30%, 50%, 70%, 80%, 90%, and 100%, <italic>v</italic>/<italic>v</italic>), subsequently dehydrated with liquid carbon dioxide and coated with platinum powder. The bacterial cell morphology was observed with SEM (Hitachi SU8010, Tokyo,Japan) [<xref rid=\"B43-ijms-21-05548\" ref-type=\"bibr\">43</xref>].</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec5-ijms-21-05548\"><title>5. Conclusions</title><p>Sphingomonads are biotechnologically interesting organisms for their potential in the bioremediation of PAHs. This study indicates that the AHL-mediated QS system in <italic>C. naphthovorans</italic> PQ-2 positively regulates the degradation of PAHs via two mechanisms: (i) by induction of PAH-degrading genes expression directly and/or indirectly and (ii) by an increase of cell surface hydrophobicity. The findings of this study improve our understanding of the involvement of QS regulation in the degradation of PAHs in Sphingomonads, therefore facilitating the development of new strategies for the bioremediation of PAHs.</p></sec></body><back><app-group><app id=\"app1-ijms-21-05548\"><title>Supplementary Materials</title><p>Supplementary Materials can be found at <uri xlink:href=\"https://www.mdpi.com/1422-0067/21/15/5548/s1\">https://www.mdpi.com/1422-0067/21/15/5548/s1</uri>.</p><supplementary-material content-type=\"local-data\" id=\"ijms-21-05548-s001\"><media xlink:href=\"ijms-21-05548-s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>Conceptualization, Z.Y., Q.M. and J.Y.; investigation, Z.H., Q.X., M.Z., N.Y. and J.L.; writing—original draft preparation, Z.Y., Z.H. and J.Y.; writing—review and editing, Z.Y., Q.M. and J.Y.; visualization, J.Y.; supervision, Z.Y. and J.Y.; project administration, Z.Y.; funding acquisition, Z.Y. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was supported by Zhejiang Provincial Natural Science Foundation of China (LY20C010002) and the National Natural Science Foundation of China (31670114).</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><glossary><title>Abbreviations</title><array orientation=\"portrait\"><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">QS</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Quorum Sensing</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AHL</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Acylated Homoserine Lactone</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PAHs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Polycyclic Aromatic Hydrocarbons</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CSH</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cell Surface Hydrophobicity</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DAP</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2,6-Daminopimelic Acid</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MATH</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Microbial Adhesion to Hydrocarbons</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CFUs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Colony-forming Units</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">OD</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Optical Denstity</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TCA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tricarboxylic Acid</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SEM</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Scanning Electron Microscopy</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TLC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Thin Layer Chromatography</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ArdR</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Aromatic Degradation Regulator</td></tr></tbody></array></glossary><ref-list><title>References</title><ref id=\"B1-ijms-21-05548\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Bassler</surname><given-names>B.L.</given-names></name><name><surname>Losick</surname><given-names>R.</given-names></name></person-group><article-title>Bacterially speaking</article-title><source>Cell</source><year>2006</year><volume>125</volume><fpage>237</fpage><lpage>246</lpage><pub-id pub-id-type=\"doi\">10.1016/j.cell.2006.04.001</pub-id><pub-id pub-id-type=\"pmid\">16630813</pub-id></element-citation></ref><ref id=\"B2-ijms-21-05548\"><label>2.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Rutherford</surname><given-names>S.T.</given-names></name><name><surname>Bassler</surname><given-names>B.L.</given-names></name></person-group><article-title>Bacterial quorum sensing: Its role in virulence and possibilities for its control</article-title><source>Cold Spring Harb. 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M1~M3, standard AHLs; S1, AHLs extracted from PQ-2; S2, AHLs extracted from <italic>E. coli</italic> BL21 expressing <italic>luxI</italic>. (<bold>C</bold>) Cross-feeding bioassay for detection of AHLs in <italic>C. naphthovorans</italic> strains. Δ<italic>luxI</italic><sup>C</sup> and Δ<italic>luxR</italic><sup>C</sup> represent the complemented version of Δ<italic>luxI</italic> and Δ<italic>luxR</italic>, respectively. (<bold>D</bold>,<bold>E</bold>) Relative expression levels of <italic>luxI</italic> (<bold>D</bold>) and <italic>luxR</italic> (<bold>E</bold>). The transcription level of the wild type was defined as 1.0. Experiments were performed in three biological replicates, and similar trends were observed. The representative data from three separate experiments are shown. Statistical significance of differences was analyzed by <italic>t</italic>-test; **, <italic>p</italic> < 0.01; ***, <italic>p</italic> < 0.001.</p></caption><graphic xlink:href=\"ijms-21-05548-g001\"/></fig><fig id=\"ijms-21-05548-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Effects of the QS system on phenanthrene degradation. (<bold>A</bold>) The degradation of PAHs by <italic>C. naphthovorans</italic> PQ-2. (<bold>B</bold>) Growth of <italic>C. naphthovorans</italic> strains in minimal medium containing phenanthrene as the sole carbon and energy source. Colony forming units (CFUs) were determined for each sample. (<bold>C</bold>,<bold>D</bold>) Percentage of residual phenanthrene in the medium after incubation for 72 h. Experiments were performed in three biological replicates, and similar trends were observed. The representative data from three separate experiments are shown. Statistical significance of differences was analyzed by <italic>t</italic>-test: *, <italic>p</italic> < 0.05; **, <italic>p</italic> < 0.01; ***, <italic>p</italic> < 0.001.</p></caption><graphic xlink:href=\"ijms-21-05548-g002\"/></fig><fig id=\"ijms-21-05548-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>The large plasmid P1 is essential for phenanthrene degradation. (<bold>A</bold>) Analysis of the PAH-degrading gene clusters in <italic>C. naphthovorans</italic> PQ-2. The red arrows show the “<italic>lux</italic> box” site in the promoter region of several PAH-degrading genes. (<bold>B</bold>) Effect of plasmid P1 removal on phenanthrene degradation.</p></caption><graphic xlink:href=\"ijms-21-05548-g003\"/></fig><fig id=\"ijms-21-05548-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>The QS system regulates the expression of PAH-degrading genes. (<bold>A</bold>) The relative transcriptional levels of PAH-degrading genes in the Δ<italic>luxI</italic> strain with or without exogenous AHLs. (<bold>B</bold>) Percentage of residual phenanthrene after three days degradation by the Δ<italic>ardR</italic> and its complemented version (Δ<italic>ardR</italic><sup>C</sup>). (<bold>C</bold>) The relative transcriptional levels of PAH-degrading genes in the Δ<italic>ardR</italic>. Experiments were performed in three biological replicates, and similar trends were observed. The representative data from three separate experiments are shown. Statistical significance of differences was analyzed by <italic>t</italic>-test: *, <italic>p</italic> < 0.05; **, <italic>p</italic> < 0.01; ***, <italic>p</italic> < 0.001.</p></caption><graphic xlink:href=\"ijms-21-05548-g004\"/></fig><fig id=\"ijms-21-05548-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>The QS system regulates bacterial cell surface properties. (<bold>A</bold>) Cell surface hydrophobicity of the Δ<italic>luxI</italic> and its complemented version (Δ<italic>luxI</italic><sup>C</sup>). (<bold>B</bold>) Bis-ANS fluorescence intensity. (<bold>C</bold>) Scanning electron micrographs. For (<bold>A</bold>,<bold>B</bold>), experiments were performed in three biological replicates, and similar trends were observed. The representative data from three separate experiments are shown. Statistical significance of differences was analyzed by <italic>t</italic>-test: **, <italic>p</italic> < 0.01; ***, <italic>p</italic> < 0.001.</p></caption><graphic xlink:href=\"ijms-21-05548-g005\"/></fig><fig id=\"ijms-21-05548-f006\" orientation=\"portrait\" position=\"float\"><label>Figure 6</label><caption><p>Model for QS regulation on phenanthrene degradation in <italic>C. naphthovorans</italic> PQ-2. The QS system positively regulates PAH degradation via multiple mechanisms. The LuxR-AHL complex may bind to the “<italic>lux</italic> box” and then directly regulate the transcriptional levels of three PAH-degrading genes, including <italic>ahdA1e</italic>, <italic>xylE</italic> and <italic>xylG</italic>. The LuxR-AHL complex may also indirectly modulate the expression of these PAH-degrading genes, which is mediated by the transcriptional regulator ArdR. Moreover, the QS system enhances bacterial cell surface hydrophobicity (CSH), thus affecting the attachment of cells to PAHs. Similar to other AHL-type QS systems, the LuxR-AHL complex in PQ-2 has a positive feedback effect on the expression of <italic>luxR</italic> and <italic>luxI</italic>. For clarity, the secretion and uptake of AHLs are omitted in the model.</p></caption><graphic xlink:href=\"ijms-21-05548-g006\"/></fig><table-wrap id=\"ijms-21-05548-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05548-t001_Table 1</object-id><label>Table 1</label><caption><p>Strains and plasmids used in this study.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Strain or Plasmid</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Description</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Source or Reference</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Strains</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>C. naphthovorans</italic> PQ-2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Wild type</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B16-ijms-21-05548\" ref-type=\"bibr\">16</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>A. tumefaciens</italic> A136 (pCF218/pCF372)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">biosensor strain for medium-/long-chain AHLs</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B19-ijms-21-05548\" ref-type=\"bibr\">19</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>E. coli</italic> DH5α</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Host strain for cloning</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Lab stock</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>E. coli</italic> BL21(DE3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Expression host for pET28b(+)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TransGen Biotech</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>E. coli</italic> WM3064</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Donor strain for conjugation</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B35-ijms-21-05548\" ref-type=\"bibr\">35</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Δ<italic>luxI</italic></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mutant of strain PQ-2 with deletion of <italic>luxI</italic></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">This study</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Δ<italic>luxR</italic></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mutant of strain PQ-2 with deletion of <italic>luxR</italic></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">This study</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Δ<italic>ardR</italic></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mutant of strain PQ-2 with deletion of <italic>ardR</italic></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">This study</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ΔP1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mutant of strain PQ-2 with deletion of the large plasmid</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">This study</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Δ<italic>luxI</italic><sup>C</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Complemented strain of Δ<italic>luxI</italic></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">This study</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Δ<italic>luxR</italic><sup>C</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Complemented strain of Δ<italic>luxR</italic></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">This study</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Δ<italic>ardR</italic><sup>C</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Complemented strain of Δ<italic>ardR</italic></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">This study</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Plasmids</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">pBBR1MCS-5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Gm<sup>r</sup>, broad-host vector</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B36-ijms-21-05548\" ref-type=\"bibr\">36</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">pET-28b(+)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">T7 expression vector</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Novagen</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">pET-28b(+)-<italic>luxI</italic></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">pET-28b(+) containing AHL synthase LuxI</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">This study</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">pAK405</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Km<sup>r</sup>, sphingomonad suicide vector</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B20-ijms-21-05548\" ref-type=\"bibr\">20</xref>]</td></tr></tbody></table></table-wrap></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32731541</article-id><article-id pub-id-type=\"pmc\">PMC7432011</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155435</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05435</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Disability-Related Questions for Administrative Datasets</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Madden</surname><given-names>Rosamond H.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05435\">1</xref><xref rid=\"c1-ijerph-17-05435\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Lukersmith</surname><given-names>Sue</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05435\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05435\">2</xref><xref rid=\"c1-ijerph-17-05435\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-9704-6771</contrib-id><name><surname>Zhou</surname><given-names>Qingsheng</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05435\">1</xref><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05435\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Glasgow</surname><given-names>Melita</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05435\">4</xref><xref ref-type=\"aff\" rid=\"af5-ijerph-17-05435\">5</xref></contrib><contrib contrib-type=\"author\"><name><surname>Johnston</surname><given-names>Scott</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05435\">4</xref><xref ref-type=\"aff\" rid=\"af6-ijerph-17-05435\">6</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05435\"><label>1</label>Centre for Disability Research and Policy, Centre for Disability Research and Policy, Sydney School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Sydney 2006, Australia; <email>Qingsheng.Zhou@health.nsw.gov.au</email></aff><aff id=\"af2-ijerph-17-05435\"><label>2</label>Research School Population Health, Australian National University, Canberra 2601, Australia</aff><aff id=\"af3-ijerph-17-05435\"><label>3</label>Western New South Wales Local Health District, Dubbo 2830, Australia</aff><aff id=\"af4-ijerph-17-05435\"><label>4</label>Public Service Commission, New South Wales, Sydney 2001, Australia; <email>Melita.Glasgow@dpmc.govt.nz</email> (M.G.); <email>scott.johnston@revenue.nsw.gov.au</email> (S.J.)</aff><aff id=\"af5-ijerph-17-05435\"><label>5</label>Department of Prime Minister and Cabinet, Wellington 6004, New Zealand</aff><aff id=\"af6-ijerph-17-05435\"><label>6</label>Revenue NSW, Parramatta 2150, Australia</aff><author-notes><corresp id=\"c1-ijerph-17-05435\"><label>*</label>Correspondence: <email>ros.madden@sydney.edu.au</email> (R.H.M.); <email>sue.lukersmith@anu.edu.au</email> (S.L.)</corresp></author-notes><pub-date pub-type=\"epub\"><day>28</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5435</elocation-id><history><date date-type=\"received\"><day>27</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>16</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>High rates of unemployment among people with disability are long-standing and persistent problems worldwide. For public policy, estimates of prevalence and population profiles are required for designing support schemes such as Australia’s National Disability Insurance Scheme; for monitoring implementation of the United Nations Convention on Rights of Persons with Disabilities; and for monitoring service access, participation, and equity for people with disability in mainstream systems including employment. In the public sector, creating a succinct identifier for disability in administrative systems is a key challenge for public policy design and monitoring. This requires concise methods of identifying people with disability within systems, producing data comparable with population data to gauge accessibility and equity. We aimed to create disability-related questions of value to the purposes of an Australian state and contribute to literature on parsimonious and respectful disability identification for wider application. The research, completed in 2017, involved mapping and identification of key disability concepts for inclusion in new questions, focus groups to refine wording of new questions, and online surveys of employees evaluating two potential new question sets on the topic of disability and environment. Recommendations for new disability-related questions and possible new data collection processes are being considered and used by the leading state authority.</p></abstract><kwd-group><kwd>disability identification</kwd><kwd>disability statistics</kwd><kwd>workplace modification</kwd><kwd>inclusive employment</kwd><kwd>inclusive workplace</kwd><kwd>diverse workplace</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05435\"><title>1. Introduction</title><p>Australia is among the countries recognising the rights of people with disability to participate fully in society and enjoy all the same opportunities, rights, and access to services as the rest of the community. In 2008, Australia ratified the United Nations Convention on the Rights of Persons with Disabilities (UNCRPD) [<xref rid=\"B1-ijerph-17-05435\" ref-type=\"bibr\">1</xref>], and now links major public policies and programs to UNCRPD objectives (e.g., National Disability Strategy, National Disability Insurance Scheme, and New South Wales (NSW) Disability Inclusion Act of 2014). Any success of these initiatives should be indicated by relative improvements in participation by people with disability in many life areas. The challenge for national statistics in any country is to produce reliable data capable of telling the participation story.</p><p>Data are therefore required on participation by people with disability compared to other citizens of the country, as well as on access to services, both specialist and mainstream. Relevant and efficient data collection requires concise methods of identifying people with disability within administrative systems, as well as the ability to compare the resulting data with population data in order to gauge accessibility and equity. Creating a succinct disability “identifier” for use in administrative systems is a key challenge for public policy design and monitoring in any country [<xref rid=\"B2-ijerph-17-05435\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijerph-17-05435\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijerph-17-05435\" ref-type=\"bibr\">4</xref>].</p><p>Meeting this challenge involves the design and adoption of short question sets, ideally a single question, in order to identify people with disability consistently within service data systems and monitor their access to, and experience of, generic services such as health and education, and to establish employment and other participatory experience in society [<xref rid=\"B5-ijerph-17-05435\" ref-type=\"bibr\">5</xref>]. A “disability flag” was designed by the Australian Institute of Health and Welfare (AIHW) to identify records of people with disability within data collections in various sectors. The flag aligns with Australian Bureau of Statistics (ABS) data about the need for assistance with self-care, mobility, or communication—a specific and less common level of disability [<xref rid=\"B6-ijerph-17-05435\" ref-type=\"bibr\">6</xref>].</p><p>The need for data development is illustrated by trends and data difficulties in important policy areas including employment and was recognised as a key need in the <italic>World Report on Disability</italic> [<xref rid=\"B2-ijerph-17-05435\" ref-type=\"bibr\">2</xref>]. Despite widespread disability policy reform in Australia and other countries, employment rates remain low, with Australia being the 21st of 29 OECD countries in 2010 ([<xref rid=\"B7-ijerph-17-05435\" ref-type=\"bibr\">7</xref>], pp. 49–51). Recent data indicate that employment of people with disability in Australia is not improving. Labour force participation and employment rates have decreased in recent years (<xref rid=\"ijerph-17-05435-t001\" ref-type=\"table\">Table 1</xref>).</p><p>The picture in the Australian state of New South Wales (NSW) public sector is no better, with the reported proportion of people with disability employed dropping from 4% in 2008 to 2.8% in 2016 (p. 32 and Figure 5.3 [<xref rid=\"B9-ijerph-17-05435\" ref-type=\"bibr\">9</xref>]). A target was set to double the representation of people with disability in the NSW public sector from an estimated 2.8% in 2016 to 5.6% by 2027 (in 2019 the disability employment target of 5.6% was announced as a Premier’s Priority for NSW and the end date to achieve the target amended to 2025) (p. 31, [<xref rid=\"B9-ijerph-17-05435\" ref-type=\"bibr\">9</xref>]). The NSW Public Service Commission (PSC) and the Department of Family and Community Services (FACS; now the NSW Department of Communities and Justice) are working to improve the inclusion of people with disability in the NSW public sector.</p><p>The decision of employees to report their disability status is complex; people with disability, when interviewed, cited reasons such as stigma, fear of discrimination, and irrelevance to work being performed in deciding not to share this information [<xref rid=\"B10-ijerph-17-05435\" ref-type=\"bibr\">10</xref>]. This issue could affect the quality of data.</p><p>The NSW PSC has a number of data collections designed to help it understand the needs of diverse employees, including people with disability:<list list-type=\"bullet\"><list-item><p>The Workforce Profile (WFP) is an annual census of the NSW Government workforce that has been collected since 1999. It captures a range of de-identified demographic information from employees of the NSW public sector that is extracted by government agencies from human resource (HR) systems. It collects information related to disability and environmental modifications—whether someone has indicated they have a disability, and whether they require an adjustment. In 2018, for example, representation of people with disability in the NSW public sector workforce was 2.5% [<xref rid=\"B9-ijerph-17-05435\" ref-type=\"bibr\">9</xref>].</p></list-item><list-item><p>The People Matter Employee Survey (PMES) is a perception survey that encompasses the whole of the NSW government and is led by the PSC. While its focus is on understanding the engagement and experiences of its workforce, it also collects information on a range of demographic information and disability. It is an anonymous survey. Each year a higher proportion of respondents declare a disability than the WFP. In 2018, for example, 3.7% of survey respondents identified as having a disability [<xref rid=\"B11-ijerph-17-05435\" ref-type=\"bibr\">11</xref>].</p></list-item></list></p><p>This project aimed to create disability-related questions of value to the purposes of the NSW state PSC and contribute to the literature on parsimonious and respectful disability identification for wider application. Research was designed around the needs of the NSW PSC to inform recommendations on the wording of items in the WFP collection, to better (a) identify people with disability working in the public sector and (b) seek information on workplace modifications needed to promote full and effective participation in the workplace.</p><p>Specifically, the project reviewed the existing questions relevant to disability in the WFP and developed and tested new potential questions about disability (D questions) and the environment (E questions) to inform recommendations for changes to existing questions in the WFP, to\n<list list-type=\"bullet\"><list-item><p>Improve the response rate to disability-related questions.</p></list-item><list-item><p>Improve the quality of data about disability and accessibility in the NSW public sector.</p></list-item><list-item><p>Compare the NSW public sector workforce with the NSW population (proportion of disability according to Australian Bureau of Statistics data).</p></list-item><list-item><p>Provide a better evidence base to inform policy interventions.</p></list-item></list></p><p>The detailed objectives of the project were to develop and trial disability-related questions and to assess how the questions were received and interpreted by NSW public sector employees, in order to ensure their appropriateness for use and inform change to questions in the WFP collection. The intention was to improve the quality of NSW public sector data on accessibility and disability representation in the WFP, assist in monitoring equity outcomes, and inform policy interventions to build a more inclusive and accessible workplace.</p></sec><sec id=\"sec2-ijerph-17-05435\"><title>2. Materials and Methods</title><p>The project was undertaken in a partnership involving the NSW PSC, University of Sydney, and Department of Family and Community Services (FACS). The advisory sub-group (DEAC ASG) of the Disability Employment Advisory Committee (DEAC) comprises public sector employees with lived experience of disability; nominees were invited to join the project team. The DEAC itself consisted of disability peak body representatives, as well as experts from academia and the private sector. It advised the NSW government on improving the representation and inclusion of people with disability in the public sector. The DEAC was invited to comment over the course of the project. Four University of Sydney researchers, expert in the field, were project advisors.</p><p>The project was completed in three stages in 2017: (1) mapping and identification of key disability concepts for inclusion in new disability-related questions, (2) focus groups with NSW public sector employees to refine wording of new draft questions, and (3) online surveys to trial two potential new question sets with a wider group of employees. The University of Sydney gave ethical approval for the focus groups (no. 2017/132) and the online survey (no. 2017/254)—Stages 2 and 3.</p><p>Stage 1: Mapping and identification of key concepts</p><p>To identify key concepts for inclusion in disability-related questions, we examined 10 instruments of potential significance and relevance to disability data collection and measurement in the NSW public sector with reference to the International Classification of Functioning, Disability and Health (ICF) [<xref rid=\"B12-ijerph-17-05435\" ref-type=\"bibr\">12</xref>]. The method involved mapping key concepts, terms, and measures in each instrument to the ICF, enabling comparisons among the instruments by referencing a single framework. This approach is a well-accepted mapping or linking method for instrument examination and comparison in the disability field [<xref rid=\"B13-ijerph-17-05435\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijerph-17-05435\" ref-type=\"bibr\">14</xref>].</p><p>The ICF is the world standard framework and classification for organising information and data about functioning and disability. It defines the main components of functioning as body functions and structures, and activities and participation. It includes a classification of environmental factors to describe the physical, social, and attitudinal environment in which people live and conduct their lives. Environmental factors have a crucial effect (as facilitators or barriers) on people’s functioning and on the creation of disability in many life areas, including the workplace. The ICF represents a biopsychosocial model of disability, combining both the medical and social models of disability, thus recognising that disability may require both individual support and social, environmental change [<xref rid=\"B15-ijerph-17-05435\" ref-type=\"bibr\">15</xref>].</p><p>The ICF enables the collection and comparison of data relating to functioning and disability in many fields and is particularly suited to the present study; it provides a framework to underpin monitoring of implementation of the UNCRPD [<xref rid=\"B4-ijerph-17-05435\" ref-type=\"bibr\">4</xref>,<xref rid=\"B12-ijerph-17-05435\" ref-type=\"bibr\">12</xref>,<xref rid=\"B16-ijerph-17-05435\" ref-type=\"bibr\">16</xref>]. The World Health Organisation (WHO) has called for its wider use to increase worldwide disability data quality and consistency [<xref rid=\"B2-ijerph-17-05435\" ref-type=\"bibr\">2</xref>,<xref rid=\"B17-ijerph-17-05435\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijerph-17-05435\" ref-type=\"bibr\">18</xref>]. Australian statistical organisations such as the ABS and the AIHW adhere to such international standards in order to produce data that enables national and international comparisons and to capture consistent administrative data nationally [<xref rid=\"B5-ijerph-17-05435\" ref-type=\"bibr\">5</xref>,<xref rid=\"B19-ijerph-17-05435\" ref-type=\"bibr\">19</xref>].</p><p>Ten instruments were selected for mapping to the ICF, on the basis of their significance to the disability field, their relevance to disability data and measurement, and Australian population data that could potentially be compared with NSW public sector data. The selected instruments were the UNCRPD [<xref rid=\"B1-ijerph-17-05435\" ref-type=\"bibr\">1</xref>]; ABS Survey of Disability [<xref rid=\"B20-ijerph-17-05435\" ref-type=\"bibr\">20</xref>], Ageing and Carers (SDAC) [<xref rid=\"B21-ijerph-17-05435\" ref-type=\"bibr\">21</xref>]; ABS short module of disability for social surveys [<xref rid=\"B22-ijerph-17-05435\" ref-type=\"bibr\">22</xref>]; Household, Income and Labour Dynamics in Australia Survey (HILDA) [<xref rid=\"B23-ijerph-17-05435\" ref-type=\"bibr\">23</xref>]; Washington Group disability questions [<xref rid=\"B24-ijerph-17-05435\" ref-type=\"bibr\">24</xref>]; the current WFP questions [<xref rid=\"B9-ijerph-17-05435\" ref-type=\"bibr\">9</xref>]; and four other selected instruments, used primarily for research rather than population data collection [<xref rid=\"B25-ijerph-17-05435\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijerph-17-05435\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijerph-17-05435\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijerph-17-05435\" ref-type=\"bibr\">28</xref>] (<xref ref-type=\"app\" rid=\"app1-ijerph-17-05435\">Supplementary Table S1</xref>): Mapping instruments to the ICF).</p><p>Two authors carried out the mapping, one doing the initial mapping and the other cross-checking the results. Points of difference were discussed and resolved to reach consensus.</p><p>Stage 2: Focus groups to refine wording of new draft questions</p><p>The key concepts and terms identified in Stage 1 were used in different combinations, to draft five D questions (designed to identify employees with disability) and five E questions (designed to identify environmental factors that can facilitate, or act as barriers to, full and effective workplace participation); these questions were tested in focus groups with NSW public sector employees (<xref ref-type=\"app\" rid=\"app1-ijerph-17-05435\">Supplementary S1</xref>). The aim was to develop agreement within each focus group on (a) question clarity and user-friendliness, bearing in mind the project aims to improve data quality, comparability, and policy relevance, and (b) the questions and terms preferred by NSW public sector employees. The results guided the design of the questions to be tested in Stage 3 online surveys.</p><p>Of the possible focus group approaches, we used the nominal group technique [<xref rid=\"B29-ijerph-17-05435\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijerph-17-05435\" ref-type=\"bibr\">30</xref>]. This technique involved a structured approach to face-to-face discussion, with responses and ideas, as well as different iterations of the D and E questions which were discussed and clarified to develop consensus [<xref rid=\"B31-ijerph-17-05435\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijerph-17-05435\" ref-type=\"bibr\">32</xref>]. A facilitator encouraged all group members to participate and contribute. The nominal group was of a manageable size to enable discussion and agreement among participants.</p><p>The method and protocol for the focus groups were developed by the University of Sydney authors in collaboration with the PSC, and in consultation with members of both public sector departments and the DEAC ASG.</p><p>Five focus groups were held over an eight-day period in March–April 2017, in three locations (Sydney city, Parramatta, and Newcastle). An email invitation for focus group volunteers was forwarded by senior office holders and key contacts in the NSW public sector for distribution within their agency cluster (administrative arrangements that bring together different but related NSW public sector agencies), and DEAC ASG members and others with specific responsibility for diversity, including disability.</p><p>At least three of the authors were present at each focus group. A senior representative of the PSC involved in the project introduced the project and facilitators; a PSC note-taker was also present, with both PSC representatives joining discussions. Facilitators were University of Sydney project members.</p><p>Each focus group commenced with an attendee orientation to the purpose of the discussion, and “rules” were explained (e.g., mutual respect of opinions, opportunities to speak, confidentiality of the information shared, including any voluntary disclosure of personal circumstances such as disability). Facilitated discussions followed, about the draft D and E questions. Participants were asked probe questions such as “<italic>Do you think the question is user friendly? What changes do you think should be made?</italic>” The facilitators employed an iterative approach in group discussions using comments and opinions expressed by group participants to identify preferred concepts and those to exclude, then develop majority agreement on the terms and phrases preferred. All participants were encouraged to engage in the discussions. At the end of each focus group, a formal voting process invited participants to indicate anonymously their preference for two D and two E questions by placing an adhesive sticker on print-outs of all questions.</p><p>The analysis of the feedback from the focus groups involved synthesis of notes from all sessions. Links between question preferences and comments about preferred concepts, terms, and phrases were identified and discussed among all authors. Two new D questions and two pairs of new E questions were then developed by</p><list list-type=\"bullet\"><list-item><p>Using the key preferred concepts as the building blocks for questions, e.g., participation, environment, barrier.</p></list-item><list-item><p>Reviewing focus group suggestions about the wording of questions and excluding or including particular terms and phrases.</p></list-item><list-item><p>Using notes from the focus groups to inform the connecting words and phrases for the questions, e.g., “having” or “experiencing” difficulties or disability.</p></list-item></list><p>The D and E questions drafted after the focus groups were further refined after comment from project advisors from the University of Sydney and informal comments from key ABS staff. Two D and two pairs of E questions to be evaluated in Stage 3 online surveys were agreed after final discussion at a full team meeting (PSC, FACS, University of Sydney).</p><p>Stage 3: Online surveys to evaluate two draft question sets</p><p>In the final stage of the project, the two D questions and two pairs of E questions produced in Stage 2 were tested in two online surveys of a wider group of NSW public sector employees (<xref ref-type=\"app\" rid=\"app1-ijerph-17-05435\">Supplementary S2</xref>: online survey questions).</p><p>Criteria for judging the suitability of the questions in light of the project aims were developed to guide survey construction and analysis of the results. The relationship between each criterion and the survey—as well as other project elements—is summarised in <xref rid=\"ijerph-17-05435-t002\" ref-type=\"table\">Table 2</xref>. If the recommended D and E questions resulting from the research met all criteria, we would meet project objectives.</p><p>The survey instrument was constructed over several iterations, involving the whole project team and further consultation with DEAC ASG members. Two surveys were prepared to allow for presentation of test questions in a different order, and to avoid preferencing particular responses (<xref ref-type=\"app\" rid=\"app1-ijerph-17-05435\">Supplementary S2</xref>). At this stage, the question “<italic>Do you have a disability?</italic>” was added to the online surveys as Q11. This addition was in response to a concern expressed by some DEAC ASG members about the wording of the D questions (e.g., an apparent focus on impairment and health conditions, also present in WFP items). Online accessibility was checked by a specialist, leading to wording changes.</p><p>An email invitation to participate in the online survey was forwarded for distribution to the key disability contacts including the DEAC sub-group members, senior office holders for distribution to diversity employee networks (including disability) in their agency, and to employees more generally. The invitation emphasised the purpose of the project and specifically the online survey. The voluntary and anonymous nature of employee contributions was clarified in the invitation and participant information statement. People who responded that they were interested in participating were then sent one of the two surveys, according to their administrative cluster. A sample size goal of 200, for each questionnaire, was set in order to enable cross-tabulation of responses by broad demographic information. Surveys 1 and 2 were sent to respondents according to their cluster; that is, to approximately equal numbers of people based on cluster sizes of public sector agencies. In practice, this approach was modified, given the low responses to Survey 2 initially, and respondents after an extended deadline were provided with Survey 2.</p></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05435\"><title>3. Results</title><p>Results for the three stages of the project informed each following stage, resulting in one recommended D question and two pairs of recommended E questions for further consideration and development by the PSC (<xref ref-type=\"boxed-text\" rid=\"ijerph-17-05435-box003\">Box 3</xref>).</p><p>Stage 1: Mapping key concepts</p><p>The 10 instruments examined all had some concordance with the ICF. Accordingly, there was considerable overlap in key concepts and terms included in several instruments, indicating potential to produce comparable data. There was variation in emphasis placed on the three ICF components (body functions and structures, activities and participation, and environmental factors), and in the language and terms used to describe and capture data in these areas. The ABS SDAC was the most comprehensive of all the instruments examined, although it, like several others, was limited in its use of the environmental factors (see <xref ref-type=\"app\" rid=\"app1-ijerph-17-05435\">Supplementary Table S1</xref> for further detail of these results).</p><p>Key concepts and terms most often included in the instruments are listed in <xref rid=\"ijerph-17-05435-t003\" ref-type=\"table\">Table 3</xref>, in three general categories broadly corresponding with three major ICF concepts.</p><p>The key concepts identified were used in various combinations to create five D and five E questions for discussion in the focus groups (<xref ref-type=\"app\" rid=\"app1-ijerph-17-05435\">Supplementary S1</xref>).</p><p>Stage 2: Focus groups</p><p>A total of 55 NSW public sector employees participated in five focus groups (32 females and 23 males), representing input from 15 agencies. Each focus group (9–16 people) ran for 2.5 h. No participant was asked to disclose whether they had lived experience of disability, although a number elected to do so; the group discussions reflected representation of the lived experience whereby participants disclosed at least 15 different disabilities and long-term health conditions amongst attendees. Given the aims of the study—namely, how best to elicit information to indicate disability—asking for such information in any form would have introduced circularity into the research.</p><p>Participant preferences on D and E questions, as voted at the end of each session, were tallied across the five groups. The most preferred D question was D4, followed by D3, and then D2; all these questions used key concepts or terms: “participation”, “impairments”, “long-term health conditions”, and “difficulties”. The preferred E question was E1, followed by E2, then E5; all these questions used key concepts or terms: “participate”, “equal”, “environment”, and “change”.</p><p>The implications about key words are difficult to interpret without the benefit of detailed comments on question structure, the format of lists used, and other expressions and terms. For example, the favourite D questions did not use the word “environment” and did use “not accommodated”, while the E questions (focused on the working environment) were preferred when they included “environment” and “enable” (E1 and E2) rather than “accommodate”.</p><p>Focus group members considered each question needed revision, with no D nor E question considered “final”. On completion of all focus groups, the project team drafted revisions on the basis of comments and opinions expressed by focus group participants (<xref ref-type=\"boxed-text\" rid=\"ijerph-17-05435-box001\">Box 1</xref>). The new questions thus developed included two D questions (D1 and D2) and four E questions (EN1 and EN2 relating to environmental barriers or changes needed now, and EP1 and EP2 relating to environmental facilitators or changes already made).</p><boxed-text id=\"ijerph-17-05435-box001\" position=\"float\" orientation=\"portrait\"><label>Box 1</label><caption><title>Sample comments made in focus groups.</title></caption><p>Use “work environment” rather than “workplace” as many people move around in their work (teachers, health professionals, transport workers).</p><p>Use a list rather than sentence for environmental factors.</p><p>Refer to “your environment” not “the environments”.</p><p>Terms such as “equality”, “on an equal basis”, or “equal opportunity” may be confused with diversity policies or imply comparisons with peers.</p><p>Mentioning “long-term health condition” is OK if used alongside “impairment”.</p><p>How to answer if you have an impairment but no everyday difficulty (because of management and environment)?</p><p>“Enable” a good word.</p><p>“Participate fully and effectively” discussed at length—and various alternatives offered.</p></boxed-text><p>The focus groups also began the process of obtaining comments on the draft introduction to the disability-related questions for use in the WFP, their likely effect on people’s responses, and the resulting data quality. Concerns were expressed about the use of data and its confidentiality, for example, concerns about potential disincentive to complete disability-related questions unless there was re-assurance on the use of data and its confidentiality, particularly regarding potential impacts on employment conditions and outcomes. Focus group participants advocated a simple explanation, which would give respondents answers to questions such as</p><list list-type=\"bullet\"><list-item><p>Why are you asking these questions, what are the data for?</p></list-item><list-item><p>What is in it for me?</p></list-item><list-item><p>What happens to the data (including who has access to it)?</p></list-item></list><p>These concerns were explored further in Stage 3 online surveys to inform implications for the introduction wording, question order, and discussion of the appropriate mechanism for collection.</p><p>Stage 3: Online surveys</p><p>Survey responses were received from a total of 533 NSW public sector employees (313 for Survey 1, 220 for Survey 2) from 41 agencies across 10 public sector clusters (27 agencies for Survey 1 and 20 for Survey 2, with 6 agencies represented in both surveys). Here, we summarise the results for D and E questions referring to survey questions by number (<xref ref-type=\"app\" rid=\"app1-ijerph-17-05435\">Supplementary Table S2</xref>). The percentages presented in this section are the percentages of valid cases, unless stated otherwise. All survey participants answered the questions, except for free text questions, resulting in the percentage of total cases being equal to the percentage of valid cases for most questions.</p><sec id=\"sec3dot1-ijerph-17-05435\"><title>3.1. Results for the D Questions</title><p>Analysis of survey responses to Q1–Q11 indicated that both D questions (D1 and D2) tested are inclusive indicators of “disability”, and lead to the inclusion of more people than the question “do you have a disability?” (Q11):<list list-type=\"bullet\"><list-item><p>A higher percentage of people responded to D1 (26.2%) and D2 (30.5%) in a way that indicated disability than those who answered “yes” to Q11 (16.6% for Survey 1 and 26.8% for Survey 2) (<xref rid=\"ijerph-17-05435-t004\" ref-type=\"table\">Table 4</xref> and <xref rid=\"ijerph-17-05435-t005\" ref-type=\"table\">Table 5</xref>, <xref ref-type=\"app\" rid=\"app1-ijerph-17-05435\">Supplementary Table S2</xref>). While disability in the NSW public sector, as reported on the basis of D1 and D2, was significantly more common than the current 3% reported in the WFP, no comparison can be made; people responding voluntarily to the online surveys are unlikely to be a random sample of NSW public sector employees.) The interpretation of disability in D1 and D2 is in line with the ICF world standard and the key concepts identified in the mapping exercise (<xref rid=\"ijerph-17-05435-t003\" ref-type=\"table\">Table 3</xref>). The results point to greater comfort answering D questions and their applicability to people who may not identify with disability, for example people with difficulties related to long-term health conditions (see data in <xref ref-type=\"app\" rid=\"app1-ijerph-17-05435\">Supplementary Table S2</xref>), and comments in <xref ref-type=\"boxed-text\" rid=\"ijerph-17-05435-box002\">Box 2</xref>).</p></list-item><list-item><p>Both D1 and D2 “picked up” approximately 85% of people explicitly stating that they have a disability in response to Q11 (44 of 52 for D1, and 50 of 59 for D2) (<xref rid=\"ijerph-17-05435-t004\" ref-type=\"table\">Table 4</xref> and <xref rid=\"ijerph-17-05435-t005\" ref-type=\"table\">Table 5</xref>).</p></list-item><list-item><p>Significantly, D1 and D2, while showing a strong relationship with Q11, “captured” more people who were dealing with potential difficulties in their daily lives via technologies and other strategies.</p></list-item><list-item><p>Of those who were not “picked up” by D1 (i.e., answered “no difficulty”), only 3.5% (8 in 231) stated later that they have a disability (in response to Q11) (for D2, only 5.9% (9 in 153)) (<xref rid=\"ijerph-17-05435-t004\" ref-type=\"table\">Table 4</xref> and <xref rid=\"ijerph-17-05435-t005\" ref-type=\"table\">Table 5</xref>).</p></list-item></list></p><boxed-text id=\"ijerph-17-05435-box002\" position=\"float\" orientation=\"portrait\"><label>Box 2</label><caption><title>Inclusiveness of D1 illustrated by survey responses.</title></caption><p>I would answer yes to D1 and no to D2 because I have mobility issues due to arthritis but don’t consider I have a disability. But am impaired somewhat with mobility.</p><p>I technically have a disability (chronic pain and fatigue, mental health conditions) but have not disclosed this in past job applications. The questions being phrased in the more open way they are above would encourage me to respond more accurately.</p></boxed-text><p>Of the two D questions, it was concluded that D1 should serve as the basis for a final recommended D question for consideration and development by the NSW PSC. Descriptive statistics illustrated that (<xref rid=\"ijerph-17-05435-t006\" ref-type=\"table\">Table 6</xref>, <xref ref-type=\"app\" rid=\"app1-ijerph-17-05435\">Supplementary Table S2</xref>):<list list-type=\"bullet\"><list-item><p>Overall, D1 was considered slightly easier to understand than D2; 93% of respondents reported that D1 was “easy” to understand, compared to 91% for D2 (Q6); 12.5% felt that D1 had words that needed explanation, compared to 15.5% for D2 (Q7).</p></list-item><list-item><p>Survey respondents were comparatively more “comfortable” answering D1 (in response to Q8)—there was little difference in Survey 1 responses; difference was more marked in Survey 2, with 63% in favour of D1 and 37% in favour of D2.</p></list-item><list-item><p>The examples provided in D1 were appreciated by respondents to both surveys (Q9, Q10). Examples of everyday life areas were considered helpful by 68% of respondents to Survey 1 and 69% to Survey 2. Examples of long-term health conditions and impairments were considered helpful by 65% of respondents to Survey 1 and 60% to Survey 2.</p></list-item></list></p><p>The text comments offered throughout the survey help explain the statistics from the online surveys. These comments generally supported the conclusions of the statistical analysis, namely, to develop D1 into a recommended D question:<list list-type=\"bullet\"><list-item><p>“Impairment” was mentioned as a word needing explanation (21 people mentioned it in response to Q7, the most mentions of key words). There were few negative comments on impairment (two negative comments in response to Q7 made by people reporting disability in terms of D1 or Q11); the focus group discussions and the great majority of responses to the online survey did not flag any concern. Impairment is a term widely used in the disability field, including in the UNCRPD and ICF. Use of only “long-term health condition” in its place was not supported by focus group comments, which indicated a preference for using both terms; the examples of “conditions and impairments” listed in D1 (favoured by survey respondents) do contain impairments. Use of impairment categories (physical, mental, intellectual, sensory) from D2 was not recommended; a number of text comments from the survey sought explanation of the bracketed words.</p></list-item><list-item><p>The use of the word “disability” in the questions appears to exclude some people from responding, e.g., those with functioning difficulties arising from long term health conditions; this is the balance of evidence from the online survey statistics, text responses, and focus group results. “Disability” would perhaps better be used in the introduction to the question.</p></list-item><list-item><p>The word “participation” was well received by participants in focus groups and in the online survey; it was not included in D1 in the survey but should be in further development of D1.</p></list-item></list></p><p>Both privacy and use of the data appeared significant concerns in text responses to the introduction and requests for “any other comments”, reinforcing focus group views. People reported lacking knowledge of and confidence in how data are used.</p></sec><sec id=\"sec3dot2-ijerph-17-05435\"><title>3.2. Results for the E Questions</title><p>On the basis of analysis of survey responses to Q13–Q21, we concluded that, of the four E questions (EN1 and EN2 relating to environmental barriers to functioning or changes needed now, and EP1 and EP2 relating to environmental facilitators of functioning or changes already made), EN1 and EP1 should serve as the basis for two final recommended E questions for consideration and further development by the PSC (<xref rid=\"ijerph-17-05435-t007\" ref-type=\"table\">Table 7</xref>, <xref ref-type=\"app\" rid=\"app1-ijerph-17-05435\">Supplementary Table S3</xref>):<list list-type=\"bullet\"><list-item><p>The great majority of survey respondents (95% or more) found all four E questions “very easy”, “easy”, or “somewhat easy” to understand (in response to Q14 and Q18). A correspondingly high proportion said no further words needed explanation (e.g., 93% for EN1 and 89% for EN2 (Q15)). A higher proportion of respondents indicated that words needed explanation in EN2 (11.4%) and EP2 (10.3%) compared to EN1 (7.1%) and EP1 (6.0%) (in response to Q15 and Q20) (<xref ref-type=\"app\" rid=\"app1-ijerph-17-05435\">Supplementary Table S3</xref>).</p></list-item><list-item><p>Respondents demonstrated no difficulty in responding across all five areas of the ICF environmental factors, and in differentiating present needs and needs met. The questions have the capacity to yield interesting information (<xref ref-type=\"app\" rid=\"app1-ijerph-17-05435\">Supplementary Table S3</xref>).</p></list-item><list-item><p>There was greater comfort answering EN1 than EN2 (in response to Q16)—59% of people in Survey 1 were more comfortable answering EN1; preferences were evenly split in Survey 2, with 51% preferring EN1 and 49% preferring EN2. The word “improvement” (in EN1) appeared to be favoured over “adjustment” (in EN2). Similarly, EP1 was preferred to EP2 (in response to Q21).</p></list-item><list-item><p>A higher percentage of people indicated that “improvements” are needed in all five aspects of the environment in response to EN1 compared with those who indicated that “adjustments” are needed in response to EN2 (Q13). Correspondingly, a lower percentage of people indicated “no improvement needed” (40.6%) in response to EN1 compared with those who indicated “no adjustment needed” (56.4%) in response to EN2. A similar but less marked pattern was seen for EP1 and EP2 (Q.18)—57% in Survey 1 reported no past improvements, and 65% in Survey 2 reported no past adjustments. The results could reflect a difference between the samples, or that EN1 and EP1 are open to wider interpretation.</p></list-item></list></p><p>The relatively small number of text comments about E questions (24 in each survey) generally supported the conclusion of the statistical analysis—namely, to develop EN1 and EP1 into two recommended E questions, with attention to suggestions for improvement. Free text comments from survey respondents, with feedback from focus groups, provided guidance for amending the questions, and could also be used to improve layout:<list list-type=\"bullet\"><list-item><p>Generally, people preferred simpler wording, without jargon and need for further explanation. Comments (in response to Q15 and Q20) reinforced preferences for the term “improvement” rather than “adjustment”, which was frequently mentioned as a word needing explanation. Other problematic terms reported include “human-made”, “enable”, and “colleagues”.</p></list-item><list-item><p>There was a suggestion from Survey 1 to simplify “what aspects of your work environment” to “at work” ... “enable you to participate fully and effectively”.</p></list-item><list-item><p>From Survey 2, suggestions included the need for further explanation or alternative terms such as “adjustments”, “EN2 asks me what help I need”, “EN1 feels like it is seeking a criticism about my workplace”, “EN2 reads like we all need adjustments, and ‘what adjustments have you and your employer made’ may be more positive”.</p></list-item></list></p><p>Overall, the free text responses about both the D and E questions, briefly illustrated in this paper, provided rich material for question redevelopment.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05435\"><title>4. Discussion</title><p>With the aim of improving the WFP questions and data, the research developed a new set of questions (<xref ref-type=\"boxed-text\" rid=\"ijerph-17-05435-box003\">Box 3</xref>) related to disability and accessibility, comprising one D question to identify disability, and two E questions about environmental modifications. The E questions enable data to be captured on improvements already made, as well as improvements that need to be made in the future. The mapping stage of the project ensured that the new questions correspond conceptually with the UNCRPD, the ICF as the world statistical standard, and ABS data.</p><p>The new questions performed well against the objectives of the project:<list list-type=\"bullet\"><list-item><p><bold>Questions are inclusive</bold>: The new questions bring into scope more people than the question “do you have a disability?”—for example, people with long-term health conditions who may not identify with disability, as well as people using technologies and other strategies to manage difficulties in their daily life.</p></list-item><list-item><p><bold>Comparable to NSW population data</bold>: The new questions use key concepts used in other data collection instruments (as well as key policy instruments), thus promoting comparability with the NSW population and labour force data.</p></list-item><list-item><p><bold>Clarity and meaning</bold>: The new questions were preferred to the alternatives as they were considered easier to understand.</p></list-item><list-item><p><bold>Comfort in answering</bold>: The new questions were preferred to the alternatives as more employees were comfortable answering them.</p></list-item></list></p><sec id=\"sec4dot1-ijerph-17-05435\"><title>4.1. Recommended D and E Questions</title><p>Following all three stages of the research, including analysis of statistical and text results of the online survey, which informed further refinement of the questions, <xref ref-type=\"boxed-text\" rid=\"ijerph-17-05435-box003\">Box 3</xref> sets out the recommended D and E questions. The inclusion of “participation” should be tested in any further refinement of the D question; while D1 was the preferred question, it did not include this popular word. <xref ref-type=\"boxed-text\" rid=\"ijerph-17-05435-box003\">Box 3</xref> illustrates how it could be included.</p><p>There was strong support for lists of examples: of health conditions and impairments that may interact with the environment to create the experience of disability, as well as for areas of the environment involved in this interaction. Example lists used were based (respectively) on the list used by the ABS in the SDAC and the environmental factors classification of the ICF; the environmental lists and examples were seen as easy to understand and to require little explanation.</p><boxed-text id=\"ijerph-17-05435-box003\" position=\"float\" orientation=\"portrait\"><label>Box 3</label><caption><title>Recommended D and E questions.</title></caption><p>\n<bold>Question D1:</bold>\n</p><p>In your everyday life, do you experience difficulty (<bold>participating)</bold>, related to a long-term health condition or impairment, in any of the following:<list list-type=\"simple\"><list-item><p>Social and community life (e.g., recreation, sport, religion).</p></list-item><list-item><p>Work, education or training (including paid or voluntary work).</p></list-item><list-item><p>Mobility (e.g., walking, moving around, handling or lifting objects, using public transport).</p></list-item><list-item><p>Self-care (e.g., washing, dressing).</p></list-item><list-item><p>Home life (e.g., shopping, cooking, caring for others).</p></list-item><list-item><p>Daily organisation (e.g., undertaking multiple tasks, making decisions, handling stress).</p></list-item><list-item><p>Communication (e.g., speaking or using communication devices).</p></list-item><list-item><p>Learning (e.g., basic learning, or applying knowledge in solving problems or making decisions).</p></list-item><list-item><p>Relationships (e.g., with friends, family, supervisors, co-workers or acquaintances).</p></list-item></list></p><p>Please select one of the following:<list list-type=\"bullet\"><list-item><p>Yes, I sometimes or always experience difficulty in at least one area, even when I use equipment, technology, assistance, or other techniques.</p></list-item><list-item><p>No, but I use equipment, technology, assistance, or other techniques to avoid difficulty.</p></list-item><list-item><p>No difficulty.</p></list-item></list></p><p><underline>Note</underline>: Long-term health condition or impairment may refer to the following:</p><p>Loss of sight or hearing;</p><p>Speech difficulties;</p><p>Shortness of breath or difficulty breathing;</p><p>Ongoing or repeated pain or discomfort;</p><p>Blackouts, seizures, or loss of consciousness;</p><p>Difficulties learning or understanding things;</p><p>Difficulties using arms or fingers;</p><p>Difficulties using feet or legs;</p><p>Mental health conditions;</p><p>Restricted in physical activities or in doing physical work;</p><p>Memory problems;</p><p>Social or behavioural difficulties;</p><p>Head injury, stroke, or other acquired brain injury;</p><p>Or any other long-term conditions.</p><p> </p><p>\n<bold>Question EN1:</bold>\n</p><p>Do aspects of your work environment need to be improved to enable you to participate fully and effectively at work? (Select all areas where improvement is needed):<list list-type=\"simple\"><list-item><p>Support and relationships (e.g., support from co-workers or supervisors).</p></list-item><list-item><p>Attitudes (e.g., attitudes or behaviour of colleagues, supervisors, or clients).</p></list-item><list-item><p>Services, systems, and policies (e.g., job design, working hours, flexible work options, transport, employment policy, training, or workplace and hiring policies).</p></list-item><list-item><p>Products and technology (e.g., work equipment, ICT, furniture, building design, and access).</p></list-item><list-item><p>Natural environment and human-made changes to environment (e.g., noise, light, air, or water)</p></list-item></list></p><p>No improvements needed.</p><p>EP1 similar to EN1 but referring to the past i.e., what has been done:</p><p>Have aspects of your work environment been improved to enable you to participate fully and effectively at work?</p></boxed-text></sec><sec id=\"sec4dot2-ijerph-17-05435\"><title>4.2. Future Research</title><p>Further analysis of text responses to the online survey and of notes taken during focus group discussions could lead to even further refinement of words and examples in D, along with further plain English editing.</p><p>Further discussions could be undertaken with agencies with interest in disability data, including the Australian Human Rights Commission. Further testing of the questions in another English-speaking country with similar national data, or use of the process described here to develop relevant national questions, may provide useful insights and comparisons in similar work-related contexts.</p><p>The strong association between the new disability questions, about difficulties participating in everyday life, and the question “Do you have a disability?” (Q11), is interesting and perhaps reassuring. Nevertheless, some analysis suggests areas of future research. Notably, D1 and D2, while showing strong relationships with Q11, “captured” more people who were dealing with potential difficulties in their daily life using technologies and other strategies. This finding reflects views expressed in the focus groups that the existing D question in the WFP (similar to Q11) may lead to self-exclusion. For example, as illustrated by the focus group and survey comments, people with difficulties related to long term health conditions or people whose difficulties are mitigated by technology or environmental facilitators may have varying views of disability and may not identify or declare they have a disability.</p><p>The apparently greater inclusivity of the new questions resonates with results from related research in a vastly different setting. Disability identification in refugee situations, for the purposes of offering services and determining and meeting needs, was found to be best done using “functionality-based questions in line with international standards” along with staff training to enable “greater sensitivity to the many different ways in which disability can manifest” ([<xref rid=\"B33-ijerph-17-05435\" ref-type=\"bibr\">33</xref>], p. 60).</p></sec></sec><sec id=\"sec5-ijerph-17-05435\"><title>5. Communication</title><p>Communication at various points will be an essential part of improving data. Communication may</p><list list-type=\"bullet\"><list-item><p>have various purposes and messages—communication about system changes; reminders about the need for updating data, the value of the data, and of participation in providing it; explanation of the importance and purpose of the questions; and accurate assurances about privacy and confidentiality; and</p></list-item><list-item><p>use various avenues—announcements, publications, presentations, and routine communication, e.g., in forms and databases including through explanation of and introduction to the questions.</p></list-item></list><p>A communication strategy could be devised on the basis of the findings of this project and decisions made about implementation in order to support the chosen paths forward on data collections relating to disability in the NSW public sector.</p><sec><title>The Purpose, Use, and Place of the D and E Questions Require Discussion</title><p>Further high-level consideration by the PSC about the purpose and use of the disability (D) and environment (E) questions is essential in order to establish the basic “why, who, when” purposes and processes. There was strong comment in both the focus groups and online survey, revealing distrust among NSW public sector employees concerning the purpose of the D question in particular and about data confidentiality.</p><p>The most appropriate data collection vehicle to gather information on disability and accessibility requires consideration. Some Australian government jurisdictions collect their disability data through HR systems and others through sample employee surveys (anonymous and confidential), possibly similar to other countries. There are advantages and disadvantages to both methods (<xref ref-type=\"app\" rid=\"app1-ijerph-17-05435\">Supplementary Table S4</xref>).</p><p>The PMES is anonymised and online, and not linked to HR systems. The PSC has control of the questions and is the sole data custodian. However, because the recommended questions are long (relative to others in that survey), so as to reflect the complexity of disability, there are advantages to using them in a context where this complexity can be explored. Therefore, the WFP collection, which draws information from HR systems, may remain the better approach, as it allows the accumulation of more in-depth data over time; the WFP collection was the focus intended at the start of this research. The questions could be asked during HR processes and stored in HR systems; if so, the purpose needs to be clear and assurances about use and privacy provided and demonstrated. While the purposes may include monitoring disability employment rates, locating the data with HR creates the capacity to cross-tabulate disability data with other relevant HR data (e.g., with position/level, age, and gender) and statistically monitor change over time.</p><p>Trust and comfort in reporting may also be influenced by the inclusiveness of the workplace, where employees feel “safe” to share their personal information. Both elements—technical and cultural—are important in developing a better picture of workplace inclusion of people with disability—having appropriate survey questions, ethical data storage and use practices, and an environment where employees feel their information is respected and acted on constructively.</p><p>The links between the D and E questions also need to be decided. It has been assumed that the D question comes first and acts as a filter to direct people to the E questions. An alternative could be to use the E question as the first question, and then ask for explanation of why environmental change is needed, even perhaps using all “diversity” questions as possible reasons. This approach has not yet been tested and has policy implications. In favour of this re-ordering is the finding that the E questions did not elicit the types of free-text comments made about the D questions. It was clearer why E questions were being asked and what actions might be taken on the basis of answers. However, there remains a question about timing; for example, people may find it hard to comment soon after recruitment on what environmental improvements they need.</p><p>The results of this project thus raise wider questions of data collection strategy. One option is to re-think the use of two questions, on disability and environment, as part of an overall strategy to improve understanding of disability and changes needed in the workforce environment. The questions could be used to monitor changes in the environment as well as in the representation of people with disability in the workforce.</p><p>The OECD noted, in contrasting disability policy reform with the lack of improvement in employment rates, “disability system reform is a huge task, for several reasons” (p. 93 [<xref rid=\"B7-ijerph-17-05435\" ref-type=\"bibr\">7</xref>]). Many months after completion of this research, the discussion about next steps is still in play. The research has been used to inform enhancements to the PMES and shared with other jurisdictions. Inevitably, key senior staff at the PSC have moved on during this period (including Johnston and Glasgow) with the resultant need for handover communication. In addition, the NSW government announced the Jobs for People with Disability Plan, which has resulted in a significant reprioritisation of work to advance disability inclusion across the NSW public sector workforce. Improving the accuracy and reliability of data remains a key focus area. Inclusion of a clear definition of disability was drafted using this research and is to be implemented into the WFP to provide more clarity of what is included. While it is understood that the WFP remains the most appropriate vehicle for the collection of detailed data public sector-wide, implementing change to the WFP is nevertheless a significant challenge—it affects administrations across the NSW public sector (with the NSW government being the largest public sector employer in Australasia). The strategic timing of such a change is made more complex by the impact on public administration in 2019–2020 of a long-term drought, severe bushfires, and now the COVID-19 virus.</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec6-ijerph-17-05435\"><title>6. Conclusions</title><p>In the public sector, creating a succinct identifier for disability in administrative systems is a key challenge for public policy design and monitoring. This requires concise methods of identifying people with disability within systems, producing data comparable with population data to gauge accessibility and equity. This paper focussed on efforts to improve data in the service of evidence-based policy. The project described here formed part of a broader program of work to improve interventions that build inclusive work environments and enable people with disability to have positive work experiences in and contribute to the NSW public sector.</p><p>We aimed to create disability-related questions of value to the purposes of an Australian state and contribute to literature on parsimonious and respectful disability identification for wider application. Both these aims were achieved. Recommendations for new disability-related questions and possible new data collection and related communication processes are under active consideration by the leading state authority, as are the additional factors identified during the collaborative research relating to the design of and communications about administrative data collections. More broadly, this research contributes methodology and findings relevant not only in the Australian public sector, but in other countries with good quality national population data with which administrative data can be compared.</p></sec></body><back><ack><title>Acknowledgments</title><p>The research reported here resulted from a collaborative project involving a “project team” from the University of Sydney: Rosamond Madden AM, Qingsheng Zhou, Sue Lukersmith; the “full team” comprising the project team and also members of the NSW Public Service Commission (PSC), Department of Family and Community Services, and the Disability Employment Advisory Committee Advisory Subgroup. University of Sydney advisers to the project team were Gwynnyth Llewellyn, Richard Madden, Nick Glozier, and Xingyan Wen. We thank all the NSW public sector employees who voluntarily participated in and greatly contributed to the research, and gratefully acknowledge the contributions of PSC members Adam Bove, Paige Neave, Helen Evans, Nicola Rossini, and Jane Spring. This paper does not necessarily reflect the views of the NSW Public Service Commission, the NSW Department of Family and Community Services, or the University of Sydney.</p></ack><app-group><app id=\"app1-ijerph-17-05435\"><title>Supplementary Materials</title><p>The following are available online at <uri xlink:href=\"https://www.mdpi.com/1660-4601/17/15/5435/s1\">https://www.mdpi.com/1660-4601/17/15/5435/s1</uri>, Supplementary S1: Questions for focus groups to discuss. Supplementary S2: Online survey questions. Table S1: Mapping 10 instruments to the ICF – overview. Table S2: Responses to disability questions. Table S3: Responses to environment questions. Table S4. Characteristics of Workforce Profile compared to People Matter Employee Survey.</p><supplementary-material content-type=\"local-data\" id=\"ijerph-17-05435-s001\"><media xlink:href=\"ijerph-17-05435-s001.zip\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>Conceptualisation, R.H.M., Q.Z., S.L.; methodology, all authors; investigation R.H.M., S.L., Q.Z., M.G., S.J.; formal analysis R.H.M., Q.Z., S.L.; writing—original draft preparation, R.H.M., Q.Z., S.L.; writing—review and editing, R.H.M., Q.Z., M.G., S.J., S.L. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>The NSW Public Service Commission contributed funding to the University of Sydney to undertake the research.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest. This project was a collaboration as outlined in the Materials and Methods section. As such, the research design and context required involvement of PSC representatives, other public sector departments and employees, and people with lived experience. Involvement and various contributions of the collaborators is detailed in the Materials and Methods section. Two representatives and employees of the PSC (M.G. and S.J.) were involved in the conduct of the research, the writing of the manuscript, and in the decision to publish the results.</p></notes><ref-list><title>References</title><ref id=\"B1-ijerph-17-05435\"><label>1.</label><element-citation publication-type=\"book\"><person-group person-group-type=\"author\"><collab>United Nations (UN)</collab></person-group><source>Convention on the Rights of Persons with Disabilities</source><comment>Treaty Ser</comment><publisher-name>United Nations (UN)</publisher-name><publisher-loc>Geneva, Switzerland</publisher-loc><year>2006</year><comment>Available online: <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.un.org/disabilities/\">http://www.un.org/disabilities/</ext-link></comment><date-in-citation content-type=\"access-date\" iso-8601-date=\"2017-02-20\">(accessed on 20 February 2017)</date-in-citation></element-citation></ref><ref id=\"B2-ijerph-17-05435\"><label>2.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Verbrugge</surname><given-names>L.M.</given-names></name><name><surname>Merrill</surname><given-names>S.S.</given-names></name><name><surname>Liu</surname><given-names>X.</given-names></name></person-group><article-title>Measuring disability with parsimony</article-title><source>Disabil. 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Stud.</source><year>2014</year><volume>28</volume><fpage>38</fpage><lpage>68</lpage><pub-id pub-id-type=\"doi\">10.1093/jrs/feu020</pub-id></element-citation></ref></ref-list></back><floats-group><table-wrap id=\"ijerph-17-05435-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05435-t001_Table 1</object-id><label>Table 1</label><caption><p>Trends in labour force participation and employment of people with disability in Australia (15–64 years).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">2009</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">2012</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">2015</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Percentage of people with disability who are in the labour force</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">54.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">±</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">52.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">±</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">53.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">±</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.5</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Percentage of people with disability who are employed</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">50.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">±</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">47.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">±</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">48.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">±</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.6</td></tr></tbody></table><table-wrap-foot><fn><p>Source: Table 15A.73 and 5A.74 [<xref rid=\"B8-ijerph-17-05435\" ref-type=\"bibr\">8</xref>].</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05435-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05435-t002_Table 2</object-id><label>Table 2</label><caption><p>Project elements to ensure project criteria met.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Criterion for D and E Questions</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Survey or Other Study Elements</th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>Improved response rate to disability questions (requiring clarity, meaningfulness, and user-friendliness) based on broad categorisations preserving non-identifiability of individuals.</p></list-item></list>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Clarity—Q6 (how easy is question D1 to understand?).<break/>Meaning—Q7 (do words need explanation); and Q9 and Q10 about preference for examples of life areas and health conditions.<break/>User-friendliness—Q8 (comfort answering).</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>Improved data quality for disability in NSW public sector (validity in terms of alignment with key current disability concepts).</p></list-item></list>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mapping to instruments (Stage 1).</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>Ability to compare NSW public sector workforce with NSW population (ABS data) (requiring use of similar concepts to those of the ABS SDAC).</p></list-item></list>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mapping to ABS data concepts (Stage 1), informal consultation with ABS (Stage 2), and question design (Stage 3).</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>Better evidence base to inform policy interventions (requires personal and policy relevance of the questions).</p></list-item></list>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mapping (Stage 1), focus groups (Stage 2), online survey (Stage 3).</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05435-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05435-t003_Table 3</object-id><label>Table 3</label><caption><p>Key concepts of disability that support data comparability, for use in surveys.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Components of Functioning</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Environment and Effects</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Disability Being Related to <break/> “Health Condition”</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Participation <break/> Activities <break/> Impairment <break/> Difficulty with Activities</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Environment <break/> Interaction <break/> Barrier <underline>in</underline> environment, <underline>hindering</underline> participation <break/> Difficulty <underline>with</underline> participation <underline>in</underline> environment <break/> … on an <underline>equal basis</underline></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Long-term health condition <break/>Impairment <break/> Restriction</td></tr></tbody></table><table-wrap-foot><fn><p>Underline in table is to emphasize the meaning.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05435-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05435-t004_Table 4</object-id><label>Table 4</label><caption><p>Responses to D1 and Q11.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th colspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Question D1 on Difficulty in Listed Areas of Life</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Question 11: Do you have a disability?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">“I sometimes or always experience difficulty in at least one area, even if…”</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">“No, but I use equipment, technology, assistance or other techniques to avoid difficulty”</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No difficulty</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">52</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">223</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">261</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">45</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">37</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">231</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">313</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05435-t005\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05435-t005_Table 5</object-id><label>Table 5</label><caption><p>Responses to D2 and Q11.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th colspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Question D2 on Difficulty in Listed Areas of Life</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Question 11: Do you have a disability?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">“I sometimes or always experience difficulty in at least one area, even if…”</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">“No, but I use equipment, technology, assistance or other techniques to avoid difficulty”</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No difficulty</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">42</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">59</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">144</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">161</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">153</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">220</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05435-t006\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05435-t006_Table 6</object-id><label>Table 6</label><caption><p>Results for questions D1 and D2 (Surveys 1 and 2).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Survey 1</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Survey 2</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">User friendliness of the question (how easy to understand) (Q6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">D1: 93.3% </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">D2: 90.9% </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Further explanation of wording needed (Q7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">D1: 12.5% </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">D2: 15.5%</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Which (question) do you feel more comfortable answering? (Q8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">D2: 52.1%, vs. D1: 47.9% </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">D1: 62.7%, vs. D2: 37.3% </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Listing of everyday life areas with examples preferred (Q9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">67.7% yes, as in D1 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">69.1% yes, as in D1 </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Examples of “long-term health condition” helpful (Q10)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">64.5% yes, as in D1 </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60.0% yes, as in D1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Whether report yes/no to “do you have a disability?” (Q11)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes: 16.6% <break/> No: 83.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes: 26.8% <break/> No: 73.2 </td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05435-t007\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05435-t007_Table 7</object-id><label>Table 7</label><caption><p>Responses to environment questions.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Survey 1</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Survey 2</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Answer to test question, i.e., need to improve/adjust aspects of environment<break/> (Q13)</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>EN1—Improvement needed in</bold>\n<list list-type=\"bullet\"><list-item><p>23.0%—support and relationships</p></list-item><list-item><p>31.0%—attitudes</p></list-item><list-item><p>28.4%—services, systems, and policies</p></list-item><list-item><p>25.6%—products and technology</p></list-item><list-item><p>20.4%—natural environment and human-made changes</p></list-item><list-item><p>40.6%—no improvement needed</p></list-item></list>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>EN2—Adjustments needed in</bold>\n<list list-type=\"bullet\"><list-item><p>16.4%—support and relationships</p></list-item><list-item><p>16.4%—attitudes</p></list-item><list-item><p>20.5%—services, systems, and policies</p></list-item><list-item><p>24.1%—products and technology</p></list-item><list-item><p>12.3%—natural environment and human-made changes</p></list-item><list-item><p>56.4%—no adjustment needed</p></list-item></list>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">How easy is this question to understand? (user friendliness) (Q14)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">EN1: 5.1% rated EN1 as “not very easy to understand”</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">EN2: 1.4% rated EN2 as “very hard to understand, or not very easy to understand”</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Further explanation of wording needed? (Q15)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes: 7.1% <break/>No: 92.9% </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes: 11.4%<break/>No: 88.6% </td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Which question do you feel more comfortable answering? (Q16)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">EN1: 58.8% <break/>EN2: 41.2% </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">EN1: 50.7% <break/>EN2: 49.3% </td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Improvements/adjustments to aspects of environment<break/>(Q18)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><bold>EP1—Improvement made in</bold><break/>14.4%—support and relationships<break/>9.3%—attitudes<break/>12.5%—services, systems, and policies<break/>20.8%—products and technology<break/>5.4%—natural environment and human-made changes<break/>57.2%—no improvement made </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><bold>EP2—Adjustment made in</bold><break/>11.6%—support and relationships<break/>5.9%—attitudes<break/>13.2%—services, systems, and policies<break/>17.3%—products and technology<break/>7.3%—natural environment and human-made changes<break/>64.5%—no adjustment made </td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">How easy is the question to understand? (user friendliness) (Q19)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">EP1: 3.9% rated it as “very hard to understand, or not very easy to understand”</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">EP2: 3.4% rated it as “not very easy to understand”</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Further explanation of wording needed? (Q20)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes: 6.0% </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes: 10.3% </td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Which question do you feel more comfortable answering? (Q21)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">EP1: 54.8% <break/>\nEP2: 45.2% </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">EP1: 52.0% <break/>\nEP2: 48.0% </td></tr></tbody></table></table-wrap></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751354</article-id><article-id pub-id-type=\"pmc\">PMC7432012</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155399</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05399</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Review</subject></subj-group></article-categories><title-group><article-title>Mechanotransduction and Stiffness-Sensing: Mechanisms and Opportunities to Control Multiple Molecular Aspects of Cell Phenotype as a Design Cornerstone of Cell-Instructive Biomaterials for Articular Cartilage Repair</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Selig</surname><given-names>Mischa</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05399\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05399\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Lauer</surname><given-names>Jasmin C.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05399\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05399\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-1876-2201</contrib-id><name><surname>Hart</surname><given-names>Melanie L.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05399\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-3275-8196</contrib-id><name><surname>Rolauffs</surname><given-names>Bernd</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05399\">1</xref><xref rid=\"c1-ijms-21-05399\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05399\"><label>1</label>G.E.R.N. Research Center for Tissue Replacement, Regeneration & Neogenesis, Department of Orthopedics and Trauma Surgery, Faculty of Medicine, Medical Center—Albert-Ludwigs-University of Freiburg, 79085 Freiburg im Breisgau, Germany; <email>mischa.selig@uniklinik-freiburg.de</email> (M.S.); <email>jasmin.lauer@uniklinik-freiburg.de</email> (J.C.L.); <email>melaniehar@gmail.com</email> (M.L.H.)</aff><aff id=\"af2-ijms-21-05399\"><label>2</label>Faculty of Biology, University of Freiburg, Schaenzlestrasse 1, D-79104 Freiburg, Germany</aff><author-notes><corresp id=\"c1-ijms-21-05399\"><label>*</label>Correspondence: <email>berndrolauffs@googlemail.com</email>; Tel.: +49-761-270-26101</corresp></author-notes><pub-date pub-type=\"epub\"><day>29</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5399</elocation-id><history><date date-type=\"received\"><day>05</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>27</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Since material stiffness controls many cell functions, we reviewed the currently available knowledge on stiffness sensing and elucidated what is known in the context of clinical and experimental articular cartilage (AC) repair. Remarkably, no stiffness information on the various biomaterials for clinical AC repair was accessible. Using mRNA expression profiles and morphology as surrogate markers of stiffness-related effects, we deduced that the various clinically available biomaterials control chondrocyte (CH) phenotype well, but not to equal extents, and only in non-degenerative settings. Ample evidence demonstrates that multiple molecular aspects of CH and mesenchymal stromal cell (MSC) phenotype are susceptible to material stiffness, because proliferation, migration, lineage determination, shape, cytoskeletal properties, expression profiles, cell surface receptor composition, integrin subunit expression, and nuclear shape and composition of CHs and/or MSCs are stiffness-regulated. Moreover, material stiffness modulates MSC immuno-modulatory and angiogenic properties, transforming growth factor beta 1 (TGF-β1)-induced lineage determination, and CH re-differentiation/de-differentiation, collagen type II fragment production, and TGF-β1- and interleukin 1 beta (IL-1β)-induced changes in cell stiffness and traction force. We then integrated the available molecular signaling data into a stiffness-regulated CH phenotype model. Overall, we recommend using material stiffness for controlling cell phenotype, as this would be a promising design cornerstone for novel future-oriented, cell-instructive biomaterials for clinical high-quality AC repair tissue.</p></abstract><kwd-group><kwd>mechanotransduction</kwd><kwd>stiffness sensing</kwd><kwd>mesenchymal stromal cells (MSCs)</kwd><kwd>chondrocyte</kwd><kwd>articular cartilage</kwd><kwd>osteoarthritis</kwd><kwd>cell shape</kwd><kwd>immunomodulation</kwd><kwd>phenotype modulation</kwd><kwd>de-differentiation</kwd><kwd>re-differentiation</kwd><kwd>biomaterials</kwd><kwd>cartilage repair</kwd><kwd>clinical</kwd><kwd>TGF-β</kwd><kwd>Rho-GTPases</kwd><kwd>Wnt</kwd><kwd>α-catenin</kwd><kwd>β-catenin</kwd><kwd>SRY-related HMG box gene 9 (SOX9)</kwd><kwd>RhoA/Rho associated protein kinase (ROCK)</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05399\"><title>1. Introduction</title><p>Microenvironmental stimuli control cell fate and function [<xref rid=\"B1-ijms-21-05399\" ref-type=\"bibr\">1</xref>]. One of the key biomechanical determinants is the stiffness of the extracellular matrix (ECM) [<xref rid=\"B2-ijms-21-05399\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijms-21-05399\" ref-type=\"bibr\">3</xref>], which is the scaffolding structure for tissues and organs that embeds the tissue-resident cells. How biophysical forces like stiffness are sensed by cells is investigated in the field of mechanobiology [<xref rid=\"B4-ijms-21-05399\" ref-type=\"bibr\">4</xref>], in which mechanotransduction studies unravel how these external forces and the intracellular forces are together converted into biochemical signals and cellular responses [<xref rid=\"B5-ijms-21-05399\" ref-type=\"bibr\">5</xref>].</p><p>Articular cartilage (AC) is a specialized tissue [<xref rid=\"B6-ijms-21-05399\" ref-type=\"bibr\">6</xref>] which primarily consists of water, collagen type II, proteoglycans, and other non-collagenous proteins and glycoproteins [<xref rid=\"B7-ijms-21-05399\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijms-21-05399\" ref-type=\"bibr\">8</xref>]. The chondrocytes (CHs) are the resident cells that build and maintain the AC matrix by synthesizing new ECM components. The CHs are present in both healthy [<xref rid=\"B9-ijms-21-05399\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijms-21-05399\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijms-21-05399\" ref-type=\"bibr\">11</xref>] and degenerative AC [<xref rid=\"B9-ijms-21-05399\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijms-21-05399\" ref-type=\"bibr\">10</xref>,<xref rid=\"B12-ijms-21-05399\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijms-21-05399\" ref-type=\"bibr\">13</xref>]. Osteoarthritis (OA) is a degenerative disease that affects the whole joint, including the AC, subchondral bone, synovial tissues as well as the menisci. A hallmark of this disease is a change in ECM stiffness [<xref rid=\"B14-ijms-21-05399\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijms-21-05399\" ref-type=\"bibr\">15</xref>], which has been associated with an altered composition of the AC matrix [<xref rid=\"B16-ijms-21-05399\" ref-type=\"bibr\">16</xref>], based on a lower proteoglycan synthesis rate, changes in the content and synthesis of the ECM collagen types [<xref rid=\"B17-ijms-21-05399\" ref-type=\"bibr\">17</xref>], an “unbundling” of prototypic collagen fibrils [<xref rid=\"B18-ijms-21-05399\" ref-type=\"bibr\">18</xref>], and damage to the collagen network with subsequent proteoglycan depletion [<xref rid=\"B19-ijms-21-05399\" ref-type=\"bibr\">19</xref>]. The underlying correlations between ECM composition and the mechanical properties of AC have been explored in detail for healthy, developing, degenerating, and post-injurious AC [<xref rid=\"B20-ijms-21-05399\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijms-21-05399\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05399\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05399\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05399\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijms-21-05399\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijms-21-05399\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijms-21-05399\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05399\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijms-21-05399\" ref-type=\"bibr\">29</xref>]. Based on OA-related changes in ECM stiffness, a number of studies have subsequently examined how biomechanical stiffness influences CH morphology and phenotype. However, even after a decade of mechanobiological research, it remains poorly understood how OA-associated ECM stiffness changes affect CH phenotype and, thus, alter cell behavior during disease progression. </p><p>Therefore, the aim of this review is to summarize how cells and specifically CHs and mesenchymal stem cells (MSCs) sense stiffness, and to answer whether the approach to control material stiffness for controlling cell fate is effective in controlling the phenotype and differentiation of CHs and MSCs, as these are key cells involved in AC repair [<xref rid=\"B30-ijms-21-05399\" ref-type=\"bibr\">30</xref>]. Secondly, we aimed to answer if or how the current designs of clinically used biomaterials for AC repair account for utilizing material stiffness in this context, and whether using material stiffness as a cue for controlling cell phenotype would be a promising design cornerstone for novel future-oriented, cell-instructive biomaterials for clinical high-quality AC repair tissue. Overall, this review presents the available data on specific stiffness-related topics in dedicated chapters, whereas the discussion chapter focuses on interpreting these data and assembling a model of the material stiffness-dependency of CH phenotype.</p></sec><sec sec-type=\"methods\" id=\"sec2-ijms-21-05399\"><title>2. Clinical Use of CHs and MSCs in AC Repair Procedures</title><p>CHs are used for autologous chondrocyte implantation (ACI), which is an established and well-accepted procedure for the treatment of large, localized full-thickness AC defects in both the knee and ankle joints [<xref rid=\"B31-ijms-21-05399\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijms-21-05399\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijms-21-05399\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijms-21-05399\" ref-type=\"bibr\">34</xref>]. Microfracture, which is one of the most commonly performed surgical AC repair procedures, relies on the influx of MSCs from the surgically penetrated subchondral bone, to initiate (fibro-)cartilaginous repair [<xref rid=\"B35-ijms-21-05399\" ref-type=\"bibr\">35</xref>] of small localized AC defects [<xref rid=\"B31-ijms-21-05399\" ref-type=\"bibr\">31</xref>]. Moreover, MSCs are being used in an emerging clinical procedure termed “autologous matrix-induced chondrogenesis” (AMIC™), which, like microfracture, utilizes the influx of MSCs from the surgically penetrated subchondral bone, but in conjunction with administration of a collagen type I/III membrane [<xref rid=\"B36-ijms-21-05399\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijms-21-05399\" ref-type=\"bibr\">37</xref>].</p></sec><sec id=\"sec3-ijms-21-05399\"><title>3. Overview: How Do Cells Sense Their Environment?</title><p>The ECM provides structural tissue integrity, tissue boundaries, and initiates mechano-sensitive signaling pathways within the attached cells, which then influence cell spreading, migration, proliferation, cell cycle progression, and stem cell differentiation [<xref rid=\"B2-ijms-21-05399\" ref-type=\"bibr\">2</xref>,<xref rid=\"B38-ijms-21-05399\" ref-type=\"bibr\">38</xref>,<xref rid=\"B39-ijms-21-05399\" ref-type=\"bibr\">39</xref>]. Each tissue has its characteristic stiffness, which is the structural property, to which a scaffold, or substrate resists deformation in response to an applied force [<xref rid=\"B40-ijms-21-05399\" ref-type=\"bibr\">40</xref>]. This stiffness is determined by the molecular composition and arrangement of the ECM and its measurement depends on its physical dimensions and boundary conditions, whereas the elastic modulus is a material property. Material stiffness can vary across multiple length scales such as the nano-, micro-, and millimeter scales [<xref rid=\"B41-ijms-21-05399\" ref-type=\"bibr\">41</xref>] but each tissue has a characteristic stiffness that serves its physiological needs. Soft tissues like the brain have a relatively low elastic modulus of approximately 1 kPa [<xref rid=\"B42-ijms-21-05399\" ref-type=\"bibr\">42</xref>], while muscle tissue have an intermediate elastic modulus of roughly 10 kPa [<xref rid=\"B43-ijms-21-05399\" ref-type=\"bibr\">43</xref>], whereas AC exhibits an elastic modulus of 70 kPa [<xref rid=\"B28-ijms-21-05399\" ref-type=\"bibr\">28</xref>] and more rigid structures like bone exhibit a relatively high elastic modulus of 100 kPa [<xref rid=\"B44-ijms-21-05399\" ref-type=\"bibr\">44</xref>]. Cells sense and respond to changes of the ECM stiffness, which is a process that is termed ‘mechanosensing‘ in mechanobiology. In the following text sections, we review proteins and structures such as focal adhesions (FAs), integrins, FA focal complex, stress fibers, Rho GTPases, and focal adhesion kinases that are involved in stiffness sensing.</p></sec><sec id=\"sec4-ijms-21-05399\"><title>4. Stiffness Sensing </title><p>Mechanical sensing occurs through probing and contraction of actin fibers, which pull and deform the surrounding ECM. Cells exert contraction forces onto their substrate and subsequently adjust their cell-ECM adhesion strength through changes in FA composition and size, and through their cytoskeletal re-arrangements [<xref rid=\"B2-ijms-21-05399\" ref-type=\"bibr\">2</xref>] by modulating endogenous cytoskeleton contractility [<xref rid=\"B45-ijms-21-05399\" ref-type=\"bibr\">45</xref>]. This leads to a homeostasis in the forces between intracellular forces due to cytoskeletal contractility and extracellular forces that resist this contractility through the stiffness of the ECM. In this context, multiple studies have focused on the mechanosensory mechanisms that range from the behavior of individual proteins or protein assemblies such as stretch-sensitive ion channels and adhesion complexes to mechanisms such as actin cytoskeleton remodeling on a more cellular scale [<xref rid=\"B46-ijms-21-05399\" ref-type=\"bibr\">46</xref>]. Although a complete picture has yet to be generated, it is thought that stiffness sensing is mediated by a kinetic mechanism, in which the loading rate on the integrin-actin linkage, which is the connection between the ECM-cell interface and the cytoskeleton, varies [<xref rid=\"B47-ijms-21-05399\" ref-type=\"bibr\">47</xref>]. While cells exert traction forces on the substrate, tension across this linkage is increased. On softer substrates, substrate movement might reduce the loading rate on the integrin-actin linkage and force increases slowly, whereas stiff surfaces with relatively low substrate movement might increase the loading rate on the integrin-actin linkage, which then leads to overall FA strengthening [<xref rid=\"B47-ijms-21-05399\" ref-type=\"bibr\">47</xref>].</p></sec><sec id=\"sec5-ijms-21-05399\"><title>5. Proteins and Structures Involved in Stiffness Sensing</title><sec id=\"sec5dot1-ijms-21-05399\"><title>5.1. Focal Adhesions</title><p>A key structure involved in force sensing and the activation of mechanotransduction pathways is the FA. FAs physically connect the actin cytoskeleton to the ECM [<xref rid=\"B48-ijms-21-05399\" ref-type=\"bibr\">48</xref>,<xref rid=\"B49-ijms-21-05399\" ref-type=\"bibr\">49</xref>,<xref rid=\"B50-ijms-21-05399\" ref-type=\"bibr\">50</xref>]. Moreover, FAs are dynamic structures because changes in the substrate affect FA size and composition through molecular assembly and disassembly by protein turnover [<xref rid=\"B51-ijms-21-05399\" ref-type=\"bibr\">51</xref>,<xref rid=\"B52-ijms-21-05399\" ref-type=\"bibr\">52</xref>,<xref rid=\"B53-ijms-21-05399\" ref-type=\"bibr\">53</xref>,<xref rid=\"B54-ijms-21-05399\" ref-type=\"bibr\">54</xref>]. Thus, cells grown on stiffer substrates have larger and more stable FAs, with increased F-actin assembly and increased adhesion [<xref rid=\"B43-ijms-21-05399\" ref-type=\"bibr\">43</xref>], whereas cells on softer substrates display small and punctuated but still dynamic FAs [<xref rid=\"B55-ijms-21-05399\" ref-type=\"bibr\">55</xref>,<xref rid=\"B56-ijms-21-05399\" ref-type=\"bibr\">56</xref>]. </p><p>The FA structure has a defined nano-scale architecture, in which the ‘integrin signaling layer’ (<xref ref-type=\"fig\" rid=\"ijms-21-05399-f001\">Figure 1</xref>) contains the cell’s integrins. In this layer, paxillin and focal adhesion kinase (FAK) localize with the integrin cytoplasmic tails [<xref rid=\"B57-ijms-21-05399\" ref-type=\"bibr\">57</xref>,<xref rid=\"B58-ijms-21-05399\" ref-type=\"bibr\">58</xref>] in order to recruit further signaling molecules and to regulate actin cytoskeleton remodeling. Paxillin is a multi-domain adaptor between the plasma membrane and the actin cytoskeleton [<xref rid=\"B59-ijms-21-05399\" ref-type=\"bibr\">59</xref>] that serves as a docking protein for recruiting signaling molecules [<xref rid=\"B60-ijms-21-05399\" ref-type=\"bibr\">60</xref>]. FAK is a cytoplasmic tyrosine kinase that localizes to FAs and regulates actin cytoskeleton remodeling for integrin-mediated processes such as cell spreading and migration [<xref rid=\"B61-ijms-21-05399\" ref-type=\"bibr\">61</xref>], for example, by participating in actin polymerization [<xref rid=\"B62-ijms-21-05399\" ref-type=\"bibr\">62</xref>,<xref rid=\"B63-ijms-21-05399\" ref-type=\"bibr\">63</xref>]. The ‘force transduction layer’ contains mechanosensitive proteins such as talin and vinculin [<xref rid=\"B64-ijms-21-05399\" ref-type=\"bibr\">64</xref>,<xref rid=\"B65-ijms-21-05399\" ref-type=\"bibr\">65</xref>], which play key roles in mechanotransduction, as recent evidence suggests that talin is the key force-sensing molecule and vinculin one of the key mechanoeffectors [<xref rid=\"B66-ijms-21-05399\" ref-type=\"bibr\">66</xref>]. Talin links integrins directly to actin, regulates integrin adhesion strength, increases FA size in response to force, and increases the affinity of integrin for ligands [<xref rid=\"B66-ijms-21-05399\" ref-type=\"bibr\">66</xref>]. Moreover, upon mechanical stretching of the molecule talin exposes otherwise not accessible binding sites for the recruitment of additional FA proteins such as vinculin. Vinculin recruitment reinforces the FAs, as recruited vinculin crosslinks an actin filament to the talin molecule [<xref rid=\"B67-ijms-21-05399\" ref-type=\"bibr\">67</xref>]. This binding of talin to actin filaments by vinculin or other FA forming molecules upon talin stretching is a critical step in mechanically linking the cell and ECM [<xref rid=\"B68-ijms-21-05399\" ref-type=\"bibr\">68</xref>]. Recently, it has been reported that talin in the FAs is under tension, that vinculin increases talin tension, and that softer substrates decrease talin tension [<xref rid=\"B47-ijms-21-05399\" ref-type=\"bibr\">47</xref>]. This appears relevant, as local talin tension correlates with F-actin stress fiber alignment [<xref rid=\"B69-ijms-21-05399\" ref-type=\"bibr\">69</xref>] and recent evidence suggests a complex relationship between talin tension, actin/vinculin localization, local actin organization, and FA dynamics [<xref rid=\"B69-ijms-21-05399\" ref-type=\"bibr\">69</xref>]. Subsequently, talin-depleted cells have a decreased ability to stiffen in response to generated tension [<xref rid=\"B70-ijms-21-05399\" ref-type=\"bibr\">70</xref>,<xref rid=\"B71-ijms-21-05399\" ref-type=\"bibr\">71</xref>]. For example, brain tumor glioblastoma multiforme cells are highly sensitive to ECM stiffness but their cytoskeletal stiffness is irrespective of ECM stiffness when talin-1 is depleted [<xref rid=\"B72-ijms-21-05399\" ref-type=\"bibr\">72</xref>]. Moreover, inhibiting the paxillin–vinculin interaction or depleting vinculin reduces FA force transmission and depletes tugging FA traction dynamics [<xref rid=\"B73-ijms-21-05399\" ref-type=\"bibr\">73</xref>]. Above the ‘force transduction layer’, the ‘actin regulatory layer’ is situated, in which proteins like α-actinin, zyxin, and vasodilator-stimulated phosphoprotein (VASP) accumulate and induce actin nucleation and polymerization to modulate the cytoskeleton. Of these proteins, zyxin facilitates cytoskeletal tension-dependent actin polymerization at FAs [<xref rid=\"B74-ijms-21-05399\" ref-type=\"bibr\">74</xref>] and α-actinin integrates mechanical forces to establish actin network symmetry [<xref rid=\"B75-ijms-21-05399\" ref-type=\"bibr\">75</xref>]. Collectively, these studies highlight the complex mechanosensitive mechanisms that enable stiffness-sensing at the FA-ECM border.</p></sec><sec id=\"sec5dot2-ijms-21-05399\"><title>5.2. FA Focal Complex</title><p>The starting point of a FA is called a focal complex, which is built at the lamellipodia of cells and regulated by Rac1 and Cdc42. A focal complex is characterized by its high throughput assembly of proteins. Upon mechanical stimulation, the focal complex matures to a FA and F-actin is assembled and cross-linked by myosin phosphatase II (myosin II) [<xref rid=\"B76-ijms-21-05399\" ref-type=\"bibr\">76</xref>]. The focal complex becomes a mature FA and it has been shown that force input induces growth and FA maturation [<xref rid=\"B77-ijms-21-05399\" ref-type=\"bibr\">77</xref>]. On substrates with increased stiffness, more integrins aggregate, which leads to enlarged FA complexes with enhanced aggregation of proteins and polymerization of more actin fibers. Spreading cells use many lamellipodia, which then establish new FA complexes [<xref rid=\"B53-ijms-21-05399\" ref-type=\"bibr\">53</xref>,<xref rid=\"B78-ijms-21-05399\" ref-type=\"bibr\">78</xref>].</p></sec><sec id=\"sec5dot3-ijms-21-05399\"><title>5.3. Integrins</title><p>In this context, integrins are FA key elements. This transmembrane receptor family consists of 18 α- and 8 β-subunits, building many heterodimers. The extracellular domain of integrins allows them to recognize ECM proteins such as fibronectin [<xref rid=\"B79-ijms-21-05399\" ref-type=\"bibr\">79</xref>], collagen [<xref rid=\"B80-ijms-21-05399\" ref-type=\"bibr\">80</xref>], laminin [<xref rid=\"B81-ijms-21-05399\" ref-type=\"bibr\">81</xref>] as well as other ECM proteins. The cytoplasmic tail of the integrins enables interactions with various FA proteins. The β-subunit binds to proteins like talin, which binds to the cytoplasmic tail of integrins [<xref rid=\"B82-ijms-21-05399\" ref-type=\"bibr\">82</xref>], to α-actinin, an actin filament crosslinking protein [<xref rid=\"B83-ijms-21-05399\" ref-type=\"bibr\">83</xref>], and to kindlin, which is also a regulator of integrin activation and cytoskeletal reorganization [<xref rid=\"B84-ijms-21-05399\" ref-type=\"bibr\">84</xref>], as integrins cannot directly bind to the actin cytoskeleton [<xref rid=\"B85-ijms-21-05399\" ref-type=\"bibr\">85</xref>,<xref rid=\"B86-ijms-21-05399\" ref-type=\"bibr\">86</xref>,<xref rid=\"B87-ijms-21-05399\" ref-type=\"bibr\">87</xref>].</p></sec><sec id=\"sec5dot4-ijms-21-05399\"><title>5.4. Focal Adhesion Kinase</title><p>FAK is a central element in mechanotransduction, as it is involved in both inside-out and outside-in signaling activation [<xref rid=\"B88-ijms-21-05399\" ref-type=\"bibr\">88</xref>,<xref rid=\"B89-ijms-21-05399\" ref-type=\"bibr\">89</xref>,<xref rid=\"B90-ijms-21-05399\" ref-type=\"bibr\">90</xref>,<xref rid=\"B91-ijms-21-05399\" ref-type=\"bibr\">91</xref>] and, thus, controls endogenous cytoskeleton contractility and multiple other cell functions (<xref ref-type=\"fig\" rid=\"ijms-21-05399-f002\">Figure 2</xref>). The recruitment of FAs leads to activation of FAK through phosphorylation [<xref rid=\"B92-ijms-21-05399\" ref-type=\"bibr\">92</xref>], which then participates in actin polymerization and, thus, is generally involved in cellular adhesion, proliferation, and cell spreading. Cell proliferation is induced through activation of extracellular signal-regulated kinases (ERKs) via FAK. In addition, cell migration is controlled by FAK, which sequesters and activates Src family kinases, leading to further phosphorylation of p130 Cas and Rac1 [<xref rid=\"B93-ijms-21-05399\" ref-type=\"bibr\">93</xref>]. Another target of activated FAK is paxillin, which initiates mitogen-activated protein kinase kinase (MEK), leading to downstream activation of ERK1/2 and the myosin light chain kinase (MLCK) to control endogenous actin contractility of the cytoskeleton. ERK1/2 controls the differentiation of stem cells into osteocytes on rigid substrates [<xref rid=\"B94-ijms-21-05399\" ref-type=\"bibr\">94</xref>] and is involved in the differentiation of cardiac fibroblasts to myofibroblasts in a stiffness-dependent manner [<xref rid=\"B95-ijms-21-05399\" ref-type=\"bibr\">95</xref>]. In addition to differentiation control, ERK1/2 mediates cell proliferation and apoptosis [<xref rid=\"B96-ijms-21-05399\" ref-type=\"bibr\">96</xref>,<xref rid=\"B97-ijms-21-05399\" ref-type=\"bibr\">97</xref>]. ERK1/2 is also regulated by the Rho protein kinases Rac, Rho, and RhoA/Rho associated protein kinase (ROCK) [<xref rid=\"B94-ijms-21-05399\" ref-type=\"bibr\">94</xref>,<xref rid=\"B98-ijms-21-05399\" ref-type=\"bibr\">98</xref>,<xref rid=\"B99-ijms-21-05399\" ref-type=\"bibr\">99</xref>,<xref rid=\"B100-ijms-21-05399\" ref-type=\"bibr\">100</xref>]. Other studies have shown that the RhoA/ROCK pathway is also capable of influencing myosin contractility and is activated through FAK and Src [<xref rid=\"B101-ijms-21-05399\" ref-type=\"bibr\">101</xref>,<xref rid=\"B102-ijms-21-05399\" ref-type=\"bibr\">102</xref>,<xref rid=\"B103-ijms-21-05399\" ref-type=\"bibr\">103</xref>].</p></sec><sec id=\"sec5dot5-ijms-21-05399\"><title>5.5. Rho GTPases </title><p>The family of Rho GTPases includes 20 protein members and the most prominent ones involved in stiffness sensing are RhoA, Rac1, and Cdc42. Biomechanical tension activates RhoA/Rho associated protein kinase (ROCK) signaling, which implies that matrix stiffness also modulates cell cytoskeletal organization. Indeed, on hard substrates, an increase of RhoA expression and its contributing effect on the formation of actin stress fibers, compared to less stiff substrates, was demonstrated [<xref rid=\"B104-ijms-21-05399\" ref-type=\"bibr\">104</xref>]. The RhoA/ROCK pathway also influences myosin contractility and is activated through FAK and Src [<xref rid=\"B101-ijms-21-05399\" ref-type=\"bibr\">101</xref>,<xref rid=\"B102-ijms-21-05399\" ref-type=\"bibr\">102</xref>,<xref rid=\"B103-ijms-21-05399\" ref-type=\"bibr\">103</xref>]. RhoA is also activated by guanine-exchange factors (GEFs) and, just as RhoA [<xref rid=\"B105-ijms-21-05399\" ref-type=\"bibr\">105</xref>], demonstrated that under mechanical force input GEF-H1 shows an increased activity. Activated RhoA increases actin nucleation and polymerization to induce formation of stress fibers [<xref rid=\"B104-ijms-21-05399\" ref-type=\"bibr\">104</xref>] via the diaphanous formins, mDia1 and mDia2, leading to long and straight actin fibers [<xref rid=\"B106-ijms-21-05399\" ref-type=\"bibr\">106</xref>]. ROCK is a serine/threonine kinase and a downstream effector of active RhoA. Through phosphorylation of the myosin-binding subunit of myosin II, cross-linking of actin filaments is initiated. Another target of ROCK is LIM kinase-1 (LIMK1). Phosphorylated LIMK1 leads to phosphorylation and therefore inactivation of cofilin, which normally inhibits actin polymerization. ROCK activity increases with substrate stiffness, as cells on stiffer substrates have a higher ROCK activity than cells cultured on softer substrates [<xref rid=\"B107-ijms-21-05399\" ref-type=\"bibr\">107</xref>]. To demonstrate that stiffness sensing on soft substrates also occurs in CHs through ROCK, one study treated primary murine CHs (mCHs) with the ROCK inhibitor Y27632 and demonstrated a complete repression of the ROCK-dependent expression of collagen type II and SRY-related HMG box-containing (SOX9) [<xref rid=\"B108-ijms-21-05399\" ref-type=\"bibr\">108</xref>], an early chondrogenic gene marker. This confirmed that ROCK plays a key role in the stiffness sensing ability of (m)CHs <sup>108</sup>. To sum it up, the RhoA/ROCK signaling pathway establishes a functional actin cytoskeleton and studies have shown that biomechanical stiffness changes expression of RhoA and concomitantly ROCK [<xref rid=\"B104-ijms-21-05399\" ref-type=\"bibr\">104</xref>]. Moreover, there is extensive crosstalk between integrins, Src-family kinases such as FAK and the Rho-family GTPases at the center of adhesion signaling [<xref rid=\"B109-ijms-21-05399\" ref-type=\"bibr\">109</xref>].</p></sec><sec id=\"sec5dot6-ijms-21-05399\"><title>5.6. Stress Fibers</title><p>Stress fibers are bundles of contractile F-actin filaments, which are mainly cross-linked by non-muscle myosin II. Four types of stress fibers have been identified: dorsal stress fibers, ventral stress fibers, transverse arcs, and the perinuclear actin cap, which is a mediator of nuclear mechanotransduction [<xref rid=\"B110-ijms-21-05399\" ref-type=\"bibr\">110</xref>]. However, this has not yet been demonstrated, as no F-actin structural sub-analysis has been performed in CHs. The filamentous singular units are monomeric G-actin and filamentation is initiated by the formin-family of actin nucleators/elongation factors, like filamin, α-actinin, and cortactin, which are activated by the Rho GTPases and phosphoinositides [<xref rid=\"B111-ijms-21-05399\" ref-type=\"bibr\">111</xref>,<xref rid=\"B112-ijms-21-05399\" ref-type=\"bibr\">112</xref>,<xref rid=\"B113-ijms-21-05399\" ref-type=\"bibr\">113</xref>]. The branched filaments are formed through actin-related protein 2/actin-related protein 3 (Arp2/3) and other nucleation promoting factors. Branched actin is then formed at the site of Arp2/3 from the existing actin filament [<xref rid=\"B114-ijms-21-05399\" ref-type=\"bibr\">114</xref>,<xref rid=\"B115-ijms-21-05399\" ref-type=\"bibr\">115</xref>] and cross-linked by myosin II. Interestingly, stiffer substrates increase intracellular contractility through an increase in stress fibers [<xref rid=\"B115-ijms-21-05399\" ref-type=\"bibr\">115</xref>].</p></sec></sec><sec id=\"sec6-ijms-21-05399\"><title>6. Material Stiffness-Regulated Cell Proliferation</title><p>ECM stiffness is able to regulate cell proliferation. Studies that increased substrate stiffness from softer to stiffer also increased the proliferation of cancer cells, fibroblasts [<xref rid=\"B116-ijms-21-05399\" ref-type=\"bibr\">116</xref>,<xref rid=\"B117-ijms-21-05399\" ref-type=\"bibr\">117</xref>,<xref rid=\"B118-ijms-21-05399\" ref-type=\"bibr\">118</xref>,<xref rid=\"B119-ijms-21-05399\" ref-type=\"bibr\">119</xref>,<xref rid=\"B120-ijms-21-05399\" ref-type=\"bibr\">120</xref>], as well as human MSCs (hMSCs) [<xref rid=\"B121-ijms-21-05399\" ref-type=\"bibr\">121</xref>] and rat CHs (rCHs) [<xref rid=\"B122-ijms-21-05399\" ref-type=\"bibr\">122</xref>]. In this context, Rho GTPases, especially Rac1, are involved in modulating the expression of cyclin D1, which promotes S-phase entry of cells needed for inducing proliferation [<xref rid=\"B39-ijms-21-05399\" ref-type=\"bibr\">39</xref>]. Additionally, proliferation can also be initiated through integrin clustering. Subsequently, recruited FAK autophosphorylates bind Src and activate p130Cas. Further downstream, either Jun NH2-terminal kinase (JNK) or Rac are then activated. Phosphorylated JNK leads to increased expression of cyclin D1 [<xref rid=\"B123-ijms-21-05399\" ref-type=\"bibr\">123</xref>,<xref rid=\"B124-ijms-21-05399\" ref-type=\"bibr\">124</xref>]. Rac1, which has been shown to be involved in FAK and p130Cas signaling [<xref rid=\"B39-ijms-21-05399\" ref-type=\"bibr\">39</xref>], is required for induction of cyclin D1 [<xref rid=\"B125-ijms-21-05399\" ref-type=\"bibr\">125</xref>]. Thus, molecular pathways involved in stiffness sensing modulate S-phase entry and control the proliferation of cells. Interestingly, proliferation can also be regulated through the transcription co-activator of the yes-associated protein (YAP) and its transcriptional co-activator with PDZ-binding motif (TAZ) [<xref rid=\"B126-ijms-21-05399\" ref-type=\"bibr\">126</xref>], as cells grown on stiff substrates build more F-actin, spread, and display active YAP/TAZ in the nucleus. In turn, active nuclear YAP/TAZ promotes the proliferation of multiple cell types [<xref rid=\"B127-ijms-21-05399\" ref-type=\"bibr\">127</xref>]. Cells cultured on relatively soft substrates accumulate YAP/TAZ in the cytoplasm and thus, show a reduction of their proliferation rate [<xref rid=\"B126-ijms-21-05399\" ref-type=\"bibr\">126</xref>,<xref rid=\"B128-ijms-21-05399\" ref-type=\"bibr\">128</xref>]. Accordingly, on soft substrates, YAP retained in the cytoplasm undergoes a degradation process [<xref rid=\"B129-ijms-21-05399\" ref-type=\"bibr\">129</xref>], whereas stiff substrates induce YAP to translocate into the nucleus [<xref rid=\"B130-ijms-21-05399\" ref-type=\"bibr\">130</xref>], due to contractile forces generated by actomyosin activity that flatten the nucleus and open up nuclear pores. However, in hMSCs active nuclear YAP/TAZ appears to have no role in proliferation but promotes osteogenesis [<xref rid=\"B121-ijms-21-05399\" ref-type=\"bibr\">121</xref>] and the effects of YAP to promote osteogenic differentiation is based on an interaction of YAP with β-catenin [<xref rid=\"B131-ijms-21-05399\" ref-type=\"bibr\">131</xref>]. In the context of endochondral ossification, proliferation of early committed CHs is increased with YAP expression [<xref rid=\"B132-ijms-21-05399\" ref-type=\"bibr\">132</xref>]. In AC rCHs YAP downregulation on soft substrates helps maintain the CH phenotype while inhibiting CH proliferation [<xref rid=\"B122-ijms-21-05399\" ref-type=\"bibr\">122</xref>]. Collectively, it has been demonstrated that harder substrates induce relatively more cell spreading and proliferation than softer substrates [<xref rid=\"B133-ijms-21-05399\" ref-type=\"bibr\">133</xref>,<xref rid=\"B134-ijms-21-05399\" ref-type=\"bibr\">134</xref>,<xref rid=\"B135-ijms-21-05399\" ref-type=\"bibr\">135</xref>], and increased adhesion and traction forces. This was shown to be also true for hMSCs [<xref rid=\"B121-ijms-21-05399\" ref-type=\"bibr\">121</xref>] and rCHs [<xref rid=\"B122-ijms-21-05399\" ref-type=\"bibr\">122</xref>]. Such a phenomenon correlate with the amount of active RhoA expression [<xref rid=\"B134-ijms-21-05399\" ref-type=\"bibr\">134</xref>,<xref rid=\"B136-ijms-21-05399\" ref-type=\"bibr\">136</xref>], as cells on softer substrates exhibit less spreading and reduced proliferation and FA assembly.</p></sec><sec id=\"sec7-ijms-21-05399\"><title>7. Material Stiffness-Regulated Cell Migration</title><p>Cells can sense substrate stiffness through probing and contraction of actin fibers and migrate towards substrate areas of higher stiffness [<xref rid=\"B137-ijms-21-05399\" ref-type=\"bibr\">137</xref>,<xref rid=\"B138-ijms-21-05399\" ref-type=\"bibr\">138</xref>]. This movement is explained by the molecular clutch hypothesis. At the leading edge of the lamellipodium, new actin monomers are incorporated into the rising actin filament. Integrins are bound to the ECM upon activation and cluster. Integrin signaling promotes actin polymerization and matures FAs, which then are composed of a number of proteins that connect the ECM and the cytoskeleton. Force is transmitted to the ECM and the lamellipodium becomes the leading edge of the cell. On softer substrates, lamellipodia are unstable and integrins are not engaged by the ECM, leading to less actin polymerization, rapid retrograde cytoskeletal flow, and no net protrusion. Thus, traction forces from the cell are not transmitted to the ECM and, consequently, the cell does not move. Depending on the magnitude of the elastic modulus, hMSCs migrate faster on softer substrates such as 3 kPa and form smaller FAs, compared to a slower movement on substrates with a higher elastic modulus such as 30 and 600 kPa [<xref rid=\"B139-ijms-21-05399\" ref-type=\"bibr\">139</xref>]. However, on gradients within the range of physiologically relevant elastic moduli for soft tissues (i.e., 1–12 kPa), hMSCs migrated to the stiffest region on each gradient and their migration speed correlated with the gradient strength [<xref rid=\"B140-ijms-21-05399\" ref-type=\"bibr\">140</xref>]. Thus, material stiffness controls the direction and speed of hMSC migration on stiffness gradients.</p></sec><sec id=\"sec8-ijms-21-05399\"><title>8. Material Stiffness-Modulated MSC Shape and Lineage Determination</title><p>Material stiffness controls many cell functions such as cell shape [<xref rid=\"B42-ijms-21-05399\" ref-type=\"bibr\">42</xref>], adhesion [<xref rid=\"B141-ijms-21-05399\" ref-type=\"bibr\">141</xref>], migration [<xref rid=\"B137-ijms-21-05399\" ref-type=\"bibr\">137</xref>], differentiation [<xref rid=\"B44-ijms-21-05399\" ref-type=\"bibr\">44</xref>], and proliferation [<xref rid=\"B142-ijms-21-05399\" ref-type=\"bibr\">142</xref>,<xref rid=\"B143-ijms-21-05399\" ref-type=\"bibr\">143</xref>,<xref rid=\"B144-ijms-21-05399\" ref-type=\"bibr\">144</xref>]. Recently, cell morphology has received re-ignited attention, as measuring, predicting, and controlling cellular shape may aid in future regenerative medicine applications [<xref rid=\"B145-ijms-21-05399\" ref-type=\"bibr\">145</xref>]. The morphology of MSCs is influenced by microenvironmental and biophysical stimuli [<xref rid=\"B44-ijms-21-05399\" ref-type=\"bibr\">44</xref>,<xref rid=\"B45-ijms-21-05399\" ref-type=\"bibr\">45</xref>,<xref rid=\"B136-ijms-21-05399\" ref-type=\"bibr\">136</xref>,<xref rid=\"B146-ijms-21-05399\" ref-type=\"bibr\">146</xref>,<xref rid=\"B147-ijms-21-05399\" ref-type=\"bibr\">147</xref>] and is defined by how the cell balances external biomechanical forces with intracellular forces. The level of internal forces is directly proportional to the biomaterial stiffness of the substrate [<xref rid=\"B45-ijms-21-05399\" ref-type=\"bibr\">45</xref>]. In one of our studies [<xref rid=\"B148-ijms-21-05399\" ref-type=\"bibr\">148</xref>], the shape of hMSCs was engineered using (i) different biomaterials with similar stiffnesses vs. using (ii) the same biomaterial materials with different stiffnesses. Indeed, higher nanoscale stiffness, compared to a lower stiffness of the same biomaterial, was associated with rounder hMSCs, high aspect ratio and circularity, and a lower solidity. Thus, hMSCs cultured on biomaterials with different stiffnesses adopted cell shapes, which are characteristic of the used biomaterial. Interestingly, when comparing the effects of biomaterial stiffness vs. cyclic tension on hMSC shape [<xref rid=\"B148-ijms-21-05399\" ref-type=\"bibr\">148</xref>], dynamic tensile forces were more effective in defining hMSC shape than substrate stiffness. However, the biomechanical effects on cell shape were transient; once the application of mechanical force had been stopped, hMSC shape ultimately reversed back to the shape dictated by substrate stiffness. In accordance to stiffness-defined alterations in the shape of MSCs, hMSCs committed to a neuronal cell type lineage on soft hydrogels and adopted the dendritic, neuron-specific cell shape, whereas hMSCs underwent osteoblast differentiation on rigid substrates and adapted the polygonal [<xref rid=\"B44-ijms-21-05399\" ref-type=\"bibr\">44</xref>]/cuboidal [<xref rid=\"B149-ijms-21-05399\" ref-type=\"bibr\">149</xref>] osteoblast-specific shape. Thus, material stiffness modulates both MSC morphology and accompanying lineage determination.</p><p>Since the stem cell ability of self-renewal and differentiation potential makes MSCs especially attractive for applications in regenerative medicine [<xref rid=\"B150-ijms-21-05399\" ref-type=\"bibr\">150</xref>], it is relevant to further explore MSC characteristics and potential therapeutic usage in the context of material stiffness. For MSCs in particular, it has been shown that matrix stiffness has a major influence on hMSCs lineage determination [<xref rid=\"B44-ijms-21-05399\" ref-type=\"bibr\">44</xref>]. This study was the first to demonstrate in a 2D system that, in the absence of exogenous soluble factors, hMSCs can be differentiated in vitro into specific tissue lineages, and that this lineage commitment depended on substrate stiffness. Moreover, the cell fate-deciding stiffness corresponded to the in vivo mechanical tissue stiffnesses. Interestingly cell adjustment to the microenvironmental material properties relied on non-muscle myosin II, together with alterations in adhesion mechanics and the actin cytoskeleton structure [<xref rid=\"B44-ijms-21-05399\" ref-type=\"bibr\">44</xref>]. In this context, multiple studies that used 2D systems confirmed that substrate stiffness controls the differentiation potential of MSCs. Cells grown on soft substrates differentiate towards the neurogenic lineage, whereas intermediate stiffnesses induce myogenic differentiation, and higher stiffnesses commit MSCs to an osteogenic fate [<xref rid=\"B94-ijms-21-05399\" ref-type=\"bibr\">94</xref>,<xref rid=\"B151-ijms-21-05399\" ref-type=\"bibr\">151</xref>,<xref rid=\"B152-ijms-21-05399\" ref-type=\"bibr\">152</xref>,<xref rid=\"B153-ijms-21-05399\" ref-type=\"bibr\">153</xref>,<xref rid=\"B154-ijms-21-05399\" ref-type=\"bibr\">154</xref>]. Some of these fate-dependent decisions may be controlled through the YAP transcription factor as we explained above. For example, adipo-osteogenic differentiation of hMSCs has been shown to be regulated in part by YAP [<xref rid=\"B155-ijms-21-05399\" ref-type=\"bibr\">155</xref>]. In this context substrate mechanics control adipogenesis through YAP phosphorylation by dictating cell spreading [<xref rid=\"B156-ijms-21-05399\" ref-type=\"bibr\">156</xref>]. Additionally, YAP is a negative regulator of chondrogenic differentiation of MSCs, as downregulation of YAP for chondrogenesis is needed to alleviate the repressing effect of nuclear YAP on chondrogenic signaling [<xref rid=\"B157-ijms-21-05399\" ref-type=\"bibr\">157</xref>].</p><p>Like MSCs, human adipose-derived stem cells (hASCs) and neural stem cells (NSCs) are also influenced by substrate stiffness. On substrates with a stiffness similar to in vivo adipose tissue, hASCs differentiate into adipocytes, whereas on substrates with a stiffness complementary to muscle tissue the cells undergo myogenic differentiation and are capable of building myotubes [<xref rid=\"B158-ijms-21-05399\" ref-type=\"bibr\">158</xref>]. For rat NSCs (rNSCs), soft substrates promote neurogenesis, whereas rNSCs on harder substrates differentiate into oligodendrocytes [<xref rid=\"B159-ijms-21-05399\" ref-type=\"bibr\">159</xref>,<xref rid=\"B160-ijms-21-05399\" ref-type=\"bibr\">160</xref>]. Together, these studies highlight lineage determination of stem cells by material stiffness.</p></sec><sec id=\"sec9-ijms-21-05399\"><title>9. Material Stiffness-Modulated CH Shape, Cytoskeleton, and Phenotype</title><p>Biomaterial stiffness might be usable as a potential regeneration-inducing determinant, as the previous text section of this review discussed how material stiffness in the context of MSCs controls cell morphology and associated cell function(s). Thus, it would be beneficial to better understand how substrate stiffness influences the behavior of healthy and diseased CHs, and how such improved insight might be translated into improved strategies for AC repair strategies. Moreover, it has been well-established that a critical decrease in ECM stiffness has been implicated in OA-related changes in CH phenotype [<xref rid=\"B14-ijms-21-05399\" ref-type=\"bibr\">14</xref>], which illustrates that material stiffness is not only a parameter relevant for CH culture but also an important aspect of the many OA pathomechanism(s).</p><p>ECM mechanical cues including ECM stiffness, cell attachment or detachment, and cellular tension are potent regulators of YAP/TAZ [<xref rid=\"B161-ijms-21-05399\" ref-type=\"bibr\">161</xref>]. A critical decrease in ECM stiffness has been implicated in OA-related changes in CH phenotype [<xref rid=\"B14-ijms-21-05399\" ref-type=\"bibr\">14</xref>]. In fibroblasts, ECM stiffness mechanoactivates YAP/TAZ, which promote the production of pro-fibrotic mediators and ECM proteins. This results in tissue stiffness-mediated YAP/TAZ signaling as a molecular link between fibrosis and cancer [<xref rid=\"B161-ijms-21-05399\" ref-type=\"bibr\">161</xref>] and illustrates how stiff substrates can contribute to inducing fibrotic changes. However, in AC, OA-associated AC degradation is in part regulated by a reciprocal inhibition of YAP/TAZ and NF-κB (nuclear factor ’kappa-light-chain-enhancer’ of activated B-cells) signaling [<xref rid=\"B162-ijms-21-05399\" ref-type=\"bibr\">162</xref>], which illustrates a potential material stiffness-mediated role of YAP/TAZ in OA AC degradation. To clarify, YAP inactivation is conducive to the maintenance of a chondrogenic phenotype [<xref rid=\"B122-ijms-21-05399\" ref-type=\"bibr\">122</xref>], because relatively stiff substrates (40 kPa) increase YAP expression and YAP accumulation in the nucleus of rCHs, concomitant with high expression levels of collagen I and almost no collagen type II expression. In turn, relatively soft substrates (4 kPa) decrease YAP expression and cytoplasmic YAP accumulation, concomitant with high expression levels of collagen type II, SOX9, and aggrecan (ACAN). Additionally, YAP knockdown of rCHs on stiff substrates displayed significantly increased collagen type II, SOX9, and ACAN and decreased collagen type I expression.</p><p>An extensive study that focused on mechanistic aspects cultured mCHs in 2D on type II collagen-coated polyacrylamide (PAA) gels with elastic moduli between 4 and 31 kPa with a constant adhesion ligand composition [<xref rid=\"B163-ijms-21-05399\" ref-type=\"bibr\">163</xref>]. In 2D, increasing the elastic modulus induced mCH catabolism, downregulation of AC ECM molecules, disrupted SOX9 nuclear localization, and decreased SOX9 transcriptional activity. Softer 2D substrates (≤7 kPa) induced a round mCH morphology and stiffer substrates (12–31 kPa) promoted FAs and stress fiber formation. In mCHs on stiff substrates, Rho and ROCK activities were increased and the inhibition of Rho with C3 transferase, of ROCK with Y27632, and of myosin II ATPase with blebbistatin, or disruption of F-actin with cytochalasin D abolished stiffening-mediated FA and stress fiber formation, upregulation of matrix-degrading enzymes, downregulation of collagen type II (COL2A1), ACAN, and SOX9, and inhibition of SOX9 activity by restoring SOX9 nuclear localization [<xref rid=\"B163-ijms-21-05399\" ref-type=\"bibr\">163</xref>]. The here discussed study also used 3D collagen matrices for increasing the elastic modulus with lysyl oxidase (LOX), also known as protein-lysine 6-oxidase, which catalyzes the conversion of lysine molecules into reactive aldehydes, which form cross-links in ECM proteins. The LOX-treatment increased the 3D collagen hydrogel elastic modulus from <65 Pa to 90 Pa and induced in the embedded CHs an increase in the mRNA expression of matrix metalloproteinase (MMP)-3, MMP-13, and a disintegrin and metalloproteinase with thrombospondin motifs 5 (ADAMTS5), and a decrease in collagen type II and ACAN expression [<xref rid=\"B163-ijms-21-05399\" ref-type=\"bibr\">163</xref>], indicating than increasing the elastic modulus in 3D has comparable effects on hCHs in 3D vs. 2D.</p><p>In the context of the role of the elastic modulus-modulated CH phenotype, one study cultivated porcine CHs (pCHs) for two weeks in 3D agarose hydrogels with different substrate elasticities (3.7 vs. 53.2 kPa) and protein-modulated adhesion site densities [<xref rid=\"B164-ijms-21-05399\" ref-type=\"bibr\">164</xref>]. Interestingly, the pCHs maintained their chondrogenic phenotype independently of the substrates, but softer gels led to higher DNA and glycosaminoglycan (GAG) contents and larger cell clusters than stiff gels. Since this occurred in both Arg-Gly-Asp (RGD)- and arginine-glycine-glutamic acid (RGE)-modified agarose, the authors hypothesized that matrix elasticity in the tested range did not influence the maintenance of the chondrogenic phenotype in 3D but rather the size of the formed cell clusters [<xref rid=\"B164-ijms-21-05399\" ref-type=\"bibr\">164</xref>]. However, another study explained such findings differently and suggested cell sensing of cell volume confinement as an adhesion-independent mechanism of mechanotransduction in 3D culture [<xref rid=\"B165-ijms-21-05399\" ref-type=\"bibr\">165</xref>]. Whether such a mechanism is subject to substrate stiffness has not been demonstrated yet. Another study investigated how matrix elasticity influences CH differentiation and phenotype. pCHs were cultured for seven days in 2D on polyacrylamide (PAA) hydrogels having lower (4 kPa) and higher elastic moduli (10, 40, and 100 kPa) [<xref rid=\"B166-ijms-21-05399\" ref-type=\"bibr\">166</xref>]. Interestingly, pCHs on 4 kPa PAA hydrogels maintained a CH phenotype, as indicated by a higher expression of collagen type II, ACAN, and lower expression of collagen type I. pCHs did not proliferate and exhibited a diffuse actin organization with round cell morphology. On hydrogels with higher elastic moduli (10, 40, and 100 kPa) the cells displayed spread morphology, organized actin fibers, and higher proliferation rates. With increasing elastic modulus, the gene expression of collagen type II decreased, whereas the expression of ACAN and collagen type I increased. Another study [<xref rid=\"B167-ijms-21-05399\" ref-type=\"bibr\">167</xref>] demonstrated that culturing hCHs in 2D on 300 g/mol poly(ethylene)glycol (PEG) substrates led to a spread morphology with distinct stress fibers, whereas culturing on 1000 g/mol PEG substrates led to cells having a round morphology, a cortical actin structure, and protein kinase C expression [<xref rid=\"B167-ijms-21-05399\" ref-type=\"bibr\">167</xref>]. Here, increasing the molecular weight or decreasing the concentration of PEG reduced the crosslinking density, which resulted in a softer hydrogel [<xref rid=\"B168-ijms-21-05399\" ref-type=\"bibr\">168</xref>]. Another study that investigated the behavior of mCHs on different polydimethylsiloxane (PDMS) stiffness substrates quantitatively with atomic force microscopy (AFM) demonstrated that a stiffer substrate tended to increase the cell spreading area and the percentages of irregular, fibroblast-like cell shapes as well as increased mechanical parameters such as elastic modulus, instantaneous modulus, relaxed modulus, and the viscosity of mCHs [<xref rid=\"B169-ijms-21-05399\" ref-type=\"bibr\">169</xref>].</p><p>In summary, a few studies demonstrated that material stiffness controls CH proliferation, morphology, phenotype, and mechanical characteristics. It is noteworthy that the used culture systems differed greatly in their stiffness values, making comparisons difficult. However, one can conclude that softer substrates foster a more chondrogenic phenotype than harder ones, and that 2D systems with an elastic modulus value of approximately 4 kPa but not ≥10 kPa appear suitable for inducing or stabilizing a chondrogenic phenotype in CHs. However, in 3D the elastic modulus values (e.g., for CHs GAG accumulation) appear much lower. Mechanistically, increasing the elastic modulus promotes FA and stress fiber formation and CH catabolism, which have been associated with the Rho-ROCK-MLC pathway (MLC: myosin light chain). In this context, ROCK and RhoA have been shown in another study to be key modulators of actin cytoskeleton tension and FA formation [<xref rid=\"B170-ijms-21-05399\" ref-type=\"bibr\">170</xref>]. Moreover, a study demonstrated an inverse correlation between cCH differentiation and the level of activated (GTP-bound) RhoA [<xref rid=\"B171-ijms-21-05399\" ref-type=\"bibr\">171</xref>].</p></sec><sec id=\"sec10-ijms-21-05399\"><title>10. Material Stiffness Changes Modulate Nuclear Shape and Nuclear Lamina and Inner Membrane Composition for Controlling mRNA Expression and MSC Differentiation</title><p>The cytoskeleton is mechanically linked to the nucleus by the linker of nucleoskeleton to cytoskeleton (LINC) complex, which consists of nuclear envelope embedded proteins [<xref rid=\"B172-ijms-21-05399\" ref-type=\"bibr\">172</xref>]. Key components of the LINC complex are lamins, which are class V intermediate filament family proteins that form the nuclear lamina under the inner nuclear membrane. Lamins occur in types, namely, A- and B-types, whereas the C-type is an isoform of A [<xref rid=\"B172-ijms-21-05399\" ref-type=\"bibr\">172</xref>]. Interestingly, it has been demonstrated that MSC differentiation into adipose tissue on soft matrix was enhanced by low lamin-A levels, whereas osteogenic differentiation on stiff matrix was enhanced by high lamin-A levels [<xref rid=\"B173-ijms-21-05399\" ref-type=\"bibr\">173</xref>]. Moreover, induced lamin-A overexpression in combination with stiff matrix and inducing media favored MSC osteogenesis [<xref rid=\"B173-ijms-21-05399\" ref-type=\"bibr\">173</xref>]. These data can be explained by evidence that suggests mRNA expression is mediated by nuclear morphology as demonstrated by previous research [<xref rid=\"B174-ijms-21-05399\" ref-type=\"bibr\">174</xref>], mediated in part by a link between the nucleoskeleton and the cytoskeleton at the nuclear envelope that provides a mechanism for transmission of mechanical forces into the nucleus [<xref rid=\"B175-ijms-21-05399\" ref-type=\"bibr\">175</xref>]. Additionally, the nuclear shape is modulated by substrate rigidity-induced changes in the actomyosin tension and, thus, a mechanically integrated nucleus-cytoskeleton is required for material stiffness sensing [<xref rid=\"B174-ijms-21-05399\" ref-type=\"bibr\">174</xref>]. In the context of this review, it is helpful to know that both A-type lamins and transcriptionally active chromatins are vertically polarized by the tension exercised by the perinuclear actin cap (or actin cap) [<xref rid=\"B176-ijms-21-05399\" ref-type=\"bibr\">176</xref>], which is a specific type of stress fiber of the cytoskeleton that is linked to the nucleus via LINC complex [<xref rid=\"B172-ijms-21-05399\" ref-type=\"bibr\">172</xref>]. This mechanical link illustrates how extracellular biophysical cues such as material stiffness impact on cell behavior via modulating the ECM–FA–cytoskeleton–actin cap–nucleus axis. In the context of material stiffness, this axis has been connected to MSC osteogenesis [<xref rid=\"B173-ijms-21-05399\" ref-type=\"bibr\">173</xref>] but not yet to CH phenotype.</p></sec><sec id=\"sec11-ijms-21-05399\"><title>11. TGF-β1-Induced Lineage Determination of MSCs is Modulated by Material Stiffness</title><p>MSCs are not only controlled by substrate stiffness, but by many other factors, including growth factors. One well-understood example is transforming growth factor β (TGF-β), which can also modulate MSC lineage differentiation. One study investigated the effect of TGF-β1 on hMSC differentiation into either smooth muscle cells (SMCs) or CHs, when cultivated on substrates with different stiffnesses [<xref rid=\"B121-ijms-21-05399\" ref-type=\"bibr\">121</xref>]. They demonstrated that the stiffness of the cell adhesion substrate modulated the effect of TGF-β1, as hMSCs on soft substrates spread less, showed fewer stress fibers, and lower proliferation rates, compared to hMSCs on stiff substrates. Moreover, hMSCs differentiated on softer substrates into the chondrogenic lineage and on substrates with intermediate stiffness into the myogenic lineage. Constitutively activated RhoA in hMSCs increased the expression of smooth muscle cell (SMC) marker genes on stiff substrates but collagen type II and lipoprotein lipase (LPL) on soft substrates, which suggested material stiffness-specific mRNA upregulation of chondrogenic and adipogenic genes through RhoA [<xref rid=\"B121-ijms-21-05399\" ref-type=\"bibr\">121</xref>].</p><p>Interestingly, in synovium-derived mesenchymal stem cells (sMSCs) on plastic, incubation with TGF-β1 induced RhoA activity and ROCK1 and 2 expression, which gradually decreased after four days. Additionally, the TGF-β1-stimulated cells showed a dramatically increased cytoplasmic stress fiber staining and chondrogenic RNA expression [<xref rid=\"B177-ijms-21-05399\" ref-type=\"bibr\">177</xref>]. When RhoA/ROCK inhibitors were added, the TGF-β1-induced cytoskeletal reorganization was interrupted, and chondrocyte-specific genes were downregulated [<xref rid=\"B177-ijms-21-05399\" ref-type=\"bibr\">177</xref>].</p></sec><sec id=\"sec12-ijms-21-05399\"><title>12. TGF-β1- and IL-1 β-Induced Changes in CH Stiffness and Traction Force are Material-Stiffness Dependent</title><p>One study examined how ECM stiffness affects the response to the chondrogenic growth factor TGF-β, an agonist of CH differentiation, and how ECM stiffness affects mechanosensitive TGF-β1 expression [<xref rid=\"B108-ijms-21-05399\" ref-type=\"bibr\">108</xref>]. The authors cultured mCHs and ATDC5 cells (a cell line derived from mouse teratocarcinoma cells) on PAA hydrogels with different elastic moduli (0.2, 0.5, and 1.1 MPa). They demonstrated that the expression levels of SOX9, collagen type II, ACAN, and endogenous TGF-β were highest on 0.5 MPa substrates in mCHs, whereas the response to the chondrogenic growth factor TGF-β measured in ATDC5 cells was higher on 0.5 MPa vs. plastic substrates. Interestingly, the study also induced the expression of collagen type II in mCHs on 1.1 MPa hydrogels by using ROCK inhibition, illustrating the stiffness-dependent effect of the mCH cytoskeleton on mCH phenotype. The authors suggested a synergistic response of TGF-β and substrate stiffness and also demonstrated that this response was dependent on p38 mitogen-activated protein kinase (MAPK) signaling rather than SMAD3 [<xref rid=\"B108-ijms-21-05399\" ref-type=\"bibr\">108</xref>]. Another study that evaluated the effects of stiffness on CHs cultivated goat CHs (gCHs) on PAA hydrogels with substrate elastic moduli of 1, 11, and 90 kPa and demonstrated that increased stiffness led to increased gCH actin stress fibers and FAs [<xref rid=\"B178-ijms-21-05399\" ref-type=\"bibr\">178</xref>]. Moreover, the study demonstrated that TGF-β1 increased cellular stiffness and traction force, while IL-1β increased cellular stiffness but lowered traction force. Interestingly, the TGF-β1 effects were potent on 90 kPa substrates and IL-1β effects on 1 kPa substrates [<xref rid=\"B178-ijms-21-05399\" ref-type=\"bibr\">178</xref>]. Although this study did not elucidate on mechanistic details, it is conceivable that the findings can be explained in part by increases in actin polymerization because it has been demonstrated that TGF-β1 treatment of synovium-derived rMSCs leads to increased F-actin stress fiber formation [<xref rid=\"B177-ijms-21-05399\" ref-type=\"bibr\">177</xref>]. Furthermore, TGF-β1 is known to induce cell stiffening in bovine CHs (bCHs) and it has been proposed that this stiffening is based on a combination of integrin activation from cellular attachment and increased actin polymerization from stimulation with TGF-β1 (and IGF-I) and subsequent increases in F-actin [<xref rid=\"B179-ijms-21-05399\" ref-type=\"bibr\">179</xref>]. Another more recent study confirmed that rabbit CHs (rabCHs) treated with TGF-β1 show enhanced F-actin [<xref rid=\"B180-ijms-21-05399\" ref-type=\"bibr\">180</xref>]. Thus, that TGF-β1 increases cellular stiffness and traction force as previously reported [<xref rid=\"B178-ijms-21-05399\" ref-type=\"bibr\">178</xref>] for gCHs can be explained by TGF-β1 increasing F-actin stress fiber formation. Mechanistically, TGF-β-induced actin reorganization appears to be mediated by Smad proteins and Rho GTPases, as demonstrated in Swiss 3T3 fibroblasts [<xref rid=\"B181-ijms-21-05399\" ref-type=\"bibr\">181</xref>]. A potential explanation for the IL-1β effects on increased gCH stiffness as reported prior [<xref rid=\"B178-ijms-21-05399\" ref-type=\"bibr\">178</xref>] can be derived from a study, which observed a disassembled appearance of actin, tubulin, vimentin, and vinculin in both healthy and OA hCHs after IL-1β stimulation [<xref rid=\"B182-ijms-21-05399\" ref-type=\"bibr\">182</xref>], as vimentin forms a tight, interconnected inner network that contributes to cytoskeletal stiffness [<xref rid=\"B183-ijms-21-05399\" ref-type=\"bibr\">183</xref>]. The effects of IL-1β on increased cell stiffness can also be explained by another study that reported increased stress fiber formation after IL-1β treatment [<xref rid=\"B184-ijms-21-05399\" ref-type=\"bibr\">184</xref>]. The effects of IL-1β on lowered traction force as reported previously [<xref rid=\"B178-ijms-21-05399\" ref-type=\"bibr\">178</xref>] can be explained by effects on multiple mechanotransducing proteins, as IL-1β is able to decrease the expression of tensin, talin, paxillin, and FAK in mCHs in an actin polymerization-dependent fashion [<xref rid=\"B185-ijms-21-05399\" ref-type=\"bibr\">185</xref>], as inhibiting the paxillin–vinculin interaction or depleting vinculin reduces FA force transmission and depletes tugging FA traction dynamics [<xref rid=\"B73-ijms-21-05399\" ref-type=\"bibr\">73</xref>]. Thus, growth factor- and pro-inflammatory cytokine-induced changes in cellular stiffness and traction force are material-stiffness dependent. The subsequent signaling is illustrated in <xref ref-type=\"fig\" rid=\"ijms-21-05399-f003\">Figure 3</xref>.</p></sec><sec id=\"sec13-ijms-21-05399\"><title>13. Substrate Stiffness-Modulated Cell Surface Growth Factor Receptor Composition</title><p>One study demonstrated that TGF-β receptors (TβR) are discretely organized to segregated spatial domains at the cell surface, and that disruption of cellular tension leads to a collapse of this spatial organization, which, in turn, drives formation of heteromeric TβRI/TβRII complexes and Smad activation [<xref rid=\"B186-ijms-21-05399\" ref-type=\"bibr\">186</xref>]. Thus, this study elucidated a novel mechanism by which cellular tension regulates TGF-β receptor organization and function, which helps to explain the observation reported by Park et al. [<xref rid=\"B121-ijms-21-05399\" ref-type=\"bibr\">121</xref>] that TGF-β1-induced lineage determination of MSCs is modulated by material stiffness. Substrate stiffness was also shown to influence the cell surface receptor composition in rat MSCs (rMSCs) [<xref rid=\"B187-ijms-21-05399\" ref-type=\"bibr\">187</xref>]. On soft substrates the bone morphogenetic protein (BMP) type I receptor, which complexes with β1 integrin, undergoes increasing activation, and is internalized through a caveolae/raft-dependent endocytosis. This internalization repressed the BMP/Smad pathway at least partially through integrin-regulated BMP receptor endocytosis, blocking the neural lineage specification of rMSCs on soft substrate. Moreover, the study suggested that ECM elasticity affects integrin activity and trafficking to modulate integrin BMP receptor internalization, which, in turn, contributes to stem cell lineage specification [<xref rid=\"B187-ijms-21-05399\" ref-type=\"bibr\">187</xref>]. CHs generate an integrated response to ECM stiffness and transforming growth factor β (TGF-β) [<xref rid=\"B108-ijms-21-05399\" ref-type=\"bibr\">108</xref>] that can be compared to the TGF-β1-induced lineage determination of MSCs described by Park et al. [<xref rid=\"B121-ijms-21-05399\" ref-type=\"bibr\">121</xref>]. However, stiffness-modulated effects of CH cellular tension on the TGF-β receptor organization and function in CHs have not yet been described.</p></sec><sec id=\"sec14-ijms-21-05399\"><title>14. Rho GTPases in Substrate Stiffness-Modulated MSC Differentiation and CH Phenotype</title><p>As demonstrated by Park et al. [<xref rid=\"B121-ijms-21-05399\" ref-type=\"bibr\">121</xref>], substrate stiffness modulates the effects of TGF-β1 on hMSC myogenic vs. chondrogenic differentiation fate. Interestingly, Rho GTPases, RhoA activity, Rho-induced stress fiber formation, and α-actin assembly were the deciding factors in lineage determination. In this context, the data in Park et al. [<xref rid=\"B121-ijms-21-05399\" ref-type=\"bibr\">121</xref>] suggested that larger amounts of activated RhoA were present in hMSCs on stiffer than softer substrates. To further elucidate, whether RhoA regulates differential gene expression, the group overexpressed constitutively active RhoA in hMSCs. RhoA activation significantly increased expression of SMC marker genes on stiff substrates but collagen type II and LPL on soft substrates, which suggested stiffness-specific mRNA upregulation of chondrogenic and adipogenic genes through RhoA [<xref rid=\"B121-ijms-21-05399\" ref-type=\"bibr\">121</xref>]. Mechanistically, another study suggested in this context that spread cells contain similar amounts of total ROCK (a kinase and downstream effector of active RhoA) comparable to round cells but higher amounts of activated ROCK and more pronounced stress fiber formation, when cells underwent osteogenic differentiation [<xref rid=\"B136-ijms-21-05399\" ref-type=\"bibr\">136</xref>]. Another study [<xref rid=\"B188-ijms-21-05399\" ref-type=\"bibr\">188</xref>] highlighted that chondrogenic or myogenic hMSC lineage determination was dependent on cell shape, Rac1, and N-cadherin. Through dose-dependent activation of Rac1, the fate decision of the hMSCs was controlled on compliant adhesion sites [<xref rid=\"B188-ijms-21-05399\" ref-type=\"bibr\">188</xref>].</p><p>In 2010, Haudenschild et al. [<xref rid=\"B189-ijms-21-05399\" ref-type=\"bibr\">189</xref>] found a consensus phosphorylation site in SOX9 for ROCK, which directly links SOX9 transcriptional activity to a ROCK–SOX9 interaction. The authors demonstrated that ROCK phosphorylates SOX9 at Ser181, which increases nuclear accumulation of SOX9 protein (e.g., in response to mechanical compression and TGF-β1) [<xref rid=\"B189-ijms-21-05399\" ref-type=\"bibr\">189</xref>]. Indeed, two other 2D studies demonstrated that the modulation of the RhoA/ROCK pathway controls the transcription of SOX9 for promoting chondrogenic differentiation [<xref rid=\"B189-ijms-21-05399\" ref-type=\"bibr\">189</xref>,<xref rid=\"B190-ijms-21-05399\" ref-type=\"bibr\">190</xref>]. At first glance, these two studies appeared to report conflicting data, as pharmacologically inhibiting ROCK, a downstream effector of active RhoA, resulted in elevated SOX9 expression levels in ATDC5 cells [<xref rid=\"B190-ijms-21-05399\" ref-type=\"bibr\">190</xref>], whereas Haudenschild et al. [<xref rid=\"B189-ijms-21-05399\" ref-type=\"bibr\">189</xref>] reported that increasing amounts of ROCK show a dose-dependent increase in SOX9 transcriptional activity in hCHs. However, this apparent conflict can be resolved by material stiffness-specific effects of ROCK, as demonstrated by Allen, Cooke, and Alliston [<xref rid=\"B108-ijms-21-05399\" ref-type=\"bibr\">108</xref>]. In that study, high SOX9 expression levels were quantified in ATDC5 cells when cells were cultured on chondrogenic elastic modulus levels of 0.5 MPa and low SOX9 expression levels on plastic culture dishes. The pharmacological inhibition of ROCK resulting in elevated SOX9 expression as reported in a previous study [<xref rid=\"B190-ijms-21-05399\" ref-type=\"bibr\">190</xref>] was observed only on plastic culture dishes, whereas ROCK inhibition of cells on chondrogenic stiffness levels resulted in decreased SOX9 expression levels, illustrating an interesting material stiffness-specific effect of ROCK on SOX9 expression. Thus, the data illustrate that higher stiffness does lead to higher SOX9 but also that higher SOX9 does not necessarily lead to increased chondrogenic gene expression. Thus, in a 2D situation, chondrogenic elastic modulus such as 0.5 MPa as reported in one study [<xref rid=\"B190-ijms-21-05399\" ref-type=\"bibr\">190</xref>] leads to SOX9 levels that act chondrogenically. Higher SOX9 levels that occur in cells cultured on higher stiffness such as on plastic act non-chondrogenically. In line with this explanation, another study demonstrated in chicken CHs (cCHs) on 2D plastic dishes an inverse correlation between CH differentiation and the level of activated (GTP-bound) RhoA [<xref rid=\"B171-ijms-21-05399\" ref-type=\"bibr\">171</xref>]. This inverse correlation has also been observed in the same study in cCHs in 3D alginate gel culture and in limb bud mesenchymal cell micromass culture, but 2D vs. 3D systems cannot be directly compared. In this context, a direct modulation of ROCK activity through material stiffness was reported by Huang et al. [<xref rid=\"B104-ijms-21-05399\" ref-type=\"bibr\">104</xref>], in which a stiffer matrix promoted increased RhoA production and also increased the activation of RhoA in the membrane but not in the cytosolic fraction, followed by subsequently increased ROCK activity on a stiffer matrix.</p><p>In summary, the roles of the Rho GTPases and of RhoA/ROCK in particular in modulating CH phenotype are not sufficiently understood. On the one hand, the ROCK–SOX9 interaction through a consensus site serves well for explaining the effects of ROCK on CH phenotype, as SOX9 is a transcription factor essential for the formation of all cartilaginous tissue [<xref rid=\"B191-ijms-21-05399\" ref-type=\"bibr\">191</xref>]. In this context, RhoA/ROCK signaling acts pro-chondrogenic. On the other hand, it has been demonstrated that ROCK induces stress fiber formation by phosphorylating MLC [<xref rid=\"B192-ijms-21-05399\" ref-type=\"bibr\">192</xref>,<xref rid=\"B193-ijms-21-05399\" ref-type=\"bibr\">193</xref>]. Other studies specified that activated RhoA increases actin polymerization to induce stress fiber formation [<xref rid=\"B104-ijms-21-05399\" ref-type=\"bibr\">104</xref>], and that ROCK inhibition supports the establishment of a CH-specific cell shape and actin organization [<xref rid=\"B190-ijms-21-05399\" ref-type=\"bibr\">190</xref>]. Interestingly, cytochalasin D, an inhibitor of actin polymerization, can reverse the de-differentiated phenotype of monolayer-passaged CHs [<xref rid=\"B194-ijms-21-05399\" ref-type=\"bibr\">194</xref>] but the subsequent mechanisms of how a chondrogenic CH phenotype is being restored are less clear. In this context, RhoA/ROCK signaling acts anti-chondrogenically and induces CH de-differentiation. How material stiffness through modulation of ROCK activity impacts this apparent balance between the pro- and anti-chondrogenic effects of RhoA/ROCK signaling remains unclear.</p></sec><sec id=\"sec15-ijms-21-05399\"><title>15. Substrate Stiffness-Modulated Integrin Subunit Expression of MSCs and CHs</title><p>Integrins are an integral part of FAs. A differential integrin expression regulated by substrate stiffness has been noted in MSCs and CHs. For example, the expression of integrin α1, α2, and α5 has been reported to be much more sensitive to stiffness in hMSCs than in human osteoblasts and hCHs [<xref rid=\"B195-ijms-21-05399\" ref-type=\"bibr\">195</xref>]. Another study [<xref rid=\"B196-ijms-21-05399\" ref-type=\"bibr\">196</xref>] investigated the expression of cell surface integrins in rCHs on hydrogels with elastic moduli of 2, 10, and 20 Pa under normoxia vs. hypoxia. Blocking various integrin subunits and assessing subsequent aggrecan (ACAN) expression, the authors concluded that the integrins α1, β1, αVβ3, and β3 were involved in mechanosensing, whereas the integrins α2, α3, and α5 were not involved. Subsequent tests of stiffness-dependency revealed in 2D an increase in the integrins α1, β1, and β3 expressions with decreasing stiffness under normoxia and also an increase in the expression of α1, β1, and β3 integrins with decreasing stiffness under hypoxia. However, the extent of increase was lower in hypoxia. In 3D, the study showed an increase in the expression of the α1, β1, and β3 integrins with decreasing stiffness under normoxia, similar to 2D, but a decrease in the expression of the α1, β1, and β3 integrins with decreasing stiffness under hypoxia [<xref rid=\"B196-ijms-21-05399\" ref-type=\"bibr\">196</xref>]. Another study with much higher substrate elastic modulus values in the MPa range [<xref rid=\"B195-ijms-21-05399\" ref-type=\"bibr\">195</xref>], in contrast to the above cited study in the kPa range, cultivated hMSCs and hCHs in 2D on hydrogels with elastic modulus values of 0.8 MPa, 4.7 MPa, 223.7 MPa, and 309.9 MPa. This study demonstrated a stiffness- and cell type-dependent expression because in hMSCs the tested expression of the integrin subunits α1, α2, α5, αv, β1, and β3 was stiffness-regulated. In CHs, the integrin subunits α1, α2, αv, β1, and β3 were stiffness-regulated. However, the subunits α1, β1, and β3 displayed a strong response, whereas α5 was not stiffness-regulated. Furthermore, this study silenced (only) the integrin subunit β1 in MSCs because that subunit mediates SOX9 and runt related transcription factor 2 (RUNX2) expression and silencing abolished mRNA expression. Combining the data from two studies [<xref rid=\"B195-ijms-21-05399\" ref-type=\"bibr\">195</xref>,<xref rid=\"B196-ijms-21-05399\" ref-type=\"bibr\">196</xref>], one can conclude that hMSCs appear to be elastic modulus-sensitive in the range from 2 Pa to 309.9 MPa and respond with differential α1, β1, and β3 expression. Moreover, a stiffness-dependent integrin subunit expression in both hMSCs and hCHs illustrates how material stiffness gives rise to differential FA compositions in these two cell types.</p></sec><sec id=\"sec16-ijms-21-05399\"><title>16. Differential MSC Behavior in 2D vs. 3D</title><p>To assess MSC differentiation in a 3D environment with low stiffness for inducing chondrogenic differentiation without use of exogeneous differentiation supplements, one study altered the composition and the mechanical properties of collagen-glycosaminoglycan scaffolds [<xref rid=\"B197-ijms-21-05399\" ref-type=\"bibr\">197</xref>]. Using substrates with elastic moduli of 0.5, 1.0, and 1.5 kPa and different glycosaminoglycan (GAG) types, they demonstrated that scaffolds with a relatively low elastic modulus of 0.5 kPa significantly upregulated SOX9. The chondrogenic differentiation of rMSCs induced by a soft 3D environment as seen in a study by Murphy et al. [<xref rid=\"B197-ijms-21-05399\" ref-type=\"bibr\">197</xref>] is in general agreement with findings in the 2D study reported above. Hence, hMSCs cultivated in 3D using hydrogels with a lower elastic modulus (3.5 kPa) caused the cells to undergo chondrogenesis [<xref rid=\"B198-ijms-21-05399\" ref-type=\"bibr\">198</xref>], whereas hydrogels with a higher elastic modulus (53.6 kPa) induced hypertrophic marker expression (collagen type X, matrix metallopeptidase 13 (MMP-13)) and osteogenic differentiation marker expression (alkaline phosphatase (ALP)) of hMSCs with increased MMP-13 and type X collagen and ALP. This expression was not modulated by ROCK but by myosin II, as blocking ROCK with Y27632 had no obvious effects, and using blebbistatin for inhibition of myosin II reduced the expression of MMP-13, type X collagen, and ALP in high crosslinking density, stiff constructs [<xref rid=\"B198-ijms-21-05399\" ref-type=\"bibr\">198</xref>]. Thus, these data are in accordance to rMSC studies on stiff substrates in 2D, which also demonstrated osteogenic differentiation marker upregulation [<xref rid=\"B152-ijms-21-05399\" ref-type=\"bibr\">152</xref>]. However, this comparison is difficult, as the substrates used in both studies had comparable stiffnesses but different biomaterial types. Regardless, we identified one study that assessed hMSC chondrogenesis on the polymers’ gelatin, chondroitin sulfate, hyaluronic acid, and polyethylene glycol in 2D vs. 3D [<xref rid=\"B199-ijms-21-05399\" ref-type=\"bibr\">199</xref>]. This study demonstrated that the expression levels of the chondrogenic differentiation markers collagen type II, ACAN, and SOX9 were comparable in 2D vs. 3D but much higher in 3D, and this behavior was observed for all four polymers. Increased chondrogenesis was always accompanied by enhanced N-cadherin expression, suggesting N-cadherin as a robust marker to, for example, select culture conditions that promote chondrogenesis. Interestingly, in another study [<xref rid=\"B199-ijms-21-05399\" ref-type=\"bibr\">199</xref>], ROCK inhibition had minimal effects in the 2D or 3D models and varying the polymer used did not change the chondrogenic response to ROCK inhibition within each culture model. However, ROCK inhibition decreased chondrogenesis in a newly developed gelatin-based microribbon (μRB) model, which is a highly macroporous scaffold, in which encapsulated cells attach to the surface of individual μRBs and exhibit rapid cell spreading upon encapsulation [<xref rid=\"B200-ijms-21-05399\" ref-type=\"bibr\">200</xref>]. Thus, it would be expectable that ROCK inhibition would decrease chondrogenesis in this model, as highly spreading cells are known to contain relatively high amounts of activated ROCK and more pronounced stress fiber formation [<xref rid=\"B136-ijms-21-05399\" ref-type=\"bibr\">136</xref>]. In turn, the limited effects of ROCK inhibition on chondrogenesis in the 2D or 3D models in previous research [<xref rid=\"B199-ijms-21-05399\" ref-type=\"bibr\">199</xref>] can perhaps be explained by the limited cell spreading known to occur in hydrogels [<xref rid=\"B201-ijms-21-05399\" ref-type=\"bibr\">201</xref>].</p></sec><sec id=\"sec17-ijms-21-05399\"><title>17. Immuno-Modulative and Angiogenic Role of Material Stiffness in MSCs</title><p>In 2011, Caplan and coworkers proposed that MSCs are released during injury from their perivascular location, become activated, and establish a regenerative microenvironment by secreting bioactive molecules and regulating the local immune response [<xref rid=\"B202-ijms-21-05399\" ref-type=\"bibr\">202</xref>]. Moreover, they termed these trophic and immunomodulatory activities as site-regulated “drugstores”. Thus, it is thought that the main mechanism for MSCs’ beneficial effects in tissue regeneration might be based on their capability to produce a large variety of bioactive trophic factors that stimulate neighboring parenchymal cells to start repairing damaged tissues [<xref rid=\"B203-ijms-21-05399\" ref-type=\"bibr\">203</xref>]. Another interesting suggestion given in 2016 was that the number of MSCs required to exert trophic actions might be less than necessary for tissue replacement [<xref rid=\"B204-ijms-21-05399\" ref-type=\"bibr\">204</xref>]. In this context, a potential association of material stiffness with trophic activities has generally not yet received much attention. However, recent evidence suggests that material stiffness modulates the paracrine signaling of a few cell types [<xref rid=\"B205-ijms-21-05399\" ref-type=\"bibr\">205</xref>,<xref rid=\"B206-ijms-21-05399\" ref-type=\"bibr\">206</xref>,<xref rid=\"B207-ijms-21-05399\" ref-type=\"bibr\">207</xref>,<xref rid=\"B208-ijms-21-05399\" ref-type=\"bibr\">208</xref>] and even the intracellular reactive oxygen species (ROS) level in human adipose-derived MSCs (ADMSCs) [<xref rid=\"B209-ijms-21-05399\" ref-type=\"bibr\">209</xref>]. One study varied either poly(ethylene)glycol diacrylate (PEGDA) hydrogel stiffness but kept the cell adhesive sites constant or varied the concentration of the cell adhesive sites. Under these conditions, matrix stiffness but not the available cell-adhesive sites played a critical role in pro-angiogenic signaling of hMSCs [<xref rid=\"B148-ijms-21-05399\" ref-type=\"bibr\">148</xref>]. Another study revealed that material stiffness modulates the expression of interleukin-8 (IL-8) as well as vascular endothelial growth factor (VEGF) of hMSCs [<xref rid=\"B210-ijms-21-05399\" ref-type=\"bibr\">210</xref>], a potent angiogenic factor, illustrating the pro-inflammatory and angiogenic cues of (increasing) material stiffness in hMSCs.</p></sec><sec id=\"sec18-ijms-21-05399\"><title>18. The Role of Material Stiffness in Inducing Re-Differentiation of CHs after Serial Expansion-Induced De-Differentiation</title><p>A significant problem in AC tissue engineering and scaffold transplantation such as performed in ACI is that scaffolds have to be seeded with a sufficient number of cells prior to surgical transplantation. For this, CHs are expanded to generate high cell numbers through serial passaging. However, this passaging leads to de-differentiation and above a certain threshold it eventually results in fibroblast-like CHs with a fibrogenic phenotype, which limits the amount of serial passages and, thus, the number of available CHs [<xref rid=\"B211-ijms-21-05399\" ref-type=\"bibr\">211</xref>,<xref rid=\"B212-ijms-21-05399\" ref-type=\"bibr\">212</xref>,<xref rid=\"B213-ijms-21-05399\" ref-type=\"bibr\">213</xref>]. Therefore, previous studies investigated how CH de-differentiation can be reversed to generate a healthy hCH phenotype [<xref rid=\"B214-ijms-21-05399\" ref-type=\"bibr\">214</xref>] by using alginate bead culture [<xref rid=\"B214-ijms-21-05399\" ref-type=\"bibr\">214</xref>], pellet culture [<xref rid=\"B212-ijms-21-05399\" ref-type=\"bibr\">212</xref>], agarose hydrogels with varying RGD adhesion site densities and mechanical properties [<xref rid=\"B215-ijms-21-05399\" ref-type=\"bibr\">215</xref>], photo-crosslinkable hydrogels [<xref rid=\"B216-ijms-21-05399\" ref-type=\"bibr\">216</xref>], chimeric Activin A/BMP2 ligand AB235 [<xref rid=\"B217-ijms-21-05399\" ref-type=\"bibr\">217</xref>], serum or growth factor cocktails [<xref rid=\"B218-ijms-21-05399\" ref-type=\"bibr\">218</xref>], low oxygen concentrations [<xref rid=\"B219-ijms-21-05399\" ref-type=\"bibr\">219</xref>], and MSC co-culture [<xref rid=\"B220-ijms-21-05399\" ref-type=\"bibr\">220</xref>]. For the interested reader, factors that are considered particularly supportive of CH expansion and re-differentiation are summarized elsewhere (see [<xref rid=\"B221-ijms-21-05399\" ref-type=\"bibr\">221</xref>]).</p><p>One study tested the re-differentiation of monolayer-expanded, de-differentiated pCHs in 3D agarose hydrogels with varying RGD adhesion site densities and mechanical properties (3.7 kPa vs. 53.2 kPa) [<xref rid=\"B215-ijms-21-05399\" ref-type=\"bibr\">215</xref>]. Unexpectedly, adhesion site availability inhibited re-differentiation and decreased in an RGD dose-dependent manner sGAG production per cell. Similarly, hydrogels with the highest RGD density remained positive for collagen type I and exhibited lowest collagen type II. Softer gels contained higher pCH numbers and ECM amounts after two weeks of culture but, interestingly, substrate stiffness did not affect re-differentiation. These results were interpreted in a way that adhesion site density, but not stiffness, influences pCH re-differentiation in 3D [<xref rid=\"B215-ijms-21-05399\" ref-type=\"bibr\">215</xref>]. As discussed above, such findings are difficult to interpret, as the data can also be explained by an adhesion-independent mechanism, in which cells sense cell volume confinement in 3D culture [<xref rid=\"B165-ijms-21-05399\" ref-type=\"bibr\">165</xref>]. Another study [<xref rid=\"B222-ijms-21-05399\" ref-type=\"bibr\">222</xref>] used very soft hydrogels (2–20 Pa) to investigate the influence of stiffness in 2D and 3D environments on sheep CH (sCH) phenotype but no de-differentiation via serial passaging was performed. The study demonstrated that the softest collagen hydrogels, used as monolayer or 3D culture system, increased the expression of ACAN, collagen type II, and SOX9. The loss of chondrogenic phenotype on stiffer hydrogels correlated with a diffuse organization of actin stress fibers [<xref rid=\"B222-ijms-21-05399\" ref-type=\"bibr\">222</xref>]. Here, the 2D experimental results of sCH differentiation were comparable to the results of the 3D environment, as sCH phenotype, morphology and organization of cytoskeleton were comparable across both systems and, importantly, stiffness-mediated. Interestingly, the elastic moduli of these hydrogels used by Sanz-Ramos et al. [<xref rid=\"B222-ijms-21-05399\" ref-type=\"bibr\">222</xref>] were much softer (2–20 Pa), compared to the other discussed studies (3.7 kPa vs. 53.2 kPa) [<xref rid=\"B164-ijms-21-05399\" ref-type=\"bibr\">164</xref>,<xref rid=\"B215-ijms-21-05399\" ref-type=\"bibr\">215</xref>], which did not find any association between chondrogenic mRNA expression and material stiffness. Another study also investigated the re-differentiation of passage de-differentiated CHs and chose infant and adult hCHs from polydactyly patients for culture on transglutaminase cross-linked hyaluronic acid hydrogels with elastic moduli of approximately 2 kPa, 5 kPa, and 8 kPa [<xref rid=\"B223-ijms-21-05399\" ref-type=\"bibr\">223</xref>]. This study demonstrated that collagen type II expression and sGAG deposition normalized to DNA content of infant hCHs were not stiffness-dependent (see Supplementary Dataset). Data on adult hCHs or for higher elastic moduli than the investigated relatively low range of 2–8 kPa were not given. Collectively, material stiffness-modulated CH phenotype regulation appears sensitive to a certain stiffness range but only a few studies are available that used serially passaged CHs for re-differentiation across a range of stiffnesses. Thus, the role of material stiffness in inducing re-differentiation of CHs after serial expansion-induced de-differentiation remains unclear.</p></sec><sec id=\"sec19-ijms-21-05399\"><title>19. The Role of Material Stiffness-Dependent β-Catenin Signaling in CH De-Differentiation</title><p>One of the molecular mechanisms involved in stiffness sensing is the Wnt/β-catenin signaling pathway (<xref ref-type=\"fig\" rid=\"ijms-21-05399-f004\">Figure 4</xref>). The Wnt/β-catenin pathway is responsible for many cell functions such as adhesion, migration, differentiation, and proliferation [<xref rid=\"B224-ijms-21-05399\" ref-type=\"bibr\">224</xref>]. A study demonstrated that relatively high material stiffness enhanced the expression level of several members of the Wnt/β-catenin pathway in both MSCs and primary mCHs [<xref rid=\"B225-ijms-21-05399\" ref-type=\"bibr\">225</xref>]. In this study the accumulation of β-catenin, an intracellular signal transducer of the Wnt signaling pathway, was increased by the integrin/FAK pathway due to high material stiffness. Accumulated β-catenin binding to the Wnt promoter region acted in a positive feedback loop, which plays a significant role in mediating Wnt signaling on stiff ECMs. Another study reported that the nuclear accumulation of β-catenin and subsequent stimulation of β-catenin-Tcf/Lef transcriptional activity causes de-differentiation of the articular CHs of two-week-old New Zealand white rabbits, characterized by decreased type II collagen expression and initiation of collagen type I expression [<xref rid=\"B226-ijms-21-05399\" ref-type=\"bibr\">226</xref>]. Moreover, α-catenin blocks the β-catenin-mediated inhibition of collagen type II expression in these rabCHs [<xref rid=\"B227-ijms-21-05399\" ref-type=\"bibr\">227</xref>] through a direct interaction between α-catenin and β-catenin [<xref rid=\"B226-ijms-21-05399\" ref-type=\"bibr\">226</xref>] and, thus, increases collagen type II expression. These studies highlight how increased material stiffness contributes to CH de-differentiation through increased β-catenin nuclear accumulation. In this context it is noteworthy to mention that elevated levels of β-catenin have been detected in human OA knee joint cartilage [<xref rid=\"B226-ijms-21-05399\" ref-type=\"bibr\">226</xref>].</p></sec><sec id=\"sec20-ijms-21-05399\"><title>20. Collagen Type II Fragment Production and Subsequent Catabolic Effects are Modulated by Rho/ROCK Activation in CHs</title><p>Interestingly, many clinical biomaterials for AC repair consist of collagen type I and/or type III, (e.g., as a fleece [<xref rid=\"B228-ijms-21-05399\" ref-type=\"bibr\">228</xref>,<xref rid=\"B229-ijms-21-05399\" ref-type=\"bibr\">229</xref>], sponge [<xref rid=\"B230-ijms-21-05399\" ref-type=\"bibr\">230</xref>,<xref rid=\"B231-ijms-21-05399\" ref-type=\"bibr\">231</xref>,<xref rid=\"B232-ijms-21-05399\" ref-type=\"bibr\">232</xref>,<xref rid=\"B233-ijms-21-05399\" ref-type=\"bibr\">233</xref>,<xref rid=\"B234-ijms-21-05399\" ref-type=\"bibr\">234</xref>], gel [<xref rid=\"B235-ijms-21-05399\" ref-type=\"bibr\">235</xref>], membrane [<xref rid=\"B236-ijms-21-05399\" ref-type=\"bibr\">236</xref>,<xref rid=\"B237-ijms-21-05399\" ref-type=\"bibr\">237</xref>], or matrix [<xref rid=\"B235-ijms-21-05399\" ref-type=\"bibr\">235</xref>,<xref rid=\"B238-ijms-21-05399\" ref-type=\"bibr\">238</xref>]) that in some cases are substituted with other materials. hCH culture on collagen type I or II promotes matrix production and turnover without significant differences between collagen types I and II, indicating that the use of collagen type I or II coating for in vitro models appears to be a sound basis for in vivo repair procedures [<xref rid=\"B239-ijms-21-05399\" ref-type=\"bibr\">239</xref>]. Nevertheless, the predominant usage of collagen type I but not type II for clinical biomaterial production is likely connected to the fact that collagen type II fragments containing the N- and C-terminal telopeptides have dose-dependent catabolic activities similar to fibronectin fragments and increase the production of NO, cytokines, and MMPs in pCHs [<xref rid=\"B240-ijms-21-05399\" ref-type=\"bibr\">240</xref>]. Moreover, in both bCHs and hCHs collagen type II fragments inhibit collagen synthesis, which has been shown in hCHs to be dose-dependent [<xref rid=\"B241-ijms-21-05399\" ref-type=\"bibr\">241</xref>]. In human AC explant collagen type II fragments perturb AC homeostasis, as the fragments suppress collagen synthesis [<xref rid=\"B242-ijms-21-05399\" ref-type=\"bibr\">242</xref>] and upregulate catabolic processes leading to a net loss of tissue mass [<xref rid=\"B241-ijms-21-05399\" ref-type=\"bibr\">241</xref>].</p><p>In the context of this review one study exposed epiphyseal rCHs to transforming growth factor α (TGF-α), which inhibits articular chondrocyte anabolic capacity, increases catabolic factors, and contributes to the development of chondrocyte clusters [<xref rid=\"B243-ijms-21-05399\" ref-type=\"bibr\">243</xref>]. Specifically, TGF-α induced actin cytoskeleton modulation, altered cell morphology, RhoA/ROCK, MAPK/ERK kinase, PI3K, and p38 MAPK signaling and downregulated collagen type II, ACAN, and SOX9 expression [<xref rid=\"B243-ijms-21-05399\" ref-type=\"bibr\">243</xref>]. Moreover, collagen type II and ACAN cleavage fragments were induced with TGF-α. Importantly, fragment production was greatly reduced by inhibiting MEK/ERK and Rho/ROCK activation, demonstrating a link between Rho/ROCK activation and collagen type II fragment generation. As Rho/ROCK activity increases with substrate stiffness [<xref rid=\"B107-ijms-21-05399\" ref-type=\"bibr\">107</xref>] and ROCK plays a key role in the stiffness sensing ability of CHs [<xref rid=\"B108-ijms-21-05399\" ref-type=\"bibr\">108</xref>], one could theoretically ask whether collagen type II fragment generation and subsequent catabolic effects might be material stiffness-sensitive. However, such data is not available to the knowledge of the authors. Another potential way to link fragment generation to material stiffness might be that the epidermal growth factor receptor (EGF) receptor, which binds fragment-inducing TGF-α, is relevant for stiffness sensing and increases spreading and contractility on stiff, but not on soft substrates [<xref rid=\"B244-ijms-21-05399\" ref-type=\"bibr\">244</xref>]. Thus, the TGF-α-binding EGF receptor is stiffness-sensitive but whether such mechanisms contribute to material stiffness-dependent catabolically acting collagen type II fragments has not yet been demonstrated.</p><p>In MSCs, the collagen type II supports chondrogenic differentiation, whereas collagen type I suppresses collagen type II expression and chondrogenic differentiation [<xref rid=\"B245-ijms-21-05399\" ref-type=\"bibr\">245</xref>,<xref rid=\"B246-ijms-21-05399\" ref-type=\"bibr\">246</xref>,<xref rid=\"B247-ijms-21-05399\" ref-type=\"bibr\">247</xref>,<xref rid=\"B248-ijms-21-05399\" ref-type=\"bibr\">248</xref>]. However, no data in a material stiffness-dependent context were found. In summary, the role of material stiffness in the generation of catabolically acting collagen type II and other ECM fragments has not been addressed sufficiently.</p></sec><sec id=\"sec21-ijms-21-05399\"><title>21. Biomaterials Used for Clinically Inducing Human AC Repair</title><p>Clinical scaffolds for cell-based or cell-free therapies should induce or stabilize a chondrogenic phenotype in both CHs and MSCs. Their use in AC repair surgery should not be too complex, facilitate the implantation of the cells, and fill the AC defect. In addition, the scaffolds have to be bio-compatible, non-toxic, resorbable, and withstand the mechanical demands within the joint. A list of the available biomaterials that have been used or are in use for clinical AC repair is given in <xref rid=\"ijms-21-05399-t001\" ref-type=\"table\">Table 1</xref> and <xref rid=\"ijms-21-05399-t002\" ref-type=\"table\">Table 2</xref>. The following text section reviews what is known about CH behavior in regard to these biomaterials but does not review the clinical performance nor assess the clinical value.</p><p>Not many studies are available that assessed basic science parameters in the context of this review. One interesting study assessed the influence of scaffold architecture on the CH distribution and behavior [<xref rid=\"B228-ijms-21-05399\" ref-type=\"bibr\">228</xref>] by comparing matrix-associated CH transplantation grafts such as Hyalograft<sup>®</sup> C autograft (Fidia Advanced Biomaterials, Italy), a hyaluronan web, Chondro-Gide<sup>®</sup> (Geistlich Biomaterials, Switzerland), a collagen type I/III fleece, CaReS<sup>®</sup> (Arthro Kinetics Biotechnology GmbH; Austria), a collagen type I gel, and Novocart<sup>®</sup> 3D (TeTeC, Germany), a collagen type I sponge containing chondroitin-sulfate. The study found that the hCHs formed cell layers, nests, or clots in the hyaluronan web, and subconfluent or confluent layers or multilayers in the collagen fleece, whereas the CHs were not situated in any groups in the collagen gel. In the collagen sponge, only a few hCHs built local aggregates and most hCHs were situated as singles, suggesting that cell–cell contacts occur in the hyaluronan web and in the collagen fleece but not in the collagen gel or sponge. In the context of AC repair with hMSCs, it might be noteworthy that a direct cell–cell contact of hMSCs with hCHs is considered a key mechanism in multipotent MSC-mediated chondrogenesis [<xref rid=\"B253-ijms-21-05399\" ref-type=\"bibr\">253</xref>]. Thus, should one consider investigating these four materials, which are intended to be used with CHs, for future MSCs or CH-MSC co-culture-based AC repair instead; it would be important to keep in mind that not all materials allow cell–cell contacts equally.</p><p>The above discussed study also showed that hCH morphology was mainly elongated and polygonal in the hyaluronan web; largely polygonal in the collagen fleece; spherical, elongated, and polygonal in the collagen gel; and mainly spherical in the collagen sponge. This is interesting, as a spherical shape indicates a differentiated phenotype, whereas an elongated shape indicates a more de-differentiated phenotype [<xref rid=\"B254-ijms-21-05399\" ref-type=\"bibr\">254</xref>,<xref rid=\"B255-ijms-21-05399\" ref-type=\"bibr\">255</xref>]. Equally interesting is the fact that comparable pCH morphologies have been previously observed (see [<xref rid=\"B166-ijms-21-05399\" ref-type=\"bibr\">166</xref>]). This study assessed how matrix elasticity influences pCH phenotype and demonstrated a round cell morphology and chondrogenic expression profiles on relatively soft PAA hydrogels and a spread morphology with decreased collagen type II and increased ACAN and collagen type I expression on harder PAA hydrogels. Thus, one would expect that clinically used biomaterials for AC repair that are associated with distinct CH morphologies would also be associated with distinct differences in the expression profiles of adhering CHs. Indeed, in a subsequent study, Nuernberger et al. [<xref rid=\"B228-ijms-21-05399\" ref-type=\"bibr\">228</xref>] then compared the mRNA expression profiles of hCHs on the four materials [<xref rid=\"B256-ijms-21-05399\" ref-type=\"bibr\">256</xref>] and found that Novocart<sup>®</sup> 3D hCHs displayed the lowest collagen type I expression, whereas collagen type II expression levels were comparable between Hyalograft<sup>®</sup> C autograft, CaReS<sup>®</sup>, and Novocart<sup>®</sup> 3D. Interestingly, the collagen type II to I ratio was comparable between Hyalograft<sup>®</sup> C autograft and Novocart<sup>®</sup> 3D but higher in CaReS<sup>®</sup>. This ratio presents the balance between a functional chondrocyte phenotype, as found in intact AC, and a modulated proliferative in vitro phenotype, and can easily be used for comparing cell phenotypes across biomaterials or across cell sources [<xref rid=\"B214-ijms-21-05399\" ref-type=\"bibr\">214</xref>]. Interestingly, these studies demonstrated that the biomaterial that was associated with a mostly spherical hCH morphology, namely Novocart<sup>®</sup> 3D, was also characterized by the lowest collagen type I expression level, highlighting the primary role of cell shape in the modulation of the CH phenotype [<xref rid=\"B145-ijms-21-05399\" ref-type=\"bibr\">145</xref>,<xref rid=\"B257-ijms-21-05399\" ref-type=\"bibr\">257</xref>]. The material with a relatively high collagen type II to I ratio was CaReS<sup>®</sup>, which can be explained by the fact that monolayer expansion is not being used in this system [<xref rid=\"B258-ijms-21-05399\" ref-type=\"bibr\">258</xref>]. Collectively, these data give rise to the question(s) whether the observed differences in CH morphology and the associated expression profiles might be related to differences in material stiffness, as the previous text sections of this review clearly demonstrate a correlation of material stiffness with resulting phenotype. However, no data on material stiffness or other mechanical parameters for the materials listed in <xref rid=\"ijms-21-05399-t001\" ref-type=\"table\">Table 1</xref> have been found by the authors.</p></sec><sec sec-type=\"discussion\" id=\"sec22-ijms-21-05399\"><title>22. Discussion</title><p>The aim of this review was (i) to summarize the current knowledge on how cells perceive and transduce material stiffness, and to answer the question whether the approach to control material stiffness for guiding cell fate is effective in the context of CH phenotype and MSC differentiation, as those cells remain the most relevant cell types for clinical cartilage repair. Moreover, we reviewed the literature (ii) to elucidate if the biomaterials that have been used or are being used for clinical cartilage repair are known to utilize material stiffness for controlling cell functions. An important insight produced by this review is that both CHs and MSCs are highly susceptible to material stiffness, as CH morphology [<xref rid=\"B167-ijms-21-05399\" ref-type=\"bibr\">167</xref>,<xref rid=\"B169-ijms-21-05399\" ref-type=\"bibr\">169</xref>], proliferation [<xref rid=\"B164-ijms-21-05399\" ref-type=\"bibr\">164</xref>], clustering [<xref rid=\"B164-ijms-21-05399\" ref-type=\"bibr\">164</xref>], and phenotype [<xref rid=\"B163-ijms-21-05399\" ref-type=\"bibr\">163</xref>,<xref rid=\"B166-ijms-21-05399\" ref-type=\"bibr\">166</xref>,<xref rid=\"B167-ijms-21-05399\" ref-type=\"bibr\">167</xref>], MSC migration [<xref rid=\"B139-ijms-21-05399\" ref-type=\"bibr\">139</xref>,<xref rid=\"B140-ijms-21-05399\" ref-type=\"bibr\">140</xref>], proliferation [<xref rid=\"B121-ijms-21-05399\" ref-type=\"bibr\">121</xref>], morphology, lineage determination and differentiation [<xref rid=\"B44-ijms-21-05399\" ref-type=\"bibr\">44</xref>,<xref rid=\"B259-ijms-21-05399\" ref-type=\"bibr\">259</xref>] as well as certain immuno-modulative and angiogenic roles of MSCs [<xref rid=\"B148-ijms-21-05399\" ref-type=\"bibr\">148</xref>,<xref rid=\"B210-ijms-21-05399\" ref-type=\"bibr\">210</xref>] are stiffness-mediated. Thus, controlling material stiffness for guiding cell fate is undoubtedly an effective approach for experimentally controlling CHs and MSCs. Arguably, this approach would also be a promising strategy for biomaterials used in the context of clinical cartilage repair but, surprisingly, this review found that information on the material stiffness of currently or previously used clinical biomaterials was not available. This suggested that clinical cartilage repair biomaterials could not have been designed with the concept in mind to control material stiffness for steering cell fate. However, the limited basic science data that is available on these biomaterials suggest that the elicited effects on CHs through a combination of material properties and architecture are effective in modulating CH phenotype. However, the authors believe that intentionally using the parameter ‘material stiffness’ as a cell-instructive cue is a not yet seized opportunity for developing novel clinical biomaterials for the future of AC repair.</p><p>The cell morphology and mRNA expression profiles of CHs adhering to graft residues of clinically used biomaterials at the time of implantation have been sufficiently investigated to allow comparisons to other studies on cell morphology and mRNA expression profiles that used non-clinical biomaterials. The rationale was to use cell shape and mRNA expression profiles as markers for the effects of material stiffness, as both cell morphology and expression are mediated by material stiffness [<xref rid=\"B143-ijms-21-05399\" ref-type=\"bibr\">143</xref>] but also by scaffold architecture [<xref rid=\"B260-ijms-21-05399\" ref-type=\"bibr\">260</xref>,<xref rid=\"B261-ijms-21-05399\" ref-type=\"bibr\">261</xref>] and biomechanical cues [<xref rid=\"B147-ijms-21-05399\" ref-type=\"bibr\">147</xref>]. For example, decreasing diameters of electrospun chitosan fibers upregulate the mRNA expression of collagen type II in CHs [<xref rid=\"B262-ijms-21-05399\" ref-type=\"bibr\">262</xref>]. In the above discussed residues of grafts used for clinical cartilage repair [<xref rid=\"B228-ijms-21-05399\" ref-type=\"bibr\">228</xref>], the CHs encompassed a range of morphologies such as an elongated shape as a marker of a de-differentiated CH, a polygonal shape as a marker of an intermediate phenotype, and a spherical shape as a marker of a fully differentiated phenotype. Interestingly, the biomaterial that was associated with a mostly spherical CH morphology was also characterized by the lowest collagen type I expression level, highlighting the important role of cell shape in modulating CH phenotype [<xref rid=\"B106-ijms-21-05399\" ref-type=\"bibr\">106</xref>,<xref rid=\"B174-ijms-21-05399\" ref-type=\"bibr\">174</xref>]. Moreover, the material with a relatively high collagen type II to I ratio contained a not further specified mixture of spherical, elongated, and polygonal CH shapes. Associating a specific cell shape with this expression profile was not possible, as the authors of this review cannot pinpoint what type of CH shape contributed most to the reported mRNA expression levels. However, these observed CH morphologies and associated mRNA expression profiles were comparable to those that were present on softer experimental substrates, which induced a chondrogenic CH phenotype [<xref rid=\"B163-ijms-21-05399\" ref-type=\"bibr\">163</xref>,<xref rid=\"B166-ijms-21-05399\" ref-type=\"bibr\">166</xref>,<xref rid=\"B167-ijms-21-05399\" ref-type=\"bibr\">167</xref>,<xref rid=\"B222-ijms-21-05399\" ref-type=\"bibr\">222</xref>]. Despite this agreement, it is impossible to relate the reported effects of the investigated clinical biomaterials on CH shape and expression to material stiffness, as we do not know the material stiffnesses of these biomaterials. Furthermore, not only their stiffnesses but also their topographies [<xref rid=\"B260-ijms-21-05399\" ref-type=\"bibr\">260</xref>,<xref rid=\"B261-ijms-21-05399\" ref-type=\"bibr\">261</xref>] would likely have contributed to modulating CH shape and expression profiles.</p><p>It is noteworthy to mention that ACI is recommended for isolated, focal AC defects [<xref rid=\"B31-ijms-21-05399\" ref-type=\"bibr\">31</xref>,<xref rid=\"B263-ijms-21-05399\" ref-type=\"bibr\">263</xref>]. Thus, future clinical biomaterials would be used in part for the phenotype stabilization of healthy CHs and to control the differentiation and immuno-modulative functions of MSCs in non-degenerative joints. In this context, this review has collected ample evidence to suggest that controlling material stiffness for guiding CH and MSC fate and functions is a highly effective approach. However, recent studies have revealed that ACI is not only being used for isolated, focal AC defects. In 34% to more than 60% of cases, ACI is also used for treating degenerative AC defects, as graded by the treating physician at the time of AC repair [<xref rid=\"B264-ijms-21-05399\" ref-type=\"bibr\">264</xref>,<xref rid=\"B265-ijms-21-05399\" ref-type=\"bibr\">265</xref>]. This is relevant for future clinical biomaterials that would utilize material stiffness as a cell-instructive stimulus, as those numbers raise the question whether material stiffness could also be used in a degenerative context. However, it appears that the role of material stiffness in an OA-related degenerative context has not been sufficiently investigated, for example, by systematically assessing the effectiveness of stiffness-induced re-differentiation of de-differentiated, serially passaged CHs, or by investigating OA CHs in this context. Another point to consider is that CHs for clinical ACI are usually derived from a standard location such as the knee joint intercondylar notch. Nevertheless, other cell sources such as CHs derived from AC lesions [<xref rid=\"B214-ijms-21-05399\" ref-type=\"bibr\">214</xref>], from the knee joint trochlea [<xref rid=\"B266-ijms-21-05399\" ref-type=\"bibr\">266</xref>], and from dissected AC fragments in joints with osteochondritis dissecans [<xref rid=\"B267-ijms-21-05399\" ref-type=\"bibr\">267</xref>,<xref rid=\"B268-ijms-21-05399\" ref-type=\"bibr\">268</xref>], as well as CHs within their native pericellular matrix termed ‘chondrons’ [<xref rid=\"B269-ijms-21-05399\" ref-type=\"bibr\">269</xref>] are investigated as additional cell sources for AC repair. Given that CH properties across human joints differ in multiple ways [<xref rid=\"B270-ijms-21-05399\" ref-type=\"bibr\">270</xref>,<xref rid=\"B271-ijms-21-05399\" ref-type=\"bibr\">271</xref>,<xref rid=\"B272-ijms-21-05399\" ref-type=\"bibr\">272</xref>,<xref rid=\"B273-ijms-21-05399\" ref-type=\"bibr\">273</xref>] and that studies on the response of CHs from these locations to material stiffness are not available, the role of material stiffness for controlling CHs from multiple locations has not yet been investigated. In contrast, the few studies on the pro-inflammatory and angiogenic cues of (increasing) material stiffness in MSCs are promising because they suggest a relevant modulatory role of material stiffness. Collectively, because CHs and MSCs are highly susceptible to material stiffness, the authors of this review speculate that a more targeted use of the material stiffness for developing novel clinical biomaterials will greatly improve controlling CH and MSC AC regenerative properties for the future of cartilage repair. In this context, and based on the available data across various species and biomaterials, the induction and/or stabilization of a chondrogenic phenotype in CHs appear to be promoted by relatively soft 2D substrates of 4 kPa to ≥10 kPa [<xref rid=\"B122-ijms-21-05399\" ref-type=\"bibr\">122</xref>,<xref rid=\"B163-ijms-21-05399\" ref-type=\"bibr\">163</xref>,<xref rid=\"B166-ijms-21-05399\" ref-type=\"bibr\">166</xref>,<xref rid=\"B222-ijms-21-05399\" ref-type=\"bibr\">222</xref>], as those induce a round mCH morphology [<xref rid=\"B163-ijms-21-05399\" ref-type=\"bibr\">163</xref>], maintain a CH phenotype indicated by a higher expression of collagen type II, ACAN, SOX9, and lower expression of collagen type I [<xref rid=\"B166-ijms-21-05399\" ref-type=\"bibr\">166</xref>,<xref rid=\"B222-ijms-21-05399\" ref-type=\"bibr\">222</xref>], and decrease YAP expression and cytoplasmic YAP accumulation [<xref rid=\"B122-ijms-21-05399\" ref-type=\"bibr\">122</xref>]. Comparisons with studies that used stiffer substrates are difficult when the stiffness ranges do not overlap (e.g., comparing these studies to [<xref rid=\"B108-ijms-21-05399\" ref-type=\"bibr\">108</xref>]). In 3D the stiffness values (e.g., for CHs GAG accumulation) appear much lower. Similarly, soft substrates appear suitable for MSC culture because substrates with 3.5 kPa induce chondrogenesis (in 3D) [<xref rid=\"B198-ijms-21-05399\" ref-type=\"bibr\">198</xref>] and with 2 kPa maintain low levels of Il-8 expression. The corresponding “ideal” molecular signaling levels supporting a chondrogenic CH phenotype have been integrated into a model of the material stiffness-dependency of CH phenotype (see <xref ref-type=\"fig\" rid=\"ijms-21-05399-f005\">Figure 5</xref>).</p><p>This review introduced the cytoskeletal structures, mechanosensitive proteins, and molecular pathways that are known to be involved in stiffness sensing to the AC-focused reader (<xref rid=\"ijms-21-05399-t003\" ref-type=\"table\">Table 3</xref>). The involved mechanosensory mechanisms range from individual proteins or protein assemblies to the cytoskeleton and the nucleus. Two mechanosensitive proteins, namely talin and vinculin, play key roles in mechanotransduction [<xref rid=\"B64-ijms-21-05399\" ref-type=\"bibr\">64</xref>,<xref rid=\"B65-ijms-21-05399\" ref-type=\"bibr\">65</xref>] because conformational changes translate mechanical deformation of for example, talin, into biochemical reactions by revealing otherwise hidden binding sites for additional partners. It has been demonstrated that rCHs express vinculin [<xref rid=\"B180-ijms-21-05399\" ref-type=\"bibr\">180</xref>], that both bCHs and mCHs express talin [<xref rid=\"B185-ijms-21-05399\" ref-type=\"bibr\">185</xref>], and that hCHs express layilin, a talin-binding receptor, which, interestingly, is downregulated by interleukin-1β [<xref rid=\"B274-ijms-21-05399\" ref-type=\"bibr\">274</xref>]. However, in terms of specific mechanosensitive molecules not much else has been investigated in CHs or in MSCs. Multiple signaling pathways are involved in stiffness sensing (<xref rid=\"ijms-21-05399-t003\" ref-type=\"table\">Table 3</xref>), of which the RhoA/ROCK pathway is perhaps the most prominent (<xref ref-type=\"fig\" rid=\"ijms-21-05399-f002\">Figure 2</xref>), as this pathway is a central regulator of MSC fate and CH phenotype. For example, MSC lineage commitment towards certain directions can be controlled by material stiffness [<xref rid=\"B44-ijms-21-05399\" ref-type=\"bibr\">44</xref>,<xref rid=\"B94-ijms-21-05399\" ref-type=\"bibr\">94</xref>,<xref rid=\"B151-ijms-21-05399\" ref-type=\"bibr\">151</xref>,<xref rid=\"B152-ijms-21-05399\" ref-type=\"bibr\">152</xref>,<xref rid=\"B153-ijms-21-05399\" ref-type=\"bibr\">153</xref>] but also by growth factors [<xref rid=\"B121-ijms-21-05399\" ref-type=\"bibr\">121</xref>] or by generating specific cell shapes, using microcontact-printed adhesion sites in conjunction with induction media [<xref rid=\"B136-ijms-21-05399\" ref-type=\"bibr\">136</xref>,<xref rid=\"B188-ijms-21-05399\" ref-type=\"bibr\">188</xref>]. Regardless, a common mechanistic denominator in 2D systems appears to be the modulation of endogenous Rho GTPases signaling [<xref rid=\"B121-ijms-21-05399\" ref-type=\"bibr\">121</xref>,<xref rid=\"B136-ijms-21-05399\" ref-type=\"bibr\">136</xref>,<xref rid=\"B188-ijms-21-05399\" ref-type=\"bibr\">188</xref>,<xref rid=\"B275-ijms-21-05399\" ref-type=\"bibr\">275</xref>]. In the context of CHs, a direct ROCK–SOX9 interaction can explain some effects of ROCK on CH phenotype because SOX9 is a potent chondrogenic transcription factor [<xref rid=\"B191-ijms-21-05399\" ref-type=\"bibr\">191</xref>]. In strong contrast, RhoA/ROCK also induces actin polymerization and subsequent stress fiber formation [<xref rid=\"B104-ijms-21-05399\" ref-type=\"bibr\">104</xref>,<xref rid=\"B192-ijms-21-05399\" ref-type=\"bibr\">192</xref>,<xref rid=\"B193-ijms-21-05399\" ref-type=\"bibr\">193</xref>]. It has also been demonstrated convincingly that RhoA/ROCK exhibits an inverse correlation with CH differentiation [<xref rid=\"B171-ijms-21-05399\" ref-type=\"bibr\">171</xref>]. Consequently, one must note (i) that RhoA/ROCK signaling appears to act both pro- and anti-chondrogenically, and (ii) that stiffness sensing appears to play a significant role in this balance between these pro- and anti-chondrogenic effects, as ROCK activity is material stiffness-dependent. Thus, we resolve this apparent contradiction of the differential effects of RhoA/ROCK on CH phenotype by suggesting that the effects of a direct ROCK–SOX9 interaction define CH phenotype at sub-chondrogenic and chondrogenic stiffness and that the stress fiber-inducing effects of ROCK and subsequent induction of de-differentiation define CH phenotype at supra-chondrogenic stiffnesses. Thus, the available molecular signaling data were integrated into a stiffness-regulated CH phenotype model, which is illustrated in <xref ref-type=\"fig\" rid=\"ijms-21-05399-f005\">Figure 5</xref>.</p><p>Moreover, material stiffness also impacts proliferation, as pathways involved in stiffness sensing modulate S-phase entry. For example, stiff substrates foster stress fibers, a spread morphology which, in turn, promotes the proliferation of many cell types [<xref rid=\"B121-ijms-21-05399\" ref-type=\"bibr\">121</xref>,<xref rid=\"B127-ijms-21-05399\" ref-type=\"bibr\">127</xref>] including AC CHs [<xref rid=\"B122-ijms-21-05399\" ref-type=\"bibr\">122</xref>] and MSCs [<xref rid=\"B121-ijms-21-05399\" ref-type=\"bibr\">121</xref>]. This link between morphology and induction of proliferation is relevant, as it might theoretically allow controlling the proliferation rate that is desired for a specific time frame via a tunable material stiffness (e.g., via optogenetics) [<xref rid=\"B276-ijms-21-05399\" ref-type=\"bibr\">276</xref>]. In contrast, biomaterials could also be developed to suppress early proliferation such as seen in early OA [<xref rid=\"B277-ijms-21-05399\" ref-type=\"bibr\">277</xref>] if desired. Stiff substrates also promote active YAP/TAZ in the nucleus, a transcription co-activator, which promotes the proliferation of multiple cell types [<xref rid=\"B127-ijms-21-05399\" ref-type=\"bibr\">127</xref>] but not of hMSCs [<xref rid=\"B121-ijms-21-05399\" ref-type=\"bibr\">121</xref>] or AC rCHs [<xref rid=\"B122-ijms-21-05399\" ref-type=\"bibr\">122</xref>]. Thus, another relevant signaling pathway is the YAP/TAZ pathway, as it not only inhibits proliferation in MSCs and CHs but also controls hMSCs lineage commitment [<xref rid=\"B155-ijms-21-05399\" ref-type=\"bibr\">155</xref>] mediated in part via substrate mechanics-regulated cell spreading [<xref rid=\"B156-ijms-21-05399\" ref-type=\"bibr\">156</xref>], and because it contributes to regulating AC homeostasis through mediating Hippo signaling [<xref rid=\"B162-ijms-21-05399\" ref-type=\"bibr\">162</xref>]. In terms of chondrogenesis, YAP is a negative regulator of chondrogenic differentiation of MSCs [<xref rid=\"B157-ijms-21-05399\" ref-type=\"bibr\">157</xref>]. Moreover, YAP inactivation is conducive to the maintenance of the chondrogenic phenotype [<xref rid=\"B122-ijms-21-05399\" ref-type=\"bibr\">122</xref>] because YAP downregulation on soft substrates helps maintain CH phenotype, and because relatively stiff substrates of 40 kPa increase YAP expression and YAP accumulation in rCH nuclei, concomitant with high expression levels of collagen I and almost no collagen type II expression. Thus, relatively high material stiffness fosters a degenerative CH phenotype through increased nuclear YAP. To explain, according to Dasgupta and McCollum [<xref rid=\"B278-ijms-21-05399\" ref-type=\"bibr\">278</xref>], stiffer substrates lead to more robust assembly of FAs and stress fibers, increased activation of the FAK kinase, increased cell spreading, and increased YAP/TAZ activity in a manner that depends on the tension-sensing focal adhesion protein talin, based on previous studies [<xref rid=\"B71-ijms-21-05399\" ref-type=\"bibr\">71</xref>,<xref rid=\"B279-ijms-21-05399\" ref-type=\"bibr\">279</xref>]. Moreover, another study [<xref rid=\"B280-ijms-21-05399\" ref-type=\"bibr\">280</xref>] suggested that YAP/TAZ activation by integrin-dependent FA formation may be linked to the activation of the RhoGEF β-PIX, the small GTPase Rac1, and its effector p21-activated protein kinase (PAK), based on previous research [<xref rid=\"B281-ijms-21-05399\" ref-type=\"bibr\">281</xref>,<xref rid=\"B282-ijms-21-05399\" ref-type=\"bibr\">282</xref>]. In particular, one study [<xref rid=\"B281-ijms-21-05399\" ref-type=\"bibr\">281</xref>] connected β1 integrin-dependent Rac/group I PAK signaling to the activation of YAP1 [<xref rid=\"B281-ijms-21-05399\" ref-type=\"bibr\">281</xref>]. In conjunction with other research [<xref rid=\"B195-ijms-21-05399\" ref-type=\"bibr\">195</xref>,<xref rid=\"B196-ijms-21-05399\" ref-type=\"bibr\">196</xref>], which found that a differential α1, β1, and β3 integrin expression is stiffness-dependent in hMSCs and in hCHs, these studies illustrate how material stiffness might activate YAP signaling in an integrin-Rac-dependent manner. Together, these studies explain how high material stiffness acts through increased YAP expression and nuclear accumulation as a negative regulator of chondrogenic differentiation of MSCs, as described by Karystinou et al. [<xref rid=\"B157-ijms-21-05399\" ref-type=\"bibr\">157</xref>], and of a healthy, chondrogenic CH phenotype, as seen elsewhere [<xref rid=\"B122-ijms-21-05399\" ref-type=\"bibr\">122</xref>]. Consolidating the material stiffness-dependent effects of both RhoA/ROCK and YAP on CH phenotype, we suggest that the effects of a direct ROCK–SOX9 interaction define CH phenotype at sub-chondrogenic and chondrogenic stiffnesses, whereas not only the stress fiber-inducing effects of ROCK but also the increased YAP expression and nuclear accumulation subsequently define the degenerative CH phenotype at supra-chondrogenic stiffnesses (see <xref ref-type=\"fig\" rid=\"ijms-21-05399-f005\">Figure 5</xref>). In line of this thought, such an assumed association between YAP and SOX9 would require that increasing levels of YAP expression and nuclear accumulation were associated with decreasing levels of SOX9. To the best knowledge of the authors, no systematic study has assessed this association. However, in support of our assumption, a study has reported decreased SOX9 expression levels concomitantly with increased YAP expression and YAP accumulation in the nucleus of rCHs on 40 kPa stiff but not on 4 kPa soft substrates [<xref rid=\"B122-ijms-21-05399\" ref-type=\"bibr\">122</xref>]. Another study on growth plate CHs also reported that high levels of phosphorylated YAP accompanied low SOX9 expression levels [<xref rid=\"B283-ijms-21-05399\" ref-type=\"bibr\">283</xref>]. In addition, in support of our assumption, another study on esophageal squamous cell carcinoma cell lines found that SOX9 is, at the same time, a downstream target as well as an upstream regulator of YAP signaling as they reported increased YAP protein levels after SOX9 knockdown [<xref rid=\"B284-ijms-21-05399\" ref-type=\"bibr\">284</xref>]. However, although it is intriguing to explain a degenerative CH phenotype on stiffer substrates by increased YAP expression and nuclear accumulation and subsequently decreased levels of SOX9, together with stress fiber-inducing effects of ROCK and subsequent de-differentiation, further studies on this topic are needed.</p><p>TGF-β1, as illustrated in <xref ref-type=\"fig\" rid=\"ijms-21-05399-f003\">Figure 3</xref>, plays major roles in stiffness-dependent hMSC chondrogenic differentiation [<xref rid=\"B121-ijms-21-05399\" ref-type=\"bibr\">121</xref>], CH phenotype regulation [<xref rid=\"B108-ijms-21-05399\" ref-type=\"bibr\">108</xref>], and mediating pro-fibrogenic activities during OA progression [<xref rid=\"B285-ijms-21-05399\" ref-type=\"bibr\">285</xref>]. Given these roles, and given how aging, mechanical stress, and inflammation contribute to altered TGF-β family signaling [<xref rid=\"B286-ijms-21-05399\" ref-type=\"bibr\">286</xref>], a material stiffness-dependent mechanism of the TGF-β receptor is rather interesting. The TGF-β receptor appears to be cell surface tension-sensitive, as altering cellular tension through ROCK inhibition or through cell culture on substrates with varying stiffness leads to a collapse of the physical separation of the receptor complexes TβRI and TβRII within the FAs and leads to multimeric TβRI/TβRII [<xref rid=\"B137-ijms-21-05399\" ref-type=\"bibr\">137</xref>]. Under such circumstances, TβRI and TβRII are no longer physically separated and, instead, they interact, which subsequently induces changes in TGF-β-induced downstream effects (e.g., Smad3 activation). Although such effects have not been demonstrated in CHs, it appears appealing to consider the discrete spatial organization of TGF-β receptors in the context of ageing or onset of OA. As discussed previously [<xref rid=\"B277-ijms-21-05399\" ref-type=\"bibr\">277</xref>], it is commonly suggested that ageing or onset of OA switches the receptor in TGF-β signaling from the classical activin receptor-like kinase 5 (ALK5)/TGF-β-RI activated Smad2/3 signaling to TGF-β-RI family member ALK1/ACVRL1 induced SMAD1/5/8 signaling, which converts TGF-β function in AC from an anabolic growth factor into a catabolic cytokine [<xref rid=\"B287-ijms-21-05399\" ref-type=\"bibr\">287</xref>]. However, to what extent material stiffness-dependent effects might be responsible remains to be seen.</p><p>The last signaling pathway that this study discusses is the Wnt/β-catenin pathway. Through the integrin/FAK pathway, material stiffness induces β-catenin nuclear accumulation [<xref rid=\"B225-ijms-21-05399\" ref-type=\"bibr\">225</xref>]. This nuclear accumulation of β-catenin and the subsequent stimulation of transcriptional activity cause rabCH de-differentiation [<xref rid=\"B226-ijms-21-05399\" ref-type=\"bibr\">226</xref>]. Thus, the Wnt/β-catenin pathway represents another material stiffness-regulated mechanism that impacts on CH phenotype. Interestingly, α-catenin can block the β-catenin-mediated inhibition of collagen type II expression in rabCHs [<xref rid=\"B227-ijms-21-05399\" ref-type=\"bibr\">227</xref>] through a direct interaction of α-catenin with β-catenin in the nucleus [<xref rid=\"B226-ijms-21-05399\" ref-type=\"bibr\">226</xref>], which experimentally reestablishes CH collagen type II expression. Thus, <xref ref-type=\"fig\" rid=\"ijms-21-05399-f005\">Figure 5</xref> illustrates the stiffness-mediated effects of β-catenin on CH de-differentiation. Theoretically, recent findings on α-catenin, β-catenin, YAP, and SOX9 might suggest that the discussed effects of these proteins on CH phenotype are connected. Although such data have not been demonstrated in CHs or MSCs, α-catenin regulates the actin-myosin contractility of cardiomyocytes, which controls YAP nuclear accumulation [<xref rid=\"B288-ijms-21-05399\" ref-type=\"bibr\">288</xref>]. α-Catenin is upregulated by high material stiffness in Madin–Darby canine kidney (MDCK) cells [<xref rid=\"B289-ijms-21-05399\" ref-type=\"bibr\">289</xref>] where it recruits actin and vinculin through a force-dependent cryptic vinculin-binding-site [<xref rid=\"B290-ijms-21-05399\" ref-type=\"bibr\">290</xref>]. In turn, vinculin reinforces FAs and nucleates actin polymerization [<xref rid=\"B291-ijms-21-05399\" ref-type=\"bibr\">291</xref>], which also promote YAP nuclear localization in cardiomyocytes [<xref rid=\"B292-ijms-21-05399\" ref-type=\"bibr\">292</xref>]. As discussed, increased YAP expression and YAP accumulation occur in the nucleus of rCHs on stiff substrates and decrease their SOX9 expression [<xref rid=\"B122-ijms-21-05399\" ref-type=\"bibr\">122</xref>]. Thus, material stiffness-triggered β-catenin and theoretically also a material stiffness-triggered interplay of α-catenin, YAP, and SOX9 impact on CH phenotype, resulting in de-differentiation in high stiffness conditions. Although designated studies have not yet been performed in CHs or MSCs, older studies have established a strong link between the cytoskeleton and collagen type I and II expression [<xref rid=\"B254-ijms-21-05399\" ref-type=\"bibr\">254</xref>,<xref rid=\"B255-ijms-21-05399\" ref-type=\"bibr\">255</xref>,<xref rid=\"B257-ijms-21-05399\" ref-type=\"bibr\">257</xref>]. Exploring such potential mechanisms in the context of this review might link the regulation of collagen type II expression through α-catenin, β-catenin, and vinculin directly to the cytoskeletal proteins and forces that are involved in CH stiffness sensing.</p><p>On a side note, the here reviewed studies were conducted in 33% of all studies on hCHs, in 16% mCHs, 11% pCHs, 8% bCHs, 8% gCHs, 8% rCHs, 8% rabChs, and in 2% cCHs and also in 2% in sCHs. In 7% of all reviewed studies, hMSCs were used and in 23% rMSCs were used. Despite the obvious usage of cells from multiple species, this review found no conflicting data between species, suggesting that CHs across species might share common stiffness sensing-mechanisms and responses. However, this insight is somewhat limited because very few studies used cells from more than one species and, thus, no head-to-head comparisons are available. Nevertheless, we suggest that future studies should not focus on differences across species but rather on differences between healthy vs. de-differentiated CHs and stiffness ranges across magnitudes of differences.</p><p>In summary, the here reviewed knowledge on the substrate stiffness-dependent behavior of CHs and MSCs has important implications for utilizing material stiffness as a phenotype-controlling parameter with the aim to create in situ environments for inducing or maintaining a healthy chondrogenic phenotype. Surgical approaches that might benefit include CH-focused methods such as ACI and other CH-focused procedures [<xref rid=\"B30-ijms-21-05399\" ref-type=\"bibr\">30</xref>], whose clinical applications are subject to algorithm-based recommendations [<xref rid=\"B31-ijms-21-05399\" ref-type=\"bibr\">31</xref>]. Procedures that might also benefit are bone marrow stimulation methods such as microfracture, nanofracture, and AMIC™.</p><p>Current clinical biomaterials were devised in a decade, in which biophysical cues such as material stiffness had not yet emerged as essential determinants of cell fate. This is in contrast to the current view, which clearly recognizes the relevance of biophysical factors because those can be equally important as biochemical and genetic factors [<xref rid=\"B293-ijms-21-05399\" ref-type=\"bibr\">293</xref>]. Acknowledging this, studies even use terms such as the “rise of mechano-transduction” [<xref rid=\"B294-ijms-21-05399\" ref-type=\"bibr\">294</xref>], “mechano-transduction: use the force” [<xref rid=\"B295-ijms-21-05399\" ref-type=\"bibr\">295</xref>], and “mechano-transduction: may the force be with you” [<xref rid=\"B296-ijms-21-05399\" ref-type=\"bibr\">296</xref>]. Importantly, the available mRNA expression data were derived from the residuals of clinical biomaterials with CHs that displayed mostly no IL-1β expression, which, in turn, suggests that the investigated CHs were derived from non-degenerative joints [<xref rid=\"B256-ijms-21-05399\" ref-type=\"bibr\">256</xref>]. Thus, despite not being optimized for using material stiffness as a cell-instructive parameter, we conclude that current clinical biomaterials control CH phenotype well in non-degenerative settings but not to equal extents. In conjunction, this review collected sufficient evidence to recommend using material stiffness for controlling cell phenotype and as a promising design cornerstone for novel future-oriented, cell-instructive biomaterials for clinical high-quality articular AC repair tissue. Since the future of clinical AC repair lies in developing solutions for degenerative AC lesions or joints, the perhaps most important insight is that material stiffness has immuno-modulative and angiogenic roles in MSCs and modulates growth factor- and pro-inflammatory cytokine-induced changes in CHs. Thus, in the future material stiffness may be used clinically to intentionally modulate a degenerative, chronic inflammatory environment, which might lead to phenotype-instructive, inflammatory response-modulating biomaterials for the future of cartilage repair.</p></sec></body><back><notes><title>Author Contributions</title><p>M.S. and B.R. wrote the initial manuscript and edited the manuscript into its final form. J.C.L., M.L.H., M.S., and B.R. interpreted the available topic-relevant data critically and J.C.L. and M.L.H. helped with wording, proofreading, and finalizing the manuscript. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was funded by the German Research Council (DFG), grant number RO2511/11-1, Freiburg University Medical Faculty (Forschungskommission), grant number ROL1193/18, and the Deutsche Arthrose-Hilfe, grant number P346-A825-Rolauffs-EP1. </p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflicts of interest.</p></notes><glossary><title>Abbreviations</title><array orientation=\"portrait\"><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">articular cartilage</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ACAN</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">aggrecan</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ACI</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">autologous chondrocyte implantation</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ALP</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">alkaline phosphatase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AMIC<sup>TM</sup></td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">autologous matrix-induced chondrogenesis</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ATF2</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">activation transcription factor 2</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Arp2/3</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">actin-related protein 2/3</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">BMP</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">bone morphogenetic protein</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CHs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">chondrocytes</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">cCHs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">chicken chondrocytes</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">COL1A2</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">collagen type I alpha II chain</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">COL2A1</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">collagen type II alpha I chain</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Col10A1</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">collagen type 10 alpha I chain</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CREB</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">cAMP response element-binding protein</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ECM</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">extracellular matrix</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">EGF</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">epidermal growth factor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ERKs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">extracellular signal-regulated kinases</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">FAK</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">focal adhesion kinase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">FAs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">focal adhesions</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GAGs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">glycosaminoglycans</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">gCHs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">goat chondrocytes</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GEF</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">guanine-exchange factor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">hASCs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">human adipose-derived stem cells</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">hMSCs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">human mesenchymal stem cells</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IL-1β</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Interleukin 1 β</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IRAK</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Interleukin-1 receptor associated kinase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">JNK</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jun NH2-terminal kinase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">LINC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Linker of nucleoskeleton to cytoskeleton</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">LOX</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">lysil oxidase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">LPL</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Lipoprotein lipase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">µRB</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">microribbon</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MMP-13</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">matrix metalloproteinase 13</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MSCs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">mesenchymal stem cells</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">mCHs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">murine chondrocytes</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MLCK</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">myosin light chain kinase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">myosin II</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">myosin phosphatase II</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NSCs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">neural stem cells</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">OA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">osteoarthritis</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PAA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">polyacrylamide</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">pCHs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">porcine chondrocytes</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PEGDA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">poly(ethylene)glycol diacrylate</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">rabCHs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">rabbit chondrocytes</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">rCHs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">rat chondrocytes</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">rMSCs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">rat mesenchymal stem cells</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">rNSCs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">rat neural stem cells</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ROCK</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rho associated protein kinase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ROS</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Reactive oxygen species</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">RUNX2</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">runt related transcription factor 2</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">sCHs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">sheep chondrocytes</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SMAD</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SMA- and MAD-related protein</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SMCs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">smooth muscle cells</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">sMSCs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">synovium-derived mesenchymal stem cells</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SOX9</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SRY-related HMG box-containing</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TAZ</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">transcriptional co-activator with PDZ-binding motif</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TGF-α</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">transforming growth factor α</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TGF-β</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">transforming growth factor β</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TGFR</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">transforming growth factor receptor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">VEGF</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">vascular endothelial growth 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This induces outside-in signaling and initiates further downstream processes for subsequent cytoskeletal remodeling.</p></caption><graphic xlink:href=\"ijms-21-05399-g001\"/></fig><fig id=\"ijms-21-05399-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Stiffness-induced integrin-dependent focal adhesion kinase (FAK) signaling. Integrin stimulation leads to an activation of various downstream effectors. One of them is FAK, which binds Src and Pyk2 and activates Ras, which further activates, Raf, MEK and extracellular signal-regulated kinase (ERK)/Jun NH2-terminal kinase (JNK). Ultimately leading to expression of runt related transcription factor 2 (RUNX2) and osteogenic differentiation of mesenchymal stromal cells (MSCs). FAK can also activate phosphoinositide 3-kinase (PI3K), which stimulates Akt and also increases expression of RUNX2. Integrins can also activate Cdc42, which activates PI3K. This leads to activation of Rac, p21-activated protein kinase (PAK), MEK kinase-1 (MEKK), MAP kinase kinase (MKK) and expression of cyclin D1 to enhance cell proliferation. Another effector of FAK is P130Cas, which also stimulates expression of cyclin D1. Integrins also activate RhoA, which binds guanosine-5’-triphosphate (GTP) and activates RhoA/Rho associated protein kinase (ROCK). ROCK activates myosin light chain kinase (MLCK) to eventually increase expression of MyoD and therefore myogenic differentiation of MSCs. FAK can also activate paxillin, which leads to further downstream signaling through MEK, phosphorylation of ERK and MLCK. MLCK then leads to enhance actin-myosin expression and myogenic differentiation.</p></caption><graphic xlink:href=\"ijms-21-05399-g002\"/></fig><fig id=\"ijms-21-05399-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Tumor necrosis factor beta (TNFα), interleukin 1 beta (IL-1β), bone morphogenetic protein (BMP), and transforming growth factor beta (TGF-β) are stiffness-dependent signaling pathways. Binding of TNFα to its receptor leads to activation of the mitogen-activated protein kinase (MAPK) pathway. MAP3K and MAP2K are activated and three groups of MAP kinases are involved: ERK, JNK, and p38 MAP kinase. Activation of p38 further activates transcription factor activation transcription factor 2 (ATF2), which enhances transcription of osteogenic genes such as RUNX2, liver/bone/kidney alkaline phosphatase (ALPL) and hypertrophic collagen Col10A1. Phosphorylation of ERK and JNK leads to activation of cAMP response element-binding protein (CREB) and also the activation of RUNX2, ALPL, Col10A1, and additionally matrix metalloproteinase 13 (MMP-13). The binding of IL-1β activates interleukin-1 receptor associated kinase (IRAK) and leads to similar downstream effectors in chondrocytes (CHs). BMP signaling occurs via the SMA- and MAD-related protein (SMAD)-dependent pathway. Signaling is initiated by binding to type I or type II serine/threonine kinase receptors and forming a heterotetrameric complex. The type I receptor is transphosphorylated by constitutively active type II receptor and activin receptor-like kinase 1 (ALK1), which activates R-SMAD1/5/8. Phosphorylated R-SMAD1/5/8 then binds to its co-receptor SMAD4 and translocates into the nucleus, where it initiates transcription of BMP-specific genes (e.g., RUNX2). TGF-β is initiated by ligand binding to receptor types I and II with ALK5. SMAD2/3 gets activated and also binds to co-activator SMAD4. The translocation into the nucleus activates transcription of chondrogenic genes like collagen type II and aggrecan (ACAN). Ligand binding also activates mitogen-activated protein kinase kinase kinase 7 (MAP3K7, also known as TAK1) and TGF-β activated kinase 1 (MAP3K7) binding protein 1 (TAB1), leading to further activation of MKK3 and phosphorylation of p38. Phosphorylated p38 leads to transcription of RUNX2 and MMP-13.</p></caption><graphic xlink:href=\"ijms-21-05399-g003\"/></fig><fig id=\"ijms-21-05399-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Material stiffness-induced Wnt signaling. Binding of a Wnt ligand to the Frizzled receptor and LRP5/6 receptor activates the Wnt signaling pathway. Wnt causes translocation of Axin and the destruction complex adenomatous polyposis coli (APC) and glycogen synthase kinase 3 (GSK3) to the plasma membrane, inhibiting β-catenin degradation through the destruction complex. Dsh becomes activated and β-catenin accumulates in the cytoplasm. β-catenin translocates into the nucleus and promotes target gene expression of Wnt genes through binding TCF/LEF co-activators and further production of β-catenin. Increasing β-catenin leads to higher expression of type I collagen and decreased expression of type II collagen in CHs.</p></caption><graphic xlink:href=\"ijms-21-05399-g004\"/></fig><fig id=\"ijms-21-05399-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>Model of the material stiffness-dependency of CH phenotype. The most prominent signaling pathway involved in stiffness sensing is the RhoA/ROCK pathway. For CHs, a direct ROCK–SOX9 interaction can explain the stiffness-mediated effects on CH phenotype, as SOX9 is a potent chondrogenic transcription factor. With increasing stiffness RhoA/ROCK induces more actin polymerization and stress fiber formation, which has an inverse correlation with CH differentiation in some studies and in others RhoA/ROCK signaling appears to act both pro- and anti-chondrogenically. We suggest resolving this apparent contradiction of the differential effects of RhoA/ROCK on CH phenotype by suggesting that the effects of a direct ROCK–SOX9 interaction define the CH phenotype at sub-chondrogenic and chondrogenic stiffnesses and that the stress fiber-inducing effects of ROCK and subsequent induction of de-differentiation define CH phenotype at supra-chondrogenic stiffnesses. The YAP/TAZ pathway is also regulated by material-stiffness. High material stiffness acts through increased YAP expression and nuclear accumulation as a negative regulator of a healthy chondrogenic CH phenotype. As already mentioned, we suggest a CH phenotype-defining effect of ROCK–SOX9 interaction at sub-chondrogenic and chondrogenic stiffnesses, whereas not only the stress fiber-inducing effects of ROCK, but also the increased YAP expression and nuclear accumulation define the degenerative CH phenotype at supra-chondrogenic stiffnesses. This assumed association between YAP and SOX9 would require increasing levels of YAP expression and nuclear accumulation with decreasing levels of SOX9. Another material-stiffness regulated mechanism is the α-catenin–β-catenin interaction. Increasing material stiffness leads to elevated nuclear β-catenin accumulation and subsequent CH de-differentiation. In addition, α-catenin is upregulated with increasing material-stiffness and can block β-catenin-mediated inhibition of collagen type II expression. The authors suggest a potential connection of α-catenin, β-catenin, YAP, and SOX9 on CH phenotype. Moreover, α-catenin also regulates actomyosin contractility and can recruit actin and vinculin through hidden binding sites. This promotes further actin polymerization and subsequent nuclear YAP localization, which decreases SOX9 expression in CHs. Thus, material stiffness-triggered increases of β-catenin and theoretically also a material stiffness-triggered interplay of α-catenin, YAP, and SOX9 may impact CH phenotype.</p></caption><graphic xlink:href=\"ijms-21-05399-g005\"/></fig><table-wrap id=\"ijms-21-05399-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05399-t001_Table 1</object-id><label>Table 1</label><caption><p>Cell-based biomaterials used for clinical articular cartilage repairs.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Product Name</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Type of Material</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Stiffness Data</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Morphology</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Gene Expression</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Porosity</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Chondro-Gide<sup>®</sup></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3D Hyaluronan web</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Some spherical; mainly elongated; polygonal</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">De-differentiated phenotype</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">High (up to 200 µm)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B228-ijms-21-05399\" ref-type=\"bibr\">228</xref>,<xref rid=\"B229-ijms-21-05399\" ref-type=\"bibr\">229</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Hyalograft C</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Autologous chondrocytes grown on a 3D hyaluronan-based scaffold</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Spherical, elongated, polygonal</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Lower ACAN and collagen type II expression</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">High (up to 200 µm)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B249-ijms-21-05399\" ref-type=\"bibr\">249</xref>,<xref rid=\"B250-ijms-21-05399\" ref-type=\"bibr\">250</xref>] </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>MACI<sup>®</sup></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Membrane of type I/III collagen</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Elongated-fibroblast like cell shape</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">High collagen type I </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B236-ijms-21-05399\" ref-type=\"bibr\">236</xref>,<xref rid=\"B237-ijms-21-05399\" ref-type=\"bibr\">237</xref>] </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Novocart<sup>®</sup>3D</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Type I collagen sponges with bilayer structure</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mainly spherical</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">High expression of collagen type II, little collagen type I, X</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">High (10-100 µm)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B230-ijms-21-05399\" ref-type=\"bibr\">230</xref>,<xref rid=\"B231-ijms-21-05399\" ref-type=\"bibr\">231</xref>,<xref rid=\"B232-ijms-21-05399\" ref-type=\"bibr\">232</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>NeoCart<sup>®</sup></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Collagen type I loaded into sponges of same material</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B233-ijms-21-05399\" ref-type=\"bibr\">233</xref>,<xref rid=\"B234-ijms-21-05399\" ref-type=\"bibr\">234</xref>] </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Novocart Inject</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Autologous CHs, hydrogel is a combination of human albumin and hyaluronic acid</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B251-ijms-21-05399\" ref-type=\"bibr\">251</xref>] </td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>RevaFlex™ (formerly DeNovo ET<sup>®</sup>)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Hyaline neocartilage discs composed of allogenic juvenile CHs</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B252-ijms-21-05399\" ref-type=\"bibr\">252</xref>] </td></tr></tbody></table></table-wrap><table-wrap id=\"ijms-21-05399-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05399-t002_Table 2</object-id><label>Table 2</label><caption><p>Cell-free biomaterials used for clinical articular cartilage repair.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Product Name</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Type of Material</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Stiffness Data</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Morphology</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Gene Expression</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Porosity</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>CaReS<sup>®</sup></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Collagen type I</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Spheroid, many elongated, polygonal</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">High collagen type II and ACAN</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Low</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B235-ijms-21-05399\" ref-type=\"bibr\">235</xref>] </td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>MaioRegen<sup>®</sup></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Collagen type Iand hydroxyapatite</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NA</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B238-ijms-21-05399\" ref-type=\"bibr\">238</xref>] </td></tr></tbody></table></table-wrap><table-wrap id=\"ijms-21-05399-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05399-t003_Table 3</object-id><label>Table 3</label><caption><p>Key molecules that regulate the material stiffness-dependency of cell phenotype.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Key Molecule</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cell Type</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Phenotype</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>ROCK-SOX9</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CH </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">stress fiber-inducing effect of ROCK leads to de-differentiation of CH phenotype at supra-chondrogenic stiffnesses [<xref rid=\"B190-ijms-21-05399\" ref-type=\"bibr\">190</xref>]</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold>RhoA/ROCK/myosin II</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CH</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">high material stiffness increases expression of stress fibers, which leads to a downregulation of collagen type II, but upregulation of SOX9 <break/>low material stiffness/disruption of actin network restores the chondrogenic phenotype [<xref rid=\"B163-ijms-21-05399\" ref-type=\"bibr\">163</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MSC </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">high material stiffness causes high cross-linking density of fibers → stiffness-specific upregulation of distinct lineage genes [<xref rid=\"B121-ijms-21-05399\" ref-type=\"bibr\">121</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ATDC5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">high material stiffness leads to upregulation of SOX9 [<xref rid=\"B190-ijms-21-05399\" ref-type=\"bibr\">190</xref>]</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold>YAP/TAZ</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CH</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">high stiffness leads to nuclear accumulation of YAP/TAZ and a degenerative CH phenotype [<xref rid=\"B162-ijms-21-05399\" ref-type=\"bibr\">162</xref>] <break/>YAP inactivation restores collagen type II levels [<xref rid=\"B122-ijms-21-05399\" ref-type=\"bibr\">122</xref>] </td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MSC</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">soft substrate leads to YAP/TAZ accumulation in the cytoplasm → no proliferation [<xref rid=\"B127-ijms-21-05399\" ref-type=\"bibr\">127</xref>]/chondrogenic differentiation [<xref rid=\"B122-ijms-21-05399\" ref-type=\"bibr\">122</xref>] <break/>stiff substrate leads to active YAP/TAZ in the nucleus → induces proliferation [<xref rid=\"B127-ijms-21-05399\" ref-type=\"bibr\">127</xref>] and osteogenic differentiation [<xref rid=\"B121-ijms-21-05399\" ref-type=\"bibr\">121</xref>] </td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold>TGF-β</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CH</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">low stiffness + TGF-β lead to elevated levels of chondrogenic gene expression [<xref rid=\"B190-ijms-21-05399\" ref-type=\"bibr\">190</xref>] <break/>higher stiffness + TGF-β increase cell stiffness and lead to higher SOX9 expression [<xref rid=\"B190-ijms-21-05399\" ref-type=\"bibr\">190</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MSC</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">differential effects of TGF-β modulated by stiffness<break/>soft material stiffness + TGF-β → chondrogenic differentiation [<xref rid=\"B121-ijms-21-05399\" ref-type=\"bibr\">121</xref>] <break/>medium material stiffness + TGF-β → myogenic differentiation [<xref rid=\"B121-ijms-21-05399\" ref-type=\"bibr\">121</xref>] </td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Lamin A</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MSC </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">soft material stiffness induces low lamin-A expression → adipogenic differentiation [<xref rid=\"B173-ijms-21-05399\" ref-type=\"bibr\">173</xref>] <break/>high material stiffness induces high lamin-A expression → osteogenic differentiation [<xref rid=\"B173-ijms-21-05399\" ref-type=\"bibr\">173</xref>] </td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Wnt/β-catenin</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CH/MSC</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">high material stiffness leads to accumulation of β-catenin and de-differentiation of CHs [<xref rid=\"B225-ijms-21-05399\" ref-type=\"bibr\">225</xref>] </td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>α-catenin</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CH</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">counteracts the β-catenin mediated inhibition of collagen type II expression [<xref rid=\"B227-ijms-21-05399\" ref-type=\"bibr\">227</xref>] </td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold>IL-1β</bold>\n<break/>\n<bold>Rac1/cyclin D1</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CH</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">elevated levels of IL-1β increase cellular stiffness [<xref rid=\"B184-ijms-21-05399\" ref-type=\"bibr\">184</xref>] </td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CH/<break/>MSC</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">high material stiffness leads to upregulation of cyclin D1 mediated by Rac1, inducing S-phase entry and proliferation [<xref rid=\"B125-ijms-21-05399\" ref-type=\"bibr\">125</xref>]</td></tr></tbody></table></table-wrap></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32731544</article-id><article-id pub-id-type=\"pmc\">PMC7432013</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155362</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05362</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Flavopereirine Inhibits Autophagy via the AKT/p38 MAPK Signaling Pathway in MDA-MB-231 Cells</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Chen</surname><given-names>Ming-Shan</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05362\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05362\">2</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05362\">†</xref></contrib><contrib contrib-type=\"author\"><name><surname>Yeh</surname><given-names>Hsuan-Te</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05362\">1</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05362\">†</xref></contrib><contrib contrib-type=\"author\"><name><surname>Li</surname><given-names>Yi-Zhen</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05362\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Lin</surname><given-names>Wen-Chun</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05362\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Lee</surname><given-names>Ying-Ray</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05362\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Tseng</surname><given-names>Ya-Shih</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijms-21-05362\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Sheu</surname><given-names>Shew-Meei</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05362\">3</xref><xref rid=\"c1-ijms-21-05362\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05362\"><label>1</label>Department of Anesthesiology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chia-Yi 60002, Taiwan; <email>06590@cych.org.tw</email> (M.-S.C.); <email>07438@cych.org.tw</email> (H.-T.Y.)</aff><aff id=\"af2-ijms-21-05362\"><label>2</label>Department of Biotechnology, Asia University, Taichung 41354, Taiwan</aff><aff id=\"af3-ijms-21-05362\"><label>3</label>Department of Medical Research, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chia-Yi 60002, Taiwan; <email>10862@cych.org.tw</email> (Y.-Z.L.); <email>13320@cych.org.tw</email> (W.-C.L.); <email>06841@cych.org.tw</email> (Y.-R.L.)</aff><aff id=\"af4-ijms-21-05362\"><label>4</label>Department of Medical Laboratory Science and Biotechnology, Chung Hwa University of Medical Technology, Tainan 71703, Taiwan; <email>shih415@mail.hwai.edu.tw</email></aff><author-notes><corresp id=\"c1-ijms-21-05362\"><label>*</label>Correspondence: <email>cych12554@gmail.com</email>; Tel.: +886-5-2765041-5568</corresp><fn id=\"fn1-ijms-21-05362\"><label>†</label><p>These authors contributed equally to this work.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>28</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5362</elocation-id><history><date date-type=\"received\"><day>12</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>23</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Autophagy is a potential target for the treatment of triple negative breast cancer (TNBC). Because of a lack of targeted therapies for TNBC, it is vital to find optimal agents that avoid chemoresistance and metastasis. Flavopereirine has anti-proliferation ability in cancer cells, but whether it regulates autophagy in breast cancer cells remains unclear. A Premo™ Tandem Autophagy Sensor Kit was used to image the stage at which flavopereirine affects autophagy by confocal microscopy. A plasmid that constitutively expresses p-AKT and siRNA targeting p38 mitogen-activated protein kinase (MAPK) was used to confirm the related signaling pathways by Western blot. We found that flavopereirine induced microtubule-associated protein 1 light chain 3 (LC3)-II accumulation in a dose- and time-dependent manner in MDA-MB-231 cells. Confocal florescent images showed that flavopereirine blocked autophagosome fusion with lysosomes. Western blotting showed that flavopereirine directly suppressed p-AKT levels and mammalian target of rapamycin (mTOR) translation. Recovery of AKT phosphorylation decreased the level of p-p38 MAPK and LC3-II, but not mTOR. Moreover, flavopereirine-induced LC3-II accumulation was partially reduced in MDA-MB-231 cells that were transfected with p38 MAPK siRNA. Overall, flavopereirine blocked autophagy via LC3-II accumulation in autophagosomes, which was mediated by the AKT/p38 MAPK signaling pathway.</p></abstract><kwd-group><kwd>flavopereirine</kwd><kwd>autophagy</kwd><kwd>breast cancer</kwd><kwd>AKT</kwd><kwd>p38 MAPK</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05362\"><title>1. Introduction</title><p>Autophagy is a cellular self-digestion pathway that removes unnecessary or dysfunctional cellular components via sequestration in double membrane vesicles, which subsequently fuse with lysosomes for acidic degradation. Basal autophagy levels in cells serve to maintain homeostasis during nutrient deprivation, including clearance of damaged organelles and nutrient recycling. In addition to the physiological roles of autophagy, autophagic dysfunction has been identified in the pathogenesis of diverse diseases, including cancer, neurodegeneration, microbial infection, and aging [<xref rid=\"B1-ijms-21-05362\" ref-type=\"bibr\">1</xref>]. In cancer treatment, autophagy also plays a critical role in the survival of breast cancer cells and is considered a novel therapeutic target [<xref rid=\"B2-ijms-21-05362\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijms-21-05362\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijms-21-05362\" ref-type=\"bibr\">4</xref>].</p><p>Breast cancer is the most frequently diagnosed cancer among women worldwide and the leading cause of cancer-related death in women [<xref rid=\"B5-ijms-21-05362\" ref-type=\"bibr\">5</xref>]. Breast cancer is highly heterogeneous. Among the different subtypes, triple negative breast cancer (TNBC) is negative for hormonal (estrogen and progesterone) receptors and human epidermal growth factor receptor 2 (HER2) and thus lacks targeted therapy [<xref rid=\"B6-ijms-21-05362\" ref-type=\"bibr\">6</xref>]. TNBC is rapidly progressive, with earlier onset of visceral metastases and poor prognosis [<xref rid=\"B7-ijms-21-05362\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijms-21-05362\" ref-type=\"bibr\">8</xref>]. Finding an optimal chemotherapy that leads to a longer metastasis-free period and overall survival for TNBC patients is urgently needed. Inducing autophagy was demonstrated to be a potential therapeutic strategy for TNBC treatment [<xref rid=\"B9-ijms-21-05362\" ref-type=\"bibr\">9</xref>]. However, it is still unclear whether autophagy in TNBC has a protective or cytotoxic role [<xref rid=\"B4-ijms-21-05362\" ref-type=\"bibr\">4</xref>].</p><p>Flavopereirine is a <italic>β</italic>-carboline alkaloid that can be extracted from the tree bark of several <italic>Geissospermum</italic> species [<xref rid=\"B10-ijms-21-05362\" ref-type=\"bibr\">10</xref>] or chemically synthesized. Synthetic flavopereirine has been reported to selectively reduce human glioblastoma (U251) cell proliferation but only slightly inhibit normal astrocytes (CRL 1656) at a high concentration of 50 μg/mL [<xref rid=\"B11-ijms-21-05362\" ref-type=\"bibr\">11</xref>]. Recent studies further demonstrated that flavopereirine significantly suppressed colorectal cancer cell growth in vitro and in vivo and induced apoptosis in breast cancer cells [<xref rid=\"B12-ijms-21-05362\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijms-21-05362\" ref-type=\"bibr\">13</xref>]. However, whether flavopereirine has an effect on autophagy in breast cancer remains unclear.</p><p>Recently, we demonstrated that flavopereirine induced TNBC (MDA-MB-231) cell cycle arrest and apoptosis through the AKT/p38 mitogen-activated protein kinase (MAPK)/extracellular regulated kinase (ERK)1/2 signaling pathway [<xref rid=\"B13-ijms-21-05362\" ref-type=\"bibr\">13</xref>]. Because autophagy has become a potential therapeutic target for breast cancer, we further investigated whether flavopereirine has a role in autophagy regulation. In this study, we used flavopereirine-treated MDA-MB-231 cells to examine the mechanism by which flavopereirine is involved in autophagy.</p></sec><sec sec-type=\"results\" id=\"sec2-ijms-21-05362\"><title>2. Results</title><sec id=\"sec2dot1-ijms-21-05362\"><title>2.1. Flavopereirine Blocked Autophagic Flux</title><p>Autophagic flux is often examined through the turnover of the autophagy marker LC3 (LC3-II) by Western blotting. We tested the significance of flavopereirine in autophagy in MDA-MB-231 cells and found that flavopereirine induced the accumulation of LC3-II in a dose- and time-dependent manner (<xref ref-type=\"fig\" rid=\"ijms-21-05362-f001\">Figure 1</xref>). To confirm the localization of LC3-II, a Premo™ Tandem Autophagy Sensor Kit (Life Technologies, Carlsbad, CA, USA) was used to visualize autophagosome fusion with lysosomes to form autolysosomes by fluorescent protein signals (LC3-II fused with green fluorescent protein (GFP) and red fluorescent protein (RFP)). The structures of autophagosomes (neutral pH) were positive for both GFP (green) and RFP (red), which are shown as yellow/orange puncta. However, the pH-sensitive fluorescence of GFP was quenched by fusion with lysosomes, causing autolysosomes (acidic pH) to appear as red puncta. As shown in <xref ref-type=\"fig\" rid=\"ijms-21-05362-f002\">Figure 2</xref>, chloroquine (CQ), an autophagy inhibitor, blocked endogenous autophagic flux, leading to the accumulation of LC3-II in autophagosomes (yellow/orange). MDA-MB-231 cells treated with flavopereirine also accumulated autophagosomes, similar to the effect of CQ. The high concentration of flavopereirine (15 μM) increased the intensity of LC3-II puncta compared with that of the low dose (10 μM). Moreover, flavopereirine further blocked aloperine-induced autophagic flux, similar to the effect of CQ. These results indicated that flavopereirine arrests autophagic flux.</p></sec><sec id=\"sec2dot2-ijms-21-05362\"><title>2.2. The Akt/p-p38 MAPK Pathway Was Targeted by Flavopereirine to Inhibit Autophagy</title><p>The Akt-mTOR pathway is a well-known pivotal negative signaling pathway that regulates autophagy. The kinase mTOR plays a core role in controlling autophagy initiation [<xref rid=\"B14-ijms-21-05362\" ref-type=\"bibr\">14</xref>]. We found that the level of p-AKT decreased with exposure to higher concentrations of flavopereirine (10 and 15 μM) (<xref ref-type=\"fig\" rid=\"ijms-21-05362-f003\">Figure 3</xref>A). After MDA-MB-231 cells were transfected with a plasmid that constitutively expressed p-AKT, flavopereirine-induced LC3-II accumulation diminished (<xref ref-type=\"fig\" rid=\"ijms-21-05362-f003\">Figure 3</xref>B). Western blot imaging also showed that flavopereirine inhibited the basal expression levels of mTOR in MDA-MB-231 cells, leading to the suppression of p-mTOR. However, transfection with the p-AKT plasmid did not obviously rescue this suppression (<xref ref-type=\"fig\" rid=\"ijms-21-05362-f003\">Figure 3</xref>B,C).</p><p>The role of MAPK in flavopereirine-mediated autophagy was explored, and the flavopereirine-induced expression of p-p38 MAPK was lower in MDA-MB-231 cells transfected with the p-AKT plasmid than in cells transfected with the control plasmid (<xref ref-type=\"fig\" rid=\"ijms-21-05362-f003\">Figure 3</xref>C). After knocking down p38 MAPK with a specific siRNA, the flavopereirine-induced levels of total and phosphorylated p38 MAPK were reduced (<xref ref-type=\"fig\" rid=\"ijms-21-05362-f003\">Figure 3</xref>D). Flavopereirine-induced LC3-II accumulation also decreased when p38 MAPK was downregulated. These results indicated that p-AKT is an upstream inhibitor of p-p38 MAPK that mediates flavopereirine-induced LC3-II accumulation.</p></sec><sec id=\"sec2dot3-ijms-21-05362\"><title>2.3. Flavopereirine Decreased Cell Viability More Than CQ</title><p>Treating MDA-MB-231 cells with 10 or 15 μM flavopereirine reduced cell viability in a dose-dependent manner, even at 24 and 48 h (<xref ref-type=\"fig\" rid=\"ijms-21-05362-f004\">Figure 4</xref>). Cell viability at these two concentrations was lower than in cells treated with 25 μM CQ. When flavopereirine and CQ were combined, cell viability was further reduced to almost complete cell death.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec3-ijms-21-05362\"><title>3. Discussion</title><p>There is a complicated interaction between autophagy and breast cancer progression, and the function of autophagy in breast cancer remains unclear [<xref rid=\"B3-ijms-21-05362\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijms-21-05362\" ref-type=\"bibr\">4</xref>]. Autophagy regulation is considered a possible treatment strategy for TNBC [<xref rid=\"B9-ijms-21-05362\" ref-type=\"bibr\">9</xref>,<xref rid=\"B15-ijms-21-05362\" ref-type=\"bibr\">15</xref>]. In the present study, we demonstrated that flavopereirine caused LC3-II accumulation in MDA-MB-231 cells in a dose- and time-dependent manner. Flavopereirine inhibited autophagic flux partially through the AKT/p38 MAPK pathway.</p><p>Treatment of TNBC with a combination of histone deacetylase inhibitors and ionizing radiation induces autophagic cell death [<xref rid=\"B16-ijms-21-05362\" ref-type=\"bibr\">16</xref>]. The small molecule RL71 can also trigger excessive autophagic cell death in TNBC [<xref rid=\"B9-ijms-21-05362\" ref-type=\"bibr\">9</xref>]. However, autophagy inhibition was more effective in TNBC than in luminal cell lines [<xref rid=\"B15-ijms-21-05362\" ref-type=\"bibr\">15</xref>]. The four TNBC cell lines analyzed, including MDA-MB-231, were sensitive to cell death through autophagy inhibition by autophagy-related gene knockdown or CQ treatment, suggesting that autophagy inhibition might be a potential therapeutic strategy for TNBC. Our results provide a candidate that can inhibit basal and aloperine-induced autophagy leading to LC3-II accumulation in MDA-MB-231 cells (<xref ref-type=\"fig\" rid=\"ijms-21-05362-f001\">Figure 1</xref> and <xref ref-type=\"fig\" rid=\"ijms-21-05362-f002\">Figure 2</xref>). In contrast to LC3-II accumulation, flavopereirine inhibited p62 in a dose-dependent manner (<xref ref-type=\"fig\" rid=\"ijms-21-05362-f001\">Figure 1</xref>). Previously, p62 was considered an autophagy indicator due to its ability to deliver polyubiquitinated proteins to autophagy degradation. Recent reports demonstrated that p62 participates in the regulation of diverse cellular signaling pathways [<xref rid=\"B17-ijms-21-05362\" ref-type=\"bibr\">17</xref>], which may interfere with p62 expression or degradation independent of autophagy. These support that the p62 level alone is not a good indicator of autophagy. Besides CQ and hydroxychloroquine (HCQ) being the only clinically available drugs that inhibit autophagy for cancer treatment in clinical trials [<xref rid=\"B18-ijms-21-05362\" ref-type=\"bibr\">18</xref>], flavopereirine presents the potential benefit of autophagy inhibition when applied to TNBC treatment.</p><p>AMP-activated protein kinase (AMPK) is the upstream energy receptor that is triggered by various stresses to activate autophagy [<xref rid=\"B19-ijms-21-05362\" ref-type=\"bibr\">19</xref>]. Activation of AMPK requires specific phosphorylation of the α subunit at Thr172. AMPKα is phosphorylated at low levels in most of the investigated breast cancer cell lines (MCF-7, MDA-MB-468, MDA-MB-231, and MDA-MB-436), especially in MDA-MB-231 cells [<xref rid=\"B20-ijms-21-05362\" ref-type=\"bibr\">20</xref>]. We also found that p-AMPKα expression was low in flavopereirine-treated MDA-MB-231 cells (data not shown), suggesting its minor role in flavopereirine-mediated autophagy inhibition. The activation of AKT/mTOR is crucial for the induction of autophagy [<xref rid=\"B14-ijms-21-05362\" ref-type=\"bibr\">14</xref>]. Flavopereirine suppressed the translation of mTOR, subsequently resulting in decreased phosphorylation of mTOR (<xref ref-type=\"fig\" rid=\"ijms-21-05362-f003\">Figure 3</xref>B, C), which did not obviously change in MDA-MB-231 cells transfected with p-AKT plasmids (<xref ref-type=\"fig\" rid=\"ijms-21-05362-f003\">Figure 3</xref>C). We further demonstrated that AKT is upstream of p38 MAPK, which is mediated by flavopereirine to induce LC3-II accumulation (<xref ref-type=\"fig\" rid=\"ijms-21-05362-f003\">Figure 3</xref>C,D). These results indicated that flavopereirine partially inhibits autophagy through nonclassical AKT/p38 MAPK signaling.</p><p>The generation of cytotoxic autophagy may either lead independently to cell death or act as a precursor to apoptosis [<xref rid=\"B3-ijms-21-05362\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijms-21-05362\" ref-type=\"bibr\">4</xref>]. In our previous study, we used a pan-caspase inhibitor (z-VAD-fmk) to pretreat MDA-MB-231 cells, which significantly reduced flavopereirine-induced apoptosis from 77.2 to 27.5% at 48 h [<xref rid=\"B13-ijms-21-05362\" ref-type=\"bibr\">13</xref>]. The proportion of cells that were positive for PI but not for annexin-V staining was 3.8%. These data suggested that other mechanisms could account for flavopereirine-mediated cytotoxicity other than caspase-dependent apoptosis. In this study, CQ, an autophagy inhibitor, decreased cell survival by more than 40% in MDA-MB-231 cells at 48 h (<xref ref-type=\"fig\" rid=\"ijms-21-05362-f004\">Figure 4</xref>). Treatment with flavopereirine inhibited endogenous autophagy (<xref ref-type=\"fig\" rid=\"ijms-21-05362-f002\">Figure 2</xref>) and resulted in lower cell survival than CQ treatment (<xref ref-type=\"fig\" rid=\"ijms-21-05362-f004\">Figure 4</xref>). MDA-MB-231 cells treated with a combination of flavopereirine and CQ showed further decreased cell survival compared to that of CQ treatment alone. These data suggested that the decrease in cell survival was due to flavopereirine-induced apoptosis and autophagy-related cell death.</p><p>In summary, flavopereirine inhibits endogenous autophagy partially through the AKT/p38 MAPK pathway, which is related to decreased cell survival.</p></sec><sec id=\"sec4-ijms-21-05362\"><title>4. Materials and Methods</title><sec id=\"sec4dot1-ijms-21-05362\"><title>4.1. Breast Cancer Cell Line and Culture Conditions</title><p>The MDA-MB-231 human breast cancer cell line was provided by Dr. Ying-Ray Lee. MDA-MB-231 cells were maintained in low glucose Dulbecco’s modified Eagle’s medium (Gibco-BRL, Carlsbad, CA, USA) supplemented with 8% fetal bovine serum and cultured in a humidified atmosphere with 5% CO<sub>2</sub> at 37 °C.</p></sec><sec id=\"sec4dot2-ijms-21-05362\"><title>4.2. Reagents and Antibodies</title><p>Flavopereirine perchlorate was obtained from ChromaDex, Inc. (Irvine, CA, USA). Aloperine (ab143290) was purchased from Abcam (Cambridge, MA, USA). A non-ATP-competitive MEK inhibitor, PD98059 (TargetMol, Boston, MA, USA), was used to suppress the phosphorylation of ERK. Antibodies targeting AKT (#4691), phospho-AKT (Ser473) (#4060), mammalian target of rapamycin (mTOR) (7C10) (#2983), phospho-mTOR (Ser2448) (#5536), p38 MAPK (#9212), phospho-p38 MAPK (Thr180/Tyr182) (#9211), p44/42 MAPK (ERK1/2) (#4695), and phospho-p44/42 MAPK (Thr202/Tyr204) (#4377) were purchased from Cell Signaling Technology, Inc. (Beverly, MA, USA). A polyclonal antibody to detect the autophagosomal marker protein microtubule-associated protein 1 light chain 3 (LC3) (PM 036) was obtained from Medical & Biological Laboratories Co. (Nagoya, Japan). An antibody against the p62 protein, also called sequestosome 1 (SQSTM1), (sc-28359) was purchased from Santa Cruz Biotechnology (Santa Cruz, CA, USA).</p></sec><sec id=\"sec4dot3-ijms-21-05362\"><title>4.3. Imaging Autophagy with the RFP-GFP-LC3B Kit</title><p>MDA-MB-231 cells (1 × 10<sup>4</sup> cells/well) were plated in a Nunc™ 177,437 Lab-Tek Chamber Slide System (Thermo Fisher Scientific, Rochester, NY, USA) and allowed to adhere overnight. The various stages of autophagy were monitored by the Premo™ Autophagy Tandem Sensor RFP-GFP LC3B Kit (Life Technologies, Carlsbad, CA, USA) according to the manufacturer’s instructions, which achieved efficient transduction using insect Baculovirus carrying the acid-sensitive LC3B-fluorescent protein chimera with a mammalian promoter. After the cells were treated overnight with 3 μL of BacMam reagents containing RFP-GFP-LC3B DNA, specified concentrations of drugs were subsequently added to the cell medium for 48 h. Aloperine (100 μM) was the positive control and induced autophagic flux, which was blocked by CQ. The media were removed, and live cell imaging solution containing Hoechst 33342 (1 µg/mL) was added and incubated for 20 min in the dark. The cells were then washed with 1× phosphate-buffered saline (PBS), covered in mounting medium (F4680) (Sigma-Aldrich, Inc. St. Louis, MO, USA), and imaged using a Zeiss laser scanning confocal microscope LSM800 (Carl Zeiss Microscopy GmbH, Jena, Germany).</p></sec><sec id=\"sec4dot4-ijms-21-05362\"><title>4.4. Western Blotting</title><p>Cells were lysed with the M-PERTM mammalian protein extraction reagent (Thermo Fisher Scientific Inc., Rockford, IL, USA) containing a 0.1% protease inhibitor cocktail. Equal amounts of each sample (40 μg of protein) were loaded and separated on sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) gels and then transferred to polyvinylidene fluoride (PVDF) membranes. Primary antibodies and horseradish peroxidase-conjugated secondary antibodies were used to target the specific proteins. The band signals were developed with Immobilon Western Chemiluminescent HRP Substrate (EMD Millipore Corporation, Billerica, MA, USA) and detected using a BioSpectrum® imaging system (UVP).</p></sec><sec id=\"sec4dot5-ijms-21-05362\"><title>4.5. Small Interfering RNA (siRNA) Transfection</title><p>The p38 MAPK siRNAs were purchased from Ambion (Life Technologies, Carlsbad, CA, USA). MDA-MB-231 cells in 6-well plates (1 × 10<sup>5</sup> cells/well) were cultured overnight and then treated with a mixture of 25 pmole siRNAs using Lipofectamine™ RNAiMAX transfection reagent (Life Technologies, Carlsbad, CA, USA) according to the manufacturer’s recommendations. The mixture contained silencer select pre-designed (ID s3585) and validated siRNAs (ID s11156) against human alpha p38 (MAPK 14) and beta p38 MAPK (MAPK11) to knockdown p38 MAPK expression. After 6 h, the cell supernatants containing the transfection reagents were replaced with fresh cell medium, and the cells were further incubated overnight. Nontargeting siRNA was also used as a transfection control. The solvent (DMSO) or 15 μM flavopereirine was separately added to MDA-MB-231 cells that were transfected with p38 MAPK or nontargeting siRNA. After incubation for 48 h, the cells were collected and harvested for Western blotting.</p></sec><sec id=\"sec4dot6-ijms-21-05362\"><title>4.6. Plasmid DNA Transfection</title><p>The wells of 6-well plates were seeded with 2 × 10<sup>5</sup> cells and further incubated overnight, and then the cells were transfected with DNA-lipid complexes using Lipofectamine™ 3000 and P3000™ transfection reagents (Life Technologies, Carlsbad, CA, USA) with modified protocols based on the manufacturer’s guidelines [<xref rid=\"B13-ijms-21-05362\" ref-type=\"bibr\">13</xref>]. Briefly, DNA-lipid complexes were prepared in two tubes that contained 125 μL of Opti-MEM™ medium. In tube one, Opti-MEM™ medium was mixed with the Lipofectamine™ 3000 reagent (4.5 μL). In tube two, plasmid DNA (1.5 μg) was diluted with Opti-MEM™ medium and then mixed with P3000™ reagent (3 μL). Before being added to the cells, the two tubes were mixed and incubated for 10 min. The pHRIG-Akt1 plasmid (pAKT, a construct with a constitutively active human Akt1) was purchased from Addgene (Watertown, MA, USA) and pBSSK was an empty vector used as a negative control. The medium changes and flavopereirine treatment were performed according to the procedures for p38 MAPK siRNA transfection. Then, the cells were collected and harvested for Western blotting.</p></sec></sec></body><back><ack><title>Acknowledgments</title><p>This work was supported by the Ditmanson Medical Foundation of Chia-Yi Christian Hospital (grant number R106-37).</p></ack><notes><title>Author Contributions</title><p>Conception and design of the experiments: Y.-R.L., Y.-S.T., and S.-M.S. Acquisition and Analysis of data: Y.-Z.L., and W.-C.L. Interpretation of data: M.-S.C., and H.-T.Y. Drafting the manuscript: M.-S.C. and H.-T.Y. Revising and approving the submitted version: S.-M.S. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research received no external funding.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijms-21-05362\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Mizushima</surname><given-names>N.</given-names></name><name><surname>Levine</surname><given-names>B.</given-names></name><name><surname>Cuervo</surname><given-names>A.M.</given-names></name><name><surname>Klionsky</surname><given-names>D.J.</given-names></name></person-group><article-title>Autophagy fights disease through cellular self-digestion</article-title><source>Nature</source><year>2008</year><volume>451</volume><fpage>1069</fpage><lpage>1075</lpage><pub-id pub-id-type=\"doi\">10.1038/nature06639</pub-id><pub-id pub-id-type=\"pmid\">18305538</pub-id></element-citation></ref><ref id=\"B2-ijms-21-05362\"><label>2.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Maxfield</surname><given-names>K.E.</given-names></name><name><surname>Macion</surname><given-names>J.</given-names></name><name><surname>Vankayalapati</surname><given-names>H.</given-names></name><name><surname>Whitehurst</surname><given-names>A.W.</given-names></name></person-group><article-title>SIK2 Restricts Autophagic Flux To Support Triple-Negative Breast Cancer Survival</article-title><source>Mol. 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Pharmacol.</source><year>2019</year><volume>10</volume><fpage>1503</fpage><pub-id pub-id-type=\"doi\">10.3389/fphar.2019.01503</pub-id><pub-id pub-id-type=\"pmid\">31969821</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijms-21-05362-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Flavopereirine induced LC3-II accumulation in dose- and time-dependent manners. (<bold>A</bold>) MDA-MB-231 cells were treated with various concentrations of flavopereirine (5-15 μM) for 24 and 48 h. (<bold>B</bold>) MDA-MB-231 cells were incubated with 15 μM flavopereirine for 18, 24, 42, and 48 h. LC3-II accumulation and p62 expression were evaluated by Western blotting. glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as the internal control. The data are representative of three independent experiments. The band signals were quantified and the ratio of LC3-II related to GAPDH is presented under the blots.</p></caption><graphic xlink:href=\"ijms-21-05362-g001\"/></fig><fig id=\"ijms-21-05362-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Flavopereirine inhibited autophagic flux in MDA-MB-231 cells. Representative fluorescent images of autophagosomes and autophagolysosomes in MDA-MB-231 cells were transduced with tandem RFP-GFP-LC3B and then independently treated with aloperine (100 μM), chloroquine (25 μM), flavopereirine, or a combination of the two drugs. In the merged images, autophagosomes are presented as yellow or orange puncta (RFP-GFP-LC3B), whereas red puncta (RFP-LC3B) indicate autophagolysosomes because acidification abolishes the green fluorescence. Images were obtained using a 63× oil immersion objective.</p></caption><graphic xlink:href=\"ijms-21-05362-g002\"/></fig><fig id=\"ijms-21-05362-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Flavopereirine inhibited autophagy through AKT/p38 MAPK signaling. (<bold>A</bold>) The expression level of p-AKT in MDA-MB-231 cells treated with increasing concentrations of flavopereirine was detected by Western blotting. (<bold>B</bold>, <bold>C</bold>) MDA-MB-231 cells transfected with plasmid constitutively expressing p-AKT (pAKT) or control plasmid (pBSSK) were treated with flavopereirine for 48 h and then analyzed for the expression of the indicated protein and their phosphorylation levels by Western blotting. (<bold>D</bold>) MDA-MB-231 cells transfected with p38 MAPK or negative control (NC) siRNAs were further treated with flavopereirine for 48 h. Western blotting was performed to detect p-p38 MAPK, p38 MAPK knockdown, and LC3 levels. The band signal was quantified and the ratio of LC3-II or p-p38 MAPK related to GAPDH is presented under the blots.</p></caption><graphic xlink:href=\"ijms-21-05362-g003\"/></fig><fig id=\"ijms-21-05362-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Cell viability of MDA-MB-231 cells treated with individual or a combination of flavopereirine with chloroquine. Cells (4 × 10<sup>5</sup>/well) were treated with different concentrations of flavopereirine, chloroquine (25 μM), or a combination of the two drugs, and cell viability was evaluated by a cell counting kit-8 (CCK-8) after treatment for 24 h (<bold>A</bold>) and 48 h (<bold>B</bold>). Histograms show the mean ± SD of three independent experiments performed in triplicate. * Indicates a significant difference from its respective untreated control as analyzed by one-way ANOVA followed by a Bonferroni test (<italic>p</italic> < 0.05).</p></caption><graphic xlink:href=\"ijms-21-05362-g004\"/></fig></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32748884</article-id><article-id pub-id-type=\"pmc\">PMC7432014</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155582</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05582</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Validation of the Women’s Views of Birth Labor Satisfaction Questionnaire (WOMBLSQ4) in the Spanish Population</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Pozo-Cano</surname><given-names>María Dolores</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05582\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-3852-9861</contrib-id><name><surname>Martín-Salvador</surname><given-names>Adelina</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05582\">2</xref><xref rid=\"c1-ijerph-17-05582\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Pérez-Morente</surname><given-names>María Ángeles</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05582\">3</xref><xref rid=\"c1-ijerph-17-05582\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Martínez-García</surname><given-names>Encarnación</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05582\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Luna del Castillo</surname><given-names>Juan de Dios</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05582\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Gázquez-López</surname><given-names>María</given-names></name><xref ref-type=\"aff\" rid=\"af5-ijerph-17-05582\">5</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-1814-5254</contrib-id><name><surname>Fernández-Castillo</surname><given-names>Rafael</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05582\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>García-García</surname><given-names>Inmaculada</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05582\">1</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05582\"><label>1</label>Faculty of Health Sciences, University of Granada, 18071 Granada, Spain; <email>pozocano@ugr.es</email> (M.D.P.-C.); <email>emartinez@ugr.es</email> (E.M.-G.); <email>rafaelfernandez@ugr.es</email> (R.F.-C.); <email>igarcia@ugr.es</email> (I.G.-G.)</aff><aff id=\"af2-ijerph-17-05582\"><label>2</label>Faculty of Health Sciences, University of Granada, 52005 Melilla, Spain</aff><aff id=\"af3-ijerph-17-05582\"><label>3</label>Faculty of Health Sciences, University of Jaen, 23071 Jaen, Spain</aff><aff id=\"af4-ijerph-17-05582\"><label>4</label>Faculty of Medicine, University of Granada, 18071 Granada, Spain; <email>jdluna@ugr.es</email></aff><aff id=\"af5-ijerph-17-05582\"><label>5</label>Faculty of Health Sciences, University of Granada, 51001 Ceuta, Spain; <email>mgazquez@ugr.es</email></aff><author-notes><corresp id=\"c1-ijerph-17-05582\"><label>*</label>Correspondence: <email>ademartin@ugr.es</email> (A.M.-S.); <email>mmorente@ujaen.es</email> (M.Á.P.-M.)</corresp></author-notes><pub-date pub-type=\"epub\"><day>02</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5582</elocation-id><history><date date-type=\"received\"><day>04</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>31</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>The satisfaction of women with the birth experience has implications for the health and wellness of the women themselves and also of their newborn baby. The objectives of this study were to determine the factor structure of the Women’s Views of Birth Labor Satisfaction Questionnaire (WOMBLSQ4) questionnaire on satisfaction with the attention received during birth delivery in Spanish women and to compare the level of satisfaction of pregnant women during the birth process with that in other studies that validated this instrument. A cross-sectional study using a self-completed questionnaire of 385 Spanish-speaking puerperal women who gave birth in the Public University Hospitals of Granada (Spain) was conducted. An exploratory factor analysis of the WOMBLSQ4 questionnaire was performed to identify the best fit model. Those items that showed commonalities higher than 0.50 were kept in the questionnaire. Using the principal components method, nine factors with eigenvalues greater than one were extracted after merging pain-related factors into a single item. These factors explain 90% of the global variance, indicating the high internal consistency of the full scale. In the model resulting from the WOMBLSQ4 questionnaire, its nine dimensions measure the levels of satisfaction of puerperal women with childbirth care. Average scores somewhat higher than those of the original questionnaire and close to those achieved in the study carried out in Madrid (Spain) were obtained. In clinical practice, this scale may be relevant for measuring the levels of satisfaction during childbirth of Spanish-speaking women.</p></abstract><kwd-group><kwd>validation study</kwd><kwd>satisfaction questionnaire</kwd><kwd>birth attention</kwd><kwd>patient satisfaction</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05582\"><title>1. Introduction</title><p>The birth of a child is one of the most significant events in the lives of women and their families. Knowing the level of satisfaction regarding the care received during the birth and postpartum periods is of special interest as it may help to improve the quality of health systems [<xref rid=\"B1-ijerph-17-05582\" ref-type=\"bibr\">1</xref>]. In Western countries, these experiences are becoming less frequent due to the drop in birth rates observed in many of them, especially in southern European countries, and it is hoped that the birth experience can become as rewarding as possible, despite not being free of serious consequences for the health of women and their newborns [<xref rid=\"B1-ijerph-17-05582\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijerph-17-05582\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijerph-17-05582\" ref-type=\"bibr\">3</xref>].</p><p>Many authors have explained the importance of women’s satisfaction with the birth process, because it influences such important aspects as the maintenance of breastfeeding [<xref rid=\"B4-ijerph-17-05582\" ref-type=\"bibr\">4</xref>], which is crucial for the health of mothers and newborns [<xref rid=\"B5-ijerph-17-05582\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijerph-17-05582\" ref-type=\"bibr\">6</xref>].</p><p>When women experience unsatisfactory or traumatic births, their memories will be of pain, anger, fear, or sadness, and they may even suffer from post-traumatic stress disorders or may not remember anything about the delivery process [<xref rid=\"B7-ijerph-17-05582\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijerph-17-05582\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05582\" ref-type=\"bibr\">9</xref>]. Furthermore, a bad experience in a previous delivery increases the anxiety and fear in subsequent deliveries [<xref rid=\"B10-ijerph-17-05582\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijerph-17-05582\" ref-type=\"bibr\">11</xref>]. The proximity of childbirth activates memories of previous traumatic experiences and abuse as well as psychiatric disorders in women that can trigger a fear of vaginal childbirth and increase the demand for caesarean births, thus increasing the risks to maternal and perinatal health [<xref rid=\"B12-ijerph-17-05582\" ref-type=\"bibr\">12</xref>].</p><p>This is why, at present, the perceived satisfaction regarding care received during the birth is considered an essential indicator to measure quality of care [<xref rid=\"B13-ijerph-17-05582\" ref-type=\"bibr\">13</xref>]. Hodnett describes the personal expectations of pregnant women, the support and quality of the relationship with health professionals, especially midwives, and the participation of women in decision making as the most influential elements [<xref rid=\"B14-ijerph-17-05582\" ref-type=\"bibr\">14</xref>].</p><p>There are various instruments that measure the satisfaction of women with childbirth [<xref rid=\"B2-ijerph-17-05582\" ref-type=\"bibr\">2</xref>,<xref rid=\"B15-ijerph-17-05582\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijerph-17-05582\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijerph-17-05582\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijerph-17-05582\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijerph-17-05582\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijerph-17-05582\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijerph-17-05582\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijerph-17-05582\" ref-type=\"bibr\">22</xref>]. The Women’s Views of Birth Labor Satisfaction Questionnaire (WOMBLSQ4) [<xref rid=\"B23-ijerph-17-05582\" ref-type=\"bibr\">23</xref>] has been used extensively in the recent literature and identifies women’s satisfaction with their birth labor and delivery experiences, as well as the pain relief received during and after. It was developed in the United Kingdom by Smith and has been translated into French and validated to be applied to French-speaking women in University Hospitals in Geneva (Switzerland), and into Spanish, where Marín-Morales et al. did the same with women who gave birth in hospitals in Madrid (Spain) [<xref rid=\"B23-ijerph-17-05582\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijerph-17-05582\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijerph-17-05582\" ref-type=\"bibr\">25</xref>].</p><p>In the Autonomous Community of Andalusia (Spain), the Public Health System is committed to achieving excellence in healthcare. This is understood as a comprehensive concept involving multiple variables, among which citizen satisfaction is an inalienable element [<xref rid=\"B26-ijerph-17-05582\" ref-type=\"bibr\">26</xref>]. Birth care is focused on women, providing them with personalized care and promoting their autonomy and their role in decision-making [<xref rid=\"B27-ijerph-17-05582\" ref-type=\"bibr\">27</xref>].</p><p>The version translated into Spanish also presents discrepancies in the number of factors with respect to the original version and its translation into French due to significant convergence problems that make it necessary to eliminate the “control” factor, thus leaving the scale in nine dimensions. In order to assess the satisfaction of Andalusian mothers in the process of birth labor and to check the structure of the instrument, the WOMBLSQ4 scale was translated into Spanish and validated.</p><p>The objectives of this study are to determine the factor structure of the WOMBLSQ4 questionnaire on satisfaction with the care received during birth in Spanish women and to compare the level of satisfaction of pregnant women during the birth process with other studies that validated this instrument.</p></sec><sec id=\"sec2-ijerph-17-05582\"><title>2. Materials and Methods</title><sec id=\"sec2dot1-ijerph-17-05582\"><title>2.1. Sample and Data Collection</title><p>A cross-sectional study was carried out between January and March 2019 in puerperal women who had given birth in the Public University Hospitals of the city of Granada (Spain). In the year prior, an average of 5000 deliveries had taken place at both hospitals. Through intentional sampling, 385 Spanish-speaking puerperal women aged 18 years old or older were selected by collaborating with midwives in the studio. The included women voluntarily agreed to participate and signed an informed consent self-completed questionnaire that was delivered in a sealed envelope and later collected by the principal investigator. Those who did not understand Spanish and had elective caesarean births were excluded.</p><p>Postpartum surveys were administered to 450 women, of whom 15 refused to complete them, 40 did not deliver babies, and 10 did not provide informed consent. The questionnaires that were complete for all items were considered valid. The final sample consisted of 385 women, which constitutes a response rate of 85.5%.</p></sec><sec id=\"sec2dot2-ijerph-17-05582\"><title>2.2. Materials</title><p>To evaluate women’s satisfaction with care received during delivery, the final version of the WOMBLSQ4 scale was used, which consists of 32 questions with Likert-type responses and 10 dimensions: professional support during the birth (5 questions), expectations of delivery (4 questions), assessment at home at the beginning of birth labor (3 questions), first contact with the newborn (3 questions), support of the husband/partner during labor (3 questions), pain relief during labor (3 questions), pain relief immediately after delivery (3 questions), continuity (2 questions), environment during delivery (2 questions), and control (2 questions). The measure of general satisfaction involved two questions [<xref rid=\"B23-ijerph-17-05582\" ref-type=\"bibr\">23</xref>]. The factorial validity of the scale was confirmed, as well as an adequate global reliability (Cronbach’s alpha 0.89), and the validity of the subscales was also shown (Cronbach’s alpha values ranged between 0.62 and 0.91). The score for each dimension was obtained by adding the values obtained in each question (some of them with an inverse score), and later on, the result was transformed so that the minimum possible score was 0 and the maximum possible one was 100 (total satisfaction in the dimension) [<xref rid=\"B23-ijerph-17-05582\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijerph-17-05582\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijerph-17-05582\" ref-type=\"bibr\">25</xref>]. Higher scores indicated greater satisfaction on the part of the women.</p><p>For this research, the scale was translated into Spanish by two English language translators and its final content was agreed upon by three midwives with extensive experience in childbirth assistance. The translated questionnaire was piloted to 50 women, and it was demonstrated that the instrument presented an excellent level of comprehension and an adequate completion time since, when collecting it, the participants were asked if they had difficulty completing it, if they understood all the questions, and if it seemed too long. </p><p>In addition, the following sociodemographic variables were incorporated: age, marital status, educational level, and employment situation.</p></sec><sec id=\"sec2dot3-ijerph-17-05582\"><title>2.3. Data Analysis</title><p>A descriptive analysis was performed in which means and standard deviations were calculated for the quantitative variables and frequencies and percentages for the qualitative ones. The factorial structure of the scale was explored by extraction of the main components followed by a Varimax rotation. In the first analysis, the Kaiser–Meyer–Olkin (KMO) sample adequacy measure was calculated, accepting values greater than 0.70 as optimal measures. Subsequently, the Bartlett sphericity test was applied to show significant differences between the items in the correlation and the unit matrix.</p><p>Next, the communality of each of the items on the scale was studied, and those that showed values less than 0.30 were eliminated, as they were poorly represented in the factorial set obtained. Those factors with eigenvalues greater than 1 were considered, and the percentage of variance explained with the said factors was determined to assess the weight of each one. After the rotation and analysis of the item saturation table, these were assigned to the dimension in which their saturation was highest. Once the items were eliminated, the previous steps were repeated in order to obtain the final factor structure. The internal consistency of each of the subscales was measured using Cronbach’s alpha. Data analysis was performed with the SPSS v. Statistical package. 26.0 (International Busines Machines Corporation (IBM), Armonk, NY, USA) for Windows.</p></sec><sec id=\"sec2dot4-ijerph-17-05582\"><title>2.4. Ethical Considerations</title><p>The study complies with the standards of good clinical practice, explicit in the European Directive 2001/20/EC and Law 14/2007 (of 3 July) on biomedical research. The treatment of personal data in health research is governed by the provisions of the Organic Law 3/2018, 5 December, Protection of Personal Data and Guarantee of Digital Rights in Spain. The research protocol obtained a favorable resolution from the Ethics and Research Committee of Health Institutions.</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05582\"><title>3. Results</title><p>The sociodemographic characteristics of the analyzed sample are reflected in <xref rid=\"ijerph-17-05582-t001\" ref-type=\"table\">Table 1</xref>. The mean age of the participants was 31.62 years (SD 5.32), with a range of 18 to 46 years old. Regarding the level of education, almost half (175, 46.2%) had a university-level education. In relation to marital status, the majority were married or had a partner (359, 94.0%).</p><p>Regarding the labor situation, 184 (47.9%) were employed workers.</p><sec id=\"sec3dot1-ijerph-17-05582\"><title>3.1. Exploratory Facial Analysis</title><p>To carry out the factor analysis, firstly, all items on the scale were considered, and a mean KMO sample adequacy of 0.80 was obtained, with the result of the Bartlett sphericity test being statistically significant (<italic>p</italic> < 0.001).</p><p>Of the 32 items in the original questionnaire, only three showed communalities below 0.50: 25 (I am satisfied with just one or two things about the labor care that I received: 0.441), 31 (I didn’t need a lot of pain relief after the birth: 0.475) and 12 (The way my labor care was provided could not have been improved: 0.476). However, they have not yet been removed from the questionnaire.</p><p>Using the main components method, nine factors that showed self-values greater than one, explaining 68.0% of the global variance, were extracted. A new dimension (3) was designed—pain during and after delivery—after merging dimensions six (pain during delivery) and seven (pain after delivery) from Smith’s original questionnaire [<xref rid=\"B23-ijerph-17-05582\" ref-type=\"bibr\">23</xref>].</p><p><xref rid=\"ijerph-17-05582-t002\" ref-type=\"table\">Table 2</xref> shows the Cronbach’s alpha and variance explained by each factor, as well as the saturations of each item, once the Varimax rotation had been performed.</p><p>Item 12 (the way my labor care was provided could not have been improved) showed a saturation of 0.55 and the generalization of its statement could be confusing. Item 25 did not saturate well with respect to the other two (0.46), and due to its statement (I am satisfied with just one or two things about labor care that I received), it did not seem to correspond to the being analyzed. Finally, both items were removed from the questionnaire.</p><p>Later on, a second analysis was performed with the remaining items, obtaining a sample adequacy of KMO of 0.86 and maintaining statistical significance in the Bartlett sphericity test (<italic>p</italic> < 0.001). This time, only items 3 and 31 showed communalities of less than 0.50, (0.44 and 0.47, respectively), although we decided to keep them in the model. The number of factors extracted by the principal component method with eigenvalues greater than 1 was also nine, which explained 70.0% of the global variance.</p><p><xref rid=\"ijerph-17-05582-t003\" ref-type=\"table\">Table 3</xref> represents the saturation level in the rotated components, the corresponding Cronbach’s alphas, and the variance explained by each factor.</p><p>In <xref rid=\"ijerph-17-05582-t003\" ref-type=\"table\">Table 3</xref>, the dimension of pain again appears to be merged. Item 31 (I didn’t need a lot of pain relief after the birth) has a saturation level close to 0.50 and continues to remain on the scale, although it is poorly associated with the other items, because its contents belong to this dimension.</p><p><xref rid=\"ijerph-17-05582-t004\" ref-type=\"table\">Table 4</xref> shows the Cronbach’s alpha values from the validation carried out in this study as well as those from the English version, the French adaptation, and the puerperal period in Madrid (Spain). It can be seen that the Cronbach’s alpha values of this study are in the range of previous studies or, in some cases, even higher.</p></sec><sec id=\"sec3dot2-ijerph-17-05582\"><title>3.2. Level of Satisfaction in the Different Versions</title><p><xref rid=\"ijerph-17-05582-t005\" ref-type=\"table\">Table 5</xref> shows the mean scores in each of the dimensions for the different versions. It can be seen that the three best valued dimensions in the four versions were professional support, support of the husband, and first contact with the newborn.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05582\"><title>4. Discussion</title><p>The response rate was 85.0%, similar to that obtained in the study by Floris et al. [<xref rid=\"B24-ijerph-17-05582\" ref-type=\"bibr\">24</xref>] in Geneva, Switzerland, and somewhat higher than that obtained by Marín-Morales et al. [<xref rid=\"B25-ijerph-17-05582\" ref-type=\"bibr\">25</xref>] in Madrid (Spain).</p><p>The scale designed in this study showed a high validity and some good psychometric characteristics for measuring childbirth satisfaction in women based on their sociocultural environment.</p><p>It can be used in primiparous and/or multiparous women; pregnant women of low, medium, and high risk; puerperal women who have had vaginal births, whether spontaneous or instrumental; and even those who have had unscheduled caesarean sections. However, it is not applicable for women who have had scheduled (elective) caesarean births since, in most cases, these women would not be able to complete some items. In these circumstances, other dimensions not considered in the original scale should be considered.</p><p>The obtained percentage of women’s satisfaction with the care received during their births by factor analysis was somewhat lower than that shown by the original scale [<xref rid=\"B23-ijerph-17-05582\" ref-type=\"bibr\">23</xref>]; however, we understand that it is adequate for identifying those aspects that can be improved.</p><p>In relation to the first dimension, “Professional support”, the psychometric characteristics of our study showed slightly lower values than those of Smith [<xref rid=\"B23-ijerph-17-05582\" ref-type=\"bibr\">23</xref>], but higher than those achieved by Floris et al. [<xref rid=\"B24-ijerph-17-05582\" ref-type=\"bibr\">24</xref>] and Marín-Morales et al. [<xref rid=\"B25-ijerph-17-05582\" ref-type=\"bibr\">25</xref>]. This is the first factor identified in all of these studies and the one that best explains women’s satisfaction. It is made up of five items, all of them stated in a positive way, and results similar to those of previous studies were obtained [<xref rid=\"B28-ijerph-17-05582\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijerph-17-05582\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijerph-17-05582\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijerph-17-05582\" ref-type=\"bibr\">31</xref>]. All of them indicate that the kind and correct treatment of professionals and good communication favor the satisfaction of women, especially highlighting the role of the midwife as the professional who provides the most support during the delivery process, describing her as “competent”, “inspiring confidence”, or “wonderful” [<xref rid=\"B32-ijerph-17-05582\" ref-type=\"bibr\">32</xref>].</p><p>In the second dimension, “Expectation”, made up of four questions expressed in a positive way, the parameters obtained are similar to those obtained in the previous dimension. In their research, Melender et al. postulated that if the expectations of the pregnant women are in accordance with their lived experiences during childbirth, their evaluation of childbirth will be satisfactory [<xref rid=\"B33-ijerph-17-05582\" ref-type=\"bibr\">33</xref>]. Many women look for information on the sensations that they may experience during the labor process and idealize how it should go. In preparation for childbirth sessions the expectations created, information from other mothers, previous experiences, and the signals of their own bodies influence the elaboration of a mental image of delivery [<xref rid=\"B34-ijerph-17-05582\" ref-type=\"bibr\">34</xref>]. In other cases, despite experiences of severe pain or complications during a previous delivery that are different from their expectations, women feel motivated and encouraged to have another child due to having received good support from the midwife during the process [<xref rid=\"B32-ijerph-17-05582\" ref-type=\"bibr\">32</xref>].</p><p>The third dimension, “Pain during and after childbirth”, integrates two dimensions of the original version, which also appears in the French version. Our results coincide with a study carried out on Spanish women from Madrid (Spain). In both cases, the items were related to pain. In this new dimension, there are 2 items stated positively and 4 negatively, which coincides with the original and French versions. The reliability of the original version is superior to that of the other studies, while the results of this investigation are superior to the version carried out with women from the center of Madrid (Spain) and partially superior to the French version.</p><p>Item 31 “I didn’t need a lot of pain relief after the birth” is the only one of these new dimensions that has an adjusted value in our study. This is probably explained by the fact that most of the women in our sample received epidural analgesia for childbirth, and the effects of this analgesia remain in the immediate postpartum period [<xref rid=\"B35-ijerph-17-05582\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijerph-17-05582\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijerph-17-05582\" ref-type=\"bibr\">37</xref>]. This fact means that puerperal women do not need many analgesics in this period.</p><p>In the early puerperium period (from 3 h after birth to 10–15 days later), women may experience pain due to uterine involution, the presence of hemorrhoids, and even breast pain [<xref rid=\"B38-ijerph-17-05582\" ref-type=\"bibr\">38</xref>,<xref rid=\"B39-ijerph-17-05582\" ref-type=\"bibr\">39</xref>]. There may also be perineal pain following or without an episiotomy [<xref rid=\"B39-ijerph-17-05582\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijerph-17-05582\" ref-type=\"bibr\">40</xref>], but in most cases, pain relief is necessary. On the other hand, this item is not relevant for those women who wanted a natural childbirth and therefore would consider it unnecessary. Therefore, it could be a potentially upgradeable question.</p><p>The fourth dimension, “Home assessment”, presents a very similar Cronbach’s alpha value in the four versions, the highest of which corresponds to the original version. In all versions, with the exception of the version from Madrid (Spain), this dimension consists of three items written in negative form. The coefficients obtained in our study were slightly lower than those obtained by Smith [<xref rid=\"B23-ijerph-17-05582\" ref-type=\"bibr\">23</xref>], but higher than those found by Marín-Morales et al. [<xref rid=\"B25-ijerph-17-05582\" ref-type=\"bibr\">25</xref>].</p><p>In Spain, as in most countries of the Organization for Economic Cooperation and Development (OCEDE) [<xref rid=\"B41-ijerph-17-05582\" ref-type=\"bibr\">41</xref>], except in the Netherlands and the United Kingdom, there is no culture of maternal care at home [<xref rid=\"B42-ijerph-17-05582\" ref-type=\"bibr\">42</xref>,<xref rid=\"B43-ijerph-17-05582\" ref-type=\"bibr\">43</xref>]. In the Spanish Health System, both public and private, most pregnant women go to hospitals when they have their first contractions to be evaluated at the beginning of labor and births are completed in them. Anecdotally, births at home are attended by professionals from the private sector.</p><p>In relation to the fifth dimension, “Support from husband”, the psychometric parameters obtained are similar to those in the original version. Item 29 “I would have preferred to have more support from my partner/husband” is negative, and its value is the lowest on our scale. This factor identified in the analysis is a component of satisfaction that, in our environment, is favored by legislation [<xref rid=\"B44-ijerph-17-05582\" ref-type=\"bibr\">44</xref>]. Along the line of humanization of perinatal care, in 1995, in the Autonomous Community of Andalusia, the right of pregnant women to be accompanied by a person they trust during the prepartum, delivery, and postpartum periods was legislated [<xref rid=\"B45-ijerph-17-05582\" ref-type=\"bibr\">45</xref>]. This legal support for the figure of the companion has been highly valued in numerous studies [<xref rid=\"B30-ijerph-17-05582\" ref-type=\"bibr\">30</xref>,<xref rid=\"B32-ijerph-17-05582\" ref-type=\"bibr\">32</xref>,<xref rid=\"B46-ijerph-17-05582\" ref-type=\"bibr\">46</xref>].</p><p>The sixth dimension, “Holding baby”, consists of two negative and one positive item, and the psychometric characteristics found in our study have somewhat higher values than those obtained in Madrid (Spain) [<xref rid=\"B25-ijerph-17-05582\" ref-type=\"bibr\">25</xref>]. Early contact with the newborn, in addition to being an indicator of women’s satisfaction, favors the establishment of an emotional bond between mother and child [<xref rid=\"B47-ijerph-17-05582\" ref-type=\"bibr\">47</xref>]. The search for greater prominence, that is granted to women in our Public Health System through the implementation of the Childbirth and Birth Plan of the Autonomous Community of Andalusia, provides mothers with the possibility of expressing their preferences during birth as the right to have their son or daughter by their side during the hospital stay [<xref rid=\"B46-ijerph-17-05582\" ref-type=\"bibr\">46</xref>,<xref rid=\"B48-ijerph-17-05582\" ref-type=\"bibr\">48</xref>]. Whenever the state of the newborn and the mother allow it, skin-to-skin contact between the two should be promoted, since it provides benefits to both the mother and the newborn: maintenance of a good body temperature, an increase in blood glucose levels, and helps to maintain breastfeeding and weight [<xref rid=\"B49-ijerph-17-05582\" ref-type=\"bibr\">49</xref>,<xref rid=\"B50-ijerph-17-05582\" ref-type=\"bibr\">50</xref>,<xref rid=\"B51-ijerph-17-05582\" ref-type=\"bibr\">51</xref>,<xref rid=\"B52-ijerph-17-05582\" ref-type=\"bibr\">52</xref>].</p><p>The seventh dimension, “Knowledge of women by professionals during birth assistance”, showed an adequate Cronbach’s alpha value. The title of this dimension is formulated in the same sense as the French version and both differently from the original version. The content of the questions is focused on the continuity raised by Australian authors [<xref rid=\"B53-ijerph-17-05582\" ref-type=\"bibr\">53</xref>,<xref rid=\"B54-ijerph-17-05582\" ref-type=\"bibr\">54</xref>] and adapted to the title formulated in our research.</p><p>The values of the parameters obtained in the eighth dimension, “Environment”, are adequate and most of them are superior to those collected the study of the Center of Spain [<xref rid=\"B25-ijerph-17-05582\" ref-type=\"bibr\">25</xref>]. This dimension consists of two questions and one of them is negative. An environment that facilitates intimacy, silence, environmental warmth, and the absence of medicalized furniture contributes to the satisfaction of women [<xref rid=\"B55-ijerph-17-05582\" ref-type=\"bibr\">55</xref>,<xref rid=\"B56-ijerph-17-05582\" ref-type=\"bibr\">56</xref>]. However, in the qualitative study carried out by Jenkins et al. in the state of New South Wales (Australia), most women did not highlight the environment as one of the three most important aspects in their care [<xref rid=\"B53-ijerph-17-05582\" ref-type=\"bibr\">53</xref>].</p><p>“Control” was the ninth dimension and showed a great relationship with the satisfaction of women and their experience in childbirth. Various authors have pointed out that perceived control over the situation increases satisfaction; this is a dimension that has been widely incorporated in Anglo-Saxon research [<xref rid=\"B53-ijerph-17-05582\" ref-type=\"bibr\">53</xref>,<xref rid=\"B55-ijerph-17-05582\" ref-type=\"bibr\">55</xref>,<xref rid=\"B57-ijerph-17-05582\" ref-type=\"bibr\">57</xref>,<xref rid=\"B58-ijerph-17-05582\" ref-type=\"bibr\">58</xref>,<xref rid=\"B59-ijerph-17-05582\" ref-type=\"bibr\">59</xref>,<xref rid=\"B60-ijerph-17-05582\" ref-type=\"bibr\">60</xref>]. However, in our study, similarly to that of Smith and Floris et al., this dimension was the last to be shown as a factor and also the one that explained the smallest percentage of satisfaction [<xref rid=\"B23-ijerph-17-05582\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijerph-17-05582\" ref-type=\"bibr\">24</xref>]. Probably for this reason, this dimension was excluded in the version carried out by Marín-Morales et al. with women from Centre of Spain [<xref rid=\"B25-ijerph-17-05582\" ref-type=\"bibr\">25</xref>]. This result shows that, despite the fact that, in recent years, it has been an incentive by the State and Health Institutions for women and families to take control and responsibility over their health through the inclusion of the Autonomy Law of the patient (2002), the change in the healthcare model based on paternalism has prevailed for so many years in our healthcare system that it is not yet something that the population considers to be of special importance [<xref rid=\"B61-ijerph-17-05582\" ref-type=\"bibr\">61</xref>].</p><p>The two questions about General Satisfaction, number 12 (the care during the delivery process could not have been better) and number 25, (I am satisfied with only one or two things about the care I received during the delivery process) were not analyzed as in the original study. Both questions, especially the last one, are so general that they suggest a certain ambiguity, since it is difficult to assess satisfaction with one or two of these aspects. However, the resulting mean scores are mostly somewhat higher than those obtained by Smith [<xref rid=\"B23-ijerph-17-05582\" ref-type=\"bibr\">23</xref>] in the original version, although they are similar to those obtained by Marín-Morales et al. [<xref rid=\"B25-ijerph-17-05582\" ref-type=\"bibr\">25</xref>] in their study carried out in Madrid (Spain).</p><p>We understand that, at present, childbirth satisfaction questionnaires should incorporate the control dimension, since, in the current context, the empowerment of women and decision-making during childbirth is a priority in healthcare [<xref rid=\"B62-ijerph-17-05582\" ref-type=\"bibr\">62</xref>].</p><sec id=\"sec4dot1-ijerph-17-05582\"><title>4.1. Limitations</title><p>Given the good understanding of the scale items and the good adaptation of the scale, it would have been interesting to have expanded the sample to other Spanish-speaking areas.</p></sec><sec id=\"sec4dot2-ijerph-17-05582\"><title>4.2. Recommendations for Future Research</title><p>It would be advisable for future research to merge dimensions 6 “pain relief during childbirth” and 7 “pain relief immediately after childbirth” into one dimension, as shown in the original version. Both dimensions are related to pain relief.</p><p>In this dimension, there is item 31, which asks about the need to relieve pain immediately after delivery. We believe that this question should be changed, as many analgesics are not necessary immediately after delivery, only if in the postpartum period.</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05582\"><title>5. Conclusions</title><p>In the resulting model from the WOMBLSQ4 questionnaire, the nine dimensions measure the level of satisfaction with childbirth care in puerperal women. The resulting average scores are mostly somewhat higher than those obtained in the original version, and close to those achieved in the study carried out in Madrid (Spain). In clinical practice, this scale is relevant for measuring satisfaction levels in Spanish-speaking women.</p></sec></body><back><ack><title>Acknowledgments</title><p>We gratefully acknowledge the women who participated in this study.</p></ack><notes><title>Author Contributions</title><p>Conceptualization, M.D.P.-C. and A.M.-S.; methodology, M.D.P.-C., A.M.-S., M.Á.P.-M., J.d.D.L.d.C. and I.G.-G.; software, J.d.D.L.d.C. and I.G.-G.; investigation, M.D.P.-C.; data curation, M.D.P.-C., E.M.-G., J.d.D.L.d.C. and I.G.-G.; writing—original draft preparation, M.D.P.-C., A.M.-S., M.Á.P.-M., M.G.-L., R.F.-C., I.G.-G.; writing—review and editing, M.D.P.-C., A.M.-S., M.ÁP.-M., E.M.-G., M.G.-L., R.F.-C., and I.G.-G; supervision, M.D.P.-C and I.G.-G. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research received no external funding.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijerph-17-05582\"><label>1.</label><element-citation publication-type=\"web\"><person-group person-group-type=\"author\"><name><surname>Peristat</surname><given-names>E.</given-names></name></person-group><article-title>Better Statistics for Better Health for Pregnant Women and Their Babies</article-title><comment>Available online: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.europeristat.com/\">https://www.europeristat.com/</ext-link></comment><date-in-citation content-type=\"access-date\" iso-8601-date=\"2020-04-22\">(accessed on 22 April 2020)</date-in-citation></element-citation></ref><ref id=\"B2-ijerph-17-05582\"><label>2.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Goodman</surname><given-names>P.</given-names></name><name><surname>Mackey</surname><given-names>M.C.</given-names></name><name><surname>Tavakoli</surname><given-names>A.S.</given-names></name></person-group><article-title>Factors related to childbirth satisfaction</article-title><source>J. 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Paraninfo Digit.</source><year>2019</year><volume>XIII</volume><fpage>e091</fpage></element-citation></ref></ref-list></back><floats-group><table-wrap id=\"ijerph-17-05582-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05582-t001_Table 1</object-id><label>Table 1</label><caption><p>Sociodemographic characteristics of the sample.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Socio-Demographic Variables</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">\n</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>x ± SD</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Range</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Age</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">31.6 ± 5.32</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18–46</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>n</italic></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>%</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Level of Education (<italic>n</italic> = 379)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">University </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">175</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">46.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Vocational training</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">87</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Secondary education</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">66</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.4</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Primary/Elementary/Basic education</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">51</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.5</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Marital Status (<italic>n</italic> =382)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Married or with a partner</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">359</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">94.0</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Single</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">23</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.0</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Labor Situation (<italic>n</italic> = 384)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Employed workers</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">184</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Housewives</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">68</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.7</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Busines women</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.8</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Unemployed</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">92</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24.0</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Other work circumstances (studying, retired, etc.)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.6</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05582-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05582-t002_Table 2</object-id><label>Table 2</label><caption><p>Analysis of each dimension and items on the scale.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">1 Professional Support (Cronbach’sAlpha = 0.867, % Variance Explained = 14.02) </th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Coefficient*</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q19</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">During labor there was always a carer to explain things so that I could understand.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.843</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q7</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">All my labor carers were very supportive.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.834</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q13</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Carers always listened very, very carefully to everything that I had to say.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.808</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q27</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">All my carers treated me in the most friendly and courteous manner possible.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.778</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q32</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">My carers couldn’t have been more helpful.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.733</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Q12</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">The way my labor care was provided could not have been improved.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.556</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n<bold>2 Expectations (Cronbach’s Alpha = 0.861, % Variance Explained = 9.19)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Q17</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">The delivery went almost completely as I had hoped that it would</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.809</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q11</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">The labor went nearly exactly as I had hoped that it would.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.794</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q22</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">My labor was just about the right length.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.719</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Q1</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">My labor went totally normally.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.710</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n<bold>3 Pain During and After the Birth (Cronbach’s Alpha = 0.781, % Variance Explained = 8.75)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q26</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">More pain relief would have made my labor easier. (−)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.719</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q6</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">I should have been offered something more to relieve the pain I had after my baby was born. (−)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.702</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q16</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">I was in a fair bit of pain immediately after the birth. (−)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.669</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q9</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">I should have been offered something more to relieve my labor pains. (−)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.668</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q20</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">I got excellent pain relief in labor.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.586</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Q31</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">I didn’t need a lot of pain relief after the birth.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.399</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n<bold>4 Home Assessment (Cronbach’s Alpha = 0.843, % Variance Explained = 7.52)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q15</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">When I thought that my labor had started, I would have liked a carer to come and see me at home to confirm that I had. (−)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.914</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q28</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Early home assessment of me in labor would have been very helpful. (−)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.904</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Q8</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">I should have had a home assessment in early labor. (−)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.761</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n<bold>5 Support from Husband (Cronbach’s Alpha = 0.750, % Variance Explained = 6.80)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q2</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">My birth partner/husband helped me to understand what was going on when I was in labor.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.937</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q23</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">My birth partner/husband couldn’t have supported me any better.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.920</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Q29</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">I could have had a bit more help from my birth partner/husband. (−)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.511</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n<bold>6 Holding Baby (Cronbach’s Alpha = 0.675, % Variance Explained = 6.74)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q18</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">I needed to hold my baby a little earlier than I did. (−)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.842</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q10</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">After my baby was born, I was not given him/her quite as soon as I wanted. (−)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.786</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Q3</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">I got to see my baby at exactly the right time after she/he was born.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.577</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n<bold>7 Knowledge of Women about Professionals During Childbirth Assistance</bold>\n<break/>\n<bold>(Cronbach’s Alpha = 0.797, % Variance Explained = 5.21)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q24</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">I knew the carer(s) present at the birth of my baby.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.855</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Q5</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">At the start of my labor I knew my carers very well.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.844</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n<bold>8 Environment (Cronbach’s Alpha = 0.711, % Variance Explained = 4.97)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q4</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">My birth room was a little impersonal and clinical. (−)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.810</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Q14</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">The area where I gave birth was very pleasant and relaxing.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.764</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n<bold>9 Control (Cronbach’s Alpha = 0.436, % Variance Explained = 4.76)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q21</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Everyone seemed to tell me what to do in labor. (−)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.753</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q30</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Labor was just a matter of doing what I was told by my carers. (−)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.729</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Q25</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">I am satisfied with just one or two things about the labor care that I received. (−)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.460</td></tr></tbody></table><table-wrap-foot><fn><p>* Correlation coefficients of each item with its subscale.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05582-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05582-t003_Table 3</object-id><label>Table 3</label><caption><p>Analysis of each dimension and items on the scale after the removal of items Q12 and Q25.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">1 Professional Support (Cronbach’s Alpha = 0.869, % Variance Explained = 13.403) </th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Coefficient*</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q19</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">During labor there was always a carer to explain things so that I could understand.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.836</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q7</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">All my labor carers were very supportive.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.830</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q13</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Carers always listened very, very carefully to everything that I had to say.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.801</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q27</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">All my carers treated me in the most friendly and courteous manner posible.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.772</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Q32</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">My carers couldn’t have been more helpful.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.722</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n<bold>2 Expectations (Cronbach’s Alpha = 0.861, % Variance Explained = 9.817)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q17</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">The delivery went almost completely as I had hoped that it would.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.819</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q11</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">The labor went nearly exactly as I had hoped that it would.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.808</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q22</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">My labor was just about the right length.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.725</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Q1</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">My labor went totally normally.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.719</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n<bold>3 Pain during and after the Birth (Cronbach’s Alpha = 0.749, % Variance Explained = 9.085)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q6</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">I should have been offered something more to relieve the pains I had after my baby was born. (−)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.717</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q26</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">More pain relief would have made my labor easier. (−)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.716</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q16</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">I was in a fair bit of pain immediately after the birth. (−)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.682</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q9</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">I should have been offered something more to relieve my labor pains. (−)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.660</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q20</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">I got excellent pain relief in labor.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.575</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Q31</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">I didn’t need a lot of pain relief after the birth.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.418</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n<bold>4 Home Assessment (Cronbach’s Alpha = 0.843, % Variance Explained = 8.026)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q15</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">When I thought that my labor had started, I would have liked a carer to come and see me at home to confirm that I had. (−)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.912</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q28</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Early home assessment of me in labor would have been very helpful. (−)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.903</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Q8</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">I should have had a home assessment in early labor. (−)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.762</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n<bold>5 Support from Husband (Cronbach’s Alpha = 0.750, % Variance Explained = 7.209)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q2</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">My birth partner/husband helped me to understand what was going on when I was in labor.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.940</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q23</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">My birth partner/husband couldn’t have supported me any better.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.927</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Q29</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">I could have had a bit more help from my birth partner/husband. (−)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.498</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n<bold>6 Holding Baby (Cronbach’s Alpha = 0.675, % Variance Explained = 7.042)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q18</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">I needed to hold my baby a little earlier than I did. (−)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.842</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q10</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">After my baby was born, I was not given him/her quite as soon as I wanted. (−)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.784</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Q3</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">I got to see my baby at exactly the right time after she/he was born.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.579</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n<bold>7 Knowledge of Women about Professionals during Childbirth Assistance (Cronbach’s Alpha = 0.797, % Variance Explained = 5.532)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q24</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">I knew the carer(s) present at the birth of my baby.</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.855</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Q5</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">At the start of my labor I knew my carers very well.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.847</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n<bold>8 Environment (Cronbach’s Alpha = 0.711, % Variance Explained = 5.297)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q4</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">My birth room was a little impersonal and clinical. (−)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.834</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Q14</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">The area where I gave birth was very pleasant and relaxing.</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.771</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n<bold>9 Control (Cronbach’s Alpha = 0.481, % Variance Explained = 4.646)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Q30</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Labor was just a matter of doing what I was told by my carers. (−)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.789</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Q21</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Everyone seemed to tell me what to do in labor. (−)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.778</td></tr></tbody></table><table-wrap-foot><fn><p>* Correlation coefficients of each item with its subscale.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05582-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05582-t004_Table 4</object-id><label>Table 4</label><caption><p>Cronbach’s alpha values from the different studies analyzed.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">V. Granada (Spanish)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">V. Original (English)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">V. Geneva (French)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">V. Madrid (Spanish)</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">C.’s Alpha</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">C.’s Alpha</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">C.’s Alpha</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">C.’s Alpha</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1 Professional support</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.869</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.91</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.84</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.74</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2 Expectations</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.861</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.90</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.86</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.80</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3 Pain in labor and Pain after the birth</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.749</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.83 & 0.65</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.79 & 0.59</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.68</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4 Home assessment</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.843</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.90</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.87</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.83</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5 Support from husband</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.750</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.83</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.56</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.61</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6 Holding baby</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.675</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.87</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.78</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.51</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7 Knowledge of women about their caretakers during the birth</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.797</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.82</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.84</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.36</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8 Environment</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.711</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.80</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.67</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.43</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9 Control</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.481</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.62</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.53</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-----</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10 General satisfaction</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.421</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.75</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.85</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-----</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05582-t005\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05582-t005_Table 5</object-id><label>Table 5</label><caption><p>Average scores in the different versions.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Dimensions</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">V. Granada<break/>(Spanish)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">V. Original<break/>(English)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">V. Geneva<break/>(French)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">V. Madrid (Spanish)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mean ± SD</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mean ± SD</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mean ± SD</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mean ± SD</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1 Professional support</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">83.71 ± 12.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">72.3 ± 20.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">80.9 ± 19.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">91.35 ± 12.9</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2 Expectations</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">64.16 ± 20.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">59.0 ± 27.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">64.2 ± 29.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">60.88 ± 29.8</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3 Pain in labor and Pain after delivery</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">65.59 ± 27.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">60.8 ± 23.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">65.15 ± 27.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">64.98 ± 22.9</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4 Home assessment</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">60.16 ± 17.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">54.3 ± 20.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">64.6 ± 24.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">69.31 ± 30.0</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5 Support from husband</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">80.42 ± 25.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">72.7 ± 21.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">75.2 ± 21.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">90.48 ± 15.9</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6 Holding baby</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">78.09 ± 25.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">74.2 ± 21.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">78.1 ± 25.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">82.61 ± 25.8</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7 Knowledge of women about their caretakers during the birth </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">51.75 ± 23.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">38.8 ± 21.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">38.0 ± 30.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">67.49 ± 27.7</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8 Environment</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">56.74 ± 19.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">61.6 ± 28.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">59.6 ± 27.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">40.18 ± 22.2</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9 Control</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">49.31 ± 21.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">53 ± 23.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">46.5 ± 27.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-----</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10 General satisfaction</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">70.11 ± 18.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">53.1 ± 22.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">66.5 ± 13.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">83.33 ± 22.2</td></tr></tbody></table></table-wrap></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32727074</article-id><article-id pub-id-type=\"pmc\">PMC7432015</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155326</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05326</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Oviduct Fluid Extracellular Vesicles Change the Phospholipid Composition of Bovine Embryos Developed In Vitro</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Banliat</surname><given-names>Charles</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05326\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05326\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Le Bourhis</surname><given-names>Daniel</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05326\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Bernardi</surname><given-names>Ophélie</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05326\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Tomas</surname><given-names>Daniel</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05326\">1</xref><xref ref-type=\"aff\" rid=\"af4-ijms-21-05326\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Labas</surname><given-names>Valérie</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05326\">1</xref><xref ref-type=\"aff\" rid=\"af4-ijms-21-05326\">4</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-4534-9949</contrib-id><name><surname>Salvetti</surname><given-names>Pascal</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05326\">3</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-8257-4224</contrib-id><name><surname>Guyonnet</surname><given-names>Benoît</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05326\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-9836-2506</contrib-id><name><surname>Mermillod</surname><given-names>Pascal</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05326\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-4569-5665</contrib-id><name><surname>Saint-Dizier</surname><given-names>Marie</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05326\">1</xref><xref ref-type=\"aff\" rid=\"af5-ijms-21-05326\">5</xref><xref rid=\"c1-ijms-21-05326\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05326\"><label>1</label>INRAE, CNRS, University of Tours, IFCE, UMR 85 PRC, F-37380 Nouzilly, France; <email>charles.banliat@inrae.fr</email> (C.B.); <email>ophelie.bernardi@inrae.fr</email> (O.B.); <email>daniel.tomas@inrae.fr</email> (D.T.); <email>valerie.labas@inrae.fr</email> (V.L.); <email>pascal.mermillod@inrae.fr</email> (P.M.)</aff><aff id=\"af2-ijms-21-05326\"><label>2</label>Union Evolution, F-35530 Noyal-Sur-Vilaine, France; <email>benoit.guyonnet@evolution-xy.fr</email></aff><aff id=\"af3-ijms-21-05326\"><label>3</label>Allice, F-37380 Nouzilly, France; <email>daniel.lebourhis@allice.fr</email> (D.L.B.); <email>pascal.salvetti@allice.fr</email> (P.S.)</aff><aff id=\"af4-ijms-21-05326\"><label>4</label>INRAE, Université de Tours, CHU de Tours, Plate-forme CIRE, F-37380 Nouzilly, France</aff><aff id=\"af5-ijms-21-05326\"><label>5</label>Department Agrosciences, Faculty of Sciences and Techniques, University of Tours, F-37200 Tours, France</aff><author-notes><corresp id=\"c1-ijms-21-05326\"><label>*</label>Correspondence: <email>marie.saint-dizier@univ-tours.fr</email>; Tel.: +33-2-47-42-75-08</corresp></author-notes><pub-date pub-type=\"epub\"><day>27</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5326</elocation-id><history><date date-type=\"received\"><day>23</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>25</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Oviduct fluid extracellular vesicles (oEVs) have been proposed as bringing key molecules to the early developing embryo. In order to evaluate the changes induced by oEVs on embryo phospholipids, fresh bovine blastocysts developed in vitro in the presence or absence of oEVs were analyzed by intact cell MALDI-TOF (Matrix assisted laser desorption ionization—Time of flight) mass spectrometry (ICM-MS). The development rates, cryotolerance, and total cell number of blastocysts were also evaluated. The exposure to oEVs did not affect blastocyst yield or cryotolerance but modified the phospholipid content of blastocysts with specific changes before and after blastocoel expansion. The annotation of differential peaks due to oEV exposure evidenced a shift of embryo phospholipids toward more abundant phosphatidylcholines (PC), phosphatidylethanolamines (PE), and sphingomyelins (SM) with long-chain fatty acids. The lipidomic profiling of oEVs showed that 100% and 33% of the overabundant masses in blastocysts and expanded blastocysts, respectively, were also present in oEVs. In conclusion, this study provides the first analysis of the embryo lipidome regulated by oEVs. Exposure to oEVs induced significant changes in the phospholipid composition of resulting embryos, probably mediated by the incorporation of oEV-phospholipids into embryo membranes and by the modulation of the embryonic lipid metabolism by oEV molecular cargos.</p></abstract><kwd-group><kwd>extracellular vesicles</kwd><kwd>exosomes</kwd><kwd>microvesicles</kwd><kwd>tubal fluid</kwd><kwd>fallopian tube</kwd><kwd>oviduct</kwd><kwd>embryo</kwd><kwd>bovine</kwd><kwd>cattle</kwd><kwd>lipidomics</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05326\"><title>1. Introduction</title><p>Understanding how the mother communicates with the early developing embryo is a scientific challenge. In ruminants as in other mammals, the oviduct is the first environment to which the embryo is exposed [<xref rid=\"B1-ijms-21-05326\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijms-21-05326\" ref-type=\"bibr\">2</xref>]. The oviductal fluid is a complex fluid containing proteins, carbohydrates, lipids, and ions that are regulated in abundance throughout the estrous cycle in domestic mammals [<xref rid=\"B3-ijms-21-05326\" ref-type=\"bibr\">3</xref>]. Recently, extracellular vesicles from the oviductal fluid (oEVs), also called oviductosomes, have been proposed as key participants of the early embryo–maternal dialog [<xref rid=\"B4-ijms-21-05326\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijms-21-05326\" ref-type=\"bibr\">5</xref>]. EVs are lipid bilayer-enclosed nanosized vesicles, including exosomes (40–100 nm) and microvesicles (100–1000 nm) [<xref rid=\"B6-ijms-21-05326\" ref-type=\"bibr\">6</xref>]. As EVs in other biological fluids, oEVs contain a range of biomolecules such as proteins, small RNAs, metabolites, and genomic DNA [<xref rid=\"B4-ijms-21-05326\" ref-type=\"bibr\">4</xref>,<xref rid=\"B7-ijms-21-05326\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijms-21-05326\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijms-21-05326\" ref-type=\"bibr\">9</xref>]. Using fluorescent-labelled oEVs and confocal microscopy, it was shown that bovine blastocysts were able to take up oEVs added to the culture medium and internalize them inside their blastomeres [<xref rid=\"B10-ijms-21-05326\" ref-type=\"bibr\">10</xref>]. Furthermore, the supplementation of culture media with oEVs has been shown to improve the quality of bovine blastocysts in terms of total cell number, cryotolerance, and hatching rates [<xref rid=\"B10-ijms-21-05326\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijms-21-05326\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijms-21-05326\" ref-type=\"bibr\">12</xref>]. Exposure to oEVs was also shown to induce changes in gene expression, miRNA content, and global DNA methylation levels in cattle embryos [<xref rid=\"B9-ijms-21-05326\" ref-type=\"bibr\">9</xref>,<xref rid=\"B11-ijms-21-05326\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijms-21-05326\" ref-type=\"bibr\">12</xref>]. In mice, oEVs from donor oviduct fluid in the transfer medium were shown to improve birth rates after transfer of in-vitro-produced embryos to recipient mothers [<xref rid=\"B13-ijms-21-05326\" ref-type=\"bibr\">13</xref>].</p><p>Phospholipids, including phosphatidylcholines (PC), phosphatidylethanolamines (PE), and sphingomyelins (SM), represent key structural components of cell membranes and also major components of EVs [<xref rid=\"B14-ijms-21-05326\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijms-21-05326\" ref-type=\"bibr\">15</xref>]. Glycerophospholipids and sphingolipids are involved in a wide range of cell-signaling pathways and act as precursors to many biomolecules such as lysophosphatidylcholines (LPC), lysoPE (LPE), and eicosanoids [<xref rid=\"B16-ijms-21-05326\" ref-type=\"bibr\">16</xref>]. Using intact-cell MALDI-TOF (Matrix assisted laser desorption ionization—Time of flight) mass spectrometry (ICM-MS), we showed earlier that the bovine oviductal fluid contains a mixture of PC, PE, LPC, LPE, and SM varying in abundance throughout the estrous cycle, and it was suggested that oEVs contribute a large part to these phospholipids [<xref rid=\"B17-ijms-21-05326\" ref-type=\"bibr\">17</xref>]. Furthermore, significant differences in various PC and SM were reported between in-vitro-produced cattle blastocysts and their in-vivo conceived counterparts [<xref rid=\"B18-ijms-21-05326\" ref-type=\"bibr\">18</xref>], indicating that the maternal environment modulates the phospholipid composition of oocytes and developing embryos.</p><p>We hypothesized that oEVs added to the culture medium would change the lipid composition of developing embryos in a way that might explain the beneficial effects of oEVs on embryo quality. The objective of this study was thus to examine the effect of oEVs on the phospholipid composition of bovine blastocysts.</p></sec><sec sec-type=\"results\" id=\"sec2-ijms-21-05326\"><title>2. Results</title><sec id=\"sec2dot1-ijms-21-05326\"><title>2.1. Oviductal Extracellular Vesicles Had No Effect on Embryo Development</title><p>The same experiment was performed in two different laboratories (see Materials and Methods Section and Figure 4). Supplementation with postovulatory oEVs during in vitro culture had no effect on embryo cleavage on day 2 or blastocyst rates from cleaved embryos on days 6, 7, and 8 (<xref rid=\"ijms-21-05326-t001\" ref-type=\"table\">Table 1</xref>).</p><p>The rates of blastocyst hatching on day 8 did not differ between groups (control vs. oEVs: 11.3 ± 3.8% vs. 19.3 ± 4.0%; data from Experiment 2). Supplementation with oEVs had no effect on mean cell number per blastocyst on day 8 (85.4 ± 5.3 vs. 97.3 ± 9.8; n = 22 and n = 12, respectively) but tended to increase this number in expanded blastocysts (133.1 ± 5.7 vs. 146.6 ± 5.2; n = 47 and n = 69, respectively; <italic>p</italic> = 0.09; data from Experiment 1).</p><p>After slow freezing and thawing, blastocyst re-expanding (control vs. oEVs: 100% vs. 94.7% and 94.7 vs. 84.2% at 24 and 48 h, respectively) and hatching (control vs. oEVs: 31.6% vs. 57.9% and 84.2% vs. 84.2% at 24 and 48 h, respectively) did not differ between groups (n = 19 blastocysts/group; data from Experiment 1).</p></sec><sec id=\"sec2dot2-ijms-21-05326\"><title>2.2. Oviductal Extracellular Vesicles Changed Embryo Phospholipid Profiles in a Stage-Specific Manner</title><p>The lipidomic analysis of fresh individual blastocysts (n = 25) and expanded blastocysts (n = 53) on day 8 detected a total of 259 peaks in the 350–900 m/z mass range, corresponding mostly after annotation to PC, PE, LPC, and SM (see all masses and corresponding annotations in <xref ref-type=\"app\" rid=\"app1-ijms-21-05326\">Supplementary data A</xref>).</p><p>The hierarchical clustering of differential m/z (<italic>p</italic>-value < 0.05) between oEV-treated and control groups showed a clear separation between blastocysts and expanded blastocysts and evidenced specific lipidomic profiles due to oEV supplementation at each stage (<xref ref-type=\"fig\" rid=\"ijms-21-05326-f001\">Figure 1</xref>; see individual profiles for blastocysts and expanded blastocysts in <xref ref-type=\"app\" rid=\"app1-ijms-21-05326\">Supplementary data B</xref>).</p><p>The Volcano plot analysis with a fold-change threshold of 1.5 between oEV-treated and control embryos evidenced 18 and 28 differentially abundant lipid m/z in blastocysts and expanded blastocysts, respectively (<xref ref-type=\"fig\" rid=\"ijms-21-05326-f002\">Figure 2</xref>). In total, 61% (11/18) and 64% (18/28) of differentially abundant m/z were increased in oEV-treated embryos compared with controls in blastocysts and expanded blastocysts, respectively. Among those, six molecular species between 756 and 788 m/z, all overabundant in oEV-treated embryos, were shared between blastocysts and expanded blastocysts and annotated as PE (36:1), PC (34:2), SM (d38:1), PC (34:1), PC (35:1), and PC (36:1). Furthermore, overabundant masses due to oEVs supplementation included a majority of PC, PE, and SM with long-chain fatty acids; whereas LPC/LPE (with one fatty acid group) were identified only among m/z found less abundant following oEV supplementation.</p><p>Relative abundance of differential masses ordered in increasing <italic>m</italic>/<italic>z</italic> values confirmed that less abundant <italic>m</italic>/<italic>z</italic> in oEV-exposed vs. control embryos included a majority of low lipid <italic>m</italic>/<italic>z</italic>; whereas overabundant masses were almost exclusively phospholipids of high molecular weight, with a cut-off around 700 and 600 <italic>m</italic>/<italic>z</italic> in blastocysts and expanded blastocysts, respectively (<xref ref-type=\"fig\" rid=\"ijms-21-05326-f003\">Figure 3</xref>).</p></sec><sec id=\"sec2dot3-ijms-21-05326\"><title>2.3. Majority of Overabundant Masses in oEV-Treated Embryos Were Also Detected in oEVs</title><p>In order to determine if the m/z found more abundant in oEV-treated compared with control embryos might originate from oEVs, the oEV sample used for in vitro embryo development (postovulatory stage, ipsilateral to ovulation) was analyzed by ICM-MS (6 replicates). A total of 234 lipid peaks in the 350–900 m/z mass range, including mostly LPC, PC, PE, and SM, were detected in oEVs (see details in <xref ref-type=\"app\" rid=\"app1-ijms-21-05326\">Supplementary data A</xref>). Of the 11 and 18 m/z found increased after oEV exposure in blastocysts and expanded blastocysts, respectively, 11 (100%) and 6 (33%) were also detected in oEVs. The lipid species shared between oEVs and embryos are indicated by an asterisk in <xref ref-type=\"fig\" rid=\"ijms-21-05326-f002\">Figure 2</xref>.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec3-ijms-21-05326\"><title>3. Discussion</title><p>The oviductal fluid is the first interface between the mother and the early developing embryo. Oviduct fluid EVs have been proposed as nanocarriers of molecules for gametes and embryos [<xref rid=\"B4-ijms-21-05326\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijms-21-05326\" ref-type=\"bibr\">5</xref>,<xref rid=\"B19-ijms-21-05326\" ref-type=\"bibr\">19</xref>], but the regulation of the embryonic lipidome by oEVs remains to be explored. Here, for the first time, we show that exposure to oEVs during in vitro culture changed the phospholipid composition of developing embryos in favor of PC, PE, and SM of high molecular masses. Oviductal EVs had specific impact on blastocyst phospholipids before and after blastocoel expansion.</p><p>Based on two successive experiments on more than 1500 zygotes, we did not observe any significant improvement in the blastocyst yield after supplementation of the culture medium with oEVs. This is in accord with some previous studies that used oEVs derived from bovine oviduct epithelial cells in vitro [<xref rid=\"B11-ijms-21-05326\" ref-type=\"bibr\">11</xref>] and oviductal fluid [<xref rid=\"B12-ijms-21-05326\" ref-type=\"bibr\">12</xref>]. In the latter, a detrimental effect of oEVs on blastocyst development was even observed at day 7 that was compensated on days 8 and 9 [<xref rid=\"B12-ijms-21-05326\" ref-type=\"bibr\">12</xref>] By contrast, a previous study reported an improvement of blastocyst rates due to oEV supplementation but with a more marked effect on day 9 than on days 7 and 8 [<xref rid=\"B10-ijms-21-05326\" ref-type=\"bibr\">10</xref>]. Here, in order to analyze blastocyst quality and phospholipid composition, we stopped embryo development at day 8, preventing us from assessing a potential later effect on embryo development. In the study from Alminana et al. [<xref rid=\"B10-ijms-21-05326\" ref-type=\"bibr\">10</xref>] and ours, frozen–thawed oEVs collected from oviductal fluids at the postovulatory stage were used. Furthermore, as previously reported [<xref rid=\"B7-ijms-21-05326\" ref-type=\"bibr\">7</xref>], the proportions of exosomes (30–100 nm: 76%) and of microvesicles (100–500 nm: 24%), as measured by transmission electron microscopy in the oEV samples used, were very similar in the two studies. However, the differences between the two studies may be due to differences in oEV content in the culture media: based on preliminary (unpublished) data comparing different doses of oEVs proteins (0.005, 0.05, 0.5 mg/mL) in the culture medium, the 0.05 dose tended to increase the rates of blastocysts at days 7–8 compared to controls without oEVs and was chosen for the present study, whereas Alminana et al. [<xref rid=\"B10-ijms-21-05326\" ref-type=\"bibr\">10</xref>] used final concentrations of 0.2–0.4 mg of proteins/mL.</p><p>A tendency to increase the total cell numbers in expanded blastocysts after oEV exposure was observed. These results are in line with previously reported positive effects of oEVs on growth and expansion of in-vitro produced cattle embryos [<xref rid=\"B4-ijms-21-05326\" ref-type=\"bibr\">4</xref>,<xref rid=\"B10-ijms-21-05326\" ref-type=\"bibr\">10</xref>]. Slow freezing in a two-step protocol is currently the method mostly used for cattle embryo cryopreservation in France. We expected a positive effect of oEVs on embryo cryosurvival after slow freezing, as previously reported by others after vitrification [<xref rid=\"B11-ijms-21-05326\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijms-21-05326\" ref-type=\"bibr\">12</xref>]. However, there was no detectable effect of pre-exposure to oEVs on blastocysts re-expansion and hatching at 24 or 48 h after thawing. It is to note that thawed blastocysts were cultured for 2 days in the presence of 1% of postovulatory cow serum. An uptake of serum EVs added for 24 h in the culture medium was previously shown to occur in bovine blastocysts [<xref rid=\"B20-ijms-21-05326\" ref-type=\"bibr\">20</xref>]. It may be that serum EVs masked the possible effects of oEVs during this culture period. It is also worth noting that the survival rates were globally very high (>94% and 84% of re-expansion at 24 and 48 h) in both groups, precluding any evaluation of the effects of oEV in suboptimal conditions. This is probably due to the selection of expanded blastocysts of excellent morphology (grade 1) for cryopreservation. Previous studies examining the effect of liposomes (containing only phospholipids and cholesterol) or of soybean lecithin (mix of phospholipids) on in vitro embryo development have resulted in slight improvements of embryo survival after slow freezing in one [<xref rid=\"B21-ijms-21-05326\" ref-type=\"bibr\">21</xref>] but not all [<xref rid=\"B22-ijms-21-05326\" ref-type=\"bibr\">22</xref>] studies, and failed to evidence any effect on blastocyst hatching and pregnancy rates after transfer to the uterus of recipient cows [<xref rid=\"B21-ijms-21-05326\" ref-type=\"bibr\">21</xref>]. Thus, the effects of oEVs on cattle embryo cryosurvival after slow freezing remains to be evaluated.</p><p>The mechanisms involved in cellular communications mediated by EVs include fusion between EV membranes and the plasma membrane of the target cells, as well as EV endocytosis via specific target cell receptors [<xref rid=\"B5-ijms-21-05326\" ref-type=\"bibr\">5</xref>]. Previous studies evidenced that oEVs are able to fuse with the membrane of spermatozoa in mice [<xref rid=\"B23-ijms-21-05326\" ref-type=\"bibr\">23</xref>] and cats [<xref rid=\"B24-ijms-21-05326\" ref-type=\"bibr\">24</xref>] in vitro, leading to the transfer of oEV membrane components to the sperm membrane [<xref rid=\"B23-ijms-21-05326\" ref-type=\"bibr\">23</xref>,<xref rid=\"B25-ijms-21-05326\" ref-type=\"bibr\">25</xref>]. Previous confocal microcopy observations in our laboratory showed that oEVs added in the culture medium of bovine embryos are able to cross the zona pellucida and localize into the cytoplasm of embryo blastomeres [<xref rid=\"B10-ijms-21-05326\" ref-type=\"bibr\">10</xref>]. Similarly, uterine EVs were shown to be internalized by trophectoderm cells of embryos at later stages of development in sheep [<xref rid=\"B26-ijms-21-05326\" ref-type=\"bibr\">26</xref>] and humans [<xref rid=\"B27-ijms-21-05326\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05326\" ref-type=\"bibr\">28</xref>]. Based on these observations, we made the hypothesis that the oEVs added to the culture medium are captured by developing embryos, leading to the incorporation of their phospholipids in the plasma membrane and/or cytoplasm of blastomeres. In line with this hypothesis, the majority (>61%) of differentially abundant m/z were overabundant in oEV-exposed embryos compared to controls. In order to evaluate which phospholipids could be brought by oEVs through internalization, we analyzed the lipidomic profile of the pool of oEVs used for the supplementation of the culture medium and detected PC, SM, and PE as major phospholipid species, in accordance with a recent lipidomic analysis of EVs isolated from the uterine fluid of ewes [<xref rid=\"B29-ijms-21-05326\" ref-type=\"bibr\">29</xref>]. A perspective of this study is to characterize the phospholipid profiles of oEVs across the cycle with a higher number of replicates. The present analysis enabled us to identify up to 100% (11/11) of overabundant masses in oEV-exposed blastocysts as also present in oEVs, which is consistent with the assumption of an internalization of oEV-derived phospholipids by developing embryos. However, this percentage was much lower (33%, 6/18) after blastocoel expansion. Furthermore, around one third of differentially abundant masses (39% and 36% in blastocysts and expanded blastocysts, respectively) were decreased in embryos after oEV supplementation, indicating that mechanisms other than the simple internalization of oEV-phospholipid cargos inside embryonic cells were involved in these changes. Of note, EVs can carry a large panel of lipid mediators and molecules potentially involved in lipid metabolism including free fatty acids, eicosanoids, proteins, and nucleic acids [<xref rid=\"B15-ijms-21-05326\" ref-type=\"bibr\">15</xref>,<xref rid=\"B30-ijms-21-05326\" ref-type=\"bibr\">30</xref>]. Previous transcriptomic analyses showed that oEV supplementation during in vitro culture altered the expression of genes involved in lipid metabolism and regulation of lipid metabolic processes in cattle blastocysts [<xref rid=\"B9-ijms-21-05326\" ref-type=\"bibr\">9</xref>,<xref rid=\"B12-ijms-21-05326\" ref-type=\"bibr\">12</xref>]. Furthermore, various miRNAs involved in fatty acid biosynthesis and metabolism were found highly abundant in bovine oEVs isolated during the periovulatory period compared to the luteal phase of cycle [<xref rid=\"B31-ijms-21-05326\" ref-type=\"bibr\">31</xref>]. Therefore, the exposure to oEVs during in vitro culture might have led to both the reorganization of blastomere plasma membrane with oEV-derived phospholipids and to the modulation of embryonic lipid metabolism by small RNAs and proteins brought by oEVs. Further experiments using EV imaging [<xref rid=\"B32-ijms-21-05326\" ref-type=\"bibr\">32</xref>] would be necessary to confirm the EV uptake and precisely determine if this uptake varies according to the developmental stage.</p><p>The embryos exposed to oEVs displayed a shift toward PC, PE, and SM of higher molecular weight, i.e., possessing fatty acids of longer chains, whereas PC and PE species of lower molecular weight and LPC were detected among less abundant phospholipids compared to control embryos. Phospholipids are important components of the bovine oviductal [<xref rid=\"B33-ijms-21-05326\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijms-21-05326\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijms-21-05326\" ref-type=\"bibr\">35</xref>] and uterine [<xref rid=\"B36-ijms-21-05326\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijms-21-05326\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijms-21-05326\" ref-type=\"bibr\">38</xref>] fluids and may serve as an energy source and as signaling precursors for the gametes and developing embryos. However, there is currently very few information on their roles for the establishment of pregnancy in ruminants. Phospholipids that are regulated in abundance in the oviductal fluid between the periovulatory and luteal stages of cycle are potential candidates for a role in early embryo development. It is noteworthy that molecular species such as PC (34:1), PC (36:4), and PC (36:3) (at m/z 760.59, 782.56, and 784.56, respectively) that were all found more abundant at the periovulatory period than at the luteal stage of the cycle [<xref rid=\"B17-ijms-21-05326\" ref-type=\"bibr\">17</xref>] were also identified as overabundant in oEV-exposed blastocysts. Furthermore, several species of PC/SM and PE identified in uterine EVs were reported to be discriminating (by hierarchical clustering) between pregnant and cyclic ewes at day 14, i.e., after blastocyst hatching and elongation [<xref rid=\"B29-ijms-21-05326\" ref-type=\"bibr\">29</xref>]. In addition, the PCs detected as increased by pregnancy in the uterine lumen of ewes at day 17, including PC (34:1), PC (34:2), PC (36:1), and PC (36:2) [<xref rid=\"B38-ijms-21-05326\" ref-type=\"bibr\">38</xref>], were also found increased in abundance in oEV-exposed blastocysts in the present study.</p><p>There is evidence that embryos developed in contact with oviduct epithelial cells, oviduct fluid, and/or oEVs are of higher quality than unexposed controls in terms of morphology, cryotolerance, and gene expression [<xref rid=\"B12-ijms-21-05326\" ref-type=\"bibr\">12</xref>,<xref rid=\"B39-ijms-21-05326\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijms-21-05326\" ref-type=\"bibr\">40</xref>,<xref rid=\"B41-ijms-21-05326\" ref-type=\"bibr\">41</xref>]. However, the mechanisms by which oviduct fluid components enhance the quality of developing embryos are poorly known. We previously showed that the steroid hormones progesterone, estradiol, and cortisol added at intraoviductal concentrations to the culture medium altered the phospholipid composition and cryosurvival of bovine embryos [<xref rid=\"B35-ijms-21-05326\" ref-type=\"bibr\">35</xref>]. Several phospholipids higher than 700 m/z that were more abundant in embryos exposed to physiological concentrations of steroid hormones—including PC (34:2), PC (36:3), SM (d40:2), PC (36:2), and PC (38:4) at m/z 758.56, 784.56, 785.58, 786.6, and 810.6, respectively—were also found overabundant in blastocysts exposed to oEVs, whereas PC (29:2) or PE (32:2) at m/z 688.43 was found less abundant in both conditions [<xref rid=\"B35-ijms-21-05326\" ref-type=\"bibr\">35</xref>]. Two previous studies compared the phospholipid composition of bovine blastocysts conceived in vivo, which have a high likelihood of pregnancy after uterine transfer to a recipient cow; and those produced in vitro, which have lower developmental potential [<xref rid=\"B18-ijms-21-05326\" ref-type=\"bibr\">18</xref>,<xref rid=\"B42-ijms-21-05326\" ref-type=\"bibr\">42</xref>]. In both studies, the MS data were acquired in the mass range above m/z 700, precluding any comparison below this threshold. However, Sudano et al. [<xref rid=\"B18-ijms-21-05326\" ref-type=\"bibr\">18</xref>] evidenced nine phospholipids differentially abundant between in-vivo- and in-vitro-developed bovine blastocysts, among which eight were found more abundant in in-vivo blastocysts. Of note, three PCs (PC (34:2), PC (36:2), and PC (36:1) of m/z 758.56, 786.6, and 788.6, respectively) found here more abundant in oEV-exposed blastocysts were also overabundant among in-vivo-conceived blastocysts [<xref rid=\"B18-ijms-21-05326\" ref-type=\"bibr\">18</xref>]. By contrast, in the study from Annes et al. [<xref rid=\"B42-ijms-21-05326\" ref-type=\"bibr\">42</xref>], a majority of phospholipids (9/12)—including the molecular species PC (32:1), PC (34:2), PC (38:4)—were absent or less abundant in in-vivo-derived blastocysts than in in-vitro-produced ones, whereas we detected them at higher abundance in oEV-exposed blastocysts or expanded blastocysts than in controls. However, in accordance with Sudano et al. [<xref rid=\"B18-ijms-21-05326\" ref-type=\"bibr\">18</xref>], PC (36:2) was more abundant in in-vivo and in-vitro oEV-exposed developed blastocysts [<xref rid=\"B42-ijms-21-05326\" ref-type=\"bibr\">42</xref>]. Further studies are now needed to know whether exposure to oEVs in vitro could lead to higher chances of implantation and pregnancy after intrauterine transfer.</p><p>In conclusion, this study provides the first analysis of the embryonic lipidome regulated by oEVs during in vitro development. Exposure to oEVs induced significant changes in the phospholipid composition of resulting embryos toward more abundant PC, PE, and SM with long-chain fatty acids. These changes were probably mediated by both the incorporation of oEV phospholipids into embryo membranes and the modulation of embryonic lipid metabolism by other oEV cargos such as mRNAs, miRNAs, and proteins.</p></sec><sec id=\"sec4-ijms-21-05326\"><title>4. Materials and Methods</title><sec id=\"sec4dot1-ijms-21-05326\"><title>4.1. In Vitro Embryo Production and oEVs Supplementation</title><p>Bovine ovaries were collected at a local slaughterhouse, transported to the laboratory at 32–35 °C, then cumulus oocyte complexes (COCs) were collected by aspirating follicles of 2–8 mm in diameter. The in-vitro maturation (IVM)/in-vitro fertilization (IVF) protocols used in the present study have been described previously [<xref rid=\"B35-ijms-21-05326\" ref-type=\"bibr\">35</xref>]. Briefly, groups of 40–50 COCs with more than three compact layers of cumulus cells were matured in 25 mM bicarbonate-buffered TCM 199 supplemented with 10% fetal calf serum (FCS; GIBCO BRL), 10 µg/mL follicle-stimulating hormone (FSH), 10 µg/mL luteinizing hormone (LH; Stimufol; SPRL Reprobiol), 1 µg/mL oestradiol 17β, and 50 µg/mL gentamycin for 22 h at 38.5 °C with 5% CO<sub>2</sub>. Matured oocytes were then fertilized in a modified Tyrode’s bicarbonate buffered solution (fert-TALP; pH 7.6) containing 10 mg/mL heparin sodium salt (H3125; Sigma), 6 mg/mL BSA, 20 mM penicillamine (P4875; Sigma), 10 mM hypotaurine (H1384; Sigma), and 1 mM adrenaline (E4250; Sigma). A single ejaculate from one Normande bull (Evolution cooperative, France) of proven fertility was used in all the IVF experiments. Frozen–thawed Bovipure-Bovidilute (Nidacon)-treated spermatozoa were coincubated with COCs at an insemination dosage of 2 × 10<sup>6</sup> spermatozoa/mL for 22 h at 38.5 °C in a humidified atmosphere with 5% CO<sub>2</sub>. The day of IVF was defined as Day 0. On Day 1, all presumptive zygotes were denuded and cultured for 8 days in 30 µL of synthetic oviductal fluid (SOF) medium [<xref rid=\"B43-ijms-21-05326\" ref-type=\"bibr\">43</xref>] supplemented with 6 g/L BSA under paraffin oil (Origio, CooperSurgical, Trumbull, USA) at 38.5 °C in a humidified atmosphere containing 5% O<sub>2</sub> and 5% CO<sub>2</sub>.</p><p>The oEVs used were obtained from a pool of bovine oviductal fluids collected at a local commercial slaughterhouse and isolated by centrifugation (12,000× <italic>g</italic>, 15 min) and two successive ultracentrifugations (100,000× <italic>g</italic>, 90 min), then characterized by transmission electron microscopy and western blotting as detailed in our previous study [<xref rid=\"B7-ijms-21-05326\" ref-type=\"bibr\">7</xref>]. Only oviducts at the postovulatory stage of cycle (recently formed corpus luteum, absence of follicles >10 mm; estimated Days 1–5 postovulation) and ipsilateral to ovulation, i.e., at the expected time and site of embryo development, were used. The same pool of oEVs isolated from 12 postovulatory ipsilateral oviducts was used for Experiments 1–2 and ICM-MS analysis. The oEVs sample was assayed for protein concentration (BCA method), aliquoted in small volumes and stored at −80 °C. In Experiments 1 and 2, oEVs were added to the culture medium at a final concentration of 0.05 mg of proteins/mL, starting on Day 1 with no renewal of the culture medium.</p></sec><sec id=\"sec4dot2-ijms-21-05326\"><title>4.2. Assessment of Embryo Development and Quality</title><p>Two successive experiments were conducted by two teams of operators (including one person in common in the two experiments) in two different IVF laboratories (Experiment 1 in the experimental unit of Allice; Experiment 2 in the PRC unit of INRAE, Nouzilly, France), allowing us to provide enough embryos for the evaluation of developmental rates (Experiments 1 and 2), embryo quality (Experiment 1), and lipidomic profiles (Experiment 2), as summarized in <xref ref-type=\"fig\" rid=\"ijms-21-05326-f004\">Figure 4</xref>. Due to the collection of expanded blastocysts at day 7 in Experiment 1, blastocyst hatching was evaluated only in Experiment 2.</p><p>In both Experiments, the number of cleaved embryos was recorded on day 2 and numbers of blastocysts, expanded blastocysts, and hatched blastocysts on days 6, 7, and 8 over 14 replicates in total (7 replicates per Experiment). Expanded blastocysts were distinguished from blastocysts according to the criteria of the International Embryo Transfer Society (IETS; larger blastocoel volume, thinner zona pellucida). In Experiment 1, subgroups of day-7-expanded blastocysts of grade 1 (according to IETS criteria) were submitted to slow freezing and thawing to assess their cryotolerance. Briefly, embryos were washed twice in the commercial Embryo holding medium (EHM, IMV Technologies, L’Aigle, France) then placed by groups of 3–6 in the Embryo freezing medium (EFM, IMV Technologies) containing 1.5 M ethylene glycol and 0.1 M of sucrose for 10 min, then the mixture was placed in a straw. Freezing at 0.3 °C/min was performed up to −32 °C thanks to a Freeze Control cryochamber (Cryologic, Blackburn, Australia), then straws were immersed in liquid nitrogen. For thawing, straws were put in a water bath at 35 °C for 30 s. Embryos were washed for 5 min in EHM then cultured for 2 days in 30 µL of SOF medium supplemented with 1% of postovulatory cow serum and 6 g/L of BSA at 38.5 °C under humidified atmosphere with 5% CO<sub>2</sub> and 5% O<sub>2</sub>. Numbers of re-expanded and hatched blastocysts were recorded at 24 and 48 h after thawing. Percentages of hatching were calculated from the total numbers of frozen–thawed blastocysts. Four replicates with 3–6 expanded blastocysts per replicate were conducted. In addition, in Experiment 1, blastocysts and expanded blastocysts at day 8 were fixed in phosphate-buffered saline (PBS) containing 4% paraformaldehyde (PBS-PAF 4%, 20 min, 34 °C) for evaluation of total cell numbers under confocal microcopy, as described in detail in our previous study [<xref rid=\"B35-ijms-21-05326\" ref-type=\"bibr\">35</xref>]. For statistical analysis, rates of development, blastocyst hatching, and re-expansion/hatching after thawing were compared between groups by Mann–Whitney tests (nonparametric data). Cell numbers per blastocyst were compared by Student t-tests (parametric data).</p></sec><sec id=\"sec4dot3-ijms-21-05326\"><title>4.3. Lipidomic Profiling of Blastocysts and oEVs by Intact Cell MALDI-TOF Mass Spectrometry</title><p>Fresh day-8 blastocysts and expanded blastocysts were washed twice in Tris-sucrose buffer (TSB: 20 mM Tris-HCl, pH 6.8, and 260 mM sucrose) before being individually analyzed for lipidomic profiling by ICM-MS, as described previously [<xref rid=\"B35-ijms-21-05326\" ref-type=\"bibr\">35</xref>]. Briefly, using a videomicroscope, individual embryos were immediately spotted onto a plate (Bruker Daltonics, Germany) and the excess of TSB was removed. A total of 0.5 µL of Methanol 100% was deposed on embryo to promote lipid extraction. After evaporation of the methanol, 2 × 0.5 µL of 2,5-Dihydroxybenzoic acid (DHB) matrix at 15 mg/mL dissolved in 90% acetonitrile/9.8% water/0.2% TFA was overlaid on embryos. For oEV analysis, 0.5 µL of oEV sample (at 11.3 mg of proteins per mL) was spotted with 0.5 µL of DHB matrix; a total of six spots were deposited on the MALDI plate. For all samples, the matrix was allowed to evaporate at room temperature for 30 min. Profiles of embryos and oEVs were acquired using a RapifleX Tissuetyper MALDI-TOF instrument (Bruker Daltonics, Bremen, Germany) equipped with a Smartbeam 3D Nd:YAG (355 mm) laser. Each spectrum was obtained in positive and reflector ion modes, at 10-kHz laser repetition rate, with a sampling rate of 1.25 GS s–1, and collected as a sum of 1000 laser shots in 3 shot steps (total of 3000 spectra). Spectra were accumulated in a random walk-on spot within the 350–900 m/z range. A total of 25 blastocysts (16 control, 9 oEV-treated) and 53 expanded blastocysts (28 control, 25 oEV-treated) were analyzed. All embryo and oEVs spots were analyzed in triplicate. External calibration was followed using a mixture of small molecules (1 µL of DHB matrix with 1 µL of calibrant solution containing Caffein, MRFA peptide, LeuEnkephalin, Reserpin, Bradykinin (fragments 2–9), Angiotensin, and Glu-1 Fibrinopeptide B).