dataesr/scientific-paragraphs-categorization huggingface dataset:
4,448 Science Direct (("Implementation OR "integration" OR "dissemination" OR "introduction" OR "adoption") AND ("Quality of Health Care "OR "innovation "OR certification OR accreditation) AND ("Nurses" OR "caregiver")) after using additional filters 4,364 Ebsco (("Implementation OR "integration" OR "dissemination" OR "introduction" OR "adoption") AND ("Quality of Health Care "OR "innovation "OR certification OR accreditation) AND ("Nurses" OR "caregiver")) after using additional filters 129 Total 9,950 Table  1  presents the search queries for each database source aligned with the output of articles. The "after using additional filters" term refers to added selection criteria to the search output, for example subject, field or domain and journal topic.
| Limitations This study had several limitations. Firstly, in terms of research output, we were limited to four research databases, which may have contributed to the low number of selected studies. However, to address this and identify maximum, quality studies, a robust threestep study selection method was incepted. Secondly, the subject of change was not specified, potentially leading to diverse and unsynchronized results. However, our interest was to map different factors and interventions, and not compare literature findings. Thus, factors responding to the same perspective were classified together, for example mentorship programmes, ongoing education and training were combined as staff skills and information under the factor or element.
, 2010) UK Changing mouth care practice To identify the prerequisites for achieving research-based mouth care practice in a district general hospital intensive care unit TA B L E 3 Included studies: A summary of the included studies Kite (1995) Authors Screening Included Eligibility USA Identification Information Systems To examine the social forces underlying Barr (2002) computer technology diffusion into Records identified from databases (n = 9950) USA Records after duplicate removal Additional records identified through other sources (n = 4) UK (n = 9369) Records' abstract screened (n = 425) Records excluded (n = 8948) Full-text articles assessed for eligibility (n = 98) Full-text articles excluded (n = 70) 15 studies relating to physicians and multidisciplinary professionals. 4 studies related to nurses or education 47 studies unrelated to change implementation (evaluation of impact, develop theories or models etc.
5, 10. 5, and 13 months. At the baseline visit, demographics, parent and household characteristics, medical history, and anthropometric measures (body weight, length, head circumference) were collected and a clinical examination was performed. At all subsequent visits, a clinical examination was conducted, and anthropometry was obtained. Lactation counseling was provided at each clinic visit for mothers of breastfed infants. Formula intake was monitored during each clinic visit and phone call. Dietary intake was assessed using a single 24-h recall conducted by a dietitian or other trained person at clinic visits at 4, 5, 6, 9 and 12 months of age as well as the 7. 5-and 10. 5-months phone call (results from the assessments conducted by phone are not presented due to potential data quality issues).
GI tolerance was assessed using the Infant Gastrointestinal Symptom Questionnaire (IGSQ)  (40)  during clinic visits at 4, 5, and 6 months of age. Blood samples were collected only for formula-fed infants at 4 and 6 months of age. Parent-reported adverse events (AE) were recorded throughout the study. This study was approved by a central research Ethics Committee (Comité de Protection des Personnes SUD-EST IV, Lyon, France) and the French national health authority (Agence Nationale de Sécurité du Medicament et des Produits du Santé). For all infants, written informed consent was obtained from the parents or legal guardian(s) (hereafter, "parents") prior to any study-related procedures. The trial was registered on clinicaltrials. gov as NCT03276663 and performed in accordance with the International Conference on Harmonization guidelines for Good Clinical Practice and the provisions of the Declaration of Helsinki and its amendments.
Results were compared against laboratory reference ranges. Dietary intake and feeding compliance were measured via feeding questionnaires at each clinic visit and phone call. Dietary intake was assessed as solid food and liquid consumption (other than study formula) over a typical day and converted into macronutrients and micronutrients using Nutrilog software (Marans, France). Compliance was determined by the reported number of days when study formula intake was interrupted. AEs (including prespecified events of interest milk allergy, cow's milk intolerance, lactose intolerance, eczema, atopic dermatitis, dry skin, seborrheic dermatitis, dermatitis, urticaria, and skin reaction) were recorded at each visit and categorized using Medical Dictionary for Regulatory Activities (MedDRA) version 19.
Statistical methods All statistical analyses were conducted using Statistical Analysis System software (SAS Institute, Cary, NC, United States) version 9. 4. The sample size was calculated based on the primary outcome of weight gain velocity (g/d). The non-inferiority margin was set at -3 g/day for boys and girls against both the WHO standards and the breastfed reference group according to guidelines from the American Academy of Pediatrics (47). Sample size calculations were performed separately by sex and by comparison group (WHO standards and breastfed infants). A Bonferroni multiplicity correction was applied to account for the two comparison groups. In a previous trial of high vs. low protein content infant formula conducted in France  (48) , boys grew slightly less than the WHO standard (-1.
The intent-to-treat (ITT) population included all subjects enrolled in either the formula-fed or breastfed group. The safety (SAF) population was comprised of all subjects from the ITT population who received at least one dose of the formula or were breastfed. The full analysis (FAS) dataset included all subjects from the SAF population with data from at least one post-enrollment visit. The per-protocol (PP) population included all subjects from the FAS population after excluding those with major protocol deviations believed to impact the primary analysis, such as interruption of study formula ≥5 consecutive days before age 4 months or ≥7 consecutive days from age 4-6 months, consumption of ≥4 teaspoons of complementary foods before age 4 months, or the 4-month clinic visit occurring outside of the visit window (113-127 days).
Results All screened infants were able to be enrolled in the trial and comprised the ITT population (n = 194, comprised of 72 formula-fed boys, 36 formula-fed girls, 51 breastfed boys, and 35 breastfed girls). During study conduct, average weight gain velocity data were monitored to assess agreement with sample size assumptions. It was observed that boys on average were growing with approximately the same velocity as the WHO standard and girls were growing a bit faster, with SDs in both groups similar to those anticipated, suggesting that the original sample size was likely overestimated. Therefore, it was decided in June 2019 to stop enrollment. The flow of subjects through the trial is shown in Figure  1. Two formula-fed infants who never received the study formula and one formula-fed infant lost to followup the day of enrollment were excluded leaving 191 in the SAF and FAS populations.
Acknowledgments We sincerely thank the parents and caregivers who consented to their infant's participation in the study, as well as the study teams at each site who substantially contributed to the successful completion of the study. We also thank  Peter Erdmann ,  Joyce Nowacki ,  Jasmin Tajeri Forman ,  Fabien Foltzer ,  Pamela Sun ,  Lionel Philippe , and  Johan Wildmoser  for assistance with study planning and operational support. EpidStrategies