</p></sec><sec id=\"sec4dot4-ijms-21-05326\"><title>4.4. Analysis of Lipidomic Data</title><p>The analysis of ICM-MS data was performed using the MALDIquant package adapted for mass spectrometry data [<xref rid=\"B44-ijms-21-05326\" ref-type=\"bibr\">44</xref>] of the R software (version 3.6.2; free software from the R. foundation), as previously described by our team [<xref rid=\"B17-ijms-21-05326\" ref-type=\"bibr\">17</xref>]. To increase mass accuracy (mass error < 0.05%), an internal calibration was subsequently applied to all spectra using flexAnalysis 4.0 software (Bruker, Bremen, Germany) and FlexAnalysis Batch Process (Bruker, Compass 2.0). For all data, a lock mass correction was performed using known PC (34:1) at 760.5851 m/z. The profile spectra were treated for baseline subtraction (SNIP method), smoothing by the Savitzky–Golay algorithm and realignment using prominent peaks and normalization on intensity using the total ion count method. Peaks were detected using a total average spectrum with a signal:background noise > 4. The precision of the ICM-MS acquisitions was determined by calculating the coefficient of variation (CV) from the normalized peak intensity values of the 3–5 technical replicates for each sample. The average CV obtained was 14.6% and 24.8% for analyses on embryos and oEVs, respectively. The MS data did not pass the normality and homogeneity tests. Thus, the nonparametric Kruskal–Wallis test was used for comparison between all groups and the Mann–Whitney test for comparison between treated and control groups at each stage. M/z values were considered differentially abundant between groups with a <italic>p</italic>-value < 0.05. Hierarchical clustering of differential m/z values was performed using Spearman correlations and the gplot package (v 3.0.1.2) of FactoMineR (v 2.1) of the R. software. The differential peaks were annotated based on theoretical masses ± 0.05 m/z using the LIPID MAPS<sup>®</sup> lipidomics gateway database [<xref rid=\"B45-ijms-21-05326\" ref-type=\"bibr\">45</xref>] or using a home databank compiled from the MS identification of lipids in bovine oviductal and follicular fluids [<xref rid=\"B17-ijms-21-05326\" ref-type=\"bibr\">17</xref>,<xref rid=\"B46-ijms-21-05326\" ref-type=\"bibr\">46</xref>].</p></sec></sec></body><back><ack><title>Acknowledgments</title><p>The authors are grateful to Marc Chodkiewicz for critical reading of this paper. M.S.D. thanks the Centre National de la Recherche Scientifique (CNRS) for full-time delegation in the PRC Unit.</p></ack><app-group><app id=\"app1-ijms-21-05326\"><title>Supplementary Materials</title><p>The supplementary materials are available online at <uri xlink:href=\"https://www.mdpi.com/1422-0067/21/15/5326/s1\">https://www.mdpi.com/1422-0067/21/15/5326/s1</uri>.</p><supplementary-material content-type=\"local-data\" id=\"ijms-21-05326-s001\"><media xlink:href=\"ijms-21-05326-s001.zip\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>Conceptualization; M.S.-D, P.M., V.L., and P.S.; methodology; M.S.-D., O.B., D.L.B., D.T., V.L., P.S., and C.B.; software; C.B., V.L., and D.T.; formal analysis; D.T., V.L., D.L.B., P.S., O.B., M.S.-D., and C.B.; writing—original draft preparation; C.B. and M.S.-D.; writing—review and editing; all authors; supervision; M.S.-D. and P.S.; project administration; M.S.-D., B.G., and P.S.; funding acquisition; M.S.-D., B.G., and P.S. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was funded by APIS-GENE under the project name “Embryomimétisme”, with additional funding from INRAE and four breeding companies (Union Evolution, Auriva, Origen+, Elitest). Charles Banliat was funded by Union Evolution and the Association Nationale de la Recherche Technologie (ANRT) as recipient of the Convention Industrielle de Formation par la Recherche N°2017/0684. The RapiFlex MALDI-TOF instrument (Up-Phenotymass project) was financed by the CPER (Contrat Plan Etat Région) Biomédicaments project supported by the “Conseil Régional du Centre”, the “Ministère de l’Education Nationale et de la Jeunesse” and the French National Institute for Agriculture, Food and Environment (INRAE).</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflicts of interest.</p></notes><glossary><title>Abbreviations</title><array orientation=\"portrait\"><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">COCs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cumulus-oocyte complexes</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ICM-MS</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Intact Cell MALDI-TOF—Mass Spectrometry</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">LPC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Lysophosphatidylcholine</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">LPE</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Lysophosphatidylethanolamine</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">oEVs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Oviductal extracellular vesicles</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Phosphatidylcholine</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PE</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Phosphatidylethanolamine</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SM</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sphingomyelin</td></tr></tbody></array></glossary><ref-list><title>References</title><ref id=\"B1-ijms-21-05326\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Coy</surname><given-names>P.</given-names></name><name><surname>Garcia-Vazquez</surname><given-names>F.A.</given-names></name><name><surname>Visconti</surname><given-names>P.E.</given-names></name><name><surname>Aviles</surname><given-names>M.</given-names></name></person-group><article-title>Roles of the oviduct in mammalian fertilization</article-title><source>Reproduction</source><year>2012</year><volume>144</volume><fpage>649</fpage><lpage>660</lpage><pub-id pub-id-type=\"doi\">10.1530/REP-12-0279</pub-id><pub-id pub-id-type=\"pmid\">23028122</pub-id></element-citation></ref><ref id=\"B2-ijms-21-05326\"><label>2.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Hunter</surname><given-names>R.H.</given-names></name></person-group><article-title>Components of oviduct physiology in eutherian mammals</article-title><source>Biol Rev. 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Carvalho</surname><given-names>A.</given-names></name><name><surname>Labas</surname><given-names>V.</given-names></name><name><surname>Blache</surname><given-names>M.C.</given-names></name><name><surname>Banliat</surname><given-names>C.</given-names></name><name><surname>Cordeiro</surname><given-names>L.A.V.</given-names></name><name><surname>Duranthon</surname><given-names>V.</given-names></name><name><surname>Papillier</surname><given-names>P.</given-names></name><etal/></person-group><article-title>Lipid Identification and Transcriptional Analysis of Controlling Enzymes in Bovine Ovarian Follicle</article-title><source>Int. J. Mol. Sci.</source><year>2018</year><volume>19</volume><elocation-id>3261</elocation-id><pub-id pub-id-type=\"doi\">10.3390/ijms19103261</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijms-21-05326-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Heatmap representation of hierarchical clustering of the differentially abundant lipid m/z according to the embryonic stage (blastocyst vs. expanded blastocyst) and exposure to oEVs (+oEVs). Each line corresponds to one molecular species. For a given species, green lines represent higher abundance while red lines represent lower abundance compared with other conditions. Black lines represent the median abundance values. The proximity between the conditions and lipid profiles are shown by the hierarchical trees on the top and left of the heatmap, respectively. A total of 25 blastocysts (16 control, 9 oEV-treated) and 53 expanded blastocysts (28 control, 25 oEV-treated) were analyzed.</p></caption><graphic xlink:href=\"ijms-21-05326-g001\"/></fig><fig id=\"ijms-21-05326-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Volcano plots of lipid m/z significantly affected by oEV supplementation in blastocysts (<bold>1</bold>) and expanded blastocysts (<bold>2</bold>) (<italic>p</italic>-value < 0.05; fold-change >1.5 or <0.67). The fold-change of treated vs. control group was plotted against the −log10 <italic>p</italic>-value. Dots in red, orange, and blue indicate significantly altered PC or PE, SM, and LPC or LPE, respectively. Tables on the right indicate the differentially abundant masses that increased (<bold>1A</bold>,<bold>2A</bold>) or decreased (<bold>1B</bold>,<bold>2B</bold>) in oEV-treated embryos compared to controls. PC, phosphatidylcholine; PE, phosphatidylethanolamine; SM, sphingomyelin; LPC, lysophosphatidylcholine; LPE, lysoPE. Note that the first number in parentheses refer to the total number of carbons and the second to the number of double bonds in all chains. * Phospholipids also detected in oEVs (see <xref ref-type=\"sec\" rid=\"sec2dot3-ijms-21-05326\">Section 2.3</xref>).</p></caption><graphic xlink:href=\"ijms-21-05326-g002\"/></fig><fig id=\"ijms-21-05326-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Relative abundance (mean ± SEM) of lipids significantly affected by oEV supplementation in blastocysts (top) and expanded blastocysts (down) and ordered in increasing m/z values (<italic>p</italic>-value < 0.05; fold-change > 1.5 or <0.67). The m/z cut-off at which lower lipid masses were less abundant after oEV supplementation and a majority of higher masses were more abundant is shown by a vertical purple dotted line. See all intensity values and <italic>p</italic>-values in <xref ref-type=\"app\" rid=\"app1-ijms-21-05326\">supplementary data A</xref>.</p></caption><graphic xlink:href=\"ijms-21-05326-g003\"/></fig><fig id=\"ijms-21-05326-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Experimental design.</p></caption><graphic xlink:href=\"ijms-21-05326-g004\"/></fig><table-wrap id=\"ijms-21-05326-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05326-t001_Table 1</object-id><label>Table 1</label><caption><p>Developmental rates of bovine embryos exposed to oviduct fluid extracellular vesicles (oEVs) during their development in vitro.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">No Experiment</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Treatment</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Number of Cumulus-Oocyte-Complexes (COCs)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cleaved on Day 2</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Blastocyst Yield, N (Mean ± SEM, %)</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">N (Mean ± SEM, %)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Day 6</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Day 7 </th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Day 8 </th></tr></thead><tbody><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Experiment 1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Control</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">374</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">318 (85.3 ± 3.2)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10 (3.6 ± 1.1)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">67 (21.5 ± 3.8)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">105 (33.1 ± 3.8)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">+oEV (treated)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">374</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">316 (85.2 ± 2.4)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18 (4.2 ± 0.6)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">85 (27.2 ± 2.8)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">115 (40.1 ± 2.6)</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Experiment 2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Control</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">587</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">468 (80.2 ± 2.0)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">21 (4.4 ± 1.4)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">77 (15.6 ± 2.8)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">85 (20.7 ± 1.8)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">+oEV (treated)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">583</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">484 (83.8 ± 1.7)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20 (4.5 ± 0.9)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">67 (15.9 ± 2.4)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">65 (17.4 ± 1.9)</td></tr></tbody></table></table-wrap></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751624</article-id><article-id pub-id-type=\"pmc\">PMC7432016</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155514</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05514</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Symptoms of Posttraumatic Stress, Anxiety, Depression, Levels of Resilience and Burnout in Spanish Health Personnel during the COVID-19 Pandemic</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-1541-4383</contrib-id><name><surname>Luceño-Moreno</surname><given-names>Lourdes</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05514\">1</xref><xref rid=\"c1-ijerph-17-05514\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Talavera-Velasco</surname><given-names>Beatriz</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05514\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>García-Albuerne</surname><given-names>Yolanda</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05514\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Martín-García</surname><given-names>Jesús</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05514\">1</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05514\"><label>1</label>Department of Social and Work Psychology and Individual Differences, Faculty of Psychology, Complutense University of Madrid, 28223 Madrid, Spain; <email>yolaga01@ucm.es</email> (Y.G.-A.); <email>jemartin@ucm.es</email> (J.M.-G.)</aff><aff id=\"af2-ijerph-17-05514\"><label>2</label>Department of Education, Faculty of Languages and Education, Nebrija University, 28015 Madrid, Spain; <email>btalavera@nebrija.es</email></aff><author-notes><corresp id=\"c1-ijerph-17-05514\"><label>*</label>Correspondence: <email>lluceno@psi.ucm.es</email>; Tel.: +34-91-934-3174</corresp></author-notes><pub-date pub-type=\"epub\"><day>30</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5514</elocation-id><history><date date-type=\"received\"><day>04</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>28</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>The number of health workers infected with COVID-19 in Spain is one of the highest in the world. The aim of this study is to analyse posttraumatic stress, anxiety and depression during the COVID-19 pandemic. Associations between burnout, resilience, demographic, work and COVID-19 variables are analysed. Cross-sectional data on 1422 health workers were analysed. A total of 56.6% of health workers present symptoms of posttraumatic stress disorder, 58.6% anxiety disorder, 46% depressive disorder and 41.1% feel emotionally drained. The profile of a health worker with greater posttraumatic stress symptoms would be a person who works in the Autonomous Community of Madrid, in a hospital, is a woman, is concerned that a person he/she lives with may be infected, and thinks that he/she is very likely to be infected. The risk variables for anxiety and depression would be a person that is a woman, working 12- or 24-h shifts, and being worried that a family member could be infected. High scores on emotional exhaustion and depersonalization are risk factors for mental health, with resilience and personal fulfilment being protective variables. Data are provided to improve preventive measures for occupational health workers.</p></abstract><kwd-group><kwd>posttraumatic stress disorder</kwd><kwd>anxiety</kwd><kwd>depression</kwd><kwd>burnout</kwd><kwd>resilience</kwd><kwd>health personnel</kwd><kwd>COVID-19</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05514\"><title>1. Introduction</title><p>The World Health Organization declared the COVID-19 outbreak as a pandemic on March 11, 2020. In Europe, Italy and Spain were the first to report a high number of deaths, as well as a rapid increase in admissions to Intensive Care Units (ICU) of patients with symptoms associated with the disease. In May 2020, Spain is one of the top five countries with the highest number of people infected, registering over 242,707 cases as of 12 June 2020, and more than 27,136 deaths [<xref rid=\"B1-ijerph-17-05514\" ref-type=\"bibr\">1</xref>]. In critical pandemic-related situations, research indicates that individuals experience a stress response associated with their fear of contracting the virus from contact with other people or objects. They also have symptoms of posttraumatic stress, such as intrusive thoughts, insomnia or nightmares [<xref rid=\"B2-ijerph-17-05514\" ref-type=\"bibr\">2</xref>]. During the epidemic of Severe Acute Respiratory Syndrome (hereinafter, SARS), a high prevalence of symptoms of posttraumatic stress, anxiety and depression was identified in emergency service professionals including hyperarousal, anger, loss of motivation at work, difficulty concentrating or trouble falling asleep [<xref rid=\"B3-ijerph-17-05514\" ref-type=\"bibr\">3</xref>]. However, not all individuals exposed to high negative impacts or crisis situations develop such symptoms, with resilience being relevant as a protective factor [<xref rid=\"B4-ijerph-17-05514\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijerph-17-05514\" ref-type=\"bibr\">5</xref>]. Resilience, the individual’s ability to deal with adversities as challenges, has been shown to reduce the impact of traumatic events, decreasing the likelihood of developing posttraumatic stress disorders [<xref rid=\"B6-ijerph-17-05514\" ref-type=\"bibr\">6</xref>]. Resilience can be understood as a process of positive adaptation to a stressful situation, in which an interaction between personal resources and the environment is established [<xref rid=\"B7-ijerph-17-05514\" ref-type=\"bibr\">7</xref>]. Resilience varies from person to person and depends on several factors, such as personality or interpersonal and social backgrounds. The strategies to cope with the current pandemic that have been identified are optimism, social support, staying actualized, avoiding information overload and maintaining online communication [<xref rid=\"B8-ijerph-17-05514\" ref-type=\"bibr\">8</xref>]. In healthcare personnel, a key factor for promoting resilience is to increase the sense of control over the adverse situation. For example, perceiving that disease prevention measures can be managed or controlling the possibility of protecting oneself with the resources that health care providers have around them to care for infected patients are some of the strategies that have been adopted in this pandemic [<xref rid=\"B9-ijerph-17-05514\" ref-type=\"bibr\">9</xref>]. In similar critical situations, such as the Severe Acute Respiratory Syndrome (SARS) epidemic, nurses who have shown higher levels of confidence in infection protection and control equipment have shown lower levels of anxiety, negative mood and emotional fatigue [<xref rid=\"B10-ijerph-17-05514\" ref-type=\"bibr\">10</xref>]. Individuals with high levels of resilience have less irritability, less concern for environmental stimuli, better interpersonal relationships, fewer headaches and musculoskeletal pains, and lower levels of depression [<xref rid=\"B11-ijerph-17-05514\" ref-type=\"bibr\">11</xref>]. If these symptoms persist over time, the feeling of a lack of control and uncertainty at work may increase, leading to burnout. This syndrome is related to work. It is characterized by high emotional exhaustion, high levels of depersonalization and low personal accomplishment [<xref rid=\"B12-ijerph-17-05514\" ref-type=\"bibr\">12</xref>]. The person may experience dysphoric symptoms, such as tiredness or emotional exhaustion. The symptoms appear in relation to work situations in individuals who previously did not show psychosocial alterations. In addition, burnout is associated with a decrease in work performance due to negative behaviors towards work [<xref rid=\"B13-ijerph-17-05514\" ref-type=\"bibr\">13</xref>]. In particular, in health workers exposed to traumatic situations during this pandemic, the presence of burnout has been detected, as well as a reduction in the ability to apply coping strategies or negative attitudes towards work. In addition to the symptoms of exhaustion, related to anxiety, depression or other symptoms related to physical pathologies (e.g., cardiovascular problems), burnout can lead to intention to leave the post, which would cause high costs [<xref rid=\"B14-ijerph-17-05514\" ref-type=\"bibr\">14</xref>].</p><p>The speed with which the disease has spread, as well as the state of confinement, has led some researchers to analyse psychological variables resulting from the situation. For example, in a recent study of the general Spanish population, in which 3480 people participated, more than 20% were found to suffer from anxiety, 18.7% revealed symptoms associated with depression, and approximately 16% suffered from posttraumatic stress. In addition, female gender was associated with greater symptomatology in anxiety, depression and posttraumatic stress, while being in the older age group was related to fewer symptoms [<xref rid=\"B15-ijerph-17-05514\" ref-type=\"bibr\">15</xref>]. This data are similar to those obtained in a survey carried out in the general population in China, in which 1210 people participated, of which 16.5% reported moderate to severe depressive symptoms and 28.8% moderate to severe anxiety [<xref rid=\"B16-ijerph-17-05514\" ref-type=\"bibr\">16</xref>]. In other European countries, such as Italy, the general population has shown high levels of anxiety, depression and stress, highlighting a higher prevalence in women, people with negative affect and individuals who had family members infected or had to work away from home [<xref rid=\"B17-ijerph-17-05514\" ref-type=\"bibr\">17</xref>].</p><p>However, despite the state of confinement, certain professional groups, as in the case of health care personnel, have performed their jobs under great stress for weeks. These professionals, together with security forces, funeral staff and others, have been highly exposed to the virus and situations with high emotional impact. They are thus more likely to suffer mental problems, especially in the first three months in which symptoms of posttraumatic stress, affective disorders, burnout or others may increase [<xref rid=\"B18-ijerph-17-05514\" ref-type=\"bibr\">18</xref>]. Spain leads the ranking of the number of health professionals infected with COVID-19 during their work. More than 40,000 health workers have tested positive for COVID-19 [<xref rid=\"B19-ijerph-17-05514\" ref-type=\"bibr\">19</xref>]. The critical situation requires the study of the psychological state of health professionals, and the potential harm to mental health caused by their exposure during the pandemic. A recent study on stress in 958 health workers from the city of Wuhan indicates that more than half had symptoms related to anxiety and depression. Specifically, 54% of the total sample experienced symptoms of anxiety, and 58% of depression, with the prevalence of stress being higher than that previously detected in health professionals who had to deal with the SARS virus [<xref rid=\"B20-ijerph-17-05514\" ref-type=\"bibr\">20</xref>]. In a study involving 1257 health workers from China, of which 760 were from Wuhan, 71.5% also showed symptoms of distress, 44.6% of anxiety, 50.4% of depression and 34% of insomnia. These symptoms were more severe in nursing staff, front-line professionals and those who worked at the epicentre of the COVID-19 outbreak (Wuhan) [<xref rid=\"B21-ijerph-17-05514\" ref-type=\"bibr\">21</xref>]. Similar results have been found in other European countries such as Germany, where health workers, specifically nurses, have reported high levels of stress, emotional fatigue and depressive symptoms [<xref rid=\"B22-ijerph-17-05514\" ref-type=\"bibr\">22</xref>]. The impact of the situation on health workers may even produce symptoms of psychotic disorder, even when this is not evident in their clinical history [<xref rid=\"B23-ijerph-17-05514\" ref-type=\"bibr\">23</xref>].</p><p>Additionally, the stress generated by the possibility of being infected with the disease also adds to the rest of the stressful conditions of these professionals. During the influenza A (H1N1) pandemic, health workers were reported to be twice as likely to be infected through contact with patients [<xref rid=\"B24-ijerph-17-05514\" ref-type=\"bibr\">24</xref>]. Working on the front line with infected people increases the likelihood of becoming infected, especially in this group [<xref rid=\"B25-ijerph-17-05514\" ref-type=\"bibr\">25</xref>]. Health professionals must work in extreme conditions, in situations where resources can be scarce. For example, they must take care of a high number of patients in disaster or epidemic situations, often without sufficient beds or staff [<xref rid=\"B26-ijerph-17-05514\" ref-type=\"bibr\">26</xref>]. In addition to their jobs in hospitals, Primary Care or Intensive Care Units, they also work in nursing homes, where the disease has had a major impact in Spain. Some authors indicate that risk factors for infection may include: (a) factors related to organization, such as the rapid development of new tasks and procedures, a shortage of protective material, frequent equipment changes or the high risk of increased demand for care by other different pathologies, in addition to COVID-19; (b) watching patients die alone; (c) fear of infecting loved ones or having to practice social distancing for an indefinite period to protect them; and (d) prioritization of care for certain patients [<xref rid=\"B27-ijerph-17-05514\" ref-type=\"bibr\">27</xref>].</p><p>On the basis of the above, health professionals must deal with possible psychological, work-related consequences during the COVID-19 crisis, such as posttraumatic stress, anxiety, depression or burnout [<xref rid=\"B28-ijerph-17-05514\" ref-type=\"bibr\">28</xref>].The aim of this study is to assess the symptoms of posttraumatic stress, anxiety, depression, levels of burnout and resilience in the Spanish health workers during the COVID-19 pandemic. It also aims to evaluate the relationship between each of the variables (demographic, work, COVID-19, burnout and resilience) and the symptoms of posttraumatic stress, depression and anxiety. It is equally intended to identify which variables have the most weight in each of the three categories (posttraumatic stress, anxiety and depression).</p><p>The main hypotheses of this study would be the following: (a) health care workers evaluated will have high levels of post-traumatic stress, anxiety, depression and burnout; (b) resilience factor will be associated with lower burnout and with symptoms related to the above three categories; (c) the female gender will be associated with symptoms of the three categories; (d) older health care personnel would have fewer symptoms; (d) health care workers in contact with other infected patients, who are highly likely to become infected and have fewer resources or protective equipment, will have more symptoms of post-traumatic stress disorder, anxiety, depression and burnout.</p></sec><sec id=\"sec2-ijerph-17-05514\"><title>2. Materials and Methods</title><sec id=\"sec2dot1-ijerph-17-05514\" sec-type=\"subjects\"><title>2.1. Participants</title><p>The sample includes 1539 subjects, recruited by non-probabilistic sampling. As criteria of exclusivity, the participants had to be in contact with patients of COVID-19. Finally, 117 were eliminated because they were not health personnel in contact with these patients. The sample of the study was made up of 1228 women (86.4%) and 194 men (13.6%). The mean age was 43.88 (<italic>SD</italic> = 10.82, ranging between 19 and 68).</p></sec><sec id=\"sec2dot2-ijerph-17-05514\"><title>2.2. Measurement Variables and Instruments</title><p>The following instruments were used:</p><p>Demographic, job-related and variables specific to COVID-19. Due to the importance of understanding how the disease affects these professionals, the researchers of this study collected information related to demographic variables, associated with the job, changes of residence, possible contact with people during work, COVID-19 tests, hospitalization, isolation, protective equipment, concern over becoming infected, concern that a family member and/or someone with whom they are living may be infected.</p><p>Posttraumatic stress: The Impact of Event Scale-Revised (IES-R) was used [<xref rid=\"B29-ijerph-17-05514\" ref-type=\"bibr\">29</xref>]. This scale was used to assess the emotional distress that accompanies a stressful life event. It is made up of 22 items distributed in three scales: intrusion (7 items, an example of this scale would be “I thought about it even when I did not want to”); avoidance (8 items, an example is “I tried not to think about the event”); and hyperarousal (7 items, a sample item is “I was easily startled and scared”). In relation to posttraumatic stress, a score of 20 was considered as the cut-off point. A total score greater than or equal to 20 on the IES-R is associated with a diagnosis of psychiatric disorder and a mean score less than or equal to 14 is associated with a non-diagnosis of a psychiatric disorder [<xref rid=\"B30-ijerph-17-05514\" ref-type=\"bibr\">30</xref>]. It shows adequate psychometric properties in its Spanish adaptation, confirming the solution of the three factors mentioned and a reliability greater than 0.70 in all subscales.</p><p>Anxiety and depression: The Spanish adaptation of the Hospital Anxiety and Depression Scale (HADS) instrument was used [<xref rid=\"B31-ijerph-17-05514\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijerph-17-05514\" ref-type=\"bibr\">32</xref>]. Consisting of 14 items that correspond to two subscales: anxiety and depression, with 7 items each, on a Likert 0–3 response scale. An example of an item in the anxiety scale is “I feel tense and nervous” and “I feel slow and awkward” in the depression scale. It evaluates symptoms of anxiety and depression in patients and in the general population. The cut-off values are between 7 and 13 possible or probable presence of a mood disorder, and greater than 14–15 for severe disorder (the range ranges from 0 to 21) for both anxiety and depression scales. Accordingly, to analyse the prevalence of symptoms in this study, the variables have been categorized as follows: < 6.99 no disorder, 7–13.99 possible or probable, > 14 severe disorder. The higher the score, the greater the prevalence of symptoms of anxiety and depression. In its Spanish adaptation, it has shown adequate psychometric properties, confirming the validity of two factors and an internal consistency of 0.77 in anxiety and 0.71 in the depression subscale [<xref rid=\"B32-ijerph-17-05514\" ref-type=\"bibr\">32</xref>]. </p><p>Burnout: The Spanish adaptation of the Maslach Burnout Inventory-MBI-HSS instrument was applied, which assesses Burnout Syndrome [<xref rid=\"B33-ijerph-17-05514\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijerph-17-05514\" ref-type=\"bibr\">34</xref>]. It consists of 22 items of seven response options on a Likert scale from 0 (never) to 6 (every day). The cut-off points for health personnel in the Spanish sample were used to analyse the prevalence of the different components of burnout in this study: emotional fatigue (low < 22, medium 22–23, high > 31); depersonalization (low < 7, medium 7–13, high > 16); and personal accomplishment (low < 30, medium 30–35, high > 35). It presents adequate psychometric characteristics, showing an appropriate fit for the three-factor solution and an internal consistency greater than 0.71 in all subscales [<xref rid=\"B35-ijerph-17-05514\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijerph-17-05514\" ref-type=\"bibr\">36</xref>]. An example of an item in the emotional exhaustion scale is “I feel emotionally drained for my work”, “I think I treat some people like impersonal objects” in the depersonalization scale, and “I easily understand how people feel” in the personal accomplishment scale.</p><p>Resilience: The Spanish adaptation of the Brief Resilience Scale (BRS) was used [<xref rid=\"B37-ijerph-17-05514\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijerph-17-05514\" ref-type=\"bibr\">38</xref>]. It evaluates the resilience construct, understood as the subject’s ability to deal with environmental obstacles and recover from stressful circumstances. It is made up of 6 items that are answered on a Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). The higher the score, the greater the degree of resilience the person shows to deal with adversities. An example of an item in this scale is “I tend to recover quickly after going through difficult times”. The Spanish adaptation presents adequate psychometric properties, corroborating the single-factor solution and an internal consistency of 0.83.</p></sec><sec id=\"sec2dot3-ijerph-17-05514\"><title>2.3. Procedure</title><p>The approval of the Deontological Committee of the Faculty of Psychology of the Complutense University of Madrid (ref. Pr_2019_038; 01/04/2020) was obtained before beginning the study. Researchers contacted both the coordinators and trade unions of health centres to inform them of this study. Due to lockdown, data were collected by means of an online survey, from 1 to 30 of April 2020, which included the instruments described above. Before starting the survey, participants had to give their informed consent in order to continue. Informed consent included the purpose of the study, those responsible for it and information on the confidentiality of the data, anonymity and the legal clause on personal data protection. Before completing the survey submission, participants were required to respond to all items. The completion time for all items was approximately 15 min.</p></sec><sec id=\"sec2dot4-ijerph-17-05514\"><title>2.4. Data Analysis</title><p>The analyses were carried out with the SPSS 26 statistical package. The proportion of cases with symptoms of the disorders mentioned above was analysed. Descriptive analyses (frequencies, mean, standard deviation) were performed for symptoms associated with posttraumatic stress disorder, anxiety, depression, burnout and resilience. Linear regression equations were used to evaluate the relationship between each of the variables (demographic, work, COVID-19, burnout and resilience) irrespective of the symptoms (posttraumatic stress, anxiety and depression), using the R<sup>2</sup> value and the standardized β coefficient. The objective was to calculate the impact of posttraumatic stress, anxiety and depression on each of the variables. Dummy variables were used for this. Finally, linear regression models were used to see which variables (demographic, work, COVID-19, burnout and resilience) were jointly related to symptoms of posttraumatic stress, anxiety and depression. The model was estimated by least squares, using the forward extraction method. </p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05514\"><title>3. Results</title><sec id=\"sec3dot1-ijerph-17-05514\"><title>3.1. Analysis of the Proportion of Health Care Cases with Symptoms Associated with Possible Posttraumatic Stress Disorders, Anxiety, Depression and Burnout</title><p>Analyses were carried out to evaluate the proportion of health care cases with symptoms associated with possible posttraumatic stress disorder, anxiety, depression and burnout at their different severity levels. The results of both genders are shown in <xref rid=\"ijerph-17-05514-t001\" ref-type=\"table\">Table 1</xref>. There are gender differences in symptoms of post-traumatic stress disorder, anxiety and depression. In addition, there are differences between men and women in depersonalization scale of burnout.</p></sec><sec id=\"sec3dot2-ijerph-17-05514\"><title>3.2. Data of Internal Consistency</title><p>The data of internal consistency and correlations between the factors evaluated with the instruments used are represented in <xref rid=\"ijerph-17-05514-t002\" ref-type=\"table\">Table 2</xref>.</p></sec><sec id=\"sec3dot3-ijerph-17-05514\"><title>3.3. Job-Related Sociodemographic Variables and Symptoms of Posttraumatic Stress, Anxiety and Depression</title><p>Regarding gender and age, being a woman is positively and significantly associated with posttraumatic stress, anxiety and depression, while age is negatively and significantly associated with symptoms of posttraumatic stress and anxiety. Working outside the Autonomous Community of Madrid and in any centre other than primary care, hospital, nursing home or day centres (other category) is negatively and significantly related to posttraumatic stress. Possessing doctoral and postgraduate studies is negatively and significantly associated with anxiety and posttraumatic stress, respectively. Being a member of the non-supervisory staff is positively and significantly associated with posttraumatic stress, just as being a doctor is negatively and significantly associated with symptoms of all the evaluated categories. The caregiver position is negatively and significantly associated with anxiety and depression. Working a fixed shift in the afternoon is positively and significantly associated with posttraumatic stress, anxiety and depression, while working a night shift is only positively and significantly associated with posttraumatic stress. With respect to rotating shifts, the late-night shift and on-call, or 12/24 h shifts are positively and significantly associated with posttraumatic stress and depression, respectively. Working part-time is positively and significantly associated with all three categories (posttraumatic stress, anxiety, and depression). Having a permanent statutory (civil servant) contract and having a training contract are negatively and significantly associated with posttraumatic stress and depression, respectively. Finally, the number of hours worked per week is positively and significantly associated with depression, and the number of on-call hours performed per month is positively and significantly associated with posttraumatic stress and depression (see <xref rid=\"ijerph-17-05514-t003\" ref-type=\"table\">Table 3</xref>).</p></sec><sec id=\"sec3dot4-ijerph-17-05514\"><title>3.4. Data on Information about COVID-19, Burnout and Resilience and its Association with Symptoms of Posttraumatic Stress, Anxiety and Depression</title><p>Changing address or living with people who are at risk is positively related to symptoms of posttraumatic stress, anxiety or depression. On the other hand, having personal protective equipment and not being very concerned that family members are infected is negatively related to symptoms of posttraumatic stress, anxiety or depression. Hospitalization for symptoms of COVID-19 and isolation due to possible contagion of the disease is positively related to posttraumatic stress. Thinking that becoming infected with COVID-19 is highly unlikely is negatively related to symptoms of posttraumatic stress and anxiety. Emotional fatigue and depersonalization are positively and significantly related to symptoms of posttraumatic stress, anxiety and depression, while personal accomplishment is negatively and significantly related to symptoms of anxiety and depression. Finally, resilience is associated in a negative and significant way with symptoms from all the evaluated categories. On the other hand, these professionals present moderate levels of resilience, while the highest possible score on resilience is 6, the mean score for these individuals is 3.02 (<italic>SD</italic> = 0.39), therefore indicating moderate levels. All associations can be seen in <xref rid=\"ijerph-17-05514-t004\" ref-type=\"table\">Table 4</xref>. </p></sec><sec id=\"sec3dot5-ijerph-17-05514\"><title>3.5. Regression Models for Posttraumatic Stress, Anxiety and Depression</title><p>As shown in <xref rid=\"ijerph-17-05514-t005\" ref-type=\"table\">Table 5</xref>, the posttraumatic stress symptom model was significant, explaining 39.6% of the variance (F(17, 1405) = 54,022, <italic>p</italic> < 0.001). It was also significant for anxiety symptoms, explaining 40.2% of the variance (F(12, 1412) = 78.593, <italic>p</italic> < 0.001). In relation to the depression model, it was significant, explaining 39.3% of the variance (F(14, 1408) = 64.932, <italic>p</italic> < 0.001). The variables common to the three models were emotional fatigue, depersonalization, resilience, gender, and concern that someone with whom they live could be infected. </p><p>The variables positively related to posttraumatic stress are emotional exhaustion, depersonalization, working in the Autonomous Community of Madrid, having a primary education, working in a hospital, being very concerned that someone with whom they live may become infected and thinking that there is a high risk of also becoming infected with COVID-19. On the other hand, the variables negatively related to posttraumatic stress are resilience, being a man, having a doctor’s degree, living with an unmarried partner, being a doctor or having another profession (mainly pharmacist or psychologist, but not a nurse, nursing assistant or caregiver), having protective equipment at work, not being concerned that someone you live with can be infected with the disease and the number of people you live with. </p><p>The variables positively and significantly related to anxiety would be emotional exhaustion, depersonalization, 12- or 24-h shifts or on-call hours and being very concerned that someone with whom they live could be infected. The variables negatively and significantly related to anxiety would be resilience, being a man, being separated, working in nursing homes or day centres, being a doctor, having a rotating morning–afternoon shift and not having been isolated due to COVID-19. </p><p>Finally, having symptoms of depression is positively and significantly related to: emotional exhaustion, depersonalization, 12- or 24-h shifts or on-call hours, the number of guards per month, being very concerned that someone with whom you live may be infected, not having a family and thinking that it is very likely that you will be infected with COVID-19. The variables negatively and significantly related to depression would be personal fulfilment, resilience, being a man and having a fixed or training contract.</p></sec><sec id=\"sec3dot6-ijerph-17-05514\"><title>3.6. Regression Models for Posttraumatic Stress, Anxiety and Depression, Separated by Gender</title><p>The gender-differentiated posttraumatic stress models were significant, in both men and women, explaining 35.2% of the variance in women (F(13, 1210) = 50,667, <italic>p</italic> < 0.001) and 53.7% in men (F(9, 183) = 23,548, <italic>p</italic> < 0.001). </p><p>On the anxiety scale, the models were significant, explaining 36.3% of the variance in women (F(11, 1212) = 64,280, <italic>p</italic> < 0.001) and 62.7% in men (F(6,168) = 52,066, <italic>p</italic> < 0.001). </p><p>For the depression scale, the models were significant, explaining 37.73% of the variance in women (F(14, 1223) = 52.310, <italic>p</italic> < 0.001). <xref rid=\"ijerph-17-05514-t006\" ref-type=\"table\">Table 6</xref> and <xref rid=\"ijerph-17-05514-t007\" ref-type=\"table\">Table 7</xref> show the regression models of post-traumatic stress, anxiety and depression, differentiated by gender.</p><p>In relation to the gender-differenciated models, in women (see <xref rid=\"ijerph-17-05514-t006\" ref-type=\"table\">Table 6</xref>):</p><p>The symptoms of posttraumatic stress are positively and significantly related to emotional exhaustion, depersonalization, working in a hospital, being very concerned that someone with whom they live may become infected and thinking that becoming infected with COVID-19 is very likely. On the other hand, the variables negatively related to posttraumatic stress are personal accomplishment, resilience, living with and unmarried partner, working in nursing homes, being a doctor or having another profession (mainly pharmacist or psychologist, but not a nurse, nursing assistant or caregiver) and thinking that is very unlikely to be infected with COVID-19. </p><p>The variables positively and significantly related to anxiety would be emotional exhaustion, depersonalization, being very concerned that someone you live with can be infected with the disease and change of residence through fear of infecting family members. The variables negatively and significantly related to anxiety would be resilience, being separated, working in nursing homes and not being at all concerned that someone you live with can be infected with the disease.</p><p>Having symptoms of depression is positively and significantly related to emotional exhaustion depersonalization, being a nurse, 12- or 24-h shifts or on-call hours, those who live with people who are at risk and being very concerned over a possible infection of a family member they do not live with. The variables negatively and significantly related to depression would be personal accomplishment, resilience, being a doctor, having a fixed or training contract.</p><p>In relation to the gender-differentiated models, in men (see <xref rid=\"ijerph-17-05514-t007\" ref-type=\"table\">Table 7</xref>):</p><p>The symptoms of posttraumatic stress are positively and significantly related to emotional exhaustion, having a primary education and being very concerned that someone with whom they live may become infected. On the other hand, the variables negatively related to posttraumatic stress are resilience, having a doctor’s degree, not having been isolated due to COVID-19 and not being concerned that someone with whom you live with can be infected with the disease. The variables positively and significantly related to anxiety would be emotional exhaustion, being a nurse and having been isolated due to COVID-19. The variables negatively and significantly related to anxiety would be resilience, having a training contract and having a statutory fixed-term employment. </p><p>Having symptoms of depression is positively and significantly related to emotional exhaustion depersonalization and being very concerned that someone with whom they live may become infected. The variables negatively and significantly related to depression would be personal accomplishment, resilience, having a doctor’s degree, having a training contract, not having been isolated due to COVID-19 and not having been hospitalized for symptoms compatible with those of coronavirus.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05514\"><title>4. Discussion</title><p>This research aimed to assess the symptoms of posttraumatic stress, anxiety, depression, burnout and resilience in Spanish health workers during the COVID-19 pandemic. It was also aimed at evaluating the relationship between each of the variables independently (demographic, work, COVID-19, burnout and resilience) and the symptoms of posttraumatic stress, depression and anxiety, as well as the variables that (together) carry more weight in each of the three categories (posttraumatic stress disorders, anxiety and depression). </p><p>The results show that 56.6% of health workers present symptoms of posttraumatic stress disorder. The number having a possible anxiety disorder is 58.6%, with 20.7% having a severe disorder. Equally, a high percentage, specifically 46%, would have a possible depressive disorder and 41% feel emotionally drained. In this sense, the first hypothesis would be fulfilled, although it would be necessary to make a thorough evaluation to determine a clinical diagnosis. Most workers present probability of developing a posttraumatic stress disorder, anxiety or depression. During the Middle East Respiratory Syndrome (MERS) or Ebola crises, among others, health professionals reported a higher number of symptoms related to posttraumatic stress [<xref rid=\"B39-ijerph-17-05514\" ref-type=\"bibr\">39</xref>], so it is necessary to pay attention to the increase in these symptoms, even more so in the situation of the COVID-19 pandemic that has not yet subsided. The demographic variables show that having a doctoral or postgraduate degree represent protective variables of anxiety and posttraumatic stress, respectively. In addition, lower-level workers show more symptoms of posttraumatic stress. This result may be due to the fact that, in lower-level jobs, control over procedures and decision-making capacity is lower than in other higher-level positions. Some authors have shown that the main differences among professions regarding the symptomatology evaluated during the COVID-19 pandemic apply to nurses and other positions, such as doctors. Nurses present more symptoms of anxiety and depression [<xref rid=\"B40-ijerph-17-05514\" ref-type=\"bibr\">40</xref>]. These differences may be associated with the contact of these professionals with infected patients. On the other hand, being a woman is associated with greater symptoms of posttraumatic stress, anxiety or depression in the sample of health workers evaluated. Younger health workers show greater levels of posttraumatic stress and anxiety. This may be due to a lack of work experience in similar stressful situations. Another possible reason is that, during the current pandemic, the lack of health care staff has required that senior students or people with fewer experience have had to deal with the demands of the COVID-19 patients. The data obtained in this study on the gender and age variables coincide with the findings of other studies in health personnel from different countries [<xref rid=\"B41-ijerph-17-05514\" ref-type=\"bibr\">41</xref>,<xref rid=\"B42-ijerph-17-05514\" ref-type=\"bibr\">42</xref>]. Some authors suggest that, both in the current situation due to COVID-19 and in similar previous situations, symptoms of stress, anxiety and depression generally increase in health professionals and also coincide in pointing out that women present more symptoms than men do. Regarding gender differences, the data obtained in this study may be due to the high number of women in positions such as nurse or nursing assistant. On the other hand, in mood disorders, which have a high comorbidity with those of anxiety, there is a high prevalence of women compared to men. For example, women present more rumination and there are hormonal differences that can explain these results [<xref rid=\"B43-ijerph-17-05514\" ref-type=\"bibr\">43</xref>]. Variables related to jobs show that those health workers who have part-time jobs have more symptoms of posttraumatic stress, anxiety and depression. The shifts most related to psychological problems are the night, afternoon and afternoon–night rotating shifts. In this regard, similar results have been identified with health personnel that indicate the association between working the night shift and having gastrointestinal problems, hormonal problems, and changes in mood and cognitive state, among others [<xref rid=\"B44-ijerph-17-05514\" ref-type=\"bibr\">44</xref>]. Regarding the work shift, other authors specify that there is a greater risk of having symptoms of depression as the number of days worked in the night shift increases [<xref rid=\"B45-ijerph-17-05514\" ref-type=\"bibr\">45</xref>]. Doctors have fewer symptoms of posttraumatic stress, anxiety, or depression, and caregivers have fewer symptoms of anxiety and depression. Health workers with a lower job category have more symptoms of posttraumatic stress, while those who work more hours a week have more symptoms associated with depression. Health workers who do more on-call hours a month have more symptoms of posttraumatic stress and depression. Therefore, the hours of rest for these professionals must be respected.</p><p>In relation to the information collected on COVID-19, it should be noted that the health workers who have had to change their residence due to the pandemic have been isolated due to possible contagion, and those who live with people who are at risk or think they may infect other people have more symptoms of posttraumatic stress, anxiety or depression, although these differences were not statistically significant in depression for the two variables: being isolated due to possible contagion and the likelihood of becoming infected with COVID-19. Regarding the hypothesis related to these variables, it would be partially fulfilled, since the association between the possibility of infection and the symptoms of depression would not be significant. Recent research indicates that one of the greatest concerns of health personnel is the possibility of infecting others, especially family members [<xref rid=\"B46-ijerph-17-05514\" ref-type=\"bibr\">46</xref>]. Believing that they are very unlikely to be infected with COVID-19 is related to fewer symptoms of posttraumatic stress and anxiety. On the other hand, as proposed in the study hypothesis, those health workers who have personal protective equipment to cope with the disease have fewer symptoms of posttraumatic stress, anxiety or depression. These results may be due to the fact that, on the one hand, contact with people who may be infected is a risk factor for imminent contagion among health workers, as a result of the high transmission of the disease [<xref rid=\"B47-ijerph-17-05514\" ref-type=\"bibr\">47</xref>]; on the other hand, the use of personal protective equipment is essential to be able to work and treat patients with COVID-19 [<xref rid=\"B48-ijerph-17-05514\" ref-type=\"bibr\">48</xref>].</p><p>The profile of a health worker with greater symptoms of posttraumatic stress would be a person who works in the Autonomous Community of Madrid, in a hospital, is a woman, has primary studies, worries that their family members may become infected and thinks that they are very likely to be infected with COVID-19. The protective variables of suffering posttraumatic stress symptoms are being a man, having a doctor’s degree, living with a partner (not married), being a doctor or working in “another position” (a category made up mainly of pharmacists and psychologists), having protective equipment at work, not being concerned about infecting the people with whom they live and not living alone. The risk variables associated with anxiety symptoms are being a woman, having a 12- or 24-h on-call shift and being worried that a person he/she lives with could be infected. Additionally, the following protective factors have been identified: being a man, being separated, working in nursing homes or day centres, being a doctor, having a rotating morning–afternoon shift and/or not being isolated by COVID-19. The profile of the health worker with greater symptoms of depression is being a woman, working 12- or 24-h shifts or on-call hours, the number of on-call hours per month, thinking that they are very likely to become infected with COVID-19, being worried about infecting someone with whom they live and having no family. The protective variables of depression are being a man, having a fixed-term or training contract, feeling professionally accomplished and not living with people who are at risk. Presenting high scores in emotional exhaustion and depersonalization are risk factors for posttraumatic stress, anxiety and depression. However, resilience would be a protective variable that reduces symptoms in all three disorders, and personal accomplishment would be a protective variable against depressive symptoms. One study points out that, in 2003, during the SARS epidemic, health personnel showed symptoms related to posttraumatic stress disorder and, in general, higher levels of psychological stress [<xref rid=\"B49-ijerph-17-05514\" ref-type=\"bibr\">49</xref>]. Recent research has indicated that, during the influenza A (H1N1) outbreak, resilience levels had a direct influence on the psychological health of health personnel [<xref rid=\"B50-ijerph-17-05514\" ref-type=\"bibr\">50</xref>]. </p><p>Regarding the gender-differentiated models, being a nurse is associated with symptoms of mental disorder, specifically depression in women and anxiety in men. In men, depersonalization and personal accomplishment are only associated with depression (although this relationship is not significant), while in women it is associated with the three scales of burnout. That is to say, women who have high scores in depersonalization would have more symptoms of mental disorder than men. </p><p>In men, personal accomplishment would not be a protective variable, while for women it would be a protective factor for posttraumatic stress, anxiety and depression. Having a primary education would be a risk variable for posttraumatic stress and having doctoral studies would be a protective variable of posttraumatic stress and depression, but only in men.</p><p>In relation to the type of contract, having a statutory fixed-term employment contract is negatively related to anxiety in men, not being a relevant variable for women. The type of shift is a relevant variable for women, but not for men. Specifically, 12- or 24-h shifts are positively related to depression in women, but not in men. Having a fixed contract is a more relevant variable for women than for men, since it is a protective variable of depression in women. With regard to the foregoing, one of the main results of this study is that the levels of resilience of the healthcare workers evaluated are moderate. Taking into account that resilience is presented as a possible protective factor of symptoms of posttraumatic stress disorder, anxiety and depression, the need to promote resilience among health personnel is highlighted. Different studies have pointed out various measures to promote resilience among these professionals during the COVID-19 pandemic. These include the following: providing psychological training to healthcare workers so that they can help patients and encouraging support within the organization by the network of personnel and train communication [<xref rid=\"B51-ijerph-17-05514\" ref-type=\"bibr\">51</xref>]. The model of intervention in psychological resilience based on peer support (Battle Buddies) developed by the US military should be highlighted. This model requires a close support partner as well as a designated mental health consultant to facilitate training in stress inoculation methods and to coordinate referral to the outpatient psychological consultation [<xref rid=\"B52-ijerph-17-05514\" ref-type=\"bibr\">52</xref>]. It might be interesting to introduce the elements mentioned in the Spanish health care system to establish measures to promote resilience in possible future waves of COVID-19. In this sense, the hypothesis proposed at the beginning would be fulfilled, since resilience would be a protective factor for such symptoms. In relation to burnout, it should be noted that a large percentage of these professionals have high scores in emotional exhaustion, but low in depersonalization and very high in personal accomplishment. The first study hypothesis, therefore, would be partially fulfilled, since it was expected that the workers would have low levels of personal accomplishment. This could be explained by the fact that health professionals have been intensely involved with patients in this situation, have felt valued by patients and society, and have realized the great importance of their profession, which may have had a very positive influence on their personal fulfilment. The variable with the greatest weight in the regression models is emotional fatigue. Hence, preventive measures to reduce this should be implemented for these workers. Emotional fatigue is the dimension that has the greatest relevance compared to depersonalization and personal fulfilment, within the burnout construct [<xref rid=\"B53-ijerph-17-05514\" ref-type=\"bibr\">53</xref>]. Other authors have also identified high levels of fatigue and negative emotions in health personnel from emergency teams in coping with the COVID-19 situation [<xref rid=\"B54-ijerph-17-05514\" ref-type=\"bibr\">54</xref>].</p></sec><sec id=\"sec5-ijerph-17-05514\"><title>5. Limitations</title><p>The study does have some limitations. The data were obtained using an online tool and people not familiar with the web could not be included in this study. In addition, the survey was carried out at the peak of the pandemic in Spain: the continuous exposure to negative stimuli and the constant information in the media about the state of health care workers and those infected and deceased by COVID-19 may have had an influence in the perception of anxiety and depression levels, due to the feeling of fear experienced [<xref rid=\"B55-ijerph-17-05514\" ref-type=\"bibr\">55</xref>].</p><p>Furthermore, there is a high proportion of women compared to men. Other studies have shown the same limitation [<xref rid=\"B56-ijerph-17-05514\" ref-type=\"bibr\">56</xref>,<xref rid=\"B57-ijerph-17-05514\" ref-type=\"bibr\">57</xref>]. In this case, one of the main reasons for this difference is that, in many positions, such as nurses and nursing assistants, the majority of the positions are occupied by women. Another limitation has to do with the cross-sectional design of the study: the pandemic has not yet finished and its influence on mental health cannot be reflected in this research, so it would also be advisable to carry out a longitudinal study that evaluates the evolution over time of the symptoms assessed in this work. On the other hand, there has not been a previous situation in Spain in which there has been a lockdown, and it is likely that after its ending, the levels of experienced symptomatology will be lower. However, during the SARS crisis, other authors have found that the symptoms of psychological problems after the quarantine period of the disease have lasted up to three years later [<xref rid=\"B3-ijerph-17-05514\" ref-type=\"bibr\">3</xref>,<xref rid=\"B58-ijerph-17-05514\" ref-type=\"bibr\">58</xref>]. In the long term, the effects of posttraumatic stress disorder, anxiety and depression will depend on the possible outbreaks of COVID-19. The measures currently being taken in order to adapt the work place to the new situation (such as providing protective equipment or increasing the number of healthcare professionals) are relevant for mitigating these symptoms. If the appropriate actions to protect health care providers are not taken, they may make medical errors in the future, present higher burnout levels associated with depressive symptoms, anxiety, suicidal ideation, have poorer interpersonal relationships or develop substance abuse [<xref rid=\"B59-ijerph-17-05514\" ref-type=\"bibr\">59</xref>]. Therefore, a follow-up study along the next few months becomes necessary.</p></sec><sec sec-type=\"conclusions\" id=\"sec6-ijerph-17-05514\"><title>6. Conclusions</title><p>This research presents a detailed description of the association between different variables and symptoms of posttraumatic stress, anxiety and depression. Previous studies for Spanish health professionals to evaluate these characteristics, including resilience, and its associations with the described variables, have not been found. Among the main uses for this study is the description of the profiles of Spanish healthcare providers that present a greater risk of suffering from post-traumatic stress, anxiety and depression, so more specific intervention measures can be designed to reduce these symptoms. On the other hand, resilience is a protective factor of the mentioned symptoms, so it would be advisable to include the promotion of resilience in the design of interventions to reduce stress, as other authors point out [<xref rid=\"B60-ijerph-17-05514\" ref-type=\"bibr\">60</xref>]. The information presented is relevant in order to protect the health of those who care for patients in future waves of COVID-19 or similar situations. It would be useful to consider the results of this study in the design of future longitudinal research that analyse the evolution of these symptoms and the risk profiles described.</p></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization, L.L.-M., B.T.-V. and J.M.-G.; methodology, L.L.-M., B.T.-V. and J.M.-G.; software, L.L.-M., B.T.-V. and J.M.-G.; validation, L.L.-M., B.T.-V. and J.M.-G.; formal analysis, L.L.-M., B.T.-V. and J.M.-G.; investigation, L.L.-M., B.T.-V. and J.M.-G.; resources, L.L.-M., B.T.-V., J.M.-G. and Y.G.-A.; data curation, L.L.-M., B.T.-V., J.M.-G. and Y.G.-A.; writing—original draft preparation, B.T.-V., J.M.-G. and L.L.-M.; writing—review and editing, B.T.-V. L.L.-M., J.M.-G. and Y.G.-A.; visualization, B.T.-V., L.M. and J.M.-G.; supervision, L.L.-M., B.T.-V. and J.M.-G.; project administration, L.L.-M. and J.M.-G. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research received no external funding</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijerph-17-05514\"><label>1.</label><element-citation publication-type=\"web\"><person-group person-group-type=\"author\"><collab>European Centre for Disease Prevention and Control (ECDC)</collab></person-group><article-title>Communicable Disease Threats Report</article-title><comment>Available online: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ecdc.europa.eu/sites/default/files/documents/Communicable-disease-threats-report-13-june-2020.pdf\">https://www.ecdc.europa.eu/sites/default/files/documents/Communicable-disease-threats-report-13-june-2020.pdf</ext-link></comment><date-in-citation content-type=\"access-date\" iso-8601-date=\"2020-06-13\">(accessed on 13 June 2020)</date-in-citation></element-citation></ref><ref id=\"B2-ijerph-17-05514\"><label>2.</label><element-citation publication-type=\"book\"><person-group person-group-type=\"author\"><name><surname>Taylor</surname><given-names>S.</given-names></name></person-group><source>The Psychology of Pandemics: Preparing for the Next Global Outbreak of Infectious Disease</source><publisher-name>Cambridge Scholars Publishing</publisher-name><publisher-loc>Newcastle upon Tyne, UK</publisher-loc><year>2019</year></element-citation></ref><ref id=\"B3-ijerph-17-05514\"><label>3.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Wu</surname><given-names>P.</given-names></name><name><surname>Fang</surname><given-names>Y.</given-names></name><name><surname>Guan</surname><given-names>Z.</given-names></name><name><surname>Fan</surname><given-names>B.</given-names></name><name><surname>Kong</surname><given-names>J.</given-names></name><name><surname>Yao</surname><given-names>Z.</given-names></name><name><surname>Liu</surname><given-names>X.</given-names></name><name><surname>Fuller</surname><given-names>C.J.</given-names></name><name><surname>Susser</surname><given-names>E.</given-names></name><name><surname>Lu</surname><given-names>J.</given-names></name><etal/></person-group><article-title>The psychological impact of the SARS epidemic on hospital employees in China: Exposure, risk perception, and altruistic acceptance of risk</article-title><source>Can. 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Health Serv.</source><year>2020</year><volume>58</volume><fpage>5</fpage><lpage>6</lpage><pub-id pub-id-type=\"doi\">10.3928/02793695-20200506-02</pub-id><pub-id pub-id-type=\"pmid\">32463905</pub-id></element-citation></ref></ref-list></back><floats-group><table-wrap id=\"ijerph-17-05514-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05514-t001_Table 1</object-id><label>Table 1</label><caption><p>Proportion of cases with symptoms associated with anxiety, depression, posttraumatic stress and burnout (<italic>n</italic> = 1422).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Variable</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Man</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Woman</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Anxiety (HADS)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No disorder</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">294 (20.7%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">64 (20.8%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">230 (78.2%)</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">X<sup>2</sup> = 26.38 **</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Possible/probable disorder</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">833 (58.6%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">108 (13%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">725 (87%)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Severe disorder</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">295 (20.7%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">22 (7.5%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">273 (92.5%)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Depression (HADS)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No disorder</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">692 (48.7%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">117 (16.9%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">575 (83.1%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>X</italic><sup>2</sup> = 13.50 **</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Possible/probable disorder</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">648 (46%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">65 (10%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">583 (90%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Severe disorder</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">82 (5.3%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12 (14.6%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">70 (85.4%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Posttraumatic stress (IES-R)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No psychiatric disorder</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">242 (17%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">55 (22.7%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">187 (77.3%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>X</italic><sup>2</sup> = 39.263 **</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Average score</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">375 (26.4%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">68 (18.1%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">307 (81.9%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Psychiatric disorder</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">805 (56.6%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">71 (8.8%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">734 (91.2%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Emotional exhaustion (MBI-HSS)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Low</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">510 (35.9%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">80 (15.7%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">430 (84.3%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>X</italic><sup>2</sup> = 3.829</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Medium</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">328 (23.1%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">36 (11%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">292 (89%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">High</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">584 (41%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">78 (13.4%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">506 (86.6%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Depersonalization (MBI-HSS)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Low</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">926 (65%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">105 (11.3%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">821 (88.7%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>X</italic><sup>2</sup> = 28.130 **</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Medium</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">280 (19.7%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35 (12.5%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">245 (87.5%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">High</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">216 (15.2%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">54 (25%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">162 (75%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Personal accomplishment (MBI-HSS)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Low</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">120 (8.4%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23 (19.2%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">97 (80.8%)</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\"><italic>X<sup>2</sup></italic> = 4.42</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Medium</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">138 (9.7%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22 (15.9%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">116 (84.1%)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">High</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1164 (81.9%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">149 (12.8%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1015 (87.2%)</td></tr></tbody></table><table-wrap-foot><fn><p>** <italic>p</italic> < 0.01.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05514-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05514-t002_Table 2</object-id><label>Table 2</label><caption><p>Correlation matrix (<italic>n</italic> = 1422).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Factors</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1. Intrusion</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2. Avoidance</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.666 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3. Hyperarousal</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.825 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.696 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4. Posttraumatic stress</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.912 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.876 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.927 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5. Anxiety</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.584 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.482 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.550 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.431 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6. Depression</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.480 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.392 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.529 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.515**</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.524 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7. Emotional exhaustion</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.374 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.345 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.423 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.420 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.512 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.484 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8. Depersonalization</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.171 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.219 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.218 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.225 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.289 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.294**</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.515 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9. Personal accomplishment</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.04</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.023</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.020</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.018</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.160 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.298 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.201 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.238 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10. Resilience</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.361**</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.324 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.434 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.412 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.461 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.460 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.324 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.161 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.259 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">α</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.866</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.828</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.740</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.932</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.852</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.848</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.876</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.660</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.791</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.829</td></tr></tbody></table><table-wrap-foot><fn><p>** <italic>p</italic> < 0.01.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05514-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05514-t003_Table 3</object-id><label>Table 3</label><caption><p>Association between sociodemographic variables relating to the workplace with symptoms of posttraumatic stress, anxiety and depression (<italic>n</italic> = 1422).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Description</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Posttraumatic Stress</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Anxiety</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Depression</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Variable</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">N (%)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">R<sup>2</sup></th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">B (β)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">IC 95%</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">R<sup>2</sup></th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">B (β)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">IC95%</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">R<sup>2</sup></th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">B (β)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">IC 95%</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Gender</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Man</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">194 (13.6%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.038 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.018 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.005, 2.249</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.10 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.547, 1.784</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Woman</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1228 (86.4%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>3.456 (0.196) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.555, 4.357</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.627 (0.135) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.165 (0.098) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Age M <italic>(SD)</italic></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">43.88 (6.06)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.003 *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.031 (−0.055) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.060<break/>−0.001</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.007 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.031 (−0.081) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.051, 0.011</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.003</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.019 (−0.051)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.039, 0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Autonomous community of the study</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.013 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.004</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.002</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Community of Madrid</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1173 (82.5%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Others</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">249 (17.5%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−1.802 (−0.092) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−2.824<break/>−0.780</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.834 (−0.062)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−1.248, 0.142</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.553 (−0.042)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−1.248, 0.142</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Completed studies</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"7\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.30 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"7\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.008 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"7\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.006</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Doctor’s Degree</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">52 (3.7%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−1.744 (−0.079) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.918, −0.571</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.012 (−0.046)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.175, 0.152</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Postgraduate Degree (Master’s)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">177 (12.4%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−4.688 (−0.145) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−6.387<break/>−2.990</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.233 (−0.019)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.930, 0.464</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.133 (0.11)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.558, 0.824</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bachelor’s Degree</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">575 (40.4%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.843 (−0.046)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.851<break/>0.165</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Baccalaureate</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">89 (6.3%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.538 (0.042)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.196<break/>1.272</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.102 (0.012)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.405, 0.609</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.077 (0.009)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.426, 0.580</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Secondary Education or Professional Training</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">460 (32.3%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.141 (0.046)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.194<break/>2.477</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.007 (0.00)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.931, 0.916</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.516 (0.030)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.400, 1.431</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Primary Education (Basic Education or equivalent)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">69 (4.8%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.926 (0.033)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.568<break/>2.421</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.835 (−0.043)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−1.868, 0.198</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.844 (−0.044)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−1.868, 0.180</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Marital status</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"8\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.007</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"8\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.004</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"8\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.002</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Married</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">698 (49.1%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Separated</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33 (2.3%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.422 (0.035)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.693<break/>3.537</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.981 (−0.036)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.429, 0.467</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">.063 (0.002)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.371, 1.498</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Divorced</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">125 (8.8%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.616 (−0.029)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.770<break/>0.537</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.346 (−0.024)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.135, 0.444</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.187 (−0.013)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.969, −0.595</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Single</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">289 (20.3%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.125 (0.008)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.706<break/>0.955</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.111 (0.011)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.458, 0.679</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.197 (0.019)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.367, 0.760</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Widower/Widow</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18 (1.3%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.824 (0.015)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.010<break/>3.659</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.875 (−0.024)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.816, 1.066</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.922 (−0.025)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.844, 1.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Living with partner, not married</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">243 (17.1%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.049</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.933<break/>−0.164</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.330 (−0.030)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.935, 0.276</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.224 (−0.021)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.824, 0.376</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Other</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16 (1.1%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.416</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−3.418<break/>2.586</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.708 (0.018)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−1.347, 2.764</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.745 (0.019)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−1.291, 2.781</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Dependent relatives</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">572 (40.2%)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.157 (0.013)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.486<break/>0.800</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.053 (0.006)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.386, 0.493</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.101 (0.012)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.335, 0.536</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">850 (59.8%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>No. of Children in your care</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">586 (41.2%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">363 (25.5%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.304 (0.022)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.491<break/>1.098</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.040 (−0.004)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.584, 0.502</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.187 (0.020)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.351, 0.725</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">389 (27.3%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.236 (−0.017)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.014<break/>0.542</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.191 (−0.021)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.723, 0.342</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.020 (0.002)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.506, 0.547</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">>2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">84 (6%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.013 (0.000)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−1.375<break/>1.402</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.010 (0.001)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.939, 0.959</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.159 (−0.009)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−1.098, 0.780</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>No. of people you live with M <italic>(SD)</italic></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.86 (1.257)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.000</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.098 (−0.020)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.349<break/>0.153</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.000</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.018 (−0.005)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.189, 0.154</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.001</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.082 (−0.025)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.252, 0.087</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Professional category</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.128 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.004</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.006</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Executive</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18 (1.2%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.953 (−0.018)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−3.853<break/>1.946</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.169 (−0.005)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.164, 1.826</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.623 (−0.017)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.595, 1.349</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Intermediate job</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">207 (14.6%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Base position</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1197 (84.2%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>2.035 (0.123) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.146<break/>2.923</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.668 (0.059)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.057, 1.279</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.806 (0.072)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.806, 1.410</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Type of centre</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hospital</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">972 (68.4%)</td><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.010 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.004</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.002</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Primary care</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">150 (10.5%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.142 (−0.007)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.181<break/>0.897</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.081 (0.006)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.632, 0.793</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.196 (0.015)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.510, 0.910</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Nursing home</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">176 (12.4%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">.269 (0.015)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.701<break/>1.240</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.002 (0.00)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−668, 0.663</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.278 (−0.022)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.937, 0.381</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Day centre</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22 (1.5%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.788 (−0.036)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−4.34<break/>0.767</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−2.070 (−0.062) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−3.821, −0.319</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.323 (−0.040)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−3.058, 0.411</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Other</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">102 (7.2%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−2.124 (−0.090) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−3.358<break/>−0.891</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.058 (−0.004)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.903, 0.788</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.072 (−0.005)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.909, 0.765</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Post</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Medical post</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">143 (10%)</td><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.210 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−2.937 (−0.096) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−4.045<break/>−1.830</td><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.012 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.959 (−0.070) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.728, −0.189</td><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.008</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−1.002 (−0.074) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.765, −0.239</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Nursing post</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">486 (34.2%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.218 (0.024)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.312, 0.750</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Assistant Nurse</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">443 (31.2%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.496 (0.038)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.269<break/>1.260</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.080 (−0.009)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.607, 0.447</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Caregiver</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">117 (8.2%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.605 (−0.027)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.804<break/>0.594</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.975 (−0.065) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.808, −0.142</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.917 (−0.061) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.743, −0.091</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Others</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">233 (16.4%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−2.464 (−0.146) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−3.391−1.536</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.575 (−0.051)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−1.220, 0.070</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.595 (−0.054)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−1.234, 0.044</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Shift</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fixed morning</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">474 (33.3%)</td><td rowspan=\"9\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.013*</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td rowspan=\"9\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.007</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td rowspan=\"9\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.008</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fixed afternoon</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">132 (9.3%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.529 (0.073) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.363, 2.694</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.029 (0.072) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.220, 1.828</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.956 (0.068) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.165, 1.747</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fixed night</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">71 (5%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.538 (0.055) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.030, 3.045</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.345 (0.018)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.689, 1.378</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.308 (0.016)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.715, 1.330</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rotating morning-afternoon</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">110 (7.7%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.185 (−0.008)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.438<break/>1.068</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.393 (−0.024)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.252, 0.467</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.200 (0.013)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.650, 1.051</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rotating morning-night</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">153 (10.8%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.951 (0.049)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.150<break/>2.053</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.343 (0.026)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.412, 1.098</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.528 (0.040)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.219, 1.275</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rotating afternoon-night</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">132 (9.3%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.779 (0.085) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.613, 2.944</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.506 (0.035)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.293, 1.305</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.274 (0.019)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.516, 1.065</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rotating morning-afternoon-night</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">145 (10.2%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.072 (0.054)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.052<break/>2.196</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.310 (0.023)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.460, 1.080</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.508 (0.037)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.255, 1.270</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12 or 24 h shifts or on-call hours</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">98 (6.3%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.266 (0.011)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.048<break/>1.580</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.582 (0.036)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.319, 1.483</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.962 (0.059) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.070, 1.854</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Other</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">107 (8.1%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.027 (0.01)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−1.240<break/>1.295</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.401 (0.026)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.468, 1.270</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.851 (0.055)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.009, 1.711</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Time</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Full time</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1273 (89.5%)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.005 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.005 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.006 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Part time</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">149 (10.5%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.426 (0.072) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.399, 2.453</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.976 (0.072) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.274, 1.678</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.997 (0.074) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.302, 1.692</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Contract type</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Statutory fixed-term employment (official)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">337 (23.7%)</td><td rowspan=\"6\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.009*</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−1.140 (−0.080) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.997 −0.283</td><td rowspan=\"6\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.004</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.282 (−0.029)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.869, 0.306</td><td rowspan=\"6\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.008</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.366 (0.038)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.215, 0.946</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Full time staff</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">442 (31.1%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Interim staff or long-term substitute</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">394 (27.7%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.083 (0.006)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.738<break/>0.905</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.441 (−0.048)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.004, 0.122</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.021 (0.002)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.535, 0.577</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Temporary employee/short-term substitute</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">180 (12.7%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.345 (−0.019)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.393<break/>0.703</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.100 (−0.008)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.818, 0.619</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.147 (0.012)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.563, 0.857</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">In training (MIR/EIR/PIR/FIR)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35 (2.5%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.309 (−0.059)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−4.391<break/>−0.228</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.201 (−0.045)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.627, 0.226</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−1.984 (−0.075) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−3.393, −0.574</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Other</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">34 (2.3%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.017 (0.00)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−2.126<break/>2.093</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.446 (0.016)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−1.00, 1.892</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.271 (0.010)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−1.157, 1.700</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Years of seniority M <italic>(SD)</italic></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9.391 (8.614)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.000</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.003 (0.005)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.033, 0.040</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.000</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.000 (0.000)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.025, 0.025</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.000</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.009 (0.019)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.016, 0.034</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Years as health workers M <italic>(SD)</italic></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16.54 (10.38)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.003 *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.032 (−0.054)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.062<break/>−0.001</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.002</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.017 (−0.042)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.038, 0.004</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.00</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.007 (−0.017)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.027, 0.014</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Weekly operating hours M <italic>(SD)</italic></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">38,42 (13.96)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.000</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.009 (−0.022)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.032<break/>0.013</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.000</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.005 (−0.017)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.020, −0.010</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.003 *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.016 (0.053) *</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.031, 0.000</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Number of on-call hours per month M <italic>(SD)</italic></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.69 (4.10)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.004 *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.031 (0.063) *</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.005,<break/>0.056</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.000</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.006 (0.019)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.011, 0.024</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.006 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.026 (0.007) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.008, 0.043</td></tr></tbody></table><table-wrap-foot><fn><p>* <italic>p</italic> < 0.05; ** <italic>p</italic> < 0.01.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05514-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05514-t004_Table 4</object-id><label>Table 4</label><caption><p>Association between COVID-19, burnout and resilience variables with symptoms of posttraumatic stress, anxiety and depression (<italic>n</italic> = 1422).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Description</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Posttraumatic Stress</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Anxiety</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Depression</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">COVID-19 Variables</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">N (%)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">R<sup>2</sup></th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">B (β)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">IC 95%</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">R<sup>2</sup></th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">B (β)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">IC 95%</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">R<sup>2</sup></th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">B (β)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">IC 95%</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Change of residence through fear of infecting family members</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">147 (10.3%)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.006 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.563 (0.079) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.530<break/>2.596</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.005 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.927 (0.068) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−220<break/>1.633</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.003 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.721</bold>\n<break/>\n<bold>(0.054) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.024 1.424</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1275 (89.7%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>At work, being with people who might have COVID-19</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1367 (96.1%)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.002</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.228 (0.039)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.406, 2.862</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.004 (0.002)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.074<break/>1.162</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.499 (−0.023)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.605<break/>0.607</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">55 (3.9%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>You tested positive for COVID-19</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">221 (3.9%)</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.003</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.086 (−0.005)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.019<break/>0.847</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.309 (−0.027)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.947<break/>0.330</td><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.244 (−0.022)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.876 0.389</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">You underwent no tests</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">609 (15.5%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No tests were performed</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">524 (36.8%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.661 (−0.053)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.369<break/>0.047</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.183 (−0.021)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.668<break/>0.301</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.068 (−0.008)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.548 0.412</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Other</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">68 (4.7%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.351 (0.012)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−1.168<break/>1.870</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.040 (−0.002)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−1.079<break/>1.000</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.069 (−0.004)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−1.099 0.960</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>You were hospitalized for symptoms compatible with those of coronavirus</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26 (1.8%)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.003 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>2.347 (0.052) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.004<break/>4.698</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.480 (0.016)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.129<break/>2.089</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.031 (0.001)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.561 1.624</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1396 (98.2%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>You were isolated due to possible infection with COVID-19</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">423 (29.7%)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.004 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.868 (0.065) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.179<break/>1.556</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.003 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.473 (0.052) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.002<break/>0.944</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.228 (0.025)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.239 0.694</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">999 (70.3%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>You have protective equipment to prevent being infected</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1177 (82.8%)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.006 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−1.245 (−0.078) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.078 −0.412</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.008 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.976 (−0.089) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.544 −0.407</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.009 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−1.046 (−0.096) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.608, −0.483</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">245 (17.2%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>You were given protective equipment at work to prevent being infected</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No, not at all</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">61 (4.3%)</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.038 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.225 (−0.008)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.765<break/>1.315</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.027 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.448 (−0.022)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.026 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.075 (−0.004)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.123 0.974</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes, the equipment necessary for the work performed</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">376 (26.4%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes, but scarce</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">985 (69.3%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−2.691 (−0.196) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−3.398<break/>−1.983</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−1.558 (−0.166) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−2.045 −1.072</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−1.507 (−0.166) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−1.989 −1.026</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Some people you live with belong to the risk group</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">550 (38.7%)</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.008 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.079</bold>\n<break/>\n<bold>(0.087) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.433<break/>1.724</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.009 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.791</bold>\n<break/>\n<bold>(0.093) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.350<break/>1.231</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.009 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.806 (0.096) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.370<break/>1.242</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">872 (61.3%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Concern over possible infection of a person you live with</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Very concerned</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">827 (58.2%)</td><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.100 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.057 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.029 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Quite concerned</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">428 (30.1%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>3.513 (0.266) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.185<break/>2.840</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.932 (0.214) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.402<break/>1.462</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.204 (0.135) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.676 −0.731</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not very concerned</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">80 (5.6%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−4.982 (−0.189) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−6.305 −3.600</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−2.452 (−0.136) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−3.377 −1.527</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−1.962 (−0.001) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.892 −1.003</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not at all concerned</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29 (2%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−6.274 (−0.146) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−8.400 −4.140</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−2.695 (−0.092) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−4.188 −1.203</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−2.147 (−0.074) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−3.647<break/>−0.647</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No family</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">58 (4.1%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−1.917 (−0.063)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−3.451 −0.383</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.937 (−0.045)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−2.010<break/>0.137</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.112 (.005)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.966<break/>1.190</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Concern over possible infection of a family member you do not live with</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Very concerned</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1069 (75.2%)</td><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.007 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.037</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.024 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Quite concerned</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">296 (20.8%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>3.421 (0.299) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.172, 2.670</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.681 (0.165) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.205, 1.156</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.484 (−0.147) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.007 −0.962</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not very concerned</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25 (1.8%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−6.098 (−0.132) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−8.412 −3.783</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−2.880 (−0.091) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−4.496 −1.265</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−1.859 (−0.60) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−3.469 −0.249</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not at all concerned</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13 (.9%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−6.511 (−0.102) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−9.703 −3.319</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−2.785 (−0.064) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−5.013 −0.557</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.022 (−0.024)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−3.243 1.198</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No family</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">19 (1.3%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−3.879 (−0.074) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−6.527 −1.232</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−1.716 (−0.048)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−3.564<break/>0.132</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.733 (−0.020)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−2.572 1.111</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Likelihood of becoming infected with COVID−19</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Very likely</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">918 (64.6%)</td><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.081 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.435 (0.034)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−7.649<break/>8.519</td><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.039 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.527 (0.166)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−4.121<break/>7.175</td><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.021 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.870 (−0.102)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−6.513 4.773</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Quite likely</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">399 (28.1%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.491 (−0.185)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−10.584 5.605</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.143 (0.016)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−5.513<break/>5.800</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.789 (−0.196)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−7.440 3.862</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not very likely</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50 (3.5%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−5.547 (−0.169)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−13.782<break/>2.688</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.260 (−0.056)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−7.014<break/>4.494</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−3.360 (−0.151)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−9.108 2.388</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not at all likely</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2 (.1%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−5.423 (0.085) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−8.607 −2.239</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−2.242 (−0.052) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−4.467<break/>−0.017</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.613 (−0.014)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.836 1.609</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Doesn’t know</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">53 (3.7%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−2.420 (−0.076)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−10.646 5.807</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.260 (−0.012)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−6.007<break/>5.488</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−1.988 (−0.092)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−7.731 3.754</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Emotional exhaustion M <italic>(SD)</italic></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">27.48 (12.62)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.177 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.202 (0.420) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.179, 0.224</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.262 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.168 (0.226) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.153, 0.183</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.234 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.157 (0.008) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.142 0.172</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Depersonalization M <italic>(SD)</italic></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.616</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.051 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.222 (0.026) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.172, 0.272</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.084 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.195 (0.289) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.162, 0.229</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.086 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.196 (0.294) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.163 0.230</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Personal accomplishment M <italic>(SD)</italic></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">39.68 (6.99)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.000</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.015 (−0.018)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.066<break/>0.030</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.026 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.095 (−0.160) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.125, −0.065</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.089 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.174 (−0.298) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.204 −0.145</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Resilience M <italic>(SD)</italic></bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3020 (0.39)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.170 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.625 (−0.412) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.697 −0.554</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.212 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.478 (−0.461) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.526, −0.430</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.212 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.473 (−0.460) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.520 −0.425</td></tr></tbody></table><table-wrap-foot><fn><p>* <italic>p</italic> < 0.05; ** <italic>p</italic> < 0.01.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05514-t005\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05514-t005_Table 5</object-id><label>Table 5</label><caption><p>Regression models for posttraumatic stress, anxiety and depression (<italic>n</italic> = 1422).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Posttraumatic stress</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Anxiety</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Depression</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Variable</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">B (β)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">IC 95%</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">B (β)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">IC 95%</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">B (β)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">IC 95%</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Emotional exhaustion (MBI-HSS)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.137 (0.285) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.112, −0.162</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.123 (0.374) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.106, 0.139</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.105 (0.324) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.089, 0.122</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Depersonalization (MBI-HSS)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.055 (0.056) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.007, −00.104</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.039 *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.007, 0.072</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.034 (0.050) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.001, 0.954</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Personal accomplishment (MBI-HSS)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.086 (0.99)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.047, 0.124</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.094 (−0.60) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.119, −0.068</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Resilience</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.395 (−0.056) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.464, −0.327</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.307 (−0.295) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.352, −0.262</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.286 (−279) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.332, −0.240</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Autonomous community (Madrid)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.896 **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.237, 1.556</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Gender (man)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−2.288 (−0.129) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.531, 3.035</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−1.118 (−0.091) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−1.623, −0.613</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.805 (−0.067) **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−1.306, −0.304</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Completed studies (doctor’s degree)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−2.254 (−0.070) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−3.688, −0.820</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Completed studies (primary education)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.215 (0.043) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.021, 2.408</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Marital status (living with partner, not married)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−1.048 (−0.065) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.718<break/>−379</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Marital status (separated)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−1.177 (−0.043) *</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−2.297, −0.058</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Centre type (hospital)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.703 (0.054) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.155, 1.252</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Centre type (residences)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>-</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.832 (−0.066) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.354, −0.309</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Centre type (day centre)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>-</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−1.469 (−0.044) *</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−2.843, −0.095</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Position (doctors)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−1405 (−0.070) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.323, −0.488</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.648 (−0.047) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.215, 1.082</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Position (others)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−1.402 (−0.086) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.096, −0.708</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">Shift (rotating morning-afternoon)</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.653 (−0.042) *</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">−1.284, −0.022</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Shift (12 or 24 shifts or on-call hours)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.698 (0.043) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.026, 1.370</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.859 (0.053) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.188, 1.530</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Shift (Other)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.909 (0.059) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.267, 1.551</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No. of On-call hours per month</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.014 (0.042) *</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.000, 0.028</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Contract type (training)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−2.254 (−0.085) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−3.347, −1.161</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Contract type (fixed-term)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.631 (−0.071) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−1.001, −0.261</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Isolated COVID-19 (NO)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.416 (−0.046) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.791, −0.042</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">You were given protective equipment at work to prevent being infected (yes, that considered necessary for the work carried out)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.857 (−0.062) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.437<break/>−0.277</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Risk groups (No)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−451 (−0.131) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.814, −0.087</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Concern over possible infection of a person you live with (very concerned)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.800 (0.146) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.234, 2.365</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.192 (0.142) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.839, 1.546</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.533 (0.064) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.137, 0.928</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Concern over possible infection of a person you live with (Not at all concerned)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−2.896 (−0.068) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−4.706<break/>−1.085</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Concern over possible infection of a family member you do not live with (No family)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.247 (0.060) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.37, 2.124</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Likelihood of infection with COVID-19 (very likely)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.176 ** (0.084)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.545, 1.807</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.529 (0.056) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.103, 0.954</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No. of people you live with</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.219 * (−0.045)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.423, −0.014</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td></tr></tbody></table><table-wrap-foot><fn><p>* <italic>p</italic> < 0.05; ** <italic>p</italic> < 0.01.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05514-t006\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05514-t006_Table 6</object-id><label>Table 6</label><caption><p>Regression models for posttraumatic stress, anxiety and depression in women (<italic>n</italic> = 1228).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Posttraumatic Stress</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Anxiety</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Depression</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Variable</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">B (β)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">IC 95%</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">B (β)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">IC 95%</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">B (β)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">IC 95%</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Emotional exhaustion (MBI-HSS)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.121 (0.261) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.095, 0.147</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.111 (0.007) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.093, 0.127</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.102 (0.318) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.085, 0.119</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Depersonalization (MBI-HSS)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.060 (0.061) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.042, 0.124</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.050 (0.071) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.013, 0.087</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.515 (0.060) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.943, −0.087</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Personal accomplishment (MBI-HSS)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.060 (−0.098) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.007, −0.113</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.84 (−1.41) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.112, −0.056</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Resilience</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.404 (−0.280) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.475, −0.332</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.293 (−0.286) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.342, −0.244</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.284 (−0.280) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.333, −0.235</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Marital status (living with partner, not married)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−1.005 (−0.067) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.696, −0.314</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Marital status (separated)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−1.461 (−0.057) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.612, −0.311</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Centre type (hospital)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.266 (0.022)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.455, 0.988</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Centre type (residences)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−1.079 (−0.063) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2067, −0.092</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.978 (−0.081) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.532, −0.424</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Position (nurse)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.496 (0.059) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.113, 0.879</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Position (doctors)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−1.869 (−0.094) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.819, −0.919</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−1.215 (−0.058) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.200, −0.230</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Position (others)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−1.154 (−0.073)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.914, −0.394</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Shift (12 or 24 shifts or on-call hours)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.785 (0.048) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.048, 1.523</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Shift (Other)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.997 (0.066) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.318, 1.675</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Contract type (training)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−2.125 (−0.081) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−3.300, −0.950</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Contract type (fixed-term)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.679 (−0.078) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.279, −0.022</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Risk groups (Yes)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.481 (0.058) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.093, 0.870</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Concern over possible infection of a person you live with (very concerned)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.826 (0.30) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.237, 2.415</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Concern over possible infection of a person you live with (Not at all concerned)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−2.870 (−0.057) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−5.150, −0.589</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Concern over possible infection of a family member you do not live with (very concerned)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.038 (0.077) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.368, 1.708</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.152 (0.139) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.763, 1.541</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.671 (0.082) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.258, 1.083</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Likelihood of infection with COVID-19 (very likely)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.22 (0.101) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.625, 1.819</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.600 (−0.072)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.703, −0.616</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Likelihood of infection with COVID-19 (unlikely)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.810 (−0.056) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−3.343, −0.276</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Likelihood of infection with COVID-19 (I do not know)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−1.058 (−0.049) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.047, 1.229</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Change of residence through fear of infecting family members</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.871 (0.064) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.245, 1.497</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr></tbody></table><table-wrap-foot><fn><p>* <italic>p</italic> < 0.05; ** <italic>p</italic> < 0.01.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05514-t007\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05514-t007_Table 7</object-id><label>Table 7</label><caption><p>Regression models for posttraumatic stress, anxiety and depression in men (n = 194).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Posttraumatic Stress</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Anxiety</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Depression</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Variable</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">B (β)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">IC 95%</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">B (β)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">IC 95%</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">B (β)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">IC 95%</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Emotional exhaustion (MBI-HSS)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.231 (0.457) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.174, 0.288</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.155 (0.510) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.125, 0.185</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.119 (0.387) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.085, 0.153</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Depersonalization (MBI-HSS)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.446 (0.165) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.393, −0.518</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Personal accomplishment (MBI-HSS)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.149 (−0.275)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.205, −0.093</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Resilience</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.373 (−0.207)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.573, −0.173</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.424 (−0.391) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.530 −0.319</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−0.253 (−0.230) **</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.393, −0.133</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Completed studies (doctor’s degree)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−5.139 (−0.198) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−7.827, −2.451</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−1.705 (−0.108) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−3.143, −0.268</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Completed studies (primary education)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>3.140 (0.133) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.724, 5.555</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Position (nurse)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.012 (0.097) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.937 −0.087</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Position (others)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−2.050 (−0.119) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−3.800<break/>−0.299</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Contract type (training)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−2.952 (−0.109) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−5.361<break/>−0.543</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−4.809 (−0.174) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−7.313, −2.305</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Contract type (Statutory fixed-term employment (official)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−1.313 (−0.131) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.219 −0.408</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Contract (Others)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−4.046 (0.094) *</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−7.947, −0.144</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Isolated COVID-19 (NO)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−1.951 (−0.129) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−3.479<break/>−0.423</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−1.332 (−0.144) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.214, −0.450</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Concern over possible infection of a person you live with (very concerned)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.881 (0.124) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.260, 3.503</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.954 (0.212) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.074, 2.835</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Concern over possible infection of a person you live with (Not at all concerned)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>−6.784 (−0.150) **</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−11.446, −2.121</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Concern over possible infection of a family member you do not live with (No family)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>2.206 (0.106) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.278, 4.135</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Isolated (Yes)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.996 (0.109) *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.180<break/>1.813</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Being hospitalized for symptoms compatible with those of coronavirus (NO)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>−2.756 (−0.100) *</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−5.317, −0.196</td></tr></tbody></table><table-wrap-foot><fn><p>* <italic>p</italic> < 0.05; ** <italic>p</italic> < 0.01.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751174</article-id><article-id pub-id-type=\"pmc\">PMC7432017</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155458</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05458</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Health System Response during the European Refugee Crisis: Policy and Practice Analysis in Four Italian Regions</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-5716-1548</contrib-id><name><surname>Mammana</surname><given-names>Leonardo</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05458\">1</xref><xref rid=\"c1-ijerph-17-05458\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Milani</surname><given-names>Chiara</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05458\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-0966-9432</contrib-id><name><surname>Bordin</surname><given-names>Paola</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05458\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Paglione</surname><given-names>Lorenzo</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05458\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Salvia</surname><given-names>Chiara</given-names></name><xref ref-type=\"aff\" rid=\"af5-ijerph-17-05458\">5</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05458\"><label>1</label>Department of Biomedical and Neuromotor Sciences, University of Bologna, 40121 Bologna, Italy</aff><aff id=\"af2-ijerph-17-05458\"><label>2</label>School of Specialization in Hygiene and Preventive Medicine, University of Florence, 50134 Florence, Italy; <email>chiara.milani@unifi.it</email></aff><aff id=\"af3-ijerph-17-05458\"><label>3</label>Postgraduate School of Hygiene and Preventive Medicine, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padua, 35128 Padova, Italy; <email>paola.bordin.2@studenti.unipd.it</email></aff><aff id=\"af4-ijerph-17-05458\"><label>4</label>Department of Civil, Building and Environmental Engineering, Sapienza University of Rome, 00184 Rome, Italy; <email>Lorenzo.paglione@uniroma1.it</email></aff><aff id=\"af5-ijerph-17-05458\"><label>5</label>Local Health Unit of Modena, 41124 Modena, Italy; <email>C.salvia@ausl.mo.it</email></aff><author-notes><corresp id=\"c1-ijerph-17-05458\"><label>*</label>Correspondence: <email>leonardo.mammana2@studio.unibo.it</email>; Tel.: +39-338-2882-256</corresp></author-notes><pub-date pub-type=\"epub\"><day>29</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5458</elocation-id><history><date date-type=\"received\"><day>28</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>21</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>The decentralization of the provision of health services at the subnational level produces variations in healthcare offered to asylum seekers (ASs) across the different Italian regions, even if they are entitled to healthcare through the national health service. The present study aims to map the healthcare path and regional policies for ASs upon arrival and identify challenges and best practices. This is a multicentric, qualitative study of migrant health policies and practices at the regional level within four Italian regions. For the analysis, a dedicated tool for the systematic comparison of policies and practices was developed. The collection and analysis of data demonstrated the presence of many items of international recommendations, even if many gaps exist and differences between regions remain. The analysis of practices permitted the identification of three models of care and access. Some aspects identified are as follows: fragmentation and barriers to access; a weakness in or lack of a governance system, with the presence of many actors involved; variability in the response between territories. The inclusion of ASs in healthcare services requires intersectoral actions, involving healthcare sectors and other actors within local social structures, in order to add value to local resources and practices, reinforce networks and contribute to social integration.</p></abstract><kwd-group><kwd>asylum seeker</kwd><kwd>health policy</kwd><kwd>regional differences</kwd><kwd>health system</kwd><kwd>primary healthcare</kwd><kwd>vulnerability</kwd><kwd>NGOs</kwd><kwd>governance</kwd><kwd>barriers in access to care</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05458\"><title>1. Introduction</title><p>Although the overall percentage of international migrants has only slightly increased, over the past several years, global migration has shown a growing trend in absolute numbers. Indeed, the total number of international migrants has increased from an estimated 173 million in 2000 to 272 million in 2019, primarily due to conflict, persecution, and environmental changes [<xref rid=\"B1-ijerph-17-05458\" ref-type=\"bibr\">1</xref>]. At the end of 2018, global displacement affected approximately 70.8 million people, more than 25 million of whom are refugees [<xref rid=\"B2-ijerph-17-05458\" ref-type=\"bibr\">2</xref>]. Europe has experienced an unprecedented influx of refugees, asylum seekers (ASs) and other migrants: about 1.5 million people arrived in Europe (EU) in 2015, including more than 1 million that applied for asylum, having fled countries affected by war, conflict or economic crisis [<xref rid=\"B3-ijerph-17-05458\" ref-type=\"bibr\">3</xref>].</p><p>Migrants are a heterogeneous group, including any person who is moving or has moved across an international border or within a state away from his or her habitual place of residence [<xref rid=\"B4-ijerph-17-05458\" ref-type=\"bibr\">4</xref>]. They may experience a number of health issues caused by the living conditions faced during their migratory journey and once in migrant reception centers. Moreover, the increase in the number of migrants is leading to the creation of a growing ethnically diverse population, with different languages, traditions, healthcare needs and prior levels of care. This increasing diversity will put a strain on healthcare systems, and will likely increase health inequalities [<xref rid=\"B5-ijerph-17-05458\" ref-type=\"bibr\">5</xref>]. Health inequalities have been defined as “differences in opportunity for different population groups which result in, for example, unequal life chances, access to health services, nutritious food, adequate housing” [<xref rid=\"B6-ijerph-17-05458\" ref-type=\"bibr\">6</xref>]. Although the right to health is encompassed in many European policies [<xref rid=\"B7-ijerph-17-05458\" ref-type=\"bibr\">7</xref>], evidence from across the EU demonstrates considerable inequalities between migrants and the local population in health and access to health services [<xref rid=\"B8-ijerph-17-05458\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05458\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijerph-17-05458\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijerph-17-05458\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05458\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijerph-17-05458\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijerph-17-05458\" ref-type=\"bibr\">14</xref>]. This is partly because of national legislation restricting access for certain groups of migrants such as ASs or undocumented migrants, but is also due to barriers that extend beyond the constraints on the legal entitlement to care [<xref rid=\"B15-ijerph-17-05458\" ref-type=\"bibr\">15</xref>]. Many factors may deter seeking care: a lack of knowledge of the national language, unfamiliarity with the healthcare system, administrative obstacles, and discrimination [<xref rid=\"B16-ijerph-17-05458\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijerph-17-05458\" ref-type=\"bibr\">17</xref>], and poverty, too, when user fees are demanded [<xref rid=\"B18-ijerph-17-05458\" ref-type=\"bibr\">18</xref>]. These factors can lead to unequal access or even to exclusion from health services [<xref rid=\"B10-ijerph-17-05458\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijerph-17-05458\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05458\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijerph-17-05458\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijerph-17-05458\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijerph-17-05458\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijerph-17-05458\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijerph-17-05458\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijerph-17-05458\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijerph-17-05458\" ref-type=\"bibr\">19</xref>].</p><p>Promoting the health of ASs, refugees and migrants has been highlighted as part of the global architecture of universal health coverage [<xref rid=\"B20-ijerph-17-05458\" ref-type=\"bibr\">20</xref>], and the European regional office of the World Health Organization (WHO) published a strategy and action plan in 2016 [<xref rid=\"B21-ijerph-17-05458\" ref-type=\"bibr\">21</xref>] in order to address the challenges of the European refugee crisis. The action plan contains recommendations for Member states to strengthen the health system’s capacity to respond to migrants and asylum seekers’ health needs at the arrival phase as well as long term [<xref rid=\"B22-ijerph-17-05458\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijerph-17-05458\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijerph-17-05458\" ref-type=\"bibr\">24</xref>]. The public health of refugees and migrants cannot be separated from the public health of the population and reflects the urgent need for the health sector to more effectively address the impact of migration and displacement on health [<xref rid=\"B25-ijerph-17-05458\" ref-type=\"bibr\">25</xref>]. Priorities for Member States are the adoption of relevant international standards and policies on refugees’ and migrants’ right to health, both in national law and in practice, and addressing social determinants of health through multisectoral public health policies. </p><p>In Italy, the foreign-born population is about 8.8% of the total population, and migration must be considered a structural phenomenon. In 2019, Italy reported an estimated 354,700 refugees including ASs, accounting for 5.7% of the total number of migrants [<xref rid=\"B26-ijerph-17-05458\" ref-type=\"bibr\">26</xref>]. According to Italian legislation, after being hosted in hotspots located near borders, ASs willing to apply for refugee status are sent to large reception centers called “hubs”, where they are expected to stay for a maximum of 30 days while their asylum claim is processed [<xref rid=\"B27-ijerph-17-05458\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijerph-17-05458\" ref-type=\"bibr\">28</xref>]. Subsequently, they are transferred to the official structures pertaining to the specific Italian system designed for the protection of ASs and refugees (SPRAR system), which offers accommodation and some integration services aimed at guaranteeing protection and facilitating integrated reception at the community level [<xref rid=\"B29-ijerph-17-05458\" ref-type=\"bibr\">29</xref>]. Given the large number of migrants coming to Italy over the last several years, additional accommodation centers (CAS) have been authorized to accommodate ASs while they wait for a response to their application. [<xref rid=\"B30-ijerph-17-05458\" ref-type=\"bibr\">30</xref>]. This national architecture responds to and aligns with European policies after 2014 and recent migratory pressures. At the regional level, regional health services (RHS) implement these policies differently, resulting in diverse and unknown models of healthcare.</p><p>The Italian Health Service (Servizio Sanitario Nazionale, SSN), founded in 1978, was reformed in the 1990s in a corporate sense, and the constitutional amendment law of 2001 specified how health is a “concurrent” matter between states and regions. Migration, on the contrary, is a matter of state competence, ensuring that the health of migrant populations is a critical element in the organizational relationships between the state and regional levels. The state, therefore, define the general regulatory framework, while the regions establish the operational procedures and laws for implementing policies, in accordance with the national legal framework [<xref rid=\"B31-ijerph-17-05458\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijerph-17-05458\" ref-type=\"bibr\">32</xref>]. Each region is divided into Local Health Organizations (LHO), each one with legal and organizational autonomy.</p><p>Italian national law guarantees universal healthcare coverage for ASs and refugees, as it does for Italian citizens [<xref rid=\"B33-ijerph-17-05458\" ref-type=\"bibr\">33</xref>]. Immediately after the regularization of the asylum request, ASs are entitled to be regularly registered in the SSN. Before the submission of their application for refugee status, migrants receive medical assistance managed by local administration. Due to this decentralization, some variations in healthcare offered across the regions are possible [<xref rid=\"B34-ijerph-17-05458\" ref-type=\"bibr\">34</xref>]. In 2017, the National Health Institute (Istituto Superiore di Sanità—ISS) along with other Italian scientific societies, released the first national guidelines on how to deal with health issues of migrants and ASs in each migratory phase, in order to homogenize the assistance in each region [<xref rid=\"B35-ijerph-17-05458\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijerph-17-05458\" ref-type=\"bibr\">36</xref>]. It recommends tackling communicable (CDs) and non-communicable diseases (NCDs) as well as detecting other vulnerable conditions. In addition, it has been suggested to organize more inclusive healthcare services through the appropriate training of personnel and specific care pathways for migrants. Health policies are full-fledged social determinants of health: they can influence aspects of the delivery of health services and the accessibility of health services, which thereby affects health outcomes [<xref rid=\"B37-ijerph-17-05458\" ref-type=\"bibr\">37</xref>].</p><p>In recent years, several studies have sought to analyze policies towards migrants’ access to healthcare with the production of analytical frameworks [<xref rid=\"B23-ijerph-17-05458\" ref-type=\"bibr\">23</xref>,<xref rid=\"B38-ijerph-17-05458\" ref-type=\"bibr\">38</xref>]. Accordingly, the management of the arrival phase—as well as that of the following phases—is key for protecting the health of ASs. However, the lack of information about policy implementation and health outcomes has made it difficult to evaluate the experience of ASs on the ground, since it is affected by the presence or absence of a government policy [<xref rid=\"B39-ijerph-17-05458\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijerph-17-05458\" ref-type=\"bibr\">40</xref>,<xref rid=\"B41-ijerph-17-05458\" ref-type=\"bibr\">41</xref>]. Moreover, no evidence has been provided yet in terms of description, continuity, and comparison in reference to international guidelines.</p><p>The objective of this paper is to thoroughly examine the policies, practices and care pathway of ASs upon their arrival in the regions, in order to assess the regional and local health system responses in Italy during the European refugee crisis. To the best of the research group’s knowledge, at the time of writing of this paper, no one has explored this field by paying particular attention to the policies and practices. </p><p>Our more specific aims are: (1) to analyze the regional health policies tailored to ASs healthcare at arrival in the various Italian regions, and compare these with national and international recommendations; (2) to evaluate the practices at the LHO level, in order to identify models of care and challenges, and to assess the local implementation of the corresponding policy. </p></sec><sec id=\"sec2-ijerph-17-05458\"><title>2. Materials and Methods </title><sec id=\"sec2dot1-ijerph-17-05458\"><title>2.1. Study Design and Setting</title><p>This is a multicentric descriptive and comparative qualitative study of migrant healthcare policies and models of care, specifically targeted at ASs in the first period after their arrival in Italy. A content analysis of the policies at the regional level was performed and data regarding local practices were collected within four Italian regions (Emilia-Romagna, Lazio, Toscana and Veneto) since the end of 2017 to present, in coherence with the Italian SSN organization and with the competences of each level, as previously discussed.</p></sec><sec id=\"sec2dot2-ijerph-17-05458\"><title>2.2. Data Collection</title><p>A preliminary context analysis of the migratory phenomenon within the investigated regions was performed with regards to the history of migration, quantification of the AS population and a description of each regional reception and accommodation organization. Then, a multiple phase methodology of data collection was used according to the following steps. </p><sec id=\"sec2dot2dot1-ijerph-17-05458\"><title>2.2.1. Policy Collection</title><p>The subject of the present collection and analysis was regional legislation [<xref rid=\"B42-ijerph-17-05458\" ref-type=\"bibr\">42</xref>,<xref rid=\"B43-ijerph-17-05458\" ref-type=\"bibr\">43</xref>]. Only policies enacted at the regional level, since the end of 2017 and focused on migrant and AS healthcare, were included in the analysis.</p><p>An organic search on the Internet was performed using institutional sources such as Wikinmp, regional official websites, and the website of the Italian Society of Migration Medicine (Società Italiana di Medicina delle Migrazioni, SIMM) searching for laws, decrees or plans specifically addressing ASs. Public health residents of the research groups in Italian regions included in the study collected the information. The same researchers analyzed and compared data from the policy collection and interviews. Policies at the national level provide the regulatory framework of the research and they were excluded from this analysis [<xref rid=\"B33-ijerph-17-05458\" ref-type=\"bibr\">33</xref>,<xref rid=\"B44-ijerph-17-05458\" ref-type=\"bibr\">44</xref>,<xref rid=\"B45-ijerph-17-05458\" ref-type=\"bibr\">45</xref>,<xref rid=\"B46-ijerph-17-05458\" ref-type=\"bibr\">46</xref>]. Similarly, regional policies addressing social determinants of health or integration issues were not considered.</p></sec><sec id=\"sec2dot2dot2-ijerph-17-05458\"><title>2.2.2. Data Collection of Practices </title><p>Data regarding practices were collected through a checklist (<xref ref-type=\"app\" rid=\"app1-ijerph-17-05458\">Supplementary Materials, Table S1</xref>). It investigated several aspects of medical care, such as first medical examination (ME), immunization, screening programs, as well as other aspects related to care provision and treatment pathways for ASs before their legal entitlement. </p><p>This evaluation followed a fixed workflow. Within the LHOs, directors of health departments and health professionals of migrant healthcare organizations and care provision were approached. At least one healthcare worker for each LHO was approached, for each LHO in each of the four regions. Then, researchers sent the checklist to explain how and why to insert information. Some instructions and technical specifications were sent in attachments to support the compilation. A semi-structured interview was carried out with these privileged observers to deepen our understanding of models of care, identify barriers to access and critical issues, and map the real organization of services with the broadest coverage and to describe pathways, strengths, and weaknesses. The information collected was carried out using the protocols of the single LHO. The process was completed in 2018. A large amount of information (contacts, protocols, challenges) was also obtained thanks to the cooperation with members of regional immigration and health groups (GrIS) of the Italian Society of Migration Medicine (SIMM). GrIS is a local network of health professionals and other members of civil society, with the aim of sharing knowledge, competencies, contacts, relationships, experiences, and practices on migrant healthcare. This organizational model is not a formal institution, but a meeting place, where voluntary participation varies according to the needs expressed by the participants themselves. </p></sec></sec><sec id=\"sec2dot3-ijerph-17-05458\"><title>2.3. Data Analysis</title><sec id=\"sec2dot3dot1-ijerph-17-05458\"><title>2.3.1. Policy Content Analysis </title><p>Based on an existing framework specifically designed for migrant health policy analysis [<xref rid=\"B23-ijerph-17-05458\" ref-type=\"bibr\">23</xref>], the research group developed a dedicated tool for the systematic comparison of policies to better focus on the population of interest. According to international and national recommendations [<xref rid=\"B21-ijerph-17-05458\" ref-type=\"bibr\">21</xref>,<xref rid=\"B35-ijerph-17-05458\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijerph-17-05458\" ref-type=\"bibr\">36</xref>], Mladowsky’s framework was adapted, introducing further categories and items specifically targeting ASs and refugees. In fact, the macro-categories (population groups targeted; data collection and research; quality and accessibility of health services; health issues addressed and implementation) were modified to define new subcategories and to integrate the international and national recommendations. This was done because the present study aims to focus on ASs and refugees upon their arrival in the regions, by comparing policies and their implementation with existing guidelines. The following macro-categories were used for the analysis [<xref rid=\"B23-ijerph-17-05458\" ref-type=\"bibr\">23</xref>,<xref rid=\"B39-ijerph-17-05458\" ref-type=\"bibr\">39</xref>]:<list list-type=\"bullet\"><list-item><p>Data collection assesses action aimed at supporting a migrant-sensitive data collection system such as the computerization of data during all the phases within ordinary data systems. Moreover, it includes other aspects of data collection, such as the aim of the collection and the typology of data collected.</p></list-item><list-item><p>Population groups refers to the subtype of migrant population included or specifically outlined in the policies. The analysis was limited to ASs and refugees in the first period after their arrival in Italy, specifying when policies contain indications towards specific categories such as unaccompanied and separated children (UASC), pregnant women, adolescents, the elderly, people with disabilities, people with mental health issues and victims of violence and torture.</p></list-item><list-item><p>Health issue addressed considers the health conditions receiving specific attention and any recommendations in the plan and policies analyzed, and it aims to identify the definition of actions specifically directed towards certain high-burden health problems, such as screening, treatment and follow up for CDs, immunization, screening, treatment and follow up for NCDs, screening and multidisciplinary diagnostic–therapeutic–rehabilitation paths for vulnerabilities, maternal and child health (MCH), counselling, health education and health promotion.</p></list-item><list-item><p>Part of health system targeted outlines specific actions concerning the organization of healthcare services, such as overcoming barriers in access to care, reinforcing comprehensive primary healthcare and health promotion, improving monitoring and governance and providing training, guidance and support to implement migrant-sensitive interventions.</p></list-item></list></p><p><xref rid=\"ijerph-17-05458-t001\" ref-type=\"table\">Table 1</xref> reports a summary of the process from the macro-category to the items investigated, through to the collection of recommendations for each category (a more detailed framework is reported in the <xref ref-type=\"app\" rid=\"app1-ijerph-17-05458\">Supplementary Materials, Table S2</xref>).</p></sec><sec id=\"sec2dot3dot2-ijerph-17-05458\"><title>2.3.2. Analysis of Practices</title><p>Data collected from interviews were transcribed in a single checklist and then reported in a unique spreadsheet following the checklist framework. Quantitative data were analyzed with statistical descriptive analysis, while qualitative data were analyzed through textual analysis and framework methods [<xref rid=\"B47-ijerph-17-05458\" ref-type=\"bibr\">47</xref>]. Each researcher collected and analyzed data from his or her regions of work. Data collected on the practices from each region were compared with regional policies. The results were shared and discussed with the other researchers.</p></sec></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05458\"><title>3. Results</title><sec id=\"sec3dot1-ijerph-17-05458\"><title>3.1. Analysis of Policies</title><p>The first survey permitted the collection of regional policies that include ASs healthcare (<xref ref-type=\"app\" rid=\"app1-ijerph-17-05458\">Supplementary Materials, Table S3</xref>) in four regions of Italy (Emilia-Romagna, Lazio, Toscana, Veneto) [<xref rid=\"B48-ijerph-17-05458\" ref-type=\"bibr\">48</xref>,<xref rid=\"B49-ijerph-17-05458\" ref-type=\"bibr\">49</xref>,<xref rid=\"B50-ijerph-17-05458\" ref-type=\"bibr\">50</xref>,<xref rid=\"B51-ijerph-17-05458\" ref-type=\"bibr\">51</xref>,<xref rid=\"B52-ijerph-17-05458\" ref-type=\"bibr\">52</xref>,<xref rid=\"B53-ijerph-17-05458\" ref-type=\"bibr\">53</xref>,<xref rid=\"B54-ijerph-17-05458\" ref-type=\"bibr\">54</xref>,<xref rid=\"B55-ijerph-17-05458\" ref-type=\"bibr\">55</xref>,<xref rid=\"B56-ijerph-17-05458\" ref-type=\"bibr\">56</xref>,<xref rid=\"B57-ijerph-17-05458\" ref-type=\"bibr\">57</xref>,<xref rid=\"B58-ijerph-17-05458\" ref-type=\"bibr\">58</xref>,<xref rid=\"B59-ijerph-17-05458\" ref-type=\"bibr\">59</xref>,<xref rid=\"B60-ijerph-17-05458\" ref-type=\"bibr\">60</xref>,<xref rid=\"B61-ijerph-17-05458\" ref-type=\"bibr\">61</xref>,<xref rid=\"B62-ijerph-17-05458\" ref-type=\"bibr\">62</xref>,<xref rid=\"B63-ijerph-17-05458\" ref-type=\"bibr\">63</xref>,<xref rid=\"B64-ijerph-17-05458\" ref-type=\"bibr\">64</xref>,<xref rid=\"B65-ijerph-17-05458\" ref-type=\"bibr\">65</xref>,<xref rid=\"B66-ijerph-17-05458\" ref-type=\"bibr\">66</xref>,<xref rid=\"B67-ijerph-17-05458\" ref-type=\"bibr\">67</xref>,<xref rid=\"B68-ijerph-17-05458\" ref-type=\"bibr\">68</xref>,<xref rid=\"B69-ijerph-17-05458\" ref-type=\"bibr\">69</xref>,<xref rid=\"B70-ijerph-17-05458\" ref-type=\"bibr\">70</xref>,<xref rid=\"B71-ijerph-17-05458\" ref-type=\"bibr\">71</xref>,<xref rid=\"B72-ijerph-17-05458\" ref-type=\"bibr\">72</xref>,<xref rid=\"B73-ijerph-17-05458\" ref-type=\"bibr\">73</xref>,<xref rid=\"B74-ijerph-17-05458\" ref-type=\"bibr\">74</xref>,<xref rid=\"B75-ijerph-17-05458\" ref-type=\"bibr\">75</xref>,<xref rid=\"B76-ijerph-17-05458\" ref-type=\"bibr\">76</xref>,<xref rid=\"B77-ijerph-17-05458\" ref-type=\"bibr\">77</xref>,<xref rid=\"B78-ijerph-17-05458\" ref-type=\"bibr\">78</xref>,<xref rid=\"B79-ijerph-17-05458\" ref-type=\"bibr\">79</xref>,<xref rid=\"B80-ijerph-17-05458\" ref-type=\"bibr\">80</xref>,<xref rid=\"B81-ijerph-17-05458\" ref-type=\"bibr\">81</xref>,<xref rid=\"B82-ijerph-17-05458\" ref-type=\"bibr\">82</xref>,<xref rid=\"B83-ijerph-17-05458\" ref-type=\"bibr\">83</xref>,<xref rid=\"B84-ijerph-17-05458\" ref-type=\"bibr\">84</xref>,<xref rid=\"B85-ijerph-17-05458\" ref-type=\"bibr\">85</xref>,<xref rid=\"B86-ijerph-17-05458\" ref-type=\"bibr\">86</xref>,<xref rid=\"B87-ijerph-17-05458\" ref-type=\"bibr\">87</xref>,<xref rid=\"B88-ijerph-17-05458\" ref-type=\"bibr\">88</xref>,<xref rid=\"B89-ijerph-17-05458\" ref-type=\"bibr\">89</xref>,<xref rid=\"B90-ijerph-17-05458\" ref-type=\"bibr\">90</xref>,<xref rid=\"B91-ijerph-17-05458\" ref-type=\"bibr\">91</xref>,<xref rid=\"B92-ijerph-17-05458\" ref-type=\"bibr\">92</xref>,<xref rid=\"B93-ijerph-17-05458\" ref-type=\"bibr\">93</xref>,<xref rid=\"B94-ijerph-17-05458\" ref-type=\"bibr\">94</xref>,<xref rid=\"B95-ijerph-17-05458\" ref-type=\"bibr\">95</xref>,<xref rid=\"B96-ijerph-17-05458\" ref-type=\"bibr\">96</xref>,<xref rid=\"B97-ijerph-17-05458\" ref-type=\"bibr\">97</xref>,<xref rid=\"B98-ijerph-17-05458\" ref-type=\"bibr\">98</xref>,<xref rid=\"B99-ijerph-17-05458\" ref-type=\"bibr\">99</xref>,<xref rid=\"B100-ijerph-17-05458\" ref-type=\"bibr\">100</xref>]. Different kinds of policies were found: regional laws and decrees, plans, protocols and guidelines. The entire process of policy analysis is reported for each single macro-area of <xref rid=\"ijerph-17-05458-t001\" ref-type=\"table\">Table 1</xref> in the <xref ref-type=\"app\" rid=\"app1-ijerph-17-05458\">Supplementary Materials, Tables S4–S7</xref>.</p><p>Concerning data collection, in all the regions that were investigated, the collection of data about healthcare at arrival is strictly recommended (<xref ref-type=\"app\" rid=\"app1-ijerph-17-05458\">Supplementary Materials, Table S4</xref>). However, only one region has planned to implement a continuative and computerized migrant-sensitive collection system and has invested in the portability of data from the first ME. Similarly, qualitative surveys and annual reports on migrants’ health status are lacking in most of the regional policies analyzed. </p><p>Several regional policies are aimed at specific population groups (<xref ref-type=\"app\" rid=\"app1-ijerph-17-05458\">Supplementary Materials, Table S5</xref>). Access to care for ASs and their right to health are formally recognized by all regions investigated through the validation of the asylum request, which gives the right to be enrolled in the RHS and, as a consequence, to have the same rights as the host community, concerning diagnosis, treatment and preventive services, with special attention paid to maternal and child health. Health protection for elderly immigrants, on the contrary, has been regulated only in two regions, alongside healthcare for migrant with disabilities or with mental health issues, and victims of violence (only in Emilia-Romagna).</p><p>Regarding policies targeting specific health issues (<xref ref-type=\"app\" rid=\"app1-ijerph-17-05458\">Supplementary Materials, Table S6</xref>), policy analysis shows that each region regulated the healthcare of ASs at arrival, identifying practices and protocols that LHOs must follow. First ME, syndromic surveillance, screening for CDs, immunization programs, despite some minimal differences, are expected in all the regions surveyed. However, our analysis revealed the lack of specific policies and protocols for screening and care for NCDs, vulnerabilities and drug abuses, and health promotion.</p><p>Policies with specific targets in the health system were collected in each region (<xref ref-type=\"app\" rid=\"app1-ijerph-17-05458\">Supplementary Materials, Table S7</xref>). All the regions have developed, during the last 20 years, dedicated services for migrant healthcare and linguistic and cultural mediation services targeted at the whole immigrant population, including ASs. Even if governing and monitoring policy contexts are widely present, only two regions have predisposed a specific government focal point for migrant health, with the aim of evaluating regional policy implementation at the LHO level and coordinating the various stakeholders involved in AS healthcare. Concerning training and technical support, only three regions have planned to provide technical guidance to LHOs and healthcare workers (HCWs).</p></sec><sec id=\"sec3dot2-ijerph-17-05458\"><title>3.2. Analysis of Practices</title><p>The second survey permitted the collection of data concerning practice at the LHO level. Thirty-four checklists were collected, reaching 65% of the total amount of LHOs present in the regions included in the study (<xref rid=\"ijerph-17-05458-t002\" ref-type=\"table\">Table 2</xref>). Covering all of the LHOs present in every region was not possible only in two regions (Lazio and Veneto) because of the logistical difficulties faced by the researchers. Different kinds of HCWs were involved in the survey as LHOs’ referees for AS healthcare, such as health managers (HMs), nurses, medical doctors (MDs).</p><p>Each LHO provides the first ME, tuberculosis (TB), sexually transmitted disease (STD), scabies and pediculosis screenings and treatments 2 to 3 days and 6 months after arrival (<xref rid=\"ijerph-17-05458-t003\" ref-type=\"table\">Table 3</xref>). Screenings for other CDs (Human Immunodeficiency Virus (HIV); hepatitis B virus (HBV); hepatitis C virus (HCV); latent tuberculosis infection (LTBI)) and for NCDs appear to be provided with more variably between LHOs, occurring during the first ME at the arrival or during the general practitioners’ intake after entitlements to the RHS have been confirmed, depending on the model adopted by the LHO or by the timeframe for entitlement to RHS. Data from the first ME, screenings, and other investigations are collected in all the LHOs, but the data collected are not continuative with the collection ordinary systems, and only one regional system has implemented a regional computer system. </p><p>Access to health services for diagnosis, treatment and follow up for CDs and NCDs are formally guaranteed in all the LHOs without cost for the first six months after arrival (12 months in Tuscany) and the entitlement to RHS is possible after the formalization of an asylum request. Moreover, maternal and child healthcare are provided in all LHOs, and some LHOs have activated multidisciplinary diagnostic–therapeutic–rehabilitation paths for people with vulnerabilities.</p><p>From our analysis of the local care pathway, three main models emerged, which are widely distributed in the regions investigated. </p><p>The first model consists of the presence of dedicated services for migrants including ASs, provided by LHOs. They offer healthcare to ASs from their arrival until their enrolment in the RHS. Another model bases the central role of the general practitioners in ASs’ care. The guarantee of access lasts from the moment of arrival until enrolment in the RHS, as in the previous model. The third model refers to the involvement of non-governmental organizations (NGOs) by LHOs in providing healthcare to vulnerable populations. As in the previous models, healthcare is provided from arrival until enrolment in the RHS. Other models of AS healthcare are more fragmented and based on the presence of different actors, such as physician involvement in the reception center, or public health professionals in the LHOs. According to the interviews, these models only focused on syndromic surveillance and they are not related to proper early intake care, even if access to services in case of need is guaranteed. </p><p>Our analysis of practices at the LHO level reveals several challenges. First, in most of the LHOs investigated, HCWs reported the presence of different kinds of barriers in access to care for ASs, dependent on linguistic–cultural factors and on legal status (<xref rid=\"ijerph-17-05458-t004\" ref-type=\"table\">Table 4</xref>). In particular, the entitlement to the RHS seemed to be impeded to the expiration of valid permission to stay or the delay of asylum requests. </p><p>Other challenging areas, reported by HCWs, are the screening and treatment for NCDs and dental health. Similarly, healthcare paths for people with vulnerabilities have been described as fragmented and inefficient, along with the continuity of care from arrival through to the next phases. The computerization of data, data collection and transmission were not well implemented, both where we expected they would be and where we did not. In all regions, health assessments of ASs and the monitoring of policy implementation, where expected, are not regularly conducted. </p><p>Difficulties in coordination between health professionals of LHOs and other stakeholders (law enforcement, NGOs, social services), as well as the lack of integration between different departments and HCWs of LHOs (primary care departments (PCDs); public health departments (PHD), general practitioners (GPs), hospitals) have been widely reported. Other issues that have been reported concern the training of health professionals, which seems to be insufficient and not regularly conducted, especially for general practitioners and health professionals involved in assistance at arrival. However, in several LHOs, training for HCWs has been provided by specific projects funded by the financial programs of the EU for migrants and ASs (European Asylum, Migration and Integration Fund (AMIF)), and managed by RHS and LHOs.</p><p>Finally, health promotion was described as one of the weakest areas of the healthcare of ASs provided by LHOs, and most of the activities related to this field have been managed by NGOs, civil society organizations (CSO) and by the spontaneous networking of health professionals.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05458\"><title>4. Discussion</title><p>This comparative content analysis of regional policies and practices towards ASs allowed for a broader examination of the gaps between regional policies and international and national recommendations, as well as the implementation of policies at the local level and the main model of healthcare adopted by the LHOs. Moreover, it allowed for the assessment of regional and local health system responses and the identification of challenges and best practices. It produced both an overview of data and an analysis within the regions and, finally, a comparison between them. </p><p>Concerning regional policy, the results showed that specific policies targeted towards AS healthcare in response to the recent migratory phenomena have been regulated in all the settings investigated. Additionally, results highlighted that policymakers gave more attention to the first phase of healthcare at arrival, syndromic surveillance, and infectious disease screening and control, than to the other phases and aspects [<xref rid=\"B101-ijerph-17-05458\" ref-type=\"bibr\">101</xref>]. From the data collected, this period appears to be rule based and particularly homogenous across the regions. However, some areas seem to be neglected by regional policy frameworks, such as the screening of NCDs and health protection for people with vulnerabilities. Consequently, actions and solutions aimed at responding to the first phase lead to fragmentation [<xref rid=\"B102-ijerph-17-05458\" ref-type=\"bibr\">102</xref>].</p><p>Our policy analysis demonstrated the presence of many items of international and national recommendations in regional policies, mostly concerning the right to health and access to care, CD prevention and control, health protection of mothers and children, cultural mediation services, and migrant-friendly services. Most of these policies are in line with national and international recommendations, but they seem to be inefficiently implemented. Specifically, results from different LHOs showed that data collection and computer systems, cultural mediation services, training and technical support, as well as monitoring and health assessment, are the weakest areas. </p><p>Evidence from migrants’ health status upon arrival showed a great burden towards NCDs, complex health needs and vulnerabilities [<xref rid=\"B103-ijerph-17-05458\" ref-type=\"bibr\">103</xref>,<xref rid=\"B104-ijerph-17-05458\" ref-type=\"bibr\">104</xref>,<xref rid=\"B105-ijerph-17-05458\" ref-type=\"bibr\">105</xref>,<xref rid=\"B106-ijerph-17-05458\" ref-type=\"bibr\">106</xref>,<xref rid=\"B107-ijerph-17-05458\" ref-type=\"bibr\">107</xref>,<xref rid=\"B108-ijerph-17-05458\" ref-type=\"bibr\">108</xref>,<xref rid=\"B109-ijerph-17-05458\" ref-type=\"bibr\">109</xref>,<xref rid=\"B110-ijerph-17-05458\" ref-type=\"bibr\">110</xref>]. This is a consequence of displacement and the migratory process and social determinants of health in the host country [<xref rid=\"B111-ijerph-17-05458\" ref-type=\"bibr\">111</xref>]. While generalizations are not possible, a few key points regarding intake into a continuous and integrated healthcare system responsible for the person from arrival emerged. The inclusion in a strong primary care system appears to reduce the healthcare burden [<xref rid=\"B112-ijerph-17-05458\" ref-type=\"bibr\">112</xref>,<xref rid=\"B113-ijerph-17-05458\" ref-type=\"bibr\">113</xref>] and reduce costs [<xref rid=\"B114-ijerph-17-05458\" ref-type=\"bibr\">114</xref>]. Moreover, it is worth noting that greater attention is paid to ensuring access and the right to health after the regularization of asylum [<xref rid=\"B39-ijerph-17-05458\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijerph-17-05458\" ref-type=\"bibr\">40</xref>,<xref rid=\"B41-ijerph-17-05458\" ref-type=\"bibr\">41</xref>,<xref rid=\"B42-ijerph-17-05458\" ref-type=\"bibr\">42</xref>,<xref rid=\"B43-ijerph-17-05458\" ref-type=\"bibr\">43</xref>,<xref rid=\"B44-ijerph-17-05458\" ref-type=\"bibr\">44</xref>,<xref rid=\"B45-ijerph-17-05458\" ref-type=\"bibr\">45</xref>,<xref rid=\"B46-ijerph-17-05458\" ref-type=\"bibr\">46</xref>,<xref rid=\"B47-ijerph-17-05458\" ref-type=\"bibr\">47</xref>,<xref rid=\"B48-ijerph-17-05458\" ref-type=\"bibr\">48</xref>,<xref rid=\"B49-ijerph-17-05458\" ref-type=\"bibr\">49</xref>,<xref rid=\"B50-ijerph-17-05458\" ref-type=\"bibr\">50</xref>,<xref rid=\"B51-ijerph-17-05458\" ref-type=\"bibr\">51</xref>,<xref rid=\"B52-ijerph-17-05458\" ref-type=\"bibr\">52</xref>,<xref rid=\"B53-ijerph-17-05458\" ref-type=\"bibr\">53</xref>,<xref rid=\"B54-ijerph-17-05458\" ref-type=\"bibr\">54</xref>,<xref rid=\"B55-ijerph-17-05458\" ref-type=\"bibr\">55</xref>,<xref rid=\"B56-ijerph-17-05458\" ref-type=\"bibr\">56</xref>,<xref rid=\"B57-ijerph-17-05458\" ref-type=\"bibr\">57</xref>,<xref rid=\"B58-ijerph-17-05458\" ref-type=\"bibr\">58</xref>,<xref rid=\"B59-ijerph-17-05458\" ref-type=\"bibr\">59</xref>,<xref rid=\"B60-ijerph-17-05458\" ref-type=\"bibr\">60</xref>,<xref rid=\"B61-ijerph-17-05458\" ref-type=\"bibr\">61</xref>,<xref rid=\"B62-ijerph-17-05458\" ref-type=\"bibr\">62</xref>,<xref rid=\"B63-ijerph-17-05458\" ref-type=\"bibr\">63</xref>,<xref rid=\"B64-ijerph-17-05458\" ref-type=\"bibr\">64</xref>,<xref rid=\"B65-ijerph-17-05458\" ref-type=\"bibr\">65</xref>,<xref rid=\"B66-ijerph-17-05458\" ref-type=\"bibr\">66</xref>,<xref rid=\"B67-ijerph-17-05458\" ref-type=\"bibr\">67</xref>,<xref rid=\"B68-ijerph-17-05458\" ref-type=\"bibr\">68</xref>,<xref rid=\"B69-ijerph-17-05458\" ref-type=\"bibr\">69</xref>,<xref rid=\"B70-ijerph-17-05458\" ref-type=\"bibr\">70</xref>,<xref rid=\"B71-ijerph-17-05458\" ref-type=\"bibr\">71</xref>,<xref rid=\"B72-ijerph-17-05458\" ref-type=\"bibr\">72</xref>,<xref rid=\"B73-ijerph-17-05458\" ref-type=\"bibr\">73</xref>,<xref rid=\"B74-ijerph-17-05458\" ref-type=\"bibr\">74</xref>,<xref rid=\"B75-ijerph-17-05458\" ref-type=\"bibr\">75</xref>,<xref rid=\"B76-ijerph-17-05458\" ref-type=\"bibr\">76</xref>,<xref rid=\"B77-ijerph-17-05458\" ref-type=\"bibr\">77</xref>,<xref rid=\"B78-ijerph-17-05458\" ref-type=\"bibr\">78</xref>,<xref rid=\"B79-ijerph-17-05458\" ref-type=\"bibr\">79</xref>,<xref rid=\"B80-ijerph-17-05458\" ref-type=\"bibr\">80</xref>,<xref rid=\"B81-ijerph-17-05458\" ref-type=\"bibr\">81</xref>,<xref rid=\"B82-ijerph-17-05458\" ref-type=\"bibr\">82</xref>,<xref rid=\"B83-ijerph-17-05458\" ref-type=\"bibr\">83</xref>,<xref rid=\"B84-ijerph-17-05458\" ref-type=\"bibr\">84</xref>,<xref rid=\"B85-ijerph-17-05458\" ref-type=\"bibr\">85</xref>,<xref rid=\"B86-ijerph-17-05458\" ref-type=\"bibr\">86</xref>,<xref rid=\"B87-ijerph-17-05458\" ref-type=\"bibr\">87</xref>,<xref rid=\"B88-ijerph-17-05458\" ref-type=\"bibr\">88</xref>,<xref rid=\"B89-ijerph-17-05458\" ref-type=\"bibr\">89</xref>,<xref rid=\"B90-ijerph-17-05458\" ref-type=\"bibr\">90</xref>,<xref rid=\"B91-ijerph-17-05458\" ref-type=\"bibr\">91</xref>,<xref rid=\"B92-ijerph-17-05458\" ref-type=\"bibr\">92</xref>,<xref rid=\"B93-ijerph-17-05458\" ref-type=\"bibr\">93</xref>,<xref rid=\"B94-ijerph-17-05458\" ref-type=\"bibr\">94</xref>,<xref rid=\"B95-ijerph-17-05458\" ref-type=\"bibr\">95</xref>,<xref rid=\"B96-ijerph-17-05458\" ref-type=\"bibr\">96</xref>,<xref rid=\"B97-ijerph-17-05458\" ref-type=\"bibr\">97</xref>,<xref rid=\"B98-ijerph-17-05458\" ref-type=\"bibr\">98</xref>,<xref rid=\"B99-ijerph-17-05458\" ref-type=\"bibr\">99</xref>,<xref rid=\"B100-ijerph-17-05458\" ref-type=\"bibr\">100</xref>,<xref rid=\"B101-ijerph-17-05458\" ref-type=\"bibr\">101</xref>,<xref rid=\"B102-ijerph-17-05458\" ref-type=\"bibr\">102</xref>,<xref rid=\"B103-ijerph-17-05458\" ref-type=\"bibr\">103</xref>,<xref rid=\"B104-ijerph-17-05458\" ref-type=\"bibr\">104</xref>,<xref rid=\"B105-ijerph-17-05458\" ref-type=\"bibr\">105</xref>,<xref rid=\"B106-ijerph-17-05458\" ref-type=\"bibr\">106</xref>,<xref rid=\"B107-ijerph-17-05458\" ref-type=\"bibr\">107</xref>,<xref rid=\"B108-ijerph-17-05458\" ref-type=\"bibr\">108</xref>,<xref rid=\"B109-ijerph-17-05458\" ref-type=\"bibr\">109</xref>,<xref rid=\"B110-ijerph-17-05458\" ref-type=\"bibr\">110</xref>,<xref rid=\"B111-ijerph-17-05458\" ref-type=\"bibr\">111</xref>,<xref rid=\"B112-ijerph-17-05458\" ref-type=\"bibr\">112</xref>,<xref rid=\"B113-ijerph-17-05458\" ref-type=\"bibr\">113</xref>,<xref rid=\"B114-ijerph-17-05458\" ref-type=\"bibr\">114</xref>,<xref rid=\"B115-ijerph-17-05458\" ref-type=\"bibr\">115</xref>], when ASs gain the same health rights as the general host population, within the universal coverage of the SSN. However, this is not within the scope of the present study.</p><p>The analysis of practices showed the full implementation of some aspects of regional policies, particularly the first ME, screening for CDs and immunization programs, as well as cooperation and collaboration with the local office of the Ministry of the Interior (Prefettura) by LHOs and managing bodies of reception centers. In general, concerning these health issues, the practices seem to be similar across LHOs of different regions. </p><p>Screenings for NCDs, health promotion activities and screening–diagnostic–rehabilitation healthcare paths for people with vulnerabilities have been developed only by some LHOs, as a part of a local policy or as a consequence of the availability of resources. </p><p>Many items that were found to be missing through content analysis do not always lack the matching of these indications and guidelines both at national and international levels. The adoption and implementation of directives and policies leads to disparities and delays, as described, and policies may not be adopted even when implementation practices come before them. </p><p>Our analysis of practice also revealed several challenges at the local level. Linguistic–cultural, administrative, and legal barriers to accessing care [<xref rid=\"B102-ijerph-17-05458\" ref-type=\"bibr\">102</xref>] are present across the regions, reflecting a lack of cultural competence in the health service [<xref rid=\"B116-ijerph-17-05458\" ref-type=\"bibr\">116</xref>,<xref rid=\"B117-ijerph-17-05458\" ref-type=\"bibr\">117</xref>,<xref rid=\"B118-ijerph-17-05458\" ref-type=\"bibr\">118</xref>]. Access to care depends on legal status [<xref rid=\"B14-ijerph-17-05458\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijerph-17-05458\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijerph-17-05458\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijerph-17-05458\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijerph-17-05458\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijerph-17-05458\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijerph-17-05458\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijerph-17-05458\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijerph-17-05458\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijerph-17-05458\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijerph-17-05458\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijerph-17-05458\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijerph-17-05458\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijerph-17-05458\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijerph-17-05458\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijerph-17-05458\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijerph-17-05458\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijerph-17-05458\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijerph-17-05458\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijerph-17-05458\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijerph-17-05458\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijerph-17-05458\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijerph-17-05458\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijerph-17-05458\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijerph-17-05458\" ref-type=\"bibr\">38</xref>,<xref rid=\"B39-ijerph-17-05458\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijerph-17-05458\" ref-type=\"bibr\">40</xref>,<xref rid=\"B41-ijerph-17-05458\" ref-type=\"bibr\">41</xref>,<xref rid=\"B42-ijerph-17-05458\" ref-type=\"bibr\">42</xref>,<xref rid=\"B43-ijerph-17-05458\" ref-type=\"bibr\">43</xref>,<xref rid=\"B44-ijerph-17-05458\" ref-type=\"bibr\">44</xref>,<xref rid=\"B45-ijerph-17-05458\" ref-type=\"bibr\">45</xref>,<xref rid=\"B46-ijerph-17-05458\" ref-type=\"bibr\">46</xref>,<xref rid=\"B47-ijerph-17-05458\" ref-type=\"bibr\">47</xref>,<xref rid=\"B48-ijerph-17-05458\" ref-type=\"bibr\">48</xref>,<xref rid=\"B49-ijerph-17-05458\" ref-type=\"bibr\">49</xref>,<xref rid=\"B50-ijerph-17-05458\" ref-type=\"bibr\">50</xref>,<xref rid=\"B51-ijerph-17-05458\" ref-type=\"bibr\">51</xref>,<xref rid=\"B52-ijerph-17-05458\" ref-type=\"bibr\">52</xref>,<xref rid=\"B53-ijerph-17-05458\" ref-type=\"bibr\">53</xref>,<xref rid=\"B54-ijerph-17-05458\" ref-type=\"bibr\">54</xref>,<xref rid=\"B55-ijerph-17-05458\" ref-type=\"bibr\">55</xref>,<xref rid=\"B56-ijerph-17-05458\" ref-type=\"bibr\">56</xref>,<xref rid=\"B57-ijerph-17-05458\" ref-type=\"bibr\">57</xref>,<xref rid=\"B58-ijerph-17-05458\" ref-type=\"bibr\">58</xref>,<xref rid=\"B59-ijerph-17-05458\" ref-type=\"bibr\">59</xref>,<xref rid=\"B60-ijerph-17-05458\" ref-type=\"bibr\">60</xref>,<xref rid=\"B61-ijerph-17-05458\" ref-type=\"bibr\">61</xref>,<xref rid=\"B62-ijerph-17-05458\" ref-type=\"bibr\">62</xref>,<xref rid=\"B63-ijerph-17-05458\" ref-type=\"bibr\">63</xref>,<xref rid=\"B64-ijerph-17-05458\" ref-type=\"bibr\">64</xref>,<xref rid=\"B65-ijerph-17-05458\" ref-type=\"bibr\">65</xref>,<xref rid=\"B66-ijerph-17-05458\" ref-type=\"bibr\">66</xref>,<xref rid=\"B67-ijerph-17-05458\" ref-type=\"bibr\">67</xref>,<xref rid=\"B68-ijerph-17-05458\" ref-type=\"bibr\">68</xref>,<xref rid=\"B69-ijerph-17-05458\" ref-type=\"bibr\">69</xref>,<xref rid=\"B70-ijerph-17-05458\" ref-type=\"bibr\">70</xref>,<xref rid=\"B71-ijerph-17-05458\" ref-type=\"bibr\">71</xref>,<xref rid=\"B72-ijerph-17-05458\" ref-type=\"bibr\">72</xref>,<xref rid=\"B73-ijerph-17-05458\" ref-type=\"bibr\">73</xref>,<xref rid=\"B74-ijerph-17-05458\" ref-type=\"bibr\">74</xref>,<xref rid=\"B75-ijerph-17-05458\" ref-type=\"bibr\">75</xref>,<xref rid=\"B76-ijerph-17-05458\" ref-type=\"bibr\">76</xref>,<xref rid=\"B77-ijerph-17-05458\" ref-type=\"bibr\">77</xref>,<xref rid=\"B78-ijerph-17-05458\" ref-type=\"bibr\">78</xref>,<xref rid=\"B79-ijerph-17-05458\" ref-type=\"bibr\">79</xref>,<xref rid=\"B80-ijerph-17-05458\" ref-type=\"bibr\">80</xref>,<xref rid=\"B81-ijerph-17-05458\" ref-type=\"bibr\">81</xref>,<xref rid=\"B82-ijerph-17-05458\" ref-type=\"bibr\">82</xref>,<xref rid=\"B83-ijerph-17-05458\" ref-type=\"bibr\">83</xref>,<xref rid=\"B84-ijerph-17-05458\" ref-type=\"bibr\">84</xref>,<xref rid=\"B85-ijerph-17-05458\" ref-type=\"bibr\">85</xref>,<xref rid=\"B86-ijerph-17-05458\" ref-type=\"bibr\">86</xref>,<xref rid=\"B87-ijerph-17-05458\" ref-type=\"bibr\">87</xref>,<xref rid=\"B88-ijerph-17-05458\" ref-type=\"bibr\">88</xref>,<xref rid=\"B89-ijerph-17-05458\" ref-type=\"bibr\">89</xref>,<xref rid=\"B90-ijerph-17-05458\" ref-type=\"bibr\">90</xref>,<xref rid=\"B91-ijerph-17-05458\" ref-type=\"bibr\">91</xref>,<xref rid=\"B92-ijerph-17-05458\" ref-type=\"bibr\">92</xref>,<xref rid=\"B93-ijerph-17-05458\" ref-type=\"bibr\">93</xref>,<xref rid=\"B94-ijerph-17-05458\" ref-type=\"bibr\">94</xref>,<xref rid=\"B95-ijerph-17-05458\" ref-type=\"bibr\">95</xref>,<xref rid=\"B96-ijerph-17-05458\" ref-type=\"bibr\">96</xref>,<xref rid=\"B97-ijerph-17-05458\" ref-type=\"bibr\">97</xref>,<xref rid=\"B98-ijerph-17-05458\" ref-type=\"bibr\">98</xref>,<xref rid=\"B99-ijerph-17-05458\" ref-type=\"bibr\">99</xref>,<xref rid=\"B100-ijerph-17-05458\" ref-type=\"bibr\">100</xref>,<xref rid=\"B101-ijerph-17-05458\" ref-type=\"bibr\">101</xref>,<xref rid=\"B102-ijerph-17-05458\" ref-type=\"bibr\">102</xref>] as well as proper response to specific needs, such as vulnerabilities [<xref rid=\"B102-ijerph-17-05458\" ref-type=\"bibr\">102</xref>]. These observations reveal the lack or the ineffectiveness of interventions aimed at overcoming these barriers and at building migrant-friendly services, focused on the migrants’ health needs and oriented to guaranteeing equitable, acceptable and adequate care [<xref rid=\"B119-ijerph-17-05458\" ref-type=\"bibr\">119</xref>,<xref rid=\"B120-ijerph-17-05458\" ref-type=\"bibr\">120</xref>,<xref rid=\"B121-ijerph-17-05458\" ref-type=\"bibr\">121</xref>] both at arrival and long term.</p><p>Other challenges reported in the interviews concern the lack of integration and cooperation between different departments of LHOs, and difficulties of continuity of care [<xref rid=\"B102-ijerph-17-05458\" ref-type=\"bibr\">102</xref>]. These aspects are already known as some of the main weaknesses of the current organization of the LHOs and are associated with low quality of care [<xref rid=\"B115-ijerph-17-05458\" ref-type=\"bibr\">115</xref>]. In migrants’ and ASs’ healthcare, the presence of barriers to access, in addition to difficulties in continuity and integration of care, can produce delays in diagnosis and treatment, inadequate care or overtreatment and disparities in health outcomes [<xref rid=\"B119-ijerph-17-05458\" ref-type=\"bibr\">119</xref>,<xref rid=\"B120-ijerph-17-05458\" ref-type=\"bibr\">120</xref>,<xref rid=\"B121-ijerph-17-05458\" ref-type=\"bibr\">121</xref>,<xref rid=\"B122-ijerph-17-05458\" ref-type=\"bibr\">122</xref>]. This requires that health service organizations play a central role in guaranteeing ASs’ right to health and their health protection, especially at the primary healthcare level, where a migrant-friendly approach seems to be more effective in reducing healthcare burdens [<xref rid=\"B112-ijerph-17-05458\" ref-type=\"bibr\">112</xref>,<xref rid=\"B113-ijerph-17-05458\" ref-type=\"bibr\">113</xref>]. </p><p>Since primary healthcare seems to play a central role in migrant healthcare, the analysis of the models of care that emerged from the analysis of local paths can offer some support to the debate on how to strengthen health services to address migrant and AS health needs. </p><p>The presence of dedicated services on migrant healthcare seems to reduce the risk of ASs being excluded from healthcare or of inappropriately using emergency services [<xref rid=\"B123-ijerph-17-05458\" ref-type=\"bibr\">123</xref>]. This is a consequence of a lack of knowledge about the healthcare system, mostly in the first period after arrival, and a lack of health coverage due to a delay in the processing of an asylum request, especially during periods of large influx [<xref rid=\"B112-ijerph-17-05458\" ref-type=\"bibr\">112</xref>,<xref rid=\"B113-ijerph-17-05458\" ref-type=\"bibr\">113</xref>]. Moreover, these services seem to have specific migrant health competences, while other RHS do not. In spite of this, the presence of dedicated services could reflect the risk of developing separate care pathways both for migrants and host communities [<xref rid=\"B123-ijerph-17-05458\" ref-type=\"bibr\">123</xref>] that could seem discriminatory. In addition, the presence of dedicated services could impede ASs’ empowerment in accessing and taking care of their health and reduces the responsibility of other services, especially of general practitioners.</p><p>The second model is based on the early intake care of ASs by general practitioners, which are the entry point of primary healthcare. As in the previous model, this one provides health protection from arrival, but it reduces the risk of creating different pathways of care for migrants and host communities [<xref rid=\"B112-ijerph-17-05458\" ref-type=\"bibr\">112</xref>,<xref rid=\"B113-ijerph-17-05458\" ref-type=\"bibr\">113</xref>]. However, according to the interviews, GPs within LHOs either lack specific training in migrant health or benefit from it disproportionately, and mediation services outside LHO facilities are not widely available.</p><p>Moreover, early intake care of ASs by GPs can result in delays or inappropriate responses to the health needs of ASs, especially in the case of complex needs and when GPs are not particularly skilled in migrant health [<xref rid=\"B124-ijerph-17-05458\" ref-type=\"bibr\">124</xref>].</p><p>Another model emerged regarding the presence of NGOs. The results showed that HCWs of NGOs are usually particularly skilled in migrant health and complex health needs. However, the risk remains in dividing the care pathway and of outsourcing some services from the SSN. In fact, many of these NGOs are not fully integrated with the network of health services and have no access to ordinary data systems. Furthermore, as stated, LHOs and RHS are not able to assess ASs’ and migrants’ health needs attended to by these NGOs. Moreover, the position of NGOs is complex and cannot be generalized. In fact, NGOs must navigate the delicate balance between providing assistance and healthcare and advocating and conditioning public system decisions [<xref rid=\"B125-ijerph-17-05458\" ref-type=\"bibr\">125</xref>]. </p><p>This variability in models of care adopted by LHOs can have more than one explanation: the regional autonomy towards healthcare and the decentralization to a subnational level of health policies and health management, in coherence with the Italian SSN organization and with the competences of each level [<xref rid=\"B32-ijerph-17-05458\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijerph-17-05458\" ref-type=\"bibr\">33</xref>]; the characteristics of the settings, such as the availability of resources or the previous policy [<xref rid=\"B123-ijerph-17-05458\" ref-type=\"bibr\">123</xref>]; geographical (urban or rural area), cultural or political aspects. In this study, the previous migratory pressure on RHS and the organizational model of the reception systems seemed to be more influential, even if more data and details are needed to better understand the possible correlation between these factors. </p><p>Since different contexts require different policies and models of care [<xref rid=\"B39-ijerph-17-05458\" ref-type=\"bibr\">39</xref>,<xref rid=\"B112-ijerph-17-05458\" ref-type=\"bibr\">112</xref>,<xref rid=\"B113-ijerph-17-05458\" ref-type=\"bibr\">113</xref>], extensive variability and heterogeneity are not wholly negative, but have positive aspects as well. On one hand, this variability could risk of hiding the gap between the policies and their implementation and the guidelines at the international and national levels. On the other hand, it could act as an engine of change with attention to the process of networking. In this regard, governance and monitoring play a key role and represent a serious lack in the health system. </p><p>The critical issues summarized above underline the need to reinforce the national and regional levels of governance towards ASs’ and migrants’ health [<xref rid=\"B21-ijerph-17-05458\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijerph-17-05458\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijerph-17-05458\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijerph-17-05458\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijerph-17-05458\" ref-type=\"bibr\">25</xref>]. A migrant-friendly approach and a stronger primary healthcare system could be a part of the solution, although it means integrating governance systems capable of challenging all the actors involved and building a community-based approach. </p><p>An essential component of building an inclusive system is to identify and constantly monitor the compliance with international recommendations and national policies and guidelines, while assessing the health needs of ASs and migrants. Accordingly, critical areas emerged from the analysis that demonstrated the importance of monitoring policy implementation. Therefore, the design and commissioning of a permanent observatory towards policies represents a useful monitoring and governance instrument [<xref rid=\"B25-ijerph-17-05458\" ref-type=\"bibr\">25</xref>]. </p><p>Concerning the latter, it is worth noting that local resources and practices, in many cases, bridge the gap, offering sustainable and inclusive practices. Moreover, many European-financed programs, as well as national and regional projects specifically devoted to ASs, fill the gaps in policies. From the interviews, it emerged that informal practices are often successful. The main factors of this process are the multiplicity of actors of the community and the local social structures, such as NGOs or volunteering, which add value to local resources and practices, act to reinforce networks and contribute to cohesion and social integration [<xref rid=\"B115-ijerph-17-05458\" ref-type=\"bibr\">115</xref>]. The adoption and implementation of directives and policies lead to disparities and delays, as explained, and policies are occasionally not adopted, even when implementation practices come before them. </p><p>The present research presents many strengths and limitations. The main limitations are: <list list-type=\"bullet\"><list-item><p>The partial coverage of the country. In fact, the study included only four Italian regions, located in the north and center of the country.</p></list-item><list-item><p>The incomplete data concerning the sources of information. Documents collected in the policies category did not consider/include projects and programs financed using European funds (such as AMIF projects).</p></list-item><list-item><p>The large amount of data collected. This made the analysis complex and some simplifications of the details were needed.</p></list-item><list-item><p>The lack of similar policy analysis studies as frameworks and tool comparisons, meaning that we needed to mix and match the Mladovsky tool [<xref rid=\"B23-ijerph-17-05458\" ref-type=\"bibr\">23</xref>] with national and international guidelines and with the difficulty of policy implementations.</p></list-item></list></p><p>The main strengths are: <list list-type=\"bullet\"><list-item><p>The relevance of the work, because of the scarcity of such research design methodologies, and few data sources. Therefore, the collection and production of data analysis on the topic appear to be relevant, as does the description of the gap referring to the international and national guidelines. These factors seem not to be present in similar studies associating policy levels to policy implementation.</p></list-item><list-item><p>The originality of both the study design (descriptive and comparative study, using a mixed methodology, both content analysis of policies and survey and semi-structured interviews with health workers to evaluate implementation), and the data collection (collaboration with local immigration groups, e.g., GrIS; proximity and closeness to the territory of the research group members; identification and engagement of key stakeholders; methodology of action–research–intervention).</p></list-item><list-item><p>The definition of a multicentric and multi-located research project, involving a regional and sub-regional detail level, unlike other similar studies [<xref rid=\"B39-ijerph-17-05458\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijerph-17-05458\" ref-type=\"bibr\">40</xref>,<xref rid=\"B115-ijerph-17-05458\" ref-type=\"bibr\">115</xref>].</p></list-item></list></p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05458\"><title>5. Conclusions</title><p>This study describes some aspects of the health system response to the recent migration phenomena in four Italian regions. Regional policies addressing migrants’ health mainly focus on the prevention and control of infectious diseases upon their arrival, while less attention is given to NCDs and to taking charge of vulnerable groups. Access to care for migrants and asylum seekers seems to still be hampered by some barriers and factors, other than those recommended by regional policies and national/international guidelines. Local realities on the ground have filled these gaps thanks to some best practices based on the strengthening of the primary healthcare services and the involvement of local governance and other stakeholders. </p><p>Since it appears that there is no one-fits-all healthcare model, each region has to adapt the healthcare policy to their specific local context. This requires interdisciplinary and intersectoral actions, engaging not only the healthcare sector, but also other local stakeholders, such as NGOs or volunteering organizations, while also involving other local resources and practices, reinforcing networks and contributing to cohesion and social integration. However, local policy implementation may lead to different types of healthcare assistance and then threaten equity and quality in healthcare for migrants. Therefore, the policies that we believe to be the most desirable directly target the empowerment of primary healthcare services for migrant assistance.</p></sec></body><back><ack><title>Acknowledgments</title><p>The authors thank Alice Corsaro, Gloria Raguzzoni, Jacopo Bianchi, Teresa Della Zuanna, Lisa Goddard and the regional groups of health and immigration (GrIS) of the Italian Society of Migration Medicine (SIMM) of Emilia-Romagna, Lazio, Toscana and Veneto for their contribution to collecting the data.</p></ack><app-group><app id=\"app1-ijerph-17-05458\"><title>Supplementary Materials</title><p>The following are available online at <uri xlink:href=\"https://www.mdpi.com/1660-4601/17/15/5458/s1\">https://www.mdpi.com/1660-4601/17/15/5458/s1</uri>, Table S1. Checklist used for semi-structured interviews; Table S2. Summary of international and national recommendation according to the Mladovsky framework and selected items for policy analysis; Table S3. Regional policies analyzed; Table S4. Policy analysis: data collection; Table S5. Policy analysis: population targeted; Table S6. Policy analysis: health issue addressed; Table S7. Policy analysis: part of health system targeted.</p><supplementary-material content-type=\"local-data\" id=\"ijerph-17-05458-s001\"><media xlink:href=\"ijerph-17-05458-s001.zip\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>Conceptualization, L.M., C.M., P.B., L.P., and C.S.; formal analysis, L.M., L.P. and C.S.; investigation, L.M., C.M., P.B., L.P. and C.S.; Methodology, L.M., C.M., P.B., L.P. and C.S.; project administration, C.S.; writing—original draft, L.M., C.M., P.B., L.P., and C.S.; writing—review and editing, L.M., C.M., P.B., L.P., and C.S. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research received no external funding.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References and Notes</title><ref id=\"B1-ijerph-17-05458\"><label>1.</label><element-citation publication-type=\"web\"><person-group person-group-type=\"author\"><collab>United Nations, Department of Economic and Social Affairs-Population Division</collab></person-group><article-title>International Migration Report</article-title><year>2019</year><comment>Available online: <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.un.org/en/development/desa/population/migration/data/estimates2/estimates19.asp\">http://www.un.org/en/development/desa/population/migration/data/estimates2/estimates19.asp</ext-link></comment><date-in-citation content-type=\"access-date\" 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Esperienze Nazionali e internazionali</source><person-group person-group-type=\"editor\"><name><surname>Maciocco</surname><given-names>G.</given-names></name></person-group><publisher-name>Carocci Editore</publisher-name><publisher-loc>Rome, Italy</publisher-loc><year>2019</year></element-citation></ref><ref id=\"B125-ijerph-17-05458\"><label>125.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Pursch</surname><given-names>B.</given-names></name><name><surname>Tate</surname><given-names>A.</given-names></name><name><surname>Legido-Quigley</surname><given-names>H.</given-names></name><name><surname>Howard</surname><given-names>N.</given-names></name></person-group><article-title>Health for all? A qualitative study of NGO support to migrants affected by structural violence in northern France</article-title><source>Soc. Sci. Med.</source><year>2020</year><volume>248</volume><fpage>112838</fpage><pub-id pub-id-type=\"doi\">10.1016/j.socscimed.2020.112838</pub-id><pub-id pub-id-type=\"pmid\">32062568</pub-id></element-citation></ref></ref-list></back><floats-group><table-wrap id=\"ijerph-17-05458-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05458-t001_Table 1</object-id><label>Table 1</label><caption><p>Framework for Policy Content Analysis.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Area </th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Summary of Recommendations</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Items/ Target Investigated</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Data collection</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Identify immediate needs during episodes of mass international migration; Use informative system to collect data during all the phase of the reception. Promote the inclusion of migrant variables in existing data collection systems; Use of a defined checklist/protocol for medical examination; Inclusion of AS data in the ordinary data system; Computerization of data. </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Continuative and computer migrant-sensitive collection system</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Collection of and access to information on the health status, modifiable risk behaviors and access to healthcare; continuous health needs assessment. Disaggregation and comparability of data is required; Enhance epidemiological surveillance capacities to include migrant-sensitive data. Use innovative approaches, including surveys and qualitative methods. </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Typology of data<break/></td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Promote the portability of health data in accordance with national law. </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Portability/Transmissibility</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Produce progress reports on the health status of refugees, asylum seekers and migrants.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Report/scope of collection</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Population groups</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Improving the health of the most vulnerable, including unaccompanied children, pregnant women, adolescents, the elderly, people with disabilities and victims of torture. Issues relating to sexual and reproductive health, family planning, gender-based violence and rape management, forced marriage and adolescent pregnancy, and mental health and care should be prioritized.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Unaccompanied children (UNCH)<break/>Pregnant women<break/>Adolescents<break/>Elderly<break/>People with disabilities<break/>People with mental issue<break/>Victims of violence (any) and torture</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Health issue addressed</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Screening during first ME: TB, malaria, STDs, parasitosis; Screening: HIV, HBV, HCV, LTBI; Involvement ASs in infectious disease prevention and control;</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Screening, treatment and follow up for CDs </td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Immunization programs for children (0-14) and adults (polio, diphtheria, tetanus, pertussis, measles, mumps, rubella, chicken poxs, HBV)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Immunization</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Screening, early access to essential primary care, accessing treatment, care and support; Screening for visual and auditory acuity, dental health, diabetes, hypertension, anemia, cervical cancer; blood tests: blood count, urine test;</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Screening, treatment and follow up for NCDs</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Screening for psychosocial disorders, drugs and alcohol abuse, nutrition disorders; Screening for violence and torture, specific and multidisciplinary diagnostic–therapeutic–rehabilitation path; </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Screening and multidisciplinary diagnostic–therapeutic–rehabilitation path for vulnerabilities</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Screening for pregnancy, access to screening programs that are in place for the host population, screening during pregnancy for neonatal diseases, access to maternal and neonatal healthcare </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Maternal and Child health</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Counselling, health education and health promotion</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Counselling, health education and health promotion</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Part of health system targeted</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Culturally sensitive health services, access to interpreters, provision of cultural mediators; Overcome administrative hurdles; Support for patient fees; Information about health entitlements and support in navigating through the system;</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Overcome barriers in access to care</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Primary care, preventive care, health promotion services, prevention, detection, treatment and monitoring of NCDs, CDs, vulnerabilities, MeCH;</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Comprehensive primary healthcare and health promotion</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Health assessment; Reporting of implementation, accountability and data collection; Government focal points, cooperation and coordination with other stakeholders; Community information and engagement;</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Monitoring and governance</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Training and continuous update with health equity and human rights-based approaches, and specific focus (es. victim of torture); Skilled health professional on migrant health/continuous professional training; Guidance, training and support tools to implement migrant sensitive interventions on CDs, NCDs, vulnerabilities;</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Continuous training, guidance and support to implement migrant sensitive interventions</td></tr></tbody></table><table-wrap-foot><fn><p>Note: Asylum seeker (AS); communicable diseases (CDs); Human Immunodeficiency Virus (HIV); hepatitis B virus (HBV); hepatitis C virus (HCV); latent tuberculosis infection (LTBI); medical examinations (ME); maternal and child health (MCH); non-communicable diseases (NCDs); sexually transmitted diseases (STDs); tuberculosis (TB); unaccompanied and separated children (UASC).</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05458-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05458-t002_Table 2</object-id><label>Table 2</label><caption><p>Summary of Checklists.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Area</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Emilia-Romagna</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Lazio</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Toscana</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Veneto</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">LHOs covered</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9/9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5/10</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3/3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3/9</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Checklist</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">HCWs</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">HMs, nurses, MDs;</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">HMs, MDs;</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">HMs, MDs;</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">HMs, MDs;</td></tr></tbody></table><table-wrap-foot><fn><p>Note: Health Managers (HMs); Local Health Organization (LHO); Medical Doctors (MDs).</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05458-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05458-t003_Table 3</object-id><label>Table 3</label><caption><p>Summary of Checklists: Practices at the LHO level.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Area</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Emilia-Romagna</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Lazio</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Toscana</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Veneto</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reception system</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Hub-Spokes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mixed</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Widespread</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Widespread</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">First ME<break/>when<break/>who<break/>where<break/>why<break/>what</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2-3d–1m;<break/>DS of LHO, NGOs, GP;<break/>LHO and NGO facilities, reception center;<break/>Syndromic surveillance and active research of health issue;</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2-3d–1m;<break/>DS of LHO, NGOs; GP;<break/>LHO and NGO facilities;<break/>Syndromic surveillance and active research of health issue;</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2-3d–1m;<break/>MD of LHO; NGOs;<break/>LHO and NGO facilities, reception center;<break/>Syndromic surveillance and active research of health issue;</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2-3d–15d;<break/>PHD, DS of LHO;<break/>PHD, DS of LHO;<break/>Syndromic surveillance, and active research of health issue;</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Screening<break/>when<break/>who<break/>where<break/>why<break/>what</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2-3 d–6 m;<break/>PHD, IDU, DS of LHO;<break/>PHD, IDU, DS of LHO;<break/>Individual and community health protection;<break/>TB, TSD, parasitosis, pediculosis, scabies, LTBI, HIV, HBV, HCV, vulnerabilities;</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2-3 d–6 m;<break/>PHD, IDU, DS of LHO;<break/>PHD, IDU, DS of LHO;<break/>Individual and community health protection;<break/>TB, parasitosis,<break/>vulnerability;</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2-3 d–6 m;<break/>PHD, IDU, PCD of LHO<break/>PHD, IDU, PCD;<break/>Individual and community health protection;<break/>Not everywhere: TB, Syphilis, HCV, HBV, HIV, vulnerabilities;</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2-3d–1m;<break/>PHD, DS of LHO;<break/>PHD, DS of LHO;<break/>Individual and community health protection;<break/>TB, LTBI, scabies and Polio;</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">IP<break/>when<break/>who<break/>where<break/>why<break/>what</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1m–6m;<break/>PHD;<break/>PHD;<break/>Individual and community health protection;<break/>National programs;</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1m–6m;<break/>PHD;<break/>PHD;<break/>Individual and community health protection;<break/>National programs;</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1m–6m;<break/>PHD;<break/>PHD;<break/>Individual and community health protection;<break/>National programs;</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1m–6m;<break/>PHD, DS of LHO;<break/>PHD, DS of LHO;<break/>Individual and community health protection;<break/>National programs;</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Data collection</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Computerized regional system and papers;</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Paper checklist</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Paper checklist</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Paper checklist</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Access to care </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Free of charge for first 6 months;</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Free of charge for first 6 months;</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Free of charge for first 12 months;</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Free of charge for first 6 months;</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">After first ME<break/>when<break/>who<break/>where<break/>why<break/>what</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Until entitlement to RHS;<break/>NGOs, DS of LHO, GP;<break/>Lack of orientation, complement of CDs screening;<break/>Access at need, take in care, CDs screening and treatment;</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Until entitlement to RHS;<break/>NGOs, DS of LHO, GP;<break/>Lack of orientation, complement of CDs screening;<break/>Access at need, take in care, CDs screening and treatment;</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Until entitlement to RHS;<break/>NGOs, DS of LHO, GP;<break/>Screening completion, answer to acute health needs and take care of chronicity and frailty;</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Until entitlement to RHS;<break/>PHD, DS of LHO, NGOs;<break/>Lack of orientation, delay on entitlements to RHS;<break/>Access at need, take in care, CDs screening and treatment;</td></tr></tbody></table><table-wrap-foot><fn><p>Note: Communicable diseases (CDs); dedicated services for migrant health (DS); general practitioner (GP); Human Immunodeficiency Virus (HIV); hepatitis B virus (HBV); hepatitis C virus (HCV); infectious diseases units of LHOs (IDU); Local Health Organization (LHO); latent tuberculosis infection (LTBI); medical examination (ME); non-communicable diseases (NCDs); non-governmental organization (NGO); primary care department (PCD); public health department (PHD); regional health service (RHS); sexually transmitted diseases (STDs); tuberculosis (TB); unaccompanied and separated children (UASC).</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05458-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05458-t004_Table 4</object-id><label>Table 4</label><caption><p>Summary of checklists: challenges.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Area</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Emilia-Romagna</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Lazio</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Toscana</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Veneto</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Access to dental healthcare; Data transmission; Cultural barriers in access to care; <break/>Delay on entitlements to RHS; Lack of multidisciplinary diagnostic–therapeutic–rehabilitation path for vulnerabilities;</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Data collection and transmission;<break/>Delay on entitlements to RHS;<break/>Integration DS of LHO and GP; <break/>lack of cultural mediators;<break/>Lack of multidisciplinary diagnostic–therapeutic–rehabilitation path for vulnerabilities;</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Lack of a computerization of health data;<break/>Heterogeneity in models of care and practices;<break/>Lack of the continuity and inclusivity of care after the first ME with the health system; </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Lack of a computerization of health data;<break/>Heterogeneity in models of care and practices;<break/>Lack of communications between stakeholder involved in take in care;<break/>Delay on entitlements to RHS;<break/>Lack of orientation;<break/>Lack of specific services;</td></tr></tbody></table></table-wrap></floats-group></article>\n"
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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751102</article-id><article-id pub-id-type=\"pmc\">PMC7432018</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155453</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05453</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Attributable Risk and Economic Cost of Cardiovascular Hospital Admissions Due to Ambient Particulate Matter in Wuhan, China</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-1954-2603</contrib-id><name><surname>Wang</surname><given-names>Xuyan</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05453\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-5467-2481</contrib-id><name><surname>Yu</surname><given-names>Chuanhua</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05453\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05453\">2</xref><xref rid=\"c1-ijerph-17-05453\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-2618-5088</contrib-id><name><surname>Zhang</surname><given-names>Yunquan</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05453\">3</xref><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05453\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Shi</surname><given-names>Fang</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05453\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-5826-5576</contrib-id><name><surname>Meng</surname><given-names>Runtang</given-names></name><xref ref-type=\"aff\" rid=\"af5-ijerph-17-05453\">5</xref></contrib><contrib contrib-type=\"author\"><name><surname>Yu</surname><given-names>Yong</given-names></name><xref ref-type=\"aff\" rid=\"af6-ijerph-17-05453\">6</xref><xref rid=\"c1-ijerph-17-05453\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05453\"><label>1</label>Department of Epidemiology and Biostatistics, School of Health Sciences, Wuhan University, Wuhan 430071, China; <email>WangxYan@whu.edu.cn</email> (X.W.); <email>18204313963@163.com</email> (F.S.)</aff><aff id=\"af2-ijerph-17-05453\"><label>2</label>Global Health Institute, Wuhan University, Wuhan 430072, China</aff><aff id=\"af3-ijerph-17-05453\"><label>3</label>Department of Epidemiology and Biostatistics, School of Public Health, Medical College, Wuhan University of Science and Technology, Wuhan 430065, China; <email>YunquanZhang@wust.edu.cn</email></aff><aff id=\"af4-ijerph-17-05453\"><label>4</label>Hubei Province Key Laboratory of Occupational Hazard Identification and Control, Wuhan University of Science and Technology, Wuhan 430065, China</aff><aff id=\"af5-ijerph-17-05453\"><label>5</label>Department of Preventive Medicine, School of Medicine, Hangzhou Normal University, Hangzhou 311121, China; <email>mengruntang@whu.edu.cn</email></aff><aff id=\"af6-ijerph-17-05453\"><label>6</label>School of Public Health and Management, Hubei University of Medicine, Shiyan 442000, China</aff><author-notes><corresp id=\"c1-ijerph-17-05453\"><label>*</label>Correspondence: <email>YuCHua@whu.edu.cn</email> (C.Y.); <email>Yongyu@hbmu.edu.cn</email> (Y.Y.)</corresp></author-notes><pub-date pub-type=\"epub\"><day>29</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5453</elocation-id><history><date date-type=\"received\"><day>12</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>25</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Although the adverse effects of ambient particulate matter (PM) on cardiovascular disease (CVD) have been previously documented, information about their economic consequence was insufficient. This study aimed to evaluate the attributable risk and economic cost of cardiovascular hospitalizations due to ambient PM. Data of CVD hospitalizations and PM concentrations from 1 January 2015 to 31 December 2017 were collected in Wuhan, China. A generalized additive model was applied to quantify the PM-attributable CVD hospitalizations, and total attributable hospitalization costs were calculated via multiplying the total attributable cases by the case-average hospitalization costs. A total of 45,714 CVD hospitalizations were included in this study. The results showed that a 10 µg/m<sup>3</sup> increase in PM<sub>2.5</sub> and PM<sub>10</sub> concentrations at lag7 day, respectively, contributed to a 1.01% (95% confidence interval: 0.67–1.34) and 0.48% (0.26–0.70) increase in CVD hospitalizations. During the study period, 1487 and 983 CVD hospitalizations were attributable to PM<sub>2.5</sub> and PM<sub>10</sub>, equaling an economic cost of 29.27 and 19.34 million RMB (1 RMB = 0.1424 USD), respectively, and significant differences in PM-attributable hospitalizations and economic burden were found between gender and age groups. Our study added evidence in heavily polluted megacities regarding the increased health risk and economic cost of CVD hospitalizations associated with ambient particulate pollution.</p></abstract><kwd-group><kwd>ambient particulate matter</kwd><kwd>cardiovascular disease</kwd><kwd>hospitalizations</kwd><kwd>attributable hospitalization costs</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05453\"><title>1. Introduction</title><p>Ambient particulate matter (PM) is typically considered to be the primary air pollutant, which has been of increasing public concern, and was estimated to have caused 40.93 million deaths and 105.67 million disability-adjusted life-years (DALYs) in 2016 globally [<xref rid=\"B1-ijerph-17-05453\" ref-type=\"bibr\">1</xref>]. Particularly, ambient PM contributed greater health impacts to Chinese populations [<xref rid=\"B2-ijerph-17-05453\" ref-type=\"bibr\">2</xref>]. Emerging studies have evaluated the adverse effects of PM on cardiovascular disease (CVD). For instance, with a 10 µg/m<sup>3</sup> increase of the PM<sub>2.5</sub> (particulate matter with aerodynamic diameter <2.5 µm) concentration, a nationwide time-series analysis in 272 Chinese cities suggested that PM<sub>2.5</sub> was related to an increase in daily cardiovascular disease mortality of 0.27% (95% confidence interval: 0.18–0.36) [<xref rid=\"B3-ijerph-17-05453\" ref-type=\"bibr\">3</xref>]; other studies [<xref rid=\"B4-ijerph-17-05453\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijerph-17-05453\" ref-type=\"bibr\">5</xref>] found that exposure to PM<sub>2.5</sub> contributed to increased cardiovascular hospital admissions in Shandong (0.40% (0.03–0.78)), and Beijing (0.30% (0.20–0.39)) as well. For PM<sub>10</sub> (particulate matter with aerodynamic diameter <10 µm), a review study reported a 0.70% (0.60–0.80) increase in cardiovascular mortality per 10 µg/m<sup>3</sup> increase of the PM<sub>10</sub> concentration [<xref rid=\"B6-ijerph-17-05453\" ref-type=\"bibr\">6</xref>]; PM<sub>10</sub> exposure was also associated with increased ischemic heart disease mortality (0.86% (0.22–1.51)) [<xref rid=\"B7-ijerph-17-05453\" ref-type=\"bibr\">7</xref>] and elevated stroke hospitalizations (1.0% (0.1–1.4)) [<xref rid=\"B8-ijerph-17-05453\" ref-type=\"bibr\">8</xref>].</p><p>The health costs of air pollution were predicted to achieve $580 billion globally by 2050 [<xref rid=\"B9-ijerph-17-05453\" ref-type=\"bibr\">9</xref>]. Meanwhile, a study indicated that PM<sub>2.5</sub> pollution should cause a loss of about 2.0% of China’s GDP by 2030 if necessary measures are not taken [<xref rid=\"B10-ijerph-17-05453\" ref-type=\"bibr\">10</xref>]. There were 290 million CVD cases in China [<xref rid=\"B11-ijerph-17-05453\" ref-type=\"bibr\">11</xref>], and CVD accounted for the highest proportion of the total curative care expenditure followed by neoplasm and respiratory diseases [<xref rid=\"B12-ijerph-17-05453\" ref-type=\"bibr\">12</xref>]. Nevertheless, few studies have determined the economic burdens of CVD attributable to ambient PM, and previous studies have mainly focused on its health outcomes (e.g., mortality and hospital admission), as described above. Therefore, the assessment of both the health and economic burden is urgently needed, which could provide more comprehensive information for the cost-effectiveness evaluation of policy-making regarding air pollution control.</p><p>It was on this basis we conducted our study to determine the association between exposure to ambient PM (including PM<sub>2.5</sub> and PM<sub>10</sub>) and cardiovascular hospitalizations in Wuhan, China. In addition, we further quantified the CVD hospitalizations and hospitalization expenses attributable to PM<sub>2.5</sub> and PM<sub>10</sub>; the preventable CVD hospitalizations and savable hospitalization costs were also estimated based on the scenarios that the concentrations of PM<sub>2.5</sub> and PM<sub>10</sub> during the study period could be maintained at relatively lower levels.</p></sec><sec sec-type=\"methods\" id=\"sec2-ijerph-17-05453\"><title>2. Methods</title><sec id=\"sec2dot1-ijerph-17-05453\"><title>2.1. Study Area</title><p>As the largest metropolis in central China, Wuhan has experienced heavy air pollution in the past few years with the development of urbanization, motorization and industrialization [<xref rid=\"B13-ijerph-17-05453\" ref-type=\"bibr\">13</xref>]. Wuhan is located at 113.7–113.1° E and 29.9–31.4° N with an 8569.15 km<sup>2</sup> total metropolitan area, and the permanent residents were 10.33 million by the end of 2019. The main climate type in Wuhan is humid subtropical monsoon, characterized by hot summers and cold winters as well as short springs and autumns.</p></sec><sec id=\"sec2dot2-ijerph-17-05453\"><title>2.2. Data Collection</title><p>Daily hospitalization admission data between 1 January 2015 and 31 December 2017 were obtained from two general hospitals in Wuhan; both the chosen hospitals are Grade-A tertiary hospitals with well-known expertise in diagnosing and treating disease. Records of hospital admissions included admission date, age, gender, principal discharge diagnosis and hospitalization costs, and the diagnosis of disease was coded according to the International Classification of Disease Tenth Revision (ICD-10). Patients hospitalized due to CVD (ICD-10: I00-I99) were included in our study; the entire study group was divided into two gender groups (male and female) and two age groups (0–64 years and +65 years). Further break-down of the 0–64 years group was not conducted because the children group (0–14 years) only accounted for 0.24% of the total cases. Besides, two subtypes of CVD including ischemic heart disease (IHD, ICD-10: I20-I25) and stroke (ICD-10: I60-I69) were also analyzed.</p><p>Data of ambient air pollutants including PM<sub>2.5</sub>, PM<sub>10</sub>, SO<sub>2</sub> (sulfur dioxide) and NO<sub>2</sub> (nitrogen dioxide) were acquired from the Hubei Environmental Protection Bureau; the daily mean concentration (µg/m<sup>3</sup>) for each air pollutant was calculated by averaging across the measurements from the 10 monitoring stations in Wuhan. Daily meteorological data were collected from the China Meteorological Data Network (<uri xlink:href=\"http://data.cma.cn\">http://data.cma.cn</uri>), including mean air temperature (°C) and relative humidity (%). The spatial distribution of included hospitals and air monitoring sites are displayed in <xref ref-type=\"app\" rid=\"app1-ijerph-17-05453\">Figure S1</xref>.</p></sec><sec id=\"sec2dot3-ijerph-17-05453\"><title>2.3. Data Analysis</title><p>A three-stage analytic approach was used to analyze the data. Firstly, we decomposed the time-series data (including PM concentration, CVD hospitalizations and hospitalization costs) to detect a potential long-term trend and seasonality. Secondly, we applied a time-series model to estimate PM-hospitalization associations. Thirdly, we calculated the hospitalizations and hospitalization costs attributable to PM<sub>2.5</sub> and PM<sub>10</sub>.</p><sec id=\"sec2dot3dot1-ijerph-17-05453\"><title>2.3.1. First-Stage Analysis</title><p>A seasonal-trend decomposed method was used to detect a potential long-term trend and seasonality of the daily concentration of PM (PM<sub>2.5</sub> and PM<sub>10</sub>), daily hospitalizations and case-average hospitalization costs. The time-series was split into three additive components, including a long-term trend during the study period, seasonal variations within years and random variation [<xref rid=\"B14-ijerph-17-05453\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijerph-17-05453\" ref-type=\"bibr\">15</xref>]; the fundamental statistical model was as shown:<disp-formula id=\"FD1-ijerph-17-05453\"><label>(1)</label><mml:math id=\"mm1\"><mml:mrow><mml:mrow><mml:msub><mml:mi>Y</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mi>T</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>S</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mi>t</mml:mi></mml:msub></mml:mrow></mml:mrow></mml:math></disp-formula>\nwhere <inline-formula><mml:math id=\"mm2\"><mml:mrow><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mtext> </mml:mtext></mml:mrow></mml:mrow></mml:math></inline-formula> is the linear trend; <inline-formula><mml:math id=\"mm3\"><mml:mrow><mml:mrow><mml:msub><mml:mi>S</mml:mi><mml:mi>t</mml:mi></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula> is the seasonal effect; <inline-formula><mml:math id=\"mm4\"><mml:mrow><mml:mrow><mml:msub><mml:mi>I</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mtext> </mml:mtext></mml:mrow></mml:mrow></mml:math></inline-formula> is the r random noise and <italic>t</italic> = 1, 2, …., N.</p></sec><sec id=\"sec2dot3dot2-ijerph-17-05453\"><title>2.3.2. Second-Stage Analysis</title><p>The associations of PM<sub>2.5</sub> and PM<sub>10</sub> with CVD hospitalizations were estimated by using a generalized additive model (GAM) with quasi-Poisson regression as shown:<disp-formula id=\"FD2-ijerph-17-05453\"><label>(2)</label><mml:math id=\"mm5\"><mml:mrow><mml:mrow><mml:mi>log</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mi>E</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:msub><mml:mi>β</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>P</mml:mi><mml:mi>M</mml:mi><mml:msub><mml:mi>C</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:mi>s</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>T</mml:mi><mml:mi>I</mml:mi><mml:mi>M</mml:mi><mml:mi>E</mml:mi><mml:mo>,</mml:mo><mml:mi>d</mml:mi><mml:mi>f</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:mi>s</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>M</mml:mi><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>d</mml:mi><mml:mi>f</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:mi>s</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>R</mml:mi><mml:mi>H</mml:mi><mml:mo>,</mml:mo><mml:mi>d</mml:mi><mml:mi>f</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:mi>D</mml:mi><mml:mi>O</mml:mi><mml:mi>W</mml:mi><mml:mo>+</mml:mo><mml:mi>a</mml:mi></mml:mrow></mml:mrow></mml:math></disp-formula>\nwhere <inline-formula><mml:math id=\"mm6\"><mml:mrow><mml:mrow><mml:msub><mml:mi>E</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula> is the expected hospitalizations on day <italic>i</italic>, <inline-formula><mml:math id=\"mm7\"><mml:mrow><mml:mrow><mml:msub><mml:mi>β</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mtext> </mml:mtext></mml:mrow></mml:mrow></mml:math></inline-formula> is the estimated slope of associations of the concentrations of PM<sub>2.5</sub> or PM<sub>10</sub> on day <italic>i</italic>, <italic>s</italic> is a spline smoothing function for the nonlinear variable (e.g., time, mean temperature and relative humidity), <italic>df</italic> is the degree of freedom—7 <italic>df</italic> per year for time trends [<xref rid=\"B15-ijerph-17-05453\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijerph-17-05453\" ref-type=\"bibr\">16</xref>] and 3 <italic>df</italic> per year for mean temperature and relative humidity were defined [<xref rid=\"B17-ijerph-17-05453\" ref-type=\"bibr\">17</xref>], <italic>DOW</italic> is a categorical variable for day of the week and <inline-formula><mml:math id=\"mm8\"><mml:mrow><mml:mi>α</mml:mi></mml:mrow></mml:math></inline-formula> is the intercept of the model.</p><p>Effect estimates across lag0 day (the day of hospital admission) to lag7 day (7 days prior to hospital admission) were investigated in our current study, which were presented as a percent change (<italic>PC</italic>, %), and the 95% confidence interval (CI) in daily CVD hospitalizations was associated with a 10 µg/m<sup>3</sup> increase in the concentrations of PM<sub>2.5</sub> or PM<sub>10</sub>. <italic>PC</italic> was calculated through Equation (3):<disp-formula id=\"FD3-ijerph-17-05453\"><label>(3)</label><mml:math id=\"mm9\"><mml:mrow><mml:mrow><mml:mi>P</mml:mi><mml:mi>C</mml:mi><mml:mo>=</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>exp</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mi>β</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>×</mml:mo><mml:mn>10</mml:mn></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mo>]</mml:mo></mml:mrow><mml:mo>×</mml:mo><mml:mn>100</mml:mn></mml:mrow></mml:mrow></mml:math></disp-formula>\nwhere <inline-formula><mml:math id=\"mm10\"><mml:mrow><mml:mrow><mml:msub><mml:mi>β</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mtext> </mml:mtext></mml:mrow></mml:mrow></mml:math></inline-formula> is the estimated slope of associations of the concentrations of PM<sub>2.5</sub> or PM<sub>10</sub> on day <italic>i</italic>, which was obtained from Formula (2).</p></sec><sec id=\"sec2dot3dot3-ijerph-17-05453\"><title>2.3.3. Third-Stage Analysis</title><p>The attributable fraction (<italic>AF</italic>) and attributable number (<italic>AN</italic>) of CVD hospitalizations due to PM<sub>2.5</sub> or PM<sub>10</sub> were estimated by using the method mentioned previously [<xref rid=\"B18-ijerph-17-05453\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijerph-17-05453\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijerph-17-05453\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijerph-17-05453\" ref-type=\"bibr\">21</xref>]. The following, Equations (4) and (5), were used:<disp-formula id=\"FD4-ijerph-17-05453\"><label>(4)</label><mml:math id=\"mm11\"><mml:mrow><mml:mrow><mml:mi>A</mml:mi><mml:msub><mml:mi>F</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:mi>exp</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mi>β</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>*</mml:mo><mml:mi>Δ</mml:mi><mml:msub><mml:mi>C</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:mfrac></mml:mrow></mml:mrow></mml:math></disp-formula>\n<disp-formula id=\"FD5-ijerph-17-05453\"><label>(5)</label><mml:math id=\"mm12\"><mml:mrow><mml:mrow><mml:mi>A</mml:mi><mml:msub><mml:mi>N</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mi>A</mml:mi><mml:msub><mml:mi>F</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>×</mml:mo><mml:msub><mml:mi>N</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mrow></mml:math></disp-formula>\nwhere <inline-formula><mml:math id=\"mm13\"><mml:mrow><mml:mrow><mml:mtext> </mml:mtext><mml:mi>A</mml:mi><mml:msub><mml:mi>F</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mtext> </mml:mtext></mml:mrow></mml:mrow></mml:math></inline-formula> is the attributable fraction on day <italic>i,</italic>\n<inline-formula><mml:math id=\"mm14\"><mml:mrow><mml:mrow><mml:mi>A</mml:mi><mml:msub><mml:mi>N</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mtext> </mml:mtext></mml:mrow></mml:mrow></mml:math></inline-formula> is the attributable number of CVD hospitalizations on day <italic>i</italic>, <inline-formula><mml:math id=\"mm15\"><mml:mrow><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mtext> </mml:mtext></mml:mrow></mml:mrow></mml:math></inline-formula> is the daily CVD hospitalizations on day <italic>i</italic> and <inline-formula><mml:math id=\"mm16\"><mml:mrow><mml:mrow><mml:msub><mml:mi>β</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mtext> </mml:mtext></mml:mrow></mml:mrow></mml:math></inline-formula> is the estimated slope of associations of the concentrations of PM<sub>2.5</sub> or PM<sub>10</sub> on day <italic>i</italic>. <inline-formula><mml:math id=\"mm17\"><mml:mrow><mml:mrow><mml:mi>Δ</mml:mi><mml:msub><mml:mi>C</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula> is the concentration difference between the observed and the reference concentrations of PM<sub>2.5</sub> or PM<sub>10</sub> on day <italic>i</italic>. The reference concentration is a threshold level at which no health effects are yet assumed, then the air quality standard proposed by the World Health Organization (WHO) was considered as the threshold (24 h average value: 25 µg/m<sup>3</sup> for PM<sub>2.5</sub> and 50 µg/m<sup>3</sup> for PM<sub>10</sub>) in our current study [<xref rid=\"B20-ijerph-17-05453\" ref-type=\"bibr\">20</xref>].</p><p>Therefore, the total attributable number was estimated by summing daily <inline-formula><mml:math id=\"mm18\"><mml:mrow><mml:mrow><mml:mi>A</mml:mi><mml:mi>N</mml:mi></mml:mrow></mml:mrow></mml:math></inline-formula>, and its ratio with total CVD hospitalizations was the total <italic>AF</italic>. We further calculated the total attributable hospitalization costs via multiplying the total attributable number by the case-average hospitalization costs during the study period [<xref rid=\"B20-ijerph-17-05453\" ref-type=\"bibr\">20</xref>,<xref rid=\"B22-ijerph-17-05453\" ref-type=\"bibr\">22</xref>]. The potential number of avoidable hospitalizations and savable hospitalization costs were also estimated based on the scenarios that the concentrations of PM<sub>2.5</sub> and PM<sub>10</sub> during the study period could be maintained at relatively lower levels. All the costs were presented as the 2020 price in Renminbi using a consumer price index for adjustment (1 RMB = 0.1424 USD).</p></sec><sec id=\"sec2dot3dot4-ijerph-17-05453\"><title>2.3.4. Sensitivity Analysis</title><p>A sensitivity analysis was performed to examine the robustness of the model through: (1) adjusting for co-pollutants (SO<sub>2</sub> and NO<sub>2</sub>) and (2) changing the degree of freedom (<italic>df</italic> = 8 and 9) for the long-term trend and seasonality.</p><p>All analyses were conducted by R software (version 3.6.1, R Foundation for Statistical Computing, Vienna, Austria), and two-sided tests with <italic>p</italic> < 0.05 were considered to be statistically significant.</p></sec></sec><sec id=\"sec2dot4-ijerph-17-05453\"><title>2.4. Ethical Approval</title><p>All the data in our study were anonymous and de-identified, and it was approved by the Ethics Committee of the Medical Department of Wuhan University to waive informed consent of the participants (No. 2019YF2037).</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05453\"><title>3. Results</title><p>A total of 45,714 CVD hospitalizations from 1 January 2015 to 31 December 2017 were included in this study, of which IHD and stroke, respectively, accounted for 20.77% and 38.29%. <xref rid=\"ijerph-17-05453-t001\" ref-type=\"table\">Table 1</xref> provides the summary statistics of daily hospitalization counts, hospitalization costs, air pollutant concentration and meteorological factors in Wuhan, China, from 2015–2017. On average, there were 42 admission cases per day for CVD, 9 cases for IHD and 16 cases for stroke. The daily CVD hospitalizations of males were obviously higher than that of females, and there were less daily CVD hospitalizations for patients aged 0–64 years compared with the older group (65+ years). The average hospitalization costs for CVD, IHD and stroke were 19,680 RMB, 19,320 RMB and 24,290 RMB, respectively. Higher mean CVD-associated costs were observed for males and patients aged 0–64 years.</p><p>The annual average concentrations of PM<sub>2.5</sub> and PM<sub>10</sub> were 58.52 µg/m<sup>3</sup> and 96.10 µg/m<sup>3</sup> in Wuhan; both exceeded the secondary standard of ambient air quality in China (annual average value: 35µg/m<sup>3</sup> for PM<sub>2.5</sub> and 70 µg/m<sup>3</sup> for PM<sub>10</sub>). In addition, the annual mean temperature and relative humidity were 17.20 °C and 80.62%, respectively. Spearman’s rank correlation indicated that PM<sub>2.5</sub> and PM<sub>10</sub> were moderately correlated with SO<sub>2</sub> and NO<sub>2</sub>, while they were slightly correlated with meteorological factors (see <xref ref-type=\"app\" rid=\"app1-ijerph-17-05453\">Table S1</xref>).</p><p>The long-term trend and seasonality of the daily concentrations of PM<sub>2.5</sub> and PM<sub>10</sub>, daily hospitalizations and case-average hospitalization costs due to CVD are shown in <xref ref-type=\"fig\" rid=\"ijerph-17-05453-f001\">Figure 1</xref>. During the study period, the daily concentrations of PM<sub>2.5</sub> and PM<sub>10</sub> had similar characteristics of periodic fluctuation with a downtrend annually, the seasonal pattern of which showed a high concentration in winter and spring while a low concentration in summer and autumn. However, daily CVD hospitalizations and case-average hospitalization costs increased over the three-year study period without notable seasonal fluctuations. Similar trends in hospitalizations and costs were also noted for IHD and stroke (see <xref ref-type=\"app\" rid=\"app1-ijerph-17-05453\">Figure S2</xref>).</p><p><xref ref-type=\"fig\" rid=\"ijerph-17-05453-f002\">Figure 2</xref> illustrates the estimated percent changes (%, 95% CI) of hospitalization risks associated with a 10 µg/m<sup>3</sup> increase in PM<sub>2.5</sub> and PM<sub>10</sub> concentrations at different lag days in single-pollutant models. We found evidence for significant positive associations for at least two exposure lag days between the PM<sub>2.5</sub> concentration and hospital admissions for all cardiovascular outcomes. The strongest effects were noted at lag7 day; the corresponding percent changes of hospitalizations for CVD, IHD and stroke were 1.01% (0.67–1.34), 1.10% (0.37–1.84) and 1.01% (0.45–1.56), respectively. With a 10 µg/m<sup>3</sup> increase in PM<sub>10</sub>, significant percent changes of cardiovascular hospitalizations first occurred at lag2 day with the exception of strokes. Likewise, estimated values reached the peak at lag7 day, and were 0.48% (0.26–0.70), 0.58% (0.11–1.07) and 0.61% (0.25–0.96) for CVD, IHD and stroke, respectively. To ease the interpretation, the lag7 day concentrations of PM<sub>2.5</sub> and PM<sub>10</sub> are further analyzed in the following paragraphs, since these days produced the largest effect estimates.</p><p>Results of subgroup analyses of CVD hospitalizations are presented in <xref ref-type=\"fig\" rid=\"ijerph-17-05453-f003\">Figure 3</xref>. There was no notable gender difference for PM<sub>2.5</sub>, whereas males were more vulnerable to PM<sub>10</sub> (0.51% (0.22–0.79)) than females (0.44% (0.11–0.77)). For age group, greater estimated values were found for the group aged 0–64 years; the percent changes of PM<sub>2.5</sub> and PM<sub>10</sub> were 1.05% (0.53–1.56) and 0.57% (0.24–0.91), respectively. Subgroup analyses of IHD and stroke are presented in <xref ref-type=\"app\" rid=\"app1-ijerph-17-05453\">Figures S3 and S4</xref>, and significant differences were found by gender and age groups.</p><p><xref rid=\"ijerph-17-05453-t002\" ref-type=\"table\">Table 2</xref> demonstrates the estimated attributable figures for different CVD observations during the study period. For all CVD, 1487 and 983 hospitalizations were, respectively, attributable to PM<sub>2.5</sub> and PM<sub>10</sub>, which corresponded to 29.27 million RMB and 19.34 million RMB. Both attributable hospitalizations and attributable hospitalization costs were higher in males than in females. In addition, there were more attributable hospitalizations due to PM<sub>2.5</sub> for the group aged 65+ years than the younger group (0–64 years), while the opposite results were produced by PM<sub>10</sub>. In addition, there were 340 and 249 IHD-related hospitalizations attributable to PM<sub>2.5</sub> and PM<sub>10</sub>, and the associated attributable hospitalization costs were 6.57 million RMB and 4.82 million RMB, respectively. For stroke, 13.35 million RMB and 8.98 million RMB were attributable to PM<sub>2.5</sub> and PM<sub>10</sub>, and the attributable hospitalizations were 550 and 465, respectively.</p><p><xref ref-type=\"fig\" rid=\"ijerph-17-05453-f004\">Figure 4</xref> illustrates the potential number of avoidable hospitalizations and savable hospitalization costs if the concentration of PM<sub>2.5</sub> and PM<sub>10</sub> during the study period could be maintained at relatively low levels. For total CVD, maintaining the PM<sub>2.5</sub> concentration at 45 µg/m<sup>3</sup> could prevent 193 hospitalizations and save 3.8 million RMB annually. In addition, if the PM<sub>10</sub> concentration could be maintained at 90 µg/m<sup>3</sup>, 39 hospitalizations could be avoided annually, reducing 0.8 million RMB correspondingly. It seems obvious that more hospital admissions and hospitalization costs could be avoided if the historical concentrations of PM<sub>2.5</sub> and PM<sub>10</sub> are kept at lower levels, such that if the PM<sub>2.5</sub> concentration is kept at 30 µg/m<sup>3</sup>, 420 hospitalizations and 8.3 million RMB could be avoided annually, and if the concentration of PM<sub>10</sub> is maintained at 60 µg/m<sup>3</sup>, 255 hospitalizations and 5.0 million RMB could be averted annually. In addition, more health and economic benefits could be obtained for stroke than IHD. For instance, if the historical concentration of PM<sub>2.5</sub> is maintained at 45 µg/m<sup>3</sup>, more stroke-associated hospitalizations and costs (68 and 1.5 million RMB, respectively) could be prevented than IHD (45 and 0.9 million RMB, respectively). More details about the subgroup analysis are showed in the <xref ref-type=\"app\" rid=\"app1-ijerph-17-05453\">supplementary material</xref> (see <xref ref-type=\"app\" rid=\"app1-ijerph-17-05453\">Figure S5</xref>).</p><p>Sensitivity analyses of adjusted co-pollutants in the model (SO<sub>2</sub> and NO<sub>2</sub>) and changed <italic>df</italic> (8 and 9) for the long-term trend and seasonality were performed, which suggested that our main models were generally robust (See <xref ref-type=\"app\" rid=\"app1-ijerph-17-05453\">Table S2</xref>).</p></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05453\"><title>4. Discussion</title><p>Some notable results were found in this time-series study in Wuhan, China. First, the daily concentration of ambient PM showed a downtrend, while daily cardiovascular hospitalizations and hospitalization costs increased during the study period. Second, exposure to PM<sub>2.5</sub> and PM<sub>10</sub> were associated with an excess risk of CVD hospitalization, and differences were found by gender and age groups. Third, substantial economic costs were attributed to PM<sub>2.5</sub> and PM<sub>10</sub> exposure; millions of hospitalization costs could be avoided if the historical PM concentration is maintained at relatively low levels.</p><p>In this study, we conducted a decomposed method to detect the potential long-term trend and seasonality of the daily concentration of ambient PM, daily hospitalizations and case-average hospitalization costs. Possibly as a result of strengthened measures taken by the local government to control ambient air pollution recently, a trend of a decline in the PM concentration from 2015 to 2017 was observed. The ambient PM concentration was relatively high in winter and spring; such a seasonal pattern could be interpreted by the increased burning of coal to provide central heating in the cold season that might cause more PM. Another reason might be that temperature inversion is common in the cold season, which would inhibit the spread of air pollutants. However, both daily CVD hospitalizations and corresponding average hospitalization costs performed uptrends during the study period, echoing a study [<xref rid=\"B23-ijerph-17-05453\" ref-type=\"bibr\">23</xref>] that suggested that the prevalence of non-communicable diseases was increasing worldwide, especially for CVD. A natural cubic spline for time with 7 <italic>df</italic> per year was commonly used to control for the long-term trend of daily hospital admissions [<xref rid=\"B15-ijerph-17-05453\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijerph-17-05453\" ref-type=\"bibr\">16</xref>,<xref rid=\"B24-ijerph-17-05453\" ref-type=\"bibr\">24</xref>]. In addition, with the growth of per-capita income and chronic disease morbidity, rates of patients leaving against medical advice and avoiding the hospital decreased [<xref rid=\"B25-ijerph-17-05453\" ref-type=\"bibr\">25</xref>], contributing to the increase of hospitalization expenses.</p><p>In line with previous studies, our results reported that PM was associated with an excess risk of CVD hospitalization. For example, with a 10 µg/m<sup>3</sup> increment of the PM concentration, a 0.87% increase in cardiovascular hospital admissions due to PM<sub>2.5</sub> [<xref rid=\"B13-ijerph-17-05453\" ref-type=\"bibr\">13</xref>] and 1.0% increase in stroke hospitalizations due to PM<sub>10</sub> [<xref rid=\"B8-ijerph-17-05453\" ref-type=\"bibr\">8</xref>] were found in Wuhan. In addition, other researchers observed consistently increased hospitalizations associated with PM<sub>2.5</sub> and PM<sub>10</sub> for CVD in some Chinese regions such as Shandong [<xref rid=\"B4-ijerph-17-05453\" ref-type=\"bibr\">4</xref>], Beijing [<xref rid=\"B5-ijerph-17-05453\" ref-type=\"bibr\">5</xref>], Shanghai [<xref rid=\"B26-ijerph-17-05453\" ref-type=\"bibr\">26</xref>] and Sichuan [<xref rid=\"B27-ijerph-17-05453\" ref-type=\"bibr\">27</xref>]. Moreover, studies [<xref rid=\"B28-ijerph-17-05453\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijerph-17-05453\" ref-type=\"bibr\">29</xref>] of Brazil, France, Iran and Italy also demonstrated the positive associations between PM exposure and cardiovascular hospitalizations. Ambient PM mainly comes from fossil fuel combustion and automobile exhaust, which contains toxic organic compounds such as benzopyrene and many heavy metals (e.g., lead, nickel and chromium) [<xref rid=\"B30-ijerph-17-05453\" ref-type=\"bibr\">30</xref>]. Previous clinical and toxicological studies have established several possible mechanisms for how PM and its components adversely impact the cardiovascular system. For instance, Cao et al. [<xref rid=\"B31-ijerph-17-05453\" ref-type=\"bibr\">31</xref>] indicated that PM could lead to cardiomyocyte apoptosis by protein kinase activation, and Kowalska et al. [<xref rid=\"B32-ijerph-17-05453\" ref-type=\"bibr\">32</xref>] suggested that PM contributed to myocardial infarction via destabilizing atherosclerotic plaques. Other researchers also found that PM was related to acute decompensated heart failure symptoms [<xref rid=\"B33-ijerph-17-05453\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijerph-17-05453\" ref-type=\"bibr\">34</xref>], decreased heart rate variability and changed autonomic tone [<xref rid=\"B35-ijerph-17-05453\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijerph-17-05453\" ref-type=\"bibr\">36</xref>].</p><p>Gender and age differences in associations of particulate pollution with cardiovascular health have been of wide interest in air pollution epidemiology. We found that the impacts of PM on CVD were significantly greater in males, which is consistent with a study in Shanghai [<xref rid=\"B26-ijerph-17-05453\" ref-type=\"bibr\">26</xref>], while other studies [<xref rid=\"B5-ijerph-17-05453\" ref-type=\"bibr\">5</xref>,<xref rid=\"B37-ijerph-17-05453\" ref-type=\"bibr\">37</xref>] showed that the gender differences were statistically insignificant. Similar with previous studies [<xref rid=\"B13-ijerph-17-05453\" ref-type=\"bibr\">13</xref>,<xref rid=\"B38-ijerph-17-05453\" ref-type=\"bibr\">38</xref>], a greater increase of CVD hospitalizations for the younger group (0–64 years) was also observed in our current study. However, a study of Powell et al. [<xref rid=\"B39-ijerph-17-05453\" ref-type=\"bibr\">39</xref>] suggested stronger associations between daily CVD hospital admissions and particulate air pollution were observed for the elderly. Considering the gender and age group specific associations were varied, the likely explanation for the heterogeneity might be various study designs, research periods and sociodemographic and economic characteristics.</p><p>Economic burden assessment is crucial for the cost-effectiveness evaluation of policy-making regarding air pollution control. A study of Tianjin [<xref rid=\"B40-ijerph-17-05453\" ref-type=\"bibr\">40</xref>] calculated that attributable CVD mortalities due to PM<sub>2.5</sub> were equivalent to 2.79% of the local GDP when converted into present value, and Cheng et al. [<xref rid=\"B16-ijerph-17-05453\" ref-type=\"bibr\">16</xref>] demonstrated that 21 million RMB CVD-associated hospitalization expenses could be saved annually in Lanzhou if the concentration of CO is kept below 1 mg/m<sup>3</sup>. Other studies in regard to mental disorders [<xref rid=\"B20-ijerph-17-05453\" ref-type=\"bibr\">20</xref>] and pneumonia [<xref rid=\"B22-ijerph-17-05453\" ref-type=\"bibr\">22</xref>] also showed that a certain amount of medical expenses could be avoided with the decline of PM concentrations. Correspondingly, our current study illustrated that 29.27 million RMB and 19.34 million RMB were, respectively, attributed to PM<sub>2.5</sub> and PM<sub>10</sub>, and more hospitalization costs could be saved if the historical concentration of PM is maintained at lower levels. To some extent, our study might fill some of the gaps of knowledge about the health economic aspects of particulate air pollution on CVD in central China, and further studies should focus on other air pollutants on cardiovascular health.</p><p>The present study had some limitations. First, the average concentration of PM in Wuhan was considered as personal exposure, which may cause measurement errors. It is a well-recognized inherent limitation of such environmental epidemiological studies [<xref rid=\"B41-ijerph-17-05453\" ref-type=\"bibr\">41</xref>] that is likely to underestimate the effects of PM [<xref rid=\"B42-ijerph-17-05453\" ref-type=\"bibr\">42</xref>]. Second, the influences of meteorological factors, day of the week, gender and age were considered in our current study, and more individual information such as socio-economic status and behavioral factors could also be considered in follow-up studies. Third, the economic burden calculated in this study tended to understate the real economic burden of CVD due to PM, since the economic cost in our results only included the direct hospitalization expenses, while the indirect medical costs and outpatient expenditures were not analyzed. Consequently, more research should be undertaken to resolve these issues.</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05453\"><title>5. Conclusions</title><p>In conclusion, we found that ambient particulate pollution increased CVD risk and cost among hospital admission patients in Wuhan, one of the heavily polluted megacities in central China. Then, we should continue to strengthen the efforts to alleviate the level of particulate pollution in Wuhan, which would certainly reduce the health and economic burden among patients with CVD. Our study also highlights the demand for evaluating the air pollution-related economic effect. It may have important implications for promoting the cost-effectiveness evaluation of measures regarding air pollution control, thereby providing informative priorities about measures with the largest benefits for local decision-makers from the public health perspective.</p></sec></body><back><app-group><app id=\"app1-ijerph-17-05453\"><title>Supplementary Materials</title><p>The following are available online at <uri xlink:href=\"https://www.mdpi.com/1660-4601/17/15/5453/s1\">https://www.mdpi.com/1660-4601/17/15/5453/s1</uri>. Figure S1: The spatial distribution of included hospitals and air monitoring sites in Wuhan, China. Figure S2: The decomposed distribution of daily hospitalizations (no.) and case-average hospitalization costs due to IHD (A and B) and stroke (C and D) in Wuhan, China, from 2015–2017. Figure S3: The estimated percent change of IHD hospitalizations of per 10 µg/m<sup>3</sup> increase in PM<sub>2.5</sub> and PM<sub>10</sub> concentrations, by gender and age group. PC: percent change, CI: confidence interval. Figure S4: The estimated percent change of stroke hospitalizations per 10 µg/m<sup>3</sup> increase in PM<sub>2.5</sub> and PM<sub>10</sub> concentrations, by gender and age group. PC: percent change, CI: confidence interval. Figure S5: The estimated annual avoidable hospitalizations and savable hospitalization costs for CVD subgroups if the historical concentrations of PM<sub>2.5</sub> and PM<sub>10</sub> could be maintained at relatively low levels. The air quality standard proposed by the WHO (24-h average value: 25 µg/m<sup>3</sup> for PM<sub>2.5</sub> and 50 µg/m<sup>3</sup> for PM<sub>10</sub>) was considered as the reference. Table S1: The coefficient of the Spearman rank correlation between particulate matter (including PM<sub>2.5</sub> and PM<sub>10</sub>) and SO<sub>2</sub>, NO<sub>2</sub> and meteorological factors in Wuhan, China. Table S2: Results of sensitivity analyses by adjusting for co-pollutants and changing the degree of freedom for the long-term trend and seasonality. Results are shown in percent change (%) per 10 µg/m<sup>3</sup> increase in PM<sub>2.5</sub> and PM<sub>10</sub> concentrations at the best lag day.</p><supplementary-material content-type=\"local-data\" id=\"ijerph-17-05453-s001\"><media xlink:href=\"ijerph-17-05453-s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>Conceptualization, X.W. and Y.Z.; data curation, X.W., C.Y., F.S. and Y.Y.; formal analysis, X.W. and Y.Y.; funding acquisition, C.Y.; methodology, X.W., C.Y., Y.Z. and Y.Y.; writing—original draft, X.W., Y.Z. and R.M.; writing—review and editing, X.W., C.Y., Y.Z., F.S., R.M. and Y.Y. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This work was funded by the National Natural Science Foundation of. China (Grant No. 81773552) and the National Key Research and Development Program of China (Grant No. 2018YFC1315302, 2017YFC1200502).</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijerph-17-05453\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><collab>GBD Collaborators</collab></person-group><article-title>Global, regional, and national comparative risk assessment of 84 behavioral, environmental and occupational, and metabolic risks or clusters of risks, 1990–2016: A systematic analysis for the Global Burden of Disease Study 2016</article-title><source>Lancet</source><year>2017</year><volume>390</volume><fpage>1345</fpage><lpage>1422</lpage><pub-id pub-id-type=\"pmid\">28919119</pub-id></element-citation></ref><ref id=\"B2-ijerph-17-05453\"><label>2.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Wang</surname><given-names>Y.</given-names></name><name><surname>Qi</surname><given-names>Y.</given-names></name><name><surname>Hu</surname><given-names>J.</given-names></name><name><surname>Zhang</surname><given-names>H.</given-names></name></person-group><article-title>Spatial and temporal variations of six criteria air pollutants in 31 provincial capital cities in China during 2013–2014</article-title><source>Environ. 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(<bold>A</bold>) daily concentration of PM<sub>2.5</sub>; (<bold>B</bold>) daily concentration of PM<sub>10</sub>; (<bold>C</bold>) daily number of hospitalizations; (<bold>D</bold>) case-average hospitalization costs.</p></caption><graphic xlink:href=\"ijerph-17-05453-g001\"/></fig><fig id=\"ijerph-17-05453-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>The estimated percent change of cardiovascular disease (CVD) hospitalizations due to a 10 µg/m<sup>3</sup> increase in PM<sub>2.5</sub> and PM<sub>10</sub> concentrations.</p></caption><graphic xlink:href=\"ijerph-17-05453-g002\"/></fig><fig id=\"ijerph-17-05453-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>The estimated percent change of CVD hospitalizations per 10 µg/m<sup>3</sup> increase in PM<sub>2.5</sub> and PM<sub>10</sub> concentrations, by gender and age group. PC: percent change; CI: confidence interval.</p></caption><graphic xlink:href=\"ijerph-17-05453-g003\"/></fig><fig id=\"ijerph-17-05453-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>The estimated annual avoidable hospitalizations and savable hospitalization costs if the historical concentrations of PM<sub>2.5</sub> and PM<sub>10</sub> could be maintained at relatively low levels. The air quality standard proposed by the WHO (24-h average value: 25 µg/m<sup>3</sup> for PM<sub>2.5</sub> and 50 µg/m<sup>3</sup> for PM<sub>10</sub>) was considered as the reference.</p></caption><graphic xlink:href=\"ijerph-17-05453-g004\"/></fig><table-wrap id=\"ijerph-17-05453-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05453-t001_Table 1</object-id><label>Table 1</label><caption><p>Summary statistics of daily hospitalization counts, hospitalization costs, air pollutant concentration and meteorological factors in Wuhan, China, from 2015–2017.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Variable</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Mean</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">SD</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Min</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\">Percentile</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Max</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">P<sub>25</sub></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">P<sub>50</sub></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">P<sub>75</sub></th></tr></thead><tbody><tr><td colspan=\"8\" align=\"center\" valign=\"middle\" rowspan=\"1\">Daily hospitalization counts</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CVD</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">42</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.79</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">41</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">53</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">97</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IHD</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.45</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Stroke</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.62</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">43</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Males</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.76</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">58</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Females</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.25</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">46</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0–64 years</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.77</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">53</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">64+ years</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.34</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60</td></tr><tr><td colspan=\"8\" align=\"center\" valign=\"middle\" rowspan=\"1\">Hospitalization costs (in 1000 RMB)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CVD</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19.68</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31.08</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.04</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.25</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19.12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1036.81</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IHD</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19.32</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25.59</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.13</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.70</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.30</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.70</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1036.81</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Stroke</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24.29</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">37.32</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.07</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.76</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.78</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23.84</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">946.95</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Males</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.22</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32.64</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.04</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.53</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.94</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.16</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1036.81</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Females</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.68</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28.79</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.08</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.95</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.01</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.49</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">728.54</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0–64 years</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.22</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34.91</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.04</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.84</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.44</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">657.65</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">64+ years</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.53</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27.78</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.07</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.58</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.41</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19.62</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1036.81</td></tr><tr><td colspan=\"8\" align=\"center\" valign=\"middle\" rowspan=\"1\">Air pollutants (µg/m<sup>3</sup>)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PM<sub>2.5</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">58.52</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38.56</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">49</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">76</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">281</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PM<sub>10</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">96.10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">51.10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">124</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">618</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SO<sub>2</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.39</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.85</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">74</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NO<sub>2</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">46.17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19.37</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">43</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">57</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">119</td></tr><tr><td colspan=\"8\" align=\"center\" valign=\"middle\" rowspan=\"1\">Meteorological factors</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mean temperature (°C)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.94</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−3.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33.9</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Relative humidity (%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">80.62</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.38</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">42</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">74</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">82</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">88</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100</td></tr></tbody></table><table-wrap-foot><fn><p>Notes: CVD, cardiovascular disease; IHD, ischemic heart disease; SD, standard deviation; P<sub>25</sub>, 25th percentile; P<sub>50</sub>, 50th percentile; P<sub>75</sub>, 75th percentile.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05453-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05453-t002_Table 2</object-id><label>Table 2</label><caption><p>The number of hospitalizations and hospitalization costs attributable to PM<sub>2.5</sub> and PM<sub>10</sub> in Wuhan, China, from 2015–2017.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Variable</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Attributable Hospitalizations (No.)</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Attributable Hospitalization Costs (RMB, Million)</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PM<sub>2.5</sub></th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PM<sub>10</sub></th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PM<sub>2.5</sub></th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PM<sub>10</sub></th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CVD</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1487 (1007, 1956)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">983 (541, 1415)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29.27 (19.81, 38.85)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19.34 (10.65, 27.85)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IHD</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">340 (119, 549)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">249 (49, 441)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.57 (2.29, 10.53)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.82 (0.94, 8.53)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Stroke</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">550 (245, 834)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">465 (199, 722)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.35 (6.16, 20.94)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.98 (3.84, 13.94)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Males</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">855 (777, 932)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">460 (388, 531)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.14 (16.48, 19.27)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.76 (8.24, 11.27)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Females</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">653 (335, 959)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">390 (95, 675)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.54 (5.91, 16.93)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.89 (1.68, 11.93)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0–64 years</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">653 (339, 955)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">497 (212, 773)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.85 (7.19, 20.40)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.55 (4.51, 16.40)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">65+ years</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1118 (1035, 1199)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">471 (393, 548)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20.71 (19.18, 22.15)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8.72 (7.28, 10.15)</td></tr></tbody></table><table-wrap-foot><fn><p>Notes: The air quality standard proposed by the WHO (24-h average value: 25 µg/m<sup>3</sup> for PM<sub>2.5</sub> and 50 µg/m<sup>3</sup> for PM<sub>10</sub>) was considered as the reference.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32718065</article-id><article-id pub-id-type=\"pmc\">PMC7432019</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155315</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05315</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Review</subject></subj-group></article-categories><title-group><article-title>Manipulation of Lateral Pharyngeal Wall Muscles in Sleep Surgery: A Review of the Literature</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-1618-0048</contrib-id><name><surname>Cammaroto</surname><given-names>Giovanni</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05315\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05315\">2</xref><xref rid=\"c1-ijerph-17-05315\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Stringa</surname><given-names>Luigi Marco</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05315\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Iannella</surname><given-names>Giannicola</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05315\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-7753-4615</contrib-id><name><surname>Meccariello</surname><given-names>Giuseppe</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05315\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Zhang</surname><given-names>Henry</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05315\">4</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-2973-9437</contrib-id><name><surname>Bahgat</surname><given-names>Ahmed Yassin</given-names></name><xref ref-type=\"aff\" rid=\"af5-ijerph-17-05315\">5</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-3580-0967</contrib-id><name><surname>Calvo-Henriquez</surname><given-names>Christian</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05315\">2</xref><xref ref-type=\"aff\" rid=\"af6-ijerph-17-05315\">6</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-9454-9464</contrib-id><name><surname>Chiesa-Estomba</surname><given-names>Carlos</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05315\">2</xref><xref ref-type=\"aff\" rid=\"af7-ijerph-17-05315\">7</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-0845-0845</contrib-id><name><surname>Lechien</surname><given-names>Jerome R.</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05315\">2</xref><xref ref-type=\"aff\" rid=\"af8-ijerph-17-05315\">8</xref></contrib><contrib contrib-type=\"author\"><name><surname>Barillari</surname><given-names>Maria Rosaria</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05315\">2</xref><xref ref-type=\"aff\" rid=\"af9-ijerph-17-05315\">9</xref></contrib><contrib contrib-type=\"author\"><name><surname>Galletti</surname><given-names>Bruno</given-names></name><xref ref-type=\"aff\" rid=\"af10-ijerph-17-05315\">10</xref></contrib><contrib contrib-type=\"author\"><name><surname>Galletti</surname><given-names>Francesco</given-names></name><xref ref-type=\"aff\" rid=\"af10-ijerph-17-05315\">10</xref></contrib><contrib contrib-type=\"author\"><name><surname>Freni</surname><given-names>Francesco</given-names></name><xref ref-type=\"aff\" rid=\"af10-ijerph-17-05315\">10</xref></contrib><contrib contrib-type=\"author\"><name><surname>Galletti</surname><given-names>Cosimo</given-names></name><xref ref-type=\"aff\" rid=\"af11-ijerph-17-05315\">11</xref></contrib><contrib contrib-type=\"author\"><name><surname>Vicini</surname><given-names>Claudio</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05315\">1</xref><xref ref-type=\"aff\" rid=\"af12-ijerph-17-05315\">12</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05315\"><label>1</label>Head and Neck Department, ENT & Oral Surgery Unit, G.B. Morgagni, L. Pierantoni Hospital, Forlì, FC 47100 ASL of Romagna, Italy; <email>giannicolaiannella@hotmail.it</email> (G.I.); <email>giuseppemec@yahoo.it</email> (G.M.); <email>claudio@claudiovicini.com</email> (C.V.)</aff><aff id=\"af2-ijerph-17-05315\"><label>2</label>Young Otolaryngologists-International Federations of Oto-rhinolaryngological Societies (YO-IFOS), 75000 Paris, France; <email>christian.calvo.henriquez@gmail.com</email> (C.C.-H.); <email>chiesaestomba86@gmail.com</email> (C.C.-E.); <email>Jerome.LECHIEN@umons.ac.be</email> (J.R.L.); <email>mariarosaria.barillari@unicampania.it</email> (M.R.B.)</aff><aff id=\"af3-ijerph-17-05315\"><label>3</label>Department of Otolaryngology, Head and Neck Surgery, University of Ferrara, FE 44121 Ferrara, Italy; <email>luigi.stringa@gmail.com</email></aff><aff id=\"af4-ijerph-17-05315\"><label>4</label>Department of Otolaryngology, Head and Neck, Royal London Hospital, London E1 1FR, UK; <email>henryzhang87@gmail.com</email></aff><aff id=\"af5-ijerph-17-05315\"><label>5</label>Department of Otorhinolaryngology, Alexandria University, Alexandria 21526, Egypt; <email>ahmedyassinbahgat@gmail.com</email></aff><aff id=\"af6-ijerph-17-05315\"><label>6</label>Department of otolaryngology, University Hospital Complex of Santiago de Compostela, 15706 Santiago de Compostela, Spain</aff><aff id=\"af7-ijerph-17-05315\"><label>7</label>Department of Otorhinolaryngology, Head & Neck Surgery, Hospital Universitario Donostia, 20014 San Sebastian, Spain</aff><aff id=\"af8-ijerph-17-05315\"><label>8</label>Department of Otolaryngology, Head & Neck Surgery, Foch Hospital, School of Medicine, UFR Simone Veil, Université Versailles Saint-Quentin-en-Yvelines (Paris Saclay University), 75000 Paris, France</aff><aff id=\"af9-ijerph-17-05315\"><label>9</label>Department of Mental and Physical Health and Preventive Medicine, University of L. Vanvitelli, CE 81100 Naples, Italy</aff><aff id=\"af10-ijerph-17-05315\"><label>10</label>Department of Adult and Development Age Human Pathology “Gaetano Barresi”, Unit of Otorhinolaryngology, University of Messina, ME 98125 Messina, Italy; <email>bgalletti@unime.it</email> (B.G.); <email>fgalletti@unime.it</email> (F.G.); <email>ffreni@unime.it</email> (F.F.)</aff><aff id=\"af11-ijerph-17-05315\"><label>11</label>Comprehensive Dentistry Department, Faculty of Dentistry, Universitat de Barcelona, L’Hospitalet de Llobregat (Barcelona), 08907 Catalonia, Spain; <email>cosimo_galletti@hotmail.it</email></aff><aff id=\"af12-ijerph-17-05315\"><label>12</label>ENT department, University of Ferrara, FE 44121 Ferrara, Italy</aff><author-notes><corresp id=\"c1-ijerph-17-05315\"><label>*</label>Correspondence: <email>giovanni.cammaroto@hotmail.com</email>; Tel.: +39-054-363-5651</corresp></author-notes><pub-date pub-type=\"epub\"><day>23</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5315</elocation-id><history><date date-type=\"received\"><day>01</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>22</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Background: Obstructive sleep apnea syndrome (OSAS) occurs due to upper airway obstruction resulting from anatomical and functional abnormalities. Upper airway collapsibility, particularly those involving the lateral pharyngeal wall (LPW), is known to be one of the main factors contributing to the pathogenesis of OSAS, leading the authors of the present study to propose different strategies in order to stiffen the pharyngeal walls to try to restore normal airflow. Methods: An exhaustive review of the English literature on lateral pharyngeal wall surgery for the treatment of OSAS was performed using the PubMed electronic database. Results: The research was performed in April 2020 and yielded approximately 2000 articles. However, considering the inclusion criteria, only 17 studies were included in the present study. Conclusions: The analyzed surgical techniques propose different parts of LPW on which to focus and a variable degree of invasivity. Despite the very promising results, no gold standard for the treatment of pharyngeal wall collapsibility has been proposed. However, thanks to progressive technological innovations and increasingly precise data analysis, the role of LPW surgery seems to be crucial in the treatment of OSAS patients.</p></abstract><kwd-group><kwd>OSA</kwd><kwd>pharyngoplasty</kwd><kwd>sleep surgery</kwd><kwd>pharynx</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05315\"><title>1. Introduction</title><p>Obstructive sleep apnea syndrome (OSAS) is a very common health problem characterized by absent or insufficient ventilation during sleep as a consequence of the multilevel structural collapse of the upper airways, which usually involves the velopharynx, base of the tongue, and lateral pharyngeal walls. The diagnosis of OSAS is the result of the integration of anamnestic and clinical evaluations with instrumental data provided by polysomnography (PSG), which, collecting physiological parameters during sleep, allows a deeper and objective assessment of sleep-related breathing disorders. In order to localize the obstacle compromising correct airflow, a specific examination (like Müller’s maneuver) can be performed when undergoing fiberoptic laryngoscopy and drug-induced sleep endoscopy (DISE). Described by different authors as a key point in the determination of airflow, the lateral pharyngeal wall (LPW) has been demonstrated to present an increased thickness and collapsibility in patients affected by OSAS, resulting in a potential cause of airway obstruction [<xref rid=\"B1-ijerph-17-05315\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijerph-17-05315\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijerph-17-05315\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijerph-17-05315\" ref-type=\"bibr\">4</xref>]. LPW includes muscular structures such as the palatoglossus muscle (PGM), the palatopharyngeal muscle (PPM), the superior pharyngeal constrictor (SPC), and, ultimately, lymphatic tissue, the palatine tonsils. Stabilizing LPW might be achieved by means of mandibular advancement devices or surgery [<xref rid=\"B5-ijerph-17-05315\" ref-type=\"bibr\">5</xref>]. Different authors have proposed surgical solutions to prevent the upper airways from collapsing [<xref rid=\"B6-ijerph-17-05315\" ref-type=\"bibr\">6</xref>], but in 2003, Cahali [<xref rid=\"B7-ijerph-17-05315\" ref-type=\"bibr\">7</xref>], for the first time, described lateral pharyngoplasty (LP), i.e., a surgical transoral approach focused on LPW, allowing, in this way, an enlarging of the oropharyngeal space and a stiffening of fibromuscular structures. Following on, surgeons have developed several techniques targeting the modification of LPW, trying to reduce surgical invasivity and complications, but none of them have been indicated as a gold standard procedure by the scientific community so far.</p><p>The efficacy of palate surgery is well documented by a meta-analysis published in 2018 by Pang et al. [<xref rid=\"B8-ijerph-17-05315\" ref-type=\"bibr\">8</xref>]. However, no reviews that are mainly oriented on lateral pharyngoplasty have been published so far. The aim of our study is to present a systematic review of the literature regarding the surgical techniques involving modifications of LPW for the treatment of OSAS in order to show their surgical stages, technical aspects, as well as their advantages and inconveniences, to better understand their applications on and contributions to the correction of upper airway obstruction.</p></sec><sec id=\"sec2-ijerph-17-05315\"><title>2. Materials and Methods </title><p>A thoughtful review of the English language literature on the surgical procedures targeting LPW for the management of sleep apnea was performed using PubMed, EMBASE, Cochrane, and CENTRAL electronic databases. Two searches using (1) pharyngoplasty sleep apnea and (2) palate surgery sleep apnea as keyword clusters were performed, and they were combined with the use of the AND function to better select the research. Each paper included in the study met the following inclusion criteria: (1) the surgical treatment of OSAS patients, (2) the application of surgical procedures designed for structural modification of LPW, (3) the presence of instrumental parameters acquired by PSG during preoperative and postoperative evaluations, (4) reports including patients previously treated with Continuous Positive Airway Pressure (CPAP), and (5) reports including patients who did not undergo previous sleep surgery procedures. Articles that were not in accordance with the inclusion criteria were excluded. The subsequent criteria were applied with the aim of excluding inappropriate studies: surgical technique reports without significant outcome data, papers consisting of meta-analyses or literature reviews on surgical procedures, studies relative to ablative techniques, and articles describing animal or cadaveric samples. In order to further reduce the risk of incomplete literature analysis, a manual search through the bibliography of the included papers was carried out.</p><p>With the purpose of obtaining an organized and exhaustive review, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria were applied first to select the papers and, secondly, to stratify them according to their level of evidence [<xref rid=\"B9-ijerph-17-05315\" ref-type=\"bibr\">9</xref>].</p></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05315\"><title>3. Results</title><p>In April 2020, the research led to the detection of approximately 2000 articles (from 1980 to 2020), but in accordance with the inclusion and exclusion criteria, 17 articles were enrolled in the present review. As the articles show detailed descriptions of surgical procedures to better clarify their singularities and effects, we grouped each article in accordance to which component of LPW is mostly involved and how deeply its modification occurs. As a result, we found four articles presenting surgical procedures in which SPC is manipulated and/or dissected, while in the remaining papers, PPM is the main structural target. In particular, five studies proposed a total or subtotal section of the muscle, four papers a partial section of the muscular fibers, and in four studies, minimal tissue handling is performed (<xref rid=\"ijerph-17-05315-t001\" ref-type=\"table\">Table 1</xref>, <xref ref-type=\"fig\" rid=\"ijerph-17-05315-f001\">Figure 1</xref>). </p><p>Data on the efficacy and complications of LPW surgeries are shown in <xref rid=\"ijerph-17-05315-t001\" ref-type=\"table\">Table 1</xref>. The majority of samples included in the study consisted of patients affected by severe OSAS (mean pre-op apnea–hypopnea index (AHI) > 30). A mean reduction of more than 50% of AHI and a mean post-op AHI < 20 were observed in all series except one [<xref rid=\"B10-ijerph-17-05315\" ref-type=\"bibr\">10</xref>]. Some authors did not specify complications and their rates.</p></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05315\"><title>4. Discussion</title><p>As suggested by the literature, some OSAS patients present structural and functional abnormalities in the upper airways. In these patients, the pharynx (specifically, LPW) is thicker and more collapsible upon exposure to negative pressure during inspiration [<xref rid=\"B4-ijerph-17-05315\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijerph-17-05315\" ref-type=\"bibr\">5</xref>]. In 1981, Fujita et al. [<xref rid=\"B6-ijerph-17-05315\" ref-type=\"bibr\">6</xref>] introduced, for the first time, the uvulopalatopharyngoplasty (UPPP)—a surgical procedure finalized at the enlargement of the pharyngeal space—but the association of postoperative complications and dissatisfactory results drove surgeons to alternative approaches. Thus, LPW acquired a central role in OSAS pathogenesis, drawing the attention of numerous studies. Several surgical techniques have been proposed to enlarge and stiffen the pharyngeal tract. All of the procedures are performed under general anesthesia, with either nasotracheal or orotracheal intubation, while the patient is placed in the supine position and a mouth gag is applied to expose the oropharynx. What differentiates these techniques is the degree of involvement of the different LPW components, and how radical their modifications are.</p><p>Taking into account the sample sizes and the average postoperative follow up of the included articles, the following findings need to be highlighted.</p><p>Our review shows that the majority of the evaluated articles included patients with severe OSAS who experienced a significant postoperative reduction of AHI. These data show that the severity of sleep apneas should not be considered an exclusion criterion for LPW surgery.</p><p>However, the selection process of surgical candidates in each study was partially investigated in this review. The choice of performing preoperative DISE might influence the outcomes, and therefore, more attention should be dedicated to this aspect in future studies.</p><p>In our opinion, the main strength of our review is its specific focus on the muscular structures that are manipulated in each surgical technique. A better understanding of the involvement of the LPW muscles and prospective comparative studies might allow sleep surgeons to select the most effective surgical procedure. </p><sec id=\"sec4dot1-ijerph-17-05315\"><title>4.1. Superior Pharyngeal Constrictor </title><p>In 2003, Cahali [<xref rid=\"B7-ijerph-17-05315\" ref-type=\"bibr\">7</xref>] presented LP as the first surgical procedure targeted to modify LPW for the treatment of pharyngeal instability, and SPC was the principal muscular structure involved. More precisely, after the individuation of SPC by bilateral tonsillectomy or mucosal removal of tonsillar fossa in the case of a previous tonsillectomy, it is split by a craniocaudal section into two flaps, one medially based, and the other, laterally based. The latter is sutured anteriorly to PGM. In order to obtain more space and simultaneously reduce traction forces, a palatal incision is performed from the lateral base of the uvula, extending laterally and superiorly, leading to the isolation of the upper part of PPM, which is partially sectioned in this portion. After that, two flaps are obtained: one superior flap, which is sutured in a Z-plasty fashion with the palatal flap, and one inferior flap, which is sutured to the anterior tonsillar pillar. The same procedure is performed on both sides. Although this innovative technique has shown satisfactory results in the correction of upper airway obstruction, it has been described to be related to transient oronasal reflux and partial taste loss. Despite the possible swallowing complication presented by LP, other authors consider SPC a successful target. José Antonio Pinto et al. [<xref rid=\"B10-ijerph-17-05315\" ref-type=\"bibr\">10</xref>], in so-called lateral-expansion pharyngoplasty, suggested the association of LP with expansion sphincter pharyngoplasty, presented for the first time by Pang and Woodson. In this way, the SPC section is combined with the section of PP muscle and the further suture of its cranial flap to the hamulus of the pterygoid process in order to improve the retropalatal obstruction. In their series of 38 samples, no patient suffered major complications. Regarding the modification of SPC, not all authors have pursued its section, and in some cases, a more conservative approach has been suggested. Huseh-Yu Li and Li-Ang Lee [<xref rid=\"B11-ijerph-17-05315\" ref-type=\"bibr\">11</xref>] introduced relocation pharyngoplasty, in which, once identified, SPC is grasped and sutured to PGM. At the same time, an elliptical cut from the lateral base of the uvula, extending superolaterally, is performed and the mucosal and submucosal adipose tissue is removed in order to gain further space, reducing tension forces. Once PPM is isolated from SPC, the posterior pillar is sewn to PGM. A distal mucosal resection of the uvula is also associated. Likewise, in anterolateral advancement pharyngoplasty by Emara et al., a relocation of SPC is performed without any muscular fiber section [<xref rid=\"B12-ijerph-17-05315\" ref-type=\"bibr\">12</xref>]. In particular, a limited dissection and partial separation of anterior and posterior parts of PPM from SPC were performed in the upper part of the tonsillar fossa. Then, SPC was plicated with a mattress-style suture and together with PPM (just inferiorly to the confluence point of its anterior and posterior parts) are anchored to the pterygomandibular raphe. Finally, the upper half of the posterior part of PPM is sutured to the levator veli palatini muscle. </p></sec><sec id=\"sec4dot2-ijerph-17-05315\"><title>4.2. Palatopharyngeal Muscle</title><p>After the very promising results showed by LP, many authors started to elaborate on new approaches aimed at the same outcomes, trying to avoid the complications. The manipulations of SPC were attributed as a significant source of postoperative swallowing problems, and in this way, PPM was considered a proper target in order to strengthen LPW. As shown below, a complete muscular fiber section is not performed in all the procedures, and a variable degree of tissue sparing is applied.</p><sec id=\"sec4dot2dot1-ijerph-17-05315\"><title>4.2.1. Total/Subtotal Section of Muscular Fibers</title><p>Three years after the introduction of LP, devising a surgical procedure that is easy to perform with a low rate of complications, K. Pang and T. Woodson [<xref rid=\"B13-ijerph-17-05315\" ref-type=\"bibr\">13</xref>] introduce the so-called expansion sphincter pharyngoplasty (ESP). In this new approach, all patients are submitted to bilateral tonsillectomy in order to identify PPM. Then, its muscular fibers are sectioned at the inferior end, and the posterior surface of the superior flap is partially detached from SPC. In order to isolate the soft palate muscles, a superolateral incision is performed on the anterior pillar. PPM is then lifted superolaterally, suturing it to the soft palate muscles. A partial uvulectomy is then performed. This technique showed very encouraging results and traced the path to more innovative approaches. A conservative modification of ESP was presented in 2013 by G. Sorrenti and O. Piccin, named functional expansion pharyngoplasty (FEP) [<xref rid=\"B14-ijerph-17-05315\" ref-type=\"bibr\">14</xref>]. The most important variation applied to the original technique is the replacement of the superolateral incision of soft palatal mucosa to expose the palatal muscles with a preparation of a tunnel through the palatal musculature from the apex to the hamulus of the tonsillar fossa of the pterygoid process. Once sectioned and rotated, the PPM flap is elevated through the tunnel and anchored to the palatine muscles close to the hamulus. A thin rim of muscular fibers of PPM is preserved medially to avoid retracting scar tissue at the posterior pillar. In this way, a more physiological widening force is applied to LPW, reducing tissue dissection and the subsequent retracting scar. Some years later, Sorrenti et al. [<xref rid=\"B25-ijerph-17-05315\" ref-type=\"bibr\">25</xref>] added some technical updates to their technique. In particular, they proposed the use of knotless barbed V-Loc sutures to fix the PPM flap to the apex of the mandibular pterygoid fold. Compared to previous FEP, this technique became easier and faster to perform with a powerful docking site. In order to reduce tissue manipulation and postoperative complications linked to tonsillectomy, A.M.M.E. Albassiouny, in 2014 [<xref rid=\"B15-ijerph-17-05315\" ref-type=\"bibr\">15</xref>], introduced the so-called soft palatal posterior pillar webbing flap palatopharyngoplasty technique in which the palatine tonsils are spared, and a transverse section of PPM is performed. Specifically, once the ventral mucosa of the posterior pillar is removed, it is sectioned in two points—one lateral, including PPM, and one medial, close to the uvula—in order to obtain two flaps. The lateral flap is then sutured to the most superolateral part of the anterior pillar, lateralizing the tonsil. After a submucosal dissection of the palatal mucosa and a shortening of the medial flap in length, it is turned up and sutured to the free margin of the soft palate. Two years later, A.M.M.E. Albassiouny [<xref rid=\"B16-ijerph-17-05315\" ref-type=\"bibr\">16</xref>] proposed two modifications to his original approach: coblation-assisted extracapsular tonsillectomy when tonsil collapse is documented by DISE, and the use of barbed STRATAFIX sutures in order to better control the tension impressed on the pharyngeal structure. In both cases, postoperative temporary velopharyngeal insufficiency and excessive postnasal discharge have been reported.</p></sec><sec id=\"sec4dot2dot2-ijerph-17-05315\"><title>4.2.2. Partial Section of Muscular Fibers</title><p>The progressive interest in the treatment of OSAS has led to the elaboration of an increasingly conservative approach, allowing its application to a more delicate population such as pediatrics. On the heels of the excellent outcomes shown first by K. Pang and T. Woodson, and later by G. Sorrenti and O. Piccin, S.O. Ulualp [<xref rid=\"B17-ijerph-17-05315\" ref-type=\"bibr\">17</xref>] presented a modified ESP, applied to a pediatric population. In fact, in order to reduce tissue removal, after bilateral tonsillectomy, a partial section of the PPM anterior fibers is performed at the junction of the upper third and mid-third. Then, superolateral tunneling of the soft palate is prepared, and the upper portion of PPM is pulled up in the muscular tunnel and sutured to it. Not only the lateral side but also the anterior portion of the soft palate has been reported to be modified during LPW surgery. In this regard, M.J. Kim et al. [<xref rid=\"B18-ijerph-17-05315\" ref-type=\"bibr\">18</xref>] introduced a modified uvulopalatal flap with a partial LP in order to widen the retropalatal space anteroposteriorly and transversely. The posterior pillar is cut at its junction with the uvula, and once trimmed, it is sutured to the anterior pillar. After having calculated the mucosa and submucosal fat to be removed by measuring the halfway point between the junction of the soft and hard palates and the tip of the uvula, a diamond-shaped area of the soft palate is removed. Then, a suture of the muscular and mucosal rims is performed. In some cases, a bilateral tonsillectomy is performed, and the patients are submitted to concomitant nasal septoplasty and turbinate surgery. Following the interest in conservative approaches, and thanks to material technology improvements, in 2015, C. Vicini et al. [<xref rid=\"B19-ijerph-17-05315\" ref-type=\"bibr\">19</xref>] introduced an innovative tissue-sparing approach based on the use of barbed knotless bidirectional reabsorbable sutures named barbed reposition pharyngoplasty (BRP). All patients are submitted to bilateral tonsillectomy, and once PPM is demarcated, a partial incision is made at its inferior portion. A triangular-shaped mucosal and submucosal portion of the tonsillar fossa at its apex is removed in order to widen the oropharyngeal inlet. Then, the barbed sutured is utilized to superolaterally add tension to the upper portion of PPM and to stiffen LPW and the palate. In particular, the first pass of the needle is introduced at the center of the palate and passed laterally, reaching the pterygomandibular raphe. Passing around the pterygomandibular raphe, the needle reaches the upper portion of PPM through the tonsillar fossa. In this way, the muscular fibers are pulled up and anchored at the pterygomandibular raphe. The same procedure is applied to the other side. Effective and simple to perform, this technique has shown important results as both a single-stage procedure and a multilevel procedure associated with other upper airway corrective surgeries like base of tongue resection, hyoid suspension, and nasal surgery [<xref rid=\"B26-ijerph-17-05315\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijerph-17-05315\" ref-type=\"bibr\">27</xref>]. To minimize mucosal and muscular resection of the uvula, M.A. Babademez et al. [<xref rid=\"B20-ijerph-17-05315\" ref-type=\"bibr\">20</xref>] suggested some modifications using a monodirectional reabsorbable barbed thread to expand the upper part of the oropharyngeal inlet in a more conservative manner. In fact, in order to pull the uvula forward and superiorly, once PPM is fixed to the pterygomandibular raphe, the suture is passed horizontally through the root of the uvula to reach the opposite tonsillar bed, and then, PPM is anchored to the pterygomandibular raphe in the same fashion.</p></sec><sec id=\"sec4dot2dot3-ijerph-17-05315\"><title>4.2.3. Minimal Handling of Muscular Fibers</title><p>In order to further reduce the complications linked to muscle fiber section and fibrotic tissue development, some authors have proposed some minimally invasive and nonresective procedures. In 2015, Mantovani et al., modifying their previous “Roman blinds technique” for the treatment of retropalatal collapse [<xref rid=\"B21-ijerph-17-05315\" ref-type=\"bibr\">21</xref>,<xref rid=\"B28-ijerph-17-05315\" ref-type=\"bibr\">28</xref>], presented the barbed Roman blinds technique (BRBT), in which complete preservation of oropharyngeal fibromuscular structures is encouraged, and stiffening of LPW and of the soft palate is obtained through the use of a bidirectional barbed suture. With the aim of exposing the muscular fibers of PPM, a mucosectomy of the tonsillar fossa is performed. The first needle of the bidirectional barbed suture is inserted in the palatal mucosa 1 cm in front of the posterior nasal spine. The suture is passed inferiorly, following the periosteal layer, to reach the periuvular extremity of PPM. Once the muscular fibers are grasped, the suture is fixed to the pterygoid hamulus and passed in a craniocaudal direction, encircling the pterygomandibular raphe. The needle is then passed medially, reaching PPM to anchor it to the pterygomandibular raphe. Once the muscular fibers are fixed laterally, the suture is directed to the hamulus, and then, to the first insertion point. A nonresective technique, barbed suspension pharyngoplasty (BSP), was also presented in 2019 by M. Barbieri et al. [<xref rid=\"B24-ijerph-17-05315\" ref-type=\"bibr\">24</xref>]. After a tonsillar fossa mucosectomy or a bilateral tonsillectomy, as in BRBT, no further tissue dissection is executed, but a bidirectional barbed suture is performed in order to lateralize LPW and to give more tension to the soft palate. In particular, the needle is first inserted in the palatal mucosa at the level of the posterior nasal spine and passed anterolaterally towards the upper part of the tonsillar fossa. Then, once PPM is anchored by multiple stitches to the anterior pillar, the suture is passed laterally towards the pterygomandibular raphe and then it medially encircles the contralateral raphe and is directed back to the ipsilateral raphe, passing through the base of the uvula. In this way, a stronger tension is applied to the soft palate, and a more secure suture is achieved. As previously reported, the pterygomandibular raphe is considered an important security point by many authors, and some of them perform a tissue dissection in order to expose it, so as to better distribute the strongpoints during the suture. Hsueh-Yu Li et al. [<xref rid=\"B23-ijerph-17-05315\" ref-type=\"bibr\">23</xref>] proposed a suspension palatoplasty, in which, after a mucosal incision from the anterior pillar rim to 1 cm in front of the center mark of the pterygomandibular raphe, the submucosal tissue is dissected to expose the fibers of the raphe and a bilateral tonsillectomy is performed. The upper third of PPM is then fixed at the pterygomandibular raphe at different points in a craniocaudal direction, and the anterior and posterior pillars are sutured together. No major complications have been reported except for a transient globus sensation. Not all the procedures acting to stabilize LPW have considered the removal of palatal lymphoid tissue as a required step. In this way, for those patients with LPW collapse without tonsillar hypertrophy, M.A. El-Ahl and M.W. El-Anwar [<xref rid=\"B22-ijerph-17-05315\" ref-type=\"bibr\">22</xref>] proposed a modified expansion pharyngoplasty without tonsillectomy and any pharyngeal tissue ablation, in which a reabsorbable submucosal suture is performed in order to fix the upper portions of PPM and PGM to the pterygoid hamulus.</p><p>In all the papers included in the review, a statistically significant improvement of the apnea–hypopnea index (AHI) was reported, showing a considerable impact of LPW surgery in the restoration of efficient airflow. Some complications have been reported, but none of them were of a major nature. Nasal regurgitation, dysphagia, foreign body sensation, velopharyngeal insufficiency, taste loss, and sensation of oral dryness have been the most noted postoperative symptoms, which resolved spontaneously after few weeks of the surgery. In a few cases, postoperative bleeding was reported, and, when a barbed suture was used, partial suture extrusion was described, without any additional problems. To better evaluate the surgical contributions to the amelioration of LPW collapse, some limits of the examined papers need to be mentioned. The small number of samples and the retrospective modality of the study are the most significant restrictions encountered, allowing, in this way, a partial estimation of the results. In addition, in some papers, the LPW surgical technique was presented as part of a multiple-level treatment and not as a single procedure, partially hiding the real impact on clinical improvement provided by the stiffening of the pharyngeal structures. Further efforts need to be made in order to clarify the authentic role of LPW surgery, and in this way, the quantification of upper airway collapsibility represents an important tool in order to perform an attentive selection of the candidate and thorough monitoring of the clinical data. The pharyngeal critical closing pressure (Pcrit) is the most accurate index of upper airway collapsibility, but the invasivity and the complexity of its measurement do not make it clinically practical. With regard to this, A.M. Osman et al. [<xref rid=\"B29-ijerph-17-05315\" ref-type=\"bibr\">29</xref>] recently presented the peak inspiratory flow percentage (PIF%) as a marker of the collapsibility of the upper airways; it is related to Pcrit and easy to obtain during a routine CPAP titration study. PIF% could represent an important opportunity to better quantify the structural collapsibility of the upper airways in clinical practice, allowing more direct estimation of the effective value of LPW surgery and optimizing, in this way, the treatment of OSAS patients. </p><p>Finally, the role of DISE as a phenotyping selection tool for surgical candidates also needs to be discussed. In fact, DISE appears to be a promising method for properly targeted therapy planning, in particular, allowing the selection or ruling-out of patients for specific surgical procedures [<xref rid=\"B30-ijerph-17-05315\" ref-type=\"bibr\">30</xref>].</p></sec></sec></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05315\"><title>5. Conclusions</title><p>Usually, upper airway collapse in patients with OSAS has a multilevel etiology, and LPW hypotonicity and flexibility represent two of the main factors. In this way, stiffening of the fibromuscular components and ablation of the redundant soft tissue of LPW constitute critical targets for many surgeons in order to restore correct airflow. Several surgical procedures acting on different fibromuscular structures have been proposed, each with different degrees of invasivity. With the improvements in surgical materials and knowledge acquisition around the physiopathology of LPW, surgeons have elaborated on increasingly efficient techniques to reduce the extension of tissue dissection and ablation. Many of them have reported important results, and while in some cases, postoperative complications have been experienced, these are usually temporary and of a small nature. Despite the high variability between the methods, all authors agree that meticulous preoperative analysis and selection of patients will reduce, as much as possible, the surgical failures. In this way, the absence of a gold standard, the possibility of utilizing the surgery of LPW in a multilevel context, the presence of a wide range of surgical options, and the short learning curve of many of them represent an opportunity to apply the most suitable method, according to the anatomical and clinical characteristics of the patient, to restore proper airflow.</p></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization: G.C. and C.V.; data curation: G.I., G.M., and C.V.; formal analysis: L.M.S., C.C.-H., and C.C.-E.; investigation: G.C., G.I., and C.G.; methodology: G.I., F.F., and M.R.B.; project administration: G.C. and A.Y.B.; resources: G.C. and L.M.S.; supervision: H.Z. and J.R.L.; validation: C.V., F.G., and B.G. 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Oto-Rhino-Laryngol.</source><year>2017</year><volume>274</volume><fpage>2307</fpage><lpage>2317</lpage><pub-id pub-id-type=\"doi\">10.1007/s00405-016-4447-x</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijerph-17-05315-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).</p></caption><graphic xlink:href=\"ijerph-17-05315-g001\"/></fig><table-wrap id=\"ijerph-17-05315-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05315-t001_Table 1</object-id><label>Table 1</label><caption><p>Clinical data of papers included in the review.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Title </th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Structural Target and Modification of LPW</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Number of Patients</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mean AHI Pre-Op</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mean AHI Post-Op</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">∆ AHI.</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Follow-Up Time (Months)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Complications Described and Relating Incidence</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Lateral Pharyngoplasty: A New Treatment for Obstructive Sleep Hypopnea Syndrome [<xref rid=\"B7-ijerph-17-05315\" ref-type=\"bibr\">7</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SPC</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">45.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">30.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Oronasal reflux (10%), taste loss (10%).</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Lateral-Expansion Pharyngoplasty: Combined Technique for the Treatment of Obstructive Sleep Apnea Syndrome [<xref rid=\"B10-ijerph-17-05315\" ref-type=\"bibr\">10</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SPC </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">38</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">22.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Relocation Pharyngoplasty for Obstructive Sleep Apnea [<xref rid=\"B11-ijerph-17-05315\" ref-type=\"bibr\">11</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SPC</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">43.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">27.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Anterolateral Advancement Pharyngoplasty: A New Technique for Treatment of Obstructive Sleep Apnea [<xref rid=\"B12-ijerph-17-05315\" ref-type=\"bibr\">12</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SPC</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">38</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">42.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Expansion Sphincter Pharyngoplasty: A New Technique for the Treatment of Obstructive Sleep Apnea [<xref rid=\"B13-ijerph-17-05315\" ref-type=\"bibr\">13</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PPM section</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">45</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">44.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">32.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Functional Expansion Pharyngoplasty in the Treatment of Obstructive Sleep Apnea [<xref rid=\"B14-ijerph-17-05315\" ref-type=\"bibr\">14</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PPM section</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">85</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">33.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">21.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Postsurgical bleeding (2.3%).</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Soft Palatal Webbing Flap Palatopharyngoplasty for Both Soft Palatal and Obstructive Sleep Apnea: A New Innovative Technique without Tonsillectomy [<xref rid=\"B15-ijerph-17-05315\" ref-type=\"bibr\">15</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PPM section</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">28</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">46.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">35.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Excessive postnasal discharge (10.7%) temporary velopharyngeal insufficiency (7.1%), sensation of oral dryness (25%). </td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Modified Barbed Soft Palatal Posterior Pillar Webbing Flap Palatopharyngoplasty [<xref rid=\"B16-ijerph-17-05315\" ref-type=\"bibr\">16</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PPM section</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">21</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">47.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">35.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Temporary velopharyngeal insufficiency (5%), excessive postnasal discharge (19%).</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Modified Expansion Sphincter pharyngoplasty for Treatment of Children with Obstructive Sleep Apnea [<xref rid=\"B17-ijerph-17-05315\" ref-type=\"bibr\">17</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PPM partial section </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">60.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">58.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Postoperative bleeding (4%).</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">A Modified Uvulopalatal Flap with Lateral Pharyngoplasty for Treatment in 92 Adults with Obstructive Sleep Apnoea Syndrome [<xref rid=\"B18-ijerph-17-05315\" ref-type=\"bibr\">18</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PPM partial section</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">92</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">39.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">21,2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Nasal regurgitation, bleeding, dysphagia, Foreign body sensation.</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Barbed Reposition Pharyngoplasty (BRP) for OSAHS: A Feasibility, Safety, Efficacy and Teachability Pilot Study. “We are on the giant’s shoulders” [<xref rid=\"B19-ijerph-17-05315\" ref-type=\"bibr\">19</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PPM partial section</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">43.65</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.57</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">30.08</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Foreign body sensation, partial thread extrusion (20%).</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Technical Update of Barbed Pharyngoplasty for Retropalatal Obstruction in Obstructive Sleep Apnoea [<xref rid=\"B20-ijerph-17-05315\" ref-type=\"bibr\">20</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PPM partial section</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">29.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">24.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Foreign body sensation.</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Barbed Roman Blinds Technique for Treatment of Obstructive Sleep Apnea: How We Do It? [<xref rid=\"B21-ijerph-17-05315\" ref-type=\"bibr\">21</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PPM minimal handling</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">32</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">36.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">23.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Expansion Pharyngoplasty by New Simple Suspension Sutures without Tonsillectomy [<xref rid=\"B22-ijerph-17-05315\" ref-type=\"bibr\">22</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PPM minimal handling</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">24</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">28.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">19.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Suspension Palatoplasty for Obstructive Sleep Apnea- Preliminary Study [<xref rid=\"B23-ijerph-17-05315\" ref-type=\"bibr\">23</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PPM minimal handling</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">39.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">24.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Globus sensation of the throat.</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Barbed Suspension Pharyngoplasty for Treatment of Lateral Pharyngeal Wall and Palatal Collapse in Patients Affected by OSAHS [<xref rid=\"B24-ijerph-17-05315\" ref-type=\"bibr\">24</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PPM minimal handling </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Transient velopharyngeal insufficiency (10%), Partial thread extrusion (25%).</td></tr></tbody></table></table-wrap></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32722374</article-id><article-id pub-id-type=\"pmc\">PMC7432020</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155360</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05360</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Increased One-Year Recurrent Ischemic Stroke after First-Ever Ischemic Stroke in Males with Benign Prostatic Hyperplasia</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-1081-760X</contrib-id><name><surname>Cheng</surname><given-names>Chun-Gu</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05360\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05360\">2</xref><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05360\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Chu</surname><given-names>Hsin</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05360\">4</xref><xref ref-type=\"aff\" rid=\"af5-ijerph-17-05360\">5</xref></contrib><contrib contrib-type=\"author\"><name><surname>Lee</surname><given-names>Jiunn-Tay</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05360\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Chien</surname><given-names>Wu-Chien</given-names></name><xref ref-type=\"aff\" rid=\"af6-ijerph-17-05360\">6</xref><xref ref-type=\"aff\" rid=\"af7-ijerph-17-05360\">7</xref><xref ref-type=\"aff\" rid=\"af8-ijerph-17-05360\">8</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-0380-7286</contrib-id><name><surname>Cheng</surname><given-names>Chun-An</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05360\">4</xref><xref rid=\"c1-ijerph-17-05360\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05360\"><label>1</label>Department of Emergency, Taoyuan Armed Forces General Hospital, Taoyuan 32549, National Defense Medical Center, Taipei 11490, Taiwan; <email>doc50015@yahoo.com.tw</email></aff><aff id=\"af2-ijerph-17-05360\"><label>2</label>Department of Emergency Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan</aff><aff id=\"af3-ijerph-17-05360\"><label>3</label>Department of Emergency and Critical Medicine, Wan Fang Hospital, Taipei Medical University, Taipei 11696, Taiwan</aff><aff id=\"af4-ijerph-17-05360\"><label>4</label>Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan; <email>hrchu@ndmctsgh.edu.tw</email> (H.C.); <email>jiunntay@gmail.com</email> (J.-T.L.)</aff><aff id=\"af5-ijerph-17-05360\"><label>5</label>Institute of Aerospace and Undersea Medicine, National Defense Medical Center, Taipei 11490, Taiwan</aff><aff id=\"af6-ijerph-17-05360\"><label>6</label>Department of Medical Research, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan; <email>chienwu@ndmctsgh.edu.tw</email></aff><aff id=\"af7-ijerph-17-05360\"><label>7</label>School of Public Health, National Defense Medical Center, Taipei 11490, Taiwan</aff><aff id=\"af8-ijerph-17-05360\"><label>8</label>Graduate Institute of Life Sciences, National Defense Medical Center, Taipei 11490, Taiwan</aff><author-notes><corresp id=\"c1-ijerph-17-05360\"><label>*</label>Correspondence: <email>cca@ndmctsgh.edu.tw</email>; Tel.: +886-2-87927173</corresp></author-notes><pub-date pub-type=\"epub\"><day>25</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5360</elocation-id><history><date date-type=\"received\"><day>21</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>22</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>(1) Background: Patients with benign prostatic hyperplasia (BPH) were questioned about quality of life and sleep. Most BPH patients were treated with alpha-1 adrenergic receptor antagonists, which could improve cerebral blood flow for 1–2 months. Patients with ischemic stroke (IS) could experience cerebral autoregulation impairment for six months. The relationship between BPH and recurrent IS remains unclear. The aim of this study was to determine the risk of one-year recurrent IS conferred by BPH. (2) Methods: We used data from the Taiwanese National Health Insurance Database to identify newly diagnosed IS cases entered from 1 January 2008 to 31 December 2008. Patients were followed until the recurrent IS event or 365 days after the first hospitalization. The risk factors associated with one-year recurrent IS were assessed using Cox proportional hazards regression. (3) Results: Patients with BPH had a higher risk of recurrent IS (12.11% versus 8.15%) (adjusted hazard ratio (HR): 1.352; 95% confidence interval (CI): 1.028–1.78, <italic>p</italic> = 0.031). Other risk factors included hyperlipidemia (adjusted HR: 1.338; 95% CI: 1.022–1.751, <italic>p</italic> = 0.034), coronary artery disease (adjusted HR: 1.487; 95% CI: 1.128–1.961, <italic>p</italic> = 0.005), chronic obstructive pulmonary disease (adjusted HR: 1.499; 95% CI: 1.075–2.091, <italic>p</italic> = 0.017), and chronic kidney disease (adjusted HR: 1.523; 95% CI: 1.033–2.244, <italic>p</italic> = 0.033). (4) Conclusion: Patients with BPH who had these risk factors had an increased risk of one-year recurrent IS. The modification of risk factors may prevent recurrent IS.</p></abstract><kwd-group><kwd>benign prostatic hyperplasia</kwd><kwd>one-year recurrent ischemic stroke</kwd><kwd>cerebral autoregulation dysfunction</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05360\"><title>1. Introduction</title><p>Incidents of recurrent ischemic stroke (IS) are higher than that of hemorrhagic stroke in Asia [<xref rid=\"B1-ijerph-17-05360\" ref-type=\"bibr\">1</xref>]. Recurrent IS is related to older age, more atherosclerotic risk factors, and cigarette smoking or drinking habits [<xref rid=\"B2-ijerph-17-05360\" ref-type=\"bibr\">2</xref>]. Recurrent IS is a challenge in terms of treatment and extended care; risk factors need to be recognized, and aggressive strategies for prevention must be considered. The traditional risk factors for atherosclerosis are well known, but some undetectable risk factors must still be identified. The patients with IS have cerebral autoregulation impairment and parasympathetic dysfunction with sympathetic overactivity [<xref rid=\"B3-ijerph-17-05360\" ref-type=\"bibr\">3</xref>]. Mild chronic IS for more than six months is associated with decreased cerebral autoregulation [<xref rid=\"B4-ijerph-17-05360\" ref-type=\"bibr\">4</xref>].</p><p>Benign prostatic hyperplasia (BPH) is a noncancerous enlargement of the prostate gland that causes symptoms by obstructing the urethra. Male patients with BPH were questioned about their quality of life and whether their condition disturbed their sleep. The majority of patients with BPH were treated with alpha-1 adrenergic receptor antagonists. A previous study found that alpha-1 adrenergic receptor antagonist therapy could improve cerebral blood flow after treatment for 4–8 weeks [<xref rid=\"B5-ijerph-17-05360\" ref-type=\"bibr\">5</xref>].</p><p>A past study revealed that symptoms of BPH were correlated with traditional cardiovascular risk factors. Endothelial dysfunction, defined as decreased levels of flow-mediated vasodilation in the brachial artery, was associated with an increased risk of moderate-to-severe lower urinary tract symptoms (LUTS) in men. Endothelial dysfunction, which initially occurs in the pathogenesis of atherosclerosis, was associated with LUTS in men. Moderate-to-severe LUTS was associated with the prevalence of coronary heart disease in men but not in women [<xref rid=\"B6-ijerph-17-05360\" ref-type=\"bibr\">6</xref>]. Nocturia was the main symptom of BPH that increased sympathetic activity and decreased nondip blood pressure during the night [<xref rid=\"B1-ijerph-17-05360\" ref-type=\"bibr\">1</xref>]. The patients with severe LUTS had increased long-term risk of major adverse cardiovascular events compared with those who were symptom-free or had mild symptoms of BPH [<xref rid=\"B7-ijerph-17-05360\" ref-type=\"bibr\">7</xref>]. A pooling study found that LUTS in patients free of cardiovascular disease were not associated with cardiovascular events in elderly patients [<xref rid=\"B8-ijerph-17-05360\" ref-type=\"bibr\">8</xref>]. The relationship between BPH and recurrent IS is unknown, and our aim is to determine if recurrent IS will be increased within one-year post-ischemic stroke in patients with BPH. We used the Taiwanese National Health Insurance Database to survey patients with recurrent IS within one year after first-ever IS.</p><p>We hypothesized that male IS patients with BPH had an increased risk of one-year recurrent IS. Patients with BPH had a 35% increased risk of recurrent IS in our study. When males with BPH suffer first-ever IS, physicians and patients must focus on the BPH symptoms and initiate aggressive lifestyle modification and therapy for atherosclerotic risk factors to reduce the occurrence of recurrent IS.</p></sec><sec id=\"sec2-ijerph-17-05360\"><title>2. Materials and Methods </title><sec id=\"sec2dot1-ijerph-17-05360\"><title>2.1. Database</title><p>The Taiwanese National Health Insurance is a single-payer health care system funded by the government and includes approximately 99% of the nation’s 23 million residents. The Longitudinal National Health Insurance Research Database (LNHID) released a cohort dataset consisting of two million randomly sampled people. In this cohort dataset, each patient’s original identification number has been encrypted to protect privacy. The National Health Insurance Research Database (NHIRD) contains outpatient and inpatient medical payment records. There are up to three International Classification of Disease, Ninth Revision, Clinical Modification (ICD-9-CM) outpatient diagnosis codes and five ICD-9-CM inpatient diagnosis codes. This study used the LNHID from 2008 to 2009 and observed the occurrence of recurrent IS within one year in ischemic stroke patients in 2008. This study was approved by the Ethics Institutional Review Board of the Tri-Service General Hospital (TSGHIRB 2-103-05-050).</p></sec><sec id=\"sec2dot2-ijerph-17-05360\"><title>2.2. Design</title><p>We used outpatient and inpatient data from the LNHID to identify newly diagnosed IS cases that were entered from 1 January 2008 to 31 December 2008 in Taiwan. The database contained patient identification numbers, outpatient visits, and admission dates. We retrieved data for patients with a first diagnosis of IS (ICD-9-CM diagnostic code 433–437). The events of recurrent IS were defined (ICD-9-CM diagnostic codes 433–437) after discharge from the first admission. Patients were followed until the recurrent IS event or 365 days after the first admission. We excluded patients who were younger than 18 years of age, died during the first hospitalization for IS, female, or were diagnosed before 2008. The date of the first diagnosis of IS was defined as the index date. The flow chart of this study is shown in <xref ref-type=\"fig\" rid=\"ijerph-17-05360-f001\">Figure 1</xref>.</p><p>Comorbidities mapped by ICD-9-CM codes included hypertension (401–405), diabetes mellitus (250), atrial fibrillation (427.31), hyperlipidemia (272), coronary artery disease (410–414), congestive heart failure (428), chronic kidney disease (580–589), peripheral artery obstructive disease (443), chronic obstructive pulmonary disease (491, 492, 496), BPH (600), and hypotension (458).</p></sec><sec id=\"sec2dot3-ijerph-17-05360\"><title>2.3. Statistical Analyses</title><p>Continuous variables were compared using the Student’s <italic>t</italic>-test and are displayed as the mean ± standard deviation. Categorical variables were compared using the chi-square test and are displayed as percentages. We performed a collinear analysis and showed 0.88 for hypertension with hyperlipidemia, 0.9 for coronary artery disease with atrial fibrillation, and 0.84 for hypotension with BPH. The risk factors associated with one-year recurrent IS or mortality were assessed using a Cox proportional hazard regression with forward stepwise selection to reduce <styled-content style=\"color:#0A0A0A\">collinearity</styled-content>. The statistical significance was set at <italic>p</italic> < 0.05. The statistical analysis was performed using SPSS version 21 software (Asia Analytics Taiwan Ltd., Taipei, Taiwan).</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05360\"><title>3. Results</title><p>The incidence of one-year recurrent IS was 9.22% (223/2390) after the first-ever IS. Patients who experienced recurrent IS within one year of the first-ever IS more frequently had BPH, hyperlipidemia, coronary artery disease, chronic obstructive pulmonary disease, chronic kidney disease, and hypotension (<xref rid=\"ijerph-17-05360-t001\" ref-type=\"table\">Table 1</xref>). The cumulative incidence of one-year recurrent IS was higher in BPH group (log-rank test: <italic>p</italic> = 0.003) (<xref ref-type=\"fig\" rid=\"ijerph-17-05360-f002\">Figure 2</xref>).</p><p>The risk factors associated with one-year recurrent IS, assessed using multivariate Cox regression with forward stepwise selection, were BPH (adjusted hazard ratio (HR): 1.352; 95% confidence interval (CI): 1.028–1.78, <italic>p</italic> = 0.031), hyperlipidemia (adjusted HR: 1.338; 95% CI: 1.022–1.751, <italic>p</italic> = 0.034), coronary artery disease (adjusted HR: 1.487; 95% CI: 1.128–1.961, <italic>p</italic> = 0.005), chronic obstructive pulmonary disease (adjusted HR:1.499; 95% CI: 1.075–2.091, <italic>p</italic> = 0.017), and chronic kidney disease (adjusted HR: 1.523; 95% CI: 1.033–2.244, <italic>p</italic> = 0.033) (<xref rid=\"ijerph-17-05360-t002\" ref-type=\"table\">Table 2</xref>). Our study found that age was an independent risk factor for BPH with an adjusted OR of 1.061 (95% CI: 1.052–1.07, <italic>p</italic> < 0.001). Other factors were hypertension (adjusted OR 1.421; 95% CI: 1.135–1.804, <italic>p</italic> = 0.002) and coronary artery disease (adjusted OR 1.333; 95% CI: 1.093–1.625, <italic>p</italic> = 0.005).</p><p>There were 309 (12.93%) male patients who died within one year after first-ever IS. The risk factors for one-year mortality were age (adjusted HR: 1.052; 95% CI: 1.04–1.064, <italic>p</italic> < 0.001), diabetes mellitus (adjusted HR: 1.678; 95% CI: 1.326–2.124, <italic>p</italic> < 0.001), chronic obstructive pulmonary disease (adjusted HR: 1.343; 95% CI: 1.03–1.751, <italic>p</italic> = 0.03), and chronic kidney disease (adjusted HR: 2.547; 95% CI: 1.935–3.352, <italic>p</italic> < 0.001) (<xref rid=\"ijerph-17-05360-t003\" ref-type=\"table\">Table 3</xref>).</p></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05360\"><title>4. Discussion</title><p>We found that BPH was associated with recurrent IS within one-year post-IS. First-ever IS with multiple comorbid conditions could cause recurrent IS within one year. Some patients who recovered from IS after treatment and still experience poor health conditions need to modify their health behaviors to prevent recurrent IS.</p><p>The circadian rhythm of BP is negatively affected in BPH patients because of increased nocturia episodes with sleep disturbances and sympathetic overactivity; nondipping blood pressure increases the risk of cardiovascular disease morbidity and mortality [<xref rid=\"B9-ijerph-17-05360\" ref-type=\"bibr\">9</xref>]. Severe LUTS with BPH increases the risk of major adverse cardiovascular events with an odds ratio of 1.68 [<xref rid=\"B7-ijerph-17-05360\" ref-type=\"bibr\">7</xref>]. A previous study found that alpha-1 adrenergic receptor antagonist therapy in elderly patients with BPH caused initial hypotension with increased IS within three weeks but improved cerebral blood flow without influencing IS later [<xref rid=\"B2-ijerph-17-05360\" ref-type=\"bibr\">2</xref>]. Increased atherosclerotic burden and endothelial dysfunction were observed in BPH patients in a recent study [<xref rid=\"B6-ijerph-17-05360\" ref-type=\"bibr\">6</xref>]. Furthermore, patients with IS had cerebral autoregulation impairment that persisted for six months in a previous study [<xref rid=\"B10-ijerph-17-05360\" ref-type=\"bibr\">10</xref>]. We found that BPH was associated with advanced age; hypertension and coronary artery disease shared risk factors of atherosclerosis. The older IS patients carried higher risk of mortality in recurrent IS. The previous studies found doxaben treatment for chronic IS patients with hypertension increased cerebral blood flow that reduced recurrent IS [<xref rid=\"B5-ijerph-17-05360\" ref-type=\"bibr\">5</xref>]. A past study of hypertension treated with doxaben in Chinese patients compared to diuretic treatment decreased lipid levels [<xref rid=\"B11-ijerph-17-05360\" ref-type=\"bibr\">11</xref>] and normalized platelet function [<xref rid=\"B12-ijerph-17-05360\" ref-type=\"bibr\">12</xref>]. Aggressive antiplatelet and lipid-lowering agent prescriptions for IS patients that can reduce coronary artery disease correlated to recurrent IS. BPH seems to have been an unrecognized risk factor of recurrent IS in the past. </p><p>We surveyed first-ever IS patients to find a relationship between BPH and one-year recurrent IS. Our study found that patients with BPH exhibited a crude odds ratio (OR) of 2.347 (95% CI: 1.409–3.907, <italic>p</italic> = 0.001) and experienced more hypotension (4.23% versus 1.88%, <italic>p</italic> = 0.001). The hypotension carried a two-fold risk of recurrent IS in univariate analysis. There were 571 patients who used alpha blockers, 31 patients who used 5-alpha reductase inhibitors, and 137 patients without medication records due to random selection of medications selection. There were 542 patients with BPH treated by single-type medication with alpha blockers that carried a higher risk of recurrent IS of crude HR 1.521 (95% CI: 1.135–2.037, <italic>p</italic> = 0.005), a higher risk of hypotension of crude OR 1.932 (95% CI: 1.083–3.45, <italic>p</italic> = 0.026), and 29 patients with double-type medications (alpha blockers and 5-alpha reductase inhibitors) with an insignificant difference (crude HR 1.672; 95% CI: 0.619–4.519, <italic>p</italic> = 0.311) not related to hypotension (crude OR 1.9; 95% CI: 0.25–14.408, <italic>p</italic> = 0.535). In a previous study of chronic IS patients with hypertension treated with doxaben, blood pressure of 150 mmHg reduced to 130 mmHg without hypotension events, and patients showed increased cerebral blood flow [<xref rid=\"B5-ijerph-17-05360\" ref-type=\"bibr\">5</xref>]. The past study found parasympathetic impairment in the acute and chronic phases of IS [<xref rid=\"B13-ijerph-17-05360\" ref-type=\"bibr\">13</xref>]. The central cholinergic effect increased cerebral blood flow for orthostatic stress-induced hypotension [<xref rid=\"B14-ijerph-17-05360\" ref-type=\"bibr\">14</xref>]. The potential mechanism was the alpha blocker treatment for BPH may induce hypotension; parasympathetic cholinergic dysfunction in IS patients induced cerebral hypoperfusion. The potential reasons for increased recurrent IS are a lack of improvement in cerebral perfusion in IS and a tendency towards hypotension.</p><p>The survival and recurrence rates in IS patients were higher than those in hemorrhagic stroke patients in a Singapore study [<xref rid=\"B15-ijerph-17-05360\" ref-type=\"bibr\">15</xref>]. The prognosis for survival of IS was important to patients, their families, and clinicians. Through aggressive preventive strategies and lifestyle modification, recurrent IS trends were reduced from 9.6% to 7.8% from 2000 to 2011 in Taiwan [<xref rid=\"B16-ijerph-17-05360\" ref-type=\"bibr\">16</xref>]. Recurrent IS was shown to be related to advanced age, hypertension, diabetes mellitus, hyperlipidemia, obstructive sleep apnea, smoking, and alcohol use [<xref rid=\"B17-ijerph-17-05360\" ref-type=\"bibr\">17</xref>]. Although these risk factors are important in recurrent IS pathophysiology and clinical presentation, no single theory is sufficient to provide an adequate explanation for all the properties of recurrent IS. The Stroke Prevention by Aggressive Reduction in Cholesterol Levels (SPARCL) trial provides evidence for the role of statins in reducing risk for future cardiovascular events in all patients with stroke, independent of cholesterol levels, and even at low-density lipoprotein cholesterol levels ≤100 mg/dL [<xref rid=\"B18-ijerph-17-05360\" ref-type=\"bibr\">18</xref>]. Our study showed similar results. In the Framingham study, the age-adjusted two-year incidence of stroke was more than double in the presence of coronary artery disease [<xref rid=\"B19-ijerph-17-05360\" ref-type=\"bibr\">19</xref>]. Our study found that coronary artery disease carried a 1.5-fold risk in one-year recurrent IS, which was lower than that of the past study. The potential reason was that BPH carried risk when interacting with other risk factors and adequate antiplatelet therapy. The main pathophysiological mechanisms of chronic obstructive pulmonary disease are systemic inflammation, hypoxia, hypercapnia, and oxidative stress [<xref rid=\"B20-ijerph-17-05360\" ref-type=\"bibr\">20</xref>]. Patients with chronic obstructive pulmonary disease tended to have incident atrial fibrillation [<xref rid=\"B21-ijerph-17-05360\" ref-type=\"bibr\">21</xref>], which could easily induce embolic stroke. A meta-analysis showed that chronic obstructive pulmonary disease caused IS with an HR of 1.31 (1.03–1.66) [<xref rid=\"B20-ijerph-17-05360\" ref-type=\"bibr\">20</xref>]. Our study showed a similar result of a 1.5-fold risk of one-year recurrent IS. One-year noncardioembolic stroke was related to old age, lower high-density lipoproteins, and chronic kidney disease with an HR 1.73 (95% CI: 1.03–2.90) in Japan [<xref rid=\"B22-ijerph-17-05360\" ref-type=\"bibr\">22</xref>]. Higher mortality risk was observed in patients with chronic kidney disease. Our study showed a lower risk of chronic kidney disease in one-year recurrent IS risk with the competing risk of death.</p><p>Age is a nonmodifiable risk factor for recurrent IS [<xref rid=\"B15-ijerph-17-05360\" ref-type=\"bibr\">15</xref>]. Prostate volume increases with age, suggesting a prostate growth rate of 2.0–2.5% per year in older men [<xref rid=\"B23-ijerph-17-05360\" ref-type=\"bibr\">23</xref>]. One study found increasing age-induced atherosclerosis and prostatic enlargement [<xref rid=\"B24-ijerph-17-05360\" ref-type=\"bibr\">24</xref>]. A one-year recurrent IS study in Sweden found that patients over 75 years of age had an increased risk [<xref rid=\"B25-ijerph-17-05360\" ref-type=\"bibr\">25</xref>]. However, our study did not show that age was related to one-year recurrent IS; a potential reason may be that older IS patients had higher mortality with an HR of 1.052 (95% CI: 1.04–1.064) competing against the risk. Diabetes may induce vascular injury through nitric oxide-dependent vasodilation, and hyperglycemia can cause ischemia in the brain by anaerobic metabolism with lactate accumulation and intracellular acidosis [<xref rid=\"B26-ijerph-17-05360\" ref-type=\"bibr\">26</xref>]. A Swedish study found that diabetes mellitus can increase one-year recurrent IS risk with an HR 1.18 (1.12–1.25) [<xref rid=\"B25-ijerph-17-05360\" ref-type=\"bibr\">25</xref>]. Our study showed no association of recurrent IS with diabetes, which was associated with a 1.7-fold increase in mortality risk. The trials of statins and blood pressure-lowering therapy suggest a new paradigm for secondary stroke prevention. The Perindopril Protection Against Recurrent Stroke Study provides active blood pressure-lowering therapy with a 28% relative risk reduction in recurrent stroke [<xref rid=\"B27-ijerph-17-05360\" ref-type=\"bibr\">27</xref>]. Our study showed no significant risk of hypertension. The potential reason may be adequate treatment according to the diagnostic codes in the claims data and alpha blockers with an antihypertensive effect. Major hemispheric stroke syndrome, atherothrombotic stroke mechanism, and atrial fibrillation were independent predictors of early recurrence [<xref rid=\"B28-ijerph-17-05360\" ref-type=\"bibr\">28</xref>]. Anticoagulant therapy reduced the risk of recurrent stroke within six months of the first-ever IS in Taiwanese patients with an odds ratio of 0.55 (95% CI: 0.44–0.8) [<xref rid=\"B29-ijerph-17-05360\" ref-type=\"bibr\">29</xref>] and within one year of the first-ever IS in Swedish patients with an HR of 0.80 (95% CI: 0.73–0.88) [<xref rid=\"B25-ijerph-17-05360\" ref-type=\"bibr\">25</xref>]. Our study did not show a significant increase in the risk of atrial fibrillation. Congestive heart failure is the end stage of cardiac disease with poor prognosis. The risk of IS in congestive heart failure patients was related to the coexistence of vascular risk factors related to a higher atherosclerotic burden and endothelial dysfunction with embolism or cerebral hypoperfusion. A previous study found that congestive heart failure was independently associated with short-term stroke recurrences at one week (adjusted OR: 2.66) and at three months (2.41) [<xref rid=\"B30-ijerph-17-05360\" ref-type=\"bibr\">30</xref>]. However, our study showed no association with congestive heart failure, and the potential reason may be related to the low prevalence of approximately 6% in our study.</p><p>It is important to recognize the mechanisms involved in recurrent IS. For secondary prevention of recurrent IS, optimal control of all vascular risk factors is essential. Although antiplatelet agents, anticoagulant agents, and increased statin use were noted in previous surveys [<xref rid=\"B16-ijerph-17-05360\" ref-type=\"bibr\">16</xref>], they are not optimal for the control of risk factors. Mechanisms of treatment of conditions, such as carotid stenosis or atrial fibrillation, need to be identified in prevention of IS recurrence. Treatment of carotid stenosis with a carotid stent or carotid endarterectomy can reduce recurrent IS [<xref rid=\"B31-ijerph-17-05360\" ref-type=\"bibr\">31</xref>]. New oral anticoagulant agents, the costs of which have been covered by insurance since 2012, reduced recurrent IS in atrial fibrillation patients [<xref rid=\"B32-ijerph-17-05360\" ref-type=\"bibr\">32</xref>]. Statin drugs have been used to treat hyperlipidemia, and they not only improved endothelial dysfunction but also reduced the risk of cardiovascular events [<xref rid=\"B33-ijerph-17-05360\" ref-type=\"bibr\">33</xref>]. Correction of hypoxia and acid–base imbalance is an effective therapy for chronic obstructive pulmonary disease patients. Cardioselective β-blockers can be prescribed for chronic obstructive pulmonary disease with atrial fibrillation [<xref rid=\"B34-ijerph-17-05360\" ref-type=\"bibr\">34</xref>]. Moderate-to-vigorous physical activity, compared with a sedentary lifestyle, reduced the risk of BPH by 25% [<xref rid=\"B35-ijerph-17-05360\" ref-type=\"bibr\">35</xref>]. Recurrent IS patients after their first-ever minor noncardioembolic IS engaged in lower levels of moderate-to-vigorous physical activity and had higher levels of visceral fat than recurrent IS-free patients in Japan [<xref rid=\"B36-ijerph-17-05360\" ref-type=\"bibr\">36</xref>]. Encouraging more aggressive physical activity to reduce obesity could prevent recurrent IS as higher levels of activity provide an increasingly protective effect. More aggressive lifestyle modification may prevent recurrent IS. </p><p>The patients with BPH used alpha-1 blockers to improve the LUTS symptoms that will enhance cerebral flow through cerebral autoregulation in hypertension patients for weeks [<xref rid=\"B5-ijerph-17-05360\" ref-type=\"bibr\">5</xref>]. The collinear analysis showed 0.84 for hypotension with BPH, but there was a lower prevalence of hypotension that was not related to recurrent IS after being adjusted for other factors. The previous study found a higher risk (HR 1.71, 95% CI: 1.10–1.30) of hypotension with the use of alpha blockers in older women [<xref rid=\"B37-ijerph-17-05360\" ref-type=\"bibr\">37</xref>]. For patients with IS and impaired parasympathetic cerebral autoregulation, alpha-1 blocker treatment for BPH carried no benefit but rather harm. If IS patients with BPH were given treatment with 5-alpha reductase inhibitors rather than alpha-1 blockers, they could avoid potential hypotension to reduce recurrent IS [<xref rid=\"B38-ijerph-17-05360\" ref-type=\"bibr\">38</xref>].</p><p>There were some limitations in our study. First, body mass index, smoking, and alcohol use were not available in the payment claims dataset of NHIRD. These factors may have an impact on recurrent IS risk. Second, a previous study found previous IS or transient ischemic attack, large vessel disease, cortical lesion, and multiple infarctions to be predictive of recurrent IS [<xref rid=\"B39-ijerph-17-05360\" ref-type=\"bibr\">39</xref>]. However, our study did not review radiological information in the claims data, which could influence the prediction of recurrent IS, unlike IS subtypes. Third, this study found BPH to be related to recurrent IS in the Taiwanese Chinese population. Whether this finding is also supported in other populations should be corroborated in the future. The higher prevalence of intracranial atherosclerosis was noted in Asia; it may be related to BPH-induced recurrent IS in the Chinese population. There were 46.6% intracranial atherosclerosis instances and 9.11% extracranial carotid stenosis instances in Chinese IS patients; the severity of intracranial artery disease is related to one-year recurrent IS [<xref rid=\"B40-ijerph-17-05360\" ref-type=\"bibr\">40</xref>]. Further clinical study is needed to confirm the relationship between BPH with hypotension and intracranial atherosclerosis in recurrent IS.</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05360\"><title>5. Conclusions</title><p>This population observation study provides a retrospective investigation of the association of BPH with one-year recurrent IS after first-ever IS. The risk factors for recurrent IS include BPH, hyperlipidemia, coronary artery disease, chronic obstructive pulmonary disease, and chronic kidney disease. Physicians must recognize stronger risk factors and provide early and continuous specific preventive strategies to reduce recurrent IS after acute IS.</p></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization, C.-A.C.; Data curation, C.-A.C.; Formal analysis, W.-C.C.; Funding acquisition, J.-T.L.; Investigation, C.-G.C.; Project administration, C.-G.C. and J.-T.L.; Resources, W.-C.C.; Software, W.-C.C.; Supervision, C.-A.C.; Validation, H.C.; Visualization, H.C.; Writing—original draft, C.-G.C.; Writing—review and editing, C.-A.C. 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BPH: benign prostatic hyperplasia.</p></caption><graphic xlink:href=\"ijerph-17-05360-g001\"/></fig><fig id=\"ijerph-17-05360-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Kaplan–Meier curve for cumulative incidence of one-year recurrent ischemic stroke stratified by benign prostatic hyperplasia using the log-rank test. BPH: benign prostatic hyperplasia.</p></caption><graphic xlink:href=\"ijerph-17-05360-g002\"/></fig><table-wrap id=\"ijerph-17-05360-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05360-t001_Table 1</object-id><label>Table 1</label><caption><p>The characteristics between recurrent ischemic stroke and non-recurrent ischemic stroke.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Risk Factors</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Recurrent Ischemic Stroke (223)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Recurrent Ischemic Stroke Free (2167)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Age</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">69.25 ± 11.22</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">67.85 ± 13.10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.082</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Benign prostatic hyperplasia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">86 (38.57%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">624 (28.8%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.002 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hyperlipidemia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">95 (42.60%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">765 (35.30%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.031 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Chronic obstructive pulmonary disease</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">49 (21.97%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">320 (14.77%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.005 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Chronic kidney disease</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31 (13.90%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">193 (8.91%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.015 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Atrial fibrillation</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27 (12.11%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">230 (10.61%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.493</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hypertension</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">176 (78.92%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1649 (76.10%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.344</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Diabetes mellitus</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100 (44.84%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">863 (39.82%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.146</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Coronary artery disease</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90 (40.36%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">614 (28.33%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Peripheral arterial occlusive disease</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6 (2.69%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">41 (1.89%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.413</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Congestive heart failure</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14 (6.28%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">133 (6.13%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.934</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Hypotension</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11 (4.93%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">50 (2.31%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.025 *</td></tr></tbody></table><table-wrap-foot><fn><p>* <italic>p</italic> < 0.05.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05360-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05360-t002_Table 2</object-id><label>Table 2</label><caption><p>Risk factors for one-year recurrent ischemic stroke.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Risk Factors</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Crude Hazard Ratio (95% C.I.)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Adjusted Hazard Ratio (95% C.I.)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Age</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.011 (1.001–1.022)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.033 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Benign prostatic hyperplasia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.502 (1.147–1.967)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.003 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.352 (1.028–1.78)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.031 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hyperlipidemia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.270 (0.974–1.655)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.078 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.338 (1.022–1.751)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.034 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Chronic obstructive pulmonary disease</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.720 (1.253–2.362)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.001 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.499 (1.075–2.091)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.017 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Chronic kidney disease</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.805 (1.235–2.638)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.002 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.523 (1.033–2.244)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.033 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Atrial fibrillation</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.190 (0.796–1.780)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.396</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hypertension</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.119 (0.811–1.543)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.494</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Diabetes mellitus</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.248 (0.958–1.625)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Coronary artery disease</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.709 (1.308–2.233)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.487 (1.128–1.961)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.005 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Peripheral arterial occlusive disease</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.483 (0.659–3.338)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.341</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Congestive heart failure</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.070 (0.623–1.839)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.806</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Hypotension</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.987 (1.084–3.643)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.044 *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr></tbody></table><table-wrap-foot><fn><p>* <italic>p</italic> < 0.05.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05360-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05360-t003_Table 3</object-id><label>Table 3</label><caption><p>Risk factors for one-year mortality after adjusted with other risk factors.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Risk Factors</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Adjusted Hazard Ratio (95% Confidence Interval)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Age</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.052 (1.04–1.064)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Diabetes mellitus</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.678 (1.326–2.124)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Chronic obstructive pulmonary disease</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.343 (1.03–1.751)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.03 *</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Chronic kidney disease</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.547 (1.935–3.352)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.001 *</td></tr></tbody></table><table-wrap-foot><fn><p>* <italic>p</italic> < 0.05.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751204</article-id><article-id pub-id-type=\"pmc\">PMC7432021</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155465</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05465</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Protocol</subject></subj-group></article-categories><title-group><article-title>HRV-Guided Training for Professional Endurance Athletes: A Protocol for a Cluster-Randomized Controlled Trial</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-5735-4908</contrib-id><name><surname>Carrasco-Poyatos</surname><given-names>María</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05465\">1</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijerph-17-05465\">†</xref></contrib><contrib contrib-type=\"author\"><name><surname>González-Quílez</surname><given-names>Alberto</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05465\">2</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijerph-17-05465\">†</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-3664-2115</contrib-id><name><surname>Martínez-González-Moro</surname><given-names>Ignacio</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05465\">3</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-1385-8386</contrib-id><name><surname>Granero-Gallegos</surname><given-names>Antonio</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05465\">1</xref><xref rid=\"c1-ijerph-17-05465\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05465\"><label>1</label>Department of Education, Health and Public Administration Research Center, University of Almeria, 04120 Almeria, Spain; <email>carrasco@ual.es</email></aff><aff id=\"af2-ijerph-17-05465\"><label>2</label>Department of Education, University of Almeria, 04120 Almeria, Spain; <email>albertillo_gq@hotmail.com</email></aff><aff id=\"af3-ijerph-17-05465\"><label>3</label>Department of Physiotherapy, Physical Exercise and Human Performance Research Group, University of Murcia, 30001 Murcia, Spain; <email>igmartgm@um.es</email></aff><author-notes><corresp id=\"c1-ijerph-17-05465\"><label>*</label>Correspondence: <email>agranero@ual.es</email></corresp><fn id=\"fn1-ijerph-17-05465\"><label>†</label><p>M.C.-P. and A.G.-Q. contributed equally to this work.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>29</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5465</elocation-id><history><date date-type=\"received\"><day>05</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>27</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Physiological training responses depend on sympathetic (SNS) and parasympathetic nervous system (PNS) balance. This activity can be measured using heart rate variability (HRV). Such a measurement method can favor individualized training planning to improve athletes’ performance. Recently, HRV-guided training has been implemented both on professional and amateur sportsmen and sportswomen with varied results. There is a dearth of studies involving professional endurance athletes following a defined HRV-guided training protocol. The objectives of the proposed protocol are: (i) to determine changes in the performance of high-level athletes after following an HRV-guided or a traditional training period and (ii) to determine differences in the athletes’ performance after following both training protocols. This will be a 12-week cluster-randomized controlled protocol in which professional athletes will be assigned to an HRV-based training group (HRV-G) or a traditional-based training group (TRAD-G). TRAD-G will train according to a predefined training program. HRV-G training will depend on the athletes’ daily HRV. The maximal oxygen uptake (VO<sub>2max</sub>) attained in an incremental treadmill test will be considered as the primary outcome. It is expected that this HRV-guided training protocol will improve functional performance in the high-level athletes, achieving better results than a traditional training method, and thus providing a good strategy for coaches of high-level athletes.</p></abstract><kwd-group><kwd>HRV</kwd><kwd>endurance training</kwd><kwd>training performance</kwd><kwd>high level athletes</kwd><kwd>VO<sub>2max</sub></kwd><kwd>running</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05465\"><title>1. Introduction</title><p>It is known that training is essential for improving physical performance [<xref rid=\"B1-ijerph-17-05465\" ref-type=\"bibr\">1</xref>] and that optimizing training for performance improvement in athletes is an important area of research within exercise physiology and sports medicine [<xref rid=\"B2-ijerph-17-05465\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijerph-17-05465\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijerph-17-05465\" ref-type=\"bibr\">4</xref>]. In this regard, different training methods for performance improvement have been tried and tested, such as intensified training [<xref rid=\"B2-ijerph-17-05465\" ref-type=\"bibr\">2</xref>,<xref rid=\"B5-ijerph-17-05465\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijerph-17-05465\" ref-type=\"bibr\">6</xref>] and submaximal tests [<xref rid=\"B7-ijerph-17-05465\" ref-type=\"bibr\">7</xref>]. However, it is also recognized that using the same standardized training program for a group of athletes can provoke a wide range of reactions in terms of performance and physiological adaptations [<xref rid=\"B8-ijerph-17-05465\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05465\" ref-type=\"bibr\">9</xref>].</p><p>As stated by Schmitt, Willis, Fardel, Coulmy, and Millet [<xref rid=\"B10-ijerph-17-05465\" ref-type=\"bibr\">10</xref>], an important component of the interindividual variability in physiological responses to standardized training is related to the balance between the parasympathetic (PNS) and sympathetic (SNS) activity of the autonomic nervous system (ANS) [<xref rid=\"B11-ijerph-17-05465\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05465\" ref-type=\"bibr\">12</xref>]. Heart rate variability (HRV) is one of the indicators that allows the noninvasive study of autonomic nervous system activity in its sympathetic and parasympathetic branches [<xref rid=\"B13-ijerph-17-05465\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijerph-17-05465\" ref-type=\"bibr\">14</xref>]. HRV is understood as the variation in the time interval between two consecutive heartbeats. It is obtained by calculating the time interval that elapses between two consecutive R waves (i.e., RR interval fluctuation) on an electrocardiogram (ECG) [<xref rid=\"B15-ijerph-17-05465\" ref-type=\"bibr\">15</xref>]. The period between beats is not constant; consequently, high HRV values are associated with an efficient ANS, which promotes behavioral adaptation and cognitive flexibility during stress [<xref rid=\"B16-ijerph-17-05465\" ref-type=\"bibr\">16</xref>], whilst low HRV values are indicative of an inefficient ANS, resulting in maladaptive responses to stress and perceived threats [<xref rid=\"B15-ijerph-17-05465\" ref-type=\"bibr\">15</xref>]. Furthermore, HRV is considered to be an indicator of cardiovascular health level [<xref rid=\"B17-ijerph-17-05465\" ref-type=\"bibr\">17</xref>].</p><p>Given that the SNS is responsible for changes in heart rate (HR) due to stress, and that the HR is one of the first parameters used to control the body’s functional capacity [<xref rid=\"B18-ijerph-17-05465\" ref-type=\"bibr\">18</xref>], HRV analysis has been established as a useful method for assessing the heart’s ability to adapt to both endogenous and exogenous loads [<xref rid=\"B19-ijerph-17-05465\" ref-type=\"bibr\">19</xref>], and can be used for the individual assessment of responses to training loads. Indeed, in recent years, HRV has been used to analyze these imbalances between SNS and PNS in athletes [<xref rid=\"B20-ijerph-17-05465\" ref-type=\"bibr\">20</xref>] and to evaluate different aspects related to training [<xref rid=\"B21-ijerph-17-05465\" ref-type=\"bibr\">21</xref>] such as exercise intensity and duration [<xref rid=\"B22-ijerph-17-05465\" ref-type=\"bibr\">22</xref>], recovery and overtraining [<xref rid=\"B23-ijerph-17-05465\" ref-type=\"bibr\">23</xref>], training load [<xref rid=\"B24-ijerph-17-05465\" ref-type=\"bibr\">24</xref>] or psychophysiological profiles [<xref rid=\"B25-ijerph-17-05465\" ref-type=\"bibr\">25</xref>].</p><p>The control of training based on HRV, as an indicator of the precompetitive physical and psychological state in athletes, enables coaches and scientists to use these HRV records to adapt the recovery and training loads to each athlete in search of a better sports result. As indicated by Ortigosa-Márquez, Reigal, Carranque, and Hernández-Mendo [<xref rid=\"B26-ijerph-17-05465\" ref-type=\"bibr\">26</xref>], high HRV values indicate more parasympathetic than sympathetic activation in an athlete and, therefore, better recovery and preparation for dealing with high-intensity training sessions.</p><p>Traditionally, HRV has been measured with ECG [<xref rid=\"B14-ijerph-17-05465\" ref-type=\"bibr\">14</xref>]. One of the ways of quantifying HRV is through rMSSD (the root mean square of successive differences between adjacent RR intervals) [<xref rid=\"B26-ijerph-17-05465\" ref-type=\"bibr\">26</xref>] since it is a temporal statistical parameter that reports those variations occurring over the short term between RR intervals [<xref rid=\"B27-ijerph-17-05465\" ref-type=\"bibr\">27</xref>] and it is used to observe the influence of the SNP on the cardiovascular system [<xref rid=\"B18-ijerph-17-05465\" ref-type=\"bibr\">18</xref>]. Currently, there are other validated tools for determining HRV that facilitate measurement, such as the Kubios HRV, Elite HRV, Mobile Lab and HRV4Training applications (apps).</p><p>In recent years, experimental studies have been carried out evaluating HRV-guided training in endurance athletes. These studies have been conducted both on elite athletes in sports such as cycling [<xref rid=\"B24-ijerph-17-05465\" ref-type=\"bibr\">24</xref>] and skiing [<xref rid=\"B10-ijerph-17-05465\" ref-type=\"bibr\">10</xref>], and with amateur endurance athletes [<xref rid=\"B28-ijerph-17-05465\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijerph-17-05465\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijerph-17-05465\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijerph-17-05465\" ref-type=\"bibr\">31</xref>]. One should take into account the scarcity of studies that have been published to date, especially on elite-level endurance athletes, as well as the absence of a common protocol to follow in this type of research.</p><p>The protocol proposed in the present study contributes to the scientific literature in this field in several ways: (i) it proposes research focused on elite athletes, a sample population for which only two experimental studies have been carried out to date; (ii) the protocol is intended to research endurance runners, for which we are not aware of any research having been carried out on this type of sample and with these characteristics at the international level. Therefore, it is a novel study aiming to provide empirical support for HRV-guided training in long-distance runners, adapting daily training to the physiological responses of each individual athlete. The performance of these athletes could be compared to that of another group of long-distance runners who carry out traditional training over the same period of time.</p><p>Until just a few years ago, conducting research of this type, involving daily HRV measurements on each athlete and then adapting training based on these data, was only possible with the collaboration of high-cost laboratories. This study tests the use of noninvasive, commercial, low-cost and publicly accessible technology to evaluate the physiological responses obtained by adapting training to HRV.</p><p>Based on everything described above, we hypothesize that HRV-guided training will: (i) improve functional performance in high-level athletes and (ii) produce better performance results than a traditional training method. The objectives of the proposed protocol are: (i) to determine changes in the performance of high-level athletes after following an HRV-guided or a traditional training period, and (ii) to determine differences in the athletes’ performance after following both training protocols.</p><p>This will be a 12-week cluster-randomized controlled trial protocol in which professional athletes are assigned to either an HRV-based training group (HRV-G) or a traditional-based training group (TRAD-G). A block randomization method will be chosen to randomly assign participants to interventions in equally sized sample groups. This protocol has been designed following the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) Statement [<xref rid=\"B32-ijerph-17-05465\" ref-type=\"bibr\">32</xref>]. To describe the intervention, the TIDieR (Template for Intervention Description and Replication) checklist by Hoffmann et al. [<xref rid=\"B33-ijerph-17-05465\" ref-type=\"bibr\">33</xref>] has been used.</p></sec><sec id=\"sec2-ijerph-17-05465\"><title>2. Materials and Methods</title><sec id=\"sec2dot1-ijerph-17-05465\"><title>2.1. Study Setting</title><p>To detect an intervention-related effect in professional athletes, other studies with similar protocols [<xref rid=\"B24-ijerph-17-05465\" ref-type=\"bibr\">24</xref>,<xref rid=\"B31-ijerph-17-05465\" ref-type=\"bibr\">31</xref>] compared athletes from two clubs or associations. Similarly, our sample will comprise athletes from two sport institutions in Almería (Spain): the C.D. Atletas de Almería, based in the city of Almería (Spain) and the Asociación Espeleológica Velezana, based in Vélez Rubio (Spain).</p></sec><sec id=\"sec2dot2-ijerph-17-05465\"><title>2.2. Eligibility Criteria</title><p>The inclusion criteria for participating in the program will be: (i) to belong to the Spanish Athletics Federation; (ii) to have been training and competing in Spanish Athletics Federation competitions for at least two years; and iii) to be in the first third of the classification for the last five races of the previous season. Regarding the exclusion criteria, the following will be taken into account: (i) having cardiovascular pathologies, abnormal blood pressure parameters or diagnosed respiratory problems; (ii) being treated for psychological problems, or regularly taking a drug(s) that has a direct or indirect effect on the nervous system (e.g., anxiolytics, antidepressants or neuroleptics); (iii) substance use that is not permitted by the International Association of Athletics Federations (IAAF); (iv) occasional consumption of medication for a disease related to the cardiorespiratory system (e.g., influenza) that might alter performance and (v) not performing at least 90% of the workouts during the intervention.</p><p>The trial steering members will be responsible for checking that the subjects interviewed meet the inclusion criteria. The Spanish Athletics Federation’s medical team will certify that the subjects do not meet any of the exclusion criteria. After being informed of the study design and potential risks, all athletes will sign a written informed consent document. The model consent form is shown in <xref ref-type=\"app\" rid=\"app1-ijerph-17-05465\">Appendix A</xref>.</p></sec><sec id=\"sec2dot3-ijerph-17-05465\"><title>2.3. Interventions</title><p>Based on the methodology used by Javaloyes et al. [<xref rid=\"B24-ijerph-17-05465\" ref-type=\"bibr\">24</xref>] and Vesterinen et al. [<xref rid=\"B31-ijerph-17-05465\" ref-type=\"bibr\">31</xref>], the intervention will be divided into two training periods for both study groups (HRV-G and TRAD-G): a four-week preparation period (PR) and an eight-week training period (TR). Both will maintain the weekly training volume. The training carried out will mainly be running. The PR period will be common to both groups and will be a familiarization phase for the training sessions and their intensities. During this period, the training intensity will gradually increase for the first three weeks and then decrease in the fourth week. This will mean three weeks of overloading and one week of recovery (3:1). The training to be carried out by the athletes is presented in more detail in <xref rid=\"ijerph-17-05465-t001\" ref-type=\"table\">Table 1</xref>. In the TR period, each group will carry out the corresponding intervention. The TRAD-G group will train according to a predefined training program, which will include sessions carried out at low intensity (approximately 50% of the total), and other sessions of moderate and high intensity, with a structure similar to that carried out during the PR period (<xref rid=\"ijerph-17-05465-t002\" ref-type=\"table\">Table 2</xref>). The training prescribed to the HRV-G group will depend on the subjects’ HRV, in accordance with authors such as Javaloyes et al. [<xref rid=\"B24-ijerph-17-05465\" ref-type=\"bibr\">24</xref>], Kiviniemi, Hautala, Kinnunen, and Tulppo [<xref rid=\"B34-ijerph-17-05465\" ref-type=\"bibr\">34</xref>], and Lamberts, Swart, Noakes, and Lambert [<xref rid=\"B35-ijerph-17-05465\" ref-type=\"bibr\">35</xref>].</p><p>To quantify the HRV, a Smartphone application known as “HRV4Training” (see <uri xlink:href=\"http://www.hrv4training.com/\">http://www.hrv4training.com/</uri>) will be used. This tool has been validated by Plews et al. [<xref rid=\"B36-ijerph-17-05465\" ref-type=\"bibr\">36</xref>], showing a low typical estimate error (CV% (90% CI) = 3.8 (3.1; 5.0)) and a clear electrocardiographical correlation (r = 1.00 (1.00; 1.00)). It provides the root mean sum of the successive differences between R – R intervals (rMSSD) data using photoplethysmography. rMSSD is more suitable and reliable than other indexes [<xref rid=\"B13-ijerph-17-05465\" ref-type=\"bibr\">13</xref>,<xref rid=\"B37-ijerph-17-05465\" ref-type=\"bibr\">37</xref>]; nonetheless, the HRV data will be transformed by taking the natural logarithm, thus allowing parametric statistical comparisons that assume a normal distribution. In this way, a 7-day rolling average will be calculated (LnrMSSD<sub>7-d</sub>). The PR period will be used as a standardized phase to obtain the baseline LnrMSSD<sub>7-d</sub> and its range of normality (upper and lower limits). Following the indications of Plews, Laursen, Kilding, and Buchheit [<xref rid=\"B38-ijerph-17-05465\" ref-type=\"bibr\">38</xref>], this will be calculated as the mean ± 0.5 × SD. During the TR period, the LnrMSSD<sub>7-d</sub> will be calculated daily in order to adapt the training prescribed to the HRV-G athletes. Moreover, the range of normality will be updated weekly. If the LnrMSSD<sub>7-d</sub> is within the range of normality, the athletes will perform a moderate or high-intensity session. If the weekly LnrMSSD<sub>7-d</sub> average falls below the normal range, a low intensity workout or rest will be undertaken. Athletes will perform a maximum of two consecutive sessions of moderate or high intensity; likewise, they will not accumulate more than two consecutive rest sessions. The modified scheme of Kiviniemi et al. [<xref rid=\"B34-ijerph-17-05465\" ref-type=\"bibr\">34</xref>] presented in <xref ref-type=\"fig\" rid=\"ijerph-17-05465-f001\">Figure 1</xref> will be followed.</p><p>In accordance with Javaloyes et al. [<xref rid=\"B24-ijerph-17-05465\" ref-type=\"bibr\">24</xref>], all participants will be instructed to measure their HRV data at home each morning after waking up and emptying their bladders. They will be instructed to lie in a supine position and not perform any further activity during the recordings. Data will be recorded over a 60-s period e.g., [<xref rid=\"B36-ijerph-17-05465\" ref-type=\"bibr\">36</xref>]. The daily control and recording of the rMSSD, as well as the LnrMSSD<sub>7-d</sub> calculation used to prescribe the training of the HRV-G athletes, will always be carried out by the trial steering members. These members will receive the information from each athlete via phone or email and, in turn, will inform the HRV-G coach of the training intensity corresponding to each athlete. This procedure will also serve as a strategy for maintaining and monitoring the athletes’ adherence to the training programs. Concomitant care, or any other intervention, will not be allowed during the trial for either the HRV-G or the TRAD-G. Athletes from both groups will carry out the training in their usual location.</p></sec><sec id=\"sec2dot4-ijerph-17-05465\"><title>2.4. Outcomes</title><p>The primary outcome of this study will be the maximal oxygen uptake (VO<sub>2max</sub>) obtained in an incremental treadmill test. The secondary outcomes will be: the maximal speed in m/s, maximal heart rate, respiratory exchange ratio, ventilatory thresholds (VT1 and VT2) and their derived speed, heart rate, respiratory exchange ratio and VO<sub>2</sub> obtained in the incremental treadmill test. Other measurements considered as secondary outcomes will be: the time, speed, heart rate, rating of perceived exertion (RPE) and lactate in the 3000 m running test. Body composition and rMSSD will be considered as other variables.</p><p>Measurements will be taken before and after the training period, which will correspond to weeks 5 (pretest) and 12 (post-test). Over the assessment weeks, care will be taken that participants do not carry out any high-intensity training sessions. Each assessment week will consist of two testing sessions with a 48-h recovery period. The first testing session will include maximal graded exercise test and body composition measurements. In the second testing session, athletes will perform a 3000 m running test. The rMSSD will be measured daily, as explained in the intervention section.</p><p>The incremental treadmill test will be performed by the Physical Exercise and Human Performance Research Group at the University of Murcia (Spain). This is a more objective way of determining physical fitness and represents the maximal performance capacity of an individual [<xref rid=\"B39-ijerph-17-05465\" ref-type=\"bibr\">39</xref>]. First, with the athlete in the supine position, a cardiovascular examination will be carried out at rest by means of cardiac auscultation, blood pressure and an electrocardiogram (ECG). The electrodes for recording the ECG and heart rate will be kept in place throughout the test. The Cardioline Cube<sup>®</sup> electrocardiograph will be used. To perform the incremental treadmill test, the Runner srl (Cavezzo Italy) treadmill will be used, as it was in other studies such as Ballesta-García, Martínez-González-Moro, Ramos-Campo, and Carrasco-Poyatos [<xref rid=\"B40-ijerph-17-05465\" ref-type=\"bibr\">40</xref>]. Similar to other studies, such as Nuuttila et al. [<xref rid=\"B30-ijerph-17-05465\" ref-type=\"bibr\">30</xref>] or Vesterinen et al. [<xref rid=\"B31-ijerph-17-05465\" ref-type=\"bibr\">31</xref>], a prior 2-min aerobic warm up will be performed at 6 km/h. The test itself will start at a velocity of 7 km/h. The speed will be increased by 0.1 km/h every 6 s. The incline will remain at 1% throughout the test. The athletes will be encouraged to perform at maximum effort. The test will end when the subject can no longer run; the subject will indicate this with a hand gesture. The recovery phase will then begin at 4km/h for 3 min followed by rest for a further 2 min. The tests will be considered maximal and valid when the theoretical heart rate (220-age) exceeds 85% and the respiratory exchange ratio (RER) is greater than 1.15 [<xref rid=\"B41-ijerph-17-05465\" ref-type=\"bibr\">41</xref>]. During the stress test, the subjects will breathe through a mask connected to a gas analyzer (Metalyzer 3b<sup>®</sup>, Cortex, Leipzig, Germany). All gas exchange parameters will be measured breath-by-breath and averaged every 30 s. The VO<sub>2max</sub> will be defined as the oxygen consumption plateau [<xref rid=\"B42-ijerph-17-05465\" ref-type=\"bibr\">42</xref>]. The aerobic (VT1) and anaerobic (VT2) thresholds will be determined. Before each test, the gas analyzer will be manually calibrated. The test’s maximal speed (V<sub>max</sub>), maximal heart rate (HR<sub>max</sub>), and respiratory exchange ratio (RER) will be recorded. The V<sub>max</sub> or HR<sub>max</sub> will be defined as the highest speed, or heart rate, reached for a finished stage. The speed, heart rate, respiratory exchange ratio and VO<sub>2</sub> at each ventilatory threshold will also be recorded as V<sub>VT1,</sub> V<sub>VT2</sub>, HR<sub>VT1</sub>, HR<sub>VT2</sub>, RER<sub>VT1</sub>, RER<sub>VT2</sub>, VO<sub>2VT1</sub>, and VO<sub>2VT2</sub>, respectively. All tests will be carried out under similar environmental conditions (an ambient temperature of 20–22 degrees).</p><p>As in other studies [<xref rid=\"B30-ijerph-17-05465\" ref-type=\"bibr\">30</xref>,<xref rid=\"B43-ijerph-17-05465\" ref-type=\"bibr\">43</xref>], the 3000 m running test will be conducted individually on a 400 m outdoor running track. Participants will be instructed to run at their maximum speed. Before the test, a 15 min standardized aerobic warm-up will be performed, consisting of running at a low to moderate intensity. Capillary blood samples (5 μL) for blood lactate concentration analysis will be taken from the fingertip using a Scout+ analyzer (SensLab GmbH, Leipzig, Germany). Lactate is considered a useful indicator for measuring the metabolic cost and intensity of effort in aerobic-anaerobic sports [<xref rid=\"B44-ijerph-17-05465\" ref-type=\"bibr\">44</xref>]. Following Ribas [<xref rid=\"B45-ijerph-17-05465\" ref-type=\"bibr\">45</xref>], it will be considered in this test to relate it to running intensity. Lactate samples will be taken at four different points in time, in accordance with Rodríguez and Valero [<xref rid=\"B46-ijerph-17-05465\" ref-type=\"bibr\">46</xref>]: (i) just before the test (Lactate<sub>pre</sub>), (ii) just after the test (Lactate<sub>post</sub>), (iii) 3 min after the test (Lactate<sub>post3</sub>) and iv) 5 min after the test (Lactate<sub>post5</sub>). Other variables, such as heart rate, time, speed, and rated perceived exertion (RPE), will also be measured. Heart rate will be recorded at five different points in time: (i) just before the test (HR<sub>pre</sub>), (ii) just after the test (HR<sub>post</sub>), (iii) 1 min after the test (HR<sub>post1</sub>), (iv) 3 min after the test (HR<sub>post3</sub>) and v) 5 min after the test (HR<sub>post5</sub>). The time will be recorded every 1000 m at three different points in time: (i) after running 1000 m, (ii) after running 2000 m and (iii) after running 3000 m (right at the end of the test). The speed will be calculated from these three points using the formula: speed = distance in m/time in seconds. The RPE will be measured at the end of the test using the modified Börg CR-10 scale of perceived exertion [<xref rid=\"B47-ijerph-17-05465\" ref-type=\"bibr\">47</xref>]. According to the authors, a 0 rating corresponds to rest; a 3 rating to moderate intensity; a 5 rating to hard intensity; a 7 rating to very hard intensity; and a 10 rating to maximal intensity. This tool has recently been determined as a stand-alone method for training load monitoring purposes in several sports and physical activities with men and women in different age categories (children, adolescents and adults) at various expertise levels [<xref rid=\"B48-ijerph-17-05465\" ref-type=\"bibr\">48</xref>].</p><p>Body composition will be analyzed just before the treadmill test using the InBody120 analyzer (Biospace Co. Ltd., Seoul, South Korea). Height will be measured using a measuring rod (Seca 213), and the body mass index (BMI) will be calculated according to the formula: BMI = kg/m<sup>2</sup>.</p><p>The time schedule for enrolment, interventions and assessments is shown in <xref ref-type=\"fig\" rid=\"ijerph-17-05465-f002\">Figure 2</xref>.</p></sec><sec id=\"sec2dot5-ijerph-17-05465\"><title>2.5. Sample Size and Power</title><p>Calculations to establish the sample size will be performed using RStudio 3.15.0 software (PBC, Boston, USA). The significance level will be set at <italic>p</italic> ≤ 0.05. According to the mean standard deviation established for VO<sub>2max</sub> in a previous study [<xref rid=\"B31-ijerph-17-05465\" ref-type=\"bibr\">31</xref>] (SD = 1.5 mL/kg/min) and an estimated error (<italic>d</italic>) of 1.1, a valid sample size providing a 95% confidence interval (CI) in each group will be 7 (<italic>n</italic> = <italic>CI</italic><sup>2</sup> × <italic>d</italic><sup>2</sup>/<italic>SD</italic><sup>2</sup>). Thus, a final sample size of 7 for each group will provide a power of 93% if between and within a variance of 2.</p></sec><sec id=\"sec2dot6-ijerph-17-05465\"><title>2.6. Recruitment</title><p>Each club or association involved in athletics in Almería (Spain) will be screened to identify the percentage of high-level or professionally federated athletes. When there are at least 7 high-level or professionally federated athletes, the club/association officers will be contacted by telephone to inform them of the study objective. Once they agree, an informative talk will be carried out with the athletes and the coach to inform them of the study objective, the time period in which it will take place, and the required commitment by the athletes to measure their daily HRV according to the established protocol, to attend the pre and post-test sessions and to attend at least 90% of the training sessions. The coaches will be informed of the required commitment to adapt each athlete’s training session to the daily HRV score if their club is randomized into the HRV-G group. If they agree to participate, then they will have to sign the written consent and meet the eligibility criteria necessary to be recruited into the study. The recruitment process will be conducted by the trial steering members.</p></sec><sec id=\"sec2dot7-ijerph-17-05465\"><title>2.7. Allocation and Blinding</title><p>A block randomization method will be used to allocate participants to the groups, which will contain equal sample sizes. The block size will be determined by the data monitoring committee according to the statistical power provided. Blocks will be chosen randomly by tossing a coin to determine the participants’ assignment into the groups. This procedure will be carried out by the data monitoring committee. The athletes and the data monitoring committee will be blinded to the exercise group assignment.</p></sec><sec id=\"sec2dot8-ijerph-17-05465\"><title>2.8. Data Analysis</title><p>Data will be analyzed using Jamovi (Jamovi Project 2018, version 0.9.1.7, Sydney, Australia) and RStudio 3.15.0 software (PBC, Boston, MA, USA). Prior to data analysis, the Shapiro Wilk test and the Levene test will be performed to determine the normal distribution of the variables and the homogeneity of variance. Descriptive data will be reported as mean ± SD and range. All the data will be analyzed based on the intention-to-treat principle (last observation carried forward). If the sample is normally distributed, Student’s t-test will be calculated to compare variables before and after the intervention. For a variable to be considered as having a normal distribution, 95% of values will have to be within two standard deviations of the mean. If the sample is nonparametric, the U-Mann Whitney test will be used to compare variables before and after the intervention. The standardized mean differences (Cohen’s effect size) will be calculated together with the 95% confidence intervals [<xref rid=\"B49-ijerph-17-05465\" ref-type=\"bibr\">49</xref>]. The effect sizes (ES) will be calculated using Cohen’s d [<xref rid=\"B49-ijerph-17-05465\" ref-type=\"bibr\">49</xref>]. The relationship between variables will be assessed using the Pearson r correlation coefficient. If r is higher than 0.7, the determination coefficient (r<sup>2</sup>) will be used to determine the percentage of Y variation with regard to the X variation. Significance will be accepted at <italic>p</italic> ≤ 0.05.</p></sec><sec id=\"sec2dot9-ijerph-17-05465\"><title>2.9. Monitoring</title><p>A data monitoring committee will be set up during the study recruitment period. Interim analyses will be supplied to the committee in strict confidence, together with any other analyses that the committee may request. Based on the data monitoring committee’s advice, the trial steering members will decide whether or not to modify the trial intake.</p><p>In our study, an adverse event will be defined as any untoward medical occurrence in a subject without regard to the possibility of a causal relationship. Adverse events will be collected after the subject has provided consent and enrolled in the study. If a subject experiences an adverse event after the informed consent document is signed (entry) but the subject has not started to receive study intervention, the event will be reported as not being related to the study’s exercise program. For this study, the following will be considered serious adverse events: severe or permanent disability, use of prohibited substances and any other significant hazard as determined by the study members. Serious adverse events occurring after a subject has stopped participating in the study will not be reported unless the researchers feel that the event may have been caused by the study protocol procedure.</p></sec><sec id=\"sec2dot10-ijerph-17-05465\"><title>2.10. Ethics and Dissemination</title><p>This protocol, the informed consent template contained in <xref ref-type=\"app\" rid=\"app1-ijerph-17-05465\">Appendix A</xref> and other requested documents (if any) will be reviewed and approved by the Bioethical Committee at the University of Almería with respect to the scientific content and compliance with applicable research and human subject regulations. Following initial review and approval, this protocol will be reviewed by the researcher at least once a year at Clinicaltrials.org, where it is registered with the ID: NCT04150952.</p><p>Any protocol modifications which might have an impact on conducting the study, potentially benefit a subject or affect a subject’s safety, or change the study objectives, study design, subject population, sample sizes, study procedures, along with significant administrative issues, will require a formal amendment to the protocol. Such an amendment will be agreed upon by the Bioethical Committee at the University of Almería prior to implementation, and the clubs/associations enrolled will be notified. Administrative changes to the protocol that are minor corrections, and/or clarifications having no effect on the way the study is to be conducted, will be agreed upon by the researchers and documented in a memorandum. The Bioethical Committee at the University of Almería may be notified of the administrative changes.</p><p>All study-related information will be stored securely at the study site. All records that contain names or other personal identifiers, such as locator forms and informed consent forms, will be stored separately from the study records and identified by a code number. Forms, lists, logbooks, appointment books and any other listings that link participant ID numbers to other identifying information will be stored in a separate, locked file.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec3-ijerph-17-05465\"><title>3. Discussion</title><p>This protocol describes the rationale, design, and methods of an HRV-guided training design for professional endurance athletes. It will allow accomplishment of a randomized controlled intervention to determine changes in the performance of high-level athletes after following an HRV-guided or a traditional training period, Moreover, the differences in the athletes’ performance after following both training protocols will be determined. To design this protocol with professional endurance athletes, the guidelines described in Kiviniemi et al. [<xref rid=\"B34-ijerph-17-05465\" ref-type=\"bibr\">34</xref>] have been followed. This procedure has also been adapted in other professional sports such as cycling [<xref rid=\"B24-ijerph-17-05465\" ref-type=\"bibr\">24</xref>,<xref rid=\"B50-ijerph-17-05465\" ref-type=\"bibr\">50</xref>] and skiing [<xref rid=\"B10-ijerph-17-05465\" ref-type=\"bibr\">10</xref>], as well as to amateur endurance athletes [<xref rid=\"B28-ijerph-17-05465\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijerph-17-05465\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijerph-17-05465\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijerph-17-05465\" ref-type=\"bibr\">31</xref>].</p><p>This is the first time that this kind of protocol will be applied in endurance elite athletes. After its implementation, we expect that both high-level athletes groups (HRV-G and TRAD-G) improve: (i) VO<sub>2max</sub> and other secondary outcomes measured in the treadmill test (the maximal speed in m/s, maximal heart rate, respiratory exchange ratio, or ventilatory thresholds), (ii) the time, speed, heart rate, rating of perceived exertion (RPE) and lactate in the 3000 m running test. Additionally, HRV-G will be better regarding performance results than the TRAD-G. These findings will suggest that training guidance balancing the sympathetic and parasympathetic autonomic nervous system leads to greater athletic performance in endurance athletes compared to standardized prescribed training. This is relevant for training optimization and for minimizing overuse and reducing injury risk.</p></sec><sec sec-type=\"conclusions\" id=\"sec4-ijerph-17-05465\"><title>4. Conclusions</title><p>Experimental research conducted in recent years shows that improvements in variables related with athletes’ performance (e.g., VO<sub>2max</sub>) can be obtained through HRV-guided training. However, accordingly to these studies, results do not allow a consensus to be established regarding the performance benefits of HRV-guided training for endurance athletes.</p><p>From studies carried out until now, this article describes a novel protocol to conduct a randomized controlled trial with endurance athletes. So far, no other HRV-guided training research has been conducted with these types of professional athletes. Besides, this protocol proposes to use emergent technologies in the training and the research fields, such as smartphone applications; in this case, HRV4training, an app scientifically validated that allows calculation of the daily HRV measurement for each athlete. Although more research is needed, the implementation of the protocol described here will contribute to this scientific field of study.</p></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization, M.C.-P., A.G.-Q. and A.G.-G.; methodology, M.C.-P., A.G.-Q., A.G.-G. and I.M.-G.-M.; investigation, M.C.-P., A.G.-G. and A.G.-Q.; writing—original draft preparation, M.C.-P. and A.G.-Q.; writing—review and editing, A.G.-G. and I.M.-G.-M. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research received no external funding.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><app-group><app id=\"app1-ijerph-17-05465\"><title>Appendix A</title><p>Mr. Antonio Granero-Gallegos and Ms. María Carrasco-Poyatos, lead researchers on the project: Physiological and psychological effects from heart rate variability-based training in professional athletes have informed:</p><p>Mr/Ms …………….………………………………… ID. …..……, about the present study’s general proceeding, its objectives, duration, purpose, inclusion and exclusion criteria, associated risks and benefits, as well as the possibility of leaving it without having to give reasons. In knowledge of all the above, and the measures that will be adopted to protect the participant’s personal data, according to the current regulations,</p><p>I CONSENT to participate in the present research.</p><p>Signed: Mr/Ms …………….………………………………… ID. …..……</p><p>Signed: Mr. Antonio Granero-Gallegos, ID 23245990M and Ms. María-Carrasco-Poyatos, ID 75096834V</p><array orientation=\"portrait\"><tbody><tr><td colspan=\"2\" align=\"center\" valign=\"top\" rowspan=\"1\">Project Leading Researche.</td></tr><tr><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Antonio Granero-Gallegos      </td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">   María Carrasco-Poyatos</td></tr></tbody></array></app></app-group><ref-list><title>References</title><ref id=\"B1-ijerph-17-05465\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Hostrup</surname><given-names>M.</given-names></name><name><surname>Bangsbo</surname><given-names>J.</given-names></name></person-group><article-title>Limitations in intense exercise performance of athletes – effect of speed endurance training on ion handling and fatigue development</article-title><source>J. 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Res.</source><year>2020</year><volume>34</volume><fpage>1511</fpage><lpage>1518</lpage><pub-id pub-id-type=\"doi\">10.1519/JSC.0000000000003337</pub-id><pub-id pub-id-type=\"pmid\">31490431</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijerph-17-05465-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>HRV-guided training schema. Modified from Kiviniemi et al. [<xref rid=\"B34-ijerph-17-05465\" ref-type=\"bibr\">34</xref>]. Note: When LnrMSSD7-d remained inside their normal range, high-intensity or moderate-intensity training sessions were prescribed. If LnrMSSD7-d fell outside their normal range (below), low intensity or rest were prescribed. HIIT/MOD = high/moderate-intensity interval training; HRV = heart rate variability; LnrMSSD7-d = 7-day rolling average of the natural logarithm of the root-mean-squared differences of successive RR intervals.</p></caption><graphic xlink:href=\"ijerph-17-05465-g001\"/></fig><fig id=\"ijerph-17-05465-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Schedule of enrolment, interventions and assessment. Note: HRV-G = heart rate variability-based training group; TRAD-G = traditional based training group; PR = preparation period; TR = training period.</p></caption><graphic xlink:href=\"ijerph-17-05465-g002\"/></fig><table-wrap id=\"ijerph-17-05465-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05465-t001_Table 1</object-id><label>Table 1</label><caption><p>Periodization and training distribution for the heart rate variability group (HRV-G) and traditional-based training group (TRAD-G) during the preparation period (PR).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Weeks</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">High Intensity</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Moderate Intensity</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Low Intensity</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90 min between VT1 and VT2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3–4 sessions between 30 and 35 min below VT1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4 × 12 min > VT2/3-min rest</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90 min between VT1 and VT2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2–3 sessions between 30 and 35 min below VT1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50 min at VT2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4 × 12 min > VT2/3-min rest</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90 min between VT1 and VT2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2–3 sessions between 30 and 35 min below VT1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3–4 sessions between 30 and 35 min below VT1</td></tr></tbody></table><table-wrap-foot><fn><p>Note: VT1 = first ventilatory threshold; VT2 = second ventilatory threshold. High-intensity and moderate-intensity sessions will be performed with a 15- to 20-min warm up and 20 min of cooling down.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05465-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05465-t002_Table 2</object-id><label>Table 2</label><caption><p>Periodization and training distribution for TRAD-G during training period (TR).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Weeks</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">High Intensity</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Moderate Intensity</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Low Intensity</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50 min at VT2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3–4 sessions between 30 and 35 min below VT1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4 × 12 min > VT2/3-min rest</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90 min between VT1 and VT2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2–3 sessions between 30 and 35 min below VT1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50 min at VT2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4 × 12 min > VT2/3-min rest</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90 min between VT1 and VT2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2–3 sessions between 30 and 35 min below VT1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3–4 sessions between 30 and 35 min below VT1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50 min al VT2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3–4 sessions between 30 and 35 min below VT1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90 min between VT1 and VT2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2–3 sessions between 30 and 35 min below VT1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50 min al VT2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4 × 12 min > VT2/3-min rest</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90 min between VT1 and VT2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2–3 sessions between 30 and 35 min below VT1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3–4 sessions between 30 and 35 min below VT1</td></tr></tbody></table><table-wrap-foot><fn><p>Note: VT1 = first ventilatory threshold; VT2 = second ventilatory threshold. High-intensity and moderate-intensity sessions will be performed with a 15- to 20-min warm up and 20 min of cooling down. Approximately 50% of the total sessions will be at low intensity.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32748844</article-id><article-id pub-id-type=\"pmc\">PMC7432022</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155538</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05538</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>A Novel Hybrid Drug Delivery System for Treatment of Aortic Aneurysms</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Yoshimura</surname><given-names>Koichi</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05538\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05538\">2</xref><xref rid=\"c1-ijms-21-05538\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Aoki</surname><given-names>Hiroki</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05538\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Teruyama</surname><given-names>Chie</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijms-21-05538\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Iijima</surname><given-names>Masumi</given-names></name><xref ref-type=\"aff\" rid=\"af5-ijms-21-05538\">5</xref><xref ref-type=\"aff\" rid=\"af6-ijms-21-05538\">6</xref></contrib><contrib contrib-type=\"author\"><name><surname>Tsutsumi</surname><given-names>Hiromori</given-names></name><xref ref-type=\"aff\" rid=\"af7-ijms-21-05538\">7</xref></contrib><contrib contrib-type=\"author\"><name><surname>Kuroda</surname><given-names>Shun’ichi</given-names></name><xref ref-type=\"aff\" rid=\"af6-ijms-21-05538\">6</xref></contrib><contrib contrib-type=\"author\"><name><surname>Hamano</surname><given-names>Kimikazu</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05538\">1</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05538\"><label>1</label>Department of Surgery and Clinical Science, Yamaguchi University Graduate School of Medicine, Ube 755-8505, Japan; <email>kimikazu@yamaguchi-u.ac.jp</email></aff><aff id=\"af2-ijms-21-05538\"><label>2</label>Graduate School of Health and Welfare, Yamaguchi Prefectural University, Yamaguchi 753-8502, Japan</aff><aff id=\"af3-ijms-21-05538\"><label>3</label>Cardiovascular Research Institute, Kurume University, Kurume 830-0011, Japan; <email>haoki@med.kurume-u.ac.jp</email></aff><aff id=\"af4-ijms-21-05538\"><label>4</label>Graduate School of Medicine, Yamaguchi University, Ube 755-8611, Japan; <email>k046uh@yamaguchi-u.ac.jp</email></aff><aff id=\"af5-ijms-21-05538\"><label>5</label>Department of Nutritional Science and Food Safety, Faculty of Applied Bioscience, Tokyo University of Agriculture, Tokyo 156-8502, Japan; <email>mi206786@nodai.ac.jp</email></aff><aff id=\"af6-ijms-21-05538\"><label>6</label>The Institute of Scientific and Industrial Research, Osaka University, Ibaraki 567-0047, Japan; <email>skuroda@sanken.osaka-u.ac.jp</email></aff><aff id=\"af7-ijms-21-05538\"><label>7</label>Department of Applied Chemistry, Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Ube 755-8611, Japan; <email>tsutsumi@yamaguchi-u.ac.jp</email></aff><author-notes><corresp id=\"c1-ijms-21-05538\"><label>*</label>Correspondence: <email>yoshimko@yamaguchi-u.ac.jp</email>; Tel.: +81-836-22-2261</corresp></author-notes><pub-date pub-type=\"epub\"><day>02</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5538</elocation-id><history><date date-type=\"received\"><day>28</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>31</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Ongoing aortic wall degeneration and subsequent aneurysm exclusion failure are major concerns after an endovascular aneurysm repair with a stent-graft. An ideal solution would be a drug therapy that targets the aortic wall and inhibits wall degeneration. Here, we described a novel drug delivery system, which allowed repetitively charging a graft with therapeutic drugs and releasing them to the aortic wall in vivo. The system was composed of a targeted graft, which was labeled with a small target molecule, and the target-recognizing nanocarrier, which contained suitable drugs. We developed the targeted graft by decorating a biotinylated polyester graft with neutravidin. We created the target-recognizing nanocarrier by conjugating drug-containing liposomes with biotinylated bio-nanocapsules. We successfully demonstrated that the target-recognizing nanocarriers could bind to the targeted graft, both in vitro and in blood vessels of live mice. Moreover, the drug released from our drug delivery system reduced the expression of matrix metalloproteinase-9 in mouse aortas. Thus, this hybrid system represents a first step toward an adjuvant therapy that might improve the long-term outcome of endovascular aneurysm repair.</p></abstract><kwd-group><kwd>drug delivery system</kwd><kwd>aortic aneurysm</kwd><kwd>endovascular aneurysm repair</kwd><kwd>bio-nanocapsule</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05538\"><title>1. Introduction</title><p>Abdominal aortic aneurysm (AAA) is a noteworthy disease that causes segmental expansion and aortic rupture [<xref rid=\"B1-ijms-21-05538\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijms-21-05538\" ref-type=\"bibr\">2</xref>]. AAA is characterized by chronic inflammation and progressive extracellular-matrix destruction, by proteolytic enzymes, like matrix metalloproteinases (MMPs), which eventually lead to fatal rupture [<xref rid=\"B3-ijms-21-05538\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijms-21-05538\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijms-21-05538\" ref-type=\"bibr\">5</xref>]. MMP-9 is the primary enzyme responsible for aortic wall degradation. We previously reported that pharmacologic treatment with hydroxymethylglutaryl-coenzyme A reductase inhibitors (statins) inhibited the secretion of MMP-9, a marker of vessel wall degeneration, from human aneurysm tissues in culture [<xref rid=\"B6-ijms-21-05538\" ref-type=\"bibr\">6</xref>]. Other clinical studies reported that statins might be associated with the attenuation of aneurysm growth [<xref rid=\"B7-ijms-21-05538\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijms-21-05538\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijms-21-05538\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijms-21-05538\" ref-type=\"bibr\">10</xref>]. We also showed in a mouse model that pharmacologic inhibition of c-Jun N-terminal kinase (JNK), a proinflammatory signaling molecule, could successfully treat aortic aneurysms [<xref rid=\"B11-ijms-21-05538\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijms-21-05538\" ref-type=\"bibr\">12</xref>]. These data and other reports have indicated the potential role of pharmacologic therapy in the treatment of AAA. </p><p>There are two ways to apply drugs in the treatment of AAA: primary therapy and adjuvant therapy. We have long awaited a primary pharmacologic treatment for small AAAs to prevent progression; currently, there is no effective therapy for patients with small AAA. However, large AAAs can be treated with endovascular aneurysm repair (EVAR), and this approach might benefit from adjuvant pharmacologic therapy [<xref rid=\"B13-ijms-21-05538\" ref-type=\"bibr\">13</xref>]. </p><p>EVAR has become widely accepted as a minimally invasive treatment for aortic aneurysms [<xref rid=\"B14-ijms-21-05538\" ref-type=\"bibr\">14</xref>]. However, after EVAR, occasionally, a late failure occurs in the aneurysm exclusion, which results in aneurysm expansion and rupture [<xref rid=\"B1-ijms-21-05538\" ref-type=\"bibr\">1</xref>,<xref rid=\"B15-ijms-21-05538\" ref-type=\"bibr\">15</xref>]. Aneurysm progression is caused by aortic wall degeneration. Therefore, an adjuvant pharmacologic intervention that stabilizes the aortic wall might prevent late EVAR failure [<xref rid=\"B13-ijms-21-05538\" ref-type=\"bibr\">13</xref>]. A previous randomized clinical trial was conducted to test doxycycline after EVAR. In that trial, doxycycline therapy showed beneficial effects in several patient groups, which suggested that pharmacologic treatment might be useful as an adjuvant therapy to improve EVAR results [<xref rid=\"B16-ijms-21-05538\" ref-type=\"bibr\">16</xref>]. However, systemic administration of doxycycline, or other drugs, could cause systemic adverse effects. Moreover, drugs with poor water solubility have limited clinical applications. Alternatively, drug-eluting stent grafts can be used to deliver drugs in combination with EVAR. With this technology, sufficiently high drug concentrations can be delivered to the aortic wall. However, once a stent graft is placed in the body, it is not possible to adjust the drug elution rate.</p><p>To address this problem, we created a new hybrid device that combined a drug delivery system and an endovascular stent graft, for treating aortic aneurysms. We called it a rechargeable drug delivery system (RDDS), because it can be charged with a drug, release the drug, and then be recharged, which provided great flexibility in drug administration. Briefly, the RDDS is composed of a target-recognizing nanocarrier that transports the drug to a targeted device (<xref ref-type=\"fig\" rid=\"ijms-21-05538-f001\">Figure 1</xref>A). Any artificial device, like a prosthetic vascular graft, can serve as a targeted device by adding target molecules to the surface. Then, after the device is implanted in the body, the target-recognizing nanocarriers can bind to the target molecules on the device. Utilizing the interaction between biotin and neutravidin, we developed a target-recognizing nanocarrier labeled with biotin and a targeted vascular graft labeled with neutravidin. Most drug types can be incorporated into the nanocarrier. Once the target-recognizing nanocarriers are filled with a suitable drug, they are administered intravenously. The nanocarriers circulate in the bloodstream, then bind to the target molecules on the vascular graft. Next, the nanocarrier shell undergoes hydrolysis, which results in the local delivery of the drug. After the nanocarrier is degraded, the target molecules are regenerated, and thus, they are available for binding to another set of nanocarriers (<xref ref-type=\"fig\" rid=\"ijms-21-05538-f001\">Figure 1</xref>B). </p><p>In the present study, we aimed to demonstrate the feasibility of the RDDS in vitro and in vivo.</p></sec><sec sec-type=\"results\" id=\"sec2-ijms-21-05538\"><title>2. Results and Discussion</title><sec id=\"sec2dot1-ijms-21-05538\"><title>2.1. Development of the Targeted Graft</title><p>First, we intended to label a prosthetic vascular graft with biotin. Initially, we biotinylated a woven polyester graft with an amine-coupling reaction (<xref ref-type=\"fig\" rid=\"ijms-21-05538-f002\">Figure 2</xref>A). Later, we developed a different biotinylated graft by coating a woven polyester graft with a biocompatible polymer, poly (2-hydroxyethyl methacrylate), which could be biotinylated, p(HEMA-biotin) (<xref ref-type=\"fig\" rid=\"ijms-21-05538-f002\">Figure 2</xref>B). The grafts biotinylated with amine-coupling contained 3.1 nmol/cm<sup>2</sup> biotin, and the grafts biotinylated with a pHEMA coating contained 245 nmol/cm<sup>2</sup> biotin. Thus, the pHEMA-coating technique greatly increased the amount of biotin on the graft (<xref ref-type=\"fig\" rid=\"ijms-21-05538-f002\">Figure 2</xref>C).</p></sec><sec id=\"sec2dot2-ijms-21-05538\"><title>2.2. Feasibility of Using the Targeted Graft in Mouse Blood Vessels</title><p>We investigated the feasibility of using the targeted graft in blood vessels in vivo. First, we placed the targeted graft, which was coated with p (HEMA-biotin), into the mouse inferior vena cava. Then, we injected fluorescence-labeled neutravidin intravenously. At 15 min after injection, neutravidin had successfully accumulated on the biotinylated graft. This result indicated that the interaction between biotin and neutravidin was preserved within blood vessels in vivo (<xref ref-type=\"fig\" rid=\"ijms-21-05538-f003\">Figure 3</xref>).</p></sec><sec id=\"sec2dot3-ijms-21-05538\"><title>2.3. Development of the Bio-Nanocapsule-Liposome Complex</title><p>The bio-nanocapsule-liposome (BNC-LP) complex was previously reported to be useful for delivering drugs to specific tissues in vivo [<xref rid=\"B17-ijms-21-05538\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijms-21-05538\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijms-21-05538\" ref-type=\"bibr\">19</xref>]. Therefore, we developed a BNC-LP complex as a target-recognizing nanocarrier. First, we selected two drugs for treating aortic aneurysms: SP60015, a JNK inhibitor, and pitavastatin, a statin, and we incorporated them into liposomes (<xref ref-type=\"fig\" rid=\"ijms-21-05538-f004\">Figure 4</xref>A,B). Both SP600125 and pitavastatin were encapsulated within liposomes at favorable concentrations (3.0 mg/mg lipid and 0.37 mg/mg lipid, respectively). The BNC, which is a hepatitis B virus surface antigen L protein with lipid bilayer, was biotinylated (<xref ref-type=\"fig\" rid=\"ijms-21-05538-f004\">Figure 4</xref>C), as described previously [<xref rid=\"B20-ijms-21-05538\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijms-21-05538\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05538\" ref-type=\"bibr\">22</xref>]. We then conjugated the biotinylated BNC with drug-containing liposomes to create the drug-containing biotinylated BNC-LP complex, as described previously [<xref rid=\"B17-ijms-21-05538\" ref-type=\"bibr\">17</xref>].</p><p>Next, we checked the binding of the biotinylated BNC-LP complex to the targeted graft, in vitro. The targeted graft was prepared by allowing DyLight488-labeled neutravidin to bind to the biotin attached to the graft (<xref ref-type=\"fig\" rid=\"ijms-21-05538-f004\">Figure 4</xref>D). Then, we demonstrated that the Cy3-labeled biotinylated BNC-LP complex could specifically bind to the neutravidin attached to the targeted graft in vitro (<xref ref-type=\"fig\" rid=\"ijms-21-05538-f004\">Figure 4</xref>D,E). </p></sec><sec id=\"sec2dot4-ijms-21-05538\"><title>2.4. Accumulation of the Target-Recognizing BNC-LP Complex on the Targeted Graft in Mouse Blood Vessels</title><p>We checked that the biotinylated BNC-LP complex could bind to the targeted graft in vivo by placing the targeted graft into the mouse inferior vena cava, then injecting the biotinylated BNC-LP complex intravenously. A non-biotinylated BNC-LP complex was used as the control (<xref ref-type=\"fig\" rid=\"ijms-21-05538-f005\">Figure 5</xref>A). At 180 min after injection, the targeted grafts had accumulated the biotinylated BNC-LP complex, but not the control BNC-LP complex (<xref ref-type=\"fig\" rid=\"ijms-21-05538-f005\">Figure 5</xref>B). </p></sec><sec id=\"sec2dot5-ijms-21-05538\"><title>2.5. Efficiency of Loading (Charging) the Targeted Graft with the Target-Recognizing BNC-LP Complex In Vivo</title><p>Next, we examined how efficiently the target-recognizing BNC-LP complex could be loaded onto the targeted graft. Here, we placed either the targeted graft or an untreated graft into the mouse inferior vena cava. The next day, we injected the biotinylated BNC-LP complex labeled with Cy3 intravenously. At 60 min after injection, the grafts were excised, and we examined the Cy3 fluorescence intensities (<xref ref-type=\"fig\" rid=\"ijms-21-05538-f006\">Figure 6</xref>A). </p><p>We found that the targeted grafts showed significantly higher fluorescence intensities (relative intensity) than the untreated grafts (targeted graft, 1.00 ± 0.11; untreated graft, 0.09 ± 0.01, <italic>p < 0.01</italic> compared to untreated graft). This result indicated that charging the targeted graft was over ten times more efficient than charging an untreated graft (<xref ref-type=\"fig\" rid=\"ijms-21-05538-f006\">Figure 6</xref>B, C).</p></sec><sec id=\"sec2dot6-ijms-21-05538\"><title>2.6. Efficiency of Recharging the Targeted Graft with the Target-Recognizing BNC-LP Complex In Vivo</title><p>Next, we examined the capacity of the targeted graft to be recharged with the target-recognizing BNC-LP complex. We placed the targeted graft into the mouse inferior vena cava. For this recharging experiment, we first intravenously injected a biotinylated BNC-LP complex without the Cy3 label, on the same day of graft placement (<xref ref-type=\"fig\" rid=\"ijms-21-05538-f007\">Figure 7</xref>A). Then, 24 h later, we injected another dose of the biotinylated BNC-LP complex, but this dose was labeled with Cy3. As a control experiment, at 24 h after graft placement, a single intravenous injection of the biotinylated BNC-LP complex labeled with Cy3 was performed. The grafts were excised at about 24 h after graft placement, and the Cy3 fluorescence intensities of the grafts were examined.</p><p>The recharged grafts showed high fluorescence intensities (<xref ref-type=\"fig\" rid=\"ijms-21-05538-f007\">Figure 7</xref>B), comparable to those of the singly charged grafts (initial charge intensity: 1.00 ± 0.11; second charge intensity: 0.95 ± 0.24, <italic>n</italic> = 3; <xref ref-type=\"fig\" rid=\"ijms-21-05538-f007\">Figure 7</xref>C). This result suggested that recharging the targeted graft with the target-recognizing BNC-LP complex was highly efficient in vivo.</p></sec><sec id=\"sec2dot7-ijms-21-05538\"><title>2.7. Effect of Releasing Drug from the Graft Charged with BNC-LP Complexes</title><p>Finally, we examined the effects of releasing drug from the graft charged with drug-containing BNC-LP complexes. For this experiment, we prepared a drug-containing graft by combining the targeted graft with the pitavastatin-containing BNC-LP complex. We stimulated the abdominal aorta with 0.5M CaCl<sub>2</sub> and then placed the pitavastatin-containing graft close to the aorta (<xref ref-type=\"fig\" rid=\"ijms-21-05538-f008\">Figure 8</xref>A). As a control, we used the targeted graft charged with BNC-LP complexes that did not contain a drug (<xref ref-type=\"fig\" rid=\"ijms-21-05538-f008\">Figure 8</xref>B).</p><p>In the control experiment, at 24 h after treatment with 0.5M CaCl<sub>2</sub>, MMP-9 was highly expressed in aortic tissues (<xref ref-type=\"fig\" rid=\"ijms-21-05538-f008\">Figure 8</xref>C). Notably, when the pitavastatin-containing graft was placed next to the CaCl<sub>2</sub>-treated aorta, the MMP-9 expression level was significantly reduced (63% ± 9% reduction, <italic>p < 0.05</italic> compared to control, <xref ref-type=\"fig\" rid=\"ijms-21-05538-f008\">Figure 8</xref>D). This result demonstrated that experimental inflammation of mouse aortic tissues was successfully inhibited by the drug released from our hybrid drug delivery system.</p></sec><sec id=\"sec2dot8-ijms-21-05538\"><title>2.8. Summary of the Results</title><p>In summary, the targeted graft was successfully prepared by combining the biotinylated graft with neutravidin. The target-recognizing nanocarrier was created by conjugating biotinylated BNCs with liposomes that contained drugs, such as SP60015 and pitavastatin. Both in vitro and in vivo, the biotinylated BNC-LP complex successfully bound to the targeted graft, but not to an untreated graft. After the target-recognizing BNC-LP complex was intravenously injected, it specifically and effectively accumulated at the targeted graft in the mouse blood vessel. In a recharging experiment, the target-recognizing BNC-LP complex accumulated again at the previously charged targeted graft. These findings indicated that the target-recognizing nanocarriers could charge and recharge the targeted graft in vivo. Finally, the targeted graft charged with pitavastatin-containing BNC-LP complexes significantly reduced MMP-9 expression in aortic tissue, which indicated that the drug had been successfully released from the graft and had treated the aortic wall in vivo. Thus, we successfully developed a novel drug delivery device system, called RDDS. Although the scale of our experimental model system with mice was too small to test the RDDS through every step of the treatment, it was sufficient for testing the system at three stages (charging, releasing, and recharging). Importantly, no mouse died, during this study, due to use of the RDDS, which included biotin, neutravidin, BNC-LP, and pitavastatin.</p></sec><sec id=\"sec2dot9-ijms-21-05538\"><title>2.9. Clinical Implications and Future Directions</title><p>Regardless of promising results in preclinical studies, to date, no AAA drug has shown beneficial effects in the clinical setting. One potential explanation for this failure might be that inappropriate doses were used in previous clinical trials [<xref rid=\"B13-ijms-21-05538\" ref-type=\"bibr\">13</xref>,<xref rid=\"B23-ijms-21-05538\" ref-type=\"bibr\">23</xref>]. Since AAAs are predominately localized to a limited site on the aorta, it is reasonable to strive for local drug delivery to increase the therapeutic efficacy and reduce systemic side effects. Recently, several studies have demonstrated the efficacy of nanoparticle therapies for treating AAAs in rodent models [<xref rid=\"B24-ijms-21-05538\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijms-21-05538\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijms-21-05538\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijms-21-05538\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05538\" ref-type=\"bibr\">28</xref>]. Those approaches could provide attractive strategies for inhibiting AAA progression. Moreover, other studies have reported the effectiveness of prolonged drug release from biodegradable systems for treating AAAs [<xref rid=\"B29-ijms-21-05538\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijms-21-05538\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijms-21-05538\" ref-type=\"bibr\">31</xref>]. Although drug-eluting stents, in combination with EVAR, might also be a means to deliver drugs to aortic aneurysms, they are likely to lack control of drug elution. In contrast, the RDDS that we developed is distinctively different from systemic drug delivery or drug-eluting stent approaches, and theoretically, the RDDS has more potential for providing great flexibility in drug administration. The RDDS could enable the safe delivery of the desired drugs to an aneurysm, based on a therapeutic marker, when necessary (<xref ref-type=\"fig\" rid=\"ijms-21-05538-f009\">Figure 9</xref>). This approach could counteract aneurysm exclusion failures and encourage AAA regression. Although further studies are necessary before the RDDS can be put into practical use, the system could provide a useful adjuvant therapy to improve the long-term results of EVAR.</p></sec></sec><sec id=\"sec3-ijms-21-05538\"><title>3. Materials and Methods </title><sec id=\"sec3dot1-ijms-21-05538\"><title>3.1. Biotinylation of a Graft Surface and In Vitro Detection of Biotin</title><p>The surface of an artificial blood vessel (UBE woven polyester graft, Ube Industries, Tokyo, Japan) was treated with 1 N sodium hydroxide, which partially hydrolyzed it and exposed free carboxyl groups. The carboxyl groups were activated with 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide hydrochloride (EDC), a cross-linker, combined with N-hydroxysuccinimide (NHS). Then, the EZ-Link Biotin-PEO3-LC Amine kit (Pierce, Rockford, IL, USA) was used to attach biotin to the graft surface. </p><p>To produce the p (HEMA-Biotin) conjugate, we performed a condensation reaction between poly (2-hydroxyethyl methacrylate) (pHEMA) and (+)-biotin. We combined pHEMA and biotin at a molar ratio of 1:5, with EDC in N,N-dimethylformamide (DMF), and incubated the reaction at room temperature for 24 h. Then, a graft was immersed in the p (HEMA-Biotin) solution for 6 h to produce a surface-biotinylated graft. The amount of biotin coupled to the graft surface was measured with the 4’-hydroxyazobenzene-2-carboxylic acid biotin quantification kit. For binding assays, the biotinylated graft was combined with DyLight488-labeled neutravidin (Pierce), which served as the target molecule that could bind a biotinylated drug carrier.</p></sec><sec id=\"sec3dot2-ijms-21-05538\"><title>3.2. Verification of the In Vivo Interaction Between Biotin and Neutravidin</title><p>Male C57BL/6 mice, obtained from Chiyoda Kaihatsu (Japan), were used for experiments at 10- to 15-weeks old. An operating microscope with 25× magnification was used for the procedure.</p><p>A mid-line abdominal incision was made. The inferior vena cava, together with the infrarenal aorta, were dissected as a unit and mobilized at levels between the renal arteries and the aortic bifurcation. The proximal and distal portions of the inferior vena cava, together with the infrarenal aorta, were clamped as a unit with an Acland microvascular clamp (B-1V). A longitudinal incision was made in the inferior vena cava between the clamps, and a tiny piece of the biotinylated graft (4 × 1 mm) was placed within the lumen of the inferior vena cava. The incision in the inferior vena cava was closed with the interrupted suture technique, performed with 10-0 nylon sutures (10V43-10R, Keisei Medical, Tokyo, Japan). The clamps were then released, and blood flow was reestablished.</p><p>Shortly thereafter, DyLight549-labeled neutravidin (Pierce) was injected through the iliac vein. The fluorescent signal was visualized with a fluorescence stereomicroscope (MVX10, Olympus, Tokyo, Japan) in real time. During the fluorescence-labeled neutravidin injection, successful reestablishment of the blood flow was confirmed when the fluorescent signal passed through the inferior vena cava. The interaction between biotin and neutravidin was considered successful when the fluorescent signal accumulated in the biotinylated graft.</p><p>All experiments in this study were performed in accordance with the Guidelines for the Care and Use of Laboratory Animals, published by the United States National Institutes of Health. All protocols were approved by the Institutional Animal Care and Use Committee of Yamaguchi University Graduate School of Medicine (No. 31-072, 01/09/2009).</p></sec><sec id=\"sec3dot3-ijms-21-05538\"><title>3.3. Drug Encapsulation in Liposomes</title><p>We used two commonly known therapeutic drugs for aortic aneurysms [<xref rid=\"B6-ijms-21-05538\" ref-type=\"bibr\">6</xref>,<xref rid=\"B11-ijms-21-05538\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijms-21-05538\" ref-type=\"bibr\">12</xref>,<xref rid=\"B32-ijms-21-05538\" ref-type=\"bibr\">32</xref>]: a JNK inhibitor (SP600125, Tocris Bioscience, Bristol, UK) and statin (pitavastatin, Santa Cruz Biotechnology, Dallas, TX, USA). We encapsulated these drugs in liposomes. Briefly, lipids (10 mg of dipalmitoylphosphatidylcholine:dipalmitoylphosphatidylethanolamine:dipalmitoylphosphatidylglycerol:cholesterol, at a ratio of: 15:15:40:30) were dissolved in a methanol/chloroform solution (2 mL). Then, the drug mixture (0.5 mg) was dissolved in this solution. The solvent was evaporated in an evaporator and heated in a water bath at 60 °C, to prepare a lipid film. The film was hydrated with a buffer (10 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES), 150 mM NaCl, pH 7.4, 1 mL). Next, the hydrated film was passed through an extruder (500 mL syringe-type, pore size 100 nm, Avestin, Ottawa, ON, Canada) 50 times. Then, gel filtration (Superdex G-50, GE Healthcare, Amersham, UK) was carried out to remove unencapsulated drugs, and thus, purified liposomes were obtained. Both SP600125 and pitavastatin exhibit specific fluorescence characteristics. Therefore, to test fluorescence measurements for detecting the released drugs, purified liposomes were destroyed in the presence of 0.1 N HCl and 0.5% sodium dodecyl sulfate (SDS). </p></sec><sec id=\"sec3dot4-ijms-21-05538\"><title>3.4. Preparation of the Biotinylated BNC-LP Complex</title><p>As described previously [<xref rid=\"B20-ijms-21-05538\" ref-type=\"bibr\">20</xref>,<xref rid=\"B33-ijms-21-05538\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijms-21-05538\" ref-type=\"bibr\">34</xref>], BNCs were overexpressed in <italic>Saccharomyces cerevisiae</italic> AH22R<sup>−</sup> cells that carried the ZZ-BNC expression plasmid, pGLD-ZZ50. Next, as described previously [<xref rid=\"B20-ijms-21-05538\" ref-type=\"bibr\">20</xref>,<xref rid=\"B35-ijms-21-05538\" ref-type=\"bibr\">35</xref>], BNCs were extracted by disrupting the cells with glass beads; then, BNCs were purified on an AKTA chromatography system (GE Healthcare). Next, BNCs were biotinylated with the EZ-Link Sulfo-N-hydroxysuccinimide-biotin kit (Pierce), according to the manufacturer’s protocol. For binding assays, biotinylated BNCs were labeled with Cy3-dye (GE Healthcare) with N-hydroxysuccinimide chemistry, as described previously [<xref rid=\"B36-ijms-21-05538\" ref-type=\"bibr\">36</xref>]. Finally, the drug-containing, biotinylated BNC-LP complex was prepared by conjugating the biotinylated BNC with drug-containing liposomes at a weight ratio of 1:35, as described previously [<xref rid=\"B17-ijms-21-05538\" ref-type=\"bibr\">17</xref>].</p></sec><sec id=\"sec3dot5-ijms-21-05538\"><title>3.5. Binding Assay In Vitro</title><p>The targeted graft was prepared by incubating the biotinylated graft with DyLight488-labeled neutravidin (0.1 mg/mL)/phosphate buffered saline (PBS) for 30 min. Next, pieces of the targeted graft (5 × 5 mm) were immersed in Tris-buffered saline with Tween20 (TBS-T), and then incubated with 5% bovine serum albumin/TBS-T for 30 min, to block non-specific binding. After washing in TBS-T, the targeted grafts were incubated with the biotinylated BNC-LP complex labeled with Cy3 (0.2 mg/mL)/PBS for 20 min. The targeted grafts were washed three times with TBS-T, then fluorescence was measured with a fluorescence stereomicroscope. Negative controls included non-biotinylated grafts, biotinylated grafts without neutravidin, and non-biotinylated BNC-LP complexes.</p></sec><sec id=\"sec3dot6-ijms-21-05538\"><title>3.6. Binding Assay In Vivo</title><p>The targeted graft was prepared by incubating the biotinylated graft with neutravidin (0.1 mg/mL)/PBS for 30 min. After non-specific blocking and washing, as described in <xref ref-type=\"sec\" rid=\"sec3dot5-ijms-21-05538\">Section 3.5</xref>, a piece of the targeted graft (4 × 1 mm) was placed within the lumen of the mouse inferior vena cava. After reestablishing blood flow, we injected the biotinylated BNC-LP complex, labeled with Cy3 (0.2 mg/mL)/PBS (250 μL) into the iliac vein. We used non-biotinylated BNC-LP complex labeled with Cy3 (0.2 mg/mL)/PBS (250 μL) in a control experiment. At 180 min after injection, the mice were sacrificed. The graft was immediately excised, together with the inferior vena cava and infrarenal aorta, as a unit. The specimen was washed with normal saline and examined with a fluorescence stereomicroscope.</p></sec><sec id=\"sec3dot7-ijms-21-05538\"><title>3.7. Assay for Charging the Targeted Graft with the Target-Recognizing BNC-LP Complex In Vivo</title><p>The targeted graft was prepared by incubating the biotinylated graft with neutravidin (0.1 mg/mL)/PBS for 90 min. After non-specific blocking and washing (<xref ref-type=\"sec\" rid=\"sec3dot5-ijms-21-05538\">Section 3.5</xref>), a piece of the targeted graft (4 × 1 mm) was placed within the lumen of the mouse inferior vena cava. Non-biotinylated grafts were used as negative controls. At 24 h after placing the graft, the biotinylated BNC-LP complex, labeled with Cy3 (0.025 mg/mL)/PBS (200 μL), was injected into the iliac vein. At 60 min after injection, the mice were sacrificed. The graft was immediately excised, together with inferior vena cava and infrarenal aorta, as a unit. The specimen was washed with normal saline and examined with a fluorescence stereomicroscope.</p></sec><sec id=\"sec3dot8-ijms-21-05538\"><title>3.8. Assay for Recharging the Targeted Graft with the Target-Recognizing BNC-LP Complex In Vivo</title><p>The targeted graft was prepared by incubating the biotinylated graft with neutravidin (0.1 mg/mL)/PBS for 90 min. After non-specific blocking and washing (<xref ref-type=\"sec\" rid=\"sec3dot5-ijms-21-05538\">Section 3.5</xref>), a piece of the targeted graft (4 × 1 mm) was placed within the lumen of the mouse inferior vena cava. For this experiment, on the same day, after reestablishing blood flow, the biotinylated BNC-LP complex, without Cy3 label, was injected into the iliac vein (first charge). Then, 24 h after the graft placement, the biotinylated BNC-LP complex labeled with Cy3 (0.025 mg/mL)/PBS (200 μL) was also injected into the iliac vein (recharge). For the control experiment, at 24 h after graft placement, a single intravenous injection of the biotinylated BNC-LP complex labeled with Cy3 (0.025 mg/mL)/PBS (200 μL). At 60 min after injecting the biotinylated BNC-LP complex labeled with Cy3, the mice were sacrificed. The graft was immediately excised, together with the inferior vena cava and infrarenal aorta, as a unit. The specimen was washed with normal saline and examined with a fluorescence stereomicroscope.</p></sec><sec id=\"sec3dot9-ijms-21-05538\"><title>3.9. Assay for Releasing Drug from the Graft Charged with BNC-LP Complex In Vivo</title><p>The targeted graft was prepared by incubating the biotinylated graft with neutravidin (0.1 mg/mL)/PBS for 90 min. The pitavastatin-containing biotinylated BNC-LP complex was prepared by conjugating biotinylated BNC with pitavastatin-containing liposomes (drug content: 0.37 mg/mg lipid, average particle diameter: 577 nm, polydispersity index: 0.399) at a weight ratio of 1:35, as described previously [<xref rid=\"B17-ijms-21-05538\" ref-type=\"bibr\">17</xref>]. Subsequently, the pitavastatin-releasing graft was charged by incubating the targeted graft (5 × 5 mm) with the pitavastatin-containing biotinylated BNC-LP complex (0.4 mg)/buffer (0.24 mL of 10 mM HEPES, 150 mM NaCl, pH 7.4) for 90 min, then washing in TBS-T three times. For the control experiment, a control graft was charged by incubating the targeted graft (5 × 5 mm) with the biotinylated BNC-LP complex without drug for 90 min, and then washing in TBS-T three times.</p><p>Eight-week-old male C57BL/6 mice were used for this experiment. A mid-line abdominal incision was made. The infrarenal aorta was dissected and mobilized at levels between the renal arteries and the aortic bifurcation. Next, the mobilized aorta was stimulated by applying periaortic 0.5 M CaCl<sub>2</sub> for 15 min, to induce inflammation in the aorta, as described previously [<xref rid=\"B11-ijms-21-05538\" ref-type=\"bibr\">11</xref>,<xref rid=\"B37-ijms-21-05538\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijms-21-05538\" ref-type=\"bibr\">38</xref>,<xref rid=\"B39-ijms-21-05538\" ref-type=\"bibr\">39</xref>]. Then, a piece of the pitavastatin-releasing graft (5 × 1.2 mm) was placed near the part of the abdominal aorta that was stimulated with CaCl<sub>2</sub>. The control graft was used in the control group. The mice were sacrificed at 24 h after the CaCl<sub>2</sub> treatment. The abdominal aorta was immediately excised and subjected to protein analysis.</p><p>Protein extraction and western blotting were performed, as described previously [<xref rid=\"B11-ijms-21-05538\" ref-type=\"bibr\">11</xref>,<xref rid=\"B40-ijms-21-05538\" ref-type=\"bibr\">40</xref>,<xref rid=\"B41-ijms-21-05538\" ref-type=\"bibr\">41</xref>]. Briefly, equal amounts of sample proteins were loaded onto individual lanes in sodium dodecyl sulfate polyacrylamide gels. Then, the proteins were separated by electrophoresis and transferred onto polyvinylidene difluoride membranes. Membranes were probed with antibodies against MMP-9 (1:1,000; R&D Systems, Minneapolis, MN, USA) and glyceraldehyde 3-phosphate dehydrogenase (GAPDH) (1:50,000; Millipore, Billerica, MA, USA).</p></sec><sec id=\"sec3dot10-ijms-21-05538\"><title>3.10. Statistical Analysis</title><p>Data are expressed as the mean ± standard deviation. Statistical analyses were performed with the Student’s <italic>t</italic>-test (<italic>n</italic> = 3) or the Mann–Whitney test (<italic>n</italic> > 3). Data were analyzed with Prism 6.0 (GraphPad Software, La Jolla, CA, USA). <italic>p</italic>-values < 0.05 were considered statistically significant.</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec4-ijms-21-05538\"><title>4. Conclusions</title><p>In conclusion, we provided a proof-of-concept for the novel RDDS. We showed that the RDDS was capable of transporting and releasing desired drugs to a specific site, repeatedly and safely. The practical development of this system might be a major step toward improving the long-term outcome after EVAR.</p></sec></body><back><ack><title>Acknowledgments</title><p>We thank Takeshi Kasuya, Koichi Yoshimaru, Yurie Kawano, Megumi Oishi, and Shizuka Nishino-Fujimoto for technical assistance.</p></ack><notes><title>Author Contributions</title><p>Conceptualization: K.Y. and H.A.; methodology: K.Y., H.T., and S.K.; validation: H.A. and K.H.; formal analysis: K.Y., C.T., and M.I.; investigation: K.Y., C.T., and M.I.; resources: K.Y., H.T., and S.K.; data curation: K.Y., C.T., and M.I.; writing—original draft preparation: K.Y.; writing—review and editing: K.Y.; visualization: K.Y.; supervision: K.Y. and K.H.; project administration: K.Y.; funding acquisition: K.Y., M.I., and S.K. All authors have read and agreed to the submitted version of the manuscript.</p></notes><notes><title>Funding</title><p>This work was supported by a Grant from New Energy and Industrial Technology Development Organization (NEDO, 08A07025a to K.Y.); KAKENHI Grant-in-Aid for Challenging Research Exploratory (25670596 to K.Y.); KAKENHI Grant-in-Aid for Scientific Research (S, 16H06314, and A, 18H03938, to S.K.; B, 20H03766 to K.Y.; C, 16K04888 and 19K05219, to M.I.); the Japan Agency for Medical Research and Development (19fk0310105h0003, to S.K.); the Uehara Memorial Foundation (to K.Y.); and Suzuki Memorial Foundation (to K.Y.) and “Dynamic Alliance for Open Innovation Bridging Human, Environment and Materials” from the Ministry of Education, Culture, Sports, Science and Technology of Japan (MEXT, to S.K.).</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest. The funders played no role in the study design; in the collection, analyses, or interpretation of data; in writing the manuscript, or in the decision to publish the results.</p></notes><glossary><title>Abbreviations</title><array orientation=\"portrait\"><tbody><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">AAA</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Abdominal aortic aneurysm</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">BNC</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Bio-nanocapsule</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">BNC-LP<break/>EDC<break/>EVAR<break/>GAPDH<break/>HEPES<break/>JNK<break/>MMP<break/>NHS<break/>PBS<break/>pHEMA<break/>RDDS<break/>SDS<break/>TBS-T</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Bio-nanocapsule-liposome<break/>1-ethyl-3-(3-dimethylaminopropyl) carbodiimide hydrochloride<break/>Endovascular aneurysm repair<break/>Glyceraldehyde 3-phosphate dehydrogenase<break/>4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid<break/>c-Jun N-terminal kinase<break/>Matrix metalloproteinase<break/>N-hydroxysuccinimide<break/>Phosphate buffered saline<break/>poly (2-hydroxyethyl methacrylate)<break/>Rechargeable drug delivery system<break/>Sodium dodecyl sulfate<break/>Tris-buffered saline with Tween20</td></tr></tbody></array></glossary><ref-list><title>References</title><ref id=\"B1-ijms-21-05538\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Sakalihasan</surname><given-names>N.</given-names></name><name><surname>Michel</surname><given-names>J.B.</given-names></name><name><surname>Katsargyris</surname><given-names>A.</given-names></name><name><surname>Kuivaniemi</surname><given-names>H.</given-names></name><name><surname>Defraigne</surname><given-names>J.O.</given-names></name><name><surname>Nchimi</surname><given-names>A.</given-names></name><name><surname>Powell</surname><given-names>J.T.</given-names></name><name><surname>Yoshimura</surname><given-names>K.</given-names></name><name><surname>Hultgren</surname><given-names>R.</given-names></name></person-group><article-title>Abdominal aortic aneurysms</article-title><source>Nat. 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After releasing the drug at the site of the aneurysm, the nanocarrier degrades, and the target molecules are regenerated.</p></caption><graphic xlink:href=\"ijms-21-05538-g001\"/></fig><fig id=\"ijms-21-05538-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Development of the biotinylated graft. (<bold>A</bold>) Schematic diagram illustrates the preparation of the biotinylated graft with an amine-coupling reaction. (<bold>B</bold>) Schematic diagram illustrates the preparation of the biotinylated graft by coating with poly (2-hydroxyethyl methacrylate) (pHEMA). (<bold>C</bold>) Quantification of the amounts of biotin on the graft surfaces.</p></caption><graphic xlink:href=\"ijms-21-05538-g002\"/></fig><fig id=\"ijms-21-05538-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Feasibility of using the targeted graft in mouse blood vessels. (Upper panels) Schematic diagrams show the main features of the procedure. (Lower, left) Bright field image shows the placement of the biotinylated graft in the mouse inferior vena cava. (Lower, middle) Fluorescence image acquired just after injection shows the passage of DyLight549-labeled neutravidin (red) through the inferior vena cava. (Lower, right) Fluorescence image acquired at 15 min after injection shows the accumulation of DyLight549-labeled neutravidin at the biotinylated graft.</p></caption><graphic xlink:href=\"ijms-21-05538-g003\"/></fig><fig id=\"ijms-21-05538-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Development of the bio-nanocapsule-liposome (BNC-LP) complex. (<bold>A, B</bold>) Incorporation of (<bold>A</bold>) SP60015 and (<bold>B</bold>) pitavastatin into liposomes. Successful incorporation was determined by measuring drug autofluorescence, before and after liposomes were dissolved with sodium dodecyl sulfate (SDS) treatment. (<bold>C</bold>) Schematic diagram illustrates the preparation of the drug-containing biotinylated BNC-LP complex. The liposome is an empty lipid micelle that can be filled with the desired drug. The bio-nanocapsule is a hollow sphere composed of hepatitis B virus surface antigen (HBsAg). Biotin reacted with HBsAg, then the BNC was conjugated to the LP. (<bold>D</bold>) Diagram shows the configuration of a BNC-LP complex labeled with Cy3. The attached biotin recognizes the neutravidin on the targeted graft. (<bold>E</bold>, left) Bright field images show the woven polyester grafts used for the in vitro binding assay. (Middle) Fluorescence images show the binding of DyLight488-labeled neutravidin (green) to the biotinylated grafts. (Right) Fluorescence images show the binding of the biotinylated Cy3-labeled BNC-LP (red) to the neutravidin on the biotinylated grafts.</p></caption><graphic xlink:href=\"ijms-21-05538-g004\"/></fig><fig id=\"ijms-21-05538-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>Accumulation of the target-recognizing BNC-LP complex on the targeted graft in mouse blood vessels. (<bold>A</bold>) Schematic diagram of the experiment: the biotinylated BNC-LP complex is compared to the non-biotinylated control. (<bold>B</bold>, left) Bright field images show the grafts excised from mice after the in vivo binding assay. (Right) Fluorescence images show the accumulation of biotinylated Cy3-labeled BNC-LP (red) on the graft.</p></caption><graphic xlink:href=\"ijms-21-05538-g005\"/></fig><fig id=\"ijms-21-05538-f006\" orientation=\"portrait\" position=\"float\"><label>Figure 6</label><caption><p>Efficient charging of the targeted graft with the target-recognizing BNC-LP complex in vivo. (<bold>A</bold>) Schematic diagram of the experiment: the targeted graft is compared to the untreated control graft. (<bold>B</bold>, left) Bright field images show the grafts excised from mice after the experiment. (Right) Fluorescence images show the accumulation of Cy3-labeled BNC-LP on the targeted graft. (<bold>C</bold>) Efficiency of charging the targeted graft with the BNC-LP complex, determined by measuring the relative fluorescence intensity. Data are the means ± standard deviations of 3 independent observations. ** <italic>p</italic> < 0.01 compared to the untreated graft.</p></caption><graphic xlink:href=\"ijms-21-05538-g006\"/></fig><fig id=\"ijms-21-05538-f007\" orientation=\"portrait\" position=\"float\"><label>Figure 7</label><caption><p>Efficient recharging of the targeted graft with the target-recognizing BNC-LP complex in vivo. (<bold>A</bold>) Schematic diagram of the experiment: an initial charge is compared to a second charge (recharge). (<bold>B,</bold> left) Bright field images show the grafts excised from mice after the experiment. (Right) Fluorescence images show the accumulation of Cy3-labeled BNC-LPs on both targeted grafts. (<bold>C</bold>) Efficiency of recharging the targeted graft with the BNC-LP complex, determined by measuring relative fluorescence intensities. Data are the means ± standard deviations of 3 independent observations.</p></caption><graphic xlink:href=\"ijms-21-05538-g007\"/></fig><fig id=\"ijms-21-05538-f008\" orientation=\"portrait\" position=\"float\"><label>Figure 8</label><caption><p>Effect of releasing drug from the graft charged with BNC-LP complexes on mouse aortic tissues. (<bold>A</bold>) Schematic diagram of the experimental design: CaCl<sub>2</sub> induces inflammation in the aorta (red); the pitavastatin-releasing graft or the control graft is placed next to the aorta. (<bold>B</bold>) Schematic diagram of the experiment: the pitavastatin-releasing graft is compared to the control graft. (<bold>C</bold>) Representative images of western blots for estimating the expression of matrix metalloproteinase-9 (MMP-9) relative to glyceraldehyde 3-phosphate dehydrogenase (GAPDH) expression (internal loading control). (<bold>D</bold>) Quantification of MMP-9 expression in mouse aortic tissues. Data are the means ± standard deviations of 5 independent observations. * <italic>p</italic> < 0.05 compared to the control.</p></caption><graphic xlink:href=\"ijms-21-05538-g008\"/></fig><fig id=\"ijms-21-05538-f009\" orientation=\"portrait\" position=\"float\"><label>Figure 9</label><caption><p>Schematic diagram of future directions for the novel hybrid drug delivery system. The system would enable the delivery of suitable drugs at aneurysms, based on a therapeutic marker when necessary. This system could serve as an adjuvant therapy to improve the long-term outcome of endovascular aneurysm repair (EVAR).</p></caption><graphic xlink:href=\"ijms-21-05538-g009\"/></fig></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32731334</article-id><article-id pub-id-type=\"pmc\">PMC7432023</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155344</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05344</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Identification and Expression Analysis of Hormone Biosynthetic and Metabolism Genes in the 2OGD Family for Identifying Genes That May Be Involved in Tomato Fruit Ripening</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Ding</surname><given-names>Qiangqiang</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05344\">1</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05344\">†</xref></contrib><contrib contrib-type=\"author\"><name><surname>Wang</surname><given-names>Feng</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05344\">1</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05344\">†</xref></contrib><contrib contrib-type=\"author\"><name><surname>Xue</surname><given-names>Juan</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05344\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Yang</surname><given-names>Xinxin</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05344\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Fan</surname><given-names>Junmiao</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05344\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Chen</surname><given-names>Hong</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05344\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Li</surname><given-names>Yi</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05344\">3</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-3305-4262</contrib-id><name><surname>Wu</surname><given-names>Han</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05344\">1</xref><xref rid=\"c1-ijms-21-05344\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05344\"><label>1</label>State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China; <email>2016204015@njau.edu.cn</email> (Q.D.); <email>wangf@njau.edu.cn</email> (F.W.); <email>2017104059@njau.edu.cn</email> (J.X.); <email>2018104055@njau.edu.cn</email> (X.Y.); <email>2018204022@njau.edu.cn</email> (J.F.)</aff><aff id=\"af2-ijms-21-05344\"><label>2</label>Jiangsu Key Laboratory for the Research and Utilization of Plant Resources, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China; <email>ch198472@163.com</email></aff><aff id=\"af3-ijms-21-05344\"><label>3</label>Department of Plant Science and Landscape Architecture, University of Connecticut, Storrs, CT 06269, USA; <email>yi.li@uconn.edu</email></aff><author-notes><corresp id=\"c1-ijms-21-05344\"><label>*</label>Correspondence: <email>wuhan@njau.edu.cn</email></corresp><fn id=\"fn1-ijms-21-05344\"><label>†</label><p>These authors contributed equally to this work.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>28</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5344</elocation-id><history><date date-type=\"received\"><day>30</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>23</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Phytohormones play important roles in modulating tomato fruit development and ripening. The 2-oxoglutarate-dependent dioxygenase (2OGD) superfamily containing several subfamilies involved in hormone biosynthesis and metabolism. In this study, we aimed to identify hormone biosynthesis and metabolism-related to 2OGD proteins in tomato and explored their roles in fruit development and ripening. We identified nine 2OGD protein subfamilies involved in hormone biosynthesis and metabolism, including the gibberellin (GA) biosynthetic protein families GA20ox and GA3ox, GA degradation protein families C19-GA2ox and C20-GA2ox, ethylene biosynthetic protein family ACO, auxin degradation protein family DAO, jasmonate hydroxylation protein family JOX, salicylic acid degradation protein family DMR6, and strigolactone biosynthetic protein family LBO. These genes were differentially expressed in different tomato organs. The GA degradation gene <italic>SlGA2ox2</italic>, and the auxin degradation gene <italic>SlDAO1</italic>, showed significantly increased expression from the mature-green to the breaker stage during tomato fruit ripening, accompanied by decreased endogenous GA and auxin, indicating that <italic>SlGA2ox2</italic> and <italic>SlDAO1</italic> were responsible for the reduced GA and auxin concentrations. Additionally, exogenous gibberellin 3 (GA<sub>3</sub>) and indole-3-acetic acid (IAA) treatment of mature-green fruits delayed fruit ripening and increased the expression of <italic>SlGA2ox2</italic> and <italic>SlDAO1</italic>, respectively. Therefore, <italic>SlGA2ox2</italic> and <italic>SlDAO1</italic> are implicated in the degradation of GAs and auxin during tomato fruit ripening.</p></abstract><kwd-group><kwd>genome-wide identification</kwd><kwd>expression analysis</kwd><kwd>2OGD family</kwd><kwd>hormone biosynthetic and metabolism genes</kwd><kwd>tomato fruit ripening</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05344\"><title>1. Introduction</title><p>Tomato is used as a model to study climacteric fruit ripening, which is mediated by the hormone ethylene. Other hormones are also involved in tomato ripening. For example, exogenous auxin treatment, or increasing the endogenous auxin level by silencing the expression of the auxin-degradation gene <italic>SlGH3.2</italic>, delayed tomato fruit ripening [<xref rid=\"B1-ijms-21-05344\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijms-21-05344\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijms-21-05344\" ref-type=\"bibr\">3</xref>]. Indeed, exogenous application of gibberellins (GAs) delayed fruit ripening, while decreasing the endogenous levels of GAs via overexpression of the GA-catabolic gene <italic>SlGA2ox1</italic> accelerated fruit ripening [<xref rid=\"B4-ijms-21-05344\" ref-type=\"bibr\">4</xref>]. Therefore, changes in hormone concentrations play important roles in tomato fruit ripening, and identification and functional analysis of hormone biosynthetic and metabolism genes are prerequisites for understanding their roles in tomato fruit ripening.</p><p>The 2-oxoglutarate-dependent dioxygenase (2OGD) superfamily is the largest enzyme family and facilitates numerous oxidative reactions, including hydroxylation, halogenation, desaturation, epimerization, etc. [<xref rid=\"B5-ijms-21-05344\" ref-type=\"bibr\">5</xref>]. The 2OGD superfamily contains many proteins involved in hormone biosynthesis and metabolism. To date, nine hormone biosynthesis and metabolism-related protein families have been identified in the 2OGD family, including GA biosynthetic protein families GA20-oxidase (GA20ox) and GA3-oxidase (GA3ox), GA degradation protein families GA2-oxidases (C19-GA2ox and C20-GA2ox), auxin degradation protein family Dioxygenase for Auxin Oxidation (DAO), ethylene biosynthetic protein family 1-aminocyclopropane-1-carboxylic acid oxidase (ACO), jasmonate (JA) hydroxylation protein family JASMONATE-INDUCED OXYGENASE (JOX), salicylic acid (SA) degradation protein family Downy Mildew Resistant6 (DMR6) and DMR6-LIKE OXYGENASE (DLO), and strigolactone (SL) biosynthetic protein family LATERAL BRANCHING OXIDOREDUCTASE (LBO). In detail, GA20oxs and GA3oxs catalyze the final two steps of GA biosynthesis: GA20oxs catalyze the conversion of GA<sub>12</sub> and GA<sub>53</sub> to GA<sub>9</sub> and GA<sub>20</sub>, which are converted by GA3oxs to bioactive GA<sub>1</sub> and GA<sub>4</sub> [<xref rid=\"B6-ijms-21-05344\" ref-type=\"bibr\">6</xref>]. GA2oxs are GA-oxidation enzymes that convert bioactive GAs or their precursors into inactive forms [<xref rid=\"B6-ijms-21-05344\" ref-type=\"bibr\">6</xref>]. DAOs catalyze the irreversible conversion of active auxin into inactive 2-oxindole-3-acetic acid (oxIAA) [<xref rid=\"B7-ijms-21-05344\" ref-type=\"bibr\">7</xref>]. ACO proteins function in the last step of ethylene biosynthesis by converting ACC into ethylene [<xref rid=\"B8-ijms-21-05344\" ref-type=\"bibr\">8</xref>]. JOX proteins hydroxylate jasmonate (JA) into inactive 12-OH-JA [<xref rid=\"B9-ijms-21-05344\" ref-type=\"bibr\">9</xref>]. DMR6s, as SA 5-hydroxylases, hydroxylate active salicylate (SA) at the C5 position of the phenyl ring to produce inactive 2,5-DHBA [<xref rid=\"B10-ijms-21-05344\" ref-type=\"bibr\">10</xref>]. In <italic>Arabidopsis</italic>, LBO converts methyl carlactonoate into an unidentified strigolactone (SL)-like compound that may be the final product of SL biosynthesis [<xref rid=\"B11-ijms-21-05344\" ref-type=\"bibr\">11</xref>]. All of these 2OGD-family hormone biosynthetic and metabolism genes play key roles in maintaining endogenous hormone homeostasis, thereby regulating plant growth and development, and the response to stresses.</p><p>2OGDs are non-heme iron-containing proteins. Their catalytic core contains a double-stranded β-helix fold (DSBH) with a highly conserved His-X-Asp-(X)<sub>n</sub>-His (HxD…H) motif, which is responsible for binding Fe (II) to form a catalytic triad [<xref rid=\"B12-ijms-21-05344\" ref-type=\"bibr\">12</xref>]. Using Fe (II) as a cofactor and 2-oxoglutarate (2OG) as a co-substrate, 2OGD proteins catalyze oxidation of the substrate and concomitant decarboxylation of 2OG to produce succinate and CO<sub>2</sub>. In addition, a conserved Arg-X-Ser/Thr (RxS/T) motif at the subfamily-conserved position within the secondary structure of the DSBH fold likely binds the C5-carboxy group of 2OG, which is the co-substrate for all known members of the subfamily except isopenicillin N synthase (IPNS), 1-aminocyclopropane-1-carboxylic acid oxidase (ACO) and (S)-2-hydroxypropylphosphonic acid epoxidase (HPPE) [<xref rid=\"B13-ijms-21-05344\" ref-type=\"bibr\">13</xref>]. 2OGD-family proteins have been identified in several species [<xref rid=\"B14-ijms-21-05344\" ref-type=\"bibr\">14</xref>]. The 2OGD superfamily can be divided into DOXA, DOXB, and DOXC subfamilies based on the amino acid sequence [<xref rid=\"B14-ijms-21-05344\" ref-type=\"bibr\">14</xref>]. DOXA proteins contain a 2OG-FeII_Oxy_2 conserved domain, and the DOXA protein AlkB of <italic>Escherichia coli</italic>, which has homologs in <italic>Arabidopsis</italic> and rice, participates in the oxidative demethylation of alkylated nucleic acids and histones [<xref rid=\"B15-ijms-21-05344\" ref-type=\"bibr\">15</xref>]. DOXB proteins typically have a conserved 2OG-FeII_Oxy_1 domain; most studies have focused on prolyl-4-hydroxylase, which is involved in the synthesis of cell-wall proteins in plants and algae [<xref rid=\"B16-ijms-21-05344\" ref-type=\"bibr\">16</xref>]. DOXC proteins, including those involved in hormone biosynthesis and metabolism, have a conserved 2OG-FeII_Oxy domain [<xref rid=\"B14-ijms-21-05344\" ref-type=\"bibr\">14</xref>].</p><p>In this study, we identified hormone biosynthesis- and metabolism-related proteins from DOXC family in tomato. Based on analysis of their structures, we predicted their motifs with the aim of determining their molecular mechanisms of action. We also analyzed the transcript levels of these genes in tomato to assess their roles in tomato growth and development, and focused on the correlations between their expression levels and tomato fruit ripening to identify proteins that degrade GAs and auxin during tomato fruit ripening.</p></sec><sec sec-type=\"results\" id=\"sec2-ijms-21-05344\"><title>2. Results</title><sec id=\"sec2dot1-ijms-21-05344\"><title>2.1. Identification and Phylogenetic Analysis of Hormone Biosynthetic and Metabolism Proteins in 2OGD Superfamily</title><p>Currently known hormone biosynthetic and metabolism proteins in the 2OGD superfamily are exclusively present in the DOXC subfamily. To identify all hormone biosynthetic and metabolism proteins of DOXC family in tomato, we used DOXC-specific 2OGD domain 2OG-FeII_Oxy (PF03171) as a key query in hmmersearch to identify all DOXC proteins in <italic>Arabidopsis</italic>, rice, and tomato. The result showed that 99, 90, and 159 proteins were identified in <italic>Arabidopsis</italic>, rice, and tomato, respectively. A phylogenic tree was constructed using the best-fit model in MEGA6.0, based on the complete sequences of the 348 identified proteins (<xref ref-type=\"app\" rid=\"app1-ijms-21-05344\">Figure S1</xref>). Nine hormone biosynthetic and metabolism protein families in DOXC family were identified: the GA biosynthetic protein families GA20ox and GA3ox, GA degradation protein families C19-GA2ox and C20-GA2ox, auxin degradation protein family DAO, ethylene biosynthetic protein family ACO, JA hydroxylation protein family JOX, SA degradation protein family DMR, and SL biosynthetic protein family LBO. The bootstrap values were >80%, suggesting high reliability of the results. The numbers of these subfamilies in <italic>Arabidopsis</italic>, rice, and tomato were as follows: 20 GA20oxs, 10 GA3oxs, 20 C19-GA2oxs, 8 C20-GA2oxs, 20 ACOs, 6 DAOs, 11 JOXs, 9 DMR6s, and 3 LBOs. A phylogenetic tree constructed using the above proteins showed that there were 11 GA20oxs, 4 GA3oxs, 9 C19-GA2oxs, 3 C20-GA2oxs, 7 ACOs, 3 DAOs, 3 JOXs, 2 DMR6s, and 1 LBO in tomato, of which 10 GA20oxs (SlGA20ox1-SlGA20ox10), 6 GA2oxs (SlGA2ox2, SlGA2ox4, SlGA2ox5, SlGA2ox7, SlGA2ox8, and SlGA2ox9), 3 DAOs (SlDAO1-SlDAO3), and 5 ACOs (SlACO1-SlACO3, SlACO4, and SlACO6) clustered together to form a monophyletic group (<xref ref-type=\"fig\" rid=\"ijms-21-05344-f001\">Figure 1</xref>). Therefore, these genes emerged via lineage-specific expansion events in tomato. In addition, the identified hormone biosynthetic and metabolism proteins in tomato comprised 104–380 amino acids, and most of them also containing a DIOX_N domain (<xref ref-type=\"app\" rid=\"app1-ijms-21-05344\">Table S1</xref>).</p></sec><sec id=\"sec2dot2-ijms-21-05344\"><title>2.2. Synteny and Duplication Analysis of Hormone Biosynthetic and Metabolism Proteins in 2OGD Superfamily</title><p>Synteny was performed to assess the relationships of the hormone biosynthetic and metabolism 2OGD genes among <italic>Arabidopsis</italic>, rice, and tomato. The result showed that there were 27 collinear gene pairs, of which 25 were between tomato and <italic>Arabidopsis</italic>: 5 pairs in the <italic>ACO</italic> family, 2 in the <italic>GA3ox</italic> family, 4 in the <italic>C19-GA2ox</italic> family, 2 in the <italic>C20-GA2ox</italic> family, 6 in the <italic>JOX</italic> family, 4 in the <italic>GA20ox</italic> family, and 2 in the <italic>DMR6</italic> family. There was only one collinear gene pair in the <italic>JOX</italic> family between tomato and rice, as and one between rice and <italic>Arabidopsis</italic> (<xref ref-type=\"fig\" rid=\"ijms-21-05344-f002\">Figure 2</xref>, <xref ref-type=\"app\" rid=\"app1-ijms-21-05344\">Table S2</xref>). This result is consistent with the evolutionary relationship between monocotyledons and dicotyledons.</p><p>The chromosomal location of the hormone biosynthetic and metabolism 2OGD genes in tomato was analyzed based on genome annotation data. The result showed that the identified hormone biosynthetic and metabolism 2OGD genes were unevenly distributed on tomato 12 chromosomes (<xref ref-type=\"app\" rid=\"app1-ijms-21-05344\">Figure S2</xref>). There was one gene on chromosomes 4, 8, and 12, seven on chromosome 2, and six on chromosome 7. Further, the genes exhibited the following duplication events: nine dispersed gene pairs in <italic>SlGA20ox</italic> (<xref ref-type=\"app\" rid=\"app1-ijms-21-05344\">Figure S2</xref>, <xref ref-type=\"app\" rid=\"app1-ijms-21-05344\">Table S3</xref>); two segmental duplication genes (one WGD or segmental duplication events) and two dispersed gene pairs in <italic>SlGA3ox</italic>; seven dispersed gene pairs in <italic>C19-SlGA2ox</italic>; two segmental duplication genes (one WGD or segmental duplication events) in <italic>C20-SlGA2ox</italic>; two tandem duplication events in <italic>SlDAO</italic>; four segmental duplication genes (two WGD or segmental duplication events) in <italic>SlACO</italic>; two segmental duplication genes (one WGD or segmental duplication events) in <italic>SlJOX</italic>; two segmental duplication genes in <italic>SlDMR6</italic> (one WGD or segmental duplication events); and one dispersed gene pair in <italic>SlLBO</italic>.</p></sec><sec id=\"sec2dot3-ijms-21-05344\"><title>2.3. Multiple Sequence Alignment and Motif Composition Analysis of Hormone Biosynthetic and Metabolism 2OGD Proteins</title><p>To determine the functional similarity of hormone biosynthetic and metabolism 2OGD proteins of tomato with those of <italic>Arabidopsis</italic> and rice, we performed multiple sequence alignments and motif composition analysis. Two 2OGD-family proteins of known three-dimensional structure—OsGA2ox3 and OsDAO [<xref rid=\"B17-ijms-21-05344\" ref-type=\"bibr\">17</xref>], and seven hormone biosynthetic and metabolism 2OGD-family proteins—AtGA20ox1, AtGA3ox1, AtGA2ox7, SlACO1, AtJOX1, AtDMR6, and AtLBO1—which have been functionally characterized were aligned to identify conserved domains or motifs in 2OGD family. The result showed that the above 2OGD proteins had the HxD…H and RxS/T conserved motifs in OsGA2ox3 and OsDAO (<xref ref-type=\"fig\" rid=\"ijms-21-05344-f003\">Figure 3</xref>a), which recruit Fe(II) as a cofactor and co-substrate. Further, among the hormone biosynthetic and metabolism 2OGD proteins in <italic>Arabidopsis</italic>, rice, and tomato, SlGA20ox8, SlGA20ox9, SlGA20ox10, SlGA2ox12, and OsACO6 did not have an HxD…H motif, while SlGA20ox7, SlGA20ox10, OsGA2ox10, SlGA2ox12, and OsACO6 lacked an RxS/T motif (<xref ref-type=\"app\" rid=\"app1-ijms-21-05344\">Figures S3–S11</xref>), suggesting that these proteins do not have 2OGD biological activity.</p><p>However, what is the difference of protein structure among different hormone biosynthetic and metabolism 2OGD protein families? Next, we used MEME to identify conserved motifs in DOXC-family proteins of <italic>Arabidopsis</italic>, rice, and tomato (<xref ref-type=\"app\" rid=\"app1-ijms-21-05344\">Tables S4 and S5</xref>). The result showed that seven hormones biosynthetic and metabolism 2OGD protein families had uniquely conserved motifs—motifs 29, 40, 35, 45, 25, 44, and 38 were unique to the GA20ox, GA3ox, C19-GA2ox, C20-GA2ox, DAO, ACO, and JOX families, respectively (<xref ref-type=\"fig\" rid=\"ijms-21-05344-f003\">Figure 3</xref>b). However, no specific conserved motif was identified in the DMR6 or LBO families (<xref ref-type=\"app\" rid=\"app1-ijms-21-05344\">Table S4</xref>). Further, sequence alignments showed that SlGA20ox7, SlGA20ox8, SlGA20ox9, and SlGA20ox10 did not have motif 29 (<xref ref-type=\"app\" rid=\"app1-ijms-21-05344\">Figure S3</xref>), OsGA2ox10 did not have motif 35 (<xref ref-type=\"app\" rid=\"app1-ijms-21-05344\">Figure S5</xref>), and OsACO4 did not have motif 25 (<xref ref-type=\"app\" rid=\"app1-ijms-21-05344\">Figure S7</xref>), suggesting that these six proteins are not related to hormone biosynthesis or metabolism. In addition, SlGA2ox6 and SlGA2ox9 were truncated proteins with several missing amino acids in the N-terminal region (<xref ref-type=\"app\" rid=\"app1-ijms-21-05344\">Figure S5</xref>). In conclusion, from the result of multiple sequence alignment and motif composition, the results suggesting that SlDAO1-SlDAO3, SlGA20ox1-SlGA20ox6, SlGA3ox1-SlGA3ox4, SlGA2ox1-SlGA2ox5, SlGA2ox7-SlGA2ox8, SlGA2ox10-SlGA2ox11, SlACO1-SlACO7, SlJOX1-SlJOX3, SlDLO1-SlDLO2, and SlLBO1 may have the ability of hormone biosynthesis and metabolism in tomato.</p></sec><sec id=\"sec2dot4-ijms-21-05344\"><title>2.4. Expression of Hormone Biosynthetic and Metabolism 2OGD Genes in Tomato</title><p>To assess the function of identified hormone biosynthetic and metabolism 2OGD genes in tomato, we analyzed online transcriptome data of tomato roots, leaves, flowers, and developing fruits. Most genes exhibited distinct spatial and temporal expression patterns (<xref ref-type=\"fig\" rid=\"ijms-21-05344-f004\">Figure 4</xref>). Three <italic>GA3ox</italic> genes exhibited the highest expression in flowers, <italic>SlGA3ox1</italic> had moderate expression in roots and early developing fruits, and <italic>SlGA3ox2</italic> had moderate expression in leaves. No <italic>GA3ox</italic> gene was expressed during fruit ripening (<xref ref-type=\"fig\" rid=\"ijms-21-05344-f004\">Figure 4</xref>a). Regarding the <italic>GA20ox</italic> family, <italic>SlGA20ox1</italic>, <italic>SlGA20ox2</italic>, and <italic>SlGA20ox3</italic> were highly expressed in flowers and early developing fruits; <italic>SlGA20ox1</italic> and <italic>SlGA20ox3</italic> were also expressed in roots, and <italic>SlGA20ox1</italic> and <italic>SlGA20ox2</italic> were expressed in leaves. <italic>SlGA20ox4</italic> was specifically expressed in unopened flowers. Only <italic>SlGA20ox3</italic> was expressed during fruit ripening, during which its expression increased continuously (<xref ref-type=\"fig\" rid=\"ijms-21-05344-f004\">Figure 4</xref>b). Five GA2ox-family genes (<italic>SlGA2ox3</italic>, <italic>4</italic>, <italic>5</italic>, <italic>7</italic>, and <italic>10</italic>) showed high expression in roots, three (<italic>SlGA2ox2</italic>, <italic>3</italic>, and <italic>10</italic>) in leaves, and six (<italic>SlGA2ox1</italic>, <italic>2</italic>, <italic>4</italic>, <italic>5</italic>, <italic>7</italic>, and <italic>10</italic>) in flowers. In addition, four genes (<italic>SlGA2ox2</italic>, <italic>4</italic>, <italic>5</italic>, and <italic>7</italic>) had high expression in early developing fruits, which increased during fruit ripening (from the mature-green stage to the breaker stage) (<xref ref-type=\"fig\" rid=\"ijms-21-05344-f004\">Figure 4</xref>c). Among the <italic>DAO</italic> family, the expression of <italic>SlDAO1</italic> was high in ripening fruits, moderate in early fruits, and low in roots, leaves, and flowers. <italic>SlDAO2</italic> was expressed mainly in flowers and early fruits, while the expression of <italic>SlDAO3</italic> was negligible in all organs. Notably, <italic>SlDAO1</italic> expression increased significantly from the mature-green to the breaker stage, suggesting a role in fruit ripening (<xref ref-type=\"fig\" rid=\"ijms-21-05344-f004\">Figure 4</xref>d). The expression of the three <italic>JOX</italic>-family genes was highest in flowers, while that of <italic>SlJOX1</italic> and <italic>SlJOX2</italic> was moderate in roots, leaves, and early developing fruits, and <italic>SlJOX2</italic> was expressed in breaker fruits (<xref ref-type=\"fig\" rid=\"ijms-21-05344-f004\">Figure 4</xref>e). Regarding the <italic>ACO</italic> family, three genes (<italic>SlACO2</italic>, <italic>3</italic>, and <italic>4</italic>) were expressed in roots, two (<italic>SlACO4</italic> and <italic>5</italic>) in leaves, and five (<italic>SlACO1</italic>, <italic>2</italic>, <italic>3</italic>, <italic>4</italic>, and <italic>6</italic>) in flowers. Further, four genes (<italic>SlACO1</italic>, <italic>3</italic>, <italic>4</italic>, and <italic>6</italic>) had high expression in early developing fruits, and the expression of four other genes (<italic>SlACO1</italic>, <italic>3</italic>, <italic>5</italic>, and <italic>6</italic>) increased from mature-green to breaker fruit (<xref ref-type=\"fig\" rid=\"ijms-21-05344-f004\">Figure 4</xref>f). The <italic>DLO</italic>-family gene <italic>SlDLO1</italic> showed high expression in roots, leaves, flowers, and early fruits, and decreased expression in ripening fruits, while <italic>SlDLO2</italic> was expressed only in flowers and early fruits (<xref ref-type=\"fig\" rid=\"ijms-21-05344-f004\">Figure 4</xref>g). The only <italic>LBO</italic> gene in tomato, <italic>SlLBO1,</italic> was expressed mainly in roots and flowers, suggesting roles in root and flower development (<xref ref-type=\"fig\" rid=\"ijms-21-05344-f004\">Figure 4</xref>h). In conclusion, a variety of 2OGD hormone biosynthetic and metabolism genes play roles in organ development and fruit ripening in tomato.</p></sec><sec id=\"sec2dot5-ijms-21-05344\"><title>2.5. Expression of SlGA2ox and SlDAO Genes during Tomato Fruit Ripening</title><p>Ethylene is the major hormone regulating tomato fruit ripening, while auxin and GAs regulate fruit ripening via the ethylene pathway [<xref rid=\"B2-ijms-21-05344\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijms-21-05344\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijms-21-05344\" ref-type=\"bibr\">4</xref>]. The endogenous auxin and GA concentration was decreased during tomato fruit ripening (<xref ref-type=\"app\" rid=\"app1-ijms-21-05344\">Figure S12</xref>) [<xref rid=\"B3-ijms-21-05344\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijms-21-05344\" ref-type=\"bibr\">4</xref>], so we investigated the roles of auxin- and GA-degradation genes on tomato fruit ripening. Tomato pericarps at four stages (mature-green, breaker, yellow-ripening, and red-ripening) were collected from the tomato cultivars ‘Ai Ji Qiao Li’ and ‘Micro-Tom’ for qPCR analysis (<xref ref-type=\"fig\" rid=\"ijms-21-05344-f005\">Figure 5</xref>a). The <italic>SlDAO1</italic> expression level was higher than that of <italic>SlDAO2</italic> in Ai Ji Qiao Li and Micro-Tom during fruit ripening (<xref ref-type=\"fig\" rid=\"ijms-21-05344-f005\">Figure 5</xref>b,d). Notably, the expression of <italic>SlDAO1</italic> significantly increased, about two-fold, in Ai Ji Qiao Li, and tenfold in Micro-Tom from the mature-green to the breaker stage; its expression level remained elevated in the yellow- and red-ripening stages. However, <italic>SlDAO2</italic> expression did not significantly change from the mature-green to the breaker stage, and remained very low in the yellow- and red-ripening stages (<xref ref-type=\"fig\" rid=\"ijms-21-05344-f005\">Figure 5</xref>b,d). Thus, <italic>SlDAO1</italic>, rather than <italic>SlDAO2</italic>, likely plays a role in the transition from the mature-green to the breaker stage and subsequent fruit ripening. In addition, the expression of <italic>SlGA2ox2</italic> was 100-fold higher than that of <italic>SlGA2ox4</italic> and <italic>SlGA2ox5</italic>, while <italic>SlGA2ox4</italic> and <italic>SlGA2ox5</italic> expression was negligible in Ai Ji Qiao Li and Micro-Tom (<xref ref-type=\"fig\" rid=\"ijms-21-05344-f005\">Figure 5</xref>c,e). <italic>SlGA2ox2</italic> expression was increased threefold in Ai Ji Qiao Li and thirty-fold in Micro-Tom from the mature-green stage to the breaker stage, and decreased slightly in the yellow- and red-ripening stages (<xref ref-type=\"fig\" rid=\"ijms-21-05344-f005\">Figure 5</xref>c,e); this suggested that <italic>SlGA2ox2</italic> participates in tomato fruit ripening.</p></sec><sec id=\"sec2dot6-ijms-21-05344\"><title>2.6. Effects of Auxin, GA<sub>3</sub>, and Ethylene on the Expression of SlDAO1, SlDAO2, and SlGA2ox2</title><p>To study the response of <italic>SlDAO1</italic>, <italic>SlDAO2</italic>, and <italic>SlGA2ox2</italic> to auxin, GAs, and ethylene, we treated Micro-Tom mature-green fruits with IAA, GA<sub>3</sub>, and ethylene, and analyzed their expression after 2 and 4 days. Consistent with previous reports, IAA and GA<sub>3</sub> delayed tomato fruit ripening (<xref ref-type=\"fig\" rid=\"ijms-21-05344-f006\">Figure 6</xref>a). Further, the expression of <italic>SlDAO1</italic> was significantly induced by IAA, but was unaffected by GA<sub>3</sub> and ethylene at 2 and 4 days, while <italic>SlDAO2</italic> expression was not significantly affected in auxin-, GA-, or ethylene-treated mature-green fruits (<xref ref-type=\"fig\" rid=\"ijms-21-05344-f006\">Figure 6</xref>b). In addition, <italic>SlGA2ox2</italic> showed higher expression in GA<sub>3</sub>-treated fruits, but similar expression in IAA- and ethylene-treated fruits, compared to the control (<xref ref-type=\"fig\" rid=\"ijms-21-05344-f006\">Figure 6</xref>b). In conclusion, the expression of <italic>SlDAO1</italic> and <italic>SlGA2ox2</italic> was induced by auxin and GAs, respectively, suggesting that <italic>SlDAO1</italic> and <italic>SlGA2ox2</italic> are responsible for regulating auxin and GA catabolism during tomato fruit ripening.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec3-ijms-21-05344\"><title>3. Discussion</title><sec id=\"sec3dot1-ijms-21-05344\"><title>3.1. Identification of Hormone Biosynthetic and Metabolism Genes from 2OGD Family</title><p>The 2OGD superfamily is widespread in microorganisms, fungi, mammals, and plants. In plants, 2OGD proteins are classified as DOXA, DOXB, and DOXC [<xref rid=\"B14-ijms-21-05344\" ref-type=\"bibr\">14</xref>]. DOXA proteins are involved in the oxidative demethylation of alkylated nucleic acids and histones, while DOXB proteins are involved in proline 4-hydroxylation in cell-wall protein synthesis, and DOXC proteins in the metabolism of various phytochemicals, such as phytohormones and flavonoids. The number of 2OGDs of the DOXA and DOXB classes is constant across plant species, whereas that of the DOXC class is extremely variable, suggesting that the latter has diversified during the evolution of land plants. The vast majority of 2OGDs from land plants are of the DOXC class, including all hormone biosynthesis- and metabolism-related proteins of the 2OGD family. In this study, the number and classifications of DOXC hormone biosynthesis- and metabolism-related proteins were consistent with the report by Kawal et al. [<xref rid=\"B14-ijms-21-05344\" ref-type=\"bibr\">14</xref>]. DOXC proteins are involved in the biosynthesis and metabolism of the phytohormones auxin, GAs, ethylene, JA, SA, and SLs, which play important roles in plant growth and development. Furthermore, the number of DOXC hormone biosynthetic and metabolism genes increases from ancient lower land plants to higher plants, consistent with the high complexity and diversity—and specialized metabolism—of higher plants.</p><p>Although the 2OGD superfamily is highly diverse, structural studies suggest that its members have a highly conserved Fe(II) binding HxD/E…H triad motif and a less conserved 2OG C5 carboxy group binding motif (RxS/T) [<xref rid=\"B13-ijms-21-05344\" ref-type=\"bibr\">13</xref>]. In this study, forty-three hormone biosynthetic and metabolism proteins of the DOXC family were identified in tomato, but five SlGA20ox7, SlGA20ox8, SlGA20ox9, SlGA20ox10 and SlGA2ox12) lacked the HxD/E…H or RxS/T motif (<xref ref-type=\"app\" rid=\"app1-ijms-21-05344\">Figures S3 and S6</xref>), suggesting a lack of 2OGD activity. In addition, we identified family-specific conserved motifs in DAOs, GA20oxs, GA3oxs, C19-GA2oxs, C20-GA2oxs, ACOs, and JOXs (<xref ref-type=\"fig\" rid=\"ijms-21-05344-f003\">Figure 3</xref>b); however, their function was unclear. A MdACO1 protein with mutated conserved Lys296 and Arg299 residues in the C-terminal helix retained only 15–30% of the activity of the wild-type, possibly because these two residues are important for ACO activity and may be involved in binding bicarbonate, the unique activator of ACOs [<xref rid=\"B18-ijms-21-05344\" ref-type=\"bibr\">18</xref>]. Notably, these two amino acids are located in the ACO-specific conserved motif identified in this study (<xref ref-type=\"app\" rid=\"app1-ijms-21-05344\">Figure S7</xref>). Therefore, the subfamily-specific conserved motifs may play important roles in the functional differentiation of 2OGD subfamilies.</p></sec><sec id=\"sec3dot2-ijms-21-05344\"><title>3.2. Functional Analysis of Hormone Biosynthetic and Metabolism Genes in 2OGD Family</title><p>GAs, ethylene, auxin, JA, SA, and SLs regulate many aspects of plant growth and development, and the response to stresses. Several 2OGD genes involved in hormone biosynthesis and metabolism have been functionally analyzed in <italic>Arabidopsis</italic> and rice, and these genes participate in the development of roots, stems, flowers, fruits, and seeds. In tomato, the <italic>SlGA20oxs</italic> GA-biosynthetic genes, particularly <italic>SlGA3oxs</italic>, which function in the final step of GA biosynthesis, were mainly expressed in tomato roots, leaves, flowers, and early developing fruits, suggesting that GAs play a role in the development of these tissues/organs (<xref ref-type=\"fig\" rid=\"ijms-21-05344-f004\">Figure 4</xref>a,b). Consistently, RNAi-mediated silencing of <italic>SlGA20ox1</italic>, <italic>SlGA20ox2</italic>, or <italic>SlGA20ox3</italic> affected the development of tomato stems, leaves, fruit, and seeds [<xref rid=\"B19-ijms-21-05344\" ref-type=\"bibr\">19</xref>], and inhibitors of GA biosynthesis decreased tomato fruit growth and fruit set; also, exogenous GA<sub>3</sub> induced parthenocarpic fruits [<xref rid=\"B20-ijms-21-05344\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijms-21-05344\" ref-type=\"bibr\">21</xref>]. The <italic>SlGA2oxs</italic> GA-metabolism proteins also play key roles in regulating endogenous GA levels. The silencing of <italic>SlGA2ox1-SlGA2ox5</italic> increased the active GA<sub>4</sub> content, induced parthenocarpic fruits, and inhibited lateral branching in tomato plants [<xref rid=\"B22-ijms-21-05344\" ref-type=\"bibr\">22</xref>]. In this study, the newly identified genes <italic>SlGA2ox7</italic> and <italic>SlGA2ox10</italic>, mainly expressed in roots, leaves, flowers, and early developing fruits (<xref ref-type=\"fig\" rid=\"ijms-21-05344-f004\">Figure 4</xref>c), had the same conserved motif as <italic>SlGA2ox1</italic> to <italic>SlGA2ox5</italic> (<xref ref-type=\"app\" rid=\"app1-ijms-21-05344\">Figure S5</xref>), suggesting a role for <italic>SlGA2ox7</italic> and <italic>SlGA2ox10</italic> in the metabolism of GAs during the development of these tissues/organs.</p><p>Although auxin regulates the growth and development of various plant tissues and organs, studies of auxin in tomato have focused on fruit set and development. Exogenous auxin treatment could induce parthenocarpic fruits, and altering the expression of auxin response genes also affected tomato fruit set and development [<xref rid=\"B21-ijms-21-05344\" ref-type=\"bibr\">21</xref>,<xref rid=\"B23-ijms-21-05344\" ref-type=\"bibr\">23</xref>]. <italic>DAO</italic>-family proteins irreversibly degrade auxin, and a <italic>dao</italic> mutant in rice displayed defective pollen fertility and seed development [<xref rid=\"B7-ijms-21-05344\" ref-type=\"bibr\">7</xref>]; meanwhile, a <italic>dao1</italic> mutant in <italic>Arabidopsis</italic> displayed larger cotyledons, increased lateral root density, and elongated pistils [<xref rid=\"B24-ijms-21-05344\" ref-type=\"bibr\">24</xref>]. <italic>DAO</italic> has three homologs in tomato; the expression of <italic>SlDAO2</italic> was higher in flowers and early developing fruits compared to <italic>SlDAO1</italic> and <italic>SlDAO3</italic>, suggesting a role in regulating the auxin level for fruit set and development (<xref ref-type=\"fig\" rid=\"ijms-21-05344-f004\">Figure 4</xref>d). Ethylene plays important roles in fruit set and development [<xref rid=\"B25-ijms-21-05344\" ref-type=\"bibr\">25</xref>], especially fruit ripening, likely due to high expression of the ethylene-biosynthetic genes <italic>SlACO1</italic>, <italic>SlACO3</italic>, and <italic>SlACO6</italic> in flower, early developing fruits, and ripening fruits (<xref ref-type=\"fig\" rid=\"ijms-21-05344-f004\">Figure 4</xref>f). Other <italic>ACO</italic> genes (<italic>SlACO2</italic> and <italic>SlACO4</italic>) may contribute to ethylene production for root and flower development. In addition, three JA-metabolism <italic>SlJOX</italic> genes showed high expression in tomato flowers (<xref ref-type=\"fig\" rid=\"ijms-21-05344-f004\">Figure 4</xref>e), indicating roles in regulating JA homeostasis for flowering [<xref rid=\"B26-ijms-21-05344\" ref-type=\"bibr\">26</xref>]. <italic>AtDMR6,</italic> the product of which degrades salicylic acid, was involved in plant growth and resistance to pathogens, and the <italic>dmr6</italic> mutant displayed smaller size, early senescence, and a loss of susceptibility to <italic>Pseudomonas syringae</italic> pv tomato DC3000 [<xref rid=\"B10-ijms-21-05344\" ref-type=\"bibr\">10</xref>]. In tomato, the homolog <italic>SlDLO1</italic> was highly expressed in roots, leaves, flowers, and fruits (<xref ref-type=\"fig\" rid=\"ijms-21-05344-f004\">Figure 4</xref>g), and CRISPR-Cas9 mediated the mutagenesis of <italic>SlDLO1</italic> in tomato conferred broad-spectrum disease resistance; however, vegetative growth and development were not significantly affected, and its role in reproductive organs was not investigated [<xref rid=\"B27-ijms-21-05344\" ref-type=\"bibr\">27</xref>]. <italic>SlDLO2</italic> is highly expressed only in flowers and fruits, suggesting roles in regulating the SA level in reproductive organs. SLs are plant hormones that regulate plant root and branch development, as well as stress tolerance [<xref rid=\"B28-ijms-21-05344\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijms-21-05344\" ref-type=\"bibr\">29</xref>]. High expression of SL biosynthetic and signaling genes in tomato or strawberry fruit indicated roles in fruit development [<xref rid=\"B30-ijms-21-05344\" ref-type=\"bibr\">30</xref>]. <italic>LBO</italic> acts in the final stages of SL biosynthesis to produce active SLs in <italic>Arabidopsis</italic>, and its homolog <italic>SlLBO1</italic> is only expressed in roots and flowers (<xref ref-type=\"fig\" rid=\"ijms-21-05344-f004\">Figure 4</xref>h). This suggests that SLs are synthesized in tomato roots and flowers, but does not mean that SLs have no effect on fruit development; they could be transported to fruit from other organs or tissues.</p></sec><sec id=\"sec3dot3-ijms-21-05344\"><title>3.3. SlGA2ox2 and SlDAO1 May Play a Role in GA and Auxin Metabolism for Normal Ripening of Tomato Fruits</title><p>Tomato is a model plant for studying the ripening of climacteric fruits, and ethylene regulates tomato fruit ripening. In this study, exogenous GA<sub>3</sub> treatment of tomato fruits at the mature-green stage delayed fruit ripening, while overexpression of the GA catabolism gene <italic>SlGA2ox1</italic> specifically in tomato fruits led to early ripening [<xref rid=\"B4-ijms-21-05344\" ref-type=\"bibr\">4</xref>]. We have previously shown that GAs play negative roles in the ethylene pathway by inhibiting the expression of ethylene biosynthetic genes (<italic>SlACS2</italic>, <italic>SlACS4,</italic> and <italic>SlACO1</italic>) and signaling genes (<italic>SlETRs</italic> and <italic>SlEINs</italic>) [<xref rid=\"B4-ijms-21-05344\" ref-type=\"bibr\">4</xref>]. Therefore, the concentration of GAs in fruits influences fruit ripening in tomato. In plants, the GA level is regulated by the balance between biosynthesis and metabolism. GA20oxs and GA3oxs catalyze the rate-limiting step of active GA biosynthesis, and GA2oxs converts bioactive GAs or their immediate precursors into inactive forms. In this study, although the expression of one <italic>GA20ox</italic> gene (<italic>SlGA20ox3</italic>) increased from the mature-green to the breaker stage (<xref ref-type=\"fig\" rid=\"ijms-21-05344-f004\">Figure 4</xref>b), no <italic>GA3ox</italic> genes, which encode enzymes that catalyze the last step of GA biosynthesis, were expressed (<xref ref-type=\"fig\" rid=\"ijms-21-05344-f004\">Figure 4</xref>a), suggesting the absence of GA biosynthesis in mature-green and breaker fruits. Further, the expression of three GA-metabolism genes (<italic>SlGA2ox2</italic>, <italic>SlGA2ox4</italic>, and <italic>SlGA2ox5</italic>) was increased, and that of <italic>SlGA2ox2</italic> was highest, and dramatically increased, from the mature-green to the breaker stage (<xref ref-type=\"fig\" rid=\"ijms-21-05344-f004\">Figure 4</xref>c). It has been reported that the concentrations of endogenous active GAs (GA<sub>1</sub> and GA<sub>4</sub>) in the fruit pericarp of tomato decrease significantly from the mature -green to the breaker stage (<xref ref-type=\"app\" rid=\"app1-ijms-21-05344\">Figure S12</xref>) [<xref rid=\"B4-ijms-21-05344\" ref-type=\"bibr\">4</xref>]. Therefore, we speculate that <italic>SlGA2ox2</italic> may be vital for GA metabolism from the mature-green to the breaker stage, and the reduced GA level caused by the increase in <italic>SlGA2ox2</italic> expression promotes tomato fruit ripening.</p><p>Auxin also negatively regulates tomato fruit ripening. Exogenous applications of IAA reduced expression of ethylene biosynthetic and consequently reduced ethylene production, and also the ethylene signaling genes, resulting in delayed tomato fruit ripening [<xref rid=\"B1-ijms-21-05344\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijms-21-05344\" ref-type=\"bibr\">2</xref>]. The concentration of endogenous auxin in tomato fruit pericarps is reduced from the mature-green to the breaker stage (<xref ref-type=\"app\" rid=\"app1-ijms-21-05344\">Figure S12</xref>) [<xref rid=\"B3-ijms-21-05344\" ref-type=\"bibr\">3</xref>]. In plants, auxin is synthesized by tryptophan (Trp)-dependent and -independent pathways [<xref rid=\"B31-ijms-21-05344\" ref-type=\"bibr\">31</xref>]. Our knowledge of the genes and intermediates of the Trp-independent pathway is limited, but the complete Trp-dependent pathway has been established. YUCCA (YUC) family proteins function in the final step of Trp-dependent auxin biosynthesis, and play a crucial role in auxin biosynthesis in various plant species. In tomato, six <italic>YUC</italic> genes were identified, the transcript levels of five of which were negligible, whereas one <italic>YUC</italic> gene (<italic>ToFZY4</italic>) displayed high expression during ripening of tomato fruit [<xref rid=\"B32-ijms-21-05344\" ref-type=\"bibr\">32</xref>]. It is not clear why the auxin concentration was decreased, but the expression of a key gene in auxin biosynthesis was increased in ripening tomato fruit. One explanation for this is that there is a change from the Trp-dependent to the Trp-independent pathway for auxin biosynthesis between the mature and red-ripe stages of tomato fruits [<xref rid=\"B33-ijms-21-05344\" ref-type=\"bibr\">33</xref>], and <italic>ToFZY4</italic> may have a novel function related to tomato fruit ripening rather than auxin biosynthesis. Auxin can be deactivated by conjugation to amino acids, or by chemical oxidation. Conjugation of IAA to amino acids is catalyzed by <italic>GH3</italic>-family proteins and yields, for instance, indole-3-acetic acid aspartic acid (IAA-Asp) and indole-3-acetic acid glutamic acid (IAA-Glu). The chemical oxidation of auxin is catalyzed by DAO-family proteins to produce oxIAA. In tomato, 24 <italic>GH3</italic> genes were identified, only 4 (<italic>SlGH3-1</italic>, <italic>SlGH3-2</italic>, <italic>SlGH3-5</italic>, and <italic>SlGH3-24</italic>) of which showed high expression during fruit ripening [<xref rid=\"B3-ijms-21-05344\" ref-type=\"bibr\">3</xref>]. Silencing of <italic>SlGH3-2</italic> in tomato increased the auxin level and reduced lycopene accumulation in ripening fruit, suggesting that <italic>SlGH3-2</italic> plays a role in deactivating free auxin to maintaining normal ripening of tomato fruit [<xref rid=\"B3-ijms-21-05344\" ref-type=\"bibr\">3</xref>]. However, oxIAA is a major IAA catabolite, where up to 10–100 folds more oxIAA than the major IAA conjugates IAA-Glu and IAA-Asp was detected in <italic>Arabidopsis</italic> [<xref rid=\"B34-ijms-21-05344\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijms-21-05344\" ref-type=\"bibr\">35</xref>]. More importantly, oxIAA oxidized by DAO is biologically inactive, and is formed rapidly and irreversibly in plant tissues [<xref rid=\"B34-ijms-21-05344\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijms-21-05344\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijms-21-05344\" ref-type=\"bibr\">36</xref>]. <italic>DAO</italic> is likely involved in maintaining the basal level of active auxin under normal growth conditions, while <italic>GH3</italic> functions in the response to various environmental factors [<xref rid=\"B37-ijms-21-05344\" ref-type=\"bibr\">37</xref>]. In this study, we identified three DAO genes in tomato. <italic>SlDAO3</italic> had lost some sequences in the N-terminal (<xref ref-type=\"app\" rid=\"app1-ijms-21-05344\">Figure S8</xref>), suggesting that it may be not involved in IAA degradation. <italic>SlDAO2</italic> expression was negligible, but that of <italic>SlDAO1</italic> was high and increased from mature-green to breaker fruits (<xref ref-type=\"fig\" rid=\"ijms-21-05344-f004\">Figure 4</xref>d); moreover, it was significantly induced by auxin in mature-green fruits (<xref ref-type=\"fig\" rid=\"ijms-21-05344-f006\">Figure 6</xref>b). These results implicate <italic>SlDAO1</italic>, rather than <italic>SlDAO2</italic> and <italic>SlDAO3</italic>, in auxin metabolism from the mature-green to the breaker stage during tomato ripening. In addition, the reduction in auxin level caused by the increase in <italic>SlDAO1</italic> expression may play an important role in maintaining normal ripening of tomato fruit.</p></sec></sec><sec id=\"sec4-ijms-21-05344\"><title>4. Materials and Methods</title><sec id=\"sec4dot1-ijms-21-05344\"><title>4.1. Identification and Phylogenetic Analysis of Hormone Biosynthesis and Metabolism Related DOXC Proteins</title><p>To find proteins belonging to DOXC family, we used 2OG-FeII_Oxy (PF03171) domain as query in hmmsearch BLAST of <italic>Arabidopsis</italic>, rice, and tomato protein databases downloaded from JGI [<xref rid=\"B38-ijms-21-05344\" ref-type=\"bibr\">38</xref>]. All sequences (length ≥ 100 aa) with an E-value cutoff 1 × 10<sup>−4</sup> were retrieved. The obtained sequences were submitted to Pfam [<xref rid=\"B39-ijms-21-05344\" ref-type=\"bibr\">39</xref>] and SMART [<xref rid=\"B40-ijms-21-05344\" ref-type=\"bibr\">40</xref>] to verify the existence of 2OG-FeII_Oxy domain. In order to better understand the relationship among all members of the DOXC and identify proteins involved in hormone biosynthesis and metabolism, we then used all verified protein sequences to construct a phylogenetic tree by MEGA6 with Maximum likelihood. The best model JTT + F was selected by Model Generator software. According to hormone biosynthesis and metabolism related genes with known function in <italic>Arabidopsis</italic> and rice, all proteins which clustered into hormone biosynthesis and metabolism related protein subfamilies were selected to construct a new phylogenetic tree.</p></sec><sec id=\"sec4dot2-ijms-21-05344\"><title>4.2. Chromosomal Location and Synteny Analysis</title><p>Genome annotation files were downloaded from the <italic>Arabidopsis</italic>, rice, and tomato databases to obtain chromosomal location information of these hormone biosynthetic and metabolism genes, then the Circos software was used to draw location pictures. A method similar to that developed for the Plant Genome Duplication Database (PGDD) [<xref rid=\"B41-ijms-21-05344\" ref-type=\"bibr\">41</xref>] was used to identify syntenic blocks in <italic>Arabidopsis</italic>, rice, and tomato. Potential homologous sequences were initially identified by BLASTP (E-value < 1 × 10<sup>−5</sup>, top 5 matches). MCScanX was used for synteny analysis [<xref rid=\"B42-ijms-21-05344\" ref-type=\"bibr\">42</xref>]. Additionally, MCScanX was further used to detect duplicate types of these biosynthetic and metabolism genes in tomato.</p></sec><sec id=\"sec4dot3-ijms-21-05344\"><title>4.3. Multiple Sequence Alignment and Motif Composition Analysis</title><p>To detect the HxD/E…H and RxS/T motifs, multiple sequence alignments were performed by submitting protein sequences to ClustalW with the default parameters in BioEdit software. Motif composition analysis was performed by submitting protein sequences to MEME [<xref rid=\"B43-ijms-21-05344\" ref-type=\"bibr\">43</xref>] with the following parameters: the maximum number of motifs was 50 and the maximum motif length was 15 amino acids.</p></sec><sec id=\"sec4dot4-ijms-21-05344\"><title>4.4. Expression Analysis</title><p>Transcriptome datasets of different tomato organs were downloaded from Tomato Functional Genomics Database [<xref rid=\"B44-ijms-21-05344\" ref-type=\"bibr\">44</xref>]. RPKM values of related genes were transformed in log<sub>2</sub> level, and a heatmap was shown using MeV4.8 software (Dana-Farber Cancer Institute, Boston, MA, USA).</p></sec><sec id=\"sec4dot5-ijms-21-05344\"><title>4.5. Plant Materials and Hormone Treatments</title><p>Two tomato cultivars Ai Ji Qiao Li grown in greenhouse and Micro-Tom grown in climate chamber were chosen as plant materials. The fruit was collected at four different ripening stages: mature-green (Mg), breaker (Br), yellow-ripening (Yr), and red-ripening (Rr). The fruit pericarp sample without placenta and seeds was collected and then immediately frozen in liquid nitrogen prior to storage at −80 °C until RNA extraction.</p><p>Tomato cultivars Micro-Tom grown in climate chamber was used for hormone treatments of fruits. Flowers were tagged at the date of pollination. After 36 days, mature-green fruits on the plants were injected with 0.1 mM IAA, 0.1 mM GA<sub>3</sub>, and 0.1 mM ethephon, respectively, distilled water was used as the control. The amount of injection was about 50 µL per fruit. Twelve fruits for each treatment were performed. The fruit pericarp without placenta and seeds was collected at two days and four days after treatments, and were immediately frozen in liquid nitrogen, and then stored at −80 °C. Plant growth conditions was: 16-h light (25 °C)/8-h dark (18 °C) photoperiod cycle and 65% relative humidity. In addition, detached mature-green fruits were injected with 0.1 mM IAA and 0.1 mM GA<sub>3</sub>, respectively, distilled water was used as the control. Then the fruit was placed under dark at 25 °C and 90% relative humidity, photos were taken after eight days.</p></sec><sec id=\"sec4dot6-ijms-21-05344\"><title>4.6. RNA Extraction and qPCR Analysis of Selected Genes</title><p>Total RNA was extracted with a modified CTAB method [<xref rid=\"B4-ijms-21-05344\" ref-type=\"bibr\">4</xref>]. cDNA library was generated by Primerscript RT reagent Kit with gDNA Erase (Takara, Beijing, China) according to the manufacturer’s protocol. qPCR was carried out using SYBR Premix Ex Taq II (Takara, Beijing, China). Primer sequences were listed in <xref ref-type=\"app\" rid=\"app1-ijms-21-05344\">Table S6</xref>. Three biologicals with triplicates were performed and results were analyzed using the 2<sup>−ΔCT</sup> method. <italic>Actin</italic> gene (gene ID: Solyc11g005330) was used as the reference.</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec5-ijms-21-05344\"><title>5. Conclusions</title><p>We have identified 43 hormone biosynthetic and metabolism genes of nine subfamilies of the 2OGD family, which were related to GAs, ethylene, auxin, JA, SA, and SLs in tomato. The subfamily-specific conserved motifs identified in this study might play roles in the functional differentiation of 2OGD subfamilies, and the different expression profiles suggest that these genes play diverse roles in tomato organ growth and development. Especially, the expression levels of the auxin-degradation gene <italic>SlDAO1</italic> and the GA-degradation gene <italic>SlGA2ox2</italic> were significantly increased from the mature-green to the breaker stage during tomato fruit ripening, accompanied by decreased endogenous IAA and GAs levels. In addition, the expression of <italic>SlDAO1</italic> and <italic>SlGA2ox2</italic> was increased by IAA and GA<sub>3</sub>, respectively, indicating that <italic>SlDAO1</italic> and <italic>SlGA2ox2</italic> may be responsible for reducing IAA and GA concentrations to maintain normal ripening of tomato fruit.</p></sec></body><back><ack><title>Acknowledgments</title><p>We thank Helin Tan for his support for the experiment in this study.</p></ack><app-group><app id=\"app1-ijms-21-05344\"><title>Supplementary Materials</title><p>Supplementary materials can be found at <uri xlink:href=\"https://www.mdpi.com/1422-0067/21/15/5344/s1\">https://www.mdpi.com/1422-0067/21/15/5344/s1</uri>, Figure S1: Phylogenetic tree of DOXC family proteins identified in <italic>Arabidopsis</italic>, rice, and tomato, Figure S2: Chromosomal location and duplication analysis of hormone biosynthetic and metabolism genes in tomato, Figure S3: Sequence alignment of GA20ox group proteins, Figure S4: Sequence alignment of GA3ox group proteins, Figure S5: Sequence alignment of GA2ox (C19) group proteins, Figure S6: Sequence alignment of GA2ox (C20) group proteins, Figure S7: Sequence alignment of ACO group proteins, Figure S8: Sequence alignment of DAO group proteins, Figure S9: Sequence alignment of Jox group proteins, Figure S10: Sequence alignment of DMR6 group proteins, Figure S11: Sequence alignment of LBO group proteins, Figure S12: The concentrations of GA<sub>1</sub>, GA<sub>4</sub>, and IAA decrease from the mature-green (Mg) to the breaker (Br) stage, Table S1: Identification and characterization of hormone biosynthetic and metabolism proteins in <italic>Arabidopsis</italic>, rice, and tomato, Table S2: Microsynteny relationships of hormone biosynthetic and metabolism genes in <italic>Arabidopsis</italic>, rice, and tomato, Table S3: Duplication modes of hormone biosynthetic and metabolism genes in tomato, Table S4: Motif composition in DOXC class of 2OGD family in <italic>Arabidopsis</italic>, rice, and tomato, Table S5: Motif sequence identified by MEME tools in <italic>Arabidopsis</italic>, rice, and tomato DOXC class of 2OGDs, Table S6: Primers used in this study.</p><supplementary-material content-type=\"local-data\" id=\"ijms-21-05344-s001\"><media xlink:href=\"ijms-21-05344-s001.zip\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>Data curation, X.Y. and H.W.; formal analysis, Q.D.; funding acquisition, H.W.; investigation, Q.D. and F.W.; methodology, Q.D. and J.X.; software, F.W. and J.F.; supervision, H.W.; validation, H.W.; visualization, H.C.; writing—original draft, Q.D. and H.W.; writing—review and editing, Y.L. and H.W. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was funded by the National Key R&D Program of China (2016YFD0100506); The Special Financial Grant Program from the China Postdoctoral Science Foundation [2016T90471]; The General Financial Grant Program from the China Postdoctoral Science Foundation [2015M581812]; The Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><glossary><title>Abbreviations</title><array orientation=\"portrait\"><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GAs </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Gibberellins</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">JA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Jasmonate</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Salicylic acid</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SL</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Strigolactone</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GA20ox</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GA20-oxidase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GA3ox</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GA3-oxidase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GA2ox</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GA2-oxidase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DAO</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dioxygenase for Auxin Oxidation</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ACO</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1-aminocyclopropane-1-carboxylic acid oxidase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">JOX</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">JASMONATE-INDUCED OXYGENASE</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DMR6</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Downy Mildew Resistant6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DLO</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DMR6-LIKE OXYGENASE</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">LBO</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">LATERAL BRANCHING OXIDOREDUCTASE</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">FPKM</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fragments Per Kilobase Per Million</td></tr></tbody></array></glossary><ref-list><title>References</title><ref id=\"B1-ijms-21-05344\"><label>1.</label><element-citation publication-type=\"journal\"><person-group 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The phylogenetic tree was constructed by MEGA6 with Maximum likelihood.</p></caption><graphic xlink:href=\"ijms-21-05344-g001\"/></fig><fig id=\"ijms-21-05344-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Synteny analysis of hormone biosynthetic and metabolism 2-oxoglutarate-dependent dioxygenase (2OGD) genes among <italic>Arabidopsis</italic>, rice, and tomato. Chromosome numbers of <italic>Arabidopsis</italic> (At), rice (Os), and tomato (Sl) are indicated on the inner side. Red, green, and blue colors represent <italic>Arabidopsis</italic>, rice, and tomato chromosomes. Gene pairs with a collinear relationship are joined by lines. Red lines represent collinear pairs between <italic>Arabidopsis</italic> and tomato, blue lines represent collinear pairs between <italic>Arabidopsis</italic> and rice, green lines represent collinear pairs between rice and tomato.</p></caption><graphic xlink:href=\"ijms-21-05344-g002\"/></fig><fig id=\"ijms-21-05344-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Sequence alignment and conserved motif analysis of functionally characterized hormone biosynthetic and metabolism 2OGD proteins. (<bold>a</bold>) Sequence alignment of functionally characterized hormone biosynthetic and metabolism 2OGD proteins in <italic>Arabidopsis</italic>, rice, tomato. The putative His-X-Asp-(X)<sub>n</sub>-His (HxD…H) and Arg-X-Ser/Thr (RxS/T) motif locations are highlighted in red and black dotted boxes, respectively. (<bold>b</bold>) The motif composition of functionally characterized hormone biosynthetic and metabolism 2OGD proteins. The motif enclosed by red boxes is specific motifs in each group.</p></caption><graphic xlink:href=\"ijms-21-05344-g003\"/></fig><fig id=\"ijms-21-05344-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Expression pattern of hormone biosynthetic and metabolism 2OGD genes in tomato. (<bold>a</bold>–<bold>h</bold>) Expression pattern of <italic>SlGA3ox</italic>, <italic>SlGA20ox</italic>, <italic>SlGA2ox</italic>, <italic>SlDAO</italic>, <italic>SlJOX</italic>, <italic>SlACO</italic>, <italic>SlDLO</italic>, and <italic>SlLBO</italic> group genes. Gray boxes represent the expression of genes was undetectable. Unopened flowers (UF); Opened flowers (F); 1 cm fruits (1 cm F); 2 cm fruits (2 cm F); 3 cm fruits (3 cm F); mature-green fruits (Mg F); breaker fruits (Br F); breaker+10 days’ fruits (Br+10 F); roots (R); leaves (L). The detailed descriptions of the stages and tissues were on the website (<uri xlink:href=\"http://ted.bti.cornell.edu/cgi-bin/TFGD/digital/home.cgi\">http://ted.bti.cornell.edu/cgi-bin/TFGD/digital/home.cgi</uri>).</p></caption><graphic xlink:href=\"ijms-21-05344-g004\"/></fig><fig id=\"ijms-21-05344-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>Expression analysis of <italic>SlDAOs</italic> and <italic>SlGA2oxs</italic> genes during tomato fruit ripening in the pericarp. (<bold>a</bold>) Different ripening stages of Ai Ji Qiao Li and Micro-Tom. (<bold>b</bold>) Expression levels of <italic>SlDAOs</italic> in Ai Ji Qiao Li. (<bold>c</bold>) Expression levels of <italic>SlGA2ox</italic> genes in Ai Ji Qi Li. (<bold>d</bold>) Expression levels of <italic>SlDAOs</italic> in Micro-Tom. (<bold>e</bold>) Expression levels of <italic>SlGA2ox</italic> genes in Micro-Tom. Mg: mature-green; Br: breaker; Yr: yellow-ripening; Rr: red-ripening. * The asterisk at the top of each column indicates a significant difference compared to Mg fruits at <italic>p</italic> < 0.05 (<italic>n</italic> = 3) by students t-test.</p></caption><graphic xlink:href=\"ijms-21-05344-g005\"/></fig><fig id=\"ijms-21-05344-f006\" orientation=\"portrait\" position=\"float\"><label>Figure 6</label><caption><p>Expression analysis of <italic>SlDAO1</italic>, <italic>SlDAO2</italic>, and <italic>SlGA2ox2</italic> after auxin, GA<sub>3</sub>, and ethylene treatments. (<bold>a</bold>) Photos of mature-green fruits after indole-3-acetic acid (IAA) and gibberellin 3 (GA<sub>3</sub>) treatment, respectively. (<bold>b</bold>) Expression analysis of <italic>SlDAO1, SlDAO2</italic>, and <italic>SlGA2ox2</italic> after auxin, GA<sub>3</sub>, and ethylene treatments. * The asterisk at the top of each column indicates a significant difference at <italic>p</italic> < 0.05 (<italic>n</italic> = 3) by students t-test.</p></caption><graphic xlink:href=\"ijms-21-05344-g006\"/></fig></floats-group></article>\n"
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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32722479</article-id><article-id pub-id-type=\"pmc\">PMC7432024</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155365</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05365</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Parents’ and Peers’ Autonomy Support and Exercise Intention for Adolescents: Integrating Social Factors from the Self-Determination Theory and the Theory of Planned Behaviour</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Sicilia</surname><given-names>Alvaro</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05365\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Águila</surname><given-names>Cornelio</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05365\">2</xref><xref rid=\"c1-ijerph-17-05365\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Posse</surname><given-names>Magalí</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05365\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Alcaraz-Ibáñez</surname><given-names>Manuel</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05365\">1</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05365\"><label>1</label>Health Research Centre and Department of Education, University of Almería, 04120 Almería, Spain; <email>asicilia@ual.es</email> (A.S.); <email>m.alcaraz@ual.es</email> (M.A.-I.)</aff><aff id=\"af2-ijerph-17-05365\"><label>2</label>Department of Education, University of Almería, 04120 Almería, Spain; <email>magaliposse@hotmail.com</email></aff><author-notes><corresp id=\"c1-ijerph-17-05365\"><label>*</label>Correspondence: <email>cornelio@ual.es</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>25</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5365</elocation-id><history><date date-type=\"received\"><day>04</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>21</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Based on the theory of planned behaviour and self-determination theory, the objective of the present study was to analyse the relationship between (i) parents’ and peers’ autonomy support, and (ii) exercise intention in adolescents, while also considering the mediating role of attitude, control, subjective norms, and descriptive norms. A total of 428 secondary school students, aged between 13 and 19 years old (<italic>M<sub>age</sub></italic> = 15.30, <italic>SD</italic> = 1.15), filled in a questionnaire assessing the variables of interest. The relationships between the study variables were examined through a mediation model with bootstrapping technique (20,000 samples) using Mplus v. 7 software. The results showed that the perception of parents’ autonomy support was positively and statistically significant associated with exercise intention; this occurring indirectly through attitude and control both in boys and girls, as well as through subjective norms in the case of girls. Conversely, the perception of peers’ autonomy support was positively and statistically significant associated with exercise intention; this occurring directly both in boys and girls, as well as indirectly through attitude in the case of girls. These findings suggest that, by involving a form of pressure (i.e., subjective/descriptive norms), perceptions of autonomy support may play a more important role than other forms of social influence in predicting exercise intention in adolescents.</p></abstract><kwd-group><kwd>exercise</kwd><kwd>adolescence</kwd><kwd>autonomy support</kwd><kwd>descriptive norms</kwd><kwd>subjective norms</kwd><kwd>mediation model</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05365\"><title>1. Introduction</title><p>There is evidence regarding the benefits of regular exercise on an individual’s physical and mental health [<xref rid=\"B1-ijerph-17-05365\" ref-type=\"bibr\">1</xref>]. Despite this, it has been found that exercise practice tends to decrease throughout life, with adolescence being the period when this decrease is most pronounced [<xref rid=\"B2-ijerph-17-05365\" ref-type=\"bibr\">2</xref>]. Based on this evidence, it is of interest to analyse the social factors that might favour exercise intention in the adolescent population. By integrating constructs from the theory of planned behaviour (TPB) and self-determination theory (SDT), the present study examines the role of parents’ and peers’ autonomy support in exercise intention in a sample of adolescents of both genders.</p><sec id=\"sec1dot1-ijerph-17-05365\"><title>1.1. TPB and Its Limitation in Capturing the Influence of Social Factors</title><p>TPB has often been applied to explain health-related behaviours such as exercise [<xref rid=\"B3-ijerph-17-05365\" ref-type=\"bibr\">3</xref>]. According to the TPB postulates, behaviour can be explained by the rational intention to engage in it, that is, the extent to which individuals consciously plan their involvement in such behaviour [<xref rid=\"B3-ijerph-17-05365\" ref-type=\"bibr\">3</xref>]. According to the TPB, the intention to carry out a certain behaviour (e.g., exercise) would be explained from three different constructs: the attitude towards the behaviour (i.e., the general evaluation, positive or negative, that the person makes regarding the possible results of the behaviour), the perceived control (i.e., the individual’s personal belief in their ability to control for difficulties when carrying out the behaviour), and the subjective norms (i.e., the social pressure, exerted by significant others, that the individual perceives in carrying out the behaviour) [<xref rid=\"B3-ijerph-17-05365\" ref-type=\"bibr\">3</xref>].</p><p>The TPB postulates have received abundant empirical support [<xref rid=\"B4-ijerph-17-05365\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijerph-17-05365\" ref-type=\"bibr\">5</xref>]. However, it has been observed that, in general, the capacity of attitude and control to explain intention is superior to that of subjective norms [<xref rid=\"B2-ijerph-17-05365\" ref-type=\"bibr\">2</xref>], which could suggest that the latter factor does not sufficiently capture the social influence posed by the TPB. One possible reason for the inferior explanatory capacity of subjective norms is that, in reflecting on what one’s significant others say should be done [<xref rid=\"B3-ijerph-17-05365\" ref-type=\"bibr\">3</xref>], this construct involves the existence of a source of social pressure. Therefore, although the TPB assumes that subjective norms contribute to explaining the intention to perform the behaviour, it is possible that the social pressure implicit in this construct may prevent, rather than favour, the performance of the behaviour [<xref rid=\"B6-ijerph-17-05365\" ref-type=\"bibr\">6</xref>].</p><p>In order to increase the explanatory capacity of the social influence exerted by the variables contemplated in the TPB, it has been suggested that descriptive norms should be incorporated into this theory [<xref rid=\"B7-ijerph-17-05365\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijerph-17-05365\" ref-type=\"bibr\">8</xref>]. Descriptive norms reflect the degree to which individuals perceive that their significant others are involved in the behaviour, which they are also expected to engage in. Thus, descriptive norms may have greater predictive power regarding the intention to engage in the behaviour than do subjective norms, since individuals are often guided more by what they see others doing rather than by what they are told they should do [<xref rid=\"B9-ijerph-17-05365\" ref-type=\"bibr\">9</xref>].</p><p>The influence of descriptive norms in adopting health-related behaviours has been previously reported. For example, the results of the meta-analysis carried out by Rivis and Sheeran [<xref rid=\"B8-ijerph-17-05365\" ref-type=\"bibr\">8</xref>] suggest that descriptive norms help to explain intention in an incremental way with respect to subjective norms, control, and attitude. However, evidence relating these constructs to other potentially healthy behaviours, such as exercise, has been inconclusive [<xref rid=\"B9-ijerph-17-05365\" ref-type=\"bibr\">9</xref>]. Specifically, the scientific literature presents evidence that supports both the absence [<xref rid=\"B10-ijerph-17-05365\" ref-type=\"bibr\">10</xref>] and the presence of a relationship between descriptive norms and exercise intention [<xref rid=\"B11-ijerph-17-05365\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05365\" ref-type=\"bibr\">12</xref>]. The absence of conclusive evidence invites further exploration of the relationship between these variables. This is even more necessary in adolescents, since this population is especially susceptible to peer pressure [<xref rid=\"B8-ijerph-17-05365\" ref-type=\"bibr\">8</xref>], a circumstance that would suggest that the role of descriptive norms might be particularly relevant at this stage of life. </p><p>However, combining descriptive norms and subjective norms within the TPB does not avoid the restriction pointed out in the literature, namely that both reflect a social influence denoting pressure. This pressure may be explained by the fact that individuals feel pushed into conforming to the group norms, either through the messages they receive from their significant others [<xref rid=\"B3-ijerph-17-05365\" ref-type=\"bibr\">3</xref>] or through the behaviour model they present [<xref rid=\"B8-ijerph-17-05365\" ref-type=\"bibr\">8</xref>]. In this sense, an important limitation of both types of norms and, by extension, of the TPB itself, is the absence of differentiation between the different sources of social influence (e.g., parents and peers).</p></sec><sec id=\"sec1dot2-ijerph-17-05365\"><title>1.2. Integrating Parents’ and Peers’ Autonomy Support into the TPB</title><p>One way of complementing the TPB and understanding the influence that social factors might have on the intention to practice, is to incorporate forms of social influence that, without involving pressure, allow differences between various agents [<xref rid=\"B13-ijerph-17-05365\" ref-type=\"bibr\">13</xref>]. From the self-determination theory (SDT) perspective, support for autonomy is a social influence construct that reflects the degree to which individuals perceive that their significant others (i.e., parents and peers) help and encourage a certain behaviour [<xref rid=\"B6-ijerph-17-05365\" ref-type=\"bibr\">6</xref>]. One of the main SDT postulates is that the interpretation of the social context can favour the adoption and maintenance of the behaviour [<xref rid=\"B6-ijerph-17-05365\" ref-type=\"bibr\">6</xref>]. Thus, autonomous interpretations of the environment involve individuals perceiving the significant others with whom they interact (i.e., parents, peers and teachers) as favouring the possibility of choice, for example, providing reasoned explanations for carrying out the actions or recognising the subject as an actor in their own right.</p><p>Autonomy support captures a form of social influence relatively free of social pressure; hence, it reflects a social influence that is differentiated from subjective and descriptive norms [<xref rid=\"B14-ijerph-17-05365\" ref-type=\"bibr\">14</xref>]. For this reason, autonomy support has been proposed as a variable that might contribute to increasing the explanatory capacity of social influence measured by the TPB variables with respect to the intention to exercise [<xref rid=\"B4-ijerph-17-05365\" ref-type=\"bibr\">4</xref>,<xref rid=\"B13-ijerph-17-05365\" ref-type=\"bibr\">13</xref>]. In fact, in behaviours where initiation and maintenance involve a considerable effort (as in exercise), it would be expected that social assistance and support could be useful regardless of the control exerted and the value attributed to the activity [<xref rid=\"B15-ijerph-17-05365\" ref-type=\"bibr\">15</xref>]. In this regard, previous studies have suggested that autonomy support could influence the intention and practice of exercise in young people and adolescents, both directly and through the TPB variables [<xref rid=\"B4-ijerph-17-05365\" ref-type=\"bibr\">4</xref>,<xref rid=\"B14-ijerph-17-05365\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijerph-17-05365\" ref-type=\"bibr\">15</xref>]. However, these studies did not consider the role of autonomy support exercised by different agents. </p><p>From the SDT perspective, the autonomy support construct allows one to differentiate the influence of different social agents. This contrasts with a limitation of the social factor included in the TPB; specifically, since the subjective and descriptive norms (i.e., the influence of one’s significant others) cover the interpersonal influences of those closest to the individual (e.g., family or parents), but not the influence of peer groups [<xref rid=\"B16-ijerph-17-05365\" ref-type=\"bibr\">16</xref>]. However, as adolescence is such an important phase for human development, peer pressure may affect the intention to exercise in a different way to that exerted by parents’ influence [<xref rid=\"B17-ijerph-17-05365\" ref-type=\"bibr\">17</xref>]. Therefore, integrating autonomy support into the TPB allows one to overcome this limitation and to examine the social influence of different signifiers (e.g., parents and peers).</p><p>To the best of our knowledge, there has only been one study to date [<xref rid=\"B13-ijerph-17-05365\" ref-type=\"bibr\">13</xref>] that has examined the influence of parents’ and peers’ autonomy support on exercise intention, taking into account the influence of the TPB variables on adolescents. Sicilia et al. [<xref rid=\"B13-ijerph-17-05365\" ref-type=\"bibr\">13</xref>] found that subjective norms and perceived control mediated the relationship between parents’ autonomy support and exercise intention in a sample of adolescents, while peers’ autonomy support predicted exercise intention through attitudes. Nonetheless, even though the Sicilia et al. study considered autonomy support from various agents, it did not include the influence of peer groups. By integrating autonomy support within the TPB, one can overcome this limitation and examine the social influence of different significant others (e.g., parents and peers). Furthermore, the Sicilia et al. study did not include the mediating role of descriptive norms, which could have contributed to a greater understanding of the role of social influence, as defined by different theoretical frameworks (i.e., TPB and SDT) nor did it examine possible differences in hypothesized relationships between boys and girls. A gender-differentiated analysis would be justified by the differences boys and girls show in exercise behaviour. In fact, research has repeatedly found that exercise frequency in women is lower than in men [<xref rid=\"B18-ijerph-17-05365\" ref-type=\"bibr\">18</xref>], this difference being especially accentuated in the adolescent stage [<xref rid=\"B2-ijerph-17-05365\" ref-type=\"bibr\">2</xref>]. In addition, studies examining gender differences within the TPB have shown that boys tend to show higher scores than girls in these constructs, especially in past exercise behaviour and attitudes towards exercise intention [<xref rid=\"B19-ijerph-17-05365\" ref-type=\"bibr\">19</xref>].</p></sec><sec id=\"sec1dot3-ijerph-17-05365\"><title>1.3. The Present Study</title><p>The potential influence of autonomy support on the intention to exercise, examined through the TPB constructs, has hardly been studied. Apart from a few exceptions [<xref rid=\"B13-ijerph-17-05365\" ref-type=\"bibr\">13</xref>], it has been considered as referring exclusively to the influence of significant others [<xref rid=\"B4-ijerph-17-05365\" ref-type=\"bibr\">4</xref>,<xref rid=\"B14-ijerph-17-05365\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijerph-17-05365\" ref-type=\"bibr\">15</xref>]. On the other hand, research to date has not considered possible gender differences in such relationships. With these limitations in mind, the aim of this study was to examine the possible influence of autonomy support from different sources (i.e., parents and peers) on the intention to exercise in a sample of adolescents of both genders, while also considering the mediating role of descriptive norms and the other TPB constructs (i.e., attitude, perceived control, and subjective norms) (see <xref ref-type=\"fig\" rid=\"ijerph-17-05365-f001\">Figure 1</xref>).</p><p>In view of the findings from previous research [<xref rid=\"B13-ijerph-17-05365\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijerph-17-05365\" ref-type=\"bibr\">14</xref>] it was hypothesized that parents’ and peers’ support for autonomy would predict exercise intention, both directly and mediated through the TPB variables. Although it is expected that the relationships between the variables may be affected by gender, given the exploratory nature of the sex-segmented analysis, no prior hypotheses were made.</p></sec></sec><sec id=\"sec2-ijerph-17-05365\"><title>2. Materials and Methods </title><sec id=\"sec2dot1-ijerph-17-05365\" sec-type=\"subjects\"><title>2.1. Participants</title><p>A total of 428 students in secondary and high school education (211 boys and 217 girls) participated in the study. They were aged between 13 and 19 years (<italic>M</italic><sub>age</sub> = 15.30, <italic>SD</italic> = 1.15) and attended two centres located in a Spanish provincial capital.</p></sec><sec id=\"sec2dot2-ijerph-17-05365\"><title>2.2. Measures </title><p>Theory of planned behaviour variables. The items of the TPB variables (attitude, subjective norms, perceived behavioural control and exercise intention) were measured using the TPB questionnaire on exercise [<xref rid=\"B20-ijerph-17-05365\" ref-type=\"bibr\">20</xref>]. These items were adapted and validated in the Spanish population by González-Cutre, Sicilia, Beas-Jiménez and Hagger [<xref rid=\"B21-ijerph-17-05365\" ref-type=\"bibr\">21</xref>]. This questionnaire consists of 15 items that are answered using a 7-point Likert response scale. The following four variables are assessed:</p><p>Attitude: This construct was measured using a seven-point scale, where at the end of five items, 5 pairs of bipolar adjectives were placed (boring/interesting, unenjoyable–enjoyable, bad–good, useless–useful, harmful–beneficial), answering the item “participating in physical activity and sport during my leisure-time”. The Cronbach’s alpha value (α) was 0.89 for this scale in the study by González-Cutre et al. [<xref rid=\"B21-ijerph-17-05365\" ref-type=\"bibr\">21</xref>].</p><p>Subjective norms: These were measured using 4 items (e.g., “most people close to me expect me to do active sports and/or physical activities during my leisure time”) which were answered on a Likert-type scale from 1 (strongly agree) to 7 (strongly disagree). The α value in the study by González-Cutre et al. [<xref rid=\"B21-ijerph-17-05365\" ref-type=\"bibr\">21</xref>] was 0.83.</p><p>Perceived behavioural control: This was measured using 3 items (e.g., “how much control do I have over doing active sports and/or physical activities in my leisure time”) that were answered on a Likert scale from 1 (no control) to 7 (total control). The α value in the study by González-Cutre et al. [<xref rid=\"B21-ijerph-17-05365\" ref-type=\"bibr\">21</xref>] was 0.84.</p><p>Exercise intention: This was measured using 3 items (e.g., “I plan to do active sports and/or physical activity during my leisure time in the next 5 weeks”) that were answered on a Likert scale from 1 (strongly agree) to 7 (strongly disagree). The α value in the study by González-Cutre et al. [<xref rid=\"B21-ijerph-17-05365\" ref-type=\"bibr\">21</xref>] was 0.84.</p><p>Descriptive norms: A direct translation of the English version by Priebe and Spink [<xref rid=\"B7-ijerph-17-05365\" ref-type=\"bibr\">7</xref>] was used, responding to two questions: “How many important people for you are engaged in physical exercise?” answered on a Likert scale from 1 (nobody) to 7 (everybody), and “Think of the important people for you; what percentage are engaged in physical exercise?” answered on a Likert scale from 1 (0%) to 7 (100%).</p><p>Parents’ and peers’ perceived autonomy support: The version validated in the Spanish context [<xref rid=\"B22-ijerph-17-05365\" ref-type=\"bibr\">22</xref>] of the Perceived Autonomy Support Scale in Exercise Settings [<xref rid=\"B23-ijerph-17-05365\" ref-type=\"bibr\">23</xref>] was used, taking into account two different social factors: parents’ and peers’ autonomy support. The scale was headed by the statement “In my physical activity or sport...” and is composed of 12 items (e.g., “Parents/peers provide me with choice and options about how to do physical activity/sport during my leisure time”), grouped into two factors (parents’ and peers’ autonomy respectively), which were answered on a Likert-type scale from 1 (strongly disagree) to 7 (strongly agree). The α value was 0.96 for parents’ autonomy support and 0.95 for peer autonomy support.</p><p>Past exercise behaviour: This was measured using a single item: “In the course of the past 6 months, how often, on average, have you participated in vigorous physical activities for 20 minutes at a time?” Responses were collected on a Likert scale ranging from 1 (not at all) to 6 (most days of the week). This measure has been used in numerous previous studies to estimate past exercise behaviour in terms of frequency [<xref rid=\"B4-ijerph-17-05365\" ref-type=\"bibr\">4</xref>,<xref rid=\"B13-ijerph-17-05365\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijerph-17-05365\" ref-type=\"bibr\">14</xref>,<xref rid=\"B21-ijerph-17-05365\" ref-type=\"bibr\">21</xref>].</p></sec><sec id=\"sec2dot3-ijerph-17-05365\"><title>2.3. Procedure</title><p>In order to translate the items assessing the descriptive norms into Spanish, the reverse translation strategy of Hambleton [<xref rid=\"B24-ijerph-17-05365\" ref-type=\"bibr\">24</xref>] was used, seeking to make the translated version equivalent to the original at a semantic level. The items were translated into Spanish by a team of two translators. Afterwards, two further translators translated the items back to their original language. The resulting version was analysed by a group of graduates in psychology (<italic>N</italic> = 1) and physical activity and sport sciences (<italic>N</italic> = 2) [<xref rid=\"B25-ijerph-17-05365\" ref-type=\"bibr\">25</xref>], who did not suggest the need for modifications.</p><p>Once the final version of the items was obtained, two public high schools were contacted to request their collaboration in the study. Since the participants were minors, they were asked for written parental authorization to participate in the study. The questionnaire was administered in the presence of a member of the research group who, after reporting the anonymous nature of the study, was available to the participants to resolve any doubts that might have arisen during the data collection process. The participants needed approximately 10 minutes to complete the questionnaire.</p></sec><sec id=\"sec2dot4-ijerph-17-05365\"><title>2.4. Data Analysis</title><p>Firstly, the existence of outliers was considered. No cases were observed in which the standardized score for any of the variables exceeded the absolute value of 4 [<xref rid=\"B26-ijerph-17-05365\" ref-type=\"bibr\">26</xref>], so all cases were used in the subsequent analyses. Secondly, descriptive statistics and bivariate correlations were obtained between the study variables (Pearson’s <italic>R</italic>), in this case using IBM SPSS v.24 software (Armonk, NY, USA). The effect size (<italic>d<sub>Cohen</sub></italic>) of the differences between girls and boys in these variables was then calculated using the procedure and interpretation criteria described by Cohen [<xref rid=\"B27-ijerph-17-05365\" ref-type=\"bibr\">27</xref>]. Thus, <italic>d<sub>Cohen</sub></italic> values below 0.20 indicate a negligible effect, between 0.20 and 0.49 a small effect, between 0.50 and 0.79 an intermediate effect, and equal to or greater than 0.80 a large effect. The hypothesized model (see <xref ref-type=\"fig\" rid=\"ijerph-17-05365-f001\">Figure 1</xref>) was then tested independently for girls and boys using a path analysis technique [<xref rid=\"B28-ijerph-17-05365\" ref-type=\"bibr\">28</xref>] in Mplus v.7 software [<xref rid=\"B29-ijerph-17-05365\" ref-type=\"bibr\">29</xref>]. In view of the results of the multivariate normality test (i.e. Mardia coefficient, <italic>p</italic> < 0.001), and in order to avoid overestimating the indirect effects of the model, a bootstrapping technique of 20,000 samples was applied to obtain the total direct and indirect effects involved, as well and their 95% bias corrected confidence interval (CI) [<xref rid=\"B30-ijerph-17-05365\" ref-type=\"bibr\">30</xref>]. CIs not comprising the zero value indicate statistically significant effects, without it being necessary for the two direct effects involved (i.e., Path a and Path b) to be statistically significant in the case of indirect effects [<xref rid=\"B30-ijerph-17-05365\" ref-type=\"bibr\">30</xref>]. Finally, the effect size (<italic>q<sub>Cohen</sub></italic>) of the differences in direct and indirect effects across girls and boys was calculated using the procedure and interpretation criteria described by Cohen [<xref rid=\"B27-ijerph-17-05365\" ref-type=\"bibr\">27</xref>]. Thus, values of <italic>q<sub>Cohen</sub></italic> below 0.10 indicate a negligible effect, between 0.10 and 0.29 a small effect, between 0.30 and 0.49 an intermediate effect, and equal to or greater than 0.50 a large effect. A statistical significance level of <italic>p</italic> < 0.05 was used in all analyses.</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05365\"><title>3. Results</title><sec id=\"sec3dot1-ijerph-17-05365\"><title>3.1. Preliminary Analysis</title><p>The results shown in <xref rid=\"ijerph-17-05365-t001\" ref-type=\"table\">Table 1</xref> reveal medium-to-high levels in all the study variables (i.e., scores above the midpoint of the respective scales). Medium-to-large-sized positive correlations were observed between all the study variables. The scores tend to favour boys, with effect sizes ranging from small (parents’ autonomy support) to medium (past exercise behaviour).</p></sec><sec id=\"sec3dot2-ijerph-17-05365\"><title>3.2. Main Analysis</title><p>The results of the mediation model are shown in <xref ref-type=\"fig\" rid=\"ijerph-17-05365-f002\">Figure 2</xref> and <xref rid=\"ijerph-17-05365-t002\" ref-type=\"table\">Table 2</xref>. These results indicate that, after controlling for the effects of past exercise behaviour, perceived parents’ autonomy support was positively associated with: (i) attitude (statistically significant for girls and boys), (ii) control (statistically significant only for boys), and (iii) subjective norms (statistically significant for girls and boys). In turn, the perception of peers’ autonomy support was positively associated with (i) attitude (statistically significant for girls only), (ii) descriptive norms (statistically significant for girls only), and (iii) subjective norms (statistically significant for boys only). Attitude and control were positively and statistically significant associated with intention in both girls and boys, while subjective norms were positively and statistically significant associated with intention in girls only. </p><p>Perceived parents’ autonomy support was positively and statistically significant associated with exercise intention, specifically through (i) attitude and control (in both girls and boys), and (ii) subjective norms (in the case of girls). Perceived autonomy support from peers was positively and statistically significant associated with exercise intention, specifically through attitude (in girls only). In addition, perceived peers’ autonomy support was positively and statistically significantly associated with exercise intention in a direct way in both girls and boys.</p><p>Past exercise behaviour over the previous six months was positively and statistically significant associated with exercise intention, both in girls (B = 0.223, CI 95% = 0.078, 0.366) and boys (B = 0.270, CI 95% = 0.128, 0.432). Overall, the considered variables explained 56% of the variance in exercise intention in the case of girls and 65% in the case of boys. </p></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05365\"><title>4. Discussion</title><p>The objective of this study was to examine the influence of parents’ and peers’ autonomy support on exercise intention in a sample of adolescent boys and girls, considering the mediating role of descriptive norms and the other TPB constructs. The results suggest that the social factors represented by autonomy support from different agents (i.e., parents and peers) may play a more relevant role in explaining exercise intention than do the social factors coming from the TPB (i.e., subjective norms and descriptive norms). On the other hand, the influence of autonomy support on exercise intention occurred via different routes. While parents’ autonomy support was associated with exercise intention indirectly, through some of the TPB constructs, peers’ autonomy support was associated with exercise intention directly on the whole. The results of the present study help to clarify the role of social influence within the TPB.</p><p>Regarding the role of social factors represented by the TPB, the results are in line with previous studies, since they suggest that these factors might explain exercise intention to a lesser extent than the rest of the TPB constructs [<xref rid=\"B10-ijerph-17-05365\" ref-type=\"bibr\">10</xref>,<xref rid=\"B14-ijerph-17-05365\" ref-type=\"bibr\">14</xref>]. In fact, the effect of subjective norms on exercise intention was only of some relevance in the case of girls, while the effect of descriptive norms was not relevant in either of the groups. Several explanations might explain these findings. First, previous research has shown a high correlation between attitudes and subjective norms [<xref rid=\"B9-ijerph-17-05365\" ref-type=\"bibr\">9</xref>], suggesting that the effect of subjective norms on exercise intention may be somewhat absorbed by attitude. Second, the influence of descriptive norms on behaviour (i.e., what I perceive other signifiers already do) has been found especially in behaviours that present a health risk, such as the consumption of alcohol and other drugs [<xref rid=\"B8-ijerph-17-05365\" ref-type=\"bibr\">8</xref>]. However, this effect may be present less in behaviours that, far from representing a health risk, promote it instead (i.e., exercise). Finally, previous research has shown that individuals tend to be influenced by the attitudes and intentions of their groups, to the extent that they identify with them [<xref rid=\"B17-ijerph-17-05365\" ref-type=\"bibr\">17</xref>,<xref rid=\"B31-ijerph-17-05365\" ref-type=\"bibr\">31</xref>]. In this sense, a possible explanation for the limited explanatory capacity shown by the descriptive and subjective norms in explaining exercise intention might come from the lack of specificity regarding the relevance of the social reference group. In fact, at the time of measuring these norms, a general question was asked about significant others, but not about specific groups of relevance. Bearing in mind that reference groups vary between individuals and depending on the type of behaviour studied [<xref rid=\"B32-ijerph-17-05365\" ref-type=\"bibr\">32</xref>], this may have affected the relationship between the social norms measured within the TPB and exercise intention. </p><p>In contrast to the limited explanatory power of the TPB social factors in explaining exercise intention, in the case of the social factors represented by the autonomy support variable, these were shown to be superior. This result may be explained by the differentiated characteristics of the social factors coming from the TPB and SDT. The TPB constructs (i.e., subjective and descriptive norms) involve a certain degree of social pressure, to the extent that they assess the degree to which individuals perceive that their significant others indicate what should be done, or serve as a model of behaviour [<xref rid=\"B7-ijerph-17-05365\" ref-type=\"bibr\">7</xref>]. In contrast, autonomy support assumes an interpersonal context that is relatively free of social pressure [<xref rid=\"B14-ijerph-17-05365\" ref-type=\"bibr\">14</xref>], since it assesses the degree to which individuals perceive possibilities of choice [<xref rid=\"B6-ijerph-17-05365\" ref-type=\"bibr\">6</xref>]. It is precisely this greater absence of pressure that might explain its greater predictive power on exercise intention. Indeed, previous research has shown that the effects of autonomy support on intentions, behaviour and attitudes are not strongly affected by social identity and the feeling of belonging to the group [<xref rid=\"B4-ijerph-17-05365\" ref-type=\"bibr\">4</xref>]. This suggests that the influence of other signifiers may further affect the intention to carry out the behaviour when this influence is perceived in the form of autonomy support, regardless of whether individuals identify with their group. Therefore, the results of the present study suggest that social factors arising from the TPB and SDT reflect different interpersonal contexts, so that including the influence of autonomy support within the TPB might contribute to an understanding of how social influences shape exercise intention [<xref rid=\"B9-ijerph-17-05365\" ref-type=\"bibr\">9</xref>,<xref rid=\"B13-ijerph-17-05365\" ref-type=\"bibr\">13</xref>]. </p><p>The results of the present study further suggest that the effects of parents’ autonomy support on exercise intention would be different from the effects of peers’ autonomy support. Thus, while parents’ autonomy support predicted exercise intention indirectly, through subjective norms (particularly in girls), the effects of peers’ autonomy support were mainly direct. These results are partially in line with those reported by Sicilia et al. [<xref rid=\"B13-ijerph-17-05365\" ref-type=\"bibr\">13</xref>], particularly since the indirect effects of parents’ and peers’ autonomy support on exercise intention were produced through the TPB variables. However, it should be noted that Sicilia et al. did not examine the possible direct effects that social influence from these agents might have on intention. In any case, the differences between the direct and indirect effects observed in the present study suggest that a more complete autonomy support, such as that seemingly received from parents, would facilitate the internalization of the behaviour to a greater extent, as well as the intention to perform it in future. In fact, previous research has shown that autonomy support facilitates consistency between affective responses and intention to perform the behaviour, which reflects a greater degree of behaviour internalization, given that the intention to perform the behaviour is produced from internal psychological states (beliefs, attitudes and emotions) [<xref rid=\"B6-ijerph-17-05365\" ref-type=\"bibr\">6</xref>,<xref rid=\"B15-ijerph-17-05365\" ref-type=\"bibr\">15</xref>]. In contrast, the direct effect of peers’ autonomy support, without the mediation of attitude, might reflect an influence perceived as not being completely autonomous, so that peers’ influence might shape intention regardless of any previous unfavourable attitudes towards exercise [<xref rid=\"B14-ijerph-17-05365\" ref-type=\"bibr\">14</xref>].</p><p>Some of the gender differences in the relationships examined are notable. First, while the indirect effect of parents’ autonomy support on exercise intention was exerted through attitude and control in the case of boys, it was exerted through attitude and subjective norms in the case of girls. This suggests that attitude (that is, the value that an individual attributes to the future behaviour to be performed) is an essential factor in both boys and girls when it comes to assessing the degree of internalization of the activity into their own identity [<xref rid=\"B15-ijerph-17-05365\" ref-type=\"bibr\">15</xref>]. However, apart from the essential mediating role that attitude seems to play in both groups, the existence of a mediation effect through subjective norms in the case of girls suggests that they could be perceiving the influence coming from parents in a more autonomous way. In fact, for girls, parents’ autonomy support explained a greater variance in subjective norms than for boys, suggesting that the former may have understood parental influence as an interest in their needs and autonomy rather than as a pressure [<xref rid=\"B16-ijerph-17-05365\" ref-type=\"bibr\">16</xref>]. In this regard, Sas-Nowosielski [<xref rid=\"B33-ijerph-17-05365\" ref-type=\"bibr\">33</xref>] found that girls are more sensitive to their families’ influence on exercise than boys are. Similarly, Abraído-Lanza, Shelton, Cunha and Crookes [<xref rid=\"B34-ijerph-17-05365\" ref-type=\"bibr\">34</xref>], in a study with Dominican women, found that those who reported greater family ties and perceived their family members as supporting them, manifested exercise practice more often. In the case of boys, the results of the present study suggest that perceived control, along with attitudes, mediates the relationship between parents’ autonomy support and exercise intention rather than subjective norms. This result may be logical if we consider that boys not only show a higher frequency of exercise than girls, but that they are more competent in this domain [<xref rid=\"B35-ijerph-17-05365\" ref-type=\"bibr\">35</xref>]. As previous research has shown [<xref rid=\"B13-ijerph-17-05365\" ref-type=\"bibr\">13</xref>,<xref rid=\"B21-ijerph-17-05365\" ref-type=\"bibr\">21</xref>], a positive perception of current competence in the context of exercise is associated with possible socio-cognitive judgments that the individual may make regarding future success in that behaviour. </p><p>On the other hand, although the effect of peers’ autonomy support on intention to exercise was mainly direct, it should be noted that there was a small attitudinal mediating effect in the case of girls. This result suggests that the influence of peers’ autonomy support may be more conducive to internalizing exercise behaviour in girls than in boys by making behaviour more dependent on the value attributed to it. As Ryan and Deci [<xref rid=\"B6-ijerph-17-05365\" ref-type=\"bibr\">6</xref>] have pointed out, this occurs when social influence is perceived by the individual as truly autonomous. Since behaviour is internalized in the individual’s identity, it is more likely that the intentions are in line with their affective system [<xref rid=\"B15-ijerph-17-05365\" ref-type=\"bibr\">15</xref>]. </p><p>It is noteworthy that, despite the small differences indicated, the relationships in the model were quite stable in both groups, reinforcing the idea that autonomy support might play a relevant role in explaining exercise intention. The fact that parents’ and peers’ autonomy support seems to affect exercise intention through different processes suggests that integrating these constructs within the TPB may lead to a more precise explanation of the influence of social factors on this variable. </p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05365\"><title>5. Conclusions</title><p>This study contributes to clarify the role of social influence within the TPB. The results suggest that the SDT social factors might play a more relevant role in explaining exercise intention in adolescents than the TPB social factors. In fact, the perception of both parents’ and peers’ autonomy support have been the social factors that showed the strongest positive relationship with exercise intention. In contrast, both subjective and descriptive norms showed a weak relationship with exercise intention. Therefore, social factors that lead individuals to perceive an absence of pressure on the intention or performance of a behaviour (such as autonomy support) might favour intention to engage in exercise, while those factors involving different forms of social pressure (such as subjective and descriptive norms), might rather deter individual from engaging in future behaviour. In any case, the results of the present study suggest that considering different social factors from different agents could increase the explanatory capacity when studying the social influence on exercise intention and behaviour.</p><p>Despite the possible contribution of the results of the present study, several limitations should be highlighted. Firstly, the transversal design employed does not allow causal relationships to be established. Secondly, although the descriptive and the subjective norms within the TPB have been included in the present study, other variables that might be affecting the relationships studied (e.g., identity or group membership) have not been measured. Future studies should examine the effects of autonomy support from different agents, considering also other constructs that might affect social influence norms such as identification with the group. Finally, it should be noted that the present study measured exercise intention rather than exercise behaviour. While it is true that exercise intention is often highly correlated with behaviour [<xref rid=\"B36-ijerph-17-05365\" ref-type=\"bibr\">36</xref>], future studies should replicate the result of this study by also assessing exercise behaviour, directly if possible. </p></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization, A.S. and M.A.-I.; methodology, A.S. and M.A.-I.; software, M.A.-I.; validation, A.S., C.Á., M.P. and M.A.-I.; formal analysis, M.A.-I.; investigation, A.S., C.Á., M.P. and M.A.-I.; resources, A.S., C.Á., M.P. and M.A.-I.; data curation, A.S., C.Á., M.P. and M.A.-I.; writing—original draft preparation, A.S.; writing—review and editing, A.S., C.Á., M.P. and M.A.-I.; visualization, A.S., C.Á., M.P. and M.A.-I.; supervision, A.S. and M.A.-I.; project administration, A.S. and M.A.-I. 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Adolesc. Health</source><year>2011</year><volume>49</volume><fpage>547</fpage><lpage>549</lpage><pub-id pub-id-type=\"doi\">10.1016/j.jadohealth.2011.03.006</pub-id><pub-id pub-id-type=\"pmid\">22018572</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijerph-17-05365-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Mediation model tested.</p></caption><graphic xlink:href=\"ijerph-17-05365-g001\"/></fig><fig id=\"ijerph-17-05365-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Direct effects and explained variance in the mediation model. Unstandardized regression coefficients shown were obtained by applying 20,000 bootstrapping iterations. The values in the top (bottom) row show the estimates for girls (boys). For the sake of clarity, the following values have not been included in the graphical representation: (a) the effects of the control variable (i.e., past exercise behaviour in the last six months), and (b) the covariance terms between parents’ and peers’ autonomy support. * Denotes a statistically significant regression coefficient (i.e., the 95% CI does not contain the zero value).</p></caption><graphic xlink:href=\"ijerph-17-05365-g002\"/></fig><table-wrap id=\"ijerph-17-05365-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05365-t001_Table 1</object-id><label>Table 1</label><caption><p>Descriptive Statistics and Bivariate Correlations among Variables.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Variable</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Range</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">α</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">\n<italic>M (SD)</italic>\n</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">\n<italic>d<sub>Cohen</sub></italic>\n</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Skewness</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Kurtosis</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">1</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">2</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">3</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">4</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">5</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">6</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">7</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">8</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Girls</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Boys</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Girls</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Boys</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Girls</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Boys</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Girls</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Boys</th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1. Past exercise behaviour (last 6 months)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1–6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.80 (1.43)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.52 (1.40)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.51</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.01</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.71</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.80</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.33</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.30</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.25</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.47</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.39</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.49</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2. Parents’ Autonomy Support</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1–7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.96</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.95</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.19 (1.44)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.50 (1.23)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.23</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.68</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.88</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.49</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.45</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.71</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.40</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.43</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.39</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.64</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.43</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3. Peers’ Autonomy Support</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1–7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.96</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.94</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.98 (1.33)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.35 (1.11)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.30</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.61</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.72</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.16</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.54</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.42</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.66</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.41</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.41</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.50</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.54</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.48</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4. Attitude</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1–7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.90</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.95</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.43 (1.49)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.82 (1.31)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.28</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.87</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-1.40</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.15</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.93</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.49</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.46</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.40</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.41</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.26</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.47</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.45</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5. Control</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1–7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.84</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.86</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.09 (1.36)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.50 (1.24)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.32</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.54</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.69</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.31</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.69</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.61</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.51</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.51</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.34</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.49</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.66</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6. Descriptive Norms</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1–7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.80</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.80</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.59 (1.27)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.92 (1.24)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.26</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.27</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.52</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.19</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.02</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.44</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.39</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.33</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.48</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.40</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.35</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7. Subjective Norms</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1–7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.89</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.85</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.21 (1.42)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.52 (1.16)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.24</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.62</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.67</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.31</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.41</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.58</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.58</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.49</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.58</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.43</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.54</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8. Exercise Intention</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1–7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.97</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.95</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.91 (1.74)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.52 (1.59)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.37</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.35</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.88</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-1.05</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.16</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.68</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.52</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.52</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.56</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.75</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.40</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.49.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td></tr></tbody></table><table-wrap-foot><fn><p>Note. The correlation values shown above the diagonal correspond to girls (<italic>n</italic> = 217). The correlation values shown below the diagonal correspond to boys (<italic>n</italic> = 211). All correlation values shown are statistically significant (<italic>p</italic> < 0.001). α = Cronbach’s alpha.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05365-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05365-t002_Table 2</object-id><label>Table 2</label><caption><p>Direct and Indirect Effects of the Tested Mediation Model.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Sex</th><th colspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Path <italic>a</italic> (IV→MV)</th><th colspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Path <italic>b</italic> (MV→IV)</th><th colspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Path <italic>c’</italic> (IV→DV)</th><th colspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Path <italic>c</italic> (IV→MV→DV)</th></tr><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Variables</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<italic>B</italic>\n</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">95% CI</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<italic>q<sub>Cohen</sub></italic>\n</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Variables</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<italic>B</italic>\n</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">95% CI</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<italic>q<sub>Cohen</sub></italic>\n</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Variables</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<italic>B</italic>\n</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">95% CI</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<italic>q<sub>Cohen</sub></italic>\n</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Variables</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<italic>B</italic>\n</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">95% CI</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<italic>q<sub>Cohen</sub></italic>\n</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Low</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Up</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Low</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Up</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Low</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Up</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Low</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Up</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Girls</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PaAS→Att</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.201 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.015</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.377</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.046</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Att→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.139 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.007</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.282</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.062</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PaAS→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.104</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.280</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.086</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.055</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PaAS→Att→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.028 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.002</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.081</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.002</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Boys</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PaAS→Att</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.254 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.095</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.420</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Att→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.217 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.060</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.409</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PaAS→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.041</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.195</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.129</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PaAS→Att→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.055 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.015</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.124</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Girls</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PaAS→Con</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.184</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.382</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.135</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Con→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.528 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.367</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.692</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.058</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PaAS→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.104</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.280</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.086</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.055</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PaAS→Con→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.097 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.003</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.208</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.070</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Boys</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PaAS→Con</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.327 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.165</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.490</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Con→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.589 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.382</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.800</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PaAS→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.041</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.195</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.129</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PaAS→Con→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.193 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.089</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.350</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Girls</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PaAS→DN</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.016</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.144</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.180</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.182</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DesN→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.014</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.187</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.145</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.003</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PaAS→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.104</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.280</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.086</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.055</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PaAS→DesN→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.020</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.012</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.002</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Boys</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PaAS→DN</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.198</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.371</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DesN→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.016</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.137</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.099</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PaAS→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.041</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.195</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.129</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PaAS→DesN→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.003</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.036</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.020</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Girls</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PaAS→SN</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.470 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.285</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.656</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.201</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SubN→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.212 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.028</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.396</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.208</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PaAS→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.104</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.280</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.086</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.055</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PaAS→SubN→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.100 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.017</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.215</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.084</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Boys</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PaAS→SN</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.291 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.115</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.461</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SubN→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.045</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.188</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.113</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PaAS→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.041</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.195</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.129</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PaAS→SubN→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.013</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.068</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.030</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Girls</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PeAS→Att</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.269 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.082</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.447</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.145</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Att→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.139 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.007</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.282</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.062</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PeAS→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.233 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.020</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.429</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.023</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PeAS→Att→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.037 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.003</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.103</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.011</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Boys</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PeAS→Att</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.117</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.068</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.301</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Att→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.217 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.060</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.409</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PeAS→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.225 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.051</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.402</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PeAS→Att→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.025</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.009</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.103</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Girls</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PeAS→Con</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.168</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.033</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.370</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.080</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Con→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.528 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.367</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.692</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.058</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PeAS→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.233 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.020</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.429</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.023</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PeAS→Con→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.089</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.014</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.225</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.031</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Boys</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PeAS→Con</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.090</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.055</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.237</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Con→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.589 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.382</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.800</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PeAS→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.225 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.051</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.402</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PeAS→Con→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.053</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.029</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.159</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Girls</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PeAS→DN</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.405 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.233</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.573</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.385</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DesN→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.014</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.187</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.145</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.003</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PeAS→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.233 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.020</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.429</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.023</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PeAS→DesN→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.006</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.085</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.057</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">0.003</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Boys</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PeAS→DN</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.076</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.103</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.245</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DesN→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.016</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.137</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.099</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PeAS→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.225 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.051</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.402</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PeAS→DesN→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.027</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.008</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Girls</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PeAS→SN</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.155</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.034</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.354</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.177</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SubN→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.212 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.028</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.396</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.208</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PeAS→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.233 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.020</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.429</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.023</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PeAS→SubN→Int</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.033</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.002</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.115</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.026</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Boys</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PeAS→SN</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.329 *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.172</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.505</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SubN→Int</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.045</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.188</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.113</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PeAS→Int</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.225 *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.051</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.402</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PeAS→SubN→Int</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.015</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.067</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.036</td></tr></tbody></table><table-wrap-foot><fn><p>Note. IV = independent variable; MV = mediating variable; DV = dependent variable; CI = confidence intervals; Low = lower bound; Up = upper bound; PaAS = parents’ autonomy support; PeAS = peers’ autonomy support; DesN = descriptive norms; SubN = subjective norms; Att = attitude; Con = control; Int = intention. Unstandardized regression coefficients (B) and confidence intervals (CI) were obtained by applying 20,000 bootstrapping iterations. * Denotes a statistically significant regression coefficient (i.e., the 95% CI does not contain the zero value).</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32752093</article-id><article-id pub-id-type=\"pmc\">PMC7432025</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155498</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05498</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Review</subject></subj-group></article-categories><title-group><article-title>Mechanisms Protecting <italic>Acinetobacter baumannii</italic> against Multiple Stresses Triggered by the Host Immune Response, Antibiotics and Outside-Host Environment</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Monem</surname><given-names>Soroosh</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05498\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Furmanek-Blaszk</surname><given-names>Beata</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05498\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Łupkowska</surname><given-names>Adrianna</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05498\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Kuczyńska-Wiśnik</surname><given-names>Dorota</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05498\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Stojowska-Swędrzyńska</surname><given-names>Karolina</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05498\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Laskowska</surname><given-names>Ewa</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05498\">1</xref><xref rid=\"c1-ijms-21-05498\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05498\"><label>1</label>Department of General and Medical Biochemistry, Faculty of Biology, University of Gdansk, Wita Stwosza 59, 80-308 Gdansk, Poland; <email>soroosh.monem@phdstud.ug.edu.pl</email> (S.M.); <email>adrianna.lupkowska@phdstud.ug.edu.pl</email> (A.Ł.); <email>dorota.kuczynska-wisnik@ug.edu.pl</email> (D.K.-W.); <email>karolina.stojowska-swedrzynska@ug.edu.pl</email> (K.S.-S.)</aff><aff id=\"af2-ijms-21-05498\"><label>2</label>Department of Microbiology, Faculty of Biology, University of Gdansk, Wita Stwosza 59, 80-308 Gdansk, Poland; <email>beata.furmanek-blaszk@ug.edu.pl</email></aff><author-notes><corresp id=\"c1-ijms-21-05498\"><label>*</label>Correspondence: <email>ewa.laskowska@ug.edu.pl</email>; Tel.:+48-58-5236060</corresp></author-notes><pub-date pub-type=\"epub\"><day>31</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5498</elocation-id><history><date date-type=\"received\"><day>29</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>30</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p><italic>Acinetobacter baumannii</italic> is considered one of the most persistent pathogens responsible for nosocomial infections. Due to the emergence of multidrug resistant strains, as well as high morbidity and mortality caused by this pathogen, <italic>A. baumannii</italic> was placed on the World Health Organization (WHO) drug-resistant bacteria and antimicrobial resistance research priority list. This review summarizes current studies on mechanisms that protect <italic>A. baumannii</italic> against multiple stresses caused by the host immune response, outside host environment, and antibiotic treatment. We particularly focus on the ability of <italic>A. baumannii</italic> to survive long-term desiccation on abiotic surfaces and the population heterogeneity in <italic>A. baumannii</italic> biofilms. Insight into these protective mechanisms may provide clues for the development of new strategies to fight multidrug resistant strains of <italic>A. baumannii</italic>.</p></abstract><kwd-group><kwd><italic>Acinetobacter baumannii</italic></kwd><kwd>biofilm</kwd><kwd>desiccation stress</kwd><kwd>multidrug resistance</kwd><kwd>persisters</kwd><kwd>proteostasis</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05498\"><title>1. Introduction</title><p>Gram-negative coccobacillus <italic>Acinetobacter baumannii</italic> belongs to a group of ESKAPE pathogens. ESKAPE is the acronym for the group of bacteria that include <italic>Enterococcus faecium</italic>, <italic>Staphylococcus aureus</italic>, <italic>Klebsiella pneumoniae</italic>, <italic>A. baumannii</italic>, <italic>Pseudomonas aeruginosa</italic>, and <italic>Enterobacter</italic> spp. Due to their ability to effectively <italic>escape</italic> antibiotic treatments, these multidrug-resistant (MDR) pathogens are common causes of life-threatening infections affecting mainly immunocompromised and critically ill patients in intensive care units (ICUs) [<xref rid=\"B1-ijms-21-05498\" ref-type=\"bibr\">1</xref>]. In recent years, the overall number of antibiotics that act on the ESKAPE pathogens decreased significantly [<xref rid=\"B1-ijms-21-05498\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijms-21-05498\" ref-type=\"bibr\">2</xref>]. In 2017, the World Health Organization (WHO) published a list of 12 “priority” pathogens encompassing the ESKAPE group, for which new antibiotics are urgently needed. The WHO classifies carbapenem-resistant <italic>A. baumannii</italic> as the number one critical pathogen. Major risk factors for the acquisition of <italic>A. baumannii</italic> include antibiotic usage, especially β-lactams—the most commonly used drugs to treat infections caused by important pathogens which cause a variety of diseases in humans and animals. The second most common risk factor is mechanical ventilation, while other risks include surgical wound infections and invasive procedures such as central venous or urinary catheters [<xref rid=\"B3-ijms-21-05498\" ref-type=\"bibr\">3</xref>]. It was demonstrated that approximately 1000,000 people globally are infected with <italic>A. baumannii</italic> every year, while the emergence of MDR strains is reported worldwide [<xref rid=\"B4-ijms-21-05498\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijms-21-05498\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijms-21-05498\" ref-type=\"bibr\">6</xref>]. Antimicrobial treatment of MDR <italic>A. baumannii</italic> infections include colistin, sulbactam, and tigecycline, used in combination with other antibiotics [<xref rid=\"B1-ijms-21-05498\" ref-type=\"bibr\">1</xref>,<xref rid=\"B7-ijms-21-05498\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijms-21-05498\" ref-type=\"bibr\">8</xref>]. A recently published global study, the Tigecycline Evaluation and Surveillance Trial (TEST), revealed that the percentage of MDR <italic>A. baumannii</italic> isolates was the highest among all analyzed Gram-negative bacteria, and it increased from 23% in 2004 to 63% in 2014 [<xref rid=\"B4-ijms-21-05498\" ref-type=\"bibr\">4</xref>]. <italic>A. baumannii</italic> causes a range of infections, including ventilator-associated pneumonia, bacteremia, meningitis, urinary tract, wound, and bone infections [<xref rid=\"B2-ijms-21-05498\" ref-type=\"bibr\">2</xref>,<xref rid=\"B9-ijms-21-05498\" ref-type=\"bibr\">9</xref>]. The risk of mortality is high and often reaches 40–50% in ICU [<xref rid=\"B10-ijms-21-05498\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijms-21-05498\" ref-type=\"bibr\">11</xref>]. <italic>A. baumannii</italic> is a life-threatening problem not only because of multidrug resistance but also its ability to evade the host immune response and survive under harsh environmental conditions. In this review, we present various mechanisms that protect <italic>A. baumannii</italic> against the innate host immune response and stresses caused by the outside host environment. We focus on (1) the ability of <italic>A. baumannii</italic> to survive long-term desiccation, (2) factors involved in maintaining proteome homeostasis in <italic>A. baumannii</italic> cells, (3) the population heterogeneity in <italic>A. baumannii</italic> biofilms, and (4) the mechanism underlying <italic>A. baumannii</italic> antibiotic resistance.</p></sec><sec id=\"sec2-ijms-21-05498\"><title>2. <italic>A. baumannii</italic> and the Host Innate Immune Response</title><sec id=\"sec2dot1-ijms-21-05498\"><title>2.1. The First Line of Host Defense against A. baumannii</title><p>Neutrophils, macrophages, antimicrobial peptides (AMPs), and complement system components are the first line of innate immune defense that <italic>A. baumannii</italic> encounters during infection. Neutrophils can kill bacteria via phagocytosis, degranulation, or NETosis—a specific type of cell death pathway resulting in the release of the neutrophil extracellular traps (NETs). In NETs, chromatin forms a web-like structure decorated with antibacterial factors, including neutrophil elastase and AMPs [<xref rid=\"B9-ijms-21-05498\" ref-type=\"bibr\">9</xref>]. Multiple studies suggest that neutrophils play a crucial role in the control of <italic>A. baumannii</italic> infection [<xref rid=\"B12-ijms-21-05498\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijms-21-05498\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijms-21-05498\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijms-21-05498\" ref-type=\"bibr\">15</xref>]; however, contradictory results showing that neutrophils do not kill <italic>A. baumannii</italic> were also reported [<xref rid=\"B16-ijms-21-05498\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijms-21-05498\" ref-type=\"bibr\">17</xref>]. After phagocytosis, neutrophil clearance of <italic>A. baumannii</italic> is mainly dependent on reduced nicotinamide adenine dinucleotide phosphate (NADPH) oxidase which generates reactive oxygen species (ROS) to kill the pathogen [<xref rid=\"B12-ijms-21-05498\" ref-type=\"bibr\">12</xref>]. The stimulation of H<sub>2</sub>O<sub>2</sub> production in the lung of a mouse model in response to <italic>A. baumannii</italic> infection confirmed this observation [<xref rid=\"B18-ijms-21-05498\" ref-type=\"bibr\">18</xref>]. It was also found that <italic>A. baumannii</italic> inhibits NETosis, in contrast to other Gram-negative bacteria that trigger NET formation [<xref rid=\"B17-ijms-21-05498\" ref-type=\"bibr\">17</xref>,<xref rid=\"B19-ijms-21-05498\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijms-21-05498\" ref-type=\"bibr\">20</xref>]. The role of macrophages in eliminating <italic>A. baumannii</italic> remains controversial. Most studies showed that macrophages play a minor role during <italic>A. baumannii</italic> infection [<xref rid=\"B14-ijms-21-05498\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijms-21-05498\" ref-type=\"bibr\">15</xref>,<xref rid=\"B21-ijms-21-05498\" ref-type=\"bibr\">21</xref>]. However, Qiu et al. demonstrated that macrophages could be the first line of defense against respiratory <italic>A. baumannii</italic> infections; the depletion of alveolar macrophages significantly enhanced the susceptibility of mice to <italic>A. baumannii</italic> [<xref rid=\"B22-ijms-21-05498\" ref-type=\"bibr\">22</xref>]. In addition, phagocytosis and killing of <italic>A. baumannii</italic> were observed in vitro, and the macrophages produced nitric oxide and secreted proinflammatory cytokines and chemokines [<xref rid=\"B22-ijms-21-05498\" ref-type=\"bibr\">22</xref>].</p><p>AMPs are expressed constitutively or induced in different types of cells and tissues. Most of the AMPs are cationic; therefore, they can easily target the negatively charged surface of bacteria [<xref rid=\"B23-ijms-21-05498\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05498\" ref-type=\"bibr\">24</xref>]. To date, 139 human host defense peptides were identified [<xref rid=\"B25-ijms-21-05498\" ref-type=\"bibr\">25</xref>]. Cathelicidin-derived LL-37 is the best-studied AMP that kills <italic>A. baumannii</italic> cells through binding to the outer membrane protein A (OmpA) [<xref rid=\"B24-ijms-21-05498\" ref-type=\"bibr\">24</xref>,<xref rid=\"B26-ijms-21-05498\" ref-type=\"bibr\">26</xref>]. The LL-37 precursor, human cationic antibacterial peptide (hCAP-18), is produced by epithelial cells and neutrophils [<xref rid=\"B13-ijms-21-05498\" ref-type=\"bibr\">13</xref>]. Recently, it was demonstrated that LL-37 forms a dimer of two anti-parallel amphipathic α-helices without supercoiling [<xref rid=\"B27-ijms-21-05498\" ref-type=\"bibr\">27</xref>]. LL-37 helices target and extract lipopolysaccharides (LPS) to form holes in the outer membrane (OM). After diffusion into the periplasmic space, LL-37 may extract lipids from the inner membrane, forming a fibril-like structure [<xref rid=\"B27-ijms-21-05498\" ref-type=\"bibr\">27</xref>]. Two other AMPs that belong to the human beta defensins, hBD-2 and hBD-3, were shown to kill <italic>A. baumannii</italic> in a concentration-dependent manner [<xref rid=\"B28-ijms-21-05498\" ref-type=\"bibr\">28</xref>].</p><p>Another element of the immune system directed against <italic>A. baumannii</italic> involves the complement system components. The complement system consists of more than 30 plasma proteins that collaborate as a cascade triggering either bacterial cell lysis or opsonization and phagocytosis [<xref rid=\"B29-ijms-21-05498\" ref-type=\"bibr\">29</xref>]. There are three pathways of complement activation: classical, lectin, and alternative pathways. The classical pathway is initiated by immune complexes, whereas the lectin pathway is triggered by pathogen-specific carbohydrates. The alternative pathway, which is responsible for the killing of <italic>A. baumannii</italic> [<xref rid=\"B30-ijms-21-05498\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijms-21-05498\" ref-type=\"bibr\">31</xref>], is permanently active due to spontaneous hydrolysis of the central complement system component, C3, enabling fast detection of pathogens [<xref rid=\"B29-ijms-21-05498\" ref-type=\"bibr\">29</xref>,<xref rid=\"B32-ijms-21-05498\" ref-type=\"bibr\">32</xref>]. Several studies reported that the depletion of the complement results in an increase of <italic>A. baumannii</italic> viability in human serum or a mouse model of <italic>A. baumannii</italic> infection [<xref rid=\"B15-ijms-21-05498\" ref-type=\"bibr\">15</xref>,<xref rid=\"B33-ijms-21-05498\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijms-21-05498\" ref-type=\"bibr\">34</xref>].</p></sec><sec id=\"sec2dot2-ijms-21-05498\"><title>2.2. Mechanisms Protecting A. baumannii against the Innate Immune Response</title><p><italic>A. baumannii</italic> uses different virulence factors or mechanisms to evade the innate immune response. Surface glycoconjugates play key roles, but other strategies, including secreted proteins and metabolic pathways, also participate in the defense against the immune system [<xref rid=\"B35-ijms-21-05498\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijms-21-05498\" ref-type=\"bibr\">36</xref>].</p><p>The first barrier that protects <italic>A. baumannii</italic> against the immune host response is an exopolysaccharide capsule. The capsule is formed from long-chain polysaccharides composed of repeated carbohydrate units (K units). The synthesis of capsular polysaccharides (CPS) is dependent on a K locus (KL), which contains genes for synthesis of activated sugar precursors, glycosyl transfer, glycan modification, and oligosaccharide repeat-unit processing [<xref rid=\"B37-ijms-21-05498\" ref-type=\"bibr\">37</xref>]. To date, 128 KL gene clusters were identified in <italic>A. baumannii</italic> [<xref rid=\"B38-ijms-21-05498\" ref-type=\"bibr\">38</xref>]. Therefore, the monosaccharide composition and CPS structure are highly variable in <italic>A. baumannii</italic> strains [<xref rid=\"B37-ijms-21-05498\" ref-type=\"bibr\">37</xref>,<xref rid=\"B39-ijms-21-05498\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijms-21-05498\" ref-type=\"bibr\">40</xref>,<xref rid=\"B41-ijms-21-05498\" ref-type=\"bibr\">41</xref>,<xref rid=\"B42-ijms-21-05498\" ref-type=\"bibr\">42</xref>,<xref rid=\"B43-ijms-21-05498\" ref-type=\"bibr\">43</xref>]. K units vary in length and may consist of two to six residues. Common neutral sugars such as <sc>d</sc>-glucose, <sc>d</sc>-galactose, <italic>N</italic>-acetyl-<sc>d</sc>-glucosamine, and <italic>N</italic>-acetyl-<sc>d</sc>-galactosamine or rare sugars, including derivatives of pseudaminic, legionaminic, or acinetaminic acid, can be incorporated into CPS. Interestingly, acinetaminic acid was never found in nature before its detection in <italic>A. baumanni</italic> isolates with K12 and K13 gene clusters [<xref rid=\"B42-ijms-21-05498\" ref-type=\"bibr\">42</xref>]. The CPS of different isolates are linear or branched and may possess non-carbohydrate substituents, including the most frequent <italic>O</italic>- and <italic>N</italic>-acyl groups [<xref rid=\"B44-ijms-21-05498\" ref-type=\"bibr\">44</xref>]. The Wzy pathway encoded by the K locus is responsible for the export and extracellular assembly of CPS [<xref rid=\"B45-ijms-21-05498\" ref-type=\"bibr\">45</xref>]. The K locus is regulated by the two-component signal transduction system BfmRS. The global BfmR regulator, along with histidine kinase BfmS, controls a variety of processes, including biogenesis of <italic>A. baumannii</italic> envelope elements, formation of biofilms, desiccation tolerance, and multiple stress responses [<xref rid=\"B46-ijms-21-05498\" ref-type=\"bibr\">46</xref>,<xref rid=\"B47-ijms-21-05498\" ref-type=\"bibr\">47</xref>,<xref rid=\"B48-ijms-21-05498\" ref-type=\"bibr\">48</xref>,<xref rid=\"B49-ijms-21-05498\" ref-type=\"bibr\">49</xref>]. Several studies demonstrated that the production of capsules affects <italic>A. baumannii</italic> virulence and persistence in the host [<xref rid=\"B50-ijms-21-05498\" ref-type=\"bibr\">50</xref>,<xref rid=\"B51-ijms-21-05498\" ref-type=\"bibr\">51</xref>]. Capsule-enriched strains cause more severe disease or higher lethality [<xref rid=\"B35-ijms-21-05498\" ref-type=\"bibr\">35</xref>,<xref rid=\"B45-ijms-21-05498\" ref-type=\"bibr\">45</xref>,<xref rid=\"B50-ijms-21-05498\" ref-type=\"bibr\">50</xref>,<xref rid=\"B52-ijms-21-05498\" ref-type=\"bibr\">52</xref>,<xref rid=\"B53-ijms-21-05498\" ref-type=\"bibr\">53</xref>]. These observations may be partly explained by the high hydrophilicity of CPS and negative charges of CPS monosaccharides that prevent phagocytosis by hindering interactions with the negatively charged surfaces of neutrophils and macrophages, preventing phagocytosis [<xref rid=\"B35-ijms-21-05498\" ref-type=\"bibr\">35</xref>]. However, it should be noted that the highly variable structure of the CPS may affect its capacity as a protective barrier against the immune response and other stresses, including complement-mediated killing, lysozyme degradation, and ROS [<xref rid=\"B45-ijms-21-05498\" ref-type=\"bibr\">45</xref>,<xref rid=\"B50-ijms-21-05498\" ref-type=\"bibr\">50</xref>,<xref rid=\"B51-ijms-21-05498\" ref-type=\"bibr\">51</xref>,<xref rid=\"B52-ijms-21-05498\" ref-type=\"bibr\">52</xref>,<xref rid=\"B53-ijms-21-05498\" ref-type=\"bibr\">53</xref>,<xref rid=\"B54-ijms-21-05498\" ref-type=\"bibr\">54</xref>].</p><p>Another barrier protecting <italic>A. baumannii</italic> against the host response is the OM. LPS, the main component of the outer layer of the OM, contains three domains: (1) lipid A, the membrane anchor glycosylated with (2) a core oligosaccharide which may provide an attachment site for (3) a long-chain <italic>O</italic>-antigenic polysaccharide [<xref rid=\"B35-ijms-21-05498\" ref-type=\"bibr\">35</xref>]. In <italic>A. baumannii</italic>, the primary component of the outer layer of the OM is lipooligosaccharide (LOS) which, in contrast to the typical LPS, lacks the <italic>O</italic>-antigen [<xref rid=\"B37-ijms-21-05498\" ref-type=\"bibr\">37</xref>]. <italic>A. baumannii</italic> LOS belongs to the group of pathogen-associated molecular patterns (PAMPs) recognized by Toll-like receptor 4 (TLR4) [<xref rid=\"B13-ijms-21-05498\" ref-type=\"bibr\">13</xref>,<xref rid=\"B55-ijms-21-05498\" ref-type=\"bibr\">55</xref>]. TLR4 is one of the pattern recognition receptors (PRRs) which function as cell-surface sensors of bacterial infection. <italic>A. baumannii</italic> LOS triggers a TLR4-mediated release of tumor necrosis factor (TNF) and interleukin 8 (IL-8) from macrophages [<xref rid=\"B56-ijms-21-05498\" ref-type=\"bibr\">56</xref>]. This inflammation response is beneficial for the host; however, if it is upregulated, it can result in a cytokine storm and septic shock [<xref rid=\"B57-ijms-21-05498\" ref-type=\"bibr\">57</xref>]. In the case of highly virulent <italic>A. baumannii</italic>, enhanced TLR4 activation is correlated to increased shedding of LOS into growth medium [<xref rid=\"B58-ijms-21-05498\" ref-type=\"bibr\">58</xref>]. <italic>A. baumannii</italic> constitutively synthesizes hepta-acylated lipid A, under standard growth conditions, in contrast to other Gram-negative bacteria that upregulate its synthesis only under stress conditions [<xref rid=\"B59-ijms-21-05498\" ref-type=\"bibr\">59</xref>]. Constitutive hepta-acylation of lipid A fortifies the OM to protect <italic>A. baumannii</italic> from cationic AMPs, lysozyme, and colistin, which is the last-resort antibiotic to treat MDR <italic>A. baumannii</italic> infections [<xref rid=\"B59-ijms-21-05498\" ref-type=\"bibr\">59</xref>]. Colistin resistance may result from a complete lack of LOS or the addition of galactosamine to LOS. LOS deficiency significantly alters the interaction of <italic>A. baumannii</italic> with the host innate immune system. The overall pro-inflammatory response to LOS-deficient <italic>A. baumannii</italic> is reduced due to the lack of TLR-4 mediated stimulation. Instead of the TLR4-dependent mechanism, the TLR2-dependent mechanism is activated [<xref rid=\"B60-ijms-21-05498\" ref-type=\"bibr\">60</xref>]. This alternative response is probably a consequence of increased expression of specific lipoproteins, transporters, and other surface components that compensate for the lack of LOS [<xref rid=\"B61-ijms-21-05498\" ref-type=\"bibr\">61</xref>]. Although LOS-deficient strains are colistin-resistant, they exhibit decreased virulence and increased susceptibility to LL-37 and lysozyme [<xref rid=\"B60-ijms-21-05498\" ref-type=\"bibr\">60</xref>,<xref rid=\"B62-ijms-21-05498\" ref-type=\"bibr\">62</xref>,<xref rid=\"B63-ijms-21-05498\" ref-type=\"bibr\">63</xref>,<xref rid=\"B64-ijms-21-05498\" ref-type=\"bibr\">64</xref>].</p><p>Apart from the surface glycoconjugates, essential virulence factors of <italic>A. baumannii</italic> are OM proteins. The best-characterized <italic>A. baumannii</italic> OM protein, OmpA porin, is responsible for adhesion and invasion of <italic>A. baumannii</italic> into human epithelial cells [<xref rid=\"B65-ijms-21-05498\" ref-type=\"bibr\">65</xref>,<xref rid=\"B66-ijms-21-05498\" ref-type=\"bibr\">66</xref>,<xref rid=\"B67-ijms-21-05498\" ref-type=\"bibr\">67</xref>,<xref rid=\"B68-ijms-21-05498\" ref-type=\"bibr\">68</xref>]. It was found that the overproduction of OmpA is a risk factor for the development of <italic>A. baumannii</italic> pneumonia and bacteraemia, as well as for an increased mortality rate [<xref rid=\"B69-ijms-21-05498\" ref-type=\"bibr\">69</xref>]. Consistently, the deletion of the <italic>ompA</italic> gene reduced <italic>A. baumannii</italic> bacterial dissemination and development of secondary pneumonia in the murine peritoneal sepsis model [<xref rid=\"B69-ijms-21-05498\" ref-type=\"bibr\">69</xref>]. OmpA is secreted and enters epithelial cells via outer membrane vesicles (OMVs) [<xref rid=\"B70-ijms-21-05498\" ref-type=\"bibr\">70</xref>,<xref rid=\"B71-ijms-21-05498\" ref-type=\"bibr\">71</xref>,<xref rid=\"B72-ijms-21-05498\" ref-type=\"bibr\">72</xref>]. OMVs are used by Gram-negative bacteria to deliver toxins, virulence factors, and other effector molecules to host cells. After entering the epithelial cell, OmpA migrates to mitochondria and stimulates the release of cytochrome <italic>c</italic>, finally promoting apoptosis [<xref rid=\"B65-ijms-21-05498\" ref-type=\"bibr\">65</xref>]. In addition, OmpA triggers cell death by inducing the expression of TLR2 and production of nitrogen oxide. Lee et al. found that high concentrations of OmpA induces ROS production, leading to early-onset apoptosis and delayed-onset necrosis in dendritic cells (DCs) [<xref rid=\"B73-ijms-21-05498\" ref-type=\"bibr\">73</xref>]. These results demonstrate that OmpA can cause the death of DSc, thereby impairing T-cell responses against <italic>A. baumannii</italic>. Other studies revealed that the interaction of OmpA with the fluid-phase complement regulator factor H (FH) enables <italic>A. baumannii</italic> to escape complement and to survive in human serum [<xref rid=\"B31-ijms-21-05498\" ref-type=\"bibr\">31</xref>]. Two other <italic>A. baumannii</italic> proteins, CipA and the protein killing factor (PKF) serine protease, also contribute to serum resistance. The recently identified CipA is a plasminogen-binding protein exposed on the OM [<xref rid=\"B32-ijms-21-05498\" ref-type=\"bibr\">32</xref>]. Plasminogen, in the complex with CipA, is cleaved and converted to the active serine protease plasmin, which degrades fibrinogen and the complement component C3b. Thus, CipA can prevent entrapment of <italic>A. baumannii</italic> in fibrin thrombi, and it facilitates dissemination of the pathogen. Regardless of plasminogen binding, CipA can also inhibit the alternative complement pathway [<xref rid=\"B32-ijms-21-05498\" ref-type=\"bibr\">32</xref>]. The PKF serine protease is involved in serum resistance possibly through degradation of yet unidentified complement components [<xref rid=\"B74-ijms-21-05498\" ref-type=\"bibr\">74</xref>]. It was suggested that both CipA and PKF are secreted via a type II secretion system (T2SS) [<xref rid=\"B75-ijms-21-05498\" ref-type=\"bibr\">75</xref>]. The T2SS enables secretion of effector proteins including multiple enzymes critical for <italic>A. baumannii</italic> virulence [<xref rid=\"B9-ijms-21-05498\" ref-type=\"bibr\">9</xref>]. The hypothesis that the T2SS participates in the secretion of CipA and PKF is based on results showing that deletion of the T2SS gene <italic>gspD</italic> resulted in significantly decreased resistance to human serum [<xref rid=\"B75-ijms-21-05498\" ref-type=\"bibr\">75</xref>].</p><p>Recent studies revealed that, in addition to the aforementioned proteins and structures, <italic>A. baumannii</italic> possesses numerous protective mechanisms against complement-mediated killing. Sanchez-Larrayoz et al. identified 50 genes essential for the survival of <italic>A. baumannii</italic> in human serum, including the Mla system, which encodes proteins required for the maintenance of OM lipid asymmetry [<xref rid=\"B54-ijms-21-05498\" ref-type=\"bibr\">54</xref>]. The Mla proteins prevent the accumulation of phospholipids in the outer leaflet of the OM by transporting them to the inner membrane. This study suggests that the accumulation of surface-exposed phospholipids in <italic>mla</italic>-deficient strains can activate the alternative pathway of the complement system [<xref rid=\"B54-ijms-21-05498\" ref-type=\"bibr\">54</xref>].</p><p>An example of a metabolic adaptation that enables <italic>A. baumannii</italic> to evade neutrophil chemotaxis is the phenylacetic acid catabolism pathway encoded by the <italic>paa</italic> system [<xref rid=\"B14-ijms-21-05498\" ref-type=\"bibr\">14</xref>]. The <italic>paa</italic> genes were identified in 16% of the sequenced bacterial species [<xref rid=\"B76-ijms-21-05498\" ref-type=\"bibr\">76</xref>,<xref rid=\"B77-ijms-21-05498\" ref-type=\"bibr\">77</xref>]. The <italic>paa</italic> operon is involved in degradation of aromatic compounds including phenylacetate to form acetyl-coenzyme A (CoA) and succinyl-CoA [<xref rid=\"B76-ijms-21-05498\" ref-type=\"bibr\">76</xref>]. Bhuiyan et al. demonstrated that the loss of function of this catabolic pathway resulted in the accumulation of phenylacetate, which acted as an attractant of neutrophils, leading finally to bacterial clearance [<xref rid=\"B14-ijms-21-05498\" ref-type=\"bibr\">14</xref>].</p><p>An interesting example of the <italic>A. baummanii</italic> virulence strategy is its ability to adhere to neutrophils without being eliminated by phagocytosis. Since neutrophils can transmigrate from the infection site to vasculature, it was proposed that the reverse migration of neutrophils can disseminate the infection to other organs [<xref rid=\"B17-ijms-21-05498\" ref-type=\"bibr\">17</xref>]. Moreover, Sato et al. demonstrated that MDR <italic>A. baumannii</italic> isolates can survive in macrophages after phagocytosis. The MDR strains induced ROS production in macrophages, and they concomitantly exhibited upregulated catalase activity which allowed them to resist oxidative stress [<xref rid=\"B78-ijms-21-05498\" ref-type=\"bibr\">78</xref>]. These results indicate that <italic>A. baumannii</italic> can spread in the infected organism using both neutrophils and macrophages.</p><p>Most of the strategies described above (<xref ref-type=\"fig\" rid=\"ijms-21-05498-f001\">Figure 1</xref>) allow <italic>A. baumannii</italic> not only to evade the innate immune response, but also to survive in the external environment.</p></sec></sec><sec id=\"sec3-ijms-21-05498\"><title>3. Mechanisms Protecting <italic>A. baumannii</italic> against Desiccation</title><p>Desiccation, as a common environmental stressor, poses challenges to bacterial cells. Water molecules, as the only nonvolatile solvent in cells, are critical in reaction mechanisms; they also confer stability to lipids, DNA, and proteins, as well as contributing structural order. Loss of membrane integrity during desiccation disrupts the respiratory chain leading to the accumulation of superoxide ions. Furthermore, the malfunction of transport proteins and destabilization of proteins with iron-sulfur clusters cause an increase in the level of intracellular iron. Superoxide radicals can participate in Fenton and Haber-Weiss reactions with ferrous or ferric ions leading to the production of highly toxic hydroxyl radicals [<xref rid=\"B79-ijms-21-05498\" ref-type=\"bibr\">79</xref>]. Therefore, oxidative damage of DNA, lipids, and proteins is one of the effects of water loss. <italic>Acinetobacter</italic> spp., compared with other Gram-negative rods, are more resistant to dry conditions [<xref rid=\"B80-ijms-21-05498\" ref-type=\"bibr\">80</xref>]. <italic>A. baumannii</italic> uniquely survives on inanimate objects and fingertips for extended periods, which explains its potency in cross-infection breakouts [<xref rid=\"B81-ijms-21-05498\" ref-type=\"bibr\">81</xref>].</p><p>The main structures that facilitate bacteria to enhance water retention are CPS and LPS/LOS [<xref rid=\"B36-ijms-21-05498\" ref-type=\"bibr\">36</xref>]. CPS were shown to contribute to desiccation tolerance in <italic>A. baumannii</italic> [<xref rid=\"B51-ijms-21-05498\" ref-type=\"bibr\">51</xref>]. However, other studies demonstrated that there is no simple correlation between the capsule thickness and survival rate under desiccation [<xref rid=\"B49-ijms-21-05498\" ref-type=\"bibr\">49</xref>]. Therefore, it seems that the type and structure of CPS or other mechanisms, including hepta-acetylated lipid A, must contribute to the outstanding desiccation tolerance of <italic>A. baumannii</italic>. Boll et al. demonstrated that the <italic>A. baumannii</italic> mutant lacking the LpxM acylsynthetaze produces penta-acylated lipid A, instead of hepta-acylated lipid A, and it exhibits decreased desiccation tolerance, probably due to increased membrane fluidity [<xref rid=\"B59-ijms-21-05498\" ref-type=\"bibr\">59</xref>].</p><p>The accumulation of organic osmolytes, generally designated as compatible solutes, is a prerequisite for the adaptation of bacteria to osmotic stress imposed by water loss. A crucial role in desiccation resistance in various microorganisms is played by the non-reducing disaccharide, trehalose. Trehalose acts as an osmolyte, chemical chaperone, and metabolite that can directly or indirectly stabilize proteins and membranes [<xref rid=\"B82-ijms-21-05498\" ref-type=\"bibr\">82</xref>,<xref rid=\"B83-ijms-21-05498\" ref-type=\"bibr\">83</xref>,<xref rid=\"B84-ijms-21-05498\" ref-type=\"bibr\">84</xref>,<xref rid=\"B85-ijms-21-05498\" ref-type=\"bibr\">85</xref>]. It seems that endogenous trehalose is not involved in desiccation tolerance in <italic>A. baumannii,</italic> but exogenous trehalose was found to efficiently protect <italic>A. baumannii</italic> on dry surfaces [<xref rid=\"B68-ijms-21-05498\" ref-type=\"bibr\">68</xref>]. In response to osmotic stress, <italic>A. baumannii</italic> also accumulates mannitol and glutamate; however, their contribution to desiccation resistance remains mostly unexplored [<xref rid=\"B81-ijms-21-05498\" ref-type=\"bibr\">81</xref>]. To counteract the effects of oxidative stress, the expression of anti-oxidant enzymes, such as catalases KatE and KatG, superoxide dismutase, and glutathione peroxidase, is induced in desiccation-stressed <italic>A. baumannii</italic> [<xref rid=\"B49-ijms-21-05498\" ref-type=\"bibr\">49</xref>,<xref rid=\"B86-ijms-21-05498\" ref-type=\"bibr\">86</xref>,<xref rid=\"B87-ijms-21-05498\" ref-type=\"bibr\">87</xref>]. A recent study by Farrow et al. proved that the global BfmR regulator contributes to that desiccation tolerance [<xref rid=\"B49-ijms-21-05498\" ref-type=\"bibr\">49</xref>].</p><p>Gayoso et al. found that desiccation stress affects the composition of the OM. The overproduction of OMPs (Omp25, DcaP-like, and CarO) was observed, indicating a shift in membrane permeability. This study also revealed that several genes encoding proteins involved in transcription and translation, including RNA polymerase subunits RpoA and RpoC, ribosome-associated proteins, and the elongation factor Tu, are downregulated in <italic>A. baumannii</italic> during desiccation. Proteins whose expression was upregulated included ribosomal recycling factor (RRF), integration host factor (IHF), and the histone-like protein HU. RRF facilitates disassembly of the ribosome at the end of translation. IHF and HU are involved in transcription regulation, and they are essential for maintaining DNA supercoiling and compaction. Consistent with this finding, the presence of an electron-dense region inside desiccation-stressed <italic>A. baumannii</italic> cells was detected [<xref rid=\"B87-ijms-21-05498\" ref-type=\"bibr\">87</xref>]. All these observations led to the conclusion that <italic>A. baumannii</italic> cells exposed to desiccation stress enter a dormant state [<xref rid=\"B87-ijms-21-05498\" ref-type=\"bibr\">87</xref>]. Under favourable conditions, dormant bacteria can recover and resume growth.</p><p>The protection of proteins is crucial for the survival of bacteria during desiccation stress and subsequent rehydration. In the next section, we discuss mechanisms that counteract protein damage caused by water loss and other stresses. Then, we present the current knowledge of biofilm formation, which is one of the main strategies used by bacterial populations to survive desiccation stress [<xref rid=\"B88-ijms-21-05498\" ref-type=\"bibr\">88</xref>].</p></sec><sec id=\"sec4-ijms-21-05498\"><title>4. Protein Homeostasis in <italic>A. baumannii</italic></title><p>Upon oxidative stress, proteins, which are the main target of ROS, are damaged by metal-catalyzed oxidation and non-enzymatic glycation [<xref rid=\"B79-ijms-21-05498\" ref-type=\"bibr\">79</xref>,<xref rid=\"B82-ijms-21-05498\" ref-type=\"bibr\">82</xref>,<xref rid=\"B89-ijms-21-05498\" ref-type=\"bibr\">89</xref>]. During desiccation, these detrimental reactions are facilitated by the reduction of the hydration shell around proteins and protein condensation, which in turn may lead to misfolding and aggregation of proteins. Apart from anti-oxidant enzymes, bacteria evolved additional mechanisms that protect proteins, including molecular chaperones and proteases. The main role of molecular chaperones is maintaining protein homeostasis (proteostasis), i.e., a proper balance of protein synthesis, folding, transport, and degradation [<xref rid=\"B90-ijms-21-05498\" ref-type=\"bibr\">90</xref>]. Molecular chaperones are highly conserved among prokaryotes and, under stress conditions, they prevent aggregation of unfolded proteins, facilitate degradation of irreversibly misfolded proteins by proteases, and enable solubilization of protein aggregates for subsequent refolding or degradation [<xref rid=\"B91-ijms-21-05498\" ref-type=\"bibr\">91</xref>,<xref rid=\"B92-ijms-21-05498\" ref-type=\"bibr\">92</xref>]. The key molecular chaperones in bacteria include the heat-shock protein 70 (Hsp70) family chaperone DnaK, its DnaJ (Hsp40) co-chaperone, and the nucleotide exchange factor GrpE, as well as the chaperonin GroEL (Hsp60) and its co-chaperone GroES (Hsp10). The efficient solubilization of aggregated proteins requires the cooperation of the DnaK–DnaJ–GrpE system with ClpB (Hsp100), IbpA/B (the small Hsp family), or Hsp33, which is the primary chaperone redox-activated upon oxidative stress. Most of these chaperones were found to be upregulated in <italic>A. baumannii</italic> submitted to stresses that impair homeostasis: heat shock (DnaK, GroEL), oxidative stress (GrpE, DnaK, GroES, GroEL), antibiotic exposure (DnaK, GroEL), and desiccation (TF, GroES, GrpE, DnaJ, DnaK, ClpX, ClpB) [<xref rid=\"B18-ijms-21-05498\" ref-type=\"bibr\">18</xref>,<xref rid=\"B86-ijms-21-05498\" ref-type=\"bibr\">86</xref>,<xref rid=\"B87-ijms-21-05498\" ref-type=\"bibr\">87</xref>,<xref rid=\"B93-ijms-21-05498\" ref-type=\"bibr\">93</xref>,<xref rid=\"B94-ijms-21-05498\" ref-type=\"bibr\">94</xref>]. Wang et al. reported that the expression of more than 50 genes encoding proteins related to proteostasis, including chaperones and the Lon protease, was increased during desiccation [<xref rid=\"B86-ijms-21-05498\" ref-type=\"bibr\">86</xref>]. The induction of proteins involved in the proteostasis system was accompanied by protein aggregation. Surprisingly, the accumulation of protein aggregates correlated positively with the ability of <italic>A. baumannii</italic> to survive desiccation. The survival rate was also increased when protein aggregation was induced prior to desiccation by a subinhibitory concentration of streptomycin, or it was enhanced by the <italic>Δlon</italic> mutation. It was also demonstrated that the model proteins sequestered in the aggregates, β-lactamase and GFP, retained their activities [<xref rid=\"B86-ijms-21-05498\" ref-type=\"bibr\">86</xref>]. These results are in agreement with previous studies showing that bacterial inclusion bodies contain functional proteins, and they confirm that aggregates may serve as compartments that protect proteins against inactivation [<xref rid=\"B95-ijms-21-05498\" ref-type=\"bibr\">95</xref>,<xref rid=\"B96-ijms-21-05498\" ref-type=\"bibr\">96</xref>]. Upon desiccation, the sequestration of native molecules into aggregates may be favored due to the gradual concentration of proteins. In contrast, high temperatures or other stressors that cause fast and abundant protein misfolding may lead to decreased survival or cell death due to the formation of aggregates enriched in non-functional proteins.</p><p>Recent studies showed that the induction of protein aggregation and disturbance of proteostasis may be an efficient strategy to kill pathogenic bacteria. Khodaparast et al. identified several peptides that induced bactericidal protein aggregation in <italic>Escherichia coli</italic> and <italic>A. baumannii</italic> [<xref rid=\"B97-ijms-21-05498\" ref-type=\"bibr\">97</xref>]. The peptides contained aggregation-prone sequences (APRs) that naturally occur in hydrophobic cores of globular proteins or on protein–protein interaction surfaces. When aggregation was nucleated in bacteria by the peptides containing APRs, it led to the lethal formation of inclusion bodies containing hundreds of proteins. The quaternary amine compounds (QACs), including benzalkonium chloride (BZK), can also trigger protein aggregation in <italic>A. baumannii</italic> when used at low concentrations [<xref rid=\"B98-ijms-21-05498\" ref-type=\"bibr\">98</xref>]. QACs are commonly used biocides that, at high concentrations, disrupt membranes. The exact mechanism of BZK action on proteostasis remains unclear, although it was found that resistance to BZK was acquired through ribosomal protein mutations that protected <italic>A. baumannii</italic> against BZK-induced protein aggregation.</p></sec><sec id=\"sec5-ijms-21-05498\"><title>5. Biofilm and Heterogeneity of <italic>A. baumannii</italic> Populations</title><sec id=\"sec5dot1-ijms-21-05498\"><title>5.1. Formation of A. baumannii Biofilms</title><p>Biofilms are multicellular consortia of single or multiple bacterial species enclosed in extracellular polymeric substances (EPS) which comprise polysaccharides, proteins, and nucleic acids secreted by bacteria. The structure of mature biofilms is often very complex with clusters of bacterial cells separated by fluid-filled channels. Diffusion of nutrients and oxygen is limited in biofilms; therefore, the environmental conditions are not homogeneous throughout a biofilm. This leads to the formation of heterogeneous cell subpopulations adapted to local microenvironments. Biofilm-dwelling bacteria are more resistant to antibiotics and other stressors than planktonic cells [<xref rid=\"B99-ijms-21-05498\" ref-type=\"bibr\">99</xref>,<xref rid=\"B100-ijms-21-05498\" ref-type=\"bibr\">100</xref>]. A number of factors are known to lead to the enhanced antibiotic resistance of biofilms, e.g., impaired drug diffusion due to microbial aggregations and overexpression of the extracellular polymeric substance (EPS) matrix, biofilm-specific efflux pumps, alterations in microbial phenotypic and genotypic features due to stress responses, and specific microenvironment conditions that inactivate antibiotics and the presence of persister cells (see below) [<xref rid=\"B101-ijms-21-05498\" ref-type=\"bibr\">101</xref>]. Antibiotics administered at concentrations below the minimum inhibitory concentration (MIC) often induce biofilm formation in a variety of bacterial species [<xref rid=\"B100-ijms-21-05498\" ref-type=\"bibr\">100</xref>,<xref rid=\"B102-ijms-21-05498\" ref-type=\"bibr\">102</xref>]. <italic>A. baumannii</italic> forms biofilms on both biotic and abiotic surfaces which contributes to its remarkable ability to survive in hospital environments. While extrinsic factors such as surface hydrophobicity, temperature, and oxygen concentration are reported to influence <italic>A. baumannii</italic> biofilms, numerous physicochemical and microbial features (e.g., capsular polysaccharides, surface appendages, adhesins, and virulence and resistance determinants) facilitate the formation and maintenance of <italic>A. baumannii</italic> biofilms (<xref ref-type=\"fig\" rid=\"ijms-21-05498-f002\">Figure 2</xref>A) [<xref rid=\"B103-ijms-21-05498\" ref-type=\"bibr\">103</xref>]. In addition to biofilms on solid surfaces, <italic>A. baumannii</italic> also forms “pellicles” at the air-liquid interface (<xref ref-type=\"fig\" rid=\"ijms-21-05498-f002\">Figure 2</xref>B) [<xref rid=\"B104-ijms-21-05498\" ref-type=\"bibr\">104</xref>,<xref rid=\"B105-ijms-21-05498\" ref-type=\"bibr\">105</xref>]. The formation of these floating biofilms is a rare trait in clinical <italic>A. baumannii</italic> isolates, and it is associated with surface-associated motility [<xref rid=\"B104-ijms-21-05498\" ref-type=\"bibr\">104</xref>,<xref rid=\"B106-ijms-21-05498\" ref-type=\"bibr\">106</xref>]. The relationship between motility and pellicles or surface-attached biofilms is complex. Although a motile state seems to be the opposite to a sedentary lifestyle in biofilms, motility may be required for the formation of microcolonies at the early stages of biofilm development and during the reorganisation of mature three-dimensional biofilm structures [<xref rid=\"B107-ijms-21-05498\" ref-type=\"bibr\">107</xref>].</p><p><italic>A. baumannii</italic> does not produce flagella; however, it can move via surface-associated motility or twitching motility [<xref rid=\"B104-ijms-21-05498\" ref-type=\"bibr\">104</xref>,<xref rid=\"B128-ijms-21-05498\" ref-type=\"bibr\">128</xref>]. Multiple genes required for surface-associated motility, including genes associated with purine and pyrimidine biosynthesis or natural competence, were recently identified [<xref rid=\"B128-ijms-21-05498\" ref-type=\"bibr\">128</xref>], but its mechanism remains poorly understood. Twitching motility is mediated by the extension and retraction of type IV pili, which are composed of helically assembled PilA subunits. PilA produced by various <italic>A. baumannii</italic> isolates differ in amino-acid sequence and <italic>O</italic>-linked glycosylation. It was proposed that, when negatively charged residues dominate on the surface of the headgroup domain of PilA, the pili retract from each other due to electrostatic repulsion and promote twitching motility. The opposite effect, i.e., pili bundling, cell–cell attachment, and biofilm formation, may occur in the case of PilA variants without negatively charged headgroup domains [<xref rid=\"B110-ijms-21-05498\" ref-type=\"bibr\">110</xref>].</p><p>Several studies showed that one of the main structures required for cell attachment and biofilm development is CPS [<xref rid=\"B129-ijms-21-05498\" ref-type=\"bibr\">129</xref>]. For example, it was shown that, in the case of the <italic>A. baumannii Δwza</italic>, capsule-deficient strain biofilm growth and adhesion to epithelial cells were reduced [<xref rid=\"B45-ijms-21-05498\" ref-type=\"bibr\">45</xref>]. However, under certain conditions, enhanced production of the capsule may be associated with biofilm reduction. It was recently reported that the Lon protease affects biofilm formation in <italic>A. baumannii</italic> [<xref rid=\"B130-ijms-21-05498\" ref-type=\"bibr\">130</xref>]. Although the Lon-deficient mutant produced thicker capsule compared to wild-type (WT) cells, it displayed lowered adherence to polystyrene surfaces, decreased motility, and formed a weak pellicle biofilm, but strongly upregulated a surface antigen, encoded by <italic>surA1</italic>. The exact mechanism of biofilm regulation by Lon remains to be elucidated. <italic>A. baumannii</italic> produces an additional surface exopolysaccharide, poly-β-(1–6)-<italic>N</italic>-acetylglucosamine (PNAG). Proteins involved in the polymerization and secretion of PNAG are encoded by the <italic>pgaABCD</italic> operon widely distributed among <italic>A. baumannii</italic> clinical isolates [<xref rid=\"B131-ijms-21-05498\" ref-type=\"bibr\">131</xref>].</p><p>The ability of <italic>A. baumannii</italic> to form biofilms on abiotic surfaces depends on the production of pili assembled via the CsuA/BABCDE chaperone–usher secretion system which is controlled by the BfmR global regulator [<xref rid=\"B109-ijms-21-05498\" ref-type=\"bibr\">109</xref>,<xref rid=\"B119-ijms-21-05498\" ref-type=\"bibr\">119</xref>]. OmpA also participates in the development of biofilms on plastic surfaces. In contrast to the CsuA/BABCDE pili system, OmpA is required during the attachment to <italic>Candida albicans</italic> filaments and human alveolar epithelial cells. After the attachment, OmpA triggers apoptosis of the eukaryotic cells [<xref rid=\"B67-ijms-21-05498\" ref-type=\"bibr\">67</xref>]. The giant Bap protein, consisting of 8621 amino acids, is involved in the formation and stabilization of the complex three-dimensional biofilm architecture on abiotic surfaces, and it plays a role in adhesion of <italic>A. baumannii</italic> to the host cell [<xref rid=\"B112-ijms-21-05498\" ref-type=\"bibr\">112</xref>,<xref rid=\"B113-ijms-21-05498\" ref-type=\"bibr\">113</xref>]. Bap possess immunoglobulin-like (Ig-like) repeats that seem to be typical for proteins involved in biofilm development, for example, Bap-like proteins, BLP1 and BLP2, produced by some <italic>A. baumannii</italic> strains [<xref rid=\"B112-ijms-21-05498\" ref-type=\"bibr\">112</xref>,<xref rid=\"B132-ijms-21-05498\" ref-type=\"bibr\">132</xref>]. Another surface adhesin in <italic>A. baumannii</italic>, the trimeric Ata autotransporter, is involved in biofilm production by binding various extracellular matrix/basal membrane (ECM/BM) components, including the basement protein laminin and collagen types I, III, IV, and V. During tissue damage, ECM/BM proteins become exposed serving as docking sites for <italic>A. baumannii</italic> and a niche supporting biofilm growth. Ata is also responsible for self-adhesion of <italic>A. baumannii</italic> cells and biofilm formation on various abiotic materials [<xref rid=\"B111-ijms-21-05498\" ref-type=\"bibr\">111</xref>,<xref rid=\"B133-ijms-21-05498\" ref-type=\"bibr\">133</xref>,<xref rid=\"B134-ijms-21-05498\" ref-type=\"bibr\">134</xref>]. Other proteins and structures contributing to <italic>A. baumannii</italic> biofilm development comprise CarO, Omp33, the resistance–nodulation–division (RND) efflux pumps, Pap pilus, and alginate [<xref rid=\"B102-ijms-21-05498\" ref-type=\"bibr\">102</xref>,<xref rid=\"B103-ijms-21-05498\" ref-type=\"bibr\">103</xref>,<xref rid=\"B108-ijms-21-05498\" ref-type=\"bibr\">108</xref>,<xref rid=\"B114-ijms-21-05498\" ref-type=\"bibr\">114</xref>,<xref rid=\"B116-ijms-21-05498\" ref-type=\"bibr\">116</xref>,<xref rid=\"B135-ijms-21-05498\" ref-type=\"bibr\">135</xref>,<xref rid=\"B136-ijms-21-05498\" ref-type=\"bibr\">136</xref>]. Depending on the experimental conditions, various structures and mechanisms responsible for pellicle formation were identified [<xref rid=\"B104-ijms-21-05498\" ref-type=\"bibr\">104</xref>,<xref rid=\"B105-ijms-21-05498\" ref-type=\"bibr\">105</xref>,<xref rid=\"B106-ijms-21-05498\" ref-type=\"bibr\">106</xref>,<xref rid=\"B117-ijms-21-05498\" ref-type=\"bibr\">117</xref>,<xref rid=\"B137-ijms-21-05498\" ref-type=\"bibr\">137</xref>]. Two independent studies demonstrated that the iron uptake systems, as well as CarO, OprD, and OprC porins, are required to develop pellicles [<xref rid=\"B105-ijms-21-05498\" ref-type=\"bibr\">105</xref>,<xref rid=\"B117-ijms-21-05498\" ref-type=\"bibr\">117</xref>] In addition, the overexpression of multiple pili systems, including Fil and Csu pili, was also observed [<xref rid=\"B105-ijms-21-05498\" ref-type=\"bibr\">105</xref>,<xref rid=\"B117-ijms-21-05498\" ref-type=\"bibr\">117</xref>,<xref rid=\"B137-ijms-21-05498\" ref-type=\"bibr\">137</xref>].</p><p>At least three two-component systems regulate surface motility and biofilm/pellicle formation: the aforementioned BmfRS pathway, GacSA, and CheA/Y [<xref rid=\"B48-ijms-21-05498\" ref-type=\"bibr\">48</xref>,<xref rid=\"B106-ijms-21-05498\" ref-type=\"bibr\">106</xref>,<xref rid=\"B120-ijms-21-05498\" ref-type=\"bibr\">120</xref>]. CheA/Y is a hybrid sensor histidine kinase/response regulator that controls the <italic>csuA</italic>/<italic>ABCDE</italic> operon and the AbaI-dependent quorum-sensing (QS) pathway [<xref rid=\"B106-ijms-21-05498\" ref-type=\"bibr\">106</xref>]. The QS system of <italic>A. baumannii</italic> consists of AbaI autoinducer synthase and the AbaR receptor protein for the autoinducer, <italic>N</italic>-acyl homoserine lactone (AHL) (for more details, see <xref ref-type=\"fig\" rid=\"ijms-21-05498-f002\">Figure 2</xref>B) [<xref rid=\"B138-ijms-21-05498\" ref-type=\"bibr\">138</xref>]. Different types of AHLs were detected in <italic>A. baumannii</italic> [<xref rid=\"B139-ijms-21-05498\" ref-type=\"bibr\">139</xref>,<xref rid=\"B140-ijms-21-05498\" ref-type=\"bibr\">140</xref>,<xref rid=\"B141-ijms-21-05498\" ref-type=\"bibr\">141</xref>]. Interestingly, C8-AHL and 3-oxo-C8-AHL were produced by both soil and nosocomial <italic>A. baumannii</italic> strains, whereas long-chain AHLs with C10, C12, C14, and C16 acyl chains were detected only in the nosocomial isolates [<xref rid=\"B140-ijms-21-05498\" ref-type=\"bibr\">140</xref>]. During biofilm formation, the AdeFGH efflux pump participates in the transport of AHLs [<xref rid=\"B116-ijms-21-05498\" ref-type=\"bibr\">116</xref>]. It was suggested that QS signals may initiate twitching motility and the attachment of <italic>A. baumannii</italic> to abiotic surfaces via the CsuA/BABCDE secretion system [<xref rid=\"B122-ijms-21-05498\" ref-type=\"bibr\">122</xref>]. Other studies reported that AbaI is required for the later stages of biofilm development [<xref rid=\"B139-ijms-21-05498\" ref-type=\"bibr\">139</xref>,<xref rid=\"B141-ijms-21-05498\" ref-type=\"bibr\">141</xref>]. Since QS signaling molecules in some bacteria are strong iron chelators, ferric iron (Fe<sup>3+</sup>) limitation increases the AHL level in <italic>A. baumannii</italic> in a dose-dependent manner, leading to a stress response and biofilm formation [<xref rid=\"B125-ijms-21-05498\" ref-type=\"bibr\">125</xref>].</p><p>Nucleotide second messengers such as cAMP, cyclic di-GMP (c-di-GMP), and penta/tetra-guanosine phosphate ((p)ppGpp) are key regulators of numerous bacterial traits including adaptation to harsh environments, as well as transition from biofilm to motility, mutualism to commensalism, acute to chronic virulence characteristics, and cell division to cell differentiation [<xref rid=\"B142-ijms-21-05498\" ref-type=\"bibr\">142</xref>,<xref rid=\"B143-ijms-21-05498\" ref-type=\"bibr\">143</xref>]. The exact functions of these messengers in <italic>A. baumannii</italic> were only partially examined. It was reported that enhanced cAMP levels, caused by the lack of cAMP phosphodiesterase, lead to the inhibition of surface-associated motility and pellicle formation [<xref rid=\"B104-ijms-21-05498\" ref-type=\"bibr\">104</xref>]. C-di-GMP is synthesized by diguanylate cyclase activity of GGDEF domain-containing proteins, while degradation of c-di-GMP into two GMP molecules is catalyzed by the phosphodiesterase activity of EAL domain-containing proteins [<xref rid=\"B144-ijms-21-05498\" ref-type=\"bibr\">144</xref>,<xref rid=\"B145-ijms-21-05498\" ref-type=\"bibr\">145</xref>]. Most c-di-GMP-dependent signalling pathways regulate the bacteria ability to interact with abiotic surfaces or with other bacterial and eukaryotic cells. Eleven genes for GGDEF/EAL proteins in the genome of the <italic>A. baumannii</italic> 17<italic>,</italic>978 strain were identified, and most of the predicted proteins were enzymatically active [<xref rid=\"B118-ijms-21-05498\" ref-type=\"bibr\">118</xref>]. It was demonstrated that distinct panels of these enzymes promote biofilm formation, macro-colony growth, and surface-associated motility [<xref rid=\"B118-ijms-21-05498\" ref-type=\"bibr\">118</xref>].</p><p>The (p)ppGpp alarmone is produced by the RelA/SpoT proteins in response to amino-acid starvation and other stresses. (p)ppGpp triggers the stringent response resulting in the downregulated transcription of most metabolic genes and the upregulation of genes responsible for amino-acid biosynthesis [<xref rid=\"B146-ijms-21-05498\" ref-type=\"bibr\">146</xref>,<xref rid=\"B147-ijms-21-05498\" ref-type=\"bibr\">147</xref>,<xref rid=\"B148-ijms-21-05498\" ref-type=\"bibr\">148</xref>]. The stringent response was linked to biofilm formation in a range of pathogens, including <italic>Acinetobacter</italic> spp. [<xref rid=\"B142-ijms-21-05498\" ref-type=\"bibr\">142</xref>,<xref rid=\"B149-ijms-21-05498\" ref-type=\"bibr\">149</xref>,<xref rid=\"B150-ijms-21-05498\" ref-type=\"bibr\">150</xref>]. The formation of biofilms is impaired [<xref rid=\"B149-ijms-21-05498\" ref-type=\"bibr\">149</xref>,<xref rid=\"B151-ijms-21-05498\" ref-type=\"bibr\">151</xref>] or enhanced [<xref rid=\"B152-ijms-21-05498\" ref-type=\"bibr\">152</xref>,<xref rid=\"B153-ijms-21-05498\" ref-type=\"bibr\">153</xref>] in (p)ppGpp-deficient bacteria. Recent studies revealed the interplay among the stringent response, QS, motility, and biofilm/pellicle formation in <italic>A. baumannii</italic>, but the exact mechanisms remain unclear (<xref ref-type=\"fig\" rid=\"ijms-21-05498-f002\">Figure 2</xref>B). It was reported that the formation of <italic>A. baumannii</italic> biofilms can be inhibited by a synthetic dodecapeptide 1081, which triggers degradation of (p)ppGpp [<xref rid=\"B149-ijms-21-05498\" ref-type=\"bibr\">149</xref>]. However, the activity of peptide 1081 and its link with the stringent response were recently questioned [<xref rid=\"B154-ijms-21-05498\" ref-type=\"bibr\">154</xref>]. The Δ<italic>relA</italic> mutation in <italic>A. baumannii</italic> results in a hypermotile phenotype, as well as in the overproduction of AbaR and acinetin-505. Acinetin-505 is a 505-Da lipopeptide that may act as a surfactant promoting surface-associated motility [<xref rid=\"B127-ijms-21-05498\" ref-type=\"bibr\">127</xref>], biofilm formation, and virulence [<xref rid=\"B121-ijms-21-05498\" ref-type=\"bibr\">121</xref>]. Numerous studies indicated that (p)ppGpp is involved in the formation of dormant persister bacteria, which are implicated in biofilm tolerance to antibiotics [<xref rid=\"B127-ijms-21-05498\" ref-type=\"bibr\">127</xref>,<xref rid=\"B155-ijms-21-05498\" ref-type=\"bibr\">155</xref>].</p></sec><sec id=\"sec5dot2-ijms-21-05498\"><title>5.2. Persisters and Heterogeneity of A. baumannii Populations</title><p>Persisters are able to survive exposure to a bactericidal drug concentration, and they usually constitute a small fraction of bacterial populations [<xref rid=\"B156-ijms-21-05498\" ref-type=\"bibr\">156</xref>]. Antibiotic persistence is a transient state, and, when persisters resume growth after drug treatment, their progeny become antibiotic susceptible. There is an ongoing debate about mechanisms underlying persister formation [<xref rid=\"B157-ijms-21-05498\" ref-type=\"bibr\">157</xref>,<xref rid=\"B158-ijms-21-05498\" ref-type=\"bibr\">158</xref>,<xref rid=\"B159-ijms-21-05498\" ref-type=\"bibr\">159</xref>,<xref rid=\"B160-ijms-21-05498\" ref-type=\"bibr\">160</xref>]. It is well known that biofilms provide conducive niches that favor the formation of persisters [<xref rid=\"B161-ijms-21-05498\" ref-type=\"bibr\">161</xref>]. Persisters can arise spontaneously or in response to stress caused by antibiotics, the host immune response, ROS, high osmolarity, pH changes, diauxic shift, desiccation, or nutrient starvation. In addition to the stringent response mentioned above, toxin–antitoxin modules, quorum signaling, efflux pumps, the SOS, and oxidative stress responses can be activated during persister formation. These pathways and stimuli may lead to decreased metabolism, depletion of ATP, protein aggregation, and inhibition of translation [<xref rid=\"B132-ijms-21-05498\" ref-type=\"bibr\">132</xref>,<xref rid=\"B162-ijms-21-05498\" ref-type=\"bibr\">162</xref>,<xref rid=\"B163-ijms-21-05498\" ref-type=\"bibr\">163</xref>]. It was recently demonstrated that, in <italic>E. coli</italic>, (p)ppGpp induces production of the ribosome modulation factor (RMF), the hibernation-promoting factor (Hpf), and the ribosome-associated inhibitor (RaiA), which convert active 70S ribosomes into inactive 70S, 90S, and 100S ribosomes, leading to translation inhibition [<xref rid=\"B164-ijms-21-05498\" ref-type=\"bibr\">164</xref>]. It should be noted that, due to diverse conditions in biofilm structures, multiple mechanisms triggering antibiotic tolerance, and stochastic effects, persister subpopulations should be considered as a heterogeneous group of cells.</p><p>The formation of <italic>A. baumannii</italic> persisters induced by polymixin B, meropenem, and ceftazidime was reported [<xref rid=\"B165-ijms-21-05498\" ref-type=\"bibr\">165</xref>,<xref rid=\"B166-ijms-21-05498\" ref-type=\"bibr\">166</xref>,<xref rid=\"B167-ijms-21-05498\" ref-type=\"bibr\">167</xref>,<xref rid=\"B168-ijms-21-05498\" ref-type=\"bibr\">168</xref>,<xref rid=\"B169-ijms-21-05498\" ref-type=\"bibr\">169</xref>]. It was also found that the ppGpp deficiency in the <italic>A. baumannii ΔrelA</italic> strain reduced formation of persister cells tolerant to colistin and rifampicin [<xref rid=\"B127-ijms-21-05498\" ref-type=\"bibr\">127</xref>]. The analysis of the transcriptome of persisters tolerant to ceftazidime revealed upregulation of two toxin–antitoxin systems HigB/HigA and DUF1044/RelB, as well as downregulation of certain metabolic pathways, including the electron transport chain and citrate cycle [<xref rid=\"B168-ijms-21-05498\" ref-type=\"bibr\">168</xref>]. Interestingly, the expression of genes associated with biodegradation pathways of aromatic compounds was detected in persistent cells. It was suggested that the degradation of aromatic rings in antibiotics could be utilized by <italic>A. baumannii</italic> persisters during nutrient starvation [<xref rid=\"B168-ijms-21-05498\" ref-type=\"bibr\">168</xref>]. Zou et al. found that the major fraction of <italic>A. baumannii</italic> persisters that survive β-lactam antibiotic treatment contains spherical non-walled, but metabolically active cells [<xref rid=\"B170-ijms-21-05498\" ref-type=\"bibr\">170</xref>]. In contrast to wall-deficient, so-called L-forms of other Gram-negative bacteria, <italic>A. baumannii</italic> non-walled cells were able to survive without any osmoprotective agent. This type of <italic>A. baumannii</italic> persister cell was also formed during antibiotic therapy in vivo in <italic>Galleria melonella</italic> larvae which were used as the infection model.</p><p>Biofilms provide a conducive environment facilitating not only phenotypic heterogeneity (e.g., persister formation) but also genetic diversification. It was demonstrated that evolution within <italic>A. baumannii</italic> biofilms can generate greater genetic diversity than in planktonic, well-mixed populations [<xref rid=\"B171-ijms-21-05498\" ref-type=\"bibr\">171</xref>]. Planktonic cells exposed to ciprofloxacin shared the same limited number of mutations in topoisomerase (the primary drug target), whereas biofilm-adapted populations acquired different types of mutations in the regulators of the efflux pumps. The emergence of a certain trade-off between fitness and resistance level was detected; biofilm-adapted clones were less drug-resistant than planktonic cells, but more fit in the absence of the drug [<xref rid=\"B171-ijms-21-05498\" ref-type=\"bibr\">171</xref>]. Other studies demonstrated that the exposure of <italic>A. baumannii</italic> biofilms to sub-inhibitory concentrations of ciprofloxacin or tetracycline led to the generation of genetic and phenotypic diversity among biofilm dispersal isolates [<xref rid=\"B172-ijms-21-05498\" ref-type=\"bibr\">172</xref>]. Dispersed cells accumulate a wide diversity of mutations that enhance biofilm formation and antibiotic resistance. For example, the efflux transport system AdeABC was upregulated in the presence of both ciprofloxacin and tetracycline, whereas the expression of RecA and UmuD, which are involved in DNA repair and mutagenesis, was increased during ciprofloxacin treatment.</p><p>Phenotypic alteration between opaque (VIR-O) and translucent (AV-T) colonies is another example of <italic>A. baumannii</italic> population heterogeneity. It was shown that both phenotypes exhibited significant differences in cell morphology, biofilm formation, surface motility, antibiotic resistance, and virulence [<xref rid=\"B173-ijms-21-05498\" ref-type=\"bibr\">173</xref>]. VIR-O cells were covered with a thicker coating of the extracellular capsule, and they were more resistant to disinfectants, ROS, antibiotics, lysozyme, and the cathelin-related antimicrobial peptide [<xref rid=\"B53-ijms-21-05498\" ref-type=\"bibr\">53</xref>]. This highly virulent subpopulation dominated in the bloodstream of human patients. The AV-T cells produced more dense biofilms and a larger quantity of OMVs in comparison with the VIR-O variant [<xref rid=\"B174-ijms-21-05498\" ref-type=\"bibr\">174</xref>]. The analysis of VIR-O and AV-T transcriptomes suggested that the AV-T subpopulation is better adapted for natural environments outside the host than VIR-O cells. The phenotype switching between VIR-O and AV-T subpopulations depends on several factors, including a TetR-type transcriptional regulator ABUW_1645, the ArpAB efflux system, the EnvZ/OmpR two-component system [<xref rid=\"B174-ijms-21-05498\" ref-type=\"bibr\">174</xref>], and ppGpp [<xref rid=\"B127-ijms-21-05498\" ref-type=\"bibr\">127</xref>].</p><p>Antibiotic heteroresistance is another common phenotype that may contribute to the heterogeneity of bacterial populations. We describe this phenomenon in the next section, discussing the broader problem of multidrug resistance <italic>of A. baumannii</italic>.</p></sec></sec><sec id=\"sec6-ijms-21-05498\"><title>6. Multidrug Resistance of <italic>A. baumannii</italic></title><p>Multidrug-resistant pathogens pose serious threats in healthcare settings worldwide. For the past number of years, antimicrobial discovery and resistance development to new antimicrobials occurred almost at the same time. Not surprisingly, <italic>A. baumannii</italic>, similarly to other bacteria, also acquired resistance to newly developed antimicrobial agents [<xref rid=\"B175-ijms-21-05498\" ref-type=\"bibr\">175</xref>]. To characterize the various patterns of resistance, the following terms are used: MDR, extensively drug-resistant (XDR), and pandrug-resistant (PDR) bacteria. According to the definition proposed by Magiorakos et al. [<xref rid=\"B176-ijms-21-05498\" ref-type=\"bibr\">176</xref>], MDR is defined as acquired non-susceptibility to at least one agent in three or more antimicrobial categories, XDR is defined as non-susceptibility to at least one agent in all but two or fewer antimicrobial categories, and PDR is defined as non-susceptibility to all agents in all antimicrobial categories. In the case of <italic>Acinetobacter</italic> spp., 22 antimicrobial agents belong to nine categories: aminoglycosides, antipseudomonal carbapenems, antipseudomonal fluoroquinolones, (antipseudomonal) penicillins + β lactamase inhibitors, extended spectrum cephalosporins, folate pathway inhibitors, polymixins, and tetracyclines. <italic>A. baumannii</italic> can become resistant to a variety of antibiotics via intrinsic and acquisition mechanisms. Its ability to acquire drug resistance genes from other human pathogens is not well understood. However, considering the capability of the <italic>A. baumannii</italic> genome to exchange genetic material both within and between species, it is quite likely that these bacteria may have evolved toward enhanced pathogenicity.</p><sec id=\"sec6dot1-ijms-21-05498\"><title>6.1. Mechanisms Responsible for A. baumannii Multidrug Resistance</title><p>The main mechanisms conferring resistance to different classes of antibiotics include the presence of β-lactamases, modifying enzymes, permeability defects, alteration of target sites, and multidrug efflux pumps [<xref rid=\"B177-ijms-21-05498\" ref-type=\"bibr\">177</xref>]. Severe hospital-acquired infections caused by <italic>A. baumannii</italic> involve the use of carbapenems as highly effective drugs of choice used for the treatment of such infections [<xref rid=\"B178-ijms-21-05498\" ref-type=\"bibr\">178</xref>]. Because of their broad spectrum, carbapenems are often active against microorganisms resistant to other antimicrobial compounds, and they are frequently used to treat complicated bacterial infections. Over the last few years, <italic>A. baumannii</italic> MDR strains became increasingly resistant to carbapenems, the drug of choice to treat severe infections caused by these bacteria. The main cause of carbapenem resistance is the presence of oxacillinases (OXA), which belong to the Ambler class D β-lactamases. Over 400 OXA enzymes encoded by chromosome- or plasmid-located genes were characterized. Other classes of β-lactamases: class A, class B (metallo-β-lactamases, MBL), and class C (AmpC) were also identified in <italic>A. baumannii</italic> strains [<xref rid=\"B6-ijms-21-05498\" ref-type=\"bibr\">6</xref>,<xref rid=\"B179-ijms-21-05498\" ref-type=\"bibr\">179</xref>,<xref rid=\"B180-ijms-21-05498\" ref-type=\"bibr\">180</xref>,<xref rid=\"B181-ijms-21-05498\" ref-type=\"bibr\">181</xref>,<xref rid=\"B182-ijms-21-05498\" ref-type=\"bibr\">182</xref>,<xref rid=\"B183-ijms-21-05498\" ref-type=\"bibr\">183</xref>,<xref rid=\"B184-ijms-21-05498\" ref-type=\"bibr\">184</xref>,<xref rid=\"B185-ijms-21-05498\" ref-type=\"bibr\">185</xref>]. The most frequent MBLs in <italic>A. baumannii</italic> are imipenemases (IMPs), Verona integron-encoded MBL (VIM), and MBL from New Delhi (NDM). Class C β-lactamases are encoded by the <italic>ampC</italic> gene. Overexpression of <italic>ampC</italic>, regulated by an upstream insertion sequence (IS) element known as IS<italic>Aba1</italic>, is the main mechanism of resistance to third-generation cephalosporins in <italic>A. baumannii</italic> [<xref rid=\"B186-ijms-21-05498\" ref-type=\"bibr\">186</xref>]. Overexpression of the OXA and AmpC enzymes due to the presence of IS elements (see below), as well as the emergence of new OXA and AmpC variants, contributes to the increasing problem of <italic>A. baumannii</italic> resistance [<xref rid=\"B187-ijms-21-05498\" ref-type=\"bibr\">187</xref>,<xref rid=\"B188-ijms-21-05498\" ref-type=\"bibr\">188</xref>].</p><p>Another mechanism of <italic>A. baumannii</italic> resistance is associated with enzymatic modification of the antimicrobial molecule. One of the best examples of resistance via modification of the drug is the presence of a large group of aminoglycoside-modifying enzymes (AMEs). These enzymes possess unique substrate specificity and modify amino- or hydroxyl- groups of the aminoglycosides. There are three different types of AMEs, acetyltransferases, nucleotidyl transferases, and phosphotransferases, while all of them were identified in <italic>A. baumannii</italic> isolates [<xref rid=\"B6-ijms-21-05498\" ref-type=\"bibr\">6</xref>].</p><p>Proteomic analysis of <italic>A. baumannii</italic> MDR strains shows protein variability that could be correlated with the appearance of resistance phenotypes, especially OMPs, which are involved in cellular drug uptake or efflux. The emergence of an antibiotic resistance level is often related to diverse variations in the expression of OMPs. It was found that, in <italic>A. baumannii</italic>, OmpA is strongly associated not only with adhesion to epithelial cells and biofilm formation, as mentioned earlier, but also with the modulation of cellular permeability and antibiotic resistance [<xref rid=\"B103-ijms-21-05498\" ref-type=\"bibr\">103</xref>]. Importantly, changes in permeability frequently result in low-level resistance; therefore, the combination with other mechanisms, such as increased expression of efflux pumps, to confer a high-level antibiotic resistance phenotype is required [<xref rid=\"B189-ijms-21-05498\" ref-type=\"bibr\">189</xref>].</p><p>Another common mechanism of antibiotic resistance in <italic>A. baumannii</italic> is alteration of the target site or cellular functions [<xref rid=\"B2-ijms-21-05498\" ref-type=\"bibr\">2</xref>]. This often results from spontaneous mutation of a bacterial gene on the chromosome. Modification of the target site results in decreased affinity for the drug molecule. One of the most known examples of target changes is enzymatic alteration of the binding site. In an alternative pathway, bacteria produce new proteins that protect the target against an antibiotic. Examples of drugs affected by this mechanism include fluoroquinolones and tetracyclines [<xref rid=\"B190-ijms-21-05498\" ref-type=\"bibr\">190</xref>]. In <italic>A. baumannii</italic>, point mutations in the <italic>gyrA/parC</italic> topoisomerases result in fluoroquinolones resistance, whereas a mutation in <italic>rpsJ</italic>, the gene that encodes the ribosomal S10 protein, is responsible for tigecycline resistance [<xref rid=\"B191-ijms-21-05498\" ref-type=\"bibr\">191</xref>]. Another interesting example is the mechanism responsible for colistin resistance. Positively charged colistin kills bacteria by interacting with the negatively charged lipid A and destabilization of the OM. Mutations in the lipid A biosynthesis genes, <italic>lpxA</italic>, <italic>lpxC</italic>, and <italic>lpxD</italic>, result in the complete loss of lipooligosaccharides, which in turn abolish interactions with colistin. The second mechanism depends on the PmrAB two-component system. Mutations in <italic>pmrA</italic> or <italic>pmrB</italic> lead to the activation of the <italic>pmrC</italic> gene located upstream of <italic>pmrAB,</italic> encoding phosphoetanolamine transferase. Phosphoethanolamine transferred to lipid A decreases the negative charge of LOS, preventing colistin binding [<xref rid=\"B62-ijms-21-05498\" ref-type=\"bibr\">62</xref>,<xref rid=\"B63-ijms-21-05498\" ref-type=\"bibr\">63</xref>].</p><p>Among several types of efflux pumps that confer multidrug resistance<italic>,</italic> the RND efflux systems (AdeABC, AdeFGH, AdeIJK) are the most prevalent in <italic>A. baumannii</italic>. The AdeABC pump, found in 80% of <italic>A. baumannii</italic> isolates, extrudes a wide range of antibiotics, including β-lactams, aminoglicosides, fluoroquinolones, tetracyclines-tigecycline, macrolides–lincosamides, and chloramphenicol [<xref rid=\"B103-ijms-21-05498\" ref-type=\"bibr\">103</xref>,<xref rid=\"B177-ijms-21-05498\" ref-type=\"bibr\">177</xref>,<xref rid=\"B192-ijms-21-05498\" ref-type=\"bibr\">192</xref>].The expression of AdeABC is tightly regulated by the AdeRS two-component system which was found to control almost 600 other genes [<xref rid=\"B192-ijms-21-05498\" ref-type=\"bibr\">192</xref>,<xref rid=\"B193-ijms-21-05498\" ref-type=\"bibr\">193</xref>]. Point mutations in the <italic>adeR–adeS</italic> genes or the presence of an IS<italic>Aba1</italic> insertion sequence upstream from the <italic>adeABC</italic> operon result in the overexpression of the AdeABC pump and multidrug resistance [<xref rid=\"B194-ijms-21-05498\" ref-type=\"bibr\">194</xref>]. The AdeFGH pump, when overexpressed, confers enhanced resistance to fluoroquinolones, tetracycline–tigecycline, chloramphenicol, clindamycin, trimethoprim, sulfamethoxazole, sodium dodecyl sulfate, and dyes such as ethidium bromide, safranin O, and acridine orange [<xref rid=\"B195-ijms-21-05498\" ref-type=\"bibr\">195</xref>]. It was found that overexpression of AdeFGH is caused by mutation in the <italic>adeL</italic> gene located upstream from the <italic>adeFGH</italic> operon, as well as the encoding of an a-Lys-type transcriptional regulator. The AdeIJK pump is produced in <italic>A. baumannii</italic> constitutively, and it is responsible for resistance to the same major drug classes as AdeABC, as well as antifolates and fusidic acid [<xref rid=\"B115-ijms-21-05498\" ref-type=\"bibr\">115</xref>].</p></sec><sec id=\"sec6dot2-ijms-21-05498\"><title>6.2. Genetic Elements Responsible for A. baumannii Multidrug Resistance</title><p>Members of the genus <italic>Acinetobacter</italic> quickly develop resistance to antimicrobial compounds. Antibiotic resistance genes can be disseminated through various mechanisms of horizontal gene transfer such as transformation, conjugation, and transduction. <italic>A. baumannii</italic> appears to use all the mechanisms; however, recent studies point to natural transformation as the mechanism playing an important role in the acquisition of the multidrug resistance phenotype [<xref rid=\"B196-ijms-21-05498\" ref-type=\"bibr\">196</xref>]. In this process, bacteria appeared to be capable of uptake, integration, and functional expression of naked fragments of extracellular DNA from the environment. Bacteria could use transformation to avoid being targeted by antibiotics by accepting the genetic variation present in their neighborhood, including drug resistance genes [<xref rid=\"B197-ijms-21-05498\" ref-type=\"bibr\">197</xref>]. Multidrug resistance of <italic>A. baumannii</italic> is mainly due to the horizontal acquisition of resistance genes, although recent studies showed that increased expression of chromosomal genes for the efflux system plays a major role in MDR [<xref rid=\"B177-ijms-21-05498\" ref-type=\"bibr\">177</xref>].</p><p>Often, <italic>A. baumannii</italic> resistance to more than one class of antibiotics occurs when genes encoding resistance to antimicrobial agents are physically located in close proximity to each other on mobile genetic elements such as plasmids, transposons, and integrons. Plasmid profiling revealed the presence of multiple plasmids of varying molecular sizes in more than 80% of <italic>Acinetobacter</italic> isolates [<xref rid=\"B198-ijms-21-05498\" ref-type=\"bibr\">198</xref>]. They constitute a reservoir of genes important not only for the dissemination of antibiotic resistance but also essential for bacteria adaptation and evolution. Recent analysis of 173 complete plasmid sequences from <italic>A. baumannii</italic> isolates originated from 17 countries revealed that 34.6% of the plasmids pose antibiotic resistance genes [<xref rid=\"B199-ijms-21-05498\" ref-type=\"bibr\">199</xref>]. Bertini et al. constructed a classification system for the <italic>A. baumannii</italic> plasmids based on the sequence of replicase genes, and they identified 19 homology groups (GR1–GR19) [<xref rid=\"B200-ijms-21-05498\" ref-type=\"bibr\">200</xref>]. Fourteen additional groups of plasmids were recently proposed [<xref rid=\"B199-ijms-21-05498\" ref-type=\"bibr\">199</xref>,<xref rid=\"B201-ijms-21-05498\" ref-type=\"bibr\">201</xref>,<xref rid=\"B202-ijms-21-05498\" ref-type=\"bibr\">202</xref>]. GR6 was the most prevalent group detected in antibiotic-resistant <italic>A</italic>. <italic>baumannii</italic> isolates from Europe [<xref rid=\"B203-ijms-21-05498\" ref-type=\"bibr\">203</xref>]. The GR6 plasmids harbor class D β-lactamase genes, including <italic>bla</italic><sub>OXA-23</sub>, <italic>bla</italic><sub>OXA-58</sub>, and <italic>bla</italic><sub>OXA-40</sub>, aminoglycoside-resistant genes (<italic>aph</italic>(3’)-Via, <italic>aadB</italic>, <italic>aadA2</italic>, <italic>strA</italic>, <italic>strB</italic>, <italic>aacA4, aph</italic>(3’)-Via), and sulfonamide (<italic>sul2</italic>) and streptomycin (<italic>strAB</italic>) resistance genes [<xref rid=\"B199-ijms-21-05498\" ref-type=\"bibr\">199</xref>,<xref rid=\"B200-ijms-21-05498\" ref-type=\"bibr\">200</xref>,<xref rid=\"B203-ijms-21-05498\" ref-type=\"bibr\">203</xref>].</p><p>IS and transposons (Tn) are able to move from one genomic location to another in the chromosome or plasmid DNA within a single cell [<xref rid=\"B204-ijms-21-05498\" ref-type=\"bibr\">204</xref>]. IS may include a strong promoter that initiates the expression of a downstream gene, e.g., IS<italic>Aba1</italic> located upstream of <italic>bla</italic><sub>OXA-51</sub> genes or the <italic>adeABC</italic> operon leading to intrinsic <italic>A. baumannii</italic> carbapenem resistance or multidrug resistance, respectively [<xref rid=\"B194-ijms-21-05498\" ref-type=\"bibr\">194</xref>,<xref rid=\"B205-ijms-21-05498\" ref-type=\"bibr\">205</xref>]. A diverse range of composite Tn, which harbor antibiotic resistance genes flanked by IS, was identified in <italic>A. baumannii</italic> isolates. These transposons encode AmpC cephalosporinases, OXA carbapenemases, aminoglycosidases, and NDM or VIM metallo-carbapenemases [<xref rid=\"B179-ijms-21-05498\" ref-type=\"bibr\">179</xref>,<xref rid=\"B186-ijms-21-05498\" ref-type=\"bibr\">186</xref>,<xref rid=\"B206-ijms-21-05498\" ref-type=\"bibr\">206</xref>,<xref rid=\"B207-ijms-21-05498\" ref-type=\"bibr\">207</xref>,<xref rid=\"B208-ijms-21-05498\" ref-type=\"bibr\">208</xref>,<xref rid=\"B209-ijms-21-05498\" ref-type=\"bibr\">209</xref>,<xref rid=\"B210-ijms-21-05498\" ref-type=\"bibr\">210</xref>]. For example, a chromosomally located Tn125-like transposon containing the <italic>bla</italic><sub>NDM-1</sub> gene was identified in NDM-1-producing <italic>A. baumannii</italic> from European countries [<xref rid=\"B179-ijms-21-05498\" ref-type=\"bibr\">179</xref>]. The <italic>bla</italic><sub>OXA-23</sub> genes with adjacent IS<italic>Aba1</italic> were detected in globally disseminated transposons Tn2006 and Tn2008, as well as in Tn2009 from <italic>A. baumannii</italic> strains isolated in China [<xref rid=\"B206-ijms-21-05498\" ref-type=\"bibr\">206</xref>,<xref rid=\"B208-ijms-21-05498\" ref-type=\"bibr\">208</xref>]. The largest antibiotic resistance gene clusters in various <italic>A. baumannii</italic> isolates are resistance islands designated AbaR1–R30. These complex transposons are located in the chromosomal <italic>comM</italic> gene (encoding the ATP-ase), and they carry heavy-metal resistance determinants apart from antibiotic resistance genes [<xref rid=\"B125-ijms-21-05498\" ref-type=\"bibr\">125</xref>,<xref rid=\"B211-ijms-21-05498\" ref-type=\"bibr\">211</xref>]. Most AbaRs from <italic>A. baumannii</italic> strains of international clone I share a backbone transposon Tn<italic>6019</italic> interrupted by a large compound transposon that contains a variable-resistance region flanked by two copies of Tn<italic>6018</italic> [<xref rid=\"B212-ijms-21-05498\" ref-type=\"bibr\">212</xref>]. AbaR1 contains genes conferring resistance to ampicillin (<italic>veb1</italic> and <italic>oxa10</italic>), sulfonamides (three copies of the <italic>sul1</italic> gene), streptomycin (two copies of <italic>aadA1</italic>; <italic>strA, strB</italic>), aminoglycoside (<italic>aadB, aacC1, aphA1b, aacA</italic>), chloramphenicol (<italic>cmlA1, cmlA5, cmlA9</italic>, and <italic>catA1</italic>), rifampin (<italic>arr2</italic>), trimethoprim (<italic>dfrA1</italic> and <italic>dfrA10</italic>), and tetracycline (<italic>tetA-A</italic> and <italic>tetA-G</italic>) [<xref rid=\"B211-ijms-21-05498\" ref-type=\"bibr\">211</xref>].</p><p>Integrons are responsible for the dissemination of antibiotic resistance, especially among Gram-negative bacteria [<xref rid=\"B213-ijms-21-05498\" ref-type=\"bibr\">213</xref>]. They can integrate into chromosomes or plasmid via site-specific recombination. These genetic elements are able to acquire, integrate, and express gene cassettes which can carry antibiotic resistance. Class 1 integrons are commonly found in <italic>A. baumannii</italic> and typically encode genes for aminoglycoside resistance, Ambler class A β-lactamases, metallo-beta-lactamases, and oxacillinases, as well resistance to antiseptics and sulfonamides [<xref rid=\"B196-ijms-21-05498\" ref-type=\"bibr\">196</xref>]. They were also reported in clinical <italic>A. baumannii</italic> strains. Many reports showed that clinical <italic>A. baumannii</italic> strains carrying integrons were significantly more resistant to all tested antibiotics than strains lacking integrons [<xref rid=\"B214-ijms-21-05498\" ref-type=\"bibr\">214</xref>]. It should be noted that some mobile genetic elements and resistance genes are disseminated worldwide (e.g., IS<italic>AbaI, bla</italic><sub>OXA-23</sub>, <italic>bla<sub>OXA-51</sub></italic>), whereas others are distributed locally across different countries or regions (e.g., integrons and class B carbapenemases are more frequently found in Asia) [<xref rid=\"B215-ijms-21-05498\" ref-type=\"bibr\">215</xref>,<xref rid=\"B216-ijms-21-05498\" ref-type=\"bibr\">216</xref>].</p></sec><sec id=\"sec6dot3-ijms-21-05498\"><title>6.3. Cross-Resistance, Co-Regulatory Resistance, and Stress-Induced Resistance to Antibiotics in A. baumannii</title><p>Antibiotics can induce selective pressure on bacterial populations, leading to antimicrobial resistance through a mechanism called cross-resistance. This mechanism confers resistance to an entire class of antibiotic and is mainly achieved via multidrug efflux pumps. In <italic>A. baumannii</italic>, efflux pumps can be specific for a single substrate or can confer resistance to multiple antimicrobials by facilitating the extrusion of a broad range of compounds including antibiotics, heavy metals, and biocides from the bacterial cell [<xref rid=\"B177-ijms-21-05498\" ref-type=\"bibr\">177</xref>]. Other studies demonstrated that treatment of <italic>A. baumannii</italic> infection with cationic microbial peptide colistin can induce not only increased resistance to antibiotics but also resistance to host cationic antimicrobials typically found at sites of inflammation. These findings showed that understanding the molecular basis of cross-resistance is important for the development of more effective therapeutic schemes [<xref rid=\"B217-ijms-21-05498\" ref-type=\"bibr\">217</xref>].</p><p>Another mechanism involved in bacterial resistance is called co-regulatory resistance. This occurs when resistant genes to antimicrobial agents are controlled by regulatory proteins [<xref rid=\"B218-ijms-21-05498\" ref-type=\"bibr\">218</xref>]. Commonality of target sites between different class of antibiotics leads to the selection of mutants and the emergence of cross-resistance, as well as the co-selection and persistence of antibiotic-resistant strains. The presence of class 1 integrons in <italic>A. baumannii</italic> strains confers several phenotypes, including resistance, to a broad range of antibiotic classes, in addition to heavy metals and biocides [<xref rid=\"B213-ijms-21-05498\" ref-type=\"bibr\">213</xref>]. In integrons, antibiotic resistance genes are under the control of a single promoter. As a result, these genes are expressed in a coordinated manner.</p><p>Some microorganisms readily acquire antibiotic resistance mechanisms in response to environmental stresses. It was shown that different physiological conditions influenced antimicrobial susceptibility and porin expression in <italic>A. baumannii</italic>. For example, putative efflux transporters were induced by the physiological concentrations of NaCl, contributing to increased tolerance of <italic>A. baumannii</italic> to aminoglycosides, carbapenems, quinolones, and colistin. Moreover, the physiological level of some cations present within the host promotes the upregulation of genes coding for multidrug efflux pumps [<xref rid=\"B219-ijms-21-05498\" ref-type=\"bibr\">219</xref>]. Such regulated changes in efflux pump expression may increase the ability of this pathogen to survive antibiotic challenge.</p><p>As mentioned, many clinical <italic>A. baumannii</italic> strains can survive drying for a prolonged period of time. However, when rehydration happens, DNA damage may occur such as various DNA lesions, cross-linking, base removal, and strand breaks. To repair some of the DNA damage, <italic>A. baumannii</italic> developed an inducible DNA damage response in which RecA plays a major regulatory role in mechanisms involved in stress survival [<xref rid=\"B220-ijms-21-05498\" ref-type=\"bibr\">220</xref>]. The RecA protein is involved in DNA damage repair and, consequently, in cellular protection against stresses induced by DNA damaging agents, several classes of antibiotics, and oxidative agents. This response increases mutagenesis and is one of the mechanisms used by <italic>A. baumannii</italic> to acquire antibiotic resistance, particularly in hospitals under clinically relevant DNA-damaging conditions [<xref rid=\"B221-ijms-21-05498\" ref-type=\"bibr\">221</xref>].</p><p>In another study, it was shown that <italic>A. baumannii</italic> cells pretreated at 45 °C for 30 min were better able to survive a subsequent streptomycin exposure than cells pretreated at 37 °C. This phenomenon may be explained by the synthesis of misfolded proteins, produced by the streptomycin-disrupted ribosome and inserted into the bacterial membrane. Treating <italic>A. baumannii</italic> cells with the aminoglycoside antibiotic streptomycin induces not only ribosomal mistranslation but also expression of the heat-shock proteins DnaK and GroEL, responsible for elimination of aberrant polypeptides, thereby reducing their toxicity to bacterial cells [<xref rid=\"B222-ijms-21-05498\" ref-type=\"bibr\">222</xref>].</p><p>The emergence of antibiotic-resistant <italic>A. baumannii</italic> strains may be preceded by the formation of persister cells. The evolution of antibiotic resistance promoted by persistence or tolerance was observed in vitro or in patients in the case of other bacterial species [<xref rid=\"B223-ijms-21-05498\" ref-type=\"bibr\">223</xref>,<xref rid=\"B224-ijms-21-05498\" ref-type=\"bibr\">224</xref>,<xref rid=\"B225-ijms-21-05498\" ref-type=\"bibr\">225</xref>,<xref rid=\"B226-ijms-21-05498\" ref-type=\"bibr\">226</xref>,<xref rid=\"B227-ijms-21-05498\" ref-type=\"bibr\">227</xref>]. Persisters, as non-dividing cells, can accumulate de novo mutations via mechanisms independent of DNA replication or via horizontal gene transfer.</p></sec><sec id=\"sec6dot4-ijms-21-05498\"><title>6.4. Heteroresistance to Antibiotics in A. baumannii</title><p>The term “heteroresistance” is defined as the presence of subpopulations of cells that have a higher MIC than the dominant population [<xref rid=\"B228-ijms-21-05498\" ref-type=\"bibr\">228</xref>,<xref rid=\"B229-ijms-21-05498\" ref-type=\"bibr\">229</xref>]. In contrast to persisters which are dormant cells, heteroresistant subpopulations can proliferate in the presence of antibiotics.</p><p><italic>A. baumannii</italic> heteroresistance to colistin in “colistin-susceptible” clinical isolates was described for the first time by Li et al. [<xref rid=\"B230-ijms-21-05498\" ref-type=\"bibr\">230</xref>]. Colistin heteroresistance was caused by mutations and the insertional inactivation of the lipid A biosynthesis genes, leading to the complete loss of lipooligosacchrides [<xref rid=\"B63-ijms-21-05498\" ref-type=\"bibr\">63</xref>,<xref rid=\"B231-ijms-21-05498\" ref-type=\"bibr\">231</xref>]. Heteroresistance to carbapenems, aminoglycosides, and trimethoprim/sulfamethoxazole in <italic>A. baumannii</italic> was also reported [<xref rid=\"B167-ijms-21-05498\" ref-type=\"bibr\">167</xref>,<xref rid=\"B232-ijms-21-05498\" ref-type=\"bibr\">232</xref>,<xref rid=\"B233-ijms-21-05498\" ref-type=\"bibr\">233</xref>]. For example, it was found that increased tobramycin resistance was an unstable phenotype that emerged due to the extensive RecA-dependent amplification of the <italic>aadB</italic> gene encoding an aminoglycoside adenylyltransferase. The <italic>aadB</italic> gene was carried on a plasmid, in the region containing four other resistance genes [<xref rid=\"B233-ijms-21-05498\" ref-type=\"bibr\">233</xref>]. Gene amplification seems to be the main mechanism conferring heteroresistance in various pathogens. Analysis of the prevalence and mechanisms of heteroresistance in <italic>A. baumannii, E. coli, Salmonella enterica</italic>, <italic>and Klebsiella pneumoniae</italic> revealed that almost 28% of clinical isolates were heteroresistant to various antibiotics. The majority of heteroresistance cases were unstable, and they resulted from tandem amplification of resistance genes [<xref rid=\"B234-ijms-21-05498\" ref-type=\"bibr\">234</xref>].</p></sec></sec><sec id=\"sec7-ijms-21-05498\"><title>7. Concluding Remarks</title><p><italic>A. baumannii</italic> is the primary species detected and isolated from hospital environments including intensive care units. The ease with which <italic>A. baumannii</italic> colonizes patients makes it problematic as these patients might transmit or become infected when the immune system is stressed. Most clinical <italic>A. baumannii</italic> isolates are naturally competent; thus, they can rapidly acquire resistance genes. The success of <italic>A. baumannii</italic> as a human pathogen is also associated with its outstanding ability to survive long-term desiccation in nosocomial environments. The formation of biofilms and antibiotic-tolerant persisters contributes to the heterogeneity of <italic>A. baumannii</italic> populations, facilitating their adaptation to fluctuating environments. It was proposed that, in response to desiccation stress, <italic>A. baumannii</italic> follows the “bust-and-boom” strategy [<xref rid=\"B87-ijms-21-05498\" ref-type=\"bibr\">87</xref>,<xref rid=\"B235-ijms-21-05498\" ref-type=\"bibr\">235</xref>]. The “bust-and-boom” strategy implies the death of the main stressed population (e.g., in the biofilm), where a few viable surviving bacteria can resume growth and restore the original population, once the environmental conditions are suitable. Persister cells and antibiotic heteroresistance are the main causes of recurrent and difficult-to-eradicate infections. Huge progress was made in the last decade toward understanding the mechanisms underlying these processes. However, there are still several issues that remain to be elucidated: (1) the controversy about the definition and metabolic state of persister cells still exists, and multiple definitions of heteroresistance used in the literature may often lead to confusing and inconsistent conclusions [<xref rid=\"B228-ijms-21-05498\" ref-type=\"bibr\">228</xref>]; (2) furthermore, different types of heteroresistant or persister cells, including viable but non-culturable bacteria and L-forms, may coexist in the same population; (3) persisters and heteroresistance are difficult to detect or diagnose with standard procedures; (4) it should also be kept in mind that most data regarding antibiotic persistence and heteroresistance originate from experiments performed in laboratory settings and animal models; therefore, they may not reflect the fate of pathogen cells in the human host; (5) inappropriate use of drugs may cause rapid development of persistence and resistance. These problems are associated with most pathogenic infections, but they are particularly important in the case of <italic>A. baumannii,</italic> due to its nosocomial origin and dramatically increasing prevalence of MDR isolates. Antibiotic persistence, population heterogeneity, and biofilm-related resistance should be considered as significant risk factors in the course of choosing an appropriate therapy. In the past few years, several strategies that eliminate <italic>A. baumannii</italic> biofilms and kill persister cells were reported. These approaches include the combination of antibiotics, natural or synthetic AMPs, stringent response inhibitors, QS antagonists, and biofilm disruptors [<xref rid=\"B149-ijms-21-05498\" ref-type=\"bibr\">149</xref>,<xref rid=\"B236-ijms-21-05498\" ref-type=\"bibr\">236</xref>,<xref rid=\"B237-ijms-21-05498\" ref-type=\"bibr\">237</xref>,<xref rid=\"B238-ijms-21-05498\" ref-type=\"bibr\">238</xref>,<xref rid=\"B239-ijms-21-05498\" ref-type=\"bibr\">239</xref>,<xref rid=\"B240-ijms-21-05498\" ref-type=\"bibr\">240</xref>,<xref rid=\"B241-ijms-21-05498\" ref-type=\"bibr\">241</xref>,<xref rid=\"B242-ijms-21-05498\" ref-type=\"bibr\">242</xref>,<xref rid=\"B243-ijms-21-05498\" ref-type=\"bibr\">243</xref>,<xref rid=\"B244-ijms-21-05498\" ref-type=\"bibr\">244</xref>,<xref rid=\"B245-ijms-21-05498\" ref-type=\"bibr\">245</xref>]. Although these results are promising, further studies are needed to implement novel therapeutic strategies or design drug candidates that will effectively combat <italic>A. baumannii</italic> infections.</p></sec></body><back><ack><title>Acknowledgments</title><p>We would like to thank Martin Blaszk for his assistance with the preparation of the manuscript.</p></ack><notes><title>Author Contributions</title><p>Conceptualization, S.M., B.F.-B. and E.L.; writing—original draft preparation, S.M., B.F.-B., A.Ł., D.K.-W., K.S.-S. and E.L.; writing—review and editing, S.M., B.F.-B. and E.L. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was funded by the National Science Center, Poland, grant number 2018/29/Z/NZ6/01040 (project funded under the Joint Program Initiative on Antimicrobial Resistance JPI EC AMR Call 2018).</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><glossary><title>Abbreviations</title><array orientation=\"portrait\"><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AHL</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>N</italic>-Acyl homoserine lactone</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AMEs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Aminoglycoside-modifying enzymes</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AMP</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Antimicrobial peptide</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">APRs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Aggregation-prone sequences</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">BZK</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Benzalkonium chloride</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CPS</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Capsular polysaccharides</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DCs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dendritic cells</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">EPS</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Extracellular polymeric substances</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ICU</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Intensive care units</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IHF</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Integration host factor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IM</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Inner membrane</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">LOS</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Lipooligosaccharides</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">LPS</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Lipopolysaccharides</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MDR</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Multidrug-resistant</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NETs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Neutrophil extracellular traps</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">OM</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Outer membrane</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">OMP</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Outer membrane protein</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">OMVs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Outer membrane vesicles</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PAMPs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Pathogen-associated molecular patterns</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PDR</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Pan-drug resistant</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PNAG</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Poly-β-(1–6)-<italic>N</italic>-acetylglucosamine</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PRRs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Pattern recognition receptors</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">QACs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Quaternary amine compounds</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">QS</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Quorum-sensing</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">QQ</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Quorum-quenching</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ROS</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Reactive oxygen species</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">RRF</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ribosomal recycling factor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">T2SS</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Type II secretion system</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TLR4</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Toll-like receptor 4</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TNF</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tumor necrosis factor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">XDR</td><td 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Agents</source><year>2019</year><volume>53</volume><fpage>337</fpage><lpage>342</lpage><pub-id pub-id-type=\"doi\">10.1016/j.ijantimicag.2018.11.004</pub-id><pub-id pub-id-type=\"pmid\">30423343</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijms-21-05498-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p><italic>Acinetobacter baumannii</italic> uses different mechanisms to evade the innate immune response. <bold>①</bold> Hepta-acylation of lipid A in lipooligosaccharide (LOS) fortifies the outer membrane (OM) and protects <italic>A. baumannii</italic> from cationic antimicrobial peptides (AMPs), colistin, and lysozyme. <bold>②</bold> Highly hydrophilic and negatively charged capsular polysaccharides (CPS) hinder interactions with negatively charged surfaces of neutrophils and macrophages; the capsule is also a barrier which protects against complement-mediated killing, lysozyme degradation, and reactive oxygen species (ROS). <bold>③</bold> Outer membrane protein A (OmpA) interacts with factor H (FH), thereby inhibiting the complement-mediated killing; OmpA induces ROS production and the death of dendritic cells (DCs). <bold>④</bold> CipA forms a complex with plasminogen/plasmin, which degrades the complement component C3b; CipA and <bold>⑤</bold> the protein killing factor (PKF) serine protease inhibit the alternative complement pathway. <bold>⑥</bold> The type II secretion system (T2SS) contributes to serum resistance, and it probably participates in CipA and PKF serine protease secretion. <bold>⑦</bold> Surface-exposed phospholipids are potential activators of the alternative complement pathway. The Mla system prevents the accumulation of phospholipids in the outer leaflet of the OM. <bold>⑧</bold> Phenylacetate, a derivative of phenylalanine and neutrophil attractant, is removed from the bacterial cell by conversion to acetyl-coenzyme A (CoA) and succinyl-CoA. <bold>⑨</bold> Enhanced catalase activity enables <italic>A. baumannii</italic> to survive in macrophages in the presence of ROS. <bold>⑩</bold>\n<italic>A. baumannii</italic> can spread during infection using neutrophils and macrophages.</p></caption><graphic xlink:href=\"ijms-21-05498-g001\"/></fig><fig id=\"ijms-21-05498-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p><italic>A. baumannii</italic> forms biofilms on solid surfaces and pellicles at the air-liquid interface. (<bold>A</bold>) Extracellular appendages involved in biofilm/pellicle formation include exopolysaccharides (capsular polysaccharides, poly-β-(1–6)-<italic>N</italic>-acetylglucosamine (PNAG), alginate), and pili [<xref rid=\"B108-ijms-21-05498\" ref-type=\"bibr\">108</xref>]. Csu pili are assembled via the chaperone-usher pathway. The CsuE adhesin, which is located at the pilus tip, exposes three hydrophobic finger-like loops that may insert into cavities in abiotic surfaces [<xref rid=\"B109-ijms-21-05498\" ref-type=\"bibr\">109</xref>]. Type IV pili (T4P) are composed of PilA subunits with variable amino-acid sequences in different <italic>A. baumannii</italic> strains. Depending on the PilA structure, the pili promote twitching motility or biofilm formation [<xref rid=\"B110-ijms-21-05498\" ref-type=\"bibr\">110</xref>]. The chaperone–usher P pili are overproduced in <italic>A. baumannii</italic> pellicles [<xref rid=\"B105-ijms-21-05498\" ref-type=\"bibr\">105</xref>]. The homotrimeric Ata autotransporter binds to extracellular matrix components and abiotic surfaces. The transmembrane anchor domain (TM) facilitates the export of the passenger domain (PSD) to the cell surface through a pore formed by the TM. Flexible PSDs allow interactions with different surfaces [<xref rid=\"B111-ijms-21-05498\" ref-type=\"bibr\">111</xref>]. Bap and Bap-like proteins (BLP1, BLP2) stabilize the three-dimensional biofilm structure on abiotic surfaces and play a role in the adhesion of <italic>A. baumannii</italic> to the host cell [<xref rid=\"B112-ijms-21-05498\" ref-type=\"bibr\">112</xref>,<xref rid=\"B113-ijms-21-05498\" ref-type=\"bibr\">113</xref>]. Three resistance-nodulation-division (RND) efflux pumps (AdeABC, AdeFGH, and AdeIJK) affect <italic>A. baumannii</italic> biofilm development [<xref rid=\"B114-ijms-21-05498\" ref-type=\"bibr\">114</xref>,<xref rid=\"B115-ijms-21-05498\" ref-type=\"bibr\">115</xref>,<xref rid=\"B116-ijms-21-05498\" ref-type=\"bibr\">116</xref>]. The AdeFGH efflux pump participates in the transport of quorum-sensing (QS) molecules [<xref rid=\"B116-ijms-21-05498\" ref-type=\"bibr\">116</xref>]. OmpA is responsible for the attachment of <italic>A. baumannii</italic> to plastic surfaces and epithelial cells [<xref rid=\"B67-ijms-21-05498\" ref-type=\"bibr\">67</xref>]. The CarO, OprC, and OprD porins may be involved in the uptake of metabolites required for the synthesis of siderophores in pellicles [<xref rid=\"B105-ijms-21-05498\" ref-type=\"bibr\">105</xref>]. The iron uptake systems, including acinetobactin and enterobactin receptors, are upregulated during pellicle formation [<xref rid=\"B105-ijms-21-05498\" ref-type=\"bibr\">105</xref>,<xref rid=\"B117-ijms-21-05498\" ref-type=\"bibr\">117</xref>]. (<bold>B</bold>) The formation of the <italic>A. baumannii</italic> biofilm and pellicle is regulated by the nucleotide second messengers, two-component signal transduction systems, and QS. cAMP inhibits pellicle formation [<xref rid=\"B104-ijms-21-05498\" ref-type=\"bibr\">104</xref>]. The synthesis of Csu pili depends on cyclic di-GMP (c-di-GMP) [<xref rid=\"B118-ijms-21-05498\" ref-type=\"bibr\">118</xref>] and the BfmRS and GacSA systems [<xref rid=\"B48-ijms-21-05498\" ref-type=\"bibr\">48</xref>,<xref rid=\"B119-ijms-21-05498\" ref-type=\"bibr\">119</xref>,<xref rid=\"B120-ijms-21-05498\" ref-type=\"bibr\">120</xref>]. The hybrid two-component regulator CheA/Y controls the expression of Csu pili and acinetin-505 via QS [<xref rid=\"B106-ijms-21-05498\" ref-type=\"bibr\">106</xref>,<xref rid=\"B121-ijms-21-05498\" ref-type=\"bibr\">121</xref>]. The QS system of <italic>A. baumannii</italic> consists of an AbaI inducer and its cognate receptor AbaR. AbaI is an autoinducer synthase producing <italic>N</italic>-acyl homoserine lactone (AHL) molecules bound by AbaR. The AbaR–AHL complexes activate the synthesis of AbaI and the expression of QS-dependent genes, which in turn triggers the production of acinetin-505 and Csu pili [<xref rid=\"B121-ijms-21-05498\" ref-type=\"bibr\">121</xref>,<xref rid=\"B122-ijms-21-05498\" ref-type=\"bibr\">122</xref>]. Biofilm formation may be inhibited by quorum-quenching (QQ) enzymes, which degrade AHLs [<xref rid=\"B123-ijms-21-05498\" ref-type=\"bibr\">123</xref>,<xref rid=\"B124-ijms-21-05498\" ref-type=\"bibr\">124</xref>], as well as high concentrations of Fe<sup>III</sup> ions that bind AHLs [<xref rid=\"B125-ijms-21-05498\" ref-type=\"bibr\">125</xref>]. On the other hand, Fe<sup>III</sup> ions are required for pellicle development [<xref rid=\"B105-ijms-21-05498\" ref-type=\"bibr\">105</xref>,<xref rid=\"B117-ijms-21-05498\" ref-type=\"bibr\">117</xref>]. The AdeABC (controlled by the two-component signal transduction AdeRS system) and AdeFGH efflux pumps participate in biofilm formation [<xref rid=\"B114-ijms-21-05498\" ref-type=\"bibr\">114</xref>,<xref rid=\"B116-ijms-21-05498\" ref-type=\"bibr\">116</xref>]. However, other studies revealed that the overproduction of efflux pumps may result in decreased biofilm/pellicle growth [<xref rid=\"B115-ijms-21-05498\" ref-type=\"bibr\">115</xref>]. ppGpp regulates the expression of genes encoding the efflux pump’s components [<xref rid=\"B126-ijms-21-05498\" ref-type=\"bibr\">126</xref>] and inhibits the production of AbaR and acinetin-505 [<xref rid=\"B127-ijms-21-05498\" ref-type=\"bibr\">127</xref>].</p></caption><graphic xlink:href=\"ijms-21-05498-g002\"/></fig></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32726948</article-id><article-id pub-id-type=\"pmc\">PMC7432026</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155387</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05387</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Review</subject></subj-group></article-categories><title-group><article-title>A Mini-Review of <italic>Ixodes</italic> Ticks Climate Sensitive Infection Dispersion Risk in the Nordic Region</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>van Oort</surname><given-names>Bob E. H.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05387\">1</xref><xref rid=\"c1-ijerph-17-05387\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Hovelsrud</surname><given-names>Grete K.</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05387\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Risvoll</surname><given-names>Camilla</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05387\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Mohr</surname><given-names>Christian W.</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05387\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Jore</surname><given-names>Solveig</given-names></name><xref ref-type=\"aff\" rid=\"af5-ijerph-17-05387\">5</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05387\"><label>1</label>CICERO Center for International Climate Research, P.O. Box 1129, Blindern, 0318 Oslo, Norway</aff><aff id=\"af2-ijerph-17-05387\"><label>2</label>Nord University and Nordland Research Institute, P.O. Box 1490, 8049 Bodø, Norway; <email>grete.hovelsrud@nord.no</email></aff><aff id=\"af3-ijerph-17-05387\"><label>3</label>Nordland Research Institute, P.O. Box 1490, 8049 Bodø, Norway; <email>cri@nforsk.no</email></aff><aff id=\"af4-ijerph-17-05387\"><label>4</label>The Norwegian Institute of Bioeconomy Research, P.O. Box 115, 1431 Ås, Norway; <email>christian.mohr@nibio.no</email></aff><aff id=\"af5-ijerph-17-05387\"><label>5</label>Norwegian Public Health Institute, P.O. Box 222 Skøyen, 0213 Oslo, Norway; <email>solveig.jore@fhi.no</email></aff><author-notes><corresp id=\"c1-ijerph-17-05387\"><label>*</label>Correspondence: <email>oort@cicero.oslo.no</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>27</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5387</elocation-id><history><date date-type=\"received\"><day>25</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>23</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Climate change in the Nordic countries is projected to lead to both wetter and warmer seasons. This, in combination with associated vegetation changes and increased animal migration, increases the potential incidence of tick-borne diseases (TBD) where already occurring, and emergence in new places. At the same time, vegetation and animal management influence tick habitat and transmission risks. In this paper, we review the literature on <italic>Ixodes ricinus</italic>, the primary vector for TBD. Current and projected distribution changes and associated disease transmission risks are related to climate constraints and climate change, and this risk is discussed in the specific context of reindeer management. Our results indicate that climatic limitations for vectors and hosts, and environmental and societal/institutional conditions will have a significant role in determining the spreading of climate-sensitive infections (CSIs) under a changing climate. Management emerges as an important regulatory “tool” for tick and/or risk for disease transfer. In particular, shrub encroachment, and pasture and animal management, are important. The results underscore the need to take a seasonal view of TBD risks, such as (1) grazing and migratory (host) animal presence, (2) tick (vector) activity, (3) climate and vegetation, and (4) land and animal management, which all have seasonal cycles that may or may not coincide with different consequences of climate change on CSI migration. We conclude that risk management must be coordinated across the regions, and with other land-use management plans related to climate mitigation or food production to understand and address the changes in CSI risks.</p></abstract><kwd-group><kwd>tick-borne diseases (TBD)</kwd><kwd>climate-sensitive infections (CSIs)</kwd><kwd><italic>Ixodes ricinus</italic></kwd><kwd>climate change</kwd><kwd>Nordic</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05387\"><title>1. Introduction</title><p>In the Nordic region, projected climate change can be summarized as increasingly warmer and wetter weather, and a higher frequency of extreme weather events, including droughts, floods and cold and heat spells [<xref rid=\"B1-ijerph-17-05387\" ref-type=\"bibr\">1</xref>]. Vegetation and animals in the Arctic adapt to the changing conditions by following the shifting ecosystem conditions to higher elevations or latitudes, as new niches open up and the climate becomes less suitable in the southern regions, and as new invading species compete with existing species. Such shifts and migrations are not limited to vegetation and animals, but also include pathogens that may follow their vectors or hosts, which means new co-existences of both pathogens and vector species in new places. Especially the warmer and wetter conditions may enable such pathogens to survive further north than before, and find new hosts in northern species, although it is not a given that pathogens will also cause disease. On the other hand, heavy precipitation, flooding, and increased numbers of hot, dry and cold spells may limit their migration. The introduction of emerging or re-emerging zoonoses may pose new risks to both animal and human health. In this paper, we discuss the climate sensitivity of such zoonoses under the term “climate-sensitive infections (CSIs)”. This is currently not an official group of diseases, but is here defined as zoonotic infections causing diseases where the spread is sensitive to changes in the climate. This can be a direct effect, e.g., through re-emergence from melting permafrost, or an indirect effect, e.g., through vector (e.g., mosquitos, flies or ticks), hosts (e.g., rodents, cervids, hare, geese) or vegetation changes [<xref rid=\"B2-ijerph-17-05387\" ref-type=\"bibr\">2</xref>].</p><p>Recent research on the presence and distribution of reindeer pathogens in northern Norway [<xref rid=\"B2-ijerph-17-05387\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijerph-17-05387\" ref-type=\"bibr\">3</xref>], historical literature on reindeer husbandry and diseases in northern Scandinavia [<xref rid=\"B4-ijerph-17-05387\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijerph-17-05387\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijerph-17-05387\" ref-type=\"bibr\">6</xref>] and information from reindeer herders and resource managers on animal diseases highlighted six potentially significant CSIs: Anaplasmosis, babesiosis, parapox virus, tularemia, necrobacillosis and anthrax. In this paper we focus on a subset of these CSIs, transmitted by the <italic>Ixodes ricinus</italic> tick [<xref rid=\"B7-ijerph-17-05387\" ref-type=\"bibr\">7</xref>], namely anaplasmosis, babesiosis and tularemia. The bacterium <italic>Anaplasma phagocytophilum</italic> has been identified in such <italic>I. ricinus</italic> tick species, but also in <italic>Ixodes persulcatus</italic> in eastern Europe and Finland, and in other ticks within the genera <italic>Dermacentor</italic> and <italic>Rhiplicephalus</italic> [<xref rid=\"B8-ijerph-17-05387\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05387\" ref-type=\"bibr\">9</xref>]. However, in most of Europe including Sweden and Norway, <italic>I. ricinus</italic> is the main vector of anaplasmosis and babesiosis [<xref rid=\"B10-ijerph-17-05387\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijerph-17-05387\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05387\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijerph-17-05387\" ref-type=\"bibr\">13</xref>]. Tularemia in the Nordic countries is primarily a waterborne disease, and is mostly explained by the presence of mosquitoes [<xref rid=\"B14-ijerph-17-05387\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijerph-17-05387\" ref-type=\"bibr\">15</xref>], but on the European continent it is also [<xref rid=\"B16-ijerph-17-05387\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijerph-17-05387\" ref-type=\"bibr\">17</xref>], or even exclusively [<xref rid=\"B18-ijerph-17-05387\" ref-type=\"bibr\">18</xref>], associated with <italic>Ixodid</italic> ticks. We will therefore consider tularemia as a potential tick-borne disease (TBD). Symptoms for anaplasmosis and babesiosis in humans typically include muscle aches, fever, chills, weakness, lethargy, jaundice, nausea and loss of appetite, whilst for tularemia there is a broad spectrum of symptoms. In animals, symptoms can range from asymptomatic to fever, reduced milk production, weight loss, urination of blood, anemia, and even death. In the context of climate change, these infections are presumably sensitive, at some or all stages of the life cycle of the pathogens, vectors and hosts, to climate variables such as increased temperatures, precipitation, changing freeze–thaw cycles and snow cover. We argue that these diseases, anaplasmosis, babesiosis and tularemia (ABT), their main vector (<italic>I. ricinus</italic>) and their hosts provide a good example of how climate can influence the migration of CSIs.</p></sec><sec id=\"sec2-ijerph-17-05387\"><title>2. Climate-Sensitive Infections: The Tick</title><p>Several authors have identified factors that contribute to the (changing) incidence and geographical distribution of diseases, including biotic and abiotic variables such as climate, vegetation, the presence of host animals, human land-use and land-use practices [<xref rid=\"B12-ijerph-17-05387\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijerph-17-05387\" ref-type=\"bibr\">13</xref>,<xref rid=\"B19-ijerph-17-05387\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijerph-17-05387\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijerph-17-05387\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijerph-17-05387\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijerph-17-05387\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijerph-17-05387\" ref-type=\"bibr\">24</xref>]. These variables are logically connected, as climate influences vegetation, which additionally is influenced by human management and grazing animals. Both ticks and hosts have preferences for certain types of vegetation, and their distribution depends both on the presence and continuity of suitable vegetations. For ticks, distribution also depends on the availability, mobility and management practices of the host animals. These different factors tend to vary spatially and seasonally and affect different stages of the vectors (in this case ticks) and hosts differently [<xref rid=\"B11-ijerph-17-05387\" ref-type=\"bibr\">11</xref>,<xref rid=\"B25-ijerph-17-05387\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijerph-17-05387\" ref-type=\"bibr\">26</xref>]. Finally, distribution depends on the responses of animal healthcare and prevention. This makes determining the role of climate inherently difficult, but as climate change becomes more pronounced and has a larger impact on the environment, there is little doubt that this will affect the geographical distribution of diseases.</p><p>This paper presents a brief literature review of the role of climate, environmental and societal factors in different stages of tick lifecycle and reindeer management that may play a role in the distribution and migration of TBDs in reindeer, in order to establish (1) changes in the occurrence of ticks, hosts and TBDs, and (2) specific links to climate variables. We focus on the Nordics, including Norway, Sweden and Finland, but use especially Norway as an example case. For the literature search, we used the ORIA database available via the University of Oslo Library, which includes all major scientific literature databases, such as Medline, PubMed, BioMed, Web of Science, SCOPUS, ScienceDirect, etc. The following general syntax was employed, in English and Norwegian, including references to TBDs, climate and the focus area: “CSI (e.g., Anaplasm * OR Babesi * OR Tularemi *) AND Climat * AND (North * OR Nord * OR Norw *)”. We also did a similar search, substituting the disease for tick occurrence, with and without references to the target area. The identified literature was scanned for the necessary environmental conditions for the survival and/or activity either of the disease, the vector or the host.</p><sec id=\"sec2dot1-ijerph-17-05387\"><title>2.1. Current Distribution, Lifecycle and Spatio-Temporal Vulnerabilities for Tick-Borne Infections</title><p>Because <italic>I. ricinus</italic> spends most of its life cycle in the external environment rather than on its hosts, ticks are highly exposed to the prevailing weather [<xref rid=\"B12-ijerph-17-05387\" ref-type=\"bibr\">12</xref>]. Thus, direct and indirect (e.g., via vegetation changes) climate change effects on the environment are likely to affect tick survival, development and reproduction, while climate effects will impact both vector (tick) abundance and distribution, and host numbers, migration patterns and diversity. Slow changes in climate, vegetation and host densities are likely to expand both abundance and activity onset in southern regions in the Nordics, and distribution opportunities north for ticks, while an increased number of extreme events and short-term weather variations may limit their abundance and distribution potential.</p><p>The <italic>I. ricinus</italic> life cycle usually spans two to three years, but can take up to six years, and includes three hosts (<xref ref-type=\"fig\" rid=\"ijerph-17-05387-f001\">Figure 1</xref>), which can be different or the same species. Ticks go through four life stages: egg, larvae, nymph and adult. Adult females drop off the third host to lay eggs after feeding, usually in autumn (1). Eggs hatch into larvae (2), which overwinter in the vegetation. In spring to early summer, the larvae quest and attach to the first host to feed for about 2–3 days, commonly a small mammal or bird (3). By late summer, engorged larvae have dropped off the first host into the vegetation and (4) molt into nymphs, usually in autumn. Nymphs overwinter in the vegetation and the following spring they quest and attach to the second host (6), again usually a small mammal. The nymphs feed on the second host for a period of around 3 to 4 days before dropping back into the vegetation again (7) to molt into adults in late summer/autumn, and overwinter in the vegetation (8). Humans are most commonly bitten by nymphs. Next spring, adult ticks seek out and attach to a third host, usually a large to medium sized mammal (including cervids and bovids) (9). Males feed for a brief period of several hours, while females normally feed only once. The sexes couple whenever they encounter each other, mostly on the hosts, and drop off the host at the latest by autumn (10) to continue the cycle. Females die upon completing oviposition.</p><p>The current distribution of ticks and ABT is constrained (or enabled) by many environmental and critical climatic conditions, which may reduce or stop the functioning of the mechanisms involved in the transmission of the disease, as well as its reservoir, reduce activity, or kill the vectors (i.e., ticks) or hosts (e.g., rodents or deer) at various life-cycle stages. Critical limits may also pertain to conditions in the surrounding environment, such as types of vegetation or microclimates (temperature and humidity) where hosts do not thrive, or find food, shade or protection [<xref rid=\"B27-ijerph-17-05387\" ref-type=\"bibr\">27</xref>]. The combined climatic, environmental and host presence requirements link ticks primarily to the coastal areas in Norway, but also inland, while in Sweden and Finland <italic>I. ricinus</italic> mostly occurs in the south-to-middle of these countries [<xref rid=\"B28-ijerph-17-05387\" ref-type=\"bibr\">28</xref>]. In Norway, the preferred vegetation is denser and preferably shaded, deciduous or mixed or fragmented forest types in general, while the highest tick densities in Sweden are in the broad-leaf vegetation types and mixed deciduous woodlands [<xref rid=\"B27-ijerph-17-05387\" ref-type=\"bibr\">27</xref>]. Ticks occur less in pine forest and least in grass and heath, explained by ticks being exposed to more desiccating weather conditions in open vegetation [<xref rid=\"B27-ijerph-17-05387\" ref-type=\"bibr\">27</xref>,<xref rid=\"B29-ijerph-17-05387\" ref-type=\"bibr\">29</xref>]. Several studies have linked the presence of ticks with the incidence of pathogens in ticks, humans, and animals [<xref rid=\"B11-ijerph-17-05387\" ref-type=\"bibr\">11</xref>,<xref rid=\"B30-ijerph-17-05387\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijerph-17-05387\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijerph-17-05387\" ref-type=\"bibr\">32</xref>]. In general, the findings confirm that not all, but varying percentages of ticks are carriers of the pathogens, sometimes more in females and in nymph stages. An increased emergence of Lyme disease, babesiosis and anaplasmosis both in time and space was found, meaning that an increase in both distribution and incidence of diseases had occurred during the period 1995–2015 [<xref rid=\"B31-ijerph-17-05387\" ref-type=\"bibr\">31</xref>]. Importantly, the prevalence of the pathogens in ticks and the incidence of the diseases in cattle and humans were both spatially and temporally correlated with (changing) exposure with regards to anaplasmosis and babesiosis, while the incidence of anaplasmosis in sheep was only weakly linked with exposure [<xref rid=\"B31-ijerph-17-05387\" ref-type=\"bibr\">31</xref>].</p></sec><sec id=\"sec2dot2-ijerph-17-05387\"><title>2.2. Ticks and Climate Requirements: Results from Literature Review</title><p><xref rid=\"ijerph-17-05387-t001\" ref-type=\"table\">Table 1</xref> presents the climatic variables, and identified limits and optimal conditions, for different development stages and activity phases for ticks. There is no consensus on which factors are the most important for tick presence and abundance, but most studies suggest that the tick host-seeking activity (questing), which is particularly important in light of disease transmission risk, has a lower limit of 7–8 °C daily maximum air temperature [<xref rid=\"B33-ijerph-17-05387\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijerph-17-05387\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijerph-17-05387\" ref-type=\"bibr\">35</xref>] for adults, or 10 °C for larvae [<xref rid=\"B12-ijerph-17-05387\" ref-type=\"bibr\">12</xref>]. While ticks may survive temperatures as low as −18.9 °C or −10 °C for periods of time, these limits do not allow for tick activity, and are thus far below transmission risk. Upper limits are related to both temperature and humidity. Qviller et al. [<xref rid=\"B12-ijerph-17-05387\" ref-type=\"bibr\">12</xref>] find that questing tick densities decrease above 15–17 °C, while Gray [<xref rid=\"B34-ijerph-17-05387\" ref-type=\"bibr\">34</xref>] showed that questing persists throughout hot dry weather at temperatures up to 35 °C, as long as appropriate vegetation cover is present to provide opportunities for rehydration. There is also evidence that summer temperatures above 30 °C can change seasonal host-seeking activity patterns and induce diapause, causing a large proportion of the tick population to become more abundant in autumn/early winter than in spring [<xref rid=\"B26-ijerph-17-05387\" ref-type=\"bibr\">26</xref>,<xref rid=\"B36-ijerph-17-05387\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijerph-17-05387\" ref-type=\"bibr\">37</xref>], thus affecting the timing of disease transmission risk. Large day-to-day fluctuations in ground surface temperature in general, and in certain months of the year, and seasonal variation in precipitation changes were found to be associated with the presence/absence of <italic>Ixodes ricinus</italic> and the pathogen <italic>Anaplasma phagocytophilum</italic>, and such an association is found as well in other vectors and diseases such as malaria [<xref rid=\"B20-ijerph-17-05387\" ref-type=\"bibr\">20</xref>]. Climate change-related changes in the absolute levels of temperature and precipitation are offset by an increase in temperature and precipitation variability, and this might be relevant not only for vector distribution, but also disease transmission [<xref rid=\"B38-ijerph-17-05387\" ref-type=\"bibr\">38</xref>].</p><p>Perhaps the most critical climatic condition for tick survival is a relative humidity of at least 80%, to prevent deadly desiccation of the free-living stages, restricting <italic>I. ricinus</italic> to areas of moderate to high rainfall with good vegetation cover and soil surface humidity through the driest times of the year [<xref rid=\"B27-ijerph-17-05387\" ref-type=\"bibr\">27</xref>,<xref rid=\"B37-ijerph-17-05387\" ref-type=\"bibr\">37</xref>,<xref rid=\"B48-ijerph-17-05387\" ref-type=\"bibr\">48</xref>,<xref rid=\"B49-ijerph-17-05387\" ref-type=\"bibr\">49</xref>]. While many studies identified temperature links with tick survival and activity, the differences in findings underline that it is not straightforward to extrapolate from seasonal temperature conditions to survival and activity, since ticks experience other temperatures and humidity conditions in the microsites (i.e., leaf litter and soil, under snow [<xref rid=\"B50-ijerph-17-05387\" ref-type=\"bibr\">50</xref>]).</p><p>Jaenson and Lindgren [<xref rid=\"B39-ijerph-17-05387\" ref-type=\"bibr\">39</xref>] found that in Sweden, tick distribution corresponded most closely to the areas representing an early start of the growing season (before May 1st) and a vegetation period length of about 170 days, and that the length of the vegetation period is determined by similar daily mean temperatures above 5 °C. Jore et al. [<xref rid=\"B20-ijerph-17-05387\" ref-type=\"bibr\">20</xref>], on the other hand, suggest that the prominent coastal distribution of <italic>I. ricinus</italic> in Norway is better explained by the length of the period without snow than the length of the growth period, as ticks overwinter on the ground and survival can be enhanced by an insulating snow cover layer, which prevents the ground temperature from dropping below zero. Cold winters and a lack of snow may also affect the survival of small mammal hosts the following year, which could mean fewer blood hosts for ticks. <italic>I. ricinus</italic> was consistently present when the period of snow cover was less than or equal to 125 days per year, and was consistently absent with a snow cover greater than or equal to 175 days per year.</p></sec></sec><sec id=\"sec3-ijerph-17-05387\"><title>3. What Does a Changing Climate Mean for Ticks and CSIs?</title><p>The climate is changing more rapidly in the Arctic than elsewhere. In the Nordic countries, annual average temperatures are currently 1.5 °C above the normal, and have consistently been warmer since the 1990s. Winter temperatures have increased by about 2 °C, while spring and autumn are about 1.5 °C warmer than normal (<uri xlink:href=\"www.met.no/vaer-og-klima/klima-siste-150-ar\">www.met.no/vaer-og-klima/klima-siste-150-ar</uri>). Precipitation has likewise increased in the last 35 years, especially in spring and winter, and more recently also summer. The snow season is shorter, precipitation increasingly falls in the form of rain, and an increased number of freeze–thaw events means changes in hydrology, type and access to vegetation. Since 1965, the temperature increase in Norway has been 0.3–0.4 °C per decade. This is almost three times faster than the global increase during the same period [<xref rid=\"B51-ijerph-17-05387\" ref-type=\"bibr\">51</xref>]. In Sweden, temperatures in the last 30–35 years have increased compared to the 1961–1990 normal, especially for winter and spring (by about 1.5 °C and 2 °C, respectively). Coastal Sweden is dryer than inland, on the Norwegian border, but all of Sweden has seen an increase in precipitation over the last 30 years (<uri xlink:href=\"www.smhi.se/klimat/framtidens-klimat\">www.smhi.se/klimat/framtidens-klimat</uri>). A study of climate change and extremes in the Nordic countries finds that the observed temperature changes are greatest for daily minimum temperatures. Important for tick activity and development, the area with a growing season over 180 days has increased, from 37,000 km<sup>2</sup> for the period 1971–2000, to 45,000 km<sup>2</sup> for the period 1985–2014 [<xref rid=\"B51-ijerph-17-05387\" ref-type=\"bibr\">51</xref>], and increased especially in coastal areas and the north of Norway, reaching up to 180 days close to 66–67° N.</p><sec id=\"sec3dot1-ijerph-17-05387\"><title>3.1. Changing Distribution Attributed to Climate Change</title><p>In our analysis of potential climate change consequences for CSI distribution, we select climatic limits from <xref rid=\"ijerph-17-05387-t001\" ref-type=\"table\">Table 1</xref> corresponding to tick activity, assuming inactive ticks would not be able to transmit the pathogens. The selected constraints include: (1) daily mean temperature between 7 °C and 35 °C; (2) daily relative humidity equal or above 80% allows tick presence; and (3) more than 175 snow cover days per year (all months) excludes tick presence. <xref ref-type=\"fig\" rid=\"ijerph-17-05387-f002\">Figure 2</xref> and <xref ref-type=\"fig\" rid=\"ijerph-17-05387-f003\">Figure 3</xref> present the number of days in which these conditions are met in both past/present (1995–2015) and future (2030–2050). Regionally downscaled geospatial meteorological data (resolution 0.11° × 0.11°) from the CORDEX project are calculated using the NorESM1-M (CMIP5) output for historical, and RCP4.5 scenarios for future climate conditions. Not included in these figures, but discussed in the paper, are other variables such as vegetation type, presence of host animals, management, etc., which play a further role in the optimal conditions for tick presence.</p><p>For 1995–2015, the mean number of days per year with optimal conditions was 29.3, 39.0 and 39.8 for Norway, Sweden and Finland respectively. In future (2030–2050) RCP4.5 scenario climate conditions, the mean numbers of days per year increase to 39.0, 46.4 and 49.7, respectively, accounting for rises of 33.1%, 19.0% and 24.8%. Increased optimal conditions in Norway are mainly at the coast, dominated by a 2–3-week increase in season (<xref ref-type=\"fig\" rid=\"ijerph-17-05387-f003\">Figure 3</xref>; most but not all red and orange areas), and less for range expansion (<xref ref-type=\"fig\" rid=\"ijerph-17-05387-f003\">Figure 3</xref>; especially the green areas). In Sweden and Finland, the figures show both a range expansion and increase in season. The increased areas with optimal climatic conditions are 9.2%, 9.4% and 11.4% of Norway, Sweden and Finland, respectively. However, some of these new areas may still have too short a season to allow realistic tick settlement.</p></sec><sec id=\"sec3dot2-ijerph-17-05387\"><title>3.2. Comparing Current and Potential Future Distribution to Observations</title><p>The warmer and wetter weather trends throughout Fenno-Scandia, especially for spring and winter, facilitate migration towards the north and a longer tick season in the south. The effect of precipitation, however, is probably not a linear one; there seems to be a positive effect at lower and moderate levels, but a negative relationship is seen with higher values (>100 mm per month) [<xref rid=\"B20-ijerph-17-05387\" ref-type=\"bibr\">20</xref>]. At high latitudes, summer temperatures may still be too low to complete tick development before the onset of winter, but particularly changes in the 7 °C questing limit, along with increased precipitation in winter and spring, improve the climate for tick presence and/or intensity of their activity, and elevate the risks for tick settlement and diseases transmission increasingly further north. An important consideration here is that while annual and seasonal climate trends might indicate increased distribution and abundance potential for ticks and hosts, extreme weather events, especially large fluctuations in temperature combined with hot and dry weather, may represent limits to tick survival when they are not protected by host or vegetation, but the effects seem to be highly dependent on season of occurrence, and in some instances could have a positive effect on tick distribution [<xref rid=\"B20-ijerph-17-05387\" ref-type=\"bibr\">20</xref>,<xref rid=\"B36-ijerph-17-05387\" ref-type=\"bibr\">36</xref>]. The effect of the reduced snow cover observed in the last 30 years in Norway, and the projected reduction in snow cover [<xref rid=\"B52-ijerph-17-05387\" ref-type=\"bibr\">52</xref>], could be a limiting factor for tick and pathogen distribution [<xref rid=\"B20-ijerph-17-05387\" ref-type=\"bibr\">20</xref>].</p><p>Vegetation co-determines both relative humidity and host occurrence, and climatic changes are causing large vegetational shifts, such as shrub encroachment, which is increasingly providing more suitable habitats for ticks and hosts [<xref rid=\"B20-ijerph-17-05387\" ref-type=\"bibr\">20</xref>,<xref rid=\"B53-ijerph-17-05387\" ref-type=\"bibr\">53</xref>]. Our projections for suitable habitats derived from climate variables show a major overlap with similar analyses of the potential distribution of ticks based on the growing season and vegetation period [<xref rid=\"B39-ijerph-17-05387\" ref-type=\"bibr\">39</xref>]. While vegetation is determined by climatic variables, it is also affected by the activity and density of grazers and browsers. Thus, vegetation is only a rough indicator, because the movement of animals between grass and woodlands also affects the distribution of ticks, complicating assessments of preferred vegetation [<xref rid=\"B20-ijerph-17-05387\" ref-type=\"bibr\">20</xref>]. Compared to the 1980s, some coastal areas in northern Norway are greening as early as April due to an earlier spring [<xref rid=\"B54-ijerph-17-05387\" ref-type=\"bibr\">54</xref>,<xref rid=\"B55-ijerph-17-05387\" ref-type=\"bibr\">55</xref>], while shrub encroachment on tundra is increasing. Earlier greening allows geese to arrive earlier to their resting sites along northern coasts, though the role of geese in carrying infected ticks, specifically for ABT, is uncertain [<xref rid=\"B56-ijerph-17-05387\" ref-type=\"bibr\">56</xref>]. This illustrates that tick range expansion may be facilitated by hosts who are just carriers and not necessarily infected themselves. Moreover, animals such as geese may cross mountains and other geophysical boundaries to reach suitable settlement areas further north. As the climate (<xref ref-type=\"fig\" rid=\"ijerph-17-05387-f002\">Figure 2</xref> and <xref ref-type=\"fig\" rid=\"ijerph-17-05387-f003\">Figure 3</xref>) and vegetation [<xref rid=\"B39-ijerph-17-05387\" ref-type=\"bibr\">39</xref>] change, we get a better idea of the risk picture for the spread of infectious pathogens.</p><p>Several Nordic studies on ticks and TBDs have shown that <italic>I. ricinus</italic> has changed its distribution in recent decennia [<xref rid=\"B20-ijerph-17-05387\" ref-type=\"bibr\">20</xref>,<xref rid=\"B45-ijerph-17-05387\" ref-type=\"bibr\">45</xref>,<xref rid=\"B57-ijerph-17-05387\" ref-type=\"bibr\">57</xref>,<xref rid=\"B58-ijerph-17-05387\" ref-type=\"bibr\">58</xref>], with a northward migration of approximately 400 km within the past 20–30 years in Norway [<xref rid=\"B58-ijerph-17-05387\" ref-type=\"bibr\">58</xref>,<xref rid=\"B59-ijerph-17-05387\" ref-type=\"bibr\">59</xref>], up to 150–250 km north of the Arctic Circle, though with a sparser presence north of 65° N [<xref rid=\"B60-ijerph-17-05387\" ref-type=\"bibr\">60</xref>,<xref rid=\"B61-ijerph-17-05387\" ref-type=\"bibr\">61</xref>]. A recent study by Hvidsten et al. [<xref rid=\"B45-ijerph-17-05387\" ref-type=\"bibr\">45</xref>] found that the northernmost permanent <italic>I. ricinus</italic> population was located at 66.22° N at the coast, although this study did not have national coverage and used only one method (flagging) with low efficiency, and cannot therefore be used to verify presence or absence [<xref rid=\"B32-ijerph-17-05387\" ref-type=\"bibr\">32</xref>]. Nevertheless, the study suggests presence at a high latitude, and the range expansion indicated in the area of 69° N (northern tip of Sweden) is in line with the distributional limit predicted previously [<xref rid=\"B20-ijerph-17-05387\" ref-type=\"bibr\">20</xref>,<xref rid=\"B28-ijerph-17-05387\" ref-type=\"bibr\">28</xref>,<xref rid=\"B31-ijerph-17-05387\" ref-type=\"bibr\">31</xref>]. Tick capture experiments and antibody measurements in sheep blood samples by Jore et al. [<xref rid=\"B20-ijerph-17-05387\" ref-type=\"bibr\">20</xref>] showed an overall increasing prevalence of <italic>Anaplasma phagocytophilum</italic> in sheep during the last 30 years, both on farmland and rough pasture areas in southern Norway. Antibody presence is not equal to presence of diseases, but may nevertheless suggest changes in tick exposure. This suggests that (more heat and drought sensitive) open vegetation, such as farmland, may become more habitable. Both in Sweden and Norway, increased <italic>I. ricinus</italic> abundance and TBD risk was associated with shrub encroachment and abandonment of fields [<xref rid=\"B20-ijerph-17-05387\" ref-type=\"bibr\">20</xref>,<xref rid=\"B27-ijerph-17-05387\" ref-type=\"bibr\">27</xref>], while in Norway the increase of TBDs seems to be greater at the coast, where humidity and spring/autumn temperatures are higher than further inland. The observations of tick distribution match well with the projections shown in <xref ref-type=\"fig\" rid=\"ijerph-17-05387-f002\">Figure 2</xref> and <xref ref-type=\"fig\" rid=\"ijerph-17-05387-f003\">Figure 3</xref>, and the increased climatic potential for tick dispersal is further matched by observations of increased TBD incidence in both humans and animals in these regions, documented both in scientific studies [<xref rid=\"B31-ijerph-17-05387\" ref-type=\"bibr\">31</xref>,<xref rid=\"B62-ijerph-17-05387\" ref-type=\"bibr\">62</xref>] and national reporting databases on TBDs (e.g., Reporting System for Infectious Diseases MSIS <uri xlink:href=\"www.fhi.no/hn/helseregistre-og-registre/msis/\">www.fhi.no/hn/helseregistre-og-registre/msis/</uri>, or the tick-center flåttsenteret.no). However, as described in a recent study by Jore et al. on human subjects [<xref rid=\"B32-ijerph-17-05387\" ref-type=\"bibr\">32</xref>], the relationship between tick bites and the risk of tick-borne diseases is poorly understood, since only a fraction of bites lead to disease, and those who develop diseases are not all diagnosed. Thus, the presence of ticks and the risk of TBDs are two different issues. The study by Hvidsten et al. [<xref rid=\"B45-ijerph-17-05387\" ref-type=\"bibr\">45</xref>] underlined this by investigating the prevalence of <italic>Borrelia</italic> spp. in the northernmost population of <italic>I. ricinus</italic>, having found a low prevalence of about 1–15%, compared to 15–27% in more southern Trøndelag populations. The prevalence of <italic>Rickettsia</italic> spp. in this population was even lower (around 1%). This suggests that while ticks may be expanding their range, the prevalence of TBDs is not equally high in all populations, and may be lower in northern populations.</p></sec></sec><sec id=\"sec4-ijerph-17-05387\"><title>4. Effects of Societal and Structural Factors on Tick Occurrences</title><p>While temperature, humidity, snow cover duration, and the changing abundance and availability of host species (especially red deer) and vegetation are amongst the determining factors for tick distribution [<xref rid=\"B20-ijerph-17-05387\" ref-type=\"bibr\">20</xref>,<xref rid=\"B30-ijerph-17-05387\" ref-type=\"bibr\">30</xref>], animal and land management variables, such as farm density, shrub encroachment and changing land cover, are also of key importance [<xref rid=\"B20-ijerph-17-05387\" ref-type=\"bibr\">20</xref>]. Land-use change is considered one of the most important reasons for the emergence of TBDs [<xref rid=\"B20-ijerph-17-05387\" ref-type=\"bibr\">20</xref>,<xref rid=\"B63-ijerph-17-05387\" ref-type=\"bibr\">63</xref>], indicating that rationalization processes and structural changes in agriculture, following changed agricultural practices, presently have a strong effect on tick survival and dispersion. In Norway, the number of grazing animals in outfield pastures has been reduced by close to 50% over the past six decades, while wild cervid populations have increased dramatically [<xref rid=\"B64-ijerph-17-05387\" ref-type=\"bibr\">64</xref>]. This gradual change from grazing livestock to browsing herbivores, attributed to climatic and vegetation changes including shrub encroachment of previous pasture lands and coastal meadows, increases the abundance of ticks [<xref rid=\"B20-ijerph-17-05387\" ref-type=\"bibr\">20</xref>,<xref rid=\"B29-ijerph-17-05387\" ref-type=\"bibr\">29</xref>,<xref rid=\"B65-ijerph-17-05387\" ref-type=\"bibr\">65</xref>].</p><p>Many regions in Europe are currently undergoing rapid alterations in land-use, and diminishing livestock-pasture management and fodder harvesting have resulted in ongoing shrub encroachment across vast areas [<xref rid=\"B29-ijerph-17-05387\" ref-type=\"bibr\">29</xref>,<xref rid=\"B64-ijerph-17-05387\" ref-type=\"bibr\">64</xref>,<xref rid=\"B65-ijerph-17-05387\" ref-type=\"bibr\">65</xref>]. Some of the increased forest cover is intentional, and subsidized through EU reforestation schemes to improve ecosystem services, such as biodiversity and carbon sequestration [<xref rid=\"B29-ijerph-17-05387\" ref-type=\"bibr\">29</xref>]. However, landscape management practices, such as woodland expansion and restoration, urban greening and implementing biodiversity policies, can also have unintended effects on vector-borne pathogen transmission, by increasing the suitable habitat for hosts and the tick vectors [<xref rid=\"B66-ijerph-17-05387\" ref-type=\"bibr\">66</xref>]. Thus, both the types and levels of human activities, active management and unintended land cover changes, and changes in the presence of domestic species (and their interactions with wild species) can have consequences for TBD incidence [<xref rid=\"B63-ijerph-17-05387\" ref-type=\"bibr\">63</xref>].</p><sec id=\"sec4dot1-ijerph-17-05387\"><title>4.1. Ticks Are a Management and Policy Issue</title><p>As outlined, there are highly complex, cumulative and partially unknown linkages between tick presence (density), the permanence of their presence and the prevalence of TBDs in these populations, related to climate change and land-use practices. While the increasing number of ticks in new areas can be explained by biotic and abiotic factors, and host behavior and abundance [<xref rid=\"B36-ijerph-17-05387\" ref-type=\"bibr\">36</xref>], the presence and migration of host animals and climate change (including climate variability and the intensity of human exposure (through activities)) also have profound effects on the incidence of tick-borne diseases. Vegetation changes are caused by climate, ecological and human land-use changes, and their interactions. Land-use change is in turn driven by a combination of environmental, political and socio-economic conditions, or reduced access for reindeer to migration routes due to restrictions to border crossings (between Norway and Sweden). Ongoing trends in agricultural and pastoral industries toward larger units with higher production yields have resulted in fundamental changes in animal husbandry both in Norway and internationally [<xref rid=\"B67-ijerph-17-05387\" ref-type=\"bibr\">67</xref>]. Traditionally, grazing livestock have kept woody shrubs down for centuries, and in Norway, sheep often grazed in the low-lying cultivated infields and mountainous commons, unlike in recent times, where sheep commonly graze at higher altitudes [<xref rid=\"B68-ijerph-17-05387\" ref-type=\"bibr\">68</xref>]. At present, a high risk of predators reduces the use of certain pastures, which combines with increasing pressure from infrastructural encroachments and human activity exacerbating shrubification [<xref rid=\"B68-ijerph-17-05387\" ref-type=\"bibr\">68</xref>,<xref rid=\"B69-ijerph-17-05387\" ref-type=\"bibr\">69</xref>]. These conditions must be seen in connection with the decreasing financial viability of the sheep farming sector, leading to fewer farms and in turn decreased grazing in certain areas. The cumulative and cascading effects of multiple changes illustrates that tick dispersal is a pertinent management and policy issue across agricultural and health sectors.</p><p>Gilbert et al. [<xref rid=\"B29-ijerph-17-05387\" ref-type=\"bibr\">29</xref>] suggest management measures such as grazing to maintain grass cover and thereby reduce TBD risk in pastoral landscapes. In addition to such biological tick control, the management of pastures and habitat modification, such as drainage, use of herbicides, controlled burning and shrub clearing, alongside the removal of leaf, litter and forest canopy, are potential measures for reducing tick density [<xref rid=\"B9-ijerph-17-05387\" ref-type=\"bibr\">9</xref>]. However, such practices will have to be repeated, are labor intensive, and can only reduce the abundance of ticks for a short period, while the risk of tick introduction from other animals or areas remains [<xref rid=\"B9-ijerph-17-05387\" ref-type=\"bibr\">9</xref>]. Other measures to reduce and prevent livestock exposure to ticks include host animal treatment with acaricide (chemical pesticide) [<xref rid=\"B29-ijerph-17-05387\" ref-type=\"bibr\">29</xref>], or the separation of livestock (e.g., with fencing) from tick-infested areas and hosts. It is also important to consider the exposure of livestock, such as sheep on pastures in spring or cattle in summer to autumn, in relation to tick questing activity (especially in spring). Finally, there are indications that selective animal breeding strategies—at least in sheep—may reduce tick infestation and infections [<xref rid=\"B70-ijerph-17-05387\" ref-type=\"bibr\">70</xref>]. Besides a focus on management measures, continued control of the prevalence of pathogens in ticks, and disease data from public health registries, are both important, and remain the main sources for the study of the risk of tick-borne diseases [<xref rid=\"B32-ijerph-17-05387\" ref-type=\"bibr\">32</xref>]. Studies on pathogen carriage in ticks thus far suggest lower pathogen load in ticks in northern Norway compared to southern Norway [<xref rid=\"B31-ijerph-17-05387\" ref-type=\"bibr\">31</xref>]. When using prevalence data from ticks, it is important to take into account the limited spatial and temporal coverage, and the potential difference of prevalence in feeding and questing ticks, as making inferences from limited or incomplete data can give an incomplete picture of risk [<xref rid=\"B20-ijerph-17-05387\" ref-type=\"bibr\">20</xref>].</p></sec><sec id=\"sec4dot2-ijerph-17-05387\"><title>4.2. Potential Risks for Migrating Reindeer</title><p>Reindeer traditionally migrate seasonally between coastal and inland pastures during Norwegian reindeer herding [<xref rid=\"B71-ijerph-17-05387\" ref-type=\"bibr\">71</xref>,<xref rid=\"B72-ijerph-17-05387\" ref-type=\"bibr\">72</xref>]. Institutional barriers (i.e., Norwegian–Swedish border) have restricted traditional seasonal migration patterns for over 100 years, and multiple environmental and societal stressors, such as icing-thawing events, increasing snowfall in high altitudes, increased encroachment (e.g., through human activity) and higher carnivore populations, are increasingly altering herding practices and reducing herders’ flexibility to move reindeer between seasonal pastures [<xref rid=\"B71-ijerph-17-05387\" ref-type=\"bibr\">71</xref>]. In Nordland county, coastal pastures have become more suitable for reindeer during winter because milder winters in the last few decades have led to less frost at the coast [<xref rid=\"B73-ijerph-17-05387\" ref-type=\"bibr\">73</xref>], while the above-mentioned drivers of change also increasingly force reindeer to graze in more low-lying areas, towards the coast [<xref rid=\"B71-ijerph-17-05387\" ref-type=\"bibr\">71</xref>].</p><p>The increase of ticks at higher latitudes, combined with their density/distribution in milder and wetter coastal regions, increases reindeer exposure to ticks and potentially TBDs. Several studies have shown the prevalence of TBDs in domesticated livestock and wild cervids [<xref rid=\"B8-ijerph-17-05387\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05387\" ref-type=\"bibr\">9</xref>,<xref rid=\"B29-ijerph-17-05387\" ref-type=\"bibr\">29</xref>], and fatal cases of <italic>A. phagocytophilum</italic> have been reported in reindeer in Norway [<xref rid=\"B8-ijerph-17-05387\" ref-type=\"bibr\">8</xref>], an ailment that was experimentally confirmed [<xref rid=\"B74-ijerph-17-05387\" ref-type=\"bibr\">74</xref>]. Other studies have also identified TBDs in reindeer in Germany [<xref rid=\"B75-ijerph-17-05387\" ref-type=\"bibr\">75</xref>] and Mongolia [<xref rid=\"B76-ijerph-17-05387\" ref-type=\"bibr\">76</xref>]. Furthermore, cases of both <italic>A. phagocytophilum</italic> and Babesia have now been reported in domesticated livestock in north Norway [<xref rid=\"B31-ijerph-17-05387\" ref-type=\"bibr\">31</xref>,<xref rid=\"B77-ijerph-17-05387\" ref-type=\"bibr\">77</xref>], identifying the spreading and increased risk of TBDs in northern latitudes. Even if not infected, reindeer can be carriers of ticks enabling tick migration, as there are anecdotal reports of a single reindeer being infested with over 400,000 individuals of the tick species <italic>Dermacentor albipictus</italic>, according to Stuen [<xref rid=\"B78-ijerph-17-05387\" ref-type=\"bibr\">78</xref>]. <italic>I. ricinus</italic> has been found on moose, deer and roe deer in coastal areas also in northern Norway [<xref rid=\"B59-ijerph-17-05387\" ref-type=\"bibr\">59</xref>], and a cross-over to reindeer is likely. This potential for reindeer as host would add to the challenges in reindeer herding, but it also increases the risk of tick and TBD dispersion to inland habitats when reindeer migrate.</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05387\"><title>5. Conclusions</title><p>This paper contributes to the discussion of the increased incidence of CSIs in the north. We agree with earlier recommendations [<xref rid=\"B20-ijerph-17-05387\" ref-type=\"bibr\">20</xref>] to “consider climatic variables year-round to disentangle important seasonal variation, climatic threshold changes, and climate variability and to consider the broader environmental change, including abiotic and biotic factors”, and [<xref rid=\"B36-ijerph-17-05387\" ref-type=\"bibr\">36</xref>] approve of the importance of “the identification of the role of particular hosts species in transmission cycles for risk assessment and risk management”. The review and analysis show that ticks, and consequently TBDs, are sensitive to (changes in) climate which, together with several other factors, co-defines their current and future range. Critical factors include temperature and humidity. Snow cover, growing season, vegetation change (as a result of climate change or land-use changes), host migration, and the suitability of new hosts or vectors (either for ticks alone or for TBDs) further play important roles in the changes of tick abundance and range.</p><p>Management emerges as an important regulatory tool for ticks and/or the risk of disease transfer. Developing policies for addressing shrub encroachment, and pasture and animal management, are particularly important. The results underscore the need to take a seasonal view of TBD risks, because the factors affecting such risks have seasonal cycles. This includes the following: (1) grazing and migratory (host) animal presence; (2) tick (vector) activity; (3) climate and vegetation; and (4) land and animal management. These cycles may or may not coincide, but it is highly likely that they have consequences for CSIs distribution. Moreover, having ticks in a location does not necessarily mean having the risk of disease in the same area, since this is dependent on the co-existence of pathogens in available hosts. The complexity of reindeer husbandry, along with herd migration across spatiotemporal scales and boundaries, national and regional husbandry management, shrub encroachment and animal–vegetation interactions, all play a role in establishing reindeer as potential new carriers of ticks, or even vectors for TBDs/CSIs and their distribution.</p><p>Given the diversity of hosts, and increasing climatic and vegetation changes providing opportunities for ticks and TBDs to settle further north, a Nordic research and management approach to monitor, identify and manage CSIs is critical [<xref rid=\"B79-ijerph-17-05387\" ref-type=\"bibr\">79</xref>]. Examples of such research networks are already present, e.g., the Scantick Innovation and CLINF projects. Management plans must be coordinated with other land-use management plans related to climate mitigation (e.g., forest, shrub encroachment) or food production (e.g., outfield grazing and local resource-use) to understand and address changes in CSI risks. The different time scales of change and local variations suggest that management plans with different temporal and spatial dimensions are needed. These include a long-term management focus on climate–vegetation changes, the short-term management of animal–vegetation interactions and disease intervention, and a management plan that incorporates and addresses how the rate and magnitude of change affects traditional and Indigenous livelihoods, such as reindeer husbandry and sheep farming.</p></sec></body><back><ack><title>Acknowledgments</title><p>We gratefully acknowledge and thank the reindeer herders who took their time to explain to us the complexity of their livelihood. In addition, we acknowledge Anna Omazic and Ann Albihn of the CLINF project for their support.</p></ack><notes><title>Author Contributions</title><p>The individual contributions from each author are as follows: conceptualization, B.E.H.v.O., G.K.H., C.R.; methodology, B.E.H.v.O., G.K.H., C.R., C.W.M.; software, C.W.M.; validation, B.E.H.v.O., C.W.M. and S.J.; formal analysis, B.E.H.v.O., G.K.H., C.R., C.W..M.; investigation, G.K.H. and C.R.; resources, G.K.H.; data curation, B.E.H.v.O.; writing—original draft preparation, B.E.H.v.O., G.K.H., C.R.; writing—review and editing, B.E.H.v.O., G.K.H., C.R. and S.J.; visualization B.E.H.v.O. and C.W.M.; supervision, not relevant; project administration, G.K.H.; funding acquisition, G.K.H. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This paper is funded through the NordForsk Nordic Centres of Excellence in Arctic Research program [Grant No. 76413] project ‘Climate change effects on the epidemiology of infectious diseases and the impacts on Northern societies’ (CLINF; <uri xlink:href=\"http://www.clinf.org\">http://www.clinf.org</uri>).</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest. 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J. Circumpolar Health</source><year>2014</year><volume>73</volume><pub-id pub-id-type=\"doi\">10.3402/ijch.v73.25163</pub-id><pub-id pub-id-type=\"pmid\">25317383</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijerph-17-05387-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>The <italic>Ixodes ricinus</italic> (tick) life cycle and linkages to its environment: Development, inactive (overwintering) and active (questing and on host) stages, climate limits and changes, preferred hosts and vegetation, and management and interventions all form boundaries for the survival and migration of ticks and the CSIs they carry. Descriptive cycle text nos. 1–10 based on <uri xlink:href=\"www.cdc.gov/dpdx/ticks/index.html\">www.cdc.gov/dpdx/ticks/index.html</uri>.</p></caption><graphic xlink:href=\"ijerph-17-05387-g001\"/></fig><fig id=\"ijerph-17-05387-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Mean number of days per year from March to November with optimal climatic conditions for tick development for past/present (1995–2015) and future (2030–2050).</p></caption><graphic xlink:href=\"ijerph-17-05387-g002\"/></fig><fig id=\"ijerph-17-05387-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Difference in number of days/year between past/present and future (RCP4.5) optimal climatic conditions.</p></caption><graphic xlink:href=\"ijerph-17-05387-g003\"/></fig><table-wrap id=\"ijerph-17-05387-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05387-t001_Table 1</object-id><label>Table 1</label><caption><p>Climatic criteria for the absence, prevalence or spread of the focus zoonoses by their vectors.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CSI</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Identified Climatic Limits</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Sources</th></tr></thead><tbody><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Anaplasmosis<break/>(NO: Sjodogg)<break/>and<break/>Babesiosis<break/>(NO: Blodpiss)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Temperature below 10 °C no development or eggs<break/>−15 °C, −18.9 °C for 24 h and −10 °C for 30 days lethal<break/>5 °C: limit for activity<break/>Tavg <5 °C for >170 days<break/>7 °C host-seeking activity (questing) for nymphs and adults<break/>10 °C host-seeking activity (questing) for larvae<break/>6.7 °C growing season mean<break/>Relative humidity 80–85%<break/>24 °C = limit for occurrence and activity.<break/>15–17 °C = reduced activity<break/>>30 °C induces diapause<break/>35 °C = questing limit<break/>Precipitation >90 mm per month in April–May reduces egg deposition.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B12-ijerph-17-05387\" ref-type=\"bibr\">12</xref>,<xref rid=\"B20-ijerph-17-05387\" ref-type=\"bibr\">20</xref>,<xref rid=\"B36-ijerph-17-05387\" ref-type=\"bibr\">36</xref>,<xref rid=\"B39-ijerph-17-05387\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijerph-17-05387\" ref-type=\"bibr\">40</xref>,<xref rid=\"B41-ijerph-17-05387\" ref-type=\"bibr\">41</xref>,<xref rid=\"B42-ijerph-17-05387\" ref-type=\"bibr\">42</xref>,<xref rid=\"B43-ijerph-17-05387\" ref-type=\"bibr\">43</xref>,<xref rid=\"B44-ijerph-17-05387\" ref-type=\"bibr\">44</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Growing season duration: 175–180 days; 160 days; 170 days</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B39-ijerph-17-05387\" ref-type=\"bibr\">39</xref>,<xref rid=\"B45-ijerph-17-05387\" ref-type=\"bibr\">45</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Snow cover duration: Below 125 days > present; Above 175 days > absent; Above 150 days > absent</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B13-ijerph-17-05387\" ref-type=\"bibr\">13</xref>,<xref rid=\"B20-ijerph-17-05387\" ref-type=\"bibr\">20</xref>,<xref rid=\"B23-ijerph-17-05387\" ref-type=\"bibr\">23</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Tularemia<break/>(NO: Harepest)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Temperature: number of days below −12 °C<break/>Growing season duration: 160–180 days<break/>Snow cover duration: absent above 175 days of snow cover</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B46-ijerph-17-05387\" ref-type=\"bibr\">46</xref>,<xref rid=\"B47-ijerph-17-05387\" ref-type=\"bibr\">47</xref>]</td></tr></tbody></table></table-wrap></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32722651</article-id><article-id pub-id-type=\"pmc\">PMC7432027</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155310</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05310</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Review</subject></subj-group></article-categories><title-group><article-title>Development of Novel Experimental Models to Study Flavoproteome Alterations in Human Neuromuscular Diseases: The Effect of Rf Therapy</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Tolomeo</surname><given-names>Maria</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Nisco</surname><given-names>Alessia</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Leone</surname><given-names>Piero</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Barile</surname><given-names>Maria</given-names></name><xref rid=\"c1-ijms-21-05310\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05310\">Department of Biosciences, Biotechnology and Biopharmaceutics, University of Bari, 70126 Bari, Italy; <email>maria.tolomeo@uniba.it</email> (M.T.); <email>alessia.nisco@gmail.com</email> (A.N.); <email>pieroleone87@gmail.com</email> (P.L.)</aff><author-notes><corresp id=\"c1-ijms-21-05310\"><label>*</label>Correspondence: <email>maria.barile@uniba.it</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>26</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5310</elocation-id><history><date date-type=\"received\"><day>04</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>21</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Inborn errors of Riboflavin (Rf) transport and metabolism have been recently related to severe human neuromuscular disorders, as resulting in profound alteration of human flavoproteome and, therefore, of cellular bioenergetics. This explains why the interest in studying the “flavin world”, a topic which has not been intensively investigated before, has increased much over the last few years. This also prompts basic questions concerning how Rf transporters and FAD (flavin adenine dinucleotide) -forming enzymes work in humans, and how they can create a coordinated network ensuring the maintenance of intracellular flavoproteome. The concept of a coordinated cellular “flavin network”, introduced long ago studying humans suffering for Multiple Acyl-CoA Dehydrogenase Deficiency (MADD), has been, later on, addressed in model organisms and more recently in cell models. In the frame of the underlying relevance of a correct supply of Rf in humans and of a better understanding of the molecular rationale of Rf therapy in patients, this review wants to deal with theories and existing experimental models in the aim to potentiate possible therapeutic interventions in Rf-related neuromuscular diseases.</p></abstract><kwd-group><kwd><italic>SLC52As</italic></kwd><kwd><italic>FLAD1</italic></kwd><kwd>mitochondrial flavoproteome</kwd><kwd>flavoprotein subunit-SDH</kwd><kwd>riboflavin</kwd><kwd>RTD</kwd><kwd>MADD</kwd><kwd>LSMFLAD</kwd><kwd>RREI</kwd><kwd>model organisms</kwd></kwd-group></article-meta></front><body><sec id=\"sec1-ijms-21-05310\"><title>1. The Human Flavoproteome</title><p>Riboflavin (Rf, also known as vitamin B2), a water-soluble vitamin belonging to the B-group vitamins, is the precursor of flavin cofactors FMN (flavin mononucleotide) and FAD (flavin adenine dinucleotide), which allow for cellular flavoproteome to become enzymatically active.</p><p>The molecule of Rf (7,8-dimethyl-10-ribitylisoalloxazine) consists of a substituted isoalloxazine ring, whose N-10 atom is bound to a ribityl residue. In FMN the 5′ end of the ribityl moiety is esterified by a single phosphoryl group; adenylation of FMN gives rise to FAD. The chemical structures of Rf and of its derived cofactors are reported in <xref ref-type=\"fig\" rid=\"ijms-21-05310-f001\">Figure 1</xref>, in the context of their transport and metabolism.</p><p>The human flavoproteome is constituted by the products of more than 90 genes, namely hundreds of different flavoenzymes, most of which (84%) utilizing, as a cofactor FAD, while only 16% depend on FMN. There are some less represented cases in which both cofactors are concomitantly required by the same enzyme, as, for example, in the case of methionine synthase reductase [<xref rid=\"B1-ijms-21-05310\" ref-type=\"bibr\">1</xref>].</p><p>The role of flavin cofactors in the enzymatic catalysis lies essentially in transferring reduction equivalents, giving rise to the semi-reduced or fully-reduced forms, thus FMN and FAD are essential molecular constituents of a large number of dehydrogenases, reductases and oxidases, mainly located in mitochondria, being involved in intermediary and terminal energetic metabolism of fatty acids, carbohydrates, amino acids, pyridoxine and choline [<xref rid=\"B2-ijms-21-05310\" ref-type=\"bibr\">2</xref>]. Nevertheless, certain non-redox flavin dependent reactions have been recently identified, as the isomerization of UDP-galactopyranose to UDP-galactofuranose by UDP-galactopyranose mutase or the reaction catalyzed by the peroxisomal alkyl-dihydroxyacetone phosphate synthase involved in the biosynthesis of ether phospholipids [<xref rid=\"B3-ijms-21-05310\" ref-type=\"bibr\">3</xref>].</p><p>The ability of flavin enzymes to catalyze a wide-range of redox reactions in a variety of biological processes relies on the electrochemical tuning of the enzyme-bound flavin redox cofactor. Enzyme active sites are able to modulate the flavin redox potential generally from +100 mV to −400 mV, spanning a 500 mV range that enables flavoenzymes to catalyze a variety of redox reactions [<xref rid=\"B4-ijms-21-05310\" ref-type=\"bibr\">4</xref>].</p><p>The versatility of flavin-dependent oxidoreductases explains why flavoenzymes play a key role in redox homeostasis in both generating and scavenging reactive oxygen species (ROS) and reactive nitrogen species (RNS) [<xref rid=\"B5-ijms-21-05310\" ref-type=\"bibr\">5</xref>]. Thus, on the one hand, we can cite the complex of NADPH oxidase, which becomes functionally active in the plasma membrane of activated macrophages [<xref rid=\"B6-ijms-21-05310\" ref-type=\"bibr\">6</xref>]. On the other hand, we can remember the flavoenzymes involved in the recycling of glutathione [<xref rid=\"B2-ijms-21-05310\" ref-type=\"bibr\">2</xref>,<xref rid=\"B7-ijms-21-05310\" ref-type=\"bibr\">7</xref>], as well as thioredoxin reductase [<xref rid=\"B8-ijms-21-05310\" ref-type=\"bibr\">8</xref>].</p><p>In the majority of cases, apo-flavoenzymes bind flavin cofactors non-covalently (90%), whereas in some cases, a covalent linkage occurs. Interestingly all the covalent flavinylated enzymes are confined inside organelles, first of all mitochondria, with succinate dehydrogenase (SDH, respiratory chain complex II, EC 1.3.5.1), dimethylglycine dehydrogenase, sarcosine dehydrogenase and monoamine oxidase being the most extensively investigated (see [<xref rid=\"B9-ijms-21-05310\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijms-21-05310\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijms-21-05310\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijms-21-05310\" ref-type=\"bibr\">12</xref>] and refs therein).</p><p>In the intermembrane space of mitochondria, the FAD-dependent apoptosis-inducing factor (AIF) triggers caspase-independent programmed cell death [<xref rid=\"B13-ijms-21-05310\" ref-type=\"bibr\">13</xref>] and also, a FAD-dependent pathway controls oxidative protein folding [<xref rid=\"B14-ijms-21-05310\" ref-type=\"bibr\">14</xref>].</p><p>An even more intensively investigated FAD-dependent pathway controls protein folding in the endoplasmic reticulum (ER) [<xref rid=\"B15-ijms-21-05310\" ref-type=\"bibr\">15</xref>], under the control of FAD trafficking [<xref rid=\"B16-ijms-21-05310\" ref-type=\"bibr\">16</xref>]. In the nucleus, FAD-dependent oxidases play some roles in chromatin remodeling and epigenetic events, controlling the expression of genes involved in energy metabolism [<xref rid=\"B17-ijms-21-05310\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijms-21-05310\" ref-type=\"bibr\">18</xref>].</p><p>Moreover, Rf has a central role in pyridoxine metabolism [<xref rid=\"B19-ijms-21-05310\" ref-type=\"bibr\">19</xref>], in folate and Vitamin B12 recycling [<xref rid=\"B20-ijms-21-05310\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijms-21-05310\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05310\" ref-type=\"bibr\">22</xref>] and therefore in one-carbon metabolisms, as well as in biosynthesis and regulation of coenzyme A, coenzyme Q<sub>10</sub>, heme, steroids and thyroxine [<xref rid=\"B23-ijms-21-05310\" ref-type=\"bibr\">23</xref>].</p></sec><sec id=\"sec2-ijms-21-05310\"><title>2. Rf Absorption and Cell Delivery</title><p>Since humans cannot synthesize Rf, its dietary supplementation is essential [<xref rid=\"B7-ijms-21-05310\" ref-type=\"bibr\">7</xref>]. Rf is present in many foods such as green vegetables, dairy products, eggs and meat. Nevertheless, a certain amount of Rf can even be supplied endogenously by some microbes residing in the large intestine. Its recommended daily intake is 1.3 mg per day for men and 1.1 mg per day for women with some variations depending on age and physical state (e.g., pregnancy or lactation). Rf status can be altered by malabsorption occurring in different conditions, such as celiac disease, malignancies, and alcoholism.</p><p>Rf absorption takes place mainly in the small intestine and partly in the large intestine [<xref rid=\"B24-ijms-21-05310\" ref-type=\"bibr\">24</xref>] and it occurs via specific carrier-mediated processes, supported by three members of the solute carrier family 52A. They are named Rf transporter 1 (RFVT1), RFVT2 and RFVT3, which perform different functional and kinetical properties [<xref rid=\"B25-ijms-21-05310\" ref-type=\"bibr\">25</xref>].</p><p><italic>SLC52A1</italic> was the first gene identified coding for a human Rf translocator [<xref rid=\"B26-ijms-21-05310\" ref-type=\"bibr\">26</xref>]: it is located on chromosome 17 at 17p13.2. Soon after <italic>SLC52A2</italic> and <italic>SLC52A3</italic> were also cloned and characterized as reported in [<xref rid=\"B27-ijms-21-05310\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05310\" ref-type=\"bibr\">28</xref>]. These two genes are located one on chromosome 8 at 8q24.3 and the other on chromosome 20 at 20p13, respectively.</p><p>In enterocyte, which allows for a trans-epithelial vectorial Rf movement to portal circulation, the vitamin is taken up by the action of RFVT3 at the apical membrane and it is released in blood by RFVT1 and RFVT2 located at the basolateral membrane [<xref rid=\"B29-ijms-21-05310\" ref-type=\"bibr\">29</xref>]. Before crossing the intestinal barrier and entering both the circulating and the peripheral cells, Rf is converted into FMN and FAD via the sequential action of riboflavin kinase (RFK, EC 2.7.1.26) and FAD synthase (FADS, EC 2.7.7.2) (see [<xref rid=\"B30-ijms-21-05310\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijms-21-05310\" ref-type=\"bibr\">31</xref>] and below).</p><p>It should be noted that RFVTs mediate the translocation of Rf, rather than FMN or FAD [<xref rid=\"B32-ijms-21-05310\" ref-type=\"bibr\">32</xref>]. FMN and FAD, derived from digested proteins, must be converted into Rf again before being delivered into enterocytes. This task is performed by extracellular diphospho- (EC 3.6.1._) and monophospho- hydrolases (EC 3.1.3.2), located on the intestinal brush border. These hydrolytic events occur later on, before Rf transport into specialized cell of peripheral tissues [<xref rid=\"B2-ijms-21-05310\" ref-type=\"bibr\">2</xref>,<xref rid=\"B19-ijms-21-05310\" ref-type=\"bibr\">19</xref>]. A comprehensive study on the identity of these “transport-preparing” hydrolases is missing at the moment. Quite unspecific activities working on the plasma membrane surface were initially proposed to perform this function by [<xref rid=\"B33-ijms-21-05310\" ref-type=\"bibr\">33</xref>]. The discovery of the big family of Nudix hydrolases (Nudix stands for <italic>“</italic><underline>nu</underline>cleoside <underline>di</underline>phosphate linked to some other moiety <underline>X</underline> hydrolase”), as well as that of a FAD-diphosphatase (FADDPase) activity “hidden” in the N-terminus domain of the bi-functional FAD synthase [<xref rid=\"B34-ijms-21-05310\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijms-21-05310\" ref-type=\"bibr\">35</xref>], suggests the possibility for the existence of a flavin-linked specific process; this necessitates further research.</p><p>Through the portal venous system, Rf reaches the liver and is taken up by hepatocytes, possibly using a Na<sup>+</sup>-independent and energy-dependent carrier regulated by intracellular Ca<sup>2+</sup>/calmodulin as showed in HepG2 cell line [<xref rid=\"B36-ijms-21-05310\" ref-type=\"bibr\">36</xref>], as well as in pancreatic β cells/islets [<xref rid=\"B37-ijms-21-05310\" ref-type=\"bibr\">37</xref>]. However, the contribution of each RFVT in the human hepatic homeostasis of Rf has not yet been fully clarified, apart from data reported in “The human protein Atlas database” suggesting the prevalence of RFVT2. Similar conclusions have been also reported in the mouse model [<xref rid=\"B38-ijms-21-05310\" ref-type=\"bibr\">38</xref>].</p><p>Circulating Rf is bound both to plasma albumin (<italic>k</italic><sub>d</sub> = 3.8–10.4 mM) and more tightly to a subfraction of immunoglobulins [<xref rid=\"B39-ijms-21-05310\" ref-type=\"bibr\">39</xref>] with median plasma concentrations of 10.5, 6.6 and 74 nmol/L for Rf, FMN and FAD [<xref rid=\"B40-ijms-21-05310\" ref-type=\"bibr\">40</xref>].</p><p>Excess of vitamin intakes or of tissue requirements is excreted in the urine as Rf or other metabolites [<xref rid=\"B41-ijms-21-05310\" ref-type=\"bibr\">41</xref>]. Due to the regulation of absorption and excretion, the circulating Rf level is subjected to a small circadian variation [<xref rid=\"B42-ijms-21-05310\" ref-type=\"bibr\">42</xref>].</p></sec><sec id=\"sec3-ijms-21-05310\"><title>3. Rf Transporters</title><sec id=\"sec3dot1-ijms-21-05310\"><title>3.1. Some Molecular Insights on RFVTs</title><p>The recently identified RFVTs have different tissue-specific expression profiles as well as functional and kinetical properties [<xref rid=\"B25-ijms-21-05310\" ref-type=\"bibr\">25</xref>]. Investigations on the expression profile of RFVTs at the transcriptional level revealed that RFVT1 is particularly abundant in the placenta and small intestine, RFVT2 is ubiquitously expressed, but it is relevant especially for the brain, while RFVT3 is expressed mostly in intestine and testis [<xref rid=\"B28-ijms-21-05310\" ref-type=\"bibr\">28</xref>].</p><p>In addition, we would like to speculate that alternative splicing of each gene, might generate different products, some of which are not yet described at the protein level. Currently, in Entrez Gene database (<uri xlink:href=\"www.ncbi.nlm.nih.gov/gene\">www.ncbi.nlm.nih.gov/gene</uri>) three RefSeq transcripts are reported for <italic>SLC52A1</italic>, all encoding for the same 448 aa protein (<xref ref-type=\"fig\" rid=\"ijms-21-05310-f002\">Figure 2</xref>). Eleven transcript variants are reported for <italic>SLC52A2</italic>, among which one is a non-coding RNA, six encode for the same 445 aa protein, three encode for the same putative 281 aa protein and one encodes for a 357 aa protein (<xref ref-type=\"fig\" rid=\"ijms-21-05310-f002\">Figure 2</xref>). According to this database, a single protein of 469 aa is encoded, starting from four different transcript variants generated by <italic>SLC52A3</italic> (<xref ref-type=\"fig\" rid=\"ijms-21-05310-f002\">Figure 2</xref>). Recently, two <italic>SLC52A3</italic> transcript variants that differ in the transcriptional start site, were described in esophageal squamous cell carcinoma (ESCC), named SLC52A3a and SLC52A3b. The first variant corresponds to NM_ 033409.4 in the RefSeq database. The second variant encodes for a protein of 415 aa (<xref ref-type=\"fig\" rid=\"ijms-21-05310-f002\">Figure 2</xref>). Immunofluorescence analysis of SLC52A3a and SLC52A3b in ESCC cell lines revealed that SLC52A3a is localized in the cell membrane and in the nucleus, while SLC52A3b is found in the cell cytoplasm. A stronger distribution of SLC52A3a in the nucleus has been correlated with poor prognosis in ESCC patients [<xref rid=\"B43-ijms-21-05310\" ref-type=\"bibr\">43</xref>].</p><p>All three RFVTs have been predicted to have 11 trans-membrane (TM) domains. Further details are reported in [<xref rid=\"B2-ijms-21-05310\" ref-type=\"bibr\">2</xref>,<xref rid=\"B44-ijms-21-05310\" ref-type=\"bibr\">44</xref>,<xref rid=\"B45-ijms-21-05310\" ref-type=\"bibr\">45</xref>]. A novel homology model of hRFVT1 is presented here (<xref ref-type=\"fig\" rid=\"ijms-21-05310-f002\">Figure 2</xref>). The <italic>N</italic>-glycosylation status of RFVT3 has been addressed in [<xref rid=\"B46-ijms-21-05310\" ref-type=\"bibr\">46</xref>]. Mutating the two predicted <italic>N</italic>-glycosylation sites at Asn<sup>94</sup> and Asn<sup>168</sup>, and the substrate-interacting residues identified by the protein-docking modeling approach, lead to impairment in Rf uptake and to intracellular (ER) retention of the mutated proteins in HuTu-80 cells, demonstrating the importance of glycosylation and structure-function relationship in physiology/biology of intestinal epithelial cells.</p><p>As far as transport mechanisms and kinetic features of Rf uptake are concerned, the first series of studies were carried out with different natural cells. For an exhaustive review on these studies, the reader is referred to [<xref rid=\"B47-ijms-21-05310\" ref-type=\"bibr\">47</xref>]. After genes’ identification, some additional features of transport have been clarified, using cells transfected with RFVTs’ cDNA. The uptake of Rf by all RFVTs was proven to be independent on extracellular Na<sup>+</sup> and Cl<sup>−</sup> [<xref rid=\"B28-ijms-21-05310\" ref-type=\"bibr\">28</xref>], in accordance with previous reports [<xref rid=\"B47-ijms-21-05310\" ref-type=\"bibr\">47</xref>], but in contrast with reports in [<xref rid=\"B48-ijms-21-05310\" ref-type=\"bibr\">48</xref>]. The pH sensitivity was also tested, indicating that the sole RFVT3-mediated uptake is pH-dependent [<xref rid=\"B27-ijms-21-05310\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05310\" ref-type=\"bibr\">28</xref>].</p><p>As for RFVTs’ inhibitors: Rf transport activity is inhibited by flavin analogs, such as the natural cofactors FMN and FAD and the artificial molecule lumiflavin (7,8,10-Trimethylisoalloxazine) in HEK-293 cells [<xref rid=\"B26-ijms-21-05310\" ref-type=\"bibr\">26</xref>]. Lumichrome (7,8-Dimethylalloxazine) and amiloride also inhibited Rf transport in NCM460 cells [<xref rid=\"B49-ijms-21-05310\" ref-type=\"bibr\">49</xref>]. In transfected HEK-293 cells, hRFVT-mediated uptake of Rf resulted in complete inhibition by an excess of Rf and lumiflavin but modest inhibition by FMN. Whereas, only hRFVT2- mediated Rf uptake resulted slightly but significantly inhibited by FAD [<xref rid=\"B28-ijms-21-05310\" ref-type=\"bibr\">28</xref>]. In addition, RFVT3-mediated Rf uptake was also inhibited by amiloride, ethidium, and methylene blue, but not by <sc>d</sc>-ribose and alloxazine [<xref rid=\"B50-ijms-21-05310\" ref-type=\"bibr\">50</xref>]. Besides all findings obtained with intact cells, the recombinant RFVT2 and the native protein extracted from fibroblasts and reconstituted in proteoliposomes are also inhibited by FMN and lumiflavin [<xref rid=\"B44-ijms-21-05310\" ref-type=\"bibr\">44</xref>]. This is the first example of studies with a purified recombinant human plasma membrane vitamin translocator.</p><p>Transcriptional mechanisms regulating Rf transport in different organs and tissues are not completely elucidated, being the intestine epithelial cells the first and the best model, because of the relevance of vitamin absorption in human nutrition. A big piece of work was made in this sense by the Said group (see below).</p><p>The first approach to address this problem was aimed to identify the minimal promoter regions of <italic>SLC52A1</italic> and <italic>SLC52A3</italic>, the main Rf intestinal transporters, together with the regulatory elements involved in their activation. Analysis performed in the promoter region of <italic>SLC52A1</italic> showed that its core activity is embedded in the region between −234 and −23 bp and it contains several putative cis-regulatory sites, including KLFs, AP-2, EGRF, and stimulating protein-1 (Sp-1). A significant decrease in promoter activity was found mutating each of the cis-regulatory sites of <italic>SLC52A1</italic> promoter, with a more pronounced reduction for Sp-1. Focusing on the Sp-1 site, Electrophoretic mobility shift assay (EMSA), super-shift and Chromatin immunoprecipitation (ChIP) analysis performed on HuTu-80 cells and studies with <italic>Drosophila</italic> SL-2 cells confirmed the important role of Sp-1 in regulating the activity of <italic>SLC52A1</italic> promoter [<xref rid=\"B51-ijms-21-05310\" ref-type=\"bibr\">51</xref>].</p><p>The same analyses performed on <italic>SLC52A3</italic> revealed the core promoter activity encoded between −199 and +8 bp, and it includes binding sites for NF-κB/cRel, KLF, and Sp-1. Among these putative cis-regulatory elements, only Sp-1 was found to play an important role in HuTu-80 cells. Studies with <italic>Drosophila</italic> SL-2 cells showed that besides Sp-1, Sp-3 can also be led to <italic>SLC52A3</italic> activation. Moreover, with the use of luciferase gene fusions, the activity of the cloned <italic>SLC52A3</italic> promoter was confirmed in vivo in transgenic mice [<xref rid=\"B52-ijms-21-05310\" ref-type=\"bibr\">52</xref>]. Differently from the bulk of the existing knowledge about the regulatory regions of <italic>SLC52A3</italic> and <italic>SLC52A1</italic>, responsive elements in the <italic>SLC52A2</italic> promoter are still not characterized. Furthermore, with the use of the Caco-2 and HuTu-80 cells, the Said group demonstrated that RFVT3 is a target for post-transcriptional regulation by miR-423-5p, which interacts with the 3′-UTR region of hRFVT3, leading to a decrease in the translational efficiency and in intestinal Rf uptake. Similar results were obtained with mouse intestinal enteroids [<xref rid=\"B53-ijms-21-05310\" ref-type=\"bibr\">53</xref>].</p></sec><sec id=\"sec3dot2-ijms-21-05310\"><title>3.2. Rf Transporters: What Else</title><p>Using human intestinal epithelial NCM460 cells grown in Rf-depleted and over-supplemented media, Said group also demonstrated that the intestinal Rf uptake process can be regulated by extracellular substrate level. Rf-deficient conditions resulted in an increased expression of hRFVT-2 and -3 (but not hRFVT-1) and of Sp-1 at both the protein and the mRNA levels. ChIP assay for histone H3 modification revealed a significant decrease in the activity of the heterochromatin marker (H3K27me3) in cells maintained in Rf-deficient conditions, suggesting the possible involvement of epigenetic changes in the <italic>SLC52A3</italic> promoter [<xref rid=\"B54-ijms-21-05310\" ref-type=\"bibr\">54</xref>].</p><p>Recently, using Caco-2 cells and mouse colonoids, sodium butyrate (NaB) has been demonstrated to up-regulate intestinal Rf uptake inducing the expression of <italic>SLC52A3</italic>, possibly due to epigenetics alteration in <italic>SLC52A3</italic> promoter after NaB treatment [<xref rid=\"B55-ijms-21-05310\" ref-type=\"bibr\">55</xref>]. Besides its important role as an energetic substrate in intestinal epithelial cells, butyrate is involved in the regulation of immune cell by modulating different processes of intestinal epithelial cells and leukocytes through the activation of G protein-coupled receptors and the modulation of the activity of enzymes and transcription factors including the histone deacetylase [<xref rid=\"B56-ijms-21-05310\" ref-type=\"bibr\">56</xref>]. An additional mode of action of NaB could involve the increase of expression of TMEM237, a transmembrane protein encoded by the <italic>ALS2CR4</italic> gene, which directly interacts with RFVT3 ensuring its stability [<xref rid=\"B57-ijms-21-05310\" ref-type=\"bibr\">57</xref>].</p><p>Another piece of work concerning regulation of RFVTs expression level has been made in relationships to modulation by TNF-α, one among the best-characterized activators upstream of NF-kB signaling pathway. TNF-α addition to Caco-2 cells reduced the expression of RFVT1 and 3 [<xref rid=\"B58-ijms-21-05310\" ref-type=\"bibr\">58</xref>]. Its effect could be also mediated via a reduction of the level of expression of the TMEM237 protein [<xref rid=\"B57-ijms-21-05310\" ref-type=\"bibr\">57</xref>]. The relevance of NF-<italic>k</italic>B in regulating RFVT3 expression, upon TNF-α stimulation due to Rf deficiency has been recently proposed also in ESCC [<xref rid=\"B43-ijms-21-05310\" ref-type=\"bibr\">43</xref>]. Altered expression patterns of <italic>SLC52As</italic> in human colorectal cancer, involving different transcriptional and post-transcriptional mechanisms specific for each transporter, have been reported ([<xref rid=\"B59-ijms-21-05310\" ref-type=\"bibr\">59</xref>] and refs. therein).</p><p>A further membrane transporter responsible for Rf movement across the plasma membrane is the less specific ABCG2 transporter, which mediates the secretion of Rf in milk and other extracellular fluids [<xref rid=\"B60-ijms-21-05310\" ref-type=\"bibr\">60</xref>]. The ABCG2 transporter belongs to the family of ABC translocators (ATP-binding cassette G2 transporter) and it is also called BCRP (breast cancer resistance protein), since it was first isolated from a multidrug-resistant breast cancer cell line. Its specific tissue distribution is closely linked to the physiological role it assumes as, for instance, limiting absorption (in the intestine), mediating distribution (in the blood-brain and blood-placental barriers) and facilitating elimination and excretion (in the liver and kidney) of a wide variety of drugs, molecules carcinogenic and food toxins [<xref rid=\"B61-ijms-21-05310\" ref-type=\"bibr\">61</xref>]. The dual action of BCRP both as a detoxifier and as an Rf transporter has been preserved in the course of evolution, so that it can be found from lower mammals to humans [<xref rid=\"B62-ijms-21-05310\" ref-type=\"bibr\">62</xref>].</p><p>A main question, still completely under-investigated, concerns the subcellular trafficking of Rf. The existence of a FAD recycling pathway, which implies a carrier-mediated process for Rf across the inner mitochondrial membrane, was demonstrated by our group in mammals, yeasts and plants [<xref rid=\"B63-ijms-21-05310\" ref-type=\"bibr\">63</xref>,<xref rid=\"B64-ijms-21-05310\" ref-type=\"bibr\">64</xref>], but the nature of the mitochondrial Rf translocator is still unknown.</p></sec></sec><sec id=\"sec4-ijms-21-05310\"><title>4. Rf Intracellular Homeostasis</title><sec id=\"sec4dot1-ijms-21-05310\"><title>4.1. Biochemical Pathways of FAD Synthesis and Degradation</title><p>Enzymes metabolizing Rf to its derived cofactors i.e., Rf kinase (RFK) and FAD synthase or FMN:ATP adenylyl transferase (FADS or FMNAT) in humans are encoded by two distinct genes, named as reported in <xref ref-type=\"fig\" rid=\"ijms-21-05310-f001\">Figure 1</xref>.</p><p><italic>RFK</italic> gene, located on chromosome 9 at 9q21.13, codes for a 17.6 kDa polypeptide consisting of a single domain, whose kinetics is largely regulated by the relative concentration of substrates/products [<xref rid=\"B65-ijms-21-05310\" ref-type=\"bibr\">65</xref>]. Its crystal structures (PDB codes: 1NB0, 1NB9, 1P4M, 1Q9S), confirmed that the nucleotide-binding motif [<xref rid=\"B30-ijms-21-05310\" ref-type=\"bibr\">30</xref>] undergoes large conformational changes upon binding of Rf, that is to say the reaction is highly regulated by the vitamin taken up [<xref rid=\"B30-ijms-21-05310\" ref-type=\"bibr\">30</xref>,<xref rid=\"B66-ijms-21-05310\" ref-type=\"bibr\">66</xref>]. These data, together with observations made in RFK-KD (knock down) cells [<xref rid=\"B18-ijms-21-05310\" ref-type=\"bibr\">18</xref>], allowed us to propose that RFK is the limiting step of the intracellular conversion of riboflavin into FAD [<xref rid=\"B2-ijms-21-05310\" ref-type=\"bibr\">2</xref>]. This flavin-based kinetic control of the flavin cofactor forming enzyme could be exerted together with other regulatory mechanisms, such as those triggered by thyroid hormones [<xref rid=\"B67-ijms-21-05310\" ref-type=\"bibr\">67</xref>].</p><p>RFK deficiency has never been described in humans, therefore it was suggested that it might be lethal, as previously reported in mice [<xref rid=\"B68-ijms-21-05310\" ref-type=\"bibr\">68</xref>]. However, hypomorphic <italic>RFK</italic> mutations might also result in a clinical presentation similar to that seen in patients with FADS deficiency [<xref rid=\"B23-ijms-21-05310\" ref-type=\"bibr\">23</xref>]. Indeed, conditional RFK knockout strains of mice have been generated to address the impact of acute Rf deficiency on host defence against <italic>Listeria monocytogenes</italic> [<xref rid=\"B69-ijms-21-05310\" ref-type=\"bibr\">69</xref>]. Moreover, knockdown of RFK combined with a Rf deficient diet has been proven to alter the levels of cryptochrome protein in mouse liver and the expression profiles of the clock and clock-controlled genes. Therefore, light-independent mechanisms depending on FAD contribute to the proper circadian oscillation of metabolic genes in mammals [<xref rid=\"B70-ijms-21-05310\" ref-type=\"bibr\">70</xref>].</p><p><italic>FLAD1</italic> gene, located on chromosome 1 at 1q21.3, codes differently sized polypeptides, consisting of either a single or two domains, corresponding to different transcript variants generated by alternative splicing of the gene. When the human gene was initially identified by our group [<xref rid=\"B31-ijms-21-05310\" ref-type=\"bibr\">31</xref>,<xref rid=\"B71-ijms-21-05310\" ref-type=\"bibr\">71</xref>], two protein isoforms (namely hFADS1 and hFADS2) were described. hFADS1, encoded by the seven exons long transcript variant 1 (GenBank Accession n: NM_025207.5), is a 587 aa protein with a predicted molecular mass of 65.3 kDa; the first 17 residues of this isoform represent a putative mitochondrial-targeting peptide, as predicted by bioinformatic analysis [<xref rid=\"B72-ijms-21-05310\" ref-type=\"bibr\">72</xref>]; hFADS2 is a 490 aa protein with a predicted molecular mass of 54.2 kDa, which lacks a 97-mer in the N-terminal region of hFADS1 with a cytosolic localization. hFADS2 is the product of transcript variant 2 (GenBank Accession n: NM_201398.3), the most abundant in tissues/cells tested so far, which derives from interruption of exon 1 by an additional intron [<xref rid=\"B72-ijms-21-05310\" ref-type=\"bibr\">72</xref>] (<xref ref-type=\"fig\" rid=\"ijms-21-05310-f003\">Figure 3</xref>).</p><p>Both hFADS1 and 2 contain an N-terminal molybdopterin binding (MPTb, recently renamed FADHy [<xref rid=\"B35-ijms-21-05310\" ref-type=\"bibr\">35</xref>] domain, since it hides a FAD hydrolase activity [<xref rid=\"B34-ijms-21-05310\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijms-21-05310\" ref-type=\"bibr\">35</xref>], which is fused with a C-terminal 3-phosphoadenosine 5-phosphosulfate reductase domain (PAPS, recently renamed FADSy [<xref rid=\"B35-ijms-21-05310\" ref-type=\"bibr\">35</xref>]), which <italic>per se</italic> performs the FADS activity [<xref rid=\"B73-ijms-21-05310\" ref-type=\"bibr\">73</xref>,<xref rid=\"B74-ijms-21-05310\" ref-type=\"bibr\">74</xref>,<xref rid=\"B75-ijms-21-05310\" ref-type=\"bibr\">75</xref>].</p><p>Isoform 2 has been over-produced and purified in its catalytically active form: it contains redox-sensitive cysteines which make hFADS2 a putative redox-sensor [<xref rid=\"B76-ijms-21-05310\" ref-type=\"bibr\">76</xref>]. In [<xref rid=\"B35-ijms-21-05310\" ref-type=\"bibr\">35</xref>], the novel feature of hFADS2 to catalyze FAD hydrolysis has been characterized especially for its ability to create a link between the flavin and the NAD world as already proposed in yeast [<xref rid=\"B7-ijms-21-05310\" ref-type=\"bibr\">7</xref>,<xref rid=\"B77-ijms-21-05310\" ref-type=\"bibr\">77</xref>]</p><p>Besides regulating FAD production/hydrolysis, FADS also takes part in cofactor delivery to the appropriate apo-flavoenzymes during holoenzyme biogenesis, operating in a flavinylation machinery as a FAD “chaperone” [<xref rid=\"B78-ijms-21-05310\" ref-type=\"bibr\">78</xref>,<xref rid=\"B79-ijms-21-05310\" ref-type=\"bibr\">79</xref>].</p><p>Of course, the two opposite processes, i.e., FAD synthesis and hydrolysis, both performed by the same polypeptide (hFADS2), can constitute a “futile cycle”, thus they cannot work contemporarily; it would be very intriguing to discover the molecular mechanisms controlling the switch of synthesis versus hydrolysis of FAD, that is to say, the changing in the relative concentration of FAD vs. FMN in vivo.</p></sec><sec id=\"sec4dot2-ijms-21-05310\"><title>4.2. The Puzzle of the Splicing Variants of FLAD1 and the Sub-Cellular Origin of FAD</title><p>The number of the transcript variants of <italic>FLAD1</italic> is far to be defined. Besides transcript variants 1 and 2, two additional RefSeq transcript variants are reported in the Entrez Gene database, as schematized in <xref ref-type=\"fig\" rid=\"ijms-21-05310-f003\">Figure 3</xref>. The transcript variant 3 (GenBank accession n: NM_00114891.2) differs in 3′ end compared to transcript variant 2 resulting in a 446 aa protein with a shorter C-terminus. It is very similar to the isoform 2, therefore it is presumably able to perform both FAD synthesis and FAD hydrolysis.</p><p>The transcript variant 4 (GenBank accession n: NM_001184892.2) has multiple differences and initiates translation at an alternative start codon, compared to variant 2. The protein product of 294 aa (isoform 4) is shorter and has distinct N- and C-termini when it is compared to hFADS2.</p><p>An additional protein (isoform 5), reported exclusively in UniprotKB, is a 338 aa protein and it is very similar to isoform 4. The last two isoforms, containing the sole FADHy domain, are expected to have hydrolytic activity, but these isoforms, as well as the protein corresponding to isoform 3, have not been either produced or characterized yet.</p><p>Moreover other three transcript variants (ENST00000295530.6, ENST00000368428.1, ENST00000368433.5) are annotated in ENSEMBL genome browser 100 (<uri xlink:href=\"https://ww.ensembl.org/index.html\">https://ww.ensembl.org/index.html</uri>).</p><p>The existence of additional “orphan” isoforms was indeed postulated since 2013 [<xref rid=\"B7-ijms-21-05310\" ref-type=\"bibr\">7</xref>] and further confirmed in 2016 [<xref rid=\"B80-ijms-21-05310\" ref-type=\"bibr\">80</xref>] when two novel transcript variants, not yet annotated, were revealed by transcriptomic analysis. They correspond to the sole C-terminus protein domain and were named isoform 5 and 6 [<xref rid=\"B80-ijms-21-05310\" ref-type=\"bibr\">80</xref>] (<xref ref-type=\"fig\" rid=\"ijms-21-05310-f003\">Figure 3</xref>). Isoform 6 was produced as a recombinant protein in its FADSy active form and its enzymatic action was characterized in some detail, confirming the absence of a hydrolase activity in this novel [<xref rid=\"B73-ijms-21-05310\" ref-type=\"bibr\">73</xref>].</p><p>The contemporary expression of different <italic>FLAD1</italic> products in the same cells [<xref rid=\"B7-ijms-21-05310\" ref-type=\"bibr\">7</xref>] was interpreted as a mode to regulate the sub-cellular compartmentation of the process of FAD delivery to apo-flavoproteins [<xref rid=\"B7-ijms-21-05310\" ref-type=\"bibr\">7</xref>,<xref rid=\"B78-ijms-21-05310\" ref-type=\"bibr\">78</xref>], but further studies are necessary to strengthen and confirm our observation/proposal.</p><p>Besides the already stressed mitochondrial localization of hFADS1, in a number of different mammalian cells, a nuclear localization for FAD forming enzymes has been demonstrated [<xref rid=\"B81-ijms-21-05310\" ref-type=\"bibr\">81</xref>] and correlated to the emerging role of FAD and derived cofactors as regulators of epigenetic redox events [<xref rid=\"B82-ijms-21-05310\" ref-type=\"bibr\">82</xref>]. In the nucleus, FAD hydrolytic events were also described, but we do not yet know if they are due to one of the bi-functional isoforms generated by <italic>FLAD1</italic> or if they occur via independent hydrolases [<xref rid=\"B81-ijms-21-05310\" ref-type=\"bibr\">81</xref>]. Unfortunately, the identification of the nuclear FADS isoform is still lacking, as well as its possible alteration in Rf-related neuromuscular disorders. An intensive effort, therefore, urges to better understand these potentially crucial nuclear events.</p><p>Moreover, some intracellular hydrolytic activities i.e., FADDPase and FMN hydrolase are localized in the inter-membrane space of mitochondria. They were characterized at the functional level, but not identified at the molecular one; they accompany Rf re-cycling in rat and human muscle mitochondria [<xref rid=\"B63-ijms-21-05310\" ref-type=\"bibr\">63</xref>,<xref rid=\"B83-ijms-21-05310\" ref-type=\"bibr\">83</xref>,<xref rid=\"B84-ijms-21-05310\" ref-type=\"bibr\">84</xref>] via a still-unidentified mitochondrial Rf transporter. Thus, the process of flavin transport across mitochondrial membrane is a matter of debate.</p><p>In humans, FAD transport across the mitochondrial membrane requires a specific inner membrane carrier encoded by <italic>SLC25A32</italic>, which is located at chromosome 8 at 8q22.3. Three RefSeq transcript variants are reported for this gene, only one encoding for a protein of 315 aa.</p><p>It has been initially identified as a mitochondrial folate transporter [<xref rid=\"B85-ijms-21-05310\" ref-type=\"bibr\">85</xref>], and then as the human orthologue of the yeast mitochondrial FAD transporter FLX1 [<xref rid=\"B86-ijms-21-05310\" ref-type=\"bibr\">86</xref>,<xref rid=\"B87-ijms-21-05310\" ref-type=\"bibr\">87</xref>] which allow cytosolically synthesized FAD to enter mitochondrial membrane and <italic>vice versa</italic> [<xref rid=\"B88-ijms-21-05310\" ref-type=\"bibr\">88</xref>].</p><p>Mutations in this gene are causative of a neuromuscular disorder responsive to Rf treatment [<xref rid=\"B89-ijms-21-05310\" ref-type=\"bibr\">89</xref>,<xref rid=\"B90-ijms-21-05310\" ref-type=\"bibr\">90</xref>], whose phenotype is described below.</p><p>The role of <italic>SLC25A32</italic> in the field of cancer research has recently emerged [<xref rid=\"B91-ijms-21-05310\" ref-type=\"bibr\">91</xref>].</p></sec></sec><sec id=\"sec5-ijms-21-05310\"><title>5. Remaining Challenges in Neuronal and Muscular Flavin Homeostasis and their Alterations</title><sec id=\"sec5dot1-ijms-21-05310\"><title>5.1. Rf Neuronal Homeostasis and BVVLS</title><p>Given the importance of vitamin B2 in oxidative metabolism crucial for nervous system economy, the mechanisms of Rf transport and homeostasis in the nervous system have been long investigated in the past (see [<xref rid=\"B92-ijms-21-05310\" ref-type=\"bibr\">92</xref>] and refs therein) and further clarified on the discovery of the human hRFVT2, which immediately appeared to be maximally expressed in the human brain and spinal cord [<xref rid=\"B28-ijms-21-05310\" ref-type=\"bibr\">28</xref>,<xref rid=\"B93-ijms-21-05310\" ref-type=\"bibr\">93</xref>].</p><p>Utilization of glucose, certain amino acid and ketone bodies’ metabolism, as well as aminoacidic neuromediator synthesis are crucial for brain (<xref ref-type=\"fig\" rid=\"ijms-21-05310-f004\">Figure 4</xref>). Utilization of fatty acids by the brain has been, for a long time, excluded based on the impermeability of the blood-brain barrier. More recently, the importance of brain economy of short-chain fatty acid, as butyrate emerged and, thus we can imagine that flavin-dependent β-oxidation is the key pathway involved in butyrate-utilizing cells [<xref rid=\"B94-ijms-21-05310\" ref-type=\"bibr\">94</xref>].</p><p>Thus, Rf deficiency is expected to slow-down all these processes, thus nervous tissue alterations/degeneration are expected as biochemical consequences of FAD forming deficiencies, as depicted by [<xref rid=\"B95-ijms-21-05310\" ref-type=\"bibr\">95</xref>]. Nevertheless, given the cellular heterogeneity of the brain, it is not surprising that different cell types have a distinctive Rf demand.</p><p>Rf enters the brain from blood at the blood-brain barrier (BBB) via a saturable system (<italic>k</italic><sub>m</sub> ~0.1 μM) and this process presumably involves RFVT2 [<xref rid=\"B48-ijms-21-05310\" ref-type=\"bibr\">48</xref>,<xref rid=\"B96-ijms-21-05310\" ref-type=\"bibr\">96</xref>], but, as far as we know, a direct demonstration of the sole presence of this transporter is lacking. Moreover, other translocators (OAT3 and ABCG2) which control Rf efflux from the Choroid Plexus seem to be additional candidates in ensuring a constant flavin supply in the extracellular space of the brain [<xref rid=\"B60-ijms-21-05310\" ref-type=\"bibr\">60</xref>,<xref rid=\"B61-ijms-21-05310\" ref-type=\"bibr\">61</xref>,<xref rid=\"B92-ijms-21-05310\" ref-type=\"bibr\">92</xref>]. In CSF (cerebrospinal fluid), total flavin concentration is reported to be ~0.1 μM, with the sole Rf concentration being ~0.02 μM [<xref rid=\"B96-ijms-21-05310\" ref-type=\"bibr\">96</xref>,<xref rid=\"B97-ijms-21-05310\" ref-type=\"bibr\">97</xref>]. A systematic study concerning alterations of these values in both control individuals and in patients suffering from Rf deficiency is still missing. The possibility that ancillary cells might be involved in Rf delivery to neurons has to be considered. In fact, the vast majority of cells in the adult human brain are astrocytes, which are intimately associated with synapses and govern synapse formation and plasticity [<xref rid=\"B98-ijms-21-05310\" ref-type=\"bibr\">98</xref>].</p><p>The importance of Rf supply for neuronal cells clearly emerged in 2010, when the alteration in <italic>SLC52A3</italic> was correlated to Brown-Vialetto-Van Laere Syndrome (BVVLS) [<xref rid=\"B99-ijms-21-05310\" ref-type=\"bibr\">99</xref>]. BVVLS is an early-onset disease characterized by progressive cranial neurons loss (resembling amyotrophic lateral sclerosis (ALS)), degeneration of spinal cord neurons and respiratory insufficiencies. This disease, now named Riboflavin Transporter Deficiency 3 (RTD3, OMIM #211530), is considered to be the same as the Fazio-Londe disease (OMIM #211500) [<xref rid=\"B100-ijms-21-05310\" ref-type=\"bibr\">100</xref>], which differs from BVVLS only for the lack of hearing loss. Not long after <italic>SLC52A3</italic> disease gene discovery, mutations in <italic>SLC52A2</italic> have also been associated with BVVLS, now named RTD2 (OMIM #614707) [<xref rid=\"B101-ijms-21-05310\" ref-type=\"bibr\">101</xref>,<xref rid=\"B102-ijms-21-05310\" ref-type=\"bibr\">102</xref>].</p><p>Other common clinical features present in RTD2 and RTD3 patients are dysarthria, weakness and hypotonia, whereas the most common differences concern facial weakness which is typical of RTD3 patients and vision loss, characteristic of RTD2 patients [<xref rid=\"B103-ijms-21-05310\" ref-type=\"bibr\">103</xref>,<xref rid=\"B104-ijms-21-05310\" ref-type=\"bibr\">104</xref>]. RTDs are sometimes responsive to high doses of Rf treatment and characterized by biochemical abnormalities in the acylcarnitine profiles, thus resembling another inborn error of metabolism mainly affecting muscle, named MADD (Multiple Acyl-CoA Dehydrogenase Deficiency) we will discuss later on [<xref rid=\"B105-ijms-21-05310\" ref-type=\"bibr\">105</xref>].</p><p>Since RTD definition, a total of 109 RTD patients have been recently exhaustively reviewed in [<xref rid=\"B103-ijms-21-05310\" ref-type=\"bibr\">103</xref>]; further nine novel cases have been reported in [<xref rid=\"B106-ijms-21-05310\" ref-type=\"bibr\">106</xref>,<xref rid=\"B107-ijms-21-05310\" ref-type=\"bibr\">107</xref>,<xref rid=\"B108-ijms-21-05310\" ref-type=\"bibr\">108</xref>,<xref rid=\"B109-ijms-21-05310\" ref-type=\"bibr\">109</xref>,<xref rid=\"B110-ijms-21-05310\" ref-type=\"bibr\">110</xref>,<xref rid=\"B111-ijms-21-05310\" ref-type=\"bibr\">111</xref>,<xref rid=\"B112-ijms-21-05310\" ref-type=\"bibr\">112</xref>,<xref rid=\"B113-ijms-21-05310\" ref-type=\"bibr\">113</xref>,<xref rid=\"B114-ijms-21-05310\" ref-type=\"bibr\">114</xref>].</p><p>A clear relationship between Rf intracellular scarcity and biochemical damages in neurons is still lacking, as well as the molecular mechanism of regulation of the flavin transport efficiency in a different area of the brain and in different types of neurons. Another unknown issue is the sub-cellular localization of both the flavin transporters and the enzymes involved in homeostasis maintenance.</p><p>First investigations, at the cellular level, to address these points concern (i) mimicking RTD deficiencies in HEK-293 transfected cells [<xref rid=\"B101-ijms-21-05310\" ref-type=\"bibr\">101</xref>] and (ii) evaluating transport defects in fibroblasts from patients [<xref rid=\"B93-ijms-21-05310\" ref-type=\"bibr\">93</xref>]. For a complete summary of this kind of study, the reader is referred to [<xref rid=\"B103-ijms-21-05310\" ref-type=\"bibr\">103</xref>].</p><p>Since mitochondrial dysfunction has widely been implicated in several neurodegenerative disorders [<xref rid=\"B115-ijms-21-05310\" ref-type=\"bibr\">115</xref>,<xref rid=\"B116-ijms-21-05310\" ref-type=\"bibr\">116</xref>], a piece of work has been carried out to link the effects of Rf transport loss of function with mitochondrial function and neuronal integrity. This was achieved by in vitro study performed on BVVL patients’ fibroblasts, muscular biopsy and induced pluripotent stem cells (iPSCs) differentiated in the motor neuron.</p><p>A significant reduction in the intracellular levels of FMN and FAD has been observed in RTD2 patient fibroblasts when grown in low extracellular Rf conditions, together with impaired electron transport chain complex I and complex II activities. [<xref rid=\"B117-ijms-21-05310\" ref-type=\"bibr\">117</xref>]. Consistently, in muscle biopsies of both types of RTD patients, mitochondrial respiratory chain deficiencies were revealed [<xref rid=\"B118-ijms-21-05310\" ref-type=\"bibr\">118</xref>,<xref rid=\"B119-ijms-21-05310\" ref-type=\"bibr\">119</xref>,<xref rid=\"B120-ijms-21-05310\" ref-type=\"bibr\">120</xref>]. All these data are consistent also with a mitochondrial myopathy, which was confirmed by muscle histopathology revealing ragged-red fibers.</p><p>The question of whether RFVT3 can be directly involved in the neuronal and muscular supply of Rf or rather a secondary intestinal absorption deficiency causes the myopathy, is still an open question [<xref rid=\"B105-ijms-21-05310\" ref-type=\"bibr\">105</xref>].</p><p>To better investigate the neuronal functional and structural consequences, RTD patient-specific iPSC-derived motoneurons have been developed. A reduction in axonal elongation, partially improved by Rf treatment, and a perturbation in neurofilament composition have been observed accompanied by a reduced autophagic/mitophagic flux [<xref rid=\"B121-ijms-21-05310\" ref-type=\"bibr\">121</xref>].</p><p>Further evidence of mitochondrial dysfunction have been described in the <italic>Drosophila melanogaster</italic> model (see [<xref rid=\"B117-ijms-21-05310\" ref-type=\"bibr\">117</xref>] and below).</p><p>Turning back to neuronal flavin homeostasis, quite surprisingly almost no data exists about neuronal RFK and FADS. The presence of the <italic>FLAD1</italic> transcript isoform 1, as well as the enrichment in these cells of a protein of about 65 kDa (as estimated by SDS-PAGE) [<xref rid=\"B122-ijms-21-05310\" ref-type=\"bibr\">122</xref>] allow us to propose that mitochondrial neuronal FAD synthase exists, as well as other isoforms, whose subcellular localizations are still obscure. Moreover, confocal microscopy experiments previously performed by our group, provided clear evidence of FADS expression in rat neonatal astrocytes, with a cytosolic and a strong nuclear localization [<xref rid=\"B81-ijms-21-05310\" ref-type=\"bibr\">81</xref>].</p><p>We would like to remind that, BVVLS is defined as a juvenile form of ALS (OMIM #105400), a fatal degenerative disease affecting upper motor neurons of the cortex and lower motor neurons of the brainstem and spinal cord. Quite interestingly, a small but significant decrease in mRNA encoding FADS, together with RFK and some proteins of the electron transport chain, was observed in the blood of patients suffering from ALS in comparison with healthy people [<xref rid=\"B123-ijms-21-05310\" ref-type=\"bibr\">123</xref>]. In the same paper, a 65 kDa FADS was recognized as the target antigen in an ALS patient who had at the same time a monoclonal gammopathy. The putative surface membrane localization of FADS on motor neurons [<xref rid=\"B123-ijms-21-05310\" ref-type=\"bibr\">123</xref>] seemed in contrast with the canonical intracellular localization [<xref rid=\"B72-ijms-21-05310\" ref-type=\"bibr\">72</xref>,<xref rid=\"B81-ijms-21-05310\" ref-type=\"bibr\">81</xref>].</p><p>Even if these results do not allow understanding the link among FADS expression or FAD isoforms and the disease, it is plausible that alterations of specific neuronal FADS isoforms could cause a sub-optimal energy metabolism that could make the patient’s motoneurons more susceptible to degeneration.</p><p>Further investigations on suitable experimental models are necessary to better describe the profile of <italic>FLAD1</italic> product expression and to establish whether altered flavin homeostasis could be listed among the several factors associated with ALS pathogenesis. Nevertheless, as discussed in a paragraph below, we got the first evidence that altering the expression of FADS can disturb cholinergic transmission [<xref rid=\"B124-ijms-21-05310\" ref-type=\"bibr\">124</xref>] in <italic>Caenorhabditis elegans</italic>, a suitable model for studying neuronal dysfunctions.</p></sec><sec id=\"sec5dot2-ijms-21-05310\"><title>5.2. Rf Muscular Homeostasis and its Alterations</title><p>Since energy demand is crucial for cardiac and skeletal muscle, which mostly rely on glucose, fatty acids, and amino acid mitochondrial metabolism, it is not surprising that alterations of flavoenzymes, flavin supply and trafficking principally affect these tissues.</p><p>As schematized in <xref ref-type=\"fig\" rid=\"ijms-21-05310-f004\">Figure 4</xref>, mitochondrial respiratory complexes I and II, the Electron Transfer Flavoprotein (ETF) and its Ubiquinone Oxidoreductase (ETF-QO, EC 1.5.5.1) together with several ETF dependent dehydrogenases, is involved in the metabolism of fatty acids and certain amino acids, which are efficient alternatives to glucose as energetic fuel for muscles. Moreover, Succinyl-CoA production, an essential process for ketone bodies utilization in muscle and hearth, also requires the flavin-dependent reaction catalyzed by the mitochondrial enzymatic complex oxoglutarate dehydrogenase (EC 1.2.4.2).</p><p>Many years ago, while investigating the mitochondrial bioenergetic alterations in an Rf-responsive patient suffering for MADD (OMIM #231680), we described human <italic>vastus lateralis</italic> muscular mitochondrial flavoproteome and its dependence on Rf supply [<xref rid=\"B84-ijms-21-05310\" ref-type=\"bibr\">84</xref>,<xref rid=\"B125-ijms-21-05310\" ref-type=\"bibr\">125</xref>]. The proteomic investigation, during the symptomatic phase, revealed a decrease or the absence of several flavoenzymes, related to flavin cofactor-dependent mitochondrial pathways and of mitochondrial or mitochondria-associated calcium-binding proteins. All deficiencies were completely rescued after Rf treatment. Our studies demonstrated for the first time, the previous hypothesis by [<xref rid=\"B126-ijms-21-05310\" ref-type=\"bibr\">126</xref>] of a profound involvement of Rf/flavin cofactors in modulating the level of a number of functionally coordinated polypeptides involved in fatty Acyl-CoA and amino acid metabolism, extending the number of enzymatic pathways known to be altered in Riboflavin Responsive (RR)-MADD.</p><p>At those times, human genes responsible for flavin cellular and sub-cellular transport, as well as for FAD metabolism were not yet identified. The recent identification of <italic>FLAD1</italic> as a novel mitochondrial disease gene [<xref rid=\"B80-ijms-21-05310\" ref-type=\"bibr\">80</xref>] confirmed the crucial role of flavin homeostasis in muscular bioenergetics. Initially identified as MADD, this novel inborn error of metabolism—resulting in Rf responsive and not responsive mitochondrial myopathy—has now been identified as LSMFLAD (lipid storage myopathy due to flavin adenine dinucleotide synthase deficiency, OMIM #255100).</p><p>As a consequence of a reduced FAD synthase enzymatic activity in fibroblasts from LSMFLAD patients, a significant reduction of flavin cofactors was detectable at the mitochondrial level lading to a reduced amount of mitochondrial flavoenzymes ETFDH and flavoprotein subunit of succinate dehydrogenase (SDHA), whereas only a slight reduction of flavin cofactors at the cellular level was observed [<xref rid=\"B80-ijms-21-05310\" ref-type=\"bibr\">80</xref>]. In fibroblasts from another patient described later, a severe reduction of cellular flavin content was observed, as associated with a drastic reduction of FAD synthase activity [<xref rid=\"B127-ijms-21-05310\" ref-type=\"bibr\">127</xref>].</p><p>The explanation of the observed residual capability to synthesize FAD, even in the case of frameshift or non-sense “hot spot” mutations in <italic>FLAD1</italic> exon 2 (expected to produce inactive hFADS isoform 1 and 2), derived from the demonstration of the existence of novel shorter transcript variants (<xref ref-type=\"fig\" rid=\"ijms-21-05310-f003\">Figure 3</xref>), whose translation starts beyond the mutated points. One of these transcripts encodes for a protein called isoform 6 or “emergency protein”, which ensures patients’ cells not to be completely deprived of FAD, and therefore still alive [<xref rid=\"B73-ijms-21-05310\" ref-type=\"bibr\">73</xref>].</p><p>In skeletal muscle biopsies from these patients, besides lipid storage caused by the derangement of fatty Acyl-CoA dehydrogenases, a global decrease of COX and/or SDH histochemical staining was described, as well as multiple respiratory chain enzyme deficiencies measured in the majority of cases tested affecting complexes I, II, III and/or IV with a variable extent. Normal muscle respiratory chain enzyme activities were found in one case. Ragged-red fibers have not been reported, so far [<xref rid=\"B80-ijms-21-05310\" ref-type=\"bibr\">80</xref>,<xref rid=\"B128-ijms-21-05310\" ref-type=\"bibr\">128</xref>,<xref rid=\"B129-ijms-21-05310\" ref-type=\"bibr\">129</xref>].</p><p>Thereafter, the number of LSMFLAD patients described at present are continuously increasing [<xref rid=\"B23-ijms-21-05310\" ref-type=\"bibr\">23</xref>,<xref rid=\"B80-ijms-21-05310\" ref-type=\"bibr\">80</xref>,<xref rid=\"B130-ijms-21-05310\" ref-type=\"bibr\">130</xref>] and this prompt us to continue our studies devoted to gain further insight into the still unclear molecular aspects concerning Rf conversion to FAD, using different human cell models, always and keeping in mind the idea that different flavoproteome expression requires different regulation of flavin homeostasis.</p><p>Concerning <italic>FLAD1</italic> gene, the main still unsolved problems are the tissue-specific expression profile of <italic>FLAD1</italic> transcripts and the sub-cellular localization of different FADS isoforms, taking into account the fact that FAD synthase must be ubiquitously expressed.</p><p>Concerning muscular isoforms of FAD synthase, nothing is known, at the moment, about their expression at the protein level and their sub-cellular localization.</p><p>Unpublished data from our laboratory, concerning this point, are shown in <xref ref-type=\"fig\" rid=\"ijms-21-05310-f005\">Figure 5</xref>: in human muscle biopsy for the first-time the simultaneous presence of gene transcript isoforms 1, 3 and 4 are detected; unfortunately, the existence of isoform 2 and 6 transcripts can be neither excluded nor proven for a technical reason. The presence of the transcript isoform 1, as well as the enrichment in muscular lysates of a protein of about 65 kDa (as estimated by SDS-PAGE) [<xref rid=\"B131-ijms-21-05310\" ref-type=\"bibr\">131</xref>] allow us to propose that a mitochondrial FAD forming process is present in human muscle, as in other sources [<xref rid=\"B72-ijms-21-05310\" ref-type=\"bibr\">72</xref>,<xref rid=\"B81-ijms-21-05310\" ref-type=\"bibr\">81</xref>], but a ton of experiments are still necessary to gain further insights into this issue and to close an old debate concerning the mitochondrial localization of FADS [<xref rid=\"B19-ijms-21-05310\" ref-type=\"bibr\">19</xref>,<xref rid=\"B80-ijms-21-05310\" ref-type=\"bibr\">80</xref>,<xref rid=\"B86-ijms-21-05310\" ref-type=\"bibr\">86</xref>,<xref rid=\"B88-ijms-21-05310\" ref-type=\"bibr\">88</xref>].</p><p>Moreover, we certainly know that human muscle mitochondria can hydrolyze FAD, via an AMP- sensitive FAD diphosphatase (EC 3.6.1._), as mitochondria and nuclei from a different origin, can do, in the so-called Rf/FAD recycling pathway [<xref rid=\"B63-ijms-21-05310\" ref-type=\"bibr\">63</xref>,<xref rid=\"B81-ijms-21-05310\" ref-type=\"bibr\">81</xref>,<xref rid=\"B84-ijms-21-05310\" ref-type=\"bibr\">84</xref>]. Quite interestingly, increased activity of FAD hydrolyzing enzyme and mitochondrial recycling have been associated with mitochondrial flavin level reduction and to an alteration in mitochondrial flavoprotein function [<xref rid=\"B84-ijms-21-05310\" ref-type=\"bibr\">84</xref>,<xref rid=\"B132-ijms-21-05310\" ref-type=\"bibr\">132</xref>]. However, we still do not know if the FAD destroying activity, we measured in mitochondrial muscle is due to either a Nudix or a not-Nudix hydrolase [<xref rid=\"B133-ijms-21-05310\" ref-type=\"bibr\">133</xref>]. The recent discovery that isoform 2 of FAD synthase discloses, under certain experimental conditions, a “hidden” not-Nudix hydrolase face (working as a bi-functional protein) leaves to future investigation the possibility that mitochondrial muscular FADDPase is a product of <italic>FLAD1</italic> gene [<xref rid=\"B34-ijms-21-05310\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijms-21-05310\" ref-type=\"bibr\">35</xref>].</p><p>Another under-investigated point, which necessitates further research to better define Rf homeostasis in muscle, is the expression profile and the sub-cellular localization of Rf transporters. RFVT2 seems to be the principal Rf transporter expressed, as in “The human protein Atlas database” (<uri xlink:href=\"https://www.proteinatlas.org/\">https://www.proteinatlas.org/</uri>). A systematic study concerning this point in the muscle of both control individuals’ patients suffering from Rf-responsive myopathies is still missing (see also [<xref rid=\"B7-ijms-21-05310\" ref-type=\"bibr\">7</xref>,<xref rid=\"B130-ijms-21-05310\" ref-type=\"bibr\">130</xref>]).</p><p>Indeed, a transient form of MADD has been related to deficiencies in RFVT1 (OMIM #615026). Nevertheless, in this case, the muscular symptoms of the newborn child are due to the altered placental distribution of flavin from the mother [<xref rid=\"B134-ijms-21-05310\" ref-type=\"bibr\">134</xref>,<xref rid=\"B135-ijms-21-05310\" ref-type=\"bibr\">135</xref>,<xref rid=\"B136-ijms-21-05310\" ref-type=\"bibr\">136</xref>].</p><p>As outlined before, another important component of the mitochondrial FAD homeostasis is the translocator firstly identified in <italic>S. cerevisiae</italic> and named <italic>FLX1</italic> [<xref rid=\"B86-ijms-21-05310\" ref-type=\"bibr\">86</xref>,<xref rid=\"B88-ijms-21-05310\" ref-type=\"bibr\">88</xref>]. The existence of a human counterpart, nowadays known as the human mitochondrial FAD translocator, SLC25A32 or MFT, was proposed for the first time at the functional level in human muscle as a component of a recycling pathway named Rf/FAD cycle [<xref rid=\"B63-ijms-21-05310\" ref-type=\"bibr\">63</xref>,<xref rid=\"B84-ijms-21-05310\" ref-type=\"bibr\">84</xref>].</p><p>The deficiency of SLC25A32 (OMIM #616839) is causative of an Rf-responsive exercise intolerance (RREI) and presents biochemical features typical of MADD [<xref rid=\"B89-ijms-21-05310\" ref-type=\"bibr\">89</xref>,<xref rid=\"B90-ijms-21-05310\" ref-type=\"bibr\">90</xref>]. In a skeletal-muscle biopsy obtained from an RREI patient, a faint SDH staining was observed together with some ragged-red fibers [<xref rid=\"B89-ijms-21-05310\" ref-type=\"bibr\">89</xref>].</p><p>Biochemical assays of complex I, complex II+III, complex IV and citrate synthetase activities was performed in muscle biopsy of another RREI patient as described before. A reduction of complex II and combined II+III activities compared to the control muscle indicated an OXPHOS complex II deficiency. Staining of muscle tissue also revealed for this patient the presence of many ragged-red fibers, as well as multiple cytochrome oxidase-negative muscle fibers [<xref rid=\"B90-ijms-21-05310\" ref-type=\"bibr\">90</xref>]. Similar alterations were found in fibroblasts [<xref rid=\"B89-ijms-21-05310\" ref-type=\"bibr\">89</xref>,<xref rid=\"B90-ijms-21-05310\" ref-type=\"bibr\">90</xref>].</p></sec><sec id=\"sec5dot3-ijms-21-05310\"><title>5.3. A Molecular Rationale for Mitochondrial Flavoproteome Derangement: the Significance of Rf Therapy</title><p>As briefly outlined in <xref ref-type=\"fig\" rid=\"ijms-21-05310-f004\">Figure 4</xref>, mitochondria are plenty of flavoproteins, whose derangements correspond to well-characterized inborn errors of metabolism. From the biochemical (or enzymological) point of view, we can distinguish between defects of β-oxidation, of AA-oxidation, of Krebs cycle components or respiratory chain complexes I and II, all affecting mitochondrial bioenergetics. A survey of all flavoproteins derangements is out of the scope of this review, but we would like to distinguish between single enzyme mutation or multiple enzymatic deficiencies [<xref rid=\"B23-ijms-21-05310\" ref-type=\"bibr\">23</xref>,<xref rid=\"B103-ijms-21-05310\" ref-type=\"bibr\">103</xref>,<xref rid=\"B137-ijms-21-05310\" ref-type=\"bibr\">137</xref>,<xref rid=\"B138-ijms-21-05310\" ref-type=\"bibr\">138</xref>].</p><p>In the case of a single enzymatic mutation very often the mutation impairs somehow the apo-protein altering the affinity for the flavin cofactor (<italic>k</italic><sub>m</sub> or <italic>k</italic><sub>d</sub>, [<xref rid=\"B139-ijms-21-05310\" ref-type=\"bibr\">139</xref>]) and in some cases the folding/stability of the apo-protein [<xref rid=\"B140-ijms-21-05310\" ref-type=\"bibr\">140</xref>,<xref rid=\"B141-ijms-21-05310\" ref-type=\"bibr\">141</xref>]. Considering these hypotheses, Rf therapy could be quite simply explained as an increase in cofactor availability, which can either compensate the higher <italic>k</italic><sub>d</sub> for the cofactor (and therefore the enzymatic activity) or prevent protein misfolding/degradation.</p><p>In the case of a multiple flavoproteome derangements, an event which is common to a number of flavoproteins should be altered: (i) transport of vitamin as in the case of RTD (ii) formation of cofactor as in the case of LSMFLAD (iii) regeneration of cofactor to allow the flavoproteome to work as in the case of MADD (iiii) cofactor delivery to nascent holo-flavoproteins [<xref rid=\"B78-ijms-21-05310\" ref-type=\"bibr\">78</xref>].</p><p>In case of accumulation of unfolded proteins within mitochondria, cells employ a transcriptional response known as the mitochondrial unfolded protein response (UPR(mt)) to promote the repair and recovery of defective mitochondria [<xref rid=\"B142-ijms-21-05310\" ref-type=\"bibr\">142</xref>,<xref rid=\"B143-ijms-21-05310\" ref-type=\"bibr\">143</xref>].</p><p>The possibility that some common transcriptional, post-transcriptional events occur and sense Rf deficiency or flavoproteome derangement presented long ago have been evolved to explain multiple enzymatic derangements both in MADD and in another mitochondrial encephalomyopathy due to alteration of AIF [<xref rid=\"B81-ijms-21-05310\" ref-type=\"bibr\">81</xref>,<xref rid=\"B124-ijms-21-05310\" ref-type=\"bibr\">124</xref>,<xref rid=\"B143-ijms-21-05310\" ref-type=\"bibr\">143</xref>,<xref rid=\"B144-ijms-21-05310\" ref-type=\"bibr\">144</xref>]. We also are considering the hypothesis that a redox-epigenetic control, presumably mediated by lysine-demethylase, can start from nuclei [<xref rid=\"B18-ijms-21-05310\" ref-type=\"bibr\">18</xref>,<xref rid=\"B81-ijms-21-05310\" ref-type=\"bibr\">81</xref>]. More recent literature concerning Rf sensing was elsewhere cited in this review [<xref rid=\"B43-ijms-21-05310\" ref-type=\"bibr\">43</xref>,<xref rid=\"B54-ijms-21-05310\" ref-type=\"bibr\">54</xref>].</p><p>Noteworthy is the case of AIF, a FAD-containing protein with NADH-dependent oxidoreductase activity. Troulinaki group and others demonstrated that AIF physically interacts and stabilizes the oxidoreductase CHCHD4/MIA40, hence assisting the correct biogenesis of the respiratory chain complexes in addition to its death-related role [<xref rid=\"B145-ijms-21-05310\" ref-type=\"bibr\">145</xref>,<xref rid=\"B146-ijms-21-05310\" ref-type=\"bibr\">146</xref>,<xref rid=\"B147-ijms-21-05310\" ref-type=\"bibr\">147</xref>,<xref rid=\"B148-ijms-21-05310\" ref-type=\"bibr\">148</xref>,<xref rid=\"B149-ijms-21-05310\" ref-type=\"bibr\">149</xref>,<xref rid=\"B150-ijms-21-05310\" ref-type=\"bibr\">150</xref>].</p><p>Deleterious mutations in the human <italic>AIFM1</italic> gene are associated with rare inherited X-linked mitochondrial disorders [<xref rid=\"B151-ijms-21-05310\" ref-type=\"bibr\">151</xref>]. The first deleterious mutation in the <italic>AIFM1</italic> gene was found in two consanguineous infant males, showing progressive mitochondrial encephalomyopathy [<xref rid=\"B151-ijms-21-05310\" ref-type=\"bibr\">151</xref>]. As a result, the recombinant mutant AIF protein is structurally unstable, shows aberrant FAD incorporation, and, consequently, impaired redox properties [<xref rid=\"B151-ijms-21-05310\" ref-type=\"bibr\">151</xref>,<xref rid=\"B152-ijms-21-05310\" ref-type=\"bibr\">152</xref>]. To date, a significant array of mutations in the <italic>AIFM1</italic> gene have been identified in patients showing a wide range of clinical presentations [<xref rid=\"B153-ijms-21-05310\" ref-type=\"bibr\">153</xref>,<xref rid=\"B154-ijms-21-05310\" ref-type=\"bibr\">154</xref>].</p><p>Focusing again on MADD (OMIM #231680), it is a well-known rare autosomal inherited disease associated with an impaired fatty acid, amino acid and choline metabolism resulting in lipid droplets accumulation in skeletal muscles, high plasmatic and urinary levels of acylcarnitine and organic acids and respiratory chain deficiency [<xref rid=\"B155-ijms-21-05310\" ref-type=\"bibr\">155</xref>,<xref rid=\"B156-ijms-21-05310\" ref-type=\"bibr\">156</xref>].</p><p>It is now well-accepted that genetic variants carrying mutations in both the apo-protein of ETF, as well as ETF-QO are responsible for the disease [<xref rid=\"B157-ijms-21-05310\" ref-type=\"bibr\">157</xref>,<xref rid=\"B158-ijms-21-05310\" ref-type=\"bibr\">158</xref>]. For the former gene, the phenotype is, generally, severe for the latter milder and often sensible to treatment with high doses of Rf. The reader is referred to [<xref rid=\"B130-ijms-21-05310\" ref-type=\"bibr\">130</xref>] for a clear distinction.</p><p>As outlined in <xref ref-type=\"fig\" rid=\"ijms-21-05310-f004\">Figure 4</xref>, ETF and ETF-QO are mitochondrial flavoproteins functionally primarily connected to β-oxidation. In the case of derangements of these oxidoreductases, the first step of β-oxidation, catalyzed by a different type of Acyl–CoA dehydrogenases cannot work correctly, since they require regeneration of FADH<sub>2</sub> deriving from oxidation of Acyl groups. Certain flavin-dependent amino acid catabolism and respiratory chain impairment in fibroblasts and muscle biopsies have been described together with alteration of ROS homeostasis and ATP shortage.</p><p>All these biochemical abnormalities were exhaustively described in cell contests [<xref rid=\"B126-ijms-21-05310\" ref-type=\"bibr\">126</xref>,<xref rid=\"B159-ijms-21-05310\" ref-type=\"bibr\">159</xref>,<xref rid=\"B160-ijms-21-05310\" ref-type=\"bibr\">160</xref>,<xref rid=\"B161-ijms-21-05310\" ref-type=\"bibr\">161</xref>]; model organism used to describe MADD are summarized in <xref rid=\"ijms-21-05310-t001\" ref-type=\"table\">Table 1</xref>, at the end of this review, together with metabolic dysfunctions induced by gene mutations.</p><p>The novel important concept emerging from the latest literature is that a secondary derangement of flavin homeostasis can be induced by damage of electron flux via ETF/ETF-QO, thus generating a sort of “flavin vicious cycle”.</p></sec></sec><sec id=\"sec6-ijms-21-05310\"><title>6. Model Organisms to Study Flavin Homeostasis Alterations</title><p>In order to better establish the biochemical alterations produced by defects in flavin metabolic pathways, two models have been initially introduced in our and other laboratories: the yeast <italic>Saccharomyces cerevisiae</italic> and the nematode <italic>Caenorhabditis elegans</italic>. More recently, murine and fruit fly <italic>Drosophila melanogaster</italic> models have been also introduced in the study of flavin linked neuromuscular disorders.</p><sec id=\"sec6dot1-ijms-21-05310\"><title>6.1. Saccharomyces Cerevisiae</title><p>The yeast <italic>Saccharomyces cerevisiae</italic> has long been used as a eukaryotic model organism mostly due to its simple and quick genetic manipulation. In this organism, the first genes involved in flavin metabolism were identified, by functional genomics approaches and cloned. Once a clear parallel to mammalian genes and biochemical processes involved in vitamin homeostasis was established, the yeast model was also useful to identify by complementation the human genes correlated to flavin homeostasis.</p><p>Besides the conservation of common fundamental processes and the significant homology of the cellular flavoproteome [<xref rid=\"B162-ijms-21-05310\" ref-type=\"bibr\">162</xref>], it should be mentioned that profound differences exist between these lower eukaryotes and men, as far flavin homeostasis is concerned.</p><p>First of all, differently from mammals, yeasts, as well as fungi, plants and bacteria, have the ability either to synthesize Rf <italic>de novo</italic> or to take it from outside [<xref rid=\"B163-ijms-21-05310\" ref-type=\"bibr\">163</xref>,<xref rid=\"B164-ijms-21-05310\" ref-type=\"bibr\">164</xref>]. All yeast enzymes required for de novo Rf biosynthesis are encoded by the <italic>RIB</italic> genes (<italic>RIB1, RIB2, RIB3, RIB4, RIB5</italic>, and <italic>RIB7</italic>) [<xref rid=\"B165-ijms-21-05310\" ref-type=\"bibr\">165</xref>], which of course have no counterpart in mammals.</p><p>Then, Rf uptake in <italic>S. cerevisiae</italic> is mediated by the product of <italic>MCH5</italic> gene [<xref rid=\"B166-ijms-21-05310\" ref-type=\"bibr\">166</xref>], which has no counterpart in mammals, since RFVTs are evolutionary recent. Mch5p is a high-affinity transporter (<italic>k</italic><sub>m</sub> = 17 µM) with a pH optimum at pH = 7.5, operating by a facilitated diffusion mechanism. The expression of <italic>MCH5</italic> is regulated by cellular Rf content. Thus, <italic>S. cerevisiae</italic> has a mechanism to sense Rf and avert Rf deficiency by increasing the expression of the plasma membrane transporter [<xref rid=\"B166-ijms-21-05310\" ref-type=\"bibr\">166</xref>].</p><p>In the <italic>S.</italic>\n<italic>cerevisiae</italic> genome, the first genes encoding for monofunctional RFK and FADS were identified and named <italic>FMN1</italic> [<xref rid=\"B167-ijms-21-05310\" ref-type=\"bibr\">167</xref>] and <italic>FAD1</italic> [<xref rid=\"B168-ijms-21-05310\" ref-type=\"bibr\">168</xref>], respectively. As mammals, yeasts use two different enzymes for FAD production, conversely, most prokaryotes depend on a single bifunctional enzyme [<xref rid=\"B169-ijms-21-05310\" ref-type=\"bibr\">169</xref>,<xref rid=\"B170-ijms-21-05310\" ref-type=\"bibr\">170</xref>,<xref rid=\"B171-ijms-21-05310\" ref-type=\"bibr\">171</xref>].</p><p><italic>FMN1</italic> encodes for a protein of 24.5 kDa which shows sequence and structure similarity to the RKF-module of prokaryotic FADS and appears largely conserved through evolution up to the human enzyme [<xref rid=\"B7-ijms-21-05310\" ref-type=\"bibr\">7</xref>]. Immunoblotting analysis of yeast subcellular fractions revealed that Fmn1p is localized in microsomes and in mitochondria [<xref rid=\"B167-ijms-21-05310\" ref-type=\"bibr\">167</xref>].</p><p>Fad1p, the sole known protein isoform generated by <italic>S. cerevisiae FAD1</italic> gene, is a 35.5 kDa, soluble enzyme, essential for yeast life, whose crystal structure was solved in a complex with FAD in the active site [<xref rid=\"B172-ijms-21-05310\" ref-type=\"bibr\">172</xref>]. Fad1p is a single-domain monofunctional enzyme containing the sole PAPS or FADSy domain, which has little or no sequence similarity to the prokaryotic FAD-forming enzymes. This feature makes prokaryotic FADS a good candidate for novel antibiotics [<xref rid=\"B31-ijms-21-05310\" ref-type=\"bibr\">31</xref>,<xref rid=\"B173-ijms-21-05310\" ref-type=\"bibr\">173</xref>].</p><p>Homology searching using yeast Fad1p as a query leads to the identification of the human <italic>FLAD1</italic> gene [<xref rid=\"B31-ijms-21-05310\" ref-type=\"bibr\">31</xref>], therefore opening the possibility to study the molecular mechanisms underlying LSMFLAD myopathies. As discussed above, the human protein isoforms are more numerous and structurally complicated than their yeast monofunctional counterpart, as the longest human isoforms are composed of two fused domains (see above, <xref ref-type=\"fig\" rid=\"ijms-21-05310-f002\">Figure 2</xref> and [<xref rid=\"B31-ijms-21-05310\" ref-type=\"bibr\">31</xref>,<xref rid=\"B71-ijms-21-05310\" ref-type=\"bibr\">71</xref>,<xref rid=\"B79-ijms-21-05310\" ref-type=\"bibr\">79</xref>]). Interestingly, the yeast protein Fad1p strongly resembles the recently discovered human isoform 6 (hFADS6): the two proteins exhibit 32% amino acid identity.</p><p>The mammalian FADHy domain has, indeed, a single domain enzymatic counterpart in yeast, namely the product of <italic>FPY1,</italic> which performs a non-Nudix diphosphatase activity [<xref rid=\"B77-ijms-21-05310\" ref-type=\"bibr\">77</xref>] capable of hydrolysing in vitro FAD, NAD(H), and ADP-ribose in presence of K<sup>+</sup> and divalent cations. Indeed, our group observed that <italic>S. cerevisiae</italic> mitochondria (SCM) are able to perform FAD hydrolysis via an enzymatic activity which is different from the already characterized Nudix hydrolases; since this activity is regulated by the NAD redox status, an experimental link between NAD and FAD mitochondrial world has been proposed [<xref rid=\"B174-ijms-21-05310\" ref-type=\"bibr\">174</xref>].</p><p>While there is no doubt about a mitochondrial localization for Fmn1p [<xref rid=\"B86-ijms-21-05310\" ref-type=\"bibr\">86</xref>,<xref rid=\"B167-ijms-21-05310\" ref-type=\"bibr\">167</xref>], the existence of a mitochondrial FADS isoform in yeast is still controversial. First, it was reported that FAD is synthesized by Fad1p exclusively in the cytosol, since no mitochondrial targeting signal is present in this protein [<xref rid=\"B86-ijms-21-05310\" ref-type=\"bibr\">86</xref>,<xref rid=\"B168-ijms-21-05310\" ref-type=\"bibr\">168</xref>], but later on, mitochondrial FAD-forming activity was revealed in yeast mitochondria [<xref rid=\"B88-ijms-21-05310\" ref-type=\"bibr\">88</xref>,<xref rid=\"B144-ijms-21-05310\" ref-type=\"bibr\">144</xref>,<xref rid=\"B175-ijms-21-05310\" ref-type=\"bibr\">175</xref>].</p><p>The yeast mitochondrial transporter of FAD was identified by Tzagoloff group [<xref rid=\"B86-ijms-21-05310\" ref-type=\"bibr\">86</xref>], who named <italic>FLX1</italic> the encoding gene; the transporter was proposed to catalyze and exchange between flavins across the mitochondrial membrane, with cytosolically synthesized FAD entering in exchange with mitochondrial FMN. The role of <italic>FLX1</italic> in catalyzing intra-mitochondrial FAD efflux to the cytosol was proposed by our group [<xref rid=\"B88-ijms-21-05310\" ref-type=\"bibr\">88</xref>]. <italic>S. cerevisiae</italic> flx1 mutant strain was used to identify, by complementation, the human orthologue <italic>SLC25A32A</italic> [<xref rid=\"B87-ijms-21-05310\" ref-type=\"bibr\">87</xref>,<xref rid=\"B91-ijms-21-05310\" ref-type=\"bibr\">91</xref>].</p><p>Despite the unsolved controversies about the direction of FAD transport catalyzed by Flx1p in yeast, this mitochondrial translocator certainly has a role in maintaining the mitochondrial flavoproteome [<xref rid=\"B162-ijms-21-05310\" ref-type=\"bibr\">162</xref>]. Flx1, null and mutated strains, were the first models used to mimic alterations of FAD homeostasis and their relationships with derangements of mitochondrial flavoprotein biogenesis [<xref rid=\"B88-ijms-21-05310\" ref-type=\"bibr\">88</xref>] as that found in human MADD [<xref rid=\"B87-ijms-21-05310\" ref-type=\"bibr\">87</xref>] or, now, better RREI [<xref rid=\"B89-ijms-21-05310\" ref-type=\"bibr\">89</xref>,<xref rid=\"B90-ijms-21-05310\" ref-type=\"bibr\">90</xref>].</p><p>The enzymatic activities of certain mitochondrial FAD-binding enzymes, i.e., lipoamide dehydrogenase and Sdh1p are altered in <italic>S. cerevisiae</italic> strain lacking <italic>FLX1</italic> [<xref rid=\"B86-ijms-21-05310\" ref-type=\"bibr\">86</xref>,<xref rid=\"B88-ijms-21-05310\" ref-type=\"bibr\">88</xref>], which resulted in a small colony and also a respiration-deficient phenotype [<xref rid=\"B144-ijms-21-05310\" ref-type=\"bibr\">144</xref>,<xref rid=\"B175-ijms-21-05310\" ref-type=\"bibr\">175</xref>]. In another set of studies, the loss of function of the SLC25 family member Flx1 was coupled to the loss of function of Hem25: this resulted in a respiratory-deficient phenotype, indicative of mitochondrial impairment [<xref rid=\"B176-ijms-21-05310\" ref-type=\"bibr\">176</xref>].</p><p>Unfortunately, other flavoenzymes, which are expected to be altered in MADD/LSMFLAD/RREI, were not assayed in yeast models. In particular, data are missing concerning the system ETFα/ETFβ/ETF-QO, which are mitochondrially located and encoded by <italic>AIM45</italic>, <italic>CIR1</italic> and <italic>CIR2</italic> in <italic>S. cerevisiae</italic>, whose alteration can be a cause of ROS unbalance [<xref rid=\"B162-ijms-21-05310\" ref-type=\"bibr\">162</xref>,<xref rid=\"B177-ijms-21-05310\" ref-type=\"bibr\">177</xref>,<xref rid=\"B178-ijms-21-05310\" ref-type=\"bibr\">178</xref>]. Conversely, the activity/level of lipoamide dehydrogenase—a component of enzymatic complexes that decarboxylate pyruvate, oxoglutarate and oxoacids deriving from branched-chain amino acids—was not tested, as well as that of ETF/ETF-QO has not yet been investigated in RREI patients’ cells.</p><p>The deletion of <italic>FLX1</italic> was accompanied by a significant ATP shortage and ROS unbalance in glycerol-grown cells [<xref rid=\"B175-ijms-21-05310\" ref-type=\"bibr\">175</xref>]. Moreover, the <italic>flx1∆</italic> strain showed H<sub>2</sub>O<sub>2</sub> hypersensitivity and decreased lifespan [<xref rid=\"B175-ijms-21-05310\" ref-type=\"bibr\">175</xref>].</p><p>In biochemical and cellular alterations induced by altered mitochondrial FAD transporter, a central role is played by the flavoprotein subunit of SDH. This is true not only in yeast, but also in human cell model recently introduced [<xref rid=\"B91-ijms-21-05310\" ref-type=\"bibr\">91</xref>].</p><p>According to our hypothesis, flx1p can act as a nutrient sensor, modulating Sdh1p biogenesis via a post-transcriptional control, that involves in a sort of “retrograde control” putative regulatory sequences located in the UTR region upstream the <italic>SDH1</italic> coding sequence [<xref rid=\"B144-ijms-21-05310\" ref-type=\"bibr\">144</xref>]. Sdh1p biogenesis can also be profoundly altered by mutations of ancillary proteins required for covalent FAD insertion into the apo-protein [<xref rid=\"B179-ijms-21-05310\" ref-type=\"bibr\">179</xref>]. The reader is referred to an excellent review on this topic [<xref rid=\"B180-ijms-21-05310\" ref-type=\"bibr\">180</xref>].</p><p>Using <italic>S. cerevisiae</italic> as a model to study flavoproteome derangements linked to alteration of Rf homeostasis, our group get the first bioinformatic evidence that the so-called transcriptional “flavin network” hypothesized in human muscle [<xref rid=\"B125-ijms-21-05310\" ref-type=\"bibr\">125</xref>] can actually exist in yeast [<xref rid=\"B175-ijms-21-05310\" ref-type=\"bibr\">175</xref>]. A search for possible cis-acting consensus motifs in the regulatory region upstream SDH1-ORF revealed the presence of two protein-binding motifs, which are conserved also in the regulatory region of genes encoding for proteins involved in flavin homeostasis. The two putative transcriptional regulators are Msn2/4 (stress response factor) and Rox1p (involved in the regulation of the expression of proteins involved in oxygen-dependent pathways, such as heme biosynthesis). As discussed in [<xref rid=\"B175-ijms-21-05310\" ref-type=\"bibr\">175</xref>], this finding strengthens the well-described relationship between oxygen/heme metabolism and flavoproteins. These elements might coordinate a response to the metabolic changes induced by altering flavin homeostasis, having as a consequence a change in the level of mitochondrial succinate, a potential epigenetic regulator [<xref rid=\"B174-ijms-21-05310\" ref-type=\"bibr\">174</xref>,<xref rid=\"B175-ijms-21-05310\" ref-type=\"bibr\">175</xref>].</p><p>If flavoproteome derangements due to the mutation in <italic>FLAD1</italic> or <italic>SLC25A32</italic> in humans is due to the lack of intramitochondrial cofactor, affecting apo-flavoprotein assembly/stability and inducing mitochondrial unfolding protein response [<xref rid=\"B143-ijms-21-05310\" ref-type=\"bibr\">143</xref>] or, rather, affecting the signaling pathway introduced as the “flavin network” in yeast [<xref rid=\"B175-ijms-21-05310\" ref-type=\"bibr\">175</xref>] is still matter of future investigation.</p><p>Finally, it should be noted that some limitations on the use of <italic>S. cerevisiae</italic> to mimic MADD/LSMFLAD consists in the observation that (i) <italic>FAD1</italic> gene deletion is lethal and (ii) β-oxidation is carried out only in the peroxisomes in yeast [<xref rid=\"B181-ijms-21-05310\" ref-type=\"bibr\">181</xref>]. Other models are more suitable for these human inborn errors of metabolism, as for example worms.</p></sec><sec id=\"sec6dot2-ijms-21-05310\"><title>6.2. Caenorhabditis Elegans</title><p><italic>C. elegans</italic> is one of the best model organisms in biology research, since in addition to the easy manipulation, it possesses physiological and pharmacological properties common to those of higher animals. Moreover, this nematode has been completely defined with respect to anatomy, genetics, development, differentiation and behavior.</p><p><italic>C. elegans</italic> is easily maintained in the laboratory on agar plates or in a liquid medium using <italic>Escherichia coli</italic> as a food source. Its life cycle from egg to adult takes about 3 days at 22 °C. The first-stage larva hatches from the egg and proceeds through three additional stages of larval development, before reaching reproductive maturity as an adult [<xref rid=\"B182-ijms-21-05310\" ref-type=\"bibr\">182</xref>]. Finally, <italic>C. elegans</italic> was the first multicellular organism for which the genome was completely sequenced [<xref rid=\"B183-ijms-21-05310\" ref-type=\"bibr\">183</xref>]. Thus, the use of this simple and inexpensive model seemed appropriate to study the relationships between FAD synthesis and flavoprotein biogenesis.</p><p><italic>C. elegans</italic>, like humans, is not able to synthesize Rf by itself, therefore its only source is the food, mainly based on bacteria. Vitamin absorption occurs in the intestine through transport systems that introduce Rf into intestinal cells.</p><p>Two potential riboflavin transporters orthologs of the human riboflavin transporters were identified in <italic>C. elegans</italic>, Y47D7A.16 (<italic>rft-1</italic>) and Y47D7A.14 (<italic>rft-2</italic>) which share 33.7 and 30.5% identity, respectively, with hRFVT3 [<xref rid=\"B184-ijms-21-05310\" ref-type=\"bibr\">184</xref>].</p><p><italic>rft-1</italic> is localized on Chromosome V and encodes a single protein isoform (RFT-1) of 427 aa, predicted to have 11 TM domains with an extensive intracellular loop between the sixth and seventh TM domains [<xref rid=\"B184-ijms-21-05310\" ref-type=\"bibr\">184</xref>]. It is expressed in the intestine and a small subset of neuronal support cells along the entire length of the animal and it has been proven to be important in embryonal development. The mediated Rf transport has an acidic pH dependence, saturability (apparent <italic>k</italic><sub>m</sub> = 1.4 ± 0.5 µM), inhibition by Rf analogs, and Na<sup>+</sup> independence. The expression of <italic>rft-1</italic> is suggested to be adaptively regulated by extracellular Rf levels. [<xref rid=\"B184-ijms-21-05310\" ref-type=\"bibr\">184</xref>].</p><p><italic>rft-2</italic> is localized on Chromosome V that generates two alternatively spliced mRNAs namely <italic>rft-2a</italic> and <italic>rft-2b,</italic> which encode for two putative protein isoforms (RFT-2a and- b) of 463 and 476 aa, respectively. Further details on structural predictions of these two isoforms are reported in [<xref rid=\"B184-ijms-21-05310\" ref-type=\"bibr\">184</xref>,<xref rid=\"B185-ijms-21-05310\" ref-type=\"bibr\">185</xref>]. At the moment other three putative shorter isoforms are reported in WormBase (<uri xlink:href=\"https://wormbase.org/species/c_elegans/gene/WBGene00021626#0-9f-10\">https://wormbase.org/species/c_elegans/gene/WBGene00021626#0-9f-10</uri>). <italic>rft-2</italic> is expressed mainly in the intestine and pharynx [<xref rid=\"B184-ijms-21-05310\" ref-type=\"bibr\">184</xref>]. No data exist about a possible subcellular localization. It is involved in maintaining the body homeostatic Rf levels at the whole animal level all through its life as demonstrated by studying the expression pattern of <italic>rft-2</italic> at different life stages. The abundance of <italic>rft-2</italic> transcript in the whole animal was upregulated in Rf-deficient conditions (10 nM) and downregulated at high doses of Rf supply (2 mM) as compared with control (10 μM). The expression of <italic>rft-2</italic> was found to be adaptively regulated in vivo when transgenic worms were maintained under different extracellular Rf levels, which was also mediated partly via changes in the <italic>rft-2</italic> levels that directs towards the possible involvement of transcriptional regulatory events [<xref rid=\"B185-ijms-21-05310\" ref-type=\"bibr\">185</xref>].</p><p>Concerning the intracellular conversion of Rf in its cofactors, little is known about flavokinase encoded by R10H10 gene in <italic>C. elegans</italic>. The R10H10 protein deduced from the cDNA sequence contains 135 aa with an estimated molecular mass of 14.7 kDa and shows 46.7% identity with the human protein.</p><p>Homology searching in <italic>C. elegans</italic> databases using hFLAD1 as template lead to the identification of the orthologue worm gene i.e., <italic>flad-1</italic> (R53.1), which is organized in 8 exons and located on chromosome II. The two products from <italic>flad-1</italic> transcription are trans-spliced and generate two proteins, which show 37% identity and 55% similarity to the human homologues [<xref rid=\"B124-ijms-21-05310\" ref-type=\"bibr\">124</xref>].</p><p>Currently, in WormBase (<uri xlink:href=\"https://wormbase.org/species/c_elegans/gene/WBGene00011271#0-9f-10\">https://wormbase.org/species/c_elegans/gene/WBGene00011271#0-9f-10</uri>) other two transcripts coding for two shorter proteins (containing the sole PAPS or FADSy domain) which show 38% identity to the human isoform 6.</p><p>Two worm models, linked to the alteration of flavin homeostasis with possible neuromuscular and neurodegenerative consequences, have been reported up to now [<xref rid=\"B124-ijms-21-05310\" ref-type=\"bibr\">124</xref>,<xref rid=\"B184-ijms-21-05310\" ref-type=\"bibr\">184</xref>].</p><p>The first model, introduced by our group, resulted from the transient silencing of the <italic>flad-1</italic> gene by feeding [<xref rid=\"B124-ijms-21-05310\" ref-type=\"bibr\">124</xref>] causing a 50% reduction of total flavin content. Phenotypical changes, among which reduced proliferation rate and impairment in locomotion behavior were observed in interfered nematodes. Besides decreased respiratory activities, ATP shortage and the reduction in the activity of several flavoenzymes, as SDH and glutathione reductase were observed in <italic>flad1</italic>-interfered nematodes. In this aspect, <italic>flad-1</italic> interfered nematodes in our opinion proved to be a suitable animal model system for studying human pathologies with alteration in flavin homeostasis/flavoenzyme biogenesis.</p><p>In many mitochondrial mutant nematodes, altered mitochondrial respiration can enhance oxidative stress and induce the formation of free radicals [<xref rid=\"B186-ijms-21-05310\" ref-type=\"bibr\">186</xref>]. Thus, we assessed reactive oxygen species (ROS) levels in flad-1-silenced animals, observing a significant increase, which presumably might cause a cellular stress response.</p><p>Proteomic studies in interfered nematodes revealed that at least 15 abundant proteins are affected by <italic>flad-1</italic> gene silencing, some of which are not flavoproteins, possibly confirming the transcriptional/post-transcriptional control exerted by FAD shortage, on mitochondrial protein expression, as already discussed for yeast [<xref rid=\"B144-ijms-21-05310\" ref-type=\"bibr\">144</xref>,<xref rid=\"B175-ijms-21-05310\" ref-type=\"bibr\">175</xref>] and for the co-ordinated network of proteins in RR-MADD patients [<xref rid=\"B125-ijms-21-05310\" ref-type=\"bibr\">125</xref>,<xref rid=\"B143-ijms-21-05310\" ref-type=\"bibr\">143</xref>,<xref rid=\"B187-ijms-21-05310\" ref-type=\"bibr\">187</xref>].</p><p>Another consequence of <italic>flad-1</italic> silencing in worms is linked to altered locomotion behavior, together with a possible reduction in neurotransmitter production, the measured alteration in aldicarb sensitivity, suggested a possible specific derangement of cholinergic transmission secondarily connected to alteration of flavin homeostasis [<xref rid=\"B124-ijms-21-05310\" ref-type=\"bibr\">124</xref>]. Experiments are currently going on in our laboratory both in worm and in human cell models to further describe the biochemical and functional consequences of inducing FAD shortage in neurons.</p><p>The second worm model was created in Said laboratory, to provide an animal model to study pathologies associated with mutations of human RFVT3, concerns the silencing of the worm plasmatic Rf transporters [<xref rid=\"B184-ijms-21-05310\" ref-type=\"bibr\">184</xref>]. The knockdown of each single or both transporter genes by RNAi resulted in reduced fertility in worms. A high concentration of exogenous Rf moderately reverted the induced Rf deficiency. Unfortunately, these strains were not characterized at the biochemical level.</p><p>More recently, Rf level in nematodes has been also critically associated with food uptake and foraging behavior. These effects are due to the regulation of specific protease gene expression and intestinal protease activity in a TORC1 mediated way, which senses ATP shortage due to altered FAD production [<xref rid=\"B188-ijms-21-05310\" ref-type=\"bibr\">188</xref>].</p><p>As stressed above, the proper mitochondrial activity and ATP production is also ensured by the regulatory role of flavoprotein AIF, which shuttles between mitochondria and nucleus in the caspase-independent apoptosis pathway. The worm AIF homolog WAH-1 is a mitochondrial protein that undergoes conformational changes according to the redox status of the surrounding milieu. Consistently <italic>wah-1</italic> downregulation (mimicking human X-linked encephalomyopathy) in <italic>C. elegans</italic> compromises oxidative phosphorylation and reduces lifespan [<xref rid=\"B145-ijms-21-05310\" ref-type=\"bibr\">145</xref>].</p><p>Notably, in agreement with the hypothesis of coordinated transcriptional regulation of flavoproteome [<xref rid=\"B125-ijms-21-05310\" ref-type=\"bibr\">125</xref>,<xref rid=\"B143-ijms-21-05310\" ref-type=\"bibr\">143</xref>,<xref rid=\"B175-ijms-21-05310\" ref-type=\"bibr\">175</xref>] a nuclear-encoded mitochondrial stress signaling pathway was identified in these worm models [<xref rid=\"B142-ijms-21-05310\" ref-type=\"bibr\">142</xref>] involving activation of <italic>hsp-6</italic> (heat-shock-protein-6), <italic>gst-4</italic> (glutathione-S-transferase), and <italic>sod-3</italic> (superoxide dismutase) promoters, associated with the enhanced expression of the HIF-1 target gene <italic>nhr-57</italic> [<xref rid=\"B145-ijms-21-05310\" ref-type=\"bibr\">145</xref>].</p><p>Despite the presence of putative FAD and NADH binding domains, it seems that WAH-1 does not incorporate cofactors. This may be a reason why the effect of Rf treatment could, unfortunately, not be established in this model.</p></sec><sec id=\"sec6dot3-ijms-21-05310\"><title>6.3. Mouse and Drosophila Melanogaster</title><p>Recently further animal models have been introduced in order to elucidate the RFVT contribution to motor neuron differentiation and the pathogenic mechanisms of BVVLS [<xref rid=\"B117-ijms-21-05310\" ref-type=\"bibr\">117</xref>,<xref rid=\"B189-ijms-21-05310\" ref-type=\"bibr\">189</xref>,<xref rid=\"B190-ijms-21-05310\" ref-type=\"bibr\">190</xref>].</p><p>Riboflavin transporters, RFVT2/<italic>Slc52a2</italic> and RFVT3/<italic>Slc52a3</italic>, have been identified in rodents. mRFVT2 is the orthologue of both hRFVT1 and hRFVT2 and it is involved in hepatic homeostasis of Rf in mice [<xref rid=\"B38-ijms-21-05310\" ref-type=\"bibr\">38</xref>]. mRFVT3 is the orthologue of hRFVT3 [<xref rid=\"B190-ijms-21-05310\" ref-type=\"bibr\">190</xref>] and it has been shown to be the main transporter involved in carrier-mediated RF uptake in the native mouse small and large intestine [<xref rid=\"B191-ijms-21-05310\" ref-type=\"bibr\">191</xref>]. Experiments performed on mouse colonoids demonstrated that mRFVT3 is up-regulated both at transcript and protein level after NaB treatment [<xref rid=\"B55-ijms-21-05310\" ref-type=\"bibr\">55</xref>]. Further, it has been shown to be essential for mouse development, with <italic>Slc52a3</italic> deficiency resulting in early embryonic lethality associated with defects in placental formation, but dispensable for neural differentiation and short-term maintenance [<xref rid=\"B190-ijms-21-05310\" ref-type=\"bibr\">190</xref>], suggesting that in BVVLS patients with mutations in RFVT3 alternative transporters may act during embryogenesis to allow full-term development. The physiological role of RFVT3 has been also investigated in [<xref rid=\"B189-ijms-21-05310\" ref-type=\"bibr\">189</xref>], in which <italic>Slc52a3</italic> knockout mice have been used. The disruption of <italic>Slc52a3</italic> gene caused neonatal mortality with hyperlipidemia and hypoglycemia owing to Rf deficiency.</p><p>To overcome the issue of early mortality in mice, which precludes phenotypic analysis at later developmental stages, [<xref rid=\"B117-ijms-21-05310\" ref-type=\"bibr\">117</xref>] turned to the fruit fly <italic>Drosophila melanogaster</italic> as novel in vivo model of BVVLS. Comparative BLAST analysis lead to the identification of <italic>Drosophila SLC52A3</italic> homologous gene, i.e., cg11576. This gene has been named <italic>drift</italic> (<underline>D</underline>rosophila <underline>ri</underline>bo<underline>f</underline>lavin <underline>t</underline>ransporter) and encodes for a protein that exhibits 36.9% amino acid identity with hRFVT3 and it is expressed in several adult tissues as head, gut, abdomen and thorax. The knockdown of <italic>drift</italic> revealed reduced levels of Rf, downstream metabolites, and electron transport chain complex I activity, which in turn resulted in abnormal mitochondrial membrane potential, respiratory chain activity and morphology. <italic>drift</italic> knockdown also resulted in severely impaired locomotor activity and reduced lifespan. These phenotypes have been partially rescued using riboflavin-5′-lauric acid monoester (RLAM), a novel esterified derivate of Rf.</p><p>In conclusion, it is well assessed that fine coordination among Rf supply, flavin cofactor homeostasis and apo-flavoproteome maintenance is necessary for efficient cellular bioenergetics. Novel experimental models are, therefore, needed to address the huge number of molecular processes and transcriptional and post-transcriptional networks not yet elucidated underlying the muscular and neuronal flavin-dependent mitochondrial oxidative pathways. This is pivotal not only for a better understanding of the molecular rationale of Rf therapy in responsive patients, but, hopefully, also in the aim to potentiate possible therapeutic intervention in Rf-related neuromuscular diseases, in cases, unfortunately, not responding to vitamin therapy.</p><table-wrap id=\"ijms-21-05310-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05310-t001_Table 1</object-id><label>Table 1</label><caption><p>Model organisms to study MADD.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Model Organism</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Gene Mutation</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Metabolic Dysfunction</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">References</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>Caenorhabditis elegans</italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>let-721</italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>let-721</italic> mutants are maternal effects either lethal or semi-sterile.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B192-ijms-21-05310\" ref-type=\"bibr\">192</xref>] </td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>Drosophila Melanogaster</italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>Etfdh</italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Biochemical defects observed in the severe forms of MADD: embryonic accumulation of short-, medium and long-chain acylcarnitines, ETF-QO activity markedly decreased, impaired cofactor association via structural destabilization and consequently enzymatic inactivation.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B193-ijms-21-05310\" ref-type=\"bibr\">193</xref>]</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Zebrafish</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>Etfdh</italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Metabolic and mitochondrial dysfunctions, alteration of plasma acylcarnitine and organic acid profiles, reduced oxidative phosphorylation, increased glycolytic flux and the upregulation of the PPARγ-ERK pathway associated to aberrant neural proliferation and motility defects.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B194-ijms-21-05310\" ref-type=\"bibr\">194</xref>] </td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>Etfa</italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pathological and biochemical features similar to those observed for MADD affected individuals, including brain, liver and kidney diseases. An increased signaling of the mechanistic target of rapamycin complex 1 (mTORC1) responsive to treatment with rapamycin was also found.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B195-ijms-21-05310\" ref-type=\"bibr\">195</xref>] </td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mouse</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>Etfdh<sup>(h)A84T</sup></italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">First RR-MADD mouse model with an <italic>Etfdh</italic> (h)p.84A > T mutation.<break/>The mice, initially normal, developed the clinical and biochemical features typical of MADD under high fat and Rf deficiency diet. Tissues from these mice exhibited a significant decrease of both FAD concentration and ETFDH protein level, which were ameliorated by Rf treatment.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B196-ijms-21-05310\" ref-type=\"bibr\">196</xref>] </td></tr></tbody></table></table-wrap></sec></sec></body><back><ack><title>Acknowledgments</title><p>The critical revision of Prof. Cesare Indiveri (University of Calabria, Arcavacata di Rende, Italy) is gratefully acknowledged.</p></ack><notes><title>Author Contributions</title><p>Writing—original draft preparation, M.T. and A.N.; writing—review and editing, P.L. and M.B.; supervision, project administration, funding acquisition, M.B. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>M.T. was supported by a research fellowship financed by “Fondazione Puglia”, entitled “Sviluppo di nuovi modelli sperimentali per studiare alterazioni del flavoproteoma in patologie neuromuscolari umane: effetto della riboflavina”, (Year 2017). This work was also partially financed by a grant from “Cure RTD” (Year 2019) <uri xlink:href=\"http://curertd.org/news/new/\">http://curertd.org/news/new/</uri>.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><glossary><title>Abbreviations</title><array orientation=\"portrait\"><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ABCG2 </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ATP-binding Cassette G2 Transporter</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AIF </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Apoptosis-Inducing Factor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AIFM </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Apoptosis-Inducing Factor Mitochondria Associated </td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ALS </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Amyotrophic Lateral Sclerosis</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AP-2 </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Activating Protein-2</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">BCRP </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Breast Cancer Resistance Protein</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">BVVLS </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Brown-Vialetto-Van Laere Syndrome</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ChIP</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Chromatin Immunoprecipitation</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">EGRF </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Epidermal Growth Factor Receptor </td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ESCC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Esophageal Squamous Cell Carcinoma </td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ETF </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Electron Transfer Flavoprotein</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ETF-QO </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Electron Transfer Flavoprotein-Ubiquinone Oxidoreductase </td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">FADS </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">FAD Synthase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">FAD </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Flavin Adenine Dinucleotide</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">FADDPase </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">FAD DiPhosphatase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">FMN </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Flavin Mononucleotide</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">LSMFLAD </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Flavin Adenine Dinucleotide Synthetase Deficiency</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">KLF </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Krupper-Like Factors</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MADD </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Multiple Acyl-CoA Dehydrogenase Deficiency </td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MPTb </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MolybdoPterin-Binding </td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NADPH </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Nicotinamide Adenine Dinucleotide Phosphate</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NF-<italic>k</italic>B </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Nuclear Factor Kappa-light-chain-enhancer of activated B cells</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Nudix </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Nucleoside Diphosphate linked to some other moiety X hydrolase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rf </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Riboflavin</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">RFK </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Riboflavin Kinase </td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">RFVT</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Riboflavin Transporter </td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ROS </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Reactive Oxygen Species </td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">RTD </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Riboflavin Transport Deficiency </td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SDH </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Succinate Dehydrogenase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SLC </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Solute Carrier </td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sp-1 </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Stimulating protein-1</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TM </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Trans-Membrane</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TNF-α </td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tumor Necrosis Factor Alpha </td></tr></tbody></array></glossary><ref-list><title>References</title><ref id=\"B1-ijms-21-05310\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Lienhart</surname><given-names>W.-D.</given-names></name><name><surname>Gudipati</surname><given-names>V.</given-names></name><name><surname>Macheroux</surname><given-names>P.</given-names></name></person-group><article-title>The human flavoproteome</article-title><source>Arch. 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Neurol.</source><year>2018</year><volume>84</volume><fpage>659</fpage><lpage>673</lpage><pub-id pub-id-type=\"doi\">10.1002/ana.25338</pub-id><pub-id pub-id-type=\"pmid\">30232818</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijms-21-05310-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Chemical structures and metabolic conversion of Riboflavin (Rf) and flavin cofactors in man. The chemical structures of Rf, flavin mononucleotide (FMN) and flavin adenine dinucleotide (FAD) and the enzymes involved in the conversion of Rf to FAD and vice versa are reported, together with the corresponding genes’ names. In humans Rf is taken up in a carrier-mediated process by three transporters named Rf transporter 1 (RFVT1), RFVT2 and RFVT3 (encoded by <italic>SLC52A1-3</italic>). Inside the cells, Rf conversion to flavin cofactors occurs in two steps catalyzed by Riboflavin Kinase (encoded by <italic>RFK</italic>) forming FMN, and FAD Synthase (encoded by <italic>FLAD1</italic>) forming FAD. Recycling of the vitamin can move from FAD in two steps, catalyzed by FAD diphosphatase and FMN phosphohydrolase.</p></caption><graphic xlink:href=\"ijms-21-05310-g001\"/></fig><fig id=\"ijms-21-05310-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Sketch representation of RFVT protein isoforms. At the top of the figure, schematic representation of genes encoding for RFVT1, RFVT2 and RFVT3. Exons are represented as black boxes, introns as lines between exons and UTRs as short white boxes. In the lower part of the figure, schematic representation of RFVT protein isoforms. In orange RFVT isoforms as reported in NCBI; in white a RFVT3 isoform, as reported in [<xref rid=\"B43-ijms-21-05310\" ref-type=\"bibr\">43</xref>]. Length as coded amino acids for each exon is reported (in brackets). Under each isoform, accession number and length for each protein. The homology structural model of hRFVT1 was built, using as a template the equilibrative nucleoside transporter 1 (PDB code 6OB7) using the SWISS-MODEL software. The protein shows the 11 transmembrane α-helical segments nearly parallel to the membrane axis.</p></caption><graphic xlink:href=\"ijms-21-05310-g002\"/></fig><fig id=\"ijms-21-05310-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Sketch representation of <italic>FLAD1</italic> transcript variants. In dark blue are indicated the four transcript variants reported in RefSeq whereas in green are indicated the two <italic>FLAD1</italic> novel transcript variants as reported in [<xref rid=\"B80-ijms-21-05310\" ref-type=\"bibr\">80</xref>]. Big colored boxes represent coding regions while lines represent introns. In transcript variant 4 light colored boxes represent different coding regions with respect to the transcript variant 1. The two functional domains of the longest isoforms are colored in the background. Homology modeling of the single domains are reported in [<xref rid=\"B7-ijms-21-05310\" ref-type=\"bibr\">7</xref>,<xref rid=\"B34-ijms-21-05310\" ref-type=\"bibr\">34</xref>,<xref rid=\"B73-ijms-21-05310\" ref-type=\"bibr\">73</xref>,<xref rid=\"B74-ijms-21-05310\" ref-type=\"bibr\">74</xref>].</p></caption><graphic xlink:href=\"ijms-21-05310-g003\"/></fig><fig id=\"ijms-21-05310-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Rf transport and FAD mitochondrial delivery in skeletal muscle and neuronal cells, in relationships with flavin-dependent mitochondrial oxidative pathways. At the top of the figure, the Rf uptake from blood into target cells (neuronal and skeletal muscle cells) presumably involving RFVT2. At the bottom of the figure, a magnification of mitochondrial flavoprotein metabolism in muscle is depicted. Flavoproteins are depicted in yellow. Abbreviated names of transporters and enzymes are indicated in the figure as listed below: RK: Riboflavin kinase (EC 2.7.1.26); RFVT2: plasma membrane riboflavin transporter 2; FADS1: FAD synthase isoform 1 (EC 2.7.7.2); mRT: mitochondrial riboflavin transporter; MFT: mitochondrial folate transporter; CACT: mitochondrial carnitine acylcarnitine translocase; OCTN2: organic cation transporter novel 2; CPT1: carnitine palmitoyltransferase 1; CPT2: carnitine palmitoyltransferase 2; I: respiratory chain complex I (NADH-ubiquinone oxidoreductase); II: respiratory chain complex II (succinate dehydrogenase); III: respiratory chain complex III (ubiquinol-cytochrome c reductase); IV: respiratory chain complex IV (cytochrome c oxidase); V: respiratory chain complex V (ATP synthase); 2-OGDH: 2-oxoglutarate dehydrogenase complex; ACADs: Acyl-CoA dehydrogenase various isoforms; BCKAD: branched-chain α-keto acid dehydrogenase complex; ETF: electron transfer flavoprotein; ETF-QO: electron transfer flavoprotein-ubiquinone oxidoreductase; GCDH: glutaryl-CoA dehydrogenase; BCAD: Acyl-CoA dehydrogenase branched chain specific (2-methyl-butyryl-CoA dehydrogenase); PDH: pyruvate dehydrogenase; SCOT: succynil-CoA:3-ketoacid-coenzyme A transferase; TCA Cycle: tricarboxylic acid cycle; β-ox: β-oxidation of fatty acids.</p></caption><graphic xlink:href=\"ijms-21-05310-g004\"/></fig><fig id=\"ijms-21-05310-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>Expression of <italic>FLAD1</italic> transcript variants in human muscle biopsy. (<bold>A</bold>) Total RNA from muscle was prepared to generate cDNA. 75 ng (left panel) or 200 ng (right panel) cDNA prepared from muscle were used as a template for PCR (35 cycles). (<bold>B</bold>) Specific primer pairs were used for the amplification of the transcripts of isoform 1, -3 or -4. No specific primer pairs could be designed for isoforms 2 and -6. Actin mRNA level was used as an internal standard.</p></caption><graphic xlink:href=\"ijms-21-05310-g005\"/></fig></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32722488</article-id><article-id pub-id-type=\"pmc\">PMC7432028</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155287</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05287</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Metabolic Response of the Yeast <italic>Candida utilis</italic> During Enrichment in Selenium</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-5836-4865</contrib-id><name><surname>Kieliszek</surname><given-names>Marek</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05287\">1</xref><xref rid=\"c1-ijms-21-05287\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Bierla</surname><given-names>Katarzyna</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05287\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-6467-6534</contrib-id><name><surname>Jiménez-Lamana</surname><given-names>Javier</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05287\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-6432-5838</contrib-id><name><surname>Kot</surname><given-names>Anna Maria</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05287\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Alcántara-Durán</surname><given-names>Jaime</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05287\">3</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-4203-4877</contrib-id><name><surname>Piwowarek</surname><given-names>Kamil</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05287\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Błażejak</surname><given-names>Stanisław</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05287\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-5644-4933</contrib-id><name><surname>Szpunar</surname><given-names>Joanna</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05287\">2</xref><xref rid=\"c1-ijms-21-05287\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05287\"><label>1</label>Department of Food Biotechnology and Microbiology, Institute of Food Sciences, Warsaw University of Life Sciences—SGGW, Nowoursynowska 159 C, 02-776 Warsaw, Poland; <email>anna_kot@sggw.edu.pl</email> (A.M.K.); <email>kamil_piwowarek@sggw.edu.pl</email> (K.P.); <email>stanislaw_blazejak@sggw.edu.pl</email> (S.B.)</aff><aff id=\"af2-ijms-21-05287\"><label>2</label>Institute of Analytical Sciences, IPREM, UMR 5254, CNRS-UPPA, Hélioparc, 2 Avenue Angot, 64053 Pau, France; <email>katarzyna.bierla@univ-pau.fr</email> (K.B.); <email>j.jimenez-lamana@univ-pau.fr</email> (J.J.-L.)</aff><aff id=\"af3-ijms-21-05287\"><label>3</label>Analytical Chemistry Research Group, Department of Physical and Analytical Chemistry, University of Jaen, 23071 Jaen, Spain; <email>jaduran@ujaen.es</email></aff><author-notes><corresp id=\"c1-ijms-21-05287\"><label>*</label>Correspondence: <email>marek_kieliszek@sggw.edu.pl</email> (M.K.); <email>joanna.szpunar@univ-pau.fr</email> (J.S.)</corresp></author-notes><pub-date pub-type=\"epub\"><day>25</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5287</elocation-id><history><date date-type=\"received\"><day>07</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>23</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Selenium (Se) was found to inhibit the growth of the yeast <italic>Candida utilis</italic> ATCC 9950. Cells cultured in 30 mg selenite/L supplemented medium could bind 1368 µg Se/g of dry weight in their structures. Increased accumulation of trehalose and glycogen was observed, which indicated cell response to stress conditions. The activity of antioxidative enzymes (glutathione peroxidase, glutathione reductase, thioredoxin reductase, and glutathione S-transferase) was significantly higher than that of the control without Se addition. Most Se was bound to water-insoluble protein fraction; in addition, the yeast produced 20–30 nm Se nanoparticles (SeNPs). Part of Se was metabolized to selenomethionine (10%) and selenocysteine (20%). The HPLC-ESI-Orbitrap MS analysis showed the presence of five Se compounds combined with glutathione in the yeast. The obtained results form the basis for further research on the mechanisms of Se metabolism in yeast cells.</p></abstract><kwd-group><kwd>selenium</kwd><kwd><italic>Candida</italic></kwd><kwd>yeast</kwd><kwd>antioxidant enzymes</kwd><kwd>selenium speciation</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05287\"><title>1. Introduction</title><p>Selenium (Se) is an important element involved in many metabolic processes [<xref rid=\"B1-ijms-21-05287\" ref-type=\"bibr\">1</xref>]. It has been recognized as an essential nutrient for its antioxidant properties [<xref rid=\"B2-ijms-21-05287\" ref-type=\"bibr\">2</xref>], and it plays an important role in thyroid hormone metabolism and immune defense mechanisms [<xref rid=\"B3-ijms-21-05287\" ref-type=\"bibr\">3</xref>]. Se deficiency is associated with many severe cardiovascular diseases [<xref rid=\"B4-ijms-21-05287\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijms-21-05287\" ref-type=\"bibr\">5</xref>] and increased susceptibility to viral or bacterial infections [<xref rid=\"B6-ijms-21-05287\" ref-type=\"bibr\">6</xref>]. The gap between the essentiality and the toxicity of Se is particularly narrow and depends on the chemical form [<xref rid=\"B7-ijms-21-05287\" ref-type=\"bibr\">7</xref>].</p><p>Inorganic Se found in soil is metabolized by plants and microorganisms to organic forms that can be eliminated by volatilization [<xref rid=\"B8-ijms-21-05287\" ref-type=\"bibr\">8</xref>] or bioaccumulated in forms that are more accessible to animals and humans [<xref rid=\"B9-ijms-21-05287\" ref-type=\"bibr\">9</xref>]. The metabolism of Se in some plants is induced in the context of bioremediation and as a source of phytochemicals for new potential therapeutic agents. The principal chemical forms of Se in plants include selenomethionine (SeMet), methyselenocysteine, and glutamyl-selenomethyl-selenocysteine [<xref rid=\"B8-ijms-21-05287\" ref-type=\"bibr\">8</xref>].</p><p>Yeast fermented in Se-rich media has been studied for a long time as the best source of Se for food and feed supplements [<xref rid=\"B10-ijms-21-05287\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijms-21-05287\" ref-type=\"bibr\">11</xref>]. The most popular yeast <italic>Saccharomyces cerevisiae</italic> grown in the presence of selenite and/or selenate can accumulate up to 3000 mg Se/kg [<xref rid=\"B10-ijms-21-05287\" ref-type=\"bibr\">10</xref>]. The total SeMet concentration of >60% has been considered as a measure of: (1) the “organic” characteristic of the Se-rich yeast; (2) the efficiency of the biotechnological enrichment process; and (3) the product quality [<xref rid=\"B12-ijms-21-05287\" ref-type=\"bibr\">12</xref>]. Other yeast varieties such as <italic>Torula</italic> (<italic>Candida utilis),</italic> which is widely used as a “natural” flavoring agent in processed foods and pet foods for replacing the flavor enhancer monosodium glutamate [<xref rid=\"B13-ijms-21-05287\" ref-type=\"bibr\">13</xref>], may not share this metabolomics pathway. Indeed, Se-rich <italic>Torula</italic> showed low (<10%) concentration of SeMet but high (>80%) concentration of selenohomolanthionine [<xref rid=\"B14-ijms-21-05287\" ref-type=\"bibr\">14</xref>]. The ability of Se to bind to the biomass of <italic>S. cerevisiae</italic> ATCC MYA-2200 and <italic>Candida utilis</italic> ATCC 9950 was compared, and it was observed that yeasts of the genus <italic>Candida</italic> were more efficient in binding Se [<xref rid=\"B15-ijms-21-05287\" ref-type=\"bibr\">15</xref>]. Effects of Se on morphological changes in cells of the yeast <italic>Candida utilis</italic> were studied [<xref rid=\"B16-ijms-21-05287\" ref-type=\"bibr\">16</xref>]. The understanding of the metabolic processes is crucial for the control of Se-enrichment biotechnologies.</p><p>High concentrations of Se are a factor of stress for yeast. Se inhibits cell proliferation through cell cycle progression, which leads to a reduction in the potential of lipid and mitochondrial membranes and eventually results in cell death [<xref rid=\"B17-ijms-21-05287\" ref-type=\"bibr\">17</xref>]. Under the appropriate growth conditions, yeasts can accumulate Se in their cellular structures and transform them into various chemical forms. The common parameters investigated include: (i) the activity of antioxidant enzymes, which ensures proper protection of metabolic processes as well as the functioning of the entire metabolic system in yeast cells; and (ii) the synthesis of carbohydrates, namely trehalose and glycogen. The function of trehalose is to protect and stabilize membranes, which prevents the loss of cellular components. Trehalose protects lipids against oxidation [<xref rid=\"B18-ijms-21-05287\" ref-type=\"bibr\">18</xref>]. The glycogen content in yeast is related to the growth rate of microorganisms [<xref rid=\"B19-ijms-21-05287\" ref-type=\"bibr\">19</xref>]. Yeast can accumulate high levels of glycogen in order to survive stress conditions caused by various environmental factors (including the presence of Se) [<xref rid=\"B20-ijms-21-05287\" ref-type=\"bibr\">20</xref>]. The development of large-scale metabolomics tools based on mass spectrometry allows monitoring hundreds of Se compounds involved in the metabolomics pathways [<xref rid=\"B21-ijms-21-05287\" ref-type=\"bibr\">21</xref>]. The interaction between Se metabolic pathways and the activity of the antioxidant system is very complicated, and only a systematic approach would allow analyzing their interrelationships. Establishing a network of interactions between these routes is a challenge.</p><p>The present study aimed to determine the effect of Se on the growth of the yeast <italic>C. utilis</italic> ATCC 9950. The metabolic response of <italic>C. utilis</italic> during enrichment in Se, including oxidative stress markers (trehalose and glycogen), was examined, together with the activity of individual antioxidative enzymes and speciation of Se in Se-enriched yeast biomass. The presence of elemental Se in the cells was also determined. To the best of our knowledge, this is the first attempt to probe the effect of Se on the content of carbohydrates and formation of Se-nanoparticles (SeNPs) in <italic>C. utilis</italic>.</p></sec><sec sec-type=\"results\" id=\"sec2-ijms-21-05287\"><title>2. Results and Discussion</title><sec id=\"sec2dot1-ijms-21-05287\"><title>2.1. Effect of Se on Yeast Growth</title><p><italic>Candida utilis</italic> ATCC 9950 <italic>C. utilis</italic> ATCC 9950 was able to grow in control and Se-supplemented media. After introducing the yeast inoculum into the control medium without the addition of Se, the optical density (OD) was approximately 0.2. In the Se medium, the yeast had an optical density of 0.3. The yeast grown in medium without Se addition showed higher OD values on the obtained growth curves in all time variants. The study showed that the concentration of Se (30 mg/L) in the culture medium significantly reduced the increase in OD. This could be due to the inhibitory effect of Se on the reproduction of yeast cells. After incubation of yeast in the presence of Se, the culture medium became slightly red in color, which indicates the reduction of selenite to red allotropes of Se (Se<sup>0</sup>)—the less toxic inorganic form of Se [<xref rid=\"B22-ijms-21-05287\" ref-type=\"bibr\">22</xref>]. As reported by Fujs et al. [<xref rid=\"B23-ijms-21-05287\" ref-type=\"bibr\">23</xref>], the presence of Se causes changes in yeast metabolic activity and expression of various genes. Oxidative stress then occurs [<xref rid=\"B24-ijms-21-05287\" ref-type=\"bibr\">24</xref>], which causes membrane depolarization, i.e., a decrease in the electrical potential difference between the cellular cytosol and the culture environment [<xref rid=\"B25-ijms-21-05287\" ref-type=\"bibr\">25</xref>]. To prevent this, yeast has developed several detoxification systems that protect cells in order to survive in adverse environmental conditions. It is believed that genes whose expression increases during stress conditions are also involved in the protection against Se stress. An example of such a process is increased biosynthesis of glutathione or antioxidant enzymes [<xref rid=\"B26-ijms-21-05287\" ref-type=\"bibr\">26</xref>]. When encountering an excess of Se concentration, yeast cells activate the glutathione biosynthesis pathway to overcome the toxicity of this element. Glutathione is produced in the sulfur cycle of microorganisms, in which the main enzyme is sulfite reductase [<xref rid=\"B27-ijms-21-05287\" ref-type=\"bibr\">27</xref>]. Glutathione plays an important role in protecting yeast against oxidative stress and has been shown to increase cell tolerance to Se.</p><p>The highest total increase in the OD of the yeast was observed in the YPD control medium after 24 h and in the medium containing Se after 26 h (<xref ref-type=\"fig\" rid=\"ijms-21-05287-f001\">Figure 1</xref>). Compared to the yeast grown in the medium with Se addition, the yeast grown in the control medium showed a fast growth rate. It should be noted that, during supplementation with Se, the yeast cells adapted to the new environment for a long time, and the duration of the adaptation phase (delay) was significantly longer (6 h) than that for the control medium (2 h). Substrate supplementation with Se at a concentration of 30 mg/L also caused a prolonged generation time. The consequence of these processes was a significant reduction of yeast budding processes in the logarithmic phase. The kinetic parameters of yeast growth are shown in <xref rid=\"ijms-21-05287-t001\" ref-type=\"table\">Table 1</xref>.</p><p>The above findings confirm the results of previous studies reporting an inhibitory effect of Se on the growth of <italic>C. utilis</italic> ATCC 9950 (determined on the basis of cell biomass yield) in a medium containing 5% glycerol as the carbon source and waste potato water as the protein source [<xref rid=\"B25-ijms-21-05287\" ref-type=\"bibr\">25</xref>]. The high concentration of Se in the culture medium significantly slowed down the growth of the tested yeast strains, as noted in this study. Estrada et al. [<xref rid=\"B28-ijms-21-05287\" ref-type=\"bibr\">28</xref>] reported that <italic>Lactobacillus</italic> isolated from Mexican cheeses were sensitive to high levels of sodium selenite; the growth of the tested bacterial strains was inhibited at the concentration of 400 mg/L sodium selenite. Miletić et al. [<xref rid=\"B29-ijms-21-05287\" ref-type=\"bibr\">29</xref>] observed a reduction in the growth of the fungus <italic>Coriolus versicolor</italic> due to the presence of Se. The studied fungi were subjected to a bioreactor culture in media with the addition of sodium selenite, and the inhibitory effect was already observed at the concentration of 10 mg Se<sup>4+</sup>/L. Similar relationships were also reported by Lusa et al. [<xref rid=\"B30-ijms-21-05287\" ref-type=\"bibr\">30</xref>] who observed that the concentration of 6 mM sodium selenite reduces the growth rate of <italic>Pseudomonas</italic> sp. T5-6-I. The presence of Se induced a significant increase in the duration of the delay phase as compared to the control culture, which was also observed in our studies.</p><p>The presence of Se in the culture medium extended the time period for the cells to adapt to new unfavorable culture conditions. It was also confirmed that Se increased the mortality rate [<xref rid=\"B30-ijms-21-05287\" ref-type=\"bibr\">30</xref>], which could mainly be associated with the occurrence of cell autolysis in the stationary growth phase. The primary importance in this phenomenon is attributed to the accumulation of metabolites (elemental form, Se<sup>0</sup>) by cells, resulting in the weakening of cytoplasmic membranes [<xref rid=\"B16-ijms-21-05287\" ref-type=\"bibr\">16</xref>].</p></sec><sec id=\"sec2dot2-ijms-21-05287\"><title>2.2. Effect of Se on the Activity of Antioxidant Enzymes</title><p>During evolution, organisms developed sophisticated antioxidant systems to maintain smooth cell function under adverse culture conditions. However, the relationship between Se and breeding stress has hardly been investigated. We observed that the presence of 30 mg/L of Se in the culture medium increased the activity of antioxidant enzymes (<xref ref-type=\"fig\" rid=\"ijms-21-05287-f002\">Figure 2</xref>) and found an increase in glutathione peroxidase (GPx) and glutathione S-transferase (GST) activities in Se-enriched biomass. Their values were 3.22 and 0.056 mU/mg, respectively. The obtained results are consistent with those of Assunção et al. [<xref rid=\"B31-ijms-21-05287\" ref-type=\"bibr\">31</xref>] where the PGx content in yeast also increased as the concentration of Se in the growth medium increased. This finding agrees with a previous study which showed that a low concentration of Se increased the activity of antioxidant enzymes (including GPx, glutathione reductase (GR), and superoxide dismutase (SOD)), which consistently inhibited lipid peroxidation [<xref rid=\"B25-ijms-21-05287\" ref-type=\"bibr\">25</xref>]. Wang et al. [<xref rid=\"B32-ijms-21-05287\" ref-type=\"bibr\">32</xref>] studied <italic>Gracilaria lemaneiformis</italic> seaweed and confirmed the hypothesis that the increasing dose of Se resulted in increased activity of GPx. Therefore, these changes in enzyme activity can be considered as indicators of stress.</p><p>In the present study, the presence of 30 mg/L of Se increased GR activity by 50% in comparison to that of the control yeast. The activity of this enzyme, which is involved in the glutathione synthesis pathway, is controlled by trehalose concentration [<xref rid=\"B33-ijms-21-05287\" ref-type=\"bibr\">33</xref>]. In addition, GR catalyzes the reduction of disulfide bond in oxidized glutathione (GSSG). The entire process is dependent on NADPH, which is very important for maintaining a pool of reduced glutathione in the cell (GSH) [<xref rid=\"B31-ijms-21-05287\" ref-type=\"bibr\">31</xref>]. In our previous study on <italic>C. utilis</italic> kept in aqueous solutions supplemented with different doses of Se, we showed that the presence of Se increased the content of antioxidant enzymes and reduced glutathione [<xref rid=\"B25-ijms-21-05287\" ref-type=\"bibr\">25</xref>]. The key strategy for yeast survival in the presence of Se is to control the dynamics of cytoplasmic membranes. If membranes are affected by changes in the content and composition of fatty acids, then they are more prone to attack by reactive oxygen species (ROS). Because of these processes, lipid peroxidation increases and changes occur in the morphological structure of cells. The consequence of such processes is a decrease in yeast viability. </p><p>Thioredoxin reductase (TRxR) in yeast also increased its activity under the influence of Se. In comparison with the control sample, the content of this enzyme increased by as much as 72%. Such a large increase in the activity of this enzyme might be due to the result of the very low tolerance of <italic>C. utilis</italic> cultured in YPD medium to the presence of Se. TRxR is present in the mitochondria of most yeast and acts as a defense mechanism against ROS damage [<xref rid=\"B34-ijms-21-05287\" ref-type=\"bibr\">34</xref>]. This enzyme is necessary to reduce ribonucleotides to deoxyribonucleotides, thereby contributing to DNA repair [<xref rid=\"B35-ijms-21-05287\" ref-type=\"bibr\">35</xref>]. In addition, this enzyme is involved in the reduction of nonspecific linkages in protein structures that have undergone oxidation [<xref rid=\"B25-ijms-21-05287\" ref-type=\"bibr\">25</xref>]. An excess of Se may reduce the intracellular pool of glutathione, causing the accumulation of ROS in yeast cell structures. Such processes can severely disrupt yeast metabolism through oxidative damage to cellular components. The production of ROS through enzymatic and nonenzymatic systems results in changes in the structure of fatty acids, causing lipid peroxidation, and thus forming lipid peroxides. However, the adaptation of yeast cells to stress conditions is very complex. Some mechanisms of stress response are still poorly understood, especially those associated with the adaptation of cells to the presence of various elements in the culture medium. Hence, it is very important to conduct further research on the effect of Se on the entire antioxidant system. Our results show that the cooperative action of the entire antioxidant enzyme system prevents the negative effect of Se on yeast cells. Based on the results obtained, we have shown similar responses of the tested antioxidant enzymes to the presence of Se.</p></sec><sec id=\"sec2dot3-ijms-21-05287\"><title>2.3. Effect of Se on Trehalose and Glycogen Content</title><p>The previously described antioxidant properties of Se-containing enzymes (including GPx and TRxR) are associated with the content of trehalose and glycogen in yeast cells. Diversified antioxidant protection in yeast resulting from the presence of Se is essential for the proper functioning of cells. We showed that the presence of 30 mg/L Se caused an increase in trehalose (17.43 mg/g) and glycogen (0.684 mg/g) content in the yeast biomass of <italic>C. utilis</italic> compared to that obtained from the control culture (<xref ref-type=\"fig\" rid=\"ijms-21-05287-f003\">Figure 3</xref>). The results obtained support the hypothesis that increased accumulation of trehalose and glycogen in cells might be associated with the presence of Se. The altered trehalose level proves that yeast cells were induced to synthesize trehalose in response to oxidative stress. Therefore, the increase in these metabolites is indicative of a protective effect against yeast cells. The task of the first sugar (trehalose) is to protect cell membranes after exposure of organisms to various environmental stresses [<xref rid=\"B36-ijms-21-05287\" ref-type=\"bibr\">36</xref>].</p><p>Trehalose protects lipids and proteins against oxidation by changing the activity of individual biochemical reactions, which has an inhibitory effect on the generation of ROS [<xref rid=\"B37-ijms-21-05287\" ref-type=\"bibr\">37</xref>]. According to Saharan and Sharma [<xref rid=\"B33-ijms-21-05287\" ref-type=\"bibr\">33</xref>], trehalose functions in coordination with glutathione to control stress associated with free radicals in cells. Furthermore, Sharma et al. [<xref rid=\"B38-ijms-21-05287\" ref-type=\"bibr\">38</xref>] demonstrated that the decrease in GSH with an increase in trehalose is associated with heat shock in both control and Se-supplemented cells. It is probably therefore a general adaptive reaction of yeast cells to proxidative stress caused by the presence of Se. Studies reported by Li et al. [<xref rid=\"B39-ijms-21-05287\" ref-type=\"bibr\">39</xref>] have shown that yeast grown under stress caused increased accumulation of trehalose in their cellular structures. Glycogen is an intracellular polymer composed of glucose subunits, thus affects the normal growth of yeast by providing constant access to a carbon source [<xref rid=\"B40-ijms-21-05287\" ref-type=\"bibr\">40</xref>]. It was found that, in yeast cells, glycogen degradation is associated with the production of sterols and unsaturated fatty acids, which are necessary to maintain cell vitality [<xref rid=\"B41-ijms-21-05287\" ref-type=\"bibr\">41</xref>]. The production and use of carbon (glycogen) reserves in cells is also a common mechanism for yeast to adapt to adverse stress conditions [<xref rid=\"B42-ijms-21-05287\" ref-type=\"bibr\">42</xref>]. According to the data presented by Possik and Pause [<xref rid=\"B43-ijms-21-05287\" ref-type=\"bibr\">43</xref>], glycogen mediates hypoosmotic-anoxic stress resistance in the nematode <italic>Caenorhabditis elegans</italic>. As reported by Chen et al. [<xref rid=\"B44-ijms-21-05287\" ref-type=\"bibr\">44</xref>], glycogen may play a protective role in yeast cells under osmotic stress when cell wall integrity is compromised. The oxidative stress resulting from the presence of Se causes cellular disorders in yeast cells, which cause changes in the functioning of individual organelles. Hence, the functioning of the entire antioxidative system in yeast is very important. It appears that Se induces various protection responses through increased accumulation of glycogen and trehalose in <italic>C. utilis</italic>. In conclusion, the way this defense mechanism works depends largely on the stress factor (Se) used to induce oxidative stress.</p></sec><sec id=\"sec2dot4-ijms-21-05287\"><title>2.4. Se Bioaccumulation</title><p>Se can accumulate in the cells of various microorganisms in organic, inorganic, or elemental form or as their mixture. The functioning of different pathways of Se accumulation in yeast may additionally affect cell growth and final cell density (OD) in culture media [<xref rid=\"B30-ijms-21-05287\" ref-type=\"bibr\">30</xref>]. There are several published studies on Se speciation in the yeast <italic>S. cerevisiae</italic>, which is widely used in human and animal supplementation [<xref rid=\"B12-ijms-21-05287\" ref-type=\"bibr\">12</xref>,<xref rid=\"B14-ijms-21-05287\" ref-type=\"bibr\">14</xref>]. The ability of Se to bind to the biomass of <italic>S. cerevisiae</italic> ATCC MYA-2200 and <italic>C. utilis</italic> ATCC 9950 was compared, and it was observed that yeasts of the genus <italic>Candida</italic> were more efficient in binding Se [<xref rid=\"B15-ijms-21-05287\" ref-type=\"bibr\">15</xref>]. The total Se content of as high as 4000 µg/g has been reported [<xref rid=\"B14-ijms-21-05287\" ref-type=\"bibr\">14</xref>]. We found that the biomass of <italic>C. utilis</italic> ATCC 9950 bound 1368 µg Se/g (<xref rid=\"ijms-21-05287-t002\" ref-type=\"table\">Table 2</xref>).</p><p>Contrary to the data published for the commercially available <italic>C. utilis</italic> strains [<xref rid=\"B14-ijms-21-05287\" ref-type=\"bibr\">14</xref>], the analyzed samples showed low content of the low-molecular-weight (LMW, water-soluble) species, which accounted for less than 10% of the total Se; the corresponding value for <italic>Saccharomyces cerevisiae</italic> is usually in the range of 12–18% [<xref rid=\"B14-ijms-21-05287\" ref-type=\"bibr\">14</xref>]. The bioaccumulation of Se by various microorganisms has been widely studied by many authors. As reported by Miletić et al. [<xref rid=\"B45-ijms-21-05287\" ref-type=\"bibr\">45</xref>], the medicinal fungus <italic>Coriolus versicolor</italic> grown in medium supplemented with 10 and 20 mg Se/L could accumulate 970 and 1300 µg/g of Se, respectively. Ullah et al. [<xref rid=\"B46-ijms-21-05287\" ref-type=\"bibr\">46</xref>] showed that the probiotic bacteria <italic>Bacillus subtilis</italic> BSN313 grown in Se-enriched medium at the dose of 12 µg/mL could bind 2.123 µg Se/g. Egressy-Molnár et al. [<xref rid=\"B47-ijms-21-05287\" ref-type=\"bibr\">47</xref>] studied the process of Se binding by <italic>Hericium erinaceus</italic> (lion’s mane mushroom). The fungus could bind 42.3 µg Se/g. An interesting course of changes in the bioaccumulation of Se in yeast biomass was presented by Zhang et al. [<xref rid=\"B26-ijms-21-05287\" ref-type=\"bibr\">26</xref>,<xref rid=\"B48-ijms-21-05287\" ref-type=\"bibr\">48</xref>]. The authors found that <italic>C. utilis</italic> CCTCC M 209298 could accumulate approximately 1010 µg Se/g after bioreactor culture in medium supplemented with 15 mg Se/L. It should be emphasized here that, although there are few studies on the binding of Se by <italic>Candida</italic> yeast, it can be concluded that the possibility of accumulation of this element is different from that in other species of microorganisms. It is worth noting that the yeast <italic>C. utilis</italic> belongs to the group of microorganisms that have the status of GRAS (Generally Recognized As Safe), i.e., safe for humans and animals [<xref rid=\"B49-ijms-21-05287\" ref-type=\"bibr\">49</xref>]. Depending on the concentration and chemical form of Se, its uptake varies between yeast species and depends on the activity of membrane transporters and physiological conditions. However, little is known about Se transport, which is the first step in the metabolism of this element, i.e., processes involving reduction, methylation, and incorporation into proteins [<xref rid=\"B50-ijms-21-05287\" ref-type=\"bibr\">50</xref>]. According to Estrada et al. [<xref rid=\"B28-ijms-21-05287\" ref-type=\"bibr\">28</xref>], <italic>Sel</italic>A and <italic>Sel</italic>D genes are involved in the bioaccumulation of Se and its incorporation into proteins. McDermott et al. [<xref rid=\"B51-ijms-21-05287\" ref-type=\"bibr\">51</xref>] showed that the transport of selenite (IV) into yeast cells is responsible for the monocarboxylic symport carboxy <italic>Jen1p</italic>. This transporter is located in the yeast cell membrane. Its function is to collect various monocarboxylic acids from the extracellular environment (e.g., pyruvic acid, acetic acid, and lactic acid). Jen1p operates on the principle of an import transporter using Na<sup>+</sup> ions. The mechanism of transport of the Se (IV) ion through Jen1p is explained by the structural similarity between Se anions and monocarboxylic acid anions [<xref rid=\"B52-ijms-21-05287\" ref-type=\"bibr\">52</xref>]. It is worth noting that these molecules have similar dissociation constants. Se uptake is also mediated by transporters with low affinity for phosphates (Pho87p, Pho90p, and Pho91p), while the other two transporters (Pho84p and Pho89p) have high affinity. The transport of Se and phosphate ions occurs through both high and low affinity channels, but with varying efficiency. In a low phosphate environment, a high affinity transporter such as Pho84p is a major contributor to the binding of selenate (IV) ions by yeast. During high phosphate concentration, the transport capacity of Se through the Pho84p channel is reduced. Under these conditions, low affinity proteins are responsible for transport. This is associated with reduced Se absorption and increased resistance of cells to toxic Se doses [<xref rid=\"B53-ijms-21-05287\" ref-type=\"bibr\">53</xref>]. In addition, the vacuolar selenodiglutathione transporter YCF1 may be responsible for the increased accumulation of Se by yeast cells. This transporter is found in the yeast vacuole membrane and is one of the best-studied subfamily transporters ABCC [<xref rid=\"B36-ijms-21-05287\" ref-type=\"bibr\">36</xref>]. The uptake of selenate by yeast can be mediated by sulfate transporters (Sul1p and Sul2p) due to the chemical similarity between selenate and sulfate [<xref rid=\"B53-ijms-21-05287\" ref-type=\"bibr\">53</xref>]. Se is involved in the metabolism of sulfur in the yeast cell structures, and, as a part of many proteins, it may modify their conformational structure, leading to toxic effects and changes in functional activity. Thus, its accumulation impairs a wide range of cellular functions, which leads to a reduction in the rate of cellular metabolism, growth, and viability of yeast.</p></sec><sec id=\"sec2dot5-ijms-21-05287\"><title>2.5. Se Speciation</title><p>The principal analytical approach to Se speciation, which is also used in this work, has been based on the fractionation of biological extracts by chromatography while specifically monitoring Se by inductively coupled plasma mass spectrometry (ICP-MS). The increase in the chromatographic resolution, the introduction of multidimensional separation approaches, and the growing sensitivity of ICP MS detection owing to collision cell and triple quadrupole mass spectrometers have led to the increasing number of unidentified peaks in HPLC–ICP MS chromatograms [<xref rid=\"B54-ijms-21-05287\" ref-type=\"bibr\">54</xref>]. The absence of retention time standards has rendered electrospray MS indispensable for standardless identification of naturally synthesized Se compounds [<xref rid=\"B54-ijms-21-05287\" ref-type=\"bibr\">54</xref>].</p><p>The widely approved method to characterize selenized yeasts to assess their “organic characteristic” is their SeMet content; it is a measure of the yeast quality with values reaching more than 60% in good quality preparations [<xref rid=\"B55-ijms-21-05287\" ref-type=\"bibr\">55</xref>]. Its determination can be validated using the SELM-1 standard reference material (National Research Council of Canada), although the physicochemical properties of yeast produced by different methods may vary from the SELM-1 yeast [<xref rid=\"B56-ijms-21-05287\" ref-type=\"bibr\">56</xref>]. SeMet content in the studied sample (<xref rid=\"ijms-21-05287-t003\" ref-type=\"table\">Table 3</xref>) was relatively low (ca. 10%), which, however, corresponds to the previous findings on <italic>Candida</italic> yeasts (syn. <italic>Torula</italic>) [<xref rid=\"B14-ijms-21-05287\" ref-type=\"bibr\">14</xref>,<xref rid=\"B57-ijms-21-05287\" ref-type=\"bibr\">57</xref>]. The other selenoamino acid, selenocysteine, which accounts for less than 5% in <italic>S. cerevisiae</italic> in the studied sample, is present at the level of more than 20% of the total Se. These data allow estimating the incorporation of Se into yeast protein fraction at ca. 30% of the total Se present in the biomass. Se in the form of the amino acid selenocysteine (SeCys) is incorporated into the catalytic center in selenoproteins. These proteins are involved in the detoxification and capture processes of ROS.</p><p>The majority of Se (ca. 60%) is present in the form of inorganic Se as water-insoluble (mostly protein) fraction (<xref ref-type=\"fig\" rid=\"ijms-21-05287-f004\">Figure 4</xref>). This species is present in the chromatograms as peak 2 (<xref ref-type=\"fig\" rid=\"ijms-21-05287-f005\">Figure 5</xref>). The nature of the binding of inorganic Se remains to be fully elucidated; it is believed to be mostly bound to cell walls, although a contribution from selenite-binding proteins can also be expected. Their presence in many organisms has been well documented, and a binding site consisting of two neighboring Cys residues was identified [<xref rid=\"B58-ijms-21-05287\" ref-type=\"bibr\">58</xref>]; such easily accessible sites are present in many yeast proteins and can scavenge (bind) inorganic Se present in Se-rich growth medium.</p><p>The speciation analysis of organoselenium metabolites obtained from the biomass of microorganisms depends on extraction conditions and on the stability of the extracted analytes [<xref rid=\"B59-ijms-21-05287\" ref-type=\"bibr\">59</xref>]. The characterization of LMW Se (water-soluble species) led to the identification of five glutathione derivatives in the 450–700 Da mass range (<xref rid=\"ijms-21-05287-t004\" ref-type=\"table\">Table 4</xref>). These species were already reported in <italic>S. cerevisiae</italic> [<xref rid=\"B21-ijms-21-05287\" ref-type=\"bibr\">21</xref>,<xref rid=\"B60-ijms-21-05287\" ref-type=\"bibr\">60</xref>]. In contrast with previous studies on commercially available <italic>Candida</italic> (syn. <italic>Torula</italic> yeast) supplements, no selenohomolanthionine has been detected [<xref rid=\"B14-ijms-21-05287\" ref-type=\"bibr\">14</xref>].</p><p>Se is present in the nature in the form of six natural isotopes, namely Se<sup>74</sup>, Se<sup>76</sup>, Se<sup>77</sup>, Se<sup>78</sup>, Se<sup>80</sup>, and Se<sup>82</sup> with natural abundances of 0.86%, 9.23%, 7.60%, 23.69%, 49.80%, and 8.82%, respectively; this ratio can be observed in mass spectra of Se-containing species and is a basis of MS data mining for selenometabolites. The characteristic isotopic patterns resulting from the presence of an Se atom in the metabolites’ molecules are shown in <xref ref-type=\"fig\" rid=\"ijms-21-05287-f006\">Figure 6</xref>. In our previous study [<xref rid=\"B61-ijms-21-05287\" ref-type=\"bibr\">61</xref>] using the UHPLC-ESI-Orbitrap-MS/MS method in <italic>C. utilis</italic> yeast biomass, we found the presence of Se compounds with mass from 195.9 to 376.9 <italic>m</italic>/<italic>z</italic>. Ward et al. [<xref rid=\"B59-ijms-21-05287\" ref-type=\"bibr\">59</xref>] showed that the commercially available yeast contained Se compounds combined with glutathione, which corroborated the earlier findings of Arnaudquilhem et al. [<xref rid=\"B21-ijms-21-05287\" ref-type=\"bibr\">21</xref>]. According to literature data [<xref rid=\"B37-ijms-21-05287\" ref-type=\"bibr\">37</xref>], cell protection against oxidative processes predominantly occurs through the functioning of glutathione as a reducing agent. The antioxidant effect of this process may involve protecting the -SH groups against irreversible oxidation and thus against irreversible loss of biological activity of many enzymes [<xref rid=\"B25-ijms-21-05287\" ref-type=\"bibr\">25</xref>].</p></sec><sec id=\"sec2dot6-ijms-21-05287\"><title>2.6. Detection and Characterization of SeNPs</title><p>The production of SeNPs by microorganisms is a widespread phenomenon in nature, which has raised considerable interest in recent years [<xref rid=\"B62-ijms-21-05287\" ref-type=\"bibr\">62</xref>]. In this context, the presence of Se-bearing NPs in the sample was investigated by single particle (SP)-ICP-MS and transmission electron microscopy (TEM). SeNPs have gained considerable interest recently owing to their antioxidant, antimicrobial, and anticancer properties [<xref rid=\"B63-ijms-21-05287\" ref-type=\"bibr\">63</xref>].</p><p>SP-ICP-MS measures the size distribution of metal-containing NPs in a single measurement [<xref rid=\"B64-ijms-21-05287\" ref-type=\"bibr\">64</xref>]. The results obtained show the presence of SeNPs of 20–40-nm diameter (<xref ref-type=\"fig\" rid=\"ijms-21-05287-f007\">Figure 7</xref>). As the size limit of detection for SeNPs is around 20 nm [<xref rid=\"B65-ijms-21-05287\" ref-type=\"bibr\">65</xref>], the occurrence of smaller nanoparticles could be missed. The presence of SeNPs in the yeast cell wall was confirmed by TEM (<xref ref-type=\"fig\" rid=\"ijms-21-05287-f008\">Figure 8</xref>). The differences in the sizes of NPs may result from the difference in Se oxidation [<xref rid=\"B27-ijms-21-05287\" ref-type=\"bibr\">27</xref>], although the results obtained are in accordance with the findings of Hamza et al. [<xref rid=\"B66-ijms-21-05287\" ref-type=\"bibr\">66</xref>]. The size of SeNPs obtained from the biomass of <italic>Yarrowia lipolytica</italic> NCIM 3589 ranged between 30 and 60 nm. Faramazi et al. [<xref rid=\"B63-ijms-21-05287\" ref-type=\"bibr\">63</xref>] reported particle sizes of 75–709 nm in <italic>S. cerevisiae</italic>. Similar results were reported by Samant et al. [<xref rid=\"B67-ijms-21-05287\" ref-type=\"bibr\">67</xref>]. TEM showed that the average particle size in the biomass of <italic>Citrobacter freundii</italic> KP6 was between 45 and 70 nm. For <italic>Magnusiomyces ingens</italic> LH-F1 (CGMCC No. 10367), the average particle size was 87.8 nm [<xref rid=\"B62-ijms-21-05287\" ref-type=\"bibr\">62</xref>].</p><p>The formation of Se nanocolloids in cellular cytosol is associated with the detoxification process [<xref rid=\"B16-ijms-21-05287\" ref-type=\"bibr\">16</xref>]. This is one of the mechanisms of protecting yeast cells against adverse culture conditions. Yeast reduces the soluble sodium selenite (Na<sub>2</sub>SeO<sub>3</sub>) to a red elemental form (Se<sup>0</sup>) [<xref rid=\"B67-ijms-21-05287\" ref-type=\"bibr\">67</xref>]. According to Lian et al. [<xref rid=\"B62-ijms-21-05287\" ref-type=\"bibr\">62</xref>], SeNPs may contain some proteins and lipids on their surface, which further stabilize their structure. The formation of SeNPs in yeast grown at different concentrations of this element causes a change in the color of the biomass of cells from bright yellow at the beginning of incubation to orange or even red. This finding confirms the result of our previous research where yeast biomass grown in high Se concentration (including 80 mg Se<sup>4+</sup>/L) showed a reddish coloration [<xref rid=\"B68-ijms-21-05287\" ref-type=\"bibr\">68</xref>].</p></sec></sec><sec id=\"sec3-ijms-21-05287\"><title>3. Materials and Methods </title><sec id=\"sec3dot1-ijms-21-05287\"><title>3.1. Biological Material</title><p>The present study used the yeast strain <italic>Candida utilis</italic> ATCC 9950 obtained from the collection of pure cultures of the Department of Food Biotechnology and Microbiology, Warsaw University of Life Sciences-SGGW. Cellular biomass of the <italic>C. utilis</italic> strain was obtained after 24 h of shaking (SM-30 Control, E. Bühler, Bodelshausen, Germany), with a vibration amplitude of 200 cycles/min) in liquid YPD medium enriched with sodium selenite (IV). The active acidity of the substrates was set at 5.0. Media and aqueous solutions of Se salts (Na<sub>2</sub>SeO<sub>3</sub>) were sterilized at 121 °C for 20 min (Hirayama Autoclave HG80, Saitama, Japan). Then, a saline working solution was added to sterile YPD media in volumes such that the final Se content in the experimental media was 30 mg/L. The obtained yeast biomass after culture was centrifuged (3000× <italic>g</italic>, 10 min, 4 °C; Centrifuge 5804R, Eppendorf, Hamburg, Germany) and then washed twice with sterile distilled water. The obtained biomass was lyophilized and stored for further analysis.</p></sec><sec id=\"sec3dot2-ijms-21-05287\"><title>3.2. Determination of Optical Density</title><p>Optical density (OD) was measured to determine the effect of Se on yeast cell growth. In brief, 270 µL of model (YPD) and experimental media (with the addition of Se<sup>4+</sup> at 30 mg/L) and 30 µL of yeast from inoculation culture were added to all wells of the Bioscreen C apparatus plate (Oy Growth Curves Ab Ltd., Helsinki, Finland). Control samples (without the addition of biological material) were simultaneously prepared. Microcultures were performed for 35 h at 28 °C with continuous shaking. Measurement and registration of the change in the OD of yeast cells were performed automatically using a wideband filter with a wavelength of 420–580 nm. From the obtained yeast growth results, the minimum and maximum OD values in the logarithmic growth phase were determined. The correct growth rate (<inline-formula><mml:math id=\"mm1\"><mml:mrow><mml:mrow><mml:msub><mml:mi>μ</mml:mi><mml:mrow><mml:mi>max</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula>) was calculated using the formula: <inline-formula><mml:math id=\"mm2\"><mml:mrow><mml:mrow><mml:msub><mml:mi>μ</mml:mi><mml:mrow><mml:mi>max</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>lnOD</mml:mi></mml:mrow><mml:mrow><mml:mi>m</mml:mi><mml:mi>a</mml:mi><mml:mi>x</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mrow><mml:mi>lnOD</mml:mi></mml:mrow><mml:mrow><mml:mi>m</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo stretchy=\"false\">/</mml:mo><mml:mi>Δ</mml:mi><mml:msub><mml:mi>t</mml:mi><mml:mrow><mml:mi>log</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula>. The generation time (<inline-formula><mml:math id=\"mm3\"><mml:mrow><mml:mi>g</mml:mi></mml:mrow></mml:math></inline-formula>) was determined from the formula: <inline-formula><mml:math id=\"mm4\"><mml:mrow><mml:mrow><mml:mi>g</mml:mi><mml:mo>=</mml:mo><mml:mi>ln</mml:mi><mml:mn>2</mml:mn><mml:mo stretchy=\"false\">/</mml:mo><mml:msub><mml:mi>μ</mml:mi><mml:mrow><mml:mi>max</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:math></inline-formula>. In addition, the total increase in the optical density of the culture (ΔOD) was determined.</p></sec><sec id=\"sec3dot3-ijms-21-05287\"><title>3.3. Preparation of Cell Extract to Determine Trehalose, Glycogen and Antioxidant Enzyme Activity</title><p>After 24 h of yeast cultivation in control and Se-enriched culture media, the cell suspension was centrifuged (3000× <italic>g</italic>, 10 min, 4 °C) using a centrifuge (5804R, Eppendorf, Hamburg, Germany). The cell biomass was washed twice with 0.1 M phosphate buffer at pH 7.4. Yeast cells were disintegrated in a laboratory mill by using 0.3–0.5 mm glass beads at 4 °C. In the process of yeast cell disintegration, depending on the method used, test buffers provided with commercial biochemical kits were used in the mill. The cell extract obtained after disintegration (homogenate) was centrifuged (11,000× <italic>g</italic>, 5 min, 4 °C), and the obtained supernatant was transferred to new tubes, frozen at −80 °C, and used for spectrophotometric determinations.</p></sec><sec id=\"sec3dot4-ijms-21-05287\"><title>3.4. Determination of Trehalose and Glycogen Content in Yeast Cell Biomass</title><p>The Trehalose Assay Kit (Megazyme, Warsaw, Poland) enzyme test was used to determine the trehalose content of the cellular biomass of <italic>C. utilis</italic> ATCC 9950. Measurements were performed at 340 nm. The content of trehalose in yeast cell biomass was expressed as milligrams of dry sugar substance per gram of yeast biomass.</p><p>The second test performed during the study was to determine the glycogen content of the cellular biomass of <italic>C. utilis</italic>. The Glycogen Assay Kit (BioVision, Mountain View, CA, USA) was used for this purpose. The absorbance of the supernatant was measured after incubating the samples in dark for 30 min at 570 nm. The glycogen content of yeast cell biomass was expressed as milligrams of dry sugar substance per gram of yeast biomass. Absorbance measurements were performed using a Multiskan SKY spectrophotometer (Thermo Scientific, Warsaw, Poland).</p></sec><sec id=\"sec3dot5-ijms-21-05287\"><title>3.5. Biochemical Determination of Antioxidant Enzymes</title><p>Thioredoxin reductase (TrxR, EC 1.8.1.9) assay was performed using commercial kits (BioVision, Mountain View, CA, USA). Measurements were conducted at 412 nm and after incubation at 25 °C for 20 min. One unit of TrxR activity was defined as the amount of enzyme that produces 1 mole (TNB) in 1 min at 25 °C.</p><p>The amount of glutathione reductase (GR, EC 1.6.4.2) in extracts obtained after the yeast disintegration process was determined using enzyme kits of BioVision. GR corresponds to the amount of enzyme that catalyzes the conversion of 1 nmol of oxidized glutathione (GSSG) to its reduced form (GSH) in 1 min at 37 °C per milligram of protein (mU/mg).</p><p>The amount of glutathione peroxidase (GPx, EC 1.11.1.9) was determined using tests developed by BioVision. The absorbance of the cell extract was measured at 340 nm. The absorbance level decreases as a result of oxidation of NADPH to NADP+, which is directly proportional to the activity of GPx. One unit of GPx activity is the amount of enzyme that catalyzes the oxidation of 1 μmol glutathione by cumene hydroperoxide in 1 min per milligram of protein (mU/mg) at 25 °C.</p><p>Glutathione S-transferase (GST, EC 2.5.1.18) activity was determined in extracts obtained after yeast disintegration by using monochlorobimane (MCB) as a substrate (BioVision). Absorbance measurements were performed at 380 nm excitation wavelength and 460 nm emission wavelength. One unit of GST activity corresponded to the amount of enzyme that catalyzed the conversion of 1 nmol of substrate into product within 1 min per milligram of protein (mU/mg).</p></sec><sec id=\"sec3dot6-ijms-21-05287\"><title>3.6. Determination of Protein Content</title><p>Protein concentration in the supernatants was determined according to the method of Kieliszek et al. [<xref rid=\"B69-ijms-21-05287\" ref-type=\"bibr\">69</xref>]. The standard curve was prepared using bovine albumin (Sigma-Aldrich, Warsaw, Poland). Absorbance measurements of all enzymatic experiments were performed using a Multiskan SKY spectrophotometer (Thermo Scientific).</p></sec><sec id=\"sec3dot7-ijms-21-05287\"><title>3.7. Yeast Observation Under an Electron Microscope</title><p>The centrifuged yeast biomass (3000× <italic>g</italic>, 10 min, 4 °C) was fixed in 2.5% glutaraldehyde at 4 °C for 2 h. The fixing process was performed in a 1% solution of osmium tetroxide for 1 h at 4 °C. After dehydration, the yeast biomass was embedded in EPON 812 epoxide and left for 24 h. The material was then incubated at 60 °C for 48 h. Blocks were cut using an ultramicrotome (LKB, Bromma, Sweden). Ultrathin sections with the biological material were contrasted with 9% uranyl acetate and 0.5% lead citrate. The obtained cell samples and SeNPs were observed under a transmission electron microscope (JEM 1220 TEM, JEOL, Tokyo, Japan).</p></sec><sec id=\"sec3dot8-ijms-21-05287\"><title>3.8. Total Se Determination</title><p>In this test, 0.2 g of sample was accurately weighed in a DigiPrep tube and left overnight with 2.5 mL of HNO<sub>3.</sub> Then, 1 mL of H<sub>2</sub>O<sub>2</sub> was added, and the sample was digested in a DigiPrep digestion system (digestion program: 0–30 min up to 65 °C, 30–240 min at 65 °C). The digests were diluted to reach HNO<sub>3</sub> concentration of 4% and analyzed by ICP-MS using the optimized conditions. The quantification was performed by the method of standard addition at 4 levels. The samples were analyzed in triplicate. The analytical blanks and SELM-1 were analyzed in parallel.</p></sec><sec id=\"sec3dot9-ijms-21-05287\"><title>3.9. Determination of Total Selenomethionine (SeMet)</title><p>A 0.2-g sample was incubated with 5 mL of a protease XIV solution (20 mg protease in 30 mM TRIS buffer, pH 7.5). Three consecutive incubations (17 h at temperature 37 °C) with fresh portions of the enzyme solution were carried out. After each incubation, the sample was centrifuged (4000× <italic>g</italic>, 4 °C, 10 min), in a 5804R centrifuge (Eppendorf, Hamburg, Germany) and the supernatant was transferred to a separate vial to which 5 μL of β-mercaptoethanol were added. Upon the completion of the whole series, the three supernatants were pooled together and analyzed by anion exchange HPLC-ICP MS in gradient elution mode. Buffer A was 20 mM acetic acid–10 mM triethylamine (pH 4.7) and Buffer B was 200 mM acetic acid–100 mM triethylamine (pH 4.7). The program was: 0–5 min: 0% B, 5–30 min: 0–100% B, 30–40 min. The quantification was carried out by the method of standard additions with SeMet at four levels. The samples were analyzed in triplicate. The analytical blanks were included in the measurements. SELM-1 was analyzed in parallel.</p></sec><sec id=\"sec3dot10-ijms-21-05287\"><title>3.10. Determination of Protein Selenocysteine (SeCys) and Selenomethionine (SeMet)</title><p>A 0.2-g sample was leached 3 times with a fresh portion 5 mL of water (by sonication during 1 h and centrifugation at 4000× <italic>g</italic>) in a 5804R centrifuge (Eppendorf, Hamburg, Germany); the solution after each washing was discarded. Two milliliters of 0.1 M TRIS buffer (pH 7.5) were added to the residue followed by addition of 30 µL of dithiothreitol (DTT) solution (0.2 M solution in 0.1 M TRIS buffer, pH 7.5) and 50 µL of iodoacetamide (IAM, 0.5 M solution in TRIS buffer). Then, a fresh 150 μL aliquot of the DTT solution was added and the mixture was shaken for 1 h in order to destroy excess of IAM. Subsequently, 10 mL of TRIS buffer an aliquot of 1 mL of a protease solution (30 mg protease XIV in 2 mL of 100 mM TRIS buffer, pH 7.5) were added and the sample. After 2-h incubation, a second aliquot of 1 mL protease solution was added and the sample was incubated overnight at 37 °C. After centrifugation, the supernatant was removed, filtrated on 2-kDa filter, and analyzed by HPLC-ICP MS. An Agilent 1200 HPLC system (Agilent, Tokyo, Japan) used as a delivery system for RP was coupled with an Agilent 7700 ICP-MS instrument (Agilent, Tokyo, Japan) fitted with Pt cones and a 1 mm i.d. injector torch using chromatographic conditions given in <xref rid=\"ijms-21-05287-t005\" ref-type=\"table\">Table 5</xref>. The quantitation of SeCys and SeMet was carried out using the method of standard (SeCys-CAM and SeMet, respectively) additions at three levels. The samples were analyzed in duplicate. The analytical blanks were measured in parallel.</p></sec><sec id=\"sec3dot11-ijms-21-05287\"><title>3.11. Identification of Se-Compounds in Water Fraction by ESI MS</title><p>Water fraction obtained in point 3.10 was analyzed by ESI MS after filtration with 2 kDa cutoff filter. HPLC separation was carried out using gradient elution (<xref rid=\"ijms-21-05287-t005\" ref-type=\"table\">Table 5</xref>) on C18 reverse phase column connected to a Dionex system (Ultimate 3000RS) and detection was performed on an Orbitrap Fusion Lumos Tribrid Mass Spectrometer (Thermo Scientific, France, <uri xlink:href=\"www.thermofisher.com\">www.thermofisher.com</uri>). The instrument was operated in the positive ionization mode at a resolution of 240,000. The electrospray voltage was set to 3.5 kV, the ion transfer tube temperature to 350 °C, and the vaporizer temperature to 450 °C. The sheath gas, auxiliary gas, and the sweep gas were set to 60, 15 and 2 units, respectively.</p></sec><sec id=\"sec3dot12-ijms-21-05287\"><title>3.12. Sample Preparation for SeNP</title><p>Enzymatic digestion. The digestion/extraction procedure included four steps: <list list-type=\"order\"><list-item><p>Two hundred milligrams of a Se-rich yeast sample were suspended in 5 mL of water, bath sonicated for 1 h in A Branson B2510 ultrasonic bath (Branson, Danbury, CT, USA), and centrifuged at 4500× <italic>g</italic> for 10 min in a 5804R centrifuge (Eppendorf, Hamburg, Germany).</p></list-item><list-item><p>The pellet was resuspended with a solution of 5 mL of 4% (<italic>m</italic>/<italic>v</italic>) Driselase (Sigma Aldrich, Saint-Quentin Fallavier, France) in 30 mM Tris (Sigma Aldrich, Saint-Quentin Fallavier, France) at pH 7.5, incubated at 25 °C for 17 h, and centrifuged at 4500× <italic>g</italic> for 10 min.</p></list-item><list-item><p>The pellet was resuspended with a solution of 5 mL of 4 mg/L protease (Sigma Aldrich, Saint-Quentin Fallavier, France) in 30 mM Tris at pH 7.5, incubated at 37° C for 17 h, and centrifuged at 4500× <italic>g</italic> for 10 min.</p></list-item><list-item><p>The pellet was resuspended with a solution of 5 mL of 4% (<italic>m</italic>/<italic>v</italic>) sodium dodecyl sulphate (SDS, Sigma Aldrich, Saint-Quentin Fallavier, France), bath sonicated for 1 h, and centrifuged at 4500× <italic>g</italic> for 10 min.</p></list-item></list></p><p>The supernatant was recovered and kept at 4 °C until analysis. An Agilent 7900 Inductively Coupled Plasma Mass Spectrometer (ICPMS) was used for detection and characterization of SeNP. The sample introduction system consisted of a concentric nebulizer and a quartz cyclonic spray chamber. Isotope <sup>80</sup>Se was monitored with a dwell time of 100 µs and a total acquisition time of 60 s. The settling time was set to 0 during analyses. H<sub>2</sub> was used as reaction cell gas at a flow rate of 5 mL/min to remove background interferences according to the method developed by Jiménez-Lamana et al. [<xref rid=\"B65-ijms-21-05287\" ref-type=\"bibr\">65</xref>]. Single Nanoparticle Application Module for ICP MS MassHunter software (Agilent Technologies, Santa Clara, CA, USA) was used for data processing.</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec4-ijms-21-05287\"><title>4. Conclusions</title><p>Se was found to negatively affect the growth of the yeast strain <italic>C. utilis</italic> ATCC 9950. The activity of antioxidant enzymes and trehalose and glycogen content increased in the presence of Se, which indicated an increased activity of the antioxidant system in the yeast. The obtained results suggest that the tested yeast was not tolerant to the used concentration of 30 mg Se<sup>4+</sup>/L. This was associated with stress conditions and detoxification processes conducted by cells. The consequence of this, among others, was the formation of SeNPs, which conferred a slightly red color to yeast biomass. High-molecular-weight Se compounds containing glutathione in their structures were observed. The formation of these compounds indicates the conditions of oxidative stress. The identification of other metabolic biochemical pathways of Se should be considered to discover the origin of the observed organic Se compounds. The obtained test results can serve as a link that connects the effect of Se metabolic transformations and progressive detoxification processes in yeast cells. They are likely to provide information on the biochemical reaction mechanisms and the possibilities of their regulation. It may be possible to obtain strains with increased tolerance to Se and stress. Finally, it should be noted that it is very important to properly control dietary supplements enriched with Se and to determine optimal breeding conditions that would not adversely affect the performance of the final product. Appropriate monitoring of the effect of this element and examination of yeast physiology and the occurrence of stress on the cells will help to control the entire production process.</p></sec></body><back><notes><title>Author Contributions</title><p>M.K. and K.B. collected and reviewed the literature and writing of manuscript; drafted and critically; M.K., K.B., J.J.-L., and J.A.-D. performed the experiments; A.M.K. and K.P. data curation; M.K., K.B. and J.S. compilation of results; S.B. review article; and M.K. and J.S. reviewed the manuscript and supervised the research. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was funded by Institute of Food Sciences in Warsaw University of Life Sciences—SGGW, Warsaw, Poland (accounting records: 505-10-092800-N00287-99) and the APC (Article Processing Charge) was funded by Institute of Analytical Sciences, IPREM, UMR 5254, CNRS-UPPA, Hélioparc, Pau, France.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijms-21-05287\"><label>1.</label><element-citation publication-type=\"book\"><person-group person-group-type=\"editor\"><name><surname>Hatfield</surname><given-names>D.L.</given-names></name><name><surname>Schweizer</surname><given-names>U.</given-names></name><name><surname>Tsuji</surname><given-names>P.A.</given-names></name><name><surname>Gladyshev</surname><given-names>V.N.</given-names></name></person-group><source>Selenium: Its Molecular Biology and Role in Human Health</source><edition>4th ed.</edition><publisher-name>Springer</publisher-name><publisher-loc>New York, NY, USA</publisher-loc><year>2016</year><isbn>978-3-319-41283-2</isbn></element-citation></ref><ref id=\"B2-ijms-21-05287\"><label>2.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Kieliszek</surname><given-names>M.</given-names></name><name><surname>Blazejak</surname><given-names>S.</given-names></name></person-group><article-title>Selenium: Significance, and outlook for supplementation</article-title><source>Nutrition</source><year>2013</year><volume>29</volume><fpage>713</fpage><lpage>718</lpage><pub-id pub-id-type=\"doi\">10.1016/j.nut.2012.11.012</pub-id><pub-id pub-id-type=\"pmid\">23422539</pub-id></element-citation></ref><ref id=\"B3-ijms-21-05287\"><label>3.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Arthur</surname><given-names>J.R.</given-names></name><name><surname>McKenzie</surname><given-names>R.C.</given-names></name><name><surname>Beckett</surname><given-names>G.J.</given-names></name></person-group><article-title>Selenium in the immune system</article-title><source>J. 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Longev.</source><year>2015</year><volume>2015</volume><fpage>659750</fpage><pub-id pub-id-type=\"doi\">10.1155/2015/659750</pub-id><pub-id pub-id-type=\"pmid\">26185592</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijms-21-05287-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Effect of selenium on the growth of <italic>Candida utilis</italic> yeast cells.</p></caption><graphic xlink:href=\"ijms-21-05287-g001\"/></fig><fig id=\"ijms-21-05287-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Effect of selenium on the activity of antioxidant enzymes in yeast biomass <italic>C. utilis</italic> (glutathione peroxidase, GPx; glutathione reductase, GR; glutathione S-transferase, GST; thioredoxin reductase, TRxR). Means with the same letter (a,b) did not differ significantly (acc. Tukey’s HSD test).</p></caption><graphic xlink:href=\"ijms-21-05287-g002\"/></fig><fig id=\"ijms-21-05287-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Effect of selenium on trehalose and glycogen content in <italic>C. utilis</italic> yeast cells. Means with the same letter (a,b) did not differ significantly (acc. Tukey’s HSD test).</p></caption><graphic xlink:href=\"ijms-21-05287-g003\"/></fig><fig id=\"ijms-21-05287-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Percentage distribution of selenium among different selenium compounds groups.</p></caption><graphic xlink:href=\"ijms-21-05287-g004\"/></fig><fig id=\"ijms-21-05287-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>C8 RP HPLC-ICP MS chromatogram of the proteolytic digest of selenized yeast: (<bold>a</bold>) <italic>C</italic><italic>. utilis</italic>; and (<bold>b</bold>) CRM SELM-1. Identification of the species based on spiking with standards of: (1) SeCys-CAM; (2) Se-CAM; (3) SeMet-CAM; and (4) SeMet.</p></caption><graphic xlink:href=\"ijms-21-05287-g005\"/></fig><fig id=\"ijms-21-05287-f006\" orientation=\"portrait\" position=\"float\"><label>Figure 6</label><caption><p>(<bold>a</bold>–<bold>e</bold>) ESI MS spectra showing isotopic pattern of Se-glutathione species detected in <italic>C. utilis</italic> water extract.</p></caption><graphic xlink:href=\"ijms-21-05287-g006\"/></fig><fig id=\"ijms-21-05287-f007\" orientation=\"portrait\" position=\"float\"><label>Figure 7</label><caption><p>Size distribution of SeNPs.</p></caption><graphic xlink:href=\"ijms-21-05287-g007\"/></fig><fig id=\"ijms-21-05287-f008\" orientation=\"portrait\" position=\"float\"><label>Figure 8</label><caption><p>TEM image of SeNPs in yeast cell wall.</p></caption><graphic xlink:href=\"ijms-21-05287-g008\"/></fig><table-wrap id=\"ijms-21-05287-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05287-t001_Table 1</object-id><label>Table 1</label><caption><p>Parameters characterizing the growth <italic>Candida utilis</italic> ATCC 9950 during culturing in the control YPD medium and experimental media enriched in selenium.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Selenium Content in the Medium </th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Δt<sub>lag</sub> (h)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Δt<sub>log</sub> (h)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">OD<sub>min lag</sub></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">OD<sub>max log</sub></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">µ<sub>max</sub> (h<sup>−1</sup>)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">G (h)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ΔOD</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 mg/L</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.54</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.063</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.09</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.934</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">30 mg/L</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.49</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.067</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.29</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.578</td></tr></tbody></table></table-wrap><table-wrap id=\"ijms-21-05287-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05287-t002_Table 2</object-id><label>Table 2</label><caption><p>Total selenium determination and speciation in <italic>Candida</italic> yeast.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Sample </th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Fraction</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Selenium (µg/g)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">RDS (%)</th></tr></thead><tbody><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">control</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">total</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.0 ± 0.12 <sup>c</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.4</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">water soluble</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.0 ± 0.13 <sup>d</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.4</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Se-enriched</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">total</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1368 ± 69.36 <sup>a</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">water soluble</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">113 ± 2.67 <sup>b</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.4</td></tr></tbody></table><table-wrap-foot><fn><p><sup>a–d</sup> Means with the same letter did not differ significantly (acc. Tukey’s HSD test).</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijms-21-05287-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05287-t003_Table 3</object-id><label>Table 3</label><caption><p>Results of selenium speciation analysis of <italic>Candida</italic> yeast.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Sample </th><th colspan=\"6\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Selenium Compounds</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Selenomethionine (SeMet, µg/g)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">RSD (%)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Selenocysteine (SeCys, µg/g)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">RSD (%)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Selenium Inorganic (Se IV, µg/g)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">RSD (%)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SELM-1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1162 ± 45.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">102 ± 7.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">132 ± 8.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.5</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>Candida utilis</italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">138 ± 4.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">279 ± 18.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">804 ± 45.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.6</td></tr></tbody></table></table-wrap><table-wrap id=\"ijms-21-05287-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05287-t004_Table 4</object-id><label>Table 4</label><caption><p>Selenium species identified in the water soluble fraction of <italic>Candida</italic> yeast.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Nr</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Formula</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Exp. Mass</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Error (µg/g)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Name Compounds</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Name</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">a</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C<sub>13</sub>H<sub>23</sub>O<sub>8</sub>N<sub>4</sub>SSe<sup>+</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">475.0387</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">selenoglutathione-cysteine</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">188609-44-1 or 117135-55-4 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">b</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C<sub>15</sub>H<sub>26</sub>O<sub>9</sub>N<sub>5</sub>SSe<sup>+</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">532.0599</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">glutathione-selenocysteinylglycine</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1357479-87-8</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">c</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C<sub>16</sub>H<sub>27</sub>O<sub>11</sub>N<sub>4</sub>SSe<sup>+</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">563.0544</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">glutathione-2,3-DHP-selenocysteine</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1006377-09-8</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">d</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C<sub>20</sub>H<sub>33</sub>O<sub>12</sub>N<sub>6</sub>SSe<sup>+</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">661.1022</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">selenoglutathione-glutathione</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">161973-63-3</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">e</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">C<sub>20</sub>H<sub>33</sub>O<sub>12</sub>N<sub>6</sub>S<sub>2</sub>Se<sup>+</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">693.0746</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−1.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">selenodiglutathione</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1052197-78-0</td></tr></tbody></table><table-wrap-foot><fn><p>* Two CAS numbers are available depending of the R, S configuration.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijms-21-05287-t005\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05287-t005_Table 5</object-id><label>Table 5</label><caption><p>Chromatographic Conditions Used.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Column</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Eluent</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Gradient Elution</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Temperature (°C)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Flow Rate (mL/min)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Sample Volume (µL)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Detection</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C8 Alltima<break/>4.6 × 250 mm</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">A: 0.1% HFBA * in water<break/>B: 0.1% HFBA in MeOH **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0–15 min 3% B<break/>15–18 min 3-40% B<break/>18–21 min 40% B<break/>21–23 min 40-3 % B<break/>23–30 min 3% B</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ICP MS</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">C18 Zorbax Eclipse XBD<break/>4.6 × 150 mm</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">A: 0.1% FA *** in water<break/>B: 0.1% FA in MeOH</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0–2.5 min 3% B<break/>2.5–15 min 3-50% B<break/>15–20 min 50% B<break/>20–20.5 min 40-3 % B<break/>20.5–30 min 3% B</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">40</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ESI MS</td></tr></tbody></table><table-wrap-foot><fn><p>* heptafluorobutyric acid; ** methanol; *** formic acid.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32731412</article-id><article-id pub-id-type=\"pmc\">PMC7432029</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155354</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05354</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Mapping the Transglycosylation Relevant Sites of Cold-Adapted β-<sc>d</sc>-Galactosidase from <italic>Arthrobacter</italic> sp. 32cB</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-8339-3230</contrib-id><name><surname>Rutkiewicz</surname><given-names>Maria</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05354\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05354\">2</xref><xref rid=\"c1-ijms-21-05354\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Wanarska</surname><given-names>Marta</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05354\">3</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-0808-1349</contrib-id><name><surname>Bujacz</surname><given-names>Anna</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05354\">1</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05354\"><label>1</label>Institute of Molecular and Industrial Biotechnology, Faculty of Biotechnology and Food Sciences, Lodz University of Technology, Stefanowskiego 4/10, 90-924 Lodz, Poland; <email>anna.bujacz@p.lodz.pl</email></aff><aff id=\"af2-ijms-21-05354\"><label>2</label>Macromolecular Structure and Interaction, Max Delbrück Center for Molecular Medicine, Robert-Rössle-Straße 10, 13125 Berlin, Germany</aff><aff id=\"af3-ijms-21-05354\"><label>3</label>Department of Molecular Biotechnology and Microbiology, Faculty of Chemistry, Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdansk, Poland; <email>marta.wanarska@pg.edu.pl</email></aff><author-notes><corresp id=\"c1-ijms-21-05354\"><label>*</label>Correspondence: <email>maria.rutkiewicz@mdc-berlin.de</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>28</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5354</elocation-id><history><date date-type=\"received\"><day>30</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>24</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>β-Galactosidase from <italic>Arthrobacter</italic> sp. 32cB (<italic>Arth</italic>βDG) is a cold-adapted enzyme able to catalyze hydrolysis of β-<sc>d</sc>-galactosides and transglycosylation reaction, where galactosyl moiety is being transferred onto an acceptor larger than a water molecule. Mutants of <italic>Arth</italic>βDG: D207A and E517Q were designed to determine the significance of specific residues and to enable formation of complexes with lactulose and sucrose and to shed light onto the structural basis of the transglycosylation reaction. The catalytic assays proved loss of function mutation E517 into glutamine and a significant drop of activity for mutation of D207 into alanine. Solving crystal structures of two new mutants, and new complex structures of previously presented mutant E441Q enables description of introduced changes within active site of enzyme and determining the importance of mutated residues for active site size and character. Furthermore, usage of mutants with diminished and abolished enzymatic activity enabled solving six complex structures with galactose, lactulose or sucrose bounds. As a result, not only the galactose binding sites were mapped on the enzyme’s surface but also the mode of lactulose, product of transglycosylation reaction, and binding within the enzyme’s active site were determined and the glucopyranose binding site in the distal of active site was discovered. The latter two especially show structural details of transglycosylation, providing valuable information that may be used for engineering of <italic>Arth</italic>βDG or other analogous galactosidases belonging to GH2 family.</p></abstract><kwd-group><kwd>β-<sc>d</sc>-Galactosidase</kwd><kwd>cold-adapted</kwd><kwd>transglycosylation</kwd><kwd>lactulose</kwd><kwd>sucrose</kwd><kwd>complex structures</kwd><kwd>crystal structures</kwd><kwd>mutants</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05354\"><title>1. Introduction</title><p>β-<sc>d</sc>-Galactosidases (βDGs), enzymes catalyzing hydrolysis of β-<sc>d</sc>-galactosides, belong to five different glycosyl hydrolase (GH) families: GH1, GH2, GH35, GH42, and GH59, as the classification was made based on protein fold not its primary activity. Their common feature is the presence of a TIM barrel fold catalytic domain; however, they differ in the number of surrounding domains possessing β architecture [<xref rid=\"B1-ijms-21-05354\" ref-type=\"bibr\">1</xref>].</p><p>β-<sc>d</sc>-Galactosidases belonging to the GH2 family are large proteins, usually ~100 kDa, of which the active forms are: tetramers [<xref rid=\"B2-ijms-21-05354\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijms-21-05354\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijms-21-05354\" ref-type=\"bibr\">4</xref>], hexamers [<xref rid=\"B5-ijms-21-05354\" ref-type=\"bibr\">5</xref>], and as recently discovered dimers [<xref rid=\"B6-ijms-21-05354\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijms-21-05354\" ref-type=\"bibr\">7</xref>]. Their catalytic site can be divided into a galactose binding subsite and a platform for weak binding of different moieties [<xref rid=\"B8-ijms-21-05354\" ref-type=\"bibr\">8</xref>]. Their primary mode of action is to catalyze the hydrolysis of lactose to <sc>d</sc>-galactose and <sc>d</sc>-glucose. Some of GH2 β-<sc>d</sc>-galactosidases are able to catalyze transglycosylation reactions when the galactosyl moiety is transferred onto an acceptor larger than a water molecule. The best studied example is <italic>lacZ</italic> β-<sc>d</sc>-galactosidase from <italic>Escherichia coli</italic>, which produces allolactose, disaccharide composed of <sc>d</sc>-galactose, and <sc>d</sc>-glucose moieties linked through a β-(1→6)-glycosidic bond, if excess of galactose occurs [<xref rid=\"B9-ijms-21-05354\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijms-21-05354\" ref-type=\"bibr\">10</xref>].</p><p>Prebiotics are non-digestible food ingredients that support development and functioning of human organisms through selective assistance of growth or activity of one or a number of bacteria species in the lower intestine [<xref rid=\"B11-ijms-21-05354\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijms-21-05354\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijms-21-05354\" ref-type=\"bibr\">13</xref>]. Ones that are used to enrich a daily diet are chosen to stimulate the growth of bacteria from <italic>Bifidobacteria</italic> or <italic>Lactobacilli</italic> families [<xref rid=\"B14-ijms-21-05354\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijms-21-05354\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijms-21-05354\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijms-21-05354\" ref-type=\"bibr\">17</xref>]. The importance of prebiotics, especially galactooligosaccharides, as an additive to infant formula has been widely tested. They support the colonization of intestines by beneficial bacteria. In consequence, they strengthen immunity by prevention of bacterial adhesion at an early stage of infection [<xref rid=\"B18-ijms-21-05354\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijms-21-05354\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijms-21-05354\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijms-21-05354\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05354\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05354\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05354\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijms-21-05354\" ref-type=\"bibr\">25</xref>]. Morerover, some oligosaccharides are a rich source of sialic acid, indispensable for proper development of the brain [<xref rid=\"B26-ijms-21-05354\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijms-21-05354\" ref-type=\"bibr\">27</xref>]. Prebiotics are also important in adults’ nutrition, as they may support absorption of minerals [<xref rid=\"B28-ijms-21-05354\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijms-21-05354\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijms-21-05354\" ref-type=\"bibr\">30</xref>], support recovery after influenza, reduce stress-related digestive problems [<xref rid=\"B31-ijms-21-05354\" ref-type=\"bibr\">31</xref>], support lipid metabolism, and also counteract the development of tumors. They augment prevention in liver encephalopathy, glycemia/insulinemia, and also have a positive effect on immunomodulation [<xref rid=\"B12-ijms-21-05354\" ref-type=\"bibr\">12</xref>,<xref rid=\"B32-ijms-21-05354\" ref-type=\"bibr\">32</xref>].</p><p>Even though prebiotics such as galactooligosaccharides (GOS) and heterooligosaccharides (HOS) have been successfully synthesized, their production in the course of enzymatic catalysis proved to be more beneficial due to the higher specificity of product and milder reaction conditions. For this purpose, glycosyltransferases (EC 2.4) or glycosidic hydrolases (EC 3.2.1) are used—enzymes that have the ability to catalyze the transfer of a galactosyl moiety to a sugar acceptor.</p><p>Nowadays, both for lactose removal and GOS/HOS synthesis, the β-<sc>d</sc>-galactosidase from <italic>Kluyveromyces lactis</italic> is predominantly used [<xref rid=\"B33-ijms-21-05354\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijms-21-05354\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijms-21-05354\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijms-21-05354\" ref-type=\"bibr\">36</xref>]. Introduction of new enzyme into food production must be preceded by a number of studies that will not only ensure its high efficiency but will also ensure the consumers’ safety. Enzyme immobilization may be the way to ensure that it will not induce allergic reactions in humans.</p><p>Nonetheless, an implementation of cold-adapted enzymes would reduce the overall process temperature bringing numerous benefits [<xref rid=\"B37-ijms-21-05354\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijms-21-05354\" ref-type=\"bibr\">38</xref>]. Lowering the temperature of the process eliminates need for heating. Therefore, not only are costs being cut, but the process becomes more environmentally friendly. Furthermore, final product quality may be improved, as conducting enzymatic reactions at low temperature in food processing prevents the loss of valuable substances and formation of undesired products of heat conversions. What is more, cold-adapted enzymes are perfect candidates to perform catalysis in organic solvent environment which is especially interesting for the pharma industry.</p><p>This broad range of benefits that can be achieved by implementation of cold-adapted enzymes provides rationale for the efforts to engineer enhanced activity cold-adapted enzymes tailored for an exact industrial applications [<xref rid=\"B39-ijms-21-05354\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijms-21-05354\" ref-type=\"bibr\">40</xref>]. The natural way of research is to provide as a first step structural information that can be further used for knowledge base enzyme engineering. However, due to the difficulty of cold-adapted enzymes’ crystallization the amount of crystal structures available in Protein Data Bank is surprisingly low. Only structures of 11 cold-adapted glycosyl hydrolases are deposited there: α-amylase from <italic>Alteromonas haloplanctis</italic> [<xref rid=\"B41-ijms-21-05354\" ref-type=\"bibr\">41</xref>], endo-1,4-beta-glucanase from <italic>Eisenia fetida</italic> [<xref rid=\"B42-ijms-21-05354\" ref-type=\"bibr\">42</xref>], psychrophilic endo-beta-1,4-xylanase [<xref rid=\"B43-ijms-21-05354\" ref-type=\"bibr\">43</xref>], β-1,4-<sc>d</sc>-mannanase from <italic>Cryptopygus antarcticus</italic> [<xref rid=\"B44-ijms-21-05354\" ref-type=\"bibr\">44</xref>], chitinase from <italic>Moritella marina</italic> [<xref rid=\"B45-ijms-21-05354\" ref-type=\"bibr\">45</xref>], endo-beta-1,4-xylanase from <italic>Aegilops speltoides</italic> subsp. speltoides [<xref rid=\"B43-ijms-21-05354\" ref-type=\"bibr\">43</xref>], β-glucosidase from <italic>Exiguobacterium antarcticum</italic> B7 [<xref rid=\"B46-ijms-21-05354\" ref-type=\"bibr\">46</xref>], xylanase from <italic>Pseudoalteromonas haloplanktis</italic> [<xref rid=\"B47-ijms-21-05354\" ref-type=\"bibr\">47</xref>], β-galactosidase from <italic>Rahnella</italic> sp. R3 [<xref rid=\"B48-ijms-21-05354\" ref-type=\"bibr\">48</xref>], β-<sc>d</sc>-galactosidase from <italic>Paracoccus</italic> sp. 32d [<xref rid=\"B6-ijms-21-05354\" ref-type=\"bibr\">6</xref>], and further discussed here β-<sc>d</sc>-galactosidase from <italic>Arthrobacter</italic> sp. 32 cB [<xref rid=\"B7-ijms-21-05354\" ref-type=\"bibr\">7</xref>]. Hence, detailed information on the structure of β-<sc>d</sc>-galactoside processing cold-adapted enzyme that could be used for prebiotics production at the industrial scale is still in demand. The modification of transglycosylase activity specificity and efficiency may be achieved by controlling reaction equilibrium or by enzyme engineering. Studies concentrated on introducing mutations into subsites of GHs showed that the modulation of hydrolysis and transglycosylation activities can be achieved by means of knowledge-based enzyme engineering. However, the role of individual amino acids in the active site must be discovered as basis for successful design of an enzyme with specific, desired activities [<xref rid=\"B49-ijms-21-05354\" ref-type=\"bibr\">49</xref>].</p><p>Psychrophilic β-<sc>d</sc>-galactosidase from <italic>Arthrobacter</italic> sp. 32cB is an interesting candidate for industrial use, as it can hydrolyze lactose at rate comparable to mesophilic βDG from <italic>Kluyveromyces lactis.</italic> Furthermore, it exhibits additional transglycosylation activity, it catalyzes synthesis of GOS and HOS, for example: lactulose and alkyl glycosides. [<xref rid=\"B50-ijms-21-05354\" ref-type=\"bibr\">50</xref>] Architecture of its five domains [<xref rid=\"B7-ijms-21-05354\" ref-type=\"bibr\">7</xref>], structural details of hydrolysis of lactose catalyzed by this enzyme [<xref rid=\"B51-ijms-21-05354\" ref-type=\"bibr\">51</xref>], and its structural features responsible for the enzyme’s cold adaptation [<xref rid=\"B52-ijms-21-05354\" ref-type=\"bibr\">52</xref>] were investigated and discussed previously. Upon the solution of native structure, E441 and E517 were assigned as catalytic residues based on superposition of <italic>Arth</italic>βDG structure with <italic>lacZ E. coli</italic> β-<sc>d</sc>-galactosidase [<xref rid=\"B7-ijms-21-05354\" ref-type=\"bibr\">7</xref>]. The role of residue E441 was previously proven: a loss-of-function mutant E441Q was designed, a biochemical activity assay was performed, and the protein was crystallized. The introduction of point mutation E441Q did not disrupt the structure of protein, yet no catalytic activity was exhibited anymore. The complex structures <italic>Arth</italic>βDG_LACs and <italic>Arth</italic>βDG_LACd, with natural substrate-lactose bound in shallow and deep mode revealed residues directly involved in the binding of galactosyl group in two modes [<xref rid=\"B51-ijms-21-05354\" ref-type=\"bibr\">51</xref>]. Especially interesting was the D207 residue, as it contributes in the deep binding of substrate by stabilizing O4 hydroxyl group but also creates a bottom of the active site limiting its size. The question of the effect of point mutation of the other catalytic residue, E517, has not been answered before. Similarly, structural elements implicated in the enzyme’s secondary activity, transglycosylation, remain elusive.</p><p>This is why, two new mutants, <italic>Arth</italic>βDG_D207A and <italic>Arth</italic>βDG_E517Q, were designed to elucidate the impact of these two residues on protein’s structure and activity. Furthermore, here we attempted to shed a light on the structural side of transglycosylation catalyzed by cold-adapted GH2 <italic>Arth</italic>βDG through usage of the loss and depletion of the function mutants for crystallization and formation of complexes with selected reaction substrates or products, especially lactulose.</p></sec><sec sec-type=\"results\" id=\"sec2-ijms-21-05354\"><title>2. Results and Discussion</title><sec id=\"sec2dot1-ijms-21-05354\"><title>2.1. Activity of ArthβDG Mutants</title><p>The hydrolytic activity of <italic>Arth</italic>βDG_D207A and <italic>Arth</italic>βDG_E517Q was measured using ONPG as a substrate. The <italic>Arth</italic>βDG_E517Q exhibited no catalytic activity, whereas activity of mutant <italic>Arth</italic>βDG_D207A was severely depleted (<xref rid=\"ijms-21-05354-t001\" ref-type=\"table\">Table 1</xref>).</p><p>The results of the activity assays show that the hydrolytic activity of the enzyme was affected not only when catalytic residues were mutated but also when the mutation of the amino acids that play a key role in building the active site was introduced. As expected of the mutation of the catalytic residue, E517 into glutamine caused loss of function of the enzyme. The mutation of the residue involved in the stabilization of galactosyl moiety within active site, D207, diminished the enzyme’s activity.</p><p>Such a drastic drop of activity related to the mutation of one of the residues contributing to binding and positioning of the substrate in the active site of enzyme suggests that the net of interactions within the <italic>Arth</italic>βDG’s active center was in a very delicate balance. This cold-adapted enzyme was already characterized by a high turnover rate, and losing contacts within its active site cripples its activity instead of further enhancement.</p></sec><sec id=\"sec2dot2-ijms-21-05354\"><title>2.2. Thermofluor Shift Assay</title><p>Results of the TSA (thermofluor shift assay) of <italic>Arth</italic>βDG_D207A did not exhibit significant differences compared with ones obtained for wild-type protein <italic>Arth</italic>βDG [<xref rid=\"B52-ijms-21-05354\" ref-type=\"bibr\">52</xref>]. The highest melting temperature (44 °C) was obtained in conditions containing 50 mM sodium phosphate pH 6.0. Similar stability was obtained for protein samples in buffers, such as PIPES, HEPES, MES BIS-TRIS propane, ADA, and MOPS, within a pH range 6.0–6.7, independently, on addition of 250 mM NaCl. Using the same set of buffers in the pH ranges 5.0–6.0 and 7.0–8.0 the melting temperature was on average 3 °C lower. Also, the stabilization effect of previously established for <italic>Arth</italic>βDG crystallization solution (37% Tascimate<sup>TM</sup> pH 8.0) augments stability of <italic>Arth</italic>βDG_D207A by 7 °C compared to 50 mM sodium phosphate pH 6.0 (<xref ref-type=\"app\" rid=\"app1-ijms-21-05354\">Figure S1</xref>).</p><p>Mutation of catalytic residue E517 into glutamine resulted in the elevation of the melting temperature of the mutant in respect to <italic>Arth</italic>βDG by 3.5–5 °C, depending on the investigated buffer solution. In the case of 50 mM sodium phosphate pH 6.0, it attained 49 °C, which means that it was higher by 4.5 °C compared to wild-type protein. The trend of increasing the melting temperature of the mutant <italic>Arth</italic>βDG_D207A by approximately 4 °C was maintained for all tested buffers in the pH range 6.0–7.0. In the pH ranges 5.0–6.0 and 7.0–8.0, the melting temperature was elevated on average by 3 °C compared to <italic>Arth</italic>βDG. Nonetheless, the impact of crystallization solution on protein stability compare to 50 mM sodium phosphate pH 6.0 was higher than in the case of <italic>Arth</italic>βDG_N440D, by 5 °C (<xref ref-type=\"app\" rid=\"app1-ijms-21-05354\">Figure S2</xref>).</p><p>The results of TSA showed that the introduced mutation did not lead to a dramatic drop of mutant’s melting temperature that would be expected if mutations would have a destabilizing effect on the protein’s structure. It also showed that they exhibited similar features to wild-type protein; thus, crystallization was attempted in conditions previously established for wild-type protein without initial screening. Such an approach allowed us to obtain mutants’ crystal almost readily without need for much of a protein sample.</p></sec><sec id=\"sec2dot3-ijms-21-05354\"><title>2.3. ArthβDG Mutants</title><p>The structures of <italic>Arth</italic>βDG mutants: <italic>Arth</italic>βDG_D207A and <italic>Arth</italic>βDG_E517Q, were solved proving that the point mutations within the active site of <italic>Arth</italic>βDG have no destabilizing effect on protein’s structure. In the case of <italic>Arth</italic>βDG_E517Q, only a slight change in character of the active site was observed (<xref ref-type=\"fig\" rid=\"ijms-21-05354-f001\">Figure 1</xref>C)—similar to previously studied loss of function mutant <italic>Arth</italic>βDG_E441Q [<xref rid=\"B51-ijms-21-05354\" ref-type=\"bibr\">51</xref>].</p><p>In the case of mutant <italic>Arth</italic>βDG_D207A, where a larger side chain was substituted with alanine residue, the change in active site size and shape was introduced (<xref ref-type=\"fig\" rid=\"ijms-21-05354-f001\">Figure 1</xref>B).</p></sec><sec id=\"sec2dot4-ijms-21-05354\"><title>2.4. Influence of Mutations within Active Site on Binding of Galactosyl Residue</title><p>The functional dimer of <italic>Arth</italic>βDG has two independent active sites. Each of them is located on the top of TIM-barrel Domain 3 and is constituted of residues from both subunits of the dimer. It can be described as a deep acidic funnel with an antechamber.</p><p>The galactosyl moiety is stabilized in the deep binding by a net of hydrogen interactions between O6 and H520 (2.8 Å), D587 (3.0 Å), D207 (2.6 Å) and sodium ion (2.4 Å); O4 and D207 (2.6 Å); O3 and E517 (2.6 Å); O2 and M481 (3.3 Å) and Q441 (2.6 Å) (<xref ref-type=\"fig\" rid=\"ijms-21-05354-f002\">Figure 2</xref>).</p><p>This interaction pattern between the galactosyl moiety bound in the deep mode in the enzyme’s active site remains uninterrupted. Single-point mutations of residues E441 and E517 introduced in the enzyme’s active site were not sufficient to change spatial arrangement of galactosyl moiety bound in the deep binding mode but were enough to abolish the enzyme’s activity. Even though the mutant <italic>Arth</italic>βDG_D207A retained partial activity of the wild-type enzyme, no crystal structure of its complex with galactose bound within the binding site was obtained. The complex structure <italic>Arth</italic>βDG_D207A/gal showed a galactose bound only in G1 and G2 sites (galactose binding sites described in detail in part 2.5). Lack of even a residual electron density that could be attributed to galactose molecule presence in the active sites was consistent for more than 15 datasets obtained under different soaking conditions in triplicates. This may indicate that even though D207 is not a catalytic residue, its presence is essential for the enzyme function. D207 assures a correct position of the substrate by retaining the net of interactions and ensuring proper shape of the active site (<xref ref-type=\"fig\" rid=\"ijms-21-05354-f003\">Figure 3</xref>).</p><p>Combining the structural data with results of activity assays suggests that the mutation of D207 effects the enzyme’s activity by disturbing the hydroxyl group O4 stabilization of galactosyl moiety. It is caused by the cumulative effect of change in the shape of active site (<xref ref-type=\"fig\" rid=\"ijms-21-05354-f001\">Figure 1</xref>) and removing possible stabilizing contacts between carboxyl group of D207 and hydroxyl O4 of galactosyl moiety.</p></sec><sec id=\"sec2dot5-ijms-21-05354\"><title>2.5. Galactose Binds on the Enzyme’s Surface</title><p>Determining the crystal structure of <italic>Arth</italic>βDG_E441Q/gal complex at atomic resolution, 1.5 Å, resulted in discovering six galactose binding sites at the surface of the protein (<xref ref-type=\"fig\" rid=\"ijms-21-05354-f004\">Figure 4</xref>A). Five of these (G2–G6) have a potential to bind larger galactosides, e.g., lactose. The question, why such a large enzyme was developed and retained by extremophilic bacteria for catalysis of relatively simple reactions, remains open. However such a strategy, to elevate the accessibility of a substrate by its weak binding on the enzyme’s surface, may be one of explanation together with the maximization of the energy gain from the surface residue–solvent interactions [<xref rid=\"B52-ijms-21-05354\" ref-type=\"bibr\">52</xref>].</p><p>Previously obtained biochemical data showed that <sc>d</sc>-galactose, one of the products of enzyme’s hydrolytic activity, has an inhibitory effect on protein’s activity. Its presence at 50 mM concentration results in the reduction of the enzyme’s activity by half [<xref rid=\"B50-ijms-21-05354\" ref-type=\"bibr\">50</xref>]. The analysis of atomic resolution <italic>Arth</italic>βDG_E441Q/gal complex revealed that galactose binding site G1 is a possible allosteric site of enzyme. What is worth noting, is the addition of galactose was used for soaking of multiple <italic>Arth</italic>βDG complex structures to inhibit the activity of enzyme and, thus, enabled complex formation. Structural analysis of these complex structures showed that the galactose molecule was present at the G1 site for all of them, namely, previously published <italic>Arth</italic>βDG_E441Q/LACd [<xref rid=\"B51-ijms-21-05354\" ref-type=\"bibr\">51</xref>] and, discussed here, <italic>Arth</italic>βDG_E517Q/lact and <italic>Arth</italic>βDG_ E441Q/lact. It should be stated that such a complex could not be successfully obtained without galactose addition.</p><p>The G1 site is located in the cleft formed at the junction of tree domains: Domain 2, 3, and 4 (<xref ref-type=\"fig\" rid=\"ijms-21-05354-f004\">Figure 4</xref>A). It is highly selective toward galactose molecules as residues N360, I361, and K357, constituting its bottom, shape it in such a way that binding of galactosyl moiety is highly preferred. The correct conformation of hydroxyl groups O2, O3, O4, and O6 is necessary for monosaccharide molecule to enter and bind within G1 site. Its limited size prevents basically any molecule larger than galactose from entering (<xref ref-type=\"fig\" rid=\"ijms-21-05354-f004\">Figure 4</xref>B).</p></sec><sec id=\"sec2dot6-ijms-21-05354\"><title>2.6. ArthβDG Mutants in Complexes with Lactulose</title><p>Crystal structures of loss of function <italic>Arth</italic>βDG_E441Q and <italic>Arth</italic>βDG_E517Q mutants in complexes with lactulose were determined both at 2.0 Å resolution. Lactulose molecule, a heterooligosaccharide composed of <sc>d</sc>-galactose and <sc>d</sc>-fructose linked through a β-(1→4)-glycosidic bond and is one of the products of transglycosylation reaction catalyzed by <italic>Arth</italic>βDG. It was bound in a deep mode in both structures (<xref ref-type=\"fig\" rid=\"ijms-21-05354-f005\">Figure 5</xref>A). The galactosyl moiety is stabilized by a net of interaction characteristic for binding of this group by <italic>Arth</italic>βDG, and described in detail in <xref ref-type=\"sec\" rid=\"sec2dot4-ijms-21-05354\">Section 2.4</xref>.</p><p>Fructose moiety of lactulose was stabilized relatively weakly in the position. In the case of a complex <italic>Arth</italic>βDG_E441Q/lact (<xref ref-type=\"fig\" rid=\"ijms-21-05354-f005\">Figure 5</xref>C), there was only one hydrogen bond between hydroxyl group O3′ of lactulose and N110 (3.2 Å). Whereas, in the case of <italic>Arth</italic>βDG_E517Q/lact (<xref ref-type=\"fig\" rid=\"ijms-21-05354-f005\">Figure 5</xref>D) there were two hydrogen bonds stabilizing fructose moiety: between O3′ of lactulose and N110 (3.3 Å) and O6’ and E441 (3.0 Å).</p><p>What is interesting, regardless of the relatively weak stabilization of the fructose moiety of lactulose, it creates more interactions within the active site of <italic>Arth</italic>βDG than glucosyl moiety of lactose, natural substrate [<xref rid=\"B51-ijms-21-05354\" ref-type=\"bibr\">51</xref>]. The hydrogen bonds, forming between sugar moiety and the amino acids at the entrance to the active site, are crucial for sugar ring stabilization indispensable for it to become a galactosyl group acceptor. Such a delicate net of interactions suggests that residues E441, N110, and possibly N440 play important roles regarding transglycosylation activity of <italic>Arth</italic>βDG.</p><p>Insight into these two complex structures enables description of positioning and binding of fructose moiety, which may act as galactosyl group acceptor, in the catalytic center of <italic>Arth</italic>βDG mutants: <italic>Arth</italic>βDG_D207A and <italic>Arth</italic>βDG_E517Q. As both these mutations are distal to space occupied by fructose moiety, one may assume that similar interactions are in place in the case of wild-type enzyme. However, the substantially higher activity of wild-type enzyme makes it impossible to capture lactulose molecule bound in its active site.</p></sec><sec id=\"sec2dot7-ijms-21-05354\"><title>2.7. Mapping the Binding Potential of the Distal Region of Active Site</title><p>Two structures of the mutants’ complexes with sucrose, a common sugar composed of <sc>d</sc>-galoctose and <sc>d</sc>-fructose linked through a β-(1→2)-glycosidic bond, were determined with resolution 1.7 Å for <italic>Arth</italic>βDG_E441Q/suc and 2.2 Å for <italic>Arth</italic>βDG_D207A/sucr. In both cases, sucrose was bound in the same place outside of the active site of enzyme, revealing a glucopyranose binding site in its distal region (<xref ref-type=\"fig\" rid=\"ijms-21-05354-f006\">Figure 6</xref>A).</p><p>In <italic>Arth</italic>βDG_D207A/sucr complex (<xref ref-type=\"fig\" rid=\"ijms-21-05354-f006\">Figure 6</xref>D), the sucrose molecule was stabilized predominantly by bonds between the glucosyl moiety interacting with residues E398, E467, E468, and G443. Fructosyl moiety was stabilized by only two hydrogen bonds created between hydroxyl O6’ and side chain oxygen of E398 (2.5 Å) and the same hydroxyl group O6’ with nitrogen of W402 indole ring (3.1 Å).</p><p>Sucrose molecule in complex structure of <italic>Arth</italic>βDG_E441Q/sucr moved a little toward the active site of enzyme (<xref ref-type=\"fig\" rid=\"ijms-21-05354-f006\">Figure 6</xref>C), and its hydroxyl 03 interacted via hydrogen bond with the mutated catalytic residue Q441 (3.1 Å). Nonetheless, it is stabilized by a similar net of interactions involving residues E398, E467, G443, and additionally E398. Hydroxyl group O3′ of fructosyl moiety was stabilized by a single hydrogen bond with indole ring nitrogen of W470.</p><p>Superposition of <italic>Arth</italic>βDG_E441Q/sucr with complex structure of the same mutant with lactose bound in deep mode, <italic>Arth</italic>βDG_E441Q/LACd (PDB ID: 6SEA [<xref rid=\"B51-ijms-21-05354\" ref-type=\"bibr\">51</xref>]) shows that the distance between anomeric carbon atom of galactosyl moiety bound in deep mode and hydroxyl group O4 of sucrose was 6.4 Å (<xref ref-type=\"fig\" rid=\"ijms-21-05354-f007\">Figure 7</xref>). This insight into sucrose binding in the distal region of active site shows how the glycosyl moiety is positioned in the vicinity of the active site, so it can become galactosyl group acceptor.</p></sec></sec><sec id=\"sec3-ijms-21-05354\"><title>3. Summary</title><p>Results of activity assays and analysis of a number of complex structures of new mutants of <italic>Arth</italic>βDG, <italic>Arth</italic>βDG_D207A and <italic>Arth</italic>βDG_E517Q, resulted in a better understanding of the importance of residues D207, E441, and E517 in the native enzyme. The inactive mutant E517Q was designed specially to enable binding of transglycosylation product—lactulose, in the active site of enzyme and thus elucidating the mode of its binding. Especially surprising was the gravity of mutation of D207 to alanine, which is part of Domain 1. It is situated on the top of a part of Domain 1, which inserts itself in a space between the top of Domain 3 TIM-barrel and highly flexible C-terminal region of Domain 3, and constitutes bottom of the active site, which means that its mutation to alanine has a significant impact on active site volume. As such, not only a stabilizing interaction between D207 and O4 hydroxyl of galactosyl moiety is lost but also the transfer of substrate from shallow to deep binding site of the enzyme is disrupted.</p><p>Furthermore, the structures of <italic>Arth</italic>βDG_D207A and <italic>Arth</italic>βDG_E517Q in complex with lactulose, which can be produced by <italic>Arth</italic>βDG in course of transglycosylation reaction, were, to best of our knowledge, the first crystal structures of galactosidase with this heterooligosaccharide, thus, providing a sought for insight in how a galactosyl group acceptor (larger than water) may approach and be accommodated (<xref ref-type=\"fig\" rid=\"ijms-21-05354-f008\">Figure 8</xref>). Such a detailed structural knowledge on the binding of lactulose pinpoints the residues that play role in the production of lactulose by <italic>Arth</italic>βDG, especially: N110. It also shows the region, where some additional hydrogen bonds, stabilizing fructosyl moiety, can be introduced to shift the reaction toward formation of lactulose instead of simple hydrolysis of glucose.</p><p>More valuable structural information that concerns transglycosylation activity of the enzyme was obtained with a solution of the complex structures with sucrose, <italic>Arth</italic>βDG_D207A/sucr and <italic>Arth</italic>βDG_E441Q/sucr. They revealed a glucopyranose binding site in the distal region of active site, showing how a galactosyl group acceptor that comprises of glucosyl moiety can approach the active site. Once again, these structures elucidate the region, mutation in which can lead to larger galactosyl group acceptor being available instead of water residue.</p><p>The results of this work can serve as a basis for knowledge-based enzyme engineering of this cold-adapted enzyme. Especially, to shift its activity toward transglycosylation reaction and improving product homogeneity. It was shown that the retention of the shape and volume of the galactosyl binding site is necessary for the enzyme’s activity. Hence, mutations, if any introduced in this region should be designed in such a way not to change the size of deep binding site, as its enlargement not only does not enhance the enzyme’s transglycosylation activity but is has a negative effect on it. It is the authors’ believe that mutations should rather be introduced in the proximal in distal regions of the active site. Especially, promoting binding of galactosyl group acceptor on the weak binding platform may prove successful in the future attempts.</p><p>Furthermore, since the architecture of the active site of galactosidases from GH2 family is highly conserved, the knowledge about the mode of binding of potential galactosyl group acceptors (larger than water) within <italic>Arth</italic>βDG and its mutant structures, can be to a certain degree transferred onto other enzymes belonging to this group, among others commonly used in the food industry β-<sc>d</sc>-galactosidase from <italic>Kluyveromyces lactis</italic>.</p></sec><sec id=\"sec4-ijms-21-05354\"><title>4. Materials and Methods</title><sec id=\"sec4dot1-ijms-21-05354\"><title>4.1. Site-Directed Mutagenesis of Gene Encoding ArthβDG</title><p>The gene encoding the <italic>Arth</italic>βDG enzyme within the pBAD-Bgal32cB plasmid [<xref rid=\"B50-ijms-21-05354\" ref-type=\"bibr\">50</xref>] has been mutated using the Q5 Site-Directed Mutagenesis Kit (New England Biolabs, Ipswich, MA, USA) following the manufacturer’s protocol. For this purpose, two pairs of mutagenic primers were designed and synthesized (Genomed, Warsaw, Poland). Primers For207AspAla 5′ GTGGAGGACCAGG<bold>C</bold>CATGTGGTGGCTT 3′ and Rev207AspAla 5′ GTAGCTGGCGGCCGAGAACTGGGCGA 3′ were used to introduce a point mutation A/C at 620 nucleotide position in the gene resulting in the D207A substitution in the <italic>Arth</italic>βDG amino acid sequence. Primers F32cBmut517 5′ TGGGTAACGGCCCCGGTGGAATGAGCGAAT 3′ and R32cBmut517 5′ TGGCATGCACATATT<bold>G</bold>GCAGAGGACAAAGGGCA 3′ were used to introduce a point mutation G/C at 1549 nucleotide position in the gene resulting in the E517Q substitution in the <italic>Arth</italic>βDG amino acid sequence.</p><p>Thermocycling conditions for PCRs were as follows: initial denaturation of pBAD-Bgal32cB plasmid at 98 °C for 30 s; then 25 cycles of PCR products amplification consisting of 10 s of DNA denaturation at 98 °C, 15 s of mutagenic primers annealing at 66 °C for For207AspAla and Rev207AspAla, or 69 °C for F32cBmut517 and R32cBmut517, and 3 min 20 s of PCR product extension at 72 °C; and the final extension at 72 °C for 7 min.</p><p>The obtained PCR products were treated with KLD Enzyme Mix (Kinase, Ligase, and <italic>Dpn</italic>I) at 22 °C for 5 min, and then used to transform NEB 5-alpha chemically competent <italic>E.coli</italic> cells (the <italic>lac</italic>Z deletion mutant, D (<italic>lac</italic>Z) M15). Transformants were spread onto selection Luria-Bertani agar plates (10 g/L of peptone K, 5 g/L of yeast extract, 10 g/L of NaCl, and 15 g/L of agar) supplemented with ampicillin (100 μg/mL), X-gal (40 μg/mL) and <sc>l</sc>-arabinose (200 μg/mL), and incubated overnight at 37 °C and then for a few hours at room temperature.</p><p>Six colonies of <italic>E. coli</italic> + pBAD-Bgal32cB_D207A (light blue colonies with weak β-<sc>d</sc>-galactosidase activity) and <italic>E. coli</italic> + pBAD-Bgal32cB_E517Q (white colonies without enzymatic activity) were selected for further studies. Recombinant pBAD-Bgal32cB_D207A and pBAD-Bgal32cB_E517Q plasmids were isolated using the Plasmid Mini Kit (A&A Biotechnology, Gdynia, Poland) and sequenced (Genomed, Warsaw, Poland).</p></sec><sec id=\"sec4dot2-ijms-21-05354\"><title>4.2. Production of ArthβDG, ArthβDG_D207A and ArthβDG_E517Q Proteins</title><p>Expression of recombinant <italic>Arth</italic>βDG, <italic>Arth</italic>βDG_D207A, and <italic>Arth</italic>βDG_E517Q mutants were performed in the <italic>E. coli</italic> LMG 194 cells (Invitrogen, Carlsbad, CA, USA) transformed with pBAD-Bgal32cB, pBAD-Bgal32cB_D207A and pBAD-Bgal32cB_E517Q plasmids, respectively, as previously described for wild-type protein [<xref rid=\"B50-ijms-21-05354\" ref-type=\"bibr\">50</xref>] and <italic>Arth</italic>βDG_E441Q mutant [<xref rid=\"B51-ijms-21-05354\" ref-type=\"bibr\">51</xref>].</p><p>Recombinant proteins were purified by a three-step chromatography comprising of ion-exchange and size-exclusion stages according to protocol developed for wild-type protein, <italic>Arth</italic>βDG [<xref rid=\"B7-ijms-21-05354\" ref-type=\"bibr\">7</xref>]. The chromatography buffer was change to storage buffer (0.05 M HEPES pH 7.0) and the samples were concentrated to 20 mg/mL using 50 kDa cutoff membrane Vivaspin filters (Sartorius, Göttingen, Germany) prior to crystallization.</p></sec><sec id=\"sec4dot3-ijms-21-05354\"><title>4.3. Determination of ArthβDG, ArthβDG_D207A and ArthβDG_E517Q Activity</title><p>The activity of <italic>Arth</italic>βDG as well as <italic>Arth</italic>βDG_D207A and <italic>Arth</italic>βDG_E517Q mutants was determined using ONPG as a substrate. 0.8 mL of ONPG solution (1 mg/mL in 20 mM potassium phosphate buffer pH 8.0) was preincubated at 28 °C for 5 min, then 0.2 mL of protein sample was added, and the reaction mixture was incubated for 1 or 10 min at 28 °C for the substrate hydrolysis. The reaction was terminated by the addition of 0.3 mL of 1 M Na<sub>2</sub>CO<sub>3</sub> and the absorbance was measured at 410 nm. One unit of β-<sc>d</sc>-galactosidase activity was defined as the quantity of enzyme releasing of 1 μmol 2-nitrophenol per min under reaction conditions.</p></sec><sec id=\"sec4dot4-ijms-21-05354\"><title>4.4. Thermofluor Shift Assay</title><p>Samples of <italic>Arth</italic>βDG_D207A and <italic>Arth</italic>βDG_E517Q were prepared at concentration of 0.3 mg/mL each and premixed with SYPRO. The CFX96 Touch<sup>TM</sup> (BioRad, Hercules, CA, USA) thermal cycles was used for TSA experiment. The applied temperature increment was 1 °C per 30 s, and the tested temperature ranged from 4 °C to 95 °C. The assay was performed for 24 buffers covering pH range from 4.0 to 10.0 and 24 corresponding buffers with addition of 250 mM NaCl [<xref rid=\"B54-ijms-21-05354\" ref-type=\"bibr\">54</xref>].</p></sec><sec id=\"sec4dot5-ijms-21-05354\"><title>4.5. Crystallization of ArthβDG Mutants and Obtaining Their Complexes’</title><p>Crystallization of <italic>Arth</italic>βDG_D207A, <italic>Arth</italic>βDG_E441Q and <italic>Arth</italic>βDG_E517Q was performed by diffusion hanging-drop method using Tacsimate<sup>TM</sup> Hampton Research (Aliso Viejo, CA, USA) at concentration range from 27% to 45% and pH range from 4.0 to 9.0 [<xref rid=\"B7-ijms-21-05354\" ref-type=\"bibr\">7</xref>]. This crystallization matrix was previously used for growing crystals of wild-type enzyme. Furthermore, the cross-seeding using seeds made of <italic>Arth</italic>βDG crystals was applied to speed up the process of obtaining diffraction quality crystals. The seed stock of crushed microcrystals diluted 10,000 times in 35% Tacsimate<sup>TM</sup> pH 7.0 was premixed in 1:40 (<italic>v/v</italic>) ratio with 20 mg/mL purified protein according to previously established protocol [<xref rid=\"B51-ijms-21-05354\" ref-type=\"bibr\">51</xref>]. The monocrystals suitable for diffraction experiment were obtained in a pH range of 6.4–8.8 and Tacsimate<sup>TM</sup> concentration varying from 24% to 40%.</p><p>The complex <italic>Arth</italic>βDG_E517Q/gal was obtained after 24 h soaking of protein crystals with addition of globotriose and <italic>Arth</italic>βDG_D207A/gal by soaking with addition of mixture of galactose and fructose. A 24h-soaking was successful in the case of complexes <italic>Arth</italic>βDG_D207A/lact and <italic>Arth</italic>βDG_E517Q/lact, where the crystals were soaked with addition of a mixture of lactulose and galactose. The complexes of <italic>Arth</italic>βDG_E441Q and <italic>Arth</italic>βDG_D207A with saccharose were obtained by same time soaking with addition of saccharose alone.</p><p>All the crystals were cryo-protected with 60% Tacsimate<sup>TM</sup> of pH corresponding to crystallization conditions prior to being flash-frozen before diffraction experiment.</p></sec><sec id=\"sec4dot6-ijms-21-05354\"><title>4.6. Data Collection, Structure Solving, and Refinement</title><p>High-resolution diffraction data were collected using state-of-the-art BESSY II beamlines 14.1 and 14.2, Berlin, Germany [<xref rid=\"B55-ijms-21-05354\" ref-type=\"bibr\">55</xref>]. The diffraction images were collected with fine slicing 0.1°. The diffraction data were processed using XDSapp [<xref rid=\"B56-ijms-21-05354\" ref-type=\"bibr\">56</xref>], to avoid alternative indexing, possible in space group P3<sub>1</sub>21, the log file for processing <italic>Arth</italic>βDG data was used as an input. Pair refinement was performed to determine optimal cutoff resolution for each data set. Crystal structures were solved by isomorphous replacement using rigid body refinement procedure, where the structure of <italic>Arth</italic>βDG (PDB ID: 6ETZ) was used as a model. Structure solving and further refinement was performed using the PHENIX.REFINE program [<xref rid=\"B57-ijms-21-05354\" ref-type=\"bibr\">57</xref>,<xref rid=\"B58-ijms-21-05354\" ref-type=\"bibr\">58</xref>] data reduction and refinement statistics are collected in <xref rid=\"ijms-21-05354-t002\" ref-type=\"table\">Table 2</xref> for <italic>Arth</italic>βDG_E517Q, <xref rid=\"ijms-21-05354-t003\" ref-type=\"table\">Table 3</xref> for <italic>Arth</italic>βDG_E441Q, and <xref rid=\"ijms-21-05354-t004\" ref-type=\"table\">Table 4</xref> for <italic>Arth</italic>βDG_D207A.</p></sec><sec id=\"sec4dot7-ijms-21-05354\"><title>4.7. Databases</title><p>The here reported crystal structures and their associated structure factor amplitudes were deposited with the Protein Data Bank under the accession codes: 6ZJP, 6ZJQ, 6ZJR, 6ZJS, 6ZJT, 6ZJU, 6ZJV, 6ZJW AND 6ZJX respectively.</p></sec></sec></body><back><ack><title>Acknowledgments</title><p>We thank Helmholtz-Zentrum Berlin für Materialien und Energie for the allocation of synchrotron radiation beamtime at BL 14.1 and BL 14.2, where the X-ray data were collected. We gratefully acknowledge the support of the Joint Berlin MX-Laboratory, especially Manfred S. Weiss, which made this work possible.</p></ack><app-group><app id=\"app1-ijms-21-05354\"><title>Supplementary Materials</title><p>The following are available online at <uri xlink:href=\"https://www.mdpi.com/1422-0067/21/15/5354/s1\">https://www.mdpi.com/1422-0067/21/15/5354/s1</uri>, Figure S1: Selected TSA data and analysis for <italic>Arth</italic>βDG_D207A mutant, Figure S2: Selected TSA data and analysis for <italic>Arth</italic>βDG_E517Q mutant.</p><supplementary-material content-type=\"local-data\" id=\"ijms-21-05354-s001\"><media xlink:href=\"ijms-21-05354-s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>M.R. purified enzymes, performed crystallization, synchrotron diffraction data collection, data processing, structure solving and refinement. M.R. and A.B. carried out structural analysis and mostly wrote the paper. M.W. designed and performed site-direct mutagenesis experiments resulting in genes encoding mutants, performed protein expression in <italic>E. coli</italic>, determined the purification protocol, and determined the activity of enzymes. A.B. coordinated the project. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was supported by National Science Centre of Poland: 2016/21/B/ST5/00555 Opus 11 grant (A.B.) and 2018/28/T/ST5/00233 Etiuda 6 scholarship (M.R.). </p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.</p></notes><glossary><title>Abbreviations</title><array orientation=\"portrait\"><tbody><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">ArthβDG</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">β-<sc>d</sc>-galactosidase from <italic>Arthrobacter</italic> sp. 32cB</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"><italic>Arth</italic>βDG_D207A</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">β-<sc>d</sc>-galactosidase from <italic>Arthrobacter</italic> sp. 32cB mutant D207A</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"><italic>Arth</italic>βDG_E441Q</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">β-<sc>d</sc>-galactosidase from <italic>Arthrobacter</italic> sp. 32cB mutant E441Q</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"><italic>Arth</italic>βDG_E517Q</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">β-<sc>d</sc>-galactosidase from <italic>Arthrobacter</italic> sp. 32cB mutant E517Q</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Gal </td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">galactose</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">GOS </td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">galactooligosaccharides</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">GH2</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">glycosyl hydrolase 2 family</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">HOS </td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">heterooligosaccharides</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Lacd </td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">lactose bound in deep mode</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Lact</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">lactulose</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">ONPG</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"><italic>o</italic>-nitrophenyl-β-<sc>d</sc>-galactopyranoside</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sucr</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">sucrose</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">TSA</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">thermofluor shift assay</td></tr></tbody></array></glossary><ref-list><title>References</title><ref id=\"B1-ijms-21-05354\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Talens-Perales</surname><given-names>D.</given-names></name><name><surname>Górska</surname><given-names>A.</given-names></name><name><surname>Huson</surname><given-names>D.H.</given-names></name><name><surname>Polaina</surname><given-names>J.</given-names></name><name><surname>Marín-Navarro</surname><given-names>J.</given-names></name></person-group><article-title>Analysis of Domain Architecture and Phylogenetics of Family 2 Glycoside Hydrolases (GH2)</article-title><source>PLoS ONE</source><year>2016</year><volume>11</volume><elocation-id>e0168035</elocation-id><pub-id pub-id-type=\"doi\">10.1371/journal.pone.0168035</pub-id><pub-id pub-id-type=\"pmid\">27930742</pub-id></element-citation></ref><ref id=\"B2-ijms-21-05354\"><label>2.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Białkowska</surname><given-names>A.M.</given-names></name><name><surname>Cieśliński</surname><given-names>H.</given-names></name><name><surname>Nowakowska</surname><given-names>K.M.</given-names></name><name><surname>Kur</surname><given-names>J.</given-names></name><name><surname>Turkiewicz</surname><given-names>M.</given-names></name></person-group><article-title>A new β-galactosidase with a low temperature optimum isolated from the Antarctic Arthrobacter sp. 20B: Gene cloning, purification and characterization</article-title><source>Arch. 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The charge distribution was calculated using the APBS [<xref rid=\"B53-ijms-21-05354\" ref-type=\"bibr\">53</xref>] plugin to PyMOL.</p></caption><graphic xlink:href=\"ijms-21-05354-g001\"/></fig><fig id=\"ijms-21-05354-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Interactions stabilizing galactose molecule in the active site of <italic>Arth</italic>βDG_E441Q/gal complex structure (<bold>A</bold>). Naming convention of galactose’s oxygen atoms presented for easier interpretation (<bold>B</bold>).</p></caption><graphic xlink:href=\"ijms-21-05354-g002\"/></fig><fig id=\"ijms-21-05354-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>The representation of the shape of active site of <italic>Arth</italic>βDG_E441Q/gal complex structure (<bold>A</bold>) and superposition of galactose molecule on the structure of <italic>Arth</italic>βDG_D207A (<bold>B</bold>) showing that this mutation introduce changes in the shape of active site, making it less selective for galactosyl moiety.</p></caption><graphic xlink:href=\"ijms-21-05354-g003\"/></fig><fig id=\"ijms-21-05354-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Galactose (violet) binding sites G1–G6 on the surface of <italic>Arth</italic>βDG (<bold>A</bold>); structure of <italic>Arth</italic>βDG_E441Q in complex with galactose (<bold>B</bold>). The galactose molecule (violet) interacting with main chain atoms at G1 site; galactose hydroxyl group O2 interacts with oxygen K357 (2.8 Å), oxygen I361 (2.9 Å); O3 with oxygen I361 (3.4 Å), nitrogen N360 (3.5 Å) and oxygen K357 (2.7 Å); O4 with N360 (2.8 Å).</p></caption><graphic xlink:href=\"ijms-21-05354-g004\"/></fig><fig id=\"ijms-21-05354-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>The position of lactulose binding by the loss of function mutants (<bold>A</bold>). <italic>2F<sub>o</sub>-F<sub>c</sub></italic> electron-density map at the 2 σ level for the lactulose molecule in <italic>Arth</italic>βDG_E517Q/lact complex (<bold>B</bold>). Lactulose bound in the active site of <italic>Arth</italic>βDG_E441Q/lact (<bold>C</bold>) and <italic>Arth</italic>βDG_E517Q/lact (<bold>D</bold>) crystal structures with hydrogen bonds marked with dashed lines.</p></caption><graphic xlink:href=\"ijms-21-05354-g005\"/></fig><fig id=\"ijms-21-05354-f006\" orientation=\"portrait\" position=\"float\"><label>Figure 6</label><caption><p>Location of sucrose molecule in the distal region of the active site in <italic>Arth</italic>βDG_E441Q/sucr complex (<bold>A</bold>). 2F<sub>o</sub>-F<sub>c</sub> electron-density map at the 2 σ level for the saccharose molecule in <italic>Arth</italic>βDG_E441Q/sucr complex (<bold>B</bold>). Sucrose bound in the active site of <italic>Arth</italic>βDG_ E441Q/sucr (<bold>C</bold>) and <italic>Arth</italic>βDG_ D207A/sucr (<bold>D</bold>) crystal structures with hydrogen bonds marked with dashed lines.</p></caption><graphic xlink:href=\"ijms-21-05354-g006\"/></fig><fig id=\"ijms-21-05354-f007\" orientation=\"portrait\" position=\"float\"><label>Figure 7</label><caption><p>Superposition of <italic>Arth</italic>βDG_E441Q/sucr (yellow/orange) with complex structure of the same mutant with lactose bound in deep mode (pale green/green), <italic>Arth</italic>βDG_E441Q/LACd (PDB ID: 6SEA), showing the positioning of sucrose molecule toward galactosyl moiety.</p></caption><graphic xlink:href=\"ijms-21-05354-g007\"/></fig><fig id=\"ijms-21-05354-f008\" orientation=\"portrait\" position=\"float\"><label>Figure 8</label><caption><p>The visualization of proximal region (weak binding platform) and distal region of active site (with glycosyl binding site) in superposed complex structures of <italic>Arth</italic>βDG_E441Q/sucr, <italic>Arth</italic>βDG_E441Q/LACd, and <italic>Arth</italic>βDG_E517Q/lact.</p></caption><graphic xlink:href=\"ijms-21-05354-g008\"/></fig><table-wrap id=\"ijms-21-05354-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05354-t001_Table 1</object-id><label>Table 1</label><caption><p>Activity of <italic>Arth</italic>βDG, <italic>Arth</italic>βDG_D207A, and <italic>Arth</italic>βDG_E517Q in hydrolysis reaction. The activity was measured with ONPG as a substrate at 28 °C and pH 8.0.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Protein</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Specific Activity (U/mg)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Arth</italic>βDG</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">212.01 ± 3.90</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>Arth</italic>βDG_D207A</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.70 ± 0.01</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>Arth</italic>βDG_E517Q</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0</td></tr></tbody></table></table-wrap><table-wrap id=\"ijms-21-05354-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05354-t002_Table 2</object-id><label>Table 2</label><caption><p>Data reduction and refinement statistics of <italic>Arth</italic>βDG_E517Q mutant’s crystal structures.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Crystal Structure</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>Arth</italic>βDG_E517Q<break/>PDB ID: 6ZJP</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>Arth</italic>βDG_E517Q/gal<break/>PDB ID: 6ZJQ</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>Arth</italic>βDG_E517Q/lact<break/>PDB ID: 6ZJR</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Diffraction source</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">BL 14.2 BESSY, Berlin, Germany</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">BL 14.2 BESSY, Berlin, Germany</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">BL 14.1 BESSY, Berlin, Germany</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Wavelength (Å)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.918400</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.918400</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.918400</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Temperature (K)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100 K</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100 K</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100 K</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Detector</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PILATUS 3S 2M</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PILATUS 3S 2M</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PILATUS 3S 2M</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rotation range per image (°)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Total rotation range (°)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">180</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">180</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Exposure time per image (s)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Space group</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">P 31 2 1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">P 31 2 1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">P 31 2 1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>a</italic>, <italic>b</italic>, <italic>c</italic> (Å)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">138.0 138.0 127.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">138.6 138.6 127.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">137.4 137.4 127.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">α, β, γ (°)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90 90 120</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90 90 120</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90 90 120</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mosaicity (°)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.13</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.09</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.09</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Resolution range (Å)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">43.6–1.8 (1.9–1.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">42.8–1.7 (1.8–1.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">46.7–2.0 (2.1–2.0)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Number of unique reflections</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">118,415 (11695)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">155,098 (15193)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">93,953 (9191)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Completeness (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99.08 (98.40)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99.85 (98.78)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99.76 (99.06)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Redundancy</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">I/σ(I)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.39 (1.52)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.5 (0.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.54 (0.78)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>R</italic><sub>meas</sub> (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.5 (81.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.6 (380.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.6 (257.7)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Overall <italic>B</italic> factor:<break/>Wilson plot/refinement (Å<sup>2</sup>)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31.06</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28.08</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38.62</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No. of reflections:<break/>working/test set</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">118,262/2096</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">155,074/2099</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">93,830/2097</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>R</italic>/<italic>R</italic><sub>free</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1691/0.1946</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1729/0.2008</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1881/0.2131</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No. of non-H atoms:<break/>Protein/Ligand/Water</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7613/7/1050</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7624/151/621</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7702/79/528</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">R.m.s. deviations:<break/>Bonds (Å)/Angles (°)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.005/0.74</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.010/1.00</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.006/0.79</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Ramachandran plot:<break/>Most favored/allowed (%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">97.06/2.94</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">97.06/2.94</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">96.96/3.04</td></tr></tbody></table><table-wrap-foot><fn><p>Values in parenthesis are given for highest resolution shell.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijms-21-05354-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05354-t003_Table 3</object-id><label>Table 3</label><caption><p>Data reduction and refinement statistics of <italic>Arth</italic>βDG_E441Q mutant’s crystal structures.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Crystal Structure</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>Arth</italic>βDG_E441Q/gal<break/>PDB ID: 6ZJS</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>Arth</italic>βDG_E441Q/lact<break/>PDB ID: 6ZJT</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>Arth</italic>βDG_E441Q/sucr<break/>PDB ID: 6ZJU</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Diffraction source</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">BL 14.2 BESSY, Berlin, Germany</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">BL 14.2 BESSY, Berlin, Germany</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">BL 14.2 BESSY, Berlin, Germany</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Wavelength (Å)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.918400</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.918400</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.918400</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Temperature (K)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100 K</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100 K</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100 K</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Detector</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PILATUS 3S 2M</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PILATUS 3S 2M</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PILATUS 3S 2M</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rotation range per image (°)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Total rotation range (°)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">180</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">180</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Exposure time per image (s)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Space group</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">P 31 2 1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">P 31 2 1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">P 31 2 1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>a</italic>, <italic>b</italic>, <italic>c</italic> (Å) </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">138.4 138.4 127.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">139.0 139.0 127.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">138.2 138.2 127.6</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">α, β, γ (°) </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90 90 120</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90 90 120</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90 90 120</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mosaicity (°) </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.03</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.18</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Resolution range (Å)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45.3–1.7 (1.8–1.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">43.8–2.0 (2.1–2.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">43.7–1.7 (1.8–1.7)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Number of unique reflections</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">224,735 (22177)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100,340 (9948)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">141,152 (13911)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Completeness (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99.91 (99.29)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99.49 (99.26)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99.87 (99.42)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Redundancy</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10,1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">I/σ(I)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10,1 (0,6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.14 (0.80)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.42 (0.56)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>R</italic><sub>meas</sub> (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13,0 (350,5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.9 (175.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.6 (425.6)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Overall <italic>B</italic> factor:<break/>Wilson plot/refinement (Å<sup>2</sup>) </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25.29</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45.01</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30.65</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Number of reflections:<break/>working/test set</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">224,710/2358</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100,137/2096</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">141,107/2099</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>R</italic>/<italic>R</italic><sub>free</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1550/0.1706</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2100/0.2362</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1786/0.1941</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Number of non-H atoms:<break/>Protein/Ligand/Water</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7662/205/721</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7615/80/337</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7627/99/751</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">R.m.s. deviations:<break/>Bonds (Å)/Angles (°)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.008/0.99</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.009/0.92</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.011/1.17</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Ramachandran plot:<break/>Most favored/allowed (%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">97.87/2.12</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">96.96/2.94</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">97.16/2.84</td></tr></tbody></table><table-wrap-foot><fn><p>Values in parenthesis are given for highest resolution shell.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijms-21-05354-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05354-t004_Table 4</object-id><label>Table 4</label><caption><p>Data reduction and refinement statistics of <italic>Arth</italic>βDG_D207A mutant’s crystal structures.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Crystal Structure</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>Arth</italic>βDG_D207A<break/>PDB ID: 6ZJV</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>Arth</italic>βDG_D207A/gal<break/>PDB ID: 6ZJW</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>Arth</italic>βDG_D207A/sucr<break/>PDB ID: 6ZJX</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Diffraction source</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">BL 14.2 BESSY, Berlin, Germany</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">BL 14.2 BESSY, Berlin, Germany</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">BL 14.2 BESSY, Berlin, Germany</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Wavelength (Å)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.918400</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.918400</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.918400</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Temperature (K)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100 K</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100 K</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100 K</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Detector</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PILATUS 3S 2M</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PILATUS 3S 2M</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PILATUS 3S 2M</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Rotation range per image (°)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Total rotation range (°)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">360</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">180</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Exposure time per image (s)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Space group</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">P 31 2 1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">P 31 2 1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">P 31 2 1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>a</italic>, <italic>b</italic>, <italic>c</italic> (Å) </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">139.5 139.5 127.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">139.3 139.3 127.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">137.6 137.6 126.8</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">α, β, γ (°) </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90 90 120</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90 90 120</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90 90 120</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mosaicity (°) </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.30</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.14</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Resolution range (Å)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47.1–2.2 (2.3–2.2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40.2–2.1 (2.2–2.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">46.6–2.2 (2.3–2.2)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Number of unique reflections</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">68,432 (6677)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">81,059 (7962)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">69,828 (6808)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Completeness (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99.25 (97.39)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99.35 (98.55)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99.72 (98.12)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Redundancy</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">I/σ(I)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.38 (1.52)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.18 (0.54)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.58 (1.28)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>R</italic><sub>meas</sub> (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.4 (184.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19.9 (200.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.8 (168.7)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Overall <italic>B</italic> factor:<break/>Wilson plot/refinement (Å<sup>2</sup>) </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50.40</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">46.09</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">42.43</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Number of reflections:<break/>working/test set</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">68,233/1094</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">80,908/2083</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">69,783/1116</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>R</italic>/<italic>R</italic><sub>free</sub></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2361/0.2623</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2258/0.2608</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1971/0.2238</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Number of non-H atoms:<break/>Protein/Ligand/Water</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7690/7/142</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7620/24/303</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7622/58/530</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">R.m.s. deviations:<break/>Bonds (Å)/Angles (°)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.005/0.84</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.003/0.66</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.004/0.66</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Ramachandran plot:<break/>Most favored/allowed (%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">97.26/2.74</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">96.56/3.44</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">96.96/3.04</td></tr></tbody></table><table-wrap-foot><fn><p>Values in parenthesis are given for highest resolution shell.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32756371</article-id><article-id pub-id-type=\"pmc\">PMC7432030</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155589</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05589</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>BCG Vaccination and Mortality of COVID-19 across 173 Countries: An Ecological Study</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-7395-5831</contrib-id><name><surname>Urashima</surname><given-names>Mitsuyoshi</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05589\">1</xref><xref rid=\"c1-ijerph-17-05589\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-2322-3405</contrib-id><name><surname>Otani</surname><given-names>Katharina</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05589\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05589\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Hasegawa</surname><given-names>Yasutaka</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05589\">1</xref><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05589\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Akutsu</surname><given-names>Taisuke</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05589\">1</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05589\"><label>1</label>Division of Molecular Epidemiology, The Jikei University School of Medicine, Tokyo 105-8461, Japan; <email>katharina@jikei.ac.jp</email> (K.O.); <email>yhearth102523@jikei.ac.jp</email> (Y.H.); <email>t-akutsu@jikei.ac.jp</email> (T.A.)</aff><aff id=\"af2-ijerph-17-05589\"><label>2</label>Advanced Therapies Innovation Department, Siemens Healthcare K.K., Tokyo 141-8644, Japan</aff><aff id=\"af3-ijerph-17-05589\"><label>3</label>Hitachi, Ltd. Research & Development Group, Tokyo 185-8601, Japan</aff><author-notes><corresp id=\"c1-ijerph-17-05589\"><label>*</label>Correspondence: <email>urashima@jikei.ac.jp</email>; Tel.: +81-3-3433-1111 (ext. 2405)</corresp></author-notes><pub-date pub-type=\"epub\"><day>03</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5589</elocation-id><history><date date-type=\"received\"><day>04</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>31</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Ecological studies have suggested fewer COVID-19 morbidities and mortalities in Bacillus Calmette–Guérin (BCG)-vaccinated countries than BCG-non-vaccinated countries. However, these studies obtained data during the early phase of the pandemic and did not adjust for potential confounders, including PCR-test numbers per population (PCR-tests). Currently—more than four months after declaration of the pandemic—the BCG-hypothesis needs reexamining. An ecological study was conducted by obtaining data of 61 factors in 173 countries, including BCG vaccine coverage (%), using morbidity and mortality as outcomes, obtained from open resources. ‘Urban population (%)’ and ‘insufficient physical activity (%)’ in each country was positively associated with morbidity, but not mortality, after adjustment for PCR-tests. On the other hand, recent BCG vaccine coverage (%) was negatively associated with mortality, but not morbidity, even with adjustment for percentage of the population ≥ 60 years of age, morbidity, PCR-tests and other factors. The results of this study generated a hypothesis that a national BCG vaccination program seems to be associated with reduced mortality of COVID-19, although this needs to be further examined and proved by randomized clinical trials.</p></abstract><kwd-group><kwd>urbanization</kwd><kwd>Bacillus Calmette–Guérin</kwd><kwd>BCG</kwd><kwd>vaccination</kwd><kwd>coronavirus disease 2019</kwd><kwd>COVID-19</kwd><kwd>SARS-CoV-2</kwd><kwd>ecological study</kwd><kwd>morbidity</kwd><kwd>mortality</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05589\"><title>1. Introduction</title><p>Currently, more than four months since declaration of the coronavirus disease 2019 (COVID-19) as a pandemic by the World Health Organization (WHO) on March 11th, 2020, more than 14 million people have been infected with the virus and more than half a million have died worldwide. Marked differences in COVID-19 mortalities have been observed in different countries. For example, the mortality per million population is till now several tens of times or even higher in Western countries, e.g., Belgium (845), the United Kingdom (UK, 664), the United States of America (USA, 426) and Germany (109), than in Asian countries, e.g., India (19), Japan (8) and China (3), as of 17 July 2020. This is quite the opposite of what was reported during the 1918–20 influenza pandemic, the so called Spanish flu, in which the population mortality was over 30-fold higher, with excess death rates observed in low-income countries, such as India, than in high-income countries, such as those in the West [<xref rid=\"B1-ijerph-17-05589\" ref-type=\"bibr\">1</xref>]. Higher morbidities and mortalities may be explained by easy access to diagnostic polymerase chain reaction tests (PCR-tests) for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in these Western countries. In contrast, they may be underestimated in middle- and low-income countries due to low capacities of PCR-testing and poor access to health care. Alternatively, there is growing concern that racial and ethnic minority, as well as socioeconomic and biological factors may influence the high morbidity and mortality. A retrospective study of integrated health care systems suggested that Black race compared with White race, increasing age, male sex, having a greater burden of comorbidities, type of public insurance, residence in a low-income area and obesity were associated with increased odds of hospital admission [<xref rid=\"B2-ijerph-17-05589\" ref-type=\"bibr\">2</xref>]. On the other hand risk of in-hospital mortality increased only in aged patients and was not associated with the Black race, sex, comorbidities, obesity and other factors after multivariate adjustment [<xref rid=\"B2-ijerph-17-05589\" ref-type=\"bibr\">2</xref>]; this phenomenon was also confirmed in other races, i.e., Asians and Hispanics, compared with the White race [<xref rid=\"B3-ijerph-17-05589\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijerph-17-05589\" ref-type=\"bibr\">4</xref>]. Moreover, since COVID-19 is an infectious disease that spreads mainly through the droplet route by close contact in dense human societies, metropolitan areas, such as New York City in the USA [<xref rid=\"B5-ijerph-17-05589\" ref-type=\"bibr\">5</xref>] and Lombardy in Italy [<xref rid=\"B6-ijerph-17-05589\" ref-type=\"bibr\">6</xref>], Paris in France [<xref rid=\"B7-ijerph-17-05589\" ref-type=\"bibr\">7</xref>], Sao Paulo in Brazil [<xref rid=\"B8-ijerph-17-05589\" ref-type=\"bibr\">8</xref>], and so on, have tended to be regional epicenters. However, associations between population dynamics, e.g., population size, density, migrants and urbanization and the morbidity/mortality of COVID-19 have not yet been well examined.</p><p>In addition to the factors listed above, several scientists proposed a hypothesis [<xref rid=\"B9-ijerph-17-05589\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijerph-17-05589\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijerph-17-05589\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05589\" ref-type=\"bibr\">12</xref>] that Bacillus Calmette–Guérin (BCG) vaccination has preventive effects not only against tuberculosis, i.e., the target disease of the vaccine, but also other non-specific infectious diseases, i.e., off-target diseases such as COVID-19, through epigenetic mechanisms [<xref rid=\"B13-ijerph-17-05589\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijerph-17-05589\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijerph-17-05589\" ref-type=\"bibr\">15</xref>]. In fact, ecological studies have suggested that both COVID-19 morbidities and mortalities were less in BCG-vaccinated countries than in BCG-non-vaccinated countries [<xref rid=\"B16-ijerph-17-05589\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijerph-17-05589\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijerph-17-05589\" ref-type=\"bibr\">18</xref>]. However, because these studies only obtained data during the early phase of the pandemic, by which time the disease load had escalated in Western countries, but not yet escalated in low- and middle-income countries where BCG is given at birth and did not adjust for any potential confounders, including PCR-test number per million population, the BCG-hypothesis needs to be reexamined now, more than four months after declaration of the pandemic, since the number of PCR-confirmed COVID-19 cases are still growing in many countries. We therefore aimed to explore whether recent BCG vaccine coverage is associated with COVID-19 morbidity and/or mortality rates, using linear regression models to explore associations between the two continuous random variables adjusted for a variety of potential confounders, such as median age and body mass index (BMI) in individual countries through this ecological study.</p></sec><sec id=\"sec2-ijerph-17-05589\"><title>2. Materials and Methods</title><sec id=\"sec2dot1-ijerph-17-05589\"><title>2.1. Ethics Statement</title><p>Institutional review board approval for this work was not sought due to the use of publicly available data obtained from open resources.</p></sec><sec id=\"sec2dot2-ijerph-17-05589\"><title>2.2. Study Design</title><p>This ecological study compared population data of each country.</p></sec><sec id=\"sec2dot3-ijerph-17-05589\"><title>2.3. Data Resources</title><p>All data were obtained from open resources: Information on total number of cases, total number of deaths and number of PCR-tests performed per million population were obtained from ‘Coronavirus Update’ [<xref rid=\"B19-ijerph-17-05589\" ref-type=\"bibr\">19</xref>], data regarding population dynamics were obtained from ‘World Population Clock’ [<xref rid=\"B20-ijerph-17-05589\" ref-type=\"bibr\">20</xref>], socioeconomic covariates were obtained from ‘the United Nations database’ [<xref rid=\"B21-ijerph-17-05589\" ref-type=\"bibr\">21</xref>], BCG vaccine coverage data were obtained from ‘The BCG World Atlas’ [<xref rid=\"B22-ijerph-17-05589\" ref-type=\"bibr\">22</xref>] and ‘The global health observatory’ on the WHO homepage [<xref rid=\"B18-ijerph-17-05589\" ref-type=\"bibr\">18</xref>] and other health related data were from the same WHO site [<xref rid=\"B23-ijerph-17-05589\" ref-type=\"bibr\">23</xref>]. Definitions of each of the covariates are described in the Supplement. Only countries that had data of both total deaths and BCG vaccine coverage were included for analyses in this study.</p></sec><sec id=\"sec2dot4-ijerph-17-05589\"><title>2.4. Outcomes and PCR-Tests</title><p>The outcomes evaluated in this study were COVID-19 morbidity, i.e., total COVID-19 cases per million population and COVID-19 mortality, i.e., total COVID-19-related deaths per million population, in each country, obtained from ‘Worldometer COVID-19 Data’ on July 17th, 2020 [<xref rid=\"B19-ijerph-17-05589\" ref-type=\"bibr\">19</xref>]. Data on the total number of PCR-tests performed per million population were simultaneously obtained from the same ‘Worldometer COVID-19 Data’ website. PCR-test positivity was simply calculated as the total COVID-19 case number divided by the total number of PCR-tests performed in each country.</p></sec><sec id=\"sec2dot5-ijerph-17-05589\"><title>2.5. BCG Vaccine Coverage</title><p>Recent BCG vaccine coverage was defined as the percentage of the vaccinated population among one-year-olds in each country (World Health Data Platform, the World Health Observatory, BCG-Immunization coverage estimates) [<xref rid=\"B23-ijerph-17-05589\" ref-type=\"bibr\">23</xref>]. For countries that have already stopped a national BCG vaccine immunization program [<xref rid=\"B22-ijerph-17-05589\" ref-type=\"bibr\">22</xref>], which includes a significant number of countries, their BCG vaccine coverage rate was counted as zero percent in this study.</p></sec><sec id=\"sec2dot6-ijerph-17-05589\"><title>2.6. Sixty-One Covariates</title><p>The covariates evaluated in this study, the definitions of which are described in the <xref ref-type=\"app\" rid=\"app1-ijerph-17-05589\">Supplement (Table S1)</xref>, included: (1) Population (n); (2) yearly change in population (%); (3) net change in population (n); (4) population density (n/km<sup>2</sup>); (5) land area (km<sup>2</sup>); (6) net number of migrants (n); (7) fertility rate (n); (8) median age (years); (9) urban population percentage (%); (10) world share (%); (11) population between age 0 to 14 years (%); (12) population ≥ 60 years of age (%); (13) population ≥ 70 years of age (%); (14) gross domestic product (GDP) (million US dollars); (15) GDP per capita (US dollars); (16) total unemployment rate (%); (17) male unemployment rate (%); (18) female unemployment rate (%); (19) total labor force participation rate (%); (20) male labor force participation rate (%); (21) female labor force participation rate (%); (22) annual incidence of tuberculosis per 100,000 population (n); (23) international health regulation (IHR) score (%); (24) universal health coverage (UHC) index; (25) hospital beds (n) per 10,000 population; (26) medical doctors (n) per 10,000 population; (27) nursing midwifery (n) per 10,000 population; (28) licensed qualified anesthesiologists who usually cover management of intensive care units actively working (n) per 10,000 population; (29) total expenditure on health as a percentage of GDP (%); (30) population with household expenditures on health greater than 10% of total household expenditure or income (%); (31) prevalence of high blood pressure (systolic ≥ 140 or diastolic ≥ 90 mmHg) (%); (32) prevalence of elevated fasting blood glucose levels (≥7.0 mmol/L or on anti-diabetic medication); (33) prevalence of elevated total cholesterol levels (≥5.0 mmol/L) (%); (34) mean BMI (body weight [kg]/height<sup>2</sup> [m<sup>2</sup>]); (35) prevalence of obesity among adults, BMI ≥ 30 kg/m<sup>2</sup> (%); (36) prevalence of ‘overweight’ people among adults, BMI ≥ 25 kg/m<sup>2</sup> (%); (37) alcohol drinking, total per capita (≥15 years of age) consumption (in liters of pure alcohol over a calendar year); (38) prevalence of smoking any tobacco product among males aged ≥ 15 years (%); (39) prevalence of smoking any tobacco product among females aged ≥ 15 years (%); (40) prevalence of insufficient physical activity among adults aged ≥ 18 years (%); (41) estimated population-based prevalence of depression (%); (42) neonatal mortality rate (n per 1000 live births); (43) infantile mortality rate (n per 1000 live births); (44) under-five mortality rate (probability of dying by the age of 5 years per 1000 live births); (45) mortality rate for 5–14-year-olds (probability of dying per 1000 children aged 5–14 years); (46) adult mortality rate (probability of dying between 15 and 60 years of age per 1000 population); (47) probability of dying between age 30 and exact age 70 years from any of the following causes: cardiovascular disease, cancer, diabetes or chronic respiratory disease; (48) life expectancy at birth (years); (49) life expectancy at age 60 years (years); (50) healthy life expectancy (HALE) at birth (years); (51) HALE at age 60 years (years); (52) death due to chronic obstructive pulmonary disease (%); (53) death due to ischemic heart disease (%); (54) death due to lower respiratory infections (%); (55) death due to stroke (%); (56) death due to tracheal, bronchial and lung cancers (%); (57) total of (52) to (56) as ambient and household air pollution-attributable death rate (n per 100,000 population); and (58) annual mean concentration of particulate matter less than 2.5 microns in diameter (PM<sub>2.5</sub>) [µg/m<sup>3</sup>] in urban areas; and (59) coverage rate with the first dose of a measles-containing-vaccine (MCV1) among one-year-olds (%) as well as (60) recent BCG coverage and (61) PCR-tests number.</p></sec><sec id=\"sec2dot7-ijerph-17-05589\"><title>2.7. Statistics</title><sec><title>Linear Regression Models</title><p>For preprocessed data, outcomes, i.e., morbidity and mortality per million population were transformed to the common logarithm (log10) to adjust for normality of the distribution, which was verified by means of kurtosis tests. When the number of total deaths was zero, these were changed to 0.01 per million population, because zero cannot be transformed to the common logarithm. Variance inflation factor (VIF) was used to detect the presence of multicollinearity. Only one variable among biologically similar variables, e.g., ‘median age (years)’, ‘≥ 60 years of age (%)’ and ‘≥ 70 years of age’, was selected to maximize adjusted R<sup>2</sup> in the multi-linear regression models to avoid the influence of collinearity. If the variance inflation factor (VIF) of certain covariates was more than 10, then the covariates were avoided in multivariate analyses because of a collinearity issue. Multiple linear regression models were used to screen potential risk or preventive factors associated with morbidity by adjusting for PCR-test numbers transformed to the common logarithm (log10) and those associated with mortality were screened by adjusting for PCR-test numbers and morbidity per million population. Considering type I error due to a multiple testing, the significance level of alfa was set as <italic>p</italic> < 0.001. Then, all the screened factors were assessed in a multi-linear regression model to determine significant factors with <italic>p</italic> < 0.05 as the cutoff point. Each model was evaluated by adjusted R<sup>2</sup> as a coefficient of determination. Pearson’s correlation coefficient for variables with normal distributions or Spearman’s rank correlation for variables with non-normal distributions, represented as rho, was used to quantify the strengths of associations between morbidity, mortality and significant factors determined by the final models, as: absolute value of rho ≥ 0.5: very strong; rho ≥ 0.4: strong; 0.4 > rho ≥ 0.2: moderate; and rho < 0.2: weak associations. Data were analyzed using Stata version 14.0 software (StataCorp LP, College Station, TX, USA).</p></sec></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05589\"><title>3. Results</title><sec id=\"sec3dot1-ijerph-17-05589\"><title>3.1. Variability of COVID-19 Morbidity and Mortality across 173 Countries</title><p>A total of 173 countries that had data of both total COVID-19 deaths and BCG vaccine coverage were included for analyses in this study. The 20 countries with highest and lowest COVID-19 morbidities (<xref rid=\"ijerph-17-05589-t001\" ref-type=\"table\">Table 1</xref>) and mortalities (<xref rid=\"ijerph-17-05589-t002\" ref-type=\"table\">Table 2</xref>), as well as their PCR-test rate per million population are shown below. Marked differences in morbidities and mortalities were observed among these countries, ranging from 1 (Papua New Guinea) to 37,566 (Qatar) and from 0 (Vietnam, etc.) to 845 (Belgium), respectively. Six and thirteen countries that do not have a BCG national vaccine program at present (indicated with bold and mark “∫”) were included in the 20 countries with the highest COVID-19 morbidity and mortality, respectively.</p><p>Histograms of morbidities and mortalities were drawn as normal density plots (<xref ref-type=\"fig\" rid=\"ijerph-17-05589-f001\">Figure 1</xref>). Although the histograms of morbidity and mortality were skewed to the right, they followed a normal distribution by transformation with the common logarithm (log10).</p></sec><sec id=\"sec3dot2-ijerph-17-05589\"><title>3.2. Associations between Morbidity, Mortality and PCR-Tests per Million Population</title><p>First, associations represented by rho and VIF between morbidity (<italic>n</italic> = 173), mortality (<italic>n</italic> = 173) and PCR-tests (<italic>n</italic> = 155) are shown (<xref ref-type=\"fig\" rid=\"ijerph-17-05589-f002\">Figure 2</xref>A). These three variables were predicted to have very strong and positive associations with each other. However, VIFs were less than 2 among these three factors. Multicollinearity is considered to be present when the VIF is higher than 5 to 10 [<xref rid=\"B24-ijerph-17-05589\" ref-type=\"bibr\">24</xref>]. Thus, any variable with a VIF < 5.0 was considered for inclusion in multiple linear regression analyses. Considering the number of PCR-tests per million population may exhibit associations with morbidity, adjustment was performed for the PCR-test number in every analysis when screening for the risk factors of COVID-19 morbidity per million population (<xref ref-type=\"fig\" rid=\"ijerph-17-05589-f002\">Figure 2</xref>B). Considering morbidities and the number of PCR-tests per million population may exhibit associations with mortality, adjustment was performed for the morbidity and PCR-test number in every analysis when screening for the risk factors of COVID-19 mortality per million population (<xref ref-type=\"fig\" rid=\"ijerph-17-05589-f002\">Figure 2</xref>C).</p><p>Since fewer PCR-tests may underestimate morbidity and mortality, the association between mortality as the outcome and morbidity as the exposure was adjusted for number of PCR-tests performed (<xref rid=\"ijerph-17-05589-t003\" ref-type=\"table\">Table 3</xref>). In this multiple regression analysis, higher morbidity was associated with higher mortality, whereas more PCR-tests were associated with lower mortality.</p><p>Evaluation of the association between PCR-test positivity and mortality, shown as a scatter plot, indicated a very strong association between them (rho = 0.54) (<xref ref-type=\"fig\" rid=\"ijerph-17-05589-f003\">Figure 3</xref>). Countries with higher PCR-positivity rates tended to have higher mortality rates. PCR-test positivity rates of countries where no deaths due to COVID-19 were observed were less than 3.5%. Minimum positivity and no deaths were observed in Vietnam.</p></sec><sec id=\"sec3dot3-ijerph-17-05589\"><title>3.3. Screening Factors Associated with Morbidity</title><p>Among the 59 covariates, plus BCG vaccine coverage and PCR-testing rate, i.e., a total of 61 factors, ‘urban population’ and ‘insufficient physical activity’ were significantly (<italic>p</italic> < 0.001) associated with morbidity after adjustment for PCR-test rate (<xref rid=\"ijerph-17-05589-t004\" ref-type=\"table\">Table 4</xref>). Next, these two significant factors were used in a multi-linear regression model to eliminate confounding (<xref rid=\"ijerph-17-05589-t005\" ref-type=\"table\">Table 5</xref>). As a result, ‘urban population’ (<italic>p</italic> = 0.02) and ‘insufficient physical activity’ (<italic>p</italic> = 0.01) remained significant factors associated with COVID-19 morbidity, even after adjustment for PCR-tests. The adjusted R<sup>2</sup> was 0.5037.</p><p>The association between ’urban population’ and morbidity, demonstrated below as a scatter plot, showed a very strong association (rho = 0.55) (<xref ref-type=\"fig\" rid=\"ijerph-17-05589-f004\">Figure 4</xref>).</p><p>The association between ‘insufficient physical activity’ and morbidity, demonstrated below as a scatter plot, also showed a very strong association (rho = 0.52) (<xref ref-type=\"fig\" rid=\"ijerph-17-05589-f005\">Figure 5</xref>).</p><p>COVID-19-related morbidity rates per million population on July 17th were transformed to the common logarithm (log10) in the graph. Countries that had never had or that had stopped a national program of BCG vaccination are indicated in red, while countries that currently follow a national BCG vaccine program are indicated in black. Selected country’s name was shown using three-letter country codes.</p></sec><sec id=\"sec3dot4-ijerph-17-05589\"><title>3.4. Screening Factors Associated with Mortality</title><p>Among the 58 covariates evaluated, plus BCG vaccine coverage, adjusted for morbidity and PCR-tests, age-related factors, i.e., median age, ≥60 years of age (%) and ≥70 years of age (%), were significantly (<italic>p</italic> < 0.001) associated with mortality (<xref rid=\"ijerph-17-05589-t004\" ref-type=\"table\">Table 4</xref>). Since these three age-related factors had collinearity for mortality, ‘≥ 60 years of age’ was selected to maximize adjusted R<sup>2</sup> of the multi-linear regression models. Moreover, ‘BCG vaccine coverage’, ‘Elevated total cholesterol levels’ and ‘Life expectancy at 60 years of age’, were also significant (<italic>p</italic> < 0.001) factors associated with mortality. Next, these significant factors were used in a multi-linear regression model to eliminate confounding (<xref rid=\"ijerph-17-05589-t006\" ref-type=\"table\">Table 6</xref>). As a result, ‘≥60 years of age’ (<italic>p</italic> < 0.001) and ‘BCG vaccine coverage’ (<italic>p</italic> = 0.002) remained significant factors associated with COVID-19 mortality, even after adjustment for morbidity and PCR-tests. The adjusted R<sup>2</sup> was 0.8254.</p><p>Evaluation of the association between ’median age’ and mortality, demonstrated as a scatter plot, showed a very strong association between these two variables (rho = 0.54) (<xref ref-type=\"fig\" rid=\"ijerph-17-05589-f006\">Figure 6</xref>). Countries with a larger population ≥ 60 years of age (%) showed a tendency toward a higher mortality rate.</p><p>Finally, evaluation of the association between ‘BCG vaccine coverage’ and mortality, demonstrated as a scatter plot, indicated a moderately negative association (rho = −0.29) (<xref ref-type=\"fig\" rid=\"ijerph-17-05589-f007\">Figure 7</xref>). Countries with higher BCG vaccine coverage showed a tendency toward lower mortality. Additionally, COVID-19 mortality rates did not have significant associations with BCG strain, e.g., Tokyo 172. Moreover, there were no significant associations between mortality and either the year of stopping or introducing a national BCG vaccine program (data not shown).</p></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05589\"><title>4. Discussion</title><p>Among the variety of parameters abstracted from open resources, ‘BCG vaccine coverage’ had a significant association with COVID-19 mortality, even after adjusting for morbidity, PCR-tests, age, universal health coverage, numbers of medical doctors, elevated total cholesterol and healthy life expectancy. On the other hand, BCG vaccination was not associated with COVID-19 morbidity. The main results of this study are consistent with a very recent article demonstrating that every 10% increase in the BCG index was associated with a 10% reduction in COVID-19 mortality [<xref rid=\"B25-ijerph-17-05589\" ref-type=\"bibr\">25</xref>]. Moreover, a retrospective cohort study suggested that BCG-vaccinated individuals were less likely to require hospital admission during the disease course [<xref rid=\"B26-ijerph-17-05589\" ref-type=\"bibr\">26</xref>]. In contrast to BCG, coverage of the measles vaccine, which is also considered to induce heterologous protection against infections through long-term boosting of innate immune responses [<xref rid=\"B9-ijerph-17-05589\" ref-type=\"bibr\">9</xref>], showed no association with the morbidity and mortality of COVID-19, which was also consistent with a very recent article showing a significant low risk of COVID-19 mortality in countries with higher BCG vaccine coverage, but not with measles vaccine coverage [<xref rid=\"B27-ijerph-17-05589\" ref-type=\"bibr\">27</xref>].</p><p>Moreover, SARS-CoV-2 is a single-stranded positive-sense RNA virus and the BCG vaccine has been shown in controlled trials to reduce the severity of infections by other viruses with such a structure [<xref rid=\"B9-ijerph-17-05589\" ref-type=\"bibr\">9</xref>]. For example, the BCG vaccine reduced yellow fever vaccine viremia by 71% in volunteers in the Netherlands [<xref rid=\"B28-ijerph-17-05589\" ref-type=\"bibr\">28</xref>]. However, some countries with a current national BCG vaccination policy have high mortality rates. Plausible reasons for this discrepancy may be: (1) low coverage of BCG vaccination in these countries (% coverage–mortality per million population), e.g., Ireland (18%—354); Portugal (32%—165); and Greece (50%—19): (2) late introduction of BCG vaccine program (year of introduction–mortality per million population), e.g., Iran, (1984–162): (3) oral delivery of the BCG vaccine, e.g., Brazil retained oral delivery of the vaccine until 1977: (4) UK administered the vaccine to older children (12 to 13 years of age) and (5) connection with endemic country by land, e.g., Mexico, Panama, Peru and Chile.</p><p>Higher morbidity, but fewer PCR-tests, were associated with higher mortality. Moreover, higher PCR-test positivity was associated with higher mortality, which was consistent with the report by Hisaka et al. [<xref rid=\"B29-ijerph-17-05589\" ref-type=\"bibr\">29</xref>]. Expanding application of PCR-tests not only to typical symptomatic cases, but also to mild or asymptomatic cases and to those who had close contact with patients, may decrease the PCR-test positivity rate. Thus, enhancing the capacity of PCR-testing may enable identification of cases, so that appropriate measures can be taken to prevent them spreading SARS-CoV-2 to others at home, at their work place or at events of mass gatherings.</p><p>In this study, the covariate of ‘urban population’ and ‘insufficient physical inactivity’ had a strong and positive association with morbidity, but not with mortality. People living in urban areas tend to have close contact with a greater number of people per day than those in rural areas, independent of age. A report on 4103 patients with COVID-19 in New York City found that obesity, which may strongly depend on the balance between physical activity and diet, was one of the clinical features leading to hospital admission [<xref rid=\"B30-ijerph-17-05589\" ref-type=\"bibr\">30</xref>]. On the other hand, older age was associated with higher mortality, which is consistent with previous articles [<xref rid=\"B2-ijerph-17-05589\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijerph-17-05589\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijerph-17-05589\" ref-type=\"bibr\">4</xref>]. From this study, the risk factors for morbidity seem to be different from those associated with mortality, suggesting that factors related to susceptibility may be different from those related to disease severity.</p><p>There are several limitations to this study. First, although we selected 61 covariates in this study, we did not evaluate range and timing of non-pharmaceutical interventions, e.g., school closures, workplace closures, cancellation of public events, restrictions on public gatherings, stay-at-home restrictions, restrictions on internal movement, international travel controls, etc., all of which would also have had significant effects on COVID-19-related morbidity and mortality. Therefore, the present study results are burdened with an extreme error. Second, the study design was ecological. Therefore, the outcome of this work should be considered highly limited, with a potential risk of high bias. Consequently, only the hypothesis that BCG vaccination mitigates COVID-19 mortality can be proposed here; cohort or case–control studies and randomized clinical trials, similar to the BCG–CORONA study [<xref rid=\"B31-ijerph-17-05589\" ref-type=\"bibr\">31</xref>], are required to test this hypothesis. Third, the COVID-19 pandemic is still ongoing, although we have confirmed the results using the latest data. However, the results may be different a few months from now. Fourth, it is clear that an extremely large number of covariates, i.e., 61, were selected for the limited number of 173 countries. This sample size could allow use of a maximum of 10 covariates in multiple regression analysis. Therefore, significant (<italic>p</italic> < 0.001) variables were at first screened after adjustment for PCR-tests and morbidity (<xref rid=\"ijerph-17-05589-t004\" ref-type=\"table\">Table 4</xref>). Then, multivariable linear regression using the screened variables were performed after adjustment for PCR-tests and morbidity (<xref rid=\"ijerph-17-05589-t005\" ref-type=\"table\">Table 5</xref> for morbidity and <xref rid=\"ijerph-17-05589-t006\" ref-type=\"table\">Table 6</xref> for mortality). Fifth, mortality in each country should be compared with excess deaths whether these two do not make a big difference. Sixth, the definition of COVID-19 cases may differ by country, e.g., including PCR confirmed, but asymptomatic cases and pneumonia cases with negative PCR-tests. Seventh, there were still residual confounders even after adjustment for PCR-tests.</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05589\"><title>5. Conclusions</title><p>Our results suggest the hypothesis that greater BCG vaccine coverage may reduce the risk of deaths due to COVID-19, which needs to be further studied by observational studies and confirmed by randomized clinical trials.</p></sec></body><back><ack><title>Acknowledgments</title><p>The authors thank the personnel who created the excellent open resources. We also thank Haruka Wada for data management at the Division of Molecular Epidemiology, Jikei University School of Medicine.</p></ack><app-group><app id=\"app1-ijerph-17-05589\"><title>Supplementary Materials</title><p>The following are available online at <uri xlink:href=\"https://www.mdpi.com/1660-4601/17/15/5589/s1\">https://www.mdpi.com/1660-4601/17/15/5589/s1</uri>, Table S1: Definitions or descriptions of all covariates.</p><supplementary-material content-type=\"local-data\" id=\"ijerph-17-05589-s001\"><media xlink:href=\"ijerph-17-05589-s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>Conceptualization, M.U.; methodology, M.U., K.O., Y.H.; validation, T.A., M.U.; formal analysis, M.U., T.A.; resources, T.A.; data curation, T.A.; writing—original draft preparation, M.U.; writing—review and editing, K.O., Y.H., T.A.; visualization, M.U. All authors have read and agreed to the published version of the manuscript. These four authors contributed equally to this work.</p></notes><notes><title>Funding</title><p>This research received no external funding.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijerph-17-05589\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Murray</surname><given-names>C.J.</given-names></name><name><surname>Lopez</surname><given-names>A.D.</given-names></name><name><surname>Chin</surname><given-names>B.</given-names></name><name><surname>Feehan</surname><given-names>D.</given-names></name><name><surname>Hill</surname><given-names>K.H.</given-names></name></person-group><article-title>Estimation of potential global pandemic influenza mortality on the basis of vital registry data from the 1918-20 pandemic: A quantitative analysis</article-title><source>Lancet</source><year>2006</year><volume>368</volume><fpage>2211</fpage><lpage>2218</lpage><pub-id pub-id-type=\"doi\">10.1016/S0140-6736(06)69895-4</pub-id><pub-id pub-id-type=\"pmid\">17189032</pub-id></element-citation></ref><ref id=\"B2-ijerph-17-05589\"><label>2.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Price-Haywood</surname><given-names>E.G.</given-names></name><name><surname>Burton</surname><given-names>J.</given-names></name><name><surname>Fort</surname><given-names>D.</given-names></name><name><surname>Seoane</surname><given-names>L.</given-names></name></person-group><article-title>Hospitalization and mortality among black patients and white patients with Covid-19</article-title><source>N. 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(<bold>A</bold>) Morbidity per million; (<bold>B</bold>) mortality per million; (<bold>C</bold>) log10 transformed morbidity; and (<bold>D</bold>) log10 transformed mortality.</p></caption><graphic xlink:href=\"ijerph-17-05589-g001\"/></fig><fig id=\"ijerph-17-05589-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>(<bold>A</bold>) Associations between morbidity, mortality and PCR-tests. Either Pearson’s correlation coefficient or Spearman’s rank correlation was applied to calculate rho; (<bold>B</bold>) associations between morbidity and risk factors were adjusted for PCR-tests per million population (log10); (<bold>C</bold>) associations between mortality and risk factors were adjusted for morbidity (log10) and PCR-tests (log10) per million population.</p></caption><graphic xlink:href=\"ijerph-17-05589-g002\"/></fig><fig id=\"ijerph-17-05589-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Scatter plot showing the association between PCR-test positivity and mortality. Mortality per million population and PCR-test positivity rates (%) on 17 July 2020 were transformed to the common logarithm (log10) in the graph. Since the variable of ‘PCR-test positivity (log10)’ showed a normal distribution, Pearson’s correlation coefficient was applied to calculate rho, to quantify the strength of the association. Countries that never had or stopped a national BCG vaccine program are indicated in red, while countries with current national BCG vaccine programs are indicated in black. Selected country names are shown using three-letter country codes.</p></caption><graphic xlink:href=\"ijerph-17-05589-g003\"/></fig><fig id=\"ijerph-17-05589-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Scatter plot showing the association between urban population and COVID-19 morbidity rates. COVID-19-related morbidity rates per million population on 17 July 2020 were transformed to the common logarithm (log10) in the graph. Since the variable of ‘urban population’ showed a non-normal distribution, Spearman’s rank correlation was applied to calculate rho, to quantify the strength of the association. Countries that had never had or that had stopped a national program of BCG vaccination are indicated in red, while countries that currently follow a national BCG vaccine program are indicated in black. Selected country names are shown using three-letter country codes.</p></caption><graphic xlink:href=\"ijerph-17-05589-g004\"/></fig><fig id=\"ijerph-17-05589-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>Scatter plot showing the association between insufficient physical activity and COVID-19 morbidity rates.</p></caption><graphic xlink:href=\"ijerph-17-05589-g005\"/></fig><fig id=\"ijerph-17-05589-f006\" orientation=\"portrait\" position=\"float\"><label>Figure 6</label><caption><p>Scatter plot showing the association between ≥60 years of age (%) of the population and mortality rate. Mortalities per million population on 17 July 2020 transformed to the common logarithm (log10) are presented in the graph. Since the variable of ‘percentage of population ≥ 60 years of age (%)’ showed a non-normal distribution, Spearman’s rank correlation was applied to calculate rho, to quantify the strength of the association. Countries that had never had or that had stopped their national BCG vaccine program are indicated in red, while countries that currently follow a national BCG vaccine program are indicated in black. Selected country names are shown using three-letter country codes.</p></caption><graphic xlink:href=\"ijerph-17-05589-g006\"/></fig><fig id=\"ijerph-17-05589-f007\" orientation=\"portrait\" position=\"float\"><label>Figure 7</label><caption><p>Scatter plot showing the association between COVID-19-related mortality and BCG vaccine coverage. Mortalities per million population on 17 July 2020 are transformed to the common logarithm (log10) in the graph. Since the variable of ‘BCG vaccine coverage’ showed a non-normal distribution, Spearman’s rank correlation was applied to calculate rho, to quantify the strength of the association. Selected country names are shown using three-letter country codes.</p></caption><graphic xlink:href=\"ijerph-17-05589-g007\"/></fig><table-wrap id=\"ijerph-17-05589-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05589-t001_Table 1</object-id><label>Table 1</label><caption><p>Twenty countries with the highest and lowest COVID-19 morbidity rates and their PCR-test rate per million population.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th colspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">The Highest 20 Countries</th><th colspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">The Lowest 20 Countries</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Rank</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Country</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Morbidity *</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PCR-Test *</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Rank</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Country *</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Morbidity *</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PCR-Test *</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Qatar</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">37,566</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">153,380</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">173</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Papua New Guinea</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">798</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Chile</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16,927</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">70,696</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">172</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Lao People’s Democratic Republic</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2990</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Kuwait</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13,496</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">105,205</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">171</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Vietnam</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2824</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Oman</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12,244</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50,512</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">170</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Myanmar</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1770</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Panama</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11,406</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40,384</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">169</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">United Republic of Tanzania</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Armenia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11,324</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47,718</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">168</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cambodia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2896</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Andorra <sup>∫</sup></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11,156</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">48,531</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">167</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Angola</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">304</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>USA <sup>∫</sup></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11,118</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">137,544</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">166</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Timor-Leste</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1189</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Peru</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10,355</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60,747</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">165</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Taiwan</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3319</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Brazil</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9464</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23,098</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">164</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Uganda</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5133</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Luxembourg <sup>∫</sup></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8438</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">502,852</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">163</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Burundi</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">563</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Singapore</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8053</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">172,506</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">162</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Syrian Arab Republic</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Sweden <sup>∫</sup></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7610</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">67,495</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">161</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fiji</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4461</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Saudi Arabia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6983</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">71,623</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">160</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Gambia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1477</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Belarus</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6945</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">123,003</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">159</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mozambique</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">44</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1360</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Spain <sup>∫</sup></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6543</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">128,893</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">158</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Niger</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">276</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">UAE</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5673</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">436,262</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">157</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Thailand</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">46</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8647</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">South Africa</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5464</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39,182</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">156</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Burkina Faso</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Belgium <sup>∫</sup></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5438</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">121,891</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">155</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yemen</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">52</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Maldives</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5234</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">118,769</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">154</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Chad</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">54</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr></tbody></table><table-wrap-foot><fn><p>* Numbers per million population, USA—United States of America, UAE—United Arab Emirates. Bold letters with mark “∫” mean no recent national BCG vaccination program for all.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05589-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05589-t002_Table 2</object-id><label>Table 2</label><caption><p>Twenty countries with the highest and lowest COVID-19 mortality rates and their PCR-test rates per million population.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th colspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">The Highest 20 Countries</th><th colspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">The Lowest 20 Countries</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Rank</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Country</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mortality *</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PCR-Test *</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Rank</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Country *</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mortality *</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PCR-Test *</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Belgium <sup>∫</sup></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">845</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">121,891</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">173</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Papua New Guinea</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">798</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Andorra <sup>∫</sup></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">673</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">48,531</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">172</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Lao People’s Democratic Republic</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2990</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>UK <sup>∫</sup></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">664</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">186,591</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">171</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Vietnam</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2824</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Spain</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">608</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">128,893</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">170</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cambodia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2896</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Italy <sup>∫</sup></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">579</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100,954</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">169</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Timor-Leste</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1189</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Sweden <sup>∫</sup></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">554</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">67,495</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">168</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Uganda</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5133</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>France <sup>∫</sup></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">462</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39,868</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">167</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fiji</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4461</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>USA <sup>∫</sup></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">426</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">137,544</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">166</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Eritrea</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Peru</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">382</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60,747</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">165</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mongolia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8688</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Chile</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">381</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">70,696</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">164</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bhutan</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">51,107</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Brazil</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">361</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23,098</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">163</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Saint Lucia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11,494</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Netherlands <sup>∫</sup></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">358</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">44,588</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">162</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Greenland</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90,957</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Ireland <sup>∫</sup></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">354</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">107,707</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">161</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dominica</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11,820</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Ecuador <sup>∫</sup></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">295</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11,023</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">160</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Saint Vincent and the Grenadines</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10,932</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mexico</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">286</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5958</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">159</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Saint Kitts and Nevis</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13,248</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Canada <sup>∫</sup></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">234</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">88,515</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">158</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Seychelles</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Switzerland <sup>∫</sup></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">227</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">81,825</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">157</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Burundi</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.08</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">563</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Panama</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">227</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40,384</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">156</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Myanmar</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1770</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Armenia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">205</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47,718</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">155</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Taiwan</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3319</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">North Macedonia</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">192</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">38,771</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">154</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mozambique</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1360</td></tr></tbody></table><table-wrap-foot><fn><p>* Numbers per million population, USA—United States of America, UK—United Kingdom, VCT—Saint Vincent and the Grenadines. Bold letters with mark “∫” mean no recent national BCG vaccination program for all.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05589-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05589-t003_Table 3</object-id><label>Table 3</label><caption><p>Association between mortality as the outcome and morbidity as the exposure, adjusted for number of PCR-tests performed.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Variable</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Coef.</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Std. Err.</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">95% CI</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Adjusted R<sup>2</sup></th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Morbidity per million population (log10)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.395</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.083</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.232 to 1.558</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.7067</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">PCR-tests per million population (log10)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.241</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.086</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.005</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.416 to −0.075</td></tr></tbody></table><table-wrap-foot><fn><p>Coef.—Coefficient; Std. Err.—Standard Error; 95% CI—95% confidence interval.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05589-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05589-t004_Table 4</object-id><label>Table 4</label><caption><p>Associations between COVID-19 morbidity and mortality per million population and world data for each country.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Variable</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>N</italic> * <sup>3</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Median, IQR</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Min (Country)<break/>Max (Country)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Morbidity * <sup>1</sup><break/>Adjusted R<sup>2</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mortality * <sup>2</sup><break/>Adjusted R<sup>2</sup></th></tr></thead><tbody><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Population, <italic>n</italic></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">173</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.02 × 10<sup>7</sup><break/>3.5 × 10<sup>6</sup>–3.3 × 10<sup>7</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.9 × 10<sup>5</sup> (Monaco)<break/>1.4 × 10<sup>9</sup> (China)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3732</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7120</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Yearly change (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">173</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.11<break/>0.35–1.96</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.35 (Lithuania)<break/>3.84 (Niger)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3822</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7172</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Net change, <italic>n</italic></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">173</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">89,516<break/>8516–447,563</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−383,840 (Japan)<break/>1.40 × 10<sup>7</sup> (India)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3713</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7073</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Population density, <italic>n</italic>/km<sup>2</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">173</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">83<break/>34–167</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (Greenland)<break/>26,337 (Monaco)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3723</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7048</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Land area, km<sup>2</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">173</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">183,630<break/>35,410–581,795</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1 (Monaco)<break/>1.6 × 10<sup>7</sup> (Russia)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3726</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7079</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Migrants, <italic>n</italic> (net)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">168</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1590<break/>−16,026–14,440.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−653,249 (Venezuela))<break/>954,806 (USA)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3885</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7460</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Fertility rate, <italic>n</italic></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">168</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.27<break/>1.71–3.65</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.11 (Korea)<break/>6.95 (Niger)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3784</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7516</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Urban population (%)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>168</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>60</bold>\n<break/>\n<bold>43–78</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>13 (Papua New Guinea)</bold>\n<break/>\n<bold>98 (Belgium)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.4246 *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7243</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">World share (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">173</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1<break/>0.05–0.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (Saint Lucia, etc.)<break/>18.5 (China)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3732</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7120</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Age</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Median age, years</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>168</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>30</bold>\n<break/>\n<bold>21.5–39</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>15 (Niger)</bold>\n<break/>\n<bold>48 (Japan)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3787</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.7811 *</bold>\n</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">0 to 14 years of age (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">165</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26.8<break/>17.6–37.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.3 (Niger)<break/>49.8 (Singapore)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3789</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7574</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>≥ 60 years of age (%)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>165</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>9.72</bold>\n<break/>\n<bold>5.30–20.48</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>2.80 (UAE)</bold>\n<break/>\n<bold>34.01 (Japan)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3802</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.7986 *</bold>\n</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>≥ 70 years of age (%)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>162</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>3.41</bold>\n<break/>\n<bold>2.03–9.17</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.53 (UAE)</bold>\n<break/>\n<bold>18.49 (Japan)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3818</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.8027 *</bold>\n</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Economy</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">GDP, million US dollars</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">170</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40,729<break/>11,135–223,780</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">393 (Sao Tome)<break/>1.90 × 10<sup>7</sup> (USA)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3701</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7095</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">GDP per capita, US dollars</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">170</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">51.4<break/>1534–15,347</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">104 (Somalia)<break/>165,421 (Monaco)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3722</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7051</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Total unemployment rate (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">164</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.85<break/>3.75–9.40</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.1 (Qatar)<break/>28.2 (Lesotho)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3933</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7436</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Male unemployment rate (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">164</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.5<break/>3–8.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (Qatar)<break/>26 (Eswatini)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3905</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7433</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Female unemployment rate (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">164</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7<break/>4–12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (Niger)<break/>38 (Syria)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4078</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7435</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Total labor force participation rate (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">164</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">62<break/>55–68</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38 (Yemen)<break/>87 (Qatar)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3939</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7597</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Male labor force participation rate (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">164</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">73<break/>66.5–79</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45 (Moldova)<break/>95 (Qatar)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3809</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7602</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Female labor force participation rate (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">164</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">53.5<break/>45–60.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6 (Yemen)<break/>84 (Rwanda)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4216</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7479</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">National BCG vaccine program</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Annual incidence of tuberculosis, <italic>n</italic>/100,000</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">164</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">57<break/>17–176</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2 (UAE)<break/>834 (South Africa)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3693</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7053</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Recent BCG vaccine coverage (%)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>173</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>93</bold>\n<break/>\n<bold>81–98</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0 (Italy, etc.)</bold>\n<break/>\n<bold>99 (Japan, etc.)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3814</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.7415 *</bold>\n</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Global health observatory</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Health policy</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">International Health Regulations score</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">170</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">64<break/>48 to 82</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17 (Central African Republic)<break/>99 (Canada, Russia)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3817</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7205</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Universal health coverage index</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>166</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.41</bold>\n<break/>\n<bold>0.36 to 0.47</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.17 (Saint Lucia)</bold>\n<break/>\n<bold>0.70 (Peru)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3812</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.7644 *</bold>\n</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Total expenditure on health as a percentage of gross domestic product</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">169</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.38<break/>4.76–8.43</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.48 (Timor-Leste)<break/>17.14 (USA)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3977</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7283</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Population with household expenditures on health greater than 10% of total household expenditure/income (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">146</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.56<break/>3.13–12.76</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.20 (Gambia)<break/>54.2 (Sierra Leone)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3956</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7906</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Medical personnel</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Hospital beds, <italic>n</italic>/10,000 population</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">164</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21<break/>11–40</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1 (Mali)<break/>134 (Japan)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3718</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7409</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Medical doctors, <italic>n</italic>/10,000 population</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>169</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>15.7</bold>\n<break/>\n<bold>3.3–29.8</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.1 (Tanzania)</bold>\n<break/>\n<bold>84.2 (Cuba)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3800</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.7351 *</bold>\n</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Nursing and midwifery personnel, <italic>n</italic>/10,000 population</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">169</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26.6<break/>9.4–64.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.06 (Cameroon)<break/>201.6 (Monaco)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3823</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7203</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Licensed qualified anesthesiologists actively working, <italic>n</italic></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">141</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">250<break/>18–1511</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (Congo)<break/>194,634 (China)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3904</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6924</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Health biomarkers</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">High blood pressure (SBP > 140 OR DBP > 90) (crude estimate) (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">167</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23<break/>20–25</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13 (Peru)<break/>41 (Croatia)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4091</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7199</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Elevated fasting blood glucose (>7.0 mmol/L or on medication) (crude estimate) (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">167</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.1<break/>5.9–9.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.6 (Burundi)<break/>16.6 (Fiji)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4069</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7162</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">E<bold>levated total cholesterol (≥5.0 mmol/L or 193 mg/dL)</bold><break/><bold>(crude estimate) (%)</bold></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>167</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>37.6</bold>\n<break/>\n<bold>25.2–52.5</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>14.8 (Niger)</bold>\n<break/>\n<bold>69.7 (Denmark)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4070</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.7401 *</bold>\n</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">BMI</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mean BMI, kg/m<sup>2</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">167</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26.2<break/>23.4–27.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.5 (Ethiopia)<break/>30.0 (Saint Lucia)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4233</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7102</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">BMI ≥ 30 kg/m<sup>2</sup> (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">167</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19.9<break/>7.5–25.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.1 (Vietnam)<break/>37.3 (USA)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4393</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7135</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">BMI ≥ 25 kg/m<sup>2</sup> (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">167</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">53.5<break/>27.8–62.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.9 (Timor-Leste)<break/>72.1 (Kuwait)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4454</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7204</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Alcohol drinking, total consumption per capita among persons aged ≥ 15 years, liters of pure alcohol over a calendar year</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">168</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.4<break/>2.6–9.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (Bangladesh)<break/>15.2 (Moldova)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3834</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7191</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Prevalence of smoking any tobacco product among males aged ≥15 years (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">119</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32<break/>23–44</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9 (Ethiopia)<break/>76 (Indonesia)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4410</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.8006</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Prevalence of smoking any tobacco product among females aged ≥ 15 years (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">119</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8<break/>3–19</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0 (Niger)<break/>40 (Serbia)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3932</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.8192</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>Prevalence of insufficient physical activity among adults aged ≥ 15 years (crude estimate) (%)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>143</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>28.7</bold>\n<break/>\n<bold>19.0–36.2</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>5.0 (Uganda)</bold>\n<break/>\n<bold>65.3 (Kuwait)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.4984 *</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7270</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Estimated population-based prevalence of depression (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">166</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.4<break/>4.0–5.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.0 (Papua New Guinea)<break/>6.3 (Ukraine)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4062</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7486</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mortality according to age group</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Neonatal mortality rate, <italic>n</italic> per 1000 live births</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">170</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.6<break/>3.5–21.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.9 (Japan)<break/>42.0 (Pakistan)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3811</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7234</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Infantile mortality rate, <italic>n</italic> per 1000 live births</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">170<break/></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.0<break/>5.9–34.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.4 (Finland)<break/>84.5 (Central African Republic)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3810</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7507</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Under-five mortality rate, probability of dying by age 5/1000 live births</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">170</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.4<break/>7.0–44.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.7 (Finland)<break/>121.5 (Somalia)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3813</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7145</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mortality rate for 5–14-year-olds, <italic>n</italic>/1000 children aged 5–14 years</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">168</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.8<break/>1.5–8.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.4 (Luxembourg)<break/>37.3 (Niger)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3781</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7117</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Adult mortality rate, probability of dying between 15 and 60 years of age/1000 population</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">167</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">150<break/>96–224</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">49 (Switzerland)<break/>484 (Lesotho)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3812</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7377</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Probability of dying between age 30 and exact age 70 from cardiovascular disease, cancer, diabetes or chronic respiratory disease</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">166</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.4<break/>14.7–22.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.8 (Korea)<break/>30.6 (Yemen)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3886</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7061</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Life expectancy at birth, years</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">At birth</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">166</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">73.4<break/>65.3–81.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">52.9 (Lesotho)<break/>84.2 (Japan)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3806</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7600</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">At age 60 years</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">166</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19.6<break/>17.2–22.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.4 (Sierra Leone)<break/>26.4 (Japan)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3838</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7630</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Healthy life expectancy (HALE), years</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">At birth</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">166</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">65.3<break/>57.5–68.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">44.9 (Central African Republic)<break/>76.2 (Singapore)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3795</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7599</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<bold>At age 60 years</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>166</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>14.8</bold>\n<break/>\n<bold>13.0–17.1</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>10.3 (Sierra Leone)</bold>\n<break/>\n<bold>21.0 (Singapore)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3825</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.7652 *</bold>\n</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Disease-specific mortality</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Chronic obstructive pulmonary disease</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">166</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.13.9–8.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.6 (Qatar)<break/>36.8 (India)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3975</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7436</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Ischemic heart disease</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">166</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.5<break/>13.4–35.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.9 (Brunei Darussalam)<break/>106.9 (Georgia)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3789</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7431</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Lower respiratory infections</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">166</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.2<break/>3.7–31.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.22 (Finland)<break/>130.3 (Chad)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3791</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7455</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Stroke</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">166</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.2<break/>5.6–16.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.4 (Qatar)<break/>49.3 (Georgia)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3963</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7726</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Tracheal, bronchial, lung cancers</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">166</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.8<break/>0.8–4.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2 (Niger)<break/>20.5 (China)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3971</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7541</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Air pollution</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Ambient and household air pollution attributable death rate /100,000 population</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">166</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">65.9<break/>38.5–97.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.5 (Georgia)<break/>184 (Brunei Darussalam)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3861</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7427</td></tr><tr><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\">Concentrations of fine particulate matter (PM<sub>2.5</sub>)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">170</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.1<break/>14.6–34.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.7 (New Zealand)<break/>94.3 (Nepal)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3940</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.7210</td></tr><tr><td valign=\"middle\" style=\"border-bottom:solid thin\" align=\"left\" rowspan=\"1\" colspan=\"1\">Coverage rate with the first dose of a measles-containing-vaccine (MCV1) among one-year-olds (%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">169</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">93<break/>84–97</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">30 (Equatorial Guinea)<break/>99 (Mongolia and 23 other countries)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.3851</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.7095</td></tr></tbody></table><table-wrap-foot><fn><p>IQR—interquartile range; GDP—gross domestic product; BMI—body mass index—weight (kg)/height (m)<sup>2</sup>; * <sup>1</sup>—adjusted for PCR tests per million population; * <sup>2</sup>—adjusted for PCR tests and COVID-19 morbidity per million population; * <sup>3</sup>—number of countries the data were able to abstract, *: <italic>p</italic> < 0.001. Bold letters mean statistically significant: <italic>p</italic> < 0.001.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05589-t005\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05589-t005_Table 5</object-id><label>Table 5</label><caption><p>Factors associated with morbidity adjusted for PCR-test rates.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Variable</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Coef.</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Std. Err.</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">95% CI</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Adjusted R<sup>2</sup></th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PCR-tests per million population (log10)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.574</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.101</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.374 to 0.774</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.5037</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Urban population (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.764</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.329</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.02</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.113 to 1.416</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Insufficient physical activity (%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.015</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.006</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.01</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.004 to 0.026</td></tr></tbody></table><table-wrap-foot><fn><p>Coef.—Coefficient; Std. Err.—Standard Error; 95% CI—95% confidence interval.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05589-t006\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05589-t006_Table 6</object-id><label>Table 6</label><caption><p>Factors associated with mortality adjusted for morbidity and PCR-test rates.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Variable</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Coef.</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Std. Err.</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">95% CI</th><th align=\"center\" valign=\"top\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Adjusted R<sup>2</sup></th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Morbidity per million population (log10)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.342</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.067</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.210 to 1.475</td><td rowspan=\"8\" align=\"center\" valign=\"top\" style=\"border-bottom:solid thin\" colspan=\"1\">0.8254</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PCR-tests per million population (log10)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.485</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.099</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.680 to −0.290</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Population ≥ 60 years of age (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.030</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.010</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.003</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.011 to 0.050</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">BCG vaccine coverage</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.004</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.002</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.006</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.007 to −0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Universal health coverage</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.473</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.549</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.008</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.387 to 2.560</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Medical doctors/10,000 population</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.003</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.004</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.47</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.005 to 0.011</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Elevated total cholesterol</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.002</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.006</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.76</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.010 to 0.014</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">HALE at age 60</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.03</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.029</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.27</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.090 to 0.025</td></tr></tbody></table><table-wrap-foot><fn><p>HALE—healthy life expectancy, Coef.—Coefficient; Std. Err.—Standard Error; 95% CI—95% confidence interval.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32731363</article-id><article-id pub-id-type=\"pmc\">PMC7432031</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155416</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05416</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Sex-Specific Energy Intakes and Physical Activity Levels According to the Presence of Metabolic Syndrome in Korean Elderly People: Korean National Health and Nutrition Examination Survey 2016–2018</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-3125-0478</contrib-id><name><surname>Jung</surname><given-names>Won-Sang</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05416\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-9901-7624</contrib-id><name><surname>Park</surname><given-names>Hun-Young</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05416\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05416\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-5976-277X</contrib-id><name><surname>Kim</surname><given-names>Sung-Woo</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05416\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Lim</surname><given-names>Kiwon</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05416\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05416\">2</xref><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05416\">3</xref><xref rid=\"c1-ijerph-17-05416\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05416\"><label>1</label>Physical Activity and Performance Institute (PAPI), Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea; <email>jws1197@konkuk.ac.kr</email> (W.-S.J.); <email>parkhy1980@konkuk.ac.kr</email> (H.-Y.P.); <email>kswrha@konkuk.ac.kr</email> (S.-W.K.)</aff><aff id=\"af2-ijerph-17-05416\"><label>2</label>Department of Sports Medicine and Science, Graduate School, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea</aff><aff id=\"af3-ijerph-17-05416\"><label>3</label>Department of Physical Education, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea</aff><author-notes><corresp id=\"c1-ijerph-17-05416\"><label>*</label>Correspondence: <email>exercise@konkuk.ac.kr</email>; Tel.: +82-2-450-3949</corresp></author-notes><pub-date pub-type=\"epub\"><day>28</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5416</elocation-id><history><date date-type=\"received\"><day>19</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>24</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>This study aimed to analyze the differences in energy intake and physical activity (PA) levels according to sex and the presence of metabolic syndrome (MetS) among elderly people in Korea. Data of 3720 elderly people (aged >65 years) were obtained from the Korean National Health and Nutrition Examination Survey (2016–2018). We analyzed PA levels (occupational and recreational PA) and energy intakes (carbohydrate, protein, and fat). The MetS group showed lower levels of moderate intensity recreational PA and place movement than the non-MetS group (<italic>p</italic> < 0.05); in the MetS group, PA levels were significantly lower in women than in men (<italic>p</italic> < 0.05). The intakes of total energy, carbohydrate, fat, and protein were lower in the MetS group than in the non-MetS group (<italic>p</italic> < 0.001). Both the non-MetS and MetS groups showed lower energy intakes in women than men (<italic>p</italic> < 0.001). Our study shows that elderly people, especially women, with MetS have significantly lower total PA levels and total energy intakes. We confirmed the importance of increased PA and proper nutritional intake in elderly people. Therefore, it is believed that practical measures such as nutrition education and nutrition guidance and PA education are urgently needed to reduce the incidence of MetS among the elderly.</p></abstract><kwd-group><kwd>energy intake level</kwd><kwd>physical activity level</kwd><kwd>metabolic syndrome</kwd><kwd>elderly people</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05416\"><title>1. Introduction</title><p>In modern societies, the increase in average life expectancy due to advances in medical technology and improvements in economic status has resulted in an increasingly aging population. Increased body fat and lower muscle mass as a result of aging cause physical problems in this population [<xref rid=\"B1-ijerph-17-05416\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijerph-17-05416\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijerph-17-05416\" ref-type=\"bibr\">3</xref>]. Furthermore, considering the deterioration in quality of life and increase in socioeconomic problems, such as chronic morbidity, medical expenses, and mortality, it is important to find ways to solve the health problems of elderly people [<xref rid=\"B2-ijerph-17-05416\" ref-type=\"bibr\">2</xref>,<xref rid=\"B4-ijerph-17-05416\" ref-type=\"bibr\">4</xref>].</p><p>The incidence of obesity, diabetes, high blood pressure, and cardiovascular diseases among elderly people is high, and metabolic syndrome (MetS), a major risk factor for chronic diseases, is on the rise [<xref rid=\"B5-ijerph-17-05416\" ref-type=\"bibr\">5</xref>]. The Korean Society of Cardio Metabolic Syndrome reported that senior citizens aged >65 years had a MetS rate of about 35% [<xref rid=\"B6-ijerph-17-05416\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijerph-17-05416\" ref-type=\"bibr\">7</xref>]. The prevalence of MetS (obesity, high blood pressure, and elevated high-density lipoprotein cholesterol (HDL-C) levels), which is a risk factor for cardiovascular diseases, has been reported to be significantly higher among elderly people in Korea than in those in the United States and Europe [<xref rid=\"B8-ijerph-17-05416\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05416\" ref-type=\"bibr\">9</xref>].</p><p>The increased risk of MetS in elderly people is mainly due to changes in body composition: an increase in body fat mass and a decrease in free fat mass and muscle strength due to aging [<xref rid=\"B10-ijerph-17-05416\" ref-type=\"bibr\">10</xref>]. Elderly people with high-calorie diets and decreased physical activity (PA) levels show a gradual increase in body fat due to excessive accumulation of body fat, and metabolic factors such as obesity, diabetes, and inflammation increase the risk of cardiovascular diseases [<xref rid=\"B6-ijerph-17-05416\" ref-type=\"bibr\">6</xref>,<xref rid=\"B11-ijerph-17-05416\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05416\" ref-type=\"bibr\">12</xref>]. In particular, a decrease in muscle mass and an increase in body fat were found to have a negative effect on the condition of the metabolism, such as decreased body function, physical strength, blood lipids, body inflammation, and hormones [<xref rid=\"B2-ijerph-17-05416\" ref-type=\"bibr\">2</xref>,<xref rid=\"B13-ijerph-17-05416\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijerph-17-05416\" ref-type=\"bibr\">14</xref>].</p><p>MetS is reported to be highly correlated with type 2 diabetes, cerebrovascular diseases, cardiovascular diseases, and cancer (colorectal, pancreatic, and breast cancer), which are the main causes of mortality among elderly people in South Korea [<xref rid=\"B15-ijerph-17-05416\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijerph-17-05416\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijerph-17-05416\" ref-type=\"bibr\">17</xref>]. These typical risk factors are associated with high levels of C-reactive protein and homocysteine, which indicate a pro-inflammatory state in the blood vessels [<xref rid=\"B18-ijerph-17-05416\" ref-type=\"bibr\">18</xref>]. The pro-inflammatory state causes hyper-viscosity by activating blood coagulation reactions, increasing the level of plasminogen activator inhibitor (PAI)-1 or fibrinogen [<xref rid=\"B19-ijerph-17-05416\" ref-type=\"bibr\">19</xref>], and increasing the level of low-density lipoprotein cholesterol (LDL-C). Additionally, the resting heart rate increases due to stress, activating the sympathetic nervous system and decreasing the activation of the parasympathetic nervous system [<xref rid=\"B20-ijerph-17-05416\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijerph-17-05416\" ref-type=\"bibr\">21</xref>]. Experimental studies have shown that these risk factors (obesity, type 2 diabetes, hypertension, etc.) for the prevalence of MetS can be modulated by increased PA and continuous exercise [<xref rid=\"B22-ijerph-17-05416\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijerph-17-05416\" ref-type=\"bibr\">23</xref>]. However, there is a paucity of research on the efficiency of PA that is based on a large sample of elderly people of both sexes.</p><p>Risk factors for MetS have been reported to also be closely related to diet and nutrition, PA levels, and exercise participation [<xref rid=\"B11-ijerph-17-05416\" ref-type=\"bibr\">11</xref>]. Nutrition in elderly people is influenced by a variety of factors, including socioeconomic levels, physical health, and emotional state and also acts as a major determinant of health [<xref rid=\"B8-ijerph-17-05416\" ref-type=\"bibr\">8</xref>]. Elderly people are prone to poor nutrition due to poor physiological function, poor digestion, poor dental conditions, economic difficulties after retirement, and depression in old age. Poor nutrition is associated with increased health risks such as decreased immunity, decreased physical function, and an increased morbidity rate [<xref rid=\"B24-ijerph-17-05416\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijerph-17-05416\" ref-type=\"bibr\">25</xref>]. In particular, fat and carbohydrate intakes are closely related to MetS, while the intake of essential fatty acids, calcium, magnesium, and dairy products has been reported to reduce the risk of and lower the prevalence rate of MetS [<xref rid=\"B26-ijerph-17-05416\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijerph-17-05416\" ref-type=\"bibr\">27</xref>]. Malnutrition in elderly people has been associated with lower income and education levels and with activity restriction or depression. Eating balanced meals can prevent the occurrence of cardiovascular disease, diabetes, hypertension, etc. and chronic degenerative diseases such as osteoporosis [<xref rid=\"B28-ijerph-17-05416\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijerph-17-05416\" ref-type=\"bibr\">29</xref>].</p><p>The level of PA, the degree of participation in exercise, and dietary and nutritional intakes have an impact on the risk factors and prevalence of MetS. Considering that these factors vary according to an elderly person’s lifestyle and sex, it is very important to examine the relationship between these factors in a large population in Korea. Moreover, cohort studies with an adequate sample size evaluating PA levels and energy intakes according to sex and the presence of MetS are insufficient. Therefore, this study aimed to analyze the differences in energy intake and PA levels by sex and the presence of MetS in a Korean elderly population, based on data from the 7th Korea National Health and Nutrition Examination Survey (2016–2018) and to investigate the relationship.</p></sec><sec id=\"sec2-ijerph-17-05416\"><title>2. Materials and Methods</title><sec id=\"sec2dot1-ijerph-17-05416\"><title>2.1. Sample and Design</title><p>This study used cross-sectional data from the Korea National Health and Nutrition Examination Survey (KNHANES) from 2016 to 2018, which was conducted by the Korea Centers for Disease Control and Prevention (KCDC). The details of the study design and data resource profiles followed the methods in the Guidelines for Use of the KNHANES Raw Data and the Final Report of sampling frame [<xref rid=\"B16-ijerph-17-05416\" ref-type=\"bibr\">16</xref>]. The KNHANES consists of a health interview survey, a nutrition survey, and a health examination and is conducted according to the Declaration of Helsinki. This survey was approved by the Institutional Review Board of the Korea Centers for Disease Control and Prevention (reference number: 2018-01-03-P-A). All participants in the survey signed an informed consent form.</p><p>Between 2016 and 2018, 20,659 individuals completed the health interview survey, nutrition survey, and health examination. Among them, 15,703 people under 65 years of age were excluded, leaving 4965 people over 65 years of age. Participants previously diagnosed with and/or treated for cancer (gastric, liver, colon, breast, cervical, lung, thyroid, and other cancers), those who had undergone surgery for other indications, and those with missing data (anthropometric, health examination, and PA data) were excluded (<xref ref-type=\"fig\" rid=\"ijerph-17-05416-f001\">Figure 1</xref>). In total, 3720 elderly people were finally included in this study.</p></sec><sec id=\"sec2dot2-ijerph-17-05416\"><title>2.2. Measures</title><p>The analytical items of this study were the height and body weight of the screening survey items from the KNHANES Raw Data. The presence or absence of MetS was determined using measurements of waist circumference, blood pressure, fasting blood glucose levels, triglyceride levels, and HDL-C levels. PA variables were evaluated using the Global Physical Activity Questionnaire (GPAQ), and PA was expressed in metabolic equivalents (MET)-minutes/week. Nutrient intake and intake rates were also analyzed. The analyzed characteristics of the participants according to sex are shown in <xref rid=\"ijerph-17-05416-t001\" ref-type=\"table\">Table 1</xref>.</p></sec><sec id=\"sec2dot3-ijerph-17-05416\"><title>2.3. Metabolic Syndrome</title><p>The diagnosis of MetS was based on the new harmonized guidelines of the National Cholesterol Education Program-Adult Treatment Panel III and the American Heart Association and the National Heart Lung and Blood Institute [<xref rid=\"B30-ijerph-17-05416\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijerph-17-05416\" ref-type=\"bibr\">31</xref>]. For waist circumference, we followed the criteria suggested by the Korean Society for the Study of Obesity [<xref rid=\"B32-ijerph-17-05416\" ref-type=\"bibr\">32</xref>]. If three or more of the following five criteria were met, participants were classified as having MetS: waist circumference > 90 cm (men) or >85 cm (women); systolic blood pressure > 130 mmHg or diastolic blood pressure > 85 mmHg; fasting triglyceride (TG) levels > 150 mg/dL; fasting HDL-C levels < 40 mg/dL (men) or <50 mg/dL (women); and fasting glucose levels > 100 mg/dL.</p></sec><sec id=\"sec2dot4-ijerph-17-05416\"><title>2.4. Physical Activity</title><p>The GPAQ comprises 16 questions grouped to capture PA undertaken in different behavioral domains: work, transport, and discretionary (also known as leisure or recreational) activities. It analyzes five domains of PA: vigorous-intensity work, moderate-intensity work, place movement, vigorous-intensity recreation, and moderate-intensity recreation. The World Health Organization (WHO) GPAQ analysis guidelines were used to analyze GPAQ data [<xref rid=\"B33-ijerph-17-05416\" ref-type=\"bibr\">33</xref>]. We estimated that, compared to while sitting quietly, a person’s caloric consumption was four times higher when they were being moderately active and eight times higher when they were being vigorously active. Therefore, when calculating the total energy consumption of an individual using GPAQ data, four METs were allocated to the time spent in moderate activity and eight METs to the time spent in vigorous activity, and the details are as follows:<list list-type=\"bullet\"><list-item><p>Vigorous intensity activity: occupational (MET) = 8.0 × vigorous intensity physical activity (day/week) × 1-day vigorous intensity physical activity (minutes/day)</p></list-item><list-item><p>Moderate intensity activity: occupational (MET) = 4.0 × moderate intensity physical activity (day/week) × 1-day moderate intensity physical activity (minutes/day)</p></list-item><list-item><p>Vigorous intensity activity: recreational (MET) = 8.0 × vigorous intensity physical activity (day/week) × 1-day vigorous intensity physical activity (minutes/day)</p></list-item><list-item><p>Moderate intensity activity: recreational (MET) = 4.0 × moderate intensity physical activity (day/week) × 1-day moderate intensity physical activity (minutes/day)</p></list-item><list-item><p>Place movement (MET) = 4.0 × place movement physical activity (day/week) × 1-day place movement physical activity</p></list-item><list-item><p>Total Physical Activity (MET) = vigorous intensity activity: occupational + moderate intensity activity: occupational + vigorous intensity activity: recreational + moderate intensity activity: recreational + place movement.</p></list-item></list></p><p>PA levels were classified into four groups: inactive (0–249 MET min/week), somewhat active (250–499 MET min/week), active (500–999 MET min/week), and very active (>1000 MET min/week). These cut-off points are based on their equivalence to the following PA thresholds: 250 MET min/week represents an energy expenditure dose equivalent to half of threshold; 500 MET min/week is equivalent to the minimal threshold; and 1000 MET min/week is equivalent to twice the minimal threshold.</p></sec><sec id=\"sec2dot5-ijerph-17-05416\"><title>2.5. Energy Intake and Intake Ratio</title><p>The nutrition survey of the KNHANES consisted of a survey of dietary habits, 1-day 24-hour recall, and food frequency questionnaire administration. The nutrition survey data were collected by trained dietitians in the homes of the participants 1 week after the health interview and health examination. The daily energy intake was calculated using the Korean Foods and Nutrients Database of the Rural Development Administration. The following items were included in the analyses: calorie intake, macronutrient (carbohydrates, proteins, fat) intake, and energy ratio.</p></sec><sec id=\"sec2dot6-ijerph-17-05416\"><title>2.6. Statistical Analysis</title><p>Continuous variables were presented as means and standard errors. Normality of distribution of all outcome variables were verified using the Kolmogorov–Smirnov test. A two-way analysis of variance (ANOVA) was used to analyze the differences in PA levels, energy intakes, intake rates, risk factors for MetS between participants with and without MetS, and between men and women. Partial eta-squared (<italic>η</italic><sup>2</sup>) values were calculated as measures of the effect size. If a significant interaction effect was found, post-hoc independent t-tests were used to compare the presence and absence of MetS and the sex-specificity of dependent variables in each group separately. Moreover, the relationships between PA levels and MetS were determined using logistic regression after controlling for covariates; logistic regression findings were presented as odds ratios (ORs) and their associated 95% confidence intervals (CIs). Statistical analyses were performed using IBM Statistical Package for Social Science (SPSS) version 25.0 for Windows (IBM Corp., Armonk, NY, USA); <italic>p</italic> < 0.05 was considered statistically significant.</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05416\"><title>3. Results</title><sec id=\"sec3dot1-ijerph-17-05416\"><title>3.1. Differences in Physical Activity Levels According to the Presence or Absence of Metabolic Syndrome and According to Sex</title><p>The differences in PA levels according to the presence or absence of MetS and according to sex are presented in <xref rid=\"ijerph-17-05416-t002\" ref-type=\"table\">Table 2</xref>. Elderly individuals without MetS had significantly higher PA levels than did those with MetS (<italic>p</italic> < 0.001; moderate intensity activity: recreational (<italic>p</italic> = 0.002) and total PA (<italic>p</italic> = 0.006)). When analyzed separately, men did not show significant differences in PA levels based on the presence or absence of MetS (<italic>p</italic> > 0.05); however, in women, individuals without MetS had significantly higher levels of total PA, (<italic>p</italic> = 0.010), place movement (<italic>p</italic> = 0.002), and moderate intensity activity: recreational (<italic>p</italic> = 0.001) than those with MetS.</p><p>Women had significantly lower levels of place movement (<italic>F</italic> = 6.889, <italic>p</italic> = 0.009), vigorous intensity activity: recreational (<italic>F</italic> = 22.790, <italic>p</italic> < 0.001), moderate intensity activity: recreational (<italic>F</italic> = 59.790, <italic>p</italic> < 0.001), and total PA (<italic>F</italic> = 34.916, <italic>p</italic> < 0.001) than did men, regardless of the presence or absence of MetS.</p></sec><sec id=\"sec3dot2-ijerph-17-05416\"><title>3.2. Difference in Energy Intakes According to the Presence or Absence of Metabolic Syndrome and According to Sex</title><p><xref rid=\"ijerph-17-05416-t003\" ref-type=\"table\">Table 3</xref> shows the differences in energy intake according to the presence or absence of MetS and according to sex. Elderly participants without MetS had significantly higher intakes of total energy (1677 vs. 1572 kcal; <italic>p</italic> < 0.001), carbohydrates (<italic>p</italic> < 0.001), proteins (<italic>p</italic> < 0.001), and fats (<italic>p</italic> < 0.001) than those with MetS. Among women, participants without MetS had significantly higher intakes of total energy (<italic>p</italic> = 0.001), carbohydrates (<italic>p</italic> = 0.008), proteins (<italic>p</italic> < 0.001), and fats (<italic>p</italic> = 0.001), while in men, there was no significant difference in energy intake regardless of the presence or absence of MetS. Looking at the differences in sex, female participants had significantly less energy intake in all variable (<italic>p</italic> < 0.001), irrespective of whether they had MetS or not.</p></sec><sec id=\"sec3dot3-ijerph-17-05416\"><title>3.3. Odds Ratios (95% CI) for MetS and MetS Components According to Physical Activity Levels</title><p>The average values according to PA levels are shown in <xref rid=\"ijerph-17-05416-t004\" ref-type=\"table\">Table 4</xref> and the odds ratio values for the MetS and MetS components according to PA levels are presented in <xref rid=\"ijerph-17-05416-t005\" ref-type=\"table\">Table 5</xref>. We found that the higher the PA level, the lower were the values for waist circumference, TG, blood pressure, and blood glucose and the higher were the values for HDL-C. When the PA activity level was above the active levels, waist circumference values significantly decreased compared to the reference value (OR = 0.80, 95% CI = 0.66–0.96), while TG (OR = 0.79, 95% CI = 0.65–0.97) and blood pressure (OR = 0.82, 95% CI = 0.69–0.99) were significantly decreased in very active levels, and HDL-C (OR = 0.64, 95% CI = 0.53–0.77) increased. Blood glucose levels decreased with increasing PA levels, but there was no significant difference.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05416\"><title>4. Discussion</title><p>In this study, we analyzed the differences in sex-specific dietary intakes and PA levels according to the presence or absence of MetS in elderly people based on data from the KNHANES. Elderly people with MetS had low PA levels and insufficient energy intakes. Elderly women had significantly lower PA levels and energy intakes and had nutritional imbalance, demonstrated by the consumption of high amounts of carbohydrates and low amounts of fat. To prevent MetS and MetS components related variables, PA levels above the active (500–999 MET min/week) or very active (>1000 MET min/week) level are required. These results suggest that to prevent MetS in elderly people, PA levels should be increased, and balanced nutrition should be assured. In particular, PA programs and education regarding balanced nutrition for elderly women may be necessary for long-term health promotion.</p><p>According to the KNHANES, the incidence of MetS in Koreans has increased by 0.6% every 10 years since 1998 [<xref rid=\"B34-ijerph-17-05416\" ref-type=\"bibr\">34</xref>]. This increase reflected the rapid recovery of the Korean society after the economic crisis and also reflected the rapid change in lifestyle, especially the adoption of high fat and high carbohydrate intake and low physical activity, which has contributed to the increase in MetS incidence. Total energy intake, especially fat and carbohydrate intakes, has been steadily increasing since 2008. Conversely, the amount of PA has decreased, with a pattern toward a decrease in moderate PA and walking rather than an increase in high-intensity PA [<xref rid=\"B35-ijerph-17-05416\" ref-type=\"bibr\">35</xref>].</p><p>In particular, elderly people with sarcopenia, increased body fat, and decreased muscle strength and muscle endurance due to aging have been found to have a high incidence of MetS due to reduced activities of daily living or the presence of chronic diseases [<xref rid=\"B2-ijerph-17-05416\" ref-type=\"bibr\">2</xref>,<xref rid=\"B36-ijerph-17-05416\" ref-type=\"bibr\">36</xref>], which in turn leads to a decrease in quality of life due to limited PA such as reduced PA as a result of cardiovascular diseases and reduced muscular strength, muscular endurance, and balance [<xref rid=\"B37-ijerph-17-05416\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijerph-17-05416\" ref-type=\"bibr\">38</xref>].</p><p>According to a recent report on the National Health and Nutrition Examination Survey, there is a significant sex-difference in MetS incidence. In men, the incidence of MetS increased with age from the 20s to 50s, but it did not increase thereafter. In contrast, the incidence rate in women increased until the 7th decade, especially in the 60s and 70s. Additionally, an analysis of MetS components showed that high blood pressure in women aged >50 years increased rapidly, and that the increase in blood pressure and blood glucose levels in men were closely related to the increase in total energy intake, carbohydrates, and fat intake [<xref rid=\"B39-ijerph-17-05416\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijerph-17-05416\" ref-type=\"bibr\">40</xref>]. Therefore, in this study, the relationships between MetS and PA levels and dietary intakes in elderly people were stratified by sex.</p><p>Overall, the amount of PA according to the presence or absence of MetS and according to sex was different in terms of recreational PA and place movement rather than occupational PA. Amagasa et al. [<xref rid=\"B41-ijerph-17-05416\" ref-type=\"bibr\">41</xref>] reported that the levels of moderate and vigorous intensity PA in elderly people in Japan did not differ significantly according to sex and that men who weighed more consumed more calories through PA than women. In the study by Davis and Fox [<xref rid=\"B42-ijerph-17-05416\" ref-type=\"bibr\">42</xref>], men aged >70 years had significantly higher PA levels than women, which is consistent with our findings. Participation in moderate and vigorous intensity PA is known to have a positive effect on the risk factors and incidence of MetS [<xref rid=\"B36-ijerph-17-05416\" ref-type=\"bibr\">36</xref>]. A study by Laaksonen et al. [<xref rid=\"B43-ijerph-17-05416\" ref-type=\"bibr\">43</xref>] reported that vigorous intensity PA (>60 min per week) was associated with a lower incidence of MetS (63% lower) after adjusting for age and body mass index. As such, we found that to reduce the incidence of MetS, PA equivalent to more than active PA (500–999 MET min/week) must be performed. Zbronska et al. [<xref rid=\"B44-ijerph-17-05416\" ref-type=\"bibr\">44</xref>] reported that 1500 MET min/week PA could bring benefits to health-related variables. This study also showed that highly active (>1000 MET min/week) PA is needed to improve the variables related to MetS. Schaller et al. reported that not only the intensity of PA but also the type of PA could affect the quality of health-related life. Pedišić et al. [<xref rid=\"B45-ijerph-17-05416\" ref-type=\"bibr\">45</xref>] reported that the quality of life decreased as occupational PA levels increased in men and women. In all, it was confirmed that health-related quality of life improved as the levels of recreational PA and place movement increased.</p><p>An analysis of dietary intake according to the presence or absence of MetS in elderly people showed that total energy intake and carbohydrate, fat, and protein intakes were significantly higher in the non-MetS group, with similar patterns being witnessed on stratification by sex. However, significant differences were found in elderly women. Similar studies have shown that elderly adults have poor nutrition, with higher levels of carbohydrate intake and lower levels of protein and fat intakes than younger age groups [<xref rid=\"B46-ijerph-17-05416\" ref-type=\"bibr\">46</xref>]. Studies on elderly people in our country have shown that they have nutritional levels below the nutritional recommendations and that the nutritional status of elderly people worsens with age [<xref rid=\"B47-ijerph-17-05416\" ref-type=\"bibr\">47</xref>,<xref rid=\"B48-ijerph-17-05416\" ref-type=\"bibr\">48</xref>,<xref rid=\"B49-ijerph-17-05416\" ref-type=\"bibr\">49</xref>]. In particular, malnutrition in elderly adults is known to cause a decrease in immune function and inflammatory reactions, increasing the risk of chronic diseases [<xref rid=\"B48-ijerph-17-05416\" ref-type=\"bibr\">48</xref>]. In this study, we suggest that lower intake may have facilitated more inflammatory reactions for reasons of lower calorie intake [<xref rid=\"B50-ijerph-17-05416\" ref-type=\"bibr\">50</xref>]. In this study, elderly people with MetS weighed more than elderly people without MetS, but given the low calorie intake, it is possible that the inflammatory reaction may have been facilitated.</p><p>According to the Korean nutritional standards, the recommended energy sources for people aged > 19 years should be as follows: 55–70% carbohydrates, 7–20% protein, and 15–25% fat. In this study, the proportion of carbohydrate intake was high and the proportions of protein and fat were low in both the MetS and non-MetS groups; these differences were bigger in the MetS group. This trend was similar between older men and older women, but significantly stronger in women.</p><p>In a prior study, it was reported that rice and mixed grains are the main energy sources of Koreans, and that there is a problem with the variety of side dishes. It was also reported that elderly people with diabetes consume a lot of carbohydrates and avoid foods with high fat content, which is why the nutritional imbalance is higher [<xref rid=\"B51-ijerph-17-05416\" ref-type=\"bibr\">51</xref>]. In Europe, on the other hand, high energy consumption and increased intake of simple carbohydrates and fat are reported, which seems to be different from characteristics of elderly Korean people caused by nutritional imbalance [<xref rid=\"B52-ijerph-17-05416\" ref-type=\"bibr\">52</xref>]. As a result, the health and nutrition problems of the elderly Korean are increasing due to the lack of energy intake and poor micronutrient intake. A prior study said that the choice of diet was not due to insufficient resources, but due to lack of information and food intake habits, and that food intake habits were established later in life [<xref rid=\"B53-ijerph-17-05416\" ref-type=\"bibr\">53</xref>]. These findings suggest the importance of developing nutrition management programs to optimize the dietary intakes of elderly people with MetS, especially women, so as to maintain an optimal nutritional status.</p><p>MetS is caused by the effects of lifestyle and genetic factors such as diet, weight, and physical activity, and it is reported that the risk of MetS can be reduced due to balanced eating habits and increased PA [<xref rid=\"B54-ijerph-17-05416\" ref-type=\"bibr\">54</xref>,<xref rid=\"B55-ijerph-17-05416\" ref-type=\"bibr\">55</xref>,<xref rid=\"B56-ijerph-17-05416\" ref-type=\"bibr\">56</xref>]. In elderly participants with MetS, malnutrition rather than an excess of nutrition appears to be the problem. Consumption of a balanced diet is more important than the lack of intake of a single nutrient intake. Therefore, future studies should evaluate and recommend best eating practices that can reduce the risk of MetS among elderly Korean people. Elderly people with MetS are also thought to have higher weight, consume less nutrients (including calories), and have lower PA levels; thus, it is believed that this information can be used as basic data in preparing countermeasures through nutrition education, nutrition guidance, and PA education for the elderly with MetS.</p><p>Combining the results of this and prior studies, the nutritional status of the elderly with MetS is poor, and the opportunity to eat quality meals and perform PA is low. Therefore, it is believed that practical measures such as nutrition education, nutrition guidance, and PA education are urgently needed to reduce the incidence of MetS among the elderly.</p><p>Based on the unique correlation between MetS and PA levels and nutrition in elderly people, which has been previously reported, the differences noted in occupational PA, recreational PA, and nutrition according to the presence or absence of MetS and according to sex in this study can be used as a basis for developing lifestyle modifications that can be applied uniquely to Korean elderly people. In particular, education and management regarding balanced nutrition are needed to address the increased carbohydrate intake and decreased fat and protein intakes seen among elderly people. Additionally, it is believed that institutional and policy support will help elderly people with MetS to safely engage in intensive PA.</p><p>This study should be interpreted considering the following limitations. First, we evaluated elderly people with MetS but did not consider the timing of MetS development or the duration of MetS. Second, the amount of PA was not assessed using heart rate measurements or using an accelerometer but quantified based on survey findings, which are prone to errors. Finally, this study reported simple differences without identifying the causality underlying the relationship between PA and nutrition.</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05416\"><title>5. Conclusions</title><p>Our study shows that elderly individuals with MetS have significantly reduced PA levels and have decreased levels of total energy and macronutrient intakes. In particular, elderly women with MetS have low PA levels and energy intakes. These findings highlight the importance of increased PA and proper nutritional intake in elderly people, especially in women. Programs aimed at increasing PA levels and education and care regarding balanced nutrition are needed.</p></sec></body><back><ack><title>Acknowledgments</title><p>This paper was supported by Konkuk University in 2019.</p></ack><notes><title>Author Contributions</title><p>Conceptualization, K.L. and H.-Y.P.; Methodology, K.L. and H.-Y.P.; Formal Analysis, S.-W.K. and W.-S.J.; Investigation, W.-S.J.; Data Curation, S.-W.K. and W.-S.J.; Writing-Original Draft Preparation, W.-S.J., K.L., and H.-Y.P.; Writing-Review and Editing, W.-S.J., H.-Y.P., and K.L.; Supervision, W.-S.J., H.-Y.P., and K.L. All authors have read and approved the final manuscript.</p></notes><notes><title>Funding</title><p>This research received no external funding.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijerph-17-05416\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Lunenfeld</surname><given-names>B.</given-names></name><name><surname>Stratton</surname><given-names>P.</given-names></name></person-group><article-title>The clinical consequences of an ageing world and preventive strategies</article-title><source>Best Pract. Res. Clin. Obstet. 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Biochem.</source><year>2019</year><volume>23</volume><fpage>45</fpage><pub-id pub-id-type=\"doi\">10.20463/jenb.2019.0016</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijerph-17-05416-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Flow diagram for the selection of study participants.</p></caption><graphic xlink:href=\"ijerph-17-05416-g001\"/></fig><table-wrap id=\"ijerph-17-05416-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05416-t001_Table 1</object-id><label>Table 1</label><caption><p>Descriptive characteristics of the participants.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Variables</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Total (<italic>n</italic> = 3720)</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Male (<italic>n</italic> = 1586)</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Female (<italic>n</italic> = 2134)</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Non-MetS<break/>(<italic>n</italic> = 2347)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MetS<break/>(<italic>n</italic> = 1373)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Non-MetS<break/>(<italic>n</italic> = 1018)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MetS<break/>(<italic>n</italic> = 586)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Non-MetS<break/>(<italic>n</italic> = 1248)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MetS<break/>(<italic>n</italic> = 886)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Age (years)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">72.8 ± 0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">73.0 ± 0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">72.7 ± 0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">72.4 ± 0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">72.8 ± 0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">73.3 ± 0.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Height (cm)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">158.2 ± 0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">157.0 ± 0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">165.3 ± 0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">165.9 ± 0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">151.7 ± 0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">151.9 ± 0.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Body weight (kg)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">57.7 ± 0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">63.4 ± 0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">62.4 ± 0.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">69.8 ± 0.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">53.4 ± 0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">59.7 ± 0.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">BMI (kg/m<sup>2</sup>)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23.0 ± 0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25.7 ± 0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22.8 ± 0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25.3 ± 0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23.2 ± 0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25.9 ± 0.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Alcohol</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">75.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">70.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">61.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">59.4</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Smoking</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">39.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">32.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">77.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">80.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Waist circumference (cm)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">81.7 ± 0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">89.7 ± 0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">83.7 ± 0.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">92.0 ± 0.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">80.0 ± 0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">88.4 ± 0.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TG (mg/dL)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">102.6 ± 1.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">173.2 ± 2.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">103.1 ± 1.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">183.3 ± 3.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">102.1 ± 1.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">167.3 ± 3.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HDL-C (mg/dL)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">52.1 ± 0.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">42.8 ± 0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">49.3 ± 0.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40.4 ± 0.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">54.6 ± 0.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">44.1 ± 0.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SBP (mmHg)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">125.5 ± 0.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">132.4 ± 0.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">123.8 ± 0.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">130.2 ± 0.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">127.0 ± 0.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">133.6 ± 0.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DBP (mmHg)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">71.7 ± 0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">73.0 ± 0.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">71.6 ± 0.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">72.4 ± 0.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">71.7 ± 0.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">73.4 ± 0.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fasting glucose (mg/dL)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100.3 ± 0.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">118.5 ± 0.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">102.6 ± 0.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">122.9 ± 1.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">98.2 ± 0.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">116.0 ± 0.9</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mets (%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">63.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">36.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">69.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">30.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">58.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">41.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Occupational activity: vigorous </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.2 ± 7.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35.6 ± 16.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32.0 ± 13.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">71.1 ± 37.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.4 ± 7.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.2 ± 13.4</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Occupational activity: moderate </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">55.0 ± 12.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">49.3 ± 10.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">55.3 ± 14.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">46.8 ± 14.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">54.62 ± 19.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50.7 ± 14.6</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Place movement</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">399.3 ± 15.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">322.4 ± 14.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">424.9 ± 24.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">362.9 ± 26.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">375.9 ± 18.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">299.2 ± 16.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Recreational activity: vigorous </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30.4 ± 7.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26.2 ± 6.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">54.2 ± 14.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">57.0 ± 14.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.7 ± 3.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.5 ± 5.4</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Recreational activity: moderate </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">108.2 ± 8.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">73.0 ± 7.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">148.0 ± 15.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">139.3 ± 16.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">71.7 ± 9.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34.8 ± 5.3</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total physical activity</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">613.1 ± 25.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">506.4 ± 29.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">714.3 ± 40.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">677.1 ± 60.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">520.3 ± 30.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">408.3 ± 30.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Total energy intake (kcal)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1677.3 ± 15.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1572.1 ± 16.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1898.2 ± 21.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1891.9 ± 28.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1475.1 ± 19.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1388.3 ± 17.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Carbohydrate intake (kcal)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1175.9 ± 10.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1114.9 ± 10.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1285.7 ± 14.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1275.0 ± 18.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1075.3 ± 15.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1022.8 ± 12.8</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Protein intake (kcal)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">252.3 ± 4.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">227.7 ± 4.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">287.4 ± 6.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">288.7 ± 9.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">220.1 ± 5.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">192.6 ± 4.9</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Fat intake (kcal)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">225.7 ± 2.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">207.5 ± 2.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">260.3 ± 4.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">255.2 ± 5.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">194.0 ± 3.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">180.1 ± 2.8</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Carbohydrate intake (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">71.3 ± 0.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">72.6 ± 0.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">68.9 ± 0.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">69.2 ± 0.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">73.5 ± 0.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">74.5 ± 0.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Protein intake (%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.4 ± 0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.1 ± 0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.6 ± 0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.3 ± 0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.1 ± 0.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.9 ± 0.1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Fat intake (%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14.4 ± 0.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.7 ± 0.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14.6 ± 0.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14.4 ± 0.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14.2 ± 0.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.2 ± 0.2</td></tr></tbody></table><table-wrap-foot><fn><p>Values are expressed as means ± standard errors; BMI, body mass index; MetS, metabolic syndrome; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; SBP, systolic blood pressure; DBP, diastolic blood pressure; MET, metabolic equivalents.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05416-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05416-t002_Table 2</object-id><label>Table 2</label><caption><p>Levels of physical activity.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Physical Activity<break/>(MET × min/Week)</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Group</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Total</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Male</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Female</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\"><italic>p</italic>-Value</th><th colspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">ANOVA</th></tr><tr><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\"><italic>F</italic>-Value</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value (<italic>η</italic><sup>2</sup>)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Power</th></tr></thead><tbody><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Occupational vigorous</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Non-MetS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.2 ± 7.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32.0 ± 13.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.4 ± 7.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.148</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">S</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.118</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.024 (0.001)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.619</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MetS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35.6 ± 16.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">71.1 ± 37.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.2 ± 13.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.162</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.678</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.195 (0.000)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.254</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-value</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.364</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.329</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.704</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">S × M</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.926</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.336 (0.000)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.161</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Occupational moderate</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Non-MetS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">55.0 ± 12.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">55.3 ± 14.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">54.62 ± 19.06</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.977</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">S</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.009</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.925 (0.000)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.051</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MetS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">49.3 ± 10.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">46.8 ± 14.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50.69 ± 14.56</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.861</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.136</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.713 (0.000)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.066</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-value</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.731</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.697</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.869</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">S × M</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.018</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.893 (0.000)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.052</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Place movement</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Non-MetS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">399.3 ± 15.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">424.9 ± 24.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">375.9 ± 18.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.112</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">S</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.889</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.009 (0.002)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.747</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MetS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">322.4 ± 14.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">362.9 ± 26.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">299.2 ± 16.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.038 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.439</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.001 (0.003)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.898</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-value</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">< 0.001 ***</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.082</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.002 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">S × M</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.116</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.733 (0.000)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.063</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Recreational vigorous</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Non-MetS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30.4 ± 7.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">54.2 ± 14.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.7 ± 3.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.002 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">S</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22.790</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.000 (0.006)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.998</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MetS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26.2 ± 6.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">57.0 ± 14.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.5 ± 5.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.018</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.894 (0.000)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.052</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-value</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.661</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.894</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.974</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">S × M</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.024</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.877 (0.000)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.053</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Recreational moderate</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Non-MetS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">108.2 ± 8.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">148.0 ± 15.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">71.7 ± 9.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">S</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">59.790</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.000 (0.016)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.000</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MetS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">73.0 ± 7.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">139.3 ± 16.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34.8 ± 5.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.811</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.051 (0.001)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.497</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-value</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.002 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.706</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.001 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">S × M</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.458</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.227 (0.000)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.227</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Total physical activity</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Non-MetS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">613.1 ± 25.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">714.3 ± 40.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">520.3 ± 30.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">S</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34.916</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.000 (0.009)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.000</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MetS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">506.4 ± 29.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">677.1 ± 60.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">408.3 ± 30.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.634</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.057 (0.001)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.478</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-value</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.006 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.593</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.010 *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">S × M</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.911</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.340 (0.000)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.159</td></tr></tbody></table><table-wrap-foot><fn><p>Values are expressed as means ± standard errors; MetS, metabolic syndrome. Main effect = S (sex) and M (metabolic syndrome); Interaction effect = S × M (sex × metabolic syndrome); * <italic>p</italic> < 0.05; ** <italic>p</italic> < 0.01; *** <italic>p</italic> < 0.001.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05416-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05416-t003_Table 3</object-id><label>Table 3</label><caption><p>Energy intake.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Variables</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Group</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Total</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Male</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Female</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\"><italic>p</italic>-Value</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">ANOVA</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th></tr><tr><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\"><italic>F</italic>-Value</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value (<italic>η</italic><sup>2</sup>)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Power</th></tr></thead><tbody><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Total energyintake (kcal)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Non-MetS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1677.3 ± 15.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1898.2 ± 21.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1475.1 ± 19.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">S</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">460.451</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.000 (0.110)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.000</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MetS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1572.1 ± 16.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1891.9 ± 28.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1388.3 ± 17.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.652</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.031 (0.001)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.578</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-value</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.001 ***</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.859</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.001 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">S × M</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.479</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.062 (0.001)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.462</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Carbohydrate intake (kcal)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Non-MetS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1175.9 ± 10.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1285.7 ± 14.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1075.3 ± 15.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">S</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">224.853</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.000 (0.057)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.000</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MetS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1114.9 ± 10.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1275.0 ± 18.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1022.8 ± 12.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.188</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.041 (0.001)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.534</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-value</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.001 ***</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.650</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.008 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">S × M</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.832</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.176 (0.000)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.272</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Fat intake (kcal)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Non-MetS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">252.3 ± 4.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">287.4 ± 6.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">220.1 ± 5.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">S</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">165.840</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.000 (0.043)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.000</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MetS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">227.7 ± 4.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">288.7 ± 9.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">192.6 ± 4.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.255</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.039 (0.001)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.541</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-value</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.001 ***</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.901</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.001 ***</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">S × M</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.177</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.023 (0.001)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.624</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Protein intake (kcal)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Non-MetS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">225.7 ± 2.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">260.3 ± 4.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">194.0 ± 3.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">S</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">376.450</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.000 (0.092)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.000</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MetS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">207.5 ± 2.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">255.2 ± 5.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">180.1 ± 2.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.834</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.009 (0.002)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.743</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-value</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.001 ***</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.422</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.001 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">S × M</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.457</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.227 (0.000)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.226</td></tr></tbody></table><table-wrap-foot><fn><p>Values are expressed as means ± standard errors, MetS = metabolic syndrome. Main effect = S (sex) and M (metabolic syndrome); Interaction effect = S × M (sex × metabolic syndrome); ** <italic>p</italic> < 0.01, *** <italic>p</italic> < 0.001.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05416-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05416-t004_Table 4</object-id><label>Table 4</label><caption><p>Classification of physical activity levels.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Physical Activity Level</th><th colspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">MET min/Week (Mean ± SE)</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>n</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Male</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>n</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Female</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Inactive (<italic>n</italic> = 2084)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32.2 ± 1.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">836</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32.2 ± 2.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1248</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32.3 ± 2.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Somewhat active (<italic>n</italic> = 439)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">402.1 ± 3.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">166</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">397.9 ± 5.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">273</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">404.6 ± 4.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Active (<italic>n</italic> = 585)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">731.0 ± 5.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">251</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">735.8 ± 8.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">334</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">727.5 ± 7.1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Very active (<italic>n</italic> = 612)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2333.2 ± 82.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">333</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2499.1 ± 114.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">279</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2135.2 ± 118.1</td></tr></tbody></table><table-wrap-foot><fn><p>Values are expressed as means ± standard errors.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05416-t005\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05416-t005_Table 5</object-id><label>Table 5</label><caption><p>Odds ratio (95% CI) for MetS and MetS components according to physical activity levels.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Physical Activity Group</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MetS</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">High Waist Circumference</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">High Triglycerides</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Low HDL-C</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">High Blood Pressure</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">High Glucose</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Inactive (<italic>n</italic> = 2084)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.00 (reference)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.00 (reference)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.00 (reference)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.00 (reference)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.00 (reference)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.00 (reference)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Somewhat active (<italic>n</italic> = 439)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.03 (0.83–1.29)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.91 (0.74–1.14)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.96 (0.77–1.21)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.88 (0.72–1.08)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.08 (0.87–1.33)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.99 (0.80–1.23)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Active (<italic>n</italic> = 585)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.81 (0.66–0.98) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.80 (0.66–0.96) *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.96 (0.79–1.18)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.86 (0.72–1.04)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.88 (0.73–1.06)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.86 (0.72–1.04)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Very active (<italic>n</italic> = 612)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.72 (0.59–0.88) **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.77 (0.64–0.93) **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.79 (0.65–0.97) *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.64 (0.53–0.77) ***</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.82 (0.69–0.99) *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.85 (0.71–1.02)</td></tr></tbody></table><table-wrap-foot><fn><p>Data presented as odds ratio (95% confidence intervals (CIs)). All ORs are adjusted for age, sex, smoking state, and alcohol. OR = odds ratio, MetS = metabolic syndrome, HDL-C = high-density lipoprotein cholesterol. * <italic>p</italic> < 0.05 vs. reference, ** <italic>p</italic> < 0.01 vs. reference, *** <italic>p</italic> < 0.001 vs. reference.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751375</article-id><article-id pub-id-type=\"pmc\">PMC7432032</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155406</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05406</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Review</subject></subj-group></article-categories><title-group><article-title>Functional Imaging for Therapeutic Assessment and Minimal Residual Disease Detection in Multiple Myeloma</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-2145-9182</contrib-id><name><surname>Jamet</surname><given-names>Bastien</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05406\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-1422-7305</contrib-id><name><surname>Zamagni</surname><given-names>Elena</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05406\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Nanni</surname><given-names>Cristina</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05406\">3</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-8313-3287</contrib-id><name><surname>Bailly</surname><given-names>Clément</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05406\">1</xref><xref ref-type=\"aff\" rid=\"af4-ijms-21-05406\">4</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-6932-7322</contrib-id><name><surname>Carlier</surname><given-names>Thomas</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05406\">1</xref><xref ref-type=\"aff\" rid=\"af4-ijms-21-05406\">4</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-0275-2575</contrib-id><name><surname>Touzeau</surname><given-names>Cyrille</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05406\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Michaud</surname><given-names>Anne-Victoire</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05406\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Moreau</surname><given-names>Philippe</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05406\">1</xref><xref ref-type=\"aff\" rid=\"af4-ijms-21-05406\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Bodet-Milin</surname><given-names>Caroline</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05406\">1</xref><xref ref-type=\"aff\" rid=\"af4-ijms-21-05406\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Kraeber-Bodere</surname><given-names>Françoise</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05406\">1</xref><xref ref-type=\"aff\" rid=\"af4-ijms-21-05406\">4</xref><xref ref-type=\"aff\" rid=\"af5-ijms-21-05406\">5</xref><xref rid=\"c1-ijms-21-05406\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05406\"><label>1</label>Nuclear Medicine/Hematology Department, Nantes University Hospital, F-44000 Nantes, France; <email>bastien.jamet@chu-nantes.fr</email> (B.J.); <email>clement.bailly@chu-nantes.fr</email> (C.B.); <email>thomas.carlier@chu-nantes.fr</email> (T.C.); <email>cyrille.touzeau@chu-nantes.fr</email> (C.T.); <email>annevictoire.michaud@chu-nantes.fr</email> (A.-V.M.); <email>philippe.moreau@chu-nantes.fr</email> (P.M.); <email>caroline.milin@chu-nantes.fr</email> (C.B.-M.)</aff><aff id=\"af2-ijms-21-05406\"><label>2</label>Seràgnoli Institute of Hematology, Bologna University School of Medicine, 40126 Bologna, Italy; <email>e.zamagni@unibo.it</email></aff><aff id=\"af3-ijms-21-05406\"><label>3</label>Nuclear Medicine Department, Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; <email>cristina.nanni@aosp.bo.it</email></aff><aff id=\"af4-ijms-21-05406\"><label>4</label>CHU de Nantes, CNRS, Inserm, CRCINA, Université de Nantes, F-44000 Nantes, France</aff><aff id=\"af5-ijms-21-05406\"><label>5</label>Nuclear Medicine Department, ICO René Gauducheau, F-44800 Saint-Herblain, France</aff><author-notes><corresp id=\"c1-ijms-21-05406\"><label>*</label>Correspondence: <email>francoise.bodere@chu-nantes.fr</email>; Tel.: +33-240084136; Fax: +33-240084218</corresp></author-notes><pub-date pub-type=\"epub\"><day>29</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5406</elocation-id><history><date date-type=\"received\"><day>27</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>28</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Serum markers and bone marrow examination are commonly used for monitoring therapy response in multiple myeloma (MM), but this fails to identify minimal residual disease (MRD), which frequently persists after therapy even in complete response patients, and extra-medullary disease escape. Positron emission tomography with computed tomography using 18F-deoxyglucose (FDG-PET/CT) is the reference imaging technique for therapeutic assessment and MRD detection in MM. To date, all large prospective cohort studies of transplant-eligible newly diagnosed MM patients have shown a strong and independent pejorative prognostic impact of not obtaining complete metabolic response by FDG-PET/CT after therapy, especially before maintenance. The FDG-PET/CT and MRD (evaluated by flow cytometry or next-generation sequencing at 10<sup>−5</sup> and 10<sup>−6</sup> levels, respectively) results are complementary for MRD detection outside and inside the bone marrow. For patients with at least a complete response, to reach double negativity (FDG-PET/CT and MRD) is a predictive surrogate for patient outcome. Homogenization of FDG-PET/CT interpretation after therapy, especially clarification of complete metabolic response definition, is currently underway. FDG-PET/CT does not allow MRD to be evaluated when it is negative at initial workup of symptomatic MM. New PET tracers such as CXCR4 ligands have shown high diagnostic value and could replace FDG in this setting. New sensitive functional magnetic resonance imaging (MRI) techniques such as diffusion-weighted MRI appear to be complementary to FDG-PET/CT for imaging MRD detection. The goal of this review is to examine the feasibility of functional imaging, especially FDG-PET/CT, for therapeutic assessment and MRD detection in MM.</p></abstract><kwd-group><kwd>MM</kwd><kwd>imaging</kwd><kwd>therapeutic assessment</kwd><kwd>FDG-PET/CT</kwd><kwd>prognostic value</kwd><kwd>MRD</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05406\"><title>1. Introduction</title><p>Multiple myeloma (MM) is a plasma cell malignancy characterized by a clonal population of bone marrow plasma cells that secrete a monoclonal paraprotein or an immunoglobulin free light chain. Whilst the detection of MM can result from a broad range of indications, including anemia, hypercalcemia, and renal failure, the most common symptom is lytic bone disease owing to an over-activation of osteoclasts, and occurs in over 80% of patients [<xref rid=\"B1-ijms-21-05406\" ref-type=\"bibr\">1</xref>]. Imaging plays a crucial role in the detection of premature lytic bone lesions and modern morphological or hybrid imaging techniques have gradually replaced conventional skeletal surveys which should no longer be performed except when these innovative imaging techniques are not available. It is now recommended [<xref rid=\"B2-ijms-21-05406\" ref-type=\"bibr\">2</xref>] to perform whole-body computed tomography (WBCT) as stand-alone CT, or as part of a positron emission tomography with CT using an 18F-deoxyglucose (FDG-PET/CT) protocol in the first instance at symptomatic MM diagnosis.</p><p>Novel and effective agent-based therapies have led to significant improvements in the treatment of MM patients, with many achieving a profound biological response. This is a crucial goal for transplant-eligible newly diagnosed MM patients, because of its major prognostic impact. Whilst serum markers and bone marrow examination is routinely used for monitoring response to therapy in MM [<xref rid=\"B3-ijms-21-05406\" ref-type=\"bibr\">3</xref>], it fails to identify minimal residual disease (MRD), which frequently persists after therapy, even in complete response patients, and extra-medullary disease escape. Modern biological diagnostic tools, such as multiparametric flow cytometry, next generation sequencing and functional imaging techniques such as FDG-PET/CT, allow the detection of increasingly lower levels of disease. This has led to a strengthening of the definition of complete response, and to the introduction of new concepts in MRD detection inside the bone marrow as well as imaging plus MRD in patients with complete response [<xref rid=\"B4-ijms-21-05406\" ref-type=\"bibr\">4</xref>].</p><p>MM induces a high spatial genomic heterogeneity, resulting in the cohabitation of distinct disease clones within a patient that exhibit various genomic profiles in the bone marrow and in bone focal lesions [<xref rid=\"B5-ijms-21-05406\" ref-type=\"bibr\">5</xref>]. Moreover, this genomic heterogeneity increases with the size of the focal bone lesions depicted by FDG-PET/CT. Detection of MRD within and outside of the bone marrow in patients with biological complete response is therefore crucial to ensure the full eradication of tumor clones in all compartments, and is clearly linked to long-term outcome. The goal of this review is to highlight the potential of functional imaging, especially FDG-PET/CT, for therapeutic assessment and MRD detection in MM.</p></sec><sec id=\"sec2-ijms-21-05406\"><title>2. FDG-PET/CT for Therapeutic Assessment of Multiple Myeloma</title><p>FDG-PET/CT is the gold-standard imaging technique for therapeutic assessment in MM [<xref rid=\"B2-ijms-21-05406\" ref-type=\"bibr\">2</xref>,<xref rid=\"B6-ijms-21-05406\" ref-type=\"bibr\">6</xref>]. FDG-PET/CT uses a radiolabeled glucose analog tracer called FDG [<xref rid=\"B7-ijms-21-05406\" ref-type=\"bibr\">7</xref>]. After intravenous injection, FDG uptake into metabolically active cells occurs via the classic GLUT family of membrane receptors. Similar to normal glucose, it is then phosphorylated by hexokinase, but it becomes trapped in the cell because the C-2 hydroxyl group has been substituted by F-18, and the molecule cannot be fully metabolized to pyruvate. FDG then allows an assessment of glucose uptake, phosphorylation and metabolic activity [<xref rid=\"B8-ijms-21-05406\" ref-type=\"bibr\">8</xref>].</p><p>The great strength of FDG-PET/CT after therapy for MM is that it allows the detection of residual active clonal plasma cells within residual lytic bone lesions. These commonly persist for a long time after the start of therapy. As shown in <xref ref-type=\"fig\" rid=\"ijms-21-05406-f001\">Figure 1</xref>, CT alone is not a suitable imaging technique for monitoring response to therapy in (all?) bone lesions.</p><p>Conversely, sclerotic changes in lytic lesions such as traditional callus formation around rib lesions or sclerotic rim development around axial and peripheral skeleton lesions can lead to the misinterpretation of FDG-PET/CT images and result in false-positive post-therapy images.</p><p>Since the seminal study published by Bartel et al. in 2009 [<xref rid=\"B9-ijms-21-05406\" ref-type=\"bibr\">9</xref>], all large-scale prospective studies of transplant-eligible newly diagnosed MM cohorts have supported the powerful prognostic value of FDG-PET/CT examinations after first-line therapy. These have revealed that a return to normal in all focal bone lesions’ uptake before the autologous stem cell transplantation process (then after chemotherapy induction cycles) is highly correlated with longer progression-free and overall survival, and can be useful as a faithful predictive surrogate for patient outcome which is primordial at age of targeted and risk-based therapies. In 2013, the same team [<xref rid=\"B10-ijms-21-05406\" ref-type=\"bibr\">10</xref>] examined a larger series of 302 patients treated using an intensive protocol consisting of two consecutive autologous stem cell transplantations. The gene expression profiles of 277 of these patients were also examined. Persistence of significant uptake of three focal bone lesions on FDG-PET/CT imaging carried out seven days after the start of induction was significantly correlated with lower progression-free and overall survival in the whole cohort, and additionally in the sub-group of patients harboring high-risk gene expression profiles. These findings imply that FDG-PET/CT imaging could be regarded as a safe instrument for very premature therapeutic adjustment. Finally, in 2018, the identical team corroborated these findings for a wider cohort including more than 500 patients encompassed in their TT4-TT6 clinical trials. This study [<xref rid=\"B11-ijms-21-05406\" ref-type=\"bibr\">11</xref>] aimed to confirm the prognostic significance of the suppression of focal bone lesion uptake at multiple time points after therapy initiation. All patients underwent serial FDG-PET/CT imaging: at baseline, seven days after the start of therapy, after induction therapy was completed, after autologous stem cell transplantation and finally before maintenance therapy. Patients with no residual uptake in focal bone lesions (compared to background bone marrow signal) seven days after the initiating treatment showed progression-free and overall survival values similar to patients without focal bone lesions at diagnosis. At later time points, patients who reached continuous total suppression of focal bone lesions uptake had statistically equivalent progression-free and overall survival values compared to patients without metabolically vibrant focal bone lesions at baseline. These important conclusions emphasized the importance of full suppression of focal bone lesions as a major therapeutic target for newly diagnosed MM patients with focal bone lesions.</p><p>In 2011, Zamagni et al. explored the prognostic relevance of FDG-PET/CT after thalidomide-dexamethasone induction therapy and double autologous stem cell transplantation in 192 newly diagnosed MM patients [<xref rid=\"B12-ijms-21-05406\" ref-type=\"bibr\">12</xref>]. Firstly, they confirmed the relevance of FDG-PET/CT imaging performed after induction therapy because patients harboring a maximum standardized uptake value (SUV<sub>max</sub>, traducing intensity of carbohydrate metabolism) of residual focal bone lesions > 4.2 had a significantly lower progression-free survival. Secondly, patients were assessed by FDG-PET/CT imaging 3 months after autologous stem cell transplantation. Complete metabolic response, defined similarly to Bartel et al. as the complete absence of residual uptake of the initial targets, was reached for 65% of patients. Progression-free and overall survival of these patients at 4 years was significantly higher than for the PET-positive patients (47% vs. 32% and 79% vs. 66%, respectively). Moreover, 23% of all patients who achieved complete biological response according to the IMWG’s regular guidelines harbored an FDG-PET/CT-positive lesion and were associated with poorer outcome, meaning residual FDG uptake after therapy could detect MRD. Multivariate analysis of all statistically significant prognostic variables by univariate analysis confirmed that FDG-PET/CT status after autologous stem cell transplantation (before maintenance therapy) was one of the most significant independent predictors of progression-free survival. Afterwards, in 2015, the same group reproduced these results in a cohort of 282 symptomatic MM patients initially treated between 2002 and 2012 [<xref rid=\"B13-ijms-21-05406\" ref-type=\"bibr\">13</xref>]. FDG-PET/CT was carried out at baseline, before maintenance therapy and at relapse. The strongest baseline variables independently correlated with progression-free and overall survival by multivariate analysis were an initial FDG-PET/CT-derived feature (SUV<sub>max</sub> of focal bone lesions >4.2), an international staging system > 2 and a failure to reach complete biological response. These three features were then combined to build a prognostic scoring system. After therapy, FDG-PET/CT negativity (defined above) was reached in 70% of patients, while only 53% percent of patients reached a conventional biological complete response. Once again, FDG-PET/CT negativity after therapy (before maintenance) clearly positively influenced progression-free and overall survival of patients. FDG-PET/CT negativity was independently correlated with extended progression-free and overall survival, even in the biological complete response patient sub-group. Of the sixty-three percent of patients who relapsed or progressed after first-line therapy, bone progression was identified exclusively by systematic FDG-PET/CT in 12% of them. Residual focal bone lesions uptake of SUV<sub>max</sub> >4.2 after first-line therapy was independently correlated with exclusive FDG-PET/CT progression.</p><p>More recently, the prospective multicenter French IMAJEM trial [<xref rid=\"B14-ijms-21-05406\" ref-type=\"bibr\">14</xref>] aimed to compare prognostic values of magnetic resonance imaging (MRI) and FDG-PET/CT. In this study, 134 patients were randomized to receive combined lenalidomide, bortezomib, and dexamethasone (RVD) with or without autologous stem-cell transplantation, followed by lenalidomide maintenance. FDG-PET/CT and MRI were carried out at diagnosis, after three cycles of RVD and before maintenance therapy. A normal MRI result after three cycles of RVD and before maintenance was not predictive of progression-free and overall survival owing to a significant number of false-positive patients. FDG-PET/CT normalization after three cycles of RVD was observed for 32% of the patients with positive initial imaging, and progression-free survival was significantly enhanced for these patients (30-months progression-free survival, 78.7% vs. 56.8%). FDG-PET/CT normalization before maintenance therapy was observed for 62% of the patients with initial positive imaging, and was strongly associated with better progression-free and overall survival compared to patients without normalization (24-months progression-free survival: 72 % vs. 57 % and 24-months overall survival: 94 % vs. 73 %, respectively). Interestingly, FDG-PET/CT complete metabolic response in this trial was defined as the residual uptake of initial focal bone lesions, bone marrow, and extra-medullary potential disease targets compared to the background liver uptake. Using multivariate analysis, a return to normal of pre-maintenance FDG-PET/CT was found to be independently correlated with longer progression-free survival, such as no extramedullary disease at baseline and the depth of biological conventional response after three cycles of induction therapy (very good partial biological response or better) suggesting pre-maintenance therapy is the best time to perform FDG-PET/CT for therapeutic assessment.</p><p>A second analysis of the IMAJEM cohort that only considered patients with baseline FDG-avid focal bone lesions and/or bone marrow involvement (determined by at least Deauville 4-point scale disease uptake) corroborated the powerful prognostic value of FDG-PET/CT results after three cycles of induction therapy [<xref rid=\"B15-ijms-21-05406\" ref-type=\"bibr\">15</xref>]. Indeed, by multivariate analysis, a decrease in the SUV<sub>max</sub> of the initial target (ΔSUV<sub>max</sub>) was the strongest independent prognostic factor regarding to hazard ratio, which was the more discriminative, especially as compared to conventional biological response. Patients with a reduction in uptake of more than 25% after three cycles of induction therapy had a significantly improved progression-free survival.</p><p>Using a smaller prospective cohort of 107 MM patients, Nanni and colleagues also analyzed the prognostic impact of FDG-PET/CT after therapy [<xref rid=\"B16-ijms-21-05406\" ref-type=\"bibr\">16</xref>]. Patients were prospectively enrolled and underwent FDG-PET/CT imaging at baseline, three months after autologous stem cell transplantation and then during follow-up every six to twelve months. Forty-seven of the 107 patients relapsed during follow-up. Twenty-two patients had a negative post-therapy FDG-PET/CT, whereas 15 had a positive post-therapy FDG-PET/CT. There was a significant difference between the median response times of the two groups; 27 months for the PET negative patients and only 18 months for the PET positive patients. Not surprisingly, among patients with a positive PET after therapy, the higher the SUV<sub>max</sub>, the shorter the time to relapse. Among patients who did not relapse during follow-up, 27 had a negative post-therapy PET, whereas 13 had a positive post-therapy PET. None of the patients who did not relapse had an SUV<sub>max</sub> progressive increase during the follow-up.</p><p>The open issue concerning FDG-PET/CT interpretation after therapy, notably before maintenance, is to find out the uptake cut-off of residual lesions corresponding to complete metabolic response definition. Indeed, in the trials summarized above, complete metabolic response was not similarly defined (<xref rid=\"ijms-21-05406-t001\" ref-type=\"table\">Table 1</xref>), and a homogenization has to be done in order to permit meaningful comparisons. This quest for better residual uptake cut-off corresponding to complete metabolic response and MRD definition is currently in progress. The initial findings of a joint analysis of two independent prospective randomized phase III European trials (Italian and French) were presented by Zamagni and colleagues at the 2018 annual meeting of the American Society of Hematology [<xref rid=\"B17-ijms-21-05406\" ref-type=\"bibr\">17</xref>]. From this combined analysis of 236 patients, the strongest independent predictor of prolonged progression-free and overall survival was a Deauville score < 4 before maintenance therapy, and was determined using focal bone lesions, bone marrow, and extramedullary disease compared to hepatic background uptake. The final results of this joint analysis are expected to be published soon.</p><p>Finally, in the large international CASSIOPET study, a companion study of CASSIOPEIA [<xref rid=\"B18-ijms-21-05406\" ref-type=\"bibr\">18</xref>], pre-maintenance complete metabolic response was defined as the residual tumor uptake < mediastinum blood pool background (<xref ref-type=\"fig\" rid=\"ijms-21-05406-f002\">Figure 2</xref>) and uncertain complete metabolic response as the residual tumor uptake <liver background. The preliminary results [<xref rid=\"B19-ijms-21-05406\" ref-type=\"bibr\">19</xref>] show that of 184 patients with pre-maintenance PET results (101 treated with daratumumab+bortezomib/thalidomide/dexamethasone (D-VTd) and 83 by Vtd only), 118 (64.1%) achieved CMR, 47 (25.5%) uCR, 17 (9.2%) partial response (PR) and 2 (1.1%) stable disease (SD). D-VTd prolonged progression-free survival in the patients with PET pre-maintenance negativity. One hundred and two and 43 patients were MRD- (evaluated by MFC or NGS) and PET/CT-negative at MRD levels of 10<sup>−5</sup> and 10<sup>−6</sup>, respectively. Concordance of the PET/CT and MRD results in ≥CR patients (PET-CR/MRD) identified 60 and 28 PET-CR-negative patients at 10<sup>−5</sup> and 10<sup>−6</sup> levels, respectively. Pre-maintenance double negativity rates for PET/CT and MRD were higher in D-VTd-treated patients compared to the Vtd patients (66.7% and 47.5% at 10<sup>−5</sup> and 39.4% and 25.0% at 10<sup>−6</sup>, respectively). Because PET and MRD results are complementary, PET-CR/MRD negativity concordance may provide insight into using both methods as a predictive surrogate for patient outcome.</p></sec><sec id=\"sec3-ijms-21-05406\"><title>3. A Novel PET Tracer (CXCR4) for Therapeutic Assessment of Multiple Myeloma</title><p>Rasche et al. recently published a study that examined the FDG-PET/CT false-negative rate within a cohort of 227 newly diagnosed MM patients, and identified the tumor-intrinsic parameters associated with this pattern [<xref rid=\"B20-ijms-21-05406\" ref-type=\"bibr\">20</xref>]. Whilst 11% of patients were FDG-PET/CT negative, this was not linked to the degree of bone marrow plasma cell infiltration or plasma cell proliferation. They then showed a statistically significant decrease in hexokinase-2 expression in this subset, an enzyme that catalyzes the first phosphorylation step of glycolysis, therefore providing a mechanistic reason. More recently, Abe et al. [<xref rid=\"B21-ijms-21-05406\" ref-type=\"bibr\">21</xref>] investigated the prognostic impact of low hexokinase-2 expression associated with false-negative FDG-PET in MM patients. Ninety patients with newly diagnosed MM were enrolled in this retrospective study and the authors confirmed that an FDG-PET negativity rate of 12% was associated with low expression of hexokinase 2. Moreover, progression-free survival of false-negative FDG-PET patients was significantly improved compared to FDG-PET positive patients. In addition, progression-free survival rates were comparable between false-negative FDG-PET patients and those without recognized high-risk FDG-PET features.</p><p>Thus, FDG-PET/CT is not suitable for MM therapeutic assessment and MRD detection in this sub-group of baseline false-negative FDG-PET patients.</p><p>The growth in molecular imaging developments is highly relevant for MM imaging. Novel PET tracers aiming to target different metabolic pathways or plasma cell receptors have demonstrated interesting preliminarily results for disease detection and could be used for MRD detection, especially in FDG-PET/CT negative patients. These include methionine, which is an amino-acid PET tracer, lipid tracers such as choline or acetate, and other promising immuno-PET targets such as CD138 and CD38. Because we recently reviewed several of these novel tracers [<xref rid=\"B22-ijms-21-05406\" ref-type=\"bibr\">22</xref>], and because data published about MM therapeutic assessment concern only <sup>68</sup>Ga-Pentixafor ligand targeting CXCR4 up until now, here we restrict our review to the potential interest of this CXCR4 tracer for therapeutic assessment and MRD detection in MM.</p><p>CXCR4 is a G-protein-coupled chemokine receptor family member [<xref rid=\"B23-ijms-21-05406\" ref-type=\"bibr\">23</xref>]. CXCL12 (stromal cell-derived factor-1) binds to CXCR4 and triggers different downstream signaling pathways. This results in a variety of responses central to tumor growth and progression including chemotaxis, cell survival and/or proliferation, and gene transcription. The CXCL12/CXCR4 pathway is also implicated in cell migration, homing of hematopoietic stem cells to the bone marrow, angiogenesis and cell proliferation. CXCR4 is overexpressed in a large number of different tumors, including MM [<xref rid=\"B24-ijms-21-05406\" ref-type=\"bibr\">24</xref>], and CXCR4 expression correlates with MM disease progression and outcome [<xref rid=\"B25-ijms-21-05406\" ref-type=\"bibr\">25</xref>]. CXCR4-directed PET imaging has predominantly been used to assess MM, and around two thirds of these patients show elevated receptor expression on the monoclonal plasma cells’ surface. <sup>68</sup>Ga-Pentixafor, a labelled peptide with high affinity for CXCR4 and an excellent signal-to-noise ratio in CXCR4-expressing patients is a promising PET ligand [<xref rid=\"B26-ijms-21-05406\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijms-21-05406\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05406\" ref-type=\"bibr\">28</xref>], especially because it is possible to theranostically target MM using this reagent. Indeed, this same PET ligand can also be labelled with therapeutic ß-emitters such as <sup>177</sup>lutetium or <sup>90</sup>yttrium. Initial CXCR4-directed endo-radiotherapy with <sup>177</sup>Lu- and <sup>90</sup>Y-labeled pentixather [<xref rid=\"B29-ijms-21-05406\" ref-type=\"bibr\">29</xref>] for progressive refractory MM with massive bone marrow and extra-medullary disease showed promising results, including good treatment tolerance and improved preliminary response rates.</p><p>There are still discrepancies between FDG and CXCR4 tracers with respect to disease detection. FDG remains more sensitive than CXCR4 for a large proportion of cases. Indeed, receptor expression on the monoclonal plasma cell surface appears to be an active process that could be deeply impacted by a preceding or concomitant chemotherapy [<xref rid=\"B30-ijms-21-05406\" ref-type=\"bibr\">30</xref>], and further studies to clarify this are required. Moreover, no prognostic data about CXCR4-directed PET imaging results before or after therapy have been reported yet.</p></sec><sec id=\"sec4-ijms-21-05406\"><title>4. New Functional MRI Approaches for Therapeutic Assessment of Multiple Myeloma</title><p>Conventional morphological MRI imaging is not a reliable tool for the therapeutic assessment of MM because focal bone abnormalities can persist a long time after the start of therapy without any vital cells, resulting in a high false-positive imaging rate during follow-up [<xref rid=\"B12-ijms-21-05406\" ref-type=\"bibr\">12</xref>]. In the attempt to improve monitoring responses to therapy by MRI, new functional MRI sequences have been developed and have shown interesting results. Diffusion-weighted magnetic resonance imaging (DW-MRI) produces images showing discrepancies of water movements in tissues in extracellular spaces, and therefore directly reflects cell density. Imaging is repeated with at least two b values (diffusion weighting), which permits the automated calculation of the apparent diffusion coefficient (ADC) for each voxel in the image and the production of a quantitative map. Tumors therefore appear as areas where water diffusion is restricted (high signal on source diffusion image and low value on ADC map). In MM, it has been demonstrated that the optimal b value for normal and diseased bone marrow contrast discrimination is around 1400 smm<sup>−2</sup> [<xref rid=\"B31-ijms-21-05406\" ref-type=\"bibr\">31</xref>]. However, reaching such a high b value causes technical issues, and a b value of 900 smm<sup>−2</sup> is commonly chosen. DW-MRI produces high quality image contrast between normal and diseased bone marrow, especially compared to conventional morphological sequences [<xref rid=\"B32-ijms-21-05406\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijms-21-05406\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijms-21-05406\" ref-type=\"bibr\">34</xref>]. Because the response to therapy induces a decrease in cellularity, the high b value signal is reduced, and the ADC values are increased [<xref rid=\"B35-ijms-21-05406\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijms-21-05406\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijms-21-05406\" ref-type=\"bibr\">37</xref>].</p><p>In MM, ADC percent changes seem to be significantly higher in good responder patients (biological complete response or very good partial response) compared to patients without a profound response (partial response, minimal response, stable or progressive disease) [<xref rid=\"B38-ijms-21-05406\" ref-type=\"bibr\">38</xref>]. Changes in ADC after therapy are now included in the recent MY-RADS MRI guidelines [<xref rid=\"B39-ijms-21-05406\" ref-type=\"bibr\">39</xref>]. Whilst these values help to define the response assessment category, ADC cut-off values should be considered with caution, because ADC values are influenced by a lot of different parameters [<xref rid=\"B40-ijms-21-05406\" ref-type=\"bibr\">40</xref>].</p><p>The potential of DW-MRI for MRD detection in MM after therapy has also been investigated [<xref rid=\"B41-ijms-21-05406\" ref-type=\"bibr\">41</xref>] in a study of 168 patients with biological complete response after first-line or salvage therapy. Multi-parametric medullary flow cytometry, FDG-PET/CT and DW- MRI were performed for each patient. DW-MRI identified more patients with residual focal bone lesions than FDG-PET/CT (21% vs. 6%, respectively). In contrast, residual focal bone lesions were only identified by FDG-PET/CT in five patients, suggesting that FDG-PET/CT and DW-MRI have additive prognostic power. Progression-free survival was significantly affected when residual focal bone lesions were detected, regardless of the imaging technique (DW-MRI or FDG-PET/CT). Prediction of outcome was significantly enhanced by the combination of intra-medullary MRD and functional imaging, resulting in double-negative and double-positive technical groups with very different outcomes. Patients identified as negative by both techniques had an excellent progression-free survival, and on the contrary, double-positive patients relapsed quickly.</p><p>Dynamic contrast-enhanced MRI (DCE-MRI) is a dynamic study of the accumulation and distribution of contrast medium (Gadolinium-DTPA) in the region of interest (in T1-weighted sequence) after a constant pump-controlled bolus injection. DCE-MRI-derived quantitative parameters such as amplitude A (correlated with blood volume) and exchange rate constant k<sub>ep</sub> (reflecting vessel permeability and perfusion) reflect bone marrow angiogenesis/microcirculation, which plays a central role in the pathogenesis of MM, notably because it is correlated to MM-related bone disease and because of its pejorative prognostic significance [<xref rid=\"B42-ijms-21-05406\" ref-type=\"bibr\">42</xref>,<xref rid=\"B43-ijms-21-05406\" ref-type=\"bibr\">43</xref>,<xref rid=\"B44-ijms-21-05406\" ref-type=\"bibr\">44</xref>]. In MM, preliminary studies have shown that the depth of the biochemical response after autologous stem cell transplantation is significantly correlated to decreased bone marrow microcirculation assessed by DCE-MRI-derived quantitative features [<xref rid=\"B45-ijms-21-05406\" ref-type=\"bibr\">45</xref>,<xref rid=\"B46-ijms-21-05406\" ref-type=\"bibr\">46</xref>].</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijms-21-05406\"><title>5. Conclusions and Perspectives</title><p>FDG-PET/CT is the preferred imaging technique for the therapeutic assessment of MM. All large prospective studies of transplant-eligible newly diagnosed MM patient cohorts showed a strong and independent pejorative prognostic impact of not achieving complete metabolic response by FDG-PET/CT after therapy, especially before maintenance therapy. FDG-PET/CT and MRD (evaluated by multi-parametric flow cytometry or next generation sequencing at 10<sup>−5</sup> and 10<sup>−6</sup> levels respectively) results are complementary for MRD detection outside and inside the bone marrow. For patients with at least a biological complete response, achieving double negativity (FDG-PET/CT and intra-medullary MRD) is a predictive surrogate for patient outcome and a trustworthy and premature biomarker of treatment effectiveness.</p><p>However, FDG-PET/CT is not yet widely used in this setting in clinical practice. In order to extend its purpose, open issues need to be addressed, especially homogenization of interpretation guidelines for reporting results. Because the definition of complete metabolic response in FDG-PET/CT is not clearly established, the final results of prospective analyses currently underway, especially the CASSIOPET study, are expected to standardize the interpretation criteria of FDG-PET/CT after therapy to better define a cut-off to determine PET positivity/negativity for MRD assessment outside the bone marrow.</p><p>FDG-PET/CT is not suitable for MM therapeutic assessment and MRD detection in a sub-group of baseline symptomatic false-negative FDG-PET/CT patients. Novel PET tracers aiming to target different metabolic pathways or plasma cell receptors like CXCR4 are being employed in current studies and are expected to alleviate the known limitations of FDG. Whilst new sensitive functional MRI sequences such as DW-MRI are not routinely used in the clinic, its use in experimental research settings suggests that it will be very useful in complementing FDG-PET/CT imaging for MRD detection. DW-MRI needs to be further studied and correlated with clinical outcomes so simultaneous acquisition with a PET-MRI systemsounds attractive. PET-MRI will allow homogeneous prospective therapeutic assessment comparisons between FDG-PET/CT and DCE-MRI as well, particularly by comparing metabolic and bone marrow angiogenesis changes.</p><p>Finally, the influence of MRD assessment on treatment approaches still has to be defined.</p></sec></body><back><ack><title>Acknowledgments</title><p>This work has been supported in part by grants from the French National Agency for Research called “Investissements d’Avenir” IRON Labex n◦ ANR-11-LABX-0018-01 and ArronaxPlus Equipex n◦ ANR-11-EQPX-0004, and by a grant INCa-DGOS-Inserm_12558 (SIRIC ILIAD).</p></ack><notes><title>Author Contributions</title><p>All authors have directly participated in the review process and in the writing of this paper, and have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This paper received no external funding.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijms-21-05406\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Kyle</surname><given-names>R.A.</given-names></name><name><surname>Gertz</surname><given-names>M.A.</given-names></name><name><surname>Witzig</surname><given-names>T.E.</given-names></name><name><surname>Lust</surname><given-names>J.A.</given-names></name><name><surname>Lacy</surname><given-names>M.Q.</given-names></name><name><surname>Dispenzieri</surname><given-names>A.</given-names></name><name><surname>Fonseca</surname><given-names>R.</given-names></name><name><surname>Rajkumar</surname><given-names>S.V.</given-names></name><name><surname>Offord</surname><given-names>J.R.</given-names></name><name><surname>Larson</surname><given-names>D.R.</given-names></name><etal/></person-group><article-title>Review of 1027 Patients with Newly Diagnosed Multiple Myeloma</article-title><source>Mayo Clin. 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Positron emission tomography with computed tomography using 18F-deoxyglucose (FDG-PET/CT) images before therapy (<bold>A</bold>) show several focal bone lesions (FLs) in maximum intensity projection (MIP) images (<bold>A1</bold>), especially the left pelvic FL (Red arrow; (<bold>A2</bold>) PET images; (<bold>A3</bold>): computed tomography (CT) images; (<bold>A4</bold>) fused images, maximum standardized uptake value (SUV<sub>max</sub>: 9)) and left collarbone (Blue arrow)/acromion FLs (Yellow arrow) in axial slices (<bold>A5</bold>/<bold>A6</bold>). Note the numerous osteolytic bone lesions without corresponding increased FDG uptake in the CT images as this patient had been pretreated, and morphological abnormalities may persist a long time after. FDG-PET/CT images after therapy (<bold>B</bold>) shows overall decrease in FL uptake in MIP images (<bold>B1</bold>). Whilst the left collarbone(Blue arrow)/acromion FL (Yellow arrow) residual uptake (<bold>B1</bold>/<bold>B2</bold>/<bold>B3</bold>/<bold>B4</bold>) is lower than the hepatic background (Deauville 3), the left pelvic FL (Red arrow) residual uptake (<bold>B1</bold>/<bold>B5</bold>/<bold>B6</bold>/<bold>B7</bold>) has decreased but is still higher than the hepatic background (Deauville 4, SUV<sub>max</sub>: 6) revealing a partial metabolic response.</p></caption><graphic xlink:href=\"ijms-21-05406-g001a\"/><graphic xlink:href=\"ijms-21-05406-g001b\"/></fig><fig id=\"ijms-21-05406-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Thirty-nine-year-old patient with a IgAk MM revealed by a thoracic spinal cord compression. FDG-PET/CT images performed at diagnosis (<bold>A1</bold>: Maximum intensity projection (MIP) images) show a T7 vertebral body destruction with corresponding increased uptake in the sagittal fused slice (Yellow arrow <bold>A2</bold>) and a highly metabolically active liver hilary lymph node (Red arrow, <bold>A3</bold> axial fused slice) suspect of extra-medullary disease (EMD). Of note, a recent right knee trans-arthroscopic reconstruction of the anterior cruciate ligament (ACL) by the Kenneth-Jones technique explaining the corresponding uptake (Black arrow in <bold>A1</bold> image). FDG-PET/CT images performed after autologous stem cell transplantation (before maintenance therapy) show complete metabolic response (<bold>B1</bold>) with no significant residual uptake (<mediastinum blood pool, Deauville 2) of T7 vertebral body in sagittal fused slice (Yellow arrow, <bold>B2</bold>) and liver hilary lymph node in axial fused slice (Red arrow, <bold>B3</bold>).</p></caption><graphic xlink:href=\"ijms-21-05406-g002\"/></fig><table-wrap id=\"ijms-21-05406-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05406-t001_Table 1</object-id><label>Table 1</label><caption><p>Definition of complete metabolic response (including Fls and/or diffuse bone marrow involvement and/or EMD when present) according to main prospective studies assessing FDG-PET/CT for response to therapy in MM.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Study</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Patients (N)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Definition of CMR</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bartel et al., 2009 [<xref rid=\"B9-ijms-21-05406\" ref-type=\"bibr\">9</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">239</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100 % normalization of FDG uptake</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Zamagni et al., 2011 [<xref rid=\"B12-ijms-21-05406\" ref-type=\"bibr\">12</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">192</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Residual FDG uptake < 4.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Usmani et al., 2013 [<xref rid=\"B10-ijms-21-05406\" ref-type=\"bibr\">10</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">302</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100 % normalization of FDG uptake</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Moreau et al., 2015 [<xref rid=\"B14-ijms-21-05406\" ref-type=\"bibr\">14</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">134</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Residual FDG uptake ≤ hepatic background (DS 1-2-3)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Zamagni et al., 2018 [<xref rid=\"B17-ijms-21-05406\" ref-type=\"bibr\">17</xref>]</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">236</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Residual FDG uptake ≤ hepatic background (DS 1-2-3)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Moreau et al., 2019 [<xref rid=\"B19-ijms-21-05406\" ref-type=\"bibr\">19</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">268</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Residual FDG uptake ≤ mediastinum background (DS 1-2) and uCMR when Residual FDG uptake ≤ hepatic background (DS 1-2-3)</td></tr></tbody></table><table-wrap-foot><fn><p>CMR indicates complete metabolic response; Fls: focal bone lesions; EMD: extra-medullary disease; FDG-PET/CT: 18F-fluorodeoxyglucose-positron emission tomography with computed tomography; DS: Deauville scale; uCR: uncertain complete metabolic response.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751712</article-id><article-id pub-id-type=\"pmc\">PMC7432033</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155512</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05512</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Evaluation of the Mitragynine Content, Levels of Toxic Metals and the Presence of Microbes in Kratom Products Purchased in the Western Suburbs of Chicago</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Prozialeck</surname><given-names>Walter C.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05512\">1</xref><xref rid=\"c1-ijerph-17-05512\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Edwards</surname><given-names>Joshua R.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05512\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-5702-2257</contrib-id><name><surname>Lamar</surname><given-names>Peter C.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05512\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Plotkin</surname><given-names>Balbina J.</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05512\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Sigar</surname><given-names>Ira M.</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05512\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-2302-8949</contrib-id><name><surname>Grundmann</surname><given-names>Oliver</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05512\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Veltri</surname><given-names>Charles A.</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05512\">3</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05512\"><label>1</label>Department of Pharmacology, College of Graduate Studies, Midwestern University, 555 31st Street, Downers Grove, IL 60515, USA; <email>jedwar@midwestern.edu</email> (J.R.E.); <email>plamar@midwestern.edu</email> (P.C.L.)</aff><aff id=\"af2-ijerph-17-05512\"><label>2</label>Department of Microbiology and Immunology, College of Graduate Studies, Midwestern University, 555 31st Street, Downers Grove, IL 60515, USA; <email>bplotk@midwestern.edu</email> (B.J.P.); <email>isigar@midwestern.edu</email> (I.M.S.)</aff><aff id=\"af3-ijerph-17-05512\"><label>3</label>Department of Pharmaceutical Sciences, College of Pharmacy Glendale, Midwestern University, 19555 N. 59th Avenue, Glendale, AZ 85308, USA; <email>ogrund@midwestern.edu</email> (O.G.); <email>cveltr@midwestern.edu</email> (C.A.V.)</aff><author-notes><corresp id=\"c1-ijerph-17-05512\"><label>*</label>Correspondence: <email>wprozi@midwestern.edu</email>; Tel.: +630-515-6385; Fax: +630-515-6295</corresp></author-notes><pub-date pub-type=\"epub\"><day>30</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5512</elocation-id><history><date date-type=\"received\"><day>30</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>27</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Kratom (<italic>Mitragyna speciosa</italic>, Korth) is a tree-like plant that is indigenous to Southeast Asia. Kratom leaf products have been used in traditional folk medicine for their unique combination of stimulant and opioid-like effects. Kratom is being increasingly used in the West for its reputed benefits in the treatment of pain, depression and opioid use disorder. Recently, the United States Food and Drug Administration and Centers for Disease Control have raised concerns regarding the contamination of some kratom products with toxic metals (Pb and Ni) and microbes such as <italic>Salmonella</italic>. To further explore this issue, eight different kratom products were legally purchased from various “head”/”smoke” shops in the Western Suburbs of Chicago and then tested for microbial burden, a panel of metals (Ni, Pb, Cr, As, Hg, Cd), and levels of the main psychoactive alkaloid mitragynine. All of the samples contained significant, but variable, levels of mitragynine (3.9–62.1 mg/g), indicating that the products were, in fact, derived from kratom. All but two of the samples tested positive for the presence of various microbes including bacteria and fungi. However, none of the samples tested positive for <italic>Salmonella</italic>. Seven products showed significant levels of Ni (0.73–7.4 µg/g), Pb (0.16–1.6 µg/g) and Cr (0.21–5.7 µg/g) while the other product was negative for metals. These data indicate that many kratom products contain variable levels of mitragynine and can contain significant levels of toxic metals and microbes. These findings highlight the need for more stringent standards for the production and sale of kratom products.</p></abstract><kwd-group><kwd>kratom</kwd><kwd>microbes</kwd><kwd>Salmonella</kwd><kwd>metals</kwd><kwd>Pb</kwd><kwd>Ni</kwd><kwd>Cr</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05512\"><title>1. Introduction</title><p>Kratom (also known as ketum) is a tree-like plant (<italic>Mitragyna speciosa</italic>, Korth, Rubiaceae) that is native to Thailand, Malaysia, Indonesia and other regions of Southeast Asia [<xref rid=\"B1-ijerph-17-05512\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijerph-17-05512\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijerph-17-05512\" ref-type=\"bibr\">3</xref>]. For generations, indigenous peoples in Southeast Asia have used kratom leaves (either unprocessed or brewed into teas or other decoctions) as a mild stimulant to stave off fatigue, or as an opioid substitute to treat pain or opioid use disorder [<xref rid=\"B4-ijerph-17-05512\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijerph-17-05512\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijerph-17-05512\" ref-type=\"bibr\">6</xref>]. Pharmacologic studies have shown that kratom leaves contain a variety of active alkaloids with the most significant being mitragynine and 7-hydroxymitragynine [<xref rid=\"B7-ijerph-17-05512\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijerph-17-05512\" ref-type=\"bibr\">8</xref>]. Mitragynine has partial agonist activity at mu-type opioid receptors and antagonist activity at delta-type opioid receptors [<xref rid=\"B7-ijerph-17-05512\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijerph-17-05512\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05512\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijerph-17-05512\" ref-type=\"bibr\">10</xref>]. In addition, it can modulate the release and synaptic actions of neurotransmitters such as norepinephrine and serotonin [<xref rid=\"B7-ijerph-17-05512\" ref-type=\"bibr\">7</xref>,<xref rid=\"B10-ijerph-17-05512\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijerph-17-05512\" ref-type=\"bibr\">11</xref>]. Even though kratom has been used in Southeast Asia for generations, it is only over the past 25 years that kratom use has expanded to Europe and North America [<xref rid=\"B1-ijerph-17-05512\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijerph-17-05512\" ref-type=\"bibr\">2</xref>,<xref rid=\"B4-ijerph-17-05512\" ref-type=\"bibr\">4</xref>,<xref rid=\"B12-ijerph-17-05512\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijerph-17-05512\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijerph-17-05512\" ref-type=\"bibr\">14</xref>]. In the US, kratom products are used extensively for the self-management of pain, depression and opioid use disorders [<xref rid=\"B15-ijerph-17-05512\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijerph-17-05512\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijerph-17-05512\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijerph-17-05512\" ref-type=\"bibr\">18</xref>]. The most widely used products include chopped, dried leaf material (either alone or in capsule or tablet form) or concentrated extracts that are formulated as liquids or capsules [<xref rid=\"B2-ijerph-17-05512\" ref-type=\"bibr\">2</xref>,<xref rid=\"B12-ijerph-17-05512\" ref-type=\"bibr\">12</xref>,<xref rid=\"B19-ijerph-17-05512\" ref-type=\"bibr\">19</xref>]. These products are widely available from internet vendors or in specialty stores commonly known as “head shops” or “smoke shops”, although some products are now being sold through chain stores that specialize in the sale of herbal supplements.</p><p>In the US, kratom is regarded as a new dietary ingredient under the United States Food and Drug Administration (FDA) and Drug Enforcement Administration policies, and is not regulated as a dietary supplement according to the Dietary Supplement Health and Education Act (for reviews see: [<xref rid=\"B12-ijerph-17-05512\" ref-type=\"bibr\">12</xref>,<xref rid=\"B19-ijerph-17-05512\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijerph-17-05512\" ref-type=\"bibr\">20</xref>]). Although it remains legal in most of the US, at the time of writing several states, such as Alabama, Florida, Indiana, Arkansas, Wisconsin and Tennessee, have passed legislation banning the local sale and possession of kratom (for reviews see [<xref rid=\"B19-ijerph-17-05512\" ref-type=\"bibr\">19</xref>]).</p><p>As kratom use in the US has increased, there has been growing concern about the safety of kratom and kratom products being sold in the US. Much of the concern focused on the increase in the number of reports to poison control centers in which kratom was mentioned as a contributing factor in adverse health effects including up to 90 deaths [<xref rid=\"B21-ijerph-17-05512\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijerph-17-05512\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijerph-17-05512\" ref-type=\"bibr\">23</xref>]. These reports of adverse effects have been cited as the basis for the Drug Enforcement Agency (DEA)’s proposal to place kratom and its mitragynine alkaloids into Schedule I of the Controlled Substance Act [<xref rid=\"B24-ijerph-17-05512\" ref-type=\"bibr\">24</xref>], which would effectively ban the sale and possession of kratom. However, there was no definitive proof that kratom was the cause of the deaths. Almost all cases involved the use of other drugs including opioids, benzodiazepines or alcohol, or the presence of serious health conditions including refractory depression [<xref rid=\"B25-ijerph-17-05512\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijerph-17-05512\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijerph-17-05512\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijerph-17-05512\" ref-type=\"bibr\">28</xref>]. In some cases, the kratom products themselves may have been adulterated with exogenous materials such as synthetic opioids [<xref rid=\"B25-ijerph-17-05512\" ref-type=\"bibr\">25</xref>,<xref rid=\"B29-ijerph-17-05512\" ref-type=\"bibr\">29</xref>]. The DEA’s proposal to ban kratom prompted a massive response by kratom users and advocates that led the DEA to place the move to Schedule I status on an indefinite hold that continues to the present day [<xref rid=\"B19-ijerph-17-05512\" ref-type=\"bibr\">19</xref>].</p><p>Recently, additional safety concerns have been raised about kratom products in the US. Two of the most important concerns involve the possible contamination of kratom products with harmful microbes, particularly <italic>Salmonella</italic> [<xref rid=\"B30-ijerph-17-05512\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijerph-17-05512\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijerph-17-05512\" ref-type=\"bibr\">32</xref>], and toxic metals, such as Ni and Pb [<xref rid=\"B33-ijerph-17-05512\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijerph-17-05512\" ref-type=\"bibr\">34</xref>]. In order to further investigate these contamination issues, we evaluated the levels of microbes, toxic metals (As, Cd, Cr, Fe, Hg, Ni, and Pb), mitragynine, and possible adulteration with benzodiazepines and opioids in a panel of eight different kratom products that were purchased in the western suburbs of Chicago.</p></sec><sec id=\"sec2-ijerph-17-05512\"><title>2. Materials and Methods</title><sec id=\"sec2dot1-ijerph-17-05512\"><title>2.1. Source of Products</title><p>The kratom products analyzed in the present study were legally purchased from “head” shops and “smoke” shops in several western suburbs of Chicago, IL (Westmont, IL, Lisle, IL, Elmhurst, IL and Aurora, IL, USA) in July and August of 2019. The samples were stored in the dark, at room temperature, in their original packaging, until processing for the various analyses. To ensure consistency of results, 2 samples of each product, which were purchased about 1 month apart, were evaluated. The samples were initially opened under sterile conditions in a laminar flow hood and portions were processed for microbial culture. The remaining portions of each sample were aseptically divided and processed for the analysis of a panel of metals, mitragynine content, and screening for 13 opioids and 8 benzodiazepines.</p></sec><sec id=\"sec2dot2-ijerph-17-05512\"><title>2.2. Microbiological Testing</title><p>To determine the prevalence of <italic>Salmonella</italic> contamination associated with the kratom samples obtained, each sample (1 or 0.5 gm) was suspended in sterile phosphate buffered saline (PBS) (1:10 dilution) and vortexed (1 min). The sample was then serially diluted and plated (<italic>n</italic> = 3) onto sheep blood agar, an enriched, differential (hemolysis) bacteriological medium used to permit the broadest range of bacteria to grow, and MacConkey agar, a medium selective for Gram negative bacteria and differential for lactose fermentation, which is consistent with bacteria that are members of the order Enterobacteriales. All lactose negative colonies were screened for oxidase production to validate <italic>Salmonella</italic> testing (oxidase negative) and MacConkey agar by standard spread plate method. Plates were incubated at 37 °C and 42 °C for 24–48 h. After 24 and 48 h incubation, the number of colonies on each medium was counted. Screening for the presence of <italic>Salmonella</italic> was done using the methods outlined in the FDA <italic>Official Methods for Bacteriological Analytical Manual</italic> [<xref rid=\"B35-ijerph-17-05512\" ref-type=\"bibr\">35</xref>]. Briefly, kratom samples from each sample (<italic>n</italic> = 16) were enriched for <italic>Salmonella</italic> in Neogen Revive/RV-M broth per the manufacturers’ directions (Reveal 2.0 for <italic>Salmonella</italic>, Neogen, Lansing, MI, USA). Portions of the enriched samples that were positive with Reveal 2.0 <italic>Salmonella</italic> were then spread plated onto both MacConkey and Hektoen Enteric (HE) agar. All lactose negative colonies were screened for oxidase production. Verification of the identity of lactose-negative colonies was performed using an API-20 test kit (BioMerieux, Durham, UK) per the manufacturer’s instructions. All suspected <italic>Salmonella</italic> colonies from HE were inoculated into Triple Sugar Iron agar and lysine iron agar slants, per FDA protocol [<xref rid=\"B35-ijerph-17-05512\" ref-type=\"bibr\">35</xref>]. All tests were negative for <italic>Salmonella</italic>.</p></sec><sec id=\"sec2dot3-ijerph-17-05512\"><title>2.3. Mitragynine Analysis</title><p>The samples (1 g) were placed in individual teabags and extracted with 12 mL of 100% methanol (ACS Grade, Fisher Scientific, Waltham, MA, USA) using microwave assisted extraction (MAE) with an Ethos EX labstation (Milestone Srl, Sorisole, Bergamo, Italy). The extraction conditions were a 15 min ramp to 65 °C, held at 65 °C for 30 min, followed by a 15 min cool-down, for a total run time of 4 °C.</p><p>For evaluation of mitragynine and 7-hydroxymitragynine concentrations and detection of benzodiazepines, extracts were diluted to 25 mg/mL, 1 mg/mL, and 0.1 mg/mL and placed in the autosampler rack of an Agilent 1260 HPLC system (Agilent Technologies, Santa Clara, CA, USA). For each HPLC separation, 1 µL was injected and chromatographed on an Agilent Poroshell 120 EC-C18 column (4.6 × 100 mm, 2.7 µm, Agilent Technologies, Santa Clara, CA, USA), equipped with the appropriate guard column, and separated at a flow rate of 0.5 mL/min. The mobile phase consisted of eluent A (LCMS grade water with 0.1% formic acid, Fisher Scientific, Waltham, MA, USA) and eluent B (LCMS grade acetonitrile with 0.1% formic acid, Fisher Scientific, Waltham, MA, USA). An isocratic elution of 25% B was started for 1 min, followed by a linear gradient applied from 25% to 75% B over 11 min, finally an isocratic wash of 75% B for 5 min was applied before a re-equilibration of 25% B for 3 min. Typically, a back pressure of <95 bar was observed at 25% acetonitrile/water.</p><p>For detection of opioids, extracts (25 mg/mL) were placed in the autosampler rack of the HPLC system. For each HPLC separation, 1 µL was injected and chromatographed on an Agilent Poroshell 120 Phenyl-hexyl column (4.6 × 100 mm, 2.7 µm, Agilent Technologies, Santa Clara, CA, USA), equipped with the appropriate guard column, and separated at a flow rate of 1.44 mL/min. The mobile phase consisted of eluent A (LCMS grade water with 0.1% formic acid, Fisher Scientific) and eluent B (LCMS grade acetonitrile with 0.1% formic acid, Fisher Scientific). An isocratic elution of 10% B was started for 1 min, followed by a linear gradient applied from 10% to 45% B over 3 min, and then from 45% to 100% B over 2 min, finally an isocratic wash of 100% B for 2.4 min was applied before a re-equilibration of 10% B for 4 min. Typically, a back pressure of <95 bar was observed at 10% acetonitrile/water.</p><p>Compounds were detected using an Accurate-Mass 6530 quadrupole time-of-flight mass analyzer (Q-TOF, Agilent Technologies, Santa Clara, CA, USA). The Q-TOF mass analyzer provided identification of compounds using accurate masses of full spectra in Targeted MS/MS mode.</p><p>Quantification of mitragynine and 7-hydroxymitragynine were performed using a series of calibration controls prepared fresh on each day of analysis at incremental concentrations ranging from 1 ng/mL to 100 µg/mL. The coefficient of determination was >0.99 for all calibration curves. The extracted kratom samples were analyzed for the presence of 13 opioids and 8 benzodiazepines by comparing the chromatograms to those of the reference mixtures Pain Management Multi-component Opiate Mixture-13 solution and Benzodiazepine Multi-component Mixture-8 solution (Sigma Aldrich, St. Louis, MO, USA). All samples were analyzed using MassHunter Software (Agilent Technologies, Santa Clara, CA, USA).</p></sec><sec id=\"sec2dot4-ijerph-17-05512\"><title>2.4. Metal Analyses</title><p>Samples of each product were analyzed for a panel of metals (As, Cd, Cr, Fe, Hg, Ni and Pb) by S.G.S. Chemical Solutions, Inc. (Harrisburg, PA, USA). The contents of the capsule samples were emptied. All samples were weighed and dried to a constant weight prior to digestion. Approximately 0.5 g of sample was analytically weighed into a microwave digestion vessel. Five mL of concentrated nitric acid and 4 mL of 30% hydrogen peroxide were added to each sample vessel. The samples were digested in a Milestone Ethos microwave digester at a final temperature of 180 °C for 20 min after being ramped to that temperature in 20 min. The sample vessels were then removed, and the contents were transferred to virgin polypropylene 50 mL flat bottom tubes. They were further diluted in these tubes to a final volume of 25 mL with American Society for Testing Materials (ASTM) Type I deionized water. The sample digestates were then analyzed using Perkin Elmer Inductively coupled plasma-mass spectrometer (ICP-MS) instruments (Fisher Scientific, Waltham, MA, USA). Different external analytical calibration ranges were established depending on the element(s) of interest. Appropriate internal standards were added to each sample aliquot prior to further dilution and subsequent analysis. Results were expressed as µg/g dry weight and µg/g wet weight.</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05512\"><title>3. Results</title><p><xref rid=\"ijerph-17-05512-t001\" ref-type=\"table\">Table 1</xref> lists the eight kratom products that were evaluated. Six of the products contained finely ground leaf material (400–600 mg) in capsules (1, 1A, 2, 2A, 4, 4A, 5, 5A, 6, 6A) or as the free powder (8, 8A). Samples 3 and 3A contained processed leaf material in capsules and samples 7 and 7A contained a concentrated alkaloid extract in capsules. These products were chosen for analysis because they appeared to be widely available and were prominently displayed in many of the shops that were visited by the lead author. In addition, informal discussions with kratom users indicated that these products were being widely used in the Chicago area.</p><p>The results of the microbial analyses are summarized in <xref rid=\"ijerph-17-05512-t002\" ref-type=\"table\">Table 2</xref>. The overall levels of microbes associated with kratom ranged from zero to in excess of a million colony forming units (CFU) on the most permissive medium (sheep blood agar) incubated at human body temperature. There was also variability in the microbial load between different samples of product obtained from the same source. Interestingly, the most processed kratom products, samples 3 and 7, exhibited the lowest level of microbial contamination (<50 CFU/mL level of sensitivity). Bacteria identified as oxidase negative, lactose negative that were isolated from samples 4 and 5 were definitively identified as <italic>Acinetobacter baumanni</italic>/<italic>calcoaceticus</italic>, <italic>Erwinia</italic> spp., <italic>Serratia</italic> spp., <italic>Pseudomonas oryzihabitans</italic>, <italic>Enterobacter</italic> spp. or <italic>Aeromonas hydrophila</italic>/<italic>caviae</italic>/<italic>sobria</italic> via the API20 test system. None of the samples tested positive for the presence of <italic>Salmonella</italic>.</p><p><xref rid=\"ijerph-17-05512-t003\" ref-type=\"table\">Table 3</xref> lists the analytical parameters that were used for the analyses of mitragynine analogs and possible adulteration with a panel of opioids and benzodiazepines. <xref ref-type=\"fig\" rid=\"ijerph-17-05512-f001\">Figure 1</xref> shows representative chromatograms that were used to identify and quantify mitragynine. <xref rid=\"ijerph-17-05512-t004\" ref-type=\"table\">Table 4</xref> summarizes the results of the mitragynine quantitation and adulteration analyses. Mitragynine was present in all samples indicating that the samples were derived from kratom. Samples 1–6 and 8 contained levels of mitragynine comparable to other commercial kratom products ranging from 3.35–11.33 mg mitragynine/g raw material while the content of mitragynine in sample 7 was much higher at 59.76 mg/g. None of the tested kratom products had detectable quantities sufficient for a positive presence of the 7-hydroxymitragynine, reference opioids and benzodiazepines.</p><p><xref rid=\"ijerph-17-05512-t005\" ref-type=\"table\">Table 5</xref> summarizes the results of the metal analyses in terms of µg of metal per gram of main product. Note that the values for the replicate samples are generally in excellent agreement with each other. All of the samples except #7 and 7A, which were concentrated kratom extracts, contained measurable levels of metals. As would be expected for leaf-based products, levels of Fe were consistently high and were similar to levels reported by Braley and Hondrogiannis [<xref rid=\"B36-ijerph-17-05512\" ref-type=\"bibr\">36</xref>]. Of the toxic metals that were evaluated, the highest levels were for Pb, Ni and Cr. Levels of several other metals including As, Cd and Hg were very low, actually at or near the level of detection.</p><p>It should be noted that, in evaluating the metal content of the samples, we primarily considered the results on the basis of the µg of the raw wet weight of each kratom product. This was done because kratom users typically base their kratom doses on grams of leaf product as obtained from vendors. Linking the metal levels to raw weight of product allows for direct estimation of the levels of metal that would occur with commonly used doses of kratom (See Discussion). The data for levels of metals based on dry weight of product (not shown) were consistently 10–15% higher than the results based on raw, wet weight. This difference was the same across all products tested.</p></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05512\"><title>4. Discussion</title><p>Of the eight kratom products that were evaluated in the present study, six were finely ground raw leaf products, either alone or in capsule form. Two of the products were concentrated (7, 7A) or semi-concentrated (3, 3A) kratom leaf extracts in capsule form. The three key findings were: (a) all of the samples contained substantial, but variable, levels of mitragynine, the main active constituent of kratom, while none of them were adulterated with detectable levels of 7-hydroxymitragynine, opioids or benzodiazepines; (b) all of the raw leaf products, but neither of the concentrated extracts, contained significant levels of microbial contamination; and (c) all of the raw leaf products contained potentially dangerous levels of toxic metals. Each of these findings has major ramifications for public health.</p><p>The fact that all of the samples contained significant levels of mitragynine is especially important. First, the levels of mitragynine that we found are comparable to those reported by other studies [<xref rid=\"B37-ijerph-17-05512\" ref-type=\"bibr\">37</xref>]. Moreover, mitragynine is a complex chemical that is difficult to synthesize [<xref rid=\"B38-ijerph-17-05512\" ref-type=\"bibr\">38</xref>]. Therefore, the presence of mitragynine in the samples strongly indicates that all of the products were derived from kratom plant material. In conjunction with the mitragynine studies, we also tried to measure levels of 7-hydroxymitragynine, which is an active constituent or metabolite of mitragynine. In addition, there is evidence that some unscrupulous purveyors of kratom may have fortified their products with exogenous 7-hydroxymitragynine [<xref rid=\"B39-ijerph-17-05512\" ref-type=\"bibr\">39</xref>]. The results of our studies indicated that all of the samples had very low or undetectable levels of 7-hydroxymitragynine (data not shown), which strongly suggests that the products were not fortified with exogenous 7-hydroxymitragynine. In conjunction with these studies, we also screened the samples for possible adulteration with opioids and benzodiazepines. Results of our studies found no evidence that any of these drugs were present in the samples. Adulteration with opioids such as oxycodone or fentanyl could increase the analgesic potency and dependency liability of kratom, creating a potential use disorder that would not occur with native kratom material alone [<xref rid=\"B25-ijerph-17-05512\" ref-type=\"bibr\">25</xref>,<xref rid=\"B27-ijerph-17-05512\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijerph-17-05512\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijerph-17-05512\" ref-type=\"bibr\">29</xref>]. Similarly, addition of an illicit benzodiazepine can enhance both the sedative and anxiolytic effects of kratom. A majority of kratom users self-treat conditions such as acute or chronic pain as well as mental or emotional disorders with the product [<xref rid=\"B13-ijerph-17-05512\" ref-type=\"bibr\">13</xref>,<xref rid=\"B17-ijerph-17-05512\" ref-type=\"bibr\">17</xref>]. Adulteration with prescription drugs would increase the potency and risk of dependence, leading to abuse.</p><p>Our finding that all of the ground-leaf preparations of kratom contained significant levels of microbes is not surprising. One would expect that any products derived from leaves of plants, under non-sterile conditions, would contain microbes. The two products that were processed extracts (3, 3A and 7, 7A) did not exhibit evidence of microbial contamination to the level of assay sensitivity. While we do not know how these samples were processed, it seems likely that the extraction procedures would involve the use of organic solvents and extreme acidic and alkaline pH’s, which would have antimicrobial activity. Previously, there have been reports of the presence of <italic>Salmonella</italic>, a pathogen associated with kratom products [<xref rid=\"B31-ijerph-17-05512\" ref-type=\"bibr\">31</xref>]. In contrast to that report, no <italic>Salmonella</italic> was isolated from any of the eight products tested.</p><p>A significant concern that has not been adequately addressed is the presence of <italic>Acinetobacter</italic> associated with loose leaf material. The presence of this intrinsically multidrug resistant opportunistic pathogen in easily aerosolized plant material could pose a threat to individuals with compromised immune systems who inhale the plant material [<xref rid=\"B40-ijerph-17-05512\" ref-type=\"bibr\">40</xref>]. Therefore, precautions should be exercised if using or dispensing loose-leaf kratom material.</p><p>One of the most important findings from this study is that all of the ground-leaf kratom products contained significant levels of toxic metals, particularly Ni and Pb. The United States FDA was the first agency [<xref rid=\"B34-ijerph-17-05512\" ref-type=\"bibr\">34</xref>] to raise concerns about the contamination of kratom products with potentially toxic levels of Ni and Pb, even though the study made no mention of other toxic metals. The results of the present analyses showed that seven of eight examined commercial kratom products contained relatively high levels of four metals including Fe, Pb, Ni and Cr, whereas they contained only trace levels of several more hazardous metals including As, Cd and Hg.</p><p>A critical question, of course, is, do any of these metals represent a hazard to kratom users? In considering this issue it is important to note that it is not uncommon for heavy kratom users to consume doses of 5–15 g of raw leaf material per day [<xref rid=\"B12-ijerph-17-05512\" ref-type=\"bibr\">12</xref>]. With this fact in mind, we believe that of the metals detected, the most problematic is Pb, which is classified as a Class I contaminant, indicating a high potential for toxicity, especially with chronic use [<xref rid=\"B41-ijerph-17-05512\" ref-type=\"bibr\">41</xref>]. Pb can cause serious neurological, psychological, cognitive, reproductive, developmental, immunologic, cardiovascular and renal effects [<xref rid=\"B42-ijerph-17-05512\" ref-type=\"bibr\">42</xref>]. The toxic neurological effects may be especially serious in children and young adults [<xref rid=\"B42-ijerph-17-05512\" ref-type=\"bibr\">42</xref>]. The permitted daily oral exposure level for Pb in foodstuffs and pharmaceuticals is only 5 µg/day [<xref rid=\"B41-ijerph-17-05512\" ref-type=\"bibr\">41</xref>]. The levels of Pb in several of the samples we tested were in the range of 0.25–1.6 µg/g product. This is significant because kratom users who commonly report consuming 5–15 g of kratom leaf per day could easily exceed the allowable daily intake of Pb (5 µg/day). This raises the possibility that some of the unusual toxicities of “kratom” products in the West may, at least in some cases, be partly attributable to Pb contamination. Further forensic studies are needed to clarify this issue.</p><p>The presence of relatively high levels of Ni (>2 µg/g) in most products could also represent a potential hazard to kratom users. Ni is classified as a Class 2A hazard, indicating that it is relatively abundant and has potential for causing serious toxicities [<xref rid=\"B41-ijerph-17-05512\" ref-type=\"bibr\">41</xref>]. Most significantly, Ni is classified as a Group I carcinogen (International Agency for Research on Cancer, IARC, 2012) and immunotoxin [<xref rid=\"B41-ijerph-17-05512\" ref-type=\"bibr\">41</xref>]. Despite these hazards of nickel, the daily allowable oral intake is set at 220 µg/day [<xref rid=\"B41-ijerph-17-05512\" ref-type=\"bibr\">41</xref>]. With the levels of Ni in the products tested in the present studies (>7.4 µg/g product), it is unlikely that even consumers who use high doses of kratom of >15 g raw leaf product per day would exceed allowable intake levels. On the other hand, it is unclear how chronic exposure to even these levels of Ni might affect human health; especially when ingested with the toxic levels of Pb in many of the samples.</p><p>Even though the levels of Cr in the leaf samples appeared to be quite high (up to 5.7 µg/g) it is unlikely that this represents a hazard to humans. Cr is classified as a Class 3 metal, with low potential for toxicity [<xref rid=\"B41-ijerph-17-05512\" ref-type=\"bibr\">41</xref>]. The permitted daily exposure level for Cr is 11,000 µg/day, which is a level of exposure that could not be achieved with any of the products even at kratom doses of over 15 g/day.</p><p>Levels of other metals (As, Cd and Hg) were well below allowable daily intake levels [<xref rid=\"B41-ijerph-17-05512\" ref-type=\"bibr\">41</xref>] and probably do not by themselves represent a significant hazard to kratom users. On the other hand, it is well established that exposure to mixtures of metals may increase risk of toxicity. Further studies are needed to determine if this is an issue with regard to kratom.</p><p>An obvious question that arises is, what is the source of the Ni and Pb in the raw-leaf samples? While we can only speculate at this time, one possibility is that the metals may break off or leach from the equipment that is used to grind, process, transport and store the kratom leaf material [<xref rid=\"B41-ijerph-17-05512\" ref-type=\"bibr\">41</xref>]. A second possibility is that the metals might be absorbed from the soil in which kratom is grown [<xref rid=\"B41-ijerph-17-05512\" ref-type=\"bibr\">41</xref>]. Ni is, in fact, an important nutrient for plants. It is noteworthy that most of the kratom sold in the United States is imported from sources in Indonesia [<xref rid=\"B2-ijerph-17-05512\" ref-type=\"bibr\">2</xref>,<xref rid=\"B43-ijerph-17-05512\" ref-type=\"bibr\">43</xref>,<xref rid=\"B44-ijerph-17-05512\" ref-type=\"bibr\">44</xref>]. The volcanic soil in many parts of Indonesia are known to contain high levels of metals, particularly Ni [<xref rid=\"B45-ijerph-17-05512\" ref-type=\"bibr\">45</xref>]. In addition, Pb pollution is a widespread problem in many regions of Indonesia [<xref rid=\"B46-ijerph-17-05512\" ref-type=\"bibr\">46</xref>,<xref rid=\"B47-ijerph-17-05512\" ref-type=\"bibr\">47</xref>]. Again, it is noteworthy that the samples that did not contain metals were concentrated extracts. It seems that the extraction procedure removed the metals. Further studies to examine the metal content of kratom samples from different geographic regions certainly seem warranted.</p><p>One limitation of the present study involves the relatively small number of products evaluated. It is unclear whether or not our finding of significant metal and microbial contamination applies to other kratom products. The American Kratom Association (AKA), a major kratom trade organization, has developed a set of “Good Manufacturing Practices (GMP)” for the kratom industry [<xref rid=\"B48-ijerph-17-05512\" ref-type=\"bibr\">48</xref>]. It is noteworthy that only one of the producers of the kratom products that were tested in the present study (O.P.M.S.) appears to have agreed to follow the American Kratom Association guidelines [<xref rid=\"B49-ijerph-17-05512\" ref-type=\"bibr\">49</xref>]. Interestingly, their products showed little or no evidence of contamination with microbes or metals (7, 7A).</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05512\"><title>5. Conclusions</title><p>We have advocated for further research on the therapeutic potential of kratom [<xref rid=\"B19-ijerph-17-05512\" ref-type=\"bibr\">19</xref>], and we stand by that position. However, we also find the present findings to be troubling. It is apparent that many of the kratom products being sold on the local level contain unknown levels of active agent (mitragynine) and are contaminated with metals, such as Pb and Ni, as well as microbes. This puts consumers at potential risk of adverse effects. Even though the AKA has adopted and advocated for GMP, it is obvious that many purveyors of kratom products have not adopted or adhered to those standards.</p><p>It is our hope that the present study will serve as a template for more extensive studies on the large number of kratom samples that are being sold in shops as well as through internet vendors. Such data are critical in formulating rational standards for the sale and production of kratom products.</p></sec></body><back><ack><title>Acknowledgments</title><p>This work has been supported by personal funds from W.C.P. that were used to purchase the kratom products. The analytical work was supported by restricted funds from Midwestern University. The authors thank Laura Phelps and Victoria Sears, of the Department of Pharmacology, for their help in preparing the manuscript. </p></ack><notes><title>Author Contributions</title><p>W.C.P. conceived of the study and coordinated the project. B.J.P. and I.M.S. performed the microbial analyses. C.A.V. and O.G. performed the mitragynine and other chemical analyses. J.R.E. assisted with the analyses and interpretation of the metal data. P.C.L. assisted with processing of samples and compilation of data. All authors participated in writing the manuscript. 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The kratom extract (100 µg/mL in methanol) was prepared and assayed as described in the methods section. (<bold>A</bold>) shows base peak chromatogram. (<bold>B</bold>) shows extracted ion chromatogram identifying chemistry with molecular formula C23H30N2O4 for mitragynine and related isomers. (<bold>C</bold>) shows extracted ion chromatogram identifying mitragynine using Targeted MS/MS transition of 399.23 to 174.09 <italic>m</italic>/<italic>z</italic> (<bold>D</bold>).</p></caption><graphic xlink:href=\"ijerph-17-05512-g001\"/></fig><table-wrap id=\"ijerph-17-05512-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05512-t001_Table 1</object-id><label>Table 1</label><caption><p>List of Kratom Products from Suburban Chicago.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Sample #</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Product</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Vendor</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1<break/>1A</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Expert Botanicals<break/>Maeng Da Capsules</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Westmont, IL, USA</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2<break/>2A</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">mK Botanicals<break/>Maeng Da Capsules</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Westmont, IL, USA</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3<break/>3A</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">OPMS<break/>Maeng Da Capsules</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Lisle, IL, USA</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4<break/>4A</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CBD Kratom<break/>Thai Maeng Da Capsules</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Elmhurst, IL, USA</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5<break/>5A</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CBD Kratom<break/>Mixed Malay Capsules</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Elmhurst, IL, USA</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6<break/>6A</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CBD Kratom<break/>Indo Red Bantuagle Capsules</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Elmhurst, IL, USA</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7<break/>7A</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">OPMS<break/>Gold Kratom Extract Capsules</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Aurora, IL, USA</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8<break/>8A</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NJOY Kratom<break/>Red Malay Powder</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Lisle, IL, USA</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05512-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05512-t002_Table 2</object-id><label>Table 2</label><caption><p>Evaluation of kratom sample microbial load.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">CFU/g Product</th></tr></thead><tbody><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">Product #</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sheep Blood Agar <sup>a</sup></td><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">MacConkey Agar <sup>b, c</sup></td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">37 °C</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">37 °C</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">42 °C</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">>1 × 10<sup>8</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.7 × 10<sup>4</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.6 × 10<sup>4</sup></td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1A</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.4 × 10<sup>5</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.5 × 10<sup>4</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3 × 10<sup>4</sup></td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.3 × 10<sup>5</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2A</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.5 × 10<sup>5</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3A</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">>1 × 10<sup>8</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.1 × 10<sup>5</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.6 × 10<sup>6</sup></td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4A</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.4 × 10<sup>5</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.2 × 10<sup>5</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.6 × 10<sup>5</sup></td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">>1 × 10<sup>8</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.7 × 10<sup>4</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5A</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.9 × 10<sup>5</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.7 × 10<sup>4</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.0 × 10<sup>4</sup></td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">>1 × 10<sup>8</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.2 × 10<sup>5</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6A</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.0 × 10<sup>5</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.7 × 10<sup>4</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.5 × 10<sup>4</sup></td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7A</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.2 × 10<sup>5</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.3 × 10<sup>4</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8 × 10<sup>2</sup></td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8A</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.1 × 10<sup>5</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.0 × 10<sup>4</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.0 × 10<sup>4</sup></td></tr></tbody></table><table-wrap-foot><fn><p><sup>a</sup> Sheep blood agar is an enriched, differential (hemolysis) bacteriological medium used to permit the broadest range of bacteria to grow. <sup>b</sup> MacConkey agar is a selective (for Gram negative bacteria) differential (lactose fermentation) medium. Lactose fermentation is consistent with bacteria that are members of the order <italic>Enterobacteriales</italic>. <sup>c</sup> All lactose negative colonies were screened for oxidase production to validate <italic>Salmonella</italic> testing (oxidase negative).</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05512-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05512-t003_Table 3</object-id><label>Table 3</label><caption><p>Retention Times, Collision Energy, and Transition States for Kratom Alkaloids, Opiates, and Benzodiazepines.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Target</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Amount<break/>(µg/mL)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MF</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Exact Mass</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">M + H</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Retention Time</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CID</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Transition</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Kratom Alkaloids</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mitragynine</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C23H30N2O4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">398.2206</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">399.2279</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.55</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">399.23 --> 174.09</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7-Hydroxymitragynine</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">C23H30N2O5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">414.2155</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">415.2228</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.58</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">30</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">415.22 --> 190.09</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Opioids</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Buprenorphine</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C29H41NO4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">467.3036</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">468.3109</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.83</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">468.3 --> 396.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Codeine</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C18H21NO3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">299.1521</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">300.1594</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.37</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">56</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">300.2 --> 165.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Fentanyl</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C22H28N2O</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">336.2202</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">337.2275</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.74</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">44</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">337.5 --> 188.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hydrocodone</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C18H21NO3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">299.1521</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">300.1594</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">300.2 --> 199.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Hydromorphone</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C17H19NO3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">285.1365</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">286.1438</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.80</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">36</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">286.2 --> 185.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Meperidine</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C15H21NO2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">247.1572</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">248.1645</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.01</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">37</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">248.2 --> 220.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(±)-Methadone</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C21H27NO</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">309.2093</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">310.2166</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.31</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">43</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">310.2 --> 105.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Morphine</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C17H19NO3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">285.1365</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">286.1438</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.63</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">44</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">286.2 --> 165.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Naloxone</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C19H21NO4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">327.1471</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">328.1544</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.27</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">328.3 --> 212.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Naltrexone</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C20H23NO4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">341.1627</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">342.1700</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.87</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">342.3 --> 212.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Oxycodone</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C18H21NO4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">315.1471</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">316.1544</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.91</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">316.2 --> 298.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Oxymorphone</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C17H19NO4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">301.1314</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">302.1387</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.69</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">302.1 --> 284.2</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">cis-Tramadol HCl</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">C16H25NO2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">263.1885</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">264.1958</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.71</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">31</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">262.2 --> 58.1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Benzodiazepines</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">Alprazolam</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">250</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">C17H13ClN4</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">308.0829</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">309.0902</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">6.83</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">309.1 --> 281.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">309.1 --> 205.1</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">Clonazepam</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">250</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">C15H10ClN3O3</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">315.0411</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">316.0484</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">6.94</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">316.1 --> 270.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">316.1 --> 214.1</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">Diazepam</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">250</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">C16H13ClN2O</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">284.0716</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">285.0789</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">8.28</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">285.1 --> 154.0</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">285.1 --> 193.1</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">Flunitrazepam</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">250</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">C16H12FN3O3</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">313.0863</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">314.0936</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">7.49</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">314.1 --> 268.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">314.1 --> 239.2</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">Lorazepam</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">250</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">C15H10Cl2N2O2</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">320.0119</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">321.0192</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">6.88</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">321.0 --> 275.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">321.0 --> 229.1</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">Nitrazepam</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">250</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">C15H11N3O3</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">281.0800</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">282.0873</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">6.33</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">282.1 --> 236.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">282.1 --> 180.1</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">Oxazepam</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">250</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">C15H11ClN2O2</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">286.0509</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">287.0582</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" colspan=\"1\">6.54</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">287.0 --> 241.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">287.0 --> 104.0</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Temazepam</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">250</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">C16H13ClN2O2</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">300.0666</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">301.0739</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">7.64</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">301.0 --> 255.1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">40</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">301.0 --> 177.1</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05512-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05512-t004_Table 4</object-id><label>Table 4</label><caption><p>Levels of Mitragynine in Kratom Products.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Product #</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mitragynine (mg/g)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Opioid Screen</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Benzodiazepine Screen</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.99</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1A</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.33</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2A</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.03</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.13</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3A</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.99</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.55</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4A</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.25</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.16</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5A</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.54</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6A</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.00</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">59.76</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7A</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60.36</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.34</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8A</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.12</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ND</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ND</td></tr></tbody></table><table-wrap-foot><fn><p>Two separate samples of each product were analyzed for levels of the mitragynine as described in the Methods section. Numerical values indicate mg of mitragynine per g of raw product. Note that the values for the replicate samples are generally in excellent agreement with each other. Sample #7 was a concentrated kratom extract. ND: not detected.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05512-t005\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05512-t005_Table 5</object-id><label>Table 5</label><caption><p>Levels of Metals in Kratom Products.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Product #</th><th colspan=\"7\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Metal Concentration (μg/g Raw Kratom Product)</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">As</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cd</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cr</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Fe</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Hg</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Ni</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pb</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.24</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.029</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">348</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.018</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.32</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1A</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.24</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.043</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">420</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.012</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.40</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.09</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.028</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">187</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.009</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.16</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2A</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.22</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.024</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">460</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.013</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.53</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.028</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">371</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.013</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.34</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3A</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.36</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.025</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">710</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.014</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.45</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.028</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.21</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">259</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.017</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.6</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4A</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.18</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.063</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">430</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.012</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.34</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.27</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.032</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">850</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.010</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.41</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5A</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.29</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.050</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">640</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.025</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.025</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.25</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.040</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">542</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.016</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.25</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6A</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.19</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.027</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">510</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.017</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.39</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7A</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ND</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.028</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.39</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">270</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.014</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.73</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.45</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8A</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.12</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.020</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.86</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">350</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.012</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.58</td></tr></tbody></table><table-wrap-foot><fn><p>Two separate samples of each product were analyzed for levels of the metals as described in the Methods section. Numerical values indicate µg of metal per g of raw product. ND indicates that the level of metal in the sample was below the level of detection. Note that the values for the replicate samples are generally in excellent agreement with each other. All of the samples except #7 which was a concentrated kratom extract contained measurable levels of metals. As would be expected for leaf-based products, levels of iron were consistently high. Of the toxic metals that were evaluated, the highest levels were for Pb, Ni and Cr.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32722060</article-id><article-id pub-id-type=\"pmc\">PMC7432034</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155324</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05324</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>The Role of Sports Practice in Young Adolescent Development of Moral Competence</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-0584-0725</contrib-id><name><surname>Bronikowska</surname><given-names>Małgorzata</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05324\">1</xref><xref rid=\"c1-ijerph-17-05324\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-4751-1193</contrib-id><name><surname>Korcz</surname><given-names>Agata</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05324\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-4534-7345</contrib-id><name><surname>Bronikowski</surname><given-names>Michał</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05324\">2</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05324\"><label>1</label>Department of Recreation, Poznan University of Physical Education, 61-871 Poznań, Poland</aff><aff id=\"af2-ijerph-17-05324\"><label>2</label>Department of Didactics of Physical Activity, Poznan University of Physical Education, 61-871 Poznań, Poland; <email>korcz@awf.poznan.pl</email> (A.K.); <email>bronikowski@awf.poznan.pl</email> (M.B.)</aff><author-notes><corresp id=\"c1-ijerph-17-05324\"><label>*</label>Correspondence: <email>bronikowska@awf.poznan.pl</email>; Tel.: +48-60-309-2510</corresp></author-notes><pub-date pub-type=\"epub\"><day>24</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5324</elocation-id><history><date date-type=\"received\"><day>18</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>21</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>The study examined the moral competence levels in adolescents participating in individual/team sports compared with those not undertaking sports at all. In total, 827 students aged 15–17 years old (45.4% boys, 54.6% girls) from randomly selected secondary schools in the Wielkopolska region in Poland participated in the study. The moral competences were assessed using the Lind’s Moral Competence Test. The students also answered questions concerning their statues of involvement in sport (not involved; amateur; professional), years of involvement, and the type of sport they partook in (individual/team). The results highlight that the moral competence level in most of the examined adolescents (71.6% girls, 76.8% boys) was low. Those who presented a high moral competence level were 10.4% in girls, 8% in boys. There was no interaction between modes of involvement in sport and moral competence when comparing adolescents. The moral competence levels were not correlated with years of training in either mode of involvement or with type of sports. Therefore, we assume that, at this age, the type of sport and the level of engagement do not differentiate moral competence level and that there must be more factors contributing to this. This opens new directions for further research on the role of external factors stimulating the socio-moral growth of youth.</p></abstract><kwd-group><kwd>adolescents</kwd><kwd>amateur/professional sport</kwd><kwd>individual/team sport</kwd><kwd>moral competence assessment</kwd><kwd>moral development evaluation</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05324\"><title>1. Introduction</title><p>It is quite early in our upbringing that we are introduced to certain standards of behavior, primarily via simple observation, later by noticing rules being applied in certain situations by significant others (grown-ups), and after that by being educated to follow those self-same rules and social conventions. Naturally, context can influence one’s moral behavior. For example, poor child–parent interactions in the early years and living in a poor neighborhood are predictors of moral disengagement in adolescent years [<xref rid=\"B1-ijerph-17-05324\" ref-type=\"bibr\">1</xref>]. Relatedly, exposure to violent video games is associated with higher level of moral disengagement, leading to worse self-control and higher levels of cheating and aggression in youth [<xref rid=\"B2-ijerph-17-05324\" ref-type=\"bibr\">2</xref>].</p><p>Universal values and cultural traditions are a common bond for overall patterns of coexistence and interaction—in school classes, local communities, religion, or sport groups—at least in theory [<xref rid=\"B3-ijerph-17-05324\" ref-type=\"bibr\">3</xref>]. However, we are aware that reality has more varied and diverse ways of influencing one’s personal growth, specifically in terms of moral development. Certain (moral) behaviors maybe an outcome of individual decisions based on a person’s maturity of moral reasoning and judgement competency or/and situational contexts, with embedded rules or principles. </p><p>In terms of education, there is also the question of teaching/coaching style. In the case of sport, Hodge and Lonsdale [<xref rid=\"B4-ijerph-17-05324\" ref-type=\"bibr\">4</xref>] report that coaches who tend to supervise athletes in a very controlled way elicit a higher level of moral disengagement, and increasing antisocial behaviors towards opponents and teammates alike, while, conversely a supportive coaching style precipitates the opposite behaviors of the young athletes. While the research findings of Theodoulides [<xref rid=\"B5-ijerph-17-05324\" ref-type=\"bibr\">5</xref>] indicate that some physical education (PE) teachers tent to reward the fittest pupils, and in their teaching perform only instrumental actions that emphasize behaviors aimed at winning, sometimes allowing for behaviors leading to unsporting/unfair play in the process. Other studies [<xref rid=\"B6-ijerph-17-05324\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijerph-17-05324\" ref-type=\"bibr\">7</xref>] found that performance-oriented goals are associated with several moral variables, such as the legitimacy of injurious acts, approval of unsportsmanlike play, verbal/physical aggression, and antisocial judgments and behaviors. Elsewhere, Stoll [<xref rid=\"B8-ijerph-17-05324\" ref-type=\"bibr\">8</xref>] observed that participation in competitive sport may sometimes cause disruption of the proper moral development of young people. A facilitative context may play an amplifying role in unethical behaviors and high dispositional moral disengagement in ethically problematic circumstances [<xref rid=\"B9-ijerph-17-05324\" ref-type=\"bibr\">9</xref>]. However, this can be counteracted with specially designed educational interventions [<xref rid=\"B10-ijerph-17-05324\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijerph-17-05324\" ref-type=\"bibr\">11</xref>].</p><p>Moral dilemmas containing specific tasks, appropriately designed and carefully interwoven in the process of PE and sport (PES), enhance pupils sound development. The school PES environment is one setting that might be challenging for a young person and can help create a valuable context for experiencing moral decision-making processes. However, Bustamante and Chaux [<xref rid=\"B12-ijerph-17-05324\" ref-type=\"bibr\">12</xref>] report that interventions containing elements of critical thinking reduce the level of moral disengagement in adolescents. Other research shows that, when combined with PE moral education programs bring positive changes in prosocial behaviors in sport-related contexts [<xref rid=\"B13-ijerph-17-05324\" ref-type=\"bibr\">13</xref>]. Another study indicates the potential benefits of involvement in sport, specifically with socially vulnerable youth to make them less vulnerable [<xref rid=\"B14-ijerph-17-05324\" ref-type=\"bibr\">14</xref>]. When such training in teaching values is provided to the teachers, it may help in facilitating both teachers’ and students’ reflection and transfer of values beyond PES [<xref rid=\"B15-ijerph-17-05324\" ref-type=\"bibr\">15</xref>].</p><p>Shields and Bredemeier [<xref rid=\"B16-ijerph-17-05324\" ref-type=\"bibr\">16</xref>] argue that participation in physical activity (PA) positively influences character development, but it might, in certain conditions, be disruptive in one’s moral growth. They report findings from research [<xref rid=\"B16-ijerph-17-05324\" ref-type=\"bibr\">16</xref>] on college athletes, among whom those with more mature moral reasoning appeared to be less approving of aggressive tactics than those with less mature reasoning. This was also noticed by coaches, who observed that athletes with “principled” moral reasoning scores were rated as significantly less aggressive [<xref rid=\"B17-ijerph-17-05324\" ref-type=\"bibr\">17</xref>]. Research shows that some psychological characteristics like aggression, especially in sport, can also be associated with genetic factors influencing sport performance [<xref rid=\"B18-ijerph-17-05324\" ref-type=\"bibr\">18</xref>], with retaliatory relational aggression [<xref rid=\"B19-ijerph-17-05324\" ref-type=\"bibr\">19</xref>], or with fatigue that triggers loss of self-control, specifically in young individuals with high moral disengagement [<xref rid=\"B20-ijerph-17-05324\" ref-type=\"bibr\">20</xref>]. However, can this explain the whole range of pro- or antisocial behaviors in youth sports? </p><p>According to the research findings, the problem is still valid despite the decades that have passed and findings are inconclusive. This proves that further research into this phenomenon is essential.</p><p>PES provide a platform for building prosocial appropriate behaviors during the most formative years of a person’s moral development. Each PES session creates countless opportunities for cooperation, decision-making, rule abiding, and fair play behaviors at various levels of competition, all within a climate of social responsibilities and sportsmanship. All these should enhance the development of moral competences. More specifically, when tasks are adjusted to the developmental period, they become valuable tools in stimulation of moral growth of youth. </p><p>However, this is not always the case. Beller and Stoll [<xref rid=\"B8-ijerph-17-05324\" ref-type=\"bibr\">8</xref>] report that in some circumstances, the behavior of college athletes actually worsens, particularly when there is too much emphasis on a “win-at-all costs” attitude. However, this largely dependents upon the motivational climate created by a PE teacher/sport coach during the training session, and thus their moral competence level (MCL) is also an issue. Henkel and Eearls [<xref rid=\"B21-ijerph-17-05324\" ref-type=\"bibr\">21</xref>] found that these agents of a child’s moral development are, on average, less developed in their moral reasoning capacities than most of their peers with a similar educational background and age. The latest research by Bronikowska and Korcz [<xref rid=\"B22-ijerph-17-05324\" ref-type=\"bibr\">22</xref>] indicates a problem—78% of PE major students at university were classified as having low MCL themselves. Interestingly, the increased number of years of professional training in a group of 19-year-old recruits of PES majors was associated with a higher MCL, specifically in male students [<xref rid=\"B23-ijerph-17-05324\" ref-type=\"bibr\">23</xref>]. Furthermore, Cummings et al. [<xref rid=\"B24-ijerph-17-05324\" ref-type=\"bibr\">24</xref>] indicate that it is a wider problem—MCLs of preservice and in-service teachers are generally relatively low. The importance of highlighting the moral aspects in training for the teaching profession and in teacher education programs has been raised in other studies [<xref rid=\"B25-ijerph-17-05324\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijerph-17-05324\" ref-type=\"bibr\">26</xref>]. Vella, Oades, and Crowe [<xref rid=\"B27-ijerph-17-05324\" ref-type=\"bibr\">27</xref>] signaled that even if sport coaches have access to formal coach training, coach education programs lack content that is relevant to positive youth development, instead maintaining a focus on technical and tactical skills.</p><p>An ambiguous question can be asked as to whether a given type of sport can play a crucial role in stimulating moral growth as, for example, team sports provide quite a different sporting context of experiences than individual sports. While comparing personality characteristics of athletes in team sports (TS) and individual sports (IS), Nia and Besharat [<xref rid=\"B28-ijerph-17-05324\" ref-type=\"bibr\">28</xref>] found that athletes from TS scored higher on agreeableness and sociotropy, whereas representatives of IS scored higher on conscientiousness and autonomy scales. According to these authors, through individual management and control in IS, one learns order and discipline and builds conscientiousness to rely on oneself, while in TS, the final outcome never depends on the performance of just one player, and unpredicted interferences of many team members mediate many contextual behaviors. These findings confirmed the earlier ones of Eyseneck et al. [<xref rid=\"B29-ijerph-17-05324\" ref-type=\"bibr\">29</xref>] on the possible psychological differences between IS and TS competitors. There is also a difference in the kind of effort—TS require working together toward common goals in a team, while in IS it is instead a case of working toward one’s own goals. The situation regarding responsibility for the result is also different (shared by many in TS vs borne solely by one person in IS). The effects of involvement in various types of sports is reflected in terms of developing different social skills, with cooperation, communication, coping with pressure, support, and responsibility, which dovetails with one’s intellectual and emotional development and can also play a role in the overall development of MCL.</p><p>For the theoretical framework of our research, we used the model of Kohlberg’s moral development [<xref rid=\"B30-ijerph-17-05324\" ref-type=\"bibr\">30</xref>] in its neo-Kohlbergian version [<xref rid=\"B31-ijerph-17-05324\" ref-type=\"bibr\">31</xref>]. Kohlberg believed that it is the setting and context that influence moral development and affect the experiences of an individual. In his works, the three-level model [<xref rid=\"B30-ijerph-17-05324\" ref-type=\"bibr\">30</xref>] consisted of a sequence of 6 stages of moral development. An individual develops morally from the pre-conventional, through conventional, to postconventional level (only a very few reach the highest stages of moral development in their entire lifetimes). Kohlberg also noticed some cases of young individuals who regressed from ostensibly principled moral judgement in their last years of high school to lower scores in their final years of college [<xref rid=\"B30-ijerph-17-05324\" ref-type=\"bibr\">30</xref>]. By contrast, Rest found [<xref rid=\"B32-ijerph-17-05324\" ref-type=\"bibr\">32</xref>] that, generally, moral development increases until early adulthood and may plateau thereafter. Later research of Rest et al. [<xref rid=\"B33-ijerph-17-05324\" ref-type=\"bibr\">33</xref>] showed that, in terms of developing moral reasoning competences, it is personal interest that is first developed in childhood. This is followed by maintaining the norms schema during the adolescent stage, with the development of post-conventional schema emerging while entering adulthood. The latter is the highest schema of critical evaluation of laws, human rights and social norms, decision making, and behaviors based on universal moral principles. Ideally, this should be the outcome and effect of school education, including PES, and should play a critical role in the moral growth of a young person. </p><p>Modern sport faces challenging issues—doping, cheating, bribery, match-fixing, and aggression—that push youth (often via resistance of their parents) away from sport or causes an increase in drop-out rates. Discovering the most critical periods for the development of moral virtues in children and youth, and relating it to the forms of sports may help discover the relationships and help counteract these negative outcomes before it is too late. For that reason, we posed the following research question: Does participation in sporting activities modify the level of moral competence in young adolescents? Therefore, based on the abovementioned findings and theoretical propositions, we developed a cross-sectional study, which aimed at assessing MCL in young adolescents participating in individual and team sports, and compared with those not participating in any sport at all. </p></sec><sec sec-type=\"methods\" id=\"sec2-ijerph-17-05324\"><title>2. Methods</title><p>Research was carried out in 2018 and included 827 students from several randomly selected secondary urban schools in the Wielkopolska (Eng. Greater Poland) region in Poland (45.4% boys, 54.6% girls aged 16.5 ± 0.6 years). Participation in the survey was voluntary, and the total return rate was 89%. Questionnaires were completed in whole-class groups during a regular school lesson in quiet classroom conditions and took approximately 30 min to complete.</p><sec id=\"sec2dot1-ijerph-17-05324\"><title>2.1. Data Analysis</title><p>Due to the lack of normal distribution, comparative analysis of differences between the groups was undertaken with the use of the U Mann–Whitney test. To analyze the potential role of involvement in sport (years of training) on MCL in male and female students a Kruskal–Wallis non-parametric analysis of variance was used. It was also examined whether, and to what extent years of training and moral competence were correlated in each of the groups (training mode: amateur/professional; type of sport: IS/TS) of boys and girls. To test the relationships between variables, a simple Spearman correlation test was used, with the value of correlation strength: ≤0.39 weak, 0.40–0.59 moderate, and ≥0.60 strong [<xref rid=\"B34-ijerph-17-05324\" ref-type=\"bibr\">34</xref>]. Significance was set at <italic>p</italic> ≤ 0.05. Statistical analysis was carried out using Statistica 13.0 software (StatSoft, Krakow, Poland).</p></sec><sec id=\"sec2dot2-ijerph-17-05324\"><title>2.2. Research Tools</title><p>To measure MCL, Lind’s Moral Competence Test was used [<xref rid=\"B35-ijerph-17-05324\" ref-type=\"bibr\">35</xref>]. To this study, a validated and certified version was used [<xref rid=\"B36-ijerph-17-05324\" ref-type=\"bibr\">36</xref>]. Participants were requested to confront two moral dilemmas and agree or disagree with the statements which were presented to them. One concerned facing a situation of illegal behavior at work, another was a life-saving dilemma. Students responded on a nine-point Likert-type scale, from −4 (totally disagree) to +4 (totally agree). Each story had 12 statements (six in favor and six against the proposed behavior). All statements corresponded to one of six stages of moral development [<xref rid=\"B30-ijerph-17-05324\" ref-type=\"bibr\">30</xref>]. The summarized score, called the <italic>C-Index</italic>, is computed and ranges from 1 to 100. It actually measures a person’s ability to assess an argument based on their moral quality or, in simpler terms, the degree to which a person allows their personal judgments to be affected by moral concerns or principals rather than their personal opinions and constructions. <italic>C-index</italic> scores below 9 are considered to be an extremely low, scores 10 to 19 are considered low, scores 20 to 29 are considered medium, 30 to 39 are considered high, 40 to 49 very high, and above 50 extremely high. </p><p>In addition, the examined students were asked to answer question concerning their statues of involvement in sport (no involvement, amateur, and professional). Professional participation meant engagement in regular system of competitions, organized by a sports federation, whereas amateurism meant taking part in sports as a hobby, for pleasure [<xref rid=\"B37-ijerph-17-05324\" ref-type=\"bibr\">37</xref>]. Then, they were asked to declare the number of years of involvement and the type of sport they partook in. This was later categorized by the research group into either a IS and TS based on the following criteria: TSs concerned activities with common efforts of one team playing against another team, which included cooperation, interaction and shared responsibilities; whereas ISs were those where the final result and responsibilities depend on the efforts of an individual and which is usually played in a one-on-one scenario. </p></sec><sec id=\"sec2dot3-ijerph-17-05324\"><title>2.3. Ethics </title><p>The investigation was carried out following the rules of the Declaration of Helsinki of 1975 and revised in 2013. The study protocol was approved by the Local Bioethics Committee of University of Medical Sciences in Poznań (decision no. 893/18) before undertaking the research.</p><p>For the students’ convenience, the information about the anonymous and voluntary nature of their participation were read out before completing the questionnaire, that the study records would be kept confidential, and that their individual contributions would be unidentifiable in the final report. Written consent was collected from all the participant and their legal careers. </p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05324\"><title>3. Results </title><p>In the examined group (<italic>N</italic> = 827), there were 139 students (43.1% boys, 56.9% girls) who declared no participation in any form of sports; 462 (66% boys, 34% girls) declared involvement on an amateur level; and 226 (51% boys, 49% girls) were involved in sport within professionally organized structures. Among those who declared an amateur level of involvement in sport, 306 (36.3% boys, 63.7% girls) related to IS and 156 (61.5% boys, 38.5% girls) with TS. Among students engaged professionally, 116 (37.9% boys, 62.1% girls) trained in IS and 110 (58.1% boys, 41.9% girls) in TS. There were no statistically significant differences (U Mann–Whitney test) between the groups in terms of the <italic>C-index</italic> of moral competences, which was established for the “no sport” group at a mean value of 15.1 ± 11.9, for the amateur group at 14.2 ± 9.6, and for the professional group at 14.0 ± 9.8. A U Mann–Whitney test indicated there were also no significant differences noticed between boys and girls. </p><p>The first step was to determine whether there was any relationship between the MCL among students taking no part in sport, those practicing in at an amateur level, and those who trained in a professional mode. ANOVA Kruskal–Wallis testing for different modes of sport involvement as an independent variable showed no significant differences between the examined groups (no sport, amateur, professional) and MCL (<italic>H</italic> (2, <italic>N</italic> = 827) = 0.15159; <italic>p</italic> = 0.927).</p><p>The second step was to investigate whether involvement in IS or TS made a difference for boys and girls in terms of MCL. The U Mann–Whitney test showed no statistical differences between the two forms of sport participation (IS/TS) in terms of the MCL between boys and girls, but a more detailed examination showed some differences (<italic>p</italic> = 0.012) between male and female students professionally training TS, with boys scoring lower than girls (<xref rid=\"ijerph-17-05324-t001\" ref-type=\"table\">Table 1</xref>).</p><p>Next step was to see whether the differences would occur in modes of sport involvement x gender analysis. For that reason a Kruskal–Wallis test (ANOVA) was used and indicated no statistically significant differentiation (<italic>F</italic>(2, 821) = 1.068; <italic>p</italic> = 0.344) between boys and girls (<xref ref-type=\"fig\" rid=\"ijerph-17-05324-f001\">Figure 1</xref>). </p><p>This was followed by an analysis of correlations of examined variables (<xref rid=\"ijerph-17-05324-t002\" ref-type=\"table\">Table 2</xref>). There were no statistically significant correlations found. Number of years of training did not correlate with the MCL in any type of sports (IS/TS), nor with the mode of sport involvement (amateur/professional) either in boys or girls. We also checked the whole sample for the potential correlation of MCL with age (the cohort included 15, 16, and 17-year-old students), but there was also no correlation found (<italic>r</italic> = 0.01; t(N-2) = 0.199; <italic>p</italic> = 0.841).</p></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05324\"><title>4. Discussion </title><p>The above study aims to portray an integrated description of involvement in sports and its potential relationship to MCL among adolescents. Research shows there is no interaction between modes of involvement in sport and MCL when comparing adolescents; this concerns both boys and girls. MCL is not correlated with number of years of training in either mode of involvement (amateur/professional) nor type of sports (IS/TS). Likewise, no correlation of MCL with age is noticed. The examined adolescent boys and girls presented similar MCL irrespective of whether they trained professionally or just recreationally as amateurs. The only significantly lower MCL is noticed in boys practicing TS in a professionalized way when compared to girls. We can assume that at the age between 15–17 years, the type of sport and the level of engagement do not differentiate the MCL and that there must be more factors contributing to this. However, the most interesting observation is the MCL itself (<italic>C-index</italic> around 15 points average for the whole cohort), which, in a majority of the examined youth (74% of the examined sample; 71.6% girls and 76.8% boys), is low (below 19 points), whereas the number of those who presented a high MCL (<italic>C-index</italic> above 29 points) is only 10.4% in girls and just 8% in boys. It seems that the findings reflect natural rate and state of development of moral competence in young adolescents. The findings are in line with observation of other authors [<xref rid=\"B38-ijerph-17-05324\" ref-type=\"bibr\">38</xref>,<xref rid=\"B39-ijerph-17-05324\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijerph-17-05324\" ref-type=\"bibr\">40</xref>]. Power and Higgins [<xref rid=\"B40-ijerph-17-05324\" ref-type=\"bibr\">40</xref>] found in their study that there was significant (but still modest) growth in moral reasoning and improvements in moral behavior in young adolescents. Moreover, Bronikowski [<xref rid=\"B41-ijerph-17-05324\" ref-type=\"bibr\">41</xref>] explains that young adolescents need sense of coherence (of understanding) to maintain their engagement in physical activity, but also to pay attention to the moral standards that are often neglected by PE teachers and thus are not considered as important in sports. This kind of involvement has limited influence on their social and moral development. Therefore, it seems that specially designed school interventions could prove effective in keeping desirable yet sustainable moral development patterns throughout the life span if they could only become meaningful for the youth [<xref rid=\"B40-ijerph-17-05324\" ref-type=\"bibr\">40</xref>]. Previous research [<xref rid=\"B42-ijerph-17-05324\" ref-type=\"bibr\">42</xref>] strongly indicates also the importance of the perceived moral atmosphere, which is linked to lower incidents of adolescent misbehavior and higher incidence of prosocial behavior observed for example during sports activities.</p><p>The PES setting is considered to be legitimate environment for the development of child and youth moral norms and attitudes [<xref rid=\"B43-ijerph-17-05324\" ref-type=\"bibr\">43</xref>], though it should not be the only context for such efforts. Behaviors presented by youth in educational settings can be stimulated, monitored, and corrected by a qualified educator, in order to develop the desired levels of awareness of moral standards because there is a social imperative for PE to teach life skills essential to functioning in everyday life outside of the sport setting [<xref rid=\"B44-ijerph-17-05324\" ref-type=\"bibr\">44</xref>]. This is what traditionalists would say, referring back to the times of Plato and the Greek <italic>arête</italic> or to more recent times of Arnold’s philosophy of moral and pedagogical education at Rugby school [<xref rid=\"B45-ijerph-17-05324\" ref-type=\"bibr\">45</xref>,<xref rid=\"B46-ijerph-17-05324\" ref-type=\"bibr\">46</xref>,<xref rid=\"B47-ijerph-17-05324\" ref-type=\"bibr\">47</xref>,<xref rid=\"B48-ijerph-17-05324\" ref-type=\"bibr\">48</xref>]. Just a 100 years ago de Coubertin, built his idea of neo-Olympism around the ancient <italic>kalokagathos</italic> (English: “beautiful” and “good” or “virtuous”), which he saw as the most universal moral values. At that time, it was still socially expected that youth presented universally accepted virtues that referred to the socially accepted moral standards (i.e., the <italic>religio athletae</italic> concept) [<xref rid=\"B49-ijerph-17-05324\" ref-type=\"bibr\">49</xref>]. To a great extent, little has changed in the last century—the same objectives of moral education are still present and well-defined in most of PE curricula around the world [<xref rid=\"B50-ijerph-17-05324\" ref-type=\"bibr\">50</xref>,<xref rid=\"B51-ijerph-17-05324\" ref-type=\"bibr\">51</xref>]. How is it that the outcome of schooling and sporting experience (in terms of moral competence) remains so low? </p><p>School education should be the most critical period for personal growth, in various dimensions. The social perception that moral competence could be and should be promoted via PE and school sport was a dominant theory till the turn of the 20th century [<xref rid=\"B52-ijerph-17-05324\" ref-type=\"bibr\">52</xref>,<xref rid=\"B53-ijerph-17-05324\" ref-type=\"bibr\">53</xref>], with such values like cooperation, fairness, a sense of responsibility, and respect becoming pivotal points for educational tasks. Recently, the questions have been raised though as to whether this could be applicable outside the sports gym, where the sportification of all spheres of life has been observed [<xref rid=\"B54-ijerph-17-05324\" ref-type=\"bibr\">54</xref>]. Indeed, one may ask whether the problem of low MCL is related to the school environment or an outcome of observed broader social changes with violence, inequality, lack of tolerance and egocentrism in all spheres coming to life more vividly recently [<xref rid=\"B55-ijerph-17-05324\" ref-type=\"bibr\">55</xref>]. Gini et al. [<xref rid=\"B56-ijerph-17-05324\" ref-type=\"bibr\">56</xref>] found that, in a school setting, both bullies and defenders show advanced moral competence, integrating information about beliefs and outcomes in judging the moral permissibility of action, while victims show delayed moral competence, focusing on outcome information alone. Interestingly, the bullies, despite their advanced moral competence, were deficient with respect to moral compassion, as least when compared to victims or defenders. It is also possible that there is something about modern sports, even in a school context that does not let youth grow past a basic MCL, and that allows young people for just a temporary engagement in sports. It is possible that “Generation Z” have developed a sense of moral relativism that is much more individualized today than it used to be [<xref rid=\"B57-ijerph-17-05324\" ref-type=\"bibr\">57</xref>]. Maybe some norms that once used to be the universal base for cultural and moral standards of social life have changed so profoundly that the so-called traditional set of core virtues is not in place anymore? It seems that there are no simple answers here and more in-depth research is needed.</p><p>The process of neglecting the traditional set of values has been recently observed more than ever, and one cannot have failed to notice the disturbances in social and cultural relationships at the more general, societal level. Children observe a grown-up world and react accordingly. Feeling like being released from the parental leash, with unlimited access to the “Mighty Internet” and less controlled in their moral conducts and behaviors, has allowed for more individual “experimenting” with moral standards. If the PES environment allows—or even encourages—behavior that neglects the moral standards of traditional values and still work in their personal favor who would not take advantage of such a situation? The outcome is easy to predict: moral values are to be incorporated when suited, and this is how they learn to see it. </p><p>However, one also needs to take into account the factor of ontogenetic developmental principles that might influence the moral maturation process. For example, some teenagers might have a different tempo of transition from one stage of moral reasoning to another or, as we have come to understand through our research, that the process of growing from conventional to nonconventional and to postconventional levels of moral reasoning does not have to be an invariant sequential process, but may, on the contrary, be a complex one, with many factors mediating the change, and might include regression as well [<xref rid=\"B58-ijerph-17-05324\" ref-type=\"bibr\">58</xref>]. Recently, Garrigan et al. [<xref rid=\"B59-ijerph-17-05324\" ref-type=\"bibr\">59</xref>] have proposed an integrative framework which combines components of earlier theoretical models of moral development including decision-making findings, contextual factors, and social neuroscience theory. The model shows that growing through moral stages does not need to be sequential, and that individuals are not consistent in their moral reasoning. This opens new alleys for further research on the role of external factors (i.e., sport setting or role of a sport coach) acting as catalysts in stimulating the onward or retrograde moral development of a child. </p><p>The study by Powell et al. [<xref rid=\"B60-ijerph-17-05324\" ref-type=\"bibr\">60</xref>] indicated that, when we ask children to judge the actions of others, they consider the costs and benefits of harm, similarly to adolescents and adults, but those aged six judge actions involving harm negatively, regardless of the benefits. A younger child may see the opportunity to cheat but rules it out and does not consider it a possible action. This may be due to the basic knowledge or alternatively while experiencing more and more situations and educational support due to the deeper understating of moral necessity, i.e., an appreciation of fairness resulting from their experiences of PES [<xref rid=\"B59-ijerph-17-05324\" ref-type=\"bibr\">59</xref>].</p><p>Opinions on this issue are ambiguous. Shields and Bredemeier [<xref rid=\"B16-ijerph-17-05324\" ref-type=\"bibr\">16</xref>] believe that PES is a more fertile ground for children’s socio-moral development and character building than competitive sports. However, some [<xref rid=\"B61-ijerph-17-05324\" ref-type=\"bibr\">61</xref>] argue that the validity and reliability of the research findings of Shields and Bredemeier might be flawed, as the authors struggled to combine two different theories and had problem with conceptualizing it in one integrated yet multifaceted concept of character, which was reduced to primarily cognitive ability of making judgment. Contrary, Carr [<xref rid=\"B62-ijerph-17-05324\" ref-type=\"bibr\">62</xref>] argues that PES, as with any other school subject, despite involving co-operation, does not qualify as a form of moral education, as there is nothing moral in teaching hockey nor football skills in itself. However, he adds that this does not mean that moral education cannot take place during these classes through, e.g., the cultivation of moral attitudes or development of moral character [<xref rid=\"B62-ijerph-17-05324\" ref-type=\"bibr\">62</xref>]. There are a lot of situations in PES which require self-critical, sometimes even intuitive thinking in order to resolve the conflicting situations arising in the “heat of the PES action.” Appropriate moral competences and awareness of a sport discipline specific code of behaviors allow youth to select between the option with “keeping the rules” seen as prima facie principle or duty of sports participants [<xref rid=\"B63-ijerph-17-05324\" ref-type=\"bibr\">63</xref>]. </p><p>On the contrary, it may also become a critical point, when PES teachers/sport coaches’ professionalization culture, or the whole education system in fact, imposes “discursive closure” in terms of morality, gender discourse [<xref rid=\"B64-ijerph-17-05324\" ref-type=\"bibr\">64</xref>], or lack of skills in critical thinking. It also may be that the problem is related to the MCL or quality of training of PE teachers/sport coaches as role models themselves [<xref rid=\"B22-ijerph-17-05324\" ref-type=\"bibr\">22</xref>]. Sanderse [<xref rid=\"B65-ijerph-17-05324\" ref-type=\"bibr\">65</xref>] signifies that role modelling is rarely used as an explicit teaching method, and only a very small percentage of school students consider teachers as role models. According to Chen and Ennis [<xref rid=\"B66-ijerph-17-05324\" ref-type=\"bibr\">66</xref>], PE teachers present the “disciplinary mastery” orientation focusing on developing performance proficiency in sport skills and understanding of performance-related knowledge. We suggest that more critical thinking skills and moral dilemma tasks need to be included in their training to improve the situation. Therefor there is a huge need to foster social responsibility and to build character as an important quality factor of PES, as participation in sports has the potential to advance moral reasoning skills among its participants [<xref rid=\"B67-ijerph-17-05324\" ref-type=\"bibr\">67</xref>]. Increasing social demand for life-skills education requires finding the ways of transforming PES combined with health education into a “sustainable development” related subject. Lake et al. (p. 474, [<xref rid=\"B68-ijerph-17-05324\" ref-type=\"bibr\">68</xref>]) makes a point on that when saying “while active living by definition is concerned with the maintenance of activity behaviours across the life span, the addition of the word ‘sustainable’ serves to emphasize environmental influences on our physical activity behaviours as well as the environmental implications of those behaviours.” Also, studies on Hellison’s model of developing responsibility through physical activity [<xref rid=\"B69-ijerph-17-05324\" ref-type=\"bibr\">69</xref>,<xref rid=\"B70-ijerph-17-05324\" ref-type=\"bibr\">70</xref>] provide both evidence and potential pathways of dealing with the matter. Hellison [<xref rid=\"B71-ijerph-17-05324\" ref-type=\"bibr\">71</xref>] even suggested some strategies (awareness, experience, choice, problem-solving, self-reflection, and counseling time) that could be easily interwoven in the process of PES by a well-trained PE teacher or a sport coach. Also, a study of Šukys et al. [<xref rid=\"B72-ijerph-17-05324\" ref-type=\"bibr\">72</xref>] confirms that personal role of coaches in moral education encompassing professional knowledge and moral competences of athletes is of great importance. Therefore, in the future, we should investigate such potential or possible covariates that were not explored in this study. </p><p>What is important is the students’ point of view; most of the studies present the researchers’ perspective, ignoring the child’s one. In a study on the moral development of children aged 10–12 with competitive youth sport experience, Stuart [<xref rid=\"B73-ijerph-17-05324\" ref-type=\"bibr\">73</xref>] revealed that the identified issues concerned three areas—fairness of adult’s actions, negative game behaviors, and negative team behaviors. Examples of each category were unfair actions by coaches, disrespecting opponents, and selfish behavior in practice. She [<xref rid=\"B73-ijerph-17-05324\" ref-type=\"bibr\">73</xref>] reports in her study that teenagers saw PES as a place where the social-conventional behaviors could be modelled and reinforced, including a variety of issues from outside sport. In a longitudinal study on American youth, Dubois [<xref rid=\"B74-ijerph-17-05324\" ref-type=\"bibr\">74</xref>] proved that participation in well-organized sport activities positively develops the moral abilities of youth. However, the problem might be the accurate assessment of a child’s readiness for participating in organized, though competitive sports, which sometimes might be the demand of an unqualified coach, or encouragement from more persistent but even less qualified parents, all leading to training misconduct (early specialization and identification of talented players, risky behaviors, injuries, burnout, peer isolation, and moral issues) [<xref rid=\"B75-ijerph-17-05324\" ref-type=\"bibr\">75</xref>]. Shields and Bredemeier [<xref rid=\"B16-ijerph-17-05324\" ref-type=\"bibr\">16</xref>] studied the differences between sport students and non-sport students in the context of moral development. Their conclusions of significantly slower rate of moral development of students playing on a college basketball team compared to students whose only activity was limited to participation in PE comes as quite contrary to the prevailing wisdom. However, such an association was not found in the case of older secondary school students. The findings from research [<xref rid=\"B16-ijerph-17-05324\" ref-type=\"bibr\">16</xref>] also pointed to the correlation between the length (in years) of training in martial arts with significantly slower rate of moral development, which also determined the level of aggression. </p><p>The problem may also be rooted in the contextual factors of the setting. In a study on a very large sample of American students in the educational context, where a low correlation was found between repeated moral behaviors concerning cheating in school tests [<xref rid=\"B76-ijerph-17-05324\" ref-type=\"bibr\">76</xref>], it was observed that students changed their behaviors from one experiment to another, and it was difficult to recognize any patterns of moral reasoning. So, it may also be the sociological context of each particular situation (different and specific for various sports) that plays a mediating role in triggering certain moral behaviors, which may be altered in the same person in various circumstances. We did not collect data on that issue in this research, which might be considered as a limitation of the study but will serve as a hint for further in-depth research. </p><p>The findings of the current study should be interpreted cautiously because of some design limitations. The size of the sample is certainly a strength of the study, while among the limitations, we can point the cross-sectional design, which indicates that we cannot infer causal relations. Data on moral competence was based on declarations by adolescents and may be subject to some bias. Additionally, no consideration was given for educational (e.g., the moral values of their PES trainers/teachers), sociocultural/socio-economic status (e.g., the opportunity to get involved in sports activities during free time), and environmental (e.g., safe neighborhood) influences that might have affected the moral competency. There is a need to analyze this dependency separately, in detail, and in a multidirectional manner. Longitudinal designs may assist researchers in addressing hypotheses of the level of moral competence in adolescents in relation to their long-time involvement in sport.</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05324\"><title>5. Conclusions</title><p>As mentioned above, our detailed examination only shows some differences between students professionally training TS, with boys scoring lower on moral competence than girls. Thus, assuming one can say that, for the 15–17 age group, the type of sport and the level of engagement do not modify the ways it affects moral competence, so there must be more factors contributing to this. This opens new paths for further research in this area on the role of external factors (i.e., sport setting, role of a sport coach in the teaching process, etc.) acting as catalysts for stimulating onward or retrograde moral development. Through the development of society, media, and technology, we cause changes in perception of the societal order of moral values. PE teachers and coaches must understand their role modeling potential and as a matter-of-fact social need to develop instructional methods and delivery teaching styles that will produce effective learning outcomes in the Millennial student in terms of their moral qualities. Future research could take into consideration also moral competence (and gender) of the PES trainers/teachers, as it is possible that the moral competence of the PES trainers/teachers may influence the behaviors of young people.</p></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization, M.B. (Małgorzata Bronikowska) and M.B. (Michał Bronikowski); Data curation, M.B. (Michał Bronikowski); Formal analysis, M.B. (Michał Bronikowski); Investigation, M.B. (Małgorzata Bronikowska); Methodology, M.B. (Małgorzata Bronikowska) and M.B. (Michał Bronikowski); Project administration, M.B. (Małgorzata Bronikowska); Resources, M.B. (Małgorzata Bronikowska), A.K. and M.B. (Michał Bronikowski); Software, M.B. (Michał Bronikowski); Supervision, M.B. (Michał Bronikowski); Validation, M.B. (Małgorzata Bronikowska), A.K. and M.B. (Michał Bronikowski); Visualization, A.K.; Writing—original draft, M.B. (Michał Bronikowski); Writing—review and editing, M.B. (Małgorzata Bronikowska). All authors contributed to the development of the research and writing of the article. 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Hum.</source><year>2012</year><volume>3</volume><fpage>780</fpage><lpage>787</lpage></element-citation></ref><ref id=\"B76-ijerph-17-05324\"><label>76.</label><element-citation publication-type=\"book\"><person-group person-group-type=\"author\"><name><surname>Cunningham</surname><given-names>C.A.</given-names></name></person-group><article-title>A certain and reasoned art: The rise and fall of character education in America</article-title><source>Character Psychology and Character Education</source><person-group person-group-type=\"editor\"><name><surname>Lapsley</surname><given-names>D.K.</given-names></name><name><surname>Power</surname><given-names>F.C.</given-names></name></person-group><publisher-name>University of Notre Dame Press</publisher-name><publisher-loc>Notre Dame, IN, USA</publisher-loc><year>2005</year><fpage>183</fpage><lpage>184</lpage></element-citation></ref></ref-list></back><floats-group><fig id=\"ijerph-17-05324-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Two way interaction between various modes of involvement in sports and the level of moral competences in boys and girls.</p></caption><graphic xlink:href=\"ijerph-17-05324-g001\"/></fig><table-wrap id=\"ijerph-17-05324-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05324-t001_Table 1</object-id><label>Table 1</label><caption><p>Differences (U Mann–Whitney values with mean ± SD) in moral competence <italic>C-index</italic> (points) between adolescent boys and girls in various modes of involvement in sport.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Variables</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">IS</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">TS</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Amateur</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Boys<break/>(<italic>n</italic> = 155)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Girls<break/>(<italic>n</italic> = 160)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Boys<break/>(<italic>n</italic> = 167)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Girls<break/>(<italic>n</italic> = 106)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>C-index</italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.5 ± 9.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14.4 ± 9.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.257</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.1 ± 9.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.4 ± 10.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.102</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Professional</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Boys<break/>(<italic>n</italic> = 44)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Girls<break/>(<italic>n</italic> = 72)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Boys<break/>(<italic>n</italic> = 64)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Girls<break/>(<italic>n</italic> = 46)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>C-index</italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.1 ± 10.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14.8 ± 11.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.797</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11.2 ± 7.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.4 ± 9.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.012</bold>\n</td></tr></tbody></table><table-wrap-foot><fn><p>Note. IS—individual sports, TS—team sports. The significant analyses (<italic>p</italic> < 0.05) may be found as bold.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05324-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05324-t002_Table 2</object-id><label>Table 2</label><caption><p>Spearman correlation between years of training (M±SD) and moral C-index in adolescents.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Type/Mode of Sport Involvement</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>N</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>C-index</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Years of Training</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">R</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">t(N-2)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th></tr></thead><tbody><tr><td colspan=\"7\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Boys</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IS and amateur training</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">111</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.5 ± 9.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.6 ± 3.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.02</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.211</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.832</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TS and amateur training</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">96</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.2 ± 9.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.5 ± 3.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.09</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.972</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.333</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IS and professional training </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">44</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.2 ± 10.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.8 ± 2.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.26</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.782</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.081</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">TS and professional training </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">64</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11.4 ± 7.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.9 ± 3.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.12</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.986</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.327</td></tr><tr><td colspan=\"7\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Girls</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IS and amateur training</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">195</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.4 ± 9.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.2±3.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.833</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.405</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TS and amateur training</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.4 ± 10.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.0±2.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.04</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.310</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.757</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IS and professional training</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">72</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.9 ± 11.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.1 ± 3.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.02</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.514</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.608</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">TS and professional training</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">46</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.4 ± 9.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.6 ± 2.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.01</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.020</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.984</td></tr></tbody></table><table-wrap-foot><fn><p>Note: IS—individual sports, TS—team sports.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751642</article-id><article-id pub-id-type=\"pmc\">PMC7432035</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155442</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05442</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Hsp22 with an N-Terminal Domain Truncation Mediates a Reduction in Tau Protein Levels</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Webster</surname><given-names>Jack M.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05442\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05442\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-2839-6452</contrib-id><name><surname>Darling</surname><given-names>April L.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05442\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05442\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Sanders</surname><given-names>Taylor A.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05442\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05442\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-9935-2660</contrib-id><name><surname>Blazier</surname><given-names>Danielle M.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05442\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05442\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Vidal-Aguiar</surname><given-names>Yamile</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05442\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05442\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Beaulieu-Abdelahad</surname><given-names>David</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05442\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05442\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Plemmons</surname><given-names>Drew G.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05442\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05442\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Hill</surname><given-names>Shannon E.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05442\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05442\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-4037-5857</contrib-id><name><surname>Uversky</surname><given-names>Vladimir N.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05442\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-9657-7725</contrib-id><name><surname>Bickford</surname><given-names>Paula C.</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05442\">3</xref><xref ref-type=\"aff\" rid=\"af4-ijms-21-05442\">4</xref><xref ref-type=\"aff\" rid=\"af5-ijms-21-05442\">5</xref></contrib><contrib contrib-type=\"author\"><name><surname>Dickey</surname><given-names>Chad A.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05442\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05442\">2</xref><xref ref-type=\"aff\" rid=\"af3-ijms-21-05442\">3</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05442\">†</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-4981-5564</contrib-id><name><surname>Blair</surname><given-names>Laura J.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05442\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05442\">2</xref><xref ref-type=\"aff\" rid=\"af3-ijms-21-05442\">3</xref><xref rid=\"c1-ijms-21-05442\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05442\"><label>1</label>Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL 33620, USA; <email>jackwebster@usf.edu</email> (J.M.W.); <email>April.Darling@Pennmedicine.Upenn.edu</email> (A.L.D.); <email>tsanders2@mail.usf.edu</email> (T.A.S.); <email>dblazier@usf.edu</email> (D.M.B.); <email>yvidalaguiar@gmail.com</email> (Y.V.-A.); <email>davidbeaulie@usf.edu</email> (D.B.-A.); <email>plemmonsd@usf.edu</email> (D.G.P.); <email>sehill2@usf.edu</email> (S.E.H.); <email>vuversky@usf.edu</email> (V.N.U.); <email>cdickey@health.usf.edu</email> (C.A.D.)</aff><aff id=\"af2-ijms-21-05442\"><label>2</label>USF Health Byrd Alzheimer’s Institute, University of South Florida, Tampa, FL 33620, USA</aff><aff id=\"af3-ijms-21-05442\"><label>3</label>Research Service, James A Haley Veterans Hospital, 13000 Bruce B Downs Blvd, Tampa, FL 33612, USA; <email>pbickfor@usf.edu</email></aff><aff id=\"af4-ijms-21-05442\"><label>4</label>Department of Neurosurgery and Brain Repair, Morsani College of Medicine, University of South Florida Health, 12901 Bruce B Downs Blvd, Tampa, FL 33612, USA</aff><aff id=\"af5-ijms-21-05442\"><label>5</label>Department of Molecular Pharmacology and Physiology, Morsani College of Medicine, University of South Florida Health, 12901 Bruce B Downs Blvd, Tampa, FL 33612, USA</aff><author-notes><corresp id=\"c1-ijms-21-05442\"><label>*</label>Correspondence: <email>laurablair@usf.edu</email>; Tel.: +1-813-396-0639</corresp><fn id=\"fn1-ijms-21-05442\"><label>†</label><p>Deceased.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>30</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5442</elocation-id><history><date date-type=\"received\"><day>16</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>28</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Misfolding, aggregation and accumulation of proteins are toxic elements in the progression of a broad range of neurodegenerative diseases. Molecular chaperones enable a cellular defense by reducing or compartmentalizing these insults. Small heat shock proteins (sHsps) engage proteins early in the process of misfolding and can facilitate their proper folding or refolding, sequestration, or clearance. Here, we evaluate the effects of the sHsp Hsp22, as well as a pseudophosphorylated mutant and an N-terminal domain deletion (NTDΔ) variant on tau aggregation in vitro and tau accumulation and aggregation in cultured cells. Hsp22 wild-type (WT) protein had a significant inhibitory effect on heparin-induced aggregation in vitro and the pseudophosphorylated mutant Hsp22 demonstrated a similar effect. When co-expressed in a cell culture model with tau, these Hsp22 constructs significantly reduced soluble tau protein levels when transfected at a high ratio relative to tau. However, the Hsp22 NTDΔ protein drastically reduced the soluble protein expression levels of both tau WT and tau P301L/S320F even at lower transfection ratios, which resulted in a correlative reduction of the triton-insoluble tau P301L/S320F aggregates.</p></abstract><kwd-group><kwd>small heat shock protein 22</kwd><kwd>tau</kwd><kwd>molecular chaperones</kwd><kwd>neurodegeneration</kwd><kwd>Alzheimer’s disease</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05442\"><title>1. Introduction</title><p>Maintenance of cellular proteostasis is critically important for cellular function and survival [<xref rid=\"B1-ijms-21-05442\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijms-21-05442\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijms-21-05442\" ref-type=\"bibr\">3</xref>], especially in neurons [<xref rid=\"B4-ijms-21-05442\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijms-21-05442\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijms-21-05442\" ref-type=\"bibr\">6</xref>]. Misfolding, aggregation and disrupted protein clearance contribute to an imbalance in proteostasis that can foster the accumulation of toxic protein aggregates [<xref rid=\"B6-ijms-21-05442\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijms-21-05442\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijms-21-05442\" ref-type=\"bibr\">8</xref>]. Aberrant accumulation of aggregated protein is often associated with neurodegenerative disease progression [<xref rid=\"B9-ijms-21-05442\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijms-21-05442\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijms-21-05442\" ref-type=\"bibr\">11</xref>]. Therefore, strategies that leverage either a direct inhibition of protein aggregation or modulation of protein clearance mechanisms may have therapeutic potential [<xref rid=\"B1-ijms-21-05442\" ref-type=\"bibr\">1</xref>,<xref rid=\"B8-ijms-21-05442\" ref-type=\"bibr\">8</xref>,<xref rid=\"B12-ijms-21-05442\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijms-21-05442\" ref-type=\"bibr\">13</xref>]. Molecular chaperones can counteract this proteostatic imbalance by facilitating proper folding or refolding, sequestration, or clearance of misfolded proteins [<xref rid=\"B14-ijms-21-05442\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijms-21-05442\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijms-21-05442\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijms-21-05442\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijms-21-05442\" ref-type=\"bibr\">18</xref>]. Small heat shock proteins (sHsps) are a class of ATP-independent molecular chaperones that associate with early misfolded proteins and sequester these aggregation-prone intermediates for processing by ATP-dependent molecular chaperone complexes that include the 70 kDa heat shock protein (Hsp70), the 90 kDa heat shock protein (Hsp90) [<xref rid=\"B19-ijms-21-05442\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijms-21-05442\" ref-type=\"bibr\">20</xref>], or other protein clearance machinery. sHsps canonically contain a conserved core α-crystallin domain (ACD) flanked by variable flexible N-terminal and C-terminal domains (NTD and CTD) [<xref rid=\"B21-ijms-21-05442\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05442\" ref-type=\"bibr\">22</xref>]. sHsps form dimers through an interface between ACDs. sHsps further multimerize to form higher order dynamic oligomers via interactions of an IXI/V sequence in the CTD with a hydrophobic groove formed by the β4 and β8 strands of the ACD, as well as through complex interactions of the NTD with neighboring sHsp subunits [<xref rid=\"B23-ijms-21-05442\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05442\" ref-type=\"bibr\">24</xref>].</p><p>In vitro, certain sHsps appear to have chaperone activity, reducing or delaying the aggregation of discrete amyloidogenic clients [<xref rid=\"B19-ijms-21-05442\" ref-type=\"bibr\">19</xref>,<xref rid=\"B23-ijms-21-05442\" ref-type=\"bibr\">23</xref>,<xref rid=\"B25-ijms-21-05442\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijms-21-05442\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijms-21-05442\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05442\" ref-type=\"bibr\">28</xref>]. The aggregation of one of these client proteins, the microtubule-associated protein tau (tau), is thought to contribute to disease progression in multiple neurodegenerative diseases, including Alzheimer’s disease (AD) and frontotemporal dementia (FTD) [<xref rid=\"B29-ijms-21-05442\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijms-21-05442\" ref-type=\"bibr\">30</xref>]. The sHsp, Hsp27, interacts directly with tau and reduces tau aggregation in vitro [<xref rid=\"B23-ijms-21-05442\" ref-type=\"bibr\">23</xref>,<xref rid=\"B26-ijms-21-05442\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijms-21-05442\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05442\" ref-type=\"bibr\">28</xref>] as well as neuronal tau accumulation in vivo [<xref rid=\"B26-ijms-21-05442\" ref-type=\"bibr\">26</xref>]. We previously demonstrated that Hsp27 reduces neuronal tau accumulation and rescues a hippocampal tau-induced deficit in long-term potentiation (LTP), a measure of hippocampal plasticity [<xref rid=\"B26-ijms-21-05442\" ref-type=\"bibr\">26</xref>]. However, overexpression of a pseudophosphorylated mutant (Hsp27 S15D/S78D/S82D) results in increased tau accumulation and a failure to rescue the hippocampal plasticity deficit, suggesting a requirement for these N-terminal residues to cycle between phosphorylated and unphosphorylated states. Freilich et al., demonstrated that while the ACD and NTD of Hsp27 are each capable to bind tau on their own, self-interactions of the NTD appear to obscure tau binding in vitro. However, the NTD was crucial for the ability of Hsp27 to delay tau aggregation [<xref rid=\"B23-ijms-21-05442\" ref-type=\"bibr\">23</xref>]. Phosphorylation of the Hsp27 NTD results in a shift to smaller Hsp27 oligomers, which may additionally free up binding sites in the NTD and ACD to interact with tau.</p><p>Hsp22 is a sHsp that lacks the IXI/V sequence commonly found in CTDs, resulting in oligomeric assemblies, which are smaller than other sHsps [<xref rid=\"B23-ijms-21-05442\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05442\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijms-21-05442\" ref-type=\"bibr\">25</xref>]. However, the β4/β8 groove of the Hsp22 ACD may still interact with other sHsp family members as well as other proteins that contain an IXI/V interaction sequence, like the Bcl-2-associated athanogene protein-3, Bag3 [<xref rid=\"B31-ijms-21-05442\" ref-type=\"bibr\">31</xref>]. Hsp22 promotes the autophagic clearance of many client proteins through an association with a multiprotein complex that includes Bag3, Hsp70 and C-terminus of Hsc70-interacting protein (CHIP) [<xref rid=\"B31-ijms-21-05442\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijms-21-05442\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijms-21-05442\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijms-21-05442\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijms-21-05442\" ref-type=\"bibr\">35</xref>]. Like Hsp27, Hsp22 contains NTD phosphorylation sites that appear to regulate the self-oligomerization state and chaperone activity for selected substrates [<xref rid=\"B36-ijms-21-05442\" ref-type=\"bibr\">36</xref>]. While a physical interaction between Hsp22 and tau has not yet been reported, Hsp22 did increase the lag time of in vitro heparin-induced aggregation for particular tau variants, including tau phosphomimetics T153E, S356E and S404E [<xref rid=\"B28-ijms-21-05442\" ref-type=\"bibr\">28</xref>]. Additionally, Bag3 overexpression in neurons reduced tau aggregation [<xref rid=\"B37-ijms-21-05442\" ref-type=\"bibr\">37</xref>], suggesting that autophagy may play a role in tau clearance and may further implicate a role for the Bag3-binding partner Hsp22 [<xref rid=\"B38-ijms-21-05442\" ref-type=\"bibr\">38</xref>]. Here, we evaluated the effects of Hsp22, a non-phosphorylatable mutant (S24A/S57A), a pseudophosphorylated mutant (S24D/S57D) and an N-terminal domain deletion (NTDΔ) mutant on tau aggregation and accumulation in vitro and in a cellular model. While Hsp22 did demonstrate inhibition of tau aggregation in vitro, the effects of Hsp22 variants with mutations at phosphorylation sites S24 and S57 were not significantly different than Hsp22 WT. In a cellular model, Hsp22 and variants with point mutations at phosphorylation sites did not significantly affect tau levels. However, co-expression of Hsp22 with an NTD deletion (NTDΔ) demonstrated a robust reduction of both WT and mutant tau P301L/S320F expression, accompanied by a reduction in triton-insoluble aggregates.</p></sec><sec sec-type=\"results\" id=\"sec2-ijms-21-05442\"><title>2. Results</title><sec id=\"sec2dot1-ijms-21-05442\"><title>2.1. Hsp22 Expression with Age and in AD</title><p>We first sought to examine Hsp22 mRNA expression profiles in the human brain to determine whether expression levels are modulated with aging or in AD. <xref ref-type=\"fig\" rid=\"ijms-21-05442-f001\">Figure 1</xref>A shows that Hsp22 mRNA expression is increased in AD relative to young controls in superior frontal gyrus, hippocampus and entorhinal cortex, but not in posterior cingulate gyrus tissue. A significant difference between AD and age matched controls was only found in the entorhinal cortex, and a significant difference between young and aged control tissue was only found in the hippocampus. Specific increases may represent a cellular stress response to intracellular protein misfolding and accumulation. Additionally, we evaluated Hsp22 protein levels in human medial temporal gyrus from AD and age-matched control brain tissue by immunohistochemistry (<xref ref-type=\"fig\" rid=\"ijms-21-05442-f001\">Figure 1</xref>B,C). Hsp22 protein levels varied between cognitively normal aged patients, with two patients having high Hsp22 levels and two patients with low Hsp22 levels. All four AD patient tissues demonstrated relatively lower Hsp22 protein levels. Additionally, Hsp22 protein levels were evaluated in control (nTg) and transgenic P301L tau mice (rTg4510). <xref ref-type=\"fig\" rid=\"ijms-21-05442-f001\">Figure 1</xref>D,E shows Hsp22 staining in the hippocampi of the transgenic model was not significantly different than the control hippocampi.</p></sec><sec id=\"sec2dot2-ijms-21-05442\"><title>2.2. Hsp22 Prevents the Formation of Tau Aggregates In Vitro</title><p>We then sought to evaluate effects of Hsp22 and variants on tau aggregation and cellular clearance. The NTD of sHsps is often flexible and disordered, making it susceptible to post-translational modifications, including phosphorylation. Phosphorylation of sHsps in the NTD has been demonstrated to regulate self-oligomerization and chaperone activity towards certain clients [<xref rid=\"B39-ijms-21-05442\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijms-21-05442\" ref-type=\"bibr\">40</xref>]. To investigate the direct effects of Hsp22 on tau aggregation, we purified Hsp22 WT and two variants with point mutations (<xref ref-type=\"fig\" rid=\"ijms-21-05442-f002\">Figure 2</xref>A). The first variant contains S24D and S57D point mutations to mimic a perpetually phosphorylated state (Hsp22 S/D). The second variant contains S24A and S57A point mutations, as a control variant with non-polar mutations (Hsp22 S/A). <xref ref-type=\"fig\" rid=\"ijms-21-05442-f002\">Figure 2</xref>B shows the predicted intrinsic disorder profile of Hsp22 and these two variants, which demonstrated a high propensity for disorder in the NTD. The disorder propensity of Hsp22 is only minimally affected by S24A/S57A and S24D/S57D point mutations that are causing local changes in disorder predisposition. In an effort to evaluate the effects of Hsp22 on tau, including FTD-related mutant forms with enhanced aggregation properties, we purified three different variants of 0N4R tau protein: tau WT, tau P301L, and tau ΔK280. To investigate whether tau aggregation could be altered by Hsp22, we conducted in vitro thioflavin T (ThT) assays. Since the Hsp22 S/D pseudophosphorylation variant was reported to have decreased chaperone activity for two substrates, insulin and rhodanese [<xref rid=\"B36-ijms-21-05442\" ref-type=\"bibr\">36</xref>], we hypothesized that the Hsp22 S/D mutant might demonstrate reduced chaperone activity for tau. Results showed that all three variants of Hsp22 were able to reduce heparin-induced aggregation of each of the tau variants tested (<xref ref-type=\"fig\" rid=\"ijms-21-05442-f003\">Figure 3</xref>A); although Hsp22 S/D effects on tau P301L aggregation were not statistically significant relative to the control with no Hsp22 variant.</p><p>We further characterized the final ThT assay aggregated protein products by visualizing them using transmission electron microscopy (TEM). Results showed that each tau variant formed large fibrillar structures, and all three variants of Hsp22 were able to dramatically reduce the size of those fibrils. It should be noted that in the Hsp22 conditions, tau fibrils were still present but the size was reduced and appeared thinner (<xref ref-type=\"fig\" rid=\"ijms-21-05442-f003\">Figure 3</xref>B). Taken together, these data indicate that Hsp22 can directly prevent the aggregation of tau WT as well as FTD-related mutants tau P301L and tau ΔK280.</p></sec><sec id=\"sec2dot3-ijms-21-05442\"><title>2.3. Hsp22 NTDΔ Exacerbates Tau P301L Aggregate Formation In Vitro</title><p>Since the effect of Hsp22 N-terminal pseudophosphorylation mutants on tau aggregation was not statistically different than that of Hsp22 WT (<xref ref-type=\"fig\" rid=\"ijms-21-05442-f003\">Figure 3</xref>A), we sought to evaluate the importance of the NTD using a truncated mutant (Hsp22 NTDΔ) as shown in <xref ref-type=\"fig\" rid=\"ijms-21-05442-f002\">Figure 2</xref>A. We performed ThT assays using tau P301L with different concentrations of Hsp22 WT or Hsp22 NTDΔ. Tau P301L aggregation was reduced with increasing concentrations of Hsp22 WT (lower tau: Hsp22 WT ratio); remarkably, an increase in aggregation measured by ThT was detected with increasing Hsp22 NTDΔ concentrations (<xref ref-type=\"fig\" rid=\"ijms-21-05442-f004\">Figure 4</xref>A). Additionally, increasing concentrations of Hsp22 NTDΔ shortened the lag phase as well as increased the rate of fibrillization detected by ThT. TEM visualization of the end product shows increased aggregated protein in the presence of Hsp22 NTDΔ, as shown in <xref ref-type=\"fig\" rid=\"ijms-21-05442-f004\">Figure 4</xref>B. Coomassie stained gels of purified recombinant protein and estimations of purity are shown in <xref ref-type=\"app\" rid=\"app1-ijms-21-05442\">Supplementary Figure S1</xref>. Melting temperatures for recombinant Hsp22 WT and Hsp22 NTDΔ were estimated using differential scanning fluorimetry (DSF), showing that truncation of the NTD does not decrease the protein stability (<xref ref-type=\"fig\" rid=\"ijms-21-05442-f004\">Figure 4</xref>C).</p></sec><sec id=\"sec2dot4-ijms-21-05442\"><title>2.4. Hsp22 NTDΔ Enhances the Clearance of Tau Protein in HEK293T Cells</title><p>To determine the effects of Hsp22 on tau in a cellular environment, HEK293T cells were co-transfected with either a tau WT or tau P301L/S320F expression plasmid and either an empty vector or an Hsp22 variant expression plasmid as indicated (at a 1:5 tau vector:Hsp22 vector ratio). In the triton-soluble fractions (<xref ref-type=\"fig\" rid=\"ijms-21-05442-f005\">Figure 5</xref>A,B, left panels), there was a slight but not significant reduction of tau WT levels in the presence of Hsp22 WT as well as the Hsp22 S/A mutant, yet only Hsp22 NTDΔ expression resulted in a significant reduction of tau WT protein. Similarly, there was a trend toward reduction of tau P301L/S320F levels in the presence of Hsp22 WT and Hsp22 S/A, which was not observed with the Hsp22 S/D variant. Only Hsp22 NTDΔ expression resulted in a significant reduction of tau P301L/S320F protein. Tau P301L/S320F was chosen for the cell assay because it forms triton-insoluble aggregates when expressed in HEK293T cells [<xref rid=\"B41-ijms-21-05442\" ref-type=\"bibr\">41</xref>], therefore triton-insoluble fractions were also evaluated for tau content (<xref ref-type=\"fig\" rid=\"ijms-21-05442-f005\">Figure 5</xref>A,B, right panels). As expected, tau WT protein was not detected in the insoluble fractions. Differences in tau P301L/S320F content in the insoluble fraction followed the same pattern of tau P301L/S320F levels with co-expressed Hsp22 variants in the triton-soluble fraction, indicating that a reduction of aggregation was likely due to a reduction in total tau protein and could not be directly attributed to an effect of the chaperones on preventing tau aggregation. It is also notable that there appear to be two tau immunoreactive bands present, with the lower band more prominent in the triton-soluble fractions and the higher band more prominent in the triton-insoluble fractions. Total protein yield in each soluble fraction was measured and shown in <xref ref-type=\"app\" rid=\"app1-ijms-21-05442\">Supplementary Figure S2</xref>, indicating that all cotransfection conditions did not result in gross cellular toxicity or impaired cell growth. GAPDH immunoblots and Ponceau S staining of PVDF membranes after transfer indicate equivalent loading of protein in each sample as well as efficient transfer (<xref ref-type=\"fig\" rid=\"ijms-21-05442-f005\">Figure 5</xref>A and <xref ref-type=\"app\" rid=\"app1-ijms-21-05442\">Supplementary Figure S2</xref>).</p><p>Triton-soluble fractions were also probed for Hsp22 expression (<xref ref-type=\"fig\" rid=\"ijms-21-05442-f005\">Figure 5</xref>A, middle panel). While Hsp22 WT and point mutation variants were expressed as expected, a lower molecular weight Hsp22 NTDΔ was initially not detected. However, the transfection of the Hsp22 NTDΔ expression plasmid had the most robust effect on tau expression or clearance, which strongly suggested this deletion variant was indeed expressed. Analysis of more concentrated triton-soluble fractions revealed that the Hsp22 NTDΔ protein was indeed produced and immunoreactive to an antibody directed to the CTD, albeit at a much lower level than the other Hsp22 variants (<xref ref-type=\"app\" rid=\"app1-ijms-21-05442\">Supplementary Figure S3</xref>). Additionally, to ensure that the low expression of Hsp22 NTDΔ in this HEK293T cell model is not dependent on the co-expression of tau protein, single transfections of Hsp22 WT and Hsp22 NTDΔ plasmids were compared demonstrating a similar impaired expression in the deletion mutant (<xref ref-type=\"app\" rid=\"app1-ijms-21-05442\">Supplementary Figure S4</xref>).</p></sec><sec id=\"sec2dot5-ijms-21-05442\"><title>2.5. Hsp22 WT and Phosphorylation Site Mutants Enhance the Clearance of Tau Protein in HEK293T Cells at High sHsp Ratios</title><p>Next, we performed experiments similar to those shown in <xref ref-type=\"fig\" rid=\"ijms-21-05442-f005\">Figure 5</xref>, but with a 1:10 ratio of tau vector to Hsp22 vector, which showed a significant reduction in soluble tau WT levels in response to Hsp22 WT, Hsp22 S/A, HSP22 S/D and Hsp22 NTDΔ (<xref ref-type=\"fig\" rid=\"ijms-21-05442-f006\">Figure 6</xref>A,B). Tau P301L/S320F protein levels were also reduced in the presence of all the Hsp22 variants but were only significantly different from empty vector controls with Hsp22 S/A and Hsp22 NTDΔ. Insoluble tau P301L/S320F levels appeared to be reduced with Hsp22 NTDΔ co-expression but the results were not statistically significant.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec3-ijms-21-05442\"><title>3. Discussion</title><p>Previous work has shown that certain molecular chaperones, including a sHsp (Hsp27), can interact with tau protein and inhibit or delay aggregation [<xref rid=\"B26-ijms-21-05442\" ref-type=\"bibr\">26</xref>,<xref rid=\"B28-ijms-21-05442\" ref-type=\"bibr\">28</xref>]. The NTD of sHsps is often a flexible disordered region that is susceptible to post-translational modifications, including phosphorylation. Phosphorylation of many sHsp NTDs appears to affect the formation and dynamic cycling between higher order oligomers, which is often correlated to chaperone activity [<xref rid=\"B39-ijms-21-05442\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijms-21-05442\" ref-type=\"bibr\">40</xref>]. Here, we evaluated the effects of Hsp22 WT, a pseudophosphorylated mutant (S/D), and a mutation with non-polar residues (S/A) on tau aggregation in vitro. While they all appeared to reduce in vitro tau aggregation, we did not find evidence for any differences in modulation of tau aggregation between Hsp22 WT and Hsp22 S/A or Hsp22 S/D. We hypothesized that one or more of the Hsp22 variants would not affect in vitro tau aggregation and serve as a negative control; since all Hsp22 variants modulated tau aggregation we cannot rule out a non-specific effect of additional bulk protein in the assay. This differs from the effect of Hsp27 on in vitro tau aggregation, as our group reported less robust inhibition of tau aggregation for a pseudophosphorylated Hsp27 variant [<xref rid=\"B26-ijms-21-05442\" ref-type=\"bibr\">26</xref>]. This suggests that the NTD of Hsp22 may have a different role than the NTD of Hsp27 with respect to the tau client. Indeed, as Hsp22 oligomers are much smaller than Hsp27 oligomers, the role of NTD phosphorylation dynamics may play a smaller role in changes to sHsp oligomer size and chaperone activity. The Hsp27 NTD appears to enable the formation of large oligomers, which may function to obscure hydrophobic regions of both the NTD and ACD in order to prevent its own aberrant aggregation, which can also functionally obscure tau binding sites. It has been suggested that in the case of Hsp27, binding to misfolded tau requires tau-sHsp interactions that compete for these oligomer-forming binding sites [<xref rid=\"B23-ijms-21-05442\" ref-type=\"bibr\">23</xref>]. Therefore, it is not surprising that in vitro tau aggregation in the presence of Hsp22 NTDΔ resulted in an increase in the formation of β-sheet containing aggregates. This effect may be due to hydrophobic regions of the Hsp22 NTDΔ that contribute to early oligomer/seed formation as well as incorporation as a constituent of the forming β-sheet-rich aggregates in this assay. This proposed in vitro mechanism is supported by the shortened lag phase, increased rate of fibrillization, and the increased total yield of fibrillized protein in the presence of Hsp22 NTDΔ, seen in <xref ref-type=\"fig\" rid=\"ijms-21-05442-f004\">Figure 4</xref>. This mechanism contrasts with Hsp22 WT, which reduced the total yield of fibrillized protein by reducing the rate of aggregation through the elongation phase. This suggests that the effects of Hsp22 WT on tau aggregation may occur at a mid to late stage of fibril elongation, while not directly impacting early tau oligomer, or seed, formation. This finding is consistent with published data demonstrating no effect on tau P301L aggregation lag time by Hsp22 or Hsp27, and an effect of Hsp27 to lengthen the lag time of tau WT (0N4R) aggregation to a greater degree than Hsp22 [<xref rid=\"B23-ijms-21-05442\" ref-type=\"bibr\">23</xref>,<xref rid=\"B28-ijms-21-05442\" ref-type=\"bibr\">28</xref>].</p><p>The most disordered part of Hsp22 is its N-terminal domain (residues 1-71), whereas in Hsp27, disorder is preferentially concentrated within the C-terminal region of the protein (residues 150-205) [<xref rid=\"B42-ijms-21-05442\" ref-type=\"bibr\">42</xref>]. Hsp27 chaperone activity for tau appears to require the NTD and is sensitive to changes in phosphorylation [<xref rid=\"B23-ijms-21-05442\" ref-type=\"bibr\">23</xref>,<xref rid=\"B26-ijms-21-05442\" ref-type=\"bibr\">26</xref>]; here, we demonstrate that the ability of Hsp22 to mediate a reduction in tau expression levels does not require the NTD. The removal of the mostly disordered N-terminal domain, NTDΔ, resulted in an Hsp22 variant with a mean disorder score of 0.43 and 26.8% disordered residues, which is noticeably more ordered than Hsp22 WT (<xref ref-type=\"fig\" rid=\"ijms-21-05442-f002\">Figure 2</xref>B).</p><p>While the robust effect of Hsp22 WT to inhibit tau aggregation in vitro suggests a potential role for direct chaperone activity of Hsp22 complexes to reduce tau aggregation, our cell co-transfection model did not provide evidence for a direct inhibition of tau aggregation. We utilized expression of tau P301L/S320F, a combination of two FTD mutations, as an accelerated model of cellular tau aggregation [<xref rid=\"B41-ijms-21-05442\" ref-type=\"bibr\">41</xref>], which demonstrated triton-insoluble aggregates that were not present with tau WT. While there were reductions in tau P301L/S320F levels in the insoluble fractions with Hsp22 variant co-expression, they correlated well with a decrease in tau P301L/S320F levels in the triton-soluble fraction. This suggests that the reduction in aggregated tau may be due more to clearance of total tau and less to direct steric inhibition of aggregation attributed to tau-binding that we find in vitro. While only Hsp22 NTDΔ significantly reduced tau levels in our standard experiment using plasmid vectors at a 1:5 ratio of tau to sHsp, further increasing the ratio of sHsp to tau vectors resulted in a significant reduction in tau WT levels for all of the Hsp22 variants examined. Overall, this suggests that Hsp22 WT, Hsp22 S/A Hsp22 S/D, and Hsp22 NTDΔ are each capable of enhancing the clearance of tau in cells; however, Hsp22 NTDΔ has a more potent effect on cellular tau clearance. This suggests a potential regulatory role of the Hsp22 NTD toward the client tau that is independent of phosphorylation at serines 24 and 57.</p><p>While Hsp22 NTDΔ resulted in exacerbated aggregation in vitro, a very different result was demonstrated in our cell culture model. The robust effect of Hsp22 NTDΔ to reduce protein levels of exogenous tau WT and tau P301L/S320F may be due to the loss of competitive inhibition to tau binding afforded by the NTD. This may result in an Hsp22 variant with increased affinity (or availability) for the intrinsically disordered tau protein. In a cellular context, exogenous Hsp22 variants likely partner with additional endogenous proteins and cellular clearance components that are absent in our in vitro aggregation assay. Since Hsp22, in complex with proteins like Bag3, plays a role in segregating clients for clearance by autophagy, a reduction in protein expression levels of client proteins, like tau, may be expected. Future studies utilizing overexpression or knock down of Hsp22-interacting proteins, like Bag3, or proteins that competitively inhibit Hsp22 interactions with Hsp70/CHIP complexes, like Bag1, may allow the regulation of a switch between an Hsp70/CHIP/Bag1 complex driving proteasomal clearance of certain clients to an Hsp70/CHIP/Bag3/Hsp22 complex driving autophagic degradation of clients [<xref rid=\"B43-ijms-21-05442\" ref-type=\"bibr\">43</xref>] like tau. The mechanism of tau protein reduction by Hsp22 NTDΔ has not been determined, but further evaluation in the presence of autophagy or proteasome inhibitors may distinguish the mechanism of tau clearance. Additionally, comparison of the effects of NTD variants or deletions on tau protein levels of other sHsps, like Hsp27, may provide insights into the role of sHsps on tau aggregation.</p><p>Hsp22 mRNA expression was upregulated in human brain regions with aging and in AD (<xref ref-type=\"fig\" rid=\"ijms-21-05442-f001\">Figure 1</xref>A). However, differences in Hsp22 mRNA between age-matched controls and AD were only statistically significant in the entorhinal cortex. Protein levels from AD and age-matched controls in another brain region, human medial temporal gyrus, demonstrated wide variability in aged brain and generally lower levels showing variability in expression among AD patients. The increase in Hsp22 mRNA expression in aging and AD may be a regulatory mechanism to address deficiencies in proteostasis and the accumulation of misfolded proteins, but we do not see an obvious upregulation of Hsp22 protein levels in brain tissue from AD patients or a mouse model of tauopathy. It is possible that Hsp22 gene expression does not lead to observable increases in Hsp22 protein due to enhanced turnover of the functioning chaperone. In fact, recombinant Hsp22 protein is unique among sHsps in that it is susceptible to proteolytic degradation when incubated in HEK293T cell lysates at 45 °C, even in the presence of protease inhibitors [<xref rid=\"B25-ijms-21-05442\" ref-type=\"bibr\">25</xref>]; therefore this protein appears to be more sensitive to post-translational regulation. Additional studies are needed to understand the dynamics of cell type specific Hsp22 expression and regulation. One recent study identified lower Hsp22 expression in excitatory neurons compared to inhibitory neurons [<xref rid=\"B44-ijms-21-05442\" ref-type=\"bibr\">44</xref>]. This is important because this same study showed that excitatory neurons are more sensitive to tau toxicity. Thus, it is possible that Hsp22 levels are not properly being upregulated in neurons that degenerate. There may be an opportunity for designing engineered sHsp variants with high chaperone activity towards tau and/or resistance to proteolytic degradation.</p><p>Modulating the expression or activity of endogenous molecular chaperones is an intriguing strategy to reduce aggregation of amyloidogenic proteins like tau in vivo. Additionally, gene therapy vectors allow the exogenous expression of molecular chaperones in the brain, as we have demonstrated with Hsp27, Aha1 and Cyp40 in an animal model of tauopathy [<xref rid=\"B14-ijms-21-05442\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijms-21-05442\" ref-type=\"bibr\">15</xref>,<xref rid=\"B26-ijms-21-05442\" ref-type=\"bibr\">26</xref>]. Utilizing exogenous chaperones will allow the incorporation of potentially beneficial mutations, engineered proteins, or fusion proteins. Continued evaluation of the mechanism by which Hsp22 NTDΔ reduces cellular tau expression and aggregation in cell models and in animal models of neurodegeneration may enhance our understanding of the role of Hsp22 in the clearance of aggregation-prone proteins and provide guidance in the development of engineered molecular chaperones as therapeutics. It is remarkable that Hsp22 NTDΔ variant has such a robust effect on tau, while demonstrating markedly reduced expression relative to Hsp22 WT and other Hsp22 variants. It is possible that Hsp22 NTDΔ itself is recognized as misfolded and cleared, which could explain the lower protein levels (an apparent shorter half-life) of this non-natural protein relative to Hsp22 WT, shown in <xref ref-type=\"app\" rid=\"app1-ijms-21-05442\">Supplementary Figure S3</xref>. Such a mechanism would alleviate potentially toxic effects that might result from potential self-aggregation of Hsp22 NTDΔ. The enhanced turnover of this chaperone fragment could also facilitate clearance of associated client proteins via the same mechanism. This mechanism may be akin to engineered antibody fragments fused with degron signal peptides that facilitate their own degradation as well as their target protein [<xref rid=\"B45-ijms-21-05442\" ref-type=\"bibr\">45</xref>,<xref rid=\"B46-ijms-21-05442\" ref-type=\"bibr\">46</xref>]. It is also notable that the reduction in tau protein was evident for both tau WT and tau P301L/S320F. This suggests that the effect of Hsp22 NTDΔ is not specific to an aggregation prone form of tau, as tau WT did not result in appreciable aggregation in our cell model. However, reduction of tau P301L/S320F levels by Hsp22 NTDΔ did result in a concomitant decrease in aggregated tau found in the insoluble fraction. This demonstrates that targeting normal soluble tau for clearance could be an adequate strategy for therapy, without the need for specifically targeting aberrant or aggregating forms. This is consistent with previous work demonstrating that when tau expression was turned off in an animal model of tau aggregation, memory function recovered and neuronal death was inhibited [<xref rid=\"B47-ijms-21-05442\" ref-type=\"bibr\">47</xref>]. If a single therapeutic molecular chaperone construct targets many variant species of tau found in neurodegenerative disease (whether these be disease-relevant mutations, post-translationally modified forms, or specific oligomeric or fibrillar species), there would be no need to stratify patients with a personalized molecular characterization of each individual presenting tauopathy.</p></sec><sec id=\"sec4-ijms-21-05442\"><title>4. Materials and Methods</title><sec id=\"sec4dot1-ijms-21-05442\"><title>4.1. Hsp22 mRNA Expression in Human Brain Tissue</title><p>Microarray postmortem tissue for mRNA analysis was obtained from 7 different ADR brain banks by UC Irvine Brain Bank (Irvine, CA, USA). Microarray analysis was undertaken on 4 brain regions (PCG, SFG, HPC and EC) from AD cases (<italic>n</italic> = 26, age 74–95, mean age 85.7 ± 6.5 yrs), age-matched controls (<italic>n</italic> = 33, age 60–99 yrs, average age 84.2 ± 6.5 yrs) and young normal cases (ages 20 to 59; <italic>n</italic> = 22, mean 35.4 ± 10.5 years) using Affymetrix (Santa Clara, CA, USA) arrays 9HgU133 plus 2.0 as described previously [<xref rid=\"B48-ijms-21-05442\" ref-type=\"bibr\">48</xref>]. All subjects were evaluated to be cognitively normal or AD based on diagnoses from medical records. mRNA expression of each sample was evaluated by gene-chip hybridization as described by Cribbs et al. [<xref rid=\"B48-ijms-21-05442\" ref-type=\"bibr\">48</xref>]. These data are deposited on the MIAME-compliant GEO database (Accession number GSE11882).</p></sec><sec id=\"sec4dot2-ijms-21-05442\"><title>4.2. Transgenic Animal Brain Tissue Preparation</title><p>Four rTg4510 mice (Jackson Laboratories, Bar Harbor, ME, USA) and four nontransgenic littermates were euthanized at 9 months by Somnasol overdose followed by transcardial perfusion with 0.9% saline. The left hemisphere was obtained from each mouse and fixed overnight in 4% paraformaldehyde followed by cryo-protection in sucrose gradients of 10%, 20% and 30%. Brains were frozen on a freezing stage and horizontally sectioned on a sliding microtome at 25 µm then stored in a PBS solution containing 0.02% NaN<sub>3</sub> at 4 °C. All animal studies were approved by the University of South Florida Institutional Animal Care and Use Committee and carried out in accordance with the National Institutes of Health (NIH) guidelines for the care and use of laboratory animals (Aproval Code M1309, approved July 15, 2015).</p></sec><sec id=\"sec4dot3-ijms-21-05442\"><title>4.3. Anti-Hsp22 Immunohistochemistry Analysis</title><p>Human tissue from the UC Irvine Brain Bank (Irvine, CA, USA) or mouse tissue was stained using free-floating technique. Sections were briefly incubated in PBS with 10% MeOH and 3% H<sub>2</sub>O<sub>2</sub> to block endogenous peroxidases followed by three PBS washes. Sections were then permeabilized for 30 min in 0.2% Triton X-100 with 1.83% lysine and 4% goat serum (Lampire Biological Laboratories, Ottsville, PA, USA) in PBS. Sections were incubated in Hsp22 primary antibody (StressMarq, SPC-181D 1:100; Victoria, BC, Canada) at room temperature overnight. After three PBS washes, sections were incubated at room temperature in biotinylated goat anti-rabbit (Southern Biotech, 1:1,000; Birmingham, AL, USA) secondary antibody for 2 h. A Vectastain ABC kit (Vector Laboratories; Burlingame, CA, USA) was used to amplify visibility. After two PBS washes and one TBS wash, sections were incubated with 0.05% diaminobenzidine and 0.5% nickel for five minutes then developed with 0.03% H<sub>2</sub>O<sub>2</sub>. Stained sections were then mounted on charged slides and allowed to air-dry overnight followed by dehydration in EtOH gradients. Slides were cleared with Histoclear (National Diagnostics; Altanta, GA, USA) then coverslipped with distyrene, plasticizer, and xylene (DPX).</p></sec><sec id=\"sec4dot4-ijms-21-05442\"><title>4.4. Analysis of Intrinsic Disorder Predisposition</title><p>Functional disorder predisposition profiles of human Hsp22 (UniProt ID: Q9UJY1) were evaluated by the D<sup>2</sup>P<sup>2</sup> platform available at: <uri xlink:href=\"http://d2p2.pro\">http://d2p2.pro</uri> [<xref rid=\"B49-ijms-21-05442\" ref-type=\"bibr\">49</xref>]. Per-residue disorder predispositions were also evaluated by a disorder meta-predictor, PONDR<sup>®</sup> FIT [<xref rid=\"B50-ijms-21-05442\" ref-type=\"bibr\">50</xref>], which was also used to study the effects of S24A/S57A, S24D/S57D, and NTDΔ mutations on the propensity of human Hsp22 for intrinsic disorder from amino acid sequence. PONDR<sup>®</sup> FIT uses outputs from PONDR<sup>®</sup> VLXT, PONDR<sup>®</sup> VSL2, PONDR<sup>®</sup> VL3, FoldIndex, IUPred, and TopIDP and shows noticeably improved prediction accuracy compared to single component predictors.</p></sec><sec id=\"sec4dot5-ijms-21-05442\"><title>4.5. Molecular Cloning</title><p>Tau WT, tau P301L, tau ΔK280, Hsp22 WT, Hsp22 S/D, Hsp22 S/A and Hsp22 NTDΔ were subcloned into the multiple cloning site of a pET28a vector (Millipore Sigma #69864; Burlington, MA, USA) modified with an additional tobacco etch virus protease cleavage site (pET28a-TEV). Mutations in Hsp22 were constructed by site-directed mutagenesis or deletion mutagenesis utilizing the Q5 high fidelity DNA polymerase. Primers were designed using NEBaseChanger online tools (New England Biolabs, Ipswich, MA, USA). Correct mutagenic constructs were confirmed by sanger sequencing. For mammalian expression studies, tau cDNA constructs were used in pRK5 (BD Biosciences #556104; San Jose, CA, USA) vectors and Hsp22 cDNA constructs were used in pCMV6-XL6 (Origene #PCMV6XL6; Rockville MD, USA) vectors.</p></sec><sec id=\"sec4dot6-ijms-21-05442\"><title>4.6. Protein Expression and Purification</title><p>One Shot BL21 Star (DE3) Chemically competent E. coli BL21 cells (ThermoFisher, Waltham, MA, USA) were transformed with 4R0N tau WT, 4R0N tau P301L, 4R0N tau ΔK280, Hsp22 WT, Hsp22 S/A, Hsp22 S/D or Hsp22 NTDΔ plasmids in a pET28a-TEV vector. The cells were then grown at 37 °C in LB media containing 50 μg/mL kanamycin. Once their OD600 reached 0.8 the cells were induced with 1mM of IPTG for 3 h. Centrifugation at 3320 <italic>g</italic> for 20 min was used to harvest the cells, which were then resuspended with 35 mL nickel chromatography running buffer (20 mM Tris-HCl pH 8.0, 500 mM NaCl, 10 mM Imidazole) containing EDTA-free protease inhibitors. The cells were then lysed using a freeze-thaw cycle followed by sonication. The lysed cells were centrifuged at 50,000 <italic>g</italic> for 30 min at 4 °C. The supernatant was affinity purified using a standard gravity column packed with 13 mL HisPur™ Ni-NTA Resin (Fisher Scientific, Waltham, MA, USA). After a wash with nickel chromatography running buffer, the protein of interest is eluted with 25 mL of elution buffer (20 mM Tris-HCl pH 8.0, 500 mM NaCl, 250 mM Imidazole). The eluted fractions were treated with 1 mL of TEV protease (2 mg/mL) for 5 h at room temperature then dialyzed back into nickel chromatography running buffer (1 L) overnight. A second nickel purification column was run wherein the flow through fractions containing the His Tag-free proteins were collected and the efficiency of the TEV cleavage was assessed by SDS-PAGE followed by Coomassie staining. For the tau constructs, size exclusion chromatography was performed using a HiLoad 16/600 Superdex 200pg column (GE Healthcare; Chicago, IL, USA) with a Bio-Rad NGC QUEST 10 Chromatography system (Bio-Rad, Hercules, CA, USA) in SEC buffer (20 mM Tris-HCl pH 7.6, 500 mM NaCl, 0.5mM EDTA, 0.5 mM DTT). Fractions of interest were pooled and concentrated. The concentrated protein was then aliquoted, flash frozen with liquid nitrogen, and frozen at −80 °C until use.</p></sec><sec id=\"sec4dot7-ijms-21-05442\"><title>4.7. Thioflavin T Fluorescence Assay</title><p>Recombinant tau and Hsp22 proteins were dialyzed into 100 mM sodium acetate buffer pH 7, overnight. Then, 10 µM of tau P301L, 10 µM of tau ΔK280, or 20 µM of tau WT was mixed with substoichiometric concentrations of WT Hsp22, Hsp22 S/A, Hsp22 S/D, or Hsp22 NTDΔ as indicated, as well as 10 µM heparin and 10 µM thioflavin T. A total of 100 µL of each assay sample was loaded onto 96-well black clear-bottom plates (Fisher Scientific, Cat#07-200-525; Hampton, NJ, USA) in triplicate. Fluorescence was then measured with 440nm excitation and 482 nm emission using a Cytation 3 multi-mode reader (BioTek; Winooski, VT, USA). Tau was induced to aggregate with 10 µM heparin and aggregation was followed over a 72-hour period taking readings every 10 min.</p></sec><sec id=\"sec4dot8-ijms-21-05442\"><title>4.8. Transmission Electron Microscopy</title><p>Ten µL of protein samples at the end of the thioflavin T assay were adsorbed onto prewashed 200 mesh formvar/carbon-coated copper grids for 5 min. The grids were washed with water (10 μL) two times, stained with 10 μL filtered 2% uranyl acetate for 1 min, then dried. The samples were analyzed with a JEOL (Peabody, MA, USA) 1400 Digital Transmission Electron Microscope, and images were captured with a Gatan (Pleasanton, CA, USA) Orius wide-field camera at the Electron Microscopy Core Facility in the College of Medicine at the University of South Florida. The fields shown are representative.</p></sec><sec id=\"sec4dot9-ijms-21-05442\"><title>4.9. Differential Scanning Fluorimetry</title><p>To assess protein thermal stability, Hsp22 WT and Hsp22 NTDΔ were diluted to a final concentration of 5–10 μM in 100 mM sodium acetate buffer pH 7.0 containing 2 mM DTT and 5× Sypro Orange dye (Invitrogen). The 30 μL mixtures were dispensed into a Bio-Rad 96-well thin-wall PCR plate and sealed with microplate adhesive film. Sypro Orange fluorescence was monitored as a function of temperature in a Bio-Rad (Hercules, CA, USA) CFX96 Touch Real-Time PCR Detection System through use of the C1000 Touch Thermal Cycler and FRET channel. Thermal melts were conducted in triplicate for each condition by heating from 25 to 95 °C in 1 °C increments for 2 min and measuring fluorescence at each temperature step. Fluorescence data were blank-subtracted, normalized to the maximum signal for each individual melt, and Boltzmann Sigmoid analysis was conducted using GraphPad Prism to determine the melting temperature (Tm) from two independent experiments.</p></sec><sec id=\"sec4dot10-ijms-21-05442\"><title>4.10. HEK 293T Co-Transfection and Fractionation</title><p>HEK293T cells (ATCC; Old Town Manassas, VA, USA) were subcultured in 6-well plates coated with poly-L-Lysine. At ~60–80% confluence, cells were co-transfected using polyethylenimine with tau and Hsp22 expression plasmids at a ratio of 1:5 and 1:10, respectively. Forty-eight hours after transfection, cells were harvested and lysed in 400 µL of Triton X-100 lysis buffer (150 mM NaCl, 50 mM Tris, 1 mM EDTA, 1% Triton X-100) supplemented with protease inhibitor cocktail, phosphatase inhibitor cocktails and PMSF (Sigma, P8340, P0044, P5726 and P7626; St. Louis, MO, USA). After a 20-minute incubation on ice, samples were cleared by centrifugation at 10,000× <italic>g</italic> for 10 min. Samples were then centrifuged at 100,000× <italic>g</italic> for 30 min to pellet triton-insoluble proteins. Supernatant was collected as the triton-soluble fraction. Pellets were washed once in 1 mL of lysis buffer, centrifuged again at 100,000× <italic>g</italic> for 30 min, resuspended in 8M urea and used as the triton-insoluble fraction.</p></sec><sec id=\"sec4dot11-ijms-21-05442\"><title>4.11. Western Blots</title><p>Triton-soluble and -insoluble fractions were diluted into 1X SDS sample buffer. Triton-soluble samples were incubated at 99 °C for 3 min and all samples were processed by SDS-PAGE in precast Any Kd gradient gels (BioRad; Hercules, CA, USA) and transferred to PVDF membranes. PVDF membranes were reversibly stained with Ponceau S to confirm proper transfer and equivalent loading of total protein in each lane. Rabbit anti-tau polyclonal antibody (Dako/Agilent, A0024; Santa Clara, CA, USA) was used to detect total tau protein. Goat anti-HspB8 (Hsp22) antibody (ThermoFisher, PA5-18103, directed to the CTD; Waltham, MA, USA) was used to detect Hsp22 protein.</p></sec><sec id=\"sec4dot12-ijms-21-05442\"><title>4.12. Statistical Analysis</title><p>Quantified data are expressed as mean ± standard error of the mean (SEM) for at least three independent experiments. Significant differences relative to controls were evaluated using a one-way analysis of variance (ANOVA) followed by either Dunnet’s or Fisher’s LSD post hoc test using Prism software (Graphpad Software, Inc. La Jolla, CA, USA).</p></sec></sec></body><back><ack><title>Acknowledgments</title><p>We would like to thank Carl Cotman and Nicole Berchtold for providing the human tissue samples and access to the mRNA data.</p></ack><app-group><app id=\"app1-ijms-21-05442\"><title>Supplementary Materials</title><p>Supplementary materials can be found at <uri xlink:href=\"https://www.mdpi.com/1422-0067/21/15/5442/s1\">https://www.mdpi.com/1422-0067/21/15/5442/s1</uri>.</p><supplementary-material content-type=\"local-data\" id=\"ijms-21-05442-s001\"><media xlink:href=\"ijms-21-05442-s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>Conceptualization, J.M.W., A.L.D., C.A.D, L.J.B. and P.C.B.; methodology, J.M.W., A.L.D., S.E.H., L.J.B., V.N.U. and P.C.B; formal analysis, J.M.W., A.L.D., S.E.H., D.M.B. and L.J.B.; investigation, J.M.W., A.L.D., T.A.S., Y.V.-A., D.B.-A., D.M.B., D.G.P. and S.E.H.; writing—original draft preparation, J.M.W.; writing—review and editing, J.M.W., A.L.D., L.J.B., V.N.U. and P.C.B.; supervision, J.M.W., L.J.B. and P.C.B.; project administration, J.M.W. and L.J.B.; funding acquisition, C.A.D., L.J.B., V.N.U. and P.C.B. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was funded by the National Institute on Aging of the National Institutes of Health, Award Number RF1AG055088. P.C.B was also supported by IKBX004214. This research was also supported by the Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development; Award Number 2I01 BX001637. Support for the University of California, Irvine, tissue bank was provided by NIH/NIA Grant P50 AG16573. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the United States government.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijms-21-05442\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Powers</surname><given-names>E.T.</given-names></name><name><surname>Morimoto</surname><given-names>R.I.</given-names></name><name><surname>Dillin</surname><given-names>A.</given-names></name><name><surname>Kelly</surname><given-names>J.W.</given-names></name><name><surname>Balch</surname><given-names>W.E.</given-names></name></person-group><article-title>Biological and chemical approaches to diseases of proteostasis deficiency</article-title><source>Annu. Rev. 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Data were analyzed by one-way ANOVA with Dunnet’s post hoc comparisons test, * <italic>p</italic> < 0.05; ** <italic>p</italic> < 0.01; *** <italic>p</italic> < 0.001). (<bold>B</bold>,<bold>C</bold>) Medial temporal gyrus tissue samples from four aged and four AD brains were evaluated by immunohistochemical staining with an antibody to Hsp22. The mean area of medial temporal gyrus Hsp22 positive staining from 10 random fields per patient is shown. Representative images from each patient are shown. Scale bars reflect 100 µm and 10 µm (inset boxes). (<bold>D</bold>) Mean % area of Hsp22 staining in the hippocampus from 4 non-transgenic and 4 rTg4510 mice. (<bold>E</bold>) Representative images of Hsp22 staining in nTg and rTg4510 brain tissue. Scale bars reflect 200 µm and 10 µm (inset boxes).</p></caption><graphic xlink:href=\"ijms-21-05442-g001\"/></fig><fig id=\"ijms-21-05442-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Hsp22 variants and intrinsic disorder propensity profiles. (<bold>A</bold>) Schematics of the four Hsp22 variants examined show the NTD (N-terminal), ACD (α-crystallin domain) and CTD (C-terminal) regions as well as the sites of point mutations. (<bold>B</bold>) Disorder predisposition profiles using PONDR<sup>®</sup> FIT are shown for the four Hsp22 variants examined in this study. Light pink shadows around the curves show error distributions for Hsp22 WT. The background shading corresponds to amino acid residues within each domain as indicated.</p></caption><graphic xlink:href=\"ijms-21-05442-g002\"/></fig><fig id=\"ijms-21-05442-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Hsp22 prevents tau aggregation in vitro. (<bold>A</bold>) WT, P301L and ΔK280 tau aggregation was monitored by Thioflavin T (ThT) fluorescence intensity over 72 h. Bars represent the mean maximum ThT intensity (± SEM) normalized to the no chaperone protein condition (black bars). Hsp22 WT (dark blue), Hsp22 S/A (S24A/S57A, medium blue), and Hsp22 S/D (S24D/S57D, light blue) were evaluated at a 20:1 tau:chaperone ratio. Data were analyzed by one-way ANOVA with Tukey’s post hoc multiple comparisons test (<italic>n</italic> = 6, two independent experiments performed in triplicate), * <italic>p</italic> < 0.05; ** <italic>p</italic> < 0.01; *** <italic>p</italic> < 0.001). (<bold>B</bold>) Representative 20,000× transmission electron microscopy images of tau alone or in the presence of the indicated Hsp22 variant. Scale bar 1 µm.</p></caption><graphic xlink:href=\"ijms-21-05442-g003\"/></fig><fig id=\"ijms-21-05442-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Hsp22 NTDΔ accentuated aggregation of tau P301L in a concentration-dependent manner. (<bold>A</bold>) Tau P301L (2.5 µM) aggregation was monitored by ThT fluorescence intensity over 40 h in the presence of either Hsp22 WT or Hsp22 NTDΔ at tau:chaperone ratios of 20:1, 10:1 or 5:1. (<bold>B</bold>) Representative 20,000× transmission electron microscopy images of tau P301L alone or in the presence of the Hsp22 NTDΔ variant. Scale bar 0.5 µM. (<bold>C</bold>) Differential scanning fluorimetry assessment of melting temperatures (Tm) for purified recombinant Hsp22 WT and Hsp 22 NTDΔ; a.u. = arbitrary units of fluorescence.</p></caption><graphic xlink:href=\"ijms-21-05442-g004\"/></fig><fig id=\"ijms-21-05442-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>Changes in cellular expression of tau WT or tau P301L/S320F with co-expression of Hsp22 and variants at a 1:5 tau vector: Hsp22 vector ratio. (<bold>A</bold>) Representative Western blot images of triton-soluble and triton-insoluble fractions from HEK293T cells co-transfected with tau WT or tau P301L/S320F and either an empty vector plasmid (EV) or one of the variant Hsp22 expression vectors as indicated. Immunoblots were with antibodies specific for tau, Hsp22 or GAPDH as indicated. (<bold>B</bold>) Quantitation of tau blots from three independent experiments, tau/GAPDH ratios were normalized to the level of tau expression in the tau P301L/S320F with empty vector co-transfection sample. Data were analyzed by one-way ANOVA with Dunnet’s post hoc comparisons test to each empty vector control (<italic>n</italic> = 3, * <italic>p</italic> < 0.05; <sup>†</sup> indicates <italic>p</italic> > 0.05 with Dunnet’s and <italic>p</italic> < 0.05 with Fisher’s LSD).</p></caption><graphic xlink:href=\"ijms-21-05442-g005\"/></fig><fig id=\"ijms-21-05442-f006\" orientation=\"portrait\" position=\"float\"><label>Figure 6</label><caption><p>Changes in cellular expression of tau WT or tau P301L/S320F with co-expression of Hsp22 and variants at a 1:10 tau vector: Hsp22 vector ratio. (<bold>A</bold>) Representative Western blot images of triton-soluble and triton-insoluble fractions from HEK293T cells co-transfected with tau WT or tau P301L/S320F and either an empty vector plasmid (EV) or one of the variant Hsp22 expression vectors as indicated. Immunoblots were with antibodies specific for tau, Hsp22 or GAPDH as indicated. (<bold>B</bold>) Quantitation of tau blots from three independent experiments, tau/GAPDH ratios were normalized to the level of tau expression in the tau P301L/S320F with empty vector co-transfection sample. Data were analyzed by one-way ANOVA with Dunnet’s post hoc comparisons test to each empty vector control (<italic>n</italic> = 3, * <italic>p</italic> < 0.05; ** <italic>p</italic> < 0.01; *** <italic>p</italic> < 0.001; ns = not significant).</p></caption><graphic xlink:href=\"ijms-21-05442-g006\"/></fig></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32731643</article-id><article-id pub-id-type=\"pmc\">PMC7432036</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155444</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05444</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Lifestyle Medicine and Psychological Well-Being Toward Health Promotion: A Cross-Sectional Study on Palermo (Southern Italy) Undergraduates</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-2466-937X</contrib-id><name><surname>Matranga</surname><given-names>Domenica</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05444\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-5406-884X</contrib-id><name><surname>Restivo</surname><given-names>Vincenzo</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05444\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-7974-3963</contrib-id><name><surname>Maniscalco</surname><given-names>Laura</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05444\">2</xref><xref rid=\"c1-ijerph-17-05444\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-3119-4445</contrib-id><name><surname>Bono</surname><given-names>Filippa</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05444\">3</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-8119-4924</contrib-id><name><surname>Pizzo</surname><given-names>Giuseppe</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05444\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Lanza</surname><given-names>Giuseppe</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05444\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Gaglio</surname><given-names>Valerio</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05444\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-5090-1366</contrib-id><name><surname>Mazzucco</surname><given-names>Walter</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05444\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Miceli</surname><given-names>Silvana</given-names></name><xref ref-type=\"aff\" rid=\"af5-ijerph-17-05444\">5</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05444\"><label>1</label>Department of Health Promotion, Mother and Infant Care, Internal and Specialized Medicine “G. D’Alessandro”, University of Palermo, 90127 Palermo, Italy; <email>domenica.matranga@unipa.it</email> (D.M.); <email>vincenzo.restivo@unipa.it</email> (V.R.); <email>giuseppeluigimarco.lanza@you.unipa.it</email> (G.L.); <email>valerio.gaglio@unipa.it</email> (V.G.); <email>walter.mazzucco@unipa.it</email> (W.M.)</aff><aff id=\"af2-ijerph-17-05444\"><label>2</label>Department of Biomedicine, Neuroscience and Advanced Diagnostics, University of Palermo, 90127 Palermo, Italy</aff><aff id=\"af3-ijerph-17-05444\"><label>3</label>Department of Economics, Business and Statistics, University of Palermo, 90128 Palermo, Italy; <email>filippa.bono@unipa.it</email></aff><aff id=\"af4-ijerph-17-05444\"><label>4</label>Department of Surgical, Oncological and Oral Sciences, University of Palermo, 90127 Palermo, Italy; <email>giuseppe.pizzo@unipa.it</email></aff><aff id=\"af5-ijerph-17-05444\"><label>5</label>Department of Psychology, Educational Science and Human Movement, University of Palermo, 90128 Palermo, Italy; <email>silvana.miceli56@unipa.it</email></aff><author-notes><corresp id=\"c1-ijerph-17-05444\"><label>*</label>Correspondence: <email>laura.maniscalco04@unipa.it</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>28</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5444</elocation-id><history><date date-type=\"received\"><day>06</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>23</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>(1) Aim: To assess the attitude toward Lifestyle Medicine and healthy behaviours among students in the healthcare area and to demonstrate its association to psychological well-being; (2) Methods: A cross-sectional study is conducted among 508 undergraduates of the University of Palermo (140 (27.6%) in the healthcare area and 368 (72.4%) in the non-healthcare area), during the academic year 2018–2019. Psychological well-being is measured through two dimensions of eudaimonia and hedonia, using the 10-item Hedonic and Eudaimonic Motives for Activities-Revised (HEMA-R) scale, with answers coded on a 7-point scale. The association between demographic and modifiable behavioural risk factors for chronic diseases is assessed through crude and adjusted Odds ratios with 95% confidence intervals; (3) Results: Orientation to both hedonia and eudaimonia is significantly associated to the Mediterranean diet (ORAdj = 2.28; 95% CI = (1.42–3.70)) and drinking spirits less than once a week (ORAdj = 1.89; 95% CI = (1.10–3.27)) and once a week or more (ORAdj = 6.02; 95% CI = (1.05–34.52)), while these conditions occur together less frequently for current smokers (ORAdj = 0.38; 95% CI = (0.18–0.81)). Students inclined to well-being consider healthcare professionals as models for their patients and all people in general (OR = 1.96, 95% CI = (1.28–3.00)); (4) Conclusions: The positive relation found between a virtuous lifestyle and psychological well-being suggests the construction, development and cultivation of individual skills are a means to succeed in counteracting at risk behaviours for health.</p></abstract><kwd-group><kwd>lifestyle medicine</kwd><kwd>chronic diseases</kwd><kwd>modifiable behaviours</kwd><kwd>risk factors</kwd><kwd>psychological well-being</kwd><kwd>public health</kwd><kwd>eudaimonia</kwd><kwd>hedonia</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05444\"><title>1. Introduction</title><p>Worldwide, countries put policy strategies and health programs in place to counteract the spread chronic diseases and to promote healthy behaviours [<xref rid=\"B1-ijerph-17-05444\" ref-type=\"bibr\">1</xref>]. Poor diet, physical inactivity, tobacco, and alcohol abuse are considered the modifiable behavioural risk factors (MBRF) determining the greatest burden of chronic diseases [<xref rid=\"B2-ijerph-17-05444\" ref-type=\"bibr\">2</xref>]. The World Health Organization [<xref rid=\"B1-ijerph-17-05444\" ref-type=\"bibr\">1</xref>] reports that tobacco accounts for over 7.2 million deaths every year, including those related to being exposed to passive smoke, and is expected to increase markedly over the coming years. It also estimates that excess salt/sodium intake is responsible for 4.1 million annual deaths and that insufficient physical activity accounts for 1.6 million deaths annually. Further, nearly 3 million annual deaths are attributable to alcohol abuse (5.3% of all deaths) [<xref rid=\"B1-ijerph-17-05444\" ref-type=\"bibr\">1</xref>]. To support the change of lifestyle as a first line of prevention, the Lifestyle Medicine (LM) approach, as defined in 2010 by the Journal of the American Medical Association, is “the evidence-based practice of assisting individuals and families to adopt and sustain behaviours that can improve health and quality of life” [<xref rid=\"B3-ijerph-17-05444\" ref-type=\"bibr\">3</xref>]. The American College of Lifestyle Medicine (ACLM) establishes six ways to take control of health through regular physical exercise, adequate and good quality sleep, cessation of smoking, stress management and maintenance of relationships. Moreover, LM advocates for a whole-food, plant-based diet for a nutritional lifestyle that is based on whole, minimally processed foods, plants, including vegetables, fruits, whole grains, legumes, seeds and nuts, and little or no intake of animal products. Additionally, there is a preference for quality or organic food and zero-miles products. Physicians are the first source of information about health and healthy behaviours for their patients [<xref rid=\"B4-ijerph-17-05444\" ref-type=\"bibr\">4</xref>] and physician counselling has been demonstrated to be effective for weight loss [<xref rid=\"B5-ijerph-17-05444\" ref-type=\"bibr\">5</xref>], smoke cessation [<xref rid=\"B6-ijerph-17-05444\" ref-type=\"bibr\">6</xref>] and alcohol intake [<xref rid=\"B7-ijerph-17-05444\" ref-type=\"bibr\">7</xref>]. Regarding the skills required to practice LM, primary care physicians are called to promote healthy behaviours as the basis of medical care, disease prevention and health promotion, as well as providing the knowledge of the mechanisms by which specific lifestyle changes can have a positive effect on patient health outcomes [<xref rid=\"B8-ijerph-17-05444\" ref-type=\"bibr\">8</xref>]. Considering this, it is essential to involve medical students in shaping and acquiring these skills through education and the alignment of medical curricula. Medical students report a lack of knowledge and poor skills to counsel their patients to improve lifestyle behaviours [<xref rid=\"B9-ijerph-17-05444\" ref-type=\"bibr\">9</xref>]. Even if the need to steer medical students to disease prevention and health promotion has been acknowledged, it must be noted that medical curricula are devoid of some content, such as positive psychology and psychological well-being [<xref rid=\"B10-ijerph-17-05444\" ref-type=\"bibr\">10</xref>], which are needed to perform counselling effectively [<xref rid=\"B11-ijerph-17-05444\" ref-type=\"bibr\">11</xref>].</p><p>Individuals experiencing a state of well-being are less likely to report alcohol and drug addiction, be on a poor diet or be sedentary [<xref rid=\"B12-ijerph-17-05444\" ref-type=\"bibr\">12</xref>], and to develop and maintain better social relationships [<xref rid=\"B13-ijerph-17-05444\" ref-type=\"bibr\">13</xref>]. Recently, the interest in well-being within the psychological field has grown rapidly to investigate the sources of happiness and the many facets of human flourishing [<xref rid=\"B14-ijerph-17-05444\" ref-type=\"bibr\">14</xref>]. Taking this perspective, we can distinguish two different approaches: “hedonia” that focuses on pleasure, happiness and the achievement of well-being through the satisfaction of one’s desires [<xref rid=\"B15-ijerph-17-05444\" ref-type=\"bibr\">15</xref>] and “eudaimonia”, according to which well-being is obtained by fulfilling one’s potential in the pursuit of complex and meaningful goals [<xref rid=\"B16-ijerph-17-05444\" ref-type=\"bibr\">16</xref>]. According to Huta [<xref rid=\"B17-ijerph-17-05444\" ref-type=\"bibr\">17</xref>], the eudaimonic orientation is defined in terms of four core elements: authenticity, meaning, excellence and growth. The first refers to the possibility of living and acting in accordance with one’s values and one’s self; the second concerns the ability to identify the meaning of things and their value; the third refers to the struggle of the human being to reach high standards of ethical behaviour; and the fourth, finally, concerns the ability to actualise one’s potential. Consequently, the eudaimonic vision is based on the concept of flourishing, true self, actualising potential, meaning, objectives, purposes, and personal expression and implies a process of continuous construction, development, and cultivation of individual skills. To contrast, the author defines the hedonic orientation as the pursuit of two elements: pleasure, that is the search for pleasant sensations and emotions; and comfort, the tendency to seek easy and painless solutions. A hedonic orientation, therefore, pushes the individual in search of what is subjectively pleasant [<xref rid=\"B17-ijerph-17-05444\" ref-type=\"bibr\">17</xref>].</p><p>Taking the perspective of medical education, the positive relation between a virtuous lifestyle and psychological well-being suggests promoting the development of curricula where a varied spectrum of LM topics, such as nutrition and exercise together with the analysis of the underlying psychological processes, are provided and developed in an integrated manner [<xref rid=\"B18-ijerph-17-05444\" ref-type=\"bibr\">18</xref>].</p><p>To assess the attitude toward LM and lifestyle behaviours among students in the healthcare area, and to demonstrate the association with psychological well-being, we conduct a cross-sectional study among undergraduates of the University of Palermo during the academic year 2018–2019. More in depth, our study assumes that the adoption of healthy behaviours could be significantly associated with psychological well-being.</p></sec><sec id=\"sec2-ijerph-17-05444\"><title>2. Materials and Methods</title><sec id=\"sec2dot1-ijerph-17-05444\"><title>2.1. Study Design</title><p>A cross-sectional study was designed to investigate the study aims on a sample of students attending health (medicine, dentistry, and other health professional courses) and non-health (economics and psychological science) degree courses. To this end, a questionnaire articulated into five sections (demographics, diet habits, smoking, physical activity, and psychological well-being) was structured. To assess the lifestyle, composite indicators were constructed for (i) eating habits, (ii) alcohol consumption, (iii) sedentary lifestyle, (iv) smoking, (v) sexual habits, and (vi) addictions. The diet habits section included questions regarding variations which occurred in the last two and five years in the respondent’s dietary habits, the food frequency test, and questions about alcohol intake [<xref rid=\"B19-ijerph-17-05444\" ref-type=\"bibr\">19</xref>], and binge drinking. The most widespread healthy diet in Italy is the Mediterranean diet that consists of “a high intake of vegetables, legumes, fruits and nuts, and cereals, and a high intake of olive oil but a low intake of saturated lipids, a moderately high intake of fish, a low-to-moderate intake of dairy products, a low intake of meat and poultry, and a regular but moderate intake of ethanol, primarily in the form of wine and generally during meals” [<xref rid=\"B20-ijerph-17-05444\" ref-type=\"bibr\">20</xref>]. Following Trichopoulou et al. [<xref rid=\"B19-ijerph-17-05444\" ref-type=\"bibr\">19</xref>], for subjects whose consumption was adherent to the Mediterranean diet, a score from 0 (minimum adherence) to 10 (maximum adherence) was assigned for each respondent and the median value was chosen as a cut-off to define the respondent’s adherence to the Mediterranean diet (adherent if the score > median and not adherent if the score ≤ median). Alcohol consumption was assessed concerning wine, beer and spirits and expressed through three frequency categories (never, less than once a week, once a week or more).</p><p>Binge drinking typically happens when men consume 5 or more drinks or women consume 4 or more drinks in about 2 hours. Binging was detected through a question about the occurrence of a binge episode in the last 12 months and expressed as a binary variable with “yes” or “no” response categories [<xref rid=\"B21-ijerph-17-05444\" ref-type=\"bibr\">21</xref>].</p><p>The smoking section was developed in agreement with the WHO guidelines about tobacco use. Smoking status was expressed as a categorical variable with three categories, non-smoker, former and current smoker [<xref rid=\"B1-ijerph-17-05444\" ref-type=\"bibr\">1</xref>]. Physical activity was defined as a binary variable with “yes” or “no” as the response categories [<xref rid=\"B22-ijerph-17-05444\" ref-type=\"bibr\">22</xref>].</p><p>The measure used to assess eudaimonic and hedonic orientations is the Hedonic and Eudaimonic Motives for Activities-Revised (HEMA-R) scale, which represents a revision of the original Hema scale [<xref rid=\"B23-ijerph-17-05444\" ref-type=\"bibr\">23</xref>]. The scale is made up of 10 items, five of which refer to eudaimonic orientation and five to hedonic orientation. Particularly, hedonia is expressed through the concepts of “pleasure” and “comfort”. Conversely, eudaimonia is demonstrated through the concepts of “authenticity”, “excellence”, “growth” and “meaning”. The subjects were asked to evaluate the degree to which they usually carry out their activities according to the intentions indicated and independently of the achievement of the objective. Participants gave ratings on various eudaimonic and hedonic motives, which were intermixed on a scale from 1 (not at all) to 7 (very much). A subject was considered oriented to hedonia or eudaimonia if the respective score was not less than the median. Well-being was expressed through a categorical variable with four categories (no orientation, hedoniac, eudaimoniac, both orientations).</p><p>To assess each student’s health, each subject was asked if he/she had ever suffered from diabetes, hypertension, hypercholesterolemia, other cardiovascular diseases, neoplasia, or other chronic diseases. The occurrence of chronic disease was expressed as a binary variable with category “yes” in the case of at least one chronic disease and “no” in the case of none.</p><p>Finally, the questionnaire included three specific questions about the role of healthcare professionals for prevention and health promotion. First, each participant was asked if healthcare professionals could be considered as models for their patients and people in general; second, if they should regularly counsel their patients for tobacco cessation and, third, if a patient has an increased probability to stop smoking if assisted by his/her practitioner or nurse. Regarding all questions, the possible answer categories were “yes” and “no”.</p><p>The study was presented by the research team during a seminar entitled “Communicative skills for the promotion of correct lifestyles and the prevention of chronic degenerative diseases” opened to students from different study areas. Then, students were invited to adhere to the survey by self-administering the questionnaire through the Google platform. The study was approved by the Ethical Committee of the “Azienda Universitaria Policlinico Paolo Giaccone” of Palermo (Reference number 08/2018).</p></sec><sec id=\"sec2dot2-ijerph-17-05444\"><title>2.2. Statistical Methods</title><p>Categorical variables were expressed as counts and percentages, and continuous variables were categorized using the median as the cut-off value. Univariable association between demographic and Modifiable Behavioural Risk Factors (MBRFs) was assessed using the Chi-square test or the Fisher exact test, as appropriate, and measured through crude Odds Ratios (OR) and 95% Confidence Intervals (CIs). Multinomial logistic regression was performed to assess the likelihood of being oriented toward eudaimonia, hedonia or both, compared to no orientation in relation to demographics and MBRFs that had significant results using univariable analysis. Results of multivariable analysis were expressed as adjusted ORs (AdjOR) and 95% CIs.</p><p>Stata IC/15.1 (StataCorp LLC, Texas, TX, USA) was used for statistical analysis, and a <italic>p</italic>-value < 0.05 was chosen as the statistical significance cut-off.</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05444\"><title>3. Results</title><p>A sample of 508 undergraduate students, 140 (27.6%) from the health area and 368 (72.4%) from the non-health area, was recruited during the study. The majority of the respondents attended the first year of study (n = 348; 68.5%), while the remaining part was divided between the second and third attendance year, 81 (15.9%) and 79 (15.6%), respectively.</p><p>The Hedonic and Eudaimonic Motives for Activities-Revised (HEMA-R) was available for all students, 325 (64.0%) females, with parents highly educated or graduated (79% fathers and 81% mothers), mostly on-site students (267, 43.8%) followed by off-site (142, 28%) and commuter (99, 15.5%) students. The attitude to be hedonic was lower for females than for males (Odds Ratios (OR) = 0.56, 95% Confidence Intervals (CI) = (0.35–0.91)). Notably, there was not any significant difference between students in the healthcare and non-healthcare area to be more inclined to either hedonia or eudaimonia (<xref rid=\"ijerph-17-05444-t001\" ref-type=\"table\">Table 1</xref>).</p><p>Using univariable analysis, students following the Mediterranean diet were more inclined to eudaimonia (OR = 2.50, 95% CI = [1.52–4.12]), while those with binge experience in the past were less prone (OR = 0.48, 95% CI = [0.29–0.79]). Overweight students were more prone to hedonia than normal weight ones (OR = 2.33, 95% CI = [1.11–4.95]), as well as students drinking beer less than once a week (OR = 2.15, 95% CI = [1.14–4.05]) and once a week or more (OR = 2.81, 95% CI = [1.23–6.42]), and for students reporting taking spirits sometimes (OR = 2.01, 95% CI = [1.17–3.46]). Furthermore, there was a statistically significant association with experiencing binge drinking (OR = 1.62 95% CI = [1.00–2.62]). Students accustomed to tobacco were less likely to be oriented to both hedonia and eudaimonia (OR = 0.47 95% CI = [0.28–0.79] for former smokers and OR = 0.40 95% CI = [0.22–0.75] for current smokers). Conversely, students drinking spirits less than once a week (OR = 1.50 95% CI = [1.00–2.24]) and once a week or more (OR = 4.29 95% CI = [1.14–16.17]) and those reporting binging experiences (OR = 1.43 95% CI = [0.98–2.09]) were more oriented to both dimensions of psychological well-being (<xref rid=\"ijerph-17-05444-t002\" ref-type=\"table\">Table 2</xref>). </p><p>Students inclined to both well–being dimensions considered healthcare professionals as models for their patients and all people in general (OR = 1.96, 95% CI = [1.28–3.00]). Students in the healthcare area were more inclined than those in non–healthcare fields to consider that healthcare professionals should counsel their patients for tobacco cessation on a regular basis (OR = 8.03; 95% CI = [1.05–61.3]) (<xref rid=\"ijerph-17-05444-t003\" ref-type=\"table\">Table 3</xref>). </p><p>Using multivariable analysis, the Mediterranean diet was confirmed as the only one statistically significant for being eudaimonic (AdjOR = 3.27; 96% CI = [1.83–5.87]). Additionally, experiencing both hedonia and eudaimonia was significantly associated to the Mediterranean diet (AdjOR = 2.28; 95% CI = [1.42–3.70]) as well as drinking spirits less than once a week (AdORj = 1.89; 95% CI = [1.10–3.27]), while these conditions occurred together less frequently for current smokers (AdjOR = 0.38; 95% CI = [0.18–0.87]) (<xref rid=\"ijerph-17-05444-t004\" ref-type=\"table\">Table 4</xref>). </p></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05444\"><title>4. Discussion</title><p>Lifestyle Medicine has produced significant changes in the concept of health, moving from a care–centred approach to an approach focused on promoting well–being. Our findings document a positive relationship between healthy behaviours and psychological well–being and suggest the construction, development, and cultivation of individual skills as a means to be successful in counteracting behaviours at risk for health. Specifically, we have deepened the link between eudaimonic well–being and healthy behaviours, with particular regard to nutrition, physical activity, alcohol, and tobacco abuse. Our results indicate that students adhering to the Mediterranean diet were more likely to be oriented toward psychological well–being, in line with other literature [<xref rid=\"B12-ijerph-17-05444\" ref-type=\"bibr\">12</xref>]. Additionally, taking alcohol less than once a week or once a week or more was significantly related to both hedonic and eudaimonic well–being. This surprising result may be explained as the occasional intake of alcohol in the young population typically occurs between meals within a social context characterized by interactions that strengthen individual identity and group cohesion [<xref rid=\"B24-ijerph-17-05444\" ref-type=\"bibr\">24</xref>]. Conversely, the result that current smokers are less oriented toward the hedonic and eudaimonic perspective can be explained as tobacco consumption is perceived as an addiction more than a condition of pleasure [<xref rid=\"B25-ijerph-17-05444\" ref-type=\"bibr\">25</xref>]. </p><p>Different from the classical approaches followed by studies conducted on medical students and residents [<xref rid=\"B26-ijerph-17-05444\" ref-type=\"bibr\">26</xref>], our study explicitly examined the role of the eudaimonic dimension in influencing the implementation of healthy lifestyles. The results are in line with the most recent developments in the literature, which have shown that the pursuit of eudaimonic objectives undoubtedly represents a protective factor from the implementation of health risk behaviours [<xref rid=\"B27-ijerph-17-05444\" ref-type=\"bibr\">27</xref>].</p><p>The result that eudaimonic students showed a trend toward improvement in their own healthy behaviours is worthy of note for the effective education of future Lifestyle Medicine (LM) practitioners, as it has been demonstrated that there is a positive association between preventive healthy habits of physicians and their patients [<xref rid=\"B28-ijerph-17-05444\" ref-type=\"bibr\">28</xref>]. Eudaimonic subjects were found to be more prone to consider healthcare professionals as a model for the whole community and students in the healthcare field were more oriented to counselling than non–healthcare students.</p><p>Ryff [<xref rid=\"B29-ijerph-17-05444\" ref-type=\"bibr\">29</xref>] suggests the science of eudaimonia shows its relevance for multiple aspects of physical functioning, as well as for health research. Eudaimonic well–being is capable of leading the individual to functional and highly adaptive behaviour patterns. The assumption of a eudaimonic perspective that focuses on the subject could indirectly lead to a change in lifestyle. Unlike many coaching programs developed for the promotion of health that mainly aim to modify specific dysfunctional behaviours, other studies [<xref rid=\"B30-ijerph-17-05444\" ref-type=\"bibr\">30</xref>] have developed interventions whose participation induced greater subjective vitality in the subjects, leading them to develop changes in their lifestyles not explicitly requested. This is the typical approach of the LM practitioner, to steer the patient to commit oneself to achieve greater awareness of oneself and of one’s life meanings, to implement virtuous and healthy behaviours. The LM practitioner has the skills to assess patient attitudes toward making healthy behaviour changes, helping them in self–managing healthy behaviours and recognising negative stress responses, also with the aid of technology and multidisciplinary teamwork. Focusing on the eudaimonic perspective, therefore, means creating a setting in which to explore oneself, understand one’s potential, one’s sense of life, far from the emotional stress derived from programs based on diets, exercise, or reduction in tobacco consumption, as examples. Promoting well–being, in any of its forms, therefore, can represent a useful goal to create conditions in which life is perceived as interesting, rewarding, and full of meaning. </p><p>The study did not find any significant difference between students attending courses in the healthcare and non–healthcare areas concerning the relation between lifestyle and well–being. This suggests that enhancing the awareness of one’s well–being is useful for the balanced growth of all individuals, but it becomes an essential skill of valued students in the healthcare field and of effective LM practitioners. Therefore, our study encourages the development of curricula for medical students that include, in addition to topics such as nutrition and exercise, the analysis of the psychological processes underlying well–being and positive mental health. This result is in line with the most recent contents of the Lifestyle Medicine Core Competencies Program (<uri xlink:href=\"https://lifestylemedicine.org/ACLM/Education/Certification\">https://lifestylemedicine.org/ACLM/Education/Certification</uri>). This program adds to core competency skills, nutrition, tobacco cessation, physical activity, sleep education, alcohol use and weight management, as well as the skills related to health and wellness coaching, emotional wellness, and mindfulness. The usual strategies used by the different countries to reduce alcohol and tobacco consumption or the traditional intervention programs aimed at pushing the population toward healthy eating behaviours have not always proved effective in the long term. </p><p>There are some flaws in this study. The first one regards the recruitment of subjects. Actually, the two student groups were different as only those in the healthcare field were compelled to the seminar where the study was presented. Therefore, it can be hypothesised that students in the non–healthcare area was disproportionately motivated to partake in the research as they are the most interested and active students, who regularly follow seminars voluntarily. This may partly explain the absence of differences between the two groups of students. Another limitation regards the lack of some important information. Actually, anthropometric data of weight and height are self–reported and not measured, leading to under–reporting of weight while height was not indicated at all by the majority of the enrolled students. It is why the Body Mass Index was not included in our results. Analogously, information regarding family income, even if requested, was not supplied by respondents. Consequently, we could not assess the role of economic well–being in the relation between psychological well–being and the student’s healthy behaviours. Taking the view of LM, further research is needed to investigate if the found positive association between healthy nutrition and the eudaimonic approach is still valid when considering the whole-food, plant-based diet.</p><p>Beyond these limits, this study contributes to LM research, as it suggests that healthy behaviour is easier to be acquired and maintained by individuals who aspire to achieve their life potential, and proposes the endowment of students in the healthcare area with the skills of counselling and well–being for a more effective spread of correct lifestyles in the general population. The protective relationship between positive psychological functioning and physical health was found, thus, promoting correct lifestyles can have an impact on health also through the enhancement of psychological well–being.</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05444\"><title>5. Conclusions</title><p>The main finding of our study was the positive relationship between virtuous lifestyle behaviours and psychological well–being. Considering terms of health promotion, the enhancement of correct lifestyles could lead to the identification of new methods aimed at preventing noncommunicable diseases and reducing the prevalence of behaviours at risk for health. The eudaimonic perspective and the Lifestyle Medicine approach imply a change of route for general practice and health promotion, from an approach based on deprivation and bans to an approach based on patient empowerment and self–management of their own lifestyle behaviours.</p></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization: D.M. and S.M.; methodology: D.M., V.R., L.M., W.M. and S.M.; software: L.M. and F.B.; validation: D.M. and S.M.; formal analysis: D.M., V.R., L.M., W.M. and S.M.; investigation: F.B., G.P., G.L., V.G.; data curation: L.M. and F.B.; writing—original draft preparation: D.M., L.M. and S.M.; writing—review and editing: D.M., W.M. and S.M.; visualization: L.M. and F.B.; supervision: D.M. and S.M.; project administration: D.M. and S.M. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research received no external funding.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijerph-17-05444\"><label>1.</label><element-citation publication-type=\"web\"><person-group person-group-type=\"author\"><collab>World Health Organization</collab></person-group><article-title>Global Status Report on Alcohol and Health</article-title><year>2018</year><comment>Available online: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.who.int/substance_abuse/publications/global_alcohol_report/gsr_2018/en/\">https://www.who.int/substance_abuse/publications/global_alcohol_report/gsr_2018/en/</ext-link></comment><date-in-citation content-type=\"access-date\" iso-8601-date=\"2020-06-03\">(accessed on 3 June 2020)</date-in-citation></element-citation></ref><ref id=\"B2-ijerph-17-05444\"><label>2.</label><element-citation publication-type=\"gov\"><person-group person-group-type=\"author\"><collab>Centers for Disease Control and Prevention (CDC)</collab></person-group><article-title>National Center for Chronic Disease Prevention and Health Promotion. 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Happiness Stud.</source><year>2011</year><volume>12</volume><fpage>769</fpage><lpage>792</lpage><pub-id pub-id-type=\"doi\">10.1007/s10902-010-9226-6</pub-id></element-citation></ref></ref-list></back><floats-group><table-wrap id=\"ijerph-17-05444-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05444-t001_Table 1</object-id><label>Table 1</label><caption><p>Psychological well-being by socio-demographic and health characteristics in a sample of 508 undergraduates of University of Palermo: univariable analysis.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\"> Characteristics <sup>§</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Eudaimonia</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Hedonia</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Both</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">N</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">n (%)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">OR (95%CI) <sup>§§</sup></th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p-</italic>Value</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">n (%)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">OR (95%CI) <sup>§§</sup></th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">n (%)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">OR (95%CI) <sup>§§</sup></th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">age (ys)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.639</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.425</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.171</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">≤20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">340</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">54 (15.88)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.00</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">58 (17.06)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">101 (29.71)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">>20</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">168</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">24 (14.29)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.88 (0.52–1.49)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">24 (14.29)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.81 (0.48–1.36)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">60 (35.71)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.31 (0.89–1.95)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">gender</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.192</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold>0.018</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.321</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">male</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">183</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23 (12.57)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39 (21.31)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">63 (34.43)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.00</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">female</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">325</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">55 (16.92)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.42 (0.84–2.40)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">43 (13.23)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.56 (0.35–0.91)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">98 (30.15)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.82 (0.56–1.21)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">chronic diseases</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.102</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.243</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.462</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">no</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">491</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">73 (14.87)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">81 (16.50)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">157 (31.98)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">55 (29.41)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.39 (0.81–7.00)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1 (5.88)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.32 (0.04–2.43)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4 (23.53)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.65 (0.21–2.04)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">year of study</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">first</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">348</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">55 (15.80)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">56 (16.09)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">109 (31.32)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">second</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">81</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9 (11.11)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.67 (0.31–1.40)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.286</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15 (18.52)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.19 (0.63–2.23)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.597</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29 (35.80)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.22 (0.74–2.03)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.437</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">third</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">79</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14 (17.72)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.15 (0.60–2.19)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.676</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11 (13.92)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.84 (0.42–1.70)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.633</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">23 (29.11)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.90 (0.53–1.54)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.702</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">father education</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">primary</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">106</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14 (13.21)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14 (13.21)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35 (33.02)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">high</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">203</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34 (16.75)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.32 (0.67–2.59)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.415</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">36 (17.73)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.42 (0.72–2.77)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.306</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">57 (28.08)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.79 (0.48–1.32)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.368</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">graduate or higher</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">198</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">30 (15.15)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.17 (0.59–2.33)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.647</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">32 (16.16)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.27 (0.64–2.50)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.494</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">68 (34.34)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.06 (0.64–1.75)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.816</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">mother education</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">primary</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">96</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13 (13.54)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16 (16.67)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34 (35.02)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">high</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">237</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32 (13.50)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.00 (0.50–2.00)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.992</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38 (16.03)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.95 (0.50–1.81)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.887</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">74 (31.22)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.83 (0.50–1.37)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.460</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">graduate or higher</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">174</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">33 (18.97)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.49 (0.74–3.01)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.257</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">28 (16.09)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.96 (0.49–1.88)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.903</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">52 (29.89)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.78 (0.46–1.32)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.351</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">type of student residency</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">on-site</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">267</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33 (12.36)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">51 (19.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">84 (31.46)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">off-site</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">142</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28 (19.72)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.74 (1.00–3.03)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.047</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19 (13.39)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.65 (0.37–1.16)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.144</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">48 (33.80)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.11 (0.72–1.72)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.630</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">commuter</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">99</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17 (17.17)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.47 (0.78–2.79)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.234</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12 (12.12)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.58 (0.30–1.15)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.117</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">29 (29.29)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.90 (0.54–1.50)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.691</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">type of course</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.215</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.914</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.770</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">non-healthcare area</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">368</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">52 (14.13)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">59 (16.03)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">118 (32.07)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">healthcare area</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">140</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">26 (18.57)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.39 (0.83–2.33)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">23 (16.43)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.03 (0.61–1.74)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">43 (30.71)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.94 (0.62–1.43)</td></tr></tbody></table><table-wrap-foot><fn><p><bold><sup>§</sup></bold> Statistics were calculated on available data; Bold values are statistically significant; <sup>§§</sup> OR: Odds ratio; CI: Confidence Interval.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05444-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05444-t002_Table 2</object-id><label>Table 2</label><caption><p>Psychological well-being by behavioral characteristics in a sample of 508 undergraduates of University of Palermo: univariable analysis.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\"> Characteristics <sup>§</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Eudaimonia</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Hedonia</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Both</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">N</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">n (%)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">OR (95%CI) <sup>§§</sup></th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p-</italic>Value</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">n (%)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">OR (95%CI) <sup>§§</sup></th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p-</italic>Value</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">n (%)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">OR (95%CI) <sup>§§</sup></th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p-</italic>Value</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">in the last 5 years, did you change your weight?</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.428</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.231</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.403</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">no</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">119</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21 (17.65)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15 (12.61)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34 (28.57)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">389</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">57 (14.65)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.80 (0.46–1.39)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">67 (17.22)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.44 (0.79–2.64)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">127 (32.65)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.21 (0.77–1.90)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">in this month, are you on a diet?</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.682</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.881</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.712</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">no</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">387</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">58 (14.99)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">63 (16.28)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">121 (31.27)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">121</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20 (16.53)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.12 (0.64–1.96)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">19 (15.70)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.96 (0.55–1.68)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">40 (33.06)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.09 (0.70–1.68)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">adherence to Mediterranean diet <sup>2</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">\n<bold><0.001</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.241</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.060</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">no</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">305</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32 (10.49)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.00</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">54 (17.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">87 (28.52)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">203</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">46 (22.66)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>2.50 (1.52–4.12)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">28 (13.79)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.74 (0.45–1.22)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">74 (36.45)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.44 (0.98–2.10)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">weight status</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">normal</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">262</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">51 (19.47)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.00</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">37 (14.12)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">87 (33.21)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">overweight</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7 (14.89)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.72 (0.31–1.71)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.461</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13 (27.66)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>2.33 (1.11–4.95)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.021</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14 (29.79)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.85 (0.43–1.68)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.646</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">obese</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">50</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7 (14.00)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.67 (0.29–1.59)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.363</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5 (10.00)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.68 (0.25–1.82)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.435</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18 (36.00)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.13 (0.60–2.13)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.702</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">smoking status</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">non–smoker</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">76</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10 (13.16)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10 (13.16)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">36 (47.37)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">former smoker</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">312</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">57 (18.27)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.48 (0.71–3.05)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.291</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47 (15.06)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.17 (0.56–2.44)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.674</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">93 (29.81)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.47 (0.28–0.79)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.004</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">current smoker</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">120</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11 (9.17)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.67 (0.27–1.66)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.380</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25 (20.83)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.74 (0.78–3.88)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.173</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">32 (26.67)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.40 (0.22–0.75)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.003</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">physical activity</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.514</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.290</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.154</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">no</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">219</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31 (14.16)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31 (14.16)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">62 (28.31)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">289</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">47 (16.26)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.18 (0.72–1.93)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">51 (17.65)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.30 (0.80–2.11)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">99 (34.26)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.32 (0.90–1.93)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">drinking wine</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">never</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">153</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26 (16.99)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20 (13.07)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">44 (28.76)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">less than once a week</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">278</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40 (14.39)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.82 (0.48–1.41)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.473</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45 (16.19)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.28 (0.73–2.27)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.388</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">91 (32.73)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.21 (0.78–1.86)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.395</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">once a week or more</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">67</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11 (16.42)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.96 (0.44–2.08)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.917</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15 (22.39)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.92 (0.91–4.06)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.083</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">23 (34.33)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.29 (0.70–2.40)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.410</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">drinking beer</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">never</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26 (17.45)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14 (9.40)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">43 (28.86)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">less than once a week</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">149</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45 (15.79)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.89 (0.52–1.51)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.657</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">52 (18.25)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>2.15 (1.14–4.05))</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.015</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">91 (31.93)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.16 (0.75–1.78)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.511</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">once a week or more</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">285</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7 (11.29)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.60 (0.25–1.48)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.263</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14 (22.58)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>2.81 (1.23–6.42)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.010</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">21 (33.87)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.26 (0.67–2.39)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.472</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">drinking spirits</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">never</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">193</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34 (17.62)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21 (10.88)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50 (25.91)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">less than once a week</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">294</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">42 (14.29)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.78 (0.48–1.28)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.322</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">58 (19.73)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>2.01 (1.17–3.46)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.010</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">101 (34.35)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.50 (1.00–2.24)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.049</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">once a week or more</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1 (10.00)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.52 (0.06–4.27)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.535</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1 (10.00)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.91 (0.11–7.58)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.931</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6 (60.00)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>4.29 (1.14–16.17)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.019</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">binging</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">no</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">255</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">51 (20.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33 (12.94)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">71 (27.84)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">253</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">27 (10.7)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.48 (0.29–0.79)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.004</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">49 (19.37)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.62 (1.00–2.62)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.049</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">90 (35.57)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.43 (0.98–2.09)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.061</bold>\n</td></tr></tbody></table><table-wrap-foot><fn><p><bold><sup>§</sup></bold> Statistics were calculated on available data. Bold values are statistically significant; <sup>§§</sup> OR: Odds ratio; CI: Confidence Interval.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05444-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05444-t003_Table 3</object-id><label>Table 3</label><caption><p>Role of healthcare professionals for prevention and health promotion using a well–being approach and type of course in a sample of 508 undergraduates of University of Palermo.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"> Characteristics <sup>§</sup></th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Do You Think that Healthcare Professionals Could Be Considered as Models for Their Patients and People in General?</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Do You Think that Healthcare Professionals Should Counsel Their Patients for Tobacco Cessation on a Regular Basis?</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Do You Think that a Patient Has an Increased Probability to Stop Smoking, if Assisted by His/Her Practitioner or Nurse?</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">N</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">n (%)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">OR (95%CI) <sup>§§</sup><break/><italic>p</italic>-Value</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">N</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">n (%)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">OR (95%CI) <sup>§§</sup><break/><italic>p</italic>-Value</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">N</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">n (%)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">OR (95%CI) <sup>§§</sup><break/><italic>p</italic>-Value</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">eudaimonia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">no</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">172</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21 (12.21)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3 (14.29)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">88</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9 (10.23)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">334</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">57 (17.07)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.48 (0.86–2.54)<break/>0.152</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">485</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">75 (15.46)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.10 (0.32–3.82)<break/>0.884</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">419</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">69 (16.47)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.73 (0.83–3.62)<break/>0.141</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">hedonia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">no</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">172</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34 (19.77)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3 (14.29)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">88</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17 (19.32)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">334</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">47 (14.07)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.66 (0.41–1.08)<break/>0.098</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">485</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">79 (16.29)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.17 (0.34–4.06)<break/>0.808</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">419</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">65 (15.51)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.77 (0.42–1.39)<break/>0.379</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">both</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">no</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">172</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39 (22.67)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9 (42.86)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">88</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29 (32.95)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">334</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">122 (36.53)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.96 (1.28–3.00)</bold>\n<break/>\n<bold>0.002</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">485</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">151 (31.13)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.60 (0.255–1.46)<break/>0.259</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">419</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">132 (31.50)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.94 (0.57–1.53)<break/>0.791</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">type of course</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">non–healthcare</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">366</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">238 (65.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">366</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">346 (94.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">367</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">296 (80.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">healthcare</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">140</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">96 (68.6)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.17 (0.77–1.78)<break/>0.451</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">140</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">139 (99.3)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>8.03 (1.05–61.3)</bold>\n<break/>\n<bold>0.013</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">140</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">123 (87.9)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.74 (0.98–3.08)<break/>0.056</td></tr></tbody></table><table-wrap-foot><fn><p><bold><sup>§</sup></bold> Statistics were calculated on available data; Bold values are statistically significant; <sup>§§</sup> OR: Odds ratio; CI: Confidence Interval.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05444-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05444-t004_Table 4</object-id><label>Table 4</label><caption><p>Multivariable analysis of well–being by modifiable behavioral characteristics in a sample of 508 undergraduates of University of Palermo.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\"> Characteristics<sup>§</sup></th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Eudaimonia</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Hedonia</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Both</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">AdjOR (95% CI) <sup>§§</sup></th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">AdjOR (95% CI) <sup>§§</sup></th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">AdjOR (95% CI) <sup>§§</sup></th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">gender</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">female</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.92 (0.49–1.71)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.788</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.60 (0.34–1.07)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.086</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.71 (0.44–1.15)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.164</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">adherence to Mediterranean diet</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>3.27 (1.83–5.87)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold><0.001</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.45 (0.80–2.63)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.221</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>2.28 (1.42–3.70)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.001</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">smoking status</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">former smoker</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.50 (0.61–3.70)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.375</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.23 (0.51–2.95)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.645</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.63 (0.32–1.21)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.163</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">current smoker</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.60 (0.20–1.77)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.356</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.03 (0.41–2.62)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.950</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.38 (0.18–0.81)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.012</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">drinking wine</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">less than once a week</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.84 (0.43–1.66)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.621</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.86 (0.43–1.72)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.674</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.89 (0.51–1.56)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.695</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">once a week or more</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.35 (0.47–3.86)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.575</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.54 (0.56–4.23)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.406</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.07 (0.44–2.59)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.878</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">drinking beer</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">less than once a week</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.42 (0.72–2.81)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.307</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.08 (0.98–4.42)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.057</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.23 (0.70–2.16)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.473</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">once a week or more</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.44 (0.43–4.86)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.553</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.88 (0.62–5.71)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.264</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.13 (0.45–2.83)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.788</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">drinking spirits</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">less than once a week</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.56 (0.81–2.99)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.185</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.98 (1.00–3.89)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.050</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>1.89 (1.10–3.27)</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>0.021</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">once a week or more</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.68 (0.21–34.33)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.449</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.83 (0.15–23.06)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.640</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>6.02 (1.05–34.52)</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.044</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">binging</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.57 (0.29–1.11)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.096</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.11 (0.58–2.09)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.758</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.11 (0.65–1.87)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.707</td></tr></tbody></table><table-wrap-foot><fn><p><sup>§</sup> Reference categories: “No” for Adherence to Mediterranean Diet, Physical activity, Binging, “Male” for Gender, “Non–smoker” for Smoking status, “Never” for Drinking wine, Drinking beer, Drinking spirits. Statistics were calculated on available data; Bold values are statistically significant; <sup>§§</sup> AdjOR: Adjusted Odds ratio; CI: Confidence Interval.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32722563</article-id><article-id pub-id-type=\"pmc\">PMC7432037</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155376</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05376</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Migrant Women’s Access to Sexual and Reproductive Health Services in Malaysia: A Qualitative Study</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-6690-000X</contrib-id><name><surname>Loganathan</surname><given-names>Tharani</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05376\">1</xref><xref rid=\"c1-ijerph-17-05376\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-9320-3437</contrib-id><name><surname>Chan</surname><given-names>Zhie X.</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05376\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-9633-1976</contrib-id><name><surname>de Smalen</surname><given-names>Allard W.</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05376\">2</xref><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05376\">3</xref><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05376\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Pocock</surname><given-names>Nicola S.</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05376\">2</xref><xref ref-type=\"aff\" rid=\"af5-ijerph-17-05376\">5</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05376\"><label>1</label>Centre for Epidemiology and Evidence-based Practice, Department of Social and Preventive Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia</aff><aff id=\"af2-ijerph-17-05376\"><label>2</label>International Institute for Global Health (UNU-IIGH), United Nations University, Kuala Lumpur 56000, Malaysia; <email>zhie.chan@unu.edu</email> (Z.X.C.); <email>allarddesmalen@gmail.com</email> (A.W.d.S.); <email>Nicola.Pocock@lshtm.ac.uk</email> (N.S.P.)</aff><aff id=\"af3-ijerph-17-05376\"><label>3</label>Maastricht Graduate School of Governance, Maastricht University, 6211 AX Maastricht, The Netherlands</aff><aff id=\"af4-ijerph-17-05376\"><label>4</label>Maastricht Economic and Social Research Institute on Innovation and Technology (UNU-MERIT), United Nations University, 6211 AX Maastricht, The Netherlands</aff><aff id=\"af5-ijerph-17-05376\"><label>5</label>Gender Violence & Health Centre, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK</aff><author-notes><corresp id=\"c1-ijerph-17-05376\"><label>*</label>Correspondence: <email>drtharani@ummc.edu.my</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>26</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5376</elocation-id><history><date date-type=\"received\"><day>25</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>24</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Providing sexual and reproductive health (SRH) services to migrant workers is key to fulfilling sustainable developmental goals. This study aims to explore key informants’ views on the provision of SRH services for migrant women in Malaysia, exploring the provision of SRH education, contraception, abortion, antenatal and delivery, as well as the management of gender-based violence. In-depth interviews of 44 stakeholders were conducted from July 2018 to July 2019. Data were thematically analysed. Migrant workers that fall pregnant are unable to work legally and are subject to deportation. Despite this, we found that insufficient SRH information and contraceptive access are provided, as these are seen to encourage promiscuity. Pregnancy, rather than sexually transmitted infection prevention, is a core concern among migrant women, the latter of which is not adequately addressed by private providers. Abortions are often seen as the only option for pregnant migrants. Unsafe abortions occur which are linked to financial constraints and cultural disapproval, despite surgical abortions being legal in Malaysia. Pregnant migrants often delay care-seeking, and this may explain poor obstetric outcomes. Although health facilities for gender-based violence are available, non-citizen women face additional barriers in terms of discrimination and scrutiny by authorities. Migrant women face extremely limited options for SRH services in Malaysia and these should be expanded.</p></abstract><kwd-group><kwd>migrant health</kwd><kwd>access to health</kwd><kwd>sexual and reproductive health</kwd><kwd>contraception</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05376\"><title>1. Introduction</title><p>Migration of women is an important component of international migration, with women comprising nearly half (48%) of the 258 million international migrants worldwide in 2017 [<xref rid=\"B1-ijerph-17-05376\" ref-type=\"bibr\">1</xref>]. In Malaysia, 19% of the 2.0 million documented migrant workers in 2019 were women [<xref rid=\"B2-ijerph-17-05376\" ref-type=\"bibr\">2</xref>]. The country also houses an estimated 2 to 3 million undocumented migrants [<xref rid=\"B3-ijerph-17-05376\" ref-type=\"bibr\">3</xref>], which increases the number of female migrants significantly. Although women represent a substantial proportion of less skilled migrant workers in Malaysia, appropriate migration and gender-sensitive policies are still lacking. As a result, female migrants are more vulnerable and prone to human rights violations [<xref rid=\"B4-ijerph-17-05376\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijerph-17-05376\" ref-type=\"bibr\">5</xref>].</p><p>Sexual and reproductive health and rights (SRHRs) are fundamental human rights, which lies with the right of individuals and couples to freely decide the number, timing, and spacing of children and have adequate information to make those decisions, and the right to attain the highest standard of sexual and reproductive health [SRH] [<xref rid=\"B6-ijerph-17-05376\" ref-type=\"bibr\">6</xref>]. SRHR was conceptualised during the 1994 International Conference on Population and Development (ICPD) in Cairo, where reproductive health was defined as a “state of complete physical, mental and social well-being, not merely the absence of disease and infirmity, in all matters relating to the reproductive system, and its functions and processes” [<xref rid=\"B6-ijerph-17-05376\" ref-type=\"bibr\">6</xref>] and was subsequently built on evolving international agreements [<xref rid=\"B7-ijerph-17-05376\" ref-type=\"bibr\">7</xref>].</p><p>SRH addresses a wide range of health issues, including contraception, unintended pregnancies, unsafe abortions, gender-based violence (GBV), pregnancy and childbirth complications, human immunodeficiency virus (HIV) and other sexually transmitted infections (STIs), and infertility and reproductive cancers, and are seen as essential elements to achieve social, economic and environmental development goals [<xref rid=\"B7-ijerph-17-05376\" ref-type=\"bibr\">7</xref>].</p><p>Maternal and child health services are the cornerstone of Malaysia’s public health system and are available nationwide as part of the integrated primary care services provided at public health clinics, maternal and child health clinics, and community clinics under the Ministry of Health (MOH), Malaysia. The scope of maternal and child health services includes maternal and perinatal health services (pre-pregnancy, antenatal, intrapartum and postnatal care), child health services (childhood immunisation and health, development and growth assessment), and woman’s health services (family planning services, and cervical and breast cancer screening) [<xref rid=\"B8-ijerph-17-05376\" ref-type=\"bibr\">8</xref>]. SRH services are also provided by the National Population and Family Development Board, under the purview of the Ministry of Women, Family and Community Development, non-governmental organisations like the Federal Reproductive Health Associations Malaysia (FRHAM), and private practitioners [<xref rid=\"B9-ijerph-17-05376\" ref-type=\"bibr\">9</xref>].</p><p>Improving access to SRH is central to development, as reflected under target 3.7 of the Sustainable Development Goals (SDGs) which calls for “universal access to sexual and reproductive health-care services, including for family planning, information, and education” by 2030 [<xref rid=\"B10-ijerph-17-05376\" ref-type=\"bibr\">10</xref>]. Although providing SRH services to marginalised communities including migrant workers are key to fulfilling the SDGs [<xref rid=\"B11-ijerph-17-05376\" ref-type=\"bibr\">11</xref>], the 2017 Voluntary National Review of SDGs by the Malaysian government did not identify migrant workers as a vulnerable group to improve delivery of healthcare services [<xref rid=\"B12-ijerph-17-05376\" ref-type=\"bibr\">12</xref>].</p><p>Women emigrating for employment face intersecting vulnerabilities of gender, social class, and ethnicity [<xref rid=\"B13-ijerph-17-05376\" ref-type=\"bibr\">13</xref>] and often encounter physical, psychological, and sexual violence [<xref rid=\"B14-ijerph-17-05376\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijerph-17-05376\" ref-type=\"bibr\">15</xref>]. Despite the ratification of the Convention on the Elimination of Discrimination against Women (CEDAW) and commitments to the ICPD Programme of Action [<xref rid=\"B16-ijerph-17-05376\" ref-type=\"bibr\">16</xref>], Malaysia has not fully recognised the migrant workers’ SRHRs [<xref rid=\"B17-ijerph-17-05376\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijerph-17-05376\" ref-type=\"bibr\">18</xref>]. Female migrant workers in Malaysia still face SRHR-related difficulties, mainly through the prohibition of pregnancy during employment [<xref rid=\"B19-ijerph-17-05376\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijerph-17-05376\" ref-type=\"bibr\">20</xref>].</p><p>In its concluding observations on the combined third to fifth periodic reports of Malaysia, the Committee on the Elimination of Discrimination against Women were concerned about the barriers faced by non-citizen women, including female migrant workers, when accessing healthcare [<xref rid=\"B21-ijerph-17-05376\" ref-type=\"bibr\">21</xref>]. Financial constraints are a major healthcare access barrier, as healthcare charges for non-citizens are considerably higher when compared to citizens for services at public facilities [<xref rid=\"B17-ijerph-17-05376\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijerph-17-05376\" ref-type=\"bibr\">18</xref>,<xref rid=\"B22-ijerph-17-05376\" ref-type=\"bibr\">22</xref>]. In addition, healthcare personnel are required to report undocumented migrants seeking medical care to the Immigration department, deterring women from seeking needed care due to fear of arrest and detention [<xref rid=\"B23-ijerph-17-05376\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijerph-17-05376\" ref-type=\"bibr\">24</xref>].</p><p>Therefore, this study aims to explore key informants’ views on the provision of SRH services for low-skilled, documented, and undocumented migrant women in Malaysia, including SRH education, contraception, abortion, antenatal and delivery, and the management of GBV.</p></sec><sec id=\"sec2-ijerph-17-05376\"><title>2. Materials and Methods</title><p>We used qualitative methods in an exploratory, iterative design to explore policy and the provision of SRH services for migrant workers in Malaysia.</p><sec id=\"sec2dot1-ijerph-17-05376\"><title>2.1. Definition of Terms</title><p>This study focuses on low-skill, low-wage migrant workers who cross international borders for employment. Documented or “regular” migrants are authorised to enter, stay, and partake in employment in a country, and also have legal documents, such as valid passports and work permits. Undocumented or “irregular” migrants are those who enter the country, reside, or partake in employment without authorisation, including those who may have entered the country legally, but have violated either the terms of their visa or over-stayed beyond the authorised period [<xref rid=\"B9-ijerph-17-05376\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijerph-17-05376\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijerph-17-05376\" ref-type=\"bibr\">11</xref>].</p><p>Refugees, asylum-seekers, foreign wives, and expatriates are not included in this study.</p></sec><sec id=\"sec2dot2-ijerph-17-05376\"><title>2.2. Sampling and Recruitment</title><p>We conducted 37 in-depth interviews with 44 individuals from July 2018 to July 2019 in Malaysia (<xref rid=\"ijerph-17-05376-t001\" ref-type=\"table\">Table 1</xref>). Most interviews were conducted on a one-on-one basis, while several were conducted in small groups of 2 to 3 participants from the same organisation.</p><p>The health and welfare of migrant workers in Malaysia are contentious, with issues concerning migrant workers’ SRHR and their immigration status being particularly sensitive. As such, we did not specifically target female migrant workers for interviews. We interviewed multiple stakeholders, including members of civil society organisations (CSOs), international organisations, academia, industry, medical doctors, and migrant representatives to obtain a broader understanding of SRHR for this vulnerable population.</p><p>Migrant representatives interviewed represented the interests of migrant workers and were able to speak broadly on migrant workers’ experiences in Malaysia. We interviewed representatives of workers from major migrant-sending countries to Malaysia (Indonesia, the Philippines, Nepal, and Bangladesh). We also interviewed medical doctors from the public sector, private sector, and CSOs, who provided SRH services to migrant populations. In addition, we interviewed representatives of CSOs that primarily worked on migrant women’s rights and welfare.</p><p>Participants were recruited purposively from a previous migrant health stakeholder workshop in Kuala Lumpur [<xref rid=\"B25-ijerph-17-05376\" ref-type=\"bibr\">25</xref>], and subsequently from snowball sampling of interviewees and further stakeholder recruitment through LinkedIn, until researchers agreed that new interviews would not yield additional information, as thematic saturation was reached.</p></sec><sec id=\"sec2dot3-ijerph-17-05376\"><title>2.3. Data Collection and Analysis</title><p>In-depth interviews averaged from 1 to 1.5 h and were conducted at physical locations chosen by participants or via telephone. Interviews were conducted either in English or Bahasa Malaysia (Malay language) depending on the participants’ preference, by the multi-lingual research team. The majority of interviews were conducted in English, with only 5 out of 37 interviews conducted in Malay.</p><p>Semi-structured interview guides were developed to seek participants’ perspectives on SRH health services for migrant women in Malaysia, and these questions were tailored towards the participants’ professional and organisational backgrounds. The interview guides were constructed based on literature review and discussion among the research team. Concurrent data analysis informed data collection and further refinement of question guides. Interviews with stakeholders from different backgrounds allowed triangulation of findings. Audio recordings were transcribed verbatim.</p><p>We conducted thematic analysis as described by Braun and Clarke, where themes or patterns of meaning within data were identified and reported using six phases: becoming familiar with the data, generating initial codes, searching for themes, reviewing themes, defining themes, and producing the report [<xref rid=\"B26-ijerph-17-05376\" ref-type=\"bibr\">26</xref>].</p><p>Data analysis was conducted in an immersive, exploratory, and inductive manner. The first and second authors reviewed and analysed transcripts independently, with regular discussions between researchers to refine codes and identify new themes. Transcripts were coded into emerging themes using NViVo 12 Plus, (QSR International, Melbourne, Australia) and quotations were extracted into Microsoft<sup>®</sup> Excel<sup>®</sup> for Office 365, (Microsoft, Redmond, WA, USA). Interviews in Bahasa Malaysia were analysed in the same language, while extracted quotations were translated to English for publication.</p></sec><sec id=\"sec2dot4-ijerph-17-05376\"><title>2.4. Reflexivity</title><p>Interviews were conducted by a medical doctor and academic researchers, who could be perceived as trusted authority figures. To counter possible power imbalances, especially among migrant workers and their representatives, participants chose interview times and their locations.</p></sec><sec id=\"sec2dot5-ijerph-17-05376\"><title>2.5. Ethics</title><p>Participant information sheets were distributed, which detailed the benefits and potential risks of the study, as well as patient rights and study procedures, including audio recording, confidentiality, and data storage. Verbal and written informed consent were sought from all participants before interviews. All participants agreed to be audio recorded and quoted anonymously in publications. Audio recordings and electronic transcripts were stored in secure data servers, while printed transcripts and notes were stored in a locked cupboard. Study participation was voluntary, and we explained that participants could refuse to answer questions or terminate interviews at any point.</p><p>This study was approved by the Medical Ethics Committee, University Malaya Medical Center, Malaysia and the Medical Research and Ethics Committee, Ministry of Health, Malaysia (Approval numbers: UM.TNC2/UMREC-238 and NMRR-18-1309-42043).</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05376\"><title>3. Results</title><p>Study results are presented on the health policy context, followed by findings on SRH services, such as SRH education and contraception, abortion, antenatal care and delivery, and GBV. <xref rid=\"ijerph-17-05376-t002\" ref-type=\"table\">Table 2</xref> summarises the major study results.</p><sec id=\"sec3dot1-ijerph-17-05376\"><title>3.1. Health Policy and Employment Contract Clauses</title><sec id=\"sec3dot1dot1-ijerph-17-05376\"><title>3.1.1. Mandatory Health Screening and the Prohibition of Pregnancy</title><p>To obtain and renew work permits in Malaysia, documented migrant workers must undergo mandatory pre-employment medical examinations within the first month of arrival, and subsequently, annual medical examinations. These medical examinations are conducted at private clinics approved by the Foreign Workers Medical Examination and Monitoring Agency (Fomema) and include screening for a list of communicable and non-communicable diseases like HIV/AIDS, syphilis, tuberculosis, leprosy, hepatitis B, malaria, diabetes mellitus, hypertension, and also pregnancy for women. Female migrant workers testing positive for pregnancy will fail their medical examinations, and consequently will be denied work permits and are subject to deportation.</p><p>Most participants agreed that prohibiting pregnancy during employment is an infringement of a woman’s reproductive rights and is discriminatory against women. This migrant representative described how pregnancy is equated with illness in mandatory health screening.</p><disp-quote><p>\n<italic>“The women who are pregnant, they are considered [as having] an illness. Pregnancy is an illness. They failed [the FOMEMA medical examination] and they have to be sent back. It is like they discriminate [against] us as a woman. This is our reproductive right.”</italic>\n</p><attrib><italic>MW-1</italic></attrib></disp-quote><p>This interviewee expressed discomfort with the government-mandated screening for pregnancy, as it does not fulfil the purpose of a pre-employment medical examination to ensure “fitness to work” and to protect the public from communicable disease.</p><disp-quote><p>\n<italic>“To get your work permit, you have to pass the medical screening, but the medical screening is not only screening for contagious disease, but also for pregnancy. For me, personally, it becomes a problem when it infringes the reproductive right [of migrants]. Other screenings make sense, that is something that is needed to ensure public health for everyone. For the workers themselves to be ‘fit to work’ and for the health of society, they have to be free from contagious disease–that makes sense! But, reproductive health issue–that concerns reproductive rights. It infringes human rights.”</italic>\n</p><attrib><italic>CSO-8</italic></attrib></disp-quote></sec><sec id=\"sec3dot1dot2-ijerph-17-05376\"><title>3.1.2. Employment Contracts Prohibit Relationships, Marriages and Pregnancy</title><p>Several participants shared that employment contracts expressly forbid sexual relationships, marriages, and pregnancy. This medical practitioner explained that while both men and women are expected to be celibate, women are especially vulnerable because of the possibility of pregnancy.</p><disp-quote><p>\n<italic>“Most of the migrant workers, especially the women, when they sign up agreements [employment contracts] with their companies, they are not allowed to get pregnant or be sexually active [throughout employment]. A lot of women have come to me and say, ‘My boss shouldn’t know this!’ Because you are not allowed to have sex. It doesn’t make sense! You are staying in this country for two years or more, and you are not allowed to have sex? Men and women are the same. But for the men, you don’t see much consequences because they don’t get pregnant! They don’t have to worry about getting pregnant! Women have a more vulnerable position because they fear they will get pregnant.”</italic>\n</p><attrib><italic>MD-12 CSO</italic></attrib></disp-quote><p>The immediate termination from employment is a direct consequence of pregnancy. This stakeholder informed that this practice, while legal, is inherently discriminatory against women.</p><disp-quote><p>\n<italic>“Migrant workers who are pregnant, they lose their job almost immediately. So, these are some of the concerns that people are afraid of… In terms of why is there a discriminative practice? If the woman is pregnant, you automatically lose the job. That is questionable.”</italic>\n</p><attrib><italic>IO-2</italic></attrib></disp-quote></sec><sec id=\"sec3dot1dot3-ijerph-17-05376\"><title>3.1.3. Prohibiting Pregnancy Forces Women to Become Undocumented</title><p>Unlike expatriates from a professional, managerial, or highly skilled technical backgrounds, less skilled migrant workers are not allowed to bring family members or to get married in Malaysia in policy. This participant explains that this denial of the right to family results in unregistered marriages among non-citizens.</p><disp-quote><p>\n<italic>“Reproductive rights, it is actually a basic of human right. You cannot say that [when] you come here, only the expatriate can have the family, non-expatriate cannot. This is human nature, you know? They got married, but they are not allowed to get married here. That is why there is a lot of ‘nikah bawah tanah’ [underground/unregistered marriages], so they get their own ‘imam’ [priest]…”</italic>\n</p><attrib><italic>IO-1</italic></attrib></disp-quote><p>Participants explained that migrant women who are pregnant and opt to keep their babies are driven to become undocumented migrants. This medical practitioner expresses surprise that many migrant women opt to deliver their babies in Malaysia despite the severe consequences.</p><disp-quote><p>\n<italic>“They will automatically be illegal migrants, because the moment they are pregnant, they will lose their visa and if they lose their visa, they become illegal migrants. But somehow, many of them do deliver locally.”</italic>\n</p><attrib><italic>MD-9 PRIVATE GP</italic></attrib></disp-quote></sec></sec><sec id=\"sec3dot2-ijerph-17-05376\"><title>3.2. Sexual and Reproductive Health Education and Contraception</title><sec id=\"sec3dot2dot1-ijerph-17-05376\"><title>3.2.1. Employers do not Provide Access to Family Planning</title><p>Although pregnancy is prohibited among low-skilled migrant women employed in Malaysia in policy, those interviewed informed that there is little support from employers in terms of providing SRH education or services, either in terms of preventing STIs or providing family planning services. This interviewee explained that the prevalent moral attitude in Malaysia—that providing family planning encourages sexual promiscuity—may explain employers’ attitudes.</p><disp-quote><p>\n<italic>“The thing that upsets me is that there is very little recognition that women migrant workers who come here are young and usually sexually active. It’s a fact of life. We have actually tried, through our NGOs, to promote the information on contraception, and access to contraception for these people. But people [employers] are very cagey about this! It all has got to do with the idea that: ‘Oh, they are only here to work, you know. They are not supposed to have boyfriends or relationships.’ And therefore, ‘Why should we give them any information on contraception? It will only make them bad workers.’ But the reality is, many of them are sexually active. And then, of course, if they don’t have access to contraception, they get unwanted pregnancies. And of course, for them to terminate their contract halfway, it’s a real waste because they made arrangements to do a two to four year contract with the factories, intending to earn and send money home. But the moment they are found to be pregnant, you know, they have two choices; They either have an abortion, or they are sent back.”</italic>\n</p><attrib><italic>MD-9 PRIVATE GP</italic></attrib></disp-quote><p>This participant implied that providing family planning services is an important investment for both employers and workers, as unwanted pregnancies may result in job loss.</p><p>Nevertheless, civil society organisations have approached employers and embassies to provide SRH awareness for migrant workers with mixed success. This participant illustrates the best practices of multinational companies that invest in their employee’s health by training migrant community leaders to ensure the continuous education of new recruits.</p><disp-quote><p>\n<italic>“There were programmes done by our NGO with a few companies, where we train their community leaders. So, we will start talking about, ‘What is the menstrual cycle? How to prevent STDs? About contraception and everything’. So, these community leaders will keep training new people [newly recruited migrant workers]. So, they know where to get contraception and will come to the clinic to get this [SRH services]”</italic>\n</p><attrib><italic>MD-12 CSO</italic></attrib></disp-quote></sec><sec id=\"sec3dot2dot2-ijerph-17-05376\"><title>3.2.2. Migrants Pay Out-of-Pocket for Sexual Reproductive Health Services</title><p>Family planning is freely available to local patients at public clinics, as part of a comprehensive package of maternal and child health services for citizens. This medical doctor explained that financial constraints may deter some female migrants from seeking contraception at public clinics, as non-citizens must pay for services.</p><disp-quote><p>\n<italic>“To be honest, migrants have to pay for the contraception-for injectable hormonal therapy or any sort of contraception-they have to pay! As opposed to locals, where contraception is free. So, the problem still comes back to financial issue. So, if they are willing to pay and can afford, and if they understand the importance to not conceive within the next two years, then they will pay for it. But most of them-no [they won’t pay].”</italic>\n</p><attrib><italic>MD-13 PUBLIC CLINIC</italic></attrib></disp-quote><p>Migrant workers pay out-of-pocket for contraception at private clinics, as SRH services are not covered by the government-mandated migrant health insurance (SPIKPA) or employer-provided healthcare.</p></sec><sec id=\"sec3dot2dot3-ijerph-17-05376\"><title>3.2.3. Private Practitioners Promote Expensive Contraceptives and Fail to Provide Information on SRH</title><p>Medical practitioners interviewed informed that although a wide range of contraceptives are available at private clinics, most migrants prefer injectable hormonal contraceptives, especially the commonly available Depo-Provera injections. This interviewee explained that private doctors do not sufficiently advise women on contraceptive options, such as on the use of long-acting contraceptives like intrauterine contraceptive devices (IUCDs) or implants, because these options are less lucrative than injectable hormonal contraceptives.</p><disp-quote><p>\n<italic>“Not many GPs [general practitioners] even want to talk about it! But they keep telling them to use Depo-Provera because it is profitable! In a year, if you are coming [to the clinic for] 4 times. So, RM 60 × 4 = RM 240. [Whereas, the] IUCD is RM 200 for 4 years. So, you are not going to see her for the next few years. It [the IUCD] is more economical for the woman, but it is less profitable for the doctors!”</italic>\n</p><attrib><italic>MD-12 CSO</italic></attrib></disp-quote><p>While the private practitioners interviewed acknowledged that there is a substantial market for contraceptives among migrant women due to perceived need, participants explained that the awareness and willingness-to-pay are low for the prevention of STIs, specifically the use of condoms.</p><disp-quote><p>\n<italic>“I got quite a number of them coming for depo injections [Depo-Provera injections]. Contraception, in the form to prevent pregnancy-yes. But to prevent STDs [sexually transmitted diseases], they have to buy la… Condoms and all that, they have to just find ways to buy it… But I have had quite a number of them who come in for depo injection. So, they do know about it, and they do come.”</italic>\n</p><attrib><italic>MD-2 PRIVATE GP</italic></attrib></disp-quote><p>This participant implied that migrants were not willing-to-pay for condoms, as this was not seen as essential. Likewise, there is very little information provided by medical practitioners regarding the use of condoms in the prevention of STI.</p></sec></sec><sec id=\"sec3dot3-ijerph-17-05376\"><title>3.3. Abortion</title><sec id=\"sec3dot3dot1-ijerph-17-05376\"><title>3.3.1. Migrant Women’s Abortion Decisions Linked with Financial Security and Employer Support</title><p>Migrant women may lose formal employment and face deportation, as a consequence of pregnancy. Migrant women who chose to continue with their pregnancy in Malaysia are likely to become undocumented. Since the economic and social costs of pregnancy are substantial, this participant explained that migrant women that opt to continue with their pregnancy are usually in stable, committed relationships with relative financial security.</p><disp-quote><p>\n<italic>“Migrants pay for antenatal care at private clinics themselves. So usually, the ones who are willing to keep a child, they know it’s going to cost them. So, they should have some ‘back up’ money or husbands who are ok, and then they can afford. Maybe he is taking home RM 1800 to RM 2000 a month. So, from all his work, he can afford it. Then they go ahead. There are some who will come and say, ‘No I can’t, I can’t afford it’. Then some are like girlfriend/boyfriend, but he might be married, she might be married, you know… ‘accidents’, you know. This group will come and ask if they can get a medical abortion.”</italic>\n</p><attrib><italic>MD-2 PRIVATE GP</italic></attrib></disp-quote><p>Migrant workers are generally in Malaysia for the short term, with employment contracts lasting 2 to 4 years. This participant explained that many of the relationships formed by migrant workers in Malaysia are impermanent. Without support from a partner, pregnancies are unwanted and result in abortion.</p><disp-quote><p>\n<italic>“Basically, when they arrive [in Malaysia], they may have a husband back home. But, after few months, no more. We heard from other Filipinos, that mostly after they separate from [their husband], they find someone else here. And then when they get pregnant, they just abort it.”</italic>\n</p><attrib><italic>MW-3</italic></attrib></disp-quote><p>Several participants shared that some domestic workers are highly valued by their employers, and that these employers are supportive of their employees’ pregnancy. Examples were given of employers sending workers back to their home countries for delivery, with the option to return to Malaysia for employment. Others gave examples of employers supporting their workers by bringing them to private clinics for antenatal follow-up. This participant shared that some employers support their domestic workers in having an abortion, as this would mean the domestic worker could keep her job.</p><disp-quote><p>\n<italic>“In terms of unwanted pregnancies, they cannot be pregnant and stay in the work. But fortunately, many of the private employers want to keep their maids. Very often their maids are quite well-paid and they [the employers] are happy with them. And if they are pregnant, the employer [would] actually bring her along [to the clinic], and then you [as the doctor], would do the termination because she wants to continue working.”</italic>\n</p><attrib><italic>MD-9 Private clinic</italic></attrib></disp-quote><p>It was unclear whether domestic workers in this position received pre-abortion counselling or advice from providers on their options.</p></sec><sec id=\"sec3dot3dot2-ijerph-17-05376\"><title>3.3.2. Health Providers have Negative Attitudes towards Abortion</title><p>Although abortion is legal in Malaysia, the prevalent negative perception of termination of pregnancies has led to the widespread belief that it is illegal, even among healthcare providers. Many healthcare providers view contraception and abortion as sensitive topics and opt not to be part of the network of private healthcare providers offering safe abortion services. This participant explained that the opposition towards abortion is related to cultural norms, as the abortion laws in Malaysia are fairly liberal.</p><disp-quote><p>\n<italic>“All of them who are at the top level [government] say: ‘Oh, yeah, we have to recognise the law.’ The law in Malaysia is almost identical to the English law on abortion. So, what happens on the ground, seems to be not so much an official policy, but all ‘cultural opposition’ to make reproductive health and particularly contraception accessible to single women, and to make safe abortion accessible to women in general.”</italic>\n</p><attrib><italic>MD-9 PRIVATE GP</italic></attrib></disp-quote><p>Abortions are rarely conducted at public healthcare facilities. While a selected number of private clinics provide safe abortions, these options are expensive and maybe unaffordable for low-wage migrant workers. Thus, migrant women may opt to perform illegal, self-induced abortions, which are likely to be unsafe. Medical practitioners interviewed informed that migrant women do present at the emergency departments of public hospitals with complications of unsafe abortions.</p><disp-quote><p>\n<italic>“I have never seen any migrants coming to us for abortions [at public clinics]. They do it by themselves–self-induced. They have their own traditional ways of doing it, you know, by drinking vinegar and certain traditional medication, or they will try to induce trauma to the stomach! So, when they do present to us, it is already–not there [pregnancy terminated]. So, we had to refer them to the [public] hospital for a D&C [Dilation and Curettage]. Curettage is to clear off whatever is left behind.”</italic>\n</p><attrib><italic>MD-13 PUBLIC CLINIC</italic></attrib></disp-quote></sec><sec id=\"sec3dot3dot3-ijerph-17-05376\"><title>3.3.3. Medical Abortion Unavailable Legally</title><p>Malaysia has not legalised the use of medical abortions (i.e., non-surgical abortions, where oral medications are given to terminate pregnancy). While “abortion pills” are available for purchase online without a doctor’s prescription, these pills are illegal and unregulated. Medical practitioners interviewed explained that medical abortions are a safe alternative and cheaper than surgical abortions. This medical doctor cautions patients against purchasing “abortion pills” from unknown sources and would make discreet referrals to a professional network of CSOs that facilitates safe abortions.</p><disp-quote><p>\n<italic>“We have to advise them on medical or a surgical abortion. A surgical abortion will cost them almost RM 700 to RM 1000, which most of them don’t have. So, instead of them harming themselves [unsafe abortion], we will actually tell them that ‘The procedure is not available here [at this clinic]. Don’t trust anybody, don’t Google, don’t find [abortion services] anywhere! Here are the contact details, where you can get pills online. But there’s a possibility of not fully recovering. You will then need to see these certain doctors [who are] providing surgical [abortion]!’ So, usually that is how we refer them to XXX.”</italic>\n</p><attrib><italic>MD-12 CSO</italic></attrib></disp-quote></sec></sec><sec id=\"sec3dot4-ijerph-17-05376\"><title>3.4. Antenatal Care and Delivery</title><sec id=\"sec3dot4dot1-ijerph-17-05376\"><title>3.4.1. Migrants OPT for Private Clinics and Traditional Midwives for Antenatal Care</title><p>Due to immigration regulations, pregnant migrant workers in Malaysia inevitably become undocumented. While healthcare providers at public healthcare facilities will not deny patients necessary medical care, they are obliged to report undocumented workers to the police and immigration authorities. This medical practitioner explains that because of these restrictions, migrant women tend to opt for private healthcare.</p><disp-quote><p>\n<italic>“They [migrants] tend not go to the ‘Klinik Kesihatan’ [public clinics for antenatal care], because they have to pay quite a bit for it. Some of them are scared that if they go there, and they [health authorities will] inform immigration department and they will be deported. So, they don’t want to go to the government side. So, they don’t get any [antenatal] follow up, they don’t get anything. Sometimes you [would] ask them, ‘Do you have antenatal records [home-based antenatal book given to patients at public clinics]?’ No records, you know, that makes it very difficult. But there are apparently some [private] clinics or some smaller maternity centres, who have their own follow-up for foreigners. So, they have their own [antenatal] book and they can go in for deliveries.”</italic>\n</p><attrib><italic>MD-2 PRIVATE GP</italic></attrib></disp-quote><p>As the lack of antenatal follow-up and records prove problematic for the management of pregnancy and delivery, some more established private maternity centres provide more detailed follow-up for non-citizens.</p><p>Mainly due to the cost of private healthcare, some migrants prefer to deliver babies at home with the help of untrained traditional birth attendants. As shared by this migrant representative, this practice is likely done out of desperation, not cultural preference, and is linked with poor obstetric outcomes.</p><disp-quote><p>\n<italic>“Some, they prefer to go to the traditional midwives. In some cases, that’s why they pass away during delivery, because they don’t want to go to the hospital. Because of the lack of documents and also because the payments are very high. So, they prefer to use the ‘dukun beranak’ [traditional midwife]. I found one [lady like that] last year, passed away in XXX. We had sent her to the hospital, but it was too late already. The baby also passed away.”</italic>\n</p><attrib><italic>MW-1</italic></attrib></disp-quote></sec><sec id=\"sec3dot4dot2-ijerph-17-05376\"><title>3.4.2. Delayed Booking, Incomplete Antenatal Follow-Up and Poor Obstetric Outcomes</title><p>Most stakeholders explained that due to healthcare costs, non-citizens tend to present late for booking and default follow-up at antenatal clinics. Doctors interviewed observed that late presentations could result in poor obstetric outcomes and avoidable complications. These complications would inevitably incur additional financial expense, as more advanced treatment may be necessary.</p><disp-quote><p>\n<italic>“For migrants, when they present, it is already 30 weeks? 32 weeks? I even had one patient last week [who] presented at 36 weeks! So, that was the first time ever that I saw her. So, whatever that has happened, has happened! It is irreversible. For example, that is something we called: IUGR, which is ‘Intrauterine growth restriction’. So, when that already occurs, nothing can be done! So, the baby may be born–with low birth weight from premature delivery. Then they will have a lot of complications! Like sepsis and all! So, all of these actually contribute to more financial burden to the patients! Because they will require a NIC [neonatal intensive care] admission for a long time!”</italic>\n</p><attrib><italic>MD-13 PUBLIC CLINIC</italic></attrib></disp-quote></sec><sec id=\"sec3dot4dot3-ijerph-17-05376\"><title>3.4.3. Hospital Delivery Linked to Deportation</title><p>The government policy to report undocumented migrants to the police has resulted in incidents of women being detained immediately after delivery. Participants explained that conditions at detention centres are unsuitable for post-partum women and newborn babies. This interviewee explains that linking healthcare with deportation is a human rights violation.</p><disp-quote><p>\n<italic>“We had a case of a migrant worker [who] was admitted to the hospital due to deliver. Within less than 24 h, both mother and the baby were already at the XXX Detention Camp. We [the CSO] needed to get intervention from the Embassy. They shouldn’t detain the baby inside there because there are not such facilities, and besides, the mother was still very fragile, and shouldn’t be detained. The Immigration Department persisted with their decision but [with] expedited repatriation. The Indonesian embassy refused to bear the expenses [of repatriation], so we [the CSO] had to find money for them. Because, the Indonesian government also has certain [financial] constraints. This was actually a very challenging situation for us.”</italic>\n</p><attrib><italic>IO-1</italic></attrib></disp-quote></sec></sec><sec id=\"sec3dot5-ijerph-17-05376\"><title>3.5. Gender-Based Violence</title><sec id=\"sec3dot5dot1-ijerph-17-05376\"><title>3.5.1. One Stop Crisis Centre Linked with Police</title><p>One Stop Crisis Centres (OSCC) were established in Malaysia since 1996 to assist survivors of gender-based violence (GBV), to obtain comprehensive care from multiple agencies in a common venue. The OSCC are located at the emergency departments of Ministry of Health hospitals in Malaysia and provide immediate treatment, while facilitating protection, counselling, medico-legal, and social support services for survivors of GBV, rape, sodomy and sexual assault, domestic violence, and child abuse.</p><p>Legally, citizens and non-citizens can use GBV-associated healthcare services, which are available free of charge regardless of citizenship status at OSCCs. However, reporting violence to the police is a pre-requisite to seeking care at the OSCC. This participant explains that the procedure for reporting to the police is not always consistent. Survivors of violence are supposed to go directly to the OSCC, and the police report should be done at the hospital. However, some survivors are asked by hospitals to go to the police station first before coming to hospital for treatment.</p><disp-quote><p>\n<italic>“I have heard different information, at different times. Previously, I have heard [that] people should just go to the emergency [department] and then be referred to the OSCC. Then the police report will be lodged there. So, the police will go [there], to take the report. But I have also heard another story when they go to the emergency [department] and want to be directed to the OSCC, and they were asked to lodge [a police] report first, before they come [into OSCC].”</italic>\n</p><attrib><italic>CSO-8</italic></attrib></disp-quote><p>This participant explained that fear of the police is a hindrance faced by many non-citizen women seeking care or justice. Law enforcement personnel were described as lacking sensitization in dealing with GBV, and migrant women face additional discrimination.</p><disp-quote><p>\n<italic>“I would say on the whole, there is definitely a lack of sensitisation amongst the police. I think in general, when it comes to gender-based violence, there is a lot of ‘victim blaming’ and those kinds of attitudes that are pretty pervasive. For non-Malaysian women, there is another layer of discrimination and some xenophobia. So, I think the quality of services is even lower for them! And then sometimes, if it is a situation where the employer has not done what they need to do to renew the work permit or the visa, then they might be afraid to go to the police because they can get reported to immigration! So, that is often a reason for women not to access help.”</italic>\n</p><attrib><italic>CSO-7</italic></attrib></disp-quote><p>Study participants informed that undocumented migrants were particularly afraid to come forward to report incidences of violence, due to their immigration status.</p></sec><sec id=\"sec3dot5dot2-ijerph-17-05376\"><title>3.5.2. Limited Shelters for Non-Citizens</title><p>While government and CSOs provide shelters, those interviewed informed that there is a shortage of shelters specifically designed for survivors of GBV. Study participants informed that government shelters provided by the Welfare Department are general shelters, which may also house the homeless or elderly populations, and may lack comprehensive case management of GBV.</p><p>Furthermore, not all government shelters accept non-citizen women. Participants informed that government shelters only accept non-citizens that have been issued protection orders by the courts.</p><disp-quote><p>\n<italic>“There are two types of shelters, shelters run by the NGO and then shelters by [the] government, especially [the] Women Ministry [Ministry of Women, Family and Community Development] and the Jabatan Kebajikan [Welfare Department]. But government shelters that takes migrants are limited to migrant workers who have already been given a protection order; after the case has been determined by [the] police and court. Let’s say the charges [pending] can be categorised as human trafficking, then… the person will be given a protection order or an interim protection order during the investigation. Only then, will they be put in the government shelters.”</italic>\n</p><attrib><italic>CSO-8</italic></attrib></disp-quote><p>Participants informed that migrants may also be reluctant to obtain refuge and protection at shelters, as they would have to make a report with the police. As government shelters are limited for migrant women, CSOs are an important source of assistance, also providing legal aid and counselling.</p></sec></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05376\"><title>4. Discussion</title><p>In Malaysia, female migrant workers are subject to regulation of their reproductive rights with pre-employment and annual screenings for pregnancy, and face termination from employment if found pregnant.</p><p>Premature dismissals from employment are financially detrimental to both employers and migrant workers. Nevertheless, we found that information and access to family planning are seldom supplied to migrant workers by employers and not provided for by the government [<xref rid=\"B20-ijerph-17-05376\" ref-type=\"bibr\">20</xref>]. The state and employers essentially deny that migrant workers are sexually active adults, with the intent of avoiding being seen as promoting promiscuity by raising the topic of SRH. This outdated approach must change towards a pragmatic one, whereby migrant workers, including men, are provided with education and access to low-cost contraceptives. The low contraception prevalence in Malaysia (33.1% for all methods and 23.3% for modern methods) compared to the global estimate for 2019 (48.5% for all methods and 44.3% for modern methods) [<xref rid=\"B27-ijerph-17-05376\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijerph-17-05376\" ref-type=\"bibr\">28</xref>] may be explained by social, cultural, and structural barriers and lack of knowledge on contraception [<xref rid=\"B29-ijerph-17-05376\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijerph-17-05376\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijerph-17-05376\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijerph-17-05376\" ref-type=\"bibr\">32</xref>].</p><p>Our findings suggest that the choice of contraceptive methods among migrants may be influenced by the perceived risk of pregnancy and its consequences borne by women; hence, female-controlled methods like injectable steroids may be preferable, with less uptake of male-controlled barrier methods like male condoms. Poor uptake of condoms may also be explained by a worrying lack of awareness of STD and HIV prevention [<xref rid=\"B33-ijerph-17-05376\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijerph-17-05376\" ref-type=\"bibr\">34</xref>].</p><p>Although Malaysia has relatively liberal abortion laws, its interpretation is subject to cultural and religious resistance in the predominantly Muslim nation [<xref rid=\"B35-ijerph-17-05376\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijerph-17-05376\" ref-type=\"bibr\">36</xref>]. The Penal Code Act 574 (revised 1997) Section 312 permits safe abortion if performed by a registered medical practitioner and the medical practitioner determines that continuance of the pregnancy endangers the life of the pregnant woman or harms her physical or mental health [<xref rid=\"B37-ijerph-17-05376\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijerph-17-05376\" ref-type=\"bibr\">38</xref>,<xref rid=\"B39-ijerph-17-05376\" ref-type=\"bibr\">39</xref>].</p><p>In 2014, a 24-year-old Nepali migrant worker who opted for an abortion for fear of losing her job was arrested when police raided the clinic where she had her abortion. This Nepali worker was the first woman charged and convicted for having an abortion in Malaysia, although her conviction was subsequently acquitted [<xref rid=\"B40-ijerph-17-05376\" ref-type=\"bibr\">40</xref>,<xref rid=\"B41-ijerph-17-05376\" ref-type=\"bibr\">41</xref>,<xref rid=\"B42-ijerph-17-05376\" ref-type=\"bibr\">42</xref>]. This case illustrates the plight of migrant women under restrictive immigration laws and labour practices, as even after her innocence was proved and despite being no longer pregnant, the Nepali worker was dismissed by employers for being a “bad role model” [<xref rid=\"B43-ijerph-17-05376\" ref-type=\"bibr\">43</xref>,<xref rid=\"B44-ijerph-17-05376\" ref-type=\"bibr\">44</xref>].</p><p>Many medical practitioners, especially public sector providers, have conservative views and exercise personal judgement that restricts a woman’s access to safe abortion [<xref rid=\"B35-ijerph-17-05376\" ref-type=\"bibr\">35</xref>]. While abortion services are available at certain private clinics, we found that financial constraints were a likely barrier for less skilled migrant women. Furthermore, the lack of information on where to obtain safe abortions and the underlying social stigma [<xref rid=\"B35-ijerph-17-05376\" ref-type=\"bibr\">35</xref>,<xref rid=\"B45-ijerph-17-05376\" ref-type=\"bibr\">45</xref>,<xref rid=\"B46-ijerph-17-05376\" ref-type=\"bibr\">46</xref>,<xref rid=\"B47-ijerph-17-05376\" ref-type=\"bibr\">47</xref>] are plausible drivers for migrant women to seek unsafe abortions, including unregulated medical abortions.</p><p>Medical abortion is a non-invasive, effective method for early pregnancy termination (within 49 days of the last menstrual period), that gives control to the woman rather than the healthcare provider [<xref rid=\"B48-ijerph-17-05376\" ref-type=\"bibr\">48</xref>]. Despite the recent classification of misoprostol and mifepristone as essential drugs–“where permitted under national law and culturally acceptable” by the World Health Organization [<xref rid=\"B49-ijerph-17-05376\" ref-type=\"bibr\">49</xref>,<xref rid=\"B50-ijerph-17-05376\" ref-type=\"bibr\">50</xref>], the Ministry of Health, Malaysia has yet to approve their use for medical abortions [<xref rid=\"B36-ijerph-17-05376\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijerph-17-05376\" ref-type=\"bibr\">37</xref>].</p><p>Notably, no participants undertook pre- or post-abortion counselling, either for their decision-making and feelings around abortion or on contraception post-abortion. We have no evidence for the latter in developed countries on increasing contraceptive uptake and acceptability [<xref rid=\"B51-ijerph-17-05376\" ref-type=\"bibr\">51</xref>].</p><p>Prohibition of pregnancy may result in avoidance of needed care due to apprehensions of job loss and deportation, and this may lead to treatment delays or unsafe abortions. It is accepted as a given by employers and healthcare providers that migrant women will want to terminate pregnancies so they can retain employment. Yet, the legal basis to prohibit pregnancy is unclear, as pregnancy as a clause for dismissal from employment is not specifically included in Malaysia’s Employment Act or the Immigration Act [<xref rid=\"B52-ijerph-17-05376\" ref-type=\"bibr\">52</xref>,<xref rid=\"B53-ijerph-17-05376\" ref-type=\"bibr\">53</xref>]. Women are effectively coerced by policy and employment contracts into abortions, and this may curtail their reproductive rights.</p><p>Migrant workers face complex barriers in accessing healthcare in Malaysia, including financial constraints, the need to present legal documents like passports and work permits at public facilities, language barriers, discrimination, and physical inaccessibility [<xref rid=\"B23-ijerph-17-05376\" ref-type=\"bibr\">23</xref>,<xref rid=\"B54-ijerph-17-05376\" ref-type=\"bibr\">54</xref>]. Immigration policies in Malaysia essentially deny maternal and child health services for migrant workers at public facilities. Previous studies support our findings that migrant women are late in initiating antenatal care, while many never attend antenatal clinics and have home births with untrained birth attendants [<xref rid=\"B19-ijerph-17-05376\" ref-type=\"bibr\">19</xref>,<xref rid=\"B55-ijerph-17-05376\" ref-type=\"bibr\">55</xref>]. These factors may lead to delivery complications, as migrant women only seek care when critically ill, necessitating more advanced and expensive care [<xref rid=\"B56-ijerph-17-05376\" ref-type=\"bibr\">56</xref>]. As seen in other settings, migrant women are at higher risk of poor obstetric outcomes, including increased maternal and neonatal mortality, as compared to local women [<xref rid=\"B57-ijerph-17-05376\" ref-type=\"bibr\">57</xref>,<xref rid=\"B58-ijerph-17-05376\" ref-type=\"bibr\">58</xref>].</p><p>Malaysia successfully lowered maternal mortality through health system strengthening and meticulous auditing of all maternal deaths, including non-citizen deaths, through confidential enquiry into maternal deaths (CEMD) [<xref rid=\"B59-ijerph-17-05376\" ref-type=\"bibr\">59</xref>,<xref rid=\"B60-ijerph-17-05376\" ref-type=\"bibr\">60</xref>,<xref rid=\"B61-ijerph-17-05376\" ref-type=\"bibr\">61</xref>]. Unfortunately, while non-citizen maternal deaths are investigated in Malaysia, maternal mortality among migrants are not captured in national statistics reported internationally [<xref rid=\"B56-ijerph-17-05376\" ref-type=\"bibr\">56</xref>,<xref rid=\"B60-ijerph-17-05376\" ref-type=\"bibr\">60</xref>], raising questions if definitive measures to reduce risk in this group have been attempted.</p><p>States have the sovereign right to govern migration within national boundaries. However, the detention of new mothers and their babies for immigration offence may conflict with international laws and conventions. According to the Bangkok Rules or “the United Nations Rules for the Treatment of Women Prisoners”, non-custodial measures are preferred to the detention of vulnerable pregnant women and minor children [<xref rid=\"B62-ijerph-17-05376\" ref-type=\"bibr\">62</xref>]. Malaysia has ratified both the CEDAW and the Convention on the Rights of the Child (CRC) [<xref rid=\"B16-ijerph-17-05376\" ref-type=\"bibr\">16</xref>] and is under obligation to provide reasonable care and cater to the special needs of pregnant women, breastfeeding mothers, and mothers with children in custody [<xref rid=\"B63-ijerph-17-05376\" ref-type=\"bibr\">63</xref>].</p><p>In Malaysia, OSCC provides integrated services for victims of GBV at public hospitals [<xref rid=\"B64-ijerph-17-05376\" ref-type=\"bibr\">64</xref>,<xref rid=\"B65-ijerph-17-05376\" ref-type=\"bibr\">65</xref>]. While services are available to all women, in theory, barriers remain in practice for non-citizen women. Migrant women, especially those with precarious legal status, are reluctant to report violence or seek medical treatment, for fear of arrest and detention for an immigration offence. Lack of uniform implementation, seen here with confusion regarding the need for victims to report violence at police stations before seeking treatment, is an example of a shortfall in service. We would like to stress the importance of gender-sensitisation training among law enforcement agents, in terms of improving gender sensitivity and reducing discrimination against vulnerable non-citizen women [<xref rid=\"B44-ijerph-17-05376\" ref-type=\"bibr\">44</xref>,<xref rid=\"B66-ijerph-17-05376\" ref-type=\"bibr\">66</xref>].</p><p>This study has several limitations. Due to the sensitive nature of this study, we had difficulties obtaining interviews with migrant workers, employers, and policy stakeholders. Nevertheless, we were able to triangulate study findings by interviewing diverse key informants, including medical doctors, representatives of civil society organisations, trade unions, and academia. While the qualitative nature of this study precludes the generalisation of findings, we were able to illustrate the landscape of SRH services for migrant women in Malaysia by examining different stakeholder viewpoints and perspectives. We were also unable to fully explore the management of STIs and HIV/AIDS among migrant populations, an important component of SRH which would need dedicated future study.</p><p>This study has several strengths. Ours is one of few studies in Malaysia that explore the access to SRH services among vulnerable female migrant workers. We hope that this work will provide a vital understanding of some of the barriers faced by this vulnerable population and opportunities for intervention. We suggest that future quantitative research be conducted to fill the gap in SRRH data in Malaysia disaggregated by citizenship, especially on contraceptive usage, abortion, utilisation of SRH services, and maternal mortality.</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05376\"><title>5. Conclusions</title><p>This study shows that the SRHR of migrant workers remains severely curtailed in Malaysia. Political will is necessary to revise restrictive immigration laws and labour policies to enable low-skill migrant workers to fulfil their SRHR. We suggest that instead of the discriminatory prohibition of pregnancy during employment, that all migrant workers are provided with access to SRH education and low-cost contraception by employers. All pregnant women, including non-citizens, should also have equal access to antenatal and delivery care at public healthcare facilities, and healthcare access should be decriminalised. A more inclusive, rights-based approach to healthcare access would have population-wide benefits, and this would put Malaysia towards the path of meeting the SDG target of 3.7 for universal access to SRH services.</p></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization, T.L.; methodology, T.L., Z.X.C. and N.S.P.; software, T.L. and Z.X.C.; validation, T.L., Z.X.C., A.W.d.S., and N.S.P.; formal analysis, T.L. and Z.X.C.; investigation, T.L., Z.X.C. and N.S.P.; resources, T.L. and N.S.P.; data curation, Z.X.C.; writing—original draft preparation, T.L.; writing—review and editing, T.L., N.S.P., Z.X.C. and A.W.d.S.; project administration, Z.X.C.; funding acquisition, T.L., and N.S.P. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was funded by The Asia Pacific Observatory (APO) on Health Systems and Policies [grant number IF034-2020] and the China Medical Board’s Equity Initiative [grant number IF055-2018].</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest. 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Malays.</source><year>2015</year><volume>70</volume><fpage>162</fpage><lpage>168</lpage></element-citation></ref></ref-list></back><floats-group><table-wrap id=\"ijerph-17-05376-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05376-t001_Table 1</object-id><label>Table 1</label><caption><p>Characteristics of the study participants (<italic>n</italic> = 44).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Participant Background</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Label</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No.</th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Medical Doctor</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MD</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Public </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Private</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"> Civil society organisation</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Civil society organisation</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CSO</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Industry</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IND</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Migrant worker <sup>1</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MW</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">International organisation</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IO</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Trade union</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TU</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Academia </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AC</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Other policy stakeholders <sup>2</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">POL</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">44</td></tr></tbody></table><table-wrap-foot><fn><p><sup>1</sup> Only 1 of the 4 migrant workers interviewed identified himself as a worker only. Others were also members of civil society organisations (2) or trade unions (1). <sup>2</sup> Government or government-linked organisation.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05376-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05376-t002_Table 2</object-id><label>Table 2</label><caption><p>Summary of major study findings.</p></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Health Policy and Employment Contract Clauses</bold>\n</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>Mandatory health screening and the prohibition of pregnancy is discriminatory towards women</p></list-item><list-item><p>Less skilled migrant workers are not allowed to bring family members or allowed to get married in Malaysia</p></list-item><list-item><p>Prohibiting pregnancy forces women to become undocumented</p></list-item></list>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Sexual and Reproductive Health Education and Contraception</bold>\n</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>Employers prohibit pregnancy but do not provide access to family planning</p></list-item><list-item><p>Providing information on sexual and reproductive health or access to contraceptive services is seen to encourage promiscuity according to prevailing attitudes</p></list-item><list-item><p>Financial constraints may deter female migrants from seeking contraception</p></list-item><list-item><p>Private practitioners tend to promote more expensive contraceptives, like injectable hormonal contraceptives</p></list-item><list-item><p>Private practitioners fail to inform on the prevention of sexually transmitted infections or encourage the use of condoms</p></list-item></list>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Abortion</bold>\n</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>Pregnancy has high economic and social costs to migrant women</p></list-item><list-item><p>Migrant women who opt to continue with pregnancy are likely to be in a stable relationship</p></list-item><list-item><p>Although abortion is legal in Malaysia, prevailing cultural norms and financial barriers force migrants to opt for unsafe abortions</p></list-item><list-item><p>Medical abortion is illegal, but ‘abortion pills’ are pragmatically recommended by some healthcare providers for purchase online</p></list-item></list>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Antenatal Care and Delivery</bold>\n</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>Migrant women opt for private care for antenatal care as public clinics report undocumented workers to the immigration department</p></list-item><list-item><p>Some opt for traditional midwives as a result of financial barriers</p></list-item><list-item><p>Delayed booking and incomplete antenatal follow-up may result in poor obstetric outcomes</p></list-item><list-item><p>Hospital delivery discouraged as linked to immigration at public facilities</p></list-item></list>\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Gender-Based Violence</bold>\n</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<list list-type=\"bullet\"><list-item><p>One Stop Crisis Centres established as a common venue for victims of gender-based violence to access care, is linked with law enforcement</p></list-item><list-item><p>Law enforcement personnel lack sensitization in gender-based violence</p></list-item><list-item><p>Migrant women face added xenophobia and fear when in using One Stop Crisis Centres, especially if undocumented</p></list-item><list-item><p>Lack of shelters available for non-citizens and shelters have limited specialisation in gender-based violence</p></list-item></list>\n</td></tr></tbody></table></table-wrap></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32731583</article-id><article-id pub-id-type=\"pmc\">PMC7432038</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155433</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05433</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Regional Variation of Suicide Mortality in South Korea</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-2057-7360</contrib-id><name><surname>Choi</surname><given-names>Minjae</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05433\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Lee</surname><given-names>Yo Han</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05433\">2</xref><xref rid=\"c1-ijerph-17-05433\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05433\"><label>1</label>Department of Public Health, Korea University, Seoul 02841, Korea; <email>choiminjae@korea.ac.kr</email></aff><aff id=\"af2-ijerph-17-05433\"><label>2</label>Graduate School of Public Health, Ajou University, Suwon 16499, Korea</aff><author-notes><corresp id=\"c1-ijerph-17-05433\"><label>*</label>Correspondence: <email>vionic@ajou.ac.kr</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>28</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5433</elocation-id><history><date date-type=\"received\"><day>26</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>24</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>South Korea’s suicide rate is the highest among the members of the Organization for Economic Cooperation and Development. This study seeks to verify regional variation in suicide rates in South Korea and to identify correlating factors. We used age-adjusted suicide rates for 252 administrative districts, and a Community Health Survey, national representative data, and other national representative data such as censuses were used to obtain information on socioeconomic, health related and social integration variables according to each administrative district. Regional variation in suicide rates was analyzed by using Extremal Quotient (EQ), and multiple linear regression analyses were used to investigate associations between variation in suicide rates and regional socioeconomic, public service factors and health related factors. The average suicide rate from 252 regions was 142.7 per 100,000 people. The highest region was Hongchun-gun (217.8) and the lowest was Gwachen-si (75.5). The EQ was 2.89, meaning that there is significant regional variation in suicide rates. Financial independence (β = −0.662, <italic>p</italic> < 0.001), social welfare budget (β = −0.754, <italic>p</italic> < 0.001) and divorce rates (β = 17.743, <italic>p</italic> < 0.001) were significant, along with other adjusted variables. This study suggests considering these factors in order to reduce suicide rates in South Korea.</p></abstract><kwd-group><kwd>suicide rate</kwd><kwd>regional variation</kwd><kwd>financial independence</kwd><kwd>social welfare budget</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05433\"><title>1. Introduction</title><p>According to the World Health Organization (WHO), suicide is defined as the act of deliberately killing oneself with knowledge of the consequence of the act [<xref rid=\"B1-ijerph-17-05433\" ref-type=\"bibr\">1</xref>]. Worldwide, the number of suicides is over 800,000 annually, which may be age-adjusted to 11.4 per 100,000 people [<xref rid=\"B2-ijerph-17-05433\" ref-type=\"bibr\">2</xref>]. The WHO is aware of this situation, and has made a plan to reduce suicide rates by 10% by 2020 [<xref rid=\"B3-ijerph-17-05433\" ref-type=\"bibr\">3</xref>]. In an Irish study, the economic cost of suicide was shown to be about 830 million euro, i.e., 1% of the nation’s gross national product [<xref rid=\"B4-ijerph-17-05433\" ref-type=\"bibr\">4</xref>]. Another study showed that the burden of suicide relative to the total burden of all diseases increased from 1.8% in 1998 to 2.4% in 2020 [<xref rid=\"B5-ijerph-17-05433\" ref-type=\"bibr\">5</xref>]. Regarding public health, the pain of those bereaved by suicide does not disappear, but is transferred to family, friends and even communities [<xref rid=\"B6-ijerph-17-05433\" ref-type=\"bibr\">6</xref>]. As many researchers are aware of the seriousness of suicide, numerous studies of risk factors have been carried out.</p><p>A wide range of risk factors for suicide have been identified, related to distal factors, such as socioeconomic and cultural factors, and proximal factors, such as psychiatric disorders and physical illnesses [<xref rid=\"B7-ijerph-17-05433\" ref-type=\"bibr\">7</xref>]. As for socioeconomic status, the lower the education level, income and social class, the higher the risk of suicide [<xref rid=\"B7-ijerph-17-05433\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijerph-17-05433\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05433\" ref-type=\"bibr\">9</xref>]. Regarding physical and psychiatric disorders, many studies have found that not only a single disease, but also multimorbidity conditions are related with suicide [<xref rid=\"B7-ijerph-17-05433\" ref-type=\"bibr\">7</xref>,<xref rid=\"B10-ijerph-17-05433\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijerph-17-05433\" ref-type=\"bibr\">11</xref>]. Empirical studies support that mental disorders such as substance abuse, schizophrenia and psychosis are associated with suicide [<xref rid=\"B12-ijerph-17-05433\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijerph-17-05433\" ref-type=\"bibr\">13</xref>]. Demographic factors such as older age and male sex are also related to suicide [<xref rid=\"B14-ijerph-17-05433\" ref-type=\"bibr\">14</xref>]. However, these studies identified an association between suicide and various risk factors at the individual level only.</p><p>Suicide is influenced by social factors that give rise to circumstances changing human behavior or making people more psychologically or physically unhealthy, which induce suicide or suicidal ideation [<xref rid=\"B15-ijerph-17-05433\" ref-type=\"bibr\">15</xref>]. So, the circumstances affecting people at the regional or community levels, and not at the individual level, need to be clarified. Durkheim developed a theory that attributed variations in suicide rates to different characteristics in different societies [<xref rid=\"B16-ijerph-17-05433\" ref-type=\"bibr\">16</xref>]. Since suicide has different meanings in different societies and circumstances, its patterns vary according to region [<xref rid=\"B17-ijerph-17-05433\" ref-type=\"bibr\">17</xref>]. Among various regional characteristics, many sociocultural factors contribute to regional variations in suicide rates [<xref rid=\"B18-ijerph-17-05433\" ref-type=\"bibr\">18</xref>]. In recent years, studies dealing with risk factors at the social level have attracted attention [<xref rid=\"B18-ijerph-17-05433\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijerph-17-05433\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijerph-17-05433\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijerph-17-05433\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijerph-17-05433\" ref-type=\"bibr\">22</xref>]. This trend has developed spatial clustering techniques which identify geographic patterns and associated characteristics across clusters [<xref rid=\"B23-ijerph-17-05433\" ref-type=\"bibr\">23</xref>].</p><p>Several studies have been conducted on suicide at the regional level. Concerning demographic factors, population density, marriage status, moving status, region, single parents and race have been associated with suicide rates [<xref rid=\"B20-ijerph-17-05433\" ref-type=\"bibr\">20</xref>,<xref rid=\"B24-ijerph-17-05433\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijerph-17-05433\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijerph-17-05433\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijerph-17-05433\" ref-type=\"bibr\">27</xref>]. Regarding socioeconomic factors, employment status and average income were shown to affect regional suicide rates [<xref rid=\"B20-ijerph-17-05433\" ref-type=\"bibr\">20</xref>,<xref rid=\"B27-ijerph-17-05433\" ref-type=\"bibr\">27</xref>]. For health-related factors, alcohol-related psychiatric illness is related with suicide rates in Slovenia [<xref rid=\"B24-ijerph-17-05433\" ref-type=\"bibr\">24</xref>]. To identify these factors, there are various methodologies such as regression analysis models, the principal component analysis model, geographic weighted regression model and Bayesian hierarchical models [<xref rid=\"B20-ijerph-17-05433\" ref-type=\"bibr\">20</xref>,<xref rid=\"B25-ijerph-17-05433\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijerph-17-05433\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijerph-17-05433\" ref-type=\"bibr\">27</xref>]. However, these studies lacked adequate degrees of variation of suicide rate and various factors. Meanwhile, few studies have been undertaken on variations in suicide rates among regions, and the few that have been published included socioeconomic factors only [<xref rid=\"B26-ijerph-17-05433\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijerph-17-05433\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijerph-17-05433\" ref-type=\"bibr\">28</xref>]. Even though various factors have been used, studies of suicide at the regional level should also focus on environmental and population aspects, such as social welfare budget [<xref rid=\"B14-ijerph-17-05433\" ref-type=\"bibr\">14</xref>].</p><p>In South Korea, the suicide mortality rate has been the highest among the members of Organization for Economic Cooperation and Development (OECD) since 2003, showing almost double the OECD average. Thus, more comprehensive understanding of sociodemographic, economic, public service and health related factors is needed to construct more effective strategies for the prevention of suicide in South Korea. The aim of this study is to verify regional variations in suicide rates and to identify associated factors at the regional level.</p></sec><sec id=\"sec2-ijerph-17-05433\"><title>2. Materials and Methods </title><sec id=\"sec2dot1-ijerph-17-05433\"><title>2.1. Study Design and Data Source</title><p>This study is an ecological study, in which the study unit is a population [<xref rid=\"B29-ijerph-17-05433\" ref-type=\"bibr\">29</xref>]. Data for this study were obtained from the 2010 Community Health Survey data, the National Death Registration from 2010–2014 and other administrative data from South Korea from 2010. The Community Health survey has been conducted annually since 2008 by the Korea Center for Disease Control and Prevention (KCDC) [<xref rid=\"B30-ijerph-17-05433\" ref-type=\"bibr\">30</xref>]. The purpose of this survey is to assess health conditions, health-related behavior and socioeconomic factors of the Korean population and to produce community health statistics [<xref rid=\"B30-ijerph-17-05433\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijerph-17-05433\" ref-type=\"bibr\">31</xref>]. In South Korea, Statistic Korea provides regional level unit information such as age-adjusted specific mortality and other sociodemographic and economic factors. These data were obtained from the National Census survey data, National death registration data and other administrative information. So, we assumed that these data were representative of the specific, Korean context which is the focus of this study. Since this information was included in a website analysis, we could access regional level information using the web-quarry system. Region-based information has different population units and we needed to unify cities because data from various data sources provided different population units. We divided the region of South Korea into 252 cities. The study was exempted from evaluation by the institutional review board of Korea University because it is an ecological study using publicly available data.</p></sec><sec id=\"sec2dot2-ijerph-17-05433\"><title>2.2. Dependent Variables: Suicide Rates </title><p>The dependent variable was the age-adjusted suicide rate in the previous five years (2010–2014). The Ministry of Statistics provides crude and age-adjusted mortality based on National Death Registration, which is census data providing cause of death by the International Classification of Diseases 10th Version (ICD-10 version), and basic sociodemographic information and National census data, providing demographic information on the South Korean population. Suicide mortality was defined when the cause of mortality was coded within the range from X60 to X84 by the ICD-10 version. Age-adjusted mortality using the 2005 census population as the standard was used in this study.</p></sec><sec id=\"sec2dot3-ijerph-17-05433\"><title>2.3. Independent Variables: Risk Factors </title><p>We used six independent variables: single household, divorce rate, financial independence, social welfare budget, depression and subjective health status. As sociodemographic variables, single household and divorce were used. Regarding economic and public services, financial independence and the social welfare budget were used. Health related factors were analyzed using depression and subjective health status variables taken from a community health survey. First, we defined sociodemographic factors as the ratio of single households to the total number of households and divorce rates to the number of divorces among the total population. Second, for economic and public service factors, financial independence was defined by the ratio of nontax revenue and local taxes for regional general accounting, which shows the economic status of each area. The social welfare budget is defined as the ratio of the social welfare budget used in regional general accounting. Third, for health-related factors, depression was measured by responses (yes or no) to the following question: “In the past year, have you felt depressed for two consecutive weeks?”, while Subjective health status was measured by the ratio of responses (very good and good) to subjective health status questions. We assumed that there would be lag effect on suicide rates, i.e., after experiencing risk factors for 1 year to 5 year, people may commit suicide. In this study, we used risk factor variables from 2010. A summary of all variables is presented in <xref rid=\"ijerph-17-05433-t001\" ref-type=\"table\">Table 1</xref>.</p></sec><sec id=\"sec2dot4-ijerph-17-05433\"><title>2.4. Statistical Analysis</title><p>We used descriptive statistics to identify general characteristics of maximum, minimum, average, and standard deviation. To identify regional variation in suicide rates, we used Extremal quotient (EQ) and Coefficient of variance (CV). EQ was calculated by the ratio of the maximum to the minimum value. CV was average divided by standard deviation. EQ is widely used to determine variations in medical procedures such as surgical procedures and examination. However, some research has used EQ and CV to determine variations in the prevalence and incidence of diseases such as hypertension and diabetes.</p><p>For bivariate analysis, Pearson’s correlation analysis was performed using suicide rates and risk factors. Correlation coefficients and variance inflation factor (VIF) are used by statisticians and epidemiologists [<xref rid=\"B32-ijerph-17-05433\" ref-type=\"bibr\">32</xref>]. Even though a VIF higher than 10 and a correlation coefficient cut off greater than 0.8 are common in measuring multicollinearity [<xref rid=\"B33-ijerph-17-05433\" ref-type=\"bibr\">33</xref>], a VIF of between 5 or 10 and a correlation coefficient cutoff higher than 0.5 are suggested [<xref rid=\"B34-ijerph-17-05433\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijerph-17-05433\" ref-type=\"bibr\">35</xref>].</p><p>Finally, the multiple linear regression model was applied to estimate associations between suicide rates and potential risk factors using single household, divorce rate, financial independence, social welfare budget, depression, and subjective health status [<xref rid=\"B14-ijerph-17-05433\" ref-type=\"bibr\">14</xref>,<xref rid=\"B36-ijerph-17-05433\" ref-type=\"bibr\">36</xref>]. The SPSS software (version 24.0) (SPSS Inc., Chicago, IL, USA) was used for data analyses. Statistical significance was verified by a two-tailed test and <italic>p</italic>-values of 0.05 were the threshold of this analyses.</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05433\"><title>3. Results</title><p>The general characteristics (suicide rate, single household rate, financial independence rate, social welfare budget, divorce rate, depression prevalence, subjective health status) of the 252 regions are presented in <xref rid=\"ijerph-17-05433-t002\" ref-type=\"table\">Table 2</xref>. The average suicide rate was 142.7 per 100,000 population. The average single household was 22%, and financial independence was 30%. The average social welfare budget was 26%. The average divorce rate was 2.2%. The average depression prevalence was 5.11%. The average subjective health status was 49%. The EQ of suicide rate was 2.88, which indicates regional variation in Korea. Among the independent variables, depression showed the greatest variation among risk factors (EQ = 55.00), followed by social welfare budget (13.05), financial independence (9.94), single household (3.25), divorce rate (3.18), subjective health status (2.04).</p><p>In a Pearson’s correlation analysis in <xref rid=\"ijerph-17-05433-t003\" ref-type=\"table\">Table 3</xref>, financial independence, social welfare budget and divorce rate were associated with suicide rates. Regarding economic and public service factors, financial independence (<italic>r</italic> = −0.435, <italic>p</italic> < 0.01), and social welfare budget (<italic>r</italic> = −0.385, <italic>p</italic> < 0.01) showed stronger associations than sociodemographic factors, e.g., divorce rate (<italic>r</italic> = 0.178, <italic>p</italic> < 0.01). Health related variables were not significantly associated with suicide rate. Correlations of all variables were less than 0.7, which proves that there is independence within variables. Also, using tolerance and VIF, we verified multicollinearity. Since tolerance was higher than 0.2 and VIF was less than 10, there was no multicollinearity among variables.</p><p><xref rid=\"ijerph-17-05433-t004\" ref-type=\"table\">Table 4</xref> shows the associations between suicide rate and risk factors at the regional level. Financial independence was associated with suicide rate at an area level (<italic>b</italic> = −0.662, <italic>p</italic> < 0.001), as was Social welfare budget (<italic>b</italic> = −0.754, <italic>p</italic> < 0.001). Divorce rate was associated with suicide rate at the regional level (<italic>b</italic> = 17.743, <italic>p</italic> < 0.001). Economic and public service factors and sociodemographic factors were associated with suicide rate after controlling other variables. Health-related variables were not significantly associated with suicide rate. To compare the degree of association, we used standardized b, which adjusts risk factors for the same condition. The strongest risk factor for suicide was shown to be financial independence (0.463), followed by social welfare budget (0.370) and divorce rate (0.282).</p></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05433\"><title>4. Discussion</title><p>We found that there is a regional variation in suicide rates in South Korea. Financial independence, social welfare budget and divorce rates were associated with suicide rates at the regional level, even after controlling other variables. To the best of our knowledge, no study has presented EQ regarding suicide rates at the regional level. Here, we show the EQ indexes from some previous studies. In Taiwan, for example, EQ was 15.68, and in England, it was 74.07, i.e., far higher than in of South Korea. These EQ values are not applicable to South Korean because each country differs in size and has different definitions of “region”. On the other hand, we could compare the EQ of suicide with other causes of death in South Korea. Statistics Korea provides the top 10 cause of death every year. In 2014, malignant neoplasm was responsible for the highest mortality, followed by heart diseases, cerebrovascular diseases, pneumonia, suicide, diabetes, chronic lower respiratory diseases, liver diseases, traffic accidents and hypertension. Compared with these causes of death, suicide has the third lowest EQ value, following malignant neoplasm and cerebrovascular diseases.</p><p>We found that financial independence was correlated with suicide mortality rates. Financial independence represents the economic status of administrative governments. Low financial independence implies financial deficit or austerity. Karanikolos et al. reviewed financial crises and health in Europe, and showed that financial difficulties among governments led to epidemics of suicide [<xref rid=\"B37-ijerph-17-05433\" ref-type=\"bibr\">37</xref>]. Another study in Europe showed that economic crises (worsening employment and gross domestic product) correlated with suicide rates [<xref rid=\"B38-ijerph-17-05433\" ref-type=\"bibr\">38</xref>]. In Greece, government austerity policy (public expenditure reduction) due to budget cuts led to increases in suicide [<xref rid=\"B39-ijerph-17-05433\" ref-type=\"bibr\">39</xref>].</p><p>We found that the social welfare budget is a strong predictor of suicide rates. In the present study, the social welfare budget was negatively correlated with suicide rates; some studies showed the same results in the U.S [<xref rid=\"B40-ijerph-17-05433\" ref-type=\"bibr\">40</xref>,<xref rid=\"B41-ijerph-17-05433\" ref-type=\"bibr\">41</xref>,<xref rid=\"B42-ijerph-17-05433\" ref-type=\"bibr\">42</xref>]. These studies compared suicide rates and public spending/expenditure in each state [<xref rid=\"B40-ijerph-17-05433\" ref-type=\"bibr\">40</xref>,<xref rid=\"B41-ijerph-17-05433\" ref-type=\"bibr\">41</xref>]. Social welfare supports people who are facing financial problems such as unemployment or underemployment [<xref rid=\"B40-ijerph-17-05433\" ref-type=\"bibr\">40</xref>]. The magnitude of the social welfare budget indicates that the government makes efforts to provide social welfare services to citizens [<xref rid=\"B40-ijerph-17-05433\" ref-type=\"bibr\">40</xref>].</p><p>Divorce rates were associated with suicide rates; with increase in divorce rates, suicide rates increased. This correlation has been observed in other studies [<xref rid=\"B43-ijerph-17-05433\" ref-type=\"bibr\">43</xref>,<xref rid=\"B44-ijerph-17-05433\" ref-type=\"bibr\">44</xref>]. Even though divorce is legal, it has a great impact on the economy and the community; e.g., people who are divorced experience changes in income and in social interactions [<xref rid=\"B45-ijerph-17-05433\" ref-type=\"bibr\">45</xref>]. In addition, divorced people report higher social isolation than married people [<xref rid=\"B46-ijerph-17-05433\" ref-type=\"bibr\">46</xref>]. These results were supported by the theory of Durkheim. According to the Durkheim, divorce can break family ties, which contributes to lower social integration, and egoistic suicide [<xref rid=\"B16-ijerph-17-05433\" ref-type=\"bibr\">16</xref>]. Moreover, divorce also increase anomic suicide [<xref rid=\"B44-ijerph-17-05433\" ref-type=\"bibr\">44</xref>].</p><p>We did not find any association between health-related factors and suicide rate at the regional level. In general, depression and subjective health status appear to be factors related to suicide rates at the individual level, but this tends to be unclear in ecological studies conducted at the regional level. An ecological study in the United States indicated that major depression was not related to suicide rates [<xref rid=\"B47-ijerph-17-05433\" ref-type=\"bibr\">47</xref>], and a Korean ecological study found that subjective health levels were not related to suicide rates [<xref rid=\"B48-ijerph-17-05433\" ref-type=\"bibr\">48</xref>]. This was probably because depression and subjective health status are accurately measured at the individual level, so they are very limited in terms of accurately reflecting regional characteristics, partly due to small sample sizes. Nonetheless, some studies which observed a relationship between regional characteristics and suicide rates excluded factors measured at the individual level [<xref rid=\"B49-ijerph-17-05433\" ref-type=\"bibr\">49</xref>,<xref rid=\"B50-ijerph-17-05433\" ref-type=\"bibr\">50</xref>,<xref rid=\"B51-ijerph-17-05433\" ref-type=\"bibr\">51</xref>].</p><p>This study has several limitations. First, since it is an ecological study, the ecological fallacy should be kept in mind. Second, we did not include age groups and gender as risk factors. In South Korea, suicide rates are far higher among males than females, and among old adults (i.e., more than 65 years). Third, more varied and accurate variables are needed. We used single household and divorce rate as demographic factors, financial independence and social welfare budget as economic and public service factors, and depression and subjective health status as health related factors. Future studies need to include indicators such as Gini’s coefficient, deprivation index and relative index of inequalities. Also, alcohol or substance abuse needs to be considered. Substance abuse disorder has been reported as an important risk factor for suicide in South Korea [<xref rid=\"B52-ijerph-17-05433\" ref-type=\"bibr\">52</xref>,<xref rid=\"B53-ijerph-17-05433\" ref-type=\"bibr\">53</xref>]. Forth, various methodologies should be used to achieve more refined results. Some methodologies have been developed which could be applied to such a study, such as geographic weighted regression and the Bayesian hierarchy model [<xref rid=\"B20-ijerph-17-05433\" ref-type=\"bibr\">20</xref>,<xref rid=\"B27-ijerph-17-05433\" ref-type=\"bibr\">27</xref>].</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05433\"><title>5. Conclusions</title><p>The present study revealed that there is substantial variation in suicide rates among South Korean regions. In addition, there are relationships between suicide mortality rates and various factors such as sociodemographic, economic, public service and health-related factors. Financial independence and social welfare budget were strong risk factors for suicide at the regional level. Adequate social policy, strengthening financial independence and social welfare budget should be more closely examined. These societal circumstances should be noted in further studies of suicide etiology and prevention.</p></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization, M.C. and Y.H.L.; methodology, M.C. and Y.H.L.; formal analysis, M.C. and Y.H.L.; investigation, M.C. and Y.H.L.; resources, M.C. and Y.H.L.; data curation, M.C. and Y.H.L.; writing—original draft preparation, M.C. and Y.H.L.; writing—review and editing, M.C. and Y.H.L. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This study was supported by the Korean Society for Preventive Medicine funded by a grant from the Korea Centers for Disease Control and Prevention (CDC Research-8(2019.1.2)) and the Korea University Graduate School Junior Fellow Research Grant.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijerph-17-05433\"><label>1.</label><element-citation publication-type=\"book\"><person-group person-group-type=\"author\"><collab>WHO</collab></person-group><source>The World Health Report 2001: Mental Health: New Understanding, New Hope</source><publisher-name>World Health Organization</publisher-name><publisher-loc>Geneva, Switzerland</publisher-loc><year>2001</year></element-citation></ref><ref id=\"B2-ijerph-17-05433\"><label>2.</label><element-citation publication-type=\"book\"><person-group person-group-type=\"author\"><collab>WHO</collab></person-group><source>Preventing Suicide: A Global Imperative</source><publisher-name>World Health Organization</publisher-name><publisher-loc>Geneva, Switzerland</publisher-loc><year>2014</year></element-citation></ref><ref id=\"B3-ijerph-17-05433\"><label>3.</label><element-citation publication-type=\"book\"><person-group person-group-type=\"author\"><collab>WHO</collab></person-group><source>Mental Health Action Plan 2013–2020</source><publisher-name>World Health Organization</publisher-name><publisher-loc>Geneva, Switzerland</publisher-loc><year>2013</year></element-citation></ref><ref id=\"B4-ijerph-17-05433\"><label>4.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Kennelly</surname><given-names>B.</given-names></name></person-group><article-title>The economic cost of suicide in ireland</article-title><source>Crisis</source><year>2007</year><volume>28</volume><fpage>89</fpage><lpage>94</lpage><pub-id pub-id-type=\"doi\">10.1027/0227-5910.28.2.89</pub-id><pub-id pub-id-type=\"pmid\">17722690</pub-id></element-citation></ref><ref id=\"B5-ijerph-17-05433\"><label>5.</label><element-citation publication-type=\"web\"><person-group person-group-type=\"author\"><collab>WHO</collab></person-group><article-title>Mental Health: Suicide Prevention (SUPRE)</article-title><comment>Available online: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.who.int/mental_health/prevention/suicide/background/en\">https://www.who.int/mental_health/prevention/suicide/background/en</ext-link></comment><date-in-citation content-type=\"access-date\" iso-8601-date=\"2018-04-26\">(accessed on 26 April 2018)</date-in-citation></element-citation></ref><ref id=\"B6-ijerph-17-05433\"><label>6.</label><element-citation publication-type=\"book\"><person-group person-group-type=\"author\"><name><surname>Clark</surname><given-names>S.</given-names></name><name><surname>Goldney</surname><given-names>R.</given-names></name></person-group><article-title>The Impact of Suicide on Relatives and Friends</article-title><source>The International Handbook of Suicide and Attempted Suicide</source><publisher-name>John Wiley & Sons</publisher-name><publisher-loc>Chichester, UK</publisher-loc><year>2000</year></element-citation></ref><ref id=\"B7-ijerph-17-05433\"><label>7.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Crump</surname><given-names>C.</given-names></name><name><surname>Sundquist</surname><given-names>K.</given-names></name><name><surname>Sundquist</surname><given-names>J.</given-names></name><name><surname>Winkleby</surname><given-names>M.A.</given-names></name></person-group><article-title>Sociodemographic, psychiatric and somatic risk factors for suicide: A swedish national cohort study</article-title><source>Psychol. 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style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Dependent Variable</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Suicide Rate</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">X60–X84 (ICD-10)</th></tr></thead><tbody><tr><td rowspan=\"6\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Independent variable</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Single household</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ratio of Single households</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Divorce rate</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ratio of divorce</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Financial independence</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ratio of local tax and nontax revenue for regional general accounting</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Social welfare budget</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ratio of social welfare budget for regional general accounting</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Depression</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ratio of depression lasting 2 weeks in the past year</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Subjective health status</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">The ratio of “very good” and “good” in subjective health status</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05433-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05433-t002_Table 2</object-id><label>Table 2</label><caption><p>Variations in suicide mortality and independent variables.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Variables</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Minimum</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Maximum</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Average</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">EQ</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CV</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Suicide mortality rate</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">75.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">217.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">142.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.88</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.70</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Single household</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.25</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.96</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Financial independence</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">82.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">9.94</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">58.35</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Social welfare budget</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.56</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">59.52</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47.03</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Divorce rate</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.18</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.71</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Depression</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">55.00</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">42.80</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Subjective health status</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">35.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">72.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">49.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.04</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14.00</td></tr></tbody></table><table-wrap-foot><fn><p>EQ: Extremal quotient; CV: Coefficient of variance.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05433-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05433-t003_Table 3</object-id><label>Table 3</label><caption><p>Correlation between suicide rate and independent variables.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Variables</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Single Households</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Divorce Rate</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Financial Independence</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Social Welfare Unit</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Depression</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Subjective Health Status</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Suicide mortality rate</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.178 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.435 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.385 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.101</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.050</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Single household</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.033</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.123</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.133 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.047</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.040</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Financial independence</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.033</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.187 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.100</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.092</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.169 **</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Social welfare budget</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.123</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.187 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.143 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.217 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.100</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Divorce rate</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.133 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.100</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.143 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.213 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.185 **</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Depression</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.047</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.092</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.217 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.213 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.520 **</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Subjective health status</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.040</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.169 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.100</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.185 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.520 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td></tr></tbody></table><table-wrap-foot><fn><p>*: <italic>p</italic> < 0.05, **: <italic>p</italic> < 0.01.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05433-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05433-t004_Table 4</object-id><label>Table 4</label><caption><p>Associated factors in regional variations in suicide rates.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Variables</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>b</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SE</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Standardized <italic>b</italic></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">95% Confidence</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">(constant)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">135.765</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16.510</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">103.244–168.285</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Single household</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.081</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.270</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.015</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.613–0.452</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.766</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Financial independence</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.662</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.075</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.463</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.810–−0.514</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Social welfare budget</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.754</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.107</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.370</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.963–−0.544</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Divorce rate</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.743</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.243</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.282</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.356–24.130</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Depression</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.970</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.706</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.083</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.420–2.361</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.170</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Subjective health status</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.082</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.218</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.022</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.346–0.511</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.706</td></tr></tbody></table></table-wrap></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32759702</article-id><article-id pub-id-type=\"pmc\">PMC7432039</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155570</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05570</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Consequences of Vitamin A Deficiency: Immunoglobulin Dysregulation, Squamous Cell Metaplasia, Infectious Disease, and Death</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Surman</surname><given-names>Sherri L.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05570\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-5317-1055</contrib-id><name><surname>Penkert</surname><given-names>Rhiannon R.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05570\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Sealy</surname><given-names>Robert E.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05570\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Jones</surname><given-names>Bart G.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05570\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-6570-7179</contrib-id><name><surname>Marion</surname><given-names>Tony N.</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05570\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Vogel</surname><given-names>Peter</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05570\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Hurwitz</surname><given-names>Julia L.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05570\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijms-21-05570\">2</xref><xref rid=\"c1-ijms-21-05570\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05570\"><label>1</label>Department of Infectious Diseases, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA; <email>sherri.surman@stjude.org</email> (S.L.S.); <email>rhiannon.penkert@stjude.org</email> (R.R.P.); <email>bob.sealy@stjude.org</email> (R.E.S.); <email>bart.jones@stjude.org</email> (B.G.J.)</aff><aff id=\"af2-ijms-21-05570\"><label>2</label>Department of Microbiology, Immunology and Biochemistry, University of Tennessee Health Science Center, Memphis, TN 38163, USA; <email>tmarion@uthsc.edu</email></aff><aff id=\"af3-ijms-21-05570\"><label>3</label>Department of Pathology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA; <email>peter.vogel@stjude.org</email></aff><author-notes><corresp id=\"c1-ijms-21-05570\"><label>*</label>Correspondence: <email>Julia.hurwitz@stjude.org</email>; Tel.: +901-595-2464; Fax: 901-595-3099</corresp></author-notes><pub-date pub-type=\"epub\"><day>04</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5570</elocation-id><history><date date-type=\"received\"><day>01</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>23</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Vitamin A is an important regulator of immune protection, but it is often overlooked in studies of infectious disease. Vitamin A binds an array of nuclear receptors (e.g., retinoic acid receptor, peroxisome proliferator-activated receptor, retinoid X receptor) and influences the barrier and immune cells responsible for pathogen control. Children and adults in developed and developing countries are often vitamin A-deficient or insufficient, characteristics associated with poor health outcomes. To gain a better understanding of the protective mechanisms influenced by vitamin A, we examined immune factors and epithelial barriers in vitamin A deficient (VAD) mice, vitamin D deficient (VDD) mice, double deficient (VAD+VDD) mice, and mice on a vitamin-replete diet (controls). Some mice received insults, including intraperitoneal injections with complete and incomplete Freund’s adjuvant (emulsified with PBS alone or with DNA + Fus-1 peptide) or intranasal inoculations with Sendai virus (SeV). Both before and after insults, the VAD and VAD+VDD mice exhibited abnormal serum immunoglobulin isotypes (e.g., elevated IgG2b levels, particularly in males) and cytokine/chemokine patterns (e.g., elevated eotaxin). Even without insult, when the VAD and VAD+VDD mice reached 3–6 months of age, they frequently exhibited opportunistic ascending bacterial urinary tract infections. There were high frequencies of nephropathy (squamous cell hyperplasia of the renal urothelium, renal scarring, and ascending pyelonephritis) and death in the VAD and VAD+VDD mice. When younger VAD mice were infected with SeV, the predominant lesion was squamous cell metaplasia of respiratory epithelium in lungs and bronchioles. Results highlight a critical role for vitamin A in the maintenance of healthy immune responses, epithelial cell integrity, and pathogen control.</p></abstract><kwd-group><kwd>vitamin A deficiency</kwd><kwd>squamous cell metaplasia</kwd><kwd>abnormal cytokines</kwd><kwd>abnormal immunoglobulins</kwd><kwd>infections</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05570\"><title>1. Introduction</title><p>Vitamin A and D deficiencies have long been recognized as dietary concerns in developing countries. For example, the increased morbidity and mortality caused by measles infections have been associated with low vitamin A levels [<xref rid=\"B1-ijms-21-05570\" ref-type=\"bibr\">1</xref>]. The WHO and other organizations support vitamin supplementation programs to correct vitamin A deficits and improve pediatric health [<xref rid=\"B2-ijms-21-05570\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijms-21-05570\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijms-21-05570\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijms-21-05570\" ref-type=\"bibr\">5</xref>].</p><p>Over the past decade, diets in the developed world have changed considerably. Vitamin A and D deficits are now prevalent among low-income families, but because clinical tests for serum retinol are not routine, the precise frequencies of vitamin deficiencies and insufficiencies are unknown. Our study of vitamins A and D among individuals in Memphis, Tennessee, using retinol binding protein (RBP) as a surrogate for blood retinol showed that the majority of tested individuals were deficient or insufficient for vitamin D, and approximately half of the individuals were deficient or insufficient for vitamin A [<xref rid=\"B6-ijms-21-05570\" ref-type=\"bibr\">6</xref>]. Some of the tested individuals may have been experiencing infections, a situation that could reduce the RBP levels, but we observed low levels in individuals without frank indications of disease. The low vitamin levels in Memphis were further associated with poor outcomes among children hospitalized with respiratory syncytial virus (RSV) or human metapneumovirus respiratory infections [<xref rid=\"B7-ijms-21-05570\" ref-type=\"bibr\">7</xref>]. </p><p>The physiological effects of vitamins A and D are vast in number. The activities influenced by vitamins A and D among structural and immune cells include proliferation, cytokine/chemokine production, and migration [<xref rid=\"B8-ijms-21-05570\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijms-21-05570\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijms-21-05570\" ref-type=\"bibr\">10</xref>]. Among their many functions, vitamin A and D metabolites serve as ligands for nuclear receptors. Heterodimeric receptors include the retinoic acid receptor-retinoid X receptor (RAR-RXR), the peroxisome proliferator-activated receptor-RXR (PPAR -RXR), and the vitamin D receptor-RXR (VDR-RXR) [<xref rid=\"B11-ijms-21-05570\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijms-21-05570\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijms-21-05570\" ref-type=\"bibr\">13</xref>]. The vitamin A metabolite all-trans retinoic acid binds RAR or PPARβ/δ, whereas 9-<italic>cis</italic>-retinoic acid binds RXR. Influenced by their binding to ligands, receptors bind DNA response elements (e.g., the retinoic acid response element, RARE). They then serve as transcription factors to regulate gene expression [<xref rid=\"B14-ijms-21-05570\" ref-type=\"bibr\">14</xref>]. We have recently found that nuclear receptors bind key switch (S) sites, promoters, and enhancers in the immunoglobulin heavy chain gene locus, suggesting a direct mechanism by which vitamins and other nuclear hormones might regulate antibody levels. If/when nuclear receptors contribute to enhanceosomes and switchosomes within regulatory regions of the immunoglobulin loci, they may influence class switch recombination (CSR) and thereby instruct the production of IgM, IgG, IgE, and IgA by activated B cells [<xref rid=\"B15-ijms-21-05570\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijms-21-05570\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijms-21-05570\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijms-21-05570\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijms-21-05570\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijms-21-05570\" ref-type=\"bibr\">20</xref>]. </p><p>To better understand the influences of vitamin deficiencies, we examined vitamin A deficient (VAD), vitamin D deficient (VDD), and double deficient (VAD+VDD) mice compared to mice on a vitamin replete diet (controls) for immunoglobulin isotypes, serum factors, and epithelial cell barrier integrity. Mice received various insults, including intraperitoneal (IP) injections of complete and incomplete Freund’s adjuvant administered with or without a DNA-Fus 1 peptide mixture [<xref rid=\"B21-ijms-21-05570\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05570\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05570\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05570\" ref-type=\"bibr\">24</xref>]. Additional insults included intranasal (IN) inoculations with a murine respiratory pathogen, Sendai virus (SeV). Results demonstrated abnormalities in the VAD and VAD+VDD mice, both before and after insults. The VAD and VAD+VDD mice suffered abnormal immunoglobulin isotype and cytokine profiles, infectious disease, and squamous cell metaplasia at mucosal sites. The VAD and VAD+VDD mice suffered frequent death, unlike their VDD and control counterparts.</p></sec><sec sec-type=\"results\" id=\"sec2-ijms-21-05570\"><title>2. Results</title><sec id=\"sec2dot1-ijms-21-05570\"><title>2.1. Abnormal Immunoglobulin and Cytokine/Chemokine Profiles in VAD and VAD+VDD Mice</title><p>Pregnant C57BL/6 mice were placed on test diets at 5 days gestation, and upon birth the pups were maintained on the same diets until adulthood and throughout experimentation. There were four diets: VAD, VDD, VAD+VDD, and the control. Experiments were begun when offspring reached adulthood (6–8 weeks of age). Test mice received consecutive intraperitoneal (IP) injections with complete and incomplete Freund’s adjuvant emulsified with PBS alone (termed CFA) or DNA + Fus-1 peptide (termed CFA/DNA/PEP, see Materials and Methods) as insults [<xref rid=\"B22-ijms-21-05570\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05570\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05570\" ref-type=\"bibr\">24</xref>]. Additional mice received PBS only.</p><p>To identify diet-associated changes, we examined the mice for their total serum immunoglobulins (<xref ref-type=\"fig\" rid=\"ijms-21-05570-f001\">Figure 1</xref>). We previously reported that in PBS-injected mice, the VAD and VAD+VDD diets affected immunoglobulin isotypes [<xref rid=\"B16-ijms-21-05570\" ref-type=\"bibr\">16</xref>].</p><p>In <xref ref-type=\"fig\" rid=\"ijms-21-05570-f001\">Figure 1</xref>, we show representative results from mice injected with PBS, CFA, or CFA/DNA/PEP. In the VAD (and VAD+VDD) mice, there were lower IgM levels compared to animals on the control diet, particularly among females (<italic>p</italic> < 0.01 for VAD females compared to the control females, Mann Whitney test). These differences were evident in animals regardless of the injection type. In male VAD mice, the IgG2b levels were significantly higher than the levels in control males (<italic>p</italic> < 0.01) or VAD females (<italic>p</italic> < 0.05). In both males and females, the VAD (and VAD+VDD) mice had higher IgG2b/IgM ratios compared to the controls (<italic>p</italic> < 0.01 for VAD females compared to the control females; <italic>p</italic> < 0.01 for VAD males compared to the control males). These features were dependent on diets and not on the type of injections that mice received.</p><p>We additionally examined the cytokine/chemokine levels known to be associated with inflammation and cell repair [<xref rid=\"B25-ijms-21-05570\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijms-21-05570\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijms-21-05570\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05570\" ref-type=\"bibr\">28</xref>]. In each of two experiments, we found that the cytokines/chemokines were altered both by the diets and by the CFA/DNA/PEP insults (results of one representative experiment are shown in <xref ref-type=\"fig\" rid=\"ijms-21-05570-f002\">Figure 2</xref>). When the “Combined VAD” mice (including VAD and VAD+VDD mice) were compared to the Non-VAD mice (including VDD and control mice) after the CFA/DNA/PEP injections, the regulated upon activation, normal T cell expressed and secreted (RANTES) chemokine concentrations were significantly reduced (<xref ref-type=\"fig\" rid=\"ijms-21-05570-f002\">Figure 2</xref>A, <italic>p</italic> = 0.0083, Mann Whitney tests). IL-1α and IL-5 were also reduced in the “Combined VAD” mice compared to the Non-VAD mice. </p><p>In contrast, in the PBS-injected animals the “Combined VAD” mice produced higher levels of interferon γ-inducible protein 10 (IP-10) compared to the Non-VAD mice (<xref ref-type=\"fig\" rid=\"ijms-21-05570-f002\">Figure 2</xref>B, <italic>p</italic> = 0.0039, Mann Whitney Test). In the animals injected with CFA/DNA/PEP, the IP-10 levels increased in all the diet groups. The “Combined VAD” mice produced higher levels of eotaxin compared to the Non-VAD mice, both following the PBS injections (<xref ref-type=\"fig\" rid=\"ijms-21-05570-f002\">Figure 2</xref>C, <italic>p</italic> < 0.0001) and the CFA/DNA/PEP injections (<xref ref-type=\"fig\" rid=\"ijms-21-05570-f002\">Figure 2</xref>C, <italic>p</italic> = 0.0002, Mann Whitney). </p></sec><sec id=\"sec2dot2-ijms-21-05570\"><title>2.2. Squamous Cell Metaplasia of the Renal Pelvis and Ascending Bacterial Urinary Tract Infections (UTI) in VAD and VAD+VDD Mice </title><p>The PBS, CFA, or CFA/DNA/PEP-injected mice on the four diets were examined for histopathologic lesions. Assessments were performed when the animals had reached 3–6 months of age. We found that many VAD and VAD+VDD mice developed squamous cell metaplasia of the renal pelvis, with ascending bacterial UTIs and renal scarring.</p><p>Disease was frequent regardless of injection (PBS, CFA, or CFA/DNA/PEP), suggesting that disease was a consequence of the diets and not the insults. In mice on the control diet, the urothelium lining of the renal pelvis and fornices consisted of a thin layer (1–3 cells thick) of polygonal cells with clearly defined nuclei (<xref ref-type=\"fig\" rid=\"ijms-21-05570-f003\">Figure 3</xref>, left image). In contrast, the urothelium in the VAD mice was a markedly thickened highly stratified squamous epithelium, with cornification of the upper layers and extensive sloughing of cornified cells into the lumen of the renal pelvis (<xref ref-type=\"fig\" rid=\"ijms-21-05570-f003\">Figure 3</xref>, right image). The urothelium in the VDD mice was normal, whereas the renal pelvis lesions in the VAD+VDD mice were indistinguishable from those of the VAD mice, being characterized by the accumulation of abundant desquamated keratinized squamous epithelium and cell debris in the lumen, with neutrophilic inflammation and bacteria.</p><p>Lesions in the renal cortex were also limited to mice with VAD or VAD+VDD. In contrast to the normal arrangement of renal tubules and glomeruli in mice on normal diets and VDD mice, there was frequent, chronic inflammation and fibrosis extending into the adjacent medulla and cortex in both the VAD and VAD+VDD mice, with extensive scarring of the renal parenchyma in some cases. Overall, these renal lesions were indicative of an ascending pyelonephritis. Nephropathies and ascending bacterial UTIs were common in the VAD and VAD+VDD mice. </p><p>When full body analyses were performed, inflammation was also observed in tissues other than the kidneys, and in some cases bacteria were identified in the lungs, heart, liver, kidney, intestine, brain, and spinal column. </p><p>The combined results from more than one experiment are shown in <xref rid=\"ijms-21-05570-t001\" ref-type=\"table\">Table 1</xref> (note that the experiments were not equally balanced for the numbers of animals per group). The frequencies of nephropathies in the “Combined VAD” and Non-VAD mice were significantly different (<italic>p</italic> < 0.001, Fisher’s exact test).</p><p>Deaths were frequent among the male and female VAD and VAD+VDD mice that had reached 3–6 months of age. Deaths occurred among mice that received any of the injections (PBS, CFA, or CFA/DNA/PEP). In one independent representative experiment, the deaths were 26/39 among the VAD mice, 14/22 among the VAD + VDD mice, 0/22 among the VDD mice, and 0/24 among the control mice. The differences between the “Combined VAD” and Non-VAD mice were significant (<italic>p</italic> < 0.0001, Fisher’s exact test).</p></sec><sec id=\"sec2dot3-ijms-21-05570\"><title>2.3. Abnormal Epithelial Cell Barriers in the Airways of VAD Mice Infected with a Respiratory Virus </title><p>We examined a second epithelial barrier, the respiratory tract lining. In this case, we evaluated the airways of young adult unmanipulated VAD and control mice, as well as mice that had been infected with SeV and sacrificed 22–29 days later (in some cases, the mice received a second dose of SeV two days prior to sacrifice, but this did not affect the outcome described below).</p><p>Upper respiratory tract lesions were not observed in the uninfected naïve or VAD control mice. However, in the SeV-infected VAD mice, there was widespread squamous metaplasia over the anterior turbinates, with patchy mild squamous metaplasia and keratinization in the larynx and trachea. </p><p>Lower respiratory tract lesions were not observed in the lungs of uninfected mice on normal diets (<xref ref-type=\"fig\" rid=\"ijms-21-05570-f004\">Figure 4</xref>A) or uninfected VAD mice (<xref ref-type=\"fig\" rid=\"ijms-21-05570-f004\">Figure 4</xref>C). In the SeV-infected mice on normal control diets, there were widespread perivascular/peribronchiolar inflammatory cell infiltrates and a mild thickening of the alveolar septa in affected areas (<xref ref-type=\"fig\" rid=\"ijms-21-05570-f004\">Figure 4</xref>B). In contrast, the lesions in SeV-infected VAD mice were characterized by reduced inflammatory cell infiltrates but extensive squamous metaplasia of the bronchiolar and alveolar epithelium (<xref ref-type=\"fig\" rid=\"ijms-21-05570-f004\">Figure 4</xref>D). The squamous metaplasia of the airway epithelium was characterized by the replacement of the normal single layer cuboidal and low columnar bronchiolar epithelium and flattened alveolar cells with multiple layers of keratinized squamous epithelium. </p><p>At higher magnifications, inflammatory cell infiltrates and epithelial changes were not present around the terminal airways of either uninfected control mice (<xref ref-type=\"fig\" rid=\"ijms-21-05570-f004\">Figure 4</xref>E) or uninfected VAD mice (<xref ref-type=\"fig\" rid=\"ijms-21-05570-f004\">Figure 4</xref>G). The SeV-infected mice on control diets showed perivascular/peribronchiolar lymphocytic infiltrates and mild accumulations of cell debris and macrophages in the peribronchiolar alveoli (<xref ref-type=\"fig\" rid=\"ijms-21-05570-f004\">Figure 4</xref>F). There were a few foci of alveolar bronchiolization in some lungs (<xref ref-type=\"fig\" rid=\"ijms-21-05570-f004\">Figure 4</xref>F). In the virus-infected VAD mice, pulmonary lesions were also centered on bronchioles, but inflammatory cell infiltrates in the VAD mice were reduced in comparison to the infected controls. The squamous metaplasia and accumulations of keratinized and cornified epithelial cells expanded and blocked the terminal airways and adjacent alveoli (<xref ref-type=\"fig\" rid=\"ijms-21-05570-f004\">Figure 4</xref>H) in affected areas. These lesions frequently coalesced with similar lesions affecting adjacent bronchioles and could involve extensive areas of the lung parenchyma.</p><p>In one set of analyses, among mice infected with SeV, the squamous metaplasia of the distal bronchiolar and proximal alveolar epithelium was observed in 5 out of 11 VAD mice and 0 out of 12 mice on the control diet (<italic>p</italic> = 0.014, Fisher’s Exact test). Taken together, our findings indicate that two different conditions, (i) VAD and (ii) a direct insult to the epithelia (in this case, virus-induced), were required in combination to induce squamous metaplasia in the lungs of young adult animals. </p></sec></sec><sec sec-type=\"discussion\" id=\"sec3-ijms-21-05570\"><title>3. Discussion</title><p>Here, we describe immune and barrier parameters among VAD, VDD, VAD+VDD, and control mice. The insults used in our study included injections with adjuvants administered with or without DNA-Fus 1 peptide mixtures, previously shown to induce inflammatory responses [<xref rid=\"B21-ijms-21-05570\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05570\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05570\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05570\" ref-type=\"bibr\">24</xref>]. Infections with SeV, a common murine respiratory pathogen, were also tested. We found that regardless of insult, the VAD and VAD+VDD mice frequently suffered squamous metaplasia, accompanied by ascending bacterial infections in the renal pelvis and death after 3–6 months of age. Squamous metaplasia was also observed in the airways of younger VAD mice infected with SeV. Abnormalities in the epithelial barriers were not surprising, given that epithelial cells are known to produce RAR-RXR, retinoic acid binding proteins [<xref rid=\"B29-ijms-21-05570\" ref-type=\"bibr\">29</xref>], and retinaldehyde dehydrogenase ALDH1A [<xref rid=\"B30-ijms-21-05570\" ref-type=\"bibr\">30</xref>] (used for the metabolism of retinal to retinoic acid), and that epithelial cells proliferate in response to vitamin A [<xref rid=\"B31-ijms-21-05570\" ref-type=\"bibr\">31</xref>]. In addition to epithelial abnormalities, the VAD and VAD+VDD mice displayed abnormal immunoglobulin and cytokine/chemokine profiles compared to the controls. The conditions of VAD and VAD+VDD clearly rendered animals vulnerable to infection, morbidity, and mortality. Our results, in combination with previous reports, emphasize the importance of vitamin A in maintaining the physical integrity of epithelial barriers and in mounting effective immune responses to infections [<xref rid=\"B32-ijms-21-05570\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijms-21-05570\" ref-type=\"bibr\">33</xref>]. The findings encourage increased attention to vitamin A levels in the clinical arena of developed and developing countries. </p><sec id=\"sec3dot1-ijms-21-05570\"><title>3.1. VAD, Keratinizing Metaplasia, UTIs and Respiratory Tract Disease</title><p>Our demonstrations of abnormalities in epithelial barriers supplement previous descriptions of poor health outcomes in the context of VAD. Children with severe VAD can suffer a chronic dry cough, increased susceptibility to respiratory infections, and pyuria, perhaps due to keratinizing metaplasia at mucosal surfaces [<xref rid=\"B32-ijms-21-05570\" ref-type=\"bibr\">32</xref>,<xref rid=\"B34-ijms-21-05570\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijms-21-05570\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijms-21-05570\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijms-21-05570\" ref-type=\"bibr\">37</xref>]. Pathologies including UTIs and pyelonephritis have been previously described in Wistar rats and also in humans with VAD [<xref rid=\"B32-ijms-21-05570\" ref-type=\"bibr\">32</xref>,<xref rid=\"B38-ijms-21-05570\" ref-type=\"bibr\">38</xref>,<xref rid=\"B39-ijms-21-05570\" ref-type=\"bibr\">39</xref>]. Acute pyelonephritis is a frequent cause of serious bacterial UTIs in infants, with renal scarring followed by hypertension and chronic renal failure being the most common long-term sequelae [<xref rid=\"B40-ijms-21-05570\" ref-type=\"bibr\">40</xref>,<xref rid=\"B41-ijms-21-05570\" ref-type=\"bibr\">41</xref>,<xref rid=\"B42-ijms-21-05570\" ref-type=\"bibr\">42</xref>,<xref rid=\"B43-ijms-21-05570\" ref-type=\"bibr\">43</xref>,<xref rid=\"B44-ijms-21-05570\" ref-type=\"bibr\">44</xref>]. Vitamin A/retinoid supplementation can be protective in some (but not all) cases [<xref rid=\"B39-ijms-21-05570\" ref-type=\"bibr\">39</xref>,<xref rid=\"B45-ijms-21-05570\" ref-type=\"bibr\">45</xref>,<xref rid=\"B46-ijms-21-05570\" ref-type=\"bibr\">46</xref>,<xref rid=\"B47-ijms-21-05570\" ref-type=\"bibr\">47</xref>,<xref rid=\"B48-ijms-21-05570\" ref-type=\"bibr\">48</xref>,<xref rid=\"B49-ijms-21-05570\" ref-type=\"bibr\">49</xref>,<xref rid=\"B50-ijms-21-05570\" ref-type=\"bibr\">50</xref>,<xref rid=\"B51-ijms-21-05570\" ref-type=\"bibr\">51</xref>,<xref rid=\"B52-ijms-21-05570\" ref-type=\"bibr\">52</xref>], perhaps in part by preventing the adhesion of bacteria to respiratory epithelium [<xref rid=\"B33-ijms-21-05570\" ref-type=\"bibr\">33</xref>]. </p><p>VAD has also been associated with the accelerated development of carcinogen-induced malignant neoplasms in the urinary tracts of rats. In one study, the administration of high-dose retinyl palmitate (≥250 IU/g diet), although unable to prevent the formation of transitional cell hyperplasia or neoplasia, prevented squamous metaplasia in animals receiving carcinogen [<xref rid=\"B53-ijms-21-05570\" ref-type=\"bibr\">53</xref>].</p><p>In our virus study, the combination of VAD with infection was necessary for the development of squamous metaplasia in the lung. This result supplemented findings by Stephensen et. al., who observed squamous cell changes in the convalescent stage after VAD mice were infected with influenza virus [<xref rid=\"B54-ijms-21-05570\" ref-type=\"bibr\">54</xref>]. Epithelial cell changes were also described in a study of cigarette smoke in VAD rats [<xref rid=\"B55-ijms-21-05570\" ref-type=\"bibr\">55</xref>]. Abnormal squamous epithelium in terminal airways may compromise pulmonary defenses against infections by impeding the clearance of pathogens and cell debris from lower alveolar regions of the lung and upregulating mucosal cytokines [<xref rid=\"B56-ijms-21-05570\" ref-type=\"bibr\">56</xref>]. </p><p>Our findings with the SeV model may have additional significance given that squamous metaplasia of the bronchial epithelium is a pre-neoplastic change that develops in response to other airway insults, including inhaled irritants such as cigarette smoke [<xref rid=\"B57-ijms-21-05570\" ref-type=\"bibr\">57</xref>,<xref rid=\"B58-ijms-21-05570\" ref-type=\"bibr\">58</xref>,<xref rid=\"B59-ijms-21-05570\" ref-type=\"bibr\">59</xref>]. Possibly, viral infections in VAD animals contribute to the development of pre-neoplastic changes in the lung that can, in some cases, progress to high-grade dysplasia and carcinoma [<xref rid=\"B60-ijms-21-05570\" ref-type=\"bibr\">60</xref>,<xref rid=\"B61-ijms-21-05570\" ref-type=\"bibr\">61</xref>]. The relationship between lung cancer and vitamin A precursors has been previously examined [<xref rid=\"B62-ijms-21-05570\" ref-type=\"bibr\">62</xref>,<xref rid=\"B63-ijms-21-05570\" ref-type=\"bibr\">63</xref>,<xref rid=\"B64-ijms-21-05570\" ref-type=\"bibr\">64</xref>,<xref rid=\"B65-ijms-21-05570\" ref-type=\"bibr\">65</xref>,<xref rid=\"B66-ijms-21-05570\" ref-type=\"bibr\">66</xref>], and (as for other cancers) vitamin supplementation as a treatment for lung cancer has yielded variable results [<xref rid=\"B67-ijms-21-05570\" ref-type=\"bibr\">67</xref>,<xref rid=\"B68-ijms-21-05570\" ref-type=\"bibr\">68</xref>,<xref rid=\"B69-ijms-21-05570\" ref-type=\"bibr\">69</xref>,<xref rid=\"B70-ijms-21-05570\" ref-type=\"bibr\">70</xref>,<xref rid=\"B71-ijms-21-05570\" ref-type=\"bibr\">71</xref>,<xref rid=\"B72-ijms-21-05570\" ref-type=\"bibr\">72</xref>,<xref rid=\"B73-ijms-21-05570\" ref-type=\"bibr\">73</xref>,<xref rid=\"B74-ijms-21-05570\" ref-type=\"bibr\">74</xref>,<xref rid=\"B75-ijms-21-05570\" ref-type=\"bibr\">75</xref>,<xref rid=\"B76-ijms-21-05570\" ref-type=\"bibr\">76</xref>,<xref rid=\"B77-ijms-21-05570\" ref-type=\"bibr\">77</xref>,<xref rid=\"B78-ijms-21-05570\" ref-type=\"bibr\">78</xref>,<xref rid=\"B79-ijms-21-05570\" ref-type=\"bibr\">79</xref>,<xref rid=\"B80-ijms-21-05570\" ref-type=\"bibr\">80</xref>,<xref rid=\"B81-ijms-21-05570\" ref-type=\"bibr\">81</xref>,<xref rid=\"B82-ijms-21-05570\" ref-type=\"bibr\">82</xref>,<xref rid=\"B83-ijms-21-05570\" ref-type=\"bibr\">83</xref>,<xref rid=\"B84-ijms-21-05570\" ref-type=\"bibr\">84</xref>,<xref rid=\"B85-ijms-21-05570\" ref-type=\"bibr\">85</xref>,<xref rid=\"B86-ijms-21-05570\" ref-type=\"bibr\">86</xref>]. </p></sec><sec id=\"sec3dot2-ijms-21-05570\"><title>3.2. Dysregulation of Adaptive and Innate Immune Parameters in VAD Mice</title><p>We examined the immunoglobulin isotype profiles and cytokines/chemokines in the blood of animals on the four diets after PBS, CFA, or CFA/DNA/PEP injections. The “Combined VAD” mice exhibited differences in isotype profiles compared to the Non-VAD mice regardless of insult. The differences in the IgG2b/IgM ratios were highly significant and were most notable among males. This may be due to direct effects of vitamin A on class switch recombination and immunoglobulin expression [<xref rid=\"B15-ijms-21-05570\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijms-21-05570\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijms-21-05570\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijms-21-05570\" ref-type=\"bibr\">18</xref>], and/or to the indirect effects of T cells and cytokines on B cell development. An indication of T cell malfunction in VAD mice was the reduced level of RANTES (and IL-1α plus IL-5). RANTES and interleukins can be produced by T cells and can also regulate T cell activities [<xref rid=\"B87-ijms-21-05570\" ref-type=\"bibr\">87</xref>,<xref rid=\"B88-ijms-21-05570\" ref-type=\"bibr\">88</xref>,<xref rid=\"B89-ijms-21-05570\" ref-type=\"bibr\">89</xref>]. The failed upregulation of RANTES in VAD animals following CFA/DNA/PEP stimuli helps to explain the poor recruitment of T cells to infected or otherwise damaged target tissues [<xref rid=\"B90-ijms-21-05570\" ref-type=\"bibr\">90</xref>,<xref rid=\"B91-ijms-21-05570\" ref-type=\"bibr\">91</xref>]. Unlike RANTES, IP-10 and eotaxin, typically produced by innate and barrier cells, were increased in the “Combined VAD” mice compared to Non-VAD mice, perhaps indicative of uncontrolled tissue damage. Abnormal immunoglobulin and cytokine patterns together mark immune dysfunction and set the stage for opportunistic infections [<xref rid=\"B88-ijms-21-05570\" ref-type=\"bibr\">88</xref>,<xref rid=\"B89-ijms-21-05570\" ref-type=\"bibr\">89</xref>,<xref rid=\"B90-ijms-21-05570\" ref-type=\"bibr\">90</xref>,<xref rid=\"B92-ijms-21-05570\" ref-type=\"bibr\">92</xref>,<xref rid=\"B93-ijms-21-05570\" ref-type=\"bibr\">93</xref>,<xref rid=\"B94-ijms-21-05570\" ref-type=\"bibr\">94</xref>,<xref rid=\"B95-ijms-21-05570\" ref-type=\"bibr\">95</xref>]. </p></sec><sec id=\"sec3dot3-ijms-21-05570\"><title>3.3. Nuclear Receptors and Cross-Regulation</title><p>As stated above, vitamins and sex hormones have influences throughout the mammalian cell, but are best known for their binding of nuclear receptors and regulation of gene expression [<xref rid=\"B11-ijms-21-05570\" ref-type=\"bibr\">11</xref>,<xref rid=\"B14-ijms-21-05570\" ref-type=\"bibr\">14</xref>]. Each factor binds a set of receptors which serve as transcription factors, influential in promoter activity, enhancer activity, and chromosome structure. Our own work has identified response elements (sites to which nuclear receptors preferentially bind) for vitamins and estrogen in key regulatory regions, including switch sites of the immunoglobulin heavy chain locus [<xref rid=\"B15-ijms-21-05570\" ref-type=\"bibr\">15</xref>]. One prominent hotspot for response elements appears in the position of the switch site Sμ, a site that is critical for the switch from IgM to other immunoglobulin isotypes. Additional response elements appear in or near promoters and enhancers of regulatory regions in the locus. Estrogen receptor binds these sites in activated B cells and shifts binding patterns (in concert with RNA polymerase) when B cells receive supplemental estrogen [<xref rid=\"B16-ijms-21-05570\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijms-21-05570\" ref-type=\"bibr\">17</xref>,<xref rid=\"B96-ijms-21-05570\" ref-type=\"bibr\">96</xref>,<xref rid=\"B97-ijms-21-05570\" ref-type=\"bibr\">97</xref>,<xref rid=\"B98-ijms-21-05570\" ref-type=\"bibr\">98</xref>]. The removal of response elements in enhancer regions reduces the capacity for B cells to switch isotypes [<xref rid=\"B96-ijms-21-05570\" ref-type=\"bibr\">96</xref>]. The response elements for vitamins and sex hormones overlap [<xref rid=\"B15-ijms-21-05570\" ref-type=\"bibr\">15</xref>], providing an explanation for the immunoglobulin isotype differences between VAD females and males (<xref ref-type=\"fig\" rid=\"ijms-21-05570-f001\">Figure 1</xref>). Perhaps the receptors for vitamins and sex hormones cross-compete for binding to switch sites and regulatory elements, explaining why isotype patterns vary among male and female VAD animals. The ultimate composition of enhanceosomes and switchosomes may predict the patterns of CSR and resultant patterns of immunoglobulin isotype expression. The immunoglobulin expression patterns may in turn affect susceptibilities to infectious disease. We emphasize that mammalian cell types differ in their responses to nuclear factors [<xref rid=\"B10-ijms-21-05570\" ref-type=\"bibr\">10</xref>], and that a composite of variables (diet, sex, genetics, and environment) will determine the host’s final outcome [<xref rid=\"B96-ijms-21-05570\" ref-type=\"bibr\">96</xref>].</p></sec><sec id=\"sec3dot4-ijms-21-05570\"><title>3.4. Defining Cause-Effect Relationships In Vivo</title><p>What are the precise cause-effect relationships among the parameters described in this report? We consider that in tissue culture settings, vitamin A directly and independently influences purified B cells and purified epithelial cells. However, parameter relationships are much more difficult to assess in vivo. A dampened immune response will render mice susceptible to infectious disease which will, in turn, damage epithelial cells. Damaged epithelial cells may increase infectious disease susceptibility and may drive immunopathological responses. These circular events are clearly detrimental to the VAD animal, particularly as the animal ages. The outcome is infection and death regardless of deliberate challenge or insult.</p><p>Of interest was the observation that squamous metaplasia in respiratory tissue was not apparent in young VAD mice, but became evident when the mice were infected with SeV. The results again portrayed complex interactions between nutritional status and pathogen invasions, in this case with an outcome of epithelial damage.</p></sec><sec id=\"sec3dot5-ijms-21-05570\"><title>3.5. A focus on the Cross Regulatory Signals of Nuclear Receptors in Clinical Studies</title><p>The difficulty in interpreting clinical trial data is likely due, in part, to a limited focus on nuclear factors. For example, baseline vitamin levels are rarely measured among clinical trial participants. Our test of vitamin supplementation for the improvement of immune responses in healthy children demonstrated: (i) a strong correlation between baseline vitamin A and antibody responses and (ii) a benefit of vitamin supplements only when children were vitamin A and D-deficient or insufficient at baseline [<xref rid=\"B99-ijms-21-05570\" ref-type=\"bibr\">99</xref>]. Our study suggested that vitamin A supplements may provide the greatest benefit if used in personalized medicine rather than with a one-size-fits-all treatment plan. One must also consider that vitamin A may convey both beneficial and harmful effects if used in patients with an acute infectious disease [<xref rid=\"B100-ijms-21-05570\" ref-type=\"bibr\">100</xref>].</p><p>Despite its intricate relationship with pathogen control, vitamin A is often overlooked as a defense against clinical UTIs or respiratory diseases, particularly in developed countries [<xref rid=\"B47-ijms-21-05570\" ref-type=\"bibr\">47</xref>,<xref rid=\"B52-ijms-21-05570\" ref-type=\"bibr\">52</xref>]. Our results illustrate the dysfunctional immune parameters and poor outcomes of VAD and encourage clinicians to consider abnormal vitamin A levels as a cause of poor outcome. Programs that improve dietary intake among malnourished populations may correct immune functions, physical barriers, and protective mechanisms against infectious disease. </p></sec></sec><sec id=\"sec4-ijms-21-05570\"><title>4. Materials and Methods </title><sec id=\"sec4dot1-ijms-21-05570\"><title>4.1. Animal Models</title><p>We followed the Association for Assessment and Accreditation for Laboratory Animal Care (AAALAC) guidelines. The protocols were approved by the Institutional Animal Care and Use Committee (IACUC) of St. Jude Children’s Research Hospital (Protocol #111, renewal 10 April 2018). To produce vitamin-deficient mice, pregnant female C57BL/6 mice were received from Jackson Laboratories (Bar Harbor, ME, USA) at day 4–5 gestation and immediately placed on test or control diets. The mice were fed characterized diets from Harlan Laboratories (Madison, WI, USA). The VAD diet was Harlan Cat# TD.10762. The VDD diet was Harlan Cat# TD.10763. The vitamin A+D double-deficient diet was Harlan Cat# TD.10616. The control diet included vitamin A palmitate (15 IU/g) and vitamin D (1.5 IU/g). This was Harlan Cat# TD.10764. Pups were maintained on the same diets as their mothers until adulthood (6–8 weeks of age, when experiments were begun) and throughout the course of the experiments. For animals depleted of vitamin D (VDD or VAD+VDD), cages were placed in dedicated cubicles with LED bulbs as the source of light to avoid UV-B irradiation. The animals were sacrificed if they were moribund during the course of the experiments. </p><p>Retinol binding protein (RBP) tests and vitamin D tests were conducted to spot-check the vitamin levels in animals, as described previously [<xref rid=\"B94-ijms-21-05570\" ref-type=\"bibr\">94</xref>]. RBP was used as a surrogate for retinol and was tested with an enzyme-linked immunosorbent assay (ELISA). The RBP tests confirmed that the VAD and VAD+VDD mice were all vitamin A-deficient (RBP was <5000 ng/mL), and there were significant differences in the RBP values when the VAD and VAD+VDD mice were compared to the VDD and control mice (unpaired <italic>t</italic>-test <italic>p</italic> < 0.0001, GraphPad Prism software, v8 [GraphPad Software, San Diego, CA, USA]). The vitamin D tests confirmed that the VDD and VAD+VDD mice were vitamin D-deficient (vitamin D was <15 nm/L), and there were significant differences in the vitamin D values when the VDD and VAD+VDD mice were compared to the VAD and control mice (unpaired <italic>t</italic>-test <italic>p</italic> < 0.0001). </p></sec><sec id=\"sec4dot2-ijms-21-05570\"><title>4.2. CFA or CFA/DNA/PEP Injections </title><p>Animal groups were labeled CFA or CFA/DNA/PEP based on injection regimens. CFA: these animals received an IP priming dose with complete Freund’s adjuvant (Thermo Scientific, Waltham, MA, USA, Cat #77140, mixed 1:1 with PBS and emulsified) and one or two subsequent booster doses with incomplete Freund’s adjuvant (Thermo scientific Cat# 77145, mixed 1:1 with PBS and emulsified), administered at approximately three-week intervals. CFA/DNA/PEP: these animals received the same adjuvants and regimens described above, but adjuvants were mixed with Fus-1, a DNA-binding 27-mer peptide (KVCRRCYARLPVRASNCRKKACGHCSN; 10 µg per mouse) from <italic>Trypanosoma cruzi</italic>, which had been added dropwise to calf thymus DNA (Invitrogen #15633-019, 100 µg per mouse) prior to emulsification [<xref rid=\"B22-ijms-21-05570\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05570\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05570\" ref-type=\"bibr\">24</xref>]. Additional animals received PBS only. Bleeds were taken 6 days to 4 weeks after the injections. The mice were sacrificed if they were moribund during the experimental course or after the final bleed. </p></sec><sec id=\"sec4dot3-ijms-21-05570\"><title>4.3. SeV infections</title><p>Young adult mice were briefly anesthetized with isoflurane and infected with SeV by intranasal inoculation (250–500 plaque forming units (pfu)). The mice were sacrificed after 22–29 days to evaluate their airway tissues. In some cases, the mice received a second dose of SeV (1.5 × 10<sup>7</sup> pfu) two days before sacrifice.</p></sec><sec id=\"sec4dot4-ijms-21-05570\"><title>4.4. Total Serum Immunoglobulin Analyses</title><p>The mouse sera, collected six days after the last booster injection, were diluted 1:25,000 and evaluated for multiple isotypes using the Luminex platform. </p><p>The samples were run on a MILLIPLEX MAP mouse immunoglobulin isotyping kit (Millipore, Burlington, MA, USA, Cat# MGAMMAG-300K) and read on a Luminex 200 Multiplexing instrument using the xPonent software. The data were further processed using the Milliplex Analyst software. If the samples were below the lower limit of detection, they received a value of “0”. If the samples were above the upper limit of detection, they were assigned the upper limit value.</p></sec><sec id=\"sec4dot5-ijms-21-05570\"><title>4.5. Histology</title><p>The mice were euthanized with isoflurane and the lungs were infused with formalin. The lungs and kidneys were then fixed by immersion in 10% neutral buffered formalin, embedded in paraffin, sectioned, and stained with hematoxylin and eosin stains. The slides were analyzed by a pathologist in the Veterinary Pathology Core Department of St. Jude Children’s Research Hospital. </p></sec><sec id=\"sec4dot6-ijms-21-05570\"><title>4.6. Cytokines</title><p>The sera were thawed, vortexed briefly, and spun at 2000 g for 1 min to pellet the debris. The sera were diluted 1:2 in an assay buffer and then assayed for Eotaxin, G-CSF, GM-CSF, interferon (IFN)γ, interleukin (IL)-1a, IL-1b, M-CSF, IL-2, IL-3, IL-4, IL-5, IL-6, IL-7, IL-10, IL-12(p40), IL-12(p70), IL-13, IL-15, IL-17, IFNγ-inducible protein (IP)-10, MIP-2, KC, LIF, LIX, MCP-1, MIP-1a, MIP-1b, MIG, regulated on activation, normal T cell expressed and secreted (RANTES), TNFα, VEGF, and IL-9 using a Mouse Cytokine/Chemokine Magnetic Bead Milliplex MAP kit (Millpore Cat# MCYTOMAG-70K-PX32). The plates were processed according to the manufacturer’s instructions and read using a Luminex 200 instrument. The results were evaluated using Excel and GraphPad Prism software, v8.</p></sec><sec id=\"sec4dot7-ijms-21-05570\"><title>4.7. Statistical Analyses</title><p>Mann Whitney, Fisher’s Exact tests, and <italic>t</italic>-tests were performed using the GraphPad Prism software.</p></sec></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization, T.N.M., P.V., and J.L.H.; methodology, S.L.S., R.R.P., T.N.M., P.V., and J.L.H.; formal analysis, R.R.P., P.V., and J.L.H.; investigation, S.L.S., R.R.P., R.E.S., B.G.J., and P.V.; writing—original draft preparation, J.L.H.; writing—review and editing, S.L.S., R.R.P., R.E.S., B.G.J., T.N.M., P.V., and J.L.H.; funding acquisition, J.L.H. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was funded in part by NIH NCI P30CA21765 and ALSAC.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest. 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Infect. Dis. J.</source><year>1996</year><volume>15</volume><fpage>777</fpage><lpage>782</lpage><pub-id pub-id-type=\"doi\">10.1097/00006454-199609000-00008</pub-id><pub-id pub-id-type=\"pmid\">8878220</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijms-21-05570-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Serum immunoglobulin isotypes in animal groups. Isotypes and isotype ratios are shown for mice that were injected with PBS, complete and incomplete Freund’s adjuvant (emulsified with PBS alone [termed CFA] or DNA + Fus-1 peptide [termed CFA/DNA/PEP]). Mice received one prime and one booster injection and were bled 6 days after the booster. Data from mice injected with PBS only were described previously [<xref rid=\"B16-ijms-21-05570\" ref-type=\"bibr\">16</xref>]. Bars represent the total immunoglobulin levels (ng/mL) for individual mice (Yellow <bold>=</bold> control mice; green <bold>=</bold> vitamin A deficient [VAD] mice; pink <bold>=</bold> vitamin D deficient [VDD] mice; blue <bold>=</bold> double deficient [VAD+VDD] mice). For each group of females or males, the bars represent mice injected with PBS (P, thin borders) CFA (C, thick borders), or CFA/DNA/PEP (D, thin borders). When the mice received similar injections, the bars were placed side-by-side with no intervening space.</p></caption><graphic xlink:href=\"ijms-21-05570-g001\"/></fig><fig id=\"ijms-21-05570-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Changes in the blood cytokines/chemokines based on diets. Results are shown from a representative experiment. Mice received consecutive injections, either with PBS or with complete and incomplete Freund’s adjuvant emulsified with DNA + Fus-1 peptide (CFA/DNA/PEP). Mice were primed and boosted and sampled approximately 3–4 weeks after the booster. Results are shown for regulated upon activation, normal T cell expressed and secreted (RANTES, (<bold>A</bold>)), interferon γ-inducible protein 10 (IP-10, (<bold>B</bold>)), and eotaxin (<bold>C</bold>). Yellow <bold>=</bold> control mice (mice on the vitamin-replete, control diet, “CON DIET”); green <bold>=</bold> vitamin A deficient (VAD) mice; red <bold>=</bold> vitamin D deficient (VDD) mice; blue <bold>=</bold> double deficient (VAD+VDD) mice. Each symbol represents a different mouse, with medians indicated by bar heights.</p></caption><graphic xlink:href=\"ijms-21-05570-g002\"/></fig><fig id=\"ijms-21-05570-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Histopathologic analyses of epithelial changes and inflammation in the renal pelvis and cortex of mice on vitamin A deficient (VAD) diets. Mice were injected with complete Freund’s adjuvant followed by incomplete Freund’s adjuvant approximately 3 weeks later and sacrificed approximately 3–4 weeks after the second injection. Representative tissue sections are shown (10× magnification). (Left) a mouse on the normal control diet. The urothelium lining the renal pelvis and fornices consisted of a thin layer (1–3 cells thick) of polygonal cells with clearly defined nuclei. (Right) in a VAD mouse, there was marked thickening of the squamous epithelium, with the cornification of upper layers (top arrow) and the extensive sloughing of cornified cells into the lumen of the renal pelvis (bottom arrow).</p></caption><graphic xlink:href=\"ijms-21-05570-g003\"/></fig><fig id=\"ijms-21-05570-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Histopathologic analysis of the epithelial changes and inflammation in the lungs of mice on control or vitamin A deficient (VAD) diets with or without Sendai virus (SeV) infections. Representative tissue sections are shown. (<bold>A</bold>) Normal lower respiratory tract in an uninfected mouse on the control diet. (<bold>B</bold>) Widespread perivascular/peribronchiolar inflammatory cell infiltrates and the mild thickening of the alveolar septa following SeV infection (after two SeV exposures, see Materials and Methods) in a mouse on a control diet. (<bold>C</bold>) Normal lower respiratory tract epithelium in an uninfected mouse on the VAD diet. (<bold>D</bold>) Reduced inflammation but extensive squamous metaplasia of the bronchiolar and alveolar epithelium in an SeV-infected mouse (after two SeV exposures) on the VAD diet. Higher magnification photos (<bold>E–H,</bold> 20× magnification) of pulmonary changes after the same treatments as (<bold>A–D,</bold> 2× magnification). (<bold>E</bold>) Normal terminal airway epithelium of an uninfected mouse on the vitamin-replete, control diet. (<bold>F</bold>) Perivascular/peribronchiolar lymphocytic infiltrates and mild accumulations of cell debris and macrophages after SeV infection in a mouse on the control diet. Small foci of alveolar bronchiolization are present (top right quadrant). (<bold>G</bold>) Absence of pulmonary lesions in an uninfected VAD mouse. (<bold>H</bold>) In an SeV-infected VAD mouse, inflammatory cell infiltrates are markedly reduced, but there is extensive squamous metaplasia and there are accumulations of keratinized epithelium, blocking the terminal airways and adjacent alveoli.</p></caption><graphic xlink:href=\"ijms-21-05570-g004\"/></fig><table-wrap id=\"ijms-21-05570-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05570-t001_Table 1</object-id><label>Table 1</label><caption><p>Frequent nephropathies in animals fed VAD or VAD+VDD diets.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Diets</th><th colspan=\"7\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Treatments</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">PBS</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">CFA</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">CFA/DNA/PEP</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">TOTAL</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Female</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Male</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Female</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Male</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Female</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Male</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">All</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>CONTROL</italic></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>Fraction</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0/8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0/11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1/10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0/13</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0/11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0/9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1/62</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>Percent</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2%</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>VAD</italic></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>Fraction</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3/5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7/11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6/8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2/7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4/7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7/10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29/48</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>Percent</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">64%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">75%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">57%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">70%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60%</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>VDD</italic></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>Fraction</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1/12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0/12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0/10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0/14</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0/7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0/14</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1/69</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>Percent</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1%</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold><italic>VAD+VDD</italic></bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<italic>Fraction</italic>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0/5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6/9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7/8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4/6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3/3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6/8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26/39</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>Percent</italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">67%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">88%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">67%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">75%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">67%</td></tr></tbody></table><table-wrap-foot><fn><p>Combined results from several experiments for which pathologies were analyzed demonstrated frequent severe kidney pathologies among the vitamin A deficient (VAD) and double deficient (VAD+VDD) animals compared to vitamin D deficient (VDD) and control animals. Female and male animals were examined at 3–6 months of age. We note that the experiments were not equally balanced for the numbers of animals per group. </p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751668</article-id><article-id pub-id-type=\"pmc\">PMC7432040</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155520</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05520</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Is Parent Engagement with a Child Health Home-Based Record Associated with Parents Perceived Attitude towards Health Professionals and Satisfaction with the Record? A Cross-Sectional Survey of Parents in New South Wales, Australia</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-7378-6302</contrib-id><name><surname>Chutiyami</surname><given-names>Muhammad</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05520\">1</xref><xref rid=\"c1-ijerph-17-05520\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-7288-2669</contrib-id><name><surname>Wyver</surname><given-names>Shirley</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05520\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Amin</surname><given-names>Janaki</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05520\">2</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05520\"><label>1</label>Faculty of Human Sciences, Macquarie University, Sydney 2109, Australia; <email>shirley.wyver@mq.edu.au</email></aff><aff id=\"af2-ijerph-17-05520\"><label>2</label>Faculty of Medicine and Health Sciences, Macquarie University, Sydney 2109, Australia; <email>janaki.amin@mq.edu.au</email></aff><author-notes><corresp id=\"c1-ijerph-17-05520\"><label>*</label>Correspondence: <email>muhammad.chutiyami@hdr.mq.edu.au</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>30</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5520</elocation-id><history><date date-type=\"received\"><day>25</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>25</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>We examined parent views of health professionals and satisfaction toward use of a child health home-based record and the influence on parent engagement with the record. A cross-sectional survey of 202 parents was conducted across New South Wales (NSW), Australia. Bivariate and multivariate logistic regressions were conducted to identify predictors of parent engagement with the record book using odds ratio (OR) at 95% confidence interval (CI) and 0.05 significance level. Parents reported utilizing the record book regularly for routine health checks (63.4%), reading the record (37.2%), and writing information (40.1%). The majority of parents (91.6%) were satisfied with the record. Parents perceived nurses/midwives as most likely to use/refer to the record (59.4%) compared to pediatricians (34.1%), general practitioners (GP) (33.7%), or other professionals (7.9%). Parents were less likely to read the record book if they perceived the GP to have a lower commitment (Adjusted OR = 0.636, 95% CI 0.429–0.942). Parents who perceived nurses/midwives’ willingness to use/refer to the record were more likely to take the record book for routine checks (Adjusted OR = 0.728, 95% CI 0.536–0.989). Both parent perceived professionals’ attitude and satisfaction significantly influenced information input in the home-based record. The results indicate that improvements in parent engagement with a child health home-based record is strongly associated with health professionals’ commitment to use/refer to the record during consultations/checks.</p></abstract><kwd-group><kwd>home-based records</kwd><kwd>parent engagement</kwd><kwd>child health</kwd><kwd>health professionals</kwd><kwd>primary healthcare</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05520\"><title>1. Introduction</title><p>In 2018, the World Health Organization (WHO) endorsed universal use of home-based records in addition to facility-based records to improve maternal, infant, and child health [<xref rid=\"B1-ijerph-17-05520\" ref-type=\"bibr\">1</xref>]. The WHO review noted the need for further research studies on provider behavior toward use of the health records as well as best way to design the records to capture useful information that ease use by parents/professionals among others. Following the WHO review, Brown et al., [<xref rid=\"B2-ijerph-17-05520\" ref-type=\"bibr\">2</xref>] added that improvements are needed in health workers’ utilization of home-based record books when working with care givers/parents, including; requesting, referencing, and updating the record. Accordingly, parents and care givers of children must be capable of retaining such records and be committed to take it to the point of service to improve potential for a good start in children’s early life [<xref rid=\"B3-ijerph-17-05520\" ref-type=\"bibr\">3</xref>].</p><p>Growing evidence indicates that a healthy early start in children’s life has the potential to affect future mental and physical wellbeing [<xref rid=\"B4-ijerph-17-05520\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijerph-17-05520\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijerph-17-05520\" ref-type=\"bibr\">6</xref>] as well as educational attainment [<xref rid=\"B7-ijerph-17-05520\" ref-type=\"bibr\">7</xref>]. Therefore, supporting parents from pregnancy through early start in life is not only significant for empowering parents, it supports health promotion for children in the future. Family-focused interventions delivered through primary healthcare settings have the ability to enhance child public health [<xref rid=\"B8-ijerph-17-05520\" ref-type=\"bibr\">8</xref>]. Accordingly, families tend to consult their primary healthcare providers including family physicians, pediatricians, family/community nurses and midwives among others, for advice about their children’s development and health [<xref rid=\"B8-ijerph-17-05520\" ref-type=\"bibr\">8</xref>]. Improving parent–professional relationship/communication is therefore essential for building strong partnerships between parents and health professionals [<xref rid=\"B9-ijerph-17-05520\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijerph-17-05520\" ref-type=\"bibr\">10</xref>]. One key area to improve such parent–professionals partnership is through the use of a home-based record, which provide frontline health practitioners with a comprehensive standardized patient health history, necessary to make informed decisions [<xref rid=\"B2-ijerph-17-05520\" ref-type=\"bibr\">2</xref>].</p><p>Child-related home-based record have been used in various countries including Australia [<xref rid=\"B11-ijerph-17-05520\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05520\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijerph-17-05520\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijerph-17-05520\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijerph-17-05520\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijerph-17-05520\" ref-type=\"bibr\">16</xref>]. These records which serve various critical roles such as keeping up to date with immunization [<xref rid=\"B2-ijerph-17-05520\" ref-type=\"bibr\">2</xref>], continuity of care [<xref rid=\"B17-ijerph-17-05520\" ref-type=\"bibr\">17</xref>], safe pregnancy practice/delivery by skilled birth attendant and knowledge of child healthcare [<xref rid=\"B18-ijerph-17-05520\" ref-type=\"bibr\">18</xref>]. In our previous review [<xref rid=\"B19-ijerph-17-05520\" ref-type=\"bibr\">19</xref>], which informed this study, we examined usefulness of the record on child health outcomes, parent knowledge, and documentation of maternal and child health data. The review identified that a vast majority of parents (average 72%) value the child health home-based record and over 60% were satisfied with using the record, yet some parents (average 40%) reported an unsupportive attitude of health professionals toward use of the record [<xref rid=\"B19-ijerph-17-05520\" ref-type=\"bibr\">19</xref>]. To our knowledge, no study has specifically assessed the association of these variables (satisfaction and perceived attitude) with parent engagement with the record book. This study therefore aims to examine parent views of health professionals and satisfaction toward use of child health home-based record and the influence on parent engagement with the record book.</p><sec><title>Hypothesis</title><p>As noted above, the hypothesis tested in this study is explicitly based on the findings of our systematic review study [<xref rid=\"B19-ijerph-17-05520\" ref-type=\"bibr\">19</xref>].\n<disp-quote><p>Parent engagement with a child health home-based record is associated with their perception of health professional commitment and their satisfaction toward use of the record.</p></disp-quote></p><p>The significance of testing this hypothesis is that it will inform policies regarding improvement of parent engagement with a child health home-based record. As the New South Wales (NSW) child health home-based record receives strong endorsement and promotion by government to emphasis its important role in child health, exploring voices of parents will inform local policies to enhance parent experiences of utilizing child health home-based record. Similarly, considering WHO plans to update of their guidelines for home-based records by 2023 [<xref rid=\"B1-ijerph-17-05520\" ref-type=\"bibr\">1</xref>], the outcome of this study will provide insight toward improving utilization of home-based records by both health professionals and parents/care givers.</p></sec></sec><sec id=\"sec2-ijerph-17-05520\"><title>2. Method</title><sec id=\"sec2dot1-ijerph-17-05520\"><title>2.1. Study Design, Population and Instrument</title><p>A community-based cross-sectional survey of parents was conducted in NSW Australia. Parents of children 0–5 years of age as at 2019 were recruited across NSW. The instrument for data collection was a semi-structured questionnaire generated from our systematic review [<xref rid=\"B19-ijerph-17-05520\" ref-type=\"bibr\">19</xref>]. It consists of a section on sociodemographic characteristics, parent personal experiences of using the record book, parent experiences with health professionals, and parent satisfaction with the record book.</p><p>This study was approved by Macquarie University Human Research Ethics Committee with a reference no. 5201933826834.</p></sec><sec id=\"sec2dot2-ijerph-17-05520\"><title>2.2. Study Sample Size</title><p>Sample size was estimated using GPower software (Heinrich Heine University, Westphalia, Germany) [<xref rid=\"B20-ijerph-17-05520\" ref-type=\"bibr\">20</xref>]. In line with the finding of a similar study that examined predictors of children’s guardians’ engagement with a home-based record [<xref rid=\"B21-ijerph-17-05520\" ref-type=\"bibr\">21</xref>], to estimate an odds ratio of 1.9, a minimum sample of 129 participants was required to generate a power of 80% at an alpha value of 0.05. The minimum required sample was exceeded with a total of 202 parent–child pairs recruited for this study.</p></sec><sec id=\"sec2dot3-ijerph-17-05520\"><title>2.3. Data Collection</title><p>Participants were approached through childcare centers and online family websites within NSW/Australia. Only parents who read and signed the participant information and consent form were included in the study. The survey instrument was then administered to parents who voluntarily agreed to participate in the study. Participants were given the option of completing an online or hard-copy version of the survey.</p><p>Responses for all engagement and perceived professionals’ attitude questions were on a four-point Likert scale (always, usually, sometimes, never). Parents were asked to rate different aspects of engagement with the record book and commitment of various health professionals based on their personal experiences of perceived willingness of health professionals to use/refer to the record during consultations and to fill-in appropriate information. Satisfaction was assessed using domains namely: ease of reading, ease of locating information, ease of understanding the words, organization of contents, and sturdiness of the child health home-based record, each using a five-point Likert scale (not satisfied, somewhat satisfied, moderately satisfied, very satisfied, excellently satisfied).</p></sec><sec id=\"sec2dot4-ijerph-17-05520\"><title>2.4. Definition of Variables</title><p>Engagement with a child health home-based record (dependent variable) was defined in terms of frequency parents indicated to have taken the record book to routine child clinic visits, write/record information in the record book, and read child data/other relevant information in the record book. The predictor (independent) variables include parent perceived attitude of health professionals (general practitioners (GPs), pediatricians, nurses/midwives, other professionals) and their satisfaction with the home-based record based on the five domain areas (ease of reading, ease of locating information, ease of understanding the words, organization of contents and sturdiness). Average satisfaction from the five domain areas was computed to identify overall satisfaction with the record book. For the purpose of regression analysis, parents were considered ‘regularly’ engaging with the home-based record if they indicated they ‘always or usually’ take the record book for routine checks, read or write in the book. Engagement with the book is considered ‘not regular’ if parents indicated ‘sometimes or never’ take the book for routine checks, read or write in the book. Furthermore, parent perceived attitude of professionals was considered ‘positive’ if parents indicated professionals ‘always or usually’ used the record book. It is considered ‘negative’ if parents indicated professionals’ attitude as ‘sometimes or never’ used the book. Similarly, parents were considered ‘highly satisfied’ if they indicated ‘excellently or very satisfied’, while it was considered ‘poorly satisfied’ if they indicated ‘moderately or somewhat satisfied’.</p></sec><sec id=\"sec2dot5-ijerph-17-05520\"><title>2.5. Data Analysis</title><p>Data collected was coded and analyzed using SPSS statistical package (IBM Corp. IBM SPSS Statistics for Macintosh, Version 25.0. Armonk, NY, USA). Descriptive statistics using proportion, mean and standard deviation were used to summarize variables for sociodemographic characteristics and parent experiences of using the record book. Spearman rho (r) correlation coefficient was used to determine the strength of association between variables of parent engagement with the record book with variables of perceived attitude of health professionals and parent satisfaction. Bivariate logistic regression was conducted to examine the influence of individual variables on parent engagement with the home-based record. Multivariate logistic regression analysis was then performed by constructing three models, each to explore interrelated predictors affecting individual parent engagement with the record book (take the record book for routine checks, read or write in the record book). Crude odds ratio (COR) and adjusted odds ratio (AOR) were respectively used as the effect-size measures in the bivariate and multivariate analyses. A <italic>p</italic>-value of 0.05 or less was considered significant at 95% confidence interval (CI).</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05520\"><title>3. Results</title><sec id=\"sec3dot1-ijerph-17-05520\"><title>3.1. Parents Characteristics</title><p>Of the 202 participants, the majority (89.9%) were females with an average age of 35.6 years. Youngest child age at the time of data collection ranged from 3 to 65 months with an average of 26.1 months. About 70% of the parents had at least a university degree, while others education was classified as ‘non-degree’ ranging from less than year 9 to holding a post-secondary qualification. The majority of parents (77.2%) identified their first language as English (<xref rid=\"ijerph-17-05520-t001\" ref-type=\"table\">Table 1</xref>).</p></sec><sec id=\"sec3dot2-ijerph-17-05520\"><title>3.2. Parent Engagement, Perception of Professionals, and Satisfaction with the Home-Based Record</title><p>As can be seen in <xref rid=\"ijerph-17-05520-t002\" ref-type=\"table\">Table 2</xref>, 63.4% of the parents always or usually take the child health home-based record for routine child checks compared to 37.2% that read or 40.1% that write information in the record book. A total of 33.7% of parents perceived GPs as always or usually use/refer to the record in comparison to pediatricians (34.1%) or nurses/midwives (59.4%). Similarly, more nurses 68.3% were perceived as always or usually recording information during routine checks compared to GPs during consultations (24.3%), pediatricians at specialist visit (28.3%) or doctors at emergency contacts (11.4%). Parents were extremely or very satisfied with ease of reading (47.6%), ease of locating information (42.1%), ease of understanding words (68.9%), ease of organization of contents (44.6%) and sturdiness of the record book (44.6%).</p></sec><sec id=\"sec3dot3-ijerph-17-05520\"><title>3.3. Correlation between Variables</title><p>Correlations between variables of parent engagement with perceived attitude of professionals and satisfaction are reported in <xref rid=\"ijerph-17-05520-t003\" ref-type=\"table\">Table 3</xref> and <xref rid=\"ijerph-17-05520-t004\" ref-type=\"table\">Table 4</xref> respectively. Significant positive correlations exist between taking the record for a routine check with parent perceived attitude of GPs or nurses to refer to the book and nurses to record information in the book. Reading the record book was significantly correlated with perceived attitude of GPs and pediatricians to refer to the record book or record information at consultations. Writing information in the record book was significantly associated with all the variables of perceived attitude except for perception of other professionals to refer to the record (<italic>p</italic> = 0.432) or doctors to record information at emergency contacts (<italic>p</italic> = 0.434). Similarly, taking the record book for routine checks was significantly correlated with satisfaction in ease of reading and organization of contents. Reading and writing information in the record was positively correlated with all variables of satisfaction except for satisfaction with sturdiness of the record book (<italic>p</italic> = 0.213). Overall satisfaction with the home-based record was significantly correlated with writing information in the record book but not taking for routine checks (<italic>p</italic> = 0.062) or reading information from the book (<italic>p</italic> = 0.051).</p></sec><sec id=\"sec3dot4-ijerph-17-05520\"><title>3.4. Regression Analysis</title><p><xref rid=\"ijerph-17-05520-t005\" ref-type=\"table\">Table 5</xref> shows outcome of logistic regression analysis, indicating association of parent engagement with the child health home-based record at bivariate and multivariate levels. Taking the record book for routine checks was significantly influenced by parents’ perceived attitude of nurses at both bivariate (COR = 0.723, 95% CI 0.544–0.961) and multivariate (AOR = 0.728, 95% CI 0.536–0.989) levels. This indicates that parents who perceived negative attitude of nurses to use/refer to the record book were less likely to take the book for routine child checks compared to parents who perceived positive nurses’ attitude. Similarly, reading information in the record book was significantly influenced by parents’ perceived attitude of GPs with both bivariate (COA = 0.635, 95% CI 0.440–0.918) and multivariate (AOR = 0.636, 95% CI 0.429–0.942) analyses. This implies that compared to parents who perceived positive attitude of GPs to use/refer to the record book, parents who perceived negative attitude of the GPs to use/refer to the record book were less likely to read what is written in the book. Writing information in the record book was significantly influenced by parent perceived attitude of GPs (COR = 0.542, 95% CI 0.373–0.786), pediatricians (COA = 0.719, 95% CI 0.564–0.916), and nurses (COR = 0.648, 95% CI 0.471–0.893) at the bivariate analysis. In the multivariate analysis, perceived attitude of all professionals significantly influenced parent to write information in the record book. Accordingly, compared to parents who were highly satisfied with the home-based record, parents who were poorly satisfied were less likely to use the home-based record (AOR = 0.717, 95% CI 0.517–0.993) by recording child health information.</p></sec><sec id=\"sec3dot5-ijerph-17-05520\"><title>3.5. Recommendation for Use of Child Health Home-Based Record</title><p>Of the participants, 115 (56.9%) recommended ways of improving utilization of child health home-based record using an open-ended question; ‘please recommend any way(s) that you think the blue book (child health home-based record) or its usage could be improved’. Many of the parents (43%) opined that it should be made electronic, which could be accessed online (as part of Australia’s My Health Record) or via a mobile app. More than a third of parents (39%) recommended re-designing the record book by reducing its bulkiness, adding more pages for unhealthy children, adding more rooms for immunization, adding more than basic information, expand eye and hearing test, remove binder and use rings, add “do’s” and “don’ts” for parents. Similarly, 14% of parents recommended encouraging health professionals using phrases such as; ‘make it mandatory for GPs’, ‘professionals ask for it’, ‘must be use by professionals particularly GPs and community nurses’ and ‘if doctors show interest’.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05520\"><title>4. Discussion</title><p>This study specifically investigated the views of parents with regards to health professional behaviour and personal satisfaction, and how both factors influence parent engagement with a child health home-based record. We previously proposed association between parent engagement with the child health home-based record and early child development or first-born status [<xref rid=\"B22-ijerph-17-05520\" ref-type=\"bibr\">22</xref>]. In a similar manner, the finding of this study indicates a link between parent perceived attitude of health professionals and parent engagement with the record book. Despite the widespread use of various forms of home-based records, the WHO universal endorsement of the records in 2018 noted the need for further research to close the knowledge gaps to achieve its ultimate goal of promoting maternal, infant and child health. With health professionals being at the forefront of achieving this goal, the findings of this study will contribute toward emphasizing the need for frontline health professionals’ commitment in the update of the WHO guidelines by 2023.</p><p>Of the three domains of parent engagement with the home-based record assessed, parents regularly took the record book for routine child health checks (63.4%) compared to reading (37.2%) or writing (40.1%) information in the book. This may not be unconnected with the fact that one of the key rationales of providing parents with a home-based record is to take it along for all health contacts [<xref rid=\"B3-ijerph-17-05520\" ref-type=\"bibr\">3</xref>,<xref rid=\"B14-ijerph-17-05520\" ref-type=\"bibr\">14</xref>,<xref rid=\"B23-ijerph-17-05520\" ref-type=\"bibr\">23</xref>], which enables continuity of care [<xref rid=\"B17-ijerph-17-05520\" ref-type=\"bibr\">17</xref>] and improved communication between parents and professionals [<xref rid=\"B24-ijerph-17-05520\" ref-type=\"bibr\">24</xref>]. Similarly, parents view nurses as the most likely professionals to use/refer to the record book during consultations in comparison to GPs and pediatricians, among others. This finding is in line with previous studies on home-based records [<xref rid=\"B14-ijerph-17-05520\" ref-type=\"bibr\">14</xref>,<xref rid=\"B17-ijerph-17-05520\" ref-type=\"bibr\">17</xref>,<xref rid=\"B23-ijerph-17-05520\" ref-type=\"bibr\">23</xref>,<xref rid=\"B25-ijerph-17-05520\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijerph-17-05520\" ref-type=\"bibr\">26</xref>], of which pediatricians [<xref rid=\"B23-ijerph-17-05520\" ref-type=\"bibr\">23</xref>] or GPs [<xref rid=\"B14-ijerph-17-05520\" ref-type=\"bibr\">14</xref>,<xref rid=\"B26-ijerph-17-05520\" ref-type=\"bibr\">26</xref>] were reported as least likely to use the record book. However, one study conducted in United Kingdom reported higher proportion of GPs who regularly use the record book compared to children nurses and pediatricians [<xref rid=\"B23-ijerph-17-05520\" ref-type=\"bibr\">23</xref>]. Overall, the findings of this study support the results of our previous systematic review indicating unsupportive attitude of health professionals despite parent’s commitment to use of the record as well as its positive health impact such as immunization uptake, antenatal care, and practice of breast feeding [<xref rid=\"B19-ijerph-17-05520\" ref-type=\"bibr\">19</xref>]</p><p>The positive association found in this study between taking the record book for routine health checks and perceived attitude of nurses to fill-in appropriate information in the book could be explained considering the guideline of the record book in NSW. In line with the recommendation of the NSW child health home-based record (known as blue book), family health nurses, GPs, or pediatricians are charged with the responsibility of conducting regular child health and development checks from birth to 4 years [<xref rid=\"B27-ijerph-17-05520\" ref-type=\"bibr\">27</xref>]. This therefore enable nurses, particularly community-based nurses, to commit to recording information in the record book at routine checks. A previous study of the NSW child health home-based record [<xref rid=\"B16-ijerph-17-05520\" ref-type=\"bibr\">16</xref>] also reported more than three-fourth of parents who regularly take the record book for schedule checks and indicated nurses as most likely to make entries. Furthermore, another study in NSW [<xref rid=\"B14-ijerph-17-05520\" ref-type=\"bibr\">14</xref>] identified that parents who regularly take the record book for routine checks were more likely to take it for GP consultations, despite less commitment of most GPs to record the information. Various factors could be associated with GPs/pediatricians lower commitment to use of the home-based record such as lack of time/workload [<xref rid=\"B28-ijerph-17-05520\" ref-type=\"bibr\">28</xref>], which is beyond the scope of this study.</p><p>Although less than half of the parents regularly write information in the record book, the writing habit was significantly associated with both variables of perceived professional’s attitude and satisfaction. While the outcome of this study cannot precisely establish the connection between these variables, it is likely to be influenced by parent perceived approval of appropriate parenting by professionals. On the other hand, reading habit as an element of parent engagement with the home-based record was found lowest in this study, of which only 37% of parents regularly read child-related data or other information in the book. This may be associated with parents’ previous experience of using the record book, of which apart from first time parents, other parents might be familiar with the content of the record book from their previous child and hence likely to affects their reading habit. Another finding of this study indicated that parent’s commitment to read the record book is significantly associated with perceived attitude of GPs to use or refer to the record during consultations. This indicates that parents who experience their GPs/family physicians recording information or referring to the record during consultations are more likely to read what is written in the book. This finding is in contrast with other studies that indicated more than three-quarters of parents who regularly read the home-based record to find important information [<xref rid=\"B16-ijerph-17-05520\" ref-type=\"bibr\">16</xref>] or read the developmental section before a schedule visit [<xref rid=\"B29-ijerph-17-05520\" ref-type=\"bibr\">29</xref>]. In a previous study in NSW [<xref rid=\"B14-ijerph-17-05520\" ref-type=\"bibr\">14</xref>], 77% of parents reported GPs willingness to use/refer to the record at consultations, although it was not reported if this was associated with parents reading habit. On the other hand, other professionals including physiotherapist, were viewed as least likely to use the record based on the findings of this study. This could be explained by the fact that other professionals do not have a pre-determine requirement to fill in information about the child, but rather as the child condition demands. Moreover, no association exists between parent engagement with the record book (in-terms of taking for regular checks and reading) and perceived attitude of other health professionals. This finding is in line with a previous study [<xref rid=\"B14-ijerph-17-05520\" ref-type=\"bibr\">14</xref>], which found other health professionals as least likely to use the home-based record compared to nurses and GPs.</p><p>Satisfaction with the record book is generally higher among parents according to the finding of this study. Satisfaction was highest in-terms of reading and ease of understanding words but lowest with ease of locating information in the record book (<xref rid=\"ijerph-17-05520-t002\" ref-type=\"table\">Table 2</xref>). Ease of reading/understanding could be associated with the fact that the NSW home-based record was made in various languages to aid understanding [<xref rid=\"B27-ijerph-17-05520\" ref-type=\"bibr\">27</xref>]. However, locating information could be difficult due to the volume of the booklet and hence, the need for it to be re-organize. In contrast to a similar study in United State of America [<xref rid=\"B30-ijerph-17-05520\" ref-type=\"bibr\">30</xref>], satisfaction with the home-based record was highest in reading and sturdiness of the record but lowest with ease of understanding words. Accordingly, parent recommendations on use of the home-based record indicated that majority of the parents were of the opinion that the record book should be made electronic. This could be associated with parent poor satisfaction toward locating information in the hardcopy book, of which a digital version would make it easier to scroll across the book. Similarly, parent desire for a digital home-based record could be associated with global advancement in information and communication technology in healthcare. Electronic health record is not only important in terms of ease of use for both professionals and service users [<xref rid=\"B31-ijerph-17-05520\" ref-type=\"bibr\">31</xref>], but improvement in patient care though provision of real-time data [<xref rid=\"B32-ijerph-17-05520\" ref-type=\"bibr\">32</xref>], completeness, and reliability of data [<xref rid=\"B31-ijerph-17-05520\" ref-type=\"bibr\">31</xref>] as well as research/epidemiological studies [<xref rid=\"B33-ijerph-17-05520\" ref-type=\"bibr\">33</xref>].</p><p>This study’s strength was in providing important evidence about parent engagement with a child health home-based record in NSW since its major update in 2013. Similarly, the study has a strength of testing its hypothesis based on findings of a published systematic review as well as its specific focus on parent views of health professionals. Despite the strengths, caution should be taking in interpreting the findings due its limitations. The cross-sectional nature of the study makes it impossible to establish a causality between parent engagement with the home-based record and perceived professionals’ attitude or satisfaction, but rather a possible association between the variables. Similarly, parent engagement may be less than reported due to a possibility of social bias in reporting. Furthermore, the participants were recruited using a non-probability sampling technique, most of which were around Sydney and not randomly distributed across NSW. Additionally, the majority of parents recruited through childcare centers were centers associated with universities, which could explain the high percentage of parents having a university degree. Therefore, findings may not be generalizable to Australian population.</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05520\"><title>5. Conclusions</title><p>Parent engagement with a child health home-based record was positive, particularly with respect to taking the record book for routine child checks. Perceived attitude of health professionals (positive or negative) was found to significantly influence parent engagement with a home-based record. GPs and nurses being frontline health professionals that look after children can influence parents to read what is recorded and take the record book to the point of service, respectively. Commitment of all health professionals and satisfaction toward use of the home-based record can significantly affect parent’s willingness to use the record book by writing appropriate child health information. The overall outcome of this study answered our hypothesis that parent-perceived attitude of health professionals and satisfaction is associated with parent engagement with the record book. Therefore, to improve parent engagement with a child health home-based record and achieve its goal of child health promotion, health professionals (particularly GPs) must be committed to use or refer to the record book during child consultations/ regular checks. Further studies could investigate health professionals-perceived barriers and motivation toward use of the home-based record.</p></sec></body><back><ack><title>Acknowledgments</title><p>This research is part of a Doctor of Philosophy (PhD) study of author M.C.</p></ack><notes><title>Author Contributions</title><p>Authors M.C. and S.W. conceptualized the study. Author M.C. developed the study instrument, collected the data, conducted the analysis, and developed the first draft of the manuscript. Authors S.W. and J.A. validated the analysis, supervised the conduct of the study, reviewed and edited the manuscript. 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Care</source><year>2013</year><volume>51</volume><fpage>S80</fpage><lpage>S86</lpage><pub-id pub-id-type=\"doi\">10.1097/MLR.0b013e31829b1d48</pub-id><pub-id pub-id-type=\"pmid\">23774512</pub-id></element-citation></ref></ref-list></back><floats-group><table-wrap id=\"ijerph-17-05520-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05520-t001_Table 1</object-id><label>Table 1</label><caption><p>Demographic characteristics of respondents.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Variables</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Response</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Parent Age</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35.6 ± 5.66</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">198</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Index child age (months)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">26.1 ± 17.33</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">194</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Parent Gender</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">199</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Females</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">179(89.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Male</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20(10.1%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Educational level</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">200</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">University degree or higher</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">139(69.5%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Non-degree </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">61(30.5%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">First language</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">202</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">English</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">156(77.2%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Others</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">46(22.8%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05520-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05520-t002_Table 2</object-id><label>Table 2</label><caption><p>Number (percentage) of reported utilization, perception and satisfaction (<italic>N</italic> = 202).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" colspan=\"1\">Variables</th><th colspan=\"6\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Frequency (%)</th></tr><tr><th align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Always</th><th align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Usually</th><th align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sometime</th><th align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Never</th><th colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">N/A</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">Parent engagement with record</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Take for routine check</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">84(41.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">44(21.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35(17.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">36(17.8)</td><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">3(1.5)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Read</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29(14.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">46(22.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">78(38.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45(22.3)</td><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">4(2.0)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Write Information</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">41(20.3)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">40(19.8)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">70(34.7)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">48(23.8)</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">3(1.5)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Parent perceived attitude</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GPs to use/refer</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27(13.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">41(20.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">79(39.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40(19.8)</td><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">15(7.5)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Pediatrician to use/refer</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31(15.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38(18.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">43(21.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24(11.9)</td><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">66(32.7)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Nurses/midwives to use/refer</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">69(34.2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">51(25.2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32(15.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16(7.9)</td><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">34(16.8)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Other professionals to use/refer</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5(2.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11(5.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26(12.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">62(30.7)</td><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">98(48.5)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Nurses to record information at routine checks</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">107(53)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31(15.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28(13.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10(5)</td><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">26(12.9)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GPs to record information at consultations</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19(9.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30(14.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90(44.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">49(24.3)</td><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">14(7)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Pediatricians to record information at specialist visit</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27(13.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30(14.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">49(24.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33(16.3)</td><td colspan=\"2\" align=\"center\" valign=\"middle\" rowspan=\"1\">63(31.2)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Doctors to record information at emergency</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9(4.5)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14(6.9)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">21(10.4)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">77(38.1)</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">81(40.1)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Satisfaction</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Extreme</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Very</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Moderate</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Somewhat</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>No</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>N/A</bold>\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ease of reading</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30(14.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">66(32.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">63(31.2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24(11.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6(3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13(6.4)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ease of locating information</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">25(12.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60(29.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">57(28.2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34(16.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12(5.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14(6.9)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ease of understanding words</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">49(24.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">90(44.6)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38(18.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10(5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2(1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13(6.4)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Organization of contents</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29(14.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">61(30.2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">63(31.2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27(13.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7(3.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15(7.4)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sturdiness of the book</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30(14.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60(29.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">48(23.8)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35(17.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16(7.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13(6.4)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Overall satisfaction</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">26(12.9)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">65(32.2)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">72(35.6)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">22(10.9)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4(2)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13(6.4)</td></tr></tbody></table><table-wrap-foot><fn><p>N/A—not available/not applicable, GPs—general practitioners.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05520-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05520-t003_Table 3</object-id><label>Table 3</label><caption><p>Correlation between parent engagement with record book and perceived attitude.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Perceived Attitude</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Taking Record Book for Routine Checks</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reading Record Book</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Writing Information in Record Book</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GPs to use/refer</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.169 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.229 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.273 ***</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Pediatrician to use/refer</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.086</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.150 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.261 ***</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Nurses/midwives to use/refer</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.289 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.116</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.221 **</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Other professionals to use/refer</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.034</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.052</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.058</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Nurses to record information at routine checks</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.320 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.109</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.241 ***</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GPs to record information at consultations</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.109</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.262 ***</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.230 ***</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Pediatricians to record information at specialist visit</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.139</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.221 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.321 ***</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Doctors to record information at emergency</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.032</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.066</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.057</td></tr></tbody></table><table-wrap-foot><fn><p>* <italic>p</italic> ≤ 0.05, ** <italic>p</italic> ≤ 0.01, *** <italic>p</italic> ≤ 0.001.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05520-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05520-t004_Table 4</object-id><label>Table 4</label><caption><p>Correlation between parent engagement with record book and satisfaction.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Satisfaction</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Taking Record Book for Routine Checks</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reading Record Book</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Writing Information in Record Book</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ease of reading</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.173 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.217 **</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.198 **</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ease of locating information</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.121</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.159 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.154 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ease of understanding words</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.092</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.164 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.168 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Organization of contents</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.145 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.184 *</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.157 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sturdiness of the book</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.068</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.092</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.056</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Overall satisfaction</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.137</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.143</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.168 *</td></tr></tbody></table><table-wrap-foot><fn><p>* <italic>p</italic> ≤ 0.05, ** <italic>p</italic> ≤ 0.01.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05520-t005\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05520-t005_Table 5</object-id><label>Table 5</label><caption><p>Logistic regression examining predictors of parent engagement with child health home-based record.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Variables</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Categories</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Taking Record Book for Routine Checks</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Reading Record Book</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Writing Information in Record Book</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Crude OR (95% CI)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Adjusted OR (95% CI)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Crude OR (95% CI)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Adjusted OR (95% CI)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Crude OR (95% CI)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Adjusted OR (95% CI)</th></tr></thead><tbody><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Perceived attitude of GPs</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Negative</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.749 (0.530–1.059)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.760 (0.526–1.097)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.635 (0.440–0.918) *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.636 (0.429–0.942) *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.542 (0.373–0.786) ***</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.597 (0.399–0.896) **</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Positive</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Perceived attitude of Pediatricians</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Negative</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.034 (0.814–1.314)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.023 (0.776–1.348)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.790 (0.619–1.009)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.796 (0.606–1.045)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.719 (0.564–0.916) **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.637 (0.478–0.850) **</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Positive</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Perceived attitude of Nurses/midwives</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Negative</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.723 (0.544–0.961) *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.728 (0.536–0.989) *</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.946 (0.708–1.263)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.053 (0.772–1.437)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.648 (0.471–0.893) **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.681 (0.485–0.956) *</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Positive</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Perceived attitude of other professionals</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Negative</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.136 (0.835–1.546)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.189 (0.842–1.681)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.762 (0.560–1.038)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.827 (0.591–1.157)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.266 (0.924–1.735)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.622 (1.117–2.356) **</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Positive</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Overall satisfaction</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Poorly satisfied</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.797 (0.594–1.071)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.778 (0.569–1.065)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.869 (0.650–1.161)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.858 (0.629–1.170)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.821 (0.616–1.094)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.717 (0.517–0.993) *</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Highly satisfied</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference</td></tr></tbody></table><table-wrap-foot><fn><p>OR odds ratio, CI confidence interval, * <italic>p</italic> ≤ 0.05, ** <italic>p</italic> ≤ 0.01, *** <italic>p</italic> ≤ 0.001.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32722514</article-id><article-id pub-id-type=\"pmc\">PMC7432041</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155290</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05290</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>TETRALEC, Artificial Tetrameric Lectins: A Tool to Screen Ligand and Pathogen Interactions</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Achilli</surname><given-names>Silvia</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05290\">1</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05290\">†</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-9855-7485</contrib-id><name><surname>Monteiro</surname><given-names>João T.</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05290\">2</xref><xref ref-type=\"author-notes\" rid=\"fn2-ijms-21-05290\">‡</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-2085-4412</contrib-id><name><surname>Serna</surname><given-names>Sonia</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05290\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Mayer-Lambertz</surname><given-names>Sabine</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05290\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Thépaut</surname><given-names>Michel</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05290\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Le Roy</surname><given-names>Aline</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05290\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Ebel</surname><given-names>Christine</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05290\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Reichardt</surname><given-names>Niels-Christian</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05290\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Lepenies</surname><given-names>Bernd</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05290\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-1194-8107</contrib-id><name><surname>Fieschi</surname><given-names>Franck</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05290\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Vivès</surname><given-names>Corinne</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05290\">1</xref><xref rid=\"c1-ijms-21-05290\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05290\"><label>1</label>Institut de Biologie Structurale, CEA, CNRS, University of Grenoble Alpes, F-38000 Grenoble, France; <email>silviaachilli20@gmail.com</email> (S.A.); <email>michel.thepaut@ibs.fr</email> (M.T.); <email>aline.le-roy@ibs.fr</email> (A.L.R.); <email>christine.ebel@ibs.fr</email> (C.E.); <email>franck.fieschi@ibs.fr</email> (F.F.)</aff><aff id=\"af2-ijms-21-05290\"><label>2</label>Immunology Unit & Research Center for Emerging Infections and Zoonoses (RIZ), University of Veterinary Medicine Hannover, 30559 Hannover, Germany; <email>terenomonteiro.joao@mh-hannover.de</email> (J.T.M.); <email>sabine-mayer1@gmx.de</email> (S.M.-L.); <email>Bernd.Lepenies@tiho-hannover.de</email> (B.L.)</aff><aff id=\"af3-ijms-21-05290\"><label>3</label>Glycotechnology Laboratory, Center for Cooperative Research in Biomaterials (CIC biomaGUNE), Basque Research and Technology Alliance (BRTA), CIBER-BBN, Paseo Miramón 182, 20014 San Sebastian, Spain; <email>sserna@cicbiomagune.es</email> (S.S.); <email>nreichardt@cicbiomagune.es</email> (N.-C.R.)</aff><author-notes><corresp id=\"c1-ijms-21-05290\"><label>*</label>Correspondence: <email>corinne.deniaud@ibs.fr</email>; Tel.: +33-457428541</corresp><fn id=\"fn1-ijms-21-05290\"><label>†</label><p>Present address: Département de Chimie Moléculaire, UMR CNRS 5250, Université Grenoble-Alpes, BP 53, 38041 Grenoble, France.</p></fn><fn id=\"fn2-ijms-21-05290\"><label>‡</label><p>Present address: Department of Pediatric Pneumology, Allergology and Neonatology and Cluster of Excellence RESIST (EXC 2155), Hannover Medical School, 30625 Hannover, Germany.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>25</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5290</elocation-id><history><date date-type=\"received\"><day>03</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>23</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>C-type lectin receptor (CLR)/carbohydrate recognition occurs through low affinity interactions. Nature compensates that weakness by multivalent display of the lectin carbohydrate recognition domain (CRD) at the cell surface. Mimicking these low affinity interactions in vitro is essential to better understand CLR/glycan interactions. Here, we present a strategy to create a generic construct with a tetrameric presentation of the CRD for any CLR, termed TETRALEC. We applied our strategy to a naturally occurring tetrameric CRD, DC-SIGNR, and compared the TETRALEC ligand binding capacity by synthetic N- and O-glycans microarray using three different DC-SIGNR constructs i) its natural tetrameric counterpart, ii) the monomeric CRD and iii) a dimeric Fc-CRD fusion. DC-SIGNR TETRALEC construct showed a similar binding profile to that of its natural tetrameric counterpart. However, differences observed in recognition of low affinity ligands underlined the importance of the CRD spatial arrangement. Moreover, we further extended the applications of DC-SIGNR TETRALEC to evaluate CLR/pathogens interactions. This construct was able to recognize heat-killed <italic>Candida albicans</italic> by flow cytometry and confocal microscopy, a so far unreported specificity of DC-SIGNR. In summary, the newly developed DC-SIGNR TETRALEC tool proved to be useful to unravel novel CLR/glycan interactions, an approach which could be applied to other CLRs.</p></abstract><kwd-group><kwd>C-type lectin</kwd><kwd>DC-SIGNR</kwd><kwd>glycan array</kwd><kwd>multivalency</kwd><kwd>pathogen recognition</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05290\"><title>1. Introduction</title><p>Glycans play critical roles in many biological processes ranging from the maintenance of cell or tissue structure, molecular signal transduction, to cell recognition. The mechanisms by which they perform these diverse functions involve the interaction of the glycan with other endogenous or exogenous molecules. For instance, many cell-cell interactions are carbohydrate driven [<xref rid=\"B1-ijms-21-05290\" ref-type=\"bibr\">1</xref>]. Detection of pathogens such as viruses, fungi and bacteria is mediated by the recognition of glycans expressed on the microorganism surface. Indeed, the human immune system possesses pattern recognition receptors (PRRs), expressed on dendritic cells, which are able to recognize molecular motifs and activate immunity [<xref rid=\"B2-ijms-21-05290\" ref-type=\"bibr\">2</xref>]. Amongst those receptors, C-type lectin receptors (CLRs) are carbohydrate-binding proteins that are specifically involved in the recognition and the uptake of altered-self and non-self glycans through their Ca<sup>2+</sup> dependent carbohydrate recognition domain (CRD) [<xref rid=\"B3-ijms-21-05290\" ref-type=\"bibr\">3</xref>]. The crucial roles of CLRs in the balance of immunity make CLR-glycan interactions attractive for pharmaceutical interventions [<xref rid=\"B4-ijms-21-05290\" ref-type=\"bibr\">4</xref>].</p><p>Human CLRs are generally characterized by a low affinity for their glycan partners. The single interaction between the protein and the isolated monosaccharide or small oligosaccharide is usually of low affinity, in the millimolar range [<xref rid=\"B5-ijms-21-05290\" ref-type=\"bibr\">5</xref>]. This apparent drawback results from the necessity to recognize a set of different ligands which include a common recognition motif. The globular structure of the CRD, in fact, does not contain any cavity, therefore the recognition of carbohydrates occurs through a largely open binding site, centered on the Ca<sup>2+</sup>, limiting in many cases the level of selectivity achieved.</p><p>Nature compensates low affinity with the concerted force of multiple binding events. The accumulation of weak affinity forces leads to an apparent strong interaction, an effect called avidity [<xref rid=\"B6-ijms-21-05290\" ref-type=\"bibr\">6</xref>]. Multivalent binding plays a crucial role in the cell-surface recognition and could be reached by different means. On the one hand, oligomerization can be achieved on the CLR side by either clustering of single CLR into membrane micro domains, multiple CRDs along a single polypeptide chain [<xref rid=\"B7-ijms-21-05290\" ref-type=\"bibr\">7</xref>] or through oligomerization of CRD-containing receptors [<xref rid=\"B8-ijms-21-05290\" ref-type=\"bibr\">8</xref>]. In some cases an oligomerization domain, termed neck, serves as a stalk to bundle several CRDs and project them from the cell membrane [<xref rid=\"B9-ijms-21-05290\" ref-type=\"bibr\">9</xref>].</p><p>On the other hand, multivalent ligands also participate in the high-avidity binding and contribute to the “glycan cluster effect” that occurs when multivalency is achieved for both protein and glycan presentation. The interaction of DC-SIGN, a CLR expressed on dendritic cells (DCs), with its ligands remarkably illustrates this multivalency enhancement. Firstly, DC-SIGN is expressed on cell surfaces in a tetrameric form and is further clustered into microdomains in lipid rafts, leading to a dense presentation of the binding sites at the cell surface [<xref rid=\"B10-ijms-21-05290\" ref-type=\"bibr\">10</xref>]. Secondly, surface glycans of the pathogens recognized by DC-SIGN, as for instance the high mannose type glycans of HIV envelope glycoprotein gp120, are also presented with an unusual density [<xref rid=\"B11-ijms-21-05290\" ref-type=\"bibr\">11</xref>].</p><p>In vitro assays performed to study CLR/glycan interactions have to be technically adapted to mimic this multivalency, otherwise they would fail to reveal low affinity monovalent binding events. At the ligand level, one way to increase valency is to present artificial multivalent ligands. Those would include both low valency compounds, such as short polymers, glycoclusters or peptide conjugates and high valency compounds, such as dendrimers, liposomes or nanoparticles [<xref rid=\"B12-ijms-21-05290\" ref-type=\"bibr\">12</xref>]. Another platform for the presentation of glycans with adjustable density for biological sample screening are glycan arrays, which consist of libraries of immobilized glycan structures spatially organized on a <xref ref-type=\"app\" rid=\"app1-ijms-21-05290\">Supporting Material</xref>. On the protein side, lectin arrays [<xref rid=\"B13-ijms-21-05290\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijms-21-05290\" ref-type=\"bibr\">14</xref>], that consist in spatially addressable collections of immobilized lectin probes with known binding specificity, and surface plasmon resonance based assays, that employ lectin functionalized sensorchips [<xref rid=\"B15-ijms-21-05290\" ref-type=\"bibr\">15</xref>], provide a multivalent lectin presentation for the study of interactions between glycoconjugates and lectins immobilized on a surface. In solution studies of protein/glycan molecular interactions can provide complementary information to surface based assays. However, when the natural protein only displays monomeric CRD, the artifices mentioned above to build up protein multivalency have to be replaced by the creation of soluble artificial multimeric lectins. One strategy commonly used is the creation of Fc-constructs. This approach based on the fusion of a CRD domain with an immunoglobin Fc domain enables dimerization [<xref rid=\"B16-ijms-21-05290\" ref-type=\"bibr\">16</xref>] of the glycan binding site. Moreover, artificial tetramers have been created by the biotinylation of the CRD followed by conjugation to tetrameric streptavidin [<xref rid=\"B17-ijms-21-05290\" ref-type=\"bibr\">17</xref>]. Here, we propose an alternative strategy to create a tetrameric complex of any lectin. The strategy is based on a site-specific biotinylation at the N-terminus of lectin CRD exploiting the “sortagging” labeling approach using the enzyme sortase A (SrtA) [<xref rid=\"B18-ijms-21-05290\" ref-type=\"bibr\">18</xref>]. The resulting biotinylated CRD is then complexed with tetrameric NeutrAvidin to obtain the final molecule exposing four glycan binding sites named hereafter TETRALEC.</p><p>To validate the approach we decided to compare the functionality of a naturally existing tetrameric lectin, termed DC-SIGNR [<xref rid=\"B19-ijms-21-05290\" ref-type=\"bibr\">19</xref>], with its artificial tetrameric counterpart. Lymph node-specific intercellular adhesion molecule-3-grabbing integrin or DC-SIGN related (L-SIGN or DC-SIGNR) is expressed in endothelial cells of lymph nodes, liver and placenta and shares 77% of amino acid homology with DC-SIGN [<xref rid=\"B20-ijms-21-05290\" ref-type=\"bibr\">20</xref>]. Both are calcium-dependent mannose-specific CLRs that act as cell adhesion and endocytic receptors involved in the recognition of a broad range of viral and bacterial pathogens [<xref rid=\"B21-ijms-21-05290\" ref-type=\"bibr\">21</xref>]. DC-SIGNR and DC-SIGN natural extracellular domains (ECD) present a tetrameric conformation exposing four CRDs [<xref rid=\"B22-ijms-21-05290\" ref-type=\"bibr\">22</xref>]. Therefore, a direct comparison in terms of ligand interaction experiments of the ECD with the TETRALEC construct could be performed. Two other DC-SIGNR constructs were analyzed in parallel: the monomeric DC-SIGNR CRD in order to evaluate the benefit of an oligomeric presentation and a more routinely used artificial construct, in other words, a dimer formed by an Fc fusion with DC-SIGNR CRD (<xref ref-type=\"fig\" rid=\"ijms-21-05290-f001\">Figure 1</xref>). We have first characterized the oligomeric status of all the multimeric constructs. For all constructs we then compared the glycan binding profiles on a microarray of synthetic N- and O- glycans to evaluate the impact of CRD presentation and geometry on the avidity-based recognition process. Besides, we also studied the interaction between DC-SIGNR and <italic>Candida albicans</italic> by flow cytometry to show that our strategy could be used to screen new host/pathogen interactions.</p></sec><sec sec-type=\"results\" id=\"sec2-ijms-21-05290\"><title>2. Results</title><sec id=\"sec2dot1-ijms-21-05290\"><title>2.1. Design and Synthesis of the TETRALEC</title><p>In the Gram-positive <italic>Staphylococcus aureus</italic>, SrtA catalyzes the anchorage of target proteins, including virulence factors, to the cell wall. This enzyme cleaves the LPXTG motif present in the proteins and links them to the amino terminal group of five glycines of the peptidoglycan [<xref rid=\"B23-ijms-21-05290\" ref-type=\"bibr\">23</xref>]. Here, SrtA was used to enzymatically couple a biotinylated peptide to the N-Terminus of the protein CRD previously extended with a poly-Gly chain (GGG) (<xref ref-type=\"fig\" rid=\"ijms-21-05290-f002\">Figure 2</xref>a). The peptidic motif LPRT-OMe recognized by the sortase A (SrtA) was appended to the biotin. Methylation of the terminal threonine residue drastically reduces the reversibility of the reaction [<xref rid=\"B23-ijms-21-05290\" ref-type=\"bibr\">23</xref>].</p><p>The biotin sortagging was followed by electrospray ionization mass spectrometry (ESI-MS). An intermediate thio-ester sortase-biotin was initially formed, followed by the transpeptidation of the biotinylated peptide onto the DC-SIGNR GGG-CRD N-terminus. As shown in <xref ref-type=\"fig\" rid=\"ijms-21-05290-f002\">Figure 2</xref>b the reaction could be considered completed after 6 h of reaction.</p><p>Biotin sortagged DC-SIGNR-CRD was then purified and complexed via its biotin moiety to a tetramer of NeutrAvidin previously labeled with Cy3 fluorophore.</p></sec><sec id=\"sec2dot2-ijms-21-05290\"><title>2.2. TETRALEC Structural Characterization</title><p>Size exclusion chromatography coupled to light scattering (SEC-LS) analysis was performed to assess the tetrameric presentation of DC-SIGNR-CRD by one molecule of tetrameric NeutrAvidin on DC-SIGNR TETRALEC, and verify DC-SIGNR ECD and DC-SIGNR Fc-CRD association states. SEC-LS combines the separation of macromolecules by size exclusion chromatography and their characterization in terms of molar mass and composition thanks to different detectors, static and dynamic light scattering, refractive index and absorbance.</p><p>For analyzing TETRALEC, in addition to the refractive index detection, acquisitions at two wavelengths were performed: 280 nm, which probes both Cy3- NeutrAvidin and DC-SIGNR CRD, and 550 nm, which only probes Cy3- NeutrAvidin. Probing for Cy3- NeutrAvidin alone, the protein eluted as a main peak at 9.2 mL with a small shoulder at 8.2 mL containing larger species (<xref ref-type=\"fig\" rid=\"ijms-21-05290-f003\">Figure 3</xref>a). The extracted molar mass along the peak decreased slightly with the elution volume revealing some heterogeneity, and reached, after 9.4 mL, a value of around 120 kDa corresponding to a dimer of the natural tetramer of NeutrAvidin (MWtheo = 2 × 58 kDa).</p><p>The TETRALEC complex eluted with a main peak at 9.1 mL, and a shoulder at 8 mL (<xref ref-type=\"fig\" rid=\"ijms-21-05290-f003\">Figure 3</xref>b). Two additional contributions, which do not absorb at 550 nm, were detected at 10.5 mL and 11.5 mL, with molecular masses of 39 kDa and 18 kDa, corresponding most probably to free dimer and monomer DC-SIGNR CRD (MWtheo = 16.5 kDa), respectively. They are however in minor amounts as indicated by the refractive index signal (black line in <xref ref-type=\"fig\" rid=\"ijms-21-05290-f003\">Figure 3</xref>b). The main peak at 9.1 mL shows a slightly decreasing molar mass with the elution volume, which could be due to a contamination by the larger species from the shoulder. The mean molar mass is about 140 kDa, close to the MWtheo = 124 kDa calculated for the 4:4 Cy3- NeutrAvidin:DC-SIGNR CRD complex. The analysis in terms of stoichiometry could not be done with certainty. Considering the theoretical extinction coefficient at 280 nm for NeutrAvidin-Cy3, the analysis supports a 4:4 complex. Unfortunately, the analysis at 550 nm provides improbable stoichiometries ranging going from 7:2 to 5:3 of Cy3- NeutrAvidin:DC-SIGNR CRD. The discrepancy between the analyses should be related to uncertainty in one or the other extinction coefficients. We consider that the presence of free Cy3- NeutrAvidin is unlikely because of the evidence, at 11.5 mL, of free biotinylated DC-SIGNR CRD in excess. The SEC-LS experiment presented here cannot, by itself, give a definitive conclusion but strongly supports the formation of the 4:4 TETRALEC stoichiometry.</p><p>Finally, we controlled the association states of the two other constructs (<xref ref-type=\"fig\" rid=\"ijms-21-05290-f004\">Figure 4</xref>). The artificial construct DC-SIGNR Fc-CRD (blue lines) gave a main contribution at 8.9 mL, with a <italic>M</italic><sub>W</sub> of 86.5 kDa and a RH of 9 nm. This contribution corresponds to a dimer of DC-SIGNR-Fc-CRD (MWtheo = 79.4 kDa). The natural tetrameric DC-SIGNR ECD (red lines) gave a main contribution at 7.4 mL, with a <italic>M</italic><sub>W</sub> of 155.6 kDa, close to the theoretical value (MWtheo = 148.8 kDa) and a RH of 7.7 nm. These results confirmed the dimeric nature of Fc-CRD and the tetrameric nature of DC-SIGNR ECD.</p></sec><sec id=\"sec2dot3-ijms-21-05290\"><title>2.3. Validation on Glycan Array</title><p>Once the oligomeric status was determined, the glycan binding profile of the constructs was assessed on glycan microarrays. The objectives were i) to examine whether the artificial constructs were functional and ii) to appraise the effect of the oligomeric status and spatial arrangement of DC-SIGNR CRDs in glycan recognition. DC-SIGNR natural ligands are N-linked high-mannose oligosaccharides, presented on several pathogens. In comparison to DC-SIGN, fucosylated oligosaccharides, such as Lewis A and Lewis X blood group epitopes found on some pathogens are not recognized [<xref rid=\"B24-ijms-21-05290\" ref-type=\"bibr\">24</xref>]. Thus, a synthetic glycan microarray (See <xref ref-type=\"app\" rid=\"app1-ijms-21-05290\">SM, Figure S1</xref>) with 135 carbohydrates containing N-glycan and O-glycan structures (See <xref ref-type=\"app\" rid=\"app1-ijms-21-05290\">SM, Table S1</xref>) was used to compare the different DC-SIGNR constructs [<xref rid=\"B25-ijms-21-05290\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijms-21-05290\" ref-type=\"bibr\">26</xref>]. DC-SIGNR CRD, ECD and TETRALEC were all labeled with Cy3 fluorophore while DC-SIGNR Fc-CRD binding was detected with Cy3 labeled secondary anti-human Fc antibody. The concentration of all constructs was adjusted to the same concentration of active CRD sites in each experiment. For this reason, while a concentration of 1 µM of the tetrameric DC-SIGNR ECD and TETRALEC was fixed for the incubation, 4 µM and 2 µM were used for the monomeric DC-SIGNR-CRD and the dimeric DC-SIGNR Fc-CRD, respectively. All constructs showed important recognition towards structures presented on glycan microarray (see structures in <xref ref-type=\"app\" rid=\"app1-ijms-21-05290\">SM, Figure S1</xref>), highlighting their functionality (<xref ref-type=\"fig\" rid=\"ijms-21-05290-f005\">Figure 5</xref>). As equal amounts of DC-SIGNR CRD sites from different constructs were employed in microarray experiments, total intensity normalization was applied to the data to overcome the difference in labeling between the detection methods employed [<xref rid=\"B27-ijms-21-05290\" ref-type=\"bibr\">27</xref>].</p><p>As binding assays were performed only at a single protein concentration for each construct, affinity constants for individual interactions could not be determined. Nevertheless, the normalized fluorescence values indicate a ranking of affinities. All constructs exhibited a similar trend in glycan recognition, high mannose and hybrid N-glycans being the preferential bound structures (<xref ref-type=\"fig\" rid=\"ijms-21-05290-f006\">Figure 6</xref>a). It is important to highlight that certain differences in binding were also observed, underlining the importance of geometrical presentation and distance between CRDs for the recognition of epitopes (<xref ref-type=\"fig\" rid=\"ijms-21-05290-f006\">Figure 6</xref>) [<xref rid=\"B28-ijms-21-05290\" ref-type=\"bibr\">28</xref>].</p><p>Natural tetrameric DC-SIGNR ECD showed recognition towards mannose containing N-glycan structures presented on the microarray, in line with previous reported specificity. The strongest binder was GL45, this high mannose N-glycan structure possesses an atypical non-natural branching pattern presenting in both antennae terminal Manα1-3(Manα1-6Man) trisaccharide residues, a well-known binding epitope for DC SIGNR [<xref rid=\"B29-ijms-21-05290\" ref-type=\"bibr\">29</xref>]. The recognition towards high mannose structures is reduced with the number of mannoses residues (GL42, GL43, GL44 versus GL45, <xref ref-type=\"fig\" rid=\"ijms-21-05290-f006\">Figure 6</xref>b), as large high mannose structures are recognized by the extended primary binding site or via interaction with secondary binding sites at different positions of DC-SIGNR [<xref rid=\"B29-ijms-21-05290\" ref-type=\"bibr\">29</xref>]. The interaction with paucimannose structures (Man1-3GlcNAc2) (GL41, <xref ref-type=\"fig\" rid=\"ijms-21-05290-f006\">Figure 6</xref>b) was negligible, due to the binding impediment with the primary binding site of DC-SIGNR caused by the presence of core β-linked GlcNAc attached to mannose residues [<xref rid=\"B30-ijms-21-05290\" ref-type=\"bibr\">30</xref>]. The multivalent presentation of carbohydrates on the microarray surface enables efficient simultaneous binding with several closely immobilized carbohydrates, and indeed even monomeric DC-SIGNR CRD showed relevant binding strength, with a similar recognition profile as ECD. The artificial CRD tetramerization in the TETRALEC led to an increased specificity in ligand recognition compared to the natural ECD tetramer and CRD monomer. While the ECD construct binds to a broader range of ligands, narrower selection of glycans was strongly recognized by DC-SIGNR TETRALEC (<xref ref-type=\"fig\" rid=\"ijms-21-05290-f005\">Figure 5</xref>). The DC-SIGN Fc-CRD construct followed the same tendency of recognition observed for the other three constructs and share the increased specific recognition for some ligands with the TETRALEC. Avidity results from the combination of both ligand rebinding and multiple attachment (chelation). All CRDs are densely packed and have the same orientation in the ECD [<xref rid=\"B31-ijms-21-05290\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijms-21-05290\" ref-type=\"bibr\">32</xref>]. Thus, this construct is probably more favorable to multiple attachment than the TETRALEC or Fc-CRD presentations where the CRDs are more distant from one another. Indeed, even if the best ligands are the same for all different constructs, the differences observed in the glycan binding profile of the weaker ligands may reflect their capacity to undergo rebinding or simultaneous multiple attachment between the CRDs.</p><p>Nevertheless, one crucial result is that all ligands recognized by DC-SIGNR TETRALEC and Fc-CRD are actual ligands of the natural constructs (ECD or CRD) indicating that the artificial oligomerization of CRDs is not affecting the binding selectivity of DC-SIGNR. For certain glycans however, the oligomerization of CRD in the artificial constructs (Fc-CRD and TETRALEC) increases binding probably by the selection of certain glycans over others by the newly multimeric architectures. For example, GL42 (<xref ref-type=\"fig\" rid=\"ijms-21-05290-f006\">Figure 6</xref>b), that displays a Manα1,2-Man disaccharide on the non- reducing end, is stronger bound by both Fc-CRD and TETRALEC compared to ECD.</p><p>Additionally, we observed how core fucose, which is not part of the recognition element, influences binding to DC-SIGNR. For example, the three hybrid type N-glycan structures with and without core fucosylation (GL54, GL65 and GL73, <xref ref-type=\"fig\" rid=\"ijms-21-05290-f006\">Figure 6</xref>b) were bound differently by the constructs. Glycan GL54 displaying a terminal Manα1-3(Manα1-6Man) displayed similar binding strengths to the four constructs, with ECD being the strongest binder. Core α-1,6 fucosylation in GL65 leads to the reduction in binding to the natural ECD but to an increase in binding to the artificial constructs Fc-CRD and TETRALEC. On the other hand, the presence of both core α-1,6 fucose and core α-1,3 fucose (GL73) reduces binding with all four constructs. The role of core fucose as a key modulator of N-glycan binding to lectins has been recognized previously and attributed to the conformational changes in the oligosaccharide conformation induced by this modification [<xref rid=\"B33-ijms-21-05290\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijms-21-05290\" ref-type=\"bibr\">34</xref>].</p></sec><sec id=\"sec2dot4-ijms-21-05290\"><title>2.4. DC-SIGNR Recognizes Heat-Killed Candida albicans</title><p>After assessing the capacity of the TETRALEC construct to recognize synthetic glycans, the possibility to use this tool to investigate CLR/pathogen interactions was explored. For this purpose, binding of the different CLR constructs to the pathogenic fungus <italic>C. albicans</italic>, a pathogen covered by N-glycans at its surface, was evaluated by flow cytometry. DC-SIGN was used as a positive control, based on its well-described role in the recognition of <italic>C. albicans</italic> and anti-fungal immune responses [<xref rid=\"B35-ijms-21-05290\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijms-21-05290\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijms-21-05290\" ref-type=\"bibr\">37</xref>]. Binding to heat-killed <italic>C. albicans</italic> (HKCA) was observed for all DC-SIGNR-Cy3 constructs, in other words, DC-SIGNR CRD, ECD and TETRALEC (<xref ref-type=\"fig\" rid=\"ijms-21-05290-f007\">Figure 7</xref>a), albeit to a minor extent when comparing DC-SIGNR CRD and ECD to the positive control, DC-SIGN ECD.</p><p>NeutrAvidin did not impact binding to <italic>C. albicans</italic>, since no binding was observed when using the negative control Cy3-NeutrAvidin. HKCA was recognized by all DC-SIGNR-Cy3 constructs independently of their oligomeric status. The binding profile to HKCA was also evaluated with CLR Fc-CRD fusion proteins (<xref ref-type=\"fig\" rid=\"ijms-21-05290-f007\">Figure 7</xref>b). The Fc fragment works as a primary antibody, hence enabling detection of binding events with an anti-Fc antibody. Oppositely to DC-SIGNR-Cy3 constructs that present direct labeling of the CLRs, the CLR Fc-CRD fusion proteins employ an indirect labeling strategy. Thus, the labeling intensity cannot be directly compared to evaluate the impact of the dimeric presentation against the monomeric or tetrameric presentations for HKCA binding. DC-SIGNR Fc-CRD also showed binding to HKCA, although to a lower degree than DC-SIGN Fc-CRD (<xref ref-type=\"fig\" rid=\"ijms-21-05290-f007\">Figure 7</xref>b). The negative controls (Fc and secondary antibody alone) were not bound to HKCA, when compared with the CLR Fc-CRD fusion proteins.</p><p>Since we detected strong binding of DC-SIGNR TETRALEC and DC-SIGNR Fc-CRD to HKCA in a flow cytometry-based assay, we applied confocal microscopy to further visualize and confirm the identified interaction (<xref ref-type=\"fig\" rid=\"ijms-21-05290-f008\">Figure 8</xref>). The results obtained showed that both DC-SIGNR constructs interact with the cell wall of HKCA. Nevertheless, this binding appears weaker than the well-described DC-SIGN binding to <italic>C. albicans</italic> [<xref rid=\"B37-ijms-21-05290\" ref-type=\"bibr\">37</xref>]. DC-SIGNR Fc-CRD interaction showed a punctuated behavior of binding to the cell wall of HKCA (<xref ref-type=\"fig\" rid=\"ijms-21-05290-f008\">Figure 8</xref>b), suggesting that the glycan structures recognized are not homogenously present at the surface of HKCA cell wall. DC-SIGN Fc-CRD, used as a positive control in the flow-cytometry experiments, showed a homogenous recognition of the entire surface of the HKCA cell wall (See <xref ref-type=\"app\" rid=\"app1-ijms-21-05290\">SM Figure S2</xref>). In line with our observation of the DC-SIGNR Fc-CRD construct binding to HKCA, DC-SIGNR TETRALEC also exhibited a non-homogenous binding to HKCA cell (<xref ref-type=\"fig\" rid=\"ijms-21-05290-f008\">Figure 8</xref>a). It is noteworthy that virtually no binding was observed for the negative control employed for the CLR Fc-CRD constructs, the Fc control, (<xref ref-type=\"fig\" rid=\"ijms-21-05290-f008\">Figure 8</xref>b) and for NeutrAvidin-Cy3, the negative control of the DC-SIGNR TETRALEC construct (<xref ref-type=\"fig\" rid=\"ijms-21-05290-f008\">Figure 8</xref>a). Interestingly, DC-SIGNR TETRALEC showed a stronger interaction with HKCA than DC-SIGNR ECD by confocal microscopy (<xref ref-type=\"fig\" rid=\"ijms-21-05290-f008\">Figure 8</xref>a), strengthening the TETRALEC construct as a relevant tool to evaluate glycan recognition via glycan array and CLR/pathogen interactions using different methodologies.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec3-ijms-21-05290\"><title>3. Discussion</title><p>The crucial roles played by CLRs in many biological processes including pathogen recognition mechanism or modulation of immune response place them as strategic targets for pharmacological intervention. Moreover, as several CLRs remain “orphan” with no ligand identified, the characterization of their carbohydrate binding specificities is required prior to any drug development. While multivalent ligand or receptor presentation can be easily achieved in surface-based assays, increasing the valency of the protein to perform in solution studies is less obvious. A common option is the creation of Fc fusion proteins that enables dimerization of the glycan binding site. Such constructs have successfully been used to screen for new CLR pathogen ligands and have for instance evidenced the following interactions: Mincle/<italic>Pneumocystis carinii</italic> [<xref rid=\"B38-ijms-21-05290\" ref-type=\"bibr\">38</xref>], Mincle/<italic>Streptococcus pneumoniae</italic> [<xref rid=\"B39-ijms-21-05290\" ref-type=\"bibr\">39</xref>], SIGNR3/<italic>Lactobacillus acidophilus</italic> [<xref rid=\"B40-ijms-21-05290\" ref-type=\"bibr\">40</xref>]. However, this method only provides limited oligomerization enhancement and a higher level of multivalency may be required for some applications. One way to create artificial tetramers is the random labeling of the protein with a biotin tag followed by its conjugation to tetrameric streptavidin. This strategy, however, presents two major drawbacks: i) the degree of labeling is random and ii) any of the accessible lysines can be targeted which may affect the lectin/sugar interactions. An alternative strategy has been developed by the Drickamer group who formed a complex between biotinylated lectin CRDs produced in <italic>Escherichia coli</italic> and streptavidin. Biotinylation of the lectin CRD was achieved by the addition of a 13 AA sequence at the C-terminal that contains a single lysine in an appropriate context, which can be biotinylated by the coexpressed bacterial biotin ligase BirA [<xref rid=\"B41-ijms-21-05290\" ref-type=\"bibr\">41</xref>]. Complexation with streptavidin enabled the creation of a molecule presenting 4 CRDs. This strategy was employed to produce tetrameric mouse DC-SIGN-related proteins for interaction studies [<xref rid=\"B42-ijms-21-05290\" ref-type=\"bibr\">42</xref>]. It was also used to study the interaction between artificial peptide-MHC oligomers with cell surface TCRs [<xref rid=\"B43-ijms-21-05290\" ref-type=\"bibr\">43</xref>].</p><p>Here, we present an alternative route for the biotinylation of DC-SIGNR CRD via sortagging with SrtA. This strategy only requires the introduction of a few (in theory even a single) glycine residues at the N-terminus of the protein of interest and the transfer under mild conditions of literally any substrate carrying a LPXTG motif [<xref rid=\"B44-ijms-21-05290\" ref-type=\"bibr\">44</xref>]. We irreversibly tagged DC-SIGNR CRD with a biotin carrying a C-terminal methyl ester (Biotin-LPXT-OMe). The reaction produces methanol instead of a short peptide with a N-terminal glycine preventing the competing back-reaction with the side product [<xref rid=\"B18-ijms-21-05290\" ref-type=\"bibr\">18</xref>]. Another advantage of sortagging is that it requires minor sequence modification of the target protein compared to the BirA approach. Besides, this approach can also be employed to incorporate other functionalities to the N-terminus of CRDs and offers very versatile applications. The enzymatic activity of SrtA has been widely used to link fluorescent tags [<xref rid=\"B45-ijms-21-05290\" ref-type=\"bibr\">45</xref>], glycosylphosphatidylinositol (GPI) mimics [<xref rid=\"B46-ijms-21-05290\" ref-type=\"bibr\">46</xref>] or even PEG chains [<xref rid=\"B46-ijms-21-05290\" ref-type=\"bibr\">46</xref>].</p><p>We also improved the homogeneity of the final TETRALEC complex by using NeutrAvidin for biotin chelation. NeutrAvidin is a deglycosylated tetrameric protein derived from avidin, its biochemical characteristics, including a near-neutral electrical point and no glycosylation, reduce non-specific binding compared to streptavidin [<xref rid=\"B47-ijms-21-05290\" ref-type=\"bibr\">47</xref>].</p><p>Although similar artificial oligomeric constructs have been used to study Glycan/CLR interactions [<xref rid=\"B38-ijms-21-05290\" ref-type=\"bibr\">38</xref>,<xref rid=\"B39-ijms-21-05290\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijms-21-05290\" ref-type=\"bibr\">40</xref>,<xref rid=\"B41-ijms-21-05290\" ref-type=\"bibr\">41</xref>,<xref rid=\"B42-ijms-21-05290\" ref-type=\"bibr\">42</xref>], to our knowledge they have not been thoroughly characterized before their use in binding studies. The objective of our approach was, besides a novel conception of artificial tetrameric lectins, to ensure that these synthetic constructs retain a capability to identify genuine ligands. Towards this end, we compared the DC-SIGNR TETRALEC glycan binding profile with the natural DC-SIGNR tetramer, the dimeric DC-SIGNR Fc-CRD and monovalent DC-SIGNR CRD binding domains. The oligomeric status of all lectin constructs was assessed by SEC-LS analysis that supports tetramer formation of the biotinylated NeutrAvidin construct.</p><p>Interestingly, the overall recognition glycan pattern was comparable for all constructs (<xref ref-type=\"fig\" rid=\"ijms-21-05290-f005\">Figure 5</xref>). The functionality of the TETRALEC construct is evidenced by the fact that its selectivity follows that of the CRD (<xref ref-type=\"fig\" rid=\"ijms-21-05290-f006\">Figure 6</xref>a) with no false positives. Besides, it provides a clear enhancement of binding measured as a gain in fluorescence intensity (2 to 3 times increase). Thus, the artificial tetramer represents a relevant tool to identify primary ligands and would present a valuable advantage over naturally non-oligomeric lectins for which in solution interaction studies are impaired by low affinity binding. The pattern recognition of the Fc-fusion construct is very comparable to that of the TETRALEC.</p><p>This systematic binding comparison of a large range of ligands to constructs offering different CRD presentations revealed that the overall selectivity is not affected, in other words, that all constructs share common ligands. However, binding to some glycans appears to be increased for some constructs as observed with GL42 or GL45 that are preferentially recognized by the two artificial multivalent constructs. This behavior suggests, on the one hand, that the number of CRDs matters as indeed the multiplicity of the CRDs within one molecule will obviously favor the interaction. On the other hand, our results suggest that the CRD specific spatial arrangement also impacts molecular recognition. In this sense, the binding variation observed between DC-SIGNR ECD that presents four CRDs pointing in the same direction and the TETRALEC construct with active sites pointing in four different directions could be explained. Besides, the dense and uniform glycan presentation of the glycan array may favor binding of the TETRALEC and therefore somehow differs from natural lectin-glycan interactions. In addition, the specificity of lectin-glycan interaction can result from the cross-linking of bivalent oligosaccharides with oligomeric lectins. These interactions, previously described, clearly depend upon the spatial geometry of the glycans and the CRDs [<xref rid=\"B48-ijms-21-05290\" ref-type=\"bibr\">48</xref>,<xref rid=\"B49-ijms-21-05290\" ref-type=\"bibr\">49</xref>]. As a matter of fact, binding differences between the various constructs are most probably due to variable contributions of the different active binding modes, in other words, rebinding and chelation that generate avidity.</p><p>We also wanted to assess whether the TETRALEC construct could be used in cellular assays and more precisely if it could help to identify new CLR/pathogen interactions. Like many pathogens, <italic>C. albicans</italic> encounter with host defense involves its detection and clearance by the innate immune system, where CLRs expressed on the surface of epithelia, endothelial and myeloid cells play a pivotal role. <italic>C. albicans</italic> has a unique and highly mannosylated cell wall, where N- and O- glycans account for more than 90% of the glycans present at the surface [<xref rid=\"B50-ijms-21-05290\" ref-type=\"bibr\">50</xref>]. Within the context of a larger screening approach to identify CLRs capable of recognizing <italic>C. albicans</italic> glycans we focused on the possibility of DC-SIGNR to interact with this fungal pathogen. Using the three different Cy3 labeled constructs of DC-SIGNR (DC-SIGNR CRD, DC-SIGNR ECD, DC-SIGNR TETRALEC) we evaluated two main points: first, if DC-SIGNR is able to bind to HKCA and, secondly, the impact of multivalent CRD presentation on the pathogen recognition. All three constructs recognized HKCA. Moreover, the TETRALEC construct presented a similar binding to the one observed for the positive control used, the DC-SIGN ECD construct. On the contrary, NeutrAvidin-Cy3, the negative control, showed no interaction, indicating that this conjugation strategy represents a useful tool to identify novel CLR-pathogen interactions, with minimal unspecific binding. Interestingly, the different oligomerizations states of DC-SIGNR-Cy3 constructs did not significantly impact recognition of HKCA by flow cytometry. It is noteworthy that both DC-SIGN ECD and DC-SIGN-Fc-CRD constructs displayed a stronger interaction to HKCA by flow cytometry, when compared to the different DC-SIGNR constructs. These binding differences between DC-SIGN and DC-SIGNR to pathogens that possess high mannose oligosaccharides may be associated with the different capacities of the CLRs to accommodate the differential spatial conformations of sugar epitopes in their binding pockets due to the distinct properties of the neck domains [<xref rid=\"B51-ijms-21-05290\" ref-type=\"bibr\">51</xref>]. Despite an overlap in mannosylated glycans recognition of DC-SIGNR with DC-SIGN [<xref rid=\"B24-ijms-21-05290\" ref-type=\"bibr\">24</xref>], no binding of DC-SIGNR and fungi has yet been described to the best of our knowledge. Therefore, in addition to proposing a new strategy to create an artificial tetrameric lectin construct with a recognition specificity close to its natural tetrameric counterpart, this study has also highlighted the utility of this construct to probe CLR/pathogens interactions, resulting in the identification of <italic>C. albicans</italic> as a pathogen recognized by DC-SIGNR.</p></sec><sec id=\"sec4-ijms-21-05290\"><title>4. Materials and Methods</title><sec id=\"sec4dot1-ijms-21-05290\"><title>4.1. Cloning</title><p>Standard pUC57 plasmids containing optimized synthetic human genes encoding human DC-SIGNR ECD (amino acids 78 to 399) and CRD (amino acids 264 to 399) designed for the efficient production in <italic>Escherichia coli</italic> were manufactured by GeneCust (Boynes, France) PCR amplification using suitable primers and restriction enzyme digestion were used to sub-clone into the pET30-b (Merck, Damstadt, Germany) DC-SIGNR ECD between the NdeI and HindIII restriction sites and DC-SIGNR CRD between the XbaI and HindIII sites. The sequencing of each construction was done by Genewiz (Leipzig, Germany).</p></sec><sec id=\"sec4dot2-ijms-21-05290\"><title>4.2. Protein Expression and Purification</title><p>DC-SIGNR ECD was expressed in <italic>E. coli</italic> BL21(DE3) in 1 L of LB medium supplemented with 50 μg·mL<sup>−1</sup> kanamycin at 37 °C. Expression was induced by addition of 1 mM isopropyl 1-thio-D-galactopyranoside (IPTG) when the culture had reached an A<sub>600 nm</sub> of 0.8 and maintained for 3 h. The protein was expressed in the bacterial cytoplasm as inclusion bodies. Cells were harvested by a 20 min centrifugation at 5000× <italic>g</italic> at 4 °C. The pellet was resuspended in 30 mL of a solution containing 150 mM NaCl, 25 mM Tris-HCl pH 8 and one anti-protease mixture tablet (Complete EDTA-free, Merck, Damstadt, Germany). Cells were disrupted by sonication and cell debris eliminated by centrifugation at 100,000× <italic>g</italic> for 45 min at 4 °C in a Beckman 45Ti rotor. The pellet was solubilized in 30 mL of 6 M guanidine-HCl containing 25 mM Tris-HCl pH 8, 150 mM NaCl and 0.01% (<italic>v</italic>/<italic>v</italic>) beta-mercaptoethanol. The mixture was centrifuged at 100,000× <italic>g</italic> for 45 min at 4 °C and the supernatant was diluted 5-fold with 25 mM Tris-HCl pH 8, 1.25 M NaCl and 25 mM CaCl<sub>2</sub> by slow addition with stirring. The diluted mixture was dialyzed against 10 volumes of 25 mM Tris-HCl pH 8, 150 mM NaCl, 4 mM CaCl<sub>2</sub> (buffer A) with 3 buffer changes. After dialysis, insoluble precipitate was removed by centrifugation at 100,000× <italic>g</italic> for 1 h at 4 °C. The supernatant containing DC-SIGNR ECD was loaded on Mannan agarose column (Merck, Damstadt, Germany) equilibrated with buffer A for purification by affinity chromatography. After loading, DC-SIGNR ECD was tightly bound to the column and eluted in the same buffer without CaCl<sub>2</sub> but supplemented with 1 mM EDTA (buffer B). This step was followed by SEC (size exclusion chromatography) using a Superose 6 column (Cytivia, Velizy-Villacoubray, France) equilibrated with buffer A. Fractions were analyzed by SDS-PAGE (12%) and DC-SIGNR ECD containing fractions were pooled and concentrated by ultrafiltration (YM10 membrane from Merck, Damstadt, Germany).</p><p>DC-SIGNR CRD was expressed in <italic>E. coli</italic> BL21(DE3) in 1 L of LB medium supplemented with 50 μg·mL<sup>−1</sup> kanamycin at 37 °C. Expression was induced by addition of 1 mM IPTG when the culture had reached an A<sub>600 nm</sub> of 0.8 and maintained for 3 h. The protein was expressed in the cytoplasm as inclusion bodies. Cells were harvested by a 20 min centrifugation at 5000× <italic>g</italic> at 4 °C. The pellet was resuspended in 30 mL of a solution containing 150 mM NaCl, 25 mM Tris-HCl pH 8 and one anti-protease mixture tablet. Cells were disrupted by sonication and cell debris eliminated by centrifugation at 100,000× <italic>g</italic> for 45 min at 4 °C in a Beckman 45Ti rotor. The pellet was solubilized in 30 mL of 6 M guanidine-HCl containing 25 mM Tris-HCl pH 8, 150 mM NaCl and 0.01% beta-mercaptoethanol. The mixture was centrifuged at 100,000× <italic>g</italic> for 45 min at 4 °C and the supernatant was diluted 5-fold with 200 mM Tris-HCl pH 8, 1.25 M NaCl and 25 mM CaCl<sub>2</sub> by slow addition under stirring. The diluted mixture was dialyzed against 10 volumes of buffer A with 3 buffer changes. After dialysis, insoluble precipitate was removed by centrifugation at 100,000× <italic>g</italic> for 1 h at 4 °C. The supernatant containing the His-tagged DC-SIGNR CRD was loaded onto a HisTrap column (Cytivia, Velizy-Villacoubray, France) at 4 °C. Unbound proteins were washed away with buffer A before DC-SIGNR CRD was eluted with buffer C (150 mM NaCl, 25 mM Tris-HCl, pH 8, 4 mM CaCl<sub>2</sub>, 0.5 M imidazole). Eluted fractions were analyzed by SDS-PAGE (15%) and the DC-SIGNR CRD containing fractions were pooled and concentrated by ultrafiltration (YM10 membrane from Amicon).</p><p>Each construct was checked by N-terminal amino acid sequencing and mass spectrometry.</p></sec><sec id=\"sec4dot3-ijms-21-05290\"><title>4.3. Labelling</title><p>A total of 250 μL of 3.77 mg·mL<sup>−1</sup> solution of DC-SIGNR ECD, 212.5 µL of 4.75 mg·mL<sup>−1</sup> solution of DC-SIGNR CRD in 25 mM HEPES pH 7.25, 4 mM CaCl<sub>2</sub> and 100 µL of 3.47 mg·mL<sup>−1</sup> solution of NeutrAvidin in PBS pH 7.4 were prepared. Then, 1 µL, 4 µL and 2 µL, respectively, of 10 mg·mL<sup>−1</sup> Cy3-NHS ester (Gene Copoeia, Rockville, MD, USA) were added to the solutions and the reactions were gently shaken at room temperature for 2 h and then at 4 °C for 4 h. Excess dye was removed by two dialyses (3.5k Z-lyser from ThermoFisher Scientific, Waltham, MA, USA) of 3 h against 25 mM Tris pH 8, 150 mM NaCl, 4 mM CaCl<sub>2</sub>. The amount of attached Cy3 was estimated spectrophotometrically based on the dye molar extinction coefficient (ε = 150,000 cm<sup>−1</sup>·M<sup>−1</sup>) and the protein extinction coefficients. The obtained degrees of labeling (DOL) were 0.4, 0.2 and 0.5 for DC-SIGNR ECD, DC-SIGNR CRD and NeutrAvidin, respectively.</p></sec><sec id=\"sec4dot4-ijms-21-05290\"><title>4.4. TETRALEC Formation</title><p>His tag cleavage. His-GGG-DC-SIGNR CRD was cleaved using factor Xa (ThermoFisher Scientific, Waltham, MA, USA) following the protocol recommended by the manufacturer: 1 µg of factor Xa per 50 µg of His-GGG-DC-SIGNR CRD protein at 1 mg·mL<sup>−1</sup>. The reaction was performed overnight at room temperature under agitation and then injected into Toyopearl exclusion chromatography column previously equilibrated in 25 mM Tris pH 8, 150 mM NaCl, 4 mM CaCl<sub>2</sub> buffer. A flow rate of 1 mL·min<sup>−1</sup> was maintained during the purification. Eluted fractions were pooled and concentrated up to 1 mg.</p><p>Biotin sortagging and TETRALEC formation. A protocol already published was used for GGG DC-SIGNR CRD biotinylation [<xref rid=\"B18-ijms-21-05290\" ref-type=\"bibr\">18</xref>]. The protein exposing three glycines at the N-terminus (1 molar equivalent) was mixed with the biotin-LPRT-OMe peptide (<italic>M</italic><sub>W</sub> = 725.9 Da, Covalab, Villeurbanne, France) (5 eq.) and His-tag sortase A (SrtA) (0.3 eq.) from <italic>Staphylococcus aureus</italic>, recombinantly produced in the laboratory, in 25 mM Tris pH 8, 150 mM NaCl, 4 mM CaCl<sub>2</sub> buffer. The reaction was incubated at 37 °C for 6 h under agitation. The kinetic of the reaction was followed by ESI-MS: 10 µL of reaction was analyzed at 0 h, 2 h, 4 h, 6 h, 8 h and overnight. When the reaction was completed, the solution was loaded onto a 1 mL HisTrap column previously equilibrated in 25 mM Tris pH 8, 150 mM NaCl, 4 mM CaCl<sub>2</sub> buffer. After column washing, the elution step was performed using 25 mM Tris pH 8, 150 mM NaCl, 4 mM CaCl<sub>2</sub>, 0.5 M imidazole buffer. A 1 mL·min<sup>−1</sup> flow rate of buffer was maintained during the purification. The His tagged sortase was retained by the HisTrap column while the untagged biotin-CRD was eluted during the washing step and was pooled and dialyzed against 25 mM Tris pH 8, 150 mM NaCl, 4 mM CaCl<sub>2</sub> to eliminate un-reacted biotinylated-peptide.</p><p>Finally, NeutrAvidin (<italic>M</italic><sub>W</sub> = 14.5 kDa, ThermoFisher Scientific, Waltham, MA, USA) sample previously labeled with Cy3-fluorophore (2.9 mg·mL<sup>−1</sup>, DOL = 0.5) was mixed to biotin-CRD with a molar ratio of 1:1 and the reaction was incubated overnight at 4 °C under agitation. The obtained CRD-TETRALEC was frozen in liquid nitrogen for storage at −80 °C.</p></sec><sec id=\"sec4dot5-ijms-21-05290\"><title>4.5. SEC-LS Analysis</title><p>SEC-LS experiments were conducted on a HPLC (Schimadzu, Kyoto, Japan) consisting of a degasser DGU-20AD, an LC-20AD pump, an autosampler SIL20-ACHT, a communication interface CBM-20A, a UV-Vis detector SPD-M20A and a fraction collector FRC-10A, a column oven XL-Therm (WynSep, Sainte Foy d’Aigrefeuille, France) and a static light scattering detector miniDawn Treos, a dynamic light scattering detector DynaPro NANOSTAR, a refractive index detector Optilab rEX (Wyatt, Santa-Barbara, CA, USA). Samples of 50 µL were injected on a KW 802.5 column (Shodex, Tokyo, Japan) equilibrated at 4 °C with 25 mM Tris pH 7.5, 150 mM NaCl, 4 mM CaCl<sub>2</sub>, at a flow rate of 0.5 mL·min<sup>−1</sup>. The analysis was made with the software ASTRA, v5.4.3.20 (Wyatt, Santa-Barbara, California, USA). It uses static light scattering and refractive index measured along the elution to derive experimental molar masses, and in the protein conjugate module, additional absorbance measurement to discriminate the molar mass of each partner in a two-component system. The refractive index increments, and extinction coefficients at 280 nm, were calculated from amino acid composition using the program Sedfit (freely available in <uri xlink:href=\"https://sedfitsedphat.nibib.nih.gov\">https://sedfitsedphat.nibib.nih.gov</uri>). We combined the refractive index detection and the 550 nm detection to determine an experimental extinction coefficient for Cy3-NeutrAvidin: 2890 cm<sup>−1</sup>·(g/L)<sup>−1</sup>.</p></sec><sec id=\"sec4dot6-ijms-21-05290\"><title>4.6. Glycan Array Analysis</title><p>Glycan microarrays were prepared as previously described [<xref rid=\"B26-ijms-21-05290\" ref-type=\"bibr\">26</xref>]. Cy3 labeled constructs were diluted in incubation buffer (25 mM Tris-HCl, 150 mM NaCl, 4 mM CaCl<sub>2</sub>, pH 7.5 containing 0.5% (<italic>w</italic>/<italic>v</italic>) bovine serum albumin (BSA) and 0.005% (<italic>v</italic>/<italic>v</italic>) Tween-20). Protein solutions (200 µL per array) were used to incubate individual wells on a glycan array slide at 4 °C for 18 h. Arrays were washed with incubation buffer without BSA, water and dried in a slide spinner. For the detection of Fc-CRD, a solution of anti-human IgG (Fc specific)-Cy3 antibody (1:1000 dilution in binding buffer was incubated in the dark for 1h. Microarrays were washed and dried as previously described. Fluorescence measurements were performed on a microarray scanner (Agilent G2565BA, Agilent Technologies, Santa Clara, CA, USA) at 10 µm resolution. Quantification of fluorescence was performed by ProScanArray Express software (Perkin Elmer, Waltham, MA, USA) employing an adaptive circle quantification method from 50 µm (minimum spot diameter) to 300 µm (maximum spot diameter). Average RFU values with local background subtraction of four spots and standard deviation of the mean were reported using Microsoft Excel and GraphPad Prism software.</p></sec><sec id=\"sec4dot7-ijms-21-05290\"><title>4.7. Generation of Human DC-SIGNR-Fc Fusion Protein</title><p>The production of human DC-SIGNR and DC-SIGN fusion proteins were performed as previously described [<xref rid=\"B52-ijms-21-05290\" ref-type=\"bibr\">52</xref>,<xref rid=\"B53-ijms-21-05290\" ref-type=\"bibr\">53</xref>]. Briefly, a human cDNA library was used as template (GE Dharmacon, Lafayette, CO, USA) and specific primers to amplify the CRD of DC-SIGNR were generated (Eurofins Genomics, Ebersberg, Germany). The DC-SIGNR and DC-SIGN primers were as follows: DC-SIGNR forward, gaattcctatcaagaactgaccgatttg; DC-SIGN forward, gaattcgtccaaggtccccagctccat; DC-SIGNR reverse ccatggattcgtctctgaagcaggc; and DC-SIGN reverse, ccatggacgcaggaggggggtttggggt. PCR was used to amplify the cDNA, followed by ligation into a pFuse-hIgG1-Fc expression vector (InvivoGen, San Diego, CA, USA). The DC-SIGNR-Fc expression vector was used to transiently transfect CHO-S cells with MAX reagent (Invitrogen, Darmstadt, Germany). After 4 days of transfection, the supernatant was collected and the fusion proteins were purified with a HisTrap protein G HP column (GE Healthcare, Piscataway, NJ, USA). Protein purity was confirmed by SDS-PAGE with subsequent Coomassie Blue staining. Western blot using an anti-human IgG-horseradish peroxidase (HRP) antibody (Dianova, Hamburg, Germany) was also performed to detect the presence of the fusion protein.</p></sec><sec id=\"sec4dot8-ijms-21-05290\"><title>4.8. Flow Cytometry-Based Binding to C. albicans</title><p>Heat-killed <italic>C. albicans</italic> (InvivoGen, San Diego, CA, USA) was stained for 15 min with 1 µM of DNA-staining dye Syto61 (ThermoFisher Scientific, Darmstadt, Germany) at 4 °C. The samples were subsequently washed two times with 1× PBS. Then, samples were incubated for 1 h either with 250 ng of the respective Fc-CRD fusion proteins in lectin-binding buffer (50 mM HEPES, 5 mM MgCl<sub>2</sub>, 5 mM CaCl<sub>2</sub>, pH 7.4) or with 1 µM of the DC-SIGNR CRD, DC-SIGNR ECD and DC-SIGNR TETRALEC constructs in their respective lectin-binding buffer (25 mM Tris, 150 mM NaCl, 4 mM CaCl<sub>2</sub>, pH 8.0). After washing once with the lectin-binding buffer, the pellet was suspended in a 1:200 PE-conjugated goat anti-human Fc antibody (Dianova, Hamburg, Germany) and incubated for 20 min at 4 °C, for detection of the bound Fc-CRD fusion proteins. Finally, cells were washed two times and flow-cytometric analysis was performed using an Attune NxT Flow Cytometer (ThermoFisher Scientific, Darmstadt, Germany). The gating strategy applied was a first gate in the <italic>C. albicans</italic> population, followed by a single cell population gating for doublet exclusion. In the single cell population gate, Syto61 positive cells were selected and further analyzed for CLR binding. The same gating strategy was performed for all experimental conditions within one experiment. Flow cytometry data were analyzed using the FlowJo version 10 software (FlowJo, Ashland, OR, USA).</p></sec><sec id=\"sec4dot9-ijms-21-05290\"><title>4.9. Confocal Fluorescence Microscopy</title><p>The preparation of slides for binding visualization of heat-killed <italic>C. albicans</italic> (Invitrogen, San Diego, CA, USA) with the different DC-SIGNR constructs was performed as described before, with minor modifications [<xref rid=\"B53-ijms-21-05290\" ref-type=\"bibr\">53</xref>]. Briefly, cover slides (ThermoFisher Scientific, Darmstadt, Germany) were cleaned with 70% ethanol and coated with poly-L-lysine solution (Sigma-Aldrich, St. Louis, MO, USA) for 30 min at 60 °C. Following that, 2 × 10<sup>7</sup> CFU/mL of HKCA was incubated for 1 h at 4 °C with 0.5 µg of DC-SIGNR Fc-CRD, DC-SIGN Fc-CRD and Fc control in lectin binding buffer. For the DC-SIGNR-Cy3 constructs, 2 µM of the constructs were incubated in lectin-binding buffer with HKCA for 1 h at 4 °C. Samples were washed two times with lectin-binding buffer and the CLR-Fc constructs were further incubated with 1:200 goat anti-human Fc Alexa Fluor (AF) 488-conjugated antibody (Dianova, Hamburg, Germany) at 4 °C for 1 h. The samples were then washed one time and applied in the poly-L-lysine-coated cover slides followed by 1 h incubation at 37 °C. Upon coating of the samples, slides were washed one time with 1× PBS, followed by a fixation step using 1% PFA for 1 h at RT. Finally, the cover slides were mounted on microscopic slides (Roth, Karlsruhe, Germany) with proLong gold antifade mountant containing DAPI (ThermoFisher Scientific, Darmstadt, Germany), sealed and stored at room temperature overnight. The next day, slides were visualized using a TCS SP5 confocal inverted-base fluorescence microscope (Leica, Nussloch, Germany) equipped with a HCX PL APO 63 × 1.4 oil immersion objective. At least three independent experiments were performed, each with three randomly selected pictures. Appropriate negative controls were also used, namely, the secondary antibody control for the Fc constructs and the NeutrAvidin-Cy3, as a control for the DC-SIGNR TETRALEC construct.</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec5-ijms-21-05290\"><title>5. Conclusions</title><p>The strategy proposed enables the irreversible functionalization of CLR CRD N-terminus with a biotin moiety and thereafter, by coupling to a NeutrAvidin molecule the generation of artificial tetrameric CLRs. As a proof of concept, DC-SIGNR was chosen by virtue of the possibility of a direct comparison with the natural tetrameric ECD. After confirmation of its oligomeric state, the ability of the synthetic TETRALEC construct to preserve binding towards genuine ligands was tested. Comparison with the other constructs proved the TETRALEC functionality and revealed ligand binding enhancement compared to monovalent CRD presentation. This would be a valuable advantage for naturally non-oligomeric CLRs. The study also underlined that not only oligomery but also the CRD spatial presentation affects ligand recognition. The TETRALEC was also considered in in vitro studies, where flow cytometry and confocal microscopy were used to analyze the interaction between DC-SIGNR and the pathogenic <italic>Candida albicans</italic>. The results revealed the ability of DC-SIGNR to recognize some surface ligands on <italic>Candida albicans</italic>, proving that the strategy could be exploited to screen new host/pathogen interactions.</p></sec></body><back><ack><title>Acknowledgments</title><p>We thank Bruce Turnbull for the kind gift of an expression construct for sortase A.</p></ack><app-group><app id=\"app1-ijms-21-05290\"><title>Supplementary Materials</title><p>Supplementary materials can be found at <uri xlink:href=\"https://www.mdpi.com/1422-0067/21/15/5290/s1\">https://www.mdpi.com/1422-0067/21/15/5290/s1</uri>.</p><supplementary-material content-type=\"local-data\" id=\"ijms-21-05290-s001\"><media xlink:href=\"ijms-21-05290-s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>Conceptualization, F.F., C.V. and M.T.; methodology, C.V., M.T., S.S., A.L.R., C.E. and S.M.-L.; validation, C.V., F.F., N.-C.R. and B.L.; formal analysis, C.V., F.F., N.-C.R. and B.L.; visualization, S.A., J.T.M., S.S., C.E., N.-C.R., B.L., F.F. and C.V.; investigation, S.A., J.T.M., S.S., A.L.R. and S.M.-L.; resources, A.L.R., C.E., S.S. and B.L.; writing—original draft preparation, S.A. and C.V.; writing—review and editing, S.A., J.T.M., S.S., C.E., N.-C.R., B.L., F.F. and C.V.; supervision, C.V.; project administration, F.F.; funding acquisition, F.F., N.-C.R. and B.L. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was supported by the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 642870 (ITN-Immunoshape). This work used the Multistep Protein Purification Platform, for human CLRs production, and the Biophysics Platform, for SEC-LS analysis, of the Grenoble Instruct-ERIC center (ISBG; UMS 3518 CNRS-CEA-UGA-EMBL) within the Grenoble Partnership for Structural Biology (PSB), supported by FRISBI (ANR-10-INBS-05-02) and GRAL, financed within the University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS (ANR-17-EURE-0003). IBS acknowledges integration into the Interdisciplinary Research Institute of Grenoble (IRIG, CEA). F.F. also acknowledges the French Agence Nationale de la Recherche (ANR) PIA for Glyco@Alps (ANR-15-IDEX-02). This project has also received funding from the Spanish Ministry of Economy and Competiveness (MINECO, grants CTQ2014-58779-R and CTQ2017–90039-R) to N.-C.R. This work was performed under the Maria de Maeztu Units of Excellence Programme–Grant No. MDM-2017-0720 Ministry of Science, Innovation and Universities.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><glossary><title>Abbreviations</title><array orientation=\"portrait\"><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CLR</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C-type Lectin Receptor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CRD</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Carbohydrate Recognition Domain</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DC-SIGN</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dendritic Cell-Specific Intercellular adhesion molecule-3-Grabbing Non-integrin</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DC-SIGNR or L-SIGN</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DC-SIGN related or Lymph node-specific intercellular adhesion molecule-3-grabbing integrin</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Dendritic Cell</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DOL</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Degree Of Labelling</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ECD</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Extracellular Domain</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ESI-MS</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Electrospray ionization mass spectrometry</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GPI</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GlycosylPhosphatidylInositol</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HKCA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Heat-Killed <italic>C. albicans</italic></td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HRP</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Horseradish Peroxidase</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IPTG</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">IsoproPyl 1-Thio-D-Galactopyranoside</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">LB</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Lysogeny broth</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PRR</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Pattern Recognition Receptors</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SEC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Size Exclusion Chromatography</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SEC-LS</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Size Exclusion Chromatography coupled to Light Scattering</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SrtA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sortase A</td></tr></tbody></array></glossary><ref-list><title>References</title><ref id=\"B1-ijms-21-05290\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Schnaar</surname><given-names>R.L.</given-names></name></person-group><article-title>Glycobiology simplified: Diverse roles of glycan recognition in inflammation</article-title><source>J. 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The peak at 15,862 kDa corresponds to un-labeled CRD and the peak at 16,556 kDa corresponds to biotinylated CRD (15,862 + 0.694 kDa). After 6 h of reaction, the biotinylation reaction is completed.</p></caption><graphic xlink:href=\"ijms-21-05290-g002\"/></fig><fig id=\"ijms-21-05290-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>SEC-LS analysis of NeutrAvidin and TETRALEC. NeutrAvidin (<bold>a</bold>) and TETRALEC (<bold>b</bold>) normalized chromatograms at 280 nm (green), at 550 nm (purple), refractive index (RI) changes (black), and molar mass (thick lines) along the elution profiles. Molar masses were determined by combining refractive index and light scattering.</p></caption><graphic xlink:href=\"ijms-21-05290-g003\"/></fig><fig id=\"ijms-21-05290-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>SEC-LS analysis of ECD construct (red) and Fc-CRD construct (blue). Normalized chromatograms at 280 nm (thin lines), and molar mass (thick lines) along the elution profiles. Molar masses were determined by combining refractive index and light scattering.</p></caption><graphic xlink:href=\"ijms-21-05290-g004\"/></fig><fig id=\"ijms-21-05290-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>Glycan binding profiles of DC-SIGNR ECD, TETRALEC, Fc-CRD and CRD. Histograms representing the normalized fluorescence values. X-axis has been labeled with glycan numbers in ascending order with only one out of five glycan names mentioned.</p></caption><graphic xlink:href=\"ijms-21-05290-g005\"/></fig><fig id=\"ijms-21-05290-f006\" orientation=\"portrait\" position=\"float\"><label>Figure 6</label><caption><p>Glycan binding profiles of DC-SIGNR ECD, TETRALEC, Fc-CRD and CRD towards selected ligands. (<bold>a</bold>) High mannose and hybrid type N glycan structures were represented in a heat map classification. (<bold>b</bold>) Histograms representing the normalized fluorescence values.</p></caption><graphic xlink:href=\"ijms-21-05290-g006\"/></fig><fig id=\"ijms-21-05290-f007\" orientation=\"portrait\" position=\"float\"><label>Figure 7</label><caption><p>Heat-killed <italic>Candida albicans</italic> recognition by DC-SIGNR. (<bold>a</bold>, left) Representative experiment of the histograms obtained for heat-killed <italic>C. albicans</italic> (HKCA) binding by the different DC-SIGNR-Cy3 constructs. DC-SIGN ECD was used as a positive control. (<bold>a</bold>, right) Mean fluorescent intensity (MFI) of DC-SIGNR-Cy3 constructs binding to HKCA. Data depicted are the average of at least three independent experiments. (<bold>b</bold>, left) Representative experiment of Fc-CRD fusion proteins recognition of HKCA. Fc and the secondary antibody were used as negative controls, while DC-SIGN Fc-CRD is the positive control. (<bold>b</bold>, right) Average of the MFI values obtained. Data showed are the average of at least five independent experiments. Statistical analysis of the MFI results was performed using the unpaired Student’s <italic>t</italic> test, where p-values of <0.05 were considered to be significant (n.s. = not significant, * <italic>p</italic> ≤ 0.05, **** <italic>p</italic> ≤ 0.0001).</p></caption><graphic xlink:href=\"ijms-21-05290-g007\"/></fig><fig id=\"ijms-21-05290-f008\" orientation=\"portrait\" position=\"float\"><label>Figure 8</label><caption><p>Binding of heat-killed <italic>C. albicans</italic> to different DC-SIGNR constructs using confocal microscopy. (<bold>a</bold>) Recognition of HKCA cell wall by DC-SIGNR TETRALEC (upper panels), DC-SIGNR ECD (middle panel) or NeutrAvidin-Cy3 (negative control, lower panels). (<bold>b</bold>) DC-SIGNR Fc-CRD binding to HKCA (upper panels) or Fc (negative control, lower panels). <italic>C. albicans</italic> cell wall was visualized by differential interference contrast (DIC). <italic>C. albicans</italic> DNA was stained using DAPI (in blue), while CLR-Fc constructs were detected using the AF488-conjugated secondary antibody (in green) and Cy3 detection of the DC-SIGNR-Cy 3 constructs is shown in red. Three random pictures were taken per independent experiment (<italic>n</italic> = 3). Scale bar indicates 10 µm.</p></caption><graphic xlink:href=\"ijms-21-05290-g008\"/></fig></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32717820</article-id><article-id pub-id-type=\"pmc\">PMC7432042</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155208</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05208</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Review</subject></subj-group></article-categories><title-group><article-title>ROS Homeostasis in Abiotic Stress Tolerance in Plants</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-9854-3219</contrib-id><name><surname>Nadarajah</surname><given-names>Kalaivani K.</given-names></name></contrib></contrib-group><aff id=\"af1-ijms-21-05208\">Department of Biological Sciences and Biotechnology, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM BANGI, Malaysia; <email>vani@ukm.edu.my</email></aff><pub-date pub-type=\"epub\"><day>23</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5208</elocation-id><history><date date-type=\"received\"><day>04</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>26</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the author.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Climate change-induced abiotic stress results in crop yield and production losses. These stresses result in changes at the physiological and molecular level that affect the development and growth of the plant. Reactive oxygen species (ROS) is formed at high levels due to abiotic stress within different organelles, leading to cellular damage. Plants have evolved mechanisms to control the production and scavenging of ROS through enzymatic and non-enzymatic antioxidative processes. However, ROS has a dual function in abiotic stresses where, at high levels, they are toxic to cells while the same molecule can function as a signal transducer that activates a local and systemic plant defense response against stress. The effects, perception, signaling, and activation of ROS and their antioxidative responses are elaborated in this review. This review aims to provide a purview of processes involved in ROS homeostasis in plants and to identify genes that are triggered in response to abiotic-induced oxidative stress. This review articulates the importance of these genes and pathways in understanding the mechanism of resistance in plants and the importance of this information in breeding and genetically developing crops for resistance against abiotic stress in plants. </p></abstract><kwd-group><kwd>antioxidative</kwd><kwd>enzymatic and non-enzymatic enzymes</kwd><kwd>ROS reactive genes</kwd><kwd>hormones</kwd><kwd>signaling</kwd><kwd>environmental stresses</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05208\"><title>1. Introduction </title><p>Abiotic stresses affect plant morphology, biochemistry, physiology, and anatomy through processes such as photosynthesis, respiration, growth, and development, where prolonged stress induces death [<xref rid=\"B1-ijms-21-05208\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijms-21-05208\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijms-21-05208\" ref-type=\"bibr\">3</xref>]. Plants have evolved physiological and metabolic mechanisms that may be instrumental in alleviating environmental stresses such as drought, cold, salinity, metal toxicity, and submergence. These processes are regulated through the activation of gene networks or pathways that result in either enhanced tolerance or resistance [<xref rid=\"B3-ijms-21-05208\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijms-21-05208\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijms-21-05208\" ref-type=\"bibr\">5</xref>]. During stress, harmful by-products that are detrimental to plants are produced. Superoxide radicals, hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>), hydroxyl radicals (OH<sup>•</sup>), and singlet oxygen (<sup>1</sup>O<sub>2</sub>) are reactive oxygen species (ROS) formed in response to the reduction of oxygen molecules <italic>in planta</italic> [<xref rid=\"B5-ijms-21-05208\" ref-type=\"bibr\">5</xref>]. These oxygen radicals affect proteins and lipids, resulting in cellular damage and death [<xref rid=\"B6-ijms-21-05208\" ref-type=\"bibr\">6</xref>]. When provided with optimal growth conditions, the ROS levels within organelles are low. However, in periods of stress, these levels are elevated due to disturbances in cellular water potential, affecting cellular homeostasis [<xref rid=\"B7-ijms-21-05208\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijms-21-05208\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijms-21-05208\" ref-type=\"bibr\">9</xref>]. Homeostasis of ROS in the cell is achieved through a balance between its production and scavenging [<xref rid=\"B9-ijms-21-05208\" ref-type=\"bibr\">9</xref>], where growth conditions, severity, and duration of stress affects cellular equilibrium [<xref rid=\"B10-ijms-21-05208\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijms-21-05208\" ref-type=\"bibr\">11</xref>]. ROS production and scavenging are somewhat opposed, where an overproduction of this molecule is toxic to the cell, while, as a signal transducer, it triggers the plant’s defense. The initial burst of ROS production activates downstream processes post-stress, which leads to defense mechanism mobilization and the management of stress [<xref rid=\"B12-ijms-21-05208\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijms-21-05208\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijms-21-05208\" ref-type=\"bibr\">14</xref>]. The ROS-scavenging mechanism plays a crucial role in protecting against stress damage in plants [<xref rid=\"B7-ijms-21-05208\" ref-type=\"bibr\">7</xref>,<xref rid=\"B14-ijms-21-05208\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijms-21-05208\" ref-type=\"bibr\">15</xref>]. Understanding the mechanism of ROS production, signaling, and scavenging allows for a powerful strategy to enhance crop tolerance toward environmental conditions [<xref rid=\"B16-ijms-21-05208\" ref-type=\"bibr\">16</xref>]. </p><p>ROS is generated in the cell as a consequence of electron leakage during photosynthesis and respiration [<xref rid=\"B17-ijms-21-05208\" ref-type=\"bibr\">17</xref>]. To moderate the overproduction of ROS and oxidative stress, plants have a well-regulated antioxidative mechanism that consists of enzymatic and non-enzymatic components that can balance ROS synthesis and scavenging and prevent cellular damage [<xref rid=\"B18-ijms-21-05208\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijms-21-05208\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijms-21-05208\" ref-type=\"bibr\">20</xref>]. Superoxide dismutase (SOD), catalase (CAT), and peroxides (POX) are among the enzymatic antioxidant systems that regulate the homeostasis of ROS within organisms [<xref rid=\"B21-ijms-21-05208\" ref-type=\"bibr\">21</xref>]. These enzymes are involved in the reduction process of O<sup>2−</sup> to H<sub>2</sub>O<sub>2</sub> [<xref rid=\"B22-ijms-21-05208\" ref-type=\"bibr\">22</xref>]. The non-enzymatic components, which are generally made up of players such as ascorbic acids, α-tocopherol, flavanoid, glutathione, carotenoids, lipids, and phenolic compounds, efficiently mitigate oxidative damage by reducing ROS activity or by working together with the enzymatic players to achieve efficient antioxidant activity via the utilization of H<sub>2</sub>O<sub>2</sub> [<xref rid=\"B19-ijms-21-05208\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijms-21-05208\" ref-type=\"bibr\">20</xref>]. The mode of action and regulation of both these antioxidant systems and the members will be discussed later.</p><p>In the past years, the importance of oxidative stress management and the role of local and systemic ROS signaling in addressing abiotic stress have been extensively studied [<xref rid=\"B15-ijms-21-05208\" ref-type=\"bibr\">15</xref>,<xref rid=\"B23-ijms-21-05208\" ref-type=\"bibr\">23</xref>]. Despite these studies, there remains a wide variation in the reports on antioxidant activities in various abiotic-stressed plants. Here, we provide an overview of the antioxidant stress management mechanisms in plants and the role they play in abiotic stress response. The regulation and key components of abiotic stress management are yet to be completely understood. This review provides an overview of the status of ROS production in plants and how the plant system achieves ROS homeostasis. Further, the signaling involved in achieving homeostasis between ROS production and scavenging within plant organelles is discussed in brief. Most importantly, this review provides a list of genes involved in ROS regulation in abiotic stress. This information is important for us to identify pathways and genes that regulate oxidative stress in plants and to determine key targets for use in the breeding and genetic engineering of crops.</p></sec><sec id=\"sec2-ijms-21-05208\"><title>2. Antioxidants and Abiotic Stress Modulation</title><p>The whole plant experiences abiotic stress. The stress experienced is then transmitted to distal tissues in the plant through signaling systems that involve hormones or molecules (abscisic acid (ABA), jasmonate salicylic acid, etc.). This, therefore, indicates the importance of stress management in mitigating the effects of stress on the whole plant [<xref rid=\"B24-ijms-21-05208\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijms-21-05208\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijms-21-05208\" ref-type=\"bibr\">26</xref>]. Reviews have focused on ROS metabolism, ROS sensory response, signaling networks [<xref rid=\"B13-ijms-21-05208\" ref-type=\"bibr\">13</xref>,<xref rid=\"B27-ijms-21-05208\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05208\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijms-21-05208\" ref-type=\"bibr\">29</xref>], and their ability to cross-talk with other molecules in addressing developmental and environmental stresses [<xref rid=\"B27-ijms-21-05208\" ref-type=\"bibr\">27</xref>,<xref rid=\"B30-ijms-21-05208\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijms-21-05208\" ref-type=\"bibr\">31</xref>]. Most reviews highlight the control over stomatal aperture, reduced CO<sub>2</sub> levels, and photosynthesis as the contributing factor toward elevated ROS levels in plants [<xref rid=\"B17-ijms-21-05208\" ref-type=\"bibr\">17</xref>,<xref rid=\"B32-ijms-21-05208\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijms-21-05208\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijms-21-05208\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijms-21-05208\" ref-type=\"bibr\">35</xref>]. Elevated levels of abiotic stress-induced ROS may be produced through a reduction in electron transport in the Calvin cycle and a higher electron leakage during photosynthesis in the Mehler reaction. Together, this results in higher respiration and lower photosynthesis and elevated ROS levels in stressed tissues [<xref rid=\"B36-ijms-21-05208\" ref-type=\"bibr\">36</xref>]. Both these reactions occur within the chloroplast and, therefore, require these organelles to be robust against ROS, which is achieved through the function of antioxidant enzymes (enzymatic regulators) that quenches ROS activity. In addition, abiotic stresses (salt, heat, and drought) also influence photo-respiration, resulting in hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) production [<xref rid=\"B17-ijms-21-05208\" ref-type=\"bibr\">17</xref>]. Within plant cells, both H<sub>2</sub>O<sub>2</sub> and O<sup>2−</sup> function as secondary messengers that control cell death, cell cycle, growth, development, hormone signaling, and stress responses [<xref rid=\"B37-ijms-21-05208\" ref-type=\"bibr\">37</xref>]. However, the scavenging systems are not reactive toward hydroxyl radicals and, at high levels, result in deleterious effects to organelles [<xref rid=\"B32-ijms-21-05208\" ref-type=\"bibr\">32</xref>,<xref rid=\"B36-ijms-21-05208\" ref-type=\"bibr\">36</xref>,<xref rid=\"B38-ijms-21-05208\" ref-type=\"bibr\">38</xref>] through lipid peroxidation, which injures membranes and causes damage to biological molecules such as lipids, nucleic acids, and proteins, ultimately resulting in cellular damage and death [<xref rid=\"B39-ijms-21-05208\" ref-type=\"bibr\">39</xref>]. </p><p>Crop production is severely impaired as a consequence of overproduction of ROS in the organs during abiotic stresses [<xref rid=\"B21-ijms-21-05208\" ref-type=\"bibr\">21</xref>]. In response to endogenous signals (hormones and signal molecules) and exogenous environmental stimuli (biotic or abiotic stresses), several enzymes such as NADPH oxidases, amine oxidases, polyamine oxidases, oxalate oxidases, and a large family of class III peroxidases that are localized at the cell surface or apoplast are induced and result in elevated ROS production [<xref rid=\"B21-ijms-21-05208\" ref-type=\"bibr\">21</xref>,<xref rid=\"B40-ijms-21-05208\" ref-type=\"bibr\">40</xref>,<xref rid=\"B41-ijms-21-05208\" ref-type=\"bibr\">41</xref>]. Therefore, to reduce loss from ROS, the antioxidant activity in the plant needs to increase [<xref rid=\"B42-ijms-21-05208\" ref-type=\"bibr\">42</xref>]. As ROS is continuously produced in plants within the chloroplast, peroxisome, and mitochondria, ROS removal systems have to be well-controlled to ensure the wellbeing of tissues and organs. Resistant lines exhibit better ROS removal systems and membrane stability toward deleterious radicals compared to susceptible ones [<xref rid=\"B28-ijms-21-05208\" ref-type=\"bibr\">28</xref>,<xref rid=\"B43-ijms-21-05208\" ref-type=\"bibr\">43</xref>]. It is, therefore, important for the homeostasis of ROS scavenging to be maintained in all stress induced (biotic or abiotic) and normal physiological responses <italic>in planta</italic> [<xref rid=\"B18-ijms-21-05208\" ref-type=\"bibr\">18</xref>,<xref rid=\"B44-ijms-21-05208\" ref-type=\"bibr\">44</xref>]. </p></sec><sec id=\"sec3-ijms-21-05208\"><title>3. Multi-facetted Mechanisms in Abiotic Stress-Related ROS Homeostasis</title><p>In unstressed conditions, ROS is generated within the cell at low levels. However, when subjected to abiotic stresses, ROS levels are elevated, which activates stress pathways within the plant cells [<xref rid=\"B27-ijms-21-05208\" ref-type=\"bibr\">27</xref>,<xref rid=\"B45-ijms-21-05208\" ref-type=\"bibr\">45</xref>]. Therefore, it is essential that the various enzymatic and non-enzymatic ROS-scavenging systems present in different organelles work together in ROS detox and achieve ROS homeostasis [<xref rid=\"B46-ijms-21-05208\" ref-type=\"bibr\">46</xref>,<xref rid=\"B47-ijms-21-05208\" ref-type=\"bibr\">47</xref>]. Ford et al. (2011), in a proteomic study conducted on wheat, showed that several antioxidative enzymes were present under stress [<xref rid=\"B48-ijms-21-05208\" ref-type=\"bibr\">48</xref>]. The most dominant enzyme was catalase (CAT), which is required for metabolizing photorespiratory H<sub>2</sub>O<sub>2</sub> when water is limited like in drought. Similar antioxidative systems were also present in <italic>Arabidopsis thaliana</italic> [<xref rid=\"B49-ijms-21-05208\" ref-type=\"bibr\">49</xref>] where core genes for redox homeostasis include antioxidative and reductant-regenerating enzymes, which respond in both a complex and specific manner. These genes that regulate enzymatic and non-enzymatic processes work in tandem within the system. As many genes are induced during ROS response, an equal number are repressed during the same process [<xref rid=\"B50-ijms-21-05208\" ref-type=\"bibr\">50</xref>,<xref rid=\"B51-ijms-21-05208\" ref-type=\"bibr\">51</xref>,<xref rid=\"B52-ijms-21-05208\" ref-type=\"bibr\">52</xref>]. </p><p>Besides the enzymatic and non-enzymatic processes, sugars are the new emerging ROS scavengers. The emerging “sugar as antioxidant” concept is based on redox balance that is achieved through their relationship in photosynthesis, mitochondrial respiration, and fatty acid β-oxidation in various organelles. Excess sugar results in increased cytosolic H<sub>2</sub>O<sub>2</sub> through metabolic pathways while sugars also result in the reduction in power that contribute toward H<sub>2</sub>O<sub>2</sub> scavenging via the oxidative pentose phosphate (OPP) pathway, which feeds the NADPH metabolism that activates the antioxidative process [<xref rid=\"B53-ijms-21-05208\" ref-type=\"bibr\">53</xref>,<xref rid=\"B54-ijms-21-05208\" ref-type=\"bibr\">54</xref>]. This is the dual nature of sugars in ROS homeostasis. Sugars like mannitol can protect from the oxidative damage of chloroplasts. These sugars affect gene expression through sugar-specific signaling cascades, which regulate the expression of abiotic stress-related genes such as superoxide oxidase (SOD), heat shock proteins (HSP) and glutathione-S-transferases (GST). In recent years, hexokinase (HXK), Snf1-related kinase 1, and INV have been identified as sugar signaling regulators. Trehalose is another sugar that has been shown to regulate abscisic acid (ABA) metabolism and protect against oxidative stress of photosystem II (PSII) during stress in plants [<xref rid=\"B55-ijms-21-05208\" ref-type=\"bibr\">55</xref>,<xref rid=\"B56-ijms-21-05208\" ref-type=\"bibr\">56</xref>]. These sugar-specific pathways together with plant hormone signaling and stress-related pathways, when integrated, are able to participate in plant defense responses. </p><p>Reports have also implicated proline in maintaining redox homeostasis by resulting in NADPH utilization. In a study involving transgenic wheat, higher proline resulted in reduced lipid peroxidation and ROS damage during abiotic stresses [<xref rid=\"B57-ijms-21-05208\" ref-type=\"bibr\">57</xref>]. Proline mitigates stress through up-regulation of proline biosynthesis, scavenging of OH<sup>•</sup> or <sup>1</sup>O<sub>2</sub>, and an active proline metabolic flux linked to other metabolic pathways. Through this, proline maintains cellular energy and NADP<sup>+</sup>/NADPH balance. This protects cells and contributes toward other pathways such as the tricarboxylic acid cycle (TCA) and GSH. Proline feeds into the electron transport chain (ETC) via proline dehydrogenase (PRODH) that results in superoxide and H<sub>2</sub>O<sub>2</sub> formation. When PRODH expression is increased due to high ROS, apoptosis and cell death results in the activation of the hypersensitive response (HR). PRODH-dependent ROS production in the mitochondria is linked to proline, pyrroline-5-carboxylate reductase (P5CR), and delta-1-pyrroline-5-carboxylate dehydrogenase (P5CDH). Therefore, an increase in PRODH/P5CDH results in an increase in proline metabolic cycling where P5C is converted to proline via P5CR and NADPH [<xref rid=\"B58-ijms-21-05208\" ref-type=\"bibr\">58</xref>].</p><p>Sharma et al. (2011) stated that proline accumulates during drought as a solute where mutants defective in proline production were sensitive to drought. Pro-Glu is also implicated in NADP[H] homeostasis [<xref rid=\"B59-ijms-21-05208\" ref-type=\"bibr\">59</xref>]. Proline and glycine betaine aids in water uptake [<xref rid=\"B60-ijms-21-05208\" ref-type=\"bibr\">60</xref>,<xref rid=\"B61-ijms-21-05208\" ref-type=\"bibr\">61</xref>] and ROS quenching, which protects against tissue damage [<xref rid=\"B62-ijms-21-05208\" ref-type=\"bibr\">62</xref>,<xref rid=\"B63-ijms-21-05208\" ref-type=\"bibr\">63</xref>]. Besides scavenging hydroxyl ions, prolines also bind redox active ions and protect against hydroxyl ion damage [<xref rid=\"B64-ijms-21-05208\" ref-type=\"bibr\">64</xref>]. Together with sugars, prolines protect the photosystems against peroxidation in drought [<xref rid=\"B65-ijms-21-05208\" ref-type=\"bibr\">65</xref>]. Recently, ABA-insensitive mutants revealed that <italic>abi4</italic> increased proline levels in stress and could not be rescued through exogenous application of ABA. However, when sucrose was supplied, the ABA response was restored, indicating that ABI4 has a role in ABA-sugar regulation of proline [<xref rid=\"B53-ijms-21-05208\" ref-type=\"bibr\">53</xref>]. Excess ROS generation may also be circumvented through alternative oxidases that divert the electron flow and reduce electron leaks that generate O<sub>2</sub><sup>•−</sup> [<xref rid=\"B66-ijms-21-05208\" ref-type=\"bibr\">66</xref>]. There are possibly other mechanisms utilized by plants to assist in the balancing of ROS levels and the energy spent in plants. These processes may include events such as leaf curling, leaf movement, and photosynthesis apparatus reassembly [<xref rid=\"B67-ijms-21-05208\" ref-type=\"bibr\">67</xref>]. Through the change in the ROS levels, plants can perceive stress and respond accordingly through complex pathways and processes [<xref rid=\"B68-ijms-21-05208\" ref-type=\"bibr\">68</xref>]. The easiest way of keeping ROS homeostasis within the plant is to remove the stress on the plant system, thus resulting in a reduction in ROS to levels non-toxic to the plant [<xref rid=\"B12-ijms-21-05208\" ref-type=\"bibr\">12</xref>]. </p></sec><sec id=\"sec4-ijms-21-05208\"><title>4. Signaling and Control in Abiotic Stress-Associated ROS Homeostasis </title><p>Plant systems activate signaling cascades that trigger downstream components to manage both biotic and abiotic stress. H<sub>2</sub>O<sub>2</sub> signaling pathways result in the accumulation of protectants that guard against the effects of the cellular redox state and the effect therein. ROS participates in stress signaling through the transduction of signals from mitogen-activated protein kinases (MAPKs) [<xref rid=\"B34-ijms-21-05208\" ref-type=\"bibr\">34</xref>,<xref rid=\"B69-ijms-21-05208\" ref-type=\"bibr\">69</xref>], which leads to the induction of several pathways, and activation of gene expression downstream. The activated MAPKs signaling cascade adjusts the levels of H<sub>2</sub>O<sub>2</sub> through the detox antioxidant systems [<xref rid=\"B70-ijms-21-05208\" ref-type=\"bibr\">70</xref>,<xref rid=\"B71-ijms-21-05208\" ref-type=\"bibr\">71</xref>]. The H<sub>2</sub>O<sub>2</sub> is the most stable and easily disseminated form of oxidative stress. These molecules act as a switch that enables the messenger to be effective. In addition, the H<sub>2</sub>O<sub>2</sub> molecules have affinity to thiol groups, which implies a role for this molecule in stress modulation [<xref rid=\"B40-ijms-21-05208\" ref-type=\"bibr\">40</xref>,<xref rid=\"B47-ijms-21-05208\" ref-type=\"bibr\">47</xref>]. Compared to the animal system, plants have a more modulatory effect on the levels of H<sub>2</sub>O<sub>2</sub> compared to completely destroying the molecule within the cell [<xref rid=\"B32-ijms-21-05208\" ref-type=\"bibr\">32</xref>,<xref rid=\"B47-ijms-21-05208\" ref-type=\"bibr\">47</xref>]. This is achieved through the role of antioxidants within the cell that regulates ROS signaling and levels in the host [<xref rid=\"B40-ijms-21-05208\" ref-type=\"bibr\">40</xref>,<xref rid=\"B72-ijms-21-05208\" ref-type=\"bibr\">72</xref>,<xref rid=\"B73-ijms-21-05208\" ref-type=\"bibr\">73</xref>]. Primary or secondary messengers trigger plant-signaling cascades. Some plant hormones like auxins (IAA), abscisic acid (ABA), ethylene (ET), cytokinins (CK), brassinosteroids (BR), gibberellins (GA), jasmonates (JA), and salicylic acid (SA) regulate plant defenses and other biological processes in response to stress [<xref rid=\"B73-ijms-21-05208\" ref-type=\"bibr\">73</xref>,<xref rid=\"B74-ijms-21-05208\" ref-type=\"bibr\">74</xref>,<xref rid=\"B75-ijms-21-05208\" ref-type=\"bibr\">75</xref>]. When under stress, the signal is amplified and stress-related genes are induced in response through signal molecules (ABA, SA, JA, and ET) [<xref rid=\"B73-ijms-21-05208\" ref-type=\"bibr\">73</xref>,<xref rid=\"B76-ijms-21-05208\" ref-type=\"bibr\">76</xref>,<xref rid=\"B77-ijms-21-05208\" ref-type=\"bibr\">77</xref>,<xref rid=\"B78-ijms-21-05208\" ref-type=\"bibr\">78</xref>]. </p><p>There is an intricate connection between ROS levels and ABA produced in plants. ABA is probably the most important signal molecule that controls a lot of physiological processes including stress response to abiotic stress [<xref rid=\"B79-ijms-21-05208\" ref-type=\"bibr\">79</xref>,<xref rid=\"B80-ijms-21-05208\" ref-type=\"bibr\">80</xref>], where it regulates osmotic balance and induces resistance to stresses [<xref rid=\"B79-ijms-21-05208\" ref-type=\"bibr\">79</xref>,<xref rid=\"B80-ijms-21-05208\" ref-type=\"bibr\">80</xref>,<xref rid=\"B81-ijms-21-05208\" ref-type=\"bibr\">81</xref>]. This is achieved through the activation of antioxidant genes (CAT, SOD, peroxidase (POX)) by ABA [<xref rid=\"B82-ijms-21-05208\" ref-type=\"bibr\">82</xref>] through ROS-induction and increased levels of NADPH oxidase [<xref rid=\"B83-ijms-21-05208\" ref-type=\"bibr\">83</xref>]. In rice, drought-hypersensitive mutants DSM1 and DSM2 have shown the ability to regulate POX expression and control ABA levels that lead to ROS quenching [<xref rid=\"B84-ijms-21-05208\" ref-type=\"bibr\">84</xref>,<xref rid=\"B85-ijms-21-05208\" ref-type=\"bibr\">85</xref>]. Further, over-expressed <italic>OsCPK4</italic> and <italic>OsSIK1</italic> genes regulate avoidance of lipid peroxidation, and results in the accumulation of SOD, CAT, and POX that acts to lower the H<sub>2</sub>O<sub>2</sub> levels in the cell [<xref rid=\"B86-ijms-21-05208\" ref-type=\"bibr\">86</xref>,<xref rid=\"B87-ijms-21-05208\" ref-type=\"bibr\">87</xref>,<xref rid=\"B88-ijms-21-05208\" ref-type=\"bibr\">88</xref>]. Up-regulated levels of antioxidant genes (CAT, SOD, POX, GST) result in reduced ROS levels. Research has also shown that the induced expression of GST genes by cold, salt, drought, and heavy metals is a common mechanism for increased tolerance to oxidative damage. In alfalfa, the over-expression of the <italic>MsGSTU8</italic> gene with higher GST activity reduces ROS accumulation by increasing other antioxidant enzyme activities to improve osmotic regulation and reduce ROS damage [<xref rid=\"B89-ijms-21-05208\" ref-type=\"bibr\">89</xref>]. Through mutant studies in Arabidopsis, two PP2C phosphatases ABI1 and ABI2 were identified as negative regulators of ABA signaling, and down-regulators of H<sub>2</sub>O<sub>2</sub> in vitro. ABI1 and ABI2 are involved in Ca<sup>2+</sup> ion channeling through ABA signaling that controls stomatal closure. ABA-induced Ca<sup>2+</sup> ion channels were disrupted in <italic>abi1-1</italic> and <italic>abi2-1</italic> mutants, where stomatal closure was affected in the <italic>abi2-1</italic> mutant, while <italic>abi1-1</italic> remained functional. However, in vivo, both genes interacted with GPX3 (glutathione peroxidase) and regulated ABA- and H<sub>2</sub>O<sub>2</sub>-induced stomatal closure. Oxidized GPX3 reduced the phosphatase activity of ABI2 and converted ABI2 to an oxidized form in vitro. ABI1 acts upstream of ROS production and ABI2 works downstream of ROS production in the cell [<xref rid=\"B90-ijms-21-05208\" ref-type=\"bibr\">90</xref>,<xref rid=\"B91-ijms-21-05208\" ref-type=\"bibr\">91</xref>,<xref rid=\"B92-ijms-21-05208\" ref-type=\"bibr\">92</xref>]. In another set of <italic>Arabidopsis</italic> genes, <italic>AtGPX3</italic> and <italic>AtGPX6</italic>, H<sub>2</sub>O<sub>2</sub> levels affect ABA signaling, ABA-responsive gene expression, CA<sup>2+</sup> channel activation, and stomatal closure, indicating that the redox status regulates all these processes [<xref rid=\"B93-ijms-21-05208\" ref-type=\"bibr\">93</xref>,<xref rid=\"B94-ijms-21-05208\" ref-type=\"bibr\">94</xref>,<xref rid=\"B95-ijms-21-05208\" ref-type=\"bibr\">95</xref>].</p></sec><sec id=\"sec5-ijms-21-05208\"><title>5. Organelles Regulation of ROS Homeostasis during Abiotic Stress</title><p>In the following sections, we will review the effect of abiotic stresses on plant cells and their effects on different organelles. <xref ref-type=\"fig\" rid=\"ijms-21-05208-f001\">Figure 1</xref>, <xref ref-type=\"fig\" rid=\"ijms-21-05208-f002\">Figure 2</xref>, <xref ref-type=\"fig\" rid=\"ijms-21-05208-f003\">Figure 3</xref> and <xref ref-type=\"fig\" rid=\"ijms-21-05208-f004\">Figure 4</xref> describe the processes ongoing in each organelle under abiotic stress.</p><sec id=\"sec5dot1-ijms-21-05208\"><title>5.1. ROS Regulation in the Chloroplast </title><p>Photosystems (PS) I and II are the reaction center in the chloroplast where ROS is produced through inhibition of CO<sub>2</sub>, and low water levels are due to stress. Control of ROS levels in the chloroplast is vital for plant survival under stress [<xref rid=\"B96-ijms-21-05208\" ref-type=\"bibr\">96</xref>]. The ETC in the PS is the chief source of ROS in chloroplasts. The ROS status in plants changes with the physiological and environmental status of the plant. When not stressed, the electrons flow from the excited PS to reduce NADP to NADPH, which then enters the Calvin cycle where CO<sub>2</sub> is reduced as the electron acceptor. Conversely, there is an overload on ETC, which results in electron leakage from ferredoxin to O<sub>2</sub>, producing O<sub>2</sub><sup>•−</sup> [<xref rid=\"B97-ijms-21-05208\" ref-type=\"bibr\">97</xref>]. When exposed to high light intensity with low CO<sub>2</sub> intake due to stomatal closure, there is a direct transfer of electron to molecular oxygen through the Mehler reaction in PSI [<xref rid=\"B38-ijms-21-05208\" ref-type=\"bibr\">38</xref>]. These superoxide radicals are converted by a membrane-bound Cu/ZnSOD to H<sub>2</sub>O<sub>2</sub>, which is further converted by a thylakoid-bound POX to water [<xref rid=\"B98-ijms-21-05208\" ref-type=\"bibr\">98</xref>]. The thylakoid-based POX then reacts with thioredoxin to protect against oxidative stress, especially in drought, and provides an alternate water–water cycle to detox the radicals in the chloroplast [<xref rid=\"B18-ijms-21-05208\" ref-type=\"bibr\">18</xref>,<xref rid=\"B99-ijms-21-05208\" ref-type=\"bibr\">99</xref>,<xref rid=\"B100-ijms-21-05208\" ref-type=\"bibr\">100</xref>]. Meanwhile, in PSII, <sup>1</sup>O<sub>2</sub> are generated through electron transfer. H<sub>2</sub>O<sub>2</sub> produced in PSI plays a role in inhibiting <sup>1</sup>O<sub>2</sub> through the water–water cycle [<xref rid=\"B101-ijms-21-05208\" ref-type=\"bibr\">101</xref>,<xref rid=\"B102-ijms-21-05208\" ref-type=\"bibr\">102</xref>,<xref rid=\"B103-ijms-21-05208\" ref-type=\"bibr\">103</xref>]. Levels of singlet oxygen when kept unchecked trigger genetic programming such as growth retardation and death through the EXECUTER pathways [<xref rid=\"B104-ijms-21-05208\" ref-type=\"bibr\">104</xref>] (<xref ref-type=\"fig\" rid=\"ijms-21-05208-f001\">Figure 1</xref>).</p></sec><sec id=\"sec5dot2-ijms-21-05208\"><title>5.2. ROS Regulation in the Mitochondria </title><p>The production of ROS is lower in the mitochondria compared to the chloroplast. There is a variation in plant and animal ROS production in mitochondria, wherein in plants, the mitochondria are also the site of photorespiration that is surrounded by carbohydrate solutes [<xref rid=\"B88-ijms-21-05208\" ref-type=\"bibr\">88</xref>]. In drought, when respiration rates are high, and transpiration and photosynthesis are low, the demand on mitochondrial ATP is high to compensate for chloroplast ATP production, which causes an increase in ROS levels in the mitochondria [<xref rid=\"B105-ijms-21-05208\" ref-type=\"bibr\">105</xref>]. ROS is produced within several sites in mitochondria. In this organelle, the O<sub>2</sub> is reduced to O<sub>2</sub><sup>•−</sup> via the NADPH dehydrogenase [complex I] [<xref rid=\"B106-ijms-21-05208\" ref-type=\"bibr\">106</xref>]. Within the mitochondria complex I and III, the electron transfer complex has sufficient energy to reduce O<sub>2</sub> to ROS from overreduction of the ubiquinone pool (UQ) [<xref rid=\"B107-ijms-21-05208\" ref-type=\"bibr\">107</xref>,<xref rid=\"B108-ijms-21-05208\" ref-type=\"bibr\">108</xref>,<xref rid=\"B109-ijms-21-05208\" ref-type=\"bibr\">109</xref>,<xref rid=\"B110-ijms-21-05208\" ref-type=\"bibr\">110</xref>]. An electron is donated to cytochrome C<sub>1</sub> when UQ is in a fully reduced state. This results in a highly unstable radical complex, which brings about electron leakage and O<sub>2</sub><sup>•−</sup> formation [<xref rid=\"B111-ijms-21-05208\" ref-type=\"bibr\">111</xref>]. When NAD is low within the complex, there is a reverse electron flow from complex II to I [<xref rid=\"B112-ijms-21-05208\" ref-type=\"bibr\">112</xref>,<xref rid=\"B113-ijms-21-05208\" ref-type=\"bibr\">113</xref>]</p><p>There are alternative ROS-producing sources in the mitochondria like aconitase [<xref rid=\"B114-ijms-21-05208\" ref-type=\"bibr\">114</xref>]. While the mitochondria are the source of ROS production, this organelle also has its detoxification system made up of alternative oxidase (AOX) and MnSOD. Aconitase causes the production of ROS while 1-galactono-γ lactone dehydrogenase (GAL) directly donates an electron to the ETC [<xref rid=\"B63-ijms-21-05208\" ref-type=\"bibr\">63</xref>]. The O<sub>2</sub><sup>•−</sup> that is formed in this organelle is quickly converted to a stable membrane-permeable H<sub>2</sub>O<sub>2</sub> by MnSOD or ascorbate peroxidase (APX). AOX together with MnSOD work at maintaining the reductive state of the UQ pool while reducing O<sub>2</sub><sup>−</sup> to O<sub>2</sub> [<xref rid=\"B115-ijms-21-05208\" ref-type=\"bibr\">115</xref>,<xref rid=\"B116-ijms-21-05208\" ref-type=\"bibr\">116</xref>]. Giraud et al. (2008) report that mutant AOX <italic>Arabidopsis</italic> plants were sensitive to drought and light stress, indicating a role for this enzyme in ROS detox [<xref rid=\"B117-ijms-21-05208\" ref-type=\"bibr\">117</xref>]. The largest amounts of ROS found in the mitochondria are as O<sub>2</sub><sup>−</sup> molecules, which are converted in the detox process by MnSOD and APX to H<sub>2</sub>O<sub>2</sub> and O<sub>2</sub> eventually [<xref rid=\"B111-ijms-21-05208\" ref-type=\"bibr\">111</xref>]. As in chloroplast, mitochondria produce ROS even in the non-stressed state at basal levels, and any form of stress causes alleviation in ROS due to ATP synthesis, leading to a reduction in the UQ pool [<xref rid=\"B18-ijms-21-05208\" ref-type=\"bibr\">18</xref>,<xref rid=\"B116-ijms-21-05208\" ref-type=\"bibr\">116</xref>,<xref rid=\"B118-ijms-21-05208\" ref-type=\"bibr\">118</xref>,<xref rid=\"B119-ijms-21-05208\" ref-type=\"bibr\">119</xref>] (<xref ref-type=\"fig\" rid=\"ijms-21-05208-f002\">Figure 2</xref>).</p></sec><sec id=\"sec5dot3-ijms-21-05208\"><title>5.3. ROS Regulation at the Peroxisomes</title><p>In drought due to reduced CO<sub>2</sub> and O<sub>2</sub> levels in the cell, there is an increase in the production of glycolates, which are then oxidized by glycolate oxidase in the peroxisome to H<sub>2</sub>O<sub>2</sub> [<xref rid=\"B88-ijms-21-05208\" ref-type=\"bibr\">88</xref>,<xref rid=\"B120-ijms-21-05208\" ref-type=\"bibr\">120</xref>,<xref rid=\"B121-ijms-21-05208\" ref-type=\"bibr\">121</xref>]. Just as in the mitochondria and chloroplast, the peroxisome produces the O<sub>2</sub><sup>•−</sup> even in their normal metabolism. Through a fine balance between scavenging and production, ROS levels in peroxisome are kept in check [<xref rid=\"B122-ijms-21-05208\" ref-type=\"bibr\">122</xref>]. There are two sites of O<sup>2−</sup> production in the peroxisomal matrix where xanthine oxidases convert xanthine and hypoxanthine to uric acid and O<sub>2</sub><sup>−</sup>, and at the proximal membrane, O<sub>2</sub> is used as an electron acceptor by NADH and Cytb to produce O<sub>2</sub><sup>−</sup> [<xref rid=\"B123-ijms-21-05208\" ref-type=\"bibr\">123</xref>]. Metabolic processes that produce H<sub>2</sub>O<sub>2</sub> in the peroxisome include β-oxidation, disproportionation of radicals, and the flavin oxidase pathway [<xref rid=\"B88-ijms-21-05208\" ref-type=\"bibr\">88</xref>,<xref rid=\"B124-ijms-21-05208\" ref-type=\"bibr\">124</xref>,<xref rid=\"B125-ijms-21-05208\" ref-type=\"bibr\">125</xref>]. CATs detox the system of H<sub>2</sub>O<sub>2</sub> while APX and ascorbic acid (AsA)-GSH scavenge H<sub>2</sub>O<sub>2</sub> in the peroxisome [<xref rid=\"B9-ijms-21-05208\" ref-type=\"bibr\">9</xref>,<xref rid=\"B126-ijms-21-05208\" ref-type=\"bibr\">126</xref>,<xref rid=\"B127-ijms-21-05208\" ref-type=\"bibr\">127</xref>]. The reduced AsA-GSH contents result in lipid peroxidation of peroxisomes. Other than these enzymes, POX, a polyamine-catabolizing enzyme, has been shown to regulate stress-responsive genes to facilitate the production and scavenging of ROS in the peroxisome [<xref rid=\"B128-ijms-21-05208\" ref-type=\"bibr\">128</xref>,<xref rid=\"B129-ijms-21-05208\" ref-type=\"bibr\">129</xref>,<xref rid=\"B130-ijms-21-05208\" ref-type=\"bibr\">130</xref>] (<xref ref-type=\"fig\" rid=\"ijms-21-05208-f003\">Figure 3</xref>).</p></sec><sec id=\"sec5dot4-ijms-21-05208\"><title>5.4. ROS Regulation in the Apoplast </title><p>Stress-induced ROS production is combined with the effect of ABA in the apoplast [<xref rid=\"B27-ijms-21-05208\" ref-type=\"bibr\">27</xref>,<xref rid=\"B131-ijms-21-05208\" ref-type=\"bibr\">131</xref>,<xref rid=\"B132-ijms-21-05208\" ref-type=\"bibr\">132</xref>]. NADPH oxidases generated in the stomatal guard cells in <italic>Arabidopsis</italic> produces ROS in the apoplast as a consequence of ABA-induced stomatal closure [<xref rid=\"B92-ijms-21-05208\" ref-type=\"bibr\">92</xref>,<xref rid=\"B133-ijms-21-05208\" ref-type=\"bibr\">133</xref>]. <italic>AtRbohD</italic> and <italic>AtRbohF</italic> are two genes that regulate NADPH oxidases in <italic>Arabidopsis</italic> [<xref rid=\"B134-ijms-21-05208\" ref-type=\"bibr\">134</xref>,<xref rid=\"B135-ijms-21-05208\" ref-type=\"bibr\">135</xref>]. Other than the NADPH oxidases, peroxidases, cell wall-linked oxidases, polyamine oxidases, and oxalate oxidases play a role in generating H<sub>2</sub>O<sub>2</sub> in the apoplast [<xref rid=\"B136-ijms-21-05208\" ref-type=\"bibr\">136</xref>]. One important source of apoplastic ROS production is the cell wall-linked oxidases [<xref rid=\"B63-ijms-21-05208\" ref-type=\"bibr\">63</xref>]. The oxalate oxidase, a cell wall germin-like protein, is known to release H<sub>2</sub>O<sub>2</sub> and CO<sub>2</sub> from oxalic acid [<xref rid=\"B137-ijms-21-05208\" ref-type=\"bibr\">137</xref>]. This enzyme is mostly involved in plant defense against biotic and abiotic stress. Amine oxidases are found in the apoplast and are contributors to plant defense through the production of H<sub>2</sub>O<sub>2</sub>. These oxidases cause the oxidative deamination of polyamines via cofactors. As observed by Heyno et al. (2011), the hydroxyl ion generation in the apoplastic region of the cell in full or in part contributes to cell wall-bound peroxidases [<xref rid=\"B138-ijms-21-05208\" ref-type=\"bibr\">138</xref>]. The increased production of H<sub>2</sub>O<sub>2</sub> results in higher levels of polyamines and Ca<sup>2+</sup>. This results in more H<sub>2</sub>O<sub>2</sub> being produced, which, in turn, activates the antioxidative machinery along with increased synthesis of higher polyamines and secondary messengers like Ca<sup>2+</sup>. ABA levels are also elevated, which leads to the activation of polyamine-activated signaling pathways in response to abiotic stresses [<xref rid=\"B128-ijms-21-05208\" ref-type=\"bibr\">128</xref>] (<xref ref-type=\"fig\" rid=\"ijms-21-05208-f004\">Figure 4</xref>). </p></sec><sec id=\"sec5dot5-ijms-21-05208\"><title>5.5. ROS Regulation at Cell Walls and Plasma Membranes</title><p>The cell walls of plants when stressed accumulates oxidative radicals OH<sup>•</sup>, O<sub>2</sub><sup>•−</sup>, H<sub>2</sub>O<sub>2</sub>, and <sup>1</sup>O<sub>2</sub>. The cell walls-localized peroxidases, lipoxygenases, oxidases, and polyamines are responsible for the generation of ROS. These oxygen radicals are also responsible for the lipid peroxidation of polyunsaturated fatty acids (PUFA) found in the cell wall of plants that produce ROS [<xref rid=\"B13-ijms-21-05208\" ref-type=\"bibr\">13</xref>]. The peroxidases found in the cell walls catalyze the formation of H<sub>2</sub>O<sub>2</sub> through NADH, through malate dehydrogenase. ROS generation by cell wall-associated peroxidases trigger biotic responses and the alteration of potassium [K] levels in drought-affected plants [<xref rid=\"B88-ijms-21-05208\" ref-type=\"bibr\">88</xref>,<xref rid=\"B111-ijms-21-05208\" ref-type=\"bibr\">111</xref>]. Liu et al. (2015), in his review, described polyamine’s role in plant stress response as one that is mediated through antioxidant systems or suppression of ROS [<xref rid=\"B12-ijms-21-05208\" ref-type=\"bibr\">12</xref>]. Therefore, the exogenous application of polyamines or the activation of polyamine-related genes would lead to the activation of antioxidant processes in situ in plants exposed to drought, salinity, nutrient deficiency, temperatures, and others [<xref rid=\"B18-ijms-21-05208\" ref-type=\"bibr\">18</xref>,<xref rid=\"B129-ijms-21-05208\" ref-type=\"bibr\">129</xref>,<xref rid=\"B139-ijms-21-05208\" ref-type=\"bibr\">139</xref>].</p><p>As for the plasma membrane, NADPH oxidases localized in the membrane are responsible for generating O<sub>2</sub><sup>•−</sup> through the transfer of an electron from NADPH to O<sub>2</sub>, which is then converted by SOD to H<sub>2</sub>O<sub>2</sub>. The NADPH oxidases in the plasma membranes are crucial players in the stress response of plant cells to environmental factors such as abiotic stresses [<xref rid=\"B18-ijms-21-05208\" ref-type=\"bibr\">18</xref>,<xref rid=\"B63-ijms-21-05208\" ref-type=\"bibr\">63</xref>]. In certain cases, multiple enzymes were found to catalyze the conversion of O<sub>2</sub> to O<sub>2</sub><sup>•−</sup>. For instance, in soybean, other than NADPH oxidase, a quinone reductase also functions in the presence of menadione to facilitate the conversion of O<sub>2</sub> to O<sub>2</sub><sup>•−</sup> [<xref rid=\"B18-ijms-21-05208\" ref-type=\"bibr\">18</xref>,<xref rid=\"B63-ijms-21-05208\" ref-type=\"bibr\">63</xref>,<xref rid=\"B140-ijms-21-05208\" ref-type=\"bibr\">140</xref>] (<xref ref-type=\"fig\" rid=\"ijms-21-05208-f004\">Figure 4</xref>).</p></sec></sec><sec id=\"sec6-ijms-21-05208\"><title>6. Genes Regulating ROS Homeostasis in Abiotic Stress</title><p>There are an array of genes identified as key players of ROS regulation in plants [<xref rid=\"B79-ijms-21-05208\" ref-type=\"bibr\">79</xref>]. Large numbers of genes are activated in response to abiotic stress in plants. These genes are highly regulated in their expression [<xref rid=\"B141-ijms-21-05208\" ref-type=\"bibr\">141</xref>]. In the following section, we will briefly expound on the roles played by some of these genes, specifically genes involved in the activation and regulation of ROS in abiotic stress response [<xref rid=\"B142-ijms-21-05208\" ref-type=\"bibr\">142</xref>]. These genes have been divided into various classes to enable a systematic illustration [<xref rid=\"B143-ijms-21-05208\" ref-type=\"bibr\">143</xref>]. <xref rid=\"ijms-21-05208-t001\" ref-type=\"table\">Table 1</xref> and <xref ref-type=\"fig\" rid=\"ijms-21-05208-f005\">Figure 5</xref> provide details of some of these genes and how these genes are interconnected in the regulation of abiotic stress.</p><sec id=\"sec6dot1-ijms-21-05208\"><title>6.1. Protein Kinases and Phosphatases </title><p>One of the important gene groups involved in ROS signaling is the mitogen-activated protein kinases (MAPK). There have been many MAPK cascades studied in plants [<xref rid=\"B144-ijms-21-05208\" ref-type=\"bibr\">144</xref>]. This gene can be activated by different external stimuli. For instance, MPK6 in <italic>Arabidopsis</italic> is activated by abiotic stress (drought) and repressed under rehydration [<xref rid=\"B145-ijms-21-05208\" ref-type=\"bibr\">145</xref>,<xref rid=\"B146-ijms-21-05208\" ref-type=\"bibr\">146</xref>]. In roses, RhMPK6 is produced at high levels during hydration, which phosphorylates and stabilizes RhACS1, resulting in ethylene production [<xref rid=\"B147-ijms-21-05208\" ref-type=\"bibr\">147</xref>]. Through signal transduction that follows, flower opening and senescence are controlled. Chen et al. (2017) also notes that RhMKK9 is involved in dehydration-dependent ethylene biosynthesis, where it works on RhMPK6-RhACS1 [<xref rid=\"B148-ijms-21-05208\" ref-type=\"bibr\">148</xref>]. In another study by Mitula et al. (2015), MAPKKK18 was reported to control stomatal aperture and development [<xref rid=\"B149-ijms-21-05208\" ref-type=\"bibr\">149</xref>]. Through mutant studies of this gene, it was concluded that MAPKKK18 controlled ABA-dependent stomatal closure under stress [<xref rid=\"B145-ijms-21-05208\" ref-type=\"bibr\">145</xref>,<xref rid=\"B150-ijms-21-05208\" ref-type=\"bibr\">150</xref>]. </p><p>GhMKK1 from cotton was able to increase stress resistance and result in ROS homeostasis. Likewise, <italic>BnMKK1</italic> (from <italic>Brassica napus</italic>), when introduced and overexpressed in transgenic tobacco, triggered ABA signaling, causing rapid water loss and drought sensitivity. This, therefore, indicates that this gene generates drought-susceptible plants [<xref rid=\"B151-ijms-21-05208\" ref-type=\"bibr\">151</xref>]. GhMKK5, however, reduced tolerance to salt and other stresses [<xref rid=\"B152-ijms-21-05208\" ref-type=\"bibr\">152</xref>]. In <italic>Arabidopsis</italic>, overexpression of GhMPK17 resulted in increased H<sub>2</sub>O<sub>2</sub> levels and osmotic stress [<xref rid=\"B145-ijms-21-05208\" ref-type=\"bibr\">145</xref>,<xref rid=\"B153-ijms-21-05208\" ref-type=\"bibr\">153</xref>]. In cotton, a novel <italic>GhMAPKKK49</italic> was induced in response to ABA or H<sub>2</sub>O<sub>2</sub> [<xref rid=\"B142-ijms-21-05208\" ref-type=\"bibr\">142</xref>]. Further, GhMAPKKK49 was also hypothesized to interact with GhMKK4 and GhMKK9 in mediating ABA- and H<sub>2</sub>O<sub>2</sub>-mediated abiotic stress responses. Wang et al. (2016) reported that GhMKK3 regulates drought tolerance through control of the water deficit. <italic>GhMKK3</italic> overexpression in tobacco effectively induced ABA-responsive stomatal closure and reduction in stomatal numbers [<xref rid=\"B154-ijms-21-05208\" ref-type=\"bibr\">154</xref>,<xref rid=\"B155-ijms-21-05208\" ref-type=\"bibr\">155</xref>]. Danquah et al. (2015) noted that GhMKK3 and GhPIP1 work in concert with GhMPK7 to generate drought and ABA-activated MAPK modules [<xref rid=\"B156-ijms-21-05208\" ref-type=\"bibr\">156</xref>]. Transgenic tobacco overexpressing <italic>ZmMKK3-1 (ZmMKK3)</italic> from maize exhibited enhanced tolerance toward stress-induced oxidative stress [<xref rid=\"B152-ijms-21-05208\" ref-type=\"bibr\">152</xref>]. <italic>ZmMKK1</italic> is induced by ABA in maize roots and results in drought and salinity tolerance in <italic>Arabidopsis</italic> [<xref rid=\"B157-ijms-21-05208\" ref-type=\"bibr\">157</xref>]. <italic>ZmMPK4-1</italic>, <italic>ZmMPK7</italic>, and <italic>ZmMPK17</italic> were also involved in the regulation of oxidative stress [<xref rid=\"B158-ijms-21-05208\" ref-type=\"bibr\">158</xref>] where <italic>ZmMKK10</italic> is jointly activated by <italic>ZmMPK7</italic> and <italic>ZmMPK3</italic> during drought in maize [<xref rid=\"B159-ijms-21-05208\" ref-type=\"bibr\">159</xref>]. Similarly, <italic>AtMPK6, AtMPK7, OsMPK5</italic>, and <italic>OsMPK</italic> show ABA-induced defense as in <italic>ZmMPK3</italic> and <italic>ZmMPK6-2</italic> [<xref rid=\"B152-ijms-21-05208\" ref-type=\"bibr\">152</xref>,<xref rid=\"B155-ijms-21-05208\" ref-type=\"bibr\">155</xref>]. Salt Intolerance 1 (<italic>SIT1</italic>), a receptor-like kinase (RLK), is expressed at high levels in rice root cells during stress and is induced when exposed to salt stress. SIT1 activation of MPK3 and MPK6 in drought and salinity was determined via immunoprecipitation assays where both kinases were reported to form a complex with SIT1 [<xref rid=\"B160-ijms-21-05208\" ref-type=\"bibr\">160</xref>], leading to the phosphorylation of MPK3 and MPK6. From the reports on various plant systems, MAPK pathways are involved in both biotic and abiotic stress modulation and act in concert with phytohormone and calcium ion signaling to activate antioxidant in plants.</p><p>Protein phosphatases are another large gene family found in plants. The sequencing project of <italic>Medicago truncatula</italic> identified and characterized PPC2 genes in the genome [<xref rid=\"B161-ijms-21-05208\" ref-type=\"bibr\">161</xref>]. Further genome analysis in other plants systems such as rice, tomato, <italic>Arabidopsis</italic>, peppers, and maize indicated the presence of <italic>PP2C</italic> gene families [<xref rid=\"B162-ijms-21-05208\" ref-type=\"bibr\">162</xref>,<xref rid=\"B163-ijms-21-05208\" ref-type=\"bibr\">163</xref>,<xref rid=\"B164-ijms-21-05208\" ref-type=\"bibr\">164</xref>]. These genes are responsible for the plants adaptation to environmental conditions and stresses [<xref rid=\"B165-ijms-21-05208\" ref-type=\"bibr\">165</xref>]. There are two main subfamilies of PP2C that are present in plants. The subfamily A of PP2Cs are involved in stress responses that are ABA-dependent, while subfamily B phosphatases are MAPK regulators [<xref rid=\"B165-ijms-21-05208\" ref-type=\"bibr\">165</xref>]. Further studies of the PP2C genes in <italic>Arabidopsis</italic> and rice indicate an active role for these genes in abiotic stresses that are ABA-dependent [<xref rid=\"B163-ijms-21-05208\" ref-type=\"bibr\">163</xref>,<xref rid=\"B166-ijms-21-05208\" ref-type=\"bibr\">166</xref>]. In <italic>M</italic>. <italic>truncatula</italic>, subfamily A genes were either up- or down-regulated during drought in an ABA-contingent manner. Through an in-depth study of the <italic>PP2C</italic> gene in <italic>M. truncatula</italic>, <italic>MtPP2C8</italic>, <italic>MtPP2C37</italic>, <italic>MtPP2C46</italic>, <italic>MtPP2C47</italic>, <italic>MtPP2C67, MtPP2C72</italic>, and <italic>MtPP2C73</italic> were reported to be homologous to the <italic>HAI</italic> PP2Cs in <italic>Arabidopsis</italic> and are induced by stress. In addition, <italic>MtPP2C92</italic> and <italic>MtPP2C65</italic> expression were induced under stress. In <italic>Arabidopsis</italic>, the <italic>HAI</italic> PP2Cs provide drought resistance and have the greatest influence on the ABA-independent low-water-potential phenotypes compared to the classical phenotypes [<xref rid=\"B166-ijms-21-05208\" ref-type=\"bibr\">166</xref>]. Further, the homologs of <italic>MtPP2C92</italic> and <italic>MtPP2C65</italic> in <italic>Arabidopsis</italic> (<italic>ABI1</italic> and <italic>ABI2</italic>, respectively) are the most well-documented PP2C genes that are ABA-dependent and are active under abiotic stresses [<xref rid=\"B163-ijms-21-05208\" ref-type=\"bibr\">163</xref>,<xref rid=\"B165-ijms-21-05208\" ref-type=\"bibr\">165</xref>]. The <italic>MP2C</italic> (homolog with <italic>AP2C1</italic>) in alfalfa acts as a negative regulator of the MAPK pathway in cold and drought [<xref rid=\"B167-ijms-21-05208\" ref-type=\"bibr\">167</xref>,<xref rid=\"B168-ijms-21-05208\" ref-type=\"bibr\">168</xref>]. The <italic>AP2C2</italic> (homolog <italic>MtPP2C72</italic>) regulates ROS levels in response to biotic and abiotic stresses [<xref rid=\"B168-ijms-21-05208\" ref-type=\"bibr\">168</xref>]. <italic>AP2C1</italic> is strongly induced in response to drought and cold, while <italic>AP2C2</italic> is less responsive [<xref rid=\"B164-ijms-21-05208\" ref-type=\"bibr\">164</xref>].</p></sec><sec id=\"sec6dot2-ijms-21-05208\"><title>6.2. Transcriptional Factors</title><p>Transcriptional factors (TFs) play an important role in stress-responsive gene regulation and expression. ERF, DREB, APETALA, WRKY, NAC, and Zn finger families play an important role in stress regulation in plants [<xref rid=\"B169-ijms-21-05208\" ref-type=\"bibr\">169</xref>,<xref rid=\"B170-ijms-21-05208\" ref-type=\"bibr\">170</xref>,<xref rid=\"B171-ijms-21-05208\" ref-type=\"bibr\">171</xref>,<xref rid=\"B172-ijms-21-05208\" ref-type=\"bibr\">172</xref>]. Zn finger family proteins (ZFP) are important in cellular function in eukaryotes and have been divided into several classes such as C2H2, CCCH, C2HC, and C2C2 based on the location of their Zn residues [<xref rid=\"B173-ijms-21-05208\" ref-type=\"bibr\">173</xref>,<xref rid=\"B174-ijms-21-05208\" ref-type=\"bibr\">174</xref>]. ZFP have been implicated as regulators of defense, development, growth, and stress in plant systems [<xref rid=\"B175-ijms-21-05208\" ref-type=\"bibr\">175</xref>,<xref rid=\"B176-ijms-21-05208\" ref-type=\"bibr\">176</xref>]. ZFPs play a key role in oxidative stress response in various plant systems [<xref rid=\"B177-ijms-21-05208\" ref-type=\"bibr\">177</xref>]. ZFPs are integral in ROS defense where genes such as <italic>ZAT7</italic>, <italic>ZAT10</italic>, and <italic>ZAT12</italic> are up-regulated in <italic>Arabidopsis</italic> knockout plants during oxidative stress [<xref rid=\"B178-ijms-21-05208\" ref-type=\"bibr\">178</xref>,<xref rid=\"B179-ijms-21-05208\" ref-type=\"bibr\">179</xref>,<xref rid=\"B180-ijms-21-05208\" ref-type=\"bibr\">180</xref>]. A drought- and salt-tolerant <italic>Arabidopsis</italic> mutant, <italic>dst</italic>, encodes a C2H2-type ZFP with the ability to negatively regulate stomatal closure. This is achieved through a DST-mediated H<sub>2</sub>O<sub>2</sub> regulation of increased stomatal closure and enhanced drought resistance [<xref rid=\"B181-ijms-21-05208\" ref-type=\"bibr\">181</xref>]. In rice, the DST complex was reported to work in association with DST Co-activator 1 [DCA1] to regulate stomata, and aperture and downstream regulation of drought-responsive genes. This complex induces genes involved in POX production and H<sub>2</sub>O<sub>2</sub> detoxification. However, overexpression of DCA1 increased sensitivity to stress [<xref rid=\"B182-ijms-21-05208\" ref-type=\"bibr\">182</xref>]. In rice, the <italic>OsAHL1</italic> gene expression improved stress tolerance through alleviation of stress at the plasma membrane [<xref rid=\"B183-ijms-21-05208\" ref-type=\"bibr\">183</xref>].</p><p><italic>ZFP36</italic>, another C2H2-type ZFP gene, is involved in antioxidant defense and enhances resistance to oxidative stress tolerance in rice [<xref rid=\"B184-ijms-21-05208\" ref-type=\"bibr\">184</xref>,<xref rid=\"B185-ijms-21-05208\" ref-type=\"bibr\">185</xref>]. This gene is a major player in the regulation of cross-talk between key players of oxidative stress such as H<sub>2</sub>O<sub>2</sub>, NADPH oxidase, MAPK, and ABA signaling [<xref rid=\"B184-ijms-21-05208\" ref-type=\"bibr\">184</xref>]. The incorporation of the <italic>ZFP245</italic> (C2H2) gene in rice increased abiotic stress tolerance through the activation of ROS-scavenging enzymes such as SOD and POX. ZFP179, ZFP182, and ZFP252 were also linked to the oxidative stress response via ROS signaling in rice [<xref rid=\"B27-ijms-21-05208\" ref-type=\"bibr\">27</xref>,<xref rid=\"B153-ijms-21-05208\" ref-type=\"bibr\">153</xref>,<xref rid=\"B186-ijms-21-05208\" ref-type=\"bibr\">186</xref>]. A tandem ZFP, OsTZF1, negatively regulates leaf senescence under drought and oxidative stress in rice through the expression of ROS homeostasis genes and scavenging enzymes [<xref rid=\"B153-ijms-21-05208\" ref-type=\"bibr\">153</xref>,<xref rid=\"B187-ijms-21-05208\" ref-type=\"bibr\">187</xref>]. Similarly, <italic>GhTZF1</italic>, a TZF gene, also modulates oxidative and senescence stress in cotton through the mediation of ROS equilibrium [<xref rid=\"B188-ijms-21-05208\" ref-type=\"bibr\">188</xref>]. </p><p>Another TF family that has a wide involvement in rice and <italic>Arabidopsis</italic> is the WRKY family, which has over 100 WRKY genes in both plants collectively. This TF regulates both biotic and abiotic stresses [<xref rid=\"B189-ijms-21-05208\" ref-type=\"bibr\">189</xref>]. The WRKY TF family is recognized by the presence of WRKYGQK heptapeptide and a zinc-finger-like motif at the N- and C-terminus, respectively [<xref rid=\"B190-ijms-21-05208\" ref-type=\"bibr\">190</xref>,<xref rid=\"B191-ijms-21-05208\" ref-type=\"bibr\">191</xref>]. These conserved domains play an important role in regulating important physiological processes by binding to promoter regions of target genes [<xref rid=\"B161-ijms-21-05208\" ref-type=\"bibr\">161</xref>,<xref rid=\"B192-ijms-21-05208\" ref-type=\"bibr\">192</xref>,<xref rid=\"B193-ijms-21-05208\" ref-type=\"bibr\">193</xref>]. WRKY, in association with ROS and ABA, functions in mitigating the effects of oxidative stress in rice. OsWRKY30 and OsWRKY45 are two WRKY genes in rice that, when overexpressed, are effective at increasing drought tolerance in rice under the regulation of OsNAC6 and SNAC1 as promoters. The OsWRKY45 also plays an important role in abiotic stress in <italic>Arabidopsis</italic> [<xref rid=\"B194-ijms-21-05208\" ref-type=\"bibr\">194</xref>,<xref rid=\"B195-ijms-21-05208\" ref-type=\"bibr\">195</xref>]. Further, when the WRKY57 gene from <italic>Arabidopsis</italic> was transferred to rice, the transgenic rice showed reduction in water loss, electrolyte leakage, and cell death. In these interactions, OsNAC1 and SNAC1 have been implicated as the promoters controlling WRKY57 function. These plants exhibit up-regulation of stress-responsive genes with higher antioxidant and proline content [<xref rid=\"B196-ijms-21-05208\" ref-type=\"bibr\">196</xref>]. In transgenic soybean, the GmWRKY27 interacts with GmMYB17 to repress promoter activity and gene expression of <italic>GmNAC29</italic>, which results in reduced ROS levels and enhanced stress tolerance [<xref rid=\"B197-ijms-21-05208\" ref-type=\"bibr\">197</xref>]. GmNAC29 negatively regulates stress where it enhances ROS production enzymes, leading to elevated stress. In another study, Yan et al. (2014) showed that GhWRKY17 reduced drought and salt tolerance in tobacco plants through mediation of cellular ROS levels and ABA signaling [<xref rid=\"B198-ijms-21-05208\" ref-type=\"bibr\">198</xref>]. Further, the <italic>BdWRKY36</italic> gene isolated from <italic>Brachypodium distachyon</italic> positively regulates abiotic stress through moderation of ROS homeostasis and regulation of stress-responsive genes [<xref rid=\"B27-ijms-21-05208\" ref-type=\"bibr\">27</xref>,<xref rid=\"B199-ijms-21-05208\" ref-type=\"bibr\">199</xref>].</p><p>NAC (for NAM, ATAF-1,-2, and CUC2) is one of the largest TF families with close to 300 members in just <italic>Arabidopsis</italic> and rice [<xref rid=\"B200-ijms-21-05208\" ref-type=\"bibr\">200</xref>,<xref rid=\"B201-ijms-21-05208\" ref-type=\"bibr\">201</xref>]. NACs, like the previously discussed proteins, regulate plant growth, development, oxidative stress, and drought tolerance [<xref rid=\"B169-ijms-21-05208\" ref-type=\"bibr\">169</xref>,<xref rid=\"B201-ijms-21-05208\" ref-type=\"bibr\">201</xref>,<xref rid=\"B202-ijms-21-05208\" ref-type=\"bibr\">202</xref>,<xref rid=\"B203-ijms-21-05208\" ref-type=\"bibr\">203</xref>,<xref rid=\"B204-ijms-21-05208\" ref-type=\"bibr\">204</xref>]. GmNAC2, a soybean NAC, negatively regulates abiotic stress through the induction of ROS signaling and expression of stress-responsive genes [<xref rid=\"B27-ijms-21-05208\" ref-type=\"bibr\">27</xref>,<xref rid=\"B205-ijms-21-05208\" ref-type=\"bibr\">205</xref>,<xref rid=\"B206-ijms-21-05208\" ref-type=\"bibr\">206</xref>,<xref rid=\"B207-ijms-21-05208\" ref-type=\"bibr\">207</xref>]. The <italic>Eleusine coracana</italic>-isolated <italic>EcNAC1</italic> gene, when transformed into tobacco, exhibited enhanced ROS scavenging and expression of abiotic stress-related genes [<xref rid=\"B208-ijms-21-05208\" ref-type=\"bibr\">208</xref>,<xref rid=\"B209-ijms-21-05208\" ref-type=\"bibr\">209</xref>]. Further, the <italic>SNAC3</italic> gene in rice positively regulates stress through ROS homeostasis and enhanced ROS-associated enzyme activity [<xref rid=\"B27-ijms-21-05208\" ref-type=\"bibr\">27</xref>,<xref rid=\"B210-ijms-21-05208\" ref-type=\"bibr\">210</xref>]. A SNAC1-regulated downstream gene, <italic>OsPP18</italic>, mediates drought resistance via ROS homeostasis [<xref rid=\"B27-ijms-21-05208\" ref-type=\"bibr\">27</xref>,<xref rid=\"B211-ijms-21-05208\" ref-type=\"bibr\">211</xref>]. The mutant <italic>ospp18</italic> was susceptible to drought and oxidative stress. As the ABA-induced expression of ABA-responsive genes was not disrupted in the <italic>ospp18</italic> mutant, the <italic>OsPP18</italic> gene expression is predicted to be regulated in an ABA-independent manner [<xref rid=\"B27-ijms-21-05208\" ref-type=\"bibr\">27</xref>]. </p><p>Another group of transcription factors that regulate multiple abiotic stress responses are the AP2/ERF (APETALA2/ethylene response factor) families, which include DREB/CBF [<xref rid=\"B212-ijms-21-05208\" ref-type=\"bibr\">212</xref>]. SUB1A, an ERF found in certain rice varieties, was able to adapt to stress and economize on energies spent in stress through ethylene and gibberellin responsiveness [<xref rid=\"B213-ijms-21-05208\" ref-type=\"bibr\">213</xref>]. Following flooding and anoxic injury, plants go through severe desiccation when the water subsides. This results in ROS accumulation in plant tissues [<xref rid=\"B213-ijms-21-05208\" ref-type=\"bibr\">213</xref>,<xref rid=\"B214-ijms-21-05208\" ref-type=\"bibr\">214</xref>] where SUB1A enhances oxidative stress tolerance through activation of ROS-scavenging genes. <italic>SUB1A</italic> is able to improve plant tolerance to abiotic stress through the induction of ABA responsiveness and activation of stress genes [<xref rid=\"B214-ijms-21-05208\" ref-type=\"bibr\">214</xref>,<xref rid=\"B215-ijms-21-05208\" ref-type=\"bibr\">215</xref>]. The <italic>JERF3</italic> gene from tomato has been reported to regulate ROS activity and, therefore, reduces the osmotic and oxidative stress response in any abiotic conditions [<xref rid=\"B216-ijms-21-05208\" ref-type=\"bibr\">216</xref>] by binding to <italic>cis</italic> elements in stress-responsive genes. This gene in tobacco increased abiotic stress tolerance [<xref rid=\"B216-ijms-21-05208\" ref-type=\"bibr\">216</xref>].</p></sec><sec id=\"sec6dot3-ijms-21-05208\"><title>6.3. ROS-Scavenging and Detoxification Proteins</title><p>Under stress, the ROS-scavenging gene families are activated in plants. Here, we present the enzymatic (processes catalyzed by enzymes) and non-enzymatic (non-enzymatic regulation) genes responsible for ROS-scavenging systems that manage the state of detoxification and homeostasis within plant cells. These genes include ascorbate peroxidase (<italic>APX</italic>), CAT, dehydroascorbate reductase (<italic>DHAR</italic>), glutathione peroxidase (<italic>GPX</italic>), glutathione reductase (<italic>GR</italic>), glutathione <italic>S</italic>-transferase (<italic>GST</italic>), monodehydroascorbate reductase (<italic>MDHAR</italic>), myo-inositol monooxygenease (MIOX), peroxiredoxin (<italic>PRX</italic>), proline synthesis, and <italic>SOD</italic>. In a study conducted on transgenic rice, the <italic>MnSOD</italic> gene expressed in chloroplast exhibited a fold increase in antioxidant levels, leading to enhanced stress tolerance. In plant systems, SODs are the frontline defense against ROS and are classified by the metal ions that are bound to their active sites such as copper and zinc (Cu/ZnSOD), manganese (MnSOD), and iron (FeSOD). Different metal ions-bound SODs are found in different cellular locations. For instance, Cu/ZnSOD are found in the cytosol and chloroplasts, while MnSOD is located in the mitochondria and peroxisomes. FeSOD is generally found in chloroplast but is significant in prokaryotes [<xref rid=\"B217-ijms-21-05208\" ref-type=\"bibr\">217</xref>]. SODs protect the photosynthetic machinery against ROS in transgenic plants compared to wild type under drought-stress [<xref rid=\"B18-ijms-21-05208\" ref-type=\"bibr\">18</xref>,<xref rid=\"B32-ijms-21-05208\" ref-type=\"bibr\">32</xref>,<xref rid=\"B99-ijms-21-05208\" ref-type=\"bibr\">99</xref>,<xref rid=\"B218-ijms-21-05208\" ref-type=\"bibr\">218</xref>]. Similarly, the Zn and Cu-containing superoxides remove oxygen radicals from plant cells. Rice transformed with Zn/Cu SOD genes had increased abiotic stress tolerance [<xref rid=\"B62-ijms-21-05208\" ref-type=\"bibr\">62</xref>,<xref rid=\"B219-ijms-21-05208\" ref-type=\"bibr\">219</xref>,<xref rid=\"B220-ijms-21-05208\" ref-type=\"bibr\">220</xref>]. Further, in transgenic alfalfa, the overexpression of MnSOD from tobacco increased the survival and yield of alfalfa over several seasons in drought conditions [<xref rid=\"B221-ijms-21-05208\" ref-type=\"bibr\">221</xref>].</p><p>The APX is involved in the initial step of the AsA-GSH cycle that scavenges ROS and protects the plant from stress [<xref rid=\"B219-ijms-21-05208\" ref-type=\"bibr\">219</xref>,<xref rid=\"B222-ijms-21-05208\" ref-type=\"bibr\">222</xref>]. This haem enzyme scavenges H<sub>2</sub>O<sub>2</sub> through the AsA-GSH cycle where H<sub>2</sub>O<sub>2</sub> is converted to water and dehydroascorbate DHA [<xref rid=\"B63-ijms-21-05208\" ref-type=\"bibr\">63</xref>,<xref rid=\"B223-ijms-21-05208\" ref-type=\"bibr\">223</xref>,<xref rid=\"B224-ijms-21-05208\" ref-type=\"bibr\">224</xref>]. In different abiotic stresses, APX activity is elevated [<xref rid=\"B225-ijms-21-05208\" ref-type=\"bibr\">225</xref>,<xref rid=\"B226-ijms-21-05208\" ref-type=\"bibr\">226</xref>,<xref rid=\"B227-ijms-21-05208\" ref-type=\"bibr\">227</xref>]. In rice, several isoforms of the APX gene have been identified in the genome. Out of these isoforms, the <italic>OsAPX2</italic> gene has been shown to protect against oxidative injury in rice seedlings [<xref rid=\"B228-ijms-21-05208\" ref-type=\"bibr\">228</xref>,<xref rid=\"B229-ijms-21-05208\" ref-type=\"bibr\">229</xref>]. The overexpression of <italic>OsAPX2</italic> in transgenic rice increases APX activity and results in a reduction in H<sub>2</sub>O<sub>2</sub> and malondialdehyde (MDA) under abiotic stress [<xref rid=\"B229-ijms-21-05208\" ref-type=\"bibr\">229</xref>]. The reduction in H<sub>2</sub>O<sub>2</sub> and malondialdehyde (MDA) is probably the reason behind the increased tolerance exhibited by transgenic rice compared to wild type in the booting stage where spikelet fertility is enhanced [<xref rid=\"B229-ijms-21-05208\" ref-type=\"bibr\">229</xref>]. <italic>OsAPX1</italic> exhibited augmented spikelet fertility under cold stress [<xref rid=\"B229-ijms-21-05208\" ref-type=\"bibr\">229</xref>]. Prakash et al. (2016) reported that both the <italic>APX3</italic> and <italic>APX8</italic> genes are responsible for drought tolerance in IR64 and Nagina [<xref rid=\"B230-ijms-21-05208\" ref-type=\"bibr\">230</xref>]. Like APX, GPX is another large family of diverse peroxidase isozymes without the haem-thiol group. This enzyme is responsible for the reduction of H<sub>2</sub>O<sub>2</sub> to water, and lipid hydroperoxides to alcohol, hence repairing lipid peroxidation and membrane damage [<xref rid=\"B120-ijms-21-05208\" ref-type=\"bibr\">120</xref>,<xref rid=\"B231-ijms-21-05208\" ref-type=\"bibr\">231</xref>]. Both the intracellular and extracellular forms of this enzyme are involved in H<sub>2</sub>O<sub>2</sub> scavenging. GPX and other peroxidase enzymes bound to plant cell walls can oxidize phenols to result in the lignification of cell walls [<xref rid=\"B46-ijms-21-05208\" ref-type=\"bibr\">46</xref>]. GPX also functions as an oxidative signal transducer in plants [<xref rid=\"B232-ijms-21-05208\" ref-type=\"bibr\">232</xref>]. In a functional study of the <italic>PgGPX</italic> (<italic>Pennisetum glauccum</italic> GPX) genes introduced into rice, the transgenic lines showed an enhanced antioxidant defense via lower H<sub>2</sub>O<sub>2</sub> and MDA content during abiotic stresses [<xref rid=\"B233-ijms-21-05208\" ref-type=\"bibr\">233</xref>].</p><p>As a haem-containing enzyme, catalase scavenges H<sub>2</sub>O<sub>2</sub> in the peroxisome, glyoxysomes, and other organelles during stress-related processes [<xref rid=\"B18-ijms-21-05208\" ref-type=\"bibr\">18</xref>,<xref rid=\"B234-ijms-21-05208\" ref-type=\"bibr\">234</xref>]. CATs respond variably to different abiotic stresses [<xref rid=\"B225-ijms-21-05208\" ref-type=\"bibr\">225</xref>,<xref rid=\"B227-ijms-21-05208\" ref-type=\"bibr\">227</xref>,<xref rid=\"B235-ijms-21-05208\" ref-type=\"bibr\">235</xref>,<xref rid=\"B236-ijms-21-05208\" ref-type=\"bibr\">236</xref>,<xref rid=\"B237-ijms-21-05208\" ref-type=\"bibr\">237</xref>]. It has a higher turnover rate minus the requirement for a reductant like APX, giving it a higher affinity and scavenging ability of H<sub>2</sub>O<sub>2</sub>. The catalase isoforms are divided into classes, which are specific to location. CAT1 and 2 are in the peroxisomes and cytosol while CAT3 is in the mitochondria [<xref rid=\"B68-ijms-21-05208\" ref-type=\"bibr\">68</xref>]. GR, another oxidoreductase, regulates the GSH redox state by catalyzing the disulphide bond formation of the GSSG pool via NADPH. This is important in maintaining the GSH at a reduced state. GR is predominantly located in the chloroplast but is also reported in cytosol and mitochondria. Both GR and GSH have been identified as enzymes linked with tolerance against various stresses in plants [<xref rid=\"B238-ijms-21-05208\" ref-type=\"bibr\">238</xref>,<xref rid=\"B239-ijms-21-05208\" ref-type=\"bibr\">239</xref>,<xref rid=\"B240-ijms-21-05208\" ref-type=\"bibr\">240</xref>].</p><p>MDHAR accompanies APX and scavenges H<sub>2</sub>O<sub>2</sub> in the mitochondria and peroxisome [<xref rid=\"B236-ijms-21-05208\" ref-type=\"bibr\">236</xref>,<xref rid=\"B237-ijms-21-05208\" ref-type=\"bibr\">237</xref>]. MDHAR accepts electrons from NADH and, together with DHAR and GR, regulates abiotic stress in rice. DHAR works on ascorbate and recycles ascorbic acid (AsA) [<xref rid=\"B241-ijms-21-05208\" ref-type=\"bibr\">241</xref>,<xref rid=\"B242-ijms-21-05208\" ref-type=\"bibr\">242</xref>]. AsA is then oxidized to form MDHA, which is further converted to DHA. DHAR through GSH reduces DHA to AsA and undergoes rapid regeneration. This regeneration of AsA is moderated through the NADPH-MDHAR cycle [<xref rid=\"B243-ijms-21-05208\" ref-type=\"bibr\">243</xref>], which is essential in ensuring and maintaining a reduced pool of AsA [<xref rid=\"B244-ijms-21-05208\" ref-type=\"bibr\">244</xref>,<xref rid=\"B245-ijms-21-05208\" ref-type=\"bibr\">245</xref>]. Maintenance of the cellular redox state of AsA is essential in abiotic stress tolerance. Increased DHAR and MDHAR activity was reported in various plants subjected to abiotic stress [<xref rid=\"B99-ijms-21-05208\" ref-type=\"bibr\">99</xref>,<xref rid=\"B238-ijms-21-05208\" ref-type=\"bibr\">238</xref>,<xref rid=\"B246-ijms-21-05208\" ref-type=\"bibr\">246</xref>,<xref rid=\"B247-ijms-21-05208\" ref-type=\"bibr\">247</xref>]. GST together with GSH can reduce POX activity in the cell through scavenging. In addition to their ability to conjugate electrophilic compounds to GSH, GST displays POX-like activities [<xref rid=\"B248-ijms-21-05208\" ref-type=\"bibr\">248</xref>]. There are over 100 GST genes that have been reported in soybean, maize, and <italic>Arabidopsis</italic> [<xref rid=\"B233-ijms-21-05208\" ref-type=\"bibr\">233</xref>] with diverse function, including cellular metabolism, hormone homeostasis, cellular detoxification, apoptosis, and various other biotic and abiotic stresses [<xref rid=\"B249-ijms-21-05208\" ref-type=\"bibr\">249</xref>,<xref rid=\"B250-ijms-21-05208\" ref-type=\"bibr\">250</xref>]. This enzyme is induced at high levels when plants are subject to abiotic stresses [<xref rid=\"B99-ijms-21-05208\" ref-type=\"bibr\">99</xref>,<xref rid=\"B146-ijms-21-05208\" ref-type=\"bibr\">146</xref>,<xref rid=\"B251-ijms-21-05208\" ref-type=\"bibr\">251</xref>,<xref rid=\"B252-ijms-21-05208\" ref-type=\"bibr\">252</xref>].</p><p>MIOX produces AsA, which results in antioxidant defense. Oxidative damage in rice is reduced through the overexpression of <italic>OsMIOX</italic> and increased ROS scavenging [<xref rid=\"B19-ijms-21-05208\" ref-type=\"bibr\">19</xref>,<xref rid=\"B253-ijms-21-05208\" ref-type=\"bibr\">253</xref>]. Another enzyme that scavenges ROS is ornithine δ–aminotransferase (δ-OAT), which synthesizes proline, a non-enzymatic ROS-scavenging system effective in abiotic conditions [<xref rid=\"B21-ijms-21-05208\" ref-type=\"bibr\">21</xref>,<xref rid=\"B254-ijms-21-05208\" ref-type=\"bibr\">254</xref>]. You et al. (2012) reported that the overexpression of <italic>OsOAT</italic> induced enzyme activities that resulted in ROS quenching and improved antioxidative activity within the plant cells [<xref rid=\"B255-ijms-21-05208\" ref-type=\"bibr\">255</xref>]. Other than proline, metallothioneins (MTs) are another low-molecular-weight protein that has metal-binding capabilities. MTs are involved in ROS detoxification and the maintenance of safe redox levels. <italic>OsMT1a</italic> expression in rice is enhanced in plants that are subjected to Zn<sup>2+</sup> treatment and drought [<xref rid=\"B256-ijms-21-05208\" ref-type=\"bibr\">256</xref>]. These plants exhibit elevated levels of APX, CAT, and POD that result in these transgenic lines exhibiting heightened tolerance to drought stress. Zn<sup>2+</sup> homeostasis is important in improved resistance in plants. OsMT1a interacts with Zn finger transcription factors that can moderate the levels of Zn<sup>2+</sup> within the cell. In cotton, <italic>GhMT3a</italic> resulted in an enhanced ability to bind metal ions, resulting in efficient ROS scavenging <italic>in planta</italic>. <italic>GhMT3a</italic>, when introduced into tobacco, resulted in improved resistance against multiple abiotic stresses through lower H<sub>2</sub>O<sub>2</sub> levels than those exhibited in wild-type plants [<xref rid=\"B27-ijms-21-05208\" ref-type=\"bibr\">27</xref>,<xref rid=\"B57-ijms-21-05208\" ref-type=\"bibr\">57</xref>,<xref rid=\"B100-ijms-21-05208\" ref-type=\"bibr\">100</xref>,<xref rid=\"B257-ijms-21-05208\" ref-type=\"bibr\">257</xref>]. </p><p>In addition to the enzymatic and non-enzymatic genes that have been mentioned above, sugars are suggested to have a role in antioxidative responses. Fructans, disaccharide, and sugar alcohols possess antioxidative abilities and can efficiently remove hydroxyl radicals within plant cells and organelles [<xref rid=\"B54-ijms-21-05208\" ref-type=\"bibr\">54</xref>,<xref rid=\"B258-ijms-21-05208\" ref-type=\"bibr\">258</xref>]. This non-enzymatic removal of the °OH radical is important as there are not any enzymatic systems available for the removal of this toxic compound [<xref rid=\"B259-ijms-21-05208\" ref-type=\"bibr\">259</xref>]. Galactinol and raffinose have demonstrated similar potential to GSH in the removal of hydroxyl ions from plant cells [<xref rid=\"B260-ijms-21-05208\" ref-type=\"bibr\">260</xref>]. The levels of these sugars in chloroplast are comparable to AsA and GSH, suggesting that these sugars play a vital role in scavenging radical ions in this organelle [<xref rid=\"B111-ijms-21-05208\" ref-type=\"bibr\">111</xref>,<xref rid=\"B239-ijms-21-05208\" ref-type=\"bibr\">239</xref>,<xref rid=\"B261-ijms-21-05208\" ref-type=\"bibr\">261</xref>]. However, the role of sucrose as antioxidants is only relevant in plants with high levels of sucrose such as beet and sugarcane, where they have been reported to remove °OH effectively [<xref rid=\"B262-ijms-21-05208\" ref-type=\"bibr\">262</xref>].</p></sec><sec id=\"sec6dot4-ijms-21-05208\"><title>6.4. Other Proteins</title><sec id=\"sec6dot4dot1-ijms-21-05208\"><title>6.4.1. Ca<sup>2+</sup> Transporters and Binding Proteins </title><p>Most of the Ca<sup>2+</sup>-binding sites contain the EF-hand motifs that are highly conserved amongst eukaryotes [<xref rid=\"B263-ijms-21-05208\" ref-type=\"bibr\">263</xref>]. The EF-hand motifs are also found in the proteins family that transport and manage intracellular Ca<sup>2+</sup> concentrations [<xref rid=\"B264-ijms-21-05208\" ref-type=\"bibr\">264</xref>]. The influx and efflux of Ca<sup>2+</sup> across the membranes is achieved through Ca<sup>2+</sup>ATPases or antiporters. Both the calcium transporters and binding proteins help to regulate processes by the altering of Ca<sup>2+</sup> levels in cells. The main players of calcium binding in plants are calmodulin [CaM], CaM-like proteins (CML), calcineurin-B-like proteins (CBL), and Ca<sup>2+</sup> dependent protein kinases (CDPK). CaM, CML, and CBL are the sensors, while CDPK are sensor responders that activate the kinase activity of this protein [<xref rid=\"B265-ijms-21-05208\" ref-type=\"bibr\">265</xref>]. As drought affects growth, development, and stress tolerance, Ca<sup>2+</sup> is implicated in the regulation of signaling involving drought-affected processes. Calcium-binding proteins bind calcium and thence activate the downstream phosphorylation cascade of gene expression. This increases the levels of Ca<sup>2+</sup> perceived by calmodulin and CDPK. <italic>OsACA6</italic>, a form of Ca<sup>2+</sup>ATPase in rice, when overexpressed, is able to moderate the reduction in ROS levels. The overexpression of this gene results in cellular homeostasis through the modulation of ROS-scavenging systems [<xref rid=\"B73-ijms-21-05208\" ref-type=\"bibr\">73</xref>,<xref rid=\"B266-ijms-21-05208\" ref-type=\"bibr\">266</xref>]. </p><p>Further, another group of proteins, annexins, are implicated in the response toward environmental stresses on growth and development. One such annexin, OsANN1, functions as an ATPase with the ability to bind Ca<sup>2+</sup> and regulate the inflow and efflux of Ca<sup>2+</sup> ions. Through the interaction with <italic>OsCDPK10</italic>, a protein kinase, <italic>OsANN1</italic> confers abiotic stress tolerance via antioxidant accumulation [<xref rid=\"B267-ijms-21-05208\" ref-type=\"bibr\">267</xref>]. Further, through yeast two-hybrid system analyses, it was demonstrated that OsANN1 interacts with OsCDPK24 and, therefore, regulates abiotic stress responses [<xref rid=\"B268-ijms-21-05208\" ref-type=\"bibr\">268</xref>]. The RNAi knockout mutant of this gene was sensitive to drought, while the overexpressing lines showed improved growth and higher expression of the gene under abiotic stress, leading to SOD and CAT activities that facilitate ROS homeostasis through a OsANN1 and H<sub>2</sub>O<sub>2</sub> feedback mechanism. </p><p>In another study involving stomatal guard cells, it was reported that cytosolic Ca<sup>2+</sup> increase and activation of ABA results in activation of the anion channel, which causes the plasma membrane of the guard cells to close. In <italic>Arabidopsis</italic>, an ABC protein, AtMRP5 was bound to the plasma membrane of guard cells, which affects the ABA and cytosolic Ca<sup>2+</sup> levels in the cell. Mutants of this gene showed loss of ability to keep the stomata closed in drought. These mutants also showed impaired ABA activity, indicating that this gene was responsible for Ca<sup>2+</sup> control over guard cell aperture [<xref rid=\"B269-ijms-21-05208\" ref-type=\"bibr\">269</xref>]. </p></sec><sec id=\"sec6dot4dot2-ijms-21-05208\"><title>6.4.2. SRO Proteins</title><p>SRO is a plant-specific protein group that has the PARP, RST and WWE domain [<xref rid=\"B270-ijms-21-05208\" ref-type=\"bibr\">270</xref>]. The <italic>rcd1</italic> [<italic>radical-induced cell death1</italic>] in <italic>Arabidopsis</italic> exhibited the ability to respond to various stimuli such as ROS stress, salt stress, and irradiation by interacting with numerous transcription factors that facilitate their involvement in developmental and stress-related responses [<xref rid=\"B27-ijms-21-05208\" ref-type=\"bibr\">27</xref>,<xref rid=\"B271-ijms-21-05208\" ref-type=\"bibr\">271</xref>,<xref rid=\"B272-ijms-21-05208\" ref-type=\"bibr\">272</xref>]. The <italic>RCD1</italic> gene regulates signaling pathways that are responsible for quantitative changes to gene expression in response to ROS [<xref rid=\"B273-ijms-21-05208\" ref-type=\"bibr\">273</xref>]. <italic>OsSRO1c</italic> is targeted by <italic>SNAC1</italic> in rice [<xref rid=\"B78-ijms-21-05208\" ref-type=\"bibr\">78</xref>], where it is induced in the guard cells during abiotic stress and results in the accumulation of H<sub>2</sub>O<sub>2</sub> and decreased stomatal aperture and water loss. Due to its involvement in stomatal aperture control and water loss, the overexpression of <italic>OsSRO1c</italic> has also been implicated in abiotic stress tolerance of rice through the regulation of the SNAC1 novel pathway and DST [<xref rid=\"B78-ijms-21-05208\" ref-type=\"bibr\">78</xref>]. Just like <italic>OsSRO1c</italic>, <italic>OsNAC5</italic> and <italic>ONAC095</italic> enhance drought and oxidative stress tolerance in rice [<xref rid=\"B274-ijms-21-05208\" ref-type=\"bibr\">274</xref>]. Further studies of the SRO protein in wheat showed that this gene was also involved in the regulation of salinity stress in addition to redox homeostasis [<xref rid=\"B27-ijms-21-05208\" ref-type=\"bibr\">27</xref>]. The regulation of salinity tolerance in wheat is achieved through the point mutation of the Ta-<italic>sro1</italic> allele. The overexpression of the <italic>Ta-sro1</italic> results in regulation of ROS through ROS-associated enzymes such as AsA-GSH and GPX that result in cellular homeostasis [<xref rid=\"B27-ijms-21-05208\" ref-type=\"bibr\">27</xref>].</p></sec><sec id=\"sec6dot4dot3-ijms-21-05208\"><title>6.4.3. ABA Metabolism-Related Proteins</title><p>ABA is involved in the response to abiotic stresses [<xref rid=\"B275-ijms-21-05208\" ref-type=\"bibr\">275</xref>]. In drought, <italic>dsm2</italic> mutants have shown impaired β-carotene hydroxylase synthesis in rice [<xref rid=\"B85-ijms-21-05208\" ref-type=\"bibr\">85</xref>]. This particular hydroxylase is a precursor of ABA and is inhibited under drought stress. However, in overexpressing DSM2 lines, this gene is expressed at high levels, leading to enhanced resistance to abiotic and oxidative stress. Other than β-carotene hydroxylase, <italic>OsABA8ox3</italic> is another hydroxylase-encoding gene that is involved in ABA catabolism and regulates oxidative stress under various abiotic stresses [<xref rid=\"B27-ijms-21-05208\" ref-type=\"bibr\">27</xref>]. RNAi-generated plants of this gene showed improved drought and oxidative stress tolerance with enhanced superoxide dismutase and catalase activities. In another study, transgenic tobacco carrying the 9-<italic>cis</italic>-epoxy carotenoid dioxygenase gene showed enhanced tolerance to abiotic stresses. This enhanced resistance has been linked to production of H<sub>2</sub>O<sub>2</sub> that induces the expression of ROS-scavenging enzymes [<xref rid=\"B276-ijms-21-05208\" ref-type=\"bibr\">276</xref>]. </p></sec></sec></sec><sec sec-type=\"conclusions\" id=\"sec7-ijms-21-05208\"><title>7. Conclusions and Future Prospective</title><p>Abiotic stresses hamper growth and development, which eventually results in low yields and productivity. Stressed plants exhibiting elevated intracellular and extracellular ROS are different organelles, leading to oxidative stress. Though the compartmentalization of antioxidant activities is well defined, the recognition, response, and balancing of ROS activity in the plant require further exploration. The coordination of the different enzymes in different compartments and the regulation of ROS levels in response to stress are questions that require further attention. The ambiguities and gaps in our knowledge are further compounded by the short half-life and the reactive nature of the molecule. From the various studies that have been conducted over the past two decades, we may conclude, in general, that ROS equilibrium involves cross-talk between ABA, Ca<sup>2+</sup>, and various other hormones and signaling molecules. ROS as a signal transducer also activates a cascade of genes that assist in abiotic stress tolerance in a ROS-dependent manner. Genes such as protein kinases and transcription factors are important upstream components that are responsible for the activation of other downstream genes involved in alleviating ROS toxicity. Genes that are involved in the regulation of ROS have been studied quite well in rice and <italic>Arabidopsis</italic>. </p><p>In this review, we have provided an overview of the oxidative and non-oxidative mechanisms involved in the reduction in ROS damage and the provision of tolerance and adaptation to abiotic stress. We are still not completely clear on the mechanism by which Ca<sup>2+</sup>, hormones, and signal molecules regulate abiotic stresses. Perhaps with the development of better imaging systems, we may be able to utilize ion markers that provide a better understanding of their role in ROS metabolism. Further, genome information has been utilized in functional and metabolome studies that provide a clearer view of the ROS network and its reactions. A combination of transcriptome, proteome, and metabolome approaches may provide a comprehensive understanding of the networks involved in ROS production, signaling, and control. These studies may result in the identification of key pathways, regulators, and genes that are responsible for ROS homeostasis in plants. Some of these genes may be developed into biomarkers to be used in plant stress-response studies. </p><p>By understanding the genes and their expression, we are then able to manipulate the endogenous ROS levels to generate plants with improved defense, growth, development, and survival in adverse abiotic stress conditions. Most of the genes identified in ROS homeostasis have been characterized through the generation of transgenic plants. Some of these transgenic lines with overexpressing genes have shown enhanced tolerance to multiple stresses [<xref rid=\"B277-ijms-21-05208\" ref-type=\"bibr\">277</xref>]. However, the networks involved in the function of these genes achieving ROS homeostasis requires further investigation and addition of any new and relevant information into the existing pathways. Some of these genes have already been used in elite cultivars and are candidates for biomarkers in the selection of abiotic-resistant crops. The location of these genes and QTLs associated with these genes is also a suitable candidate for use in the breeding and genetic engineering of resistant cultivars. </p></sec></body><back><ack><title>Acknowledgments</title><p>The authors would like to sincerely thank Ilakiya Kumar [<uri xlink:href=\"https://orcid.org/0000-0002-9968-9156\">https://orcid.org/0000-0002-9968-9156</uri>] for the technical assistance.</p></ack><notes><title>Funding</title><p>This work was supported by the Universiti Kebangsaan Malaysia [DCP-2017-004/1] and Ministry of Higher Education [FRGS/1/2019/STG03/UKM/01/2].</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The author declares no conflict of interest. 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Biol.</source><year>2018</year><volume>6</volume><fpage>2</fpage><pub-id pub-id-type=\"doi\">10.3389/fcell.2018.00002</pub-id><pub-id pub-id-type=\"pmid\">29503816</pub-id></element-citation></ref><ref id=\"B277-ijms-21-05208\"><label>277.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Huang</surname><given-names>H.</given-names></name><name><surname>Ullah</surname><given-names>F.</given-names></name><name><surname>Zhou</surname><given-names>D.X.</given-names></name><name><surname>Yi</surname><given-names>M.</given-names></name><name><surname>Zhao</surname><given-names>Y.</given-names></name></person-group><article-title>Mechanisms of ROS regulation of plant development and stress responses</article-title><source>Front. Plant Sci.</source><year>2019</year><volume>10</volume><fpage>800</fpage><pub-id pub-id-type=\"doi\">10.3389/fpls.2019.00800</pub-id><pub-id pub-id-type=\"pmid\">31293607</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijms-21-05208-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>A diagrammatic representation of the processes in the chloroplast during stress where reactive oxygen species (ROS) is produced through inhibition of CO<sub>2</sub>, and low water levels are due to stress. The electron transport chain (ETC) in the photosystem (PS) is the main source of ROS in chloroplast.</p></caption><graphic xlink:href=\"ijms-21-05208-g001\"/></fig><fig id=\"ijms-21-05208-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>The process of redox in the mitochondria. Any form of stress causes alleviation in ROS due to ATP synthesis, leading to a reduction in the ubiquinone pool (UQ) pool. Several enzymes work together to manage ROS levels in mitochondria.</p></caption><graphic xlink:href=\"ijms-21-05208-g002\"/></fig><fig id=\"ijms-21-05208-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>The process of redox in peroxisome during stress. Xanthine oxidases convert xanthine and hypoxanthine to uric acid and O<sub>2</sub><sup>−</sup>, while the proximal membrane produces O<sub>2</sub><sup>−</sup> via NADH and Cytb.</p></caption><graphic xlink:href=\"ijms-21-05208-g003\"/></fig><fig id=\"ijms-21-05208-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>The processes involved in the production and control of ROS in different organelles within a plant system. The above is observed during stress response in plants. All these organelles are collectively responsible at maintaining ROS homeostasis in the cell.</p></caption><graphic xlink:href=\"ijms-21-05208-g004\"/></fig><fig id=\"ijms-21-05208-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>The pathway taken from the time of stimulus to the activation of genes downstream and the genes involved in the regulation of this pathway.</p></caption><graphic xlink:href=\"ijms-21-05208-g005\"/></fig><table-wrap id=\"ijms-21-05208-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05208-t001_Table 1</object-id><label>Table 1</label><caption><p>List of mitogen-activated protein (MAP) kinases and the processes they are implicated to regulate.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MAP Kinase</th><th align=\"center\" valign=\"top\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Phosphorylated Amino Acids</th><th align=\"center\" valign=\"top\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">List of MAP Kinases</th><th align=\"center\" valign=\"top\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">These MAP Kinases Respond or are Involved in These Processes</th></tr></thead><tbody><tr><td align=\"left\" valign=\"top\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MAPKKK</td><td align=\"left\" valign=\"top\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Serine Threonine</td><td align=\"left\" valign=\"top\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MEKK1, MEKK2, MEKK3, MEKK4,<break/><italic>MAPKKK18</italic>, <italic>GhMAPKKK49</italic><break/><italic>DSM1</italic>, <italic>DSM2</italic></td><td align=\"left\" valign=\"top\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Influences oxidative, abiotic, and biotic stress.<break/>Hormones: Abscisic acid;</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">MAPKK</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Threonine/tyrosine</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">MKK1, MKK2, MKK6, GhMKK1,</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Influences oxidative, abiotic, and biotic stresses and cell division.<break/>Hormones: Salicylic acid;</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">MKK3, GhMKK3,</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Influences oxidative, abiotic, and biotic stresses and cell division.<break/>Hormones: Salicylic acid;</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">MKK4, MKK5 GhMKK4, GhMKK5,</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Influences oxidative, abiotic, and biotic stresses<break/>Hormones: Jasmonic acid.</td></tr><tr><td align=\"left\" valign=\"top\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MKK7, MKK8, MKK9, MKK10, RhMKK9,<break/>GhMKK9 ZmMKK10</td><td align=\"left\" valign=\"top\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Influences oxidative and biotic stresses, Hormones: Ethylene</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">MAPK</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Serine/Threonine/<break/>Tyrosine</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">MPK3, MPK6, MPK10<break/>OsMPK6, ZmMPK3, RhMPK6, ZmMPK6-2, OsMPK3,<break/>ZmMPK3</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Influences oxidative, abiotic, and biotic stresses.<break/>Hormones: Jasmonic acid and ethylene</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">MPK4, MPK5, MPK11, MPK12, MPK13 OsMPK4ZmMPK4-1, OsMPK5, OsMPK5, ZmMPK5</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Influences oxidative, abiotic, and biotic stresses and cell division.<break/>Hormones: Salicylic acid;</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">MPK1, MPK2, MPK7,<break/>MPK14, ZmMPK7, OsMPK2AtMPK7, OsMPK7, GhMPK7</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Influences oxidative, abiotic, and biotic stresses. Circadian-rhythm-regulated.<break/>Hormones: Jasmonic acid, abscisic acid.</td></tr><tr><td align=\"left\" valign=\"top\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MPK8, MPK9,<break/>MPK15/16/17/18/19/20 GhMPK17, ZmMPK17</td><td align=\"left\" valign=\"top\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Influences oxidative, abiotic, and biotic stresses.<break/>Hormones: Jasmonic acid</td></tr></tbody></table></table-wrap></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32708026</article-id><article-id pub-id-type=\"pmc\">PMC7432043</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155292</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05292</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Comparison of the Level of Awareness about the Transmission of Echinococcosis and Toxocariasis between Pet Owners and Non-Pet Owners in Greece</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Kantarakia</surname><given-names>Christina</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05292\">1</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijerph-17-05292\">†</xref></contrib><contrib contrib-type=\"author\"><name><surname>Tsoumani</surname><given-names>Maria E.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05292\">1</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijerph-17-05292\">†</xref></contrib><contrib contrib-type=\"author\"><name><surname>Galanos</surname><given-names>Antonis</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05292\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Mathioudakis</surname><given-names>Alexander G.</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05292\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Giannoulaki</surname><given-names>Eleni</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05292\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-5639-0528</contrib-id><name><surname>Beloukas</surname><given-names>Apostolos</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05292\">1</xref><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05292\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Voyiatzaki</surname><given-names>Chrysa</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05292\">1</xref><xref rid=\"c1-ijerph-17-05292\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05292\"><label>1</label>Laboratory of Molecular Microbiology & Immunology, Department of Biomedical Sciences, University of West Attica, 12243 Athens, Greece; <email>ml12113@uniwa.gr</email> (C.K.); <email>mtsoumani@uniwa.gr</email> (M.E.T.); <email>egiannoul@uniwa.gr</email> (E.G.); <email>abeloukas@uniwa.gr</email> (A.B.)</aff><aff id=\"af2-ijerph-17-05292\"><label>2</label>Laboratory of Research of the Musculoskeletal System, School of Medicine, University of Athens, 12243 Athens, Greece; <email>galanostat@yahoo.gr</email></aff><aff id=\"af3-ijerph-17-05292\"><label>3</label>Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, The University of Manchester, Manchester M23 9LT, UK; <email>Alexander.Mathioudakis@Manchester.ac.uk</email></aff><aff id=\"af4-ijerph-17-05292\"><label>4</label>Institute of Infection and Global Health, University of Liverpool, Liverpool L 69 7BE, UK</aff><author-notes><corresp id=\"c1-ijerph-17-05292\"><label>*</label>Correspondence: <email>cvoyiatz@uniwa.gr</email>; Tel.: +30-210-538-5745</corresp><fn id=\"fn1-ijerph-17-05292\"><label>†</label><p>These authors contributed equally to this work and they will jointly share the first authorship.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>22</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5292</elocation-id><history><date date-type=\"received\"><day>31</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>19</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Ζoonotic parasitic diseases that can occur through animal contact pose risks to pets, their owners and to their bond. This study aims to assess the level of knowledge about zoonoses, specifically echinococcosis and toxocariasis, among cat/dog owners and non-pet owners in Greece. Multiple-choice questionnaires were designed to obtain data regarding the knowledge of pet and non-pet owners on echinococcosis and toxocariasis, including signs and symptoms of these zoonoses, ways of transmission and precautions that need to be taken into account in order to avoid it. A total of 185 questionnaires were retrieved and data was expressed as absolute (Ν) and relative frequencies (%). Associations between pet ownership, residence and outcome variables were evaluated using the Fisher exact test and Chi-squared test, respectively. Multifactorial linear regression analysis was used to investigate the cross-sectional association between demographic characteristics and the awareness of helminthic zoonoses. All tests were two-sided and statistical significance was set at <italic>p</italic> < 0.05. Our study revealed a disturbing lack of awareness of echinococcosis and toxocariasis (mean zoonotic knowledge score 8.11 ± 3.18) independently of pet ownership. Surprisingly, in some cases the ignorance of pet owners exceeded that of non-pet owners. Given the progressive impact of toxocariasis in public health and the high prevalence of echinococcosis in the Mediterranean region, measures should be taken to inform people about zoonoses and eliminate their putative transmission.</p></abstract><kwd-group><kwd>awareness</kwd><kwd>cats</kwd><kwd>dogs</kwd><kwd>echinococcosis</kwd><kwd>parasites</kwd><kwd>pet owners</kwd><kwd>toxocariasis</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05292\"><title>1. Introduction</title><p>Nowadays, many households own at least one pet, with dogs and cats being the most common ones, followed by fish, birds, rabbits and hamsters [<xref rid=\"B1-ijerph-17-05292\" ref-type=\"bibr\">1</xref>]. Humans and pets develop a strong emotional relationship, known as the “human-animal bond”, and many studies support the benefits it offers in terms of socialization, physical and mental health [<xref rid=\"B2-ijerph-17-05292\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijerph-17-05292\" ref-type=\"bibr\">3</xref>]. Additionally, it is widely accepted that pets also contribute to improving recovery rates for a large number of diseases such as cardiovascular or respiratory problems, anxiety and depression [<xref rid=\"B4-ijerph-17-05292\" ref-type=\"bibr\">4</xref>]. However, this close relationship between animals and humans may result in an increased risk of exposure to infectious diseases since pets are a potential source for more than 60 zoonotic agents [<xref rid=\"B5-ijerph-17-05292\" ref-type=\"bibr\">5</xref>]. In fact, dogs and cats play a major role in spreading zoonoses such as echinococcosis and toxocariasis, which are transmitted directly from pets to the human environment, without involving vectors or other intermediate hosts [<xref rid=\"B1-ijerph-17-05292\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijerph-17-05292\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijerph-17-05292\" ref-type=\"bibr\">3</xref>]. </p><p>Human toxocariasis, a neglected parasitic infection, can develop serious syndromes, known as visceral larva migrans, ocular larva migrans, neurotoxocariasis, and covert or common toxocariasis. Following accidental ingestion of infective eggs containing third stage larvae of roundworms <italic>Toxocara cati</italic> and <italic>Toxocara canis</italic>, or larvae in under-cooked infected organ or muscle tissues (rare), Toxocara larvae hatches, penetrates the intestinal mucosa and migrates via the blood circulation to various tissues (e.g., liver, heart, brain, lungs, skeletal muscle and eyes) causing local inflammation and granuloma [<xref rid=\"B6-ijerph-17-05292\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijerph-17-05292\" ref-type=\"bibr\">7</xref>]. Visceral larva migrans (VLM) is the most common syndrome in infected people, particularly children. Although most infections are light, with clinical signs such as fever, pulmonary congestion and eosinophilia, in heavy infections some people develop lymphadenopathy, granulomatous hepatitis, nephritis, and arthritis, elevation of serum immunoglobulin E (IgE)concentration, presence of allergen-specific IgE, asthma and promotion of pulmonary fibrosis [<xref rid=\"B8-ijerph-17-05292\" ref-type=\"bibr\">8</xref>]. Ocular larva migrans (OLM) commonly reported in children 3–16 years of age, can be caused by even a single <italic>Toxocara</italic> spp.</p><p>Larva become entrapped in the eye and characterized by significant visual disability, photophobia, retinitis, granulomata, and blindness [<xref rid=\"B9-ijerph-17-05292\" ref-type=\"bibr\">9</xref>]. Neurotoxocariasis, a rare syndrome in middle-aged people, relates to the migration of <italic>T. canis</italic> larvae in the Central Nervous System with clinical signs of meningitis, encephalitis and myelitis [<xref rid=\"B10-ijerph-17-05292\" ref-type=\"bibr\">10</xref>]. Covert toxocariasis in children or common toxocariasis in adult people is characterized by nebulous symptoms such as fever, headache, vomiting, nausea, abdominal pain, lymphadenitis, hepatomegaly and pulmonary dysfunction [<xref rid=\"B7-ijerph-17-05292\" ref-type=\"bibr\">7</xref>]. Human echinococcosis is a parasitic zoonotic disease, caused by the larval form of taeniid cestodes <italic>Echinococcus granulosus sensulato</italic> (cystic echinococcosis), <italic>E. multilocularis</italic> (alveolar echinococcosis), <italic>E. vogeli</italic> and <italic>E. oligarthus</italic> (polycystic echinococcosis). The two most important forms, which are of medical and public health relevance in humans, are cystic echinococcosis and alveolar echinococcosis. The clinical potential of two other <italic>Echinococcus</italic> species (<italic>E. shiquicus</italic> and <italic>E. felidis</italic>) is unknown [<xref rid=\"B11-ijerph-17-05292\" ref-type=\"bibr\">11</xref>]. Echinococcosis is contracted by accidental ingestion of fully developed infectious eggs (containing an oncosphere larva) from the feces of dogs or other carnivores, with humans serving as intermediate hosts instead of sheep, cattle, mice or other herbivores as well as pigs. It must be noted that the eggs excreted by defecation can be dispersed from the deposition site either by being washed away or carried by flies and other vectors. Additionally, <italic>Echinococcus</italic> eggs may be widely dispersed by adhering to tyres, shoes or animal paws, resulting in contamination of the environment, including human dwellings [<xref rid=\"B12-ijerph-17-05292\" ref-type=\"bibr\">12</xref>]. Hydatids or larval cysts are formed in many tissues, producing lesions and symptoms of which the incubation period and clinical picture depends on the location, number and state of the cyst, the tissues affected etc. The liver, the most commonly invaded organ, may take twenty years to present symptoms, such as jaundice. The lungs, mediastinum, peritoneum, kidneys, spleen, vertebral column and brain, may also be affected, producing symptoms ranging from respiratory distress and kidney dysfunction to seizures. Hydatid cyst rupture can produce anaphylactic shock. Eosinophilia accompanies the infection [<xref rid=\"B13-ijerph-17-05292\" ref-type=\"bibr\">13</xref>]. It is worth noting that the World Health Organization (WHO) has listed echinococcosis as one of the 17 neglected diseases targeted for control or elimination by 2050 [<xref rid=\"B14-ijerph-17-05292\" ref-type=\"bibr\">14</xref>].</p><p>Awareness of both echinococcosis and toxocariasis, and their transmission to humans, attaches great importance to the design of elimination plans and prevention strategies. Prevention requires an ‘One Health’ approach, persisting training of veterinarians and continuing education of pet owners [<xref rid=\"B12-ijerph-17-05292\" ref-type=\"bibr\">12</xref>]. The support of education programs that promote adopting healthy practices (for instance, not feeding raw offal to dogs in order to prevent cystic echinococcosis) is important for the success of zoonotic disease control programs [<xref rid=\"B15-ijerph-17-05292\" ref-type=\"bibr\">15</xref>].</p><p>Evidence from several studies suggest that awareness of the perception of the zoonotic potential of some parasites by pet owners is limited. A study published in 2010 reported that only 56% of dog owners in Texas, USA were cognizant of the zoonotic potential of intestinal helminthes [<xref rid=\"B16-ijerph-17-05292\" ref-type=\"bibr\">16</xref>]. Similar results were derived in the most recently conducted study in Portugal in 2016 [<xref rid=\"B17-ijerph-17-05292\" ref-type=\"bibr\">17</xref>]. In a study conducted in 2012 in Ontario, Canada, where questionnaires were also distributed, only 27% of the responders recalled being informed by a veterinarian about zoonoses [<xref rid=\"B1-ijerph-17-05292\" ref-type=\"bibr\">1</xref>]. Finally, a study carried out in 2008 in Nigeria, using a survey, revealed an inadequate knowledge of parasitic diseases in dogs, in spite of the high prevalence of ectoparasites (60%) and intestinal helminths (68%) in the region. Public information regarding zoonoses was poor and the diseases were not considered of high concern as a health issue [<xref rid=\"B18-ijerph-17-05292\" ref-type=\"bibr\">18</xref>].</p><p>There are several, mainly epidemiological, studies concerning cases of these particular helminthic infestations in Greece. A seroepidemiological investigation of <italic>Toxocara canis</italic> in a female Greek pregnant population in the area of Athens in 2016 concluded that the prevalence of <italic>Toxocara canis</italic> infection in a population of Greek pregnant women was found to be at a rate of 17% and the soil contamination rate was 17% [<xref rid=\"B19-ijerph-17-05292\" ref-type=\"bibr\">19</xref>]. In another seroepidemiologic survey, involving children, a remarkably high percentage (13%) reacted positively to this method. Sixteen (3%) out of 511 sera showed immunoglobulin G (IgG) antibodies, 43 (8%) showed immunoglobulin M (IgM), while 5 (1%) showed both IgG and IgM antibodies against <italic>T. canis</italic> excretory–secretory (ES) antigen. Females were significantly more infected than males. Seropositivity rate was highest in children over the age of 10 and was not found to differ significantly with age (<italic>p</italic> ≥ 0.05). Males and females were found to differ significantly only in the sero-prevalence of IgM antibodies in the first age group (less than 5 years old) [<xref rid=\"B20-ijerph-17-05292\" ref-type=\"bibr\">20</xref>]. In addition, in a study published in 2018, concerning soil contamination in the Attica region, <italic>T. canis</italic> eggs were isolated from 31 (94%) of the examined public areas. Of the total samples, <italic>T. canis</italic> ova were recovered from 258 samples, suggesting an overall <italic>T. canis</italic> ova contamination of 17%. The areas of higher socioeconomic status presented lower percentages of soil contamination at a statistically significant level, compared to the areas of lower socioeconomic status. <italic>T. canis</italic> IgG seropositivity was detected in 40 (16%) serum samples. Similar rates were established among <italic>T. canis</italic> seropositivity and soil contamination within the same geographical areas. The proportion of seropositive samples in the group of children was significantly higher compared to the proportion of adults (48% versus 8%, <italic>p</italic> < 0.001) [<xref rid=\"B21-ijerph-17-05292\" ref-type=\"bibr\">21</xref>]. In Crete, Greece, totals of 879 dog and 264 cat fecal samples were examined in 2017. In dogs, the overall prevalence was 0.8% (CI: 0.2–1.4) for taeniid eggs. In cats, the prevalence was 8.3% (CI: 5.0–11.7) for <italic>Toxocara</italic> spp.; 0.8% (CI: 0–1.8) for taeniid eggs [<xref rid=\"B22-ijerph-17-05292\" ref-type=\"bibr\">22</xref>]. A review gathering incidents of cystic echinococcosis in Northern Greece reports that in the 1970s, 123 cases of cystic echinococcosis were dealt with; in the 1980s, the number of cases decreased to 54; in the period between 1990 and 2003, cases decreased to 8; and in the period 2004–2009, there were 2 recorded cases. This listing referred to children ranging from 2 to 14 years, with an average age of 9.2 years. The youngest patient was 23 months of age [<xref rid=\"B23-ijerph-17-05292\" ref-type=\"bibr\">23</xref>].</p><p>Nevertheless, to the best of our knowledge, a survey study assessing awareness and knowledge of these parasitic zoonoses has never been conducted in Greece, despite the higher risk of echinococcosis in the Mediterranean region [<xref rid=\"B24-ijerph-17-05292\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijerph-17-05292\" ref-type=\"bibr\">25</xref>] and the high rates of environmental contamination with <italic>Toxocara</italic> eggs in Europe [<xref rid=\"B26-ijerph-17-05292\" ref-type=\"bibr\">26</xref>]. The aim of this study was to evaluate the awareness and knowledge of cat and/or dog owners in Greece regarding the zoonotic potential of helminthic parasites, especially <italic>Echinococcus</italic> spp. and <italic>Toxocara</italic> spp., which dogs and cats can harbor, in comparison to non-pet owners.</p></sec><sec id=\"sec2-ijerph-17-05292\"><title>2. Materials and Methods </title><sec id=\"sec2dot1-ijerph-17-05292\"><title>2.1. Survey Protocol and Design</title><p>From September to October 2018, we distributed anonymous self-administered, primarily multiple-choice paper questionnaires at the University of West Attica, in Athens, the Technological Educational Institute (TEI) of Thessaly in Larissa, and via the internet, through an anonymous questionnaire platform. The questionnaire, which consisted of 3 pages, was developed with the cooperation and guidance of biomedical scientists, veterinarians and statisticians (see <xref ref-type=\"app\" rid=\"app1-ijerph-17-05292\">Supplementary Material</xref>). The questionnaire collated information such as demographics (i.e., gender, age, residency and education level), participants’ knowledge of echinococcosis and toxocariasis, their signs and symptoms, consequences and therapy, participants’ sources of information, pet and generally animal contact-related attitudes, types of pet owned (if any), reported cases of echinococcosis or toxocariasis, knowledge about ways of transmission and precautions that need to be taken into account in order to avoid them. Pet owners were asked to enlist their pets’ residence as ‘only indoors’, ‘only outdoors or ‘combined’. The use of these categories in the questionnaire was based on previous studies [<xref rid=\"B1-ijerph-17-05292\" ref-type=\"bibr\">1</xref>,<xref rid=\"B17-ijerph-17-05292\" ref-type=\"bibr\">17</xref>,<xref rid=\"B27-ijerph-17-05292\" ref-type=\"bibr\">27</xref>]. The study was approved by the Research Ethics Board of the University of West Attica (ref 3/11.02.2016). </p></sec><sec id=\"sec2dot2-ijerph-17-05292\"><title>2.2. Statistical Data Analysis</title><p>Demographic and other data are summarized using absolute and relative frequencies. Simple comparisons of the relevant distributions across different levels of other categorical variables are based on chi-square tests. Associations between pet ownership, residence and outcome variables were evaluated using the Fisher exact test and Chi-squared test, respectively. Unifactorial analysis was performed using the One way Analysis of Variance model with the Bonferroni test for pairwise comparisons. Multifactorial linear regression analysis was used to investigate the cross-sectional association between demographic characteristics and the awareness of helminthic zoonoses. All variables were included in the model, using the Enter method, to determine the influence of independent predictors on awareness of helminthic zoonoses. All assumptions of linear regression analysis (homoscedasticity, linearity, normality and independence of error terms, as well as multicollinearity of independent variables) were examined. All tests were two-sided and the statistical significance was set at <italic>p</italic> < 0.05. All analyses were carried out using the statistical package SPSS version 17.0 (Statistical Package for the Social Sciences, SPSS Inc, Chicago, IL, USA).</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05292\"><title>3. Results</title><p>Out of a total of 200 respondents, 15 were excluded from the study because they owned rodents or rabbits as pets and not cats or dogs and this may have affected their answers. Consequently, data analysis was restricted to only dog and/or cat owners and non-pet owners, in a total of 185 (93%) respondents.</p><p>The rate of dog or cat owners and non-pet owners of those responded the questionnaire was almost equally shared, as 55% of them owned at least one pet (dog or cat) and 45% were non-pet owners. The majority of the respondents were women (77%). Most of the participants were young (86% were between 18 and 39 years old), highly educated (83%—university as education level) and lived in a city (74%). The majority of pet owners had dogs as pets (67%) whereas 17 of the respondents owned both cat(s) and dog(s). Of the 101 pet owners, 50% kept their pet(s) indoors, 21% kept them outdoors while 35% of them kept them both indoors and outdoors (<xref ref-type=\"app\" rid=\"app1-ijerph-17-05292\">Supplementary Table S1</xref>).</p><p>Regarding the social perception of zoonoses, the majority reported that they were somewhat concerned about contracting a disease, followed by those who were not at all concerned (54% and 23%, respectively). The percentages of the answers concerning the sources of information about zoonoses were almost evenly shared, with veterinarians and the internet being the most frequent answers (22% and 21%, respectively). Forty-seven of the 185 participants (25%) were satisfied with their knowledge about zoonoses, and a high proportion (45%) replied that they were satisfied with their awareness of the precautions needed to avoid the diseases’ transmission. Only five respondents reported that there was a case of echinococcosis or toxocariasis in their close circle (<xref rid=\"ijerph-17-05292-t001\" ref-type=\"table\">Table 1</xref>).</p><p>Furthermore, we assessed the knowledge of the respondents concerning echinococcosis and toxocariasis. Total scores can range from 0 to 15, with higher scores indicating a higher degree of knowledge. Overall, participants had a middle level of consciousness of these two helminthic zoonoses (mean score ± SD; 8.11 ± 3.18). The zoonotic disease knowledge score did not differ between pet and non-pet owners (<xref rid=\"ijerph-17-05292-t002\" ref-type=\"table\">Table 2</xref>). It is worth noting that unifactorial analysis revealed that people who graduated from University or College presented higher levels of awareness of helminthic zoonoses compared with those who graduated from High school. All other variables (including ownership) did not affect the dependent variable (<xref rid=\"ijerph-17-05292-t002\" ref-type=\"table\">Table 2</xref>).</p><p>In <xref rid=\"ijerph-17-05292-t003\" ref-type=\"table\">Table 3</xref>, we report the answers of the participants, most of which were used for calculating the mean zoonotic disease knowledge score (please see the appendix provided as <xref ref-type=\"app\" rid=\"app1-ijerph-17-05292\">Supplementary Material</xref>). Most of the respondents answered correctly that consumption of raw/uncooked food from pets enhances the probability of infection (75%). Eighty-three of the participants (44.9%) knew that immunosuppressed people are more vulnerable to zoonoses, while 78 of them (42%) reported that they did not know the right answer. More than a half of the participants declared ignorance when asked whether the inhabitants of the Mediterranean region have an increased risk of echinococcosis and the same answer’s rate was also high when they were asked about the appropriate treatment for echinococcosis and toxocariasis (37% and 50%, respectively). In addition, 65.4% of the respondents think that the risk of transmission for these zoonoses is raised after contact with soil in areas accessible to dogs/cats. Approximately one-third out of a total of 101 pet owners claimed that they deworm their pet(s) every 6 months. Regarding the questions enquiring specifically about the transmission, high risk age group, and affected organs from the zoonoses, there was a great percentage that reported unfamiliarity for both zoonoses, with that choice gathering the highest rate in many of them (<xref rid=\"ijerph-17-05292-t003\" ref-type=\"table\">Table 3</xref>).</p><p>Subsequently, we assessed the level of awareness regarding echinococcosis and/or toxocariasis transmission and prevention (<xref rid=\"ijerph-17-05292-t004\" ref-type=\"table\">Table 4</xref>). The two most common ways of transmission of the zoonoses, i.e., consumption of contaminated food/water and through the fecal–oral route, were the ones that marked the lowest rates of correct answers (<xref rid=\"ijerph-17-05292-t004\" ref-type=\"table\">Table 4</xref>). Importantly, the level of awareness regarding transmission was similar between pet and non-pet owners (<xref rid=\"ijerph-17-05292-t004\" ref-type=\"table\">Table 4</xref>). On the contrary, high rates of correct answers about precautions were noted, with exception of the answer for the vaccination of pets, where participants provided the wrong answer, meaning that vaccination of pets is a way that can be used for prevention (<xref rid=\"ijerph-17-05292-t004\" ref-type=\"table\">Table 4</xref>).</p><p>The percentages of correct answers regarding the zoonotic disease knowledge and animal contact-related attitudes of respondents are shown in <xref rid=\"ijerph-17-05292-t005\" ref-type=\"table\">Table 5</xref>. Despite the fact that there were no statistically significant differences among pet and non-pet owners, it is important to highlight the very low rates of correct answers in the majority of these questions. Finally, no statistically significant results were found in the responses of pet owners in relation to the pet’s place of residence (inside, outside or mixed) (<xref ref-type=\"app\" rid=\"app1-ijerph-17-05292\">Supplementary Table S2</xref>).</p><p>Finally, we employed a multiple regression model with the Enter method in order to examine the independent contribution of demographic variables to the score of awareness of helminthic zoonoses. Regression analysis accounted for 10% of the variance of the dependent variable [R<sup>2</sup> = 0.101, F(5179) = 3.6, <italic>p</italic> = 0.004]. According to our results, higher levels of Education (Beta coefficient ± SE: 2.38 ± 0.65; <italic>p</italic> < 0.005; R<sup>2</sup> = 0.09) were statistically significantly associated with higher awareness of helminthic zoonoses, while Age (Beta coefficient ± SE: 0.06 ± 0.56; <italic>p</italic> = 0.695; R<sup>2</sup> = 0.001), Gender (Beta coefficient ± SE: −0.28 ± 0.72; <italic>p</italic> = 0.695; R<sup>2</sup> < 0.001), Location of Residence (Beta coefficient ± SE: −0.41 ± 0.53; <italic>p</italic> = 0.443; R<sup>2</sup> = 0.004) and Pet-ownership (Beta coefficient ± SE: 0.11 ± 0.47; <italic>p</italic> = 0.822; R<sup>2</sup> < 0.001) did not have a statistically significant influence on the dependent variable (<xref rid=\"ijerph-17-05292-t006\" ref-type=\"table\">Table 6</xref>).</p></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05292\"><title>4. Discussion</title><p>Echinococcosis and toxocariasis constitute some of the most important and common zoonotic infections threatening human populations in Europe [<xref rid=\"B12-ijerph-17-05292\" ref-type=\"bibr\">12</xref>]. We aim to assess the knowledge of cat and/or dog owners about echinococcosis and toxocariasis, in comparison to non-pet owners, in addition to their beliefs and attitudes with respect to pet ownership, in Greece. Although this subject is of major significance for public and personal health, a similar study has never been carried out in Greece. </p><p>We found a poor knowledge about both parasites, regardless of whether respondents were pet owners, or not. Similarly, in a study conducted in 2016 in Portugal, it was noted that 56.5% of the pet owners were familiar with zoonoses as a term, but only 35.2% understood what it means. However, the higher the educational level, the better the understanding about zoonoses was [<xref rid=\"B19-ijerph-17-05292\" ref-type=\"bibr\">19</xref>]. This is consistent with our results showing that education is the only variable that significantly affects the zoonotic parasitic knowledge score.</p><p>Regarding <italic>Toxocara</italic> spp., in our study, the rates of correct answers in most of the questions were very low. In a recent study, a high percentage of pediatricians stated that they were not confident that their knowledge of toxocariasis was updated. In addition, the majority of respondents could not discriminate between toxoplasmosis and toxocariasis and also, they were not able to correctly identify prevention strategies to decrease risks of acquiring <italic>Toxocara</italic> infection. In an effort to combat this gap, the Centers for Disease Control and Prevention (CDC) have developed downloadable resources for the public and for physicians, available at <uri xlink:href=\"https://www.cdc.gov/parasites/toxocariasis/printresources.html\">https://www.cdc.gov/parasites/toxocariasis/printresources.html</uri> [<xref rid=\"B28-ijerph-17-05292\" ref-type=\"bibr\">28</xref>].</p><p>In our study, veterinarians (21.6%) and the internet (20.7%) were the most frequently reported source of information about zoonoses, which is consistent with a similar previously conducted study [<xref rid=\"B1-ijerph-17-05292\" ref-type=\"bibr\">1</xref>]. In a similar survey conducted in 2012 in Ontario, Canada, where questionnaires were also distributed, only 27% of the responders recalled having been informed by a veterinarian about zoonoses and over 30% of them were not concerned about catching a disease from their pets [<xref rid=\"B1-ijerph-17-05292\" ref-type=\"bibr\">1</xref>].</p><p>Veterinarians could be the most important educators regarding the transfer of knowledge of zoonoses to stakeholders. However, it seems that they hesitate to provide this information to their clients to avoid alarming them and leading them to give up their pets. In fact, a national survey of intestinal parasites in dogs and cats in Australia revealed that most veterinarians would not discuss with their clients the zoonotic risk of gastrointestinal parasites their pets may bear, resulting in the owners’ nescience. Nevertheless, they suggested regular anthelminthic treatment throughout a pet’s life. Moreover, pet owners were unaware of the existence of zoonoses [<xref rid=\"B29-ijerph-17-05292\" ref-type=\"bibr\">29</xref>].</p><p>We found a considerably low level of satisfaction about the knowledge of zoonoses (25.4%). It seems that respondents are not properly using the sources of knowledge, highlighting the imperative need for awareness campaigns among pet owners in Greece.</p><p>It should be mentioned that during the distribution of the questionnaires, supplementary questions (e.g., about their knowledge on which diseases are capable of being transmitted to humans through contact with cats and dogs) were made during the discussions with those interested in participating in the study. It is worth noting that there were respondents thinking that it is possible to be infected with HIV (Human Immunodeficiency Virus) (three participants) and hepatitis B virus (HBV) (seven participants) through animal contact.</p><p>Since the adequate knowledge of zoonotic diseases, and particularly echinococcosis that is endemic in Mediterranean region [<xref rid=\"B30-ijerph-17-05292\" ref-type=\"bibr\">30</xref>], is key to preventing them from spreading, the limited awareness is highly concerning. It is also crucial to mention that a heavily poor acknowledgement of the increased localization for these particular zoonoses in the Mediterranean Basin was observed, with only 27 of the 185 participants being cognizant of the elevated risk. In addition, the most common ways of transmission of echinococcosis and toxocariasis (fecal–oral route and consumption of contaminated food/water) marked the lowest rates of correct answers. It seems that there is a gap between owners’ and non-owners’ answers in whether the avoidance of infected people is useful in the prevention of helminthic transmission. Last but not least, almost 70% of the respondents erroneously believe that vaccination contributes to the eradication of these parasites.</p><p>The rate of correct answers was significantly decreased in questions regarding organs affected, the pet involved in the transmission, most vulnerable age group, treatment and deworming frequency. Veterinarians could play a critical role in the attempt to increase pet owners’ understanding of the importance of frequent deworming. In addition, the need to properly dispose of their pet’s feces will also help to highlight toxocariasis [<xref rid=\"B31-ijerph-17-05292\" ref-type=\"bibr\">31</xref>]. The most recent European Scientific Counsel Companion Animal Parasites (ESCCAP) guidelines provide research-based independent advice regarding risk factors and recommended deworming frequency [<xref rid=\"B32-ijerph-17-05292\" ref-type=\"bibr\">32</xref>]. In the literature, it has been reported that compliance to veterinary and guideline advice is poor, resulting in wrong decisions on behalf of pet-owners regarding routine deworming [<xref rid=\"B33-ijerph-17-05292\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijerph-17-05292\" ref-type=\"bibr\">34</xref>]. Indeed, very recently, it has been shown that pet-owners from five countries in Europe (France, Germany, Spain, Sweden and the UK) do not deworm their dogs and cats as frequently as is recommended by ESCCAP or required in order to reduce zoonotic risk and improve pet health [<xref rid=\"B35-ijerph-17-05292\" ref-type=\"bibr\">35</xref>].</p><p>It is surprising that the results obtained from the comparison of dog or/and cat owners and non-pet owners indicate no statistically significant difference among these two groups. In contradiction to what was expected, there were many questions whose results presented an exceeding ignorance of pet owners, since their rates of incorrect answers were higher compared to those of non-pet owners (e.g., the pet contributing to the spread of the disease, ways of transmission and precautions, etc.). In deworming frequency, although it is not statistically different between the pet’s residence, the percentage is quite low apropos the right answers.</p><p>Unlike the livestock parasites, the anthelmintic drug resistance in companion animals’ parasites has been detected to be of slow emergence, probably due to individual or small numbers in animal keeping, correct dosage to dogs and cats on a body weight basis and appropriate frequency of deworming. However, there are the exceptions of anthelmintic resistance of larvae <italic>Dirofilaria immitis</italic> [<xref rid=\"B36-ijerph-17-05292\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijerph-17-05292\" ref-type=\"bibr\">37</xref>], the proved anthelmintic resistance that had evolved in the canine hookworm <italic>Ancylostoma caninum</italic> [<xref rid=\"B38-ijerph-17-05292\" ref-type=\"bibr\">38</xref>,<xref rid=\"B39-ijerph-17-05292\" ref-type=\"bibr\">39</xref>], as well as praziquantel resistance in the zoonotic cestode <italic>Dipylidium caninum</italic> [<xref rid=\"B40-ijerph-17-05292\" ref-type=\"bibr\">40</xref>]. As yet, there are no reports confirming antihelmintic resistance in <italic>Toxocara</italic> species, or in other dog or cat internal parasite species. However, the increased frequency or underdosing of anthelmintic treatment could favor selection pressure for resistance, like in the case of the shelters and breeding kennels, where there may be concomitant administration of the same antiparasitic drug to all the dogs or cats [<xref rid=\"B41-ijerph-17-05292\" ref-type=\"bibr\">41</xref>].</p><p>Last but not least, taking into account the sad truth that Greece is a country with a major issue of stray animals, it is a matter of urgency for medical care programs to be carried out regularly (vaccinations, deworming, etc.). In Greece, ESCCAP guidelines are not widely known and thus not followed, highlighting that the veterinarians should take the responsibility to educate pet owners about the transmission routes of zoonotic parasites, as well as prevention and parasite control practices [<xref rid=\"B12-ijerph-17-05292\" ref-type=\"bibr\">12</xref>]. Conclusively, it is of paramount importance for the country to initiate educational campaigns about zoonoses and precautions, transmission and treatment, and for veterinarians to inform pet owners about the importance of appropriate antihelminthic treatment and monitor the frequency in order to avoid belated administration.</p><p>Our survey has the limitation of a restricted sample size since it was retrieved mainly from two geographic areas (Attica and Larissa). From the data analysis, it was observed that the participants were mainly young adults and, despite the easy access to many sources of information, we observed a lack of knowledge and awareness. Further investigation is required in order to see if these results will be confirmed in different populations. </p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05292\"><title>5. Conclusions</title><p>In conclusion, this particular study aimed to demonstrate the knowledge and attitudes with respect to pet ownership and zoonotic diseases in Greece. As occurred from the study, awareness regarding the risk of contamination with the parasites <italic>Echinococcus</italic> spp. and <italic>Toxocara</italic> spp. was very limited even among pet owners. In fact, there was no statistically significant difference between pet owners and non-pet owners’ knowledge concerning the parasites. It is an undeniable fact that the acknowledgement of ways of transmission and risk factors can prevent the perpetuation of such zoonoses. Precautions, such as more active involvement of veterinarians in informing pet owners of zoonoses, should be applied or enhanced. In addition, introduction of the matter in the educational system is a necessity, since misinformation or ignorance is dangerous, both for animals and humans. Moreover, campaigns should be held, for instance television spots, leaflets in vet clinics, pet shops and physicians’ offices, informing individuals about zoonoses with specific information about which diseases are considered zoonoses, their transmission, risk factors and necessary preventive measures in order to eliminate the spread without causing panic and mistreatment towards animals.</p></sec></body><back><ack><title>Acknowledgments</title><p>The authors thank all responders for their voluntary participation in the study.</p></ack><app-group><app id=\"app1-ijerph-17-05292\"><title>Supplementary Materials</title><p>The following are available online at <uri xlink:href=\"https://www.mdpi.com/1660-4601/17/15/5292/s1\">https://www.mdpi.com/1660-4601/17/15/5292/s1</uri>. Table S1: Descriptive characteristics of study participants; Table S2: Zoonotic disease knowledge and animal contact-related attitudes in relation to pet’s place of residence.</p><supplementary-material content-type=\"local-data\" id=\"ijerph-17-05292-s001\"><media xlink:href=\"ijerph-17-05292-s001.rar\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>M.E.T., A.B. and C.V. conceived and designed the study. C.K., M.E.T. and A.G. acquired the data. M.E.T., A.G., A.G.M. and C.V. conducted statistical analyses. C.K., M.E.T., A.G.M., A.B., E.G. and C.V. drafted and revised the manuscript. A.G.M., E.G., A.B. and C.V. reviewed the manuscript for important intellectual content. 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How concerned are you that the pet(s) in your family/social environment could transfer a disease to you or your family/friends?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Very concerned</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Concerned</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Somewhat concerned</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Not at all concerned</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">32</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">100</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">42</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.9%</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">17.3%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">54.1%</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">22.7%</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Q2. Sources of information about the diseases that may occur through animal contact (multiple answers allowed)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Family- friends</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Veterinarian</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Medical Staff</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Internet</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Books</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Other</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">85</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">124</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">71</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">119</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">93</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">83</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">45.9%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">67.0%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">38.4%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">64.3%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">50.3%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">44.9%</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Q3. Are you satisfied with the comprehension and the knowledge about the diseases that can occur through animal contact?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Yes</td><td colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">I don’t know</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">81</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">47</td><td colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">57</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">43.8%</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">25.4%</td><td colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">30.8%</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Q4. Are you satisfied with the knowledge about the precautions that need to be taken in order to minimize the risk of disease transmission that can occur through animal contact?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Yes</td><td colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">I don’t know</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">37</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">84</td><td colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">64</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20.0%</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">45.4%</td><td colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">34.6%</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Q5. Has anyone in your family/inner circle ever been infected with echinococcosis or toxocariasis?</td><td colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">No</td><td colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Yes</td></tr><tr><td colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">180</td><td colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">5</td></tr><tr><td colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">97.3%</td><td colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">2.7%</td></tr></tbody></table><table-wrap-foot><fn><p>All variables are presented as absolute (Ν) and relative frequencies (%).</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05292-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05292-t002_Table 2</object-id><label>Table 2</label><caption><p>Unifactorial analysis of knowledge of the zoonoses echinococcosis and toxocariasis.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>N</italic>\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mean ± SD</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th></tr></thead><tbody><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Gender</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Male</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">43</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.78 ± 3.20</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.450</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Female</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">142</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8.20 ± 3.18</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Age</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18–29</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">128</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8.38 ± 3.20</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.123</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">30–39</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">31</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.93 ± 3.35</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">40+</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">26</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.00 ± 2.74</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Education</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">High School</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">32</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.06 ± 3.09</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\"><0.005</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">University/College</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">153</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8.54 ± 3.05</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Location of Residence</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">City</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">137</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8.26 ± 3.19</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.283</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Other</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">48</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.68 ± 3.16</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Pet-owner</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">84</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8.25 ± 3.23</td><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.589</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">101</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8.00 ± 3.16</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Pet </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Dog</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">68</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8.00 ± 3.30</td><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">0.720</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cat</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8.47 ± 2.90</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Both</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.72 ± 2.92</td></tr></tbody></table><table-wrap-foot><fn><p>“Other” in the Location of residence includes: Suburban area (<italic>n</italic> = 21)/Village (<italic>n</italic> = 11)/Island (<italic>n</italic> = 16).</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05292-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05292-t003_Table 3</object-id><label>Table 3</label><caption><p>Investigation of the population’s knowledge about the zoonoses echinococcosis and toxocariasis.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Question</th><th colspan=\"6\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Answers</th></tr></thead><tbody><tr><td rowspan=\"3\" align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Q1. Consumption of raw or insufficiently cooked food from pets increases the risk of echinococcosis?</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">No</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Yes</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">I don’t know</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">7</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">139</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">39</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">3.8%</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">75.1%</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">21.1%</td></tr><tr><td rowspan=\"3\" align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Q2. Are the immunosuppressed people (due to disease, syndrome, radiation or chemotherapy etc.) more susceptible to echinococcosis and/or toxocariasis? </td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">No</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Yes</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">I don’t know</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">24</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">83</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">78</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">13.0%</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">44.9%</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">42.1%</td></tr><tr><td rowspan=\"3\" align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Q3.The inhabitants of the Mediterranean region have increased risk of developing echinococcosis?</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">No</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Yes</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">I don’t know</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">54</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">27</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">104</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">29.2%</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">14.6%</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">56.2%</td></tr><tr><td rowspan=\"3\" align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Q4. Children’s contact with soil/sand in areas accessible to dogs/cats increases the risk of echinococcosis and/or toxocariasis?</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">No</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Yes</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">I don’t know</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">23</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">121</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">41</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">12.4%</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">65.4%</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">22.2%</td></tr><tr><td rowspan=\"3\" align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Q5. How frequently should dogs and cats be dewormed?</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Monthly</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Every 3 months</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Every 6 months</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Every 12 months</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">19</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">28</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">32</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">22</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">18.8%</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">27.7%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">31.7%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">21.8%</td></tr><tr><td rowspan=\"3\" align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Q6. Contact with which animal is more likely to lead to the development of cystic echinococcosis in humans?</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Both cats and dogs</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Cat</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Dog</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">I don’t know</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">56</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">23</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">52</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">54</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">30.3%</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">12.4%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">28.1%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">29.2%</td></tr><tr><td rowspan=\"3\" align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Q7. Which age group has the greatest risk of developing echinococcosis? </td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Children</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Adults</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Elderly</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">All ages</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">I don’t know</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">40</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">89</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">47</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">21.6%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0 %</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.9%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">48.1%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25.4%</td></tr><tr><td rowspan=\"3\" align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Q8. Which organs/ tissues of humans may be damaged in cases of echinococcosis?</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Lungs</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Liver</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Eyes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Brain</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">I don’t know</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">53</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">41</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">28</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">50</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">29.0%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">22.0%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.0%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.0%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">27.0%</td></tr><tr><td rowspan=\"3\" align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Q9.Which is the appropriate treatment of echinococcosis?</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Medication</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Surgical Removal</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Combination</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">I don’t know</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">53</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">11</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">52</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">69</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">29.0%</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">6.0%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">28.0%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">37.0%</td></tr><tr><td rowspan=\"3\" align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Q10. The contact with which animal is more likely to lead to development of toxocariasis in humans? </td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Both cats and dogs</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Cat</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Dog</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">I don’t know</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">45</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">58</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">71</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">24.3%</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">31.4%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.9%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">38.4%</td></tr><tr><td rowspan=\"3\" align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Q11. Which age group has the greatest risk of developing toxocariasis? </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Children</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Adults</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Elderly</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">All ages</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">I don’t know</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">38</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">69</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">67</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20.5%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.7%</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">3.2%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">37.3%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">36.2%</td></tr><tr><td rowspan=\"3\" align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Q12.Which organs/ tissues of humans may be damaged in cases of toxocariasis?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Lungs</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Liver</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Eyes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Brain</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">I don’t know</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">26</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">41</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">32</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">33</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">53</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14.0%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">22.0%</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">17.0%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18.0%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">29.0%</td></tr><tr><td rowspan=\"3\" align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Q13.Which is the appropriate treatment of toxocariasis?</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Medication</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Surgical Removal</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Combination</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">I don’t know</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">61</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">28</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">92</td></tr><tr><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">33.0%</td><td colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">2.0%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.0%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">50.0%</td></tr></tbody></table><table-wrap-foot><fn><p>All variables are presented as absolute (Ν) and relative frequencies (%).</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05292-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05292-t004_Table 4</object-id><label>Table 4</label><caption><p>Participants’ awareness regarding echinococcosis and/or toxocariasis transmission and prevention in relation to pet ownership.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Question</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Possible Options</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Correct Answer</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Total<break/><italic>N</italic> (%)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pet Owners<break/>(<italic>n</italic> = 101)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Non Pet Owners (<italic>n</italic> = 84)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th></tr></thead><tbody><tr><td rowspan=\"6\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Q1. Possible way(s) of echinococcosis and/or toxocariasis transmission</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">a. Through fecal–oral route</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">136 (73.5%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">73 (72.2%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">63 (75.0%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.314</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">b. Consumption of contaminated food/water</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">127 (68.6%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">67 (66.3%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">60 (71.4%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.201</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">c. Sexual intercourse</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">176 (95.1%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">98 (97.0%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">78 (92.8%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.733</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">d. By blood</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">165 (89.2%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">91 (90.1%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">74 (88.0%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.479</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">e. Contact with infected person</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">165 (89.2%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">94 (93.0%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">71 (84.5%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.636</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">f. I don’t know</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>-</italic>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">158 (85.4%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">85 (84.1%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">73 (86.9%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.802</td></tr><tr><td rowspan=\"8\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Q2. Precautions for preventing the development/transmission of echinococcosis and/or toxocariasis</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">a. Vaccination of individual</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">138 (74.6%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">76 (75.2%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">62 (73.8%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.614</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">b. Vaccination of pet</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">58 (31.4%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">33 (32.6%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25 (29.7%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">c. Deworming of pet</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">125 (67.61%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">74 (73.2%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">51 (60.7%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.269</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">d. Observance of personal hygiene rules (frequent handwashing etc.)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">152 (82.2%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">85 (84.1%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">67 (79.7%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">e. Appropriate cooking/washing of food</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">128 (69.2%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">71 (70.2%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">57 (67.8%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.873</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">f. Avoiding the burden of land near homes and playgrounds with dog feces</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">106 (57.3%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">58 (57.4%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">48 (57.1%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.656</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">g. Avoidance of contact with infected person</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">153 (82.7%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">91 (90.0%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">62 (73.8%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.077</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">h. Informing the population about these diseases</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">133 (71.9%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">74 (73.2%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">59 (70.2%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.87</td></tr></tbody></table><table-wrap-foot><fn><p>All variables are presented as absolute (Ν) and relative frequencies (%).</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05292-t005\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05292-t005_Table 5</object-id><label>Table 5</label><caption><p>Zoonotic disease knowledge and animal contact-related attitudes of respondents.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Question </th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Correct Answer</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pet Owners<break/>(<italic>n</italic> = 101)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Non Pet Owners <break/>(<italic>n</italic> = 84)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Q1. Do the residents of Mediterranean region have increased risk of developing echinococcosis?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15 (14.8%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12 (14.2%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.944</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Q2. Children’s contact with soil/sand in areas accessible to dogs/cats increases the risk of echinococcosis and/or toxocariasis?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">64 (63.3%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">57 (67.8%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.211</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Q3. The contact with which animal is more likely to lead to the development of cystic echinococcosis in humans?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Dog</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">28 (27.7%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">24 (28.5%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.682</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Q4. The contact with which animal is more likely to lead to the development of toxocariasis in humans?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Both dogs and cats</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">22 (21.7%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">23 (27.3%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.254</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Q5. Which age group has the greatest risk of developing echinococcosis?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">All ages</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">55 (54.4%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">34 (40.4%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.141</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Q6. Which age group has the greatest risk of developing toxocariasis?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Children</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">24 (23.7%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14 (16.6%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.332</td></tr></tbody></table><table-wrap-foot><fn><p>All variables are presented as absolute (Ν) and relative frequencies (%) of right answers.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05292-t006\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05292-t006_Table 6</object-id><label>Table 6</label><caption><p>Multifactorial analysis of awareness of the zoonoses echinococcosis and toxocariasis.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Variable</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reference Category</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">R<sup>2</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Beta<break/>Coefficient</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SE</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Constant</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">---</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.443</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.048</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.031</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Age (40+)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18–39</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.06</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.56</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.919</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Gender (female)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Male</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.28</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.72</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.695</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Education (university)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">High School</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.095</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.38</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.65</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.005</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Location of Residence<break/>(Other *)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">City</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.004</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.41</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.53</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.443</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pet-owner (yes)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.11</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.47</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.822</td></tr></tbody></table><table-wrap-foot><fn><p>* The word Other in the variable Location of residence refers to Suburban area/Village/Island, SE: Standard Error.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751072</article-id><article-id pub-id-type=\"pmc\">PMC7432044</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155376</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05376</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Review</subject></subj-group></article-categories><title-group><article-title>Life and Death of Fungal Transporters under the Challenge of Polarity</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Dimou</surname><given-names>Sofia</given-names></name><xref rid=\"c1-ijms-21-05376\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Diallinas</surname><given-names>George</given-names></name><xref rid=\"c1-ijms-21-05376\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05376\">Department of Biology, National and Kapodistrian University of Athens, Panepistimioupolis, 15784 Athens, Greece</aff><author-notes><corresp id=\"c1-ijms-21-05376\"><label>*</label>Correspondence: <email>sodimou@biol.uoa.gr</email> (S.D.); <email>diallina@biol.uoa.gr</email> (G.D.)</corresp></author-notes><pub-date pub-type=\"epub\"><day>29</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5376</elocation-id><history><date date-type=\"received\"><day>06</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>27</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Eukaryotic plasma membrane (PM) transporters face critical challenges that are not widely present in prokaryotes. The two most important issues are proper subcellular traffic and targeting to the PM, and regulated endocytosis in response to physiological, developmental, or stress signals. Sorting of transporters from their site of synthesis, the endoplasmic reticulum (ER), to the PM has been long thought, but not formally shown, to occur via the conventional Golgi-dependent vesicular secretory pathway. Endocytosis of specific eukaryotic transporters has been studied more systematically and shown to involve ubiquitination, internalization, and sorting to early endosomes, followed by turnover in the multivesicular bodies (MVB)/lysosomes/vacuole system. In specific cases, internalized transporters have been shown to recycle back to the PM. However, the mechanisms of transporter forward trafficking and turnover have been overturned recently through systematic work in the model fungus <italic>Aspergillus nidulans</italic>. In this review, we present evidence that shows that transporter traffic to the PM takes place through Golgi bypass and transporter endocytosis operates via a mechanism that is distinct from that of recycling membrane cargoes essential for fungal growth. We discuss these findings in relation to adaptation to challenges imposed by cell polarity in fungi as well as in other eukaryotes and provide a rationale of why transporters and possibly other housekeeping membrane proteins ‘avoid’ routes of polar trafficking. </p></abstract><kwd-group><kwd>traffic</kwd><kwd>endocytosis</kwd><kwd>sorting</kwd><kwd><italic>Aspergillus nidulans</italic></kwd><kwd>UapA</kwd><kwd>Golgi</kwd><kwd>fungi</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05376\"><title>1. Introduction</title><p>The pioneer biochemical work of Ronald Kaback on the LacY permease before the era of crystallographic analysis of transporters (<2003) had a huge impact on all of us who decided to devote our research to understanding how transporters work. His ingenious experimental inventions for studying a prokaryotic transporter, which led to the establishment of the generally accepted ‘alternating access mechanism of transport’ [<xref rid=\"B1-ijms-21-05376\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijms-21-05376\" ref-type=\"bibr\">2</xref>], coupled with a wealth of parallel findings on fungal transporter regulation of expression and physiological function (1970–1990s), coming mostly from the work of Marcel Grenson [<xref rid=\"B3-ijms-21-05376\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijms-21-05376\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijms-21-05376\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijms-21-05376\" ref-type=\"bibr\">6</xref>], Claudio Scazzocchio [<xref rid=\"B7-ijms-21-05376\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijms-21-05376\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijms-21-05376\" ref-type=\"bibr\">9</xref>], and Rosine Haguenauer-Tsapis [<xref rid=\"B10-ijms-21-05376\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijms-21-05376\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijms-21-05376\" ref-type=\"bibr\">12</xref>], offers new insights into more complex issues of transporters at the molecular and cellular level. Two novel aspects concerning transporters that we are particularly interested in since 1998 are how substrate specificity is determined and evolves at the molecular level, and which are the signals and mechanisms that regulate transporter trafficking and turnover in eukaryotic cells. To investigate both issues, the model genetic system of the filamentous ascomycete <italic>Aspergillus nidulans</italic> has been used, which eventually emerged as a unique organism for studying eukaryotic transporters [<xref rid=\"B13-ijms-21-05376\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijms-21-05376\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijms-21-05376\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijms-21-05376\" ref-type=\"bibr\">16</xref>]. In the most recent reviews, we discussed our basic ideas concerning how transporter specificity might be determined. Here, we present our views, based on very recent findings and related references, on how transporters traffic to the plasma membrane (PM) and how they are downregulated in response to environment signals [<xref rid=\"B17-ijms-21-05376\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijms-21-05376\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijms-21-05376\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijms-21-05376\" ref-type=\"bibr\">20</xref>]. This article does not intend to be a general account of fungal transporter trafficking, but rather it aims to highlight how work with selected transporters of <italic>A. nidulans</italic> is changing our views on transporter biogenesis and turnover. </p></sec><sec id=\"sec2-ijms-21-05376\"><title>2. Brief Account on the Trafficking of Membrane Cargoes via Conventional Secretion </title><p>The first step in the biogenesis of nascent eukaryotic membrane proteins, including transporters and other polytopic transmembrane proteins, is their co-translational translocation from ribosomes to the membrane of the endoplasmic reticulum (ER) [<xref rid=\"B21-ijms-21-05376\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05376\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05376\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05376\" ref-type=\"bibr\">24</xref>]. This is a rather mechanistic process that is not regulated in response to environmental signals. However, proper folding of membrane proteins during or after co-translational translocation into the ER is not only a prerequisite but also serves as a protein quality step. Misfolding can occur due to mutations, temperature or chemical stress, heterologous expression, or stochastically. Misfolded membrane proteins are trapped in the ER membrane and eventually turned over by ER-associated degradation (ERAD) or selective autophagy [<xref rid=\"B25-ijms-21-05376\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijms-21-05376\" ref-type=\"bibr\">26</xref>]. Once correctly folded in the ER, membrane proteins are sorted into specialized nascent microdomains called ER-exit sites (ERes), where they interact with components of the COPII complex. Assembly of the COPII complex on the ER membrane occurs in a stepwise fashion, beginning with recruitment of the GTPase Sar1, which recruits the heterodimeric Sec23–Sec24, which in turn interacts with the membrane. Sec24 is the principle cargo-binding COPII component. Following cargo-Sec23/Sec24 complex formation, heterodimers of Sec13–Sec31 are recruited via interaction between Sec23 and Sec31. Sec13–Sec31 drive membrane curvature and budding of COPII vesicles, aided by the oligomerization of Sec23–Sec24 and concentrative cargo homo-oligomerization. After vesicle fission, downstream events lead to the uncoating of transport vesicles and recycling of the COPII coat components back to the ER [<xref rid=\"B27-ijms-21-05376\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05376\" ref-type=\"bibr\">28</xref>,<xref rid=\"B29-ijms-21-05376\" ref-type=\"bibr\">29</xref>]. In several cases, the process of ER exit requires specific autonomous or context-dependent sequence motifs in cargoes, most commonly located at their cytosolic termini, essential for recognition by Sec24 [<xref rid=\"B16-ijms-21-05376\" ref-type=\"bibr\">16</xref>]. Such motifs are usually short di-acidic (D/E-X-D/E), hydrophobic, and aromatic sequences (FF, YY, LL, FY, ΦXΦXΦ). </p><p>Membrane cargo–Sec24 interaction and packaging into COPII vesicles is often assisted by specific ER-resident chaperones and/or cargo receptors. One of the best characterized ER-exit membrane chaperone is the <italic>Saccharomyces cerevisiae</italic> Shr3 protein, which mediates COPII–cargo interactions required specifically for the packaging of amino acid transporters into vesicles [<xref rid=\"B30-ijms-21-05376\" ref-type=\"bibr\">30</xref>]. Shr3 has been shown to assist in folding amino acid permeases, thus preventing precocious ERAD [<xref rid=\"B31-ijms-21-05376\" ref-type=\"bibr\">31</xref>]. Additional ER membrane-localized chaperones are specific for the trafficking of distinct fungal transporters [<xref rid=\"B30-ijms-21-05376\" ref-type=\"bibr\">30</xref>,<xref rid=\"B32-ijms-21-05376\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijms-21-05376\" ref-type=\"bibr\">33</xref>]. Other types of ER proteins that are necessary for COPII packaging and trafficking of specific cargoes are receptors that interact with both the cargo and the Sec24-Sec23 complex. The best characterized cargo receptor is Erv14, which has been shown to be essential for the ER exit and trafficking of tens of membrane proteins, including mostly transporters and polytopic transmembrane proteins [<xref rid=\"B34-ijms-21-05376\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijms-21-05376\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijms-21-05376\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijms-21-05376\" ref-type=\"bibr\">37</xref>]. Exit from the ER additionally requires specific interactions of the cargo and COPII vesicular machinery components with specific ER lipids [<xref rid=\"B27-ijms-21-05376\" ref-type=\"bibr\">27</xref>,<xref rid=\"B29-ijms-21-05376\" ref-type=\"bibr\">29</xref>,<xref rid=\"B38-ijms-21-05376\" ref-type=\"bibr\">38</xref>]. In mammalian cells, an intermediate compartment between the ER and the <italic>cis-</italic>Golgi has been defined and called the ER-intermediate compartment (ERGIC) [<xref rid=\"B39-ijms-21-05376\" ref-type=\"bibr\">39</xref>]. After successful ER or ERGIC exit, uncoated vesicles fuse with the <italic>cis</italic>-Golgi and then ‘reach’ the trans-Golgi network (TGN) via Golgi maturation [<xref rid=\"B27-ijms-21-05376\" ref-type=\"bibr\">27</xref>,<xref rid=\"B40-ijms-21-05376\" ref-type=\"bibr\">40</xref>,<xref rid=\"B41-ijms-21-05376\" ref-type=\"bibr\">41</xref>,<xref rid=\"B42-ijms-21-05376\" ref-type=\"bibr\">42</xref>]. Membrane cargoes exit from the TGN, after recruitment of the small GTPase RabE<sup>Rab11</sup>, package in AP-1/clathrin-coated vesicles, which translocate to the PM either directly or indirectly via highly motile endosomes [<xref rid=\"B40-ijms-21-05376\" ref-type=\"bibr\">40</xref>,<xref rid=\"B43-ijms-21-05376\" ref-type=\"bibr\">43</xref>,<xref rid=\"B44-ijms-21-05376\" ref-type=\"bibr\">44</xref>]. AP-1/clathrin-coated vesicles carrying membrane cargoes move on tubulin tracts of the cytoskeleton [<xref rid=\"B18-ijms-21-05376\" ref-type=\"bibr\">18</xref>,<xref rid=\"B43-ijms-21-05376\" ref-type=\"bibr\">43</xref>,<xref rid=\"B45-ijms-21-05376\" ref-type=\"bibr\">45</xref>]. The final step of membrane cargo forward traffic is carried out by the exocyst, an octameric protein complex that is involved in the tethering of secretory vesicles to the PM prior to fusion, mediated by soluble SNAREs [<xref rid=\"B46-ijms-21-05376\" ref-type=\"bibr\">46</xref>]. In filamentous fungi, vesicles carrying apical membrane cargoes and secretory proteins move to the so-called Spitzenkörper (SPK), an aggregation of numerous vesicles and rich in actin microfilaments, positioned under the tip of growing hyphae, from where they fuse to the PM via the exocyst complex. The SPK has been suggested to be a transfer station from cytoplasmic microtubules to actin microfilaments [<xref rid=\"B47-ijms-21-05376\" ref-type=\"bibr\">47</xref>,<xref rid=\"B48-ijms-21-05376\" ref-type=\"bibr\">48</xref>,<xref rid=\"B49-ijms-21-05376\" ref-type=\"bibr\">49</xref>]. </p></sec><sec id=\"sec3-ijms-21-05376\"><title>3. Do Transporters Reach the PM via Conventional Golgi-Dependent Secretion? </title><p>Noticeably, however, the aforementioned brief account on membrane cargo forward trafficking towards the PM is heavily based on cargoes that are not transporters or other polytopic transmembrane proteins (e.g., channels or receptors). In fact, very few studies have addressed directly and systematically how transporters reach the PM. The dogma seems to be that, being transmembrane proteins, transporters, channels, and receptors use the conventional Golgi- and post-Golgi-dependent vesicular secretion route, described above. However, some lines of evidence supported that specific transporters might not follow known conventional secretion routes. For instance, the insulin-regulated glucose transporter GLUT4 accumulated at the PM, rather than being sequestered in the Golgi or other intracellular compartments, after the deletion of proteins involved in TGN-dependent membrane cargo sorting (e.g., Arfrp1, golgin-160, or AP-1), suggesting the presence of alternative routes out of the TGN [<xref rid=\"B50-ijms-21-05376\" ref-type=\"bibr\">50</xref>]. In line with this, kinesin motor proteins or microtubule disruption had a moderate or no effect on GLUT4 accumulation at the PM [<xref rid=\"B51-ijms-21-05376\" ref-type=\"bibr\">51</xref>,<xref rid=\"B52-ijms-21-05376\" ref-type=\"bibr\">52</xref>]. Compelling direct evidence has been recently obtained showing that neosynthesized GLUT4 is indeed sorted to the PM from an early secretory compartment, bypassing the TGN [<xref rid=\"B53-ijms-21-05376\" ref-type=\"bibr\">53</xref>]. In another example, the mammalian potassium channel Kv2.1 has been shown to translocate to the PM of the initial segment (AIS) of neurons via a mechanism that also bypasses the Golgi [<xref rid=\"B54-ijms-21-05376\" ref-type=\"bibr\">54</xref>]. In a recent report, atypical glycosylation of surface neuronal proteins, including a plethora of synaptic receptors, was attributed to a bypass or a hypo-function of the Golgi apparatus [<xref rid=\"B55-ijms-21-05376\" ref-type=\"bibr\">55</xref>]. Noticeably also, a specific form of the cystic fibrosis transmembrane conductance regulator (CFTR), namely ΔF508-CFTR, has been shown to translocate to the PM via the Golgi bypass under specific stress conditions [<xref rid=\"B56-ijms-21-05376\" ref-type=\"bibr\">56</xref>,<xref rid=\"B57-ijms-21-05376\" ref-type=\"bibr\">57</xref>]. Although these examples concerning mammalian transporters, channels, or receptors might be considered as exceptional cases of unconventional trafficking of specific cargoes in mammals, our very recent work with fungal nutrient transporters pointed to the rather provocative and original view that states that transporter forward trafficking occurs by a major mechanism that bypasses Golgi functioning and post-Golgi routes [<xref rid=\"B20-ijms-21-05376\" ref-type=\"bibr\">20</xref>]. Before coming to the experimental details that support such a conclusion, let us consider what has been known on the localization of fungal transporters before. </p><p>All studied fungal transporters involved in the uptake or efflux of solutes or ions are known to localize in a rather homogeneous manner all along the PM. Some appear in distinct foci, whereas other evenly mark the entire PM. For example, in <italic>S. cerevisiae</italic>, several transporters specific for amino acids (Can1, Mup1, Tat2) or uracil (Fur4) transporters are preferentially located in PM microdomains of 200-300 nm called MCC (membrane compartments of Can1), which do not overlap with other microdomains, called MCP or MCL, defined by distinct transporters, such as the H<sup>+</sup>-ATPase Pma1 or sterol transporters Ltc3/4, respectively [<xref rid=\"B58-ijms-21-05376\" ref-type=\"bibr\">58</xref>]. Still, other transporters do not define PM microdomains (e.g., the general amino acid permease Gap1, several sugar transporters, etc.). In <italic>A. nidulans</italic>, several nucleobase transporters of the NCS1 family also do not define microdomains, at least within the limits of conventional epifluorescence microscopy [<xref rid=\"B59-ijms-21-05376\" ref-type=\"bibr\">59</xref>,<xref rid=\"B60-ijms-21-05376\" ref-type=\"bibr\">60</xref>,<xref rid=\"B61-ijms-21-05376\" ref-type=\"bibr\">61</xref>,<xref rid=\"B62-ijms-21-05376\" ref-type=\"bibr\">62</xref>], but members of the AzgA and NAT/NCS2 family appear as distinct foci when expressed at moderate levels [<xref rid=\"B20-ijms-21-05376\" ref-type=\"bibr\">20</xref>,<xref rid=\"B25-ijms-21-05376\" ref-type=\"bibr\">25</xref>,<xref rid=\"B63-ijms-21-05376\" ref-type=\"bibr\">63</xref>]. In general, however, there is no evidence that transporters in fungi localize in a polar fashion. This is better established in filamentous fungi, which maintain high polarity, growing as continuously elongating hyphal cells with morphologically and functionally distinct apical and sub-apical regions. The non-polar homogeneous localization of transporters in fungi is well established in <italic>A. nidulans</italic>, where >30 different transporters specific for purines, pyrimidines, amino acids, sugars, carboxylic acids, or drugs have been studied at the level of subcellular localization via epifluorescent microscopy. </p><p>The non-polar localization of <italic>Aspergillus</italic> transporters highly contrasts the polar localization of several other membrane cargoes involved in continuous plasma membrane or cell wall synthesis at the apex of growing hyphae, such as, for example, chitin synthase ChsB, lipid flippases DfnA and DfnB, synaptobrevin SynA<sup>Snc1</sup>, or components of the exocyst complex, needed for regulation of apical recycling and repositioning of specific cargoes necessary for growth [<xref rid=\"B47-ijms-21-05376\" ref-type=\"bibr\">47</xref>,<xref rid=\"B64-ijms-21-05376\" ref-type=\"bibr\">64</xref>,<xref rid=\"B65-ijms-21-05376\" ref-type=\"bibr\">65</xref>,<xref rid=\"B66-ijms-21-05376\" ref-type=\"bibr\">66</xref>,<xref rid=\"B67-ijms-21-05376\" ref-type=\"bibr\">67</xref>]. Thus, it is logical to consider that the biogenesis of specific membrane polar cargoes destined to the fungal apical region might differ mechanistically with that of transporters, localized non-polarly all along the PM (<xref ref-type=\"fig\" rid=\"ijms-21-05376-f001\">Figure 1</xref>). The distinct localization of fungal membrane cargoes might present some analogies to distinct cargo localization in basolateral and apical membranes in metazoa [<xref rid=\"B68-ijms-21-05376\" ref-type=\"bibr\">68</xref>,<xref rid=\"B69-ijms-21-05376\" ref-type=\"bibr\">69</xref>]. In all cases, the simplest scenario is that cargoes themselves contain intrinsic information for distinct subcellular localization. This, however, leaves open whether basic trafficking mechanisms and routes are the same for cargoes destined to different segments of membranes. </p><p>In the course of studying fungal solute transporters since the 1990s, and particularly after having started using GFP-tagged versions of <italic>A. nidulans</italic> transporters, we noticed that transporters are localized principally in two compartments: The PM (including septa) and in vacuoles, the latter reflecting the fraction of transporters undergoing constitutive or signal-elicited degradation after endocytosis ([<xref rid=\"B19-ijms-21-05376\" ref-type=\"bibr\">19</xref>]; see also later). In some cases, internalized transporters are also detected in highly motile transient cytoplasmic structures, which proved to be early endosomes [<xref rid=\"B70-ijms-21-05376\" ref-type=\"bibr\">70</xref>,<xref rid=\"B71-ijms-21-05376\" ref-type=\"bibr\">71</xref>]. Some transporters can also be detected marking the perinuclear or cortical ER, but this occurs only when overexpressed via strong promoters [<xref rid=\"B20-ijms-21-05376\" ref-type=\"bibr\">20</xref>]. Partially misfolded transporters due to mutations are also blocked in the ER [<xref rid=\"B25-ijms-21-05376\" ref-type=\"bibr\">25</xref>]. Notably, however, we are not aware of any case where a transporter is blocked in cytoplasmic puncta characteristic of fungal early (cis) or late (trans) Golgi structures, as these are identified using fluorescent tagging of relative resident proteins [<xref rid=\"B45-ijms-21-05376\" ref-type=\"bibr\">45</xref>,<xref rid=\"B72-ijms-21-05376\" ref-type=\"bibr\">72</xref>,<xref rid=\"B73-ijms-21-05376\" ref-type=\"bibr\">73</xref>]. In fact, we could not find any reference on transporters trapped in the Golgi, for any reason, in any eukaryotic system. Surprisingly, a thorough search of the literature showed that indeed there is no formal evidence of de novo made transporters ‘passing’ from the Golgi on their way to the PM. In cases where PM transporters have been convincingly shown to be transiently sorted to the Golgi/TGN, the relative experiments could not distinguish whether transporters in Golgi/TGN are de novo made or recycled from the PM. Finally, another indirect indication that several fungal transporters might not be sorted to the Golgi is the fact that they are not glycosylated (unpublished observations and Bruno Andre pers. com.). The aforementioned observations prompted us to recently investigate the mechanism of transporter trafficking in <italic>A. nidulans</italic> via a systematic approach.</p></sec><sec id=\"sec4-ijms-21-05376\"><title>4. Evidence for Translocation of <italic>Aspergillus</italic> Transporters to the PM by Golgi-Independent Transfer from the ER</title><p>To identify the trafficking pathways of nutrient transporters in <italic>A. nidulans</italic>, we made use of two approaches. Both involved following the localization of selected transporters, functionally tagged with fluorescent epitopes, in growing germling and hyphal cells (i.e., in vivo). In the first approach, we blocked the synthesis of proteins involved in conventional secretion via the tight repression of a regulatable promoter. In particular, we followed transporter localization in strains that did not express Sec24 or Sec13 (COPII generation), SedV<sup>Sed5</sup> or GeaA<sup>Gea1</sup> (early-Golgi functioning), HypB<sup>Sec7</sup> (TGN functioning), RabE<sup>Rab11</sup>, AP-1<sup>σ</sup> or clathrin ClaH<sup>Chc1</sup> (post-Golgi secretion), RabA/B<sup>Rab5</sup> (early and recycling endosomes), or SsoA<sup>Sso1</sup> (major PM tethering t-SNARE). Notice that although persistent transcriptional repression (i.e., >24 h) of secretion eventually leads to <italic>A. nidulans</italic> cell death, given that in our system full repression needed 10–12 h to occur, this provided a period of time for conidiospore germination and the development of germlings, and also sufficient time, after full repression of secretion, for inducing and studying the trafficking of transporters [<xref rid=\"B20-ijms-21-05376\" ref-type=\"bibr\">20</xref>]. Besides using strains where proteins of the secretory route could be repressed, we also examined transporter traffic in the presence of drugs that block microtubule or actin filament polymerization (benomyl and Latrunculin B, respectively), processes reported as essential in conventional secretion. In the second approach, we estimated quantitatively the degree of co-localization of transporters with proteins resident of COPII, early Golgi, late-Golgi, post-Golgi vesicles, recycling endosomes, or the SPK, all marked with distinct fluorescent epitopes. For both approaches, we primarily used, as a model transporter cargo, the well-studied uric acid-xanthine UapA transporter. Subsequently, we also examined the trafficking of other nutrient transporters, namely AzgA (purines) and FurA (allantoin), representing structurally and functionally distinct transporter families [<xref rid=\"B20-ijms-21-05376\" ref-type=\"bibr\">20</xref>]. </p><p>Using these approaches, we showed that trafficking of de novo made transporters initiates by COPII-packaging (i.e., Sec23 and Sec13 dependent) and subsequently requires clathrin heavy chain and the PM t-SNARE SsoA but is not dependent on Golgi functioning (i.e., SedV, GeaA, and HypB independent) or key effectors for post-Golgi conventional secretion (RabE and AP-1), microtubule polymerization (benomyl-insensitive), or early and recycling endosome formation (i.e., RabA/B independent). Actin polymerization was shown to be required for transporter trafficking (i.e., Latrunculin B-sensitive), surprisingly due to a previously non-described essential role in COPII formation. Co-localization studies strongly supported the presence of nascent transporters in COPII structures and the absence of transporters from the late Golgi/TGN. These findings contrasted the dynamic trafficking of model apical cargoes (e.g., SynA or ChsB), followed in parallel by analogous approaches, which showed a clear dependence on early and late-Golgi/TGN and post-Golgi secretory routes (RabE, AP-1, and microtubule dependence). <xref ref-type=\"fig\" rid=\"ijms-21-05376-f002\">Figure 2</xref> depicts the principle points of trafficking transporters versus apical cargoes. </p><p>The essential role of clathrin heavy chain (but not of clathrin light chain; [<xref rid=\"B18-ijms-21-05376\" ref-type=\"bibr\">18</xref>]) on transporter trafficking, despite the observed lack of relative co-localization, pointed to an undefined role of clathrin, other than vesicle budding from the TGN. The essential role of clathrin heavy chain raised several issues that will need to be addressed. Considering that transporters pack in COPII vesicles, there must be an uncoating step before vesicles tether to the PM. If we consider that clathrin coats transporter vesicles at some point, this suggests there must be some ‘missing’ events of uncoating (COPII) and coating (clathrin), before the fusion of vesicles to the PM. This made us consider the possibility of formation of an intermediate compartment between the ERes and the PM, which is probably too transient for detection with standard fluorescence microcopy [<xref rid=\"B20-ijms-21-05376\" ref-type=\"bibr\">20</xref>]. Interestingly, clathrin light chain has been found to be redundant for the forward trafficking of both transporters and apical cargoes [<xref rid=\"B18-ijms-21-05376\" ref-type=\"bibr\">18</xref>], but it is essential specifically for the endocytosis of transporters ([<xref rid=\"B17-ijms-21-05376\" ref-type=\"bibr\">17</xref>]; see later).</p><p>The above findings, and in particular the common dependence of transporters and apical cargoes on Sec24 and Sec13, led to a simple but major assumption of considering the existence of distinct subpopulations of COPII vesicles as elements of different trafficking routes. Thus, the main question to address is to identify the particular features of distinct subpopulations of COPII and understand what makes these vesicles traffic through different routes and ending at different sections of the fungal membrane. </p><sec><title>Endocytosis of Apical Cargoes and Transporters Occurs by Distinct Mechanisms that Serve Polar Growth and Nutrition, Respectively</title><p>PM transmembrane proteins can undergo regulated endocytosis in response to physiological or developmental signals or stress. This a ubiquitous biological phenomenon that serves adaptation to changes of the cell environment (mostly related to nutrition, pH regulation, or excretion of toxic metabolites or drugs), signaling (e.g., synaptic neurotransmission or hormonal response), and renewal of membrane components, especially under chemical and biological stress, senescence, or aging. Endocytosis of membrane proteins is followed by sorting to early endosomes, which in most cases mature to late endosomes and multivesicular bodies (MVBs) that eventually fuse with the lysosome (animals) or vacuole (plants/fungi), where membrane protein degradation takes place. Notably, several membrane cargoes can recycle back the PM instead of being degraded [<xref rid=\"B74-ijms-21-05376\" ref-type=\"bibr\">74</xref>,<xref rid=\"B75-ijms-21-05376\" ref-type=\"bibr\">75</xref>,<xref rid=\"B76-ijms-21-05376\" ref-type=\"bibr\">76</xref>]. Recycling of specific membrane cargoes serves eukaryotic cell polarity establishment, polarity maintenance in specialized cells, differentiation, and growth. In filamentous fungi, in particular, it has been rigorously established that spatially and temporarily synchronized cargo trafficking towards growing hyphal tips coupled with apical endocytosis and recycling are absolutely essential for maintaining polar cell growth [<xref rid=\"B47-ijms-21-05376\" ref-type=\"bibr\">47</xref>,<xref rid=\"B48-ijms-21-05376\" ref-type=\"bibr\">48</xref>,<xref rid=\"B49-ijms-21-05376\" ref-type=\"bibr\">49</xref>]. Although most recycled membrane cargoes are related to plasma membrane or cell wall synthesis and renewal (e.g., lipid flippases, synaptobrevin, fungal chitin synthases secretory enzymes, etc.), specific transporters are also known to recycle. One of the best studied examples of transporter regulation by recycling is the mammalian insulin-responsive GLUT4 glucose transporter [<xref rid=\"B77-ijms-21-05376\" ref-type=\"bibr\">77</xref>,<xref rid=\"B78-ijms-21-05376\" ref-type=\"bibr\">78</xref>,<xref rid=\"B79-ijms-21-05376\" ref-type=\"bibr\">79</xref>]. GLUT4 molecules destined to the PM are kept in specific intracellular vesicles referred to as GLUT4 storage vesicles (GSVs). The donor compartment from which newly made GVS form remains uncertain, but it is seemingly the ER or ERGIC [<xref rid=\"B53-ijms-21-05376\" ref-type=\"bibr\">53</xref>]. Insulin stimulation mobilizes GSVs to fuse with the PM, but in its continuous presence, GLUT4 molecules are internalized and recycled back to the PM in endosomal vesicles that are distinct from GSVs. Recycling of fungal nutrient transporters has also been reported when growth conditions change ([<xref rid=\"B76-ijms-21-05376\" ref-type=\"bibr\">76</xref>,<xref rid=\"B80-ijms-21-05376\" ref-type=\"bibr\">80</xref>,<xref rid=\"B81-ijms-21-05376\" ref-type=\"bibr\">81</xref>,<xref rid=\"B82-ijms-21-05376\" ref-type=\"bibr\">82</xref>] and references therein).</p><p>Studies in fungi, mostly in <italic>S. cerevisiae</italic> and <italic>A. nidulans</italic>, have led to important findings concerning the molecular mechanism underlying endocytosis, turnover, or recycling of nutrient transporters. In most cases, the primary molecular signal preceding endocytosis is ubiquitylation of cytoplasmically exposed terminal regions of transporters [<xref rid=\"B83-ijms-21-05376\" ref-type=\"bibr\">83</xref>,<xref rid=\"B84-ijms-21-05376\" ref-type=\"bibr\">84</xref>]. Transporter ubiquitination is carried out by HECT-type ubiquitin ligases of the Nedd4/Rsp5 type recruited to transporter tails by adaptor proteins called α-arrestins [<xref rid=\"B83-ijms-21-05376\" ref-type=\"bibr\">83</xref>,<xref rid=\"B85-ijms-21-05376\" ref-type=\"bibr\">85</xref>,<xref rid=\"B86-ijms-21-05376\" ref-type=\"bibr\">86</xref>]. How arrestins recognize specific sequence or structural motifs, and thus recruit ubiquitin ligases to the tails of transporters, is little known [<xref rid=\"B87-ijms-21-05376\" ref-type=\"bibr\">87</xref>,<xref rid=\"B88-ijms-21-05376\" ref-type=\"bibr\">88</xref>]. Signals that lead to transporter endocytosis include nutrient starvation, changes in the carbon and nitrogen source, the pH or the temperature of the growth medium, or the presence of excess substrate, drugs, or oxidizing agents [e.g., azoles, amphotericin B, rapamycin, cycloheximide, dimethyl sulphoxide (DMSO), or dithiothreitol (DTT)]. Such physiological or stress signals lead to activation or/and recruitment of the α-arrestin adaptors and thus to increased ubiquitination and endocytic turnover of transporters [<xref rid=\"B19-ijms-21-05376\" ref-type=\"bibr\">19</xref>,<xref rid=\"B71-ijms-21-05376\" ref-type=\"bibr\">71</xref>,<xref rid=\"B89-ijms-21-05376\" ref-type=\"bibr\">89</xref>,<xref rid=\"B90-ijms-21-05376\" ref-type=\"bibr\">90</xref>,<xref rid=\"B91-ijms-21-05376\" ref-type=\"bibr\">91</xref>,<xref rid=\"B92-ijms-21-05376\" ref-type=\"bibr\">92</xref>]. Interestingly, increased ubiquitination and endocytic turnover highly depend on transporter conformational changes associated with transport catalysis, so that several transporters are much more vulnerable to internalization when actively translocate their substrates, a phenomenon known as activity-dependent or substrate-dependent endocytosis [<xref rid=\"B81-ijms-21-05376\" ref-type=\"bibr\">81</xref>,<xref rid=\"B92-ijms-21-05376\" ref-type=\"bibr\">92</xref>,<xref rid=\"B93-ijms-21-05376\" ref-type=\"bibr\">93</xref>,<xref rid=\"B94-ijms-21-05376\" ref-type=\"bibr\">94</xref>,<xref rid=\"B95-ijms-21-05376\" ref-type=\"bibr\">95</xref>]. Finally, dynamic lateral PM compartmentalization of transporters is also crucial for their ubiquitin-dependent internalization and turnover [<xref rid=\"B96-ijms-21-05376\" ref-type=\"bibr\">96</xref>,<xref rid=\"B97-ijms-21-05376\" ref-type=\"bibr\">97</xref>,<xref rid=\"B98-ijms-21-05376\" ref-type=\"bibr\">98</xref>,<xref rid=\"B99-ijms-21-05376\" ref-type=\"bibr\">99</xref>]. </p><p>Following transporter ubiquitination, several proteins are involved in subsequent steps necessary for fission of endocytic vesicles from the PM and sorting into early or recycling endosomes [<xref rid=\"B100-ijms-21-05376\" ref-type=\"bibr\">100</xref>,<xref rid=\"B101-ijms-21-05376\" ref-type=\"bibr\">101</xref>]. The best-characterized mechanism of endocytosis in all eukaryotes is based on clathrin-coated vesicles (clathrin-mediated endocytosis or CME). Cargoes are sorted into clathrin vesicles with the help of the heterotetrameric AP-2 adaptor complex, which recognizes clathrin and short di-hydrophobic motifs on cytoplasm-facing domains of cargoes [<xref rid=\"B43-ijms-21-05376\" ref-type=\"bibr\">43</xref>,<xref rid=\"B102-ijms-21-05376\" ref-type=\"bibr\">102</xref>]. In <italic>S. cerevisiae</italic>, a detailed temporal scheme of arrival and departure of over 60 proteins at sites of endocytosis has been established [<xref rid=\"B103-ijms-21-05376\" ref-type=\"bibr\">103</xref>]. Endocytic proteins are organized into modules according to their function and timing of recruitment and disappearance as early proteins, middle and late coat proteins, myosin and actin proteins, and fission-related proteins [<xref rid=\"B101-ijms-21-05376\" ref-type=\"bibr\">101</xref>]. AP-2 and clathrin are considered early endocytic proteins. Several other early middle yeast endocytic proteins, namely Ede1, Sla1, Sla2, and Ent1/Ent2 (mammalian AP-180 homologues), are known to have domains for ubiquitin and/or PtdIns(4,5)P2 lipid binding. Myosins Myo3p and Myo5p and actin-binding protein Abp1 are also later key regulators for fission [<xref rid=\"B104-ijms-21-05376\" ref-type=\"bibr\">104</xref>,<xref rid=\"B105-ijms-21-05376\" ref-type=\"bibr\">105</xref>]. Whether the PM bends before or after actin arrives is still debated. Finally, endocytic proteins (e.g., Abp1, Sla1, and Sla2) are also substrates of kinases, including cyclin-dependent kinase 1 (Cdk1), the master cell cycle regulator [<xref rid=\"B106-ijms-21-05376\" ref-type=\"bibr\">106</xref>]. Deubiquitylation of internalized transporters and other membrane cargoes, probably occurring at the endosomal compartment, is critical for sorting to MVBs or recycling [<xref rid=\"B75-ijms-21-05376\" ref-type=\"bibr\">75</xref>].</p><p>As far as it concerns fungi, the above brief account on endocytosis has been based on the studies concerning the internalization of selected transporters and polar markers. However, a surprising result came from studies with <italic>A. nidulans</italic> transporters, which showed that internalization from the PM is AP-2 independent, despite being clathrin dependent, as probably expected [<xref rid=\"B17-ijms-21-05376\" ref-type=\"bibr\">17</xref>]. A further surprise came when, in sharp contrast to transporter endocytosis, the internalization of <italic>Aspergillus</italic> polarly localized (apical) membrane proteins, such as chitin synthase, lipid flippases, or synaptobrevin A, proved to be AP-2 dependent but clathrin independent [<xref rid=\"B17-ijms-21-05376\" ref-type=\"bibr\">17</xref>]. Thus, similarly to distinct biosynthetic trafficking routes described earlier, there are distinct endocytic mechanisms for polar membrane cargoes versus non-polar house-keeping proteins, such as nutrient transporters. The findings in <italic>A. nidulans</italic> showed formally that clathrin-dependent and clathrin-independent mechanisms of endocytosis do exist in lower eukaryotes, similar to metazoa. Furthermore, these findings very probably concern all higher fungi, since the σ subunit of the AP-2 complex lacks the entire C-terminal domain containing the putative clathrin-binding box in all cases [<xref rid=\"B17-ijms-21-05376\" ref-type=\"bibr\">17</xref>,<xref rid=\"B19-ijms-21-05376\" ref-type=\"bibr\">19</xref>]. Overall, these results supported that the AP-2 complex of fungi has acquired, in the course of evolution, a specialized clathrin-independent function in apical cargo internalization followed by recycling necessary for fungal polar growth, while clathrin evolved to function independently of AP-2 in generalized non-polar endocytosis of transporters and probably other non-polar membrane cargoes destined for signal-elicited degradation. In this latter mechanism, α-arrestins might function as the direct adaptor of clathrin, but this remains to be shown. Which is, if any, the protein that “replaces” clathrin in AP-2-dependent apical protein endocytosis remains unknown. The distinct endocytic mechanisms for transporters and apical markers in fungi are depicted in a simplified model in <xref ref-type=\"fig\" rid=\"ijms-21-05376-f003\">Figure 3</xref><bold>.</bold>\n</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec5-ijms-21-05376\"><title>5. Conclusions and Perspective </title><p>Work with <italic>A. nidulans</italic> transporters revealed that transporters homogenously localized all along the PM and polarly localized apical cargoes follow distinct forward trafficking and endocytic routes. In endocytosis, the distinct mechanisms followed seem to depend on the involvement of AP-2 (apical markers) vs. clathrin (transporters). What makes a cargo follow different biosynthetic trafficking routes (i.e., Golgi dependent vs. Golgi independent), implicating distinct COPII subpopulations, remains less clear. What is, however, apparent is that trafficking and endocytosis are both highly dependent on intrinsic information contained in membrane cargoes. Specific sequence or structural motifs/domains, the number and length of transmembrane segments, interactions with membrane specific chaperones, receptors or lipids, oligomerization, and partitioning in specialized membrane microdomains might all play roles in determining the route and mechanism of trafficking followed. </p><p>In mammalian neurons, most cargoes involved in neurotransmission are polarly localized in the synaptic region by conventional Golgi-dependent secretion, but specific cargoes serving dendrite, soma homeostasis, or the initial segment (AIS), such as glutamate receptor GluA1, neuroligin, or the potassium channel Kv2.1, are sorted via mechanisms bypassing the Golgi [<xref rid=\"B54-ijms-21-05376\" ref-type=\"bibr\">54</xref>,<xref rid=\"B107-ijms-21-05376\" ref-type=\"bibr\">107</xref>,<xref rid=\"B108-ijms-21-05376\" ref-type=\"bibr\">108</xref>,<xref rid=\"B109-ijms-21-05376\" ref-type=\"bibr\">109</xref>]. In polarized epithelial cells, distinct cargo-dependent subcellular trafficking routes are known to exist for cargoes destined to basolateral versus apical membranes [<xref rid=\"B16-ijms-21-05376\" ref-type=\"bibr\">16</xref>,<xref rid=\"B110-ijms-21-05376\" ref-type=\"bibr\">110</xref>,<xref rid=\"B111-ijms-21-05376\" ref-type=\"bibr\">111</xref>,<xref rid=\"B112-ijms-21-05376\" ref-type=\"bibr\">112</xref>,<xref rid=\"B113-ijms-21-05376\" ref-type=\"bibr\">113</xref>]. The recent discovery of distinct forward trafficking routes of mammalian glucose transporters serving different physiological needs [<xref rid=\"B53-ijms-21-05376\" ref-type=\"bibr\">53</xref>] further exemplifies a general necessity for a multiplicity of trafficking mechanisms of membrane cargoes. </p><p>Given the plethora of cellular roles cargoes have, the assumption that trafficking is cargocentric has a strong physiological rationale. For example, most transporters serve the cell nutrient supply so that there is no obvious physiological need to drive transporters to a specialized domain of the PM. This is well reflected in fungi and other organisms, where transporters are localized non-polarly and rather homogenously all over the PM of hyphae. In contrast to transporters, membrane cargoes that serve membrane or cell wall synthesis, polarity maintenance, and apical growth in fungi need to be polarly localized at the hyphal tips, and thus face the challenge of long-distance sorting, coupled with recycling, which apparently need the involvement of conventional vesicular secretion via the Golgi/endosome and microtubules. Similarly, endocytosis of transporters occurs all along the PM and most often leads to vacuolar degradation, whereas apical cargo endocytosis needs to be restricted at the fungal apical region to serve rapid recycling at the apical tip necessary for growth. In the case of transporters, by4passing the Golgi might also serve the need to avoid polar localization at the tip, and thus avoid competition for PM translocating with apical cargoes needed for polar growth. </p></sec></body><back><ack><title>Acknowledgments</title><p>We thank all previous and present members of the <italic>Aspergillus</italic> group (<uri xlink:href=\"http://scholar.uoa.gr/diallina\">http://scholar.uoa.gr/diallina</uri>) for sharing their results and comments. Research described in the article has been generally supported by the <italic>Fondation Santé</italic> and the <italic>Stavros Niarchos Foundation</italic>.</p></ack><notes><title>Funding</title><p>This research received no external funding.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijms-21-05376\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Guan</surname><given-names>L.</given-names></name><name><surname>Kaback</surname><given-names>H.R.</given-names></name></person-group><article-title>Lessons from lactose permease</article-title><source>Annu. Rev. Biophys. Biomol. 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Subcellular localization of transporters (UapA or AzgA) and apical membrane proteins (synaptobrevin SynA or chitin synthase ChsB) functionally tagged with green fluorescent protein (GFP) in hyphal cells of <italic>A. nidulans.</italic> Transporters localize homogenously along the plasma membrane, while apical cargoes are located at the hyphal membrane in a hemisphere extending from the apex to the endocytic collar, as well as in the Spitzenkörper and numerous secretory vesicles.</p></caption><graphic xlink:href=\"ijms-21-05376-g001\"/></fig><fig id=\"ijms-21-05376-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Conventional and transporter-related trafficking routes. Schematic depiction of the distinct trafficking routes of transporters and apical membrane proteins in <italic>A. nidulans.</italic> Apical cargoes are polarly secreted to the plasma membrane (PM) through the conventional pathway. Briefly, they exit the endoplasmic reticulum (ER) in COPII vesicles, which fuse with early Golgi cisternae after uncoating and pass via Golgi maturation to late-Golgi cisternae. From there, apical cargoes get packed in AP-1/clathrin-coated vesicles with the recruitment of the small GTPase RabE and move along microtubules towards the Spitzenkörper (SPK). The final step of this pathway involves fusion to the PM via the exocyst complex. Transporters are sorted to the PM via a distinct non-polar pathway that bypasses the Golgi and does not necessitate Rab GTPases, AP adaptors, or microtubules. This route requires functional COPII vesicles, actin network, and the PM t-SNARE SsoA, without excluding the existence of an ER-to-PM intermediate compartment [<xref rid=\"B20-ijms-21-05376\" ref-type=\"bibr\">20</xref>].</p></caption><graphic xlink:href=\"ijms-21-05376-g002\"/></fig><fig id=\"ijms-21-05376-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Endocytosis of transporter versus apical cargoes in <italic>A. nidulans.</italic> Model highlighting the endocytic process of transporters and apical membrane cargoes in a growing hyphal tip of <italic>A. nidulans.</italic> After reaching the PM, apical cargoes diffuse laterally to the actin-enriched collar region where they undergo AP-2-dependent, but clathrin-independent, endocytosis with the involvement of several endocytosis-related proteins (SagA, SlaB). Endocytic vesicles fuse to sorting endosomes (SEs) and from there undergo retrograde traffic to late-Golgi cisternae and then travel via AP-1/clathrin-coated vesicles to the SPK and finally to the PM. This model does not exclude (not shown) that a fraction of apical cargoes could be sorted directly to vacuoles to undergo degradation. The ongoing recycling process of apical cargoes ensures constant polarity maintenance and polar cell tip growth. Transporters, which are not polarly localized at the PM, are not cargoes of the AP-2 pathway, but instead follow α-arrestin- and clathrin-dependent endocytosis. Specifically, as a response to physiological or stress signals, transporters are recognized and ubiquitinated by HulA ubiquitin ligase, assisted by α-arrestin adaptors (ArtA). Internalization takes place by a clathrin-dependent, but AP-2 independent, pathway with the recruitment of several endocytosis-related proteins (e.g., SagA, SlaB), followed by sorting into early endosomes (EEs) and eventually degradation to multivesicular bodies (MVBs) [<xref rid=\"B17-ijms-21-05376\" ref-type=\"bibr\">17</xref>].</p></caption><graphic xlink:href=\"ijms-21-05376-g003\"/></fig></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32727103</article-id><article-id pub-id-type=\"pmc\">PMC7432045</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155403</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05403</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>One Month into the Reinforcement of Social Distancing due to the COVID-19 Outbreak: Subjective Health, Health Behaviors, and Loneliness among People with Chronic Medical Conditions</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Elran-Barak</surname><given-names>Roni</given-names></name><xref rid=\"c1-ijerph-17-05403\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Mozeikov</surname><given-names>Maya</given-names></name></contrib></contrib-group><aff id=\"af1-ijerph-17-05403\">School of Public Health, Faculty of Social Welfare and Health Sciences, University of Haifa, Haifa 3498838, Israel; <email>smayal@hotmail.com</email></aff><author-notes><corresp id=\"c1-ijerph-17-05403\"><label>*</label>Correspondence: <email>relranbar@univ.haifa.ac.il</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>27</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5403</elocation-id><history><date date-type=\"received\"><day>09</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>24</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>We sought to examine how the near-lockdown measures, announced by the Israeli government in an effort to contain the COVID-19 outbreak, impacted the self-rated health (SRH), health behaviors, and loneliness of people with chronic illnesses. An online cross-sectional survey was carried out about one month (20–22 April 2020) after the Israeli government reinforced the severe social distancing regulations, among a convenience sample of 315 participants (60% women) with chronic conditions (27% metabolic, 17% cardiovascular, 21% cancer/autoimmune, 18% orthopedic/pain, 12% mental-health). Results suggested that about half of the participants reported a decline in physical or mental SRH, and as many as two-thirds reported feeling lonely. A significant deterioration in health behaviors was reported, including a decrease in vegetable consumption (<italic>p</italic> = 0.008) and physical activity (<italic>p</italic> < 0.001), an increase in time spent on social media (<italic>p</italic> < 0.001), and a perception among about half of the participants that they were eating more than before. Ordinal regression suggested that a decline in general SRH was linked with female gender (<italic>p</italic> = 0.016), lack of higher education (<italic>p</italic> = 0.015), crowded housing conditions (<italic>p</italic> = 0.001), longer illness duration (<italic>p</italic> = 0.010), and loneliness (<italic>p</italic> = 0.008). Findings highlight the important role of loneliness in SRH during the COVID-19 lockdown period. Future studies are warranted to clarify the long-term effects of social-distancing and loneliness on people with chronic illnesses.</p></abstract><kwd-group><kwd>COVID-19</kwd><kwd>self-rated health</kwd><kwd>health behaviors</kwd><kwd>loneliness</kwd><kwd>chronic illness</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05403\"><title>1. Introduction</title><p>The World Health Organization (WHO) declared the coronavirus 2019 (COVID-19) outbreak an international public health emergency on January 30, 2020 and a pandemic on March 11, 2020 [<xref rid=\"B1-ijerph-17-05403\" ref-type=\"bibr\">1</xref>]. In an effort to contain the COVID-19 outbreak, the Israeli government announced a number of new restrictions aimed at reinforcing social distancing [<xref rid=\"B2-ijerph-17-05403\" ref-type=\"bibr\">2</xref>]. On March 12, Israel announced that all universities and schools would close. On March 16, all non-critical government and local authority workers were placed on paid leave, and private sector firms were required to reduce the staff presence in the workplace [<xref rid=\"B3-ijerph-17-05403\" ref-type=\"bibr\">3</xref>]. On March 19, Prime Minister Benjamin Netanyahu declared a national state of emergency [<xref rid=\"B4-ijerph-17-05403\" ref-type=\"bibr\">4</xref>], saying that existing restrictions would henceforth be legally enforceable, and violators would be fined. Israelis were not allowed to leave their homes unless absolutely necessary, putting a near-lockdown into effect. Essential services—including grocery stores, pharmacies, and banks—remained open, but people were prohibited from venturing more than 100 m from their homes, apart from under certain circumstances (e.g., stocking up on food and medicine). Non-essential stores were required to close, and parks were to remain shut. People were required not to participate in any social gatherings and to limit face-to-face interactions with individuals outside the immediate household [<xref rid=\"B5-ijerph-17-05403\" ref-type=\"bibr\">5</xref>].</p><p>Self-rated health (SRH)—also known as subjective health, self-assessed health, or perceived health—is a simple and straightforward construct that has a strong predictive capacity regarding various health-related outcomes, including mortality [<xref rid=\"B6-ijerph-17-05403\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijerph-17-05403\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijerph-17-05403\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05403\" ref-type=\"bibr\">9</xref>]. Numerous studies suggest that, among people with chronic illnesses, SRH encompasses many of the objective aspects of health including clinical, cognitive, and functional aspects [<xref rid=\"B10-ijerph-17-05403\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijerph-17-05403\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05403\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijerph-17-05403\" ref-type=\"bibr\">13</xref>]. For example, studies among individuals with Type 2 diabetes suggest that SRH is highly associated with clinical status (e.g., glycemic control, BMI, and blood pressure) [<xref rid=\"B14-ijerph-17-05403\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijerph-17-05403\" ref-type=\"bibr\">15</xref>], and provides additional information regarding mortality risk, independent of demographic, socioeconomic, and medical risk factors. In addition, studies about changes in SRH suggest that a decline (or improvement) in SRH can independently predict better (or worse) long-term prognoses among people with chronic illnesses [<xref rid=\"B16-ijerph-17-05403\" ref-type=\"bibr\">16</xref>]. Therefore, and based on the extant literature highlighting the importance of SRH and changes in SRH in the assessment and understanding of the medical status of people with chronic illnesses, we aimed to investigate whether people with chronic illnesses experienced changes in SRH after one month of the social distancing reinforcement due to the coronavirus outbreak. Changes in health behaviors (e.g., physical activity, food consumption) were also assessed, as these behaviors may have been affected by the lockdown situation [<xref rid=\"B3-ijerph-17-05403\" ref-type=\"bibr\">3</xref>]. It is important to assess changes in health behaviors given that these behaviors can have detrimental effects on health [<xref rid=\"B17-ijerph-17-05403\" ref-type=\"bibr\">17</xref>], especially among people with chronic medical conditions [<xref rid=\"B18-ijerph-17-05403\" ref-type=\"bibr\">18</xref>]. A recent study conducted in France and Switzerland among the general population assessed whether changes in physical activity and sedentary behaviors during the COVID-19 lockdown were associated with changes in mental and physical health [<xref rid=\"B19-ijerph-17-05403\" ref-type=\"bibr\">19</xref>]. Results of this study showed that the lockdown in these countries resulted in an increase in sedentary behaviors, which was associated with a decrease in physical health, mental health, and subjective vitality [<xref rid=\"B19-ijerph-17-05403\" ref-type=\"bibr\">19</xref>]. Although no research has yet been conducted regarding the effect of the COVID-19 crisis on health behaviors in people with chronic illnesses, it may be presumed that the disruption in usual activity would be even more harmful in this population.</p><p>Loneliness is an important determinant of health [<xref rid=\"B20-ijerph-17-05403\" ref-type=\"bibr\">20</xref>], and studies suggest that loneliness and social isolation are major risk factors for morbidity and mortality, risk factors comparable in importance to obesity, sedentary lifestyles, and possibly even smoking [<xref rid=\"B21-ijerph-17-05403\" ref-type=\"bibr\">21</xref>]. For example, a longitudinal study conducted among a U.S. nationally representative sample reported that loneliness was associated with an increased mortality risk [<xref rid=\"B22-ijerph-17-05403\" ref-type=\"bibr\">22</xref>]. Specifically, among people with chronic illnesses, severity of illness can predict enhanced feelings of loneliness, and loneliness, in turn, can aggravate the medical condition [<xref rid=\"B23-ijerph-17-05403\" ref-type=\"bibr\">23</xref>]. Nevertheless, despite the understanding that lockdown measures can increase loneliness, specifically among vulnerable populations [<xref rid=\"B24-ijerph-17-05403\" ref-type=\"bibr\">24</xref>], studies examining the impact of loneliness on the health of people with chronic medical conditions during the COVID-19 outbreak are, as yet, scarce.</p><p>We also aimed to examine factors contributing to changes in SRH. The literature suggests that various factors, including sociodemographic and medical-related factors, can contribute to changes in SRH [<xref rid=\"B25-ijerph-17-05403\" ref-type=\"bibr\">25</xref>]. For example, there is some evidence that lower socioeconomic status, as reflected for instance by lower education level, or higher number of people living in the household, is often correlated with worse SRH [<xref rid=\"B26-ijerph-17-05403\" ref-type=\"bibr\">26</xref>]. Furthermore, females may be more vulnerable to psychological distress than are males [<xref rid=\"B27-ijerph-17-05403\" ref-type=\"bibr\">27</xref>], and this psychological distress may be linked with a stronger decline in mental SRH during stressful occasions (e.g., the COVID-19 outbreak). Indeed, Mazza et al. [<xref rid=\"B28-ijerph-17-05403\" ref-type=\"bibr\">28</xref>] found that female gender was associated with higher levels of depression, anxiety, and stress during the COVID-19 lockdown in Italy. In addition, studies among patients with chronic illnesses suggest that those with higher illness severity, as represented for instance in higher BMI, more medical visits, and longer illness duration, are more vulnerable to a decline in SRH [<xref rid=\"B10-ijerph-17-05403\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijerph-17-05403\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijerph-17-05403\" ref-type=\"bibr\">12</xref>]. Therefore, in the current study we sought to find those characteristics of people with chronic illnesses that might be linked with a stronger decline in SRH.</p><p>An online survey was conducted one month into the reinforcement of social distancing (between 20 April and 22 April 2020) among people with various chronic medical conditions in order to address the following research questions:<list list-type=\"order\"><list-item><p>Did people with chronic illnesses experience changes in their SRH, one month into the reinforcement of social distancing?</p></list-item><list-item><p>Did people with chronic illnesses report changes in loneliness and health behaviors, one month into the reinforcement of social distancing?</p></list-item><list-item><p>Did people with different chronic illnesses experience different changes in self-rated health, loneliness, and health behaviors one month into the reinforcement of social distancing?</p></list-item><list-item><p>Were there specific characteristics, such as sociodemographic and medical-related factors, that were linked with a decline in SRH one month into the reinforcement of social distancing among people with chronic illnesses?</p></list-item></list></p><p>It is important to address these questions, as chronic medical conditions may increase the risk of having a fatal reaction to COVID-19 [<xref rid=\"B29-ijerph-17-05403\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijerph-17-05403\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijerph-17-05403\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijerph-17-05403\" ref-type=\"bibr\">32</xref>]. That is, these vulnerable individuals (who were advised to take extra social-distancing measures) are under additional stresses and likely more confused about how to handle their ongoing medical issues in the context of COVID-19 [<xref rid=\"B1-ijerph-17-05403\" ref-type=\"bibr\">1</xref>]. Relatedly, the fact that non-emergency medical services were completely shut down or reduced to an absolute minimum during the examined period must have been particularly challenging for people with chronic illnesses, who have no choice but to make use of these services on a regular basis. Hence, findings from the current study may help us understand how the reinforcement of social distancing might have influenced the psychological and physical health [<xref rid=\"B33-ijerph-17-05403\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijerph-17-05403\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijerph-17-05403\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijerph-17-05403\" ref-type=\"bibr\">36</xref>] of people with chronic illnesses, in order to inform policymakers about the effect of social distancing on the health of vulnerable populations.</p></sec><sec sec-type=\"methods\" id=\"sec2-ijerph-17-05403\"><title>2. Methods</title><sec id=\"sec2dot1-ijerph-17-05403\"><title>2.1. Procedure </title><p>Information was collected using an online, self-report survey, which was advertised through Camoni (Hebrew for “like me”, <uri xlink:href=\"http://www.camoni.co.il/\">http://www.camoni.co.il/</uri>). Camoni is the first Israeli medical social network to have been established, and it includes several virtual health communities for people who share similar medical conditions, such as diabetes, cancer, pain, depression, or eating disorders. These are moderated communities—maintained by a governmental organization, the Gertner Institute for Epidemiology and Health Policy Research (<uri xlink:href=\"http://www.gertnerinst.org.il/\">http://www.gertnerinst.org.il/</uri>)—that are free of charge and accessible to everyone. Users can log into the communities to share in-the-moment feelings or to seek momentary support from moderators and other users. The communities do not present themselves or intend to be seen as alternatives to professional treatment, and the moderators of the communities encourage community users to receive professional treatment. The invitation to participate in the study was advertised by the maintainer of Camoni through a newsletter, thereby using a convenience-sampling method. </p><p>On the first page of the online survey and prior to the start of the questionnaire, participants were asked to press “continue” if they were over the age of 18 and agreed to participate in the survey. Participants were also informed that they could choose not to participate in the study or to stop participating at any stage. In addition, they were told that the questionnaire was anonymous, and that no personal information would be collected (e.g., name or address). The contact details of the primary investigator (R.E.B.) were provided, as well as the name of the funding organization. Following consent, participants were provided with a link to a designated website which contained the anonymous survey. Participants who wished to participate in a lottery were invited to provide their contact information (e.g., email/telephone) at the end of the survey. A 50-shekel (Israeli currency) cash gift was the prize, equivalent to about 15 U.S. dollars. The study was approved by the university’s institutional review board IRB. Ethical approval number 042011.</p></sec><sec id=\"sec2dot2-ijerph-17-05403\"><title>2.2. Statistical Analyses</title><p>Data were analyzed using IBM (New York, NY, USA) SPSS-25. Chi<sup>2</sup> and one-way ANOVA with LSD post-hoc tests were conducted to examine differences among the five medical condition categories. Paired T-tests were conducted to examine changes in health behaviors. Ordinal logistic regressions were fitted to examine which factors contributed to perceived changes in physical, mental, and general health occurring during the first month of the outbreak. The orthopedic/pain-related condition was selected as a reference group in the regression models, given that univariate analyses suggested that people with this condition reported the greatest decline in SRH. The independent variable “crowded living” was dichotomized in the regression models to not at all (74.5%) versus somewhat (25.5%), as its distribution was not normal.</p></sec><sec id=\"sec2dot3-ijerph-17-05403\"><title>2.3. Measures</title><p>Individual and Socioeconomic Variables: These variables consisted of gender (female, male), age group (18–25, 26–35, 36–45, 46–55, 56–65, 66–75, 76–85, >85 years), marital status (single, married or live with a partner, divorced, separated, widowed), education (12 years or less, vocational education, college/university degree), work status during and before the COVID-19 crisis (full-time, part-time, unemployed, on sick leave, retired), economic status (bad, very bad, pretty good, good, very good), religiosity (secular, traditional, religious), number of people in household (1, 2, 3, >3), and perception of crowded living (“To what extent do you agree with the following statement: I live in crowded housing conditions” with five response options from completely disagree [<xref rid=\"B1-ijerph-17-05403\" ref-type=\"bibr\">1</xref>] to completely agree [<xref rid=\"B5-ijerph-17-05403\" ref-type=\"bibr\">5</xref>]). Categories containing few responses were combined.</p><sec id=\"sec2dot3dot1-ijerph-17-05403\"><title>2.3.1. Medical Condition</title><p>Participants self-selected their one main medical condition from a list of predefined conditions, based on the different communities within the Camoni platform, with an option to add an additional condition not mentioned in the list. In addition, participants reported the duration of their medical condition (less than one, one to three, three to five, more than five years), whether they generally received medical care for their medical condition (yes/no), and their Body Mass Index (BMI, Kg/m<sup>2</sup>). In addition, one item was included to examine frequency of medical appointments/visits: “How often did you meet with your medical team before the COVID-19 crisis (daily, weekly, monthly, bi-yearly, yearly, less than once a year)?</p></sec><sec id=\"sec2dot3dot2-ijerph-17-05403\"><title>2.3.2. Health Behaviors</title><p>Participants were asked to report the frequency of their health-related behaviors, during and before the COVID-19 crisis via the following questions—“On average, how many times a week do you participate in any exercise/sports activity for half an hour or longer?” This item was based on the 36-item short-form (SF-36) Medical Outcomes Study (MOS) [<xref rid=\"B37-ijerph-17-05403\" ref-type=\"bibr\">37</xref>]. “On average, how many times a day do you eat fresh fruit?” “On average, how many times a day do you eat fresh vegetables?”—with answers ranging from 0–10 times per day, based on the 2-item Serving Fruits and Vegetables Scale (2-Serving FVS) [<xref rid=\"B38-ijerph-17-05403\" ref-type=\"bibr\">38</xref>]. Participants were also asked to compare the amount of food they consumed during and before the COVID-19 crisis, with answers ranging from 1 (a lot more than before) to 5 (a lot less than before).</p></sec><sec id=\"sec2dot3dot3-ijerph-17-05403\"><title>2.3.3. Disease Management</title><p>Disease management was defined to participants as actions that patients need to take routinely in order to maintain their health, for example, going to medical appointments, adhering to nutrition recommendations, taking medications, and engaging in physical activity. Perceived disease management, during and before the COVID-19 crisis, was measured by a single item: “In my opinion, I manage my illness optimally,” rated on a 5-point Likert scale with answers ranging from 1 (strongly disagree) to 5 (strongly agree). This item was adopted from previously validated disease-specific self-efficacy measures [<xref rid=\"B39-ijerph-17-05403\" ref-type=\"bibr\">39</xref>] and from the Challenges to Illness Management Scale [<xref rid=\"B40-ijerph-17-05403\" ref-type=\"bibr\">40</xref>].</p></sec><sec id=\"sec2dot3dot4-ijerph-17-05403\"><title>2.3.4. Time Spent on Social Media</title><p>Total hours per day spent on social media, during and before the COVID-19 crisis, were measured by two single items: “How many hours per day, on average, do you spend on social media in general?” and “How many hours per day, on average, do you spend on online health communities to receive or provide information about the medical condition you are dealing with?” Participants were asked to answer this question in regard to the week before the outbreak and the previous week. Hours per day were measured on a seven-point scale: (0; <1; 1–2; 2–4; 4–6; 6–10; >10 hours per day). These items were adopted from the Technology Use Questionnaire [<xref rid=\"B41-ijerph-17-05403\" ref-type=\"bibr\">41</xref>,<xref rid=\"B42-ijerph-17-05403\" ref-type=\"bibr\">42</xref>].</p></sec><sec id=\"sec2dot3dot5-ijerph-17-05403\"><title>2.3.5. Self-Rated Health</title><p>Five items were included to examine SRH before and during the outbreak, revised from the SF-36 MOS [<xref rid=\"B37-ijerph-17-05403\" ref-type=\"bibr\">37</xref>] and adapted for the COVID-19 pandemic: (1) “In your opinion, as of today, your physical health condition is?” with answers ranging from 1 (very bad) to 5 (very good). (2) “In your opinion, as of today, your mental health condition is?” with answers ranging from 1 (very bad) to 5 (very good). (3) “Has your medical condition improved or worsened in the last month?” with answers ranging from 1 (greatly improved) to 5 (greatly worsened). (4) “How much do you feel that the COVID-19 crisis has affected your physical health?” with answers ranging from 1 (greatly improved) to 5 (greatly worsened). (5) “How much do you feel that the COVID-19 crisis has affected your mental health?” with answers ranging from 1 (greatly improved) to 5 (greatly worsened).</p></sec><sec id=\"sec2dot3dot6-ijerph-17-05403\"><title>2.3.6. Loneliness</title><p>The 3-item version of the Revised UCLA Loneliness Scale [<xref rid=\"B43-ijerph-17-05403\" ref-type=\"bibr\">43</xref>,<xref rid=\"B44-ijerph-17-05403\" ref-type=\"bibr\">44</xref>] was used to assess participants’ sense of loneliness, during and before the COVID-19 crisis. The Hebrew-translated version of the scale was used [<xref rid=\"B45-ijerph-17-05403\" ref-type=\"bibr\">45</xref>]. Items are as follows: “How often do you feel that you lack companionship?” “How often do you feel left out?” and “How often do you feel isolated from others?” with answers ranging from 1 (hardly ever) to 2 (some of the time) or 3 (often). The three items are summed to form a total score ranging from 3-9. Participants were asked to rate their current and past loneliness (i.e., before the pandemic) using these three items.</p></sec></sec><sec id=\"sec2dot4-ijerph-17-05403\" sec-type=\"subjects\"><title>2.4. Participants</title><p>Participants comprised 315 individuals (60% were women). Inclusion criteria included subjects over the age of 18 with a chronic medical condition, who use the Camoni website for themselves and not for another person (e.g., for a family member). One quarter of the participants were aged 18–45 years, and the rest were older (14% were 46–55, 22% were 56–65, 34% were 66–75, and 11% were older than 76 years). The average BMI was 27.4 Kg/m<sup>2</sup>. More than half (57%) of the participants reported being unemployed before the COVID-19 crisis compared to as many as 70% one month into the crisis. Specifically, before the crisis, a quarter (25%) of participants worked full-time away from home, and 3% worked full-time at home, compared to 9% away from home and 7% at home after the crisis; 14% worked part-time away from home and 2% worked part-time at home before the crisis, compared to 7% away from home and 9% at home after the crisis; 3% were on sick leave; 10% were unemployed, and 41% were retired before the crisis. After the crisis, 17% were on unpaid leave and 2% were let go due to the crisis; about half (48%) reported not working before the crisis or currently. As many as three quarters (74.5%) of the participants reported that they completely disagreed with the statement “I live in crowded housing conditions” and the rest reported that they partly disagreed (10.7%), did not agree/disagree (6.9%), partly agreed (1.4%) or completely agreed (5.2%). Additional characteristics of the study sample are presented in <xref rid=\"ijerph-17-05403-t001\" ref-type=\"table\">Table 1</xref>.</p><p>Participants had one of six medical conditions: (1) Mental health conditions (<italic>n</italic> = 36, 12%): depression and anxiety (<italic>n</italic> = 25), eating disorders (<italic>n</italic> = 3), posttraumatic stress disorder (<italic>n</italic> = 2), attention deficit disorder (<italic>n</italic> = 4), schizoaffective disorder (<italic>n</italic> = 1), and personality disorder (<italic>n</italic> = 1). (2) Metabolic conditions (<italic>n</italic> = 83, 27%): obesity (<italic>n</italic> = 16), diabetes (<italic>n</italic> = 58), kidney disease (<italic>n</italic> = 5), digestive issues (<italic>n</italic> = 2), liver disease (<italic>n</italic> = 1), thyroid conditions (<italic>n</italic> = 1). (3) Cardiovascular conditions (<italic>n</italic> = 53, 17%): heart-related diseases (<italic>n</italic> = 33), blood pressure disorders (<italic>n</italic> = 17), stroke (<italic>n</italic> = 3). (4) Cancer and autoimmune conditions (<italic>n</italic> = 64, 21%): cancer (all types) (<italic>n</italic> = 18), Crohn’s disease and ulcerative colitis (<italic>n</italic> = 11), multiple sclerosis (<italic>n</italic> = 17), psoriasis (<italic>n</italic> = 5), Sjogren’s (<italic>n</italic> = 2), Lupus (<italic>n</italic> = 4), HIV (<italic>n</italic> = 1), rheumatic diseases (<italic>n</italic> = 5), endometriosis (<italic>n</italic> = 1), Guillain-Barré (<italic>n</italic> = 1). (5) Orthopedic/pain-related conditions (<italic>n</italic> = 54, 18%): orthopedic conditions (<italic>n</italic> = 21), osteoporosis (<italic>n</italic> = 12), osteoarthritis (<italic>n</italic> = 4), pain (<italic>n</italic> = 9), fibromyalgia (<italic>n</italic> = 8). (6) Other conditions (<italic>n</italic> = 17, 5%): organ transplants (<italic>n</italic> = 3), Cushing’s syndrome (<italic>n</italic> = 1): epilepsy (<italic>n</italic> = 1), eye conditions (<italic>n</italic> = 1), Alzheimer’s disease (<italic>n</italic> = 1), smoking (<italic>n</italic> = 1), non-cancerous prostate conditions (<italic>n</italic> = 2), not specified (<italic>n</italic> = 7).</p><p>About two-thirds (65%, <italic>n</italic> = 201) of the participants had been dealing with their medical condition for over five years, and the vast majority (86%, <italic>n</italic> = 267) received medical treatment for their condition. Frequency of medical appointments (before the outbreak) ranged from weekly (10.7%, <italic>n</italic> = 31) to yearly (8.6%, <italic>n</italic> = 25).</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05403\"><title>3. Results</title><p><xref rid=\"ijerph-17-05403-t002\" ref-type=\"table\">Table 2</xref> provides an answer to the first research question. About half of the participants reported a decline in mental and physical health during the first month of the COVID-19 outbreak. Regarding changes in physical SRH, 8% reported feeling greatly worsened, 39% reported feeling slightly worse, 44% reported feeling no change, 5% reported feeling slightly better, and 4% reported feeling greatly improved. Regarding changes in mental SRH, 10% reported feeling greatly worsened, 41% reported feeling slightly worse, 42% reported feeling no change, 4% reported feeling slightly better, and 3% reported feeling greatly improved. Regarding changes in general SRH, 7% reported feeling greatly worsened, 20% reported feeling slightly worse, 60% reported feeling no change, 8% reported feeling slightly better, and 5% reported feeling greatly improved.</p><p><xref rid=\"ijerph-17-05403-t003\" ref-type=\"table\">Table 3</xref> provides an answer to the second research question by demonstrating changes in health behaviors and loneliness. One month into the reinforcement of social distancing, participants reported a decrease in their perceived disease management abilities (T = −5.42, <italic>p</italic> < 0.001), and a decline in physical activity (T = 4.51, <italic>p</italic> < 0.001). In addition, total time spent on social media (T = 13.29, <italic>p</italic> < 0.001) and on online health communities (T = 3.76, <italic>p</italic> < 0.001) increased significantly. Over 50% of the participants reported eating more than they did before the outbreak. Furthermore, one month into the reinforcement of social distancing, participants reported a higher sense of loneliness (T = 12.76, <italic>p</italic> < 0.001). Whereas only one-third of the participants reported that they experienced loneliness before the outbreak, as many as two-thirds of them reported feeling lonely one month after the reinforcement of social distancing. Specifically, in regard to the question “How often do you feel that you lack companionship?” only 6% of the participants reported almost always before the outbreak, relative to as many as 27% during the outbreak. Similarly, in regard to the question “How often do you feel left out?” only 7% of the participants reported almost always before the outbreak, relative to as many as 20% during the outbreak. Finally, in regard to the question “How often do you feel isolated from others?” only 7% of the participants reported almost always before the outbreak, relative to as many as 21% during the outbreak. </p><p><xref rid=\"ijerph-17-05403-t004\" ref-type=\"table\">Table 4</xref> provides an answer to the third research question by demonstrating differences in background variables and SRH measures between each of the five medical condition categories. There were no group differences in current physical health (F = 0.49, <italic>p</italic> = 0.79), but current mental SRH was lowest among participants with mental health conditions (F = 5.64, <italic>p</italic> < 0.001). Similarly, the feeling of loneliness was highest among participants with mental health conditions (F = 4.57, <italic>p</italic> < 0.001). </p><p><xref rid=\"ijerph-17-05403-t005\" ref-type=\"table\">Table 5</xref> provides an answer to the fourth research question by presenting three ordinal logistic regressions to predict changes in SRH. The first model shows that a decline in physical SRH was predicted by crowded housing conditions (<italic>p</italic> = 0.005), higher BMI (<italic>p</italic> = 0.006), and higher frequency of medical visits before the crisis (<italic>p</italic> = 0.011). In addition, participants with orthopedic/pain conditions were more likely to experience a decline in their physical SRH relative to participants with mental (<italic>p</italic> = 0.002), metabolic (<italic>p</italic> = 0.008), and cardiovascular (<italic>p</italic> = 0.022) conditions. The second model shows that a decline in mental SRH was predicted by female gender (<italic>p</italic> = 0.049), crowded housing conditions (<italic>p</italic> = 0.002), and higher BMI (<italic>p</italic> = 0.005). In addition, participants with orthopedic/pain conditions were more likely to experience a decline in their mental SRH relative to participants with metabolic (<italic>p</italic> = 0.025) conditions. The third model shows that a decline in general SRH was predicted by female gender (<italic>p</italic> = 0.016), higher education (<italic>p</italic> = 0.015), crowded housing conditions (<italic>p</italic> = 0.001), and illness duration (<italic>p</italic> = 0.010).</p></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05403\"><title>4. Discussion</title><p>The present study investigated how people with chronic medical conditions perceived their health status one month into the reinforcement of social distancing due to the COVID-19 outbreak. We conducted an online survey among more than 300 people with chronic illnesses exactly one month (20–22 April 2020) after the Israeli government reinforced severe social-distancing regulations, and two days before these regulations were reduced. The survey included self-report information about SRH, health behaviors, and loneliness. Several interesting findings were identified. First, as many as about half of the participants with a chronic medical condition reported a decline in their physical SRH (47.2%) or mental SRH (50.5%) during the first month of the social distancing reinforcement. Second, a significant deterioration in health behaviors was reported, including a decrease in vegetable consumption and physical activity, an increase in time spent on social media, and a perception among about half of the participants (50.2%) that they were eating more than they had been before. Third, whereas only one-third of the participants reported that they had felt lonely before the outbreak, as many as two-thirds of the participants reported feeling lonely one month after the reinforcement of social distancing. Last, a decline in general SRH was linked with female gender, lack of higher education, crowded housing conditions, longer illness duration, and loneliness.</p><p>Data indicated that as many as about half of the participants in this study experienced a decline in their SRH after one month of social distancing reinforcement (first research question). To the best of our knowledge, the current study is the first to report changes in SRH during the coronavirus crisis among people with chronic illnesses [<xref rid=\"B33-ijerph-17-05403\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijerph-17-05403\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijerph-17-05403\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijerph-17-05403\" ref-type=\"bibr\">36</xref>]. Contrary to the current findings, in a study by Lei et al. (2020), conducted during the lockdown among 1593 healthy Chinese individuals, only 2.4% perceived their SRH as bad or very bad, and over two-thirds perceived their SRH as good or very good [<xref rid=\"B46-ijerph-17-05403\" ref-type=\"bibr\">46</xref>]. This difference may be explained by the fact that our study exclusively included participants with chronic medical conditions, who likely ordinarily have poorer SRH than do healthy individuals [<xref rid=\"B47-ijerph-17-05403\" ref-type=\"bibr\">47</xref>], whereas the Chinese sample was recruited from the general population. Furthermore, this discrepancy between our findings and those of Lei et al. (2020) may imply that the outbreak and the reinforcement of social distancing may have had a stronger negative impact on people with a chronic illness. Relatedly, Wang et al. (2020), who collected data at two timepoints during the COVID-19 outbreak [<xref rid=\"B48-ijerph-17-05403\" ref-type=\"bibr\">48</xref>], reported that poor SRH status, physical symptoms, and a history of chronic illness may have contributed to higher levels of stress, anxiety, and depression. It could be that there was a vicious cycle in which during the COVID-19 outbreak, chronic illness and poor SRH contributed to higher levels of stress, anxiety, and depression, which contributed (in a circular manner) to a decline in SRH.</p><p>Findings suggest a deterioration in health behaviors, including less exercising, less consumption of fresh fruits, and more time spent on social media (second research question). These findings can be explained by the limitations imposed by the social distancing reinforcement: People were not allowed to venture more than 100 m from their homes, and health-clubs, gyms, and recreational parks, as well as many stores and markets, were closed [<xref rid=\"B3-ijerph-17-05403\" ref-type=\"bibr\">3</xref>]. Although supermarkets and pharmacies did remain open, and local shortages of fresh produce were unusual [<xref rid=\"B49-ijerph-17-05403\" ref-type=\"bibr\">49</xref>], people with chronic medical conditions were discouraged from leaving the house [<xref rid=\"B50-ijerph-17-05403\" ref-type=\"bibr\">50</xref>], making it difficult to purchase fresh, healthy food. The reported decline in people’s ability to optimally manage their illnesses may have been related to the disruption in healthcare access, including the transition to online medical care and the absence of non-emergency medical surgeries due to the outbreak [<xref rid=\"B51-ijerph-17-05403\" ref-type=\"bibr\">51</xref>]. These disruptions may have posed specific difficulties (e.g., cancellations of routine medical appointments) for people with chronic medical conditions. Some people may also have had poor access to or difficulties in mastering the new technologies in remote care that were introduced by healthcare providers [<xref rid=\"B52-ijerph-17-05403\" ref-type=\"bibr\">52</xref>]. Several studies have already examined compliance to health behaviors directly related to COVID-19 (i.e., washing hands, avoiding social gatherings, self-isolating) [<xref rid=\"B53-ijerph-17-05403\" ref-type=\"bibr\">53</xref>,<xref rid=\"B54-ijerph-17-05403\" ref-type=\"bibr\">54</xref>], but limited information has been available regarding routine health behaviors such as nutrition, exercise, or pre-existing disease management during the pandemic among people with chronic illnesses. A single study identified health-related behaviors significantly associated with mental health among people in quarantine due to COVID-19 in Brazil: diet, tele-psychotherapy participation, and exercise level. The results from Brazil highlighted the role of health behaviors during the outbreak by showing that specific health behaviors, including balanced meals, exercising, and the use of tele-psychotherapy, may have impacted stress, depression, and anxiety levels [<xref rid=\"B55-ijerph-17-05403\" ref-type=\"bibr\">55</xref>]. </p><p>Our findings demonstrated some similarities and some dissimilarities between people with different medical conditions in terms of SRH (third research question). For example, the adjusted model suggests that people with orthopedic/pain conditions experienced more deterioration in physical SRH relative to people with other medical conditions (i.e., mental, metabolic, or cardiovascular conditions). However, medical condition did not contribute to the variability we detected in general SRH declines. In Israel and the rest of the world, pain treatment and physical therapy centers closed their doors following the outbreak of the COVID-19 pandemic. Although telemedicine is being used to treat patients with many different medical conditions, pain management providers face a challenge in delivering services through video or other eHealth methods [<xref rid=\"B51-ijerph-17-05403\" ref-type=\"bibr\">51</xref>], preventing people with orthopedic/pain conditions from participating in treatments such as physical therapy. Moreover, the decline in physical activity reported in the entire sample may have more negatively affected people with orthopedic/pain conditions than those without, as these individuals are more vulnerable to lessened muscular strength, muscular endurance, and joint flexibility [<xref rid=\"B56-ijerph-17-05403\" ref-type=\"bibr\">56</xref>,<xref rid=\"B57-ijerph-17-05403\" ref-type=\"bibr\">57</xref>]. </p><p>As expected, we found that participants felt lonelier during the first month of the outbreak than they had previously, and that loneliness was an important contributor to a decline in SRH (fourth research question). This finding is not surprising given that previous studies have already highlighted the importance of loneliness in health [<xref rid=\"B18-ijerph-17-05403\" ref-type=\"bibr\">18</xref>,<xref rid=\"B20-ijerph-17-05403\" ref-type=\"bibr\">20</xref>]. For example, a longitudinal study conducted before the coronavirus outbreak among a U.S. nationally representative sample reported that loneliness was associated with an increased mortality risk over a six-year period, and that this association (between loneliness and mortality) was explained by health outcomes [<xref rid=\"B22-ijerph-17-05403\" ref-type=\"bibr\">22</xref>]. Furthermore, a study conducted during the COVID-19 lockdown period in Spain found that higher reported loneliness was associated with higher distress [<xref rid=\"B24-ijerph-17-05403\" ref-type=\"bibr\">24</xref>]. In line with findings from the current study, Brodeur et al. (2020) used Google Trends to show a significant increase in searches for loneliness, worry, and sadness before and during the lockdown in Europe and the U.S. [<xref rid=\"B58-ijerph-17-05403\" ref-type=\"bibr\">58</xref>].</p><p>In the current study, the “crowded housing conditions” variable was a strong predictor of decline in both physical and mental SRH (fourth research question). Although a lower number of people living in the household is often correlated with worse SRH [<xref rid=\"B26-ijerph-17-05403\" ref-type=\"bibr\">26</xref>], even after adjusting for socioeconomic status, it could be that the social distancing regulations that forced people to stay in their homes for an extended period of time increased the negative effect of crowded housing conditions on participants’ health. In line with the current findings, Wang et al. (2020) reported that respondents staying in a household with three or more people one month into the COVID-19 outbreak had significantly higher posttraumatic stress disorder scores compared to respondents who lived alone [<xref rid=\"B49-ijerph-17-05403\" ref-type=\"bibr\">49</xref>]. Furthermore, female gender, higher BMI, higher frequency of medical visits (before the outbreak), and longer illness duration were also linked with a decline in SRH. These findings are not surprising given that females may be more vulnerable to psychological distress than are males [<xref rid=\"B27-ijerph-17-05403\" ref-type=\"bibr\">27</xref>], and this psychological distress may be linked with a stronger decline in mental SRH. Indeed, Mazza et al. [<xref rid=\"B28-ijerph-17-05403\" ref-type=\"bibr\">28</xref>] found that female gender was associated with higher levels of depression, anxiety, and stress during the COVID-19 lockdown in Italy [<xref rid=\"B28-ijerph-17-05403\" ref-type=\"bibr\">28</xref>]. Moreover, it has been reported that the COVID-19 pandemic has increased the care burden of women, negatively impacting women and their families [<xref rid=\"B59-ijerph-17-05403\" ref-type=\"bibr\">59</xref>]. In addition, higher BMI, more medical visits (before the outbreak), and longer illness duration are all likely to be linked with illness severity [<xref rid=\"B60-ijerph-17-05403\" ref-type=\"bibr\">60</xref>,<xref rid=\"B61-ijerph-17-05403\" ref-type=\"bibr\">61</xref>]. Therefore, our findings may imply that among people with a chronic illness, poorer medical condition (as indicated by higher BMI, more medical visits, and longer illness duration) is linked with a stronger decline in SRH. Future studies are warranted to clarify the long-term effects of social distancing on the health of this vulnerable population.</p><p>This study has several strengths, including the investigation of people with chronic illnesses exactly one month after the Israeli government decided to carry out an aggressive response to the COVID-19 outbreak. We used real-time data (gathered over three days) from a relatively large number of respondents at the peak of the COVID-19 epidemic and reinforcement of social distancing, thereby contributing preliminary information regarding the effect of such regulations on people suffering from chronic medical conditions. Several limitations should be noted. First, although participants were asked to report about their past and present situation, the cross-sectional design of the study does not allow us to determine causality. Second, participants were asked to provide retrospective data about their health status prior to the lockdown, as we did not have access to their pre-pandemic records. A recent study [<xref rid=\"B62-ijerph-17-05403\" ref-type=\"bibr\">62</xref>] examining the validity of a retrospective measurement (recall) of health, by using a test-retest design to measure reliability and agreement between prospective and retrospective patient-reported health, suggests that a retrospective measurement of health is a valid alternative to using reference data for the purpose of estimating past health status. Third, we relied on self-reported data and not on clinical records. For example, height and weight were assessed by self-report, which is likely less accurate than objective measurement. However, studies show that web responders usually provide accurate information about themselves [<xref rid=\"B63-ijerph-17-05403\" ref-type=\"bibr\">63</xref>]. Relatedly, we do not have information about participants’ specific medical diagnoses, and it may be that such information could have provided important insights into the detected variability in SRH. In addition, SRH is a subjective measure and may not always reflect objective measures of health, although a strong predictive capacity regarding various health-related outcomes is well established [<xref rid=\"B6-ijerph-17-05403\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijerph-17-05403\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijerph-17-05403\" ref-type=\"bibr\">8</xref>]. Fourth, the sampling design is subject to biases of internet-based surveys. Participants voluntarily selected to take the survey online, and although the current sample was large and heterogeneous, it was not a representative sample.</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05403\"><title>5. Conclusions</title><p>Findings highlight the important role of loneliness in health [<xref rid=\"B58-ijerph-17-05403\" ref-type=\"bibr\">58</xref>], while demonstrating how the near-lockdown measures, announced by the Israeli government in response to the COVID-19 outbreak, may have had a negative impact on the health of people with chronic illnesses. Future longitudinal studies are warranted to clarify the long-term effects of loneliness on the health of people with chronic medical condition during the COVID-19 outbreak.</p></sec></body><back><ack><title>Acknowledgments</title><p>We would like to thank Tami Shifter from the Gertner Institute, for her kind co-operation and help with data collection. </p></ack><notes><title>Author Contributions</title><p>Data curation, R.E.-B. and M.M.; Formal analysis, R.E.-B. and M.M.; Funding acquisition, R.E.-B.; Investigation, R.E.-B.; Methodology, R.E.-B.; Project administration, M.M.; Writing—original draft, R.E.-B. and M.M.; Writing—review & editing, R.E.-B. 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Disord.</source><year>2020</year><volume>21</volume><fpage>1</fpage><lpage>10</lpage><pub-id pub-id-type=\"doi\">10.1186/s12891-020-03434-8</pub-id></element-citation></ref></ref-list></back><floats-group><table-wrap id=\"ijerph-17-05403-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05403-t001_Table 1</object-id><label>Table 1</label><caption><p>Demographic and medical characteristics.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Characteristics</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th></tr></thead><tbody><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Gender</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), Female</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">59.5 (178)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), Male</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">40.5 (121)</td></tr><tr><td rowspan=\"5\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Age <sup>2</sup></td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), 18–45</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">19.2 (60)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), 46–55</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.8 (43)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), 56–65</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">22.1 (69)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), 66–75</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">34.3 (107)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), ≥76</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.6 (33)</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Marital status <sup>2</sup></td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), Married </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">37.5 (118)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), Unmarried</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">62.5 (197)</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Education</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), 12 years or less</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">26.8 (84)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), Vocational education</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">24.6 (77)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), University degree</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">48.6 (152)</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Work status <break/>(before COVID-19) <sup>2</sup></td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), Employed</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">42.8 (131)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), Unemployed</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">57.2 (175)</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Work status <break/>(during COVID-19) <sup>2</sup></td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), Employed</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">29.9 (83)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), Unemployed</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">70.1 (195)</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Economic status <sup>2</sup></td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), Bad/very bad</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">28.8 (90)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), pretty good</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">35.3 (110)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), Good/very good</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">35.9 (112)</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Religiosity</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), Secular </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">70.9 (222)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), Traditional </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18.8 (59)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), Religious</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.2 (32)</td></tr><tr><td rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Num. of people in the household <sup>2</sup></td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), One </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18.5 (58)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), Two</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">47.0 (147)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), More than two</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">34.5 (108)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Crowded housing conditions <sup>1</sup></td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Average (Std)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.50 (1.05)</td></tr><tr><td rowspan=\"6\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Main medical condition</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), Mental health </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11.7 (36)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), Metabolic </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">27.0 (83)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), Cardiovascular </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17.3 (53)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), Cancer & autoimmune </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20.8 (64)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), Orthopedic/pain </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17.6 (54)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), Other</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.5 (17)</td></tr><tr><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Duration of medical condition</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), <1 year</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.2 (23)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), 1–3 years</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16.7 (52)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), 3–5 years</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11.3 (35)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), >5 years</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">64.6 (201)</td></tr><tr><td rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Receive medical care for the condition</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), Yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">85.6 (267)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14.4 (45)</td></tr><tr><td rowspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Medical visit frequency <sup>2</sup></td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), Weekly or more</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.7 (31)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), Monthly</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">33.8 (98)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), Every six months</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">46.2 (134)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (n), Yearly or less</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8.6 (25)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">BMI</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Average (Std)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">27.4 (6.2)</td></tr></tbody></table><table-wrap-foot><fn><p>BMI = Body Mass Index. <sup>1</sup> Items rated on a 5-point scale; higher scores represent a more crowded living perception. <sup>2</sup> Original categories combined due to few responses.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05403-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05403-t002_Table 2</object-id><label>Table 2</label><caption><p>Self-rated health (SRH) measures.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SRH Measures</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Title</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">% (<italic>n</italic>)</th></tr></thead><tbody><tr><td rowspan=\"3\" align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Current Physical SRH</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Bad/very bad</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14.6 (46)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Neither good nor bad</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">46.7 (147)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Good/very good</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">38.7 (122)</td></tr><tr><td rowspan=\"3\" align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Current Mental SRH</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Bad/very bad</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14.2 (44)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Neither good nor bad</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">30.3 (94)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Good/very good</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">55.5 (172)</td></tr><tr><td rowspan=\"3\" align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Change in Physical SRH during the outbreak</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Improved</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8.9 (28)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Unchanged</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">43.9 (138)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Worsened</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">47.2 (148)</td></tr><tr><td rowspan=\"3\" align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Change in Mental SRH during the outbreak</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Improved</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.3 (21)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Unchanged</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">42.3 (129)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Worsened</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">50.5 (154)</td></tr><tr><td rowspan=\"3\" align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">Change in general SRH during the outbreak</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Improved</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12.5 (39)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Unchanged</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">60.1 (187)</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Worsened</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">27.4 (85)</td></tr></tbody></table><table-wrap-foot><fn><p>SRH = Self-Rated Health.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05403-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05403-t003_Table 3</object-id><label>Table 3</label><caption><p>Changes in health behaviors and loneliness one month into the COVID-19 outbreak among 315 adults with chronic medical conditions.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"/><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Range</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Before<break/>(Average, Std)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">During<break/>(Average, Std)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">T-Test</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Disease management <sup>1</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1–5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.9 (1.0)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.6 (1.1)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.42</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Time spent on social media (hours/day) <sup>2</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1–7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.2 (1.1)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.9 (1.2)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.29</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Time spent on online health communities (hours/day) <sup>2</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1–7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.0 (0.7)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.2 (0.9)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.76</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Physical activity (times/week) <sup>3</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0–10</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.5 (2.4)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.8 (2.4)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.51</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Fruit consumption (units/day)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0-10</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.7 (2.0)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.6 (2.0)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.89</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.060</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Vegetable consumption (units/day)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0–10</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.2 (2.4)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.0 (2.3)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.66</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.008</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Loneliness (Total score) <sup>4</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3–9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.3 (1.7)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.6 (2.0)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12.76</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.001</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Food Consumption <sup>5</sup></td><td colspan=\"3\" align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">% (<italic>n</italic>) Much more than before</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">19.7 (62)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td colspan=\"3\" align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">% (<italic>n</italic>) A little more than before</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">30.5 (96)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td colspan=\"3\" align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">% (<italic>n</italic>) Same as before</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">40.0 (126)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td colspan=\"3\" align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">% (<italic>n</italic>) A little less than before</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.0 (22)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td colspan=\"3\" align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">% (<italic>n</italic>) Much less than before</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.9 (9)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr></tbody></table><table-wrap-foot><fn><p><sup>1</sup> “In my opinion, I manage my illness optimally” with answers ranging from 1 (strongly disagree) to 5 (strongly agree). <sup>2</sup> Answers ranging from 1 (not at all) to 7 (more than 10 hours per day) <sup>3</sup> Half an hour or more of physical activity. <sup>4</sup> Revised UCLA Loneliness Scale. <sup>5</sup> “Relative to the amount of food I consumed before the outbreak, I estimate that I currently consume” 1 (a lot more than before) to 5 (a lot less than before).</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05403-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05403-t004_Table 4</object-id><label>Table 4</label><caption><p>Background, Loneliness, and Self-Rated Health (SRH) by Medical Condition Category.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Background, Loneliness, <break/>and Self-Rated Health</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mental<break/><italic>n</italic> = 36</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Metabolic<break/><italic>n</italic> = 83</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cardiovascular<break/><italic>n</italic> = 53</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Autoimmune<break/><italic>n</italic> = 64</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Orthopedic/Pain<break/><italic>n</italic> = 54</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Test-Statistics</th></tr></thead><tbody><tr><td colspan=\"7\" align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">\n<bold>Background Characteristics</bold>\n</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Age (years), average (Std)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">42.3 (14.3) <sup>a</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">63.1 (13.1) <sup>c</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">65.1 (16.1) <sup>c</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">55.2 (16.0) <sup>b</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">57.8 (14.2) <sup>b</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16.35, <italic>p</italic> < 0.001</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Female, % (n)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">71.43 (25) <sup>c</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">43.04 (34) <sup>b</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">30.43 (14) <sup>a</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">79.03 (49) <sup>c</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">70.37 (38) <sup>c</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">39.40, <italic>p</italic> < 0.001</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">BMI, average (Std)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25.58 (6.26) <sup>a</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">30.30 (7.38) <sup>b</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">27.58 (4.48) <sup>a</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25.87 (4.52) <sup>a</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25.99 (5.10) <sup>a</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.61, <italic>p</italic> < 0.001</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Duration, average (Std)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.46 (1.15) <sup>ab</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.49 (0.92) <sup>b</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.09 (1.01) <sup>a</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.22 (1.06) <sup>ab</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.07 (1.42) <sup>a</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.54, <italic>p</italic> = 0.179</td></tr><tr><td colspan=\"7\" align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\"><bold>Current SRH and Loneliness</bold>, average (Std)\n</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SRH physical (current) <sup>1</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.08 (0.97)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.29 (0.82)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.28 (0.86)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.25 (0.67)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.17 (0.86)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.49, <italic>p</italic> = 0.787</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SRH mental (current) <sup>1</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.92 (1.03) <sup>a</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.63 (0.96) <sup>b</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.75 (0.90) <sup>b</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.66 (0.85) <sup>b</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.26 (0.81) <sup>b</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.64, <italic>p</italic> < 0.001</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Loneliness (current) <sup>2</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.75 (2.11) <sup>c</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.46 (1.81) <sup>ab</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.24 (2.09) <sup>a</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.66 (2.15) <sup>ab</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.00 (1.90) <sup>b</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.57, <italic>p</italic> < 0.001</td></tr><tr><td colspan=\"7\" align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\"><bold>Changes in SRH</bold>, average (Std)\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Change in physical SRH <sup>3</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.36 (0.96) <sup>ab</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.29 (0.93) <sup>a</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.34 (0.81) <sup>a</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.53 (0.78) <sup>ab</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.70 (0.82) <sup>b</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.91, <italic>p</italic> = 0.092</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Change in mental SRH <sup>3</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.72 (1.03) <sup>b</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.35 (0.91) <sup>a</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.48 (0.70) <sup>ab</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.48 (0.77) <sup>ab</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.73 (0.64) <sup>b</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.96, <italic>p</italic> = 0.084</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Change in general SRH <sup>3</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.22 (1.02) <sup>ab</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.13 (0.79) <sup>ab</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.19 (0.66) <sup>ab</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.08 (0.82) <sup>a</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.40 (1.01) <sup>b</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.96, <italic>p</italic> = 0.440</td></tr></tbody></table><table-wrap-foot><fn><p>SRH = Self-Rated Health. <sup>abc</sup> Different superscript values indicate that the absolute values differ significantly. <sup>1</sup> Items rated on a scale from 1 (very bad) to 5 (very good). <sup>2</sup> Total score; items rated on a scale from 1 (hardly ever) to 3 (often). <sup>3</sup> Items rated on a scale from 1 (greatly improved) to 5 (greatly worsened).</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05403-t005\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05403-t005_Table 5</object-id><label>Table 5</label><caption><p>Ordinal logistic regression predicting decline in self-rated health (SRH) one month into the Covid-19 outbreak among 292 people with chronic illnesses.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"3\" colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\">Independent Variables</th><th colspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Decline in Physical SRH <sup>1</sup></th><th colspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Decline in Mental SRH <sup>2</sup></th><th colspan=\"4\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Decline in General SRH <sup>3</sup></th></tr><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">EXP (B)</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\"><italic>p</italic>-Value</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">95% Wald Confidence Interval for Exp(B)</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">EXP (B)</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\"><italic>p</italic>-Value</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">95% Wald Confidence Interval for Exp (B)</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">EXP (B)</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\"><italic>p</italic>-Value</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">95% Wald Confidence Interval for Exp (B)</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Lower</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Upper</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Lower</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Upper</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Lower</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Upper</th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Female gender</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.04</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.893</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.60</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.79</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.79</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.049</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.00</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>3.19</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>2.07</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.016</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.15</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>3.74</bold>\n</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Age</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.04</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.734</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.84</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.29</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.93</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.524</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.74</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.16</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.07</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.564</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.86</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.33</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Higher education</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.02</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.938</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.57</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.85</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.85</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.610</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.45</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.59</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>2.20</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.015</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.16</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>4.18</bold>\n</td></tr><tr><td colspan=\"2\" align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Crowded housing conditions</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.39</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.005</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.21</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.76</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.35</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.002</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.17</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.69</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.32</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.001</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.16</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.64</bold>\n</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">BMI </td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.07</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.006</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.02</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.12</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.07</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.005</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.02</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.12</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.04</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.107</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.99</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.09</td></tr><tr><td colspan=\"2\" align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Medical visit frequency <sup>4</sup></td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.68</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.011</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.51</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.92</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.84</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.258</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.62</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.14</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.83</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.255</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.60</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.14</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Illness duration</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.93</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.577</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.74</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.19</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.13</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.353</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.87</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.47</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.43</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.010</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.09</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.88</bold>\n</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Medical condition <sup>5</sup>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mental</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.19</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.002</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.07</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.55</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.53</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.248</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.18</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.55</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.40</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.105</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.13</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.21</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Metabolic</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.34</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.008</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.16</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.76</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.40</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.025</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.18</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.89</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.77</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.527</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.33</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.76</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cardiovascular</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.36</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.022</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.15</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.86</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.71</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.457</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.29</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.75</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.27</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.619</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.49</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.28</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Autoimmune</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.62</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.235</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.29</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.36</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.47</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.066</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.21</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.05</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.66</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.329</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.28</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.53</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Orthopedic/pain</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Ref.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Ref.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Ref.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td colspan=\"2\" align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Loneliness—current</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.23</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.002</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.08</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.40</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.58</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.000</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.37</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.82</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.21</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>0.008</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.05</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>1.39</bold>\n</td></tr></tbody></table><table-wrap-foot><fn><p>SRH = Self-Rated Health. BMI = Body Mass Index. Significant values appear in bold. <sup>1</sup> “How much do you feel that the COVID-19 crisis has affected your physical health?” with answers ranging from 1 (greatly improved) to 5 (greatly worsened).<sup>2</sup> “How much do you feel that the COVID-19 crisis has affected your mental health?” with answers ranging from 1 (greatly improved) to 5 (greatly worsened). <sup>3</sup> “Has your medical condition improved or worsened in the last month?” with answers ranging from 1 (greatly improved) to 5 (greatly worsened). <sup>4</sup> Before the outbreak. <sup>5</sup> Participants with “other” medical condition were omitted from the analyses.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32759711</article-id><article-id pub-id-type=\"pmc\">PMC7432046</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155612</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05612</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Design and Validation of the Adaptation to Change Questionnaire: New Realities in Times of COVID-19</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-5950-5175</contrib-id><name><surname>Pérez-Fuentes</surname><given-names>María del Carmen</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05612\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05612\">2</xref><xref rid=\"c1-ijerph-17-05612\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-9187-1474</contrib-id><name><surname>Molero Jurado</surname><given-names>María del Mar</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05612\">1</xref><xref rid=\"c1-ijerph-17-05612\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-6794-3906</contrib-id><name><surname>Martos Martínez</surname><given-names>África</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05612\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-8582-6631</contrib-id><name><surname>Fernández-Martínez</surname><given-names>Elena</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05612\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Franco Valenzuela</surname><given-names>Raquel</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05612\">4</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-5183-5679</contrib-id><name><surname>Herrera-Peco</surname><given-names>Iván</given-names></name><xref ref-type=\"aff\" rid=\"af5-ijerph-17-05612\">5</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-1169-0428</contrib-id><name><surname>Jiménez-Rodríguez</surname><given-names>Diana</given-names></name><xref ref-type=\"aff\" rid=\"af6-ijerph-17-05612\">6</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-8458-5314</contrib-id><name><surname>Méndez Mateo</surname><given-names>Inmaculada</given-names></name><xref ref-type=\"aff\" rid=\"af7-ijerph-17-05612\">7</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-7376-4170</contrib-id><name><surname>Santillán García</surname><given-names>Azucena</given-names></name><xref ref-type=\"aff\" rid=\"af8-ijerph-17-05612\">8</xref></contrib><contrib contrib-type=\"author\"><name><surname>Simón Márquez</surname><given-names>María del Mar</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05612\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Gázquez Linares</surname><given-names>José Jesús</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05612\">1</xref><xref ref-type=\"aff\" rid=\"af9-ijerph-17-05612\">9</xref><xref rid=\"c1-ijerph-17-05612\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05612\"><label>1</label>Department of Psychology, Faculty of Psychology, University of Almería, 04120 Almería, Spain; <email>amm521@ual.es</email> (Á.M.M.); <email>msm112@ual.es</email> (M.d.M.S.M.)</aff><aff id=\"af2-ijerph-17-05612\"><label>2</label>Department of Psychology, Faculty of Psychology, Universidad Politécnica y Artística del Paraguay, Asunción 1628, Paraguay</aff><aff id=\"af3-ijerph-17-05612\"><label>3</label>SALBIS Research Group, Nursing and Physiotherapy Department, Faculty of Health Sciences, University of León, 24401 Ponferrada, Spain; <email>elena.fernandez@unileon.es</email></aff><aff id=\"af4-ijerph-17-05612\"><label>4</label>Hospital Universitario Mútua de Terrassa, 08001 Barcelona, Spain; <email>rfranco@mutuaterrassa.cat</email></aff><aff id=\"af5-ijerph-17-05612\"><label>5</label>Nursing Department, Health Sciences Collegue, Alfonso X El Sabio University, 28691 Madrid, Spain; <email>iherrpec@uax.es</email></aff><aff id=\"af6-ijerph-17-05612\"><label>6</label>Department of Nursing, Physiotherapy and Medicine, University of Almería, 04120 Almería, Spain; <email>d.jimenez@ual.es</email></aff><aff id=\"af7-ijerph-17-05612\"><label>7</label>Department of Evolutionary and Educational Psychology, University of Murcia, 30100 Murcia, Spain; <email>inmamendez@um.es</email></aff><aff id=\"af8-ijerph-17-05612\"><label>8</label>Department of Cardiology, Burgos University Hospital, 09006 Burgos, Spain; <email>ebevidencia@gmail.com</email></aff><aff id=\"af9-ijerph-17-05612\"><label>9</label>Department of Psychology, Universidad Autónoma de Chile, Providencia 7500000, Chile</aff><author-notes><corresp id=\"c1-ijerph-17-05612\"><label>*</label>Correspondence: <email>mpf421@ual.es</email> (M.d.C.P.-F.); <email>mmj130@ual.es</email> (M.d.M.M.J.); <email>jlinares@ual.es</email> (J.J.G.L)</corresp></author-notes><pub-date pub-type=\"epub\"><day>04</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5612</elocation-id><history><date date-type=\"received\"><day>10</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>03</day><month>8</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Emotional and cognitive-behavioral factors influence people’s adaptability to change. Based on this premise, the objective of this study was to develop, evaluate and validate the Adaptation to Change Questionnaire (ADAPTA-10) for identifying those who show poor adaptability to adverse situations, such as those caused by COVID-19. This study was carried out in a sample of 1160 adults and produced a 10-item instrument with good reliability and validity indices. It is an effective tool useful in research and in clinical practice. Calculation tables are provided for the general Spanish population and by sex to evaluate adaptability to change. The two-dimensional structure proposed in the original model was confirmed. This instrument will enable the needs for adaptation to the new reality associated with COVID-19 to be detected and also other situations in which the subject becomes immersed which demand adaptation strategies in the new situation lived in.</p></abstract><kwd-group><kwd>adaptability to change</kwd><kwd>COVID-19</kwd><kwd>general population</kwd><kwd>design</kwd><kwd>validation</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05612\"><title>1. Introduction</title><p>The disease caused by the coronavirus, SARS-CoV-2, called COVID-19 [<xref rid=\"B1-ijerph-17-05612\" ref-type=\"bibr\">1</xref>], was declared a pandemic by the World Health Organization in March 2020 [<xref rid=\"B2-ijerph-17-05612\" ref-type=\"bibr\">2</xref>] because of its rapid spread and high death toll [<xref rid=\"B3-ijerph-17-05612\" ref-type=\"bibr\">3</xref>].</p><p>The risk of this pandemic is not only a healthcare problem, but also has severe socioeconomic and psychological implications [<xref rid=\"B4-ijerph-17-05612\" ref-type=\"bibr\">4</xref>]. Therefore, the approach to the COVID-19 public healthcare emergency should try to minimize both the negative physical and psychological impacts of the virus [<xref rid=\"B5-ijerph-17-05612\" ref-type=\"bibr\">5</xref>]. Ignoring the immediate psychological effects of this global situation would have a disproportionate mid- to long-term impact [<xref rid=\"B6-ijerph-17-05612\" ref-type=\"bibr\">6</xref>]. Its severity and persistence are still unknown, and therefore, how long public restrictions and measures should be maintained is also unknown [<xref rid=\"B7-ijerph-17-05612\" ref-type=\"bibr\">7</xref>]. What is clear is that the transition to the new normality will be a process that will put the adaptability of every individual to the test.</p><sec id=\"sec1dot1-ijerph-17-05612\"><title>1.1. Adaptation to Change</title><p>In situations of adversity, some individuals are at greater risk of developing psychological alterations, such as posttraumatic stress or biopsychosocial disorders, while others resist and adapt well [<xref rid=\"B8-ijerph-17-05612\" ref-type=\"bibr\">8</xref>]. In psychology, the concept of adaptation refers to functional change in response to environmental stimuli, whether in terms of sensorial, behavioral, cognitive or emotional functioning. These changes must provide benefits to the subject, improving adjustment to the current or future environment [<xref rid=\"B9-ijerph-17-05612\" ref-type=\"bibr\">9</xref>]. Positive adaptation to adversity is not a completely innate trait, so this ability can be learned and developed by actively reformulating life’s challenges [<xref rid=\"B10-ijerph-17-05612\" ref-type=\"bibr\">10</xref>]. From a perspective of virtue, based on committed action, the individual’s practical wisdom and courage, resilience and adaptability may be understood as the transformation of adversity into opportunity [<xref rid=\"B11-ijerph-17-05612\" ref-type=\"bibr\">11</xref>]. In research, psychological adaptation has often been analyzed in the scope of natural disasters due to the direct impact and high losses undergone by a high percentage of the population in such situations. These losses may be permanent or temporary, total or partial, depending on the capacity of strength of the individual and the number of stressors to cope with [<xref rid=\"B12-ijerph-17-05612\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijerph-17-05612\" ref-type=\"bibr\">13</xref>]. Women seem to adapt the worst to highly stressful events [<xref rid=\"B14-ijerph-17-05612\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijerph-17-05612\" ref-type=\"bibr\">15</xref>]. High financial losses and the death of loved ones are the two factors which have the strongest long-term repercussions on adaptation and subjective wellbeing of individuals [<xref rid=\"B14-ijerph-17-05612\" ref-type=\"bibr\">14</xref>,<xref rid=\"B16-ijerph-17-05612\" ref-type=\"bibr\">16</xref>]. Thus, psychological adaptability enables adjustment to or acceptance of difficult situations and is of great value in learning to struggle with the limitations of daily life [<xref rid=\"B17-ijerph-17-05612\" ref-type=\"bibr\">17</xref>]. On the contrary, absence of psychological adaptability has been linked to the presence of internalization (somatic complaints, anxiety or depression) and externalization (behavioral problems) symptoms [<xref rid=\"B18-ijerph-17-05612\" ref-type=\"bibr\">18</xref>]. Therefore, after a significant event, the wellbeing of an individual may not return to baseline or may take many years to do so, and the adaptation responses of each subject may differ enormously [<xref rid=\"B19-ijerph-17-05612\" ref-type=\"bibr\">19</xref>].</p></sec><sec id=\"sec1dot2-ijerph-17-05612\"><title>1.2. Factors in Adapting to Change</title><p>The hedonic adaptation model by Graham and Oswald [<xref rid=\"B20-ijerph-17-05612\" ref-type=\"bibr\">20</xref>] states that individuals tend to remain at a certain stable level of endogenous wellbeing, recovering from harmful events and becoming used to the good ones. Thus, when exogenous events threaten their wellbeing, people may recover, safeguarding their adjustment if they can control the situation to a certain extent through the flow of psychological resources for coping with it. Based on integration of individuals in their setting, this could generate different styles and strategies for approaching the situation [<xref rid=\"B21-ijerph-17-05612\" ref-type=\"bibr\">21</xref>]. According to the Threat Appraisal and Coping Theory [<xref rid=\"B22-ijerph-17-05612\" ref-type=\"bibr\">22</xref>], people who are exposed to stressful situations may respond with adaptive behavior which provides them with immediate and long-term wellbeing, or, with maladaptive coping, which distracts them or relieves them, making them feel good temporarily, but generating psychological distress later. According to Black and Hendy [<xref rid=\"B23-ijerph-17-05612\" ref-type=\"bibr\">23</xref>], the choice in many cases is related to the perceived ability to do something about the situation. Thus, although exposure to stressful factors may not always be avoidable, if one perceives that something can be done to change the situation, a more adaptive coping strategy, mainly related to proactive efforts to change the situation or its meaning, will be chosen [<xref rid=\"B24-ijerph-17-05612\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijerph-17-05612\" ref-type=\"bibr\">25</xref>]. Thus, staying in control of events goes beyond the resources for coping. The sense of control may be internal or external. Beliefs about stronger internal control buffer the effects of stressors, so people who feel more able to control stressful events adapt better, as they are more able to face them, even though they may be more strongly exposed to this type of event [<xref rid=\"B22-ijerph-17-05612\" ref-type=\"bibr\">22</xref>,<xref rid=\"B26-ijerph-17-05612\" ref-type=\"bibr\">26</xref>]. However, other authors note that although at first sight it might be thought that the internal locus of control is related directly with wellbeing, it is not always that way. When people face completely uncontrollable situations, maintaining a high perception of internal control of events could be a negative strategy for adaptation, generating emotional distress [<xref rid=\"B27-ijerph-17-05612\" ref-type=\"bibr\">27</xref>].</p><p>Another of the factors involved in an individual’s adaptability is tolerance to uncertainty. Uncertainty is present in daily life (e.g., Will it rain today?), at existential moments and important decisions (personal and professional), in relations with the world (e.g., the future and unemployment during an economic crisis) at significant times (e.g., illness of a family member) and in nature (e.g., fear of natural disasters in a prone geographic area). So, the way in which people perceive and cope with uncertainty is relevant to their adaptability [<xref rid=\"B28-ijerph-17-05612\" ref-type=\"bibr\">28</xref>]. Tolerance to uncertainty has been defined as the way in which people understand and process information in uncertain situations and how they respond with cognitive-behavioral and emotional reactions [<xref rid=\"B29-ijerph-17-05612\" ref-type=\"bibr\">29</xref>]. Repetitive, expected events do not usually awaken fear, but apprehension. Worry and uncertainty usually appear when the causes of an event cannot be explained [<xref rid=\"B30-ijerph-17-05612\" ref-type=\"bibr\">30</xref>]. Similarly, situations of psychological uncertainty are usually coupled with anxiety symptoms due to the agitation from anticipation of threat, and with stress, which refers to persistent irritability, impatience and tension; therefore, its management is important in order to lower psychological stress and ensure adjustment [<xref rid=\"B31-ijerph-17-05612\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijerph-17-05612\" ref-type=\"bibr\">32</xref>]. Along with stress management, the capacity for regulating emotions is fundamental to adapting later to indispensable situations [<xref rid=\"B33-ijerph-17-05612\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijerph-17-05612\" ref-type=\"bibr\">34</xref>]. Thus, people who show better ability to regulate their emotions have a stronger capacity for adapting and responding to a changing environment [<xref rid=\"B35-ijerph-17-05612\" ref-type=\"bibr\">35</xref>].</p><p>Depression is another emotional component to be kept in mind in an individual’s adaptation. Perception of the inability to cope with demands is linked to dysphoric feelings and depressive symptoms [<xref rid=\"B36-ijerph-17-05612\" ref-type=\"bibr\">36</xref>]. It has also been associated with cognitive inflexibility, which is transformed into problems for adapting reactions to new situations [<xref rid=\"B37-ijerph-17-05612\" ref-type=\"bibr\">37</xref>]. Thus, cognitive flexibility is another component to be considered in adaptation to change [<xref rid=\"B38-ijerph-17-05612\" ref-type=\"bibr\">38</xref>]. This refers to the ability to modify cognitive and behavioral strategies in response to changes in environmental demands [<xref rid=\"B39-ijerph-17-05612\" ref-type=\"bibr\">39</xref>]. Increase in psychological flexibility has been shown to diminish stress and anxiety that handicap effective response and provide benefits for wellbeing [<xref rid=\"B40-ijerph-17-05612\" ref-type=\"bibr\">40</xref>]. This capacity in turn depends on the ability to detect characteristics and changes in situations [<xref rid=\"B38-ijerph-17-05612\" ref-type=\"bibr\">38</xref>]. Monitoring conflicts is linked to the capacity for cognitive control, which facilitates assimilation and accommodation of conflict, and in turn, orientation toward specific objectives and resolution of potentially problematic or incongruent situations [<xref rid=\"B41-ijerph-17-05612\" ref-type=\"bibr\">41</xref>]. In a stressful situation, this could involve concentrating on information related to the threat and the one that leads to eliminating stressors, distancing oneself from nonessential information [<xref rid=\"B38-ijerph-17-05612\" ref-type=\"bibr\">38</xref>]. Thus, the mechanisms of control and conscious awareness enable detection and adaptation to situations in which information is conflictive [<xref rid=\"B42-ijerph-17-05612\" ref-type=\"bibr\">42</xref>].</p><p>The effects of awareness on the adaptability to change are mediated by perceived social support, which favors redefinition of stressful situations so they are not perceived as such or supply resources that enable the severity of such events to be reduced [<xref rid=\"B43-ijerph-17-05612\" ref-type=\"bibr\">43</xref>,<xref rid=\"B44-ijerph-17-05612\" ref-type=\"bibr\">44</xref>]. Thus, counting on strong perceived social support provides material sustenance and emotional comfort to people, in addition to helping them to reduce the negative evaluation of events, enabling them to alleviate distress and improve adaptation [<xref rid=\"B45-ijerph-17-05612\" ref-type=\"bibr\">45</xref>]. Along this line, the study by Koffer et al. [<xref rid=\"B26-ijerph-17-05612\" ref-type=\"bibr\">26</xref>] found that beliefs about control in stressful situations increase with age, postulating that this result could be due to the decrease in availability and efficacy of psychosocial resources.</p><p>Interest in knowing the capacity of individuals to adapt to change has led to studies seeking to establish the cognitive and emotional dimensions giving rise to this variable. However, analysis of the factors that enable success in new situations and unexpected changes in one’s environment have focused mainly on the job context [<xref rid=\"B46-ijerph-17-05612\" ref-type=\"bibr\">46</xref>,<xref rid=\"B47-ijerph-17-05612\" ref-type=\"bibr\">47</xref>]. Hedonic adaptation to important life events has also been analyzed [<xref rid=\"B48-ijerph-17-05612\" ref-type=\"bibr\">48</xref>], but not to changes in environmental demands with less transcendence in life than the birth of a child or the death of a family member. Thus, to date, the factors determining adaptability to everyday events and demands have not been established. Therefore, the following model was hypothesized as a starting point for the design and validation of an evaluation scale for adaptability to change. The factors included on it are those mentioned above, differentiating between those that pertain to the emotional dimension because of their repercussion on feelings experienced during adaptation (social support, anxiety, stress and tolerance to uncertainty) and those pertaining to the cognitive-behavioral dimension because of its involvement in management, control and action on it (that is, stress management, locus of control, state of alertness, coping, emotional management, cognitive flexibility and tolerance to uncertainty) (<xref ref-type=\"fig\" rid=\"ijerph-17-05612-f001\">Figure 1</xref>). The latter (tolerance to uncertainty) is included in both dimensions because it includes both emotional and cognitive responses [<xref rid=\"B29-ijerph-17-05612\" ref-type=\"bibr\">29</xref>].</p></sec><sec id=\"sec1dot3-ijerph-17-05612\"><title>1.3. Objective</title><p>There are many gaps in our knowledge of control, treatment or even socioeconomic effects derived from the COVID-19 pandemic [<xref rid=\"B49-ijerph-17-05612\" ref-type=\"bibr\">49</xref>]. Along with the strong perception of uncertainty and threat caused by the pandemic and by the new measures that must be adopted in daily life [<xref rid=\"B50-ijerph-17-05612\" ref-type=\"bibr\">50</xref>,<xref rid=\"B51-ijerph-17-05612\" ref-type=\"bibr\">51</xref>,<xref rid=\"B52-ijerph-17-05612\" ref-type=\"bibr\">52</xref>], they can affect behavioral efficacy and the capacity for management and coping [<xref rid=\"B53-ijerph-17-05612\" ref-type=\"bibr\">53</xref>]. Adaptability to change is fundamental to avoid psychological alterations linked to the accumulation of stressors [<xref rid=\"B15-ijerph-17-05612\" ref-type=\"bibr\">15</xref>], however, there is no instrument that specifically evaluates this capacity in the individual. Therefore, based on the model conceptualized above, and in view of its relevance for ensuring adjustment to change in daily scenarios, the objective of this study was to develop, evaluate and validate the Adaptation to Change Questionnaire.</p></sec></sec><sec id=\"sec2-ijerph-17-05612\"><title>2. Materials and Methods</title><sec id=\"sec2dot1-ijerph-17-05612\" sec-type=\"subjects\"><title>2.1. Participants</title><p>The sample was made up of 1168 adult Spaniards. The questionnaire included control questions for detecting random or incongruent answers, which led to the elimination of eight subjects, so that the final sample was comprised of 1160 people. The mean age of the sample was 38.29 years (standard deviation (SD) = 13.71) in a range of 18 to 82. Of these, 69.9% (<italic>n =</italic> 811) were women and 30.1% (<italic>n</italic> = 349) were men, with mean ages of 37.05 (SD = 13.34) and 41.16 years (SD = 14.14), respectively.</p></sec><sec id=\"sec2dot2-ijerph-17-05612\"><title>2.2. Instruments</title><p>The sociodemographic data were collected in an ad hoc questionnaire, which included items on age, sex, marital status and education.</p><p>The General Health Questionnaire-28 (GHQ-28) [<xref rid=\"B54-ijerph-17-05612\" ref-type=\"bibr\">54</xref>,<xref rid=\"B55-ijerph-17-05612\" ref-type=\"bibr\">55</xref>] was used for evaluating general health and related functional symptoms. This questionnaire consists of 28 items grouped in four subscales with seven items each: Subscale A (somatic symptoms), Subscale B (anxiety and insomnia), Subscale C (social dysfunction) and Subscale D (severe depression). Each question has four gradually worsening answer choices. The subject must mark the answer chosen based on recent weeks.</p><p>The Adaptation to Change Questionnaire (ADAPTA-10) was designed to evaluate an individual’s adaptability to the demands of novel situations. This questionnaire is made up of 17 items related to the individual’s disposition to achieve successful adjustment to unknown situations or events. It includes items linked to emotions of distress and anxiety that could appear when faced with changes or others related to the capacity for controlling, managing and acting in different situations, that is: social support, anxiety experienced, depression, stress management, awareness and state of alertness, coping concentrated on the problem, tolerance to uncertainty, emotion management, mental flexibility and locus of control. The answers are rated on a five-point Likert-type scale (from “not at all” to “very much”).</p></sec><sec id=\"sec2dot3-ijerph-17-05612\"><title>2.3. Procedure</title><p>This cross-sectional study was done with snowball sampling carried out on social networks and instant messaging during the seventh and eighth week of confinement of the Spanish population, specifically from 1 to 12 May 2020. The participants filled out the tests individually in a time estimated at 5–10 min.</p><p>The stages that led to the conceptualization and development of the ADAPTA-10 Questionnaire for evaluating adaptability to change are described below. The study was approved by the University of Almeria Ethics Committee (UALBIO2020/032, 06-25-2020). All the subjects in the study participated voluntarily and gave their written informed consent prior to filling out the questionnaire, after being informed of the objectives of the research and the anonymous nature of their answers. The data were collected and processed respecting all of the rights and guarantees as provided for in EU Regulation 2016/679 and Organic Law 3/2018 of 5 December on Protection of Personal Information and guarantee of digital rights.</p><p>The questionnaire was implemented as a CAWI (Computer Aided Web Interviewing) interview, in which the participants expressly gave their consent by marking a box for the purpose before going to the questionnaire screen.</p><p>The first step was an analysis of the scientific literature on the subject of adaptability to change in an adult population. Search machines were used to collect studies that could contribute to the development of the items on the questionnaire.</p><p>After the review of the literature on the subject, experts were consulted to evaluate a first proposal of possible constructs for the final repertoire of indicators. The result of this stage was a list of constructs which we took as the starting point to develop the content of the items: social support, stress management, alertness, coping, tolerance to uncertainty, emotion management, locus of control, cognitive flexibility, anxiety and depression. Following this, a specific search was made on measurement of each of the proposals.</p><p>The next step was to write the items, which were in first person because it was to be a self-informed questionnaire. To check the intelligibility and clarity of this first set of items, a pilot questionnaire was drafted and distributed to a sample of 30 subjects selected by snowball sampling, all of them adults over 18 years of age. Then, the content and wording of the items were reviewed considering their observations, making minor modifications to reduce the answer bias or misunderstanding.</p><p>The questionnaire was comprised of 17 items and the answers for each item were rated on a five-point Likert-type scale (1 = not at all, 2 = a little, 3 = somewhat, 4 = quite a lot, 5 = very much).</p><p>Finally, the questionnaire was validated by administering it to a representative sample of adults (see sample characteristics in the section on Participants). Although the scale was designed with several theoretical constructs as the basis, we could not determine any latent factor models until the measurement structure was proven statistically based on the original theoretical model proposed.</p></sec><sec id=\"sec2dot4-ijerph-17-05612\"><title>2.4. Data Analysis</title><p>Data were analyzed in two stages following the validation steps recommended by Pérez-Fuentes et al. [<xref rid=\"B56-ijerph-17-05612\" ref-type=\"bibr\">56</xref>]. The first stage dealt with the study of the structure according to the original theoretical basis of the Adaptation to Change Scale. To approach this objective, the sample was first divided at random into two homogeneous independent subsamples. The first sample was used for calibration (<italic>n</italic> = 578) in the exploratory (EFA) and confirmatory factor analyses (CFA) of the proposed theoretical Adaptation to Change model. The confirmatory factor analysis was done for the original model taking the following indices of fit as measures: χ2/df (Degrees of freedom), Comparative Fit Index (CFI), Tucker-Lewis index (TLI) and Root Mean Square Error of Approximation (RMSEA), with their confidence interval (CI) at 90%. Values below five were considered acceptable for the χ2/df index [<xref rid=\"B57-ijerph-17-05612\" ref-type=\"bibr\">57</xref>] for the CFI and TLI over or near 0.90, and for the RMSEA, below or very near 0.08 [<xref rid=\"B58-ijerph-17-05612\" ref-type=\"bibr\">58</xref>]. As a general rule, fit of the model is considered to be good when the χ2/df ≤ 3, TLI > 0.90, CFI > 0.95 and RMSEA ≤ 0.05. The appropriate re-specifications were made of the model, which showed good indices of fit, considering theoretical and statistical criteria (change index, error of estimation, standardized error of measurement), but the model was not improved. The Akaike Information Criteria [<xref rid=\"B59-ijerph-17-05612\" ref-type=\"bibr\">59</xref>] was used for model selection. Then, the re-specified model was validated based on the second subsample (<italic>n</italic> = 583), used as the validation sample. The Cronbach’s Alpha [<xref rid=\"B60-ijerph-17-05612\" ref-type=\"bibr\">60</xref>], Spearman-Brown and intraclass correlation coefficient were used for the reliability analysis of the new scale.</p><p>Finally, in the second stage, an analysis was done that supports the invariance of the factor structure proposed across sex (men/women). First, the goodness of fit of these structures was tested in both subsamples separately (Model M0a—Men and Model M0b—Women). The result was four nested models which were evaluated: (A) Model 1: both samples together simultaneously with free estimation of the parameters, (B) Model 2: metric invariance shown, (C) Model 3: scalar invariance shown, (D) Model 4: strict invariance. With no criterion of consensus to determine the criteria to be used to evaluate the difference in fit between the nested models [<xref rid=\"B61-ijerph-17-05612\" ref-type=\"bibr\">61</xref>], for evaluation of fit, this study used the ΔCFI. Thus, the model is completely invariant if the ΔCFI is below 0.01 [<xref rid=\"B62-ijerph-17-05612\" ref-type=\"bibr\">62</xref>]. Similarly, the validity of the construct was evaluated by analyzing the correlation of the items and factors with other instruments that measure related aspects.</p><p>The analyses were performed using the e SPSS Statistical Package, version 23.0, for Windows (IBM, Armonk, NY, USA) and the AMOS 22 Program (IBM, Chicago, IL, USA).</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05612\"><title>3. Results</title><sec id=\"sec3dot1-ijerph-17-05612\"><title>3.1. Preliminary Analyses </title><p>In the first place, the data show that the items in the original ADAPTA-17GF (general factor) model have a distribution within the limits of normality according to the criteria of Finney and DiStefano [<xref rid=\"B63-ijerph-17-05612\" ref-type=\"bibr\">63</xref>], for whom 2 and 7 are the maximums permitted for skewness and kurtosis, which in our case were 1.28 and 2.74, respectively (<xref rid=\"ijerph-17-05612-t001\" ref-type=\"table\">Table 1</xref>).</p></sec><sec id=\"sec3dot2-ijerph-17-05612\"><title>3.2. Confirmatory Factor Analysis of the Original Model</title><p><xref rid=\"ijerph-17-05612-t002\" ref-type=\"table\">Table 2</xref> shows the fit of the various questionnaire models according to the original ADAPTA 17 model (with a general adaptation factor and two other factors: emotional and cognitive-behavioral). This model was re-specified considering theoretical and statistical criteria (indices of change, errors of estimation, standardized errors of measurement).</p><p>It may be observed that both the original 17-item model and the 12-item model show values that could be improved. The two-factor model with a general adaptation factor and 10 items is the best one after analysis. Thus, the ADAPTA-10GF Model showed much better fit in the calibration sample. There is also a smaller difference between the AIC default model = 141,996 and the AIC Saturated model = 110,000, showing that it is probably the best model according to the Akaike model selection criteria.</p></sec><sec id=\"sec3dot3-ijerph-17-05612\"><title>3.3. Exploratory Factor Analysis of the ADAPTA-10GF Model</title><p>The Principal Components Analysis revealed the existence of two components with eigenvalues over 1. The Scree Test showed the presence of two factors (<xref ref-type=\"fig\" rid=\"ijerph-17-05612-f002\">Figure 2</xref>). Thus, we see in that in <xref rid=\"ijerph-17-05612-t003\" ref-type=\"table\">Table 3</xref>, there are two components corresponding to the Emotional Component and the Cognitive-Behavioral Component in the original model, with five items each, all with weights over 0.65, and explaining 59.55% of the variance (<xref rid=\"ijerph-17-05612-t003\" ref-type=\"table\">Table 3</xref>).</p><p>Reliability of the model was analyzed with the Spearman-Brown coefficient <italic>p</italic> = 0.73 and the Cronbach’s Alpha, which for the whole scale was α = 0.84. The intraclass correlation coefficient (ICC) and its confidence interval (CI) were used for the analysis of temporal stability, with the following results for adaptation to change: 0.84 (CI = 0.82–0.86).</p><p>Confirmatory Factor Analysis data for the model proposed (<xref ref-type=\"fig\" rid=\"ijerph-17-05612-f003\">Figure 3</xref>) with the validation sample (<italic>n</italic> = 583) showed the following measures of fit: <italic>χ<sup>2</sup>/df =</italic> 3.21, CFI = 0.970, TLI = 0.956 and RMSEA= 0.062 (0.048–0.076), which were all adequate.</p><p>The values in <xref rid=\"ijerph-17-05612-t004\" ref-type=\"table\">Table 4</xref> for the six different models in the analysis of variance across sex show that in all cases, the ΔCFI is less than 0.01, and therefore, configural, metric, strict and strong invariance are accepted.</p><p>With regard to construct validity, <xref ref-type=\"fig\" rid=\"ijerph-17-05612-f004\">Figure 4</xref> shows that the correlations in the direct scores on the GHQ-28 health questionnaires and the ADAPTA-10 questionnaire are significant (<italic>p</italic> < 0.01) and negative in all cases, backing the validity of the ADAPTA-10 construct. A higher score on the GHQ-28 shows more problems in each of the health dimensions.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05612\"><title>4. Discussion</title><p>Adaptation to change is an important concept in psychology, as it depends on the adjustment of functioning and the responses with which one copes with the diversity of environmental demands [<xref rid=\"B9-ijerph-17-05612\" ref-type=\"bibr\">9</xref>,<xref rid=\"B17-ijerph-17-05612\" ref-type=\"bibr\">17</xref>]. Its absence has been related to psychological alterations [<xref rid=\"B18-ijerph-17-05612\" ref-type=\"bibr\">18</xref>]. Given the speed with which daily scenarios vary and the number of novel demands which must be coped with in short periods of time, knowing the effects of the capacity of adaptation to change of the population may be beneficial to both immediate and long-term psychological health [<xref rid=\"B6-ijerph-17-05612\" ref-type=\"bibr\">6</xref>]. In this respect, models have been proposed to establish the dimensions and factors that intervene in the process of adaptation to change, but linked to transcendental life events (such as the appearance of a disability, birth of a child or death of a spouse) or employment demands [<xref rid=\"B46-ijerph-17-05612\" ref-type=\"bibr\">46</xref>,<xref rid=\"B47-ijerph-17-05612\" ref-type=\"bibr\">47</xref>]. This study proposed construction of a model of adaptation to change in everyday events and circumstances, which would enable a scale to be designed for evaluating the response to challenges and changing circumstances.</p><p>In the validation of the Adaptation to Change Questionnaire, ADAPTA-10, in the general population, the exploratory and confirmatory factor analyses showed the existence of the two dimensions previously found in the original model: emotional (linked to feelings that arise during adaptation) and cognitive-behavioral (related to cognitive management and behaviors for that purpose). Based on the factors analyzed and according to Bjorklund [<xref rid=\"B9-ijerph-17-05612\" ref-type=\"bibr\">9</xref>], it seems that the capacity for adapting to change includes both types of response, which would be in line with the model that showed the best fit. However, although the index of this two-factor model was adequate, after performing the corresponding re-specifications following theoretical and statistical criteria, seven items were eliminated from different factors in the original model. Specifically, items pertaining to the social support, stress management, locus of control and flexibility items were eliminated. In social support, the item eliminated may have been related, as mentioned by Kim et al. [<xref rid=\"B43-ijerph-17-05612\" ref-type=\"bibr\">43</xref>], with support being a mediator in the individual adaptation process, positively promoting one’s resources to cope with challenges, but not as a factor directly involved in this capacity. With regard to the stress management item, it may not be part of the validation process, since in situations in which the response must be rapidly modified or adjusted, a certain level of stress can eliminate lethargy or paralysis and generate the drive necessary to make the appropriate modifications. Furthermore, as items referring to emotional management were entered, negative thoughts and feelings that could arise with the appearance of stress (such as anxiety, irritability and so forth) and diminish the capacity for adaptation, could have been covered by that factor. Concerning the locus of control, even though items related to internal and external control which could diminish the capacity for adaptation were included, they did not form part of the final model. This may have been due to the perception of control and cause of events, although generating a stronger feeling of capacity for managing situations [<xref rid=\"B22-ijerph-17-05612\" ref-type=\"bibr\">22</xref>,<xref rid=\"B26-ijerph-17-05612\" ref-type=\"bibr\">26</xref>] may not be directly related to one’s possibility to adapt. That is, the capacity for adjusting to daily situations may be independent of the control that one feels one has over them. Finally, the item referring to cognitive flexibility was also eliminated from the two-factor, ten-item model. This result may be due to its being a relevant factor or variable with a heavy weight which acts as a mediator in the adaptation process but does not affect it directly. However, due to the wide presence in the scientific literature of this factor as the one which provides the most possibility of modifying strategies and reactions to meet changing demands to ensure adjustment [<xref rid=\"B37-ijerph-17-05612\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijerph-17-05612\" ref-type=\"bibr\">38</xref>,<xref rid=\"B39-ijerph-17-05612\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijerph-17-05612\" ref-type=\"bibr\">40</xref>], this relationship must be reexamined in the future.</p><p>Therefore, based on the results, two dimensions were extracted from the ADAPTA-10 questionnaire. The first of these, the emotional dimension, would be linked to feelings experienced during adaptation. The five items that form part of this dimension pertain to the anxiety, depression and tolerance to uncertainty factors. Studies have shown the presence of anxiety symptoms, such as agitation from the need for adaptation to new demands [<xref rid=\"B30-ijerph-17-05612\" ref-type=\"bibr\">30</xref>]. Feelings of dysphoria and depressive symptoms are also present when the challenges one is faced with put the capacity to cope effectively with them to the test [<xref rid=\"B36-ijerph-17-05612\" ref-type=\"bibr\">36</xref>]. The cognitive-behavioral dimension refers to competence for managing and undertaking action to respond appropriately to daily situations that can be challenging. State of alertness enables conflictive situations to be detected and directs one’s attention toward specific objectives that must be met or problems that must be solved [<xref rid=\"B41-ijerph-17-05612\" ref-type=\"bibr\">41</xref>]. Coping concentrated on the problem means that efforts made are directly related to modifying the situation or its meaning, enabling its functional and adaptive management [<xref rid=\"B24-ijerph-17-05612\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijerph-17-05612\" ref-type=\"bibr\">25</xref>]. The emotional management factor may facilitate the regulation of negative feelings that appear because of the uncertainty, threat or perceived inability [<xref rid=\"B35-ijerph-17-05612\" ref-type=\"bibr\">35</xref>]. In the end, tolerance to uncertainty, as mentioned, formed part of the two dimensions through two different items. These referred to the emotional reaction and behavior in situations where one does not have all the information [<xref rid=\"B29-ijerph-17-05612\" ref-type=\"bibr\">29</xref>].</p><p>Thus, the Adaptation to Change Questionnaire, ADAPTA-10, is a short instrument, easily applied, which enables finding out the individual’s ability to adjust the best way possible to new demands based on two dimensions. Even so, there are some limitations. It should be mentioned that most of the sample was made up of women, although the questionnaire showed good invariance across sex, and could be reflecting populational characteristics in Spain. Another limitation derived from the way data were collected, as the mean age was low with respect to the reality of the Spanish population, since fewer older people use the new technology tools with which the questionnaire was publicized and data were collected. In future, when the health situation so permits, these age groups should be approached to include more such subjects, although as observed in the section on participants, older people also answered correctly. Future research could validate our findings even more through the use of a more general sample. Another of the limitations is derived from the study design, because, as a cross-sectional study, there were variables which could not be controlled. The performance of a longitudinal study would solve this limitation by evaluating longitudinal invariance of the questionnaire.</p><p>Although this is not a tool specific to COVID-19, it is a contextualized tool, so it would be necessary to analyze it again when the special situation of the health emergency ends. Meanwhile, its use along with other instruments evaluating psychological variables in the context of the COVID-19 pandemic can have very useful clinical applications. Evaluation of threat [<xref rid=\"B52-ijerph-17-05612\" ref-type=\"bibr\">52</xref>] or perceived risk from COVID-19 [<xref rid=\"B64-ijerph-17-05612\" ref-type=\"bibr\">64</xref>,<xref rid=\"B65-ijerph-17-05612\" ref-type=\"bibr\">65</xref>,<xref rid=\"B66-ijerph-17-05612\" ref-type=\"bibr\">66</xref>], combined with the capacity for adaptation to change, can help develop risk profiles and mental health protection measures in the mid- to long-term.</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05612\"><title>5. Conclusions</title><p>The Adaptation to Change Questionnaire, ADAPTA-10, for the general population possesses favorable psychometric properties. The internal consistency of both the total scale and the two factors (emotional and cognitive-behavioral) is adequate, and therefore, the general fit is acceptable. However, it is recommended that the goodness and fit of the model for testing the psychometric properties of the instrument continue to be analyzed in other specific collectives or contexts. The construction of this scale can contribute to the analysis of the consequences associated with the presence of low adaptability to change. The analysis of this construct emerged during the pandemic from the SARS-CoV-2 coronavirus, which has been mentioned by various authors as both a physical and psychological health emergency due to the high impact of the illness on people’s daily lives. This is because, to a greater or lesser extent, everyone must adapt to a highly changing environment. The absence of the capacity to recover one’s previous state of wellbeing in transcendental life circumstances has shown to have long-term psychological effects. This scale can provide further knowledge of this ability and its repercussions in uncertain everyday situations, not necessarily linked to such events. It can also be valid for establishing the level of this variable in individuals, enabling development of intervention programs to strengthen adaptability, and thereby, promote better adjustment to demands. Therefore, the psychometric indicators, both for the factors and the global scale, reveal that it is a reliable, valid measurement instrument for use in research. Likewise, it is thought that it can be of maximum usefulness for the prevention and early diagnosis of problems related to mental health (such as depression, anxiety, development of health-risk behaviors or use of maladaptive coping strategies) in the general population derived from poor adaptation to adverse situations, similar to those triggered by the COVID-19 pandemic.</p></sec></body><back><ack><title>Acknowledgments</title><p>The present study was undertaken in collaboration with Excma, Diputación Provincial de Almería.</p></ack><notes><title>Author Contributions</title><p>M.d.C.P.-F., M.d.M.M.J. and Á.M.M. contributed to the concept, design, analysis and interpretation of the data. M.d.M.S.M. contributed to the technical details and manuscript preparation. E.F.-M., R.F.V., I.H.-P., D.J.-R., I.M.M. and A.S.G. contributed to collecting the data. J.J.G.L. contributed to critically revising the manuscript for important intellectual content and the final approval of the version to be published. All authors accept and agree that the work is original, any methods and data presented are described accurately and honestly and any relevant interests have been disclosed. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research received no external funding.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijerph-17-05612\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Lai</surname><given-names>C.C.</given-names></name><name><surname>Shih</surname><given-names>T.P.</given-names></name><name><surname>Ko</surname><given-names>W.C.</given-names></name><name><surname>Tang</surname><given-names>H.J.</given-names></name><name><surname>Hsueh</surname><given-names>P.R.</given-names></name></person-group><article-title>Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): The epidemic and the challenges</article-title><source>Int. 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J. Ment. Health Addict.</source><year>2020</year><fpage>1</fpage><lpage>9</lpage><pub-id pub-id-type=\"doi\">10.1007/s11469-020-00270-8</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijerph-17-05612-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Theoretical explanatory model of the adaptability to change construct.</p></caption><graphic xlink:href=\"ijerph-17-05612-g001\"/></fig><fig id=\"ijerph-17-05612-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Scree plot of factor analysis for the ADAPTA-10 GF Model.</p></caption><graphic xlink:href=\"ijerph-17-05612-g002\"/></fig><fig id=\"ijerph-17-05612-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Confirmatory factor analysis ADAPTA-10GF Model (<italic>N</italic> = 583).</p></caption><graphic xlink:href=\"ijerph-17-05612-g003\"/></fig><fig id=\"ijerph-17-05612-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>ADAPTA-10 questionnaire correlations and General Health Questionnaire-28.</p></caption><graphic xlink:href=\"ijerph-17-05612-g004\"/></fig><table-wrap id=\"ijerph-17-05612-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05612-t001_Table 1</object-id><label>Table 1</label><caption><p>Descriptive statistics. Calibration sample (<italic>n</italic> = 578). SD: standard deviation, Std. Error: standard error.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Items</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">\n<italic>n</italic>\n</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">M</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">SD</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Skewness</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Kurtosis</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Statistics</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Std. Error</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Statistic</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Std. Error</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ADAPTA 1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">578</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.83</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.21</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.41</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.20</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ADAPTA 2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">578</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.00</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.19</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.03</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.85</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.20</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ADAPTA 3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">578</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.95</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.05</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.87</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.20</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ADAPTA 4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">578</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.22</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.26</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.22</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.95</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.20</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ADAPTA 5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">578</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.57</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.44</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.78</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.20</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ADAPTA 6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">578</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.00</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.01</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.90</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.24</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.20</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ADAPTA 7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">578</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.73</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.28</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.24</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.94</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.20</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ADAPTA 8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">578</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.75</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.07</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.79</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.17</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.20</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ADAPTA 9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">578</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.18</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.22</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.80</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.20</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ADAPTA 10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">578</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.59</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.94</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.41</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.20</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ADAPTA 11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">578</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.66</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.02</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.64</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.00</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.20</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ADAPTA 12</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">578</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.30</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.73</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.16</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.34</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.20</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ADAPTA 13</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">578</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.30</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.75</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.28</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.74</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.20</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ADAPTA 14</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">578</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.65</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.04</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.79</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.19</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.20</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ADAPTA 15</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">578</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.06</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.93</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.09</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.07</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.20</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ADAPTA 16</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">578</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.01</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.83</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.83</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.95</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.20</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ADAPTA 17</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">578</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.54</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.94</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.44</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.10</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.09</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.20</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05612-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05612-t002_Table 2</object-id><label>Table 2</label><caption><p>Fit indices for the models proposed (calibration sample <italic>n</italic> = 578).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Model</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">CFI</th><th rowspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">TLI</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">RMSEA</th></tr><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">χ<sup>2</sup> (df)</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" colspan=\"1\">χ<sup>2</sup>/df</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">RMSEA</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">CI90%</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Lower</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\">Upper</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Original ADAPTA-17GF Model</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">690,331 (118)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.85</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.801</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.770</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.092</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.085</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.098</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ADAPTA-12GF Model</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">432,200 (53)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.154</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.846</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.808</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.111</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.102</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.126</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ADAPTA-10GF Model</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">91,996 (30)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.066</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.969</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.954</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.06</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.046</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.074</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ADAPTA-10 Model</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">106,175 (31)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.425</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.963</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.946</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.065</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.052</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.079</td></tr></tbody></table><table-wrap-foot><fn><p>CFI = Comparative fit index; TLI = Tucker-Lewis index; RMR = Root mean square residual; RMSEA = Root Mean Square Error of Approximation; CI = Confidence Interval; <italic>df</italic> = Degrees of freedom; Est. = Estimation.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05612-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05612-t003_Table 3</object-id><label>Table 3</label><caption><p>Factor structure, communalities (<italic>h<sup>2</sup></italic>) eigenvalues, Cronbach’s alpha and percentage of explained variance (<italic>n</italic> = 583). Extraction method: Factoring of principal components.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Items</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">F1</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">F2</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>h<sup>2</sup></italic>\n</th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Item 1. I feel nervous, tense and irritable</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.867</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.752</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Item 2. I am worried and it’s hard for me to relax</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.887</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.794</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Item 3. I feel like I don’t have enough energy to cope with everyday life</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.824</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.678</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Item 4. I’ve lost hope of recovering my normal life</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.676</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.458</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Item 5. I’m calm, but I don’t know what is going to happen at any moment</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.671</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.432</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.498</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Item 6. I can act in any situation, even though I don’t have all the information</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.695</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.509</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Item 7. I consider myself smart, I am aware of what is happening around me</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.774</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.606</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Item 8. When I have a problem, I make an effort to solve it</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.773</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.604</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Item 9. I recognize my emotions, those of others and act accordingly</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.756</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.575</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Item 10. I control my emotions when I think they could make things worse for me</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.446</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.652</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.482</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Percentage of explained variance</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">41.08%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.46%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Kaiser-Meyer-Olkin</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.85</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Barlett’s sphericity</td><td colspan=\"3\" align=\"center\" valign=\"middle\" rowspan=\"1\">χ<sup>2</sup>(45) = 2276.17, <italic>p</italic> < 0.000</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cronbach’s Alpha</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.85</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.78</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.84</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05612-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05612-t004_Table 4</object-id><label>Table 4</label><caption><p>Multigroup analysis of invariance across sex (men/women).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Model</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">χ<sup>2</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">df</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">χ<sup>2</sup> / df</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Δχ<sup>2</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CFI</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">ΔCFI</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">IFI</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">RMSEA (IC 90%)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M0a (men)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">124.169 (<italic>p</italic> = 0.000)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.069</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.971</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.971</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.043 (0.032–0.054)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M0b (women)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">124.169 (<italic>p</italic> = 0.000)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.069</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.971</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.971</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.043 (0.032–0.054)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M1 (base model)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">138.387 (<italic>p</italic> = 0.000)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">68</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.035</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.034</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.968</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.968</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.042 (0.032–0.052)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M2 (FS)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">139.905 (<italic>p</italic> = 0.000)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">69</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.027</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.008</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.968</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.968</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0. 042 (0.032–0.052)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M3 (FS + Int)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">141.494 (<italic>p</italic> = 0.000)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">71</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.993</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.034</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.968</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.968</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0. 042 (0.032–0.052)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">M4 (FS + Int + Err)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">156.330 (<italic>p</italic> = 0.000)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">85</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.839</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.154</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.968</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.968</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0. 041 (0.031–0.051)</td></tr></tbody></table><table-wrap-foot><fn><p>FS = Factor saturations, Int = Intercepts, Err = Errors.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751702</article-id><article-id pub-id-type=\"pmc\">PMC7432047</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155452</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05452</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Review</subject></subj-group></article-categories><title-group><article-title>Molecular Basis of Mitochondrial and Peroxisomal Division Machineries</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Imoto</surname><given-names>Yuuta</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05452\">1</xref><xref rid=\"c1-ijms-21-05452\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Itoh</surname><given-names>Kie</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05452\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Fujiki</surname><given-names>Yukio</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05452\">2</xref><xref ref-type=\"aff\" rid=\"af3-ijms-21-05452\">3</xref><xref rid=\"c1-ijms-21-05452\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05452\"><label>1</label>Department of Cell Biology, Johns Hopkins University School of Medicine, 725 N. Wolfe Street, Baltimore, MD 21205, USA; <email>kito5@jhmi.edu</email></aff><aff id=\"af2-ijms-21-05452\"><label>2</label>Division of Organelle Homeostasis, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan</aff><aff id=\"af3-ijms-21-05452\"><label>3</label>Institute of Rheological Functions of Food, Hisayama-cho, Fukuoka 811-2501, Japan</aff><author-notes><corresp id=\"c1-ijms-21-05452\"><label>*</label>Correspondence: <email>yimoto1@jhmi.edu</email> (Y.I.); <email>yfujiki@kyudai.jp</email> (Y.F.)</corresp></author-notes><pub-date pub-type=\"epub\"><day>30</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5452</elocation-id><history><date date-type=\"received\"><day>30</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>28</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Mitochondria and peroxisomes are ubiquitous subcellular organelles that are highly dynamic and possess a high degree of plasticity. These organelles proliferate through division of pre-existing organelles. Studies on yeast, mammalian cells, and unicellular algae have led to a surprising finding that mitochondria and peroxisomes share the components of their division machineries. At the heart of the mitochondrial and peroxisomal division machineries is a GTPase dynamin-like protein, Dnm1/Drp1, which forms a contractile ring around the neck of the dividing organelles. During division, Dnm1/Drp1 functions as a motor protein and constricts the membrane. This mechanochemical work is achieved by utilizing energy from GTP hydrolysis. Over the last two decades, studies have focused on the structure and assembly of Dnm1/Drp1 molecules around the neck. However, the regulation of GTP during the division of mitochondrion and peroxisome is not well understood. Here, we review the current understanding of Dnm1/Drp1-mediated divisions of mitochondria and peroxisomes, exploring the mechanisms of GTP regulation during the Dnm1/Drp1 function, and provide new perspectives on their potential contribution to mitochondrial and peroxisomal biogenesis.</p></abstract><kwd-group><kwd>mitochondrial division</kwd><kwd>peroxisomal division</kwd><kwd>dynamin-related protein Dnm1/Drp1</kwd><kwd>nucleoside-diphosphate kinase</kwd><kwd>local GTP generation</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05452\"><title>1. Introduction</title><p>Mitochondria and peroxisomes play vital roles in cellular metabolism. Double-membrane-bounded mitochondria contain their own DNA derived from an endosymbiotic ancestor and are the powerhouse of eukaryotic cells, playing roles in the regulation of the cellular redox state, Ca<sup>2+</sup> homeostasis, and apoptosis [<xref rid=\"B1-ijms-21-05452\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijms-21-05452\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijms-21-05452\" ref-type=\"bibr\">3</xref>]. Single-membrane-bounded peroxisomes were originally defined as a carrier of flavin-oxidases, producing H<sub>2</sub>O<sub>2</sub> and catalase [<xref rid=\"B4-ijms-21-05452\" ref-type=\"bibr\">4</xref>]. Subsequently, peroxisomes were found to exert important metabolic functions in lipid homeostasis [<xref rid=\"B5-ijms-21-05452\" ref-type=\"bibr\">5</xref>]. Mitochondria and peroxisomes are metabolically linked organelles and share matrix enzyme activities, including fatty-acid β-oxidation enzymes [<xref rid=\"B6-ijms-21-05452\" ref-type=\"bibr\">6</xref>]. Since mitochondria and peroxisomes share functions, their morphogenesis and number need to be coordinated. Importantly, the number of both organelles is maintained by the division of pre-existing organelles using the same division machinery [<xref rid=\"B7-ijms-21-05452\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijms-21-05452\" ref-type=\"bibr\">8</xref>]. A range of pathological conditions, including cancer, aging, neurodegeneration and metabolic diseases, are associated with disorders in mitochondrial and peroxisomal division [<xref rid=\"B9-ijms-21-05452\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijms-21-05452\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijms-21-05452\" ref-type=\"bibr\">11</xref>]. Thereby, the divisions of mitochondria and peroxisomes have been studied extensively over the last two decades with various model systems including yeast, mammals, and algae. There are two important findings in this field. (1) The identification of dynamin-like protein Dnm1/Drp1 (Dnm1 in yeast and algae; Drp1, DRP1, or DLP1 in mammals), which catalyzes the membrane fission of mitochondria [<xref rid=\"B12-ijms-21-05452\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijms-21-05452\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijms-21-05452\" ref-type=\"bibr\">14</xref>] and peroxisomes [<xref rid=\"B7-ijms-21-05452\" ref-type=\"bibr\">7</xref>] in yeast and mammalian cells as a model system. Dnm1/Drp1 is a dynamin family member as well as a classical dynamin involved in the endocytosis [<xref rid=\"B15-ijms-21-05452\" ref-type=\"bibr\">15</xref>]. Similar to classical dynamin, Dnm1/Drp1 contains a GTPase domain (G-domain) and forms a helical polymer to constrict the membrane tubules upon binding to and hydrolyzing GTP [<xref rid=\"B16-ijms-21-05452\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijms-21-05452\" ref-type=\"bibr\">17</xref>]. (2) The identification of an electron-dense “ring” around the neck of the dividing organelles. This electron-dense ring was first identified around the neck of dividing mitochondrion in a unicellular alga, <italic>Cyanidioschyzon merolae</italic> [<xref rid=\"B18-ijms-21-05452\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijms-21-05452\" ref-type=\"bibr\">19</xref>]. It was also observed later around the neck of a dividing peroxisome [<xref rid=\"B20-ijms-21-05452\" ref-type=\"bibr\">20</xref>]. A similar electron-dense neck has also been observed around the constriction site of mitochondrion in yeast [<xref rid=\"B16-ijms-21-05452\" ref-type=\"bibr\">16</xref>] and mammalian cells [<xref rid=\"B21-ijms-21-05452\" ref-type=\"bibr\">21</xref>]. These findings raise a view that mitochondria and peroxisomes divide via the constriction of the ring-shaped division machinery composed of Dnm1/Drp1 [<xref rid=\"B22-ijms-21-05452\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05452\" ref-type=\"bibr\">23</xref>]. Research in this field has previously focused on the receptor-mediated recruitment of the Dnm1/Drp1-based division machinery to membrane fission sites [<xref rid=\"B24-ijms-21-05452\" ref-type=\"bibr\">24</xref>]. In addition to exploring of the function of Dnm1/Drp1 receptors, a recent study on <italic>C. merolae</italic> demonstrated that the energy source of Dnm1/Drp1, GTP is spatio-temporally regulated during the divisions of mitochondrion and peroxisome, raising an idea that the energy source for the organelle division machinery is locally generated [<xref rid=\"B25-ijms-21-05452\" ref-type=\"bibr\">25</xref>]. In this review, we briefly compile the current knowledge about membrane remodeling of mitochondria and peroxisomes in yeast, algae, and mammalian cells. We also address molecular mechanisms underlying the energetic regulation of Dnm1/Drp1-based machineries, in regards to mitochondrial and peroxisomal division.</p></sec><sec id=\"sec2-ijms-21-05452\"><title>2. Mitochondrial Dynamics</title><sec id=\"sec2dot1-ijms-21-05452\"><title>2.1. In Yeast and Mammals</title><p>In yeast and mammalian cells, membrane fission and fusion are responsible for maintaining the morphology and number of mitochondria [<xref rid=\"B26-ijms-21-05452\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijms-21-05452\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05452\" ref-type=\"bibr\">28</xref>]. The core component of the mitochondrial fission machinery is Dnm1/Drp1 that was first identified as Dnm1 by yeast mutagenesis [<xref rid=\"B12-ijms-21-05452\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijms-21-05452\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijms-21-05452\" ref-type=\"bibr\">14</xref>]. Dnm1 is a dynamin-related GTPase, which is recruited to the mitochondrial outer membrane (MOM), where it self-assembles via GTP binding followed by membrane constriction (<xref ref-type=\"fig\" rid=\"ijms-21-05452-f001\">Figure 1</xref>A). Loss of Dnm1 results in long interconnected mitochondrial networks [<xref rid=\"B13-ijms-21-05452\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijms-21-05452\" ref-type=\"bibr\">14</xref>]. Receptors with transmembrane domains are involved in recruitment of Dnm1 to the MOM. Genetics studies in yeast identified the receptor proteins of Dnm1, namely mitochondrial fission 1 protein (Fis1) [<xref rid=\"B29-ijms-21-05452\" ref-type=\"bibr\">29</xref>], mitochondrial division protein 1 (Mdv1) [<xref rid=\"B30-ijms-21-05452\" ref-type=\"bibr\">30</xref>], and CCR4-associated factor 4 (Caf4) [<xref rid=\"B31-ijms-21-05452\" ref-type=\"bibr\">31</xref>]. In mammals, a Dnm1 ortholog, Drp1 [<xref rid=\"B32-ijms-21-05452\" ref-type=\"bibr\">32</xref>], and its receptor protein, Fis1, have been identified [<xref rid=\"B21-ijms-21-05452\" ref-type=\"bibr\">21</xref>]. However, it has been reported that Fis1 is dispensable for Drp1-mediated mitochondrial fission in mammalian cells [<xref rid=\"B33-ijms-21-05452\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijms-21-05452\" ref-type=\"bibr\">34</xref>] and important under a specific physiological condition, such as stress-induced mitophagy [<xref rid=\"B35-ijms-21-05452\" ref-type=\"bibr\">35</xref>]. More recent studies have shown that Fis1 negatively regulates mitochondrial fusion [<xref rid=\"B36-ijms-21-05452\" ref-type=\"bibr\">36</xref>]. As such, Fis1 may exert functions other than in the recruitment of Drp1 in mammalian cells. Mdv1 and Caf4 are not conserved in mammals [<xref rid=\"B37-ijms-21-05452\" ref-type=\"bibr\">37</xref>]. Instead, recruitment of Drp1 likely depends on other receptors, such as mitochondrial fission factor (Mff) [<xref rid=\"B33-ijms-21-05452\" ref-type=\"bibr\">33</xref>], which was originally identified by the small interfering RNAscreening of the cultured <italic>Drosophila melanogaster</italic> cells [<xref rid=\"B38-ijms-21-05452\" ref-type=\"bibr\">38</xref>], and mitochondrial dynamics proteins of 49 kDa (MiD49) and 51 kDa/mitochondrial elongation factor 1 (MiD51/MIEF1) conserved in vertebrates [<xref rid=\"B39-ijms-21-05452\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijms-21-05452\" ref-type=\"bibr\">40</xref>]. In addition, the mitochondrial distribution and morphology protein 36 (Mdm36), and a cortical protein, nuclear migration protein 1 (Num1), are also known to regulate mitochondrial fission and distribution in yeast [<xref rid=\"B41-ijms-21-05452\" ref-type=\"bibr\">41</xref>,<xref rid=\"B42-ijms-21-05452\" ref-type=\"bibr\">42</xref>]. Mammalian orthologs of Mdm36 and Num1 have not yet been identified.</p><p>Mitochondrial fusion is also regulated by dynamin-related proteins. The first identified mitochondrial fusion gene is <italic>fuzzy onions</italic> (<italic>fzo1</italic>) in <italic>D. melanogaster</italic> [<xref rid=\"B43-ijms-21-05452\" ref-type=\"bibr\">43</xref>]. Fzo1 is a transmembrane protein with its GTPase domain that is exposed to the cytoplasm. A molecular genetic study using yeast found that <italic>fzo1</italic> mutation causes a fragmented mitochondrial phenotype as a result of blocking the fusion of MOM [<xref rid=\"B44-ijms-21-05452\" ref-type=\"bibr\">44</xref>]. <italic>Fzo1</italic> and <italic>Dnm1</italic> double-mutations alter this fragmented phenotype to wild-type mitochondrial morphology, indicating that the balance between fission and fusion plays an important role in the morphology of mitochondria [<xref rid=\"B14-ijms-21-05452\" ref-type=\"bibr\">14</xref>]. In yeast, the fusion of mitochondrial inner membrane (MIM) is regulated by a dynamin-related GTPase, Mgm1 [<xref rid=\"B45-ijms-21-05452\" ref-type=\"bibr\">45</xref>]. In mammalian cells, the fusion of MOM is regulated by Fzo1 orthologs, namely mitofusins 1 and 2 (Mfn1 and Mfn2) [<xref rid=\"B46-ijms-21-05452\" ref-type=\"bibr\">46</xref>], and MIM is regulated by the Mgm1 ortholog optic atrophy-1 (OPA-1) [<xref rid=\"B47-ijms-21-05452\" ref-type=\"bibr\">47</xref>,<xref rid=\"B48-ijms-21-05452\" ref-type=\"bibr\">48</xref>].</p><p>In the initiation of mitochondrial dynamics, the endoplasmic reticulum (ER) plays an important role. During mitochondrial division, ER tubules encircle and constrict mitochondrial tubules prior to the recruitment of Dnm1/Drp1 to the mitochondria [<xref rid=\"B49-ijms-21-05452\" ref-type=\"bibr\">49</xref>]. At the ER-marked mitochondrial division site, an ER-associated formin, INF2, facilitates the polymerization of actin to generate small patches of the actin–myosin II cytoskeleton [<xref rid=\"B50-ijms-21-05452\" ref-type=\"bibr\">50</xref>,<xref rid=\"B51-ijms-21-05452\" ref-type=\"bibr\">51</xref>,<xref rid=\"B52-ijms-21-05452\" ref-type=\"bibr\">52</xref>]. Since Mff functions via its affinity to membrane curvature and recruits Drp1 in mammalian cells, it is proposed that the ER tubules and actin cytoskeleton trigger a mechanical force that recruits Mff and Drp1 [<xref rid=\"B53-ijms-21-05452\" ref-type=\"bibr\">53</xref>]. Interestingly, the Drp1 oligomers, Mff and Fis1, are located on the ER membrane as puncta, and these fission components are transferred to mitochondrial division site upon the ER–mitochondria contact site [<xref rid=\"B54-ijms-21-05452\" ref-type=\"bibr\">54</xref>]. Therefore, the ER most likely serves as both an initiator of mitochondrial division and a platform of mitochondrial fission machinery. Similar to mitochondrial division, mitochondrial fusion also occurs at the ER-mitochondria contact site [<xref rid=\"B55-ijms-21-05452\" ref-type=\"bibr\">55</xref>]. It is consistent with the fusion protein of MOM, Mfn2, which is involved in mitochondrial–ER tethering [<xref rid=\"B56-ijms-21-05452\" ref-type=\"bibr\">56</xref>].</p><p>In addition to the receptor proteins and the ER-actin cytoskeleton, a mitochondria-specific polyanionic phospholipid cardiolipin also regulates mitochondrial dynamics. Cardiolipin is primarily localized at the MIM, but it is also found at the MOM [<xref rid=\"B57-ijms-21-05452\" ref-type=\"bibr\">57</xref>] where Drp1 is found. Cardiolipin directly binds to an insert B region of Drp1 and promotes its oligomerization followed by stimulation of its GTPase activity, the so-called assembly-stimulated GTPase activity [<xref rid=\"B58-ijms-21-05452\" ref-type=\"bibr\">58</xref>,<xref rid=\"B59-ijms-21-05452\" ref-type=\"bibr\">59</xref>]. Overexpression of an insert B mutant of Drp1 with a reduced binding affinity to cardiolipin-containing membranes does not rescue the defect of mitochondrial fission in Drp1-knockout cells [<xref rid=\"B60-ijms-21-05452\" ref-type=\"bibr\">60</xref>]. Cardiolipin has been also shown to interact with mitochondrial fusion proteins, such as yeast Mgm1 and human Opa1, and stimulate their GTPase activity, although these dynamin-like proteins do not contain PH domain [<xref rid=\"B61-ijms-21-05452\" ref-type=\"bibr\">61</xref>,<xref rid=\"B62-ijms-21-05452\" ref-type=\"bibr\">62</xref>]. Thereby, cardiolipin is important for both the fission and fusion of mitochondria.</p><p>Mutations in the mitochondrial division genes are associated with human disease [<xref rid=\"B9-ijms-21-05452\" ref-type=\"bibr\">9</xref>,<xref rid=\"B63-ijms-21-05452\" ref-type=\"bibr\">63</xref>]. The first reported patient was a newborn female patient with microcephaly, abnormal brain development, optic atrophy and hypoplasia, persistent lactic acidemia, and a mildly elevated plasma concentration of very-long-chain fatty acids. The patient had a dominant negative heterozygous mutation at G395D in the <italic>DRP1</italic> gene, manifested as a severe fission defect of mitochondria and died one month after birth [<xref rid=\"B64-ijms-21-05452\" ref-type=\"bibr\">64</xref>]. Patients with mutations in the <italic>Mff</italic> gene are associated with early-onset Leigh-like basal ganglia disease [<xref rid=\"B65-ijms-21-05452\" ref-type=\"bibr\">65</xref>]. In cells derived from these patients, fission defect in the mitochondria and impaired DRP1 recruitment are observed. Several clinical diseases have also been shown to be associated with mutations in mitochondrial fusion genes. <italic>Mfn2</italic> is mutated in patients with Charcot–Marie–Tooth type 2A [<xref rid=\"B66-ijms-21-05452\" ref-type=\"bibr\">66</xref>], while mutations of the <italic>OPA1</italic> gene are associated with dominant optic atrophy [<xref rid=\"B67-ijms-21-05452\" ref-type=\"bibr\">67</xref>,<xref rid=\"B68-ijms-21-05452\" ref-type=\"bibr\">68</xref>].</p></sec><sec id=\"sec2dot2-ijms-21-05452\"><title>2.2. In C. merolae</title><p>Unlike yeast and mammalian cells which contain numerous mitochondria and peroxisomes, the unicellular red algae <italic>C. merolae</italic> contains a single mitochondrion and peroxisome per cell [<xref rid=\"B19-ijms-21-05452\" ref-type=\"bibr\">19</xref>]. The division of these organelles is highly synchronized by the cycles of light/dark stimulation [<xref rid=\"B20-ijms-21-05452\" ref-type=\"bibr\">20</xref>,<xref rid=\"B69-ijms-21-05452\" ref-type=\"bibr\">69</xref>], allowing for both snapshots of sequential events of individual mitochondrial and peroxisomal division and the visualization of the entire picture of the division machinery. Furthermore, during synchronization, division machineries can be isolated in bulk; this allows the components of the division machinery to be identified by mass spectrometry and 100% sequenced for their genomic information [<xref rid=\"B70-ijms-21-05452\" ref-type=\"bibr\">70</xref>,<xref rid=\"B71-ijms-21-05452\" ref-type=\"bibr\">71</xref>]. In <italic>C. merolae</italic>, mitochondria only divide and do not fuse, unlike the mitochondria in yeast and mammalian cells. Consistent with this membrane remodeling, <italic>C. merolae</italic> does not contain any mitochondrial fusion genes, such as <italic>Mfn</italic> and <italic>OPA-1</italic> [<xref rid=\"B70-ijms-21-05452\" ref-type=\"bibr\">70</xref>]. The division machinery of the mitochondrion, called mitochondrion-dividing (MD) machinery, consists of three types of ring-shaped structures: the mitochondrion-dividing (MD) ring, the dynamin ring, and the FtsZ ring [<xref rid=\"B23-ijms-21-05452\" ref-type=\"bibr\">23</xref>] (<xref ref-type=\"fig\" rid=\"ijms-21-05452-f001\">Figure 1</xref>B). Using transmission electron microscopy, the MD ring was identified as an electron-dense ring-like structure wrapped around the neck of dividing mitochondria [<xref rid=\"B18-ijms-21-05452\" ref-type=\"bibr\">18</xref>]. There are two types of MD rings: an outer MD ring, which forms at the cytoplasmic side of the MOM, and an inner MD ring, which forms within the matrix beneath the MIM. The outer MD ring is composed of a bundle of polyglucan nanofilaments (~5 nm in width) and glycosyltransferase MITOCHONDRION-DIVIDING RING1 (MDR1) that regulates the synthesis of the filaments [<xref rid=\"B72-ijms-21-05452\" ref-type=\"bibr\">72</xref>]. Molecular details of the inner MD ring are not yet clear. The dynamin ring is composed of Dnm1 and formed around the MD ring [<xref rid=\"B73-ijms-21-05452\" ref-type=\"bibr\">73</xref>]. In the cytoplasm, Dnm1-positive signals are observed as 10–20 cytoplasmic puncta (dynamin patches), and Dnm1 is likely to be recruited from the dynamin patches to the mitochondrial division site [<xref rid=\"B73-ijms-21-05452\" ref-type=\"bibr\">73</xref>]. The dynamin patches do not contain GTP; thus, Dnm1 is probably in a GTP-unbound form before recruitment [<xref rid=\"B74-ijms-21-05452\" ref-type=\"bibr\">74</xref>]. The Dnm1 receptor proteins encoded in <italic>C. merolae</italic> are mitochondrial division apparatus 1 (Mda1) and Fis1 [<xref rid=\"B70-ijms-21-05452\" ref-type=\"bibr\">70</xref>,<xref rid=\"B71-ijms-21-05452\" ref-type=\"bibr\">71</xref>]. Mda1 is a WD40 repeat protein homologous to yeast Mdv1 and Caf4. Mda1 localizes to the mitochondrial division site prior to Dnm1 recruitment and its stable homo-oligomer is a core structure of the MD machinery [<xref rid=\"B75-ijms-21-05452\" ref-type=\"bibr\">75</xref>]. Fis1 is assumed to be an adaptor between Dnm1 and Mda1, but its function in <italic>C. merolae</italic> remains unclear. The nucleoside diphosphate kinase (NDPK) protein DYNAMO1 was recently identified as an interacting partner of Dnm1 [<xref rid=\"B25-ijms-21-05452\" ref-type=\"bibr\">25</xref>]. NDPK domain catalyzes the GTP generating reaction by transferring γ-phosphate from ATP [<xref rid=\"B76-ijms-21-05452\" ref-type=\"bibr\">76</xref>]. DYNAMO1 is essential for the recruitment of Dnm1 and the constriction of Dnm1-dependent mitochondrial division by facilitating G-domain activity (both GTP binding and hydrolysis) of Dnm1 and providing GTP to Dnm1, respectively. The FtsZ ring is composed of an alphaproteobacterial-type filamenting temperature sensitive mutant Z1 (FtsZ1), a remnant of bacterial cell division apparatus [<xref rid=\"B77-ijms-21-05452\" ref-type=\"bibr\">77</xref>,<xref rid=\"B78-ijms-21-05452\" ref-type=\"bibr\">78</xref>]. FtsZ ring formation depends on the interaction between FtsZ1 and a bacterial ZapA-like protein, ZED [<xref rid=\"B79-ijms-21-05452\" ref-type=\"bibr\">79</xref>]. An exact role of FtsZ ring in mitochondrial division is not fully understood, but is thought to play an important role in the constriction of MIM and the positioning of the MD machinery [<xref rid=\"B73-ijms-21-05452\" ref-type=\"bibr\">73</xref>].</p><p>The molecular mechanisms of the initiation of mitochondrial division are not yet well understood in <italic>C. merolae</italic>. In yeast and mammals, the ER and actin–myosin cytoskeleton are involved in the initiation of mitochondrial division, as discussed in the previous section. In <italic>C. merolae</italic>, the ER extends towards the mitochondrial division site [<xref rid=\"B80-ijms-21-05452\" ref-type=\"bibr\">80</xref>]. However, since <italic>C. merolae</italic> lacks an actin–myosin system [<xref rid=\"B70-ijms-21-05452\" ref-type=\"bibr\">70</xref>,<xref rid=\"B71-ijms-21-05452\" ref-type=\"bibr\">71</xref>,<xref rid=\"B81-ijms-21-05452\" ref-type=\"bibr\">81</xref>], it remains unclear whether the ER is involved in the initiation of mitochondrial division. Instead of the ER and actin–myosin-mediated initiation of the mitochondrial division, the FtsZ ring is thought to play a pivotal role in the positioning of the MD machinery [<xref rid=\"B73-ijms-21-05452\" ref-type=\"bibr\">73</xref>]. Light/dark cycle-induced synchronization captures the sequential event of mitochondrial division and demonstrates that the FtsZ ring forms on the matrix side of MIM followed by the recruitment of Mda1 and Dnm1 at the same site on MOM [<xref rid=\"B73-ijms-21-05452\" ref-type=\"bibr\">73</xref>,<xref rid=\"B75-ijms-21-05452\" ref-type=\"bibr\">75</xref>]. The exact molecular mechanism of the FtsZ ring function is not yet understood, particularly how the FtsZ ring engages in the crosstalk with the division proteins over the MIM and MOM, and it is not known what signal induces FtsZ ring formation. Understanding of these issues would be an exciting topic for the investigation in future studies.</p></sec></sec><sec id=\"sec3-ijms-21-05452\"><title>3. Peroxisomal Dynamics</title><sec id=\"sec3dot1-ijms-21-05452\"><title>3.1. In Yeast and Mammals</title><p>Peroxisomal proliferation by “growth and division” is a widely accepted dogma in yeast and mammals [<xref rid=\"B82-ijms-21-05452\" ref-type=\"bibr\">82</xref>,<xref rid=\"B83-ijms-21-05452\" ref-type=\"bibr\">83</xref>]. This process involves the synthesis of peroxisomal proteins on cytosolic free-polyribosomes and their post-translational transportation into the peroxisome matrix and membrane, followed by peroxisomal division. Peroxisomal membrane protein 11 (PEX11) is the first protein to be identified that plays an important role in peroxisomal division (<xref ref-type=\"fig\" rid=\"ijms-21-05452-f002\">Figure 2</xref>A). PEX11 was originally identified as Pmp27p using yeast mutagenesis [<xref rid=\"B84-ijms-21-05452\" ref-type=\"bibr\">84</xref>]. The disruption of this gene in yeast caused a significant reduction in peroxisome abundance, while its overexpression yielded the opposite result [<xref rid=\"B84-ijms-21-05452\" ref-type=\"bibr\">84</xref>,<xref rid=\"B85-ijms-21-05452\" ref-type=\"bibr\">85</xref>,<xref rid=\"B86-ijms-21-05452\" ref-type=\"bibr\">86</xref>]. In mammals, the PEX11 isoform, PEX11β, mediates membrane growth by remodeling, deforming, and elongating the peroxisomal membrane prior to fission [<xref rid=\"B87-ijms-21-05452\" ref-type=\"bibr\">87</xref>,<xref rid=\"B88-ijms-21-05452\" ref-type=\"bibr\">88</xref>]. In addition to its membrane-shaping function, PEX11β is also involved in the recruitment of division factors. Peroxisome division in mammals is regulated by Drp1 [<xref rid=\"B7-ijms-21-05452\" ref-type=\"bibr\">7</xref>,<xref rid=\"B89-ijms-21-05452\" ref-type=\"bibr\">89</xref>], Fis1 [<xref rid=\"B8-ijms-21-05452\" ref-type=\"bibr\">8</xref>,<xref rid=\"B90-ijms-21-05452\" ref-type=\"bibr\">90</xref>], and Mff [<xref rid=\"B38-ijms-21-05452\" ref-type=\"bibr\">38</xref>,<xref rid=\"B91-ijms-21-05452\" ref-type=\"bibr\">91</xref>]. Pex11β interacts with Mff in a Drp1-dependent manner, suggesting that Mff plays a key role in the fission of the peroxisomal membrane in a concerted manner with Pex11β and Drp1 [<xref rid=\"B91-ijms-21-05452\" ref-type=\"bibr\">91</xref>]. A functional complex comprising Pex11β, Mff, and Drp1 promotes the Mff-mediated fission during peroxisomal division [<xref rid=\"B91-ijms-21-05452\" ref-type=\"bibr\">91</xref>,<xref rid=\"B92-ijms-21-05452\" ref-type=\"bibr\">92</xref>]. The involvement of PEX11 in the regulation of peroxisomal number is conserved between yeast and mammals, although the manner of Dnm1/Drp1 action is different in yeast. In the yeast <italic>Saccharomyces cerevisiae</italic>, dynamin-like protein vacuolar protein sorting 1 (Vps1) is involved in peroxisomal division [<xref rid=\"B93-ijms-21-05452\" ref-type=\"bibr\">93</xref>], while Dnm1, Fis1, and Mdv1 are required for division when cells are under the peroxisomal growth condition in the presence of oleate [<xref rid=\"B94-ijms-21-05452\" ref-type=\"bibr\">94</xref>]. On the other hand, a study using the yeast <italic>Hansenula polymorpha</italic> showed that Dnm1, but not Vps1, plays a crucial role in peroxisomal division [<xref rid=\"B95-ijms-21-05452\" ref-type=\"bibr\">95</xref>]. In <italic>H</italic>. <italic>polymorpha</italic>, PEX11 directly binds to Dnm1 and functions as a GTPase-activating protein (GAP) [<xref rid=\"B96-ijms-21-05452\" ref-type=\"bibr\">96</xref>]. Therefore, Dnm1 function may be dispensable in a subset of cell types or environment in yeast.</p><p>As described in the previous section, the ER plays an important role in the initiation of mitochondrial division and the recruitment of Dnm1/Drp1, and also plays a pivotal role in peroxisomal proliferation. In yeast and mammalian cells, peroxisomal membrane peroxins 3 and 16 (Pex3 and Pex16) have been observed emerging from ER [<xref rid=\"B97-ijms-21-05452\" ref-type=\"bibr\">97</xref>,<xref rid=\"B98-ijms-21-05452\" ref-type=\"bibr\">98</xref>,<xref rid=\"B99-ijms-21-05452\" ref-type=\"bibr\">99</xref>,<xref rid=\"B100-ijms-21-05452\" ref-type=\"bibr\">100</xref>,<xref rid=\"B101-ijms-21-05452\" ref-type=\"bibr\">101</xref>,<xref rid=\"B102-ijms-21-05452\" ref-type=\"bibr\">102</xref>]. Thereby, Pex-containing pre-peroxisomal vesicle is proposed to play an important role in the biogenesis of peroxisomes followed by Dnm1/Drp1-mediated membrane fission [<xref rid=\"B103-ijms-21-05452\" ref-type=\"bibr\">103</xref>]. The mechanism by which Pex-containing pre-peroxisomal vesicles participate in peroxisomal biogenesis is not yet clearly understood. However, it has been proposed that the vesicles fuse with each other or with pre-existing peroxisome to generate a larger matured peroxisome [<xref rid=\"B104-ijms-21-05452\" ref-type=\"bibr\">104</xref>,<xref rid=\"B105-ijms-21-05452\" ref-type=\"bibr\">105</xref>]. A recent study suggested that Pex3 is targeted to mitochondria, and Pex3-containing vesicles bud off from MOM [<xref rid=\"B106-ijms-21-05452\" ref-type=\"bibr\">106</xref>]. This study also suggested that Pex3-containing vesicles fuse to Pex16-containing vesicles from the ER for peroxisome assembly. Thus, mitochondria may also be involved in the proliferation of peroxisomes, in addition to the ER. The identification of other factors involved in the regulation of the fusion of Pex-containing vesicles is another interesting topic for future studies.</p><p>As for mitochondrial division proteins, mutations in peroxisomal division proteins are also responsible for human diseases. The first reported mutation was a Chinese hamster ovary (CHO) cell mutant, ZP121, which was impaired in DRP1 with one-point temperature-sensitive and dominant negative mutation at G363D in the middle region [<xref rid=\"B89-ijms-21-05452\" ref-type=\"bibr\">89</xref>]. In terms of the manifestation of peroxisomal dysmorphogenesis in humans, only three patients have been identified with a different defect in two proteins involved in the division of peroxisomes. The first patient was a severely affected female patient with a dominant negative heterozygous mutation at G395D in DLP1, who manifested a severe fission defect of both peroxisomes and mitochondria and died one month after birth [<xref rid=\"B64-ijms-21-05452\" ref-type=\"bibr\">64</xref>]. The second patient with a dysfunctional DRP1 harboring a G362D mutation was more recently reported [<xref rid=\"B107-ijms-21-05452\" ref-type=\"bibr\">107</xref>]. A patient with defective peroxisomal division due to a homozygous nonsense mutation in the <italic>PEX11β</italic> gene was reported as the 14th complementation group of the peroxisome biogenesis disorders [<xref rid=\"B108-ijms-21-05452\" ref-type=\"bibr\">108</xref>,<xref rid=\"B109-ijms-21-05452\" ref-type=\"bibr\">109</xref>].</p></sec><sec id=\"sec3dot2-ijms-21-05452\"><title>3.2. In C. merolae</title><p>In a unicellular red algae <italic>C. merolae</italic>, the division of a peroxisome occurs during M phase and always takes place after mitochondrial division. The peroxisome morphology is plastic, as in mammalian cells, and is a result of the physical interaction of peroxisomes with mitochondria during mitosis [<xref rid=\"B110-ijms-21-05452\" ref-type=\"bibr\">110</xref>]. Morphological changes take place in peroxisomes during their physical interaction with mitochondria, including an increase in their volume before peroxisomal division. The post-translational import of catalase is linked to an increase in the volume of a peroxisome [<xref rid=\"B110-ijms-21-05452\" ref-type=\"bibr\">110</xref>]. Thus, a peroxisome in <italic>C. merolae</italic> proliferates via its growth and division like in yeast and mammalian cells. As such, the interaction of peroxisome with the mitochondrion may play an important role in its the morphological plasticity. The division machinery of the peroxisome, called the peroxisomal-dividing (POD) machinery, consists of two types of ring-shaped structures, namely a filamentous ring and a dynamin-based (DB) ring. Both are formed at the cytoplasmic side of peroxisomal membrane constriction site [<xref rid=\"B20-ijms-21-05452\" ref-type=\"bibr\">20</xref>] (<xref ref-type=\"fig\" rid=\"ijms-21-05452-f002\">Figure 2</xref>B). The POD machinery does not contain a FtsZ ring or an electron-dense ring in the matrix side. The filamentous ring is composed of a bundle of 4-nm wide filaments, which is similar to the structure of the MD ring, but its components remain unknown. The DB ring contains Dnm1 [<xref rid=\"B20-ijms-21-05452\" ref-type=\"bibr\">20</xref>] and DYNAMO1 [<xref rid=\"B25-ijms-21-05452\" ref-type=\"bibr\">25</xref>], thus sharing components with the dynamin ring of the MD machinery. The DB ring is formed from a single spot on the POD machinery, called the dynamin-based ring organizing center (DOC), which functions as the nucleation site of Dnm1 [<xref rid=\"B74-ijms-21-05452\" ref-type=\"bibr\">74</xref>]. GTP binding is likely to play an essential role in the assembly of Dnm1. Similar to the dynamin ring in the MD machinery, Dnm1 is recruited from the dynamin patches to the division site of the peroxisome [<xref rid=\"B20-ijms-21-05452\" ref-type=\"bibr\">20</xref>,<xref rid=\"B74-ijms-21-05452\" ref-type=\"bibr\">74</xref>]. DYNAMO1 provides GTP to Dnm1 on the DB ring, which is critical for generating the constriction force of the POD machinery, as well as the constriction of the MD machinery. Unlike mitochondrial division, DYNAMO1 is not involved in recruiting Dnm1 to the membrane fission site during peroxisomal division. Future studies will elucidate how <italic>C. merolae</italic> regulates Dnm1 recruitment during the peroxisomal division. <italic>C. merolae</italic> encodes <italic>Pex11β</italic> and thus Pex11β-Dnm1 interaction may be sufficient for the recruitment of Dnm1 to the peroxisomal division site.</p></sec></sec><sec id=\"sec4-ijms-21-05452\"><title>4. Regulation of GTP during Dnm1/Drp1 Function</title><p>Dnm1/Drp1 is a core component in the division machineries of the mitochondria and peroxisomes in yeast, mammals, and <italic>C. merolae</italic> [<xref rid=\"B11-ijms-21-05452\" ref-type=\"bibr\">11</xref>,<xref rid=\"B22-ijms-21-05452\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05452\" ref-type=\"bibr\">23</xref>]. The key regulatory mechanisms of the Dnm1/Drp1-based division machinery include the step of recruitment of Dnm1/Drp1 to the membrane, the receptor-mediated protein recruitment, and the mobilization of GTP. We are certain that various Dnm1/Drp1 receptor proteins described in the previous sections are required for Dnm1/Drp1 recruitment. However, their regulation of GTP remains unclear. GTP is the only energy source used for Dnm1/Drp1 recruitment and membrane constriction, and GTP binding is important in the recruitment of Dnm1/Drp1. In yeast, the binding of GTP to the G-domain alters the structure of Dnm1 by exposing the insert B region involved in the membrane interaction [<xref rid=\"B111-ijms-21-05452\" ref-type=\"bibr\">111</xref>]. After GTP binding, Dnm1 is able to form a highly ordered helix-shaped structure [<xref rid=\"B16-ijms-21-05452\" ref-type=\"bibr\">16</xref>]. Mdv1 is known to preferentially interact with GTP-bound Dnm1 [<xref rid=\"B112-ijms-21-05452\" ref-type=\"bibr\">112</xref>]. In mammals, GTP binding alters the conformation of Drp1 and allows for the interaction between Drp1 and Mid49 [<xref rid=\"B113-ijms-21-05452\" ref-type=\"bibr\">113</xref>], followed by polymerization with Mff on the mitochondrial membrane [<xref rid=\"B114-ijms-21-05452\" ref-type=\"bibr\">114</xref>]. Moreover, GTP and GDP influence the binding kinetics between Drp1 and actin filaments [<xref rid=\"B115-ijms-21-05452\" ref-type=\"bibr\">115</xref>]. During the constriction, the Dnm1/Drp1-helical polymer is thought to demonstrate ratchet motion [<xref rid=\"B17-ijms-21-05452\" ref-type=\"bibr\">17</xref>] similar to classical dynamin, which requires continuous consumption of GTP until the diameter of the membrane reaches its limit, at ~4 nm, for spontaneous membrane fission [<xref rid=\"B116-ijms-21-05452\" ref-type=\"bibr\">116</xref>,<xref rid=\"B117-ijms-21-05452\" ref-type=\"bibr\">117</xref>]. Although GTP binding is important for the recruitment of Dnm1/Drp1, its affinity to GTP is weak. That is, the <italic>K<sub>m</sub></italic> of basal G-domain is at least 1 mM [<xref rid=\"B118-ijms-21-05452\" ref-type=\"bibr\">118</xref>]. Dnm1/Drp1 has a relatively high rate of GTP hydrolysis on the membrane [<xref rid=\"B16-ijms-21-05452\" ref-type=\"bibr\">16</xref>,<xref rid=\"B118-ijms-21-05452\" ref-type=\"bibr\">118</xref>,<xref rid=\"B119-ijms-21-05452\" ref-type=\"bibr\">119</xref>], at least 5000-fold higher than that of small GTPases [<xref rid=\"B120-ijms-21-05452\" ref-type=\"bibr\">120</xref>]. Indeed, a study using yeast Dnm1 reported that significant constriction of lipid tubules mediated by the Dnm1-helical polymer requires extraordinarily high levels of GTP concentration (~1 mM) [<xref rid=\"B17-ijms-21-05452\" ref-type=\"bibr\">17</xref>], consistent with the idea that the allosteric enhancer of G-domain is required to elevate the affinity of Dnm1 to GTP [<xref rid=\"B118-ijms-21-05452\" ref-type=\"bibr\">118</xref>]. Several earlier studies have showed that Dnm1/Drp1 binding proteins, such as Mdv1 and Mff, enhance G-domain function [<xref rid=\"B121-ijms-21-05452\" ref-type=\"bibr\">121</xref>,<xref rid=\"B122-ijms-21-05452\" ref-type=\"bibr\">122</xref>]. In addition to these Dnm1/Drp1 binding proteins present on the MOM, cytoplasmic proteins also participate in the enhancement of the G-domain function. A study in mammalian cells found that mitochondrial fragmentation is induced by cytoplasmic cyclin C, which is released from cell nucleus in response to oxidative stress [<xref rid=\"B123-ijms-21-05452\" ref-type=\"bibr\">123</xref>]. Cyclin C directly binds to Drp1 and increases the affinity of Drp1 to GTP. As GTP binding to Drp1 is required for the interaction of Drp1 with MiD49 [<xref rid=\"B113-ijms-21-05452\" ref-type=\"bibr\">113</xref>], enhancing the affinity of Drp1 to GTP may play an important role in the recruitment of Drp1 to MOM from the cytoplasm. In <italic>C. merolae</italic>, the ortholog of Drp1, Dnm1, is likely recruited from the dynamin patches in the cytoplasm [<xref rid=\"B73-ijms-21-05452\" ref-type=\"bibr\">73</xref>]. The Dnm1 in the dynamin patches is likely a GTP-unbound form [<xref rid=\"B74-ijms-21-05452\" ref-type=\"bibr\">74</xref>]. The GTP-unbound form of Dnm1 is thought to be altered to GTP-bound form during the recruitment in <italic>C. merolae</italic>. Consistent with this, a recent study found that the NDPK protein DYNAMO1 facilitates the G-domain function and regulates the recruitment of Dnm1 to the division site of mitochondrion [<xref rid=\"B25-ijms-21-05452\" ref-type=\"bibr\">25</xref>]. This indicates that GTP is needed during Drp1 recruitment and that DYNAMO1 functions as an enhancer of the G-domain function of Dnm1. The enzyme activity of NDPK is not required for the recruitment, but it is essential during the membrane constriction. During the membrane constriction in a mitochondrion and a peroxisome, DYNAMO1 localizes to the MD and POD machineries and locally generates GTP from ATP. Abolishing this activity results in stalling of the constriction of a mitochondrion and a peroxisome. Thus, DYNAMO1 is an essential GTP regulator for the Dnm1-based division machinery of a mitochondrion and a peroxisome. It is not known whether NDPK orthologs, if any, are important in Dnm1/Drp1 function in yeast and mammalian cells, while NDPK function is reported to be required during mitochondrial fusion. One of the NDPK isoforms in mammals, non-metastatic cells 4 (NME4), has been suggested to provide GTP for the OPA-1 function during MIM fusion [<xref rid=\"B124-ijms-21-05452\" ref-type=\"bibr\">124</xref>,<xref rid=\"B125-ijms-21-05452\" ref-type=\"bibr\">125</xref>]. Recently, another isoform NME3, was found to regulate the function of Mfn1 and Mfn2 during MOM fusion. However, in this case, GTP generation activity was not required for Mfn1 and Mfn2 functions [<xref rid=\"B126-ijms-21-05452\" ref-type=\"bibr\">126</xref>]. Thus, mitochondrial fusion in mammals seems to be regulated by the NDPK protein. Fission and fusion occur in rapid succession at the same region on mitochondria [<xref rid=\"B127-ijms-21-05452\" ref-type=\"bibr\">127</xref>]. Moreover, these two opposing membrane remodeling processes frequently occur at the ER–mitochondrial contact sites [<xref rid=\"B49-ijms-21-05452\" ref-type=\"bibr\">49</xref>,<xref rid=\"B55-ijms-21-05452\" ref-type=\"bibr\">55</xref>,<xref rid=\"B128-ijms-21-05452\" ref-type=\"bibr\">128</xref>], where Drp1 and Mfn1 are colocalized [<xref rid=\"B129-ijms-21-05452\" ref-type=\"bibr\">129</xref>]. Therefore, NDPK protein may also be accessible to Drp1, in addition to Mfn1. In future studies, the issue as to whether local GTP generation during mitochondrial fission is conserved in yeast and mammals will need to be addressed.</p></sec><sec id=\"sec5-ijms-21-05452\"><title>5. Molecular Mechanisms Underlying Local GTP Generation around the Organelle Division Machinery</title><p>Local GTP generation on dynamin family proteins has been reported in both classical dynamin and dynamin-related proteins. In <italic>D. melanogaster</italic>, a mutation of the NDPK gene, called <italic>abnormal wing disc</italic> (<italic>Awd</italic>), is identified as an enhancer of the <italic>shibire</italic> mutant phenotype, which has a defect in dynamin-dependent synaptic vesicle endocytosis [<xref rid=\"B130-ijms-21-05452\" ref-type=\"bibr\">130</xref>]. In mammals, NDPK proteins are important for both clathrin-mediated and clathrin-independent endocytosis [<xref rid=\"B131-ijms-21-05452\" ref-type=\"bibr\">131</xref>,<xref rid=\"B132-ijms-21-05452\" ref-type=\"bibr\">132</xref>], during which the NDPK isoforms, NME1 and NME2, bind to classical dynamin and generate GTP locally on the endocytic sites. Thus, NDPK function is essential for classical dynamin. For the mitochondrial dynamics, another isoform, NME4, produces GTP on OPA-1 during MIM fusion [<xref rid=\"B124-ijms-21-05452\" ref-type=\"bibr\">124</xref>,<xref rid=\"B125-ijms-21-05452\" ref-type=\"bibr\">125</xref>]. For the Dnm1-dependent division of a mitochondrion and a peroxisome, the NDPK ortholog DYNAMO1 generates GTP on Dnm1 in <italic>C. merolae</italic> [<xref rid=\"B25-ijms-21-05452\" ref-type=\"bibr\">25</xref>]. Local GTP generation by the NDPK protein is a conserved phenomenon among dynamin family proteins, although molecular mechanisms of GTP generation have a discrepancy between the two proposed models: one is that GTP is channeled within the complex of dynamin family proteins and NDPK [<xref rid=\"B133-ijms-21-05452\" ref-type=\"bibr\">133</xref>,<xref rid=\"B134-ijms-21-05452\" ref-type=\"bibr\">134</xref>], and the other is that GTP concentration is enriched locally around the membrane fission site [<xref rid=\"B25-ijms-21-05452\" ref-type=\"bibr\">25</xref>].</p><p>In the channeling model (<xref ref-type=\"fig\" rid=\"ijms-21-05452-f003\">Figure 3</xref>), GTP generation within the dynamin family protein-NDPK protein complex maximizes the efficiency of GTP delivery to the G-domain of Dnm1. Maximizing enzyme kinetics usually takes place in a spatial proximity within the complex of multifunctional enzymes to be separated from the diffusion equilibrium, a process known as “channeling” [<xref rid=\"B135-ijms-21-05452\" ref-type=\"bibr\">135</xref>]. A well-known example of this channeling is the glycolysis reaction between glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and the phosphoglycerate kinase (PGK) complex [<xref rid=\"B136-ijms-21-05452\" ref-type=\"bibr\">136</xref>], and it is also proposed for the NDPK reaction [<xref rid=\"B133-ijms-21-05452\" ref-type=\"bibr\">133</xref>]. The caveat of the channeling model is the unbalanced kinetics between the generation and hydrolysis of GTP. The turnover of NDPK is <italic>K<sub>cat</sub></italic> = 600/s [<xref rid=\"B137-ijms-21-05452\" ref-type=\"bibr\">137</xref>], which suggests >500–2000-fold higher enzyme kinetics than the GTPase activity of the membrane-bound classical dynamin or Dnm1 orthologs in yeast, algae, and mammals [<xref rid=\"B118-ijms-21-05452\" ref-type=\"bibr\">118</xref>,<xref rid=\"B119-ijms-21-05452\" ref-type=\"bibr\">119</xref>,<xref rid=\"B138-ijms-21-05452\" ref-type=\"bibr\">138</xref>]. Thus, the catalytic domains of the NDPK protein and the G-domain of Dnm1 need to be in close proximity to secure a high GTP concentration ratio and to promote GTP hydrolysis without leakage of the nucleotides. However, the GTP-holding pocket in the G-domain faces each other between the adjusting helical turns in a helical polymer of Dnm1/Drp1, as well as in classical dynamin [<xref rid=\"B17-ijms-21-05452\" ref-type=\"bibr\">17</xref>,<xref rid=\"B116-ijms-21-05452\" ref-type=\"bibr\">116</xref>]. In this case, the interaction between the NDPK domain and the G-domain would compete with the formation of the G-domain dimer. Structural studies of the NDPK protein-dynamin family protein complex are required for further analysis and discussion of the channeling model.</p><p>The enrichment model (<xref ref-type=\"fig\" rid=\"ijms-21-05452-f003\">Figure 3</xref>) reconciles the fact that the dynamin family proteins, including Dnm1/Drp1, have a low affinity to GTP and a high rate of GTP-hydrolysis, which cannot be supported by the physiological levels of GTP. As mentioned above, the enzyme kinetics of NDPK is much higher than that of the GTPase activity of dynamin family proteins. Thus, a balance between GTP generation and consumption may not exist around NDPK-enriched membrane fission sites. In cells, NDPK forms a tetramer or a hexamer [<xref rid=\"B139-ijms-21-05452\" ref-type=\"bibr\">139</xref>]. The functional membrane fission ring contains ~100 molecules of Drp1 during mitochondrial fission in mammalian cells, based on the quantification of endogenous GFP-tagged proteins [<xref rid=\"B140-ijms-21-05452\" ref-type=\"bibr\">140</xref>], which are ~200 nm in diameter [<xref rid=\"B53-ijms-21-05452\" ref-type=\"bibr\">53</xref>]. An accurate model for the number of G-domain dimers and the number of helix turns is required in the future studies. Given the parameters of enzyme kinetics between NDPK and Dnm1 or classical dynamin, excess of GTP may be generated around the membrane fission sites to increase the concentration of local GTP. However, the diffusion coefficient of nucleotides is ~360 µm<sup>2</sup>/s [<xref rid=\"B141-ijms-21-05452\" ref-type=\"bibr\">141</xref>], and it is uncertain whether GTP can be locally enriched by overcoming the diffusion kinetics. This could be understood by carefully considering the coupling between the generation/consumption and diffusion of GTP. One way to locally enrich GTP is to manipulate the diffusion coefficient, as seen during the diffusion of calcium ions, whose diffusion coefficient is slowed down ~10 times as a result of molecular crowding or the restriction of movement by the cytoskeleton and cellular organelles [<xref rid=\"B142-ijms-21-05452\" ref-type=\"bibr\">142</xref>,<xref rid=\"B143-ijms-21-05452\" ref-type=\"bibr\">143</xref>]. Another mechanism by which local enrichment can be achieved is liquid-phase separation. Recently, the liquid-phase separation of cycling GMP-AMP synthase (cGAS) that converts GTP and ATP to cAMP is found in mammalian cells [<xref rid=\"B144-ijms-21-05452\" ref-type=\"bibr\">144</xref>]. In this study, the enrichment of ATP or GTP within the cGAS-DNA liquid droplets is demonstrated. Thus, even small molecules, such as ATP and GTP, could be phase separated. However, there is only little information on the buffering elements that can slow down the diffusion coefficient of GTP, or on intrinsically disordered proteins that help in the phase separation of GTP around membrane fission sites. For example, transmission electron microscopy images of platinum replicas of an unroofed cell show that the mitochondria are surrounded with a dense cytoskeletal network in mammalian cells [<xref rid=\"B52-ijms-21-05452\" ref-type=\"bibr\">52</xref>]. During clathrin-mediated endocytosis, early stage endocytic proteins have been shown to form phase separated droplets on the endocytic sites [<xref rid=\"B145-ijms-21-05452\" ref-type=\"bibr\">145</xref>]. Future studies would need to examine a mobile fraction of GTP around the division sites of mitochondrion and peroxisome.</p></sec><sec sec-type=\"conclusions\" id=\"sec6-ijms-21-05452\"><title>6. Conclusions and Perspectives</title><p>In this review, we summarize the most recent studies regarding GTP regulation during the Dnm1/Drp1-dependent membrane fission of mitochondria and peroxisomes. In addition to the importance of receptor-mediated Dnm1/Drp1 recruitment during the division of mitochondria and peroxisomes, the emerging studies have highlighted the importance of the replenishment of GTP during membrane fission. We also discussed the issues regarding the molecular mechanism underlying how local GTP generation is generated by NDPK protein on the division machineries of mitochondria and peroxisomes, as exampled by GTP channeling and GTP enrichment models. To elucidate these two models, various approaches need to be developed in future studies. To characterize the GTP channeling model, the structure of the interface between dynamin family protein and NDPK protein on the membrane needs to be elucidated. For the GTP enrichment model, the visualization of GTP concentration is vital. Recently, a circularly permutated YFP (cpYFP)-based GTP sensor was developed [<xref rid=\"B146-ijms-21-05452\" ref-type=\"bibr\">146</xref>]. GTP imaging using the cpYFP could open another door in the field of NDPK protein and dynamin protein research. Currently, the working model of these proteins remains unknown. Several questions remain to be answered, such as when does local GTP generation start? How many molecules of NDPK protein are required per membrane fission machinery? What is the geometry of the NDPK proteins in the membrane fission machinery? To address these issues, the structure of the membrane fission machinery needs to be visualized using in situ cryo-electron tomography and subtomogram averaging techniques. Although these represent considerable challenges, the results will provide a new direction of research towards the membrane fission events conserved across eukaryotic cells.</p></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization, Y.I. and Y.F.; writing—original draft preparation, Y.I.; writing—review and editing, Y.F. and K.I.; supervision, Y.I. and Y.F.; project administration, Y.I. and Y.F.; funding acquisition, Y.I. and Y.F. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was funded by the Japan Society for the Promotion of Science Postdoctoral Research Fellowship for Research Abroad and in part by grants from the Japan Society for the Promotion of Science Fellowships grant number 14J04556. Ministry of Education, Culture, Sports, Science, and Technology of Japan, Grants-in-Aid for Scientific Research grant numbers JP24247038, JP25112518, JP25116717, JP26116007, JP15K14511, JP15K21743, and JP17H03675. Takeda Science Foundation. Naito Foundation. (Japan Foundation for Applied Enzymology and Novartis Foundation (Japan) for the Promotion of Science.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijms-21-05452\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Ernster</surname><given-names>L.</given-names></name><name><surname>Schatz</surname><given-names>G.</given-names></name></person-group><article-title>Mitochondria: A Historical Review</article-title><source>J. 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Methods</source><year>2017</year><volume>14</volume><fpage>1003</fpage><lpage>1009</lpage><pub-id pub-id-type=\"doi\">10.1038/nmeth.4404</pub-id><pub-id pub-id-type=\"pmid\">28869758</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijms-21-05452-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Mitochondria are divided by Dnm1/Drp1-based membrane fission machinery. Schematic images represent dividing mitochondria and their cross sections at the division plane. (<bold>A</bold>), Mitochondrial division in yeast and mammals is initiated at the endoplasmic reticulum (ER)-mitochondrion contact site. At the contact site, ER tubules encircle the mitochondria and polymerized actin is formed. Receptor proteins Fis1 and Mdv1/Caf4 are involved in the recruitment of Dnm1 to mitochondrial outer membrane (MOM) in yeast, while Fis1, Mff, and MiD49/51 are involved in the recruitment of Drp1 in mammals. In mammals, the ER–mitochondrion contact site also participates in the recruitment of Drp1. Polymerized Dnm1/Drp1 constricts and pinches off mitochondrial division site. (<bold>B</bold>) In <italic>C. merolae</italic>, a mitochondrion is divided by mitochondrion-dividing (MD) machinery (FtsZ ring, MD ring and dynamin ring). The first event of the mitochondrial division is formation of the FtsZ ring. ZED is important for the FtsZ ring formation on the matrix side of MIM followed by the formation of MD ring. MDR1 is involved in the formation of MD ring. Dynamin ring is composed of Dnm1, and Mdv1/Caf4 ortholog Mda1 is involved in the recruitment of Dnm1. Dnm1 is likely recruited from cytosolic dynamin patches. During the constriction of MD machinery, DYNAMO1 generates GTP and supports the GTPase activity of Dnm1.</p></caption><graphic xlink:href=\"ijms-21-05452-g001\"/></fig><fig id=\"ijms-21-05452-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Peroxisomes are divided by Dnm1/Drp1-based membrane fission machinery. (<bold>A</bold>) Peroxisomal division in yeast and mammals are initiated by PEX11. In yeast <italic>S. cerevisiae</italic>, Vps1 and PEX11 are involved in the peroxisomal division in normal conditions. In growth condition, Dnm1, PEX11, Fis1, and Mdv1 are involved in the division. In mammals, Drp1, PEX11β, Fis1, and Mff are involved in the division. (<bold>B</bold>) In <italic>C. merolae</italic>, a peroxisome is divided by peroxisomal-dividing (POD) machinery (filamentous ring and dynamin-based ring). The filamentous ring is thought to be formed first at the division site followed by formation of the dynamin-based ring. Dnm1 is likely recruited from cytosolic dynamin patches. DYNAMO1 regulates both recruitment of Dnm1 and GTP-dependent constriction of dynamin-based ring.</p></caption><graphic xlink:href=\"ijms-21-05452-g002\"/></fig><fig id=\"ijms-21-05452-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>DYNAMO1 locally generates GTP for the GTPase activity of Dnm1 during the division of mitochondrion and peroxisome in <italic>C. merolae</italic>. During the recruitment of Dnm1 to mitochondrial membrane, DYNAMO1 binds to Dnm1 and promotes the recruitment by enhancing the G-domain function of Dnm1. During the constriction of Dnm1-based membrane fission machinery, DYNAMO1 is thought to provide GTP locally to Dnm1 by mechanisms called a channeling model or an enrichment model. In the channeling model, DYNAMO1 provides GTP in close proximity to G-domain of Dnm1. In the enrichment model, DYNAMO1 elevates local GTP concentration around the membrane fission site.</p></caption><graphic xlink:href=\"ijms-21-05452-g003\"/></fig></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32731408</article-id><article-id pub-id-type=\"pmc\">PMC7432048</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155352</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05352</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Age-Related Memory Impairment Is Associated with Increased zif268 Protein Accumulation and Decreased Rpt6 Phosphorylation</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-4396-5334</contrib-id><name><surname>Trask</surname><given-names>Sydney</given-names></name></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-9930-4287</contrib-id><name><surname>Dulka</surname><given-names>Brooke N.</given-names></name></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-3413-2418</contrib-id><name><surname>Helmstetter</surname><given-names>Fred J.</given-names></name><xref rid=\"c1-ijms-21-05352\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05352\">Department of Psychology, The University of Wisconsin-Milwaukee, P.O. Box 413, Milwaukee, WI 53201, USA; <email>trask@uwm.edu</email> (S.T.); <email>dulka@uwm.edu</email> (B.N.D.)</aff><author-notes><corresp id=\"c1-ijms-21-05352\"><label>*</label>Correspondence: <email>fjh@uwm.edu</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>28</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5352</elocation-id><history><date date-type=\"received\"><day>08</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>25</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Aging is associated with cognitive decline, including impairments in the ability to accurately form and recall memories. Some behavioral and brain changes associated with aging are evident as early as middle age, making the understanding of associated neurobiological mechanisms essential to aid in efforts aimed at slowing cognitive decline throughout the lifespan. Here, we found that both 15-month-old and 22-month-old rats showed impaired memory recall following trace fear conditioning. This behavioral deficit was accompanied by increased zif268 protein accumulation relative to 3-month-old animals in the medial prefrontal cortex, the dorsal and ventral hippocampi, the anterior and posterior retrosplenial cortices, the lateral amygdala, and the ventrolateral periaqueductal gray. Elevated zif268 protein levels corresponded with decreases in phosphorylation of the Rpt6 proteasome regulatory subunit, which is indicative of decreased engagement of activity-driven protein degradation. Together, these results identify several brain regions differentially impacted by aging and suggest that the accumulation of proteins associated with memory retrieval, through reduced proteolytic activity, is associated with age-related impairments in memory retention.</p></abstract><kwd-group><kwd>aging</kwd><kwd>immediate early gene</kwd><kwd>protein degradation</kwd><kwd>memory</kwd><kwd>proteasome</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05352\"><title>1. Introduction</title><p>Normal human aging is accompanied by several changes in cognitive function [<xref rid=\"B1-ijms-21-05352\" ref-type=\"bibr\">1</xref>], including impairments in memory formation and retention [<xref rid=\"B2-ijms-21-05352\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijms-21-05352\" ref-type=\"bibr\">3</xref>]. Age-related deficits have also been observed in the rodent literature, with aged animals demonstrating impairments in several memory paradigms, including the Morris water maze [<xref rid=\"B4-ijms-21-05352\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijms-21-05352\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijms-21-05352\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijms-21-05352\" ref-type=\"bibr\">7</xref>], the radial arm maze [<xref rid=\"B8-ijms-21-05352\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijms-21-05352\" ref-type=\"bibr\">9</xref>], active and passive avoidance learning [<xref rid=\"B9-ijms-21-05352\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijms-21-05352\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijms-21-05352\" ref-type=\"bibr\">11</xref>], object recognition [<xref rid=\"B12-ijms-21-05352\" ref-type=\"bibr\">12</xref>], and the objects in updated locations task [<xref rid=\"B13-ijms-21-05352\" ref-type=\"bibr\">13</xref>]. Maintaining unimpaired cognitive ability in older adults is considered one of the major components to healthy aging [<xref rid=\"B14-ijms-21-05352\" ref-type=\"bibr\">14</xref>], and understanding the neurobiological mechanisms that contribute to cognitive dysfunction is crucially important to facilitating healthy aging in a senescent human population [<xref rid=\"B15-ijms-21-05352\" ref-type=\"bibr\">15</xref>].</p><p>Age-related memory deficits are also evident following trace fear conditioning (TFC), a type of classical conditioning in which the conditional stimulus (CS) and unconditional stimulus (UCS) are separated by a brief period of time known as a the “trace interval”. Older animals show reduced conditional responding (e.g., freezing) following CS–UCS pairings compared to young adult counterparts [<xref rid=\"B16-ijms-21-05352\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijms-21-05352\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijms-21-05352\" ref-type=\"bibr\">18</xref>]. Interestingly, this age-related deficit is not seen in delay fear conditioning, in which a similar CS coterminates with the UCS [<xref rid=\"B19-ijms-21-05352\" ref-type=\"bibr\">19</xref>]. This behavioral finding is believed to result from the age-related degeneration of a larger neural circuit required for the successful formation and recall of a trace, as opposed to delay, fear memory. The TFC circuit includes the hippocampus [<xref rid=\"B20-ijms-21-05352\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijms-21-05352\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05352\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05352\" ref-type=\"bibr\">23</xref>], retrosplenial cortex (RSC; [<xref rid=\"B24-ijms-21-05352\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijms-21-05352\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijms-21-05352\" ref-type=\"bibr\">26</xref>]), and medial prefrontal cortex (mPFC; [<xref rid=\"B27-ijms-21-05352\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05352\" ref-type=\"bibr\">28</xref>]) in addition to brain areas such as the lateral amygdala (LA) and the ventrolateral periaqueductal gray (vlPAG), which are also necessary for the delay version of the procedure [<xref rid=\"B29-ijms-21-05352\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijms-21-05352\" ref-type=\"bibr\">30</xref>]. Interestingly, the basolateral amygdala (BLA; [<xref rid=\"B16-ijms-21-05352\" ref-type=\"bibr\">16</xref>]), dorsal hippocampus (DH; [<xref rid=\"B16-ijms-21-05352\" ref-type=\"bibr\">16</xref>,<xref rid=\"B31-ijms-21-05352\" ref-type=\"bibr\">31</xref>]), ventral hippocampus (VH; [<xref rid=\"B31-ijms-21-05352\" ref-type=\"bibr\">31</xref>]), mPFC [<xref rid=\"B6-ijms-21-05352\" ref-type=\"bibr\">6</xref>,<xref rid=\"B16-ijms-21-05352\" ref-type=\"bibr\">16</xref>,<xref rid=\"B32-ijms-21-05352\" ref-type=\"bibr\">32</xref>], and RSC [<xref rid=\"B33-ijms-21-05352\" ref-type=\"bibr\">33</xref>] all show neurobiological changes in aged rodents relative to their young-adult counterparts. These changes range from impairments in protein degradation associated with memory retrieval [<xref rid=\"B16-ijms-21-05352\" ref-type=\"bibr\">16</xref>] to changes in resting state neural activity [<xref rid=\"B33-ijms-21-05352\" ref-type=\"bibr\">33</xref>]. While decreases in protein degradation processes occur as a function of normal aging [<xref rid=\"B34-ijms-21-05352\" ref-type=\"bibr\">34</xref>], impairments in protein homeostasis and degradation are primary factors underlying age-associated protein accumulation [<xref rid=\"B35-ijms-21-05352\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijms-21-05352\" ref-type=\"bibr\">36</xref>]. Importantly, protein degradation following memory retrieval is necessary for the successful formation and stabilization of long-term memory [<xref rid=\"B37-ijms-21-05352\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijms-21-05352\" ref-type=\"bibr\">38</xref>].</p><p>The current experiment was designed to examine age-related brain changes throughout the trace fear circuit. We measured the degree of impaired trace fear recall as well as associated molecular changes throughout brain regions known to support TFC in young (3-month-old), middle-aged (15-month-old), and aged (22-month-old) rats. However, not all aged animals show deficits in TFC [<xref rid=\"B17-ijms-21-05352\" ref-type=\"bibr\">17</xref>]. Several studies have demonstrated that memory impairments, and not age per se, correspond with molecular changes [<xref rid=\"B5-ijms-21-05352\" ref-type=\"bibr\">5</xref>,<xref rid=\"B16-ijms-21-05352\" ref-type=\"bibr\">16</xref>,<xref rid=\"B39-ijms-21-05352\" ref-type=\"bibr\">39</xref>]. Aged animals that show no behavioral impairment often resemble young-adult counterparts on molecular endpoints. We predicted that deficits in memory retrieval would correspond with alterations in protein accumulation associated with memory retrieval. Specifically, we measured expression of the zinc finger transcription factor zif268 in several brain regions important for TFC (mPFC, anterior RSC, posterior RSC, DH, VH, BLA, and ventrolateral periaqueductal gray, or vlPAG). Zif268 is an immediate-early gene (IEG) that is necessary for the active process of memory retrieval and memory consolidation [<xref rid=\"B40-ijms-21-05352\" ref-type=\"bibr\">40</xref>,<xref rid=\"B41-ijms-21-05352\" ref-type=\"bibr\">41</xref>,<xref rid=\"B42-ijms-21-05352\" ref-type=\"bibr\">42</xref>,<xref rid=\"B43-ijms-21-05352\" ref-type=\"bibr\">43</xref>,<xref rid=\"B44-ijms-21-05352\" ref-type=\"bibr\">44</xref>,<xref rid=\"B45-ijms-21-05352\" ref-type=\"bibr\">45</xref>], and it also can serve as a general marker of increased neural activity [<xref rid=\"B46-ijms-21-05352\" ref-type=\"bibr\">46</xref>,<xref rid=\"B47-ijms-21-05352\" ref-type=\"bibr\">47</xref>,<xref rid=\"B48-ijms-21-05352\" ref-type=\"bibr\">48</xref>]. Memory retrieval-related increases in zif268 are greater for aged animals than their young adult counterparts despite showing poorer recall, while baseline age differences are not typically seen in naïve controls [<xref rid=\"B35-ijms-21-05352\" ref-type=\"bibr\">35</xref>,<xref rid=\"B49-ijms-21-05352\" ref-type=\"bibr\">49</xref>]. Based on this, we predicted that age-related deficits in fear recall would be associated with increases in zif268 protein accumulation.</p><p>We were also interested in how changes in the ubiquitin proteasome system (UPS), which is critically needed for protein degradation and synaptic plasticity following memory activation, corresponded with differences in memory performance across the lifespan and how this related to the predicted elevation in zif268 protein expression. The UPS is a major regulatory pathway that is responsible for the recognition and clearance of unneeded, abnormal, or damaged proteins [<xref rid=\"B50-ijms-21-05352\" ref-type=\"bibr\">50</xref>]. Work from our lab [<xref rid=\"B38-ijms-21-05352\" ref-type=\"bibr\">38</xref>,<xref rid=\"B51-ijms-21-05352\" ref-type=\"bibr\">51</xref>] and others [<xref rid=\"B52-ijms-21-05352\" ref-type=\"bibr\">52</xref>] has previously shown that the UPS is also important for engaging reconsolidation-related synaptic mechanisms that are associated with memory retrieval, and the functionality of this system decreases with age [<xref rid=\"B34-ijms-21-05352\" ref-type=\"bibr\">34</xref>,<xref rid=\"B36-ijms-21-05352\" ref-type=\"bibr\">36</xref>]. Decreased UPS function has also been associated with increases in protein accumulation. For example [<xref rid=\"B53-ijms-21-05352\" ref-type=\"bibr\">53</xref>], it has been demonstrated that increased protein levels of the IEG Activity Regulated Cytoskeleton Associated Protein (ARC) corresponded with decreased ubiquitin-mediated degradation. Increased phosphorylation of Rpt6 protein (i.e., pRpt6), a regulatory subunit of the proteasome important for protein degradation, is observed following learning [<xref rid=\"B50-ijms-21-05352\" ref-type=\"bibr\">50</xref>,<xref rid=\"B54-ijms-21-05352\" ref-type=\"bibr\">54</xref>], and age-related decreases in retrieval-induced Rpt6 phosphorylation have been associated with impaired performance in a trace fear conditioning paradigm [<xref rid=\"B16-ijms-21-05352\" ref-type=\"bibr\">16</xref>]. Further, cognitive deficits in at least one mouse model of Alzheimer’s disease can be reversed by manipulations that increase the phosphorylation of the Rpt6 subunit [<xref rid=\"B55-ijms-21-05352\" ref-type=\"bibr\">55</xref>]. We predicted that increases in the accumulation of zif268 protein in each region of interest (ROI) would be accompanied by a reduction in activity-driven phosphorylation of Rpt6, which is a proxy measure for activity-driven engagement of the UPS [<xref rid=\"B50-ijms-21-05352\" ref-type=\"bibr\">50</xref>].</p></sec><sec sec-type=\"results\" id=\"sec2-ijms-21-05352\"><title>2. Results</title><sec id=\"sec2dot1-ijms-21-05352\"><title>2.1. Middle-Aged (15-Month-Old) and Old (22-Month-Old) Animals Show Behavioral Impairment during Trace Fear Retrieval Relative to Young (3-Month-Old) Animals</title><p>A 3 (Group: 3 mo, 15 mo, 22 mo) × Time Period (Pre-CS, CS–UCS, Post-CS) ANOVA was conducted to assess responding throughout the acquisition session (Means/SD were as follows. CS–UCS: 3 mo: 793.54/975.33; 15 mo: 156.36/66.52; 22 mo: 177.00/99.64. Post-CS: 3 mo: 831.54/1089.63, 15 mo: 137.56/63.65, 22 mo: 180.43/104.30). This found a main effect of time period, <italic>F</italic><sub>(2, 34)</sub> = 3.96, Mean Square Error (MSE) = 130796.71, <italic>p</italic> < 0.001, <italic>η<sub>p</sub></italic><sup>2</sup> = 0.19, but no effect of group nor an interaction, largest <italic>F</italic> = 2.59, <italic>p</italic> = 0.11, indicating that all groups increased their freezing throughout the session in response to shock. Although the interaction was not significant, this effect appeared to be largest in the young animals, which may suggest that animals may acquire the learning differently as they age.</p><p>Results from the memory retrieval test are depicted in <xref ref-type=\"fig\" rid=\"ijms-21-05352-f001\">Figure 1</xref>A. A 3 (Group: 3 mo, 15 mo, 22 mo) × Time Period (CS, ITI, Post-CS) ANOVA conducted to assess responding during this session found a main effect of time period, <italic>F</italic><sub>(2, 34)</sub> = 6.46, MSE = 1067.50, <italic>p</italic> = 0.004, <italic>η<sub>p</sub></italic><sup>2</sup> = 0.28, a marginally significant interaction, <italic>F</italic><sub>(3, 34)</sub> = 2.57, MSE = 1067.50, <italic>p</italic> = 0.06, <italic>η<sub>p</sub></italic><sup>2</sup> = 0.23, and no effect of group, <italic>F</italic> = 1.86, <italic>p</italic> = 0.19, despite a clear visual trend showing decreased freezing in middle-aged and old animals. Since group differences were not observed between 15-month-old and 22-month-old animals and animals have shown deficits in fear conditioning tasks as early as middle age [<xref rid=\"B19-ijms-21-05352\" ref-type=\"bibr\">19</xref>], these groups were combined to increase statistical power and compared to young adult controls to provide a clearer follow up on the marginal interaction. This 2 (Group: 3 mo, 15 mo, and 22 mo) × Time Period (CS, ITI, Post-CS) ANOVA found a significant effect of time period, <italic>F</italic><sub>(2, 36)</sub> = 10.30, MSE = 1021.98, <italic>p</italic> < 0.001, <italic>η<sub>p</sub></italic><sup>2</sup> = 0.36, an interaction, <italic>F</italic><sub>(2, 36)</sub> = 5.12, MSE = 1021.98, <italic>p</italic> = 0.01, <italic>η<sub>p</sub></italic><sup>2</sup> = 0.22, and a marginal effect of group, <italic>F</italic><sub>(1, 18)</sub> = 3.94, MSE = 26570.66, <italic>p</italic> = 0.06. Planned comparisons showed that groups did not differ during the CS period, <italic>p</italic> = 0.16, showed a trend toward significance in the ITI, <italic>p</italic> = 0.06, and differed significantly during the Post-CS period, <italic>p</italic> = 0.04. Together, these results demonstrate that freezing was lower in the middle-aged and old animals compared to young animals.</p></sec><sec id=\"sec2dot2-ijms-21-05352\"><title>2.2. The Amount of zif268 Protein Increases and the Phosphorylation of Rpt6 Decreases as a Function of Age in Brain Regions that Support Trace Fear Learning</title><p>A one-way ANOVA comparing age group was conducted for each ROI for zif268 expression (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f001\">Figure 1</xref>) as well as for changes in pRpt6 (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f002\">Figure 2</xref>). Post-hoc Tukey’s Least Significant Difference (LSD) tests were conducted to examine group differences following the ANOVA. </p></sec><sec id=\"sec2dot3-ijms-21-05352\"><title>2.3. Medial Prefrontal Cortex</title><p>We first examined changes in zif268 accumulation and Rpt6 phosphorylation in the mPFC, which is a region that is important for working memory [<xref rid=\"B56-ijms-21-05352\" ref-type=\"bibr\">56</xref>]. In line with this, the mPFC is needed to link related events separated by time in trace conditioning [<xref rid=\"B28-ijms-21-05352\" ref-type=\"bibr\">28</xref>] and also shows age-related disruptions in UPS function [<xref rid=\"B16-ijms-21-05352\" ref-type=\"bibr\">16</xref>]. Group differences in zif268 accumulation were found in the mPFC (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f001\">Figure 1</xref>B), <italic>F</italic><sub>(2, 17)</sub> = 25.67, <italic>p</italic> < 0.001. Post-hoc tests showed that while 3-month-old animals were lower than 15-month-old (<italic>p</italic> < 0.001) and 22-month-old (<italic>p</italic> < 0.001) animals, the 15- and 22-month-old groups did not differ from each other (<italic>p</italic> = 0.21). While the ANOVA examining pRpt6 in the mPFC (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f002\">Figure 2</xref>A), <italic>F</italic><sub>(2, 15)</sub> = 2.44, <italic>p</italic> = 0.12, was not significant, 22-month-old animals showed decreases in pRpt6 relative to 3-month-old animals, <italic>p</italic> = 0.04. No other between-group differences were found; the smallest <italic>p</italic> = 0.28. No group differences were found in total Rpt6, <italic>F</italic> < 1, or in actin, <italic>F</italic> = 1.06, <italic>p</italic> = 0.32, suggesting that the changes may be specific to proteins linked to memory-related neural activity. </p></sec><sec id=\"sec2dot4-ijms-21-05352\"><title>2.4. Retrosplenial Cortex</title><p>We next examined age-related changes in the RSC, which is important in the retrieval of trace, but not delay, fear conditioning [<xref rid=\"B24-ijms-21-05352\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijms-21-05352\" ref-type=\"bibr\">25</xref>] as well as spatial and contextual processing more generally [<xref rid=\"B57-ijms-21-05352\" ref-type=\"bibr\">57</xref>]. Group differences were found in zif268 accumulation in the anterior RSC (aRSC; <xref ref-type=\"fig\" rid=\"ijms-21-05352-f001\">Figure 1</xref>C), <italic>F</italic><sub>(2, 17)</sub> = 34.93, <italic>p</italic> < 0.001. Post-hoc tests again showed that while accumulation in the 3-month-old animals was lower than that in both the 15-month-old (<italic>p</italic> < 0.001) and 22-month-old (<italic>p</italic> < 0.001) animals, these two groups did not differ from each other (<italic>p</italic> = 0.69). The ANOVA conducted to assess zif268 accumulation in the posterior RSC (pRSC) also was significant (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f001\">Figure 1</xref>D), <italic>F</italic><sub>(2, 15)</sub> = 50.61, <italic>p</italic> < 0.001. Post-hoc tests revealed that all groups differed significantly from each other (3 mo vs. 15 mo: <italic>p</italic> < 0.001; 3 mo vs. 22 mo: <italic>p</italic> < 0.001; 15 mo vs. 22 mo: <italic>p</italic> = 0.001), with increases in expression during aging. When examining pRpt6 in fractionated samples that included tissue from both the anterior and posterior RSC (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f002\">Figure 2</xref>B), the ANOVA was significant, <italic>F</italic><sub>(2, 9)</sub> = 7.63, MSE = 680.79, <italic>p</italic> = 0.01. Both 15-month-old, <italic>p</italic> = 0.01, and 22-month old animals, <italic>p</italic> = 0.006, showed reduced pRpt6 relative to 3-month-old animals but did not differ from each other, <italic>p</italic> = 0.75. There were no differences in total Rpt6 subunit protein or actin, <italic>F</italic>s < 1.</p></sec><sec id=\"sec2dot5-ijms-21-05352\"><title>2.5. Dorsal Hippocampus</title><p>In the DH (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f001\">Figure 1</xref>E), a region recruited for trace fear conditioning [<xref rid=\"B21-ijms-21-05352\" ref-type=\"bibr\">21</xref>] and spatial learning [<xref rid=\"B58-ijms-21-05352\" ref-type=\"bibr\">58</xref>], the ANOVA examining zif268 accumulation was again significant, <italic>F</italic><sub>(2, 17)</sub> = 6.51, <italic>p</italic> = 0.008. As in the mPFC and the aRSC, post-hoc tests demonstrated that while 3-month-old animals differed from both 15-month-old (<italic>p</italic> = 0.03) and 22-month-old (<italic>p</italic> = 0.003) animals, these two groups did not differ from each other (<italic>p</italic> = 0.35). No systematic changes in pRpt6 were seen in the DH (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f002\">Figure 2</xref>C), <italic>F</italic> < 1, and no group differences were observed, smallest <italic>p</italic> = 0.83. The same was true of total Rpt6, <italic>F</italic> < 1, and of actin, <italic>F</italic> = 2.83, <italic>p</italic> = 0.12.</p></sec><sec id=\"sec2dot6-ijms-21-05352\"><title>2.6. Ventral Hippocampus</title><p>Then, we examined zif268 accumulation and pRpt6 in the ventral hippocampus (VH), which is a region needed for both the acquisition and expression of trace fear [<xref rid=\"B23-ijms-21-05352\" ref-type=\"bibr\">23</xref>] as well as spatial processing [<xref rid=\"B58-ijms-21-05352\" ref-type=\"bibr\">58</xref>]. Groups also differed in zif268 accumulation in the VH (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f001\">Figure 1</xref>F), <italic>F</italic><sub>(2, 15)</sub> = 25.74, <italic>p</italic> <0.001, with post-hoc tests revealing that all groups differed from each other (3 mo vs. 15 mo: <italic>p</italic> = 0.004; 3 mo vs. 22 mo: <italic>p</italic> < 0.001; 15 mo vs. 22 mo: <italic>p</italic> = 0.005). When examining pRpt6 in the VH (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f002\">Figure 2</xref>D), there was a marginal interaction, <italic>F</italic><sub>(2, 15)</sub> = 2.98, MSE = 2153.87, <italic>p</italic> = 0.08. The 22-month-old animals showed less pRpt6 than the 3-month-old animals, <italic>p</italic> = 0.04, and there was a marginal decrease between the 3- and 15-month-old animals, <italic>p</italic> = 0.07, while the 15-month-old and 22-month-old animals did not differ from each other, <italic>p</italic> = 0.73. No differences were observed in total Rpt6 or actin, <italic>F</italic>s < 1.</p></sec><sec id=\"sec2dot7-ijms-21-05352\"><title>2.7. Lateral Amygdala</title><p>In the LA (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f001\">Figure 1</xref>G), a region important for both trace and delay fear conditioning [<xref rid=\"B59-ijms-21-05352\" ref-type=\"bibr\">59</xref>,<xref rid=\"B60-ijms-21-05352\" ref-type=\"bibr\">60</xref>], the ANOVA to test for differences in zif268 accumulation was significant, <italic>F</italic><sub>(2, 17)</sub> = 41.99, <italic>p</italic> < 0.001. Post-hoc tests revealed that all groups again differed from each other (3 mo vs. 15 mo: <italic>p</italic> < 0.001; 3 mo vs. 22 mo: <italic>p</italic> < 0.001; 15 mo vs. 22 mo: <italic>p</italic> = 0.04). When examining pRpt6 in tissue that included the entire basolateral amygdala complex (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f002\">Figure 2</xref>E), the ANOVA was not significant, <italic>F</italic><sub>(2,15)</sub> = 1.07, <italic>p</italic> = 0.37, and group differences were not observed, smallest <italic>p</italic> = 0.21. There were also no differences in total Rpt6 or actin, <italic>F</italic>s < 1.</p></sec><sec id=\"sec2dot8-ijms-21-05352\"><title>2.8. Ventrolateral Periaqueductal Gray</title><p>Finally, we examined zif268 accumulation and Rpt6 phosphorylation in the ventrolateral periaqueductal gray (vlPAG), which is a region that is important for timing and generation of the fear response [<xref rid=\"B61-ijms-21-05352\" ref-type=\"bibr\">61</xref>] as well as error correction during memory acquisition [<xref rid=\"B30-ijms-21-05352\" ref-type=\"bibr\">30</xref>]. Groups differed in zif268 accumulation in the vlPAG (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f001\">Figure 1</xref>H), <italic>F</italic><sub>(2, 15)</sub> = 41.07, <italic>p</italic> < 0.001. Post-hoc tests showed that all groups differed from each other (3 mo vs. 15 mo: <italic>p</italic> < 0.001; 3 mo vs. 22 mo: <italic>p</italic> < 0.001; 15 mo vs. 22 mo: <italic>p</italic> = 0.001), in a pattern similar to the pRSC, VH, and LA. The ANOVA testing for differences in pRpt6 was not significant in the vlPAG (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f002\">Figure 2</xref>F), <italic>F</italic><sub>(2, 12)</sub> = 1.62, <italic>p</italic> = 0.24, with a trend in differences only between the 3-month-old and 15-month-old animals, <italic>p</italic> =0.10, but no others, smallest <italic>p</italic> = 0.32. No differences were observed in total Rpt6, <italic>F</italic> = 1.25, <italic>p</italic> =0.32, or actin, <italic>F</italic> = 1.46, <italic>p</italic> = 0.24, indicating once again that protein accumulation was specific to proteins actively engaged by memory retrieval.</p><p>Together, these results show that aging results in an increase in expression of the zif268 protein throughout the trace fear circuit, and several of these regions have corresponding decreases in the phosphorylation of the Rpt6 proteasome regulatory subunit.</p></sec><sec id=\"sec2dot9-ijms-21-05352\"><title>2.9. Age-Related Changes in zif268 and pRpt6 are Associated with Degree of Behavioral Impairment</title><p>Based on the above findings, we hypothesized that decreased memory retention, as indicated by reduced freezing, would be associated with increased zif268 expression. To test this, animals were split into two groups based on a median split of their Post-CS freezing during the test [<xref rid=\"B16-ijms-21-05352\" ref-type=\"bibr\">16</xref>,<xref rid=\"B39-ijms-21-05352\" ref-type=\"bibr\">39</xref>]. The high freezers (Group Unimpaired; <italic>n</italic> = 10) consisted of six 3-month-old, three 15-month-old, and one 22-month-old animal. The low freezers (Group Impaired; <italic>n</italic> = 10) were comprised of one 3-month-old, three 15-month-old, and six 22-month-old animals. A schematic representing group membership after the median split was applied as depicted in <xref ref-type=\"fig\" rid=\"ijms-21-05352-f003\">Figure 3</xref>. All data were analyzed using independent samples t-tests. As defined by a median split on post-CS freezing behavior, impaired animals froze less than the unimpaired (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f004\">Figure 4</xref>A), <italic>t</italic><sub>(18)</sub> = 2.43, <italic>p</italic> = 0.03. Impaired animals showed greater zif268 expression than unimpaired animals in the mPFC (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f004\">Figure 4</xref>B; <italic>t</italic><sub>(18)</sub> = 2.74, <italic>p</italic> = 0.01), pRSC (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f004\">Figure 4</xref>C; <italic>t</italic><sub>(16)</sub> = 2.32, <italic>p</italic> = 0.005), VH (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f004\">Figure 4</xref>F; <italic>t</italic><sub>(16)</sub> = 2.47, <italic>p</italic> = 0.03), LA (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f004\">Figure 4</xref>G; <italic>t</italic><sub>(18)</sub> = 2.52, <italic>p</italic> = 0.02), and vlPAG (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f004\">Figure 4</xref>H; <italic>t</italic><sub>(16)</sub> = 2.75, <italic>p</italic> = 0.01). While this visual trend appeared to hold on the aRSC (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f004\">Figure 4</xref>D; <italic>t</italic><sub>(18)</sub> = 1.75, <italic>p</italic> = 0.10) and the DH (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f004\">Figure 4</xref>C; <italic>t</italic><sub>(18)</sub> = 1.32, <italic>p</italic> = 0.20), neither reached significance. </p><p>When examining the data as a function of behavioral performance using the median split procedure describe above, impaired animals showed decreases in pRpt6 in the mPFC (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f005\">Figure 5</xref>A, <italic>t</italic><sub>(16)</sub> = 2.15, <italic>p</italic> = 0.047) as compared to unimpaired animals. There was a trend in the RSC (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f005\">Figure 5</xref>B, <italic>t</italic><sub>(10)</sub> = 1.96, <italic>p</italic> = 0.08) and the VH (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f005\">Figure 5</xref>D, <italic>t</italic><sub>(16)</sub> = 2.04, <italic>p</italic> = 0.06), but this was not the case in the DH, (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f005\">Figure 5</xref>C, <italic>t</italic><sub>(15)</sub> = 0.99, <italic>p</italic> = 0.34), LA (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f005\">Figure 5</xref>E, <italic>t</italic><sub>(16)</sub> = 0.66, <italic>p</italic> = 0.52), and vlPAG (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f005\">Figure 5</xref>F, <italic>t</italic><sub>(13)</sub> = 0.54, <italic>p</italic> = 0.60).</p><p>In order to test if memory impairment was correlated with zif268 expression, post-CS behavior during retrieval was transformed using a Log10 transformation (to compress extreme scores) and Pearson’s bivariate correlations were run with each region of interest. Zif268 expression was negatively correlated with behavior in the mPFC (<italic>r</italic> = −0.38, <italic>p</italic> = 0.05), pRSC (<italic>r</italic> = −0.50, <italic>p</italic> = 0.02), VH (<italic>r</italic> = −0.50, <italic>p</italic> = 0.02), and LA (<italic>r</italic> = −0.37, <italic>p</italic> = 0.05), demonstrating that decreases in performance corresponded with increased protein. While this trend appeared in the aRSC (<italic>r</italic> = −0.33, <italic>p</italic> = 0.08), DH (<italic>r</italic> = −0.24, <italic>p</italic> = 0.15), and vlPAG (<italic>r</italic> = −0.31, <italic>p</italic> = 0.11), it was not significant. Similar correlations examined the relationship between pRpt6 and behavior. Rpt6 phosphorylation was positively correlated with behavior in the RSC (<italic>r</italic> = 0.68, <italic>p</italic> = 0.008) and the VH (<italic>r</italic> = 0.45, <italic>p</italic> = 0.03), demonstrating that reductions in pRpt6 were associated with impaired behavioral performance. This was not the case in the mPFC (<italic>r</italic> = 0.27, <italic>p</italic> = 0.14), DH (<italic>r</italic> = −0.27, <italic>p</italic> = 0.11), LA (<italic>r</italic> = −0.06, <italic>p</italic> = 0.40), or vlPAG (<italic>r</italic> = 0.02, <italic>p</italic> = 0.47). </p></sec><sec id=\"sec2dot10-ijms-21-05352\"><title>2.10. Decreased pRpt6 Corresponds with Increased Levels of zif268 in Several Brain Regions</title><p>In each region of interest, we next tested if Rpt6 phosphorylation was associated with increases in zif268 accumulation (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f006\">Figure 6</xref>). This was the case in the RSC (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f006\">Figure 6</xref>B; <italic>r</italic> = −0.77, <italic>p</italic> = 0.002) and the VH (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f006\">Figure 6</xref>D; <italic>r</italic> = −0.64, <italic>p</italic> = 0.004), with a trend toward this association in the mPFC (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f006\">Figure 6</xref>A; <italic>r</italic> = −0.34, <italic>p</italic> = 0.08) and the vlPAG (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f006\">Figure 6</xref>F; <italic>r</italic> = −0.42, <italic>p</italic> = 0.08). Decreases in pRpt6 were not associated with zif268 accumulation in the DH (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f006\">Figure 6</xref>C; <italic>r</italic> = −0.22, <italic>p</italic> = 0.20) or the LA (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f006\">Figure 6</xref>E; <italic>r</italic> = −0.12, <italic>p</italic> = 0.32).</p></sec><sec id=\"sec2dot11-ijms-21-05352\"><title>2.11. Age-Related zif268 and pRpt6 Changes Hold throughout the Trace Fear Circuit</title><p>Each animal’s levels of zif268 expression and pRpt6 within each ROI and subject were examined in the current study and averaged. An ANOVA examining zif268 expression throughout the brain was significant (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f007\">Figure 7</xref>A), <italic>F</italic><sub>(2, 17)</sub> = 104.02, MSE = 5.08, <italic>p</italic> < 0.001. All groups differed from each other (3 mo vs. 15 mo: <italic>p</italic> < 0.001; 3 mo vs. 22 mo: <italic>p</italic> < 0.001; 15 mo vs. 22 mo: <italic>p</italic> = 0.001). A similar ANOVA assessing pRpt6 levels (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f007\">Figure 7</xref>B) throughout the whole brain was also significant, <italic>F</italic><sub>(2, 17)</sub> = 4.95, MSE = 681.02, <italic>p</italic> = 0.02. Both 15-month-old (<italic>p</italic> = 0.05) and 22-month-old animals (<italic>p</italic> = 0.007) showed less pRpt6 than the 3-month controls but did not differ from each other (<italic>p</italic> = 0.42), demonstrating that Rpt6 phosphorylation is reduced with age.</p><p>Then, we examined whether global changes throughout the brain were associated with behavior using the median split analysis described above. Impaired memory performance corresponded with increased zif268 accumulation (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f007\">Figure 7</xref>C), <italic>t</italic><sub>(18)</sub> = 2.84, <italic>p</italic> = 0.01, and decreased pRpt6 (<xref ref-type=\"fig\" rid=\"ijms-21-05352-f007\">Figure 7</xref>D), <italic>t</italic><sub>(18)</sub> = 2.88, <italic>p</italic> = 0.10, relative to unimpaired animals. Finally, we examined how 22-month-old animals differed from young and middle-aged animals as a group on these measures. The 22-month-old animals showed increased zif268, <italic>t</italic><sub>(18)</sub> = 4.52, <italic>p</italic> < 0.001, and decreased pRpt6, <italic>t</italic><sub>(18)</sub> = 2.14, <italic>p</italic> = 0.046.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec3-ijms-21-05352\"><title>3. Discussion</title><p>The present findings demonstrate age-related changes in zif268 accumulation and Rpt6 phosphorylation throughout the brain. Aging resulted in increases in zif268 protein content, with both the 15-month and 22-month groups showing increased zif268 accumulation relative to the 3-month animals in all selected ROIs. However, increased zif268 accumulation in the 22-month-old animals relative to the 15-month-old animals was evident in several regions. In the posterior RSC, VH, LA, and the vlPAG, 22-month-old animals showed elevated zif268 expression relative to the 15-month-old animals in addition to the 3-month-old animals. This was not the case in the mPFC, aRSC, and the DH. Interestingly, all regions of interest showed an association between freezing during the test and zif268 expression where lower levels of freezing were associated with higher levels of zif268 expression, except for the DH [<xref rid=\"B6-ijms-21-05352\" ref-type=\"bibr\">6</xref>] and the aRSC. These results are also consistent with hypotheses that suggest age-related memory impairments result from an increase in IEG accumulation as a result of reduced proteolytic activity and subsequent protein degradation rather than an overall decrease in number of neurons [<xref rid=\"B35-ijms-21-05352\" ref-type=\"bibr\">35</xref>,<xref rid=\"B62-ijms-21-05352\" ref-type=\"bibr\">62</xref>]. These age-related decreases in zif268 degradation were largely accompanied by decreases in pRpt6. The clearest effects were observed in regions known to be critical for trace conditioning, such as the mPFC, RSC, and VH. Interestingly, the age-related differences we observed in several brain regions following trace fear retrieval were specific to proteins associated with memory processing (zif268 and pRpt6). Total levels of Rpt6 and actin, proteins that are typically not affected by memory retrieval, were unaffected. Together, these results suggest that the IEG-clearing process following memory retrieval, rather than global protein accumulation, is reduced in older animals. Thus, cognitive impairments might instead arise from decreases in activity-driven protein degradation rather than protein accumulation itself. </p><p>The current results support prior work demonstrating that memory impairments in aged animals are associated with increases in retrieval-induced zif268 protein expression [<xref rid=\"B49-ijms-21-05352\" ref-type=\"bibr\">49</xref>], as well as other IEGs [<xref rid=\"B63-ijms-21-05352\" ref-type=\"bibr\">63</xref>] and provide a likely mechanism through which protein accumulation occurs. These results are consistent with others that have found indications of increased neural activity in older animals that show memory deficits. For example, age-related deficits in the Morris water maze corresponded with increased mRNA levels in the hippocampus [<xref rid=\"B64-ijms-21-05352\" ref-type=\"bibr\">64</xref>,<xref rid=\"B65-ijms-21-05352\" ref-type=\"bibr\">65</xref>]. One interesting discrepancy in this literature is that following TFC learning, zif268 expression in aged mice is reduced relative to young adult controls [<xref rid=\"B66-ijms-21-05352\" ref-type=\"bibr\">66</xref>]. While the current results might be at odds with this finding, they also may suggest that the pattern of zif268 activity differs between initial memory consolidation and later memory retrieval, although to our knowledge, this has yet to be systematically tested. </p><p>These results are the first to show memory-related protein accumulation and UPS engagement associated in brain regions known to support trace fear across three different age groups. The results from the 15-month age group are especially interesting because of the intermediate nature of both their behavioral performance and their biological outcomes. Recall that while overall middle-aged animals seemed to resemble old animals, an equal number of 15-month animals were classified as unimpaired or impaired when groups were constructed on a median split of memory performance. Furthermore, when examining zif268 accumulation in several brain regions, 15-month-old animals were more likely to be in an intermediate zone between the 3-month-old and 22-month-old animals. As is the case in the current study, the inclusion of a “middle-aged” group in studies on age-related cognitive decline often suggests that impairments develop over time and might exist on a spectrum. Recent findings from our own laboratory have demonstrated that using a stronger TFC protocol than the one employed in the current experiment (i.e., 10 CS–UCS pairings instead of 6), the 15-month-old animals demonstrated a less robust behavioral impairment than the 22-month-old animals [<xref rid=\"B16-ijms-21-05352\" ref-type=\"bibr\">16</xref>]. Together, these results suggest that there are important boundary conditions on behavioral impairments and their associated cellular changes that are relatively understudied in middle-aged groups. Understanding the conditions that produce deficits in middle-aged cohorts may allow for more targeted preventative treatments before age-related decline becomes pathological.</p><p>One interesting dissociation observed in the present experiment is that while the 15-month-old and 22-month-old animals did not differ in zif268 expression in the anterior portion of the RSC, they did differ in zif268 expression in the posterior region. Other work in our lab has demonstrated dissociable roles for these regions in the acquisition of TFC in that the aRSC seems to be important for acquiring CS-related information, whereas the pRSC seems to be important for acquiring context-related information [<xref rid=\"B67-ijms-21-05352\" ref-type=\"bibr\">67</xref>]. In the current experiment, the behavioral impairment associated with learning about the CS (as all testing occurred in a novel context) corresponded with activity in the aRSC in both 15-month-old and 22-month-old animals, but the 22-month-old animals showed greater activity than the 15-month-old animals in the pRSC. This suggests that if tested for conditional responding to acquisition context, 22-month-old animals might show an even more robust behavioral impairment that 15-month-old animals. A similar dissociation was demonstrated in zif268 and pRpt6 expression between the DH and VH, which is in line with other experiments that have demonstrated different functions of these regions in TFC [<xref rid=\"B23-ijms-21-05352\" ref-type=\"bibr\">23</xref>,<xref rid=\"B68-ijms-21-05352\" ref-type=\"bibr\">68</xref>]. </p><p>Some clear limitations exist in the present study. Since we did not include a no-retrieval control condition, it is impossible to determine if the increases in zif268 accumulation observed were pre-existing or instead a specific result of memory retrieval. One explanation of the current data is that retrieval results in zif268 protein expression that is equivalent between groups, but failures in protein degradation in middle-aged and aged animals result in greater protein accumulation in the latter groups. A second explanation could be that aged brains create more IEG protein in general, and this process is independent of retrieval. However, recall that retrieval resulted in increases in zif268 protein based on age group, which is an effect that is not observed in naïve animals [<xref rid=\"B49-ijms-21-05352\" ref-type=\"bibr\">49</xref>]. This suggests that the effect observed here is more likely driven by active memory retrieval processes than baseline differences in protein expression. Furthermore, the effects in the present manuscript were exclusive to proteins associated with memory retrieval (zif268 and pRpt6), as actin and total Rpt6 did not differ as a function of age in any region. </p><p>Additionally, brain regions that have not been shown to be important in trace fear conditioning were not examined in the present study. Thus, it is impossible to tell if the pattern observed in the current dataset is unique to regions that are involved in TFC, or if this pattern holds throughout the entire brain even in regions not typically associated with the trace conditioning task. However, it should be noted that the strongest effects in the present experiment were in regions that are preferentially recruited during TFC and were weaker in regions that are recruited in both TFC and DFC, a task in which aged animals show no deficits [<xref rid=\"B19-ijms-21-05352\" ref-type=\"bibr\">19</xref>].</p><p>Finally, given that older animals show increases in baseline freezing relative to young animals in both of our sampled time points (acquisition and retrieval), we must acknowledge that freezing is an inherently confounded measure when analyzing memory across age groups, and therefore, the behavioral conclusions must be approached with appropriate caution as increases in nonassociative freezing might result in a ceiling effect that leaves us unable to detect group differences. While we aimed to eliminate this confound using a within-subject correction to account for differences in pre-CS freezing, baseline freezing and CS-elicited freezing measures do often interact [<xref rid=\"B69-ijms-21-05352\" ref-type=\"bibr\">69</xref>]. Therefore, the present behavioral data should be taken in concert with the corresponding descriptive molecular data, as well as understood within the greater context in cognitive decline overall as a function of age. Regardless of whether or not the observed behavioral effects were due to a ceiling effect, behavioral performance using the baseline-corrected metric, and not necessarily age, was tightly associated with zif268 accumulation and pRpt6 throughout the brain (see <xref ref-type=\"fig\" rid=\"ijms-21-05352-f007\">Figure 7</xref>C,D). </p></sec><sec sec-type=\"methods\" id=\"sec4-ijms-21-05352\"><title>4. Methods</title><sec id=\"sec4dot1-ijms-21-05352\"><title>4.1. Subjects</title><p>Subjects were 20 male Fisher 344 (F344) rats obtained from the National Institute on Aging (Charles River; Raleigh, NC, USA) at the ages of 3 (<italic>n</italic> = 7), 15 (<italic>n</italic> = 6), and 22 months (<italic>n</italic> = 7) old at the time of delivery. Animals were housed individually in plastic cages with chip bedding and free access to food in water. The room where animals were housed was maintained on a 14:10 light/dark cycle. All animals were run with approval from the University of Wisconsin-Milwaukee Institutional Animal Care and Use Committee (Protocol 2019-31, Approved 30 April 2019) in an AAALAC-accredited facility.</p></sec><sec id=\"sec4dot2-ijms-21-05352\"><title>4.2. Behavioral Procedure</title><p>For TFC, animals were placed in a Med Associates (St. Albans, VT, USA) conditioning chamber (30.5 × 24.1 × 29.2 cm) housed in individual sound attenuating chambers. Chambers were illuminated with an incandescent house light, and exhaust fans provided a 65-dB background noise. A scent was created by cleaning each chamber with a 5% acetic acid solution immediately before the animal was placed in the chamber. Following a six-minute baseline period, rats received six CS–UCS pairings. The CS was a 10-s 72 dB white noise stimulus played from a speaker mounted to the wall of the sound-attenuating chamber. The UCS was a 1 mA footshock. A 20-s trace interval period separated each CS and UCS and the intertrial interval (ITI) between these pairings was on average 240 s. Animals remained in the chamber for four minutes following the final footshock. </p><p>Animals were tested in a novel context (20.5 × 26.5 × 21 cm) for conditional freezing to the white noise CS. These conditioning boxes were housed in a sound-attenuating chamber in a separate room in the laboratory. The chamber was not illuminated, and the conditioning box had plexiglass flooring. To create a scent, it was cleaned with a 100% ethanol solution immediately before the animals were placed inside. Following a four-minute baseline period, animals received four 30-s CS presentations (with an average ITI of 60 s). Animals remained in the chamber for one minute following the final CS presentation. </p><p>Freezing was defined as the cessation of all movement excluding respiration and was automatically scored in real time with FreezeScan 1.0 detection software (Clever Sys, Inc., Reston, VA, USA) calibrated to a trained human observer. To account for differences observed in baseline freezing to the chambers in older animals (Acquisition Context (mean/SD): 3 mo: 22.59/19.75; 15 mo: 53.88/30.45; 22 mo: 40.86/21.38. Testing Context: 3 mo: 32.14/35.56; 15 mo: 61.96/29.36; 22 mo: 65.26/24.31), results are shown as a percentage of each animal’s pre-CS responding throughout the test session [<xref rid=\"B70-ijms-21-05352\" ref-type=\"bibr\">70</xref>]. This minimizes potential variation in behavior produced by factors unrelated to associative learning and instead captures the amount of behavior change produced by recall of the CS–UCS learning.</p></sec><sec id=\"sec4dot3-ijms-21-05352\"><title>4.3. Immunofluorescence (IF)</title><p>Animals were deeply anesthetized with isoflurane 60 min following the retrieval session. Previous research in our lab has demonstrated that zif268 expression peaks at 60 min following fear learning [<xref rid=\"B71-ijms-21-05352\" ref-type=\"bibr\">71</xref>]. Brains were immediately removed and stored at −80 °C until sliced in 20-micron sections and mounted onto charged slides. Slides were rehydrated in wash buffer (PBS + 0.05% Tween-20) and permeabilized (PBS + 0.3% Triton X) for 15 min and incubated in blocking solution (PBS + 0.7% NGS). Then, slides were incubated in zif268/Erg-1 antibody (Cell Signaling, 1:500, #4153, Beverly, MA, USA) solution (PBS + 0.3% Triton X + 5% NGS) overnight at 4 °C. The next day, slides were incubated in secondary antibody solution for 2 h and rinsed with wash buffer, a DAPI counterstain was applied, and slides were cover slipped. Images were captured on an Olympus Fluoview FV1200 (Olympus, Center Valley, PA, USA) confocal microscope using a 20× objective lens. Serial z-stack images covered a depth of 4.55 μm through five consecutive sections (0.91 μm per section) and were acquired using Olympus Fluoview software. Zif268 activity was normalized as a proportion of DAPI present in the same section. The total amount DAPI did not differ between groups in any ROI, <italic>F</italic>s < 1, indicating that the overall amount of cells present did not depend on age. Representative images from the DH are depicted in <xref ref-type=\"fig\" rid=\"ijms-21-05352-f008\">Figure 8</xref>.</p></sec><sec id=\"sec4dot4-ijms-21-05352\"><title>4.4. Crude Synaptosomal Membrane Fractionation</title><p>Synaptosomal membrane fractions were obtained using methods previously described [<xref rid=\"B38-ijms-21-05352\" ref-type=\"bibr\">38</xref>,<xref rid=\"B46-ijms-21-05352\" ref-type=\"bibr\">46</xref>] with minor alterations noted below. Tissue samples were homogenized in TEVP buffer with 320 mM Sucrose and centrifuged at 1000× <italic>g</italic> for 10 min at 4 °C. The supernatant was collected and spun at 10,000× <italic>g</italic> for 10 min at 4 °C. The resulting pellet containing the synaptosomal fraction was resuspended in phospho-homogenization buffer (50 mM Tris-HCl, 6 mM sodium deoxycholate, 150 mM NaCl, 1 mM NaF, two mini EDTA-free complete protease inhibitor tablets, 0.1% SDS, 1 mM sodium orthovanadate) and measured using a 660 nm protein assay (Pierce, Rockford, IL, USA). </p></sec><sec id=\"sec4dot5-ijms-21-05352\"><title>4.5. Western Blotting</title><p>Following synaptosomal preparation, protein levels were normalized and loaded onto a 7.5% SDS/PAGE gel and then to a membrane using a transfer apparatus (Bio-Rad, Richmond, CA, USA). Membranes were incubated in blocking buffer for 1 h before being incubated in pRpt6 (ProSci, 1:850, Poway, CA, USA), total Rpt6 (Abcam, 1:1000, Cambridge, MA, USA), or βactin (Cell Signaling, 1:1000, Beverly, MA, USA) primary solutions overnight at 4 °C. Then, membranes were incubated in the appropriate secondary (Cell Signaling, 1:20,000) antibody for either four hours at 4 °C (pRpt6 and tRpt6) or one hour at room temperature (βactin) and prepped in a chemiluminescence solution for 3 min. Images were captured and densitometry was performed using NIH Genesys. The pRpt6 antibody was generated commercially (ProSci) against a synthetic peptide (NH2-CALRND(pS)YTLHK-OH) as described previously [<xref rid=\"B16-ijms-21-05352\" ref-type=\"bibr\">16</xref>]. Western blotting was conducted in the same regions as IF images were taken except as follows. Tissue punches from both the anterior and posterior RSC were fractionated together into one sample and from the entire basolateral amygdala complex rather than just the lateral amygdala. Then, 15 μg of protein was loaded into each well per animal per ROI for blotting.</p></sec><sec id=\"sec4dot6-ijms-21-05352\"><title>4.6. Statistical Analysis</title><p>All results were analyzed using analyses of variance (ANOVAs) with planned comparisons to examine between-group differences following repeated measures ANOVAs and Tukey’s LSD to examine between-group differences following one-way ANOVAs, t-tests, or one-tailed Pearson’s bivariate correlation using SPSS (Statistical Package for Social Sciences, Version 25; IBM) software, with alpha set to <italic>p</italic> < 0.05. </p></sec></sec><sec sec-type=\"conclusions\" id=\"sec5-ijms-21-05352\"><title>5. Conclusions</title><p>In conclusion, these findings increase our understanding of the molecular mechanisms associated with age-related cognitive decline by examining IEG activity throughout the trace fear circuit across an age spectrum in rats. While these results suggest that some brain regions (i.e., mPFC, aRSC, and DH) stop showing age-related changes at earlier ages, others (i.e., pRSC, VH, LA, and vlPAG) continue to change from middle age to senescent populations. Furthermore, they suggest that one likely mechanism through which increased protein expression might occur is through decreases in activity-driven protein degradation processes and subsequent protein accumulation. </p></sec></body><back><ack><title>Acknowledgments</title><p>We thank Nicole Ferrara and Hanna Yousuf for their comments on the manuscript.</p></ack><notes><title>Author Contributions</title><p>Conceptualization, S.T., B.N.D., and F.J.H.; data collection, S.T. and B.N.D.; formal analysis, S.T.; resources, F.J.H.; data curation, S.T.; writing—original draft preparation, S.T.; writing—review and editing, B.N.D. and F.J.H.; funding acquisition, F.J.H. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This work was supported by NIH grants R21AG053854 (FJH), R01MH112141 (FJH), and F32MH120938 (ST).</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijms-21-05352\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Anstey</surname><given-names>K.J.</given-names></name><name><surname>Low</surname><given-names>L.F.</given-names></name></person-group><article-title>Normal cognitive changes in aging</article-title><source>Aust. Fam. 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Panels (<bold>B</bold>–<bold>H</bold>) show zif268 expression reported as a percentage (mean and SEM) of total DAPI present in the same section. Zif268 was increased in both 15-month-old and 22-month-old animals relative to 3-month-old animals in the medial prefrontal cortex (mPFC) (<bold>B</bold>), the aRSC (<bold>C</bold>), pRSC (<bold>D</bold>), dorsal hippocampus (DH) (<bold>E</bold>), ventral hippocampus (VH) (<bold>F</bold>), LA (<bold>G</bold>), and ventrolateral periaqueductal gray (vlPAG) (<bold>H</bold>). In the pRSC, VH, LA, and vlPAG, 22-month-old animals also showed increased zif268 relative to 15-month-old animals. Asterisks indicate <italic>p</italic> < 0.05; pound signs indicate <italic>p</italic> < 0.10. White dots represent individual rats.</p></caption><graphic xlink:href=\"ijms-21-05352-g001\"/></fig><fig id=\"ijms-21-05352-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Rpt6 protein (pRpt6) reported as optical density as a percentage of 3-month-old control animals (mean and SEM) with representative images below group name. The 22-month-old animals showed decreased pRpt6 relative to the 3-month-old animals in the medial prefrontal cortex (mPFC) (<bold>A</bold>). In the retrosplenial cortex (RSC) (<bold>B</bold>), both the 15-month-old and 22-month-old animals showed decreased pRpt6. No differences were observed in the DH (<bold>C</bold>). The 22-month-old animals showed decreased pRpt6 in the VH (<bold>D</bold>), where a trend toward a decrease was seen in the 15-month-old animals. We found no differences in the basolateral amygdala (BLA) (<bold>E</bold>), and a trend toward a decrease in 15-month-old animals in the vlPAG (<bold>F</bold>). Asterisks indicate <italic>p</italic> < 0.05; pound signs indicate <italic>p</italic> < 0.10. White dots represent individual rats.</p></caption><graphic xlink:href=\"ijms-21-05352-g002\"/></fig><fig id=\"ijms-21-05352-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Schematic showing animals from each group classified as unimpaired or impaired based on behavioral performance. Each square represents an individual animal.</p></caption><graphic xlink:href=\"ijms-21-05352-g003\"/></fig><fig id=\"ijms-21-05352-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Animals were divided into two groups (high freezers and low freezers) based on a median split of performance in the Post-CS (conditional stimulus) period during the retrieval test. Results from the Post-CS period during the retrieval test (mean and SEM) are shown in Panel (<bold>A</bold>), where impaired animals froze less than unimpaired animals. Panels (<bold>B</bold>–<bold>H</bold>) show zif268 expression reported as a percentage (mean and SEM) of total DAPI present in the same section. Impaired animals had increased zif268 relative to unimpaired in the mPFC (<bold>B</bold>). While there was a trend in the aRSC (<bold>C</bold>), impaired animals showed increased zif268 in the pRSC (<bold>D</bold>). No differences were seen in the DH (<bold>E</bold>), but impaired animals had increased zif268 relative to unimpaired animals in the VH (<bold>F</bold>). This finding held in the LA (<bold>G</bold>) and the vlPAG (<bold>H</bold>). Asterisks indicate <italic>p</italic> < 0.05; pound signs indicate <italic>p</italic> < 0.10. White dots represent individual rats.</p></caption><graphic xlink:href=\"ijms-21-05352-g004\"/></fig><fig id=\"ijms-21-05352-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>Animals were divided into two groups based on a median split of performance in the Post-CS period during the retrieval test. High freezers were designated as unimpaired, whereas low freezers were designated as impaired. Panels (<bold>A</bold>–<bold>F</bold>) show pRpt6 reported as a percentage (mean, SEM) in 3-month-old animals. Impaired animals showed less pRpt6 than unimpaired in both the mPFC (<bold>A</bold>) and the RSC (<bold>B</bold>). As before, there were no differences in the DH (<bold>C</bold>). Impaired memory performance corresponded with a trend toward a decrease in the VH (<bold>D</bold>). No differences between unimpaired and impaired animals were observed in the LA (<bold>E</bold>) or the vlPAG (<bold>F</bold>). Asterisks indicate <italic>p</italic> < 0.05; pound signs indicate <italic>p</italic> < 0.10. White dots represent individual rats.</p></caption><graphic xlink:href=\"ijms-21-05352-g005\"/></fig><fig id=\"ijms-21-05352-f006\" orientation=\"portrait\" position=\"float\"><label>Figure 6</label><caption><p>Scatterplots depicting correlations between phosphorylated Rpt6 and zif268 accumulation in the mPFC (<bold>A</bold>), RSC (<bold>B</bold>), DH (<bold>C</bold>), VH (<bold>D</bold>), LA (<bold>E</bold>), and vlPAG (<bold>F</bold>). Unimpaired animals are represented by gray dots, and impaired animals are represented by white dots.</p></caption><graphic xlink:href=\"ijms-21-05352-g006\"/></fig><fig id=\"ijms-21-05352-f007\" orientation=\"portrait\" position=\"float\"><label>Figure 7</label><caption><p>Levels of zif268 expression and pRpt6 in each ROI were averaged throughout the entire brain for each animal. Panels (<bold>A</bold>,<bold>B</bold>) represent zif268 (<bold>A</bold>) and pRpt6 (<bold>B</bold>) levels with groups constructed based on age group. zif268 levels differ between all age groups (<bold>A</bold>). Both 15-month-old and 22-month-old animals showed less pRpt6 than 3-month-old animals, but did not differ from each other. (<bold>B</bold>) Panels (<bold>C</bold>,<bold>D</bold>) represent zif268 (<bold>C</bold>) and pRpt6 (<bold>D</bold>) levels with groups constructed based on behavioral performance defined by a median split. Animals with impaired memory performance showed increased zif268 (<bold>C</bold>) and decreased pRpt6 (<bold>D</bold>) relative to unimpaired animals. Asterisks indicate <italic>p</italic> < 0.05. White dots represent individual rats.</p></caption><graphic xlink:href=\"ijms-21-05352-g007\"/></fig><fig id=\"ijms-21-05352-f008\" orientation=\"portrait\" position=\"float\"><label>Figure 8</label><caption><p>Schematic of coronal section where dorsal hippocampus (DH) slices were taken at −3.00 mm relative to bregma (Panel <bold>A</bold>). Representative images from taken at 20× 3-month-old (Panel <bold>B</bold>), 15-month-old (Panel <bold>C</bold>), and 22-month-old (Panel <bold>D</bold>) animals have DAPI depicted in blue and zif268 depicted in red. Insets of the highlighted region are shown in the bottom right corner for each image.</p></caption><graphic xlink:href=\"ijms-21-05352-g008\"/></fig></floats-group></article>\n"
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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32722472</article-id><article-id pub-id-type=\"pmc\">PMC7432049</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155367</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05367</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Physical Literacy, Physical Activity, and Health Indicators in School-Age Children</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Caldwell</surname><given-names>Hilary A.T.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05367\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05367\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Di Cristofaro</surname><given-names>Natascja A.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05367\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Cairney</surname><given-names>John</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05367\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Bray</surname><given-names>Steven R.</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05367\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-9851-3610</contrib-id><name><surname>MacDonald</surname><given-names>Maureen J.</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05367\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Timmons</surname><given-names>Brian W.</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05367\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05367\">2</xref><xref rid=\"c1-ijerph-17-05367\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05367\"><label>1</label>Child Health and Exercise Medicine Program, Department of Pediatrics, McMaster University, Hamilton, ON L8S 4L8, Canada; <email>caldweha@mcmaster.ca</email> (H.A.T.C.); <email>dalimona@mcmaster.ca</email> (N.A.D.C.)</aff><aff id=\"af2-ijerph-17-05367\"><label>2</label>Department of Kinesiology, McMaster University, Hamilton, ON L8S 4L8, Canada; <email>sbray@mcmaster.ca</email> (S.R.B.); <email>macdonmj@mcmaster.ca</email> (M.J.M.)</aff><aff id=\"af3-ijerph-17-05367\"><label>3</label>School of Human Health and Nutritional Sciences, University of Queensland, St Lucia QLD 4072, Australia; <email>j.cairney@uq.edu.au</email></aff><author-notes><corresp id=\"c1-ijerph-17-05367\"><label>*</label>Correspondence: <email>timmonbw@mcmaster.ca</email>; Tel.: +905-521-2100 (ext. 77615)</corresp></author-notes><pub-date pub-type=\"epub\"><day>25</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5367</elocation-id><history><date date-type=\"received\"><day>24</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>21</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>It has been theorized that physical literacy is associated with physical activity and health. The purpose of this study is to investigate the associations between physical literacy and health, and if this relationship is mediated by moderate-to-vigorous physical activity (MVPA). Two hundred and twenty-two children (113 girls, 10.7 ± 1.0 years old) participated in this cross-sectional study. A physical literacy composite score was computed from measures of PLAYfun, PLAYparent, and PLAYself. Physical activity was measured over seven days with accelerometers, expressed as MVPA (min/day). Health indicators included: body composition (percent body fat), aerobic fitness (treadmill time and 60s heart rate recovery), resting systolic blood pressure, and quality of life. Physical literacy was significantly associated (<italic>p</italic> < 0.001) with percent body fat (R<sup>2</sup> = 0.23), treadmill time (R<sup>2</sup> = 0.21), 60 s heart rate recovery (R<sup>2</sup> = 0.36), systolic blood pressure (R<sup>2</sup> = 0.11), and quality of life (R<sup>2</sup> = 0.11). The relationships between physical literacy and aerobic fitness, but not other health indicators, were directly mediated by MVPA. Higher physical literacy in children is associated with favorable health indicators, and the relationships between physical literacy and aerobic fitness were influenced by MVPA. Future work should examine these relationships longitudinally and determine if changes in physical literacy leads to changes in health. </p></abstract><kwd-group><kwd>youth</kwd><kwd>aerobic fitness–body composition</kwd><kwd>blood pressure</kwd><kwd>quality of life</kwd><kwd>mediation</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05367\"><title>1. Introduction</title><p>Physical literacy is theorized to be the foundation of lifetime physical activity participation and is defined as the motivation, confidence, physical competence, knowledge and, understanding to value and take responsibility for engagement in physical activities for life [<xref rid=\"B1-ijerph-17-05367\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijerph-17-05367\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijerph-17-05367\" ref-type=\"bibr\">3</xref>]. This definition of physical literacy includes four interconnected elements (affective, physical, cognitive, and behavioral) that change and adapt across the lifespan [<xref rid=\"B3-ijerph-17-05367\" ref-type=\"bibr\">3</xref>]. In childhood, increased physical activity participation is associated with numerous health benefits, including decreased adiposity, reductions in cardiometabolic disease risk, increases in aerobic fitness and muscular strength, and higher quality of life [<xref rid=\"B4-ijerph-17-05367\" ref-type=\"bibr\">4</xref>]. The majority of Canadian children and youth are not participating in enough physical activity to achieve health benefits and innovative strategies are needed to increase participation [<xref rid=\"B5-ijerph-17-05367\" ref-type=\"bibr\">5</xref>]. If physical literacy is the gateway to increasing physical activity, then physical literacy may be an indirect determinant of health, as increased physical activity is associated with health benefits [<xref rid=\"B6-ijerph-17-05367\" ref-type=\"bibr\">6</xref>]. The enthusiasm for physical literacy in physical education, public health, sport, and recreation has out-paced research on this topic [<xref rid=\"B7-ijerph-17-05367\" ref-type=\"bibr\">7</xref>]. As such, further empirical evidence about the relationships between physical literacy, physical activity, and health indicators is necessary to advance knowledge in this field. </p><p>In Margaret Whitehead’s text, Physical Literacy Throughout the Lifecourse, she theorizes that physical literacy ought to be associated with weight status, fitness, physical activity, and motor competence [<xref rid=\"B2-ijerph-17-05367\" ref-type=\"bibr\">2</xref>]. More recently, it has been suggested that physical literacy be considered a determinant of health through the following reciprocal pathways: elevated physical literacy leads to greater physical activity participation, which leads to positive physiological, social, and psychosocial adaptations, resulting in improved physical, mental, and social health; this pathway would be present and dynamic across the lifespan from early childhood to old age [<xref rid=\"B6-ijerph-17-05367\" ref-type=\"bibr\">6</xref>]. Another important part of this proposed model is that some relationships are bidirectional. For example, physical literacy is proposed to be an important determinant of physical activity participation, but development of physical literacy is suggested to occur through structured and unstructured physical activity opportunities [<xref rid=\"B6-ijerph-17-05367\" ref-type=\"bibr\">6</xref>]. As this proposed pathway is novel, limited empirical research has explored these relationships.</p><p>Based on the above definitions and theories, physical literacy has gained attention as the foundation for lifelong physical activity participation, which would, in turn, lead to desirable physical and psychosocial benefits [<xref rid=\"B3-ijerph-17-05367\" ref-type=\"bibr\">3</xref>,<xref rid=\"B6-ijerph-17-05367\" ref-type=\"bibr\">6</xref>,<xref rid=\"B8-ijerph-17-05367\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05367\" ref-type=\"bibr\">9</xref>]. Several studies have outlined these associations in a large sample of Canadian children whose physical literacy was assessed with the Canadian Assessment of Physical Literacy (CAPL) [<xref rid=\"B10-ijerph-17-05367\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijerph-17-05367\" ref-type=\"bibr\">11</xref>]. In those studies, healthy weight children demonstrated slightly higher overall physical literacy and higher scores in modified physical competence, daily behavior, motivation and confidence, and knowledge and understanding domains than children who were overweight or obese [<xref rid=\"B12-ijerph-17-05367\" ref-type=\"bibr\">12</xref>]. Children and youth who met the Canadian Physical Activity Guidelines of 60 min of daily moderate-to-vigorous physical activity (MVPA) demonstrated higher physical competence, and motivation and confidence scores than those who did not meet the Physical Activity Guideline, while the knowledge and understanding score was not associated with meeting the Physical Activity Guideline [<xref rid=\"B13-ijerph-17-05367\" ref-type=\"bibr\">13</xref>]. Higher physical literacy scores were associated with higher cardiorespiratory fitness, but the associations between physical literacy and other aspects of fitness (i.e., muscle endurance and muscle strength) were not studied [<xref rid=\"B14-ijerph-17-05367\" ref-type=\"bibr\">14</xref>]. </p><p>The Physical Literacy Assessment for Youth (PLAY) tools are another measure of individual physical literacy and includes several workbooks to assess different domains of physical literacy [<xref rid=\"B15-ijerph-17-05367\" ref-type=\"bibr\">15</xref>]. PLAYfun is a measure of individual movement competence with several domains, including running, locomotor, upper and lower body object control, and balance [<xref rid=\"B15-ijerph-17-05367\" ref-type=\"bibr\">15</xref>]. Physical activity participation (measured with pedometers) was significantly associated with the PLAYfun total score, locomotor domain score, and balance domain score in 7–14 year-old children [<xref rid=\"B16-ijerph-17-05367\" ref-type=\"bibr\">16</xref>]. The relationships between physical literacy, as assessed with the PLAY Tools, and fitness, body composition, blood pressure, and health-related quality of life have not been previously studied. </p><p>In light of the gaps in knowledge, the purpose of this study was to examine the cross-sectional associations between physical literacy and body composition, fitness, blood pressure, and health-related quality of life, and to determine if these relationships are mediated by physical activity participation in Canadian school-age children (8–13-year-old children). It was hypothesized that physical literacy would be positively associated with aerobic fitness and health-related quality of life, negatively associated with body composition and blood pressure, and that these relationships would be mediated by physical activity participation. </p></sec><sec id=\"sec2-ijerph-17-05367\"><title>2. Materials and Methods</title><sec id=\"sec2dot1-ijerph-17-05367\" sec-type=\"subjects\"><title>2.1. Participants and Design</title><p>Participants in this study were part of the school-age kids health from early investment in physical activity (SKIP) study, a 3-year longitudinal cohort study of physical activity and health outcomes in school-age boys and girls. The SKIP study was a follow-up to the health outcomes and physical activity in preschoolers (HOPP) study, described previously [<xref rid=\"B17-ijerph-17-05367\" ref-type=\"bibr\">17</xref>]. At enrollment, children with diagnosed medical conditions or known developmental or cognitive delays were excluded. Physical literacy assessments were added to the third and final year of the SKIP study. The Hamilton Integrated Research Ethics Board provided ethical approval for the study. All parents provided informed, written consent and all children provided written, informed assent. </p></sec><sec id=\"sec2dot2-ijerph-17-05367\"><title>2.2. Measures</title><sec id=\"sec2dot2dot1-ijerph-17-05367\"><title>2.2.1. Years from Peak Height Velocity (YPHV)</title><p>YPHV was calculated with validated equations that included the following variables: gender, date of birth, date of measurement, standing height, sitting height, and weight. Assessment of YPHV is a non-invasive, practical method to assess maturity status and the equations have been tested and cross-validated in longitudinal samples. The mean difference between actual and predicted maturity was 0.243 ± 0.650 years for boys and 0.001 ± 0.678 years in girls, allowing an accurate prediction of biological age [<xref rid=\"B18-ijerph-17-05367\" ref-type=\"bibr\">18</xref>]. Due to the age of participants in this study, models were adjusted for YPHV, rather than for chronological age. </p></sec><sec id=\"sec2dot2dot2-ijerph-17-05367\"><title>2.2.2. Body Mass Index (BMI)</title><p>Height and weight were measured using standard procedures [<xref rid=\"B17-ijerph-17-05367\" ref-type=\"bibr\">17</xref>]. BMI was calculated as weight/height<sup>2</sup> (kg/m<sup>2</sup>). BMI percentiles, based on sex and age, were calculated using Centre for Disease Control growth charts for descriptive purposes [<xref rid=\"B19-ijerph-17-05367\" ref-type=\"bibr\">19</xref>].</p></sec><sec id=\"sec2dot2dot3-ijerph-17-05367\"><title>2.2.3. Physical Literacy</title><p>The PLAY Tools were developed by Sport for Life and represent a series of assessment tools to assess the multiple domains of physical literacy [<xref rid=\"B15-ijerph-17-05367\" ref-type=\"bibr\">15</xref>]. The PLAY Tools were designed for children 7 years and older. In combination, the PLAYfun, the PLAYself, and the PLAYparent tools provide a multi-perspective assessment of a participant’s physical literacy [<xref rid=\"B15-ijerph-17-05367\" ref-type=\"bibr\">15</xref>]. Participants in the SKIP Study completed PLAYfun and PLAYself and a parent or guardian of each participant completed PLAYparent. </p><p>The PLAYfun assessment includes 18 movement skills within five domains: running, locomotor, object control (upper body, object control) lower body, and balance, stability, and body control. PLAYfun was administered with the same methods as previously described [<xref rid=\"B7-ijerph-17-05367\" ref-type=\"bibr\">7</xref>,<xref rid=\"B20-ijerph-17-05367\" ref-type=\"bibr\">20</xref>]. The total score is the average score of all 18 task scores [<xref rid=\"B15-ijerph-17-05367\" ref-type=\"bibr\">15</xref>]. All PLAYfun assessments were administered and scored by one of two investigators (HC and ND). </p><p>The PLAYself questionnaire is a 22-item self-evaluation of a child’s perception of their own physical literacy [<xref rid=\"B15-ijerph-17-05367\" ref-type=\"bibr\">15</xref>]. The PLAYself questionnaire includes four subsections: environment, physical literacy self-description, relative rankings of literacies (literacy, numeracy, physical literacy) and fitness. The PLAYself score was calculated by adding up the totals of the subsections and dividing by 27, as outlined in the PLAYself workbook [<xref rid=\"B15-ijerph-17-05367\" ref-type=\"bibr\">15</xref>].</p><p>The PLAYparent questionnaire was used to assess a parent’s perception of their child’s level of physical literacy, including questions about the child’s ability, confidence, and participation. PLAYparent provided researchers with an additional perspective and identified positive and negative factors that affect the child’s ability to lead a healthy lifestyle. The PLAYparent questionnaire is divided into five subsections: physical literacy VAS, cognitive domain, environment, motor competence (locomotor and object control) and fitness [<xref rid=\"B15-ijerph-17-05367\" ref-type=\"bibr\">15</xref>]. The PLAYparent questionnaire was scored by summing the parents’ responses and multiplying by 2.63 to give a total out of 150, as outlined in the PLAYparent workbook [<xref rid=\"B15-ijerph-17-05367\" ref-type=\"bibr\">15</xref>]. </p><p>A physical literacy composite score was calculated using the standardized scores of PLAYfun, PLAYparent, and PLAYself. The standardized scores were summed, with higher values suggesting greater physical literacy. </p></sec><sec id=\"sec2dot2dot4-ijerph-17-05367\"><title>2.2.4. Body Composition</title><p>Percent body fat (%BF) was measured by bioelectrical impedance analysis (BIA; RJL Quantum 2, Tanita Corporation, Japan). Fat free mass (FFM) was calculated using an equation that was validated against DEXA in children [<xref rid=\"B21-ijerph-17-05367\" ref-type=\"bibr\">21</xref>]; %BF was then calculated as ((body weight- FFM)/body weight] × 100). </p></sec><sec id=\"sec2dot2dot5-ijerph-17-05367\"><title>2.2.5. Physical Activity</title><p>Physical activity was assessed using Actigraph GT3X accelerometers (Fort Walton Beach, FL, USA). The accelerometers recorded raw accelerations at 30 Hz during waking hours for seven days, except during swimming or bathing. Participants wore the accelerometer on a belt over their right hip. Participants and/or parents were instructed to record the times the accelerometer was put on and taken off in the provided logbook. Accelerometer data were downloaded in 3s epochs, visually inspected for any spurious activity counts, and processed with Actilife Software (Version 6.11.9, Actigraph, Pensacola, FL). A non-wear period was defined as 60 min or more of continuous zero counts or if the logbook indicated device removal. Only participants who wore the accelerometer for at least 3 days with a minimum wear time of 10 h per day were included in the analyses. This minimum wear time provides a reliability coefficient of 0.9 for children [<xref rid=\"B22-ijerph-17-05367\" ref-type=\"bibr\">22</xref>]. Daily minutes of MVPA were calculated using Evenson et al. (2008) cut-points (≥574 counts/15-sec) [<xref rid=\"B23-ijerph-17-05367\" ref-type=\"bibr\">23</xref>] that are recommended to estimate time spent in different intensities of physical activity in children and adolescents [<xref rid=\"B24-ijerph-17-05367\" ref-type=\"bibr\">24</xref>]. Cut-points were divided by 5 to account for the 3 s epoch used in the current study [<xref rid=\"B25-ijerph-17-05367\" ref-type=\"bibr\">25</xref>]. </p></sec><sec id=\"sec2dot2dot6-ijerph-17-05367\"><title>2.2.6. Aerobic Fitness</title><p>Aerobic fitness was assessed using a modified Bruce Protocol, a progressive treadmill test that increases in speed and grade every 3 min [<xref rid=\"B26-ijerph-17-05367\" ref-type=\"bibr\">26</xref>]. To ensure participant safety, participants were given the option to hold the handrails during the test and a researcher was positioned behind the treadmill. Participants were fitted with a heart rate (HR) monitor (Polar Electro, Kepele, Finland) to continuously monitor HR during the test and seated recovery. The test was terminated when the participants were exhausted, could no longer keep up with the speed of the treadmill and/or showed signs of emotional distress and/or refused to continue. In our sample, the average peak HR was 202 ± 7 bpm (183–226 bpm), suggesting participants were at, or near, exhaustion. Time to exhaustion with the Bruce Protocol is highly reproducible in school-age children (correlation coefficient = 0.94) [<xref rid=\"B27-ijerph-17-05367\" ref-type=\"bibr\">27</xref>]. Upon termination of the treadmill test, the participants were immediately seated and asked to remain as still as possible for 2 min. The second indicator of aerobic fitness was 60 s HR recovery (HRR), calculated as the difference between the peak HR (single beat highest value) and HR 60s into recovery. Higher values indicate faster autonomic recovery [<xref rid=\"B28-ijerph-17-05367\" ref-type=\"bibr\">28</xref>]. </p></sec><sec id=\"sec2dot2dot7-ijerph-17-05367\"><title>2.2.7. Blood Pressure</title><p>Automated measures of seated blood pressure (Dinamap Pro 100; Critikon Inc) were obtained from the right arm at least 4 times with a 1 min delay between each measure. The 2nd, 3rd, and 4th measures were averaged if within 5 mmHg; additional measures were taken if the measures differed by more than 5 mm Hg [<xref rid=\"B29-ijerph-17-05367\" ref-type=\"bibr\">29</xref>]. Blood pressure was expressed as seated systolic blood pressure (SBP), as SBP is a better predictor of hypertension and cardiovascular events compared to diastolic blood pressure [<xref rid=\"B30-ijerph-17-05367\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijerph-17-05367\" ref-type=\"bibr\">31</xref>]. </p></sec><sec id=\"sec2dot2dot8-ijerph-17-05367\"><title>2.2.8. Health-Related Quality of Life (HRQOL)</title><p>Participants completed the self-reported Pediatric Quality of Life (PedsQL) 4.0 Child Self-Report for 8–12-year-old children, a reliable, valid, 23-item questionnaire that evaluates children’s quality of life in 4 core domains: physical (8 items), emotional (5 items), social (5 items), and school functioning (5 items). Children were asked to rate how problematic each item had been in the previous month on a 5-point scale (never a problem, almost never a problem, sometimes a problem, often a problem, almost always a problem). The PedsQL outcomes are an aggregate score, with higher scores suggesting better HRQOL [<xref rid=\"B32-ijerph-17-05367\" ref-type=\"bibr\">32</xref>]. PedsQL cut-off scores for designating an at-risk status for impaired HRQOL and minimal clinically important difference values are also available to aid in the interpretation of results [<xref rid=\"B33-ijerph-17-05367\" ref-type=\"bibr\">33</xref>]. </p></sec></sec><sec id=\"sec2dot3-ijerph-17-05367\"><title>2.3. Statistical Analyses</title><p>All statistical analyses were performed in STATA (Version 14.2). A <italic>p</italic>-value of 0.05 was used to specific statistical significance. Descriptive statistics (mean, standard deviation, minimum and maximum) of the participant’s age, YPHV, sex, percent body fat, treadmill time, 60s HRR, blood pressure, HRQOL, and PLAY Tools were calculated. Sex-dependent variation in all measures were examined with <italic>t</italic>-tests. Physical literacy z-scores were calculated for PLAYfun, PLAYself, and PLAYparent as the individual values minus the group mean, divided by the standard deviation to achieve variables that had a mean of 0 and a standard deviation of 1. The physical literacy composite score was calculated as the sum of the PLAYfun, PLAYparent, and PLAYself z-scores. </p><p>Linear regression was used to determine the relationships between physical literacy composite score and percent body, treadmill time, 60 s HRR, blood pressure, and HRQOL in independent models. Regression models were adjusted for participant’s sex and YPHV. Normality and skewness were assessed with the Shapiro–Wilk Test for Normality, the Skewness/Kurtosis Test for Normality, and visual inspection of P-P plots, Q-Q plots, and histograms. Collinearity between variables was assessed with the variance inflation factor. </p><p>To further explore Cairney et al.’s model (2019), mediation analyses were conducted to determine if the associations between physical literacy and the various health indicators were mediated by MVPA. The tests for mediation effects were conducted independently for each health outcome using the SEM command in STATA. For each model, the physical literacy composite score was entered as the independent (X) variable, MVPA as the mediator (M) and health indicator as the dependent variable (Y), with sex and YPHV included as covariates. Bootstrapping was set to 10,000 samples [<xref rid=\"B34-ijerph-17-05367\" ref-type=\"bibr\">34</xref>]. Sex was included as a covariate because male children and youth engage in more habitual physical activity than females, as demonstrated by our results (<xref rid=\"ijerph-17-05367-t001\" ref-type=\"table\">Table 1</xref>) and in the literature [<xref rid=\"B5-ijerph-17-05367\" ref-type=\"bibr\">5</xref>]. YPHV was included because it is recommended that pediatric exercise science studies control for the effects of maturity on their results [<xref rid=\"B35-ijerph-17-05367\" ref-type=\"bibr\">35</xref>]. </p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05367\"><title>3. Results</title><sec id=\"sec3dot1-ijerph-17-05367\" sec-type=\"subjects\"><title>3.1. Participants</title><p>Two hundred and forty-nine participants (121 girls, 128 boys) took part in the lab-based assessments of year 3 of the SKIP study, and 222 completed consent and assent forms to participate in the physical literacy assessments (113 girls, 109 boys, 10.7 ± 1.0 years). Only participants with consent and assent for the physical literacy assessments were included in subsequent analysis. Reasons for not participating in the physical literacy assessments included: the visit was scheduled before ethics approval was granted for these additional assessments (<italic>n</italic> = 3), participant and/or parent denied participation in the extra assessment (<italic>n</italic> = 19), or an assessor trained in the assessment of physical literacy was unavailable (<italic>n</italic> = 5). One participant who provided consent and assent became distressed in the visit and withdrew participation from PLAYfun and PLAYself, and the parents of two participants did not complete PLAYparent. Treadmill data (treadmill time and 60s HRR) from 11 participants was excluded from analyses for the following reasons: 1 participant did not participate in the treadmill test, 8 participants began the test but refused to continue and ended the test before reaching the termination criteria, and two participants stopped prematurely due to pre-existing musculoskeletal injuries. Two hundred and eight (93.7%) of the 222 participants met the accelerometer wear time criteria. Six children aged 8 did not complete the Peds-QL as per the study’s protocol that only participants ≥9 years old completed questionnaires. Two participants were missing blood pressure measures because one participant declined the measurement and one did not complete the vascular component of testing. Missing data were not imputed, and pairwise deletion was used for analysis. </p><p>Descriptive statistics are included in <xref rid=\"ijerph-17-05367-t001\" ref-type=\"table\">Table 1</xref>. The Shapiro–Wilk test for normality showed that all variables, except 60s HRR, were not consistent with a normal distribution (<italic>p</italic> > 0.05). Visual interpretation of histograms further revealed that HRQOL (median: 78.26) and physical literacy composite (median: 0.07) were negatively skewed. Age was evenly distributed between 8–13 years old. YPHV, %BF, SBP, and MVPA appeared to be consistent with the normal distribution. Treadmill time was irregularly distributed and was not consistent with a normal or skewed distribution (median: 10.36). Variance inflation factors did not reveal collinearity among the independent variables. </p><p>There were no differences between boys and girls in age, height, weight, BMI or BMI%ile (<italic>p</italic> = 294–0.904). Girls had smaller YPHV values, suggesting that they were more mature than boys (<italic>p</italic> < 0.001), and displayed a higher %BF than the boys (<italic>p</italic> < 0.001). The boys exhibited longer treadmill times (<italic>p</italic> = 0.005) and faster 60 s HRR (<italic>p</italic> < 0.001). The girls self-reported higher HRQOL total scores than the boys (<italic>p</italic> = 0.014), which were attributable to significantly higher psychosocial composite scale scores (includes emotional, social, and school functioning scales) in the girls versus the boys (76.58 ± 13.03 versus 72.02 ± 12.18, <italic>p</italic> = 0.008, respectively). On average, participants wore their accelerometers for 12.76 ± 0.70 h per day, and there were no differences in wear time between boys and girls. Boys participated in more MVPA than girls (<italic>p</italic> < 0.001). </p></sec><sec id=\"sec3dot2-ijerph-17-05367\"><title>3.2. Physical Literacy and Health</title><p>Boys displayed higher PLAYfun scores than girls (<xref rid=\"ijerph-17-05367-t002\" ref-type=\"table\">Table 2</xref>; <italic>p</italic> = 0.017), but there were no differences in PLAYself and PLAYparent between boys and girls (<italic>p</italic> = 0.423–0.820). The physical literacy composite score ranged from −8.8 to 5.6, with no difference between boys and girls (<italic>p</italic> = 0.151). </p><p>The physical literacy composite score was significantly associated with each health indicator (<xref rid=\"ijerph-17-05367-t003\" ref-type=\"table\">Table 3</xref>). The physical literacy composite score and YPHV were associated with %BF (R<sup>2</sup> = 0.228, F (3,205) = 20.19, <italic>p</italic> < 0.001) and MVPA (R<sup>2</sup> = 0.235, F (3,192) = 16.61, <italic>p</italic> < 0.001). The physical literacy composite score, sex, and YPHV were associated with SBP (R<sup>2</sup> = 0.109, F (3,204) = 8.31, <italic>p</italic> < 0.001).</p></sec><sec id=\"sec3dot3-ijerph-17-05367\"><title>3.3. Mediation Analyses</title><p>As outlined above, physical literacy had a direct effect on all health indicators. Next, physical activity was explored as a mediator of the relationship between physical literacy and health indicators. With %BF as the dependent variable, there was a direct effect of physical literacy on MVPA (β = 3.20, 95% confidence interval (CI): 2.21–4.18, <italic>p</italic> < 0.001) and a non-significant direct effect of MVPA on %BF (β = −0.03, 95% CI: −0.07–0.001, <italic>p</italic> = 0.057). There was a non-significant indirect effect of MVPA on the relationship between physical literacy and %BF (β = −0.11, 95% CI: −0.22–0.003, <italic>p</italic> = 0.06), and this model explained 37% of the variance (R<sup>2</sup> = 0.37) in %BF. Similar results were found for HRQOL, with a direct effect of physical literacy on MVPA (β = 3.17, 95% CI: 2.07–4.27, <italic>p</italic> < 0.001), a non-significant direct effect of MVPA on HRQOL (β = 0.03, 95% CI: −004, 0.10, <italic>p</italic> = 0.36), and a non-significant indirect effect of MVPA on the relationship between physical literacy and HRQOL (β = 0.10, 95% CI: −0.11–0.31, <italic>p</italic> = 0.36). This model explained 34% of the variance (R<sup>2</sup> = 0.34) in HRQOL. For TM time and 60 s HRR, there was a direct effect of physical literacy on MVPA (TM Time: β = 3.04, 95% CI: 1.85–4.23; <italic>p</italic> < 0.001; 60 s HRR: β = 3.04, 95% CI: 1.85–4.23, <italic>p</italic> < 0.001), and a direct effect of MVPA on treadmill time (β = 0.04, 95% CI: 0.01–0.06, <italic>p</italic> = 0.002) and on 60 s HRR (β = 0.09, 95% CI: 0.02–0.16, <italic>p</italic> = 0.01). There was an indirect effect of MVPA on the relationship between physical literacy and treadmill time (β = 0.12, 95% CI: 0.02–0.22, <italic>p</italic> = 0.02), and between physical literacy and 60 s HRR (β = 0.27, 95% CI: 0.02–0.53, <italic>p</italic> = 0.03). These results suggest that aerobic fitness, expressed as a treadmill time or as 60s HRR, is partially mediated by participation in MVPA and that these models explained 32% (R<sup>2</sup> = 0.32) and 45% (R<sup>2</sup> = 0.45) of the variance in treadmill time and 60 s HRR, respectively. Lastly, for SBP, there was a direct effect of physical literacy on MVPA (β = 3.22, 95% CI: 1.98–4.46, <italic>p</italic> < 0.001), a non-significant direct effect of MVPA on SBP (β = 0.02, 95% CI: −0.02, 0.06, <italic>p</italic> = 0.31), and a non-significant indirect effect of MVPA on the relationship between physical literacy and SBP (β = 0.06, 95% CI: −0.06, 0.18, <italic>p</italic> = 0.32). This model explained 33% of the variance (R<sup>2</sup> = 0.33) in SBP. </p></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05367\"><title>4. Discussion</title><p>This is one of the first studies to empirically assess the relationships between physical literacy and health (body composition, fitness, blood pressure, and HRQOL) in school-aged children. The physical literacy composite score, a combination of PLAYfun, PLAYself, and PLAYparent, was associated with all health indicators. The strongest association was observed between PL and 60s HRR, an indicator of aerobic fitness. It was also determined that MVPA mediated the associations between physical literacy and aerobic fitness, as indicated by either treadmill time or 60 s HRR. These findings provide initial support for theories that position physical literacy as a determinant of health across the lifespan [<xref rid=\"B6-ijerph-17-05367\" ref-type=\"bibr\">6</xref>]. Evidence to support the proposed associations between physical literacy and health is necessary to move this field beyond physical education, recreation, and sport. </p><p>To date, most research on the topic of physical literacy and health in children was conducted with a cross-Canadian sample of over 10,000 school-age children using the CAPL to assess physical literacy and field-based measures of health indicators [<xref rid=\"B11-ijerph-17-05367\" ref-type=\"bibr\">11</xref>,<xref rid=\"B36-ijerph-17-05367\" ref-type=\"bibr\">36</xref>]. A weak relationship was observed between indicators of aerobic fitness and children’s perceived adequacy and predilection toward physical activity, but not with other components of the CAPL [<xref rid=\"B37-ijerph-17-05367\" ref-type=\"bibr\">37</xref>]. The physical competence domain of CAPL and the total CAPL scores were associated with cardiorespiratory fitness, assessed with the PACER 20 m shuttle run test [<xref rid=\"B14-ijerph-17-05367\" ref-type=\"bibr\">14</xref>], similar to the associations we observed between physical literacy composite score and aerobic fitness. Lastly, it was observed that children who were a healthy weight had higher CAPL scores than children who were overweight or obese [<xref rid=\"B12-ijerph-17-05367\" ref-type=\"bibr\">12</xref>]. In the current study, while we did not classify participants based on weight status, we did observe that physical literacy was negatively associated with %BF. </p><p>To represent physical literacy, a composite score of PLAYfun, PLAYparent, and PLAYself was generated. In a review of 50 studies, core attributes of physical literacy were identified as movement competence, motivation, confidence, self-esteem, knowledge and understanding, and value and responsibility for physical activity [<xref rid=\"B8-ijerph-17-05367\" ref-type=\"bibr\">8</xref>]. This work, in addition to the International Consensus Statement on Physical Literacy, suggests that a composite score, rather than a single PL assessment tool, may better reflect the multiple domains of PL. Through confirmatory factor analysis, it was determined that several domains (perceived competence, motivation, enjoyment, and motor skills), work synergistically to produce physical literacy in school-age children; however, the domains were assessed with tools not specially designed to measure components of physical literacy [<xref rid=\"B38-ijerph-17-05367\" ref-type=\"bibr\">38</xref>]. In previous work, a composite physical literacy score was generated from a combination of physical literacy measures (PLAYfun) and validated questionnaires that assessed motivation, confidence, knowledge, and understanding related to physical activity. In that study, the physical literacy composite score increased in the intervention group and decreased in the control group following 11 weeks of physical literacy-enriched programming for university students [<xref rid=\"B39-ijerph-17-05367\" ref-type=\"bibr\">39</xref>]. Our study is the first study to generate a physical literacy composite score using the PLAY Tools, that were specifically designed to assess physical literacy. </p><p>Physical literacy is proposed to be the foundation of an active future and as a precursor to physical activity participation [<xref rid=\"B2-ijerph-17-05367\" ref-type=\"bibr\">2</xref>]. It has been observed that children who met the Canadian physical activity guideline of 60 min of daily MVPA displayed a higher physical competence and motivation and confidence physical literacy domain scores, as measured by the CAPL [<xref rid=\"B13-ijerph-17-05367\" ref-type=\"bibr\">13</xref>]. When physical literacy was assessed with PLAYfun, a positive relationship with pedometer-measured PA was reported (R<sup>2</sup> = 0.30, <italic>p</italic> < 0.05) [<xref rid=\"B16-ijerph-17-05367\" ref-type=\"bibr\">16</xref>]. Similar relationships have been reported between PLAYfun and self-reported PA in another Canadian study [<xref rid=\"B40-ijerph-17-05367\" ref-type=\"bibr\">40</xref>]. The current findings confirm that MVPA, measured objectively with accelerometers, is associated with the physical literacy composite score. While it is beneficial to better understand the associations between physical literacy and physical activity, it remains to be determined how physical literacy can be effectively fostered in children and youths. The next significant step in this field is to design and implement interventions based on the concepts of physical literacy and assess if they have an impact on physical activity levels and contribute to better health indicators. The results of this study suggest that physical literacy is associated with indicators of aerobic fitness, and that this relationship is influenced by children’s participation in MVPA. </p><p>This study has potential limitations that should be addressed in future research. This study was conducted in Canadian children, and results may not be comparable to other populations. The composite score of PL was novel and psychometric properties are not available; therefore, it is unclear if the method used to combine PLAYfun, PLAYparent, and PLAYself was most appropriate. Based on the definition of physical literacy [<xref rid=\"B41-ijerph-17-05367\" ref-type=\"bibr\">41</xref>], it was not appropriate to use the scores of PLAYfun, PLAYparent, and PLAYself individually. Future work should consider how various PLAY tools can be combined into one score that reflects the multiple domains of physical literacy. With this study’s cross-sectional design, it was not possible to determine the causal relationships between physical literacy, physical activity, and health. Mediation analysis would have been more appropriate if the physical literacy, physical activity, and health indicators were not measured at the same timepoint, but the results do help us understand these novel relationships that have not previously been investigated. Aerobic fitness was not assessed with the gold standard, VO2max, because the methodology of this study was developed for the participant’s young age (3 to 5-years-old) at the beginning of the study [<xref rid=\"B17-ijerph-17-05367\" ref-type=\"bibr\">17</xref>]. The direct measurement of VO2max would not have been feasible in that young sample. Rather, time to exhaustion with the modified Bruce Protocol was used, and is strongly correlated with direct VO2max in children [<xref rid=\"B27-ijerph-17-05367\" ref-type=\"bibr\">27</xref>]. The accelerometers were not waterproof, and participants were asked to remove the devices for swimming, therefore underestimating the physical activity levels of some participants. Lastly, success on the PLAYfun or aerobic fitness assessments could have been impacted by a participant’s motivation, and not all participants were similarly motivated to perform their best on the assessment, despite the continued encouragement from the assessors.</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05367\"><title>5. Conclusions</title><p>The present study determined that physical literacy was associated with health, represented as body composition, fitness, blood pressure, and HRQOL, and that the association between PL and aerobic fitness was mediated by MVPA. To our knowledge, this was the first study to explore these relationships using the PLAY Tools to assess physical literacy and lab-based measures to assess health. This study generated novel information that can inform future research in this field and contribute to the growing evidence that PL is the foundation for an active future [<xref rid=\"B2-ijerph-17-05367\" ref-type=\"bibr\">2</xref>], and that physical literacy is correlated with several health indicators [<xref rid=\"B6-ijerph-17-05367\" ref-type=\"bibr\">6</xref>]. Future research is needed to explore these relationships over time, and in additional demographics. </p></sec></body><back><ack><title>Acknowledgments</title><p>This study would not have been possible without the ongoing support and contributions of the members of the Child Health & Exercise Medicine Program and Vascular Dynamic Lab at McMaster University. Lastly, we would like to thank the many participants and their families who contributed to the SKIP Study as participants in our research study. </p></ack><notes><title>Author Contributions</title><p>Conceptualization, H.A.T.C., J.C., S.R.B., M.J.M. and B.W.T.; Data curation, H.A.T.C. and N.A.D.C.; Funding acquisition, H.A.T.C., J.C., S.R.B., N.A.D.C. and B.W.T.; Investigation, H.A.T.C., N.D., J.C., S.R.B., M.J.M. and B.W.T.; Project administration, N.A.D.C. and B.W.T.; Supervision, B.W.T.; Writing—original draft, H.A.T.C.; Writing—review and editing, N.A.D.C., J.C., S.R.B., M.J.M. and B.W.T. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was funded by Canadian Institutes for Health Research (CIHR Award#: MOP 137026) and the North America Society for Pediatric Exercise Medicine Marco Cabrera Student Research Award Program. BWT is supported by a Tier II Canada Research Chair in Child Health and Exercise Medicine. H.A.T.C. was supported by an Ontario Graduate Scholarship. </p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest. 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Phys. Educ. Exerc. Sci.</source><year>2018</year><volume>23</volume><fpage>47</fpage><lpage>57</lpage><pub-id pub-id-type=\"doi\">10.1080/1091367X.2018.1500368</pub-id></element-citation></ref><ref id=\"B41-ijerph-17-05367\"><label>41.</label><element-citation publication-type=\"web\"><person-group person-group-type=\"author\"><collab>International Physical Literacy Association</collab></person-group><article-title>Canada’s Physical Literacy Consensus Statement</article-title><comment>Available online: <ext-link ext-link-type=\"uri\" xlink:href=\"https://physicalliteracy.ca/physical-literacy/consensus-statement/\">https://physicalliteracy.ca/physical-literacy/consensus-statement/</ext-link></comment><date-in-citation content-type=\"access-date\" iso-8601-date=\"2020-07-25\">(accessed on 25 July 2020)</date-in-citation></element-citation></ref></ref-list></back><floats-group><table-wrap id=\"ijerph-17-05367-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05367-t001_Table 1</object-id><label>Table 1</label><caption><p>Participant demographics and descriptive statistics.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">\n</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Whole Sample</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Girls</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Boys</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\"><italic>p</italic>-Value</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mean (SD)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Min</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Max</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mean (SD)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mean (SD)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Age (years)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.76 (1.04)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.38</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.66</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.83 (1.01)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.69 (1.07)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.330</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">YPHV (years)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.75 (1.24)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−4.07</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.61</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.96 (1.01)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.57 (0.86)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Height (cm)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">145.71 (9.36)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">124.45</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">174.65</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">145.78 (9.92)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">145.63 (8.79)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.904</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Weight (kg)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38.03 (10.38)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22.30</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">91.75</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">38.53 (10.43)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">37.52 (10.35)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.470</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">BMI (m/kg<sup>2</sup>)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.68 (3.13)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.02</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31.40</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.89 (3.03)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.45 (3.23)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.294</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">BMI%ile</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47.87 (30.55)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.44</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">99.04</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">49.52 (30.17)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">46.17 (31.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.415</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Percent Body Fat</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.08 (6.60)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39.71</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22.93 (6.18)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19.16 (6.50)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Treadmill Time (min)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.91 (2.88)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.27</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.47</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10.36 (2.68)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.47 (2.98)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.005 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60 s HRR (bpm)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">56 (14)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">27</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50 (13)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">61 (12)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">SBP (mmHg)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100 (7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">84</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">125</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">100 (7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">101.0 (7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.103</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HRQOL</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">76.83 (11.27)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30.43</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">96.74</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">78.68 (11.42)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">74.93 (10.84)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.014 *</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">MVPA (min/day)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">62.39 (21.07)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18.43</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">124.86</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">56.73 (19.91)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">68.39 (20.70)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.001 *</td></tr></tbody></table><table-wrap-foot><fn><p>SD: standard deviation; YPHV: years from peak height velocity; BMI: body mass index; HR: heart rate; HRR: heart rate recovery; bpm: beats per minute; HRQOL: health-related quality of life. <italic>p</italic>-value represents the results of independent <italic>t</italic>-tests to determine differences between boys and girls; * <italic>p</italic> < 0.05.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05367-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05367-t002_Table 2</object-id><label>Table 2</label><caption><p>PLAYfun, PLAYself, PLAYparent, and physical literacy composite scores.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">\n</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Whole Sample</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Girls</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Boys</th><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\"><italic>p</italic>-Value</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mean (SD)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Min</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Max</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mean (SD)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mean (SD)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PLAYfun</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">49.09 (7.56)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.22</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">67.67</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47.91 (7.13)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50.33 (7.82)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.017 *</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PLAYself</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">63.50 (10.54)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39.43</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">97.52</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">73.66 (10.76)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">73.33 (10.35)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.820</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">PLAYparent</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">127.98 (16.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">76.27</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">149.91</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">127.13 (15.94)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">128.87 (16.08)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.423</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Physical Literacy Composite Score</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.007 (2.19)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−8.78</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.56</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−0.22 (2.05)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.22 (2.33)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.151</td></tr></tbody></table><table-wrap-foot><fn><p>SD: standard deviation; PLAY: Physical Literacy Assessment for Youth. <italic>p</italic>-value represents the results of independent <italic>t</italic>-tests to determine differences between boys and girls; * <italic>p</italic> < 0.05.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05367-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05367-t003_Table 3</object-id><label>Table 3</label><caption><p>Associations between physical literacy composite score and percent body fat, physical activity, aerobic fitness, blood pressure, and HRQOL.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">β (95% CI)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">T Statistic</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">R<sup>2</sup></th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Percent Body Fat</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.228</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Physical literacy composite </td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.56 (−0.93, −1.94)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−3.02</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.003</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sex</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.18 (2.36, 2.00)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.16</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.869</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">YPHV</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.27 (1.340, 3.14)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.13</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Constant</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25.16 (23.76, 26.55)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">35.59</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MVPA</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.235</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Physical literacy composite</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.19 (2.00, 4.40)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.25</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sex</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.66 (−0.58, 13.90)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.81</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.071</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">YPHV</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−3.46 (−6.35, −0.56)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.35</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.020</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Constant</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">53.91 (49.36, 58.45)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">23.39</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Treadmill Time</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.212</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Physical literacy composite</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.52 (0.36, 0.69)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.21</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sex</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.61 (−0.36, 1.57)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.49</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.219</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">YPHV</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.33 (−0.72, 0.07)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.20</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.101</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Constant</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10.01 (9.38, 10.65)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.32</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">60 s HRR</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.357</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Physical literacy composite</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.92 (0.22, 1.61)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.59</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.010</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sex</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.44 (−3.62, 4.51)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.22</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.829</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">YPHV</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−6.12 (−7.76, −4.48)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−7.37</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Constant</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">44.38 (41.71, 47.04)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">32.80</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Systolic Blood Pressure</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.109</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Physical literacy composite</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.54 (−0.93, −0.15)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−2.73</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.007</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sex</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.40 (2.08, 6.72)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.74</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">YPHV</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.04 (1.11, 2.97)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.472</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Constant</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">101.56 (100.08, 103.05)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">134.93</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HRQOL</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.156</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Physical literacy composite</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.73 (1.05, 2.40)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.06</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sex</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−3.68 (−7.76, 0.40)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−1.78</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.077</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">YPHV</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.68 (−0.96, 2.33)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.83</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.412</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Constant</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">79.70 (77.13, 82.26)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">61.32</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr></tbody></table><table-wrap-foot><fn><p>YPHV: years from peak height velocity, MVPA: moderate-to-vigorous physical activity; HRQOL: health-related quality of life.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32722601</article-id><article-id pub-id-type=\"pmc\">PMC7432050</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155304</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05304</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Review</subject></subj-group></article-categories><title-group><article-title>Hematopoietic Stem Cell Transplantation in Neuromyelitis Optica-Spectrum Disorders (NMO-SD): State-of-the-Art and Future Perspectives</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Ceglie</surname><given-names>Giulia</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05304\">1</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05304\">†</xref></contrib><contrib contrib-type=\"author\"><name><surname>Papetti</surname><given-names>Laura</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05304\">2</xref><xref ref-type=\"author-notes\" rid=\"fn1-ijms-21-05304\">†</xref></contrib><contrib contrib-type=\"author\"><name><surname>Valeriani</surname><given-names>Massimiliano</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05304\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Merli</surname><given-names>Pietro</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05304\">1</xref><xref rid=\"c1-ijms-21-05304\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05304\"><label>1</label>Department of Hematology/Oncology, Cell and Gene Therapy, Bambino Gesù Children’s Hospital, 00165 Rome, Italy; <email>giulia.ceglie@opbg.net</email></aff><aff id=\"af2-ijms-21-05304\"><label>2</label>Department of Neurology, Bambino Gesù Children’s Hospital, 00165 Rome, Italy; <email>laura.papetti@opbg.net</email> (L.P.); <email>Massimiliano.valeriani@opbg.net</email> (M.V.)</aff><author-notes><corresp id=\"c1-ijms-21-05304\"><label>*</label>Correspondence: <email>pietro.merli@opbg.net</email>; Tel.: +39-06-6859-2623</corresp><fn id=\"fn1-ijms-21-05304\"><label>†</label><p>These authors contributed equally to this work.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>26</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5304</elocation-id><history><date date-type=\"received\"><day>01</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>23</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Neuromyelitis optica (NMO) and neuromyelitis optica spectrum disorders (NMOSD) are a group of autoimmune inflammatory disorders of the central nervous system (CNS). Understanding of the molecular basis of these diseases in the last decades has led to an important improvement in the treatment of this disease, in particular, to the use of immunotherapeutic approaches, such as monoclonal antibodies and Hematopoietic Stem Cell Transplantation (HSCT). The aim of this review is to summarize the pathogenesis, biological basis and new treatment options of these disorders, with a particular focus on HSCT applications. Different HSCT strategies are being explored in NMOSD, both autologous and allogeneic HSCT, with the new emergence of therapeutic effects such as an induction of tolerance to auto-antigens and graft versus autoimmunity effects that can be exploited to hopefully treat a disease that still has prognosis.</p></abstract><kwd-group><kwd>NMO</kwd><kwd>NMOSD</kwd><kwd>HSCT</kwd><kwd>monoclonal antibodies</kwd><kwd>multiple sclerosis</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05304\"><title>1. Introduction</title><p>Neuromyelitis optica (NMO) and neuromyelitis optica spectrum disorders (NMOSD) are a group of immune-mediated diseases characterized by inflammatory damage of the myelin and axonal damage. Typically, the most frequently affected areas are the optic nerve and the spinal cord. From a serological point of view, the most specific marker of the disease is the presence of IgG antibodies against aquaporin-4 (AQP4) [<xref rid=\"B1-ijms-21-05304\" ref-type=\"bibr\">1</xref>].</p><p>The NMO is estimated to represent approximately 1% of CNS inflammatory demyelinating diseases in Europe, with an estimated prevalence of 1–2/100,000 [<xref rid=\"B1-ijms-21-05304\" ref-type=\"bibr\">1</xref>]. NMO is more frequent in women than in men, and it usually begins in young adults even if an onset in childhood is possible [<xref rid=\"B2-ijms-21-05304\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijms-21-05304\" ref-type=\"bibr\">3</xref>].</p><p>Some familiar forms of NMO have been described, but they cannot clearly be linked to any disease-specific genetic mutation and AQP4 polymorphism [<xref rid=\"B4-ijms-21-05304\" ref-type=\"bibr\">4</xref>].</p><p>The diagnosis of NMO/NMOSD requires a history of typical clinical presentations and findings on neuroimaging. The analysis of cerebrospinal fluid (CSF) and the research of AQP4-IgG serum autoantibody are mandatory [<xref rid=\"B5-ijms-21-05304\" ref-type=\"bibr\">5</xref>].</p><p>NMO therapy is often a challenge for the clinician, and it is based on relapse therapy (corticosteroids and plasmapheresis) and preventive treatment. To date, there is little evidence on which type of treatment is most effective for controlling NMO. The most commonly used drugs are immunosuppressors such as azathioprine, rituximab, methotrexate or mycophenolate [<xref rid=\"B6-ijms-21-05304\" ref-type=\"bibr\">6</xref>].</p><p>However, in recent years, new scenarios have opened up with new monoclonal antibodies for NMOSD therapy [<xref rid=\"B7-ijms-21-05304\" ref-type=\"bibr\">7</xref>]. Scientific evidence from case reports and series also support the use of hematopoietic stem cell transplantation for forms refractory to standard treatments [<xref rid=\"B8-ijms-21-05304\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijms-21-05304\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijms-21-05304\" ref-type=\"bibr\">10</xref>]. These data will be reviewed in the present manuscript.</p></sec><sec id=\"sec2-ijms-21-05304\"><title>2. Materials and Methods</title><p>The authors conducted a literature search describing the issue of Hematopoietic Stem Cell Transplantation (HSCT) in NMOSD. Research studies were selected based on research topics. The search terms used in PubMed were “HSCT” or “bone marrow transplantation” and “NMO” or “NMOSD”, “Allogeneic HSCT”, “Autologous HSCT” and “haploidentical HSCT”. Only papers written in English were considered, and those published from the year 2000 up to June 2020 were mostly selected. We included reviews, case series and research studies that were classified according to their relevance. No abstracts were included.</p><p>The information found in the selected studies, with particular attention to epidemiology, histological and biological characteristics, symptoms, diagnosis and treatment were carefully evaluated and are described and discussed in the following sections.</p></sec><sec id=\"sec3-ijms-21-05304\"><title>3. Pathogenesis of NMOSD</title><p>Brain and spinal lesions of NMO patients show the presence of necrotic lesions with infiltration of neutrophils and eosinophils, deposits of IgM and IgG, and complement activated fractions on the wall of blood vessels as well as reduced expression of AQ4 on astrocytes [<xref rid=\"B6-ijms-21-05304\" ref-type=\"bibr\">6</xref>]. These data suggest the involvement of different mechanisms in the pathogenesis of NMOSD. The damage caused by these immune-mediated mechanisms involves both grey and white matter, including axons and oligodendrocytes. Demyelination and loss of neuronal cells occur as a consequence of astrocytic damage [<xref rid=\"B6-ijms-21-05304\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijms-21-05304\" ref-type=\"bibr\">7</xref>].</p><p>The immune target is represented by aquaporin-4 (AQP4). The water channel AQP4 is the target of the immunoglobulin G autoantibody (AQP4-IgG) in NMOSD. The AQP4-IgG is an IgG1 autoantibody, and it has been identified in the serum of about 75% of NMOSD patients. Detection of AQP4-IgG is less sensitive in cerebrospinal fluid (CSF) than in serum, suggesting that a large proportion of AQP4-IgG originates in peripheral lymphoid tissues. Therefore, the most effective test for seropositive NMOs is the determination of serum Ig AQ4 [<xref rid=\"B11-ijms-21-05304\" ref-type=\"bibr\">11</xref>]. The AQP4-IgG titre also correlates with disease activity and relapses [<xref rid=\"B12-ijms-21-05304\" ref-type=\"bibr\">12</xref>]. The presence of AQ4 IgG in serum is highly predictive of evolution in NMO in patients with optic neuritis or transverse myelitis and is associated with a high risk of future relapse. Clinical relapses are generally preceded by a progressive increase in the Ig AQ4 titre, and the latter levels decrease during immunosuppressive treatments [<xref rid=\"B12-ijms-21-05304\" ref-type=\"bibr\">12</xref>].</p><p>Another type of antibody called MOG-Ab (myelin oligodendrocyte glycoprotein antibody) has been identified in approximately 5–10% of NMOSDs negative for AQP4-Ab [<xref rid=\"B13-ijms-21-05304\" ref-type=\"bibr\">13</xref>]. The MOG-IgG is considered a potentially pathogenic biomarker for a similar but separate disease entity (anti-MOG syndrome) rather than for a subgroup of NMOSD patients [<xref rid=\"B14-ijms-21-05304\" ref-type=\"bibr\">14</xref>].</p><p>AQP4 constitutes the main mechanism for the passage of transmembrane water at the brain, spinal cord, retina, inner ear and musculature. It is the most abundant aquaporin in the brain of mammals, and it is expressed at major concentrations near the blood–brain barrier and at the level of the pedicels of astrocytes [<xref rid=\"B15-ijms-21-05304\" ref-type=\"bibr\">15</xref>]. It is also present at the level of ependymal cells and, at lower level concentrations, at the level of endothelial cells of the brain tissue and of the nuclei supraoptics of the hypothalamus and periventricular structures [<xref rid=\"B16-ijms-21-05304\" ref-type=\"bibr\">16</xref>]. On the pedicels of astrocytes, AQP4 is associated with the potassium channel (Kir 4.1), which is involved in the regulation of extracellular K concentration [<xref rid=\"B17-ijms-21-05304\" ref-type=\"bibr\">17</xref>]. In addition, its localization is also associated with the molecule involved in the transport of glutamate (GLT-1). Glutamate represents one of the main excitatory neurotransmitters of astrocytes [<xref rid=\"B17-ijms-21-05304\" ref-type=\"bibr\">17</xref>].</p><p>Such strategic localization, in association with Kir 4.1 and GLT-1 at perivascular levels and subependymal, gives AQP4 a potential role of severe damage at the level of myelin and axons of vulnerable brain areas, such as the optic nerve or the spinal cord [<xref rid=\"B17-ijms-21-05304\" ref-type=\"bibr\">17</xref>]. Also significant is the fact that the distribution of areas rich in AQP4 in the CNS (the central part of the spinal cord, the hypothalamus, the periventricular areas and periaqueductal areas) has a high correspondence with the location of the lesions in the NMO [<xref rid=\"B16-ijms-21-05304\" ref-type=\"bibr\">16</xref>].</p><p>AQP4-IgG crosses the blood–brain barrier (BBB) and binds to the AQP4 proteins expressed by astrocytes. This process activates the complement and other effector cells which are then responsible for the cytotoxicity that damages the astrocytes [<xref rid=\"B18-ijms-21-05304\" ref-type=\"bibr\">18</xref>]. Complement activation and astrocyte damage recruit inflammatory cells such as eosinophils, neutrophils and macrophages that locally determine the release of cytokines (IL-17 and IL-8), proteases and radicals that contribute to both vascular and parenchymal damage [<xref rid=\"B18-ijms-21-05304\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijms-21-05304\" ref-type=\"bibr\">19</xref>]. In particular, the damage of the BBB promotes further entry of AQP4 into the CNS [<xref rid=\"B19-ijms-21-05304\" ref-type=\"bibr\">19</xref>]. However, the presence of plasma AQP4-IgG alone is insufficient to damage the BBB. This is also suggested by the evidence that the severity of NMO is not correlated with serum AQP4-Ab levels [<xref rid=\"B20-ijms-21-05304\" ref-type=\"bibr\">20</xref>] and that the injection of IgG of patients with NMO into the brain of naïve mice does not cause NMO-like lesions [<xref rid=\"B21-ijms-21-05304\" ref-type=\"bibr\">21</xref>]. These data therefore suggest the involvement of other immunological mechanisms (e.g., cellular immunity) in combination with AQP4-IgG in the pathogenesis of the disease. In particular, B cells seems to play a key role in the NMO pathology and, on this basis, many therapies for NMO deplete B cells or modulate their activity. Strategies such as plasmapheresis, depletion of B lymphocytes by rituximab and interference with IL-6 (which acts also as a growth and differentiation factor of B lymphocytes) are the most effective strategies to prevent relapses [<xref rid=\"B22-ijms-21-05304\" ref-type=\"bibr\">22</xref>].</p><p>AQP4-Abs are produced by B lymphocytes with specific CD19int, CD27high and CD38high phenotypes which are selectively increased in the blood of subjects with NMO, especially during relapse [<xref rid=\"B23-ijms-21-05304\" ref-type=\"bibr\">23</xref>]. B cells are involved in the pathogenesis of NMO through various mechanisms [<xref rid=\"B20-ijms-21-05304\" ref-type=\"bibr\">20</xref>]. Both central and peripheral tolerance defects should be responsible for the presence of autoreactive B lymphocytes. Moreover, B cells induce the activation of T cell responses trough mechanisms of antigen presentation or cytokine secretion [<xref rid=\"B22-ijms-21-05304\" ref-type=\"bibr\">22</xref>,<xref rid=\"B24-ijms-21-05304\" ref-type=\"bibr\">24</xref>]. In a second phase of NMO pathogenesis, cells of the adaptive immune system extravasate into the affected tissue. Myeloid antigen (Ag)-presenting cells (APCs) process AQP4 and present Ag to CD4-T cells [<xref rid=\"B22-ijms-21-05304\" ref-type=\"bibr\">22</xref>,<xref rid=\"B24-ijms-21-05304\" ref-type=\"bibr\">24</xref>]. Consequently, the activated AQP4-specific T cells damage the BBB and allow further entry of AQP4-IgG and other effectors into tissues containing astrocytes expressing AQP4 [<xref rid=\"B23-ijms-21-05304\" ref-type=\"bibr\">23</xref>]. In NMOSD, activated T helper lymphocytes present a Th17 phenotype dominance [<xref rid=\"B25-ijms-21-05304\" ref-type=\"bibr\">25</xref>]. This differentiation is promoted by the high production of IL-6 that is observed in patients with NMOSD [<xref rid=\"B23-ijms-21-05304\" ref-type=\"bibr\">23</xref>]. In turn, the Th17 activation causes the production of cytokines such as IL-17, Tumor Necrosis Factor-alpha (TNF) and granulocyte-macrophage colony-stimulating factor (GM-CSF) which promote tissue damage [<xref rid=\"B26-ijms-21-05304\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijms-21-05304\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05304\" ref-type=\"bibr\">28</xref>]. Moreover, activated T cells cross-activate antigen-specific B cells that mature into plasma cells and memory B cell producing AQP4-Abs. The antibodies in turn keep the initial phase of this immune and inflammatory cascade active by amplifying the astrocytic damage [<xref rid=\"B28-ijms-21-05304\" ref-type=\"bibr\">28</xref>]. The binding between AQP4-IgG and AQP4 on astrocytes causes complement activation through the classical pathway [<xref rid=\"B15-ijms-21-05304\" ref-type=\"bibr\">15</xref>]. Complement-dependent cytotoxicity combined with antibody-dependent cell-mediated cytotoxicity (ADCC) and internalization of the glutamate transporter EAAT-2 cause astrocytes to lose the ability of osmotic regulation and glutamate uptake [<xref rid=\"B29-ijms-21-05304\" ref-type=\"bibr\">29</xref>]. Finally, astrocytic damage results in a lack of support for oligodendrocytes and neurons with demyelination and axonal loss responsible for a patient’s disability [<xref rid=\"B15-ijms-21-05304\" ref-type=\"bibr\">15</xref>,<xref rid=\"B28-ijms-21-05304\" ref-type=\"bibr\">28</xref>].</p></sec><sec id=\"sec4-ijms-21-05304\"><title>4. Clinical Features and Diagnostic Criteria</title><p>NMO has long been considered a disease characterized clinically by the association of recurrent episodes of optic neuritis and longitudinally extensive transverse myelitis (LETM). The discovery of the AQP4-IgG and the description of new clinical and neuroradiological pictures allowed to expand the phenotype with the definition of NMOSDs. The spectrum of NMO therefore refers to clinical pictures in which the positivity of AQP4-IgG is associated with other clinical pictures besides optic neuritis and myelitis such as diencephalic, brainstem or other cerebral syndromes [<xref rid=\"B1-ijms-21-05304\" ref-type=\"bibr\">1</xref>,<xref rid=\"B5-ijms-21-05304\" ref-type=\"bibr\">5</xref>]. In 2015, an international consensus was published which established the criteria for the diagnosis of NMOSD. The six core clinical pictures included 1) optic neuritis; 2) acute myelitis; 3) area postrema syndrome, which manifests with persistent hiccups or nausea and vomiting; 4) acute brainstem syndrome; 5) symptomatic narcolepsy or acute diencephalic clinical syndrome; and 6) symptomatic cerebral syndrome. The last two clinical manifestations required the simultaneous presence of NMOSD-typical brain lesions detected in MRIs [<xref rid=\"B5-ijms-21-05304\" ref-type=\"bibr\">5</xref>]. The episodes of neuritis are more commonly unilateral than bilateral, but the presence of bilateral simultaneous optic neuritis is high specific for NMO [<xref rid=\"B2-ijms-21-05304\" ref-type=\"bibr\">2</xref>]. In patients with positive AQP4-IgG, a clinical core is sufficient for diagnosis. In patients without evidence of serum AQP4-IgG, the diagnosis can be established only if there are two clinical events with core features [<xref rid=\"B5-ijms-21-05304\" ref-type=\"bibr\">5</xref>].</p><p>Magnetic resonance imaging (MRI) of the brain is often diriment for the diagnosis of NMOSD. The most suggestive MRI pictures include an alteration of the optic nerve (<xref ref-type=\"fig\" rid=\"ijms-21-05304-f001\">Figure 1</xref>A) or a spinal cord injury that extends for more than three metamers (LETM) (<xref ref-type=\"fig\" rid=\"ijms-21-05304-f001\">Figure 1</xref>B). However, in other cases, lesions in other locations such as medulla/area postrema lesions or periependymal brainstem can be found [<xref rid=\"B30-ijms-21-05304\" ref-type=\"bibr\">30</xref>].</p><p>The course of NMO should be monophasic or relapsing [<xref rid=\"B2-ijms-21-05304\" ref-type=\"bibr\">2</xref>]. The monophasic course occurs in about 25% of patients, with concomitant involvement of either unilateral/bilateral optic neuritis and a single episode of transverse myelitis [<xref rid=\"B31-ijms-21-05304\" ref-type=\"bibr\">31</xref>]. About 75% of patients develop a relapsing course with recurrent optic neuritis and myelitis with significant increasing disability [<xref rid=\"B31-ijms-21-05304\" ref-type=\"bibr\">31</xref>]. The most frequent disabilities included permanent vision loss and motor dysfunctions. Subjects who are younger at disease onset have a higher probability of visual disability; otherwise, patients who are older at onset have a higher chance of motor disability [<xref rid=\"B13-ijms-21-05304\" ref-type=\"bibr\">13</xref>].</p><p>In patients with anti-MOG antibodies, the most frequent clinical presentations include acute disseminated acute encephalomyelitis (ADEM) with optic neuritis (ADEM-NO) or relapses of optic neuritis, although in most cases, the course remains monophasic [<xref rid=\"B14-ijms-21-05304\" ref-type=\"bibr\">14</xref>].</p></sec><sec id=\"sec5-ijms-21-05304\"><title>5. Therapy</title><p>NMOSD therapy is based on the acute treatment of relapses and therapy that prevents relapses.</p><p>The acute treatment of relapses is based on the use of high dose corticosteroids (methylprednisolone), intravenous immunoglobulins and plasmapheresis [<xref rid=\"B32-ijms-21-05304\" ref-type=\"bibr\">32</xref>].</p><p>Therapy that prevents relapses and modifies the course of the disease can use different drugs. Generally, the most used immunosuppressant drugs are azathioprine (AZA) and mycophenolate (MMF) [<xref rid=\"B11-ijms-21-05304\" ref-type=\"bibr\">11</xref>,<xref rid=\"B33-ijms-21-05304\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijms-21-05304\" ref-type=\"bibr\">34</xref>]. Despite the effectiveness of these drugs, their long-term use can be associated with numerous adverse effects [<xref rid=\"B35-ijms-21-05304\" ref-type=\"bibr\">35</xref>]. Their use has been gradually overshadowed with growing evidence of the efficacy of rituximab (RTX) for the treatment of NMOSD [<xref rid=\"B35-ijms-21-05304\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijms-21-05304\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijms-21-05304\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijms-21-05304\" ref-type=\"bibr\">38</xref>,<xref rid=\"B39-ijms-21-05304\" ref-type=\"bibr\">39</xref>].</p><p>Positive MOG patient relapses are treated in the same way as NMOSD relapses. For chronic treatment, however, therapeutic strategies include long-term treatment with low doses of prednisolone; monthly intravenous immunoglobulins cycles; as well as, for refractory patients, immunosuppressive therapies with rituximab, mycophenolate mofetil, methotrexate or azathioprine [<xref rid=\"B13-ijms-21-05304\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijms-21-05304\" ref-type=\"bibr\">14</xref>]. Notably, for patients with NMOSD, there is a general consensus that treatment was effective regardless of serostatus (AQP4- IgG positive or negative) [<xref rid=\"B11-ijms-21-05304\" ref-type=\"bibr\">11</xref>,<xref rid=\"B33-ijms-21-05304\" ref-type=\"bibr\">33</xref>].</p><p>RTX is a monoclonal antibody directed to CD20 antigen on B cells, and it causes a rapid depletion of circulating CD20<sup>+</sup> B cells [<xref rid=\"B36-ijms-21-05304\" ref-type=\"bibr\">36</xref>]. RTX has been often used now as first-line treatment in highly active NMOSD [<xref rid=\"B40-ijms-21-05304\" ref-type=\"bibr\">40</xref>,<xref rid=\"B41-ijms-21-05304\" ref-type=\"bibr\">41</xref>]. In patients affected by NMOSD, RTX causes a removal of B cells as antigen-presenting cells and it also produces a reduction in the CD20<sup>+</sup> early plasmablast population generating AQP4-IgG [<xref rid=\"B42-ijms-21-05304\" ref-type=\"bibr\">42</xref>].</p><p>RTX has been progressively used as a first-line therapy with a suggested better control of disease activity compared to AZA and possibly MMF [<xref rid=\"B35-ijms-21-05304\" ref-type=\"bibr\">35</xref>,<xref rid=\"B37-ijms-21-05304\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijms-21-05304\" ref-type=\"bibr\">38</xref>,<xref rid=\"B43-ijms-21-05304\" ref-type=\"bibr\">43</xref>]. Finally, the efficacy and safety of RTX in NMOSD has recently been evaluated in a multicentre, randomized, double-blind, placebo-controlled (RIN-1) study. This study included 38 patients AQP4 antibody-positive treated with RTX (375 mg/m²). RTX prevented relapses for 72 weeks in patients with NMOSD AQP4 positive [<xref rid=\"B44-ijms-21-05304\" ref-type=\"bibr\">44</xref>]. RTX has acceptable tolerance, reduces the relapse frequency and improves disability in most patients with NMOSD [<xref rid=\"B45-ijms-21-05304\" ref-type=\"bibr\">45</xref>]. Maintaining the depletion of memory B cells through repeated treatment courses may be pivotal to the clinical effects of RTX in patients with NMO [<xref rid=\"B45-ijms-21-05304\" ref-type=\"bibr\">45</xref>].</p><p>Recently, the therapeutic scenario has opened up to three new monoclonal antibodies subjected to analysis in clinical trials: eculizumab [<xref rid=\"B46-ijms-21-05304\" ref-type=\"bibr\">46</xref>], inebilizumab [<xref rid=\"B47-ijms-21-05304\" ref-type=\"bibr\">47</xref>] and satralizumab [<xref rid=\"B48-ijms-21-05304\" ref-type=\"bibr\">48</xref>].</p><p>In 2019, The U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA) approved eculizumab injection for intravenous use for the treatment of NMOSD in adult patients who were AQP4 antibody positive. Eculizumab is a long-acting humanized monoclonal antibody targeted against complement C5. It inhibits the cleavage of C5 into C5a and C5b and hence inhibits deployment of the terminal complement system [<xref rid=\"B49-ijms-21-05304\" ref-type=\"bibr\">49</xref>]. The rationale behind testing a complement inhibitor in NMOSD is based on the pathology of NMO lesions showing extensive complement deposition, and it derives from the study of the role of complement in pathogenesis of the disease [<xref rid=\"B50-ijms-21-05304\" ref-type=\"bibr\">50</xref>]. The effectiveness of eculizumab for the treatment of NMOSD was demonstrated in the phase III PREVENT study (prevention of relapses in neuromyelitis optica). This randomized, double-blind, placebo-controlled study included 143 patients with anti-AQP4-positive NMOSD. Eculizumab was administered intravenously at doses of 900 mg weekly for the first four doses and then 1200 mg every 2 weeks from the following week. At 48 weeks, 98% of eculizumab-treated patients had no relapse compared to 63% of placebo-treated patients. This effect was observed for 144 weeks of treatment, with 96% of patients receiving eculizumab without relapses compared to 45% of patients in the placebo arm. Eculizumab also reduced hospitalization rates and the need for treatment of acute attacks with corticosteroids and plasma exchange [<xref rid=\"B46-ijms-21-05304\" ref-type=\"bibr\">46</xref>]. Notably, eculizumab is associated with an increased susceptibility to opportunistic meningococcal infection (<italic>N. meningitidis</italic>) (EMA 2019).</p><p>Inebilizumab is a humanized, affinity-optimized, afucosylated IgG1 kappa monoclonal antibody that acts as an anti-CD19 monoclonal antibody that targets circulating B cells, differing from anti-CD20 therapy by depleting pro-B cells and plasmablasts in addition to pre-B cells through memory B cells [<xref rid=\"B51-ijms-21-05304\" ref-type=\"bibr\">51</xref>].</p><p>Inebilizumab has recently shown efficacy in a randomized double-blinded, placebo-controlled clinical trial with 174 participants receiving inebilizumab and 56 participants receiving a placebo. Intravenous inebilizumab or placebo was administered on days 1 and 15 (the total dose of inebilizumab in the randomised controlled period was 600 mg, with no further doses occurring after day 15 in this study period). The trial tested inebilizumab as a monotherapy in NMOSD patients with or without AQP4-IgG, and it showed that 12% of participants receiving inebilizumab had an attack versus 39% participants receiving the placebo (<italic>p</italic> < 0.0001). The secondary outcome of decreasing disability worsening compared to placebo was also reached. In detail, AQP4-IgG positive patients were those with the greatest evidence of efficacy. The most frequent side effects were infusion reactions [<xref rid=\"B47-ijms-21-05304\" ref-type=\"bibr\">47</xref>]. To date, inebilizumab is under FDA review for approval use in NMOSD [<xref rid=\"B52-ijms-21-05304\" ref-type=\"bibr\">52</xref>].</p><p>Satralizumab is a monoclonal antibody that acts by antagonizing IL-6, and it was evaluated in a randomized, double-blind, placebo-controlled trail (phase 3) enrolling 83 patients with NMOSD (41 in the satralizumab group vs. 42 in the placebo group). Satralizumab was administered subcutaneously at baseline and then later at 2 weeks and at every 4 weeks. The concomitant use of stable immunosuppressant treatment was allowed. The results of the trial showed that relapse occurred in 20% of patients under satralizumab treatment vs. 43% of the patients with placebo (hazard ratio, 0.38). At 48 weeks, 89% on satralizumab and 66% on placebo were relapse-free. In the analysis of subgroups, the AQP4-positive subjects presented a relapse in 11% of satralizumab patients vs. 43% of placebo patients. Adverse events were more frequently observed in the placebo group [<xref rid=\"B48-ijms-21-05304\" ref-type=\"bibr\">48</xref>]. Another drug acting against IL-6 is a humanized anti-IL-6 receptor called tocilizumab, which inhibits both classical and trans IL-6 signalling. To date, tocilizumab showed efficacy only in a pilot study conducted on few cases of NMO and it is considered as a third-line treatment for severe cases [<xref rid=\"B53-ijms-21-05304\" ref-type=\"bibr\">53</xref>,<xref rid=\"B54-ijms-21-05304\" ref-type=\"bibr\">54</xref>].</p><p>Even though these new immunotherapeutic strategies hold steady progress in the treatment of NMOSD, a curative approach is not yet available and the vast majority of patients have a temporary control of the disease and require multiple lines of therapy. Hematopoietic Stem Cell Transplantation (HSCT) fits into this context, providing an alternative and possibly curative form of immunotherapy.</p></sec><sec id=\"sec6-ijms-21-05304\"><title>6. Historical and Biological Basis of HSCT in Neurological Autoimmune Disorders</title><p>Hematopoietic Stem Cell Transplantation (HSCT) is a complex procedure that includes the substitution of the host hematopoietic system through chemo- and/or radiotherapy with a new one, thus completely erasing the immune system. The first and most used applications of the procedure were in the treatment of onco-haematological disease [<xref rid=\"B55-ijms-21-05304\" ref-type=\"bibr\">55</xref>], but its role in the cure of autoimmune diseases (AD) has recently emerged and has been demonstrated in several diseases, including neurological ones [<xref rid=\"B56-ijms-21-05304\" ref-type=\"bibr\">56</xref>]. According to the donor of the hematopoietic stem cells (HSCs), HSCT procedures can be divided into autologous (auHSCT) or allogeneic (alHSCT). In auHSCT, the cells of the patients are harvested and frozen, and after a radio- and/or chemotherapy regimen, the cells are reinfused in the patients (that acts both as the donor and the recipient) with the aim of rescuing him/her from long-lasting cytopenia. AlHSCT, on the other hand, involves two subjects: the (healthy) donor and the (patient) recipient. So far, in the treatment of severe autoimmune diseases, auHSCT has been preferred over alHSCT mainly because of the lower toxicity due to the absence alloreactivity (i.e., of Graft-versus-Host Disease (GVHD) [<xref rid=\"B57-ijms-21-05304\" ref-type=\"bibr\">57</xref>] and lower Transplant Related Mortality (TRM), defined as death due to causes unrelated to the underlying disease but directly consequent to the transplant procedure [<xref rid=\"B58-ijms-21-05304\" ref-type=\"bibr\">58</xref>]).</p><p>The biological bases of HSCT treatment is that the neuroinflammation is due to an immune system error dependent on immunological memory. HSCT aims to destroy the immune system, thus erasing its erroneous response towards self-antigens and allowing the reconstitution of a new, self-tolerant immune system [<xref rid=\"B59-ijms-21-05304\" ref-type=\"bibr\">59</xref>]. In this context, HSCT stands as a potentially curative treatment with the notable consequence that the patients potentially would not require additional therapy after the procedure.</p><p>In the early 1990s, the first studies on the effects of both autologous and allogeneic HSCT for experimental autoimmune encephalomyelitis (EAE) were conducted in rodent models [<xref rid=\"B60-ijms-21-05304\" ref-type=\"bibr\">60</xref>,<xref rid=\"B61-ijms-21-05304\" ref-type=\"bibr\">61</xref>]. In these studies, both immune-ablation through Total Body Irradiation (TBI) and cyclophosphamide (Cy) followed by infusion of syngeneic bone marrow from healthy rats or different resistant strains were tested and proved to be efficient in both clinical and histopathological terms [<xref rid=\"B62-ijms-21-05304\" ref-type=\"bibr\">62</xref>,<xref rid=\"B63-ijms-21-05304\" ref-type=\"bibr\">63</xref>]. Indeed, the treated animals did not reach the state of paralysis like non-transplanted animals; moreover, the histopathological specimen obtained showed no significant inflammation. Concerning the different role of different type of HSCT, it was observed that auHSCT was only effective when performed very early after induction of the disease, thus proving to be effective only in the first stages of the disease [<xref rid=\"B64-ijms-21-05304\" ref-type=\"bibr\">64</xref>]. Taking the first steps from these animal studies in 1995, the first auHSCT was performed in a patient with Multiple Sclerosis (MS) [<xref rid=\"B65-ijms-21-05304\" ref-type=\"bibr\">65</xref>], shortly followed by numerous other experiences [<xref rid=\"B66-ijms-21-05304\" ref-type=\"bibr\">66</xref>,<xref rid=\"B67-ijms-21-05304\" ref-type=\"bibr\">67</xref>,<xref rid=\"B68-ijms-21-05304\" ref-type=\"bibr\">68</xref>]. These seminal efforts, although at first sometimes disappointing, over the course of less than two decades led to the first consensus recommendation in 2012 that HSCT should be considered as a therapeutic option at second line or beyond for patients with relapsing-remitting MS who deteriorate despite standard therapy [<xref rid=\"B69-ijms-21-05304\" ref-type=\"bibr\">69</xref>]. Although most of the experiences on HSCT in autoimmune neurological diseases was achieved in MS for prevalence reasons, in the following years, the field extended to other diseases, such as NMO and NMOSD.</p></sec><sec id=\"sec7-ijms-21-05304\"><title>7. Autologous HSCT in NMOSD</title><p>AuHSCT does not carry the same TRM risk as alHSCT, and thus, it is a relatively safe procedure in specialized centres. The first step of auHSCT is mobilization: to mobilize HSCs from the bone marrow into the peripheral blood, different drugs (most frequently, granulocyte colony-stimulating factor (GCSF)) are administered. The mobilized stem cells are then harvested through leukapheresis, and the high-dose conditioning can be started: chemotherapy (and less frequently radiation) are given to delete the self-reacting immune system of the patient. Since lymphocytes (of both T and B lineage) are the population responsible for the abnormal immune reaction, conditioning regimens used frequently to incorporate antibodies (e.g., antithymocyte globulin (ATG) and/or rituximab) able to eliminate these cells both in the recipient. Finally, autologous HSCs are reinfused; since ATG and rituximab half-lives are prolonged, their persistence in the patient delete in vivo T and B lymphocytes present in the graft. It is important to notice that the therapeutic potential of the procedure is not dependent on the HSC product. The actual therapeutic phase is the conditioning that acts as a highly active immunosuppressive therapy that allows for the complete reset of the immune system. HSCs are, in this case, a mere support product that speeds the hematopoietic recovery that may not be achieved or delayed in the absence of a reinfusion.</p><p>The first report of auHSCT in NMO dates back to 2010 when Peng and colleagues performed it in a 23-year-old female patient with a very severe disease course characterized by paraparesis, vision loss, radicular pain and dysesthesia [<xref rid=\"B70-ijms-21-05304\" ref-type=\"bibr\">70</xref>]. Stem cells were harvested from peripheral blood, and the procedure was performed 2 years after diagnosis. After 12 months, she was stable without any further relapses. Motor and sensory functions were restored except for visual acuity (but atrophy of the optic nerve was present before treatment). Also, imaging abnormalities appeared to be improved.</p><p>In 2014, the European Group for Blood and Marrow Transplantation Autoimmune Diseases Working Party (EBMT ADWP) presented data from a retrospective multicentre study [<xref rid=\"B9-ijms-21-05304\" ref-type=\"bibr\">9</xref>] with 16 patients diagnosed with NMO or NMOSD mainly treated with the BEAM (BCNU, Etoposide, Ara-C, Melphalan)-ATG regimen followed by HSC infusion. Three cases remained progression- and treatment-free, while in 13 patients, anti-AQP-4Ab antibodies persisted, leading to relapse requiring further treatment. Moreover, one patient died from disease progression 14 months after HSCT.</p><p>Recently, Aouad and colleagues reported on a 47-year-old female patient that underwent autologous HSCT with a disease duration of 11 years [<xref rid=\"B71-ijms-21-05304\" ref-type=\"bibr\">71</xref>]. Rituximab was administered as part of the conditioning regimen (cyclophosphamide + Anti-thymocyte Globulin, ATG). At 12 months follow-up, she had a sustained clinical, radiological and immunopathological remission. Also, recent data from the Northwestern University [<xref rid=\"B72-ijms-21-05304\" ref-type=\"bibr\">72</xref>] supported the use of a Cy-based (ATG and rituximab) auHSCT to obtain a prolonged drug-free remission in 12 patients with NMOSD; all of them achieved clearance of anti-AQP-4Ab. Of the 12 patients, 11 were female, 8 were Caucasian and 4 were African American. Mean age was 42 years, and mean duration of disease before HSCT was 84 months. Before transplant, 11 had clinical attacks of optic neuritis, 12 had myelitis, 1 had area postrema syndrome and 11 of 12 were AQP4-IgG-positive. The mean pretransplant EDSS (Expanded Disability Status Scale) score was 4.3. The patents were followed up for 5 years and achieved neurological improvements; in particular, EDSS score improved from a baseline mean of 4.4 to 3.3, and quality of life significantly improved. The median day of white blood cell engraftment (defined as neutrophil count >1000/μL) was day +9. The treatment appeared to be very well tolerated, with few toxicities and with only 1 inpatient infection (<italic>Clostridium difficile</italic>). Grade 3 toxicities were reported, the most frequent one being hypophosphatemia, followed by neutropenic fever, hypocalcemia, nausea and vomiting. No grade 4 toxicities were reported. The number of infections after HSCT was 0.18 per year per patient. Interestingly, 2 patients developed new autoimmune diseases: one developed myasthenia gravis that occurred with NMOSD relapse, and the other one developed hyperthyroidism.</p><p>Other reports have investigated the outcome of auHSCT in the context of NMOSD, with less structured conclusions [<xref rid=\"B73-ijms-21-05304\" ref-type=\"bibr\">73</xref>,<xref rid=\"B74-ijms-21-05304\" ref-type=\"bibr\">74</xref>]. A Chinese study [<xref rid=\"B73-ijms-21-05304\" ref-type=\"bibr\">73</xref>] treated 20 patients with Opticospinal Multiple Sclerosis (OSMS), a disease comparable to NMOSD, with auHSCT. Modified BEAM conditioning regimens were administered (tiniposide, melphalan, carmustin and cytosine arabinoside). Outcome was evaluated with ExpandedDisability Status Scale (EDSS) scores; for all patients, the overall EDSS score decreased significantly after transplantation while visual functions had no significant improvement. Confronting the OSMS relapse rate with conventional multiple sclerosis, progressive OSMS had a higher relapse rate. Matiello and colleagues [<xref rid=\"B74-ijms-21-05304\" ref-type=\"bibr\">74</xref>] described a case of a woman with relapsing NMO and who experienced a relapse of myelitis 4 months after auHSCT for a lymphoma that developed while receiving AZA therapy. Also, a marked increase of NMO-IgG was documented coinciding with the relapse.</p><p>All this considered, the recently published EBMT-updated guidelines on the indication of auHSCT in MS and other immune-mediated neurological disorders, including NMO and NMOSD, recommend auHSCT in patients with refractory NMO (level II recommendation) [<xref rid=\"B10-ijms-21-05304\" ref-type=\"bibr\">10</xref>]. What emerges from the available data is that auHSCT can reduce inflammation in NMO especially in the long term but that a number of patients will relapse within 5 years. Conditioning regimens containing rituximab might improve prognosis but the very few cases described prohibit drawing firm conclusions.</p></sec><sec id=\"sec8-ijms-21-05304\"><title>8. Allogeneic HSCT in NMOSD</title><p>AlHSCT is less explored in AD and mainly restricted to the paediatric setting, this restriction mainly deriving from the lower risk of TRM and, in general, of transplant-related morbidities in children compared to adults [<xref rid=\"B75-ijms-21-05304\" ref-type=\"bibr\">75</xref>]. Also, the more frequent indication for alHSCT is immune cytopenia, mainly affecting paediatric patients [<xref rid=\"B76-ijms-21-05304\" ref-type=\"bibr\">76</xref>].</p><p>AuHSCT is a relatively safe and relatively effective treatment for NMOSD. In particular, auHSCT has been proven to maintain a good temporary control of the disease but scarce control in the long term. These issues are of utmost importance when considering treatment options in very young or paediatric patients, when a stable and durable control of the disease is fundamental in light of the relatively long life-expectation compared to adult patients.</p><p>From a biological point of view, the main difference between alHSCT and auHSCT is the fact that a healthy donor is required as a source of HSCs. HSCs can be collected from the peripheral blood after a mobilization phase or can be harvested from the bone marrow of the donor. The patient undergoes a conditioning regimen, usually more intensive than those used in auHSCT in order to cross the HLA (Human Leukocyte Antigen) barrier, and it is followed by infusion of the HSCs of the donor. In this setting, the conditioning regimen not only has the role of resetting the immune system of the patient but also is necessary to “make space” for the new hematopoietic system to avoid the risk of rejection and to allow for the engraftment of the new cells. Indeed, differently from auHSCT, the bidirectional alloreactivitiy (namely host-versus-graft and graft-versus-host), which depends on several factors (including HLA disparity, age, sex mismatch, intensity of the conditioning, etc.), can cause graft rejection or graft-versus-host-disease (GVHD). This is why, in alHSCT, prophylaxis of GVHD is necessary, consisting either of drugs (mostly methotrexate and calcineurin inhibitors) and/or manipulation of the graft.</p><p>As for the donor types, an HLA-matched family donor (MFD) is generally considered the best option, because of prompt availability and lower risk of GVHD, but such a donor is available in less than 25% of cases [<xref rid=\"B76-ijms-21-05304\" ref-type=\"bibr\">76</xref>]. Also, fully matched unrelated donors (MUD) have been used in the setting of AID (Auto-Immune Diseases), while alHSCT from haploidentical donors (i.e., those sharing only one haplotype, thus, with a high degree of HLA mismatch) is still considered highly experimental. Potential HLA-haploidentical donors are biological parents, biological children, full or half siblings, or even other family members. One of the main advantages of this type of HSCT is (i) the fact that virtually every patient has a haploidentical donor and (ii) its fast availability (since the donor is usually close to the patient and highly motivated). The main disadvantage is HLA mismatch, thus increasing the risk of both graft rejection and severe GVHD. Because of these issues, innovative prophylactic measures have been used, including graft manipulation in order to deplete the alloreactive T cells (T-cell depleted transplants) [<xref rid=\"B77-ijms-21-05304\" ref-type=\"bibr\">77</xref>] or the use of posttransplantation cyclophosphamide (T-cell repleted transplants) [<xref rid=\"B78-ijms-21-05304\" ref-type=\"bibr\">78</xref>].</p><p>Limited experiences are available regarding the use of alHSCT in NMOSD, but they appear to be promising. The first report of the use of alHSCT in patients with NMO is that of Greco et al., who successfully treated with al-HSCT the first 2 patients (see also <xref rid=\"ijms-21-05304-t001\" ref-type=\"table\">Table 1</xref>) [<xref rid=\"B79-ijms-21-05304\" ref-type=\"bibr\">79</xref>]. Both patients had already undergone auHSCT with insufficient results. One of the 2 patients received an HLA identical HSCT from an MFD, while the second patient received the transplant from an unrelated donor with a 9/10 HLA match. In both cases, the conditioning regimen consisted in fludarabine and treosulfan. GVHD prophylaxis consisted in ATG, cyclosporine and a short course of methotrexate for the patient transplanted from MUD and mycophenolate and rapamycin and in the other one. In vivo B cell depletion was also performed with rituximab. Both the patients achieved negative AQ-P4 antibodies and neuroradiological stability without any new lesions documented at MRI follow-up. Also, EDSS dropped from 6 to 3.5 in the first patient and from 8.5 to 7.5 in the second one. The immunological and radiological improvements paralleled clinical improvement with a decreased grade of disability.</p><p>Also, our group recently reported the first case of a paediatric patients with NMO treated with an HLA-haploidentical HSCT after ex vivo TCRαβ/CD19 depletion of the graft [<xref rid=\"B8-ijms-21-05304\" ref-type=\"bibr\">8</xref>]. Even in this case, the patients achieved clinical, neurological and immunological improvements. Notably, in our case, the disappearance of AQ-P4 antibodies took a long time (6 months after transplantation); however, despite a flare of disease immediately after the transplant, the patient did not experience relapse of the disease. Although the experience is limited, in all cases, alHSCT appeared to be superior in maintaining long-term stabilization of the disease compared to auHSCT.</p><p>The reasons for this apparent increased efficiency in the control of the disease could be several. For instance, because of the bidirectional alloreactivity of T cells, in alHSCT, more aggressive myeloablative/immune suppressive regimens are used than auHSCT. This could be more effective in eradicating the patient’s immune system, particularly the autoreactive clones. Moreover, it has been hypothesized that alloreactive T cells of the donor might act similarly to GVHD with a subclinical graft-versus-host anti-autoimmune reaction that could eradicate autoreactive B and T cells. Indeed, both experimental [<xref rid=\"B80-ijms-21-05304\" ref-type=\"bibr\">80</xref>] and clinical data [<xref rid=\"B81-ijms-21-05304\" ref-type=\"bibr\">81</xref>] support this hypothesis.</p><p>As already discussed, in the treatment of autoimmune diseases, the lower toxicity of auHSCT has made it the preferred option over alHSCT mainly because of the absence of GVHD and TRM. We have discussed how allogeneic donor T cells may eliminate autoreactive host lymphocytes and therefore may mediate an immunotherapeutic approach (“graft-versus-autoimmunity”) (see also <xref ref-type=\"fig\" rid=\"ijms-21-05304-f002\">Figure 2</xref>). This association was confirmed in a meta-analysis using patients’ data [<xref rid=\"B81-ijms-21-05304\" ref-type=\"bibr\">81</xref>]. A paradigmatic case was described by Slavin and colleagues [<xref rid=\"B82-ijms-21-05304\" ref-type=\"bibr\">82</xref>]; a patient affected by chronic myelogenous leukaemia and systemic psoriasis with polyarthritis was treated with alHSCT following non-myeloablative conditioning. Initially, both leukaemia and the autoimmune disease were cured. However, later, a recurrence of polyarthritis and psoriasis was observed at the same time as an increase in the proportion of the host DNA (i.e., mixed chimerism). Notably, concomitant reappearance of leukemic minimal residual disease (MRD) was observed. Both molecular MRD positivity and autoimmune manifestations were successfully resolved by the discontinuation of anti-GVHD prophylaxis with Cyclosporine A (CSA) that resulted in the activation of alloreactive T cells leading to GVHD and to Graft-versus-Leukemia (GVL) and graft-versus-autoimmunity effect. This observation corroborates the hypothesis that donor lymphocytes have a role in eliminating self-reactive host T cells. The risk of GVHD has to be carefully evaluated in this context, giving the clinician the hint of a reactive immune system that may also act against the autoimmunity. Indeed, GVHD is a severe and potentially lethal condition, so its manifestations must be promptly recognized and treated. Other complications, including infections, graft rejection, veno-occlusive-disease (VOD) and massive haemorrhage [<xref rid=\"B58-ijms-21-05304\" ref-type=\"bibr\">58</xref>], accounting for considerable morbidity and mortality of alHSCT must be carefully weighed against its therapeutic benefits.</p></sec><sec sec-type=\"conclusions\" id=\"sec9-ijms-21-05304\"><title>9. Conclusions</title><p>The NMOSD includes clinical pictures of variable severity, many at high risk of recurrence and strongly disabling which might lead to loss of visual or motor functions in a short time. In recent years, much effort has been put into better understanding the pathogenesis of the disease and, in particular, the different immune mechanisms involved in NMOSD besides the production of AQP4- IgG. This has promoted the development of new therapeutic frontiers that can change the course of the disease. Starting from the off-label use of immunosuppressive drugs in NMOSD, more recently, new monoclonal antigens have been tested in specific NMOSD trials. In addition, in the refractory forms of immunosuppressive treatments, HSCT must be addressed and it should be considered as a possible therapeutic option in the most severe form of NMOSD.</p></sec></body><back><notes><title>Author Contributions</title><p>G.C. and L.P., writing—original draft preparation; M.V. and P.M., writing—review and editing. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research received no external funding.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><glossary><title>Abbreviations</title><array orientation=\"portrait\"><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NMO</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">neuromyelitis optica</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">NMOSD</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">neuromyelitis optica spectrum disorders</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CNS</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">central nervous system</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HSCT</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">hematopoietic stem cell transplantation</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AQP4</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">aquaporin-4</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CSF</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">cerebrospinal fluid</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MOG-Ab</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">myelin oligodendrocyte glycoprotein antibody</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">BBB</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">the blood–brain barrier</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GM-CSF</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">granulocyte-macrophage colony-stimulating factor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">LETM</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">longitudinally extensive transverse myelitis</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MRI</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">magnetic resonance imaging</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AZA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">azathioprine</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MMF</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">mycophenolate mofetil</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">RTX</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">rituximab</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">FDA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Food and Drug Administration</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">EMA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">European Medicine Agency</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HR</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">hazard ratio</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AuHSCT</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">autologous HSCT</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AlHSCT</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">allogeneic HSCT</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HSCs</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">hematopoietic stem cells</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GVHD</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">graft versus host disease</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TRM</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">transplant-related mortality</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">EAE</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">experimental autoimmune encephalomyelitis</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TBI</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">total body irradiation</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cy</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">cyclophosphamide</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MS</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">multiple sclerosis</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ATG</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">antithymocyte globulin</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">EDSS</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Expanded Disability Status Scale</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">OSMS</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">opticospinal multiple sclerosis</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">EBMT</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">European Group for Blood and Marrow Transplantation</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AID</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">autoimmune diseases</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HLA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">human leukocyte antigen</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MFD</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">matched family donor</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">MUD</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">matched unrelated donors</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Flu</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">fludarabine</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Threo</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">threosulfan</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">VOD</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" 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Hematol.</source><year>2000</year><volume>28</volume><fpage>853</fpage><lpage>857</lpage><pub-id pub-id-type=\"doi\">10.1016/S0301-472X(00)00172-7</pub-id><pub-id pub-id-type=\"pmid\">10907647</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijms-21-05304-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>MRI of a 11-year-old patient with Neuromyelitis optica (NMO): (<bold>A</bold>) the brain axial Flair-T2 weighted image shows hyperintensity of the left optic nerve and (<bold>B</bold>) the spinal T2 weighted image shows cervical hyperintense lesions extending longitudinally from C3 to C7 (longitudinally extensive transverse myelitis (LETM).</p></caption><graphic xlink:href=\"ijms-21-05304-g001\"/></fig><fig id=\"ijms-21-05304-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>(<bold>A</bold>) Schematic representation of neuromyelitis optica spectrum disorder (NMOSD) pathogenesis: currently available treatments and their targets are reported. ADCC, antibody-dependent cell-mediated cytotoxicity; AQP4-IgG, anti-aquaporin-4 immunoglobulin G; NK, natural killer; MMF, mycophenolate mofetil; aza, azathioprine. (<bold>B</bold>) Allogeneic HSCT rationale and immunological implications.</p></caption><graphic xlink:href=\"ijms-21-05304-g002\"/></fig><table-wrap id=\"ijms-21-05304-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05304-t001_Table 1</object-id><label>Table 1</label><caption><p>Summary of patients’ characteristics of reported allogeneic hematopoietic stem cells (alHSCT).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Characheristics</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pt1</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pt2</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pt3</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Age</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sex</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">M</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">F</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">F</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Conditioning</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Flu/Threo</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Flu/Threo</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Flu/Threo/TT</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GVHD prophilaxis</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ATG/Cyclosporine/Mtx</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mycophenolate/Rapamycin</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ATG/αβ depletion</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Previous AuHSCT</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes (2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">No</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">EDSS improv</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6–3.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.5–7.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.5–5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AQP4 negativity after HSCT</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Donor Type</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HLA-id sibling</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HLA matched (9/10) MUD</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">HLA-Haploidentical</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Follow-up</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3 years</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3 years</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2 years</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Reported in</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Greco et al.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Greco et al.</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Ceglie et al.</td></tr></tbody></table><table-wrap-foot><fn><p>alHSCT, allogeneic HSCT; HSCT, hematopoietic stem cell transplantation; MTX, Methotrexate; Flu, Fludarabine; Threo, Threosulfan; TT, Thiotepa; HLA, Human Leukocyte Antigen; id, identical; MUD, Matched Unrelated Donor; ATG, Antithymocyte Globulin, EDSS, Expanded Disability Status Scale, Pt, Patients.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32759871</article-id><article-id pub-id-type=\"pmc\">PMC7432051</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155624</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05624</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Synthetic Bone Grafting in Aseptic Loosening of Acetabular Cup: Good Clinical and Radiological Outcomes in Contained Bone Defects at Medium-Term Follow Up</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Parchi</surname><given-names>Paolo Domenico</given-names></name></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-3368-8919</contrib-id><name><surname>Simonetti</surname><given-names>Matteo</given-names></name><xref rid=\"c1-ijerph-17-05624\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Bonicoli</surname><given-names>Enrico</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Piolanti</surname><given-names>Nicola</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Scaglione</surname><given-names>Michelangelo</given-names></name></contrib></contrib-group><aff id=\"af1-ijerph-17-05624\">1st Orthopedic Division, Department of Translational Research and new technology in medicine and surgery, University of Pisa, 56124 Pisa, Italy; <email>paolo.parchi@unipi.it</email> (P.D.P.); <email>enrico.bonicoli@gmail.com</email> (E.B.); <email>piolanti.nicola@gmail.com</email> (N.P.); <email>michelangelo.scaglione@gmail.com</email> (M.S.)</aff><author-notes><corresp id=\"c1-ijerph-17-05624\"><label>*</label>Correspondence: <email>matteo.simonetti90@gmail.com</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>04</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5624</elocation-id><history><date date-type=\"received\"><day>08</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>31</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Restoring bone loss is one of the major challenges when facing hip revision surgery. To eliminate the risk of disease transmission and antigenicity of allografts and donor-morbidity of autografts, the use of synthetic bioceramics has become popular in the last decade. Our study investigated the effectiveness of impaction bone grafting (IBG) of contained acetabular defects (Paprosky 2 and 3a) using a porous ceramic-based hydroxyapatite bone substitute (Engipore, provided by Finceramica Faenza S.p.A., Faenza, Italy) mixed with a low percentage of autologous bone (obtained from reaming when available). We retrospectively assessed 36 patients who underwent acetabular revision using IBG using a porous ceramic-based hydroxyapatite bone substitute with cementless implants with a mean follow-up of 4.4 years. We evaluated, at regular intervals, patients clinically (using the Hip Harris Score and Oxford Score) and radiologically to evaluate the rate of incorporation of the graft, the presence of radiolucent lines or migrations of the cup. Clinical scores significantly improved (WOMAC improved from 49.7–67.30, and the HSS from 56–89). The rate of implants’ survival was 100% at our medium follow-up (4.4 years). We reported five cases of minor migration of the cup, and radiolucent lines were visible in seven patients at the last-follow up. The graft was well-incorporated in all patients. The results presented in this study suggest the HA bone substitute is an effective and safe bone graft when facing hip revision surgery; thus, longer follow-up studies are required.</p></abstract><kwd-group><kwd>bone grafting</kwd><kwd>bone substitutes</kwd><kwd>total hip arthroplasty revision</kwd><kwd>acetabular cup</kwd><kwd>hydroxyapatite</kwd><kwd>osteointegration</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05624\"><title>1. Introduction</title><p>With the ageing of the population and a life expectancy increase in the Western world, there will be a greater demand for total hip replacement procedures and, subsequently, of hip revisions.</p><p>In the United States, more than 300,000 total hip replacements (THAs) are performed yearly and, according to the National Inpatient Sample of the US, over 250,000 THA revisions were performed between 2009 and 2013 [<xref rid=\"B1-ijerph-17-05624\" ref-type=\"bibr\">1</xref>]. Given the premises, the revision surgery rate is expected to grow by 137% by the end of 2030 [<xref rid=\"B2-ijerph-17-05624\" ref-type=\"bibr\">2</xref>].</p><p>The main indications for revision surgery are hip instability and aseptic loosening of the implant, accounting for 42% of all revision procedures [<xref rid=\"B3-ijerph-17-05624\" ref-type=\"bibr\">3</xref>].</p><p>The greatest concerns for orthopedic surgeons dealing with a THA revision is how to address bone loss and poor bone regenerative potential to restore the anatomical hip center and improve joint biomechanics.</p><p>Impaction bone grafting (IBG) has been described for the treatment of contained defects where a good primary stability could be obtained [<xref rid=\"B4-ijerph-17-05624\" ref-type=\"bibr\">4</xref>]. Although long-term results with this procedure are mainly related to the surgical technique, good outcomes have been reported in the literature.</p><p>IBG of the acetabulum, first performed by Parker et al. in 1975 [<xref rid=\"B5-ijerph-17-05624\" ref-type=\"bibr\">5</xref>], was made popular by Slooff et al. in 1984 using a cemented acetabular component [<xref rid=\"B6-ijerph-17-05624\" ref-type=\"bibr\">6</xref>]. More recently, favorable results have been reported when morcellized allografts were used in combination with uncemented cups [<xref rid=\"B7-ijerph-17-05624\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijerph-17-05624\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05624\" ref-type=\"bibr\">9</xref>].</p><p>There are multiple viable sources for bone grafting. Autografts are described as the gold standard source to treat bone loss in terms of the properties of osteoconduction, osteoinduction and osteogenesis, but they are rarely used in THA revisions due to high rates of morbidity at harvest sites and their limited availability [<xref rid=\"B10-ijerph-17-05624\" ref-type=\"bibr\">10</xref>]. Allografts retain osteoconductive properties and may exhibit osteoinductivity potential. For these reasons, although the option presents high costs and there are still concerns about the related risks of disease transmission and antigenicity [<xref rid=\"B11-ijerph-17-05624\" ref-type=\"bibr\">11</xref>], they could be considered the second-choice option to address bone defects in THA revision procedures.</p><p>All the above concerns have led to growing research in developing synthetic bone substitutes for natural bone stock replacement.</p><p>Bioactive ceramics are synthetic bone substitutes which have received great attention recently due to their potential in stimulating cell proliferation, differentiation and bone tissue regeneration [<xref rid=\"B12-ijerph-17-05624\" ref-type=\"bibr\">12</xref>]. Several synthetic ceramics have been tested and used, such as calcium phosphate, tricalcium phosphate, calcium sulphate and hydroxyapatite (HA) [<xref rid=\"B13-ijerph-17-05624\" ref-type=\"bibr\">13</xref>].</p><p>In this scenario, hydroxyapatite (HA), a major component of natural bone, can combine with tissues by chemical bonds to form new bone tissue when implanted [<xref rid=\"B14-ijerph-17-05624\" ref-type=\"bibr\">14</xref>].</p><p>The ceramic-based hydroxyapatite bone graft substitute Engipore (provided by Finceramica Faenza S.p.A., Faenza, Italy) consists of porous hydroxyapatite that allows the product to serve as a scaffold to guide bone regeneration, fostering cell attachment and proliferation and promoting osteointegration [<xref rid=\"B15-ijerph-17-05624\" ref-type=\"bibr\">15</xref>]. The aim of this retrospective study was to analyze the osteointegrative properties of Engipore bone grafts in revision surgery after primary THA.</p></sec><sec id=\"sec2-ijerph-17-05624\"><title>2. Materials and Methods</title><p>This is a retrospective case series of patients who underwent an acetabular revision associated with the use of the bone substitute Engipore between January 2014 and December 2016.</p><p>In this period, 105 THA revision procedures were performed: we selected 36 patients whose indication for THA revision was aseptical loosening of the acetabulum after primary THA with a contained bone defect (Paprosky 2 and 3a) treated with cementless implants.</p><p>There were 21 women (58.3%) and 15 men (41.7%). The mean age at surgery was 72 (age range: 40–81 years). Patients with an uncontained defect requiring the use of mesh or a cage to reconstruct the acetabulum were excluded from this analysis. It was the first revision in all cases.</p><p>We performed a postero-lateral approach to the hip in all patients, obtaining a good exposure of the acetabulum. After implant removal, necrotic and soft tissue surrounding the acetabulum and the bone defect were carefully removed in order to evaluate the severity of bone loss according to Paprosky’s classification (see <xref rid=\"ijerph-17-05624-t001\" ref-type=\"table\">Table 1</xref>) [<xref rid=\"B16-ijerph-17-05624\" ref-type=\"bibr\">16</xref>].</p><p>We prepared the acetabulum with progressive hemispherical reamers and then packed the bone loss with reverse reaming and trial prosthesis using a mixture of Engipore chips mixed with the patient’s blood and autologous bone recovered from reaming when available.</p><p>The bone substitute Engipore is a biomimetic and biocompatible porous stoichiometric hydroxyapatite bone substitute comprised of calcium ions, phosphate ions and hydroxyl groups, which is very similar in microstructure and chemical composition to the mineral component of human bones. The trabecular structure, which resembles the mineralized phase of natural bone, is characterized by a 90% porosity rate, allowing physiological fluids absorption, the promotion of cell migration and adhesion for mineral matrix synthesis, thus offering an ideal environment for new bone formation and tissue restoration [<xref rid=\"B17-ijerph-17-05624\" ref-type=\"bibr\">17</xref>]. The composition, shape and handling properties of this bone substitute make it an ideal bone graft candidate for hip revision surgery. Moreover, this material can be safely mixed with autologous bone. The chips provided by Finceramica used in our study came in the size of 2–4 mm.</p><p>Our case series is comprised of selected patients with contained defects, so it was possible to achieve a primary press-fit stability placing uncemented cups in all cases (Regenerex Revision Shell in 17 cases, Delta TT Revision Cup in 19 cases), 1–2 mm larger than the last trial used. Regardless of the primary stability of the implant, a median of 4 screws (2–6) was used to fix the shell (<xref ref-type=\"fig\" rid=\"ijerph-17-05624-f001\">Figure 1</xref>). The post-operative rehabilitation protocol included: mobilization of the hip, avoiding luxation movements, and partial weight bearing was given for 6 weeks and then gradually progressed to full weight bearing and was performed by all the patients. Clinical and radiographical evaluations of all patients were performed pre-operatively and post-operatively at regular intervals (1, 3, 6 months and yearly after). The radiological follow-up included anterior-posterior X-rays of the pelvis and antero-posterior and lateral X-rays of the hip. The clinical evaluation was conducted using the Harris Hip Score (HHS) [<xref rid=\"B18-ijerph-17-05624\" ref-type=\"bibr\">18</xref>] and the West Ontario and McMaster Universities Osteoarthritis Index (WOMAC) [<xref rid=\"B19-ijerph-17-05624\" ref-type=\"bibr\">19</xref>]. We eventually reached by phone patients (or their relatives) who did not fully accomplish follow-up visits to assess whether they had underwent further surgery. Radiographic analysis was carried out by two senior surgeons (N.P. and M.S.), assessing graft incorporation, bone resorption or migration of the implant. We used the De Lee and Charnley classification [<xref rid=\"B20-ijerph-17-05624\" ref-type=\"bibr\">20</xref>] to assess lines of radiolucency around the acetabular component. A radiolucent line wider than 2 mm was considered significant. We considered the surgery to have failed if the cup had migrated 3 mm or more or if revision was necessary. The grade of heterotopic ossification was evaluated according to Brooker et al. [<xref rid=\"B21-ijerph-17-05624\" ref-type=\"bibr\">21</xref>]. The mean follow-up was 4.4 years (minimum 3.1—maximum 5.8 years). Given the small sample size, statistical testing of correlations was not determined to be useful.</p><sec><title>Ethical Statement</title><p>This is retrospective study which was approved by internal revision board and for these reason needs only a tacital approval of the ethical committee—ethical approval was not needed for this study.</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05624\"><title>3. Results</title><p>Thirty-six patients met the inclusion and exclusion criteria defined for our retrospective case analysis. Five patients died of disease not related to the surgical procedures. Among the remaining 31 patients, 25 patients returned a complete questionnaire; meanwhile, a full radiological follow-up was available in 28 cases.</p><p>We recorded a significant improvement in clinical function: the WOMAC score changed from 49.7 pre-operatively to 67.3 post-operatively, and the HHS changed from 56.1 pre-operatively to 89.4 post-operatively. At the final follow-up, most of the patients (67%) stated they would undergo surgery again. Data about survivorship of the implant were available for all patients. There were no cases of acetabular revision in the selected patients, so the rate of survivorship of the implant at our medium follow-up was 100%. We reported five cases of acetabular migrations wider than 3 mm (range: 3–6 mm), but surgery was not necessary because four patients reported no functional impairment and one patient had functional impairment but refused further surgery. All migrations occurred within one year after surgery and then remained stable during the follow-up and were not associated with significant worsening of the clinical scores. According to De Lee and Charnley et al. [<xref rid=\"B20-ijerph-17-05624\" ref-type=\"bibr\">20</xref>], we found at the initial follow-up two patients with radiolucent lines bigger than 1 mm in zone 1, three patients in zone 2, one patient in zone 3, two patients both in zone 1 and 2 and one patient in all three zones (see <xref rid=\"ijerph-17-05624-t002\" ref-type=\"table\">Table 2</xref>). No significant association between the presence of radiolucent lines and clinical outcome was noticed.</p><p>In the last X-ray evaluation, radiolucent lines remained still visible and no signs of progression were detectable, except for two cases, in which radiolucent lines were filled by, presumably, the formation of new bone (see <xref ref-type=\"fig\" rid=\"ijerph-17-05624-f002\">Figure 2</xref>).</p><p>Mild heterotopic ossification (type 1–2 according to the Brooker classification) was found in three patients. Early complications (during the in-patient stay) occurred in five of the 31 patients. These included a superficial wound infection in two patients, treated successfully with antibiotic therapy; one case of deep infection treated successfully with DAIR (debridement, antibiotics and implant retention); one case of deep-vein thrombosis and one case of postoperative early dislocation, which was successfully treated with close reduction. One patient sustained a periprosthetic fracture of the femur (type B1 according to the Vancouver Classification) after falling and was treated successfully with osteosynthesis with a plate.</p></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05624\"><title>4. Discussion</title><p>There is a great debate in the literature regarding clinical and radiological outcomes of hip revision surgery using synthetic bone substitutes. Kurien et al. [<xref rid=\"B22-ijerph-17-05624\" ref-type=\"bibr\">22</xref>], in a systematic review about the evidence of the use of bone graft substitutes, stated that there are few synthetic graft substitutes with level I evidence. A certain number of papers have reported good results on the use of bone substitutes in hip revision surgery, but they were all a heterogenous case series mix with different kinds of implants, wide bone loss severity and type of bone substitutes used. Our case series, even if small, has an asset of being a homogeneous court of patients characterized by an isolated acetabular contained defect treated with hydroxyapatite (mixed with autologous bone and blood obtained from reaming when available) and with cementless implants. IBG is recognized as an efficient method to treat contained acetabular defects [<xref rid=\"B16-ijerph-17-05624\" ref-type=\"bibr\">16</xref>,<xref rid=\"B23-ijerph-17-05624\" ref-type=\"bibr\">23</xref>], even if the procedure is described as successful for selected cases of the Paprosky 3 type [<xref rid=\"B24-ijerph-17-05624\" ref-type=\"bibr\">24</xref>]. A porous, cementless coated socket would lead to bony ingrowth and osteointegration, providing a stable and solid fixation [<xref rid=\"B7-ijerph-17-05624\" ref-type=\"bibr\">7</xref>]. Few authors have reported the results of the use of an isolated bone graft substitute, not augmented with allografts or autografts. Oonishi et al. [<xref rid=\"B25-ijerph-17-05624\" ref-type=\"bibr\">25</xref>] documented the use of hydroxyapatite granules to fill massive bone loss in 40 patients using cemented sockets, obtaining good clinical and radiological results in a 4–10 year follow-up. They reported three cup migrations associated with mild clinical impairment. Good osteointegration to native bone was observed in all 40 patients. Schwartz et al. [<xref rid=\"B26-ijerph-17-05624\" ref-type=\"bibr\">26</xref>] used a biphasic phosphor-calcium bone substitute to face severe acetabular bone loss using both jumbo cups and a screwed support ring. At a mean 10 year follow-up, they reported no cases of migration of the cup in living patients and good bone osteointegration. Coralline hydroxyapatite was used by Wasielewski et al. [<xref rid=\"B27-ijerph-17-05624\" ref-type=\"bibr\">27</xref>] in complex acetabular revision surgery, reporting one case of failure. No resorption of the graft was noticed and all cases showed good osteointegration. Our clinical results encourage the choice of synthetic bone substitutes, rather than a metallic augment, when facing THA revision. Patients were eventually satisfied in terms of pain relief and functional recovery, and most of them stated they would undergo surgery again. The radiographical results are quite in contrast to the clinical outcomes: we registered five cases of acetabular migration, of which one was considered a frank radiological failure but not associated with worsening of clinical scores. The incidence of radiolucent lines we reported around the acetabular shells was concerning, but similar to other studies in the literature [<xref rid=\"B27-ijerph-17-05624\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijerph-17-05624\" ref-type=\"bibr\">28</xref>]. Although these radiolucent lines showed up early after surgery, they did not become wider with the progress of time; thus, we agree with Schmalzried et al. [<xref rid=\"B29-ijerph-17-05624\" ref-type=\"bibr\">29</xref>], and we consider that these radiolucent lines could represent a predictive factor of future aseptic loosening, so patients need to be kept monitored. The rate of graft incorporation in our series was satisfactory, similar to others reported [<xref rid=\"B25-ijerph-17-05624\" ref-type=\"bibr\">25</xref>] or even higher compared to other series in the literature [<xref rid=\"B30-ijerph-17-05624\" ref-type=\"bibr\">30</xref>]. We reported only one patient with graft incorporation failure in all three zones, but the case was not associated with any clinical impairment. Synthetic bioceramics such as Engipore offer several advantages over autografts and allografts in terms of safety [<xref rid=\"B10-ijerph-17-05624\" ref-type=\"bibr\">10</xref>]. The synthetic fabrication of Engipore makes the product free of any risk of disease transmission or immunoreaction, and the availability of a wide range of shapes and formats makes it a valid option for different surgical applications. Moreover, its availability off-the-shelf eliminates the donor-morbidity of autograft harvests and reliance on bone banks for allograft supply.</p><p>Based on this clinical experience, as a general comment, the identification of the most appropriate surgery, together with a proper application and packing of the chips in the surgical implantation phase, are key aspects for a successful outcome.</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05624\"><title>5. Conclusions</title><p>The clinical and radiological results presented in our study suggest that the bioceramic bone substitute Engipore can be used as an ideal bone substitute in THA acetabular revision surgery, even if mixed with a low percentage of autologous bone. The product is safe, with no risk of disease transmission or an antigenic response. It led to a low rate of failure and a satisfactory grade of osteointegration. The results presented in the study must be interpreted with caution due to the low number of patients enrolled and the retrospective design of the study and follow-up period. Prospective, randomized controlled clinical studies would be beneficial to confirm its safety and efficacy at a longer follow-up.</p></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization, P.D.P. and M.S. (Matteo Simonetti); methodology, N.P.; validation, E.B.; formal analysis, E.B. and N.P.; investigation, M.S. (Michelangelo Scaglione).; data curation, M.S. (Matteo Simonetti); writing—original draft preparation, M.S. (Matteo Simonetti); writing—review and editing, M.S. (Matteo Simonetti); supervision, M.S. (Michelangelo Scaglione) and P.D.P.; project administration, P.D.P. and M.S. (Michelangelo Scaglione). All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research received no external funding.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest. 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Br.</source><year>2010</year><volume>92</volume><fpage>617</fpage><lpage>623</lpage><pub-id pub-id-type=\"doi\">10.1302/0301-620X.92B5.23044</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijerph-17-05624-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Clinic case: 76 y female with loosening of the acetabular cup (<bold>a</bold>) Pre-operative x-rays, (<bold>b</bold>) Intraoperative pictures show the acetabulum impacted with Engipore chips filling the bone defect (Paprosky 3a), (<bold>c</bold>) Post-operative x-rays at the last follow-up (3.2 years) showing good osteointegration of the implant.</p></caption><graphic xlink:href=\"ijerph-17-05624-g001\"/></fig><fig id=\"ijerph-17-05624-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>X-rays show good osteointegration of the implant and formation in a 72-year-old woman treated with cup revision for a Paprosky 3a. (<bold>a</bold>) One month follow-up, presence of radiolucent lines, (<bold>b</bold>) One year follow-up, shows filling of the radiolucent line with the formation of new bone.</p></caption><graphic xlink:href=\"ijerph-17-05624-g002\"/></fig><table-wrap id=\"ijerph-17-05624-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05624-t001_Table 1</object-id><label>Table 1</label><caption><p>Paprosky’s classification of acetabular bone loss.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Type</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Description</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Patients</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mean Age</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Minimal deformity, intact rim</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2a</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Superior bone lysis with intact superior rim</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">69 years</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2b</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Absent superior rim, superolateral migration</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">70 years</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2c</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Localized destruction of medial wall</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">73 years</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3a</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bone loss from 10 am to 2 pm around rim with 30–60% of the supporting bone stock destroyed. There is superolateral cup migration</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">76 years</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3b</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bone loss from 9 am to 5 pm around rim with up to 60% of the supporting bone stock destroyed. There is superomedial cup migration</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Pelvic discontinuity</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05624-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05624-t002_Table 2</object-id><label>Table 2</label><caption><p>Clinical and radiological results at the mean follow-up (4.4 years).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Clinical and Radiological Results</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No.</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mean pre-operative WS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">49.7 (39–57)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mean post-operative WS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">67.3 (53–81)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mean pre-operative HHS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">56.1 (41–75)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Mean post-operative HHS</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">89.4 (62–91)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Radiolucent lines at the last follow-up</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7 pz</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Migration >3 mm (radiological failure)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Clinical failure</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Eterotopic ossifications</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3</td></tr></tbody></table><table-wrap-foot><fn><p>HHS, Harris Hip Score; WS, WOMAC Score.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751484</article-id><article-id pub-id-type=\"pmc\">PMC7432052</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155503</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05503</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Coronavirus-Related Health Literacy: A Cross-Sectional Study in Adults during the COVID-19 Infodemic in Germany</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-1714-4783</contrib-id><name><surname>Okan</surname><given-names>Orkan</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05503\">1</xref><xref rid=\"c1-ijerph-17-05503\" ref-type=\"corresp\">*</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-0147-6890</contrib-id><name><surname>Bollweg</surname><given-names>Torsten Michael</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05503\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Berens</surname><given-names>Eva-Maria</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijerph-17-05503\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Hurrelmann</surname><given-names>Klaus</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijerph-17-05503\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Bauer</surname><given-names>Ullrich</given-names></name><xref ref-type=\"aff\" rid=\"af5-ijerph-17-05503\">5</xref></contrib><contrib contrib-type=\"author\"><name><surname>Schaeffer</surname><given-names>Doris</given-names></name><xref ref-type=\"aff\" rid=\"af6-ijerph-17-05503\">6</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05503\"><label>1</label>Interdisciplinary Centre for Health Literacy Research, Faculty of Educational Science, Bielefeld University, 33615 Bielefeld, Germany</aff><aff id=\"af2-ijerph-17-05503\"><label>2</label>Centre for Prevention and Intervention in Childhood and Adolescence, Faculty of Educational Science, Bielefeld University, 33615 Bielefeld, Germany; <email>torsten.bollweg@uni-bielefeld.de</email></aff><aff id=\"af3-ijerph-17-05503\"><label>3</label>School of Public Health, Interdisciplinary Centre for Health Literacy Research, Bielefeld University, 33615 Bielefeld, Germany; <email>eva-maria.berens@uni-bielefeld.de</email></aff><aff id=\"af4-ijerph-17-05503\"><label>4</label>Department of Public Health and Education, Hertie School of Governance, 10117 Berlin, Germany; <email>hurrelmann@hertie-school.org</email></aff><aff id=\"af5-ijerph-17-05503\"><label>5</label>Interdisciplinary Centre for Health Literacy Research, Centre for Prevention and Intervention in Childhood and Adolescence, Faculty of Educational Science, Bielefeld University, 33615 Bielefeld, Germany; <email>ullrich.bauer@uni-bielefeld.de</email></aff><aff id=\"af6-ijerph-17-05503\"><label>6</label>Interdisciplinary Centre for Health Literacy Research, School of Public Health, Bielefeld University, 33615 Bielefeld, Germany; <email>doris.schaeffer@uni-bielefeld.de</email></aff><author-notes><corresp id=\"c1-ijerph-17-05503\"><label>*</label>Correspondence: <email>orkan.okan@uni-bielefeld.de</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>30</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5503</elocation-id><history><date date-type=\"received\"><day>20</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>24</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>There is an “infodemic” associated with the COVID-19 pandemic—an overabundance of valid and invalid information. Health literacy is the ability to access, understand, appraise, and apply health information, making it crucial for navigating coronavirus and COVID-19 information environments. A cross-sectional representative study of participants ≥ 16 years in Germany was conducted using an online survey. A coronavirus-related health literacy measure was developed (HLS-COVID-Q22). Internal consistency was very high (α = 0.940; ρ = 0.891) and construct validity suggests a sufficient model fit, making HLS-COVID-Q22 a feasible tool for assessing coronavirus-related health literacy in population surveys. While 49.9% of our sample had sufficient levels of coronavirus-related health literacy, 50.1% had “problematic” (15.2%) or “inadequate” (34.9%) levels. Although the overall level of health literacy is high, a vast number of participants report difficulties dealing with coronavirus and COVID-19 information. The participants felt well informed about coronavirus, but 47.8% reported having difficulties judging whether they could trust media information on COVID-19. Confusion about coronavirus information was significantly higher among those who had lower health literacy. This calls for targeted public information campaigns and promotion of population-based health literacy for better navigation of information environments during the infodemic, identification of disinformation, and decision-making based on reliable and trustworthy information.</p></abstract><kwd-group><kwd>health literacy</kwd><kwd>infodemic</kwd><kwd>survey</kwd><kwd>coronavirus 2</kwd><kwd>SARS-CoV-2</kwd><kwd>COVID-19</kwd><kwd>Germany</kwd><kwd>adult population</kwd><kwd>HLS-EU-Q</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05503\"><title>1. Introduction</title><p>The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel virus in the coronavirus family, causing the coronavirus disease (COVID-19). It was first reported in December 2019 and has since evolved into a global pandemic that created a social and economic crisis as well as a humanitarian catastrophe [<xref rid=\"B1-ijerph-17-05503\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijerph-17-05503\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijerph-17-05503\" ref-type=\"bibr\">3</xref>,<xref rid=\"B4-ijerph-17-05503\" ref-type=\"bibr\">4</xref>]. While the human and economic costs certainly are the most significant consequences of the COVID-19 pandemic, they are tackled by challenges related to information overload and an ongoing “infodemic” [<xref rid=\"B5-ijerph-17-05503\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijerph-17-05503\" ref-type=\"bibr\">6</xref>].</p><p>Infodemic is short for “information epidemic”, a phenomenon that portrays the rapid spread and amplification of vast amounts of valid and invalid information on the internet or through other communication technologies [<xref rid=\"B5-ijerph-17-05503\" ref-type=\"bibr\">5</xref>,<xref rid=\"B7-ijerph-17-05503\" ref-type=\"bibr\">7</xref>]. Although the term was first coined during the SARS outbreak of 2003 [<xref rid=\"B7-ijerph-17-05503\" ref-type=\"bibr\">7</xref>], it has not been used much in the scientific literature before 2020 [<xref rid=\"B8-ijerph-17-05503\" ref-type=\"bibr\">8</xref>]. Since the beginning of the COVID-19 pandemic, both the production and consumption of information have increased rapidly and significantly [<xref rid=\"B9-ijerph-17-05503\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijerph-17-05503\" ref-type=\"bibr\">10</xref>]. The World Health Organization [<xref rid=\"B6-ijerph-17-05503\" ref-type=\"bibr\">6</xref>], amongst others [<xref rid=\"B11-ijerph-17-05503\" ref-type=\"bibr\">11</xref>], has stressed that the infodemic is a serious threat to public health, public action, social cohesion, and the political landscape as a whole. Baines and Elliott conclude that “(i) the infodemic is unprecedented in its size and velocity; (ii) unexpected forms of false information are emerging daily; and (iii) no global consensus exists on how best to classify the types of false messages being encountered” [<xref rid=\"B12-ijerph-17-05503\" ref-type=\"bibr\">12</xref>]. On the individual level, the infodemic creates confusion among recipients of information, specifically in relation to the identification of reliable information [<xref rid=\"B13-ijerph-17-05503\" ref-type=\"bibr\">13</xref>,<xref rid=\"B14-ijerph-17-05503\" ref-type=\"bibr\">14</xref>,<xref rid=\"B15-ijerph-17-05503\" ref-type=\"bibr\">15</xref>] as well as disinformation, misinformation, and malinformation [<xref rid=\"B12-ijerph-17-05503\" ref-type=\"bibr\">12</xref>]. In the current pandemic, negative information bias (causing “catastrophic thinking”) and positive information bias (causing “unrealistic optimism”) are among the many consequences and risks posed by the infodemic [<xref rid=\"B16-ijerph-17-05503\" ref-type=\"bibr\">16</xref>]. Altogether, this constitutes a global scientific challenge [<xref rid=\"B13-ijerph-17-05503\" ref-type=\"bibr\">13</xref>].</p><p>Media literacy and information literacy are critical competencies in the context of infodemics [<xref rid=\"B13-ijerph-17-05503\" ref-type=\"bibr\">13</xref>]. Since the current infodemic is a health-related infodemic, information literacy must be focused on health—this is known as health literacy [<xref rid=\"B17-ijerph-17-05503\" ref-type=\"bibr\">17</xref>]. Although health information about COVID-19 dominates most communication channels [<xref rid=\"B18-ijerph-17-05503\" ref-type=\"bibr\">18</xref>], health literacy has thus far remained both an underestimated concept and a neglected field of research in the context of the COVID-19 infodemic [<xref rid=\"B19-ijerph-17-05503\" ref-type=\"bibr\">19</xref>]. Researchers from various disciplines have urged the need to consider the health literacy of citizens, policymakers, governments, and information producers and providers [<xref rid=\"B9-ijerph-17-05503\" ref-type=\"bibr\">9</xref>,<xref rid=\"B16-ijerph-17-05503\" ref-type=\"bibr\">16</xref>,<xref rid=\"B18-ijerph-17-05503\" ref-type=\"bibr\">18</xref>,<xref rid=\"B20-ijerph-17-05503\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijerph-17-05503\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijerph-17-05503\" ref-type=\"bibr\">22</xref>].</p><p>Health literacy can facilitate distinguishing between reliable information on COVID-19 and dis- and misinformation on the topic, it helps navigating sources of health information and health services, and health literacy empowers people to make informed health decisions and to practice healthy and protective behaviors in the time of the coronavirus and COVID-19 pandemic [<xref rid=\"B9-ijerph-17-05503\" ref-type=\"bibr\">9</xref>,<xref rid=\"B19-ijerph-17-05503\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijerph-17-05503\" ref-type=\"bibr\">20</xref>]. In general, health literacy is defined as the motivation, knowledge, and competence used to access, understand, appraise, and apply health information and make health-related decisions [<xref rid=\"B23-ijerph-17-05503\" ref-type=\"bibr\">23</xref>]. However, health literacy is a relational concept and the system-level health literacy, e.g., the health literacy of information suppliers and service providers is as important as the health literacy of individuals [<xref rid=\"B24-ijerph-17-05503\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijerph-17-05503\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijerph-17-05503\" ref-type=\"bibr\">26</xref>]. Past research has shown that low health literacy is associated with various adverse health outcomes [<xref rid=\"B27-ijerph-17-05503\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijerph-17-05503\" ref-type=\"bibr\">28</xref>] and higher health care costs [<xref rid=\"B29-ijerph-17-05503\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijerph-17-05503\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijerph-17-05503\" ref-type=\"bibr\">31</xref>]. In addition, several studies have demonstrated the existence of a social gradient in low health literacy with financial deprivation, education, and social status being the strongest predictors [<xref rid=\"B28-ijerph-17-05503\" ref-type=\"bibr\">28</xref>,<xref rid=\"B32-ijerph-17-05503\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijerph-17-05503\" ref-type=\"bibr\">33</xref>]. Population-based studies on health literacy have shown that great proportions of European [<xref rid=\"B28-ijerph-17-05503\" ref-type=\"bibr\">28</xref>], North American [<xref rid=\"B34-ijerph-17-05503\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijerph-17-05503\" ref-type=\"bibr\">35</xref>], and Asian [<xref rid=\"B36-ijerph-17-05503\" ref-type=\"bibr\">36</xref>] populations have difficulties dealing with health-related information. The European Health Literacy Survey (HLS-EU), which was conducted in eight countries, found that almost 50% of all adults have “problematic” or “inadequate” health literacy, meaning that it is potentially difficult for them to access, understand, appraise, and apply information to promote or protect their health [<xref rid=\"B28-ijerph-17-05503\" ref-type=\"bibr\">28</xref>]. In Germany, this proportion is even higher, with 54.3% of all participants [<xref rid=\"B37-ijerph-17-05503\" ref-type=\"bibr\">37</xref>]. A recent study on fear of COVID-19 in medical students has shown that higher levels of health literacy may lower the level of fear [<xref rid=\"B38-ijerph-17-05503\" ref-type=\"bibr\">38</xref>]. In another study, higher health literacy levels have shown protective effects against COVID-19 related depression [<xref rid=\"B39-ijerph-17-05503\" ref-type=\"bibr\">39</xref>]. Understanding of public health recommendations, applying protective measures against infection with coronavirus, and navigating COVID-19-related health information environments are currently of elevated importance [<xref rid=\"B18-ijerph-17-05503\" ref-type=\"bibr\">18</xref>,<xref rid=\"B19-ijerph-17-05503\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijerph-17-05503\" ref-type=\"bibr\">20</xref>], which is why there is a need to explore coronavirus-related health literacy. A recent study in Germany is focussing on university students’ digital health literacy in relation to coronavirus and COVID-19 information [<xref rid=\"B40-ijerph-17-05503\" ref-type=\"bibr\">40</xref>]. This study is part of a global health literacy research network, which measures the digital health literacy, health information behavior, fear of COVID-19, and wellbeing of students during university lockdowns in 45 countries [<xref rid=\"B41-ijerph-17-05503\" ref-type=\"bibr\">41</xref>]. Together, these studies will contribute to creating evidence on health literacy in the time of COVID-19.</p><p>The aim of this study was to (1) test the feasibility of a newly developed assessment tool for coronavirus-related health literacy, (2) assess coronavirus-related health literacy of German adults, (3) examine differences in health literacy in relation to sociodemographic variables, and (4) explore how participants feel with regard to the overabundance of health information during the COVID-19 infodemic.</p></sec><sec sec-type=\"methods\" id=\"sec2-ijerph-17-05503\"><title>2. Methods</title><sec id=\"sec2dot1-ijerph-17-05503\"><title>2.1. Study Design and Target Group</title><p>A cross-sectional online survey was conducted from 31 March to 7 April 2020 among adult internet users (≥16 years) in Germany. The sample is representative of German-speaking internet users regarding age, sex, education, and state of residence. The survey was conducted by the Interdisciplinary Centre for Health Literacy Research at Bielefeld University, Germany, in collaboration with Allensbach Institute, Allensbach, Germany. Participants were recruited by the Allensbach Institute from an online panel of 180.000 individuals, in which all participants gave their consent. Using a stratified random sampling strategy, invitations were sent by email to <italic>n</italic> = 4329 prospective participants, of which <italic>n</italic> = 1461 responded and <italic>n</italic> = 308 were excluded due to recruitment quota was reached. Finally, <italic>n</italic> = 1153 participants received a link to complete the self-administered questionnaire, which was implemented as an online survey due to COVID-19 restrictions. Participants received an incentive upon completion.</p></sec><sec id=\"sec2dot2-ijerph-17-05503\"><title>2.2. Measures</title><sec id=\"sec2dot2dot1-ijerph-17-05503\"><title>2.2.1. Sociodemographic Indicators</title><p>Participants were asked about their sex, age, education, net household income, region of residence, and whether they had a child aged 18 or younger.</p></sec><sec id=\"sec2dot2dot2-ijerph-17-05503\"><title>2.2.2. Feeling Informed or Confused about the Coronavirus</title><p>Two general items were used to address individual perceptions of information on the coronavirus and COVID-19. First, the question “Overall, how well do you feel informed about the coronavirus or the coronavirus-epidemic?” was answered on a 4-point Likert scale: 1—“not well at all”, 2—“not so well”, 3—“well”, and 4—“very well”. The second question was: “Some people feel confused by all the information about the coronavirus or the coronavirus epidemic, and they don’t know what information to believe anymore. What about you: do you feel confused by all the information?” Responses were given on a 3-point scale: 1—“yes, very confused”, 2—“yes, somewhat confused”, and 3—“no, barely confused/not confused at all”.</p></sec><sec id=\"sec2dot2dot3-ijerph-17-05503\"><title>2.2.3. Health Literacy</title><p>Coronavirus-related health literacy was assessed based on the European Health Literacy Survey Questionnaire (HLS-EU-Q). The HLS-EU-Q is a self-report instrument for measuring a comprehensive model of health literacy [<xref rid=\"B42-ijerph-17-05503\" ref-type=\"bibr\">42</xref>]. It assesses participants’ perceived difficulty or ease when accessing, understanding, appraising, and applying health information [<xref rid=\"B43-ijerph-17-05503\" ref-type=\"bibr\">43</xref>]. Originally, the HLS-EU-Q consisted of 47 items (HLS-EU-Q47), but over the years, short forms have been developed. The 16-item version, the HLS-EU-Q16 [<xref rid=\"B44-ijerph-17-05503\" ref-type=\"bibr\">44</xref>], has shown high internal consistency (Cronbach’s α = 0.90) in a German representative sample [<xref rid=\"B45-ijerph-17-05503\" ref-type=\"bibr\">45</xref>]. The HLS-EU-Q16 comprises sixteen questions starting with “How easy or difficult is it for you to?” followed by specific questions, e.g., “…find information on treatments of illnesses that concern you?” It is answered on 4-point scale ranging from 1 (very difficult) to 4 (very easy). Usually, the questionnaire is administered as a (computer-assisted) personal interview, but telephone, paper and pencil, and online versions have been used as well [<xref rid=\"B42-ijerph-17-05503\" ref-type=\"bibr\">42</xref>,<xref rid=\"B43-ijerph-17-05503\" ref-type=\"bibr\">43</xref>]. In order to explore how German adults deal with information regarding coronavirus and COVID-19 during the pandemic, the German HLS-EU-Q16 [<xref rid=\"B44-ijerph-17-05503\" ref-type=\"bibr\">44</xref>,<xref rid=\"B45-ijerph-17-05503\" ref-type=\"bibr\">45</xref>] was adapted. After the first draft was commented and approved by the authors, it was sent to the Allensbach Institute for critical review by experts in questionnaire development. The comments were discussed and consented by the study group and the Allensbach Institute. The final questionnaire, HLS-COVID-Q22 [<xref rid=\"B46-ijerph-17-05503\" ref-type=\"bibr\">46</xref>], comprises 22 items organized in four subscales: accessing (six items), understanding (six items), appraising (five items), and applying (five items) health-related information in the context of the coronavirus pandemic.</p><p>HLS-COVID-Q22 mean scores are based on the responses ranging from 1 to 4, meaning that no transformation of scores was conducted, as suggested by Sørensen and colleagues [<xref rid=\"B28-ijerph-17-05503\" ref-type=\"bibr\">28</xref>,<xref rid=\"B42-ijerph-17-05503\" ref-type=\"bibr\">42</xref>]. In this way, mean scores can easily be interpreted based on the response range. The scoring system suggested in the original HLS-EU study [<xref rid=\"B37-ijerph-17-05503\" ref-type=\"bibr\">37</xref>,<xref rid=\"B42-ijerph-17-05503\" ref-type=\"bibr\">42</xref>,<xref rid=\"B43-ijerph-17-05503\" ref-type=\"bibr\">43</xref>,<xref rid=\"B44-ijerph-17-05503\" ref-type=\"bibr\">44</xref>,<xref rid=\"B45-ijerph-17-05503\" ref-type=\"bibr\">45</xref>] was adapted: Sørensen and colleagues [<xref rid=\"B42-ijerph-17-05503\" ref-type=\"bibr\">42</xref>] propose three cut-off values resulting in four levels of health literacy (<xref rid=\"ijerph-17-05503-t001\" ref-type=\"table\">Table 1</xref>). However, Röthlin et al. (2013) have found limited discrimination of the HLS-EU-Q16 between participants in the higher-end range of scores [<xref rid=\"B44-ijerph-17-05503\" ref-type=\"bibr\">44</xref>]. Thus, they have suggested merging the highest health literacy levels, discarding the highest cut-off value and the level “excellent health literacy”. In this study, we use the following cut-off values and levels:<list list-type=\"bullet\"><list-item><p>Mean score of ≤2.5: “inadequate health literacy”</p></list-item><list-item><p>Mean score of >2.5–<3: “problematic health literacy”</p></list-item><list-item><p>Mean score of ≥3: “sufficient health literacy”</p></list-item></list></p></sec></sec><sec id=\"sec2dot3-ijerph-17-05503\"><title>2.3. Data Analyses</title><p>By implementing iterative proportional fitting (IPF), the dataset was weighted for sex, education, and age based on the German Mikrozensus 2018 [<xref rid=\"B47-ijerph-17-05503\" ref-type=\"bibr\">47</xref>]. Confirmatory factor analysis (CFA) was performed using SPSS AMOS 22 (IBM, SPSS Inc., Chicago, IL, USA) [<xref rid=\"B48-ijerph-17-05503\" ref-type=\"bibr\">48</xref>]. Commonly reported cut-off values were used for the model fit [<xref rid=\"B49-ijerph-17-05503\" ref-type=\"bibr\">49</xref>], which was evaluated using the following indices: relative χ<sup>2</sup>(χ<sup>2</sup>/df ≤ 3), the normed-fit index (NFI ≥ 0.90), the incremental fit index (IFI ≥ 0.90), the comparative fit index (CFI ≥ 0.90), the Tucker-Lewis index (TLI ≥ 0.90), the root-mean-square error of approximation (RMSEA ≤ 0.05–0.08), and the standardized root mean square residual (SRMR ≤ 0.1). Factor loadings were interpreted based on the following cut-offs, as suggested by Tabachnick and Fidell: 0.32 (poor), 0.45 (fair), 0.55 (good), 0.63 (very good), and 0.71 (excellent) [<xref rid=\"B50-ijerph-17-05503\" ref-type=\"bibr\">50</xref>]. Internal consistency was examined by calculating Cronbach’s α and the Spearman-Brown split-half reliability coefficient using SPSS Statistics 22 (IBM, SPSS Inc., Chicago, IL, USA) [<xref rid=\"B51-ijerph-17-05503\" ref-type=\"bibr\">51</xref>]. For the latter indicators, values ≥ 0.7 indicate satisfactory reliability [<xref rid=\"B52-ijerph-17-05503\" ref-type=\"bibr\">52</xref>]. Mean scores and relative frequencies are reported for health literacy levels and HLS-COVID-Q22 items. Bivariate correlations are reported for the relationship between HLS-COVID-Q22 subscales. Also, <italic>p</italic>-values for nonparametric correlations between the items on feeling informed or confused, and sociodemographic indicators are reported. Bivariate analyses were employed to calculate correlations: the Spearman-Rho correlation coefficient was used for ordinal variables; the Pearson correlation coefficient was used for metric variables. Differences in mean scores related to sociodemographic indicators were checked for significance with the Mann-Whitney-U test, as the assumptions of normality and homoscedasticity had been violated for HLS-COVID-Q22 mean scores in most groups.</p></sec><sec id=\"sec2dot4-ijerph-17-05503\"><title>2.4. Ethical Considerations</title><p>This study was approved by the Bielefeld University Ethics Board (Reference No 2020-060_S). All personal data obtained was anonymized by Allensbach, and the Bielefeld University study group received a fully anonymized dataset. Data protection procedures were covered in adherence to the European Union General Data Protection Regulation (EU) (GDPR). This study was carried out by the Interdisciplinary Centre for Health Literacy Research and within the Health Literacy in Childhood and Adolescence (HLCA) Consortium (<uri xlink:href=\"www.hlca-consortium.com\">www.hlca-consortium.com</uri>), funded by the German Federal Ministry of Education and Research (grant number: 01EL1824A).</p></sec></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05503\"><title>3. Results</title><sec id=\"sec3dot1-ijerph-17-05503\"><title>3.1. Sample Characteristics</title><p>A total of <italic>n</italic> = 1153 participants received a link to complete the self-administered questionnaire, of which <italic>n</italic> = 99 participants dropped out before completing the whole survey, and <italic>n</italic> = 17 were excluded because of concerns about the reliability of the data. Finally, a total of <italic>n</italic> = 1037 adults aged ≥16 years in all 16 German federal states were interviewed. The mean age was 45.6 years (SD = 15.8). 31.5% of the participants reported a household income under 1750 €, and between 1750–2999 €, while 37% had an income of at least 3000 € per month. Education was divided into three categories: 25.5% had “low” school education (no or secondary school certificate “Hauptschulabschluss”), 33.5% had “medium” school education (secondary school certificate “Realschulabschluss”), and 41.0% had “high” school education (university entrance qualification or university degree). A total of 80.2% of participants lived in federal states of former Western Germany (including Berlin), whereas 19.8% lived in the area of former East Germany. Also, 24.8% of the sample had children (<18 years).</p></sec><sec id=\"sec3dot2-ijerph-17-05503\"><title>3.2. Psychometric Properties</title><sec id=\"sec3dot2dot1-ijerph-17-05503\"><title>3.2.1. Reliability</title><p>Internal consistency for the HLS-COVID-Q22 was very high (α = 0.94; ρ = 0.891). The four subscales also showed high internal consistency (subscale “access”: α = 0.814; “understand”: α = 0.858; “appraise”: α = 0.823; “apply”: α = 0.83).</p></sec><sec id=\"sec3dot2dot2-ijerph-17-05503\"><title>3.2.2. Validity</title><p>Model fit indices for a 4-factorial model representing the action areas ”access“, “understand“, ”appraise“, and “apply“ are displayed in <xref rid=\"ijerph-17-05503-t002\" ref-type=\"table\">Table 2</xref>.</p><p>All model fit indices, except SRMR, suggest an insufficient model fit for the unmodified model (Model 1). Thus, modification indices were used to revise the model. In Model 2, we allowed the residuals of items 1 and 2, 19 and 20, and 20 and 21 to correlate, which was suggested as the modifications within factors with the most significant decrease in χ² by SPSS Amos. In Model 2, the model fit indices NFI, TLI, CFI, IFI, RMSEA, and SRMR suggest a sufficient model fit, while χ²/df does not. However, if applying the more strict cut-off values recommended by Hooper and colleagues [<xref rid=\"B53-ijerph-17-05503\" ref-type=\"bibr\">53</xref>], only RMSEA and SRMR suggest a sufficient model fit. We refrained from further modifications to evaluate a model close to our original assumptions. Factor loadings between items and latent factors (not shown here) were 0.52–0.70 for the latent factor “access”, 0.63–0.78 for “understand”, 0.60–0.79 for “appraise”, and 0.58–0.80 for the latent factor “apply”. Correlations between the overall score and subscales are very high (r > 0.850), as well as correlations between subscales (r = 0.647 to 0.742) (<xref rid=\"ijerph-17-05503-t003\" ref-type=\"table\">Table 3</xref>).</p></sec></sec><sec id=\"sec3dot3-ijerph-17-05503\"><title>3.3. Health Literacy</title><p>The health literacy mean score of the total sample was 2.99 (SD = 0.49), meaning that it was, on average, “easy” for participants to deal with information related to coronavirus. A total of 15.2% of participants were found to have “inadequate health literacy”, 34.9% had “problematic health literacy”, and 49.9% had “sufficient health literacy”. We found no significant differences in mean scores related to sex, age, education, household income, living together with children <18 years, or region of residence (<xref rid=\"ijerph-17-05503-t004\" ref-type=\"table\">Table 4</xref>). “Inadequate” and “problematic” levels were significantly more prevalent among those who felt “not so well informed/not well informed at all” about the coronavirus compared to those who reported feeling “well informed” (<italic>p</italic> < 0.001), or “very well informed” (<italic>p</italic> < 0.001). Also, “inadequate” and “problematic” health literacy levels were significantly more prevalent among those who felt “very confused” compared to those who reported feeling “somewhat confused” (<italic>p</italic> < 0.01), or “barely confused/not confused at all” (<italic>p</italic> < 0.001).</p><p>While most tasks addressed by the HLS-COVID-Q22 are easy for most respondents (mean score close to 3, <xref rid=\"ijerph-17-05503-t005\" ref-type=\"table\">Table 5</xref>), it is easiest to “find information about the coronavirus on the internet” (mean = 3.33) and “find information on the internet about protective behaviors that can help to prevent infection with the coronavirus” (mean = 3.24). On the contrary, an equal number of participants report that it is either “very difficult/difficult” or “easy/very easy” to “judge if information on coronavirus and the coronavirus epidemic in the media is reliable” (mean = 2.53), and “judge if [they] have been infected with coronavirus” (mean = 2.5), making those the most difficult tasks. Significant associations (<italic>p</italic> < 0.01; <italic>p</italic> < 0.05) for some items were observed mostly for education but partially related to age, sex, and fewer for income and having children <18 years (<xref ref-type=\"app\" rid=\"app1-ijerph-17-05503\">Supplement 1, Table S1</xref>).</p></sec><sec id=\"sec3dot4-ijerph-17-05503\"><title>3.4. Feeling Informed or Confused about the Coronavirus during the Infodemic</title><p>The vast majority (<italic>n</italic> = 934; 90%) feels well or very well informed about coronavirus (<xref ref-type=\"fig\" rid=\"ijerph-17-05503-f001\">Figure 1</xref>). We found no significant differences related to sex, age, education, living with a child <18 years, or region. Participants with higher household income felt significantly better informed about the coronavirus (<italic>p</italic> < 0.05).</p><p>A total of 56.7% of the participants (<italic>n</italic> = 578) reported feeling confused by the variety of information on COVID-19 (<xref ref-type=\"fig\" rid=\"ijerph-17-05503-f002\">Figure 2</xref>), with female participants reporting feeling more confused compared to their male counterparts (<italic>p</italic> < 0.01). Confusion was found to be more widespread among younger people (<italic>p</italic> < 0.001): 14% of people >45 years reported to feel very confused, while another 47% were somewhat confused by all the information on coronavirus, compared to 7% and 39% among people aged ≥60 years. Adults with children <18 years felt significantly more confused (<italic>p</italic> < 0.001). Confusion was not significantly correlated with education, income, and region. Both items were found to be negatively associated (r = −0.224; <italic>p</italic> < 0.001). Thus, people who feel better informed tend to be less confused about the information on the coronavirus and vice versa.</p></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05503\"><title>4. Discussion</title><p>This study represents the first population-based survey on comprehensive health literacy in relation to coronavirus information and the first to adapt the HLS-EU-Q to assess coronavirus-related health literacy. It is also the first study analyzing health literacy in the context of the COVID-19 infodemic in order to gain insights in relation to feeling informed about the coronavirus or confused by the sheer amount of coronavirus information.</p><sec id=\"sec4dot1-ijerph-17-05503\"><title>4.1. Coronavirus-Related Health Literacy</title><p>We found that 50.1% of our sample had “problematic” (15.2%) or “inadequate” (34.9%) coronavirus-related health literacy, whereas 49.9% had “sufficient” health literacy. Mean scores show it was, on average, “easy” for participants to deal with coronavirus information. However, coronavirus-related health literacy should still be increased, as a significant share of participants score below the average health literacy level. Although an adapted questionnaire, the HLS-COVID-Q22, was used, these findings are in line with previous studies on adults’ general health literacy in Germany: The Health Literacy Survey Germany (HLS-GER) [<xref rid=\"B37-ijerph-17-05503\" ref-type=\"bibr\">37</xref>] found that 54.3% of the population (<italic>n</italic> = 2000) had “inadequate” and “problematic” health literacy, while the German Health Update (GEDA) study [<xref rid=\"B45-ijerph-17-05503\" ref-type=\"bibr\">45</xref>] resulted in a slightly lower estimate (44.2% of <italic>n</italic> = 4854). Further, our findings are comparable to what has been found in previous studies on adults’ general health literacy in Europe (47%, “inadequate” or “problematic” health literacy [<xref rid=\"B28-ijerph-17-05503\" ref-type=\"bibr\">28</xref>]). However, while a social gradient of health literacy has been observed in multiple studies [<xref rid=\"B28-ijerph-17-05503\" ref-type=\"bibr\">28</xref>,<xref rid=\"B32-ijerph-17-05503\" ref-type=\"bibr\">32</xref>,<xref rid=\"B36-ijerph-17-05503\" ref-type=\"bibr\">36</xref>,<xref rid=\"B54-ijerph-17-05503\" ref-type=\"bibr\">54</xref>], this was not the case in this study. The missing social gradient could be based on the sample characteristics of the original panel that might not be representative of those with lower social status. However, a missing social gradient could also indicate that ample information is available, and that the given information environment makes it easy to access, understand, appraise, and apply health information in everyday life. During the first wave of the COVID-19 outbreak, when this survey was conducted, adherence to public health interventions and government policies was very high in Germany [<xref rid=\"B55-ijerph-17-05503\" ref-type=\"bibr\">55</xref>,<xref rid=\"B56-ijerph-17-05503\" ref-type=\"bibr\">56</xref>], i.e., diverse information on coronavirus and recommendations were followed appropriately. This might also explain the missing differences in health literacy regarding sex, age, and state of residence. In addition, information and recommendations were simple and therefore easy-to-understand and easy-to-apply (e.g., hand washing, physical distance, wearing masks, avoid public gatherings, staying home). However, adherence is already declining significantly due to the various adverse effects on the economy, occupation, health, and social life [<xref rid=\"B57-ijerph-17-05503\" ref-type=\"bibr\">57</xref>]. This makes lower health literacy a threat to the effectiveness of public health measures to contain the virus the longer the pandemic lasts, with people less likely to follow official public health recommendations.</p></sec><sec id=\"sec4dot2-ijerph-17-05503\"><title>4.2. Information Tasks and Challenges</title><p>Most participants report little difficulty with accessing information about coronavirus and protective behaviors on the internet, or understanding information provided by health professionals, authorities, and family members (items # 1–3, 7–11, 15, 19–20, 22). The most significant association was found for education in relation to tasks associated with all four action areas (# 1, 2, 3, 7, 9, 14, 15, 19).</p><p>However, there are also a number of items that highlight critical aspects of dealing with coronavirus-related health information. Between ~20% and 52% of participants report that it is difficult or very difficult to access, understand, appraise, and apply coronavirus information (# 4–6, 12–14, 16–18, 21). Those “difficult” action areas are concerned primarily with medical information regarding infection (# 4, 17), help-seeking (item 5), evaluation of personal risk (# 6, 12, 14, 16), and media information (# 4, 5, 17).</p><p>For 20.9%, it is difficult to decide how to protect themselves from infection with the coronavirus based on media information. Even more citizens (32.1%) report that it is difficult to use media information to decide how to act in case of coronavirus infection. Most strikingly, 47.8% of participants report that it is difficult or very difficult to judge whether they can trust media information on the coronavirus, which is similar to findings of the HLS-GER in relation to disease-related media information [<xref rid=\"B37-ijerph-17-05503\" ref-type=\"bibr\">37</xref>]. Trustworthy information about the virus is vital for the population’s ability to take part in informed, healthy, and responsible behavior as well as for an effective system-wide response in line with government policies on coronavirus containment and burden reduction for the healthcare system and professionals [<xref rid=\"B19-ijerph-17-05503\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijerph-17-05503\" ref-type=\"bibr\">20</xref>]. Low trust in health information can cause serious problems during the pandemic, especially regarding the ongoing infodemic and the associated spread of dis- and misinformation regarding COVID-19, potentially leading to irrational behavior endangering the success of prevention measures [<xref rid=\"B19-ijerph-17-05503\" ref-type=\"bibr\">19</xref>]. Further, if the information on the pandemic and related preventive measures is not perceived as reliable, communication between national and local governments, including public health and research institutions on the one hand and citizens on the other hand, is impaired, casting doubt on the broad acceptance of future national and evidence-based strategies for protecting public health. Thus, there is a need for measures increasing the reliability of available and accessible coronavirus information and related actions, as well as increasing the trust of the public in such information. Another approach to enabling the general population to judge whether or not they can trust media information on the coronavirus is to promote critical health literacy [<xref rid=\"B22-ijerph-17-05503\" ref-type=\"bibr\">22</xref>], which can help people identify reliable sources of information and investigate which information might be misleading.</p><p>Accessing, understanding, and appraising information about risks associated with coronavirus, coronavirus infection, and adverse health behaviors pose further problems. More than 20% of participants find it difficult to understand media-based risk information about coronavirus. Deciding which behaviors can help most to lower infection risk is difficult for 22% of the population. More than 30% of adults perceive difficulties judging if they belong to a high-risk group for infection, and 33% even have difficulties finding information on risks. These results call for increased and improved risk communication, adapted to the needs of the population.</p><p>Further, it is difficult for almost 40% of the participants to find information on how to recognize a likely infection with coronavirus, and almost 52% say it is difficult to judge whether they may have been infected. These findings are worrying, as knowledge of one’s own infection status may be an important determinant of whether people adhere to distancing rules and other preventive measures [<xref rid=\"B16-ijerph-17-05503\" ref-type=\"bibr\">16</xref>]. Although evidence on immunity after being infected with COVID-19 is inconclusive [<xref rid=\"B58-ijerph-17-05503\" ref-type=\"bibr\">58</xref>], people that assume to have already been infected, e.g., based on symptoms similar to those of COVID-19, might believe to be immune and put themselves and others at risk by not adhering to preventive measures based on a false sense of safety [<xref rid=\"B19-ijerph-17-05503\" ref-type=\"bibr\">19</xref>]. It has been shown that even health professionals and researchers face difficulties providing accurate information on symptoms, which is why controversial information is available on the matter [<xref rid=\"B8-ijerph-17-05503\" ref-type=\"bibr\">8</xref>,<xref rid=\"B11-ijerph-17-05503\" ref-type=\"bibr\">11</xref>,<xref rid=\"B59-ijerph-17-05503\" ref-type=\"bibr\">59</xref>,<xref rid=\"B60-ijerph-17-05503\" ref-type=\"bibr\">60</xref>]. In addition, information on COVID-19 changes rapidly, which may contribute to people having difficulties judging what information is reliable [<xref rid=\"B18-ijerph-17-05503\" ref-type=\"bibr\">18</xref>,<xref rid=\"B20-ijerph-17-05503\" ref-type=\"bibr\">20</xref>]. Going forward, this could potentially jeopardize public adherence to restrictions and preventive measures, especially in the case of a second wave of infections.</p><p>Finally, participants also report having difficulties accessing information on how to find professional help in case of coronavirus infection. This can pose a serious threat in emergency situations. Therefore, efforts are needed to improve the transparency and accessibility of the healthcare system regarding diagnosis and treatment options regarding COVID-19.</p></sec><sec id=\"sec4dot3-ijerph-17-05503\"><title>4.3. Infodemic and Associated Influences on Information Uptake and Use</title><sec id=\"sec4dot3dot1-ijerph-17-05503\"><title>4.3.1. Being Informed about Coronavirus</title><p>A total of 90% of participants feel well-informed or very well-informed about coronavirus, irrespective of sex, age, education, or living with a child <18 years. However, participants with a lower income and participants living in the federal states of former East Germany were found to feel significantly less informed. This points to the presence of an economic as well as a regional gradient. While our data does not provide further clues on reasons for these differences, a number of reasons are plausible. Regarding economic differences, it is possible that the information on coronavirus available is sufficient for the majority, but that low-income participants have specific information needs that are not fully satisfied by the information circulating in major news channels. For instance, specific questions might arise more frequently among low-income participants during the pandemic concerning topics such as unemployment, social benefits, and social security, or youth services. The regional differences, however, may result from differences in patterns and frequencies of internet use in eastern and western Germany. While the latest Eurostat data shows that 9 out of every 10 households have internet access [<xref rid=\"B61-ijerph-17-05503\" ref-type=\"bibr\">61</xref>], there are differences in internet use between households in the east and west of Germany with people living in eastern regions using the internet significantly less than their counterparts [<xref rid=\"B62-ijerph-17-05503\" ref-type=\"bibr\">62</xref>]. Another explanation could be that the outbreak of COVID-19 started in the west of Germany [<xref rid=\"B63-ijerph-17-05503\" ref-type=\"bibr\">63</xref>], which is why at the time of this survey, people living in the east of Germany may have felt less concerned about coronavirus and less engaged with seeking information.</p><p>However, the overall level of feeling informed is high, which might, at least partly, be attributed to government-led public health and media campaigns early on in the pandemic using different channels including social media. This finding may also reflect how well German public health authorities and public broadcasting agencies have reacted to the pandemic and associated challenges for society and individuals.</p></sec><sec id=\"sec4dot3dot2-ijerph-17-05503\"><title>4.3.2. Being Confused about Coronavirus</title><p>Despite the findings, more than half of the German population (56%) feels somewhat confused or very confused about the amount of coronavirus information. Although it was expected that confusion and feeling informed were diametrically opposed indicators, this was not always the case. Even though a negative correlation was found between those variables, it was only small in size (r = −0.224). This means that there are a number of participants that feel well-informed and, at the same time, confused by all the information. Hence, it is not only necessary to provide citizens with coronavirus information they need so that they feel well-informed, but also to help them make sense of and evaluate additional and potentially conflicting information to avoid confusion.</p><p>We found that women reported feeling confused by the amount of coronavirus information more often than men. While the virus and virus information are not gender-specific, it may be related to the unequal distribution of care responsibilities, which is still prevalent in Germany [<xref rid=\"B64-ijerph-17-05503\" ref-type=\"bibr\">64</xref>]. As women in Germany are more often engaged in the upbringing of children, providing childcare and care of elderly family members [<xref rid=\"B64-ijerph-17-05503\" ref-type=\"bibr\">64</xref>,<xref rid=\"B65-ijerph-17-05503\" ref-type=\"bibr\">65</xref>], and more likely than men to seek online health information [<xref rid=\"B66-ijerph-17-05503\" ref-type=\"bibr\">66</xref>], it is possible that they are confronted more often with confusing coronavirus information.</p><p>Further, we found that younger people were more confused than older people by coronavirus information. This can partly be explained by a more prevalent media use among younger people and thus the higher exposure to a broad range of potentially conflicting information, but there are probably other reasons that are unaccounted for in our data, such as frequently using social media websites (e.g., Twitter, Facebook, and Instagram), reading more about coronavirus, having fear of COVID-19, being stressed by the pandemic, and feeling uncertain regarding their future and the many changes that arise from the impact of COVID-19 on their lives.</p></sec><sec id=\"sec4dot3dot3-ijerph-17-05503\"><title>4.3.3. Health Literacy and Infodemic</title><p>Regarding the relationship between feeling informed or confused and health literacy, we found that people with “inadequate” and “problematic” health literacy were significantly less likely to feel well-informed and more likely to feel confused by the amount of coronavirus information. It has to be noted that assumptions about cause and effect are difficult here, as “feeling informed” could be regarded as equivalent to two main dimensions of health literacy as measured by the HLS-COVID-Q22, namely the perceived ease in accessing and understanding health information. The same could be assumed for “feeling confused” and another core dimension of health literacy, namely the perceived ease or difficulty in appraising health information.</p><p>It has been highlighted that health education and teaching health literacy are critical prevention and health promotion measures for mitigating the adverse effects of the COVID-19 infodemic [<xref rid=\"B10-ijerph-17-05503\" ref-type=\"bibr\">10</xref>,<xref rid=\"B13-ijerph-17-05503\" ref-type=\"bibr\">13</xref>]. The benefits of fostering health literacy, the acquisition of competencies, and the confidence to handle the sheer amount of information in an infodemic are not restricted to infectious diseases. Moreover, they will also be helpful in different public health areas, such as increasing critical thinking, promoting healthy behavior and managing risk behavior, informed decision making, empowerment, and health outcomes, as well as improving health care interaction, health communication, and adherence.</p><p>In order to facilitate health information seeking and understanding, information suppliers and providers, such as official public health organizations and health authorities, have a critical role in providing the public with high-quality health information [<xref rid=\"B59-ijerph-17-05503\" ref-type=\"bibr\">59</xref>]. When designing and providing coronavirus and COVID-19-related information, providers must ensure that information is based on health literacy principles such as being easy-to-access, easy-to-understand, easy-to-use, culturally appropriate, and relevant to various populations [<xref rid=\"B20-ijerph-17-05503\" ref-type=\"bibr\">20</xref>]. Accessing valid and reliable health information on the internet is among the greatest challenges for internet users [<xref rid=\"B60-ijerph-17-05503\" ref-type=\"bibr\">60</xref>,<xref rid=\"B67-ijerph-17-05503\" ref-type=\"bibr\">67</xref>]. Social media providers, who are important actors in the infodemic [<xref rid=\"B4-ijerph-17-05503\" ref-type=\"bibr\">4</xref>,<xref rid=\"B11-ijerph-17-05503\" ref-type=\"bibr\">11</xref>,<xref rid=\"B68-ijerph-17-05503\" ref-type=\"bibr\">68</xref>,<xref rid=\"B69-ijerph-17-05503\" ref-type=\"bibr\">69</xref>,<xref rid=\"B70-ijerph-17-05503\" ref-type=\"bibr\">70</xref>,<xref rid=\"B71-ijerph-17-05503\" ref-type=\"bibr\">71</xref>,<xref rid=\"B72-ijerph-17-05503\" ref-type=\"bibr\">72</xref>], must be urged to act responsibly, support the provision of reliable health information, and inhibit the spread of dis- and misinformation on their websites [<xref rid=\"B13-ijerph-17-05503\" ref-type=\"bibr\">13</xref>]. In this regard, government support is needed to implement policies that hold social media and tech giants accountable, e.g., by fact-checking and flagging false information.</p><p>The perpetual stream of information overload that is driven by actors across society amplifies the COVID-19 infodemic. The infodemic must be acknowledged as a meta-risk in its own right, which interferes with people’s health literacy and their health outcomes. Limited health literacy in individuals, populations, and systems can cause adverse effects for parts and even the whole of society. When people lack the competencies to critically appraise health information, suppliers cannot ensure the means to protect valid and meaningful information against the many sources that spread invalid information. This can cause panic, destabilize the effectiveness of information distribution and public health interventions, and even threaten social cohesion and the political landscape. As Eysenbach highlighted in the context of infodemiology studies, “an epidemic of fear may exhibit similar characteristics as a true epidemic” [<xref rid=\"B73-ijerph-17-05503\" ref-type=\"bibr\">73</xref>], which is why investment in population and system-level health literacy must be considered absolutely critical for public health responses against toxic health threats, such as coronavirus and COVID-19. This is supported by a recent study on health literacy and fear of COVID-19 in medical students, showing that higher levels of health literacy may protect from fear during the pandemic [<xref rid=\"B38-ijerph-17-05503\" ref-type=\"bibr\">38</xref>].</p></sec></sec><sec id=\"sec4dot4-ijerph-17-05503\"><title>4.4. Feasibility of the Questionnaire</title><p>The HLS-COVID-Q22 is a novel tool in the HLS-family and the first to measure coronavirus-related health literacy. Indicators for internal consistency demonstrate satisfactory reliability (α = 0.940 and ρ = 0.891), suggesting the success of the adaptation process and the feasibility of the instrument. Internal consistency estimates found in this study are in line with what has been found in previous studies using the HLS-EU-Q16 in German language (α = 0.96) [<xref rid=\"B45-ijerph-17-05503\" ref-type=\"bibr\">45</xref>], which served as the original blueprint for developing the HLS-COVID-Q22. Internal consistency for the four subscales was also satisfactory, which means that they can be used to assess subjective coronavirus-related health literacy related to the areas of accessing, understanding, appraising, and applying coronavirus-related health information.</p><p>Construct validity for the four subscales suggested that the model fit was only moderate. However, the model fit indices clearly did not indicate an excellent model fit, neither based on the moderate cut-off values [<xref rid=\"B49-ijerph-17-05503\" ref-type=\"bibr\">49</xref>] that were applied nor based on strict cut-off values as suggested by Hooper and colleagues [<xref rid=\"B53-ijerph-17-05503\" ref-type=\"bibr\">53</xref>]. An acceptable model fit was only achieved in some indicators after a slight modification. In this modification step, we allowed the residuals of items 1 and 2, 19 and 20, and 20 and 21 to correlate, expressing a common source of variance. These modifications were acceptable from a theoretical and content-related standpoint, as those item pairs are concerned with finding information on the internet (items 1 & 2), following or using information received from the doctor (items 19 & 20), and using information on how to handle an infection with coronavirus (items 20 & 21). After allowing the correlation between the item’s residuals using moderate model parameters [<xref rid=\"B49-ijerph-17-05503\" ref-type=\"bibr\">49</xref>], the model fit indices NFI, TLI, IFI, CFI, RMSEA, and SRMR of Model 2 suggested a sufficient model fit, whereas χ<sup>2</sup>/df did not. To reach an excellent model fit based on both the strict [<xref rid=\"B53-ijerph-17-05503\" ref-type=\"bibr\">53</xref>] or the more moderate [<xref rid=\"B49-ijerph-17-05503\" ref-type=\"bibr\">49</xref>] values employed here, further modifications or even a revision of the underlying model might be necessary. However, we chose not to apply further modifications, such as correlations of residuals across factors (as suggested by SPSS Amos), as this would have implied further, potentially atheoretical deviation from the model which suggests 4 distinct factors. However, the high correlations (r = 0.65–0.90) found between the subscales (and thus, factors) conflict with the assumption of four independent factors and point to a second-order common factor, i.e., content-wise similarities between the highly correlated factors. This might contribute to less-than-optimal model fit and warrant further investigation of the underlying factor structure.</p><p>Earlier studies with the HLS-EU-Q have reported similar problems regarding model fit for the four-factorial model, assuming a rather unidimensional model [<xref rid=\"B74-ijerph-17-05503\" ref-type=\"bibr\">74</xref>]. Unfortunately, most studies using the HLS-EU-Q do not provide data on construct validity. In a Norwegian study, the model fit was reported for the HLS-EU-Q16 for a one- and three-dimensional model with values for TLI = 0.911 and 0.939, CFI = 0.923 and 0.949, SRMR = 0.080 and 0.070, and RMSEA = 0.118 and 0.103, respectively [<xref rid=\"B74-ijerph-17-05503\" ref-type=\"bibr\">74</xref>]. In a study conducted in six Asian countries using the HLS-EU-Q47 [<xref rid=\"B36-ijerph-17-05503\" ref-type=\"bibr\">36</xref>], the model fit indices were reported for a three-factorial model representing the areas of health promotion, disease prevention, and health care. In each domain, the four-factor model then included accessing, understanding, appraising, and applying health information. For all six countries, RMSEA values ranged from 0.05–0.10, CFI ranged from 0.90–0.97, IFI ranged from 0.90–0.97, NFI ranged from 0.87–0.96, χ<sup>2</sup>/df ranged from 2.55–21.85. In a Japanese study using the HLS-EU-Q47, CFI and RMSEA were reported for the three domains with values for CFI 0.937 and RMSEA 0.075 (health care), 0.943 and 0.079 (disease prevention), and 0.934 and 0.078 (health promotion) [<xref rid=\"B75-ijerph-17-05503\" ref-type=\"bibr\">75</xref>]. The model fit indices of the HLS-COVID-22 are comparable to earlier studies. This calls for a more detailed approach to operationalising the four action areas, better reflecting the actions taken to achieve them, i.e., deconstructing the steps necessary to access information.</p><p>This study was conducted during the first weeks of the pandemic in Germany while people were already overburdened and stressed with the many changes they had to cope with and apply in everyday life and at the workplace. Therefore, we decided to keep the questionnaire rather short and did not include further scales and variables, such as COVID-19-related health knowledge and behavioral aspects. Based on the HLS-EU-Q, the HLS-COVID-Q22 is a self-report measure and does not assess the performance-based health literacy capabilities of individuals [<xref rid=\"B76-ijerph-17-05503\" ref-type=\"bibr\">76</xref>]. For a better understanding of the associations and possible relationships between health literacy on the one hand and knowledge as well as health behavior on the other, we plan to include additional questions to the follow-up surveys (waves 2 and 3 of this measurement) to allow comparisons between subjective health literacy and objective performance-based skills and abilities. These waves will be implemented in 2020 as a three-country survey in Germany, Austria, and Switzerland in order to analyze change over time (German Federal Ministry of Health, grant number: ZMVI1-2520COR009).</p></sec></sec><sec id=\"sec5-ijerph-17-05503\"><title>5. Limitations</title><p>This study was conducted during the first wave of the COVID-19 outbreak in Germany when information on the topic was easy to understand and omnipresent in the media and internet, supported by government campaigns and campaigns by public health agencies. This may have contributed to the overall good results, i.e., the high satisfaction with information and the perceived ease regarding most information tasks. These results may not be transferred to other countries that use different public information and communication strategies. Future studies should also consider measuring behavioral accounts and assessing the health literacy of different populations, e.g., children, students, migrants, patients, and people with chronic conditions. Further, coronavirus-related health literacy was assessed by employing a self-report measure, which generally is a reliable method to measure subjective indicators. However, participants’ actual ability to access, understand, appraise, and apply coronavirus-related health information was not tested, and it is up to future research to determine to what extent the indicators “perceived ease” and “ability” correspond. Also, future research may need to include additional scales to widen the scope of analysis. Lastly, the study sample is representative of German internet users, which excludes non-users who may feel different about the infodemic.</p></sec><sec sec-type=\"conclusions\" id=\"sec6-ijerph-17-05503\"><title>6. Conclusions</title><p>The HLS-COVID-Q22 has proven to be a feasible and reliable tool to assess adults’ coronavirus-related health literacy. During the first wave of the COVID-19 pandemic and the associated infodemic, more than half of the German population was found to have “inadequate” or “problematic” levels of health literacy. The judgment of media-based information on COVID-19, especially, seems to be difficult. There is a need for promoting critical health literacy needed to navigate the infodemic, identify disinformation and misinformation, and make decisions based on reliable and trustworthy information. Political attention must be paid to policies supporting the enhancement of population health literacy and sustaining easy-to-access and easy-to-use information. More research on health literacy will facilitate a better understanding of population information needs, i.e., with further surveys, monitoring and surveillance systems, and longitudinal studies for informing public health and policymaking. Health literacy should also be considered in the COVID-19 public health response framework and public health should be prompted to further research health literacy with policies to support such endeavours. As COVID-19 has put an unprecedented burden on the healthcare system and workforce, addressing health literacy in such a response framework will be of great value for healthcare, health promotion, and prevention. Information supply as a public health intervention should be considered in future emergency and non-emergency response frameworks. Additionally, linking research on health literacy with evidence from information literacy, media literacy, risk literacy and infodemiology may help to widen the understanding how people seek for and use information in everyday life.</p></sec></body><back><ack><title>Acknowledgments</title><p>We would like to thank Steffen de Sombre and his team at Allensbach Institute, and Jürgen Pelikan and Jürgen Griebler from Public Health Austria. We thank Gloria Glinphratum from Bielefeld University for language editing services.</p></ack><app-group><app id=\"app1-ijerph-17-05503\"><title>Supplementary Materials</title><p>The following are available online at <uri xlink:href=\"https://www.mdpi.com/1660-4601/17/15/5503/s1\">https://www.mdpi.com/1660-4601/17/15/5503/s1</uri>, Table S1: Correlations between health literacy items (HLS-COVID-22) and sociodemographic variables, Table S2: Correlation between information variables and sociodemographic indicators.</p><supplementary-material content-type=\"local-data\" id=\"ijerph-17-05503-s001\"><media xlink:href=\"ijerph-17-05503-s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>Conceptualization, O.O. and T.M.B.; methodology, O.O., T.M.B., and E.-M.B.; software, O.O. and T.M.B.; validation, O.O., T.M.B., and E.-M.B.; formal analysis, O.O. and T.M.B.; investigation, O.O., E.-M.B., K.H., U.B., and D.S.; resources, U.B.; data curation, O.O. and T.M.B.; writing—original draft preparation, O.O. and T.M.B.; writing—review and editing, O.O., T.M.B., E.-M.B., K.H., U.B., and D.S.; visualization, O.O. and T.M.B.; supervision, K.H., U.B., and D.S.; project administration, O.O., T.M.B., E.-M.B., K.H., U.B., and D.S.; funding acquisition, U.B. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was funded by Federal Ministry of Education and Research: Health Literacy in Childhood and Adolescence (HLCA), grant number 01EL1824A.</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijerph-17-05503\"><label>1.</label><element-citation publication-type=\"web\"><person-group person-group-type=\"author\"><collab>European Centre for Disease Prevention and Control</collab></person-group><article-title>ECDC Communicable Disease Threats Report (CDTR)</article-title><comment>Available online: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ecdc.europa.eu/sites/default/files/documents/Communicable-disease-threats-report-21-mar-2020-PUBLIC.pdf\">https://www.ecdc.europa.eu/sites/default/files/documents/Communicable-disease-threats-report-21-mar-2020-PUBLIC.pdf</ext-link></comment><date-in-citation content-type=\"access-date\" iso-8601-date=\"2020-03-22\">(accessed on 22 March 2020)</date-in-citation></element-citation></ref><ref id=\"B2-ijerph-17-05503\"><label>2.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Casadevall</surname><given-names>A.</given-names></name><name><surname>Pirofski</surname><given-names>L.-A.</given-names></name></person-group><article-title>The convalescent sera option for containing COVID-19</article-title><source>J. 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Health Commun.</source><year>2014</year><volume>19</volume><fpage>1481</fpage><lpage>1496</lpage><pub-id pub-id-type=\"doi\">10.1080/10810730.2014.954083</pub-id><pub-id pub-id-type=\"pmid\">25491583</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"ijerph-17-05503-f001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>How well are you informed about coronavirus? * <italic>p</italic> < 0.05; Spearman correlation, <italic>n</italic> = 1037, see <xref ref-type=\"app\" rid=\"app1-ijerph-17-05503\">supplement 1, Table S2</xref>.</p></caption><graphic xlink:href=\"ijerph-17-05503-g001\"/></fig><fig id=\"ijerph-17-05503-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Do you feel confused about coronavirus information? ** <italic>p</italic> <.01; *** <italic>p</italic> < 0.001; Spearman correlation, <italic>n</italic> = 1037, see <xref ref-type=\"app\" rid=\"app1-ijerph-17-05503\">supplement 1, Table S2</xref>.</p></caption><graphic xlink:href=\"ijerph-17-05503-g002\"/></fig><table-wrap id=\"ijerph-17-05503-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05503-t001_Table 1</object-id><label>Table 1</label><caption><p>Health literacy scores and cut-off values.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Tools</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"># of Items</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Score</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Answers<break/> Dichotomized *</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Scores Transformed †</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cut-Off Values (Relative)</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cut-Off Values (Absolute)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">HLS-EU-Q47 [<xref rid=\"B28-ijerph-17-05503\" ref-type=\"bibr\">28</xref>,<xref rid=\"B42-ijerph-17-05503\" ref-type=\"bibr\">42</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">47</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mean</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">a ≤ 50% (½) 50% < b < 66% (~⅔)<break/>66.6% ≤ c < 84% (~⅚)<break/>84 ≤ d</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0 < a ≤ 25<break/>25 < b < 33<break/>33 ≤ c < 42<break/>42 ≤ d</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">HLS-EU-Q16 [<xref rid=\"B44-ijerph-17-05503\" ref-type=\"bibr\">44</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Sum</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Yes</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">a ≤ 50%<break/>50% < b < 81.3%<break/>81.3% ≤ c</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0 < a < 9<break/>9 ≤ b < 13<break/>13 ≤ c</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">HLS-COVID-Q22 [<xref rid=\"B46-ijerph-17-05503\" ref-type=\"bibr\">46</xref>]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">22</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mean</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">≤ 50%<break/>50% < b < 66%<break/>66% ≤ c</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">a ≤ 2.5<break/>2.5 < b < 3<break/>3 ≤ c</td></tr></tbody></table><table-wrap-foot><fn><p>Notes. * = In their study, Röthlin et al. [<xref rid=\"B44-ijerph-17-05503\" ref-type=\"bibr\">44</xref>] dichotomized the answers to “very difficult/rather difficult” vs. “rather easy/very easy”; † = Transformed with the formula y = (mean − 1) × (50 ÷ 3); a = “inadequate health literacy”; b = “problematic health literacy”; c = “sufficient health literacy”; d = “excellent health literacy”.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05503-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05503-t002_Table 2</object-id><label>Table 2</label><caption><p>Model fit indices for a 4-factorial model representing the HLS-EU model structure [<xref rid=\"B23-ijerph-17-05503\" ref-type=\"bibr\">23</xref>,<xref rid=\"B42-ijerph-17-05503\" ref-type=\"bibr\">42</xref>].</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Model</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">χ<sup>2</sup>/df</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">NFI</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">IFI</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">TLI</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">CFI</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">RMSEA [95% CI]</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">SRMR</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Model 1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.664</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.872</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.887</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.871</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.887</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.080 [0.077–0.084]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.048</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Model 2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5.996</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.902</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.917</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.904</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.916</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.069 [0.066–0.073]</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.043</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05503-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05503-t003_Table 3</object-id><label>Table 3</label><caption><p>Correlations between the overall health literacy score and the subscales.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Questionnaire and Subscales</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Subscale “Access”</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Subscale “Understand”</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Subscale “Appraise”</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Subscale “Apply”</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">HLS-COVID-Q22</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.880 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.897 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.876 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.874 **</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Subscale<break/>“access”</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.742 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.690 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.647 **</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Subscale<break/>“understand”</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.675 **</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.736 **</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Subscale<break/>“appraise”</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.729 **</td></tr></tbody></table><table-wrap-foot><fn><p>Notes. ** = <italic>p</italic> < 0.01 (two-tailed). <italic>n</italic> = 1037 for all correlations (Pearson coefficient).</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05503-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05503-t004_Table 4</object-id><label>Table 4</label><caption><p>Health literacy levels and mean scores by participant characteristics.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" colspan=\"1\">Study Population</th><th colspan=\"3\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Health Literacy Levels [%]</th><th colspan=\"2\" align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\">Health Literacy Scores</th></tr><tr><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">“Inadequate”</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">“Problematic”</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">“Sufficient”</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mean (SD)</th><th align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>\n</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><bold>Total</bold> (<bold><italic>n</italic> = 1037</bold>)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">34.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">49.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.99 (0.49)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Sex</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Female (<italic>n</italic> = 505; 48.7%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.99 (0.49)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ref.</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Male (<italic>n</italic> = 532; 51.3%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">35.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">49.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.99 (0.49)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.904</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Age</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16–29 (<italic>n</italic> = 226; 21.8%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">46.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.96 (0.48)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.326</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30–44 (<italic>n</italic> = 267; 25.7%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">52.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.99 (0.50)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ref.</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">45–59 (<italic>n</italic> = 319; 30.8%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">32.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">52.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.02 (0.50)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.877</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">60+ (<italic>n</italic> = 225; 21.7%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">38.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">47.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.98 (0.48)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.614</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Education</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Low (<italic>n</italic> = 264; 25.5%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">46.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.94 (0.52)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.597</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Medium (<italic>n</italic> = 347; 33.5%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">49.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.98 (0.49)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ref.</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">High (<italic>n</italic> = 425; 41%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">33.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">52.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.03 (0.48)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.161</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Household income</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Below 1750 € (<italic>n</italic> = 327; 31.5%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">47.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.96 (0.52)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.496</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1750–2999 € (<italic>n</italic> = 327; 31.5%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">34.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">51.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.00 (0.48)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ref.</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3000 € + (<italic>n</italic> = 383; 37%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">35.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">50.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.01 (0.48)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.409</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Do you have children younger than 18?</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Yes (<italic>n</italic> = 257; 24.8%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">30.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">51.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.96 (0.54)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ref.</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">No (<italic>n</italic> = 780; 75.2%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">36.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">49.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.00 (0.48)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.633</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Region</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Former Western Germany (<italic>n</italic> = 832; 80.3%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">15.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">50.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.99 (0.49)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ref.</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Former East Germany (<italic>n</italic> = 205; 19.7%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">39.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">46.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.97 (0.49)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.322</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>How well are you informed about the coronavirus?</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Very well (<italic>n</italic> = 304; 29.3%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">67.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.23 (0.50)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Well (<italic>n</italic> = 631; 60.9%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">13.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">39.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">46.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.95 (0.41)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ref.</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Not so well/not well at all (<italic>n</italic> = 102; 9.8%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">56.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24.5</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.51 (0.51)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Do you feel confused about COVID-19 information?</bold>\n</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" style=\"border-top:solid thin\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Very confused (<italic>n</italic> = 115; 11.1%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.72 (0.57)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.01</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Somewhat confused (<italic>n</italic> = 463; 44.6%)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">17.0</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">42.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.90 (0.43)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ref.</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Barely confused/not confused at all (<italic>n</italic> = 459; 44.3%)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">28.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">64.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.15 (0.47)</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr></tbody></table><table-wrap-foot><fn><p>Notes. ref. = reference group; <italic>p</italic> = asymptotic significance (2-tailed). To transfer mean scores to frequently reported metric ranging from 0–50, this formula can be used: y = (mean − 1) × (50 ÷ 3). The inverse formula, y = (3 × mean ÷ 50) + 1, transforms scores on the transformed metric to mean scores on the original response format (1–4).</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05503-t005\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05503-t005_Table 5</object-id><label>Table 5</label><caption><p>Frequencies and mean scores for HLS-Covid-Q22 items.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">#</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">How Easy or Difficult is It for You to…</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Related HLS-EU-Q Item <sup>a</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Very Difficult</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Difficult</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Easy</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Very Easy</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Mean (SD)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Find information about the coronavirus on the internet?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1; 2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">49.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">42.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.34 (0.65)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Find information on the internet about protective behaviors that can help to prevent infection with the coronavirus?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13; 33</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">52.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">36.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.25 (0.67)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Find information in newspapers, magazines and on TV about behaviors that can help to prevent infection with the coronavirus?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13; 33</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">54.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">26.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.06 (0.72)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Find information on how to recognize if I have likely become infected with the coronavirus?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8; 18</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">27.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">46.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">19.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.80 (0.82)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Find information on how to find professional help in case of coronavirus infection?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2; 4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">27.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">46.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.81 (0.83)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Find information on how I much I am at risk for being infected with the coronavirus?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2; 4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">28.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">46.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.84 (0.79)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Understand your doctor’s, pharmacist’s or nurse’s instructions on protective measures against coronavirus infection?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4; 8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">57.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">31.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.20 (0.65)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Understand recommendations of authorities regarding protective measures against coronavirus infection?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10; 23</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">53.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">30.7</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.13 (0.73)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Understand advice from family members or friends regarding protective measures against coronavirus infection?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14; 37</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">58.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">27.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.10 (0.69)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Understand information in the media on how to protect myself against coronavirus infection?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15; 39</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">58.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">29.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.15 (0.66)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Understand risks of the coronavirus that I find on the internet?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">9; 21</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">1.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">55.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">25.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.05 (0.71)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Understand risks of the coronavirus that I find in newspapers, magazines or on TV?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3; 5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">56.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">23.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.01 (0.71)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Judge if information on the coronavirus and the coronavirus epidemic in the media is reliable?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11; 28</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">36.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">39.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.54 (0.86)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Judge which behaviors are associated with a higher risk of coronavirus infection?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16; 43</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">53.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">24.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.99 (0.76)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Judge what protective measures you can apply to prevent a coronavirus infection?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5; 11</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">15.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">54.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">27.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.07 (0.73)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">16</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Judge how much I am at risk for a coronavirus infection?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11; 43</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">27.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">45.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">23.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.88 (0.80)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Judge if I have been infected with coronavirus?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11; 43</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">40.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">34.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">13.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.50 (0.86)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">18</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Decide how you can protect yourself from coronavirus infection based on information in the media?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12; 31</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">57.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">21.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.97 (0.73)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Follow instructions from your doctor or pharmacist regarding how to handle the coronavirus situation?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">7; 16</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">58.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">27.8</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.11 (0.69)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Use information the doctor gives you to decide how to handle an infection with the coronavirus?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6; 13</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17.0</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">59.4</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">21.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.00 (0.69)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">21</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Use media information to decide how to handle an infection with the coronavirus?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">6; 13</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4.2</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">27.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">50.3</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">17.6</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.81 (0.77)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">22</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">to behave in a way to avoid infecting others?</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">12; 31</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">2.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">11.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">52.1</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">33.5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">3.17 (0.73)</td></tr></tbody></table><table-wrap-foot><fn><p>Notes. Items were used in German but have been translated for the purpose of reporting; health literacy action areas: access (#1–6), understand (#7–12), appraise (#13–17), and apply health-related information (#18–22); <sup>a</sup> = the first number indicates based on what item in the HLS-EU-Q16 the HLS-Covid-Q22 item was adapted, the second number relates to the item number in the HLS-EU-Q47.</p></fn></table-wrap-foot></table-wrap></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Environ Res Public Health</journal-id><journal-id journal-id-type=\"publisher-id\">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type=\"ppub\">1661-7827</issn><issn pub-type=\"epub\">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32707989</article-id><article-id pub-id-type=\"pmc\">PMC7432053</article-id><article-id pub-id-type=\"doi\">10.3390/ijerph17155286</article-id><article-id pub-id-type=\"publisher-id\">ijerph-17-05286</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>A Clustering Approach to Classify Italian Regions and Provinces Based on Prevalence and Trend of SARS-CoV-2 Cases</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-2655-8574</contrib-id><name><surname>Maugeri</surname><given-names>Andrea</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05286\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-0905-5003</contrib-id><name><surname>Barchitta</surname><given-names>Martina</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05286\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-4405-8162</contrib-id><name><surname>Agodi</surname><given-names>Antonella</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijerph-17-05286\">1</xref><xref ref-type=\"aff\" rid=\"af2-ijerph-17-05286\">2</xref><xref rid=\"c1-ijerph-17-05286\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijerph-17-05286\"><label>1</label>Department of Medical and Surgical Sciences and Advanced Technologies “GF Ingrassia”, University of Catania, 95123 Catania, Italy; <email>andrea.maugeri@unict.it</email> (A.M.); <email>martina.barchitta@unict.it</email> (M.B.)</aff><aff id=\"af2-ijerph-17-05286\"><label>2</label>Azienda Ospedaliero-Universitaria “Policlinico-Vittorio Emanuele”, 95123 Catania, Italy</aff><author-notes><corresp id=\"c1-ijerph-17-05286\"><label>*</label>Correspondence: <email>agodia@unict.it</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>22</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>17</volume><issue>15</issue><elocation-id>5286</elocation-id><history><date date-type=\"received\"><day>08</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>18</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>While several efforts have been made to control the epidemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Italy, differences between and within regions have made it difficult to plan the phase two management after the national lockdown. Here, we propose a simple and immediate clustering approach to categorize Italian regions working on the prevalence and trend of SARS-CoV-2 positive cases prior to the start of phase two on 4 May 2020. Applying both hierarchical and k-means clustering, we identified three regional groups: regions in cluster 1 exhibited higher prevalence and the highest trend of SARS-CoV-2 positive cases; those classified into cluster 2 constituted an intermediate group; those in cluster 3 were regions with a lower prevalence and the lowest trend of SARS-CoV-2 positive cases. At the provincial level, we used a similar approach but working on the prevalence and trend of the total SARS-CoV-2 cases. Notably, provinces in cluster 1 exhibited the highest prevalence and trend of SARS-CoV-2 cases. Provinces in clusters 2 and 3, instead, showed a median prevalence of approximately 11 cases per 10,000 residents. However, provinces in cluster 3 were those with the lowest trend of cases. K-means clustering yielded to an alternative cluster solution in terms of the prevalence and trend of SARS-CoV-2 cases. Our study described a simple and immediate approach to monitor the SARS-CoV-2 epidemic at the regional and provincial level. These findings, at present, offered a snapshot of the epidemic, which could be helpful to outline the hierarchy of needs at the subnational level. However, the integration of our approach with further indicators and characteristics could improve our findings, also allowing the application to different contexts and with additional aims.</p></abstract><kwd-group><kwd>COVID-19</kwd><kwd>clustering</kwd><kwd>positive cases</kwd><kwd>epidemiology</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijerph-17-05286\"><title>1. Introduction</title><p>The novel coronavirus (SARS-CoV-2) epidemic began to spread in the Hubei province (China) at the end of 2019, and then to more than 200 countries worldwide [<xref rid=\"B1-ijerph-17-05286\" ref-type=\"bibr\">1</xref>]. At the end of February 2020, two distinct outbreaks of SARS-CoV-2 occurred in two small Italian areas within the Lombardy and Veneto regions [<xref rid=\"B2-ijerph-17-05286\" ref-type=\"bibr\">2</xref>]. The epidemic, since then, spread at different times and with different intensities across all the Italian regions [<xref rid=\"B3-ijerph-17-05286\" ref-type=\"bibr\">3</xref>]. On 10 March 2020, the Italian government has promptly reacted to the epidemic by adopting a first set of national restrictions and recommendations (e.g., travel restrictions, quarantine and contact precautions) [<xref rid=\"B4-ijerph-17-05286\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijerph-17-05286\" ref-type=\"bibr\">5</xref>], which were strengthened on 23 March by avoiding non-essential industrial productions and social interactions [<xref rid=\"B4-ijerph-17-05286\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijerph-17-05286\" ref-type=\"bibr\">5</xref>]. While these efforts have been made to control the epidemic [<xref rid=\"B4-ijerph-17-05286\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijerph-17-05286\" ref-type=\"bibr\">5</xref>], differences between and within regions have made it difficult to plan the phase two management after the national lockdown. As a consequence of the control measures adopted, there has been a 5.3% decrease in SARS-CoV-2 positive patients during the week preceding the beginning of phase two in Italy, 4 May 2020 [<xref rid=\"B3-ijerph-17-05286\" ref-type=\"bibr\">3</xref>]. This resulted in a total of 100,179 active cases throughout the Italian territory on 3 May 2020 [<xref rid=\"B3-ijerph-17-05286\" ref-type=\"bibr\">3</xref>]. However, these patients were not evenly distributed across Italian regions, with three regions (i.e., Lombardy, Piedmont, and Emilia-Romagna) that presented more than 60% of the total active cases [<xref rid=\"B3-ijerph-17-05286\" ref-type=\"bibr\">3</xref>]. Several modelling studies evaluated the efficacy of control measures adopted in Italy and predicted future scenarios to help policymakers in designing the phase two strategy against the SARS-CoV-2 epidemic [<xref rid=\"B6-ijerph-17-05286\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijerph-17-05286\" ref-type=\"bibr\">7</xref>,<xref rid=\"B8-ijerph-17-05286\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijerph-17-05286\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijerph-17-05286\" ref-type=\"bibr\">10</xref>]. However, identifying different regional and provincial clusters would be just as crucial to plan strategies for relaxing the restrictions in accordance with specific situations and needs. As for other emerging diseases, useful and innovative tools could be able to provide a snapshot of the epidemic at the regional and provincial level. Here, we used two of the most common clustering methods (i.e., hierarchical clustering and k-means algorithm) [<xref rid=\"B11-ijerph-17-05286\" ref-type=\"bibr\">11</xref>] to categorize Italian regions and provinces into several groups. At the regional level, this clustering approach works directly on the prevalence and trend of active cases in each region, prior to the start of phase two on 4 May 2020. Specifically, it has been applied to regional data on the prevalence of SARS-CoV-2 positive cases on 3 May 2020 and their trend in the previous week from the 27 April to the 3 May 2020. At the provincial level, we applied a similar approach, but working on the prevalence and trend of total SARS-CoV-2 cases, due to the limited availability of data on positive cases. Thus, our aim was to apply a simple clustering approach in order to classify Italian regions and provinces based on the prevalence and trend of SARS-CoV-2 cases prior to the start of phase two on 4 May 2020.</p></sec><sec id=\"sec2-ijerph-17-05286\"><title>2. Materials and Methods </title><p>We first obtained data on SARS-CoV-2 positive cases and on the number of tests performed, which were collected and released by Italy’s Civil Protection of the Italian Ministry of Health from 27 April to 3 May 2020. These data, deposited on GitHub [<xref rid=\"B12-ijerph-17-05286\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijerph-17-05286\" ref-type=\"bibr\">13</xref>], were freely available and provided at the regional and provincial level. On the dedicated website [<xref rid=\"B14-ijerph-17-05286\" ref-type=\"bibr\">14</xref>], it is also possible to consult the daily prevalence of SARS-CoV-2 cases, their weekly trend and other information on the epidemic in Italy. We also obtained the number of residents of each region and province from the Italian National Institute of Statistics (ISTAT, Istituto Nazionale di Statistica) [<xref rid=\"B15-ijerph-17-05286\" ref-type=\"bibr\">15</xref>]. Thus, our study did not contain data at the individual level that would require ethical approval. Using the aforementioned data, we calculated the following regional and provincial indicators:<list list-type=\"bullet\"><list-item><p>The regional prevalence of SARS-CoV-2 positive cases on 3 May 2020 (i.e., expressed as the number of positive cases per 10,000 residents);</p></list-item><list-item><p>The weekly regional trend of SARS-CoV-2 positive cases from 27 April to 3 May 2020 (expressed as the percentage of increment/decrement of positive cases);</p></list-item><list-item><p>The provincial prevalence of SARS-CoV-2 cases on 3 May 2020 (i.e., expressed as the number of total cases per 10,000 residents);</p></list-item><list-item><p>The weekly provincial trend of SARS-CoV-2 cases from 27 April to 3 May 2020 (expressed as percentage of increment of total cases);</p></list-item><list-item><p>The number of tests performed per 10,000 residents (i.e., only at the regional level).</p></list-item></list></p><p>We then inspected the structure of the regional dataset by a three-dimensional scatter plot and tested for normality using the Kolmogorov–Smirnov test. According to the skewed distribution of the aforementioned indicators, we tested for correlations using the Spearman’s rank correlation coefficient. We separately tested the linear relationship of the number of tests performed per 10,000 residents (independent variable) with a prevalence or trend of SARS-CoV-2 cases (dependent variables) using linear regression models. Variables included in the regression models were log-transformed before being analyzed and the results were reported as β and its standard error (SE). Since data on the prevalence and trend of SARS-CoV-2 positive cases were related to different testing strategies between Italian regions, we then used the residuals of linear regressions to adjust both indicators for the number of tests performed per 10,000 residents. To account for different scales between the indicators, the adjusted values were further standardized using the Z-Score formula: z = (x − μ)/σ, where x was the log-transformed indicator, μ was its national log-transformed mean, and σ was the standard deviation. Working on these values (i.e., the log-transformed prevalence and trend of SARS-CoV-2 positive cases), we then applied hierarchical clustering using Ward’s criterion. The distance between regions—and hence their similarity—was determined by calculating their squared Euclidean distance in the two-dimensional plane. We also applied the Euclidean distance but obtaining the same cluster solution (data not shown). This clustering was shown by a hierarchical tree—also called dendrogram—in which the height of the branches indicated the distance between the clusters [<xref rid=\"B11-ijerph-17-05286\" ref-type=\"bibr\">11</xref>]. The optimal number of clusters—k—was determined using the silhouette method. Specifically, we derived four clustering solutions, which differed for the number of clusters <italic>k,</italic> into a range from two to five clusters (2 ≤ <italic>k</italic> ≤ 5). For each clustering solution, we calculated the silhouette value that represented how each region was cohered to its own cluster and separated from the others. We chose the optimal clustering solution that maximized the silhouette value [<xref rid=\"B16-ijerph-17-05286\" ref-type=\"bibr\">16</xref>]. Since slight differences in the clustering solution could be obtained using different methods [<xref rid=\"B11-ijerph-17-05286\" ref-type=\"bibr\">11</xref>], we consolidated the hierarchical clustering by applying the k-means algorithm. This method partitioned <italic>n</italic> observations into <italic>k</italic> clusters by minimizing intra-cluster variation [<xref rid=\"B11-ijerph-17-05286\" ref-type=\"bibr\">11</xref>]. In contrast to hierarchical clustering, the k-means clustering required a predefined number of clusters, which was determined according to the silhouette method applied to hierarchical clustering. Finally, we evaluated the inter-cluster variability using the Kruskal–Wallis test.</p><p>The provincial dataset on the prevalence and trend of total SARS-CoV-2 cases was analyzed in the same way, except for the preliminary adjustment for the number of tests performed. In fact, there were no accurate and complete data on the number of tests performed at provincial level. In brief, the prevalence and trend of total SARS-CoV-2 cases at the provincial level were first log-transformed and standardized using the Z-Score formula. Then, we used hierarchical clustering based on the squared Euclidean distance and Ward’s criterion to derive four clustering solutions (2 ≤ <italic>k</italic> ≤ 5). The optimal clustering solution was chosen to maximize the silhouette value [<xref rid=\"B16-ijerph-17-05286\" ref-type=\"bibr\">16</xref>]. Finally, we consolidated the hierarchical clustering solution by applying the k-means algorithm with a predefined number of clusters. All the analyses were performed on the SPSS software (version 23.0, SPSS, Chicago, IL, USA), with a significance level α of 0.05.</p></sec><sec sec-type=\"results\" id=\"sec3-ijerph-17-05286\"><title>3. Results</title><sec id=\"sec3dot1-ijerph-17-05286\"><title>3.1. Description of Data</title><p>The prevalence of SARS-CoV-2 positive cases on 3 May 2020, their trend in the week before, and the number of tests performed in each region are reported in <xref rid=\"ijerph-17-05286-t001\" ref-type=\"table\">Table 1</xref>.</p><p>In <xref ref-type=\"fig\" rid=\"ijerph-17-05286-f001\">Figure 1</xref>A, we show how Italian regions were distributed in a three-dimensional scatter plot of these indicators. Although this plot showed no clear separation between the regions, we found that six and eight regions were above the national average for the prevalence and trend of SARS-CoV-2 positive cases, respectively. In contrast, 10 regions were below the national average for the number of tests performed per 10,000 residents. We also found a positive correlation between the number of tests performed and the prevalence of SARS-CoV-2 positive cases on 3 May 2020 (Spearman’s correlation coefficient = 0.527; <italic>p</italic> = 0.015). In contrast, we noted a significant negative correlation between the number of tests performed and the trend of SARS-CoV-2 positive cases in the week from 27 April to 3 May 2020 (Spearman’s correlation coefficient = −0.613; <italic>p</italic> = 0.003; <xref ref-type=\"fig\" rid=\"ijerph-17-05286-f001\">Figure 1</xref>B). Specifically, we found a positive linear relationship between the log-transformed number of tests performed and the log-transformed prevalence of SARS-CoV-2 positive cases (β = 0.786; SE = 0.292; <italic>p</italic> = 0.015; <xref ref-type=\"fig\" rid=\"ijerph-17-05286-f002\">Figure 2</xref>A). By contrast, the log-transformed trend of SARS-CoV-2 positive cases was negatively associated with the log-transformed number of tests performed (β = −0.201; SE = 0.067; <italic>p</italic> = 0.007; <xref ref-type=\"fig\" rid=\"ijerph-17-05286-f002\">Figure 2</xref>B).</p></sec><sec id=\"sec3dot2-ijerph-17-05286\"><title>3.2. Regional Clusters</title><p>We then applied hierarchical clustering, yielding to the dendrogram depicted in <xref ref-type=\"fig\" rid=\"ijerph-17-05286-f003\">Figure 3</xref>. After the visual inspection of the hierarchical tree and based on silhouette values, we decided to partition Italian regions into three clusters. Indeed, the clustering solution with a number of three clusters was that with the highest average silhouette width (<xref rid=\"ijerph-17-05286-t002\" ref-type=\"table\">Table 2</xref>).</p><p>According to this clustering solution, Lombardy, Piedmont, Liguria and Marche tended to be grouped into cluster 1, Umbria and Valle d’Aosta into cluster 3, and the remaining regions into cluster 2 (<xref ref-type=\"fig\" rid=\"ijerph-17-05286-f004\">Figure 4</xref>). Differences between the clusters, in terms of the prevalence and trend of SARS-CoV-2 positive cases, are reported in <xref rid=\"ijerph-17-05286-t003\" ref-type=\"table\">Table 3</xref>. Specifically, the regions in cluster 1 exhibited the highest prevalence and trend of SARS-CoV-2 positive cases. In contrast, the regions in cluster 3 were those with the lowest prevalence and trend of SARS-CoV-2 positive cases. Regions in cluster 2 were those with an intermediate prevalence and trend of SARS-CoV-2 positive cases. We then consolidated this partition by applying a k-means algorithm with a predefined number of three clusters. However, all the regions maintained the same allocation of hierarchical clustering.</p></sec><sec id=\"sec3dot3-ijerph-17-05286\"><title>3.3. Provincial Clusters</title><p>We then applied a similar approach to identify different clusters among 107 Italian provinces, based on the prevalence and trend of total SARS-CoV-2 cases. The hierarchical clustering yielded to the dendrogram shown in <xref ref-type=\"fig\" rid=\"ijerph-17-05286-f005\">Figure 5</xref>.</p><p>According to the highest silhouette value reported in <xref rid=\"ijerph-17-05286-t004\" ref-type=\"table\">Table 4</xref>, we obtained an optimal clustering solution by partitioning the provincial dataset into three clusters. The hierarchical clusters’ composition is summarized in <xref rid=\"ijerph-17-05286-t005\" ref-type=\"table\">Table 5</xref>.</p><p>Specifically, the provinces in cluster 1 exhibited the highest prevalence and trend of SARS-CoV-2 cases; the provinces in cluster 2 had a median prevalence of 11.2 cases per 10,000 residents (IQR = 14.0) and a median increment of 3.9% (IQR = 2.4); and the provinces in cluster 3 were those with the lowest trend and a median prevalence of 11.6 cases per 10,000 residents (IQR = 12.7) (<xref ref-type=\"fig\" rid=\"ijerph-17-05286-f006\">Figure 6</xref>A and <xref rid=\"ijerph-17-05286-t006\" ref-type=\"table\">Table 6</xref>). We further consolidated the hierarchical clustering by applying the k-means algorithm with a predefined number of three clusters. This approach yielded to the alternative clustering solution summarized in <xref rid=\"ijerph-17-05286-t007\" ref-type=\"table\">Table 7</xref>. In particular, the provinces in cluster 1 exhibited the highest prevalence of SARS-CoV-2 cases and a median increment of 3.2% (IQR = 1.1); the provinces in cluster 2 had a median prevalence of 42.7 cases per 10,000 residents (IQR = 44.0) and the highest increment; the provinces in cluster 3 were those with the lowest prevalence and trend of SARS-CoV-2 cases (<xref ref-type=\"fig\" rid=\"ijerph-17-05286-f006\">Figure 6</xref>B and <xref rid=\"ijerph-17-05286-t006\" ref-type=\"table\">Table 6</xref>).</p></sec></sec><sec sec-type=\"discussion\" id=\"sec4-ijerph-17-05286\"><title>4. Discussion</title><p>Data on the prevalence and trend of total cases could be useful to monitor the SARS-CoV-2 epidemic at the regional level [<xref rid=\"B17-ijerph-17-05286\" ref-type=\"bibr\">17</xref>]. To the best of our knowledge, our study was the first applying two clustering methods to categorize the Italian regions into different groups, based on the prevalence and trend of SARS-CoV-2 positive cases prior to the starting of phase two. Specifically, we first used hierarchical clustering [<xref rid=\"B11-ijerph-17-05286\" ref-type=\"bibr\">11</xref>] to identify the groups with a similar prevalence of SARS-CoV-2 positive cases on 3 May and trend from 27 April to 3 May 2020. According to this method, Lombardy and Piedmont—the most hit Italian regions [<xref rid=\"B3-ijerph-17-05286\" ref-type=\"bibr\">3</xref>]—were grouped with Umbria and Marche into cluster 1, which was characterized by a high prevalence and trend of SARS-CoV-2 positive cases. Looking at the data, the Umbria and Marche were characterized by the values of prevalence and trend that were hybrid between cluster 1 and 2. Most regions, instead, were classified into a more heterogeneous cluster, with an intermediate prevalence and trend of positive cases. Finally, Umbria and Valle d’Aosta—those regions that simultaneously exhibited a low prevalence and trend of active cases [<xref rid=\"B3-ijerph-17-05286\" ref-type=\"bibr\">3</xref>]—were grouped into cluster 3. We further applied the k-means algorithm, a partitioning clustering used for splitting the dataset into a predefined number of groups [<xref rid=\"B11-ijerph-17-05286\" ref-type=\"bibr\">11</xref>]. Our combined approach allowed to improve the robustness of our findings by using a mixed algorithm that produced a better clustering solution [<xref rid=\"B11-ijerph-17-05286\" ref-type=\"bibr\">11</xref>]. In this case, however, all the regions maintained the same allocation of hierarchical clustering.</p><p>Our approach did not intend to explore the causality of the observed differences between clusters, nor to account for the spatial distribution of SARS-CoV-2 cases within the Italian territory. Actually, we aimed to provide a simple tool to monitor the SARS-CoV-2 epidemic across Italian regions. Indeed, our findings offered a snapshot of the epidemic, which could be helpful to outline the hierarchy of needs at the regional level. Specifically, some Italian regions—Lombardy and Piedmont, surely, but also Liguria and Marche—required the highest level of attention. For all the other regions, there were hopeful signs that the epidemic was slowly going towards a resolution. Among these, interestingly, Umbria and Valle d’Aosta were those with the best scenario. There were several explanations as to why Italian regions exhibited different prevalences and trends of SARS-CoV-2 cases. First, the epidemic initially started in Lombardy and Veneto and only after spread to the other regions with different times and intensities [<xref rid=\"B2-ijerph-17-05286\" ref-type=\"bibr\">2</xref>,<xref rid=\"B3-ijerph-17-05286\" ref-type=\"bibr\">3</xref>]. Thus, temporal and spatial distributions of SARS-CoV-2 cases represented the first reason motivating the differences observed across Italian regions. Beyond this, it is possible to hypothesize other grounds to support our clustering solution, for instance, comparing the epidemic spread between Lombardy and Veneto regions. Indeed, the first outbreaks concomitantly but independently occurred in two small Italian areas within the Lombardy and Veneto regions [<xref rid=\"B2-ijerph-17-05286\" ref-type=\"bibr\">2</xref>]. However, the scenario observed in Lombardy on the 3 May 2020 was much more serious than that in Veneto. Interestingly, Veneto imposed a wider testing campaign than Lombardy (771 vs. 409 tests per 10,000 residents), which may have contributed to a more rapid resolution of the epidemic [<xref rid=\"B18-ijerph-17-05286\" ref-type=\"bibr\">18</xref>]. Moreover, it is worth mentioning that performing a lower number of tests might alter the statistics and sustain the epidemic spread through contacts of positive but undocumented SARS-CoV-2 patients [<xref rid=\"B19-ijerph-17-05286\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijerph-17-05286\" ref-type=\"bibr\">20</xref>]. Equally important, the variability in the level of compliance with national restrictions and recommendations between and within regions could have influenced their efficacy at the regional level, yielding to the observed differences between clusters [<xref rid=\"B21-ijerph-17-05286\" ref-type=\"bibr\">21</xref>]. Finally, the demographic structure of each region, as well as environmental factors, could represent more suggestive explanations underlying a part of the variation between regions.</p><p>Our study also intended to provide an optimal clustering solution at the provincial level, despite that the data released by Italian authorities were less accurate. Whilst it would have been proper to work on positive cases, we used available data on the prevalence of total cases on 3 May and their trend from 27 April to 3 May 2020. Most provinces, actually, were first included in a cluster with the highest prevalence and trend of total cases. The remaining provinces, instead, were partitioned into two clusters with a similar prevalence of SARS-CoV-2 cases (approximately 11 cases per 10,000 residents) but a different trend from 27 April to 3 May: provinces in cluster 2 exhibited a median increment of 3.9%, while those in cluster 3 had the lowest trend (i.e., 1.5%). An alternative clustering solution obtained by the k-means algorithm categorized Italian provinces in a cluster with the lowest prevalence and trend of SARS-CoV-2 cases and in two clusters with the highest prevalence or the highest trend, respectively. The aforementioned reasons supporting the differences between regions could be equally applied to the partition of Italian provinces.</p><p>Our study had several limitations that should be considered when interpreting our results. First, it did not intend to investigate the causality of the observed differences, but rather to provide a simple tool to classify the regions and provinces according to the prevalence and trend of SARS-CoV-2 cases prior to the start of phase two in Italy. In line, our approach did not take into account the temporal and spatial distributions of SARS-CoV-2 cases within the Italian territory. Moreover, it considered only a part of the available data and knowledge, and hence further analyses could include additional indicators, characteristics and epidemic parameters (e.g., demographic structure, environmental factors, adherence to national restrictions, mobility data) at the regional and provincial level that could improve our cluster solutions and motivate the observed partitions [<xref rid=\"B22-ijerph-17-05286\" ref-type=\"bibr\">22</xref>]. A main limitation was with regard to the accuracy of the data used in our analysis and the differences in ascertainment and reporting capabilities between and within regions. For instance, we did not consider the proportion of unreported cases [<xref rid=\"B20-ijerph-17-05286\" ref-type=\"bibr\">20</xref>,<xref rid=\"B23-ijerph-17-05286\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijerph-17-05286\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijerph-17-05286\" ref-type=\"bibr\">25</xref>], though previous studies suggested how undocumented SARS-CoV-2 cases could silently sustain the epidemic spread [<xref rid=\"B23-ijerph-17-05286\" ref-type=\"bibr\">23</xref>]. Indeed, the testing strategy adopted in Italy might have caused an underestimation of infectious individuals, especially those with mild or no symptoms [<xref rid=\"B19-ijerph-17-05286\" ref-type=\"bibr\">19</xref>]. For this reason, we adjusted the prevalence and trend indicators for the regional number of tests performed per 10,000 residents.</p></sec><sec sec-type=\"conclusions\" id=\"sec5-ijerph-17-05286\"><title>5. Conclusions</title><p>In view of these considerations, our study described a simple and immediate approach to monitor the SARS-CoV-2 epidemic at the regional and provincial level. Interestingly, we partitioned both Italian regions and provinces into several groups according to the prevalence and trend of SARS-CoV-2 cases. These findings, at present, could be important to support policymakers in monitoring and planning future strategies against the epidemic. Indeed, they offered a snapshot of the epidemic, which could be helpful to outline the hierarchy of needs at the subnational level. However, the integration of our approach with further indicators and characteristics (i.e., the spatial and temporal distribution of SARS-CoV-2 cases, compliance with national control measures, demographic structure, environmental factors, and mobility data) could improve our findings, also allowing the application to different contexts and with additional aims. To do that, further studies working on additional and more accurate data at the regional and provincial level should be encouraged.</p></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization, A.M. and A.A.; methodology, A.M. and M.B.; software, A.M.; formal analysis, A.M. and M.B.; data curation, A.M.; writing—original draft preparation, A.M. and M.B.; writing—review and editing, A.M., M.B. and A.A.; visualization, A.M.; supervision, A.A.; funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This research was funded by the Assessorato della Salute, Regione Siciliana—Progetti Obiettivo di Piano Sanitario Nazionale (PSN 2014—4.9.2).</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijerph-17-05286\"><label>1.</label><element-citation publication-type=\"web\"><person-group person-group-type=\"author\"><collab>World Health Organization</collab></person-group><article-title>Coronavirus Disease (COVID-19) Dashboard</article-title><comment>Available online: <ext-link ext-link-type=\"uri\" xlink:href=\"https://covid19.who.int/\">https://covid19.who.int/</ext-link></comment><date-in-citation content-type=\"access-date\" iso-8601-date=\"2020-05-04\">(accessed on 4 May 2020)</date-in-citation></element-citation></ref><ref id=\"B2-ijerph-17-05286\"><label>2.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Day</surname><given-names>M.</given-names></name></person-group><article-title>Covid-19: Italy confirms 11 deaths as cases spread from north</article-title><source>BMJ</source><year>2020</year><volume>368</volume><fpage>m757</fpage><pub-id pub-id-type=\"doi\">10.1136/bmj.m757</pub-id><pub-id pub-id-type=\"pmid\">32102793</pub-id></element-citation></ref><ref id=\"B3-ijerph-17-05286\"><label>3.</label><element-citation publication-type=\"gov\"><person-group person-group-type=\"author\"><collab>Italian Ministry of Health</collab></person-group><article-title>Covid-19. 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Indicators are reported as log-transformed values.</p></caption><graphic xlink:href=\"ijerph-17-05286-g002\"/></fig><fig id=\"ijerph-17-05286-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Dendrogram of the hierarchical clustering of regions based on Ward’s criterion.</p></caption><graphic xlink:href=\"ijerph-17-05286-g003\"/></fig><fig id=\"ijerph-17-05286-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Scatter plot illustrating how the egional clusters were distributed on the prevalence of SARS-CoV-2 positive cases and their trend. Clustering solution was obtained by the hierarchical clustering and consolidated by the k-means algorithm.</p></caption><graphic xlink:href=\"ijerph-17-05286-g004\"/></fig><fig id=\"ijerph-17-05286-f005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>Dendrogram of the hierarchical clustering of the provinces based on Ward’s criterion.</p></caption><graphic xlink:href=\"ijerph-17-05286-g005\"/></fig><fig id=\"ijerph-17-05286-f006\" orientation=\"portrait\" position=\"float\"><label>Figure 6</label><caption><p>Scatter plot illustrating how provincial clusters were distributed on the prevalence of SARS-CoV-2 cases and their trend: (<bold>A</bold>) the clustering solution obtained by hierarchical clustering; and (<bold>B</bold>) the clustering solution obtained using the k-means algorithm.</p></caption><graphic xlink:href=\"ijerph-17-05286-g006\"/></fig><table-wrap id=\"ijerph-17-05286-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05286-t001_Table 1</object-id><label>Table 1</label><caption><p>Characteristics of the Italian regions.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Regions</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Residents</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Prevalence of Positive Cases (Per 10,000 Residents) <sup>a</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Trend of Positive Cases (%) <sup>b</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Number of Tests (Per 10,000 Residents) <sup>a</sup></th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Abruzzo</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1,315,196</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−8.00%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">309.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Apulia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4,048,242</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.50%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">164.1</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Basilicata</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">567,118</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−10.60%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">250.6</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bolzano <sup>c</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">527,750</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−29.30%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">838.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Calabria</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1,956,687</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−10.20%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">198.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Campania</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5,826,860</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−5.30%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">148.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Emilia-Romagna</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4,452,629</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−26.00%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">442.6</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Friuli Venezia Giulia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1,215,538</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−13.60%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">616.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Lazio</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5,896,693</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−3.90%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">255.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Liguria</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1,556,981</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">22.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−0.80%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">350</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Lombardy</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">10,036,258</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">36.8</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.20%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">409.4</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Marche</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1,531,753</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−3.40%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">420.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Molise</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">308,493</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.9</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−9.50%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">229.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Piedmont</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4,375,865</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">35.7</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.80%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">393.5</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sardinia</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1,648,176</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−11.20%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">168.3</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Sicily</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5,026,989</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.80%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">170.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Trento <sup>c</sup></td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">539,898</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−27.00%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">761.2</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Tuscany</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3,736,968</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">14.3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−11.00%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">403.8</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Umbria</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">884,640</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−36.20%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">438.9</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Valle d’Aosta</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">126,202</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">8.6</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−53.60%</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">641.8</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Veneto</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">4,905,037</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">14.9</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">−17.60%</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">771.0</td></tr></tbody></table><table-wrap-foot><fn><p><sup>a</sup> Data are referred to 3 May 2020. <sup>b</sup> Weekly trend from 27 April to 3 May 2020. <sup>c</sup> Autonomous provinces.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05286-t002\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05286-t002_Table 2</object-id><label>Table 2</label><caption><p>Average silhouette width for the hierarchical clustering of regions.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Number of Clusters</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Average Silhouette Width<break/>(Standard Deviation)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.566 (0.166)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.632 (0.099)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.493 (0.197)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.530 (0.248)</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05286-t003\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05286-t003_Table 3</object-id><label>Table 3</label><caption><p>Comparisons of the prevalence of SARS-CoV-2 positive cases, their trend and the number of tests between regional clusters.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Clusters</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Prevalence of Positive Cases Per 10,000 Residents <sup>a</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Trend of Positive Cases <sup>b</sup></th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Number of Tests Per 10,000 Residents <sup>a</sup></th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cluster 1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">29.3 (15.2)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0% (6.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">401.5 (56.9)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cluster 2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.4 (9.9)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−10.6% (12.3)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">255.9 (446.0)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cluster 3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.4 (6.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">−44.9% (17.4)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">540.4 (202.9)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.011</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.007</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.356</td></tr></tbody></table><table-wrap-foot><fn><p>Results are reported as the median (interquartile range), with <italic>p</italic>-values based on the Kruskal–Wallis test. <sup>a</sup> Data are referred to 3 May 2020 <sup>b</sup> Weekly trend from 27 April to 3 May 2020.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05286-t004\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05286-t004_Table 4</object-id><label>Table 4</label><caption><p>Average silhouette width for the hierarchical clustering of provinces.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Number of Clusters</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Average Silhouette Width<break/>(Standard Deviation)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.399 (0.169)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.402 (0.229)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.368 (0.214)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">0.377 (0.225)</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05286-t005\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05286-t005_Table 5</object-id><label>Table 5</label><caption><p>Hierarchical clusters’ composition of provinces.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Clusters</th><th align=\"left\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Provinces</th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cluster 1</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Alessandria; Aosta; Asti; Belluno; Bergamo; Biella; Bologna; Brescia; Como; Cremona; Cuneo; Fermo; Firenze; Forlì-Cesena; Genova; Imperia; La Spezia; Lecco; Lodi; Mantova; Massa Carrara; Milano; Modena; Monza e della Brianza; Novara; Parma; Pavia; Pesaro e Urbino; Pescara; Piacenza; Reggio nell’Emilia; Rimini; Savona; Sondrio; Torino; Trento; Trieste; Varese; Venezia; Verbano-Cusio-Ossola; Vercelli; Verona; Vicenza</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cluster 2</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Arezzo; Avellino; Bari; Barletta-Andria-Trani; Benevento; Brindisi; Cagliari; Caltanissetta; Catania; Chieti; Enna; Ferrara; Foggia; Gorizia; Latina; Lecce; Livorno; Lucca; Macerata; Matera; Messina; Napoli; Nuoro; Palermo; Pistoia; Pordenone; Potenza; Prato; Ragusa; Rieti; Roma; Siracusa; Taranto; Terni; Treviso; Vibo Valentia; Viterbo</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cluster 3</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Agrigento; Ancona; Ascoli Piceno; Bolzano; Campobasso; Caserta; Catanzaro; Cosenza; Crotone; Frosinone; Grosseto; Isernia; L’Aquila; Oristano; Padova; Perugia; Pisa; Ravenna; Reggio di Calabria; Rovigo; Salerno; Sassari; Siena; Sud Sardegna; Teramo; Trapani; Udine</td></tr></tbody></table></table-wrap><table-wrap id=\"ijerph-17-05286-t006\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05286-t006_Table 6</object-id><label>Table 6</label><caption><p>Comparisons of the prevalence of SARS-CoV-2 cases and their trend between clusters of provinces.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Clusters</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Prevalence of Total Cases Per 10,000 Residents</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Trend of Total Cases (%)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>Hierarchical Clustering</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cluster 1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">61.0 (31.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">5.7% (4.4)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cluster 2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.2 (14.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.9% (2.4)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cluster 3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">11.6 (12.7)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.5% (0.9)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n<bold>K-means Clustering</bold>\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">\n</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cluster 1</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">53.0 (38.1)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.2% (1.1)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cluster 2</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">42.7 (44.0)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">6.7% (3.3)</td></tr><tr><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cluster 3</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7.4 (8.5)</td><td align=\"center\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2.2% (2.0)</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><italic>p</italic>-Value</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.001</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr></tbody></table><table-wrap-foot><fn><p>Results are reported as the median (interquartile range), with <italic>p</italic>-values based on the Kruskal–Wallis test. Data are referred to 3 May 2020. Weekly trend from 27 April to 3 May 2020.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"ijerph-17-05286-t007\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijerph-17-05286-t007_Table 7</object-id><label>Table 7</label><caption><p>K-means clusters’ composition of provinces.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Clusters</th><th align=\"left\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Provinces</th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cluster 1</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Ancona; Aosta; Bergamo; Biella; Bolzano; Brescia; Cremona; Enna; Ferrara; Forlì-Cesena; La Spezia; Lecco; Lodi; Lucca; Macerata; Mantova; Massa Carrara; Modena; Padova; Parma; Pesaro e Urbino; Pordenone; Prato; Ravenna; Reggio nell’Emilia; Rieti; Rimini; Sondrio; Treviso; Trieste; Vercelli</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Cluster 2</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Alessandria; Arezzo; Asti; Avellino; Belluno; Bologna; Brindisi; Caltanissetta; Chieti; Como; Cuneo; Fermo; Firenze; Foggia; Genova; Gorizia; Imperia; Matera; Milano; Monza e della Brianza; Novara; Palermo; Pavia; Pescara; Piacenza; Pistoia; Roma; Savona; Terni; Torino; Trento; Varese; Venezia; Verbano-Cusio-Ossola; Verona; Vicenza</td></tr><tr><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Cluster 3</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Agrigento; Ascoli Piceno; Bari; Barletta-Andria-Trani; Benevento; Cagliari; Campobasso; Caserta; Catania; Catanzaro; Cosenza; Crotone; Frosinone; Grosseto; Isernia; L’Aquila; Latina; Lecce; Livorno; Messina; Napoli; Nuoro; Oristano; Perugia; Pisa; Potenza; Ragusa; Reggio di Calabria; Rovigo; Salerno; Sassari; Siena; Siracusa; Sud Sardegna; Taranto; Teramo; Trapani; Udine; Vibo Valentia; Viterbo</td></tr></tbody></table></table-wrap></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32756504</article-id><article-id pub-id-type=\"pmc\">PMC7432054</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155564</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05564</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Early Transcriptomic Changes upon Thalidomide Exposure Influence the Later Neuronal Development in Human Embryonic Stem Cell-Derived Spheres</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Kikegawa</surname><given-names>Mami</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05564\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-0110-6849</contrib-id><name><surname>Qin</surname><given-names>Xian-Yang</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05564\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Ito</surname><given-names>Tomohiro</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05564\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Nishikawa</surname><given-names>Hiromi</given-names></name><xref ref-type=\"aff\" rid=\"af4-ijms-21-05564\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Nansai</surname><given-names>Hiroko</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05564\">3</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-7162-6455</contrib-id><name><surname>Sone</surname><given-names>Hideko</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05564\">1</xref><xref ref-type=\"aff\" rid=\"af3-ijms-21-05564\">3</xref><xref ref-type=\"aff\" rid=\"af5-ijms-21-05564\">5</xref><xref rid=\"c1-ijms-21-05564\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05564\"><label>1</label>Laboratory of Kampo Pharmacology, Yokohama University of Pharmacy, Yokohama 245-0066, Japan; <email>m.takigawa@hamayaku.ac.jp</email></aff><aff id=\"af2-ijms-21-05564\"><label>2</label>Liver Cancer Prevention Research Unit, RIKEN Cluster for Pioneering Research, Wako, Saitama 351-0198, Japan; <email>xyqin@riken.jp</email></aff><aff id=\"af3-ijms-21-05564\"><label>3</label>Center for Health and Environmental Risk Research, National Institute for Environmental Studies, Tsukuba, Ibaraki 305-8506, Japan; <email>itotomo@nies.go.jp</email> (T.I.); <email>zahahi@yahoo.co.jp</email> (H.N.)</aff><aff id=\"af4-ijms-21-05564\"><label>4</label>Department of Psychiatry and Behavioral Science, Kanazawa University School of Medicine, Kanazawa, Ishikawa 920-0942, Japan; <email>hiromi.a.rac@gmail.com</email></aff><aff id=\"af5-ijms-21-05564\"><label>5</label>Environmental Health and Prevention Research Unit, Yokohama University of Pharmacy, Yokohama 245-0066, Japan</aff><author-notes><corresp id=\"c1-ijms-21-05564\"><label>*</label>Correspondence: <email>hideko.sone@yok.hamayaku.ac.jp</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>03</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5564</elocation-id><history><date date-type=\"received\"><day>18</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>30</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Stress in early life has been linked with the development of late-life neurological disorders. Early developmental age is potentially sensitive to several environmental chemicals such as alcohol, drugs, food contaminants, or air pollutants. The recent advances using three-dimensional neural sphere cultures derived from pluripotent stem cells have provided insights into the etiology of neurological diseases and new therapeutic strategies for assessing chemical safety. In this study, we investigated the neurodevelopmental effects of exposure to thalidomide (TMD); 2,2′,4,4′-tetrabromodiphenyl ether; bisphenol A; and 4-hydroxy-2,2′,3,4′,5,5′,6-heptachlorobiphenyl using a human embryonic stem cell (hESC)-derived sphere model. We exposed each chemical to the spheres and conducted a combinational analysis of global gene expression profiling using microarray at the early stage and morphological examination of neural differentiation at the later stage to understand the molecular events underlying the development of hESC-derived spheres. Among the four chemicals, TMD exposure especially influenced the differentiation of spheres into neuronal cells. Transcriptomic analysis and functional annotation identified specific genes that are TMD-induced and associated with ERK and synaptic signaling pathways. Computational network analysis predicted that TMD induced the expression of DNA-binding protein inhibitor ID2, which plays an important role in neuronal development. These findings provide direct evidence that early transcriptomic changes during differentiation of hESCs upon exposure to TMD influence neuronal development in the later stages.</p></abstract><kwd-group><kwd>thalidomide</kwd><kwd>environmental chemicals</kwd><kwd>neuronal differentiations</kwd><kwd>human embryonic stem cells</kwd><kwd>computational network analysis</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05564\"><title>1. Introduction</title><p>There has been worldwide concern over the increasing number of patients with depression and children with developmental disorders [<xref rid=\"B1-ijms-21-05564\" ref-type=\"bibr\">1</xref>,<xref rid=\"B2-ijms-21-05564\" ref-type=\"bibr\">2</xref>]. Recent studies suggest that the increasing prevalence of developmental disability in children is due to not only genetic factors but also some environmental factors [<xref rid=\"B3-ijms-21-05564\" ref-type=\"bibr\">3</xref>]. Environmental factors including exposure to chemicals such as pesticides and air pollutants during the developmental age could play a role in the development of neurodevelopmental diseases [<xref rid=\"B4-ijms-21-05564\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijms-21-05564\" ref-type=\"bibr\">5</xref>]. In addition, the developing brain has been shown to be more sensitive to environmentally hazardous chemicals than the adult brain [<xref rid=\"B6-ijms-21-05564\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijms-21-05564\" ref-type=\"bibr\">7</xref>]. Recent studies indicate that in vitro models are starting to replace traditional in vivo models for evaluation of the effects of external substances on fetuses and for the assessment of neurotoxicity to chemical exposure; this alternative approach can bridge the mechanistic gap between humans and animals and can be used to elucidate new therapeutic approaches [<xref rid=\"B8-ijms-21-05564\" ref-type=\"bibr\">8</xref>,<xref rid=\"B9-ijms-21-05564\" ref-type=\"bibr\">9</xref>,<xref rid=\"B10-ijms-21-05564\" ref-type=\"bibr\">10</xref>]. Therefore, systems using human embryonic stem cells (hESCs) and human induced pluripotent stem cells (hiPSCs) have been developed to directly predict human risks; the development of these systems would provide important information to elucidate the neurodevelopmental toxicities of numerous environmental chemicals.</p><p>We previously developed in vitro models using hESCs for studying the neurodevelopmental toxicities caused by various environmental pollutants [<xref rid=\"B11-ijms-21-05564\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijms-21-05564\" ref-type=\"bibr\">12</xref>,<xref rid=\"B13-ijms-21-05564\" ref-type=\"bibr\">13</xref>]. Our previous work also showed that thalidomide (TMD) inhibits the development of dopaminergic neurons from neuronal progenitor cells [<xref rid=\"B14-ijms-21-05564\" ref-type=\"bibr\">14</xref>]. Anti-depressant-like effects of maternal exposure to TMD was also observed in mice [<xref rid=\"B15-ijms-21-05564\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijms-21-05564\" ref-type=\"bibr\">16</xref>]. Early exposure to TMD in the pregnant period caused developmental abnormality in the human brain [<xref rid=\"B17-ijms-21-05564\" ref-type=\"bibr\">17</xref>,<xref rid=\"B18-ijms-21-05564\" ref-type=\"bibr\">18</xref>]. TMD is currently used to treat multiple myeloma, while it is a known teratogen and neurodevelopmental toxicant. Although it was recently shown that the degradation of spalt-like transcription factor 4 (SALL4) may be an essential component of TMD-induced teratogenicity that causes severe birth defects in the fetus [<xref rid=\"B19-ijms-21-05564\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijms-21-05564\" ref-type=\"bibr\">20</xref>], this mechanism is not enough to explain developmental neurotoxicity of TMD observed in in vitro and in vivo experiments. In addition, 2,2′,4,4′-tetrabromodiphenyl ether (BDE-47), bisphenol A (BPA), and 4-hydroxy-2,2′,3,4′,5,5′,6-heptachlorobiphenyl (4OH-PCB187) were also included in this study in comparison to TMD, since they show a high association with neuronal developmental disorders in epidemiologic studies and in animal and cellular experiments [<xref rid=\"B21-ijms-21-05564\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05564\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05564\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05564\" ref-type=\"bibr\">24</xref>]. Grandjean and Landrigan suggested polybrominated diphenyl ethers (PBDEs) as one group of newly recognized developmental neurotoxicants including organophosphate pesticides, herbicides, fungicides and manganese. Bisphenol A is also suggested as another suspected developmental neurotoxicant [<xref rid=\"B25-ijms-21-05564\" ref-type=\"bibr\">25</xref>]. 4OH-PCB187 is one of main metabolites for PCBs and they concentration was found in blood at a higher concentration, rather than other congeners [<xref rid=\"B26-ijms-21-05564\" ref-type=\"bibr\">26</xref>]. PCB and their metabolites are very similar to the structures of PBDEs and their hydroxyl metabolites [<xref rid=\"B27-ijms-21-05564\" ref-type=\"bibr\">27</xref>].</p><p>Collectively, to understand the molecular events underlying the neurodevelopmental effects of environmental chemicals including drugs, endocrine disruptors, and flame retardants, we have studied the effects of TMD and three environmental pollutants including BDE-47, BPA, and 4OH-PCB187 on global gene expression during neurosphere formation and during the following differentiation into neuronal cells.</p></sec><sec sec-type=\"results\" id=\"sec2-ijms-21-05564\"><title>2. Results and Discussion</title><sec id=\"sec2dot1-ijms-21-05564\"><title>2.1. Morphological Analysis of the Effect of Chemical Exposure at the Early Stage of Development on the Neuronal Differentiation from hESC-Derived Spheres</title><p>In order to investigate the neurodevelopmental effects, we generated a protocol for sphere formation from hESCs and differentiation to neuronal cells (<xref ref-type=\"fig\" rid=\"ijms-21-05564-f001\">Figure 1</xref>A). In this model, we confirmed the differentiation of spheres into neuronal cells on Day 28 by immunostaining with anti-microtubule-associated protein 2 (MAP2) and anti-tyrosine hydroxylase (TH) antibodies (<xref ref-type=\"app\" rid=\"app1-ijms-21-05564\">Figure S1A</xref>). Next, we exposed the spheres to each chemical, and examined the effects of chemical exposure on differentiation potency. Our results showed that TMD significantly increased the total cell numbers, and the presence of MAP2-positive and TH-positive neuronal cells in a dose-dependent manner (<xref ref-type=\"fig\" rid=\"ijms-21-05564-f001\">Figure 1</xref>B and <xref ref-type=\"app\" rid=\"app1-ijms-21-05564\">Figure S1A</xref>). Slight induction of TH-positive cells but not MAP2-positive cells was observed with exposure to 10<sup>−8</sup> M BDE-47 (<xref ref-type=\"fig\" rid=\"ijms-21-05564-f001\">Figure 1</xref>C). No adverse effects were observed upon exposure to BPA and 4OH-PCB187 (<xref ref-type=\"fig\" rid=\"ijms-21-05564-f001\">Figure 1</xref>D,E, respectively). Furthermore, the normalized MAP2-positive area and TH-positive area with the total cell number showed that the neuronal differentiation-promoting effect was only observed with TMD (<xref ref-type=\"app\" rid=\"app1-ijms-21-05564\">Figure S1B</xref>). However, there was no significant change in TH/MAP2 ratio, which may be due to the promotion of nervous system differentiation or the expansion of the sphere itself. At that expansion stage, the TMD-treated cells in spheres may not be involved in promoting or suppressing the differentiation of TH.</p><p>The increase in number of neuronal cells upon TMD exposure was not consistent with another study that has investigated high-concentration exposure of this chemical [<xref rid=\"B28-ijms-21-05564\" ref-type=\"bibr\">28</xref>]. These results indicate that the early exposure to TMD in vitro promotes neuronal development at a later stage of hESCs, contrary to previous reports that hESC-derived neuronal progenitor cells were exposed at the later stage [<xref rid=\"B14-ijms-21-05564\" ref-type=\"bibr\">14</xref>]. The increase in TH-neuronal cells in the BDE-47 exposure was consistent with another study that the similar dose range of BDE-47 slightly increased the TUBB3 expression associated with dopaminergic neuron [<xref rid=\"B29-ijms-21-05564\" ref-type=\"bibr\">29</xref>]. These results highlighted the importance to evaluate risk of environmental chemicals, such as the effect on neuronal development, in the hESC-derived sphere model at realistic blood concentration levels and timing of exposure.</p></sec><sec id=\"sec2dot2-ijms-21-05564\"><title>2.2. Transcriptional Analysis of the Effect of Chemical Exposure at the Early Stage of Neuronal Development Derived from hESCs</title><p>Next, to explore the molecular mechanism underlying the effects of chemical exposure on the differentiation of hESCs, we performed microarray-based transcriptome analysis to examine the global gene expression changes at the sphere stage (on Day 7). The chemical concentration for the microarray analysis was determined according to the results of the morphological analysis. For BPA and PCB, the highest concentrations were chosen since there were no significant effects at any concentration. For TMD and BDE, the lowest observed effect concentrations were chosen. Each chemical induced the changes of gene expression at a comparable level. For this comparison, only genes with a fold change of more than two compared to vehicle control were considered (<xref ref-type=\"fig\" rid=\"ijms-21-05564-f002\">Figure 2</xref>A). Further, exposure to different chemicals showed that there are similar functional annotations of differentially expressed genes (<xref ref-type=\"fig\" rid=\"ijms-21-05564-f002\">Figure 2</xref>B). The percentage of differentially expressed transcriptional factors was approximately 9% in all chemical cases (<xref ref-type=\"fig\" rid=\"ijms-21-05564-f002\">Figure 2</xref>B). These results suggest that our experimental strategy was able to capture the similar impact of different chemical exposures on transcriptomic changes.</p><p>To further understand the functional difference upon each different chemical exposure, the differentially expressed genes were imported into the IPA program. Canonical pathway analysis showed the strongest impact of TMD exposure in regulating multiple biological pathways as compared to other chemicals (<xref ref-type=\"fig\" rid=\"ijms-21-05564-f002\">Figure 2</xref>C). In accordance with the findings in the morphological analysis (<xref ref-type=\"fig\" rid=\"ijms-21-05564-f001\">Figure 1</xref>B), the gene expression of <italic>MAP2</italic> was selectively induced upon TMD exposure (<xref ref-type=\"app\" rid=\"app1-ijms-21-05564\">Figure S1C</xref>). In addition, among the differentially expressed genes induced upon TMD exposure, we could identify an enrichment of genes encoding molecules regulating neuronal functions, such as “nNOS Signaling in Neurons”, “Extrinsic Prothrombin Activation Pathway”, “Circadian Rhythm Signaling”, and “Synaptogenesis Signaling Pathway” (<xref ref-type=\"fig\" rid=\"ijms-21-05564-f002\">Figure 2</xref>C). Gene expression profiling showed that TMD exposure selectively modulated the expression of <italic>CACNB4, CDH6, CPLX3, CREB5, EPHB1, GRIA3, GRIA4, GRIN2A, KALRN, NRXN1, PRKCD, SYT15,</italic> and <italic>SYT4</italic> (<xref ref-type=\"fig\" rid=\"ijms-21-05564-f002\">Figure 2</xref>D and <xref ref-type=\"app\" rid=\"app1-ijms-21-05564\">Figure S2</xref>).</p></sec><sec id=\"sec2dot3-ijms-21-05564\"><title>2.3. TMD-Specific Effect on Gene Expression at the Early Stage of Neuronal Development Derived from hESCs</title><p>The differentially expressed genes that are induced by TMD but not the other chemicals were selected if they have a fold change higher than the fold change of 2. TMD-specific genes with 377 candidates were selected after the comparison between TMD and each of the three chemicals correspondingly (<xref ref-type=\"fig\" rid=\"ijms-21-05564-f003\">Figure 3</xref>A and <xref ref-type=\"app\" rid=\"app1-ijms-21-05564\">Table S1</xref>) and was imported into IPA for pathway analysis. Interestingly, top network function analysis showed that a wide range of signaling pathways associated with neurological disease and embryonic development were specifically affected by TMD exposure (<xref ref-type=\"fig\" rid=\"ijms-21-05564-f003\">Figure 3</xref>B). The most highly populated network entitled “Neurological Disease, Organismal Injury and Abnormalities, Cell Morphology” indicated the central role of extracellular signal-regulated kinases ERK1/2 in controlling the transcriptional response of hESC-derived sphere in response to TMD exposure (<xref ref-type=\"fig\" rid=\"ijms-21-05564-f003\">Figure 3</xref>C). Further upstream causal network analysis confirmed that TMD might be exerting its neurodevelopmental effect via the suppression of ERK1/2 activation (<xref ref-type=\"app\" rid=\"app1-ijms-21-05564\">Figure S3</xref>). In accordance with our findings in the hESC-derived sphere model, a previous study in the human neural stem cell model showed that inhibition of ERK by chemical inhibitors promoted neuronal generation, especially of TH-positive neurons [<xref rid=\"B30-ijms-21-05564\" ref-type=\"bibr\">30</xref>]. To further understand the neurodevelopmental effect of TMD, the effect of TMD on biological pathways related to embryonic development was evaluated according to the activation z-score, which is a statistical measure in IPA and can be used to predict the activation state (activated or inhibited) of a biological molecule or function based on a statistically significant pattern match of up- and down-regulated gene expression [<xref rid=\"B31-ijms-21-05564\" ref-type=\"bibr\">31</xref>]. Interestingly, the function of “Differentiation of embryonic cells” was predicted to be activated by TMD based on the expression of enriched genes such as FRZB, TET2, CoL12A1, ID2, NRP1, HHEX, IL6ST, RUNX1, KDM2B, NPY1R, FOXA2, NODAL, and ANGPT1 (<xref ref-type=\"fig\" rid=\"ijms-21-05564-f003\">Figure 3</xref>D,E).</p></sec><sec id=\"sec2dot4-ijms-21-05564\"><title>2.4. Integrated Network Analysis of Transcriptional and Morphological Changes during Neuronal Differentiation from hESC-Derived Spheres</title><p>Finally, integrated network analysis was performed to determine the connection between the genetic and morphological actions of TMD during neuronal differentiation from hESC-derived spheres. Candidate feature genes involved in the function of “Differentiation of embryonic cells” (<xref ref-type=\"fig\" rid=\"ijms-21-05564-f003\">Figure 3</xref>E) with correctly predicted expression patterns in comparison with previous findings in the Ingenuity Knowledge Base were selected for network analysis (<xref ref-type=\"app\" rid=\"app1-ijms-21-05564\">Table S2</xref>). As the result, expression data of four genes (TET2, HHEX, ID2 and NRP1) and two morphological measures of TH and MAP2 staining after chemical exposure were used for network analysis using three approaches: correlation-based network analysis, Bayesian network analysis and physiological network analysis. The correlation network was generated by calculating the Pearson correlation coefficient between each pair of gene and morphological parameters (<xref ref-type=\"fig\" rid=\"ijms-21-05564-f004\">Figure 4</xref>A). Either the four genes or the two morphological measures were correlated with each other. For the relationship between the gene and morphological parameters, NRP1 was correlated with TH and MAP2, while HHEX was correlated with MAP2.</p><p>However, a limitation of correlation networks is that they can be confounded by indirect relationships. In contrast, methods that infer the data such as a whole, such as the Bayesian network, includes only direct effects and is considered more biologically interpretable due to removal of indirect correlations [<xref rid=\"B31-ijms-21-05564\" ref-type=\"bibr\">31</xref>]. The integrative Bayesian network showed that the node of a transcriptional regulator ID2 was located at the top of the network hierarchy and was positively related to MAP2 (<xref ref-type=\"fig\" rid=\"ijms-21-05564-f004\">Figure 4</xref>B and <xref ref-type=\"app\" rid=\"app1-ijms-21-05564\">Figure S4</xref>). The enzyme TET2 was not connected to the network, suggesting that the inferred network using a Bayesian algorithm was able to remove indirect relationships. Finally, to verify the biological relevance of the inferred connections, a physiological network was generated based on findings of from previous studies using the Ingenuity Knowledge Base. In accordance with Bayesian network, the physiological network showed that there were no direct relationships between TET2 and the functions of either “Neurogenesis” or “Differentiation of embryonic stem cells”, whereas ID2 was connected to “Neurogenesis” (<xref ref-type=\"fig\" rid=\"ijms-21-05564-f004\">Figure 4</xref>C). It was reported that induction of the ID2 gene expression, which was also observed in hESC-derived sphere after TMD treatment in the present study, increased differentiation of Tuj1 and GFAP-positive neurosphere cells [<xref rid=\"B32-ijms-21-05564\" ref-type=\"bibr\">32</xref>]. Three environmental chemicals other than TMD had very limited or no significant changes in neuronal differentiation in this model. BDE47 has recently been reported to have inhibitory effects on human ES-derived neuronal cells similar to our model [<xref rid=\"B21-ijms-21-05564\" ref-type=\"bibr\">21</xref>,<xref rid=\"B33-ijms-21-05564\" ref-type=\"bibr\">33</xref>]. Similarly, BPA has been found to have suppressive effects on neuronal stem cells [<xref rid=\"B34-ijms-21-05564\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijms-21-05564\" ref-type=\"bibr\">35</xref>]. Regarding OH-PCB187 or mother compounds of PCB, epidemiological studies suggest a negative relationship with IQ and a relationship with ADHD [<xref rid=\"B36-ijms-21-05564\" ref-type=\"bibr\">36</xref>], but no report has been made in vitro. This is the first report of exposure of OH-PCB187 to human ES.</p></sec></sec><sec id=\"sec3-ijms-21-05564\"><title>3. Materials and Methods</title><sec id=\"sec3dot1-ijms-21-05564\"><title>3.1. Ethics Statement</title><p>Experiments using hESCs were approved by the ethics committees of the National Institute for Environmental Studies in accordance with to the guideline of the Japanese Ministry of Education, Culture, Sports, Science, and Technology (Notification number 20 of Ministry of Education, Culture, Sports, Science and Technology No. 857, 10 October 2008). The experimental plan was registered on the NIES ethics committee (Notification number: 2008-1, Reception number: 2007-1, 15 October 2008).</p></sec><sec id=\"sec3dot2-ijms-21-05564\"><title>3.2. Chemical Exposures, Culture and Neuronal Differentiation of Human ESCs</title><p>Dimethyl sulfoxide (DMSO) and BPA were obtained from Sigma–Aldrich Co. (St. Louis, MO, USA). TMD were obtained from Wako Pure Chemicals (Tokyo, Japan); 4OH-PCB187 and BDE-47 were obtained from AccuStandard (New Haven, CT, USA). DMSO was used as the primary solvent for all chemicals. The final concentrations of DMSO in the media did not exceed 0.1% (<italic>v</italic>/<italic>v</italic>). Human embryonic stem cells (khES3) were maintained and differentiated as described previously [<xref rid=\"B10-ijms-21-05564\" ref-type=\"bibr\">10</xref>,<xref rid=\"B11-ijms-21-05564\" ref-type=\"bibr\">11</xref>]. The hESC line, KhES-3 (XY genotype), was provided by Dr. Hirofumi Suemori, Research Center of Stem Cells, Institute for Frontier Medical Science, Kyoto University according to the NIES institutional guidelines for the use of human ES research [<xref rid=\"B37-ijms-21-05564\" ref-type=\"bibr\">37</xref>]. All experiments using hESCs were approved by the ethics committees of the National Institute for Environmental Studies and the University of Tokyo in accordance with the guidelines of the Japanese Ministry of Education, Culture, Sports, Science, and Technology. The procedures for the maintenance of hESCs were performed as described previously [<xref rid=\"B37-ijms-21-05564\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijms-21-05564\" ref-type=\"bibr\">38</xref>,<xref rid=\"B39-ijms-21-05564\" ref-type=\"bibr\">39</xref>]. MEFs were used as feeder cells for the culture and passage of the hESC line KhES3 in the DMEM/F12 media containing 20% KSR, 100 µM NEAA, 2 mM <sc>l</sc>-glutamine, 100 µM 2-ME, and 5 ng/mL bFGF. After five times of passages with additional MEFs, the MEFs were eliminated by a brief enzymatic treatment. The hESC colonies left on the dishes were harvested. The hESCs (purity > 99%) were seeded at 9.0 × 10<sup>3</sup> cells/well in the medium containing DMEM/F12, 20% KSR, 100 µM NEAA, 2 mM <sc>l</sc>-glutamine, 100 µM 2-ME, and 10 µM of ROCK inhibitor Y-27632 (Day 1). The generated EBs were cultured for 7 days in the medium, which was exchanged every two days, followed by growth in the medium without Y-27632 for two days. The growing EBs were cultured for 2 additional days in NIM containing DMEM/F12: Neurobasal<sup>®</sup> Medium (1:1), N-2 Supplement, B-27<sup>®</sup> Supplement, GlutaMAX™-I, Penicillin-Streptomycin to promote neuronal differentiation. Then, EBs were re-plated onto O/L-coated 24-well-plates at 20 EBs/well. They were cultured for 7 days in neuronal proliferation medium (NPM) containing DMEM/F12: Neurobasal<sup>®</sup> Medium (1:1), two-fold concentrations of N-2 Supplement, two-fold concentrations of B-27<sup>®</sup> Supplement, GlutaMAX™-I, Penicillin-Streptomycin, 20 ng/mL bFGF. The medium was exchanged every 3 days. hESCs were allowed to form embryoid bodies (EBs) in the round bottom 96-well plate (Falcon 351177). The EBs were seeded onto ornithine–laminin (O/L)-coated 24-well plates to promote neuronal differentiation with the sequential exchange of authentic appropriate neuronal differentiation media every other day. The schedules for the formation of sphere and neuronal differentiation of hESCs are summarized in <xref ref-type=\"fig\" rid=\"ijms-21-05564-f001\">Figure 1</xref>A. Briefly, hESCs were allowed to form sphere in the round bottom 96 well plate for 7 days. Cells were exposed to each chemical from Day 3 to 7 during sphere formation. Doses of chemicals used here were within the clinical dose range for TMD, blood, urinary or breast milk concentrations reported in population studies for BPA, 4OH-PCB187 and BDE-47 [<xref rid=\"B40-ijms-21-05564\" ref-type=\"bibr\">40</xref>,<xref rid=\"B41-ijms-21-05564\" ref-type=\"bibr\">41</xref>,<xref rid=\"B42-ijms-21-05564\" ref-type=\"bibr\">42</xref>,<xref rid=\"B43-ijms-21-05564\" ref-type=\"bibr\">43</xref>,<xref rid=\"B44-ijms-21-05564\" ref-type=\"bibr\">44</xref>,<xref rid=\"B45-ijms-21-05564\" ref-type=\"bibr\">45</xref>]. The spheres were then seeded onto poly-ornithine/laminin 111-coated 24-well plates to promote proliferation and neuronal differentiation for 21 days, and the medium was refreshed every 3 days, as reported previously [<xref rid=\"B11-ijms-21-05564\" ref-type=\"bibr\">11</xref>].</p></sec><sec id=\"sec3dot3-ijms-21-05564\"><title>3.3. Immunocytochemistry and Image Analysis</title><p>Differentiated cells on day 28 were immunolabeled with human anti-MAP2 (M4403, 1:200, Sigma-Aldrich) or anti-TH (AB152, 1:200, Millipore, Burlington, MA, USA) antibodies, followed by staining with Alexa 546-conjugated secondary antibody (1:1000, Invitrogen, Carlsbad, CA, USA). The nuclei were stained with Hoechst 33,342 solution (Dojindo, Tokyo, Japan). The values for the area of fluorescent signals were analyzed using an IN Cell Analyzer 1000 (GE Healthcare UK Ltd., Buckinghamshire, UK), as previously reported [<xref rid=\"B11-ijms-21-05564\" ref-type=\"bibr\">11</xref>,<xref rid=\"B46-ijms-21-05564\" ref-type=\"bibr\">46</xref>]. In brief, immunofluorescent images were automatically acquired using the IN Cell Analyzer to quantify the differences in the cellular nuclei and cellular phenotypes. Fluorescent microphotographs (12 fields per well of a 24-well plate) were obtained automatically. Hoechst-positive nuclei and MAP2-positive or TH-positive neurites were recognized using IN Cell Developer Software. Removal of apoptotic or necrotic cells was performed by measuring the Hoechst-positive nuclei’s morphological features with nuclei fragmentation and chromatin condensation. Viable cells and apoptotic cells were classified according to nuclei size and the nuclei fluorescence signal density. The software was also used to identify neurons that stained positive with both nuclear stain and MAP2 antibodies, and to characterize the neurites extending from these cells. The length of the neurite for each identified cell was measured, respectively. Data were expressed as the mean neurite area per cell. Cells were cultured and stained in 6 independent wells for each condition, 12 fields per well were observed and values were calculated. Statistical analyses for cellular morphology were performed with Excel statistics (Microsoft 2016). All data were expressed relative to the means of the control groups. All results were represented as mean ± standard error (SE). All data were analyzed by one-way analysis of variance (ANOVA), followed by Fisher’s least significant difference (LSD) post hoc test, to compare the effect of each dose with the DMSO control groups. <italic>p</italic>-values less than 0.05 were considered statistically significant.</p></sec><sec id=\"sec3dot4-ijms-21-05564\"><title>3.4. Microarray Gene Expression Profiling</title><p>Four spheres from different experimental groups were pooled together separately. RNAs were isolated from each group on day 7. To detect changes in gene expression in spheres after the chemical exposure, microarray analyses were performed on the RNA sample using a microarray. Total RNA from spheres was isolated with the RNeasy Mini Kit (Qiagen, Hilden, Germany). Fifty nanograms of total RNA pooled from three independent samples was fluorescently labeled and hybridized to Agilent 8 × 60 K-Human Genome Microarrays (Sureprint G3 Human GE 8 × 60 K Ver.2.0 1color 4; Agilent Technologies Inc., Santa Clara, CA, USA). The arrays were hybridized and scanned in accordance with the manufacturer’s directions at the facility of Hokkaido System Science Co., Ltd. (Sapporo, Japan), as reported previously [<xref rid=\"B10-ijms-21-05564\" ref-type=\"bibr\">10</xref>]. The raw data was filtered based on signal intensity values and in the lowest 20 percentile and then filtered by FLAG-tag to remove entities that were not detected using GeneSpring GX12.10 software (Agilent Technologies Inc., Santa Clara, CA, USA). The microarray data were submitted to Gene Expression Omnibus (GEO) and registered as GSE151239 [<xref rid=\"B47-ijms-21-05564\" ref-type=\"bibr\">47</xref>].</p></sec><sec id=\"sec3dot5-ijms-21-05564\"><title>3.5. Knowledge-Based Pathway Analysis and Network Analysis</title><p>To explore the biological interpretation of the transcriptome data, the canonical pathway analysis, disease and bio-function annotation, and upstream causal network analysis were performed using the knowledge-based functional analysis software, Ingenuity Pathways Analysis (IPA, Ingenuity Systems, Redwood City, CA, USA). In IPA analysis, the fold change is a ratio (case/control), and it is up-regulated as it is between 1 and +infinity, and the value (x) between 0 and 1 is converted as “−1/x”. It is down-regulated with a distribution from −infinity to −1. Correlations of gene expression and morphological measures were calculated according to the Pearson correlation coefficients method in R Bioconductor (<uri xlink:href=\"https://www.bioconductor.org/\">https://www.bioconductor.org/</uri>). Bayesian network analysis was applied based on using the TAO-Gen algorithm using the web-based RX-Taogen software (<uri xlink:href=\"http://extaogen.nies.go.jp/\">http://extaogen.nies.go.jp/</uri>). The replicate exchange time was set as 20,000, as reported previously [<xref rid=\"B39-ijms-21-05564\" ref-type=\"bibr\">39</xref>]. The networks were visualized using the Gephi software (<uri xlink:href=\"https://gephi.org/\">https://gephi.org/</uri>).</p></sec></sec><sec sec-type=\"conclusions\" id=\"sec4-ijms-21-05564\"><title>4. Conclusions</title><p>The recent technology using three-dimensional neuronal sphere models derived from pluripotent stem cells has provided new insights into the etiology of neurological diseases and new therapeutic strategies for assessing chemical safety. In this study, we explored the comparative effects of TMD and environmental chemicals such as BDE47, BPA, and 4OH-PCB187 at realistic blood concentration levels on the neuronal differentiation of hESC-derived spheres. In conclusion, exposure to TMD, but not other chemicals, at the early stage of development influenced neuronal differentiation of MAP2-positive and TH-positive neuronal cells from hESC-derived spheres. Transcriptomic analysis and functional annotation at the early stage of neural differentiation showed specific TMD-induction genes associated with the ERK and synaptic signaling pathways. Computational network analysis of genetic and morphological actions of TMD during neuronal differentiation predicted that TMD-induced expression of DNA-binding protein inhibitor ID2 played an important role in neuronal development from hESC-derived spheres. These findings provide direct evidence that early transcriptomic changes during differentiation of hESCs by chemical exposure influenced neuronal development in later stages.</p></sec></body><back><app-group><app id=\"app1-ijms-21-05564\"><title>Supplementary Materials</title><p>Supplementary Materials can be found at <uri xlink:href=\"https://www.mdpi.com/1422-0067/21/15/5564/s1\">https://www.mdpi.com/1422-0067/21/15/5564/s1</uri>. Figure S1: Effects of chemical exposure on cellular morphologies and <italic>MAP2</italic> gene expression during the neuronal differentiation from human embryonic stem cell (hESC)-derived spheres. Figure S2: Synaptogenesis Signaling Pathway. Figure S3: Upstream causal network. Figure S4: Representative network with probability values in Bayesian network analysis. Networks were generated based on the TAO-Gen algorithm using the web-based RX-Taogen software. Table S1: Lists of the TMD-specific genes selected after the comparison between TMD and each of the three chemicals correspondingly for IPA-pathway analysis. Table S2: Prediction of expression patterns of genes involved in the function of “Differentiation of embryonic cells” by IPA.</p><supplementary-material content-type=\"local-data\" id=\"ijms-21-05564-s001\"><media xlink:href=\"ijms-21-05564-s001.zip\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></app></app-group><notes><title>Author Contributions</title><p>Conceptualization, H.S.; methodology, H.S., H.N. (Hiromi Nishikawa), H.N. (Hiroko Nansai).; software, H.S., X.-Y.Q. and M.K.; validation, H.S., X.-Y.Q. and T.I.; investigation, H.S. and H.N. (Hiromi Nishikawa), H.N. (Hiroko Nansai).; resources, H.S.; data curation, M.K. and Q.Y.; writing—original draft preparation, H.S., X.-Y.Q., T.I. and M.K.; writing—review and editing, H.S., X.-Y.Q., T.I. and M.K.; visualization, X.-Y.Q. and M.K.; supervision, H.S.; project administration, H.S.; funding acquisition, H.S. All authors have read and agreed to the published version of the manuscript. </p></notes><notes><title>Funding</title><p>This study was supported in part by the Environmental Technology Development Fund (to H.S.) from the Ministry of the Environment, and by a Grant-in-Aid for Scientific Research (A) (15H01749) by Japan Society for the Promotion of Science. </p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><glossary><title>Abbreviations</title><array orientation=\"portrait\"><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">BDE-47</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2,2′,4,4′-tetrabromodiphenyl ether</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">BPA</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">bisphenol A</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">DMSO</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">dimethyl sulfoxide</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">hESC</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">human embryonic stem cell</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4OH-PCB187</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4-hydroxy-2,2′,3,4′,5,5′,6-heptachlorobiphenyl</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TMD</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">thalidomide</td></tr></tbody></array></glossary><ref-list><title>References</title><ref id=\"B1-ijms-21-05564\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Patel</surname><given-names>V.</given-names></name><name><surname>Saxena</surname><given-names>S.</given-names></name><name><surname>Lund</surname><given-names>C.</given-names></name><name><surname>Thornicroft</surname><given-names>S.G.</given-names></name><name><surname>Baingana</surname><given-names>F.</given-names></name><name><surname>Bolton</surname><given-names>P.</given-names></name><name><surname>Chisholm</surname><given-names>D.</given-names></name><name><surname>Collins</surname><given-names>P.Y.</given-names></name><name><surname>Cooper</surname><given-names>J.L.</given-names></name><name><surname>Eaton</surname><given-names>J.</given-names></name><etal/></person-group><article-title>The Lancet Commission on global mental health and sustainable development</article-title><source>Lancet</source><year>2018</year><volume>392</volume><fpage>1553</fpage><lpage>1598</lpage><pub-id pub-id-type=\"doi\">10.1016/S0140-6736(18)31612-X</pub-id><pub-id pub-id-type=\"pmid\">30314863</pub-id></element-citation></ref><ref id=\"B2-ijms-21-05564\"><label>2.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Zablotsky</surname><given-names>B.</given-names></name><name><surname>Black</surname><given-names>L.I.</given-names></name><name><surname>Maenner</surname><given-names>M.J.</given-names></name><name><surname>Schieve</surname><given-names>L.A.</given-names></name><name><surname>Blumberg</surname><given-names>S.J.</given-names></name></person-group><article-title>Estimated Prevalence of Autism and Other Developmental Disabilities Following Questionnaire Changes in the 2014 National Health Interview Survey</article-title><source>Natl. 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(<bold>A</bold>) Experimental protocol of sphere formation and neuronal differentiation from hESCs. Dose-dependent effects of exposure to (<bold>B</bold>) thalidomide (TMD), (<bold>C</bold>) 2,2′,4,4′-tetrabromodiphenyl ether (BDE-47), (<bold>D</bold>) bisphenol A (BPA), and (<bold>E</bold>) 4-hydroxy-2,2′,3,4′,5,5′,6-heptachlorobiphenyl (4OH-PCB187) at the early stage of development on the number of cell nuclei and microtubule-associated protein 2 (MAP2)-positive and tyrosine hydroxylase (TH)-positive areas during neuronal differentiation from hESC-derived spheres. The data are represented as mean ± standard deviation of at least three biological replicates. * <italic>p</italic> < 0.05, analysis of variance (ANOVA) test.</p></caption><graphic xlink:href=\"ijms-21-05564-g001\"/></fig><fig id=\"ijms-21-05564-f002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Transcriptome analysis of the effect of chemical exposure on gene expression in hESC-derived spheres. (<bold>A</bold>) Summary of the number and (<bold>B</bold>) functional annotation of differentially expressed genes in hESC-derived spheres after exposure to 1 μM TMD, 0.01 μM BDE47, 0.1 μM BPA, and 0.01 μM 4OH-PCB187. (<bold>C</bold>) Canonical pathway analysis of differentially expressed genes generated using the knowledge-based functional analysis software Ingenuity Pathways Analysis (IPA). (<bold>D</bold>) Expression of enriched genes involved in the synaptogenesis signaling pathway.</p></caption><graphic xlink:href=\"ijms-21-05564-g002\"/></fig><fig id=\"ijms-21-05564-f003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>TMD-specific effect on gene expression in hESC-derived spheres. (<bold>A</bold>) Comparison of differentially expressed genes in hESC-derived spheres upon exposure to 1 μM TMD, 0.01 μM BDE47, 0.1 μM BPA, or 0.01 μM 4OH-PCB187. The red number indicates TMD-specific genes. (<bold>B</bold>) The biological functions of the top populated networks generated in IPA associated with differentially expressed genes specifically induced by TMD exposure. (<bold>C</bold>) A representative network related to neurogenesis entitled “Neurological Disease, Organismal Injury and Abnormalities, Cell Morphology”. Upregulated genes under control of TMD are indicated in red symbols, downregulated genes under control of TMD indicated in green symbols, and genes that were not annotated in this study, but are part of this network were indicated in white symbols. Solid lines indicate direct relationships, and dotted lines indicate indirect gene-gene relationships within the represented network. (<bold>D</bold>) The biological pathways of embryonic development generated in IPA were ranked by z-score, which can be used to find the likely regulating molecules based on a statistically significant pattern match of up- and down-regulation, and also to predict the activation state (activated or inhibited) of a putative regulator. (<bold>E</bold>) Expression of enriched genes involved in the “Differentiation of embryonic cells” pathway.</p></caption><graphic xlink:href=\"ijms-21-05564-g003\"/></fig><fig id=\"ijms-21-05564-f004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Integrated network analysis of transcriptomic and morphological changes during neuronal differentiation from hESC-derived spheres. (<bold>A</bold>) Pearson correlation analysis. The links with an absolute Pearson correlation coefficient of more than 0.7 are shown. The network was visualized using Gephi software. (<bold>B</bold>) Bayesian network analysis. The network was generated based on the TAO-Gen algorithm using the web-based RX-Taogen software and was visualized using Gephi software. The β-value of the Bayesian model is expressed as a red arrow if positively relative (β > 1), and a blue arrow if negatively relative (β < 1). (<bold>C</bold>) Physiological network generated using the Ingenuity Knowledge Base of the IPA software. Upregulated genes induced by TMD exposure are indicated in red symbols that are representing molecules. The solid line indicates direct interactions gene-gene association. Dot lines indicate indirect interactions to “Neurogenesis” or “Differentiation of embryonic stem cells”.</p></caption><graphic xlink:href=\"ijms-21-05564-g004\"/></fig></floats-group></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Int J Mol Sci</journal-id><journal-id journal-id-type=\"publisher-id\">ijms</journal-id><journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title></journal-title-group><issn pub-type=\"epub\">1422-0067</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32751556</article-id><article-id pub-id-type=\"pmc\">PMC7432055</article-id><article-id pub-id-type=\"doi\">10.3390/ijms21155432</article-id><article-id pub-id-type=\"publisher-id\">ijms-21-05432</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Review</subject></subj-group></article-categories><title-group><article-title>Extracellular Vesicles-Based Drug Delivery Systems: A New Challenge and the Exemplum of Malignant Pleural Mesothelioma</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-9497-5612</contrib-id><name><surname>Burgio</surname><given-names>Stefano</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05432\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Noori</surname><given-names>Leila</given-names></name><xref ref-type=\"aff\" rid=\"af2-ijms-21-05432\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0002-1683-3803</contrib-id><name><surname>Marino Gammazza</surname><given-names>Antonella</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05432\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Campanella</surname><given-names>Claudia</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05432\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Logozzi</surname><given-names>Mariantonia</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05432\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Fais</surname><given-names>Stefano</given-names></name><xref ref-type=\"aff\" rid=\"af3-ijms-21-05432\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Bucchieri</surname><given-names>Fabio</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05432\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Cappello</surname><given-names>Francesco</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05432\">1</xref><xref ref-type=\"aff\" rid=\"af4-ijms-21-05432\">4</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0001-8260-8471</contrib-id><name><surname>Caruso Bavisotto</surname><given-names>Celeste</given-names></name><xref ref-type=\"aff\" rid=\"af1-ijms-21-05432\">1</xref><xref ref-type=\"aff\" rid=\"af4-ijms-21-05432\">4</xref><xref rid=\"c1-ijms-21-05432\" ref-type=\"corresp\">*</xref></contrib></contrib-group><aff id=\"af1-ijms-21-05432\"><label>1</label>Department of Biomedicine, Neuroscience and Advanced Diagnostics (BIND), Section of Human Anatomy, University of Palermo, 90127 Palermo, Italy; <email>stefano.burgio94@gmail.com</email> (S.B.); <email>antonella.marinogammazza@unipa.it</email> (A.M.G.); <email>claudia.campanella@unipa.it</email> (C.C.); <email>fabio.bucchieri@unipa.it</email> (F.B.); <email>francesco.cappello@unipa.it</email> (F.C.)</aff><aff id=\"af2-ijms-21-05432\"><label>2</label>Department of Anatomy, School of Medicine, Tehran University of Medical Science, Tehran 141 765 3911, Iran; <email>leili.noori.1359@gmail.com</email></aff><aff id=\"af3-ijms-21-05432\"><label>3</label>Department of Oncology and Molecular Medicine, National Institute of Health, 00161 Rome, Italy; <email>mariantonia.logozzi@iss.it</email> (M.L.); <email>stefano.fais@iss.it</email> (S.F.)</aff><aff id=\"af4-ijms-21-05432\"><label>4</label>Euro-Mediterranean Institute of Science and Technology (IEMEST), 90139 Palermo, Italy</aff><author-notes><corresp id=\"c1-ijms-21-05432\"><label>*</label>Correspondence: <email>celestebavisotto@gmail.com</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>30</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>8</month><year>2020</year></pub-date><volume>21</volume><issue>15</issue><elocation-id>5432</elocation-id><history><date date-type=\"received\"><day>06</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>27</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>Research for the most selective drug delivery to tumors represents a fascinating key target in science. Alongside the artificial delivery systems identified in the last decades (e.g., liposomes), a family of natural extracellular vesicles (EVs) has gained increasing focus for their potential use in delivering anticancer compounds. EVs are released by all cell types to mediate cell-to-cell communication both at the paracrine and the systemic levels, suggesting a role for them as an ideal nano-delivery system. Malignant pleural mesothelioma (MPM) stands out among currently untreatable tumors, also due to the difficulties in achieving an early diagnosis. Thus, early diagnosis and treatment of MPM are both unmet clinical needs. This review looks at indirect and direct evidence that EVs may represent both a new tool for allowing an early diagnosis of MPM and a potential new delivery system for more efficient therapeutic strategies. Since MPM is a relatively rare malignant tumor and preclinical MPM models developed to date are very few and not reliable, this review will report data obtained in other tumor types, suggesting the potential use of EVs in mesothelioma patients as well.</p></abstract><kwd-group><kwd>extracellular vesicles</kwd><kwd>exosomes</kwd><kwd>drug delivery systems</kwd><kwd>malignant pleural mesothelioma</kwd></kwd-group></article-meta></front><body><sec sec-type=\"intro\" id=\"sec1-ijms-21-05432\"><title>1. Introduction</title><p>Cancer is a complex and multifactorial disease with both high incidence and mortality rates. Approximately 1 out of 6 deaths are caused by cancer, making it the second leading cause of death worldwide, with 9.6 million of deaths in 2018 [<xref rid=\"B1-ijms-21-05432\" ref-type=\"bibr\">1</xref>]. Moreover, lung-associated cancers, with respectively 2.09 million worldwide cases and 1.76 million deaths in 2018 hold the primacy in terms of tumor-related morbidity and mortality [<xref rid=\"B1-ijms-21-05432\" ref-type=\"bibr\">1</xref>]. In recent years, a new multidirectional approach has been adopted in oncology, by combining basic research aimed at enhancing our knowledge of the genetic and molecular characteristics that drive tumorigenesis and tumor growth with the attempt to develop new molecular targeting strategies for the treatment of pathology [<xref rid=\"B2-ijms-21-05432\" ref-type=\"bibr\">2</xref>].</p><p>Over the years, therapies targeting tumor pathologies have become increasingly pioneering, in an attempt to overcome the limits imposed by classic chemotherapies, such as high toxicity, poor specificity and, consequently, a plethora of side effects. Such an approach, which is increasingly gaining ground, mainly concerns new delivery models and new therapeutic strategies that must be as specific and selective as possible, taking full advantage of the genetic/biomolecular differences of cancer cells compared to healthy tissues. Although survival rates have increased in recent years, and promising clinical trials for targeted drug delivery are ongoing, current treatments for many cancers remain ineffective and require the development of improved delivery methods. Indeed, in this review, we highlight the current “target-therapy” strategies, focusing our attention on the strengths and weaknesses of each approach. Moreover, we will focus on a particular tumor, the malignant pleural mesothelioma (MPM). MPM is a tumor with a poor prognosis. The surgical approach has significantly high perioperative mortality and high recurrence rate. In addition, chemotherapy is frequently only a palliative treatment due to the onset of chemoresistance, characteristic of this tumor. We therefore suggest the exploitation of extracellular vesicles (EVs) for the active delivery of therapeutic molecules. In fact, the intrinsic molecular characteristics of EVs, such as the high editability and low immunogenicity, make them the most suitable candidate. Currently there are no accessory therapies for MPM to be implemented if the patient develops resistance or conventional therapies do not prove effective. Therefore, since MPM has a “chameleon-like” extracellular profile that does not exhibit specific markers to be exploited for selective targeting, it is difficult to diagnose promptly. In this context, the use of a “Trojan horse”, self-produced by the tumor itself as a paracrine communication mechanism, is a real possibility. In fact, we reiterate that none of the current therapies or the ongoing clinical trials succeeds in what is the “unmet medical need”, that is, of therapeutic strategies aimed at prolonging the overall survival of the affected population, whose current average is estimated between 4 and 18 months [<xref rid=\"B3-ijms-21-05432\" ref-type=\"bibr\">3</xref>].</p><p>The concept of “targeted therapy” was born due to the insights of Paul Ehrlich at the end of the 1800s, when he first theorized the use of a “magic bullet” [<xref rid=\"B4-ijms-21-05432\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijms-21-05432\" ref-type=\"bibr\">5</xref>]. Even though the idea of combining a cytotoxic molecule and a courier acting as a selective “bullet” was conceived for antimicrobial application purposes [<xref rid=\"B4-ijms-21-05432\" ref-type=\"bibr\">4</xref>,<xref rid=\"B5-ijms-21-05432\" ref-type=\"bibr\">5</xref>,<xref rid=\"B6-ijms-21-05432\" ref-type=\"bibr\">6</xref>,<xref rid=\"B7-ijms-21-05432\" ref-type=\"bibr\">7</xref>], over time it has increasingly become suitable in the cancer field.</p><p>The strategies adopted in recent years in order to accomplish the expectations on targeted therapy can be divided into two macro groups: on one hand, the first group involves the use of compounds or antibodies, which can interfere directly with the biology of the cancerous tissue or the tumor microenvironment; on the other, the second one concerns the developing of new drug delivery strategies, which can improve the biodistribution on the tumor site. However, together with the problems related to the best choice of a molecule to target in/on tumor cells, there are some issues that were and are unfortunately disregarded in thinking about future cancer therapies. The first and most important issue is related to the resistance of tumors to both chemical and biological treatments. Initially, this was thought to be due to a mechanism called multidrug resistance, through which a cancer cell is not responsive to most known drugs because of a group of proteins that are able to pump the drugs outside the cells; the most studied among these proteins was the P glycoprotein-1. This phenomenon of resistance of tumors to treatments has been described and characterized in several in vitro models. The experimentation in tumor patients is ongoing and it is aimed to improve the efficiency of anticancer drug delivery, overcoming the not responsiveness of tumor cells to conventional therapy [<xref rid=\"B8-ijms-21-05432\" ref-type=\"bibr\">8</xref>]. In patients with some forms of leukemia, the drug resistance has shown to be dependent on the membrane-to-cytoskeleton connection through a family of proteins called ezrin/radixin/moesin [<xref rid=\"B9-ijms-21-05432\" ref-type=\"bibr\">9</xref>]. This failure of scientific research seems to be due to the disregard of two important facts: (i) a chemical drug enters within a cell due to a pH gradient and (ii) all solid tumors live in an acidic microenvironment. In fact, being the vast majority of chemotherapeutics chemical drugs with an electric charge, and being virtually all weak bases, when they get to the acidic tumor microenvironment, they are protonated and therefore blocked outside the cells [<xref rid=\"B8-ijms-21-05432\" ref-type=\"bibr\">8</xref>]. To support this finding, both buffers, such as sodium bicarbonate, and antiacidic drugs could highly improve the antiproliferative effect of antitumor drugs [<xref rid=\"B8-ijms-21-05432\" ref-type=\"bibr\">8</xref>,<xref rid=\"B10-ijms-21-05432\" ref-type=\"bibr\">10</xref>]. Particularly, proton pump inhibitors have been shown to significantly improve the effectiveness of antitumor drugs of different natures, including classical chemotherapeutics [<xref rid=\"B11-ijms-21-05432\" ref-type=\"bibr\">11</xref>,<xref rid=\"B12-ijms-21-05432\" ref-type=\"bibr\">12</xref>], drugs targeting surrogate tumor molecules [<xref rid=\"B13-ijms-21-05432\" ref-type=\"bibr\">13</xref>] and small molecules as well [<xref rid=\"B14-ijms-21-05432\" ref-type=\"bibr\">14</xref>]. Some clinical trials have supported the preclinical evidence [<xref rid=\"B15-ijms-21-05432\" ref-type=\"bibr\">15</xref>,<xref rid=\"B16-ijms-21-05432\" ref-type=\"bibr\">16</xref>,<xref rid=\"B17-ijms-21-05432\" ref-type=\"bibr\">17</xref>], thus suggesting that this should represent at least one of the future anticancer strategies.</p><p>In this review, we provide the current state-of-the-art of strategies in cancer targeted therapy, with particular attention to EVs as new potential innovative nanocarriers with high targeting efficiency. We summarize the current knowledge on EVs’ biogenesis and features, believing in the importance of improving the understanding of their unique composition, in order to exploit them in clinical applications. Furthermore, we focus on the MPM whose management could be implemented by the application of EVs as a drug delivery strategy.</p></sec><sec id=\"sec2-ijms-21-05432\"><title>2. Strategies in Cancer Targeted Therapy</title><p>Patients’ response to treatments is related to the ability of the therapeutic system to be selective and to reach only the target tissue without affecting healthy sites. In order to improve response selectivity and to achieve a safer and more efficient systemic delivery, new technologies have been developed. These strategies, in particular in the oncological field, need to include a system able to release the active agent in a controlled manner and to target specific sites in the body, in order to realize an interface between the patient and the drug and then increase the effectiveness and reduce systemic effects.</p><p>Many new materials and approaches for drug delivery systems (DDS) are being developed. Among these, EVs appear to be promising candidates as nanocarriers over the conventional synthetic systems, considering their intrinsic features, including their tropism for specific organs or cells, their key role in intercellular communication and their non-toxicity. The concept of drug delivery is rooted in the early beginning of the 1950s. The first generation of DDS laid the groundwork of the controlled release of compounds, but it was from the second generation of systems (1980–2010) that researchers began to focus their attention on the setting up of smart DDS. Subsequently, from the third generation of DDS (from 2010), researchers tried to overcome both biological and physiochemical barriers, such as poor solubility of certain drugs, large molecular weight of protein-based drugs and the uncertain drug release in some kinds of formulations [<xref rid=\"B18-ijms-21-05432\" ref-type=\"bibr\">18</xref>]. </p><p>Therefore, in the last ten years precision medicine and nanotechnologies gave a strong contribution to the setting up of new delivery strategies, which may lead to a significant improvement of biodistribution and biocompatibility.</p><sec id=\"sec2dot1-ijms-21-05432\"><title>2.1. Nanoparticles</title><p>In the context of safe and effective therapeutic approaches, nanoparticles have received extensive interest as promising DDS for cancer treatment in recent years. Nanoparticles are the product of the applied research of nanotechnology, which is trying to cross both physical and biological barriers of the body in order to improve the drug delivery directly on the target site. In fact, when drugs are encapsulated in structures that are larger than five nanometers, this can easily prevent renal excretion, improving bioavailability [<xref rid=\"B19-ijms-21-05432\" ref-type=\"bibr\">19</xref>].</p><p>Generally, nanoparticles can be defined as round nanospheres, built from synthesized nanomaterials, ranging between 100 and 1000 nanometers [<xref rid=\"B20-ijms-21-05432\" ref-type=\"bibr\">20</xref>]. As they are nanosized, the uptake of nanostructures by cells is facilitated, allowing efficient drug delivery and ensuring target-specific action [<xref rid=\"B21-ijms-21-05432\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05432\" ref-type=\"bibr\">22</xref>]. A plethora of materials have been used in nanosized carriers for cancer therapy, including polymers, lipids, protein–drug conjugates, viral nanoparticles, inorganic molecules and metal nanoparticles [<xref rid=\"B23-ijms-21-05432\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05432\" ref-type=\"bibr\">24</xref>] (<xref rid=\"ijms-21-05432-t001\" ref-type=\"table\">Table 1</xref>). Nanoparticles offer great advantages in nanomedicine applications, both in the diagnostic and therapeutic fields. In fact, the use of nano-systems is applied in early diagnosis, through non-invasive imaging modalities, for instance, by the use of fluorescent or magnetic systems. In the therapeutic field, research is trying to exploit their capability to trap, protect and deliver drugs to the target site, due to highly specific binding and internalization capabilities. Furthermore, it is possible to synthesize nanoparticles that have the possibility to integrate diagnostic and therapeutic entities within a single formulation, thus obtaining a single system capable of simultaneously satisfying the criteria of theranostic approaches and providing real-time feedback on pharmacokinetics, including the drugs’ target site localization and the off-target accumulation. These features are achieved due to both the co-loading of nanoparticles with drugs and with contrast agents, and the intrinsic ability of nanomaterials, such as gold and iron oxide-based nanoparticles, to be suitable to be used for the imaging [<xref rid=\"B25-ijms-21-05432\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijms-21-05432\" ref-type=\"bibr\">26</xref>].</p><p>However, it is important to underline that the use of some materials can be potentially risky for patients’ health [<xref rid=\"B27-ijms-21-05432\" ref-type=\"bibr\">27</xref>]. In fact, carbon nanotubes, metal-based nanoparticles or even polymeric nanoparticles have been widely investigated and used for possible clinical applications. Despite the versatility of these therapeutic nanoscale agents used for the synthesis of nanomedicines, there are potential adverse side effects induced by the materials used [<xref rid=\"B27-ijms-21-05432\" ref-type=\"bibr\">27</xref>]. In this regard, “green chemistry” approaches are increasingly considered in order to identify biocompatible/biodegradable nanomaterials capable of eliminating the toxicological impact and potential side effects deriving from some materials used, such as carbon nanotubes, metal nanoparticles or polymeric nanoparticles [<xref rid=\"B27-ijms-21-05432\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05432\" ref-type=\"bibr\">28</xref>] (<xref rid=\"ijms-21-05432-t001\" ref-type=\"table\">Table 1</xref>).</p><sec id=\"sec2dot1dot1-ijms-21-05432\"><title>2.1.1. Liposomes</title><p>Liposomes are lipid-based nanoparticles and were the nanoparticles used in nanomedicine [<xref rid=\"B29-ijms-21-05432\" ref-type=\"bibr\">29</xref>]. They consist of a single or multiple lipid bilayer that is engineered to encapsulate, in the lipid bilayer or in the internal aqueous phase, hydrophobic or hydrophilic drugs or small molecules, depending on their polarity features [<xref rid=\"B30-ijms-21-05432\" ref-type=\"bibr\">30</xref>]. Liposomes are the most investigated nanocarriers for targeted drug delivery, because of their morphological similarity with cellular membranes, allowing biocompatibility and minimal toxicity [<xref rid=\"B31-ijms-21-05432\" ref-type=\"bibr\">31</xref>]. Furthermore, due to the possibility to modify their lipid bilayer characteristics and to build a large aqueous center, these nanoparticles permit to deliver a wide variety of macromolecules [<xref rid=\"B32-ijms-21-05432\" ref-type=\"bibr\">32</xref>]. There are different classes of liposomes used as drug delivery systems, which are classified on the basis of size and number of layers; based on the composition, and on the method of preparation [<xref rid=\"B33-ijms-21-05432\" ref-type=\"bibr\">33</xref>]. Moreover, in the last few years new liposome formulations have emerged, which are stimulus-sensitive and offer a more efficient drug release [<xref rid=\"B34-ijms-21-05432\" ref-type=\"bibr\">34</xref>]. In a wide range of pathologies, liposomes seem to be effective as a target-therapy approach; indeed, it has been observed that they are able to improve and control pharmacokinetics and pharmacodynamics, to enhance drug activity, and are target selective. In cancer therapy, the use of liposomes offers several advantages, including efficient internalization by cancer cells that exploits the passive targeting effect, a phenomenon known as enhanced permeability and retention [<xref rid=\"B35-ijms-21-05432\" ref-type=\"bibr\">35</xref>]. The in vitro and in vivo delivery efficacy of different liposomes formulations has been extensively studied in anticancer therapy and several data demonstrated low systemic toxicity and the capability to suppress tumor growth [<xref rid=\"B36-ijms-21-05432\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijms-21-05432\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijms-21-05432\" ref-type=\"bibr\">38</xref>,<xref rid=\"B39-ijms-21-05432\" ref-type=\"bibr\">39</xref>].</p><p>Despite the potential use of liposomes in the area of drug delivery, clinical applications have so far been negligible. In fact, there are some disadvantages limiting the development of liposome-based therapies, such as sensitivity to sterilization methods [<xref rid=\"B40-ijms-21-05432\" ref-type=\"bibr\">40</xref>], stability issues [<xref rid=\"B41-ijms-21-05432\" ref-type=\"bibr\">41</xref>], reproducibility in drug encapsulation, particle size control [<xref rid=\"B42-ijms-21-05432\" ref-type=\"bibr\">42</xref>], short shelf-life and stability in blood circulation [<xref rid=\"B43-ijms-21-05432\" ref-type=\"bibr\">43</xref>] (<xref rid=\"ijms-21-05432-t001\" ref-type=\"table\">Table 1</xref>).</p></sec><sec id=\"sec2dot1dot2-ijms-21-05432\"><title>2.1.2. Polymer-Conjugated Drugs for Selective Delivery</title><p>Ringsdorf properly described the concept of polymers and biopolymers-conjugated drugs in 1975 [<xref rid=\"B44-ijms-21-05432\" ref-type=\"bibr\">44</xref>]. Water-soluble polymers are an efficient drug delivery system that can highly increase the half-life of compounds, significantly improve the general tolerance to high doses of drugs (such as chemotherapeutic drugs) and implement specificity. The first example reported in the literature of polymer-conjugated drugs is the dextran-methotrexate conjugate [<xref rid=\"B45-ijms-21-05432\" ref-type=\"bibr\">45</xref>]. Kidney excretion of the chemotherapeutic drug was prevented, improving also the drug’s circulation in the blood stream. </p><p>There are plenty of examples of peptides used as drug delivery vectors, which show promising results. Among these, the multiple antigen peptides (MAP) are one of them as they have both the specificity of an antibody and the versatility of a peptide. The branched structure of MAP peptides was originally designed to be used in the vaccination field [<xref rid=\"B46-ijms-21-05432\" ref-type=\"bibr\">46</xref>,<xref rid=\"B47-ijms-21-05432\" ref-type=\"bibr\">47</xref>]. However, in recent years the multimeric bond has been further analyzed in order to implement the avidity of the molecule. In fact, despite having a short amino acidic sequence and being in a branched form, it still was not able to trigger an effective immune response; the same cannot be said for the property of these molecules in terms of avidity and specificity. Therefore, if supported by a solid affinity and interaction study, target-peptide could become a reliable drug delivery model.</p><p>In this regard, an example could be the NT4, a tetra-branched form of neurotensin, which in the MAP form is correlated to a high affinity for the sulphate groups present in the glycosaminoglycans (GAGs) chains, highly expressed in most solid tumors [<xref rid=\"B48-ijms-21-05432\" ref-type=\"bibr\">48</xref>,<xref rid=\"B49-ijms-21-05432\" ref-type=\"bibr\">49</xref>,<xref rid=\"B50-ijms-21-05432\" ref-type=\"bibr\">50</xref>].</p><p>It is possible to combine different chemotherapeutic drugs to the tetra-branched core of NT4, exponentially increasing their selectivity [<xref rid=\"B48-ijms-21-05432\" ref-type=\"bibr\">48</xref>,<xref rid=\"B51-ijms-21-05432\" ref-type=\"bibr\">51</xref>], as drug delivery to the target is strictly related to the carrier and with how much specificity it binds to its receptor (<xref rid=\"ijms-21-05432-t001\" ref-type=\"table\">Table 1</xref>).</p><p>Progress achieved in recent years has made it possible to better implement and structure the NT4-based delivery system. In fact, the tetra branched peptide conjugated with the chemotherapy compound paclitaxel (PTX) was compared in an in vitro study with the PTX counterpart in the unconjugated form, demonstrating how the NT4-PTX complex can induce tumor regression, while the free PTX just led to a slowing down of tumor proliferation [<xref rid=\"B51-ijms-21-05432\" ref-type=\"bibr\">51</xref>]. The delivery system was then refined and implemented, conjugating the NT4 peptide with NIR-emitting quantum dots nanoparticles (QD). The two tested formulations, NT4-QD and NT4-QD-PTX, have demonstrated effectiveness in vitro both in diagnosis/identification of the tumor itself and in theranostics on HT-29 cells, showing a higher cytotoxicity compared to the QD-PTX control-formulation used [<xref rid=\"B52-ijms-21-05432\" ref-type=\"bibr\">52</xref>].</p><p>There are many possible applications of MAP peptides, including practical applications in the diagnosis of solid tumors [<xref rid=\"B53-ijms-21-05432\" ref-type=\"bibr\">53</xref>]. However, practical applications are not limited to the oncological field: the properties of the multimeric bond are in fact provided by a huge variety of clinical contexts, such as in the implementation of the half-life of antibody fragments (Fab) injected intravitreally [<xref rid=\"B54-ijms-21-05432\" ref-type=\"bibr\">54</xref>].</p><p>In this regard, the research group of Whitney Shatz et al., starting from a polyethylene glycol structure (PEG) backbone, synthetized an octameric Fab-PEG structure, capable of being administered intravitreally in order to treat age-related blindness pathologies. [<xref rid=\"B54-ijms-21-05432\" ref-type=\"bibr\">54</xref>].</p></sec></sec><sec id=\"sec2dot2-ijms-21-05432\"><title>2.2. Small Molecules, Peptides and Antibody in Targeted Therapy</title><p>Small molecules are compounds whose molecular weight is generally <900 Daltons. Due to their small size, they can easily diffuse inside the cells, bind specific targets and expound their therapeutic function [<xref rid=\"B55-ijms-21-05432\" ref-type=\"bibr\">55</xref>]. Small molecules in cancer carry out their function in different ways, such as intruding on the cell cycle, triggering apoptotic signals, and interfering with key enzymes, crucial for cell metabolism and slowing down tumor invasion and metastasis. Those molecules can also interfere with the tumor microenvironment, blocking the angiogenesis, boosting or regulating the immune system [<xref rid=\"B56-ijms-21-05432\" ref-type=\"bibr\">56</xref>,<xref rid=\"B57-ijms-21-05432\" ref-type=\"bibr\">57</xref>].</p><p>An analogue function is absolved by the monoclonal antibodies (mABs), largely used in clinics for a great variety of tumoral conditions. Those antibodies are developed starting from the hybridoma technology [<xref rid=\"B58-ijms-21-05432\" ref-type=\"bibr\">58</xref>] and have been used in clinical trials since the 1980s [<xref rid=\"B59-ijms-21-05432\" ref-type=\"bibr\">59</xref>]. MABs are usually produced for extracellular targets, and can fulfill their therapeutic function in both a direct and an indirect way. In the first case, the antibody is produced to recognize specific structures exposed on the tumor extracellular membrane, such as growth factor receptors or membrane bound proteins [<xref rid=\"B60-ijms-21-05432\" ref-type=\"bibr\">60</xref>].</p><p>In the second case, mAB can be useful tools for the handling of tumor microenvironment, i.e., interrupting the interactions between ligands and their receptors (EGF, endothelial growth factor; vascular endothelial growth factor, VEGF; etc.), or promoting the recruitment of the immune system, in order to facilitate a direct and specific attack to cancerous cells.</p><p>Antibody-based therapy has deep roots in the history of biotechnologies. In fact, starting from serotherapy institutes (such as The Institute of Serotherapy and Vaccination of Tuscany), where active immunization against specific antigens was induced in large animals, such as horses, in order to use their plasma as a medical tool, subsequently we finally reached a more sophisticated biotechnological product [<xref rid=\"B4-ijms-21-05432\" ref-type=\"bibr\">4</xref>,<xref rid=\"B61-ijms-21-05432\" ref-type=\"bibr\">61</xref>].</p><p>The production of monoclonal antibodies originated in the early 1970s. Due to the two researchers Georges Kohler and Ceasar Milstein, the massive production of mABs was assessed using the hybridoma technology. The technology developed by the two researchers was never patented and it might be considered as a milestone in the large-scale production of mABs for therapeutic purposes. Therefore, due to the hybridoma technology, it was possible to produce and market the first murine mAB in 1986, “muromonab-CD3”, a full mouse mAB used to prevent kidney transplant rejection [<xref rid=\"B62-ijms-21-05432\" ref-type=\"bibr\">62</xref>,<xref rid=\"B63-ijms-21-05432\" ref-type=\"bibr\">63</xref>,<xref rid=\"B64-ijms-21-05432\" ref-type=\"bibr\">64</xref>].</p><p>However, the clinical use of full murine antibodies has several limitations. The murine structure of the antibody might be recognized as “not self” from the patient, which may lead to a consistent immune reaction against the injected mAB.</p><p>Technological evolution and knowledge acquired in the field of genetic engineering have subsequently allowed one to create antibodies made of both murine genetic sequences and human genetic inserts, in a ratio of 33%–66%. Essentially these antibodies, which were defined as “chimeric”, presented a human Fc fragment, and a murine Fab. Antibodies thus constituted were less immunogenic than the murine counterpart was. The first chimeric antibody with these characteristics was approved for antithrombotic therapy in 1994, with the commercial name of Reopro<sup>®</sup> (Abciximab) [<xref rid=\"B4-ijms-21-05432\" ref-type=\"bibr\">4</xref>,<xref rid=\"B64-ijms-21-05432\" ref-type=\"bibr\">64</xref>].</p><p>With the refinement of technology, it was possible to obtain chimeric antibodies containing less and less murine DNA, up to the so-called humanized antibodies consisting of 90–95% of human DNA sequences. In this typology of antibodies, the murine regions were located mostly in correspondence of the complementary-determining regions (CDRs). The first humanized antibody approved by the FDA in 1997, to prevent rejection following kidney transplants, was Zenapax<sup>®</sup> (Daclizumab) [<xref rid=\"B4-ijms-21-05432\" ref-type=\"bibr\">4</xref>,<xref rid=\"B64-ijms-21-05432\" ref-type=\"bibr\">64</xref>].</p><p>Another important step forward in the production of mAB has been made through the introduction of the phage display technique. This technique allows the study of protein–protein interactions, starting from the genetic sequence of interest inserted within a viral vector, generally the bacteriophage M13. This protein of interest is exposed on the outer shape of the phage, preserving the genetic sequence of the interest inside. These phages are then subjected to interaction screening with the target protein, making possible large-scale affinity screening starting from genetic libraries, consisting of variants of the gene of interest [<xref rid=\"B4-ijms-21-05432\" ref-type=\"bibr\">4</xref>,<xref rid=\"B64-ijms-21-05432\" ref-type=\"bibr\">64</xref>,<xref rid=\"B65-ijms-21-05432\" ref-type=\"bibr\">65</xref>].</p><p>Later in 2006, due to the phage display technology, it was possible to produce and market the first completely human monoclonal antibody, Humira<sup>®</sup> (Adalimumab). The fully human antibody structure (100% compared to 90–95% of humanized ones) takes a step forward in the field of immunological therapy: murine CDR of humanized antibodies has been replaced by fully human CDRs, so that Adalimumab effectively improved compatibility and avoided structure-related immunogenicity.</p><p>The applications of mAB are various and can be used as powerful drug-carriers. Antibody-drug conjugates (ADC) are interesting tools in precision medicine, in which the mAB is covalently linked with a cytotoxic compound in order to be driven directly on the tumor site [<xref rid=\"B60-ijms-21-05432\" ref-type=\"bibr\">60</xref>,<xref rid=\"B66-ijms-21-05432\" ref-type=\"bibr\">66</xref>,<xref rid=\"B67-ijms-21-05432\" ref-type=\"bibr\">67</xref>,<xref rid=\"B68-ijms-21-05432\" ref-type=\"bibr\">68</xref>]. Of course, the above issue of acidity-mediated drug resistance still stands in the case of more targeted or smaller molecules, but for biological compounds as well, inasmuch as it appears highly conceivable that the acidic microenvironment may well hamper the affinity of a mAB for its specific epitope. Moreover, recent evidence has shown that microenvironmental pH markedly changes the lipid composition of tumor cells, thus hampering the hypothetical binding between a ligand and its receptor [<xref rid=\"B69-ijms-21-05432\" ref-type=\"bibr\">69</xref>] (<xref rid=\"ijms-21-05432-t001\" ref-type=\"table\">Table 1</xref>).</p></sec><sec id=\"sec2dot3-ijms-21-05432\"><title>2.3. ADC</title><p>The therapeutic potential of antibodies has been deepened between the late 1800s and the early 1900s, when horses and oxen were exposed to an antigen in order to produce specific antibodies to it.</p><p>Despite the remarkable risk of rejection that the administration of animal immunoglobulins entailed, it is undeniable that such a practice has helped to encourage future studies and biotechnological innovations related to the engineering of antibodies for therapeutic purposes. Additionally, in the oncological field, the therapeutic applications of antibodies are several, as they can act as single entities, conjugated with a drug or an enzyme that in turn is capable of activating a drug, administered systemically [<xref rid=\"B4-ijms-21-05432\" ref-type=\"bibr\">4</xref>]. There are several examples of antibodies that work as single entities, targeting a specific aberrant or overexpressed receptor component in neoplasms. An example may be Herceptin<sup>®</sup>, a humanized mAB for the treatment of metastatic breast cancer: it exerts its function binding the tyrosine-kinase receptor HER-2 present on the cell membrane.</p><p>Another example could be AVASTIN<sup>®</sup> (Bevacizumab), a mAB aimed at the VEGF-A, proangiogenic growth factor. In combination with chemotherapy, it is approved for the treatment of advanced colorectal cancer, advanced non-small cell lung cancer and metastatic breast cancer [<xref rid=\"B70-ijms-21-05432\" ref-type=\"bibr\">70</xref>,<xref rid=\"B71-ijms-21-05432\" ref-type=\"bibr\">71</xref>,<xref rid=\"B72-ijms-21-05432\" ref-type=\"bibr\">72</xref>].</p><p>Besides working as independent entities, several mABs exert their function conjugated with chemotherapeutic drugs or with radioactive ligands [<xref rid=\"B73-ijms-21-05432\" ref-type=\"bibr\">73</xref>,<xref rid=\"B74-ijms-21-05432\" ref-type=\"bibr\">74</xref>]. Indeed, numerous antibody-drug conjugates have been developed for the selective delivery of chemotherapeutic agents, and many of them have been tested in clinical trials; however, for some of them there was no significant improvement of the patient’s outcomes, compared to classical therapies [<xref rid=\"B75-ijms-21-05432\" ref-type=\"bibr\">75</xref>,<xref rid=\"B76-ijms-21-05432\" ref-type=\"bibr\">76</xref>,<xref rid=\"B77-ijms-21-05432\" ref-type=\"bibr\">77</xref>,<xref rid=\"B78-ijms-21-05432\" ref-type=\"bibr\">78</xref>] (<xref rid=\"ijms-21-05432-t001\" ref-type=\"table\">Table 1</xref>).</p></sec><sec id=\"sec2dot4-ijms-21-05432\"><title>2.4. Reconfigurable Organisms as Drug Carriers</title><p>A new promising study driven by the team of S. Kriegman et al. [<xref rid=\"B79-ijms-21-05432\" ref-type=\"bibr\">79</xref>] opens up to a new way of conceiving nanomachines and, by extension, drug delivery. The research group focused its studies on a completely innovative model, which, starting from different biological tissues originating from <italic>Xenopus laevis</italic> embryos, gives rise to biological machines from scratch. Using compiler algorithms, 3D structures have been designed by the computer whose “blocks” are constituted by the epithelial and cardiac cells of the frog embryo. The most promising models are thus selected and used through the help of high precision manipulators, following the model path suggested by the computer. These “Xenobots”, thus defined by the research group, have several interesting peculiarities, including the ability to automatically self-repair, preserving their own integrity. The high motility is conferred by the cardiomyocytes content because the embedded cardiac cells make the structure self-propelled and establish a predictable path a priori. These structures, although still being in the early stages of research, have enormous potential, especially in the “drug delivery”, as they allow following a pre-established path and managing a load within the core [<xref rid=\"B79-ijms-21-05432\" ref-type=\"bibr\">79</xref>] (<xref rid=\"ijms-21-05432-t001\" ref-type=\"table\">Table 1</xref>).</p></sec></sec><sec id=\"sec3-ijms-21-05432\"><title>3. EVs</title><p>EVs have recently entered solid tumor research, regarding the new possible targeted drug delivery systems, offering considerable advantages due to their intrinsic characteristics. Given this, and as the EVs are the object of our study, we devote a separate section for their discussion in this review.</p><p>EVs are lipid membrane vesicles actively secreted by all human cells and are involved in a plethora of cell-to-cell communication processes, both in pathological and physiological conditions. Although they were thought to be “garbage disposals” that eliminate unwanted cellular components, such as misfolded proteins or metabolic wastes [<xref rid=\"B80-ijms-21-05432\" ref-type=\"bibr\">80</xref>,<xref rid=\"B81-ijms-21-05432\" ref-type=\"bibr\">81</xref>], decades of research have defined their pivotal role in coagulation, cell-to-cell communication (both paracrine and systemic levels), vascular injuries and, more in general, in cellular maintenance of homeostasis [<xref rid=\"B82-ijms-21-05432\" ref-type=\"bibr\">82</xref>].</p><p>EVs are actively secreted from all cell types, including cancer cells, and are highly heterogeneous in size, lipid layer composition and cargo (such as nucleic acids, proteins and lipids). In addition, EVs are classified according to their size: (1) microvesicles (100–1000 nm in diameter); (2) apoptotic blebs (1000–5000 nm in diameter) and (3) exosomes (diameter 20–150 nm). On one hand, the first two subclasses: they are a wide and heterogeneous population of vesicles originated from the outward budding of the cell membrane. On the other side, exosomes follow a different path, originating from the invagination of endosomal membranes. This invagination gives rise to multivesicular bodies defined by the presence of intraluminal vesicles (ILVs). The endosomal sorting complex required for transport (ESCRT) complex family mediates the inward folding of the membrane of the early endosomes, resulting in ILVs formation into the lumen [<xref rid=\"B83-ijms-21-05432\" ref-type=\"bibr\">83</xref>,<xref rid=\"B84-ijms-21-05432\" ref-type=\"bibr\">84</xref>,<xref rid=\"B85-ijms-21-05432\" ref-type=\"bibr\">85</xref>,<xref rid=\"B86-ijms-21-05432\" ref-type=\"bibr\">86</xref>]. The ESCRT is composed of four distinct proteins (ESCRT-0, -I, -II and -III), that accurately regulate biogenesis and cargo loading into the exosomes [<xref rid=\"B87-ijms-21-05432\" ref-type=\"bibr\">87</xref>]. It is hypothesized that different structures for sorting molecules from cytoplasm toward exosomes may depend on their source cell. Additionally, the function of the cells can be understood via their released EV content [<xref rid=\"B88-ijms-21-05432\" ref-type=\"bibr\">88</xref>].</p><p>Due to the heterogeneity existing within the EVs family, where boundaries between the various groups are often not easily distinguishable, a distinction is still problematic since the characteristics of these membranous vesicles overlap with each other, so in this review, we refer to EVs and exosomes commonly.</p><p>However, a specific type of EVs might be more represented rather than others, depending on the isolation method used. The EVs isolation methods development represents one of the greatest challenges in the case of the exploitation of EVs as therapeutic tools. Owing to the fact that the isolation protocol should achieve high efficiency, high purity and reproducibility. Among them, the gold standard in EVs isolation methods is represented by the differential ultracentrifugation, based on isolation by size. Over the years alternative methods have been developed, based on the size and the hydrodynamic radius, such as ultrafiltration, hydrostatic dialysis and gel filtration. Other approaches are precipitation methods, which exploit the variation of EVs solubility and/or their aggregation when placed in polymeric solutions, and lastly, methods utilizing affinity interactions, such as immunoaffinity techniques. Further information on technical issues on the isolation methods of EVs can be found in recent papers [<xref rid=\"B89-ijms-21-05432\" ref-type=\"bibr\">89</xref>,<xref rid=\"B90-ijms-21-05432\" ref-type=\"bibr\">90</xref>,<xref rid=\"B91-ijms-21-05432\" ref-type=\"bibr\">91</xref>,<xref rid=\"B92-ijms-21-05432\" ref-type=\"bibr\">92</xref>].</p><p>Each of these methods has its own advantages and weakness and the choice should be based on the sample type from which isolate EVs and depending on the final application. Nevertheless, due to the complexity of biological fluids from which EVs are being isolated, all the methods developed allow one to obtain heterogeneous mixtures of EVs and other extracellular space components. One solution may be the use of multiple isolation methods consecutively, in order to enrich a particular EVs population. Since the EV isolation efficiency is dependent on the nature of biological fluids, it is most important to standardize a particular method for isolation of EVs, taking into account specific characteristics of the sample, such as viscosity, typical of blood plasma and serum and, the presence of specific proteins, e.g., THP in the urine. Besides, the characterization of the obtained EV preparations, through the combination of different methods, such as electron microscopy, light scattering, flow cytometry and immunohistochemical analysis for the markers specific, is recommended to confirm the EV morphology, physical features and biochemical composition [<xref rid=\"B83-ijms-21-05432\" ref-type=\"bibr\">83</xref>].</p><p>Exosomes are the main type studied among the family of EVs. They mediate the cell communication, and can be found in a large spectra of body fluids, such as blood, seminal fluid, urine, saliva, breast milk, ascitic fluid, bronchoalveolar lavage, malignant effusions, cerebrospinal fluid and amniotic fluid [<xref rid=\"B88-ijms-21-05432\" ref-type=\"bibr\">88</xref>,<xref rid=\"B93-ijms-21-05432\" ref-type=\"bibr\">93</xref>,<xref rid=\"B94-ijms-21-05432\" ref-type=\"bibr\">94</xref>].</p><p>The intravesicular cargo may variate depending on the cell type that secretes them and according to their specific function on the recipient cell, such as cytokines [<xref rid=\"B95-ijms-21-05432\" ref-type=\"bibr\">95</xref>], hormones [<xref rid=\"B96-ijms-21-05432\" ref-type=\"bibr\">96</xref>], transcription factors, growth factors [<xref rid=\"B97-ijms-21-05432\" ref-type=\"bibr\">97</xref>] and heat shock proteins [<xref rid=\"B98-ijms-21-05432\" ref-type=\"bibr\">98</xref>,<xref rid=\"B99-ijms-21-05432\" ref-type=\"bibr\">99</xref>,<xref rid=\"B100-ijms-21-05432\" ref-type=\"bibr\">100</xref>,<xref rid=\"B101-ijms-21-05432\" ref-type=\"bibr\">101</xref>,<xref rid=\"B102-ijms-21-05432\" ref-type=\"bibr\">102</xref>,<xref rid=\"B103-ijms-21-05432\" ref-type=\"bibr\">103</xref>,<xref rid=\"B104-ijms-21-05432\" ref-type=\"bibr\">104</xref>,<xref rid=\"B105-ijms-21-05432\" ref-type=\"bibr\">105</xref>,<xref rid=\"B106-ijms-21-05432\" ref-type=\"bibr\">106</xref>,<xref rid=\"B107-ijms-21-05432\" ref-type=\"bibr\">107</xref>]. Moreover, there is a large number of proteins that compose the structure of exosomes, which are directly involved in cell trafficking and their specificity: exosomes are enriched in proteins involved in the vesicles’ trafficking, cell surface receptors such as tumor susceptibility gene 101 (TSG101); integrin and a number of tetraspanins such as CD9, CD53, CD63, CD81 and CD82 [<xref rid=\"B108-ijms-21-05432\" ref-type=\"bibr\">108</xref>,<xref rid=\"B109-ijms-21-05432\" ref-type=\"bibr\">109</xref>]. Tetraspanin proteins are a functional ubiquitous region in the endosomal membrane, which assist in sorting of the cytosolic component into ILVs [<xref rid=\"B110-ijms-21-05432\" ref-type=\"bibr\">110</xref>].</p><p>The release of EVs by tumor cells is believed to play a major role in intercellular communication, facilitating signaling to surrounding tumor cells and to distant sites via blood or other biological fluid transportation. Indeed, EVs cancerous cells can communicate with other tumor cells, with fibroblasts that surround the tumor, with endothelial cells and inflammatory cells, such as monocytes or T-cells [<xref rid=\"B83-ijms-21-05432\" ref-type=\"bibr\">83</xref>].</p><p>As concerns fibroblasts, under tumorigenic conditions, they can be induced to change their morphological features, promoting a myofibroblast-like phenotype, more motile than the normal fibroblasts that surround the tumoral stroma. It has been proved that through the active secretion of EVs, tumoral cells can drive the polarization of normal fibroblasts into activated cancer-associated fibroblasts [<xref rid=\"B111-ijms-21-05432\" ref-type=\"bibr\">111</xref>,<xref rid=\"B112-ijms-21-05432\" ref-type=\"bibr\">112</xref>,<xref rid=\"B113-ijms-21-05432\" ref-type=\"bibr\">113</xref>,<xref rid=\"B114-ijms-21-05432\" ref-type=\"bibr\">114</xref>].</p><p>Strong evidence also underlines the EVs’ involvement in the angiogenesis in a plethora of tumor types [<xref rid=\"B111-ijms-21-05432\" ref-type=\"bibr\">111</xref>,<xref rid=\"B115-ijms-21-05432\" ref-type=\"bibr\">115</xref>,<xref rid=\"B116-ijms-21-05432\" ref-type=\"bibr\">116</xref>,<xref rid=\"B117-ijms-21-05432\" ref-type=\"bibr\">117</xref>,<xref rid=\"B118-ijms-21-05432\" ref-type=\"bibr\">118</xref>]. The angiogenic activity is flanked by a pivotal role in the promotion of cell migration and metastasis in many tumor types [<xref rid=\"B115-ijms-21-05432\" ref-type=\"bibr\">115</xref>], and the main mediators of this activity seem to be miR-9, miR-105, miR-142-3p, miR-210, miR-19a and H19 lncRNA [<xref rid=\"B100-ijms-21-05432\" ref-type=\"bibr\">100</xref>,<xref rid=\"B119-ijms-21-05432\" ref-type=\"bibr\">119</xref>,<xref rid=\"B120-ijms-21-05432\" ref-type=\"bibr\">120</xref>,<xref rid=\"B121-ijms-21-05432\" ref-type=\"bibr\">121</xref>,<xref rid=\"B122-ijms-21-05432\" ref-type=\"bibr\">122</xref>,<xref rid=\"B123-ijms-21-05432\" ref-type=\"bibr\">123</xref>].</p><p>For what may concern the immunomodulation, the literature has plenty of discordant results, which reflect the heterogeneity of EVs and their cell-specific function. Zhou M. and his research group show how pancreatic derived EVs mediated the expression of Toll-like receptor 4 (TLR4) in dendritic cells, promoting an antitumor immune response [<xref rid=\"B86-ijms-21-05432\" ref-type=\"bibr\">86</xref>]. This strong evidence, taking in account the presence of major histocompatibility complexes on the surface of EVs that are also able to display cancer-derived peptides, suggested the possibility to develop an EV-based anticancer vaccine [<xref rid=\"B124-ijms-21-05432\" ref-type=\"bibr\">124</xref>,<xref rid=\"B125-ijms-21-05432\" ref-type=\"bibr\">125</xref>].</p><p>Despite those promising results, other evidence underlines how EVs have an inhibitory effect on immunomodulatory cells, especially on macrophages and dendritic cells [<xref rid=\"B126-ijms-21-05432\" ref-type=\"bibr\">126</xref>,<xref rid=\"B127-ijms-21-05432\" ref-type=\"bibr\">127</xref>].</p><p>Considering all the above, the possible therapeutic use of EVs in clinics becomes clear. </p><p>Exosomes might be used as possible therapeutic targets in cancer, due to their confirmed role in tumorigenic progression, cancer cell communication and metastasis. In triple negative breast cancer (TNBC), for example, it has been proved that exosomes are involved in both the promotion of drug resistance and in the transferring of phenotypic traits from a progenitor cancer cell to another cell, thusly promoting cancer progression and metastasis [<xref rid=\"B115-ijms-21-05432\" ref-type=\"bibr\">115</xref>]. The inhibition of exosome production in TNBC has been deepened through the blocking of ESCRT-independent pathways, or silencing the Rab27, which mediate another exosome-production pathway: in both cases, experimental evidence shows a slowing down of cell proliferation rate and reduction of local growth and metastasis [<xref rid=\"B128-ijms-21-05432\" ref-type=\"bibr\">128</xref>,<xref rid=\"B129-ijms-21-05432\" ref-type=\"bibr\">129</xref>].</p><p>Nevertheless, mostly and coherently with the aim of this review, exosomes might be a great potential drug delivery system [<xref rid=\"B130-ijms-21-05432\" ref-type=\"bibr\">130</xref>,<xref rid=\"B131-ijms-21-05432\" ref-type=\"bibr\">131</xref>]. Differently from what we have seen with the other abovementioned delivery systems, which in a few cases turned out to be poor in efficiency and high in immunogenicity and general tolerance [<xref rid=\"B132-ijms-21-05432\" ref-type=\"bibr\">132</xref>], the endogenous origin of exosomes gives them a “boost” in terms of low immunogenicity and high specificity [<xref rid=\"B115-ijms-21-05432\" ref-type=\"bibr\">115</xref>,<xref rid=\"B133-ijms-21-05432\" ref-type=\"bibr\">133</xref>,<xref rid=\"B134-ijms-21-05432\" ref-type=\"bibr\">134</xref>]. The intrinsic specificity of cancer-associated exosomes, which, as mentioned before, are the main characters in cancer cell-to-cell communication, make them powerful tools in the cancer-drug delivery system. Moreover, exosomes can be highly modified in order to enhance or modify their tissue-specificity, widening the amount of feasible therapeutic strategies [<xref rid=\"B135-ijms-21-05432\" ref-type=\"bibr\">135</xref>]. Notably, preclinical studies have shown that exosomes are able to shuttle different kinds of molecules, including chemotherapeutics [<xref rid=\"B136-ijms-21-05432\" ref-type=\"bibr\">136</xref>], drugs possibly useful to work as both effective molecules and tracers, representing a prototype for “Theranostics” [<xref rid=\"B137-ijms-21-05432\" ref-type=\"bibr\">137</xref>,<xref rid=\"B138-ijms-21-05432\" ref-type=\"bibr\">138</xref>], but nanoparticles as well [<xref rid=\"B139-ijms-21-05432\" ref-type=\"bibr\">139</xref>], thus really supporting their exploitation as a natural delivery system for both diagnostics and therapeutics [<xref rid=\"B140-ijms-21-05432\" ref-type=\"bibr\">140</xref>,<xref rid=\"B141-ijms-21-05432\" ref-type=\"bibr\">141</xref>]. Moreover, their structure allows them to deliver active molecules as well, and this has been shown in in vitro models [<xref rid=\"B142-ijms-21-05432\" ref-type=\"bibr\">142</xref>], and in vivo as well [<xref rid=\"B143-ijms-21-05432\" ref-type=\"bibr\">143</xref>]. Lastly, it has been shown that exosome release and size are profoundly changed by extracellular acidity [<xref rid=\"B144-ijms-21-05432\" ref-type=\"bibr\">144</xref>], suggesting that when using EVs or exosomes as a delivery system for the treatment of cancers the source of these should always be chosen very carefully [<xref rid=\"B7-ijms-21-05432\" ref-type=\"bibr\">7</xref>] (<xref rid=\"ijms-21-05432-t001\" ref-type=\"table\">Table 1</xref>).</p><sec><title>EV Bioengineering and Drug Loading</title><p>To consider developing EV-based therapy, it is necessary to take into account the cellular source from which to obtain them. Actually, although it is known that all cells can produce EVs, they should have some physical and biochemical characteristics that make them suitable as drug carriers. Among them, several features have been investigated, such as composition, influencing immunogenicity and targeting; loading capability, which affects therapeutic efficiency [<xref rid=\"B145-ijms-21-05432\" ref-type=\"bibr\">145</xref>].</p><p>Currently, mesenchymal stem cell-derived EVs (MSC-EVs) are being studied and many experimental data have confirmed that these vesicles mimic the immune-regulating function and regenerative capacity of MSCs. The therapeutic potential of MSC-EVs, which exploit some intrinsic EV features, has been found in preclinical studies in various tissues, e.g., nervous tissue, cartilage and bone [<xref rid=\"B146-ijms-21-05432\" ref-type=\"bibr\">146</xref>,<xref rid=\"B147-ijms-21-05432\" ref-type=\"bibr\">147</xref>,<xref rid=\"B148-ijms-21-05432\" ref-type=\"bibr\">148</xref>,<xref rid=\"B149-ijms-21-05432\" ref-type=\"bibr\">149</xref>]. The mechanism of action of this type of EVs includes the activation of the immune system and the promotion of injury healing. In addition to the MSC-EVs, EVs from cells such as embryonic stem cells, induced pluripotent stem cells [<xref rid=\"B150-ijms-21-05432\" ref-type=\"bibr\">150</xref>] and cardiomyocytes [<xref rid=\"B151-ijms-21-05432\" ref-type=\"bibr\">151</xref>] have good therapeutic potential. In addition, dendritic cells have been used as EV cell sources in therapy, since they preserve the immunostimulatory functions of parental cells [<xref rid=\"B152-ijms-21-05432\" ref-type=\"bibr\">152</xref>] and are able to cross biological barriers [<xref rid=\"B153-ijms-21-05432\" ref-type=\"bibr\">153</xref>] with minor side effects [<xref rid=\"B154-ijms-21-05432\" ref-type=\"bibr\">154</xref>]. Similar anticancer effects have been demonstrated for macrophage-derived EVs that modulate an immune response, including cell-mediated response against cancer cell growth [<xref rid=\"B155-ijms-21-05432\" ref-type=\"bibr\">155</xref>,<xref rid=\"B156-ijms-21-05432\" ref-type=\"bibr\">156</xref>]. Nevertheless, the role of macrophage-derived EVs appears ambiguous and it has been observed that they can favor both antitumoral and tumoral immune system stimulations [<xref rid=\"B157-ijms-21-05432\" ref-type=\"bibr\">157</xref>].</p><p>As stated above, EVs, being carriers of natural active molecules including lipids, proteins, nucleic acids and other metabolites, have a broad therapeutic potential per se. However, for therapeutic applications, EVs should be obtained in significant amounts. Certainly, the achievement of clinically relevant doses highly influences EVs based therapy. Obtaining a sufficient quantity of EVs for clinical applications is influenced not only by the source, but also by the isolation processes and by the sample preservation and manipulation. Furthermore, these methods must ensure a high degree of quality, safety and consistency mandatory for clinical applications [<xref rid=\"B158-ijms-21-05432\" ref-type=\"bibr\">158</xref>].</p><p>Although cell culture-derived EVs have been mainly used, other types of EVs can be easily isolated and manipulated, both in direct and indirect ways, to carry and deliver therapeutic molecules.</p><p>A possible approach consists of the isolation and engineering of cells from the patient, starting from body fluids [<xref rid=\"B139-ijms-21-05432\" ref-type=\"bibr\">139</xref>,<xref rid=\"B159-ijms-21-05432\" ref-type=\"bibr\">159</xref>] or from various foods including milk, fruits and vegetables [<xref rid=\"B160-ijms-21-05432\" ref-type=\"bibr\">160</xref>,<xref rid=\"B161-ijms-21-05432\" ref-type=\"bibr\">161</xref>]. Therapeutic compounds can be loaded within these EVs and infused into the patient.</p><p>The efficiency of this strategy is dependent on the kind of biological sample. Among the body fluids, blood plasma represents the most investigated EVs-source, both as a diagnostic tool (through a low invasive liquid biopsy) and as EVs producing cells reservoir. This because of the low invasiveness in obtaining it and because it contains cells that can be isolated and expanded in vitro for EVs production, and also because it contains a large amount of EVs produced by all the cells of our body and potentially shed into the bloodstream. For instance, monocyte-derived macrophages present in the blood have been shown to represent a safe and valuable source for therapeutic exosomes [<xref rid=\"B162-ijms-21-05432\" ref-type=\"bibr\">162</xref>], and platelet-derived EVs that are rich in growth factors and noncoding RNAs [<xref rid=\"B163-ijms-21-05432\" ref-type=\"bibr\">163</xref>,<xref rid=\"B164-ijms-21-05432\" ref-type=\"bibr\">164</xref>]. These evidences support the idea that blood constitutes an almost ideal “EVs-source reservoir”, compared with other bodily fluids, for the production and isolation of clinical-grade EVs [<xref rid=\"B158-ijms-21-05432\" ref-type=\"bibr\">158</xref>] both in terms of large-scale production and clinical feasibility [<xref rid=\"B165-ijms-21-05432\" ref-type=\"bibr\">165</xref>].</p><p>Regarding EVs purified from foods, such as plant-derived or milk-derived EVs, despite that they can offer an easy and large-scale production and result particularly stable, may raise different concerns than human source-based therapeutics EVs. It has been shown that food-derived EVs are carriers of toxins and allergens that may determine immune reactions [<xref rid=\"B166-ijms-21-05432\" ref-type=\"bibr\">166</xref>,<xref rid=\"B167-ijms-21-05432\" ref-type=\"bibr\">167</xref>,<xref rid=\"B168-ijms-21-05432\" ref-type=\"bibr\">168</xref>] (<xref ref-type=\"fig\" rid=\"ijms-21-05432-f001\">Figure 1</xref>).</p><p>Bioengineering applied to EVs is a tool used in order to induce EVs to uptake an exogenous cargo, and to drive it into specific body districts for therapeutic purposes (<xref rid=\"ijms-21-05432-t001\" ref-type=\"table\">Table 1</xref>).</p><p>Actually, there are two main different approaches to manipulate EVs: a pre and a post-loading method. These two are technically and theoretically different. The pre-loading approach consists of using a pre-existing endogenous cargo as the therapeutic molecule [<xref rid=\"B169-ijms-21-05432\" ref-type=\"bibr\">169</xref>] (<xref ref-type=\"fig\" rid=\"ijms-21-05432-f001\">Figure 1</xref>).</p><p>The pre-loading method is the easiest way to obtain therapeutic EVs, because the potential therapeutic molecule is already inside the vesicles. One of the main issues of this methodology is the impossibility to control the amount of cargo loaded into the EVs. If it is true that endogenous molecules, such as miRNA, have a high potential in therapeutic purposes, it is also true that for nucleic acids natural EV content is very poor [<xref rid=\"B170-ijms-21-05432\" ref-type=\"bibr\">170</xref>,<xref rid=\"B171-ijms-21-05432\" ref-type=\"bibr\">171</xref>,<xref rid=\"B172-ijms-21-05432\" ref-type=\"bibr\">172</xref>] (<xref ref-type=\"fig\" rid=\"ijms-21-05432-f001\">Figure 1</xref>).</p><p>Therefore, it is clear that a deeper understanding of the sorting mechanisms that regulate the miRNA and other nucleic acids loaded into the EVs is very important: several research groups are trying to clarify this process, indicating for example Annexin A2 and Y-box binding protein 1 (also called as nuclease-sensitive element-binding protein 1) as two coworkers able to bind miRNA sequences mediating their EV’s loading (the first one in a sequence independent manner and the second one related with the binding with miRNA-223) [<xref rid=\"B171-ijms-21-05432\" ref-type=\"bibr\">171</xref>,<xref rid=\"B173-ijms-21-05432\" ref-type=\"bibr\">173</xref>,<xref rid=\"B174-ijms-21-05432\" ref-type=\"bibr\">174</xref>].</p><p>These modifications made on EV-producing cells can be various and are somewhere between a “pre loading” and a “post loading” method. In fact, through genetic engineering techniques, it is possible to induce an overexpression of therapeutic molecules, such as miRNA, siRNA or proteins on the producing cell [<xref rid=\"B171-ijms-21-05432\" ref-type=\"bibr\">171</xref>,<xref rid=\"B175-ijms-21-05432\" ref-type=\"bibr\">175</xref>] (<xref ref-type=\"fig\" rid=\"ijms-21-05432-f001\">Figure 1</xref>). The fusion of therapeutic proteins with EV’s localized proteins is an efficient strategy that might drive the expression of the interest protein onto the EVs surface: this specific technique is called “Exosome display technology” [<xref rid=\"B175-ijms-21-05432\" ref-type=\"bibr\">175</xref>,<xref rid=\"B176-ijms-21-05432\" ref-type=\"bibr\">176</xref>]. Another promising approach is the use of specific functionalized domains in order to induce the expression of exogenous proteins: for this purpose the constant region one and two (C1C2 domain) of Milk Fat Globule-EGF Factor 8 Protein (also called Lactaderin) might be used, and promising works show the efficacy of this technique [<xref rid=\"B171-ijms-21-05432\" ref-type=\"bibr\">171</xref>,<xref rid=\"B176-ijms-21-05432\" ref-type=\"bibr\">176</xref>,<xref rid=\"B177-ijms-21-05432\" ref-type=\"bibr\">177</xref>] (<xref ref-type=\"fig\" rid=\"ijms-21-05432-f001\">Figure 1</xref>).</p><p>The post-loading method consists of the encapsulation of therapeutic molecules on the EVs after their isolation. It is more efficient than the pre-loading one, because manipulating EV’s cargo after the isolation gives the operator a deeper control on the encapsulation efficiency (<italic>EE</italic>%) and loading capacity (<italic>LC</italic>%) of the final product. Those two are parameters used to calculate the efficiency of the final product in DDS [<xref rid=\"B178-ijms-21-05432\" ref-type=\"bibr\">178</xref>,<xref rid=\"B179-ijms-21-05432\" ref-type=\"bibr\">179</xref>], according to the following equations:<disp-formula id=\"FD1-ijms-21-05432\"><label>(1)</label><mml:math id=\"mm1\"><mml:mrow><mml:mrow><mml:mi>EE</mml:mi><mml:mo>%</mml:mo><mml:mo> </mml:mo><mml:mo>=</mml:mo><mml:mo> </mml:mo><mml:mfrac><mml:mrow><mml:mi>M</mml:mi><mml:mi>o</mml:mi><mml:mi>l</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>u</mml:mi><mml:mi>l</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi><mml:mo> </mml:mo><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:mi>r</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>t</mml:mi><mml:mi>l</mml:mi><mml:mi>y</mml:mi><mml:mo> </mml:mo><mml:mi>e</mml:mi><mml:mi>n</mml:mi><mml:mi>c</mml:mi><mml:mi>a</mml:mi><mml:mi>p</mml:mi><mml:mi>s</mml:mi><mml:mi>u</mml:mi><mml:mi>l</mml:mi><mml:mi>a</mml:mi><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi><mml:mo> </mml:mo><mml:mi>w</mml:mi><mml:mi>i</mml:mi><mml:mi>t</mml:mi><mml:mi>h</mml:mi><mml:mo> </mml:mo><mml:mi>t</mml:mi><mml:mi>h</mml:mi><mml:mi>e</mml:mi><mml:mo> </mml:mo><mml:mi>d</mml:mi><mml:mi>r</mml:mi><mml:mi>u</mml:mi><mml:mi>g</mml:mi></mml:mrow><mml:mrow><mml:mi>T</mml:mi><mml:mi>o</mml:mi><mml:mi>t</mml:mi><mml:mi>a</mml:mi><mml:mi>l</mml:mi><mml:mo> </mml:mo><mml:mi>a</mml:mi><mml:mi>m</mml:mi><mml:mi>o</mml:mi><mml:mi>u</mml:mi><mml:mi>n</mml:mi><mml:mi>t</mml:mi><mml:mo> </mml:mo><mml:mi>o</mml:mi><mml:mi>f</mml:mi><mml:mo> </mml:mo><mml:mi>m</mml:mi><mml:mi>o</mml:mi><mml:mi>l</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>u</mml:mi><mml:mi>l</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:mfrac><mml:mo>×</mml:mo><mml:mn>100</mml:mn></mml:mrow></mml:mrow></mml:math></disp-formula>\n<disp-formula id=\"FD2-ijms-21-05432\"><label>(2)</label><mml:math id=\"mm2\"><mml:mrow><mml:mrow><mml:mi>LC</mml:mi><mml:mo>%</mml:mo><mml:mo> </mml:mo><mml:mo>=</mml:mo><mml:mo> </mml:mo><mml:mfrac><mml:mrow><mml:mi>M</mml:mi><mml:mi>o</mml:mi><mml:mi>l</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>u</mml:mi><mml:mi>l</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi><mml:mo> </mml:mo><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:mi>r</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>t</mml:mi><mml:mi>l</mml:mi><mml:mi>y</mml:mi><mml:mo> </mml:mo><mml:mi>e</mml:mi><mml:mi>n</mml:mi><mml:mi>c</mml:mi><mml:mi>a</mml:mi><mml:mi>p</mml:mi><mml:mi>s</mml:mi><mml:mi>u</mml:mi><mml:mi>l</mml:mi><mml:mi>a</mml:mi><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi><mml:mo> </mml:mo><mml:mi>w</mml:mi><mml:mi>i</mml:mi><mml:mi>t</mml:mi><mml:mi>h</mml:mi><mml:mo> </mml:mo><mml:mi>t</mml:mi><mml:mi>h</mml:mi><mml:mi>e</mml:mi><mml:mo> </mml:mo><mml:mi>d</mml:mi><mml:mi>r</mml:mi><mml:mi>u</mml:mi><mml:mi>g</mml:mi></mml:mrow><mml:mrow><mml:mi>A</mml:mi><mml:mi>m</mml:mi><mml:mi>o</mml:mi><mml:mi>u</mml:mi><mml:mi>n</mml:mi><mml:mi>t</mml:mi><mml:mo> </mml:mo><mml:mi>o</mml:mi><mml:mi>f</mml:mi><mml:mo> </mml:mo><mml:mi>c</mml:mi><mml:mi>a</mml:mi><mml:mi>r</mml:mi><mml:mi>r</mml:mi><mml:mi>i</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mo> </mml:mo><mml:mi>t</mml:mi><mml:mi>h</mml:mi><mml:mi>a</mml:mi><mml:mi>t</mml:mi><mml:mo> </mml:mo><mml:mi>c</mml:mi><mml:mi>a</mml:mi><mml:mi>r</mml:mi><mml:mi>r</mml:mi><mml:mi>i</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi><mml:mo> </mml:mo><mml:mi>d</mml:mi><mml:mi>r</mml:mi><mml:mi>u</mml:mi><mml:mi>g</mml:mi><mml:mo> </mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>E</mml:mi><mml:mi>V</mml:mi><mml:mi>s</mml:mi><mml:mo> </mml:mo><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mo> </mml:mo><mml:mi>t</mml:mi><mml:mi>h</mml:mi><mml:mi>i</mml:mi><mml:mi>s</mml:mi><mml:mo> </mml:mo><mml:mi>c</mml:mi><mml:mi>a</mml:mi><mml:mi>s</mml:mi><mml:mi>e</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:mfrac><mml:mo>×</mml:mo><mml:mn>100</mml:mn></mml:mrow></mml:mrow></mml:math></disp-formula></p><p>It is possible to intervene on EVs cargo through three different ways: chemical-treating EVs, through physical methods or using electroporation (<xref ref-type=\"fig\" rid=\"ijms-21-05432-f001\">Figure 1</xref>).</p><p>Sometimes the lipid bilayer prevents passive encapsulation of therapeutic compounds, so using chemical solvents in order to create pores on the lipid surface, or using some freezing–thawing cycles, can facilitate the drug loading into EVs [<xref rid=\"B180-ijms-21-05432\" ref-type=\"bibr\">180</xref>,<xref rid=\"B181-ijms-21-05432\" ref-type=\"bibr\">181</xref>]. In reality, the gold standard for the manipulation of EVs is electroporation: the electric pulse can induce the formation of pores on the surface of the vesicles, allowing migration of therapeutic compounds into them [<xref rid=\"B177-ijms-21-05432\" ref-type=\"bibr\">177</xref>]. However, electroporation and approaches that affect EVs membrane stability and integrity present limitations due to the creation of aggregates, leading to an overestimation of the loading efficiency [<xref rid=\"B135-ijms-21-05432\" ref-type=\"bibr\">135</xref>,<xref rid=\"B182-ijms-21-05432\" ref-type=\"bibr\">182</xref>].</p><p>In cancer therapy, there are several examples of EVs encapsulated with chemotherapeutic drugs, such as doxorubicin or patitaxel, which are currently ongoing on preclinical trials [<xref rid=\"B183-ijms-21-05432\" ref-type=\"bibr\">183</xref>,<xref rid=\"B184-ijms-21-05432\" ref-type=\"bibr\">184</xref>].</p><p>It is important to underline how the choosing of bioengineering methods is strictly related with the properties of each compound used.</p><p>The possibility of exploitation of EVs as therapeutic nanocarriers has entered the field of anticancer therapy in the last decade, due to the similitude with liposomes, which have been studied for a long time for their capability to encapsulate drugs. Considering some physical features and the structure of the lipid envelope, EVs and liposomes appear similar [<xref rid=\"B185-ijms-21-05432\" ref-type=\"bibr\">185</xref>]. Moreover, it is possible to synthesize liposomes so that they share many of the membrane properties of exosomes [<xref rid=\"B186-ijms-21-05432\" ref-type=\"bibr\">186</xref>,<xref rid=\"B187-ijms-21-05432\" ref-type=\"bibr\">187</xref>]. Despite this, although several liposome-based drug products are currently available in the market [<xref rid=\"B188-ijms-21-05432\" ref-type=\"bibr\">188</xref>], they lack in selectivity and they can induce side effects, so much so that the clinical trials of liposomes turned out to be poorly effective [<xref rid=\"B188-ijms-21-05432\" ref-type=\"bibr\">188</xref>]. Compared to liposomes, EVs have a unique lipid and protein composition, which favors targeting and organotropism. In fact, as mentioned above, EVs play a key role in cross-talking between cells and, due to the biogenesis mechanism, they have a very similar membrane to the cells that produced them; consequently, this would represent a characteristic to be exploited, for example in autologous therapy, to reduce renal clearance. In addition, immune compatibility, due to the EVs’ membrane features, constitutes an advantage that establishes them as a suitable target-therapy strategy, when compared with liposomes. Similarly, to liposomes, when injected intravenously EVs are rapidly cleared by the reticuloendothelial system in the liver and spleen, reducing drug efficacy. On the contrary, exosomes administered in a tumor in situ have a major rate of association to cancer cells and besides, autologous EVs show an enhanced organotropism [<xref rid=\"B189-ijms-21-05432\" ref-type=\"bibr\">189</xref>]. In terms of immune compatibility between the EVs donor and receiving host, the allogeneic EVs applications seem to be feasible, for instance, taking into account the typical practices in hematopoietic transplantation (HLA, human leukocyte antigens-matching) [<xref rid=\"B165-ijms-21-05432\" ref-type=\"bibr\">165</xref>,<xref rid=\"B190-ijms-21-05432\" ref-type=\"bibr\">190</xref>,<xref rid=\"B191-ijms-21-05432\" ref-type=\"bibr\">191</xref>,<xref rid=\"B192-ijms-21-05432\" ref-type=\"bibr\">192</xref>]. Currently, strong evidence that can demonstrate the immune effects in allogeneic administration of EVs is lacking. Thus, the complete characterization of EVs and the source of EVs may lead to a robust definition of the mechanisms underlying the therapeutic effect of EVs [<xref rid=\"B169-ijms-21-05432\" ref-type=\"bibr\">169</xref>].</p><p>The intrinsic ability of EVs to bind target cells, as mentioned before, is a particular point on targeted therapy. However, it is also possible to think to improve this natural feature, modifying the structure of those vesicles in order to improve the delivery to target tissues: for instance, it is possible to improve the blood circulation of EVs coating them with polyethylene glycol, or increasing the expression of CD47 on the vesicle surface, or using specific residues (like the C1C2 domain of Lactadherin) in order to conjugate specific antibodies on the EV surface, switching and driving the specificity of the complex [<xref rid=\"B171-ijms-21-05432\" ref-type=\"bibr\">171</xref>,<xref rid=\"B193-ijms-21-05432\" ref-type=\"bibr\">193</xref>,<xref rid=\"B194-ijms-21-05432\" ref-type=\"bibr\">194</xref>].</p><p>An emerging drug delivery strategy, based on nanoparticles, that has demonstrated great potential in the treatment of different diseases and in regenerative medicine [<xref rid=\"B135-ijms-21-05432\" ref-type=\"bibr\">135</xref>], is to create a hybrid carrier between exosomes and liposomes, in order to combine the features that may increase the effectiveness of anticancer therapies. These exciting new approaches consist of membrane fusion methods and have the aim to combine the high yield and high drug-loading capacity of liposomes with the EVs’ intrinsic properties, such as low immunogenicity, stability in circulation and high efficiency in target reaching and in tissue retention [<xref rid=\"B195-ijms-21-05432\" ref-type=\"bibr\">195</xref>,<xref rid=\"B196-ijms-21-05432\" ref-type=\"bibr\">196</xref>,<xref rid=\"B197-ijms-21-05432\" ref-type=\"bibr\">197</xref>]. The freeze–thaw method has been used to induce the mixing of liposome and exosome membranes, which have been embedded with a specific membrane protein. This has determined the enhancing of the delivery function of the exosomes by changing the lipid composition or the properties of the exosome by membrane fusion [<xref rid=\"B179-ijms-21-05432\" ref-type=\"bibr\">179</xref>,<xref rid=\"B198-ijms-21-05432\" ref-type=\"bibr\">198</xref>]. This process seems to be exploiting the biocompatibility of EVs, allowing the camouflage of liposomes by enriching their lipid bilayer and inner compartment with biogenic molecules and on the other side, improve the EV drug loading, proper of liposomes [<xref rid=\"B170-ijms-21-05432\" ref-type=\"bibr\">170</xref>,<xref rid=\"B198-ijms-21-05432\" ref-type=\"bibr\">198</xref>]. More recently, the functionalization of exosomes, to increase their ability to deliver various contents into cells, has been successfully tested using nanogel systems that ensure their effective delivery in a functionally intact state [<xref rid=\"B199-ijms-21-05432\" ref-type=\"bibr\">199</xref>]. Other interesting technologies have developed artificial nanovesicles, with features resembling those of EVs membrane and obtained from broken cells [<xref rid=\"B200-ijms-21-05432\" ref-type=\"bibr\">200</xref>,<xref rid=\"B201-ijms-21-05432\" ref-type=\"bibr\">201</xref>]. Despite the exciting potential of engineered EVs as drug delivery systems, the feasibility, and scalability of EVs and EVs-like systems are strongly dependent on some aspects that need to be addressed to accelerate the translation into the clinic. The issues that must be defined include the characteristic of the EVs source that entails the question of effectiveness and biocompatibility. Important are the isolation, storage and quality control procedures of EVs that affect the use of clinically relevant doses and EVs-based therapy safety. Moreover, ethical issues are noteworthy.</p><p>Considering the growing interest and the breakthrough data that are continuously provided in the field of EVs-based therapy, much progress is being taken to develop standardized protocols and to establish efficacy and biosecurity criteria, to the aim to translate this platform into reality to treat diseases like cancer.</p></sec></sec><sec id=\"sec4-ijms-21-05432\"><title>4. Malignant Pleural Mesothelioma: The Lack of Efficient Therapeutic Strategies and Possible Drug Delivery Strategies</title><p>Malignant mesothelioma (MM) is an insidious neoplasm arising from mesothelial surfaces such as pleura and peritoneal cavities, pericardium and tunica vaginalis. The distribution of the tumor may involve lining cells in a continuous manner. Among all different subtypes of MM, MPM is the most common one [<xref rid=\"B202-ijms-21-05432\" ref-type=\"bibr\">202</xref>,<xref rid=\"B203-ijms-21-05432\" ref-type=\"bibr\">203</xref>,<xref rid=\"B204-ijms-21-05432\" ref-type=\"bibr\">204</xref>,<xref rid=\"B205-ijms-21-05432\" ref-type=\"bibr\">205</xref>,<xref rid=\"B206-ijms-21-05432\" ref-type=\"bibr\">206</xref>].</p><p>MPM is an aggressive, rare tumor, with increasing incidence and poor prognosis. The massive use of asbestos after the Second World War exposed a significant number of people to asbestos, dramatically increasing the risk of developing MPM in 1960. Although the use of asbestos was significantly reduced or abandoned in the western world in the 1980s, because of the long latency between the exposure to the contaminant agent (asbestos) and the onset of the disease (from 15 to 60 years), mortality due to MPM continued to rise [<xref rid=\"B207-ijms-21-05432\" ref-type=\"bibr\">207</xref>,<xref rid=\"B208-ijms-21-05432\" ref-type=\"bibr\">208</xref>,<xref rid=\"B209-ijms-21-05432\" ref-type=\"bibr\">209</xref>,<xref rid=\"B210-ijms-21-05432\" ref-type=\"bibr\">210</xref>]. The global incidence of MPM is uncertain: this is due to a poor compatibility of databases from different countries, and few international data is available [<xref rid=\"B208-ijms-21-05432\" ref-type=\"bibr\">208</xref>]. It is estimated that every year around 43,000 people die due to this tumor [<xref rid=\"B202-ijms-21-05432\" ref-type=\"bibr\">202</xref>,<xref rid=\"B208-ijms-21-05432\" ref-type=\"bibr\">208</xref>]. In Italy, according to epidemiological studies, MM represents 0.4% of the total amount of tumors diagnosed in men and 0.2% in women (AIRTUM). As mentioned before, in the last ten years there has been a significant increase in the diagnosis of this rare neoplasia, also due to the mean latency of the tumor of 44.6 years in 2544 cases diagnosed in the period from 1993 to 2001 [<xref rid=\"B202-ijms-21-05432\" ref-type=\"bibr\">202</xref>,<xref rid=\"B208-ijms-21-05432\" ref-type=\"bibr\">208</xref>].</p><p>A macroscopic observation is not enough for diagnosing MPM, as it is not always easy to distinguish between a benign pleural lesion and a malignant one. Certainly, the best way to evaluate the malignancy is to analyze the presence of tumor invasion. Furthermore, some MPM patients are not eligible for pleural biopsy, and diagnosis must be conducted on pleural effusions (PE) [<xref rid=\"B211-ijms-21-05432\" ref-type=\"bibr\">211</xref>]. Considering the above, finding affordable biomarkers that can ease the recognition of malignant subtypes seems to be necessary. Unfortunately, current methods of detection, based on the most common alteration documented on MPM, lack in reliability and are generally not recommended. In fact, it is possible to say that currently there are no standardized diagnostic methods based on the most common genetic MPM’s alterations. Nevertheless, a transversal analysis conducted by Bruno R. et al. [<xref rid=\"B212-ijms-21-05432\" ref-type=\"bibr\">212</xref>] has investigated the possibility to find new suitable biomarkers: first, the classical approach, involving standard immunohistochemical biomarkers such as glucose transporter 1, p53, desmin, epithelial membrane antigens and insulin-like growth factor mRNA binding proteins [<xref rid=\"B211-ijms-21-05432\" ref-type=\"bibr\">211</xref>,<xref rid=\"B212-ijms-21-05432\" ref-type=\"bibr\">212</xref>,<xref rid=\"B213-ijms-21-05432\" ref-type=\"bibr\">213</xref>,<xref rid=\"B214-ijms-21-05432\" ref-type=\"bibr\">214</xref>,<xref rid=\"B215-ijms-21-05432\" ref-type=\"bibr\">215</xref>], has been combined with soluble PE biomarkers, then it has been evaluated through ELISA assay (mesotelin and fibulin-3). They have also evaluated new emerging biomarkers, recently introduced in the clinical practice (BAP1, breast cancer associated protein 1, through immunohistochemistry and p16 through, fluorescence in situ hybridization) [<xref rid=\"B216-ijms-21-05432\" ref-type=\"bibr\">216</xref>]. Despite promising results, Bruno’s team has demonstrated that none of the tissues and soluble markers are highly sensitive enough to distinguish benign from malign pleural lesions. Only BAP1 and p16 showed a high specificity in discerning pleural lesions, both in PEs and tissues. Those two markers, BAP1 and p16, result exclusively unexpressed or deleted only in MPM. Considering that BAP1 and p16 are not deleted in all MPMs and their sensitivity can variate between 43% and 93% for p16 and 61–67% for BAP1, negative results can sometimes be inconsistent in order to exclude the malignancy [<xref rid=\"B204-ijms-21-05432\" ref-type=\"bibr\">204</xref>,<xref rid=\"B217-ijms-21-05432\" ref-type=\"bibr\">217</xref>,<xref rid=\"B218-ijms-21-05432\" ref-type=\"bibr\">218</xref>]. Therefore, these markers may be not specific to MPM, but may aid diagnosis and may have prognostic significance [<xref rid=\"B216-ijms-21-05432\" ref-type=\"bibr\">216</xref>,<xref rid=\"B217-ijms-21-05432\" ref-type=\"bibr\">217</xref>,<xref rid=\"B218-ijms-21-05432\" ref-type=\"bibr\">218</xref>].</p><p>Another important field of increasing interest concerns the link between MPM and EVs, especially exosomes. Currently, the role that exosomes, secreted by MPM, exert in tumoral progression, and their effector functions are increasingly being investigated. The analysis of samples of PE obtained from various patients proved to be enriched by EVs secreted by the tumor itself and, in particular, by exosomes [<xref rid=\"B70-ijms-21-05432\" ref-type=\"bibr\">70</xref>,<xref rid=\"B219-ijms-21-05432\" ref-type=\"bibr\">219</xref>,<xref rid=\"B220-ijms-21-05432\" ref-type=\"bibr\">220</xref>]. However, currently, there are very few works in the literature about the function of exosomes secreted by the MPM. In order to identify the function and catalog the mesothelial-derived EVs, a close proteomic analysis was carried out on MM-derived exosomes. From an initial pool of 2178 proteins present in the exosomal isolate, all proteins common to all exosomes or proteins involved in their biogenesis (such as ESCRT or tetraspanins) have been excluded. Following this, it was possible to underline several proteins that constitute a sort of molecular “fingerprint” of MPM: the list of potential candidates as mesothelioma biomarkers includes, but is not limited to, Piruvate Kinase, Annexin A1 and A2, Heat Shock Cognate 71, Heat Shock Protein 90, Alpha Enolase, glucose6-phosphate1-dehydrogenase and 5 tubulin isotypes (such as TUBB4A, Q8IWP6 and B3KPS3) [<xref rid=\"B219-ijms-21-05432\" ref-type=\"bibr\">219</xref>].</p><p>These and other potential candidates constitute the so-called “mesothelioma exosomal signature”, a potential pool of 570 proteins that can be used as markers for early and minimally invasive diagnoses, or as a basis for deepening the signaling mediated by the MPM-derived exosomes, and the way in which the secreted EVs influence the tumor microenvironment [<xref rid=\"B219-ijms-21-05432\" ref-type=\"bibr\">219</xref>].</p><p>Anyway, as for the other tumor types, some specific functions mediated by the active secretion of exosomes by MPM have been analyzed and reported in the literature. In fact, it has been shown how MPM-derived-exosomes are involved in the formation of “tunneling nanotubes”, which are actin-based cellular extensions that act as channels for the transport of cellular material, in order to implement cell–cell communication [<xref rid=\"B221-ijms-21-05432\" ref-type=\"bibr\">221</xref>]. In addition, MPM-derived exosomes also mediate immune evasion through the downregulation of the Natural Killer (NK) group 2 member D receptor, which is normally involved in the killing mediated by NK cells and by CD8+ T cells [<xref rid=\"B222-ijms-21-05432\" ref-type=\"bibr\">222</xref>]. Finally, it has been observed how, through the exosome-mediated expression of CD39 and CD73, MPM can induce the extracellular expression of adenosine that, in turn, behaves as an immunomodulator exerting an anti-inflammatory action by suppressing the T-cell mediated response [<xref rid=\"B223-ijms-21-05432\" ref-type=\"bibr\">223</xref>].</p></sec><sec id=\"sec5-ijms-21-05432\"><title>5. Future Perspective: The Challenge in the Exploitation of EVs in MPM Therapy</title><p>Concerning treatments, the handling and management of MPM therapies are a challenge for clinicians. First-line approaches include surgical intervention, chemotherapy or both of them. However, none of the actual standard therapies approved could be considered as an ultimate cure, but more as a palliative treatment. Moreover, currently, a standardized second-line therapy, meant as a different and effective treatment, does not exist and the overall survival rate after a two year follow-up remains poor [<xref rid=\"B224-ijms-21-05432\" ref-type=\"bibr\">224</xref>,<xref rid=\"B225-ijms-21-05432\" ref-type=\"bibr\">225</xref>].</p><p>In the oncological field, lines of treatment are dramatically important. These refer to different approaches to handle the cancer at different times. First line treatment is the first approach indicated by international guidelines. Other lines of treatment are usually exploited to treat tumors in which the first line approach does not work, side effects of the first line are not well-tolerated by the patient or new approaches seem to be more effective [<xref rid=\"B226-ijms-21-05432\" ref-type=\"bibr\">226</xref>].</p><p>Therefore, the importance of new strategies that may lead to a more efficient and proficient handling of MPM is clear.</p><p>Several trials have been performed with the aim of increasing MPM treatment options for patients that respond negatively to the first line treatment.</p><p>An interesting ongoing clinical trial concerns the possibility of using pegylated phospholipid vesicles as a carrier for doxorubicin [<xref rid=\"B227-ijms-21-05432\" ref-type=\"bibr\">227</xref>]. This study is proposed as a possible first-line treatment for the handling of MPM, by implementing the uptake of doxorubicin directly in the tumor site, consequently reducing the side effects of traditional systemic therapy, with acceptable toxicity values that improve the patient’s quality of life [<xref rid=\"B227-ijms-21-05432\" ref-type=\"bibr\">227</xref>].</p><p>Another phase II study, conducted by Szlosarek et al., concerns the use of pegylated arginine deaminase (ADI-PEG20) [<xref rid=\"B228-ijms-21-05432\" ref-type=\"bibr\">228</xref>]. In fact, preclinical studies demonstrate how arginosuccinate synthetase 1 (ASS1) enzyme deficient tumors are sensitive to arginine deprivation, leading to cell death. The trial showed that the progression free-survival (PFS) of patients treated with the compound ADI-PEG20 increased compared to the control group (15.7 months in the ADI-PEG20 group versus 12.1 months of PFS in the control group) [<xref rid=\"B228-ijms-21-05432\" ref-type=\"bibr\">228</xref>].</p><p>Promising results also come from the use of a humanized anti-EGFR (epidermal growth factor receptor) antibody, Nimotuximab (h-R3) [<xref rid=\"B61-ijms-21-05432\" ref-type=\"bibr\">61</xref>]. In the study in question, the researchers wanted to evaluate antibody biodistribution, uptake on the tumor site and binding affinity for EGFR. The study was conducted in vivo on MPM xenograft models, radiolabeling the antibody with Gallium-67, demonstrating a remarkable uptake of the antibody-radiolabeled complex at the tumor site [<xref rid=\"B61-ijms-21-05432\" ref-type=\"bibr\">61</xref>].</p><p>Concerning the above-mentioned possible therapeutic tools in drug delivery, it might be tempting to consider EVs, and exosomes more specifically, as a potential therapeutic tool in the handling of MPM. Nevertheless, the small amount of works in the literature leads us to further investigate the connection between EVs and MPM. In this regard, once the effective relevance of the production of exosomes by the MPM has been assessed, it would be interesting to discover whether it is possible, and if so how, to exploit this system of cancer communication in order to deliver exogenous molecules, possibly with therapeutic functions, using the natural tumor network to attack itself with its own messengers.</p><p>The idea we want to propose is the exploitation of EVs isolated from primary cell cultures of MPM, in order to be able to modify them through post-loading techniques. In this way, even though the isolation efficiency might be lower if compared to therapies with recombinant proteins (>5 g/L of culture medium) [<xref rid=\"B171-ijms-21-05432\" ref-type=\"bibr\">171</xref>,<xref rid=\"B229-ijms-21-05432\" ref-type=\"bibr\">229</xref>], the final product would be far more representative of the actual secretome of the MPM. This would allow an expression of surface receptors of tumoral exosomes closer to the physiological one; it would also theoretically allow a more efficient drug loading, through post-loading techniques, and drug delivery.</p><p>Among other things, exosome surface proteins define their tropism, and today the pivotal role of EVs on tumor signaling is largely confirmed [<xref rid=\"B230-ijms-21-05432\" ref-type=\"bibr\">230</xref>].</p><p>Due to the relative simplicity and low cost of EV manipulation techniques, they represent a potentially suitable model of DDS. The prospect of attacking the tumor by exploiting its own messengers is not only fascinating from a scientific point of view, but also clinically remarkable: the biocompatibility of a biopharmaceutical like this, based on a loading into the vesicles of a drug, it would be complete.</p><p>In addition, unlike the strategies adopted in other drug delivery models, in which specificity is entrusted to a single molecule present on the surface of the target cell (e.g., monoclonal antibodies and peptide-based nanocarriers), EVs can rely on a much more layered and complex vehiculation system. Of course, a careful evaluation of the tumor microenvironment, the choice of an ad hoc delivery system and the electric charge of the nanocarrier have to be taken into account.</p></sec></body><back><notes><title>Author Contributions</title><p>Conceptualization, S.B., C.C.B., A.M.G., C.C., F.B. and F.C.; writing—original draft preparation, S.B., L.N., M.L., S.F. and C.C.B.; supervision, C.C.B. and F.C. All authors have read and agreed to the published version of the manuscript.</p></notes><notes><title>Funding</title><p>This work was funded in part by the Italian National Operational Program (PON) “Imprese e Competitività” 2014-2020 FESR, grant awarded by the Italian Ministry of Economic Development to the project titled “Gestione di un servizio integrato multicentrico di diagnostica e terapia personalizzata in oncologia” (Project code: F/090012/01-02/X36).</p></notes><notes notes-type=\"COI-statement\"><title>Conflicts of Interest</title><p>Authors declare that they have no conflict of interest.</p></notes><ref-list><title>References</title><ref id=\"B1-ijms-21-05432\"><label>1.</label><element-citation publication-type=\"web\"><person-group person-group-type=\"author\"><collab>World Health Organization</collab></person-group><article-title>Cancer</article-title><comment>Available online: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.who.int/news-room/fact-sheets/detail/cancer\">https://www.who.int/news-room/fact-sheets/detail/cancer</ext-link></comment><date-in-citation content-type=\"access-date\" iso-8601-date=\"2020-05-07\">(accessed on 7 May 2020)</date-in-citation></element-citation></ref><ref id=\"B2-ijms-21-05432\"><label>2.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Gotwals</surname><given-names>P.</given-names></name><name><surname>Cameron</surname><given-names>S.</given-names></name><name><surname>Cipolletta</surname><given-names>D.</given-names></name><name><surname>Cremasco</surname><given-names>V.</given-names></name><name><surname>Crystal</surname><given-names>A.</given-names></name><name><surname>Hewes</surname><given-names>B.</given-names></name><name><surname>Mueller</surname><given-names>B.</given-names></name><name><surname>Quaratino</surname><given-names>S.</given-names></name><name><surname>Sabatos-Peyton</surname><given-names>C.</given-names></name><name><surname>Petruzzelli</surname><given-names>L.</given-names></name><etal/></person-group><article-title>Prospects for combining targeted and conventional cancer therapy with immunotherapy</article-title><source>Nat. 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The figure schematizes the approaches currently used in EVs engineering or in the manipulation of their content. (<bold>1</bold>) The EVs are promising candidates in the treatment of numerous pathologies and there are various reliable sources. EVs can be isolated from the cell culture supernatant of various producing cell lines, from body fluids and also from food. (<bold>2</bold>) The EVs molecular composition is complex and it depends on the cellular source. They can contain different classes of proteins (membrane-bound tetraspanins CD9, CD81 and CD63; receptors, heat shock proteins), ncRNAs (RNAs; MicroRNAs) and lipids (lysobisphosphatidic acid; phosphatidylcholine; phosphatidylethanolamine and sphingomyelin). (<bold>3</bold>) The different methods to manipulate the EVs content include the preloading approach, in which a pre-existing endogenous cargo is the therapeutic molecule. In the display technology, the EV-producing cells can be engineered with a plasmid in order to induce the expression of exogenous proteins. The post-loading method consists in the direct introduction of drug molecules into EVs after their isolation. (<bold>4</bold>) The engineered EVs may also be manipulated to be more bioactive and bioavailable and can be administered to patients for the MPM therapy.</p></caption><graphic xlink:href=\"ijms-21-05432-g001\"/></fig><table-wrap id=\"ijms-21-05432-t001\" orientation=\"portrait\" position=\"float\"><object-id pub-id-type=\"pii\">ijms-21-05432-t001_Table 1</object-id><label>Table 1</label><caption><p>The advantages and disadvantages of main drug delivery approaches. Abbreviations: ADC, antibody-drug conjugated; EVs, extracellular vesicles.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Strategy</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Advantages</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">Disadvantages</th><th align=\"center\" valign=\"middle\" style=\"border-top:solid thin;border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">References</th></tr></thead><tbody><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Nanoparticles</bold>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">• Flexibility<break/>• High stability in vivo<break/>• Increased compound half-life<break/>• Hydrophilic and lipophilic compounds can be both loaded<break/>• Enhanced permeability of tumor vasculature facilitates nanoparticle delivery on the tumor site</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">• Depends on the nanoparticle type<break/>• Inorganic nanoparticles may trigger the immune-system</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B19-ijms-21-05432\" ref-type=\"bibr\">19</xref>,<xref rid=\"B20-ijms-21-05432\" ref-type=\"bibr\">20</xref>,<xref rid=\"B21-ijms-21-05432\" ref-type=\"bibr\">21</xref>,<xref rid=\"B22-ijms-21-05432\" ref-type=\"bibr\">22</xref>,<xref rid=\"B23-ijms-21-05432\" ref-type=\"bibr\">23</xref>,<xref rid=\"B24-ijms-21-05432\" ref-type=\"bibr\">24</xref>,<xref rid=\"B25-ijms-21-05432\" ref-type=\"bibr\">25</xref>,<xref rid=\"B26-ijms-21-05432\" ref-type=\"bibr\">26</xref>,<xref rid=\"B27-ijms-21-05432\" ref-type=\"bibr\">27</xref>,<xref rid=\"B28-ijms-21-05432\" ref-type=\"bibr\">28</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Liposomes</bold>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">• Similarity with cell membrane<break/>• High variety of drug encapsulation<break/>• Low systemic toxicity</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">• Sensitivity to sterilization methods<break/>• Low stability in circulation<break/>• Low reproducibility in liposome loading and size control<break/>• Short shelf-life</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B29-ijms-21-05432\" ref-type=\"bibr\">29</xref>,<xref rid=\"B30-ijms-21-05432\" ref-type=\"bibr\">30</xref>,<xref rid=\"B31-ijms-21-05432\" ref-type=\"bibr\">31</xref>,<xref rid=\"B32-ijms-21-05432\" ref-type=\"bibr\">32</xref>,<xref rid=\"B33-ijms-21-05432\" ref-type=\"bibr\">33</xref>,<xref rid=\"B34-ijms-21-05432\" ref-type=\"bibr\">34</xref>,<xref rid=\"B35-ijms-21-05432\" ref-type=\"bibr\">35</xref>,<xref rid=\"B36-ijms-21-05432\" ref-type=\"bibr\">36</xref>,<xref rid=\"B37-ijms-21-05432\" ref-type=\"bibr\">37</xref>,<xref rid=\"B38-ijms-21-05432\" ref-type=\"bibr\">38</xref>,<xref rid=\"B39-ijms-21-05432\" ref-type=\"bibr\">39</xref>,<xref rid=\"B40-ijms-21-05432\" ref-type=\"bibr\">40</xref>,<xref rid=\"B41-ijms-21-05432\" ref-type=\"bibr\">41</xref>,<xref rid=\"B42-ijms-21-05432\" ref-type=\"bibr\">42</xref>,<xref rid=\"B43-ijms-21-05432\" ref-type=\"bibr\">43</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Polymer Conjugated Drugs</bold>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">• Increased compound half-life<break/>• Increased high-dose drug tolerance<break/>• Increased specificity</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">• Deep knowledge of polymer–receptor molecular interactions required<break/>• Poor information about long-term side effects</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B44-ijms-21-05432\" ref-type=\"bibr\">44</xref>,<xref rid=\"B45-ijms-21-05432\" ref-type=\"bibr\">45</xref>,<xref rid=\"B46-ijms-21-05432\" ref-type=\"bibr\">46</xref>,<xref rid=\"B47-ijms-21-05432\" ref-type=\"bibr\">47</xref>,<xref rid=\"B48-ijms-21-05432\" ref-type=\"bibr\">48</xref>,<xref rid=\"B49-ijms-21-05432\" ref-type=\"bibr\">49</xref>,<xref rid=\"B50-ijms-21-05432\" ref-type=\"bibr\">50</xref>,<xref rid=\"B51-ijms-21-05432\" ref-type=\"bibr\">51</xref>,<xref rid=\"B52-ijms-21-05432\" ref-type=\"bibr\">52</xref>,<xref rid=\"B53-ijms-21-05432\" ref-type=\"bibr\">53</xref>,<xref rid=\"B54-ijms-21-05432\" ref-type=\"bibr\">54</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Small molecules, peptides and antibodies</bold>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">• Various anticancer effects<break/>• High cell permeability<break/>• Low systemic toxicity</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">• Depends on the small molecule type<break/>• Size-influenced pharmacokinetics<break/>• Short half-life</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B55-ijms-21-05432\" ref-type=\"bibr\">55</xref>,<xref rid=\"B56-ijms-21-05432\" ref-type=\"bibr\">56</xref>,<xref rid=\"B57-ijms-21-05432\" ref-type=\"bibr\">57</xref>,<xref rid=\"B58-ijms-21-05432\" ref-type=\"bibr\">58</xref>,<xref rid=\"B59-ijms-21-05432\" ref-type=\"bibr\">59</xref>,<xref rid=\"B60-ijms-21-05432\" ref-type=\"bibr\">60</xref>,<xref rid=\"B61-ijms-21-05432\" ref-type=\"bibr\">61</xref>,<xref rid=\"B62-ijms-21-05432\" ref-type=\"bibr\">62</xref>,<xref rid=\"B63-ijms-21-05432\" ref-type=\"bibr\">63</xref>,<xref rid=\"B64-ijms-21-05432\" ref-type=\"bibr\">64</xref>,<xref rid=\"B65-ijms-21-05432\" ref-type=\"bibr\">65</xref>,<xref rid=\"B66-ijms-21-05432\" ref-type=\"bibr\">66</xref>,<xref rid=\"B67-ijms-21-05432\" ref-type=\"bibr\">67</xref>,<xref rid=\"B68-ijms-21-05432\" ref-type=\"bibr\">68</xref>,<xref rid=\"B69-ijms-21-05432\" ref-type=\"bibr\">69</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>ADC</bold>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">• High specificity<break/>• Remarkable results for AB working as single entities</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">• May trigger the immune system<break/>• High production costs<break/>• Some clinical trials showed no significant improvement of patient outcomes compared to canonical therapies</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B4-ijms-21-05432\" ref-type=\"bibr\">4</xref>,<xref rid=\"B70-ijms-21-05432\" ref-type=\"bibr\">70</xref>,<xref rid=\"B71-ijms-21-05432\" ref-type=\"bibr\">71</xref>,<xref rid=\"B72-ijms-21-05432\" ref-type=\"bibr\">72</xref>,<xref rid=\"B73-ijms-21-05432\" ref-type=\"bibr\">73</xref>,<xref rid=\"B74-ijms-21-05432\" ref-type=\"bibr\">74</xref>,<xref rid=\"B75-ijms-21-05432\" ref-type=\"bibr\">75</xref>,<xref rid=\"B76-ijms-21-05432\" ref-type=\"bibr\">76</xref>,<xref rid=\"B77-ijms-21-05432\" ref-type=\"bibr\">77</xref>,<xref rid=\"B78-ijms-21-05432\" ref-type=\"bibr\">78</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>Reconfigurable Organisms</bold>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">• Extremely configurable organisms<break/>• High drug loading efficiency<break/>• Autonomous, self-repairing system</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">• Machine learning is still unripe<break/>• Research in this field is still in the embryonic stage</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B79-ijms-21-05432\" ref-type=\"bibr\">79</xref>]</td></tr><tr><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">\n<bold>EVs</bold>\n</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">• High biocompatibility due to the endogenous origin<break/>• High specificity<break/>• Can be highly modified in order to enhance or modify tissue specificity<break/>• Due to the phospholipid bilayer, they can easily fuse directly to the target’s plasma membrane<break/>• Presence of specific surface markers that preserve them from phagocytosis<break/>• Functional complexity, not easy to artificially replicate</td><td align=\"left\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">• Generally low efficiency of EVs isolation methods<break/>• Lack of clinical evaluations<break/>• Lack of standardized isolation methods<break/>• Deepening of the understanding of intracellular production/packaging mechanisms still ongoing</td><td align=\"center\" valign=\"middle\" style=\"border-bottom:solid thin\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"B80-ijms-21-05432\" ref-type=\"bibr\">80</xref>,<xref rid=\"B81-ijms-21-05432\" ref-type=\"bibr\">81</xref>,<xref rid=\"B82-ijms-21-05432\" ref-type=\"bibr\">82</xref>,<xref rid=\"B83-ijms-21-05432\" ref-type=\"bibr\">83</xref>,<xref rid=\"B84-ijms-21-05432\" ref-type=\"bibr\">84</xref>,<xref rid=\"B85-ijms-21-05432\" ref-type=\"bibr\">85</xref>,<xref rid=\"B86-ijms-21-05432\" ref-type=\"bibr\">86</xref>,<xref rid=\"B87-ijms-21-05432\" ref-type=\"bibr\">87</xref>,<xref rid=\"B88-ijms-21-05432\" ref-type=\"bibr\">88</xref>,<xref rid=\"B89-ijms-21-05432\" ref-type=\"bibr\">89</xref>,<xref rid=\"B90-ijms-21-05432\" ref-type=\"bibr\">90</xref>,<xref rid=\"B91-ijms-21-05432\" ref-type=\"bibr\">91</xref>,<xref rid=\"B92-ijms-21-05432\" ref-type=\"bibr\">92</xref>,<xref rid=\"B93-ijms-21-05432\" ref-type=\"bibr\">93</xref>,<xref rid=\"B94-ijms-21-05432\" ref-type=\"bibr\">94</xref>,<xref rid=\"B95-ijms-21-05432\" ref-type=\"bibr\">95</xref>,<xref rid=\"B96-ijms-21-05432\" ref-type=\"bibr\">96</xref>,<xref rid=\"B97-ijms-21-05432\" ref-type=\"bibr\">97</xref>,<xref rid=\"B98-ijms-21-05432\" ref-type=\"bibr\">98</xref>,<xref rid=\"B99-ijms-21-05432\" ref-type=\"bibr\">99</xref>,<xref rid=\"B100-ijms-21-05432\" ref-type=\"bibr\">100</xref>,<xref rid=\"B101-ijms-21-05432\" ref-type=\"bibr\">101</xref>,<xref rid=\"B102-ijms-21-05432\" ref-type=\"bibr\">102</xref>,<xref rid=\"B103-ijms-21-05432\" ref-type=\"bibr\">103</xref>,<xref rid=\"B104-ijms-21-05432\" ref-type=\"bibr\">104</xref>,<xref rid=\"B105-ijms-21-05432\" ref-type=\"bibr\">105</xref>,<xref rid=\"B106-ijms-21-05432\" ref-type=\"bibr\">106</xref>,<xref rid=\"B107-ijms-21-05432\" ref-type=\"bibr\">107</xref>,<xref rid=\"B108-ijms-21-05432\" ref-type=\"bibr\">108</xref>,<xref rid=\"B109-ijms-21-05432\" ref-type=\"bibr\">109</xref>,<xref rid=\"B110-ijms-21-05432\" ref-type=\"bibr\">110</xref>,<xref rid=\"B111-ijms-21-05432\" ref-type=\"bibr\">111</xref>,<xref rid=\"B112-ijms-21-05432\" ref-type=\"bibr\">112</xref>,<xref rid=\"B113-ijms-21-05432\" ref-type=\"bibr\">113</xref>,<xref rid=\"B114-ijms-21-05432\" ref-type=\"bibr\">114</xref>,<xref rid=\"B115-ijms-21-05432\" ref-type=\"bibr\">115</xref>,<xref rid=\"B116-ijms-21-05432\" ref-type=\"bibr\">116</xref>,<xref rid=\"B117-ijms-21-05432\" ref-type=\"bibr\">117</xref>,<xref rid=\"B118-ijms-21-05432\" ref-type=\"bibr\">118</xref>,<xref rid=\"B119-ijms-21-05432\" ref-type=\"bibr\">119</xref>,<xref rid=\"B120-ijms-21-05432\" ref-type=\"bibr\">120</xref>,<xref rid=\"B121-ijms-21-05432\" ref-type=\"bibr\">121</xref>,<xref rid=\"B122-ijms-21-05432\" ref-type=\"bibr\">122</xref>,<xref rid=\"B123-ijms-21-05432\" ref-type=\"bibr\">123</xref>,<xref rid=\"B124-ijms-21-05432\" ref-type=\"bibr\">124</xref>,<xref rid=\"B125-ijms-21-05432\" ref-type=\"bibr\">125</xref>,<xref rid=\"B126-ijms-21-05432\" ref-type=\"bibr\">126</xref>,<xref rid=\"B127-ijms-21-05432\" ref-type=\"bibr\">127</xref>,<xref rid=\"B128-ijms-21-05432\" ref-type=\"bibr\">128</xref>,<xref rid=\"B129-ijms-21-05432\" ref-type=\"bibr\">129</xref>,<xref rid=\"B130-ijms-21-05432\" ref-type=\"bibr\">130</xref>,<xref rid=\"B131-ijms-21-05432\" ref-type=\"bibr\">131</xref>,<xref rid=\"B132-ijms-21-05432\" ref-type=\"bibr\">132</xref>,<xref rid=\"B133-ijms-21-05432\" ref-type=\"bibr\">133</xref>,<xref rid=\"B134-ijms-21-05432\" ref-type=\"bibr\">134</xref>,<xref rid=\"B135-ijms-21-05432\" ref-type=\"bibr\">135</xref>,<xref rid=\"B136-ijms-21-05432\" ref-type=\"bibr\">136</xref>,<xref rid=\"B137-ijms-21-05432\" ref-type=\"bibr\">137</xref>,<xref rid=\"B138-ijms-21-05432\" ref-type=\"bibr\">138</xref>,<xref rid=\"B139-ijms-21-05432\" ref-type=\"bibr\">139</xref>,<xref rid=\"B140-ijms-21-05432\" ref-type=\"bibr\">140</xref>,<xref rid=\"B141-ijms-21-05432\" ref-type=\"bibr\">141</xref>,<xref rid=\"B142-ijms-21-05432\" ref-type=\"bibr\">142</xref>,<xref rid=\"B143-ijms-21-05432\" ref-type=\"bibr\">143</xref>,<xref rid=\"B144-ijms-21-05432\" ref-type=\"bibr\">144</xref>,<xref rid=\"B145-ijms-21-05432\" ref-type=\"bibr\">145</xref>,<xref rid=\"B146-ijms-21-05432\" ref-type=\"bibr\">146</xref>,<xref rid=\"B147-ijms-21-05432\" ref-type=\"bibr\">147</xref>,<xref rid=\"B148-ijms-21-05432\" ref-type=\"bibr\">148</xref>,<xref rid=\"B149-ijms-21-05432\" ref-type=\"bibr\">149</xref>,<xref rid=\"B150-ijms-21-05432\" ref-type=\"bibr\">150</xref>,<xref rid=\"B151-ijms-21-05432\" ref-type=\"bibr\">151</xref>,<xref rid=\"B152-ijms-21-05432\" ref-type=\"bibr\">152</xref>]</td></tr></tbody></table></table-wrap></floats-group></article>\n"
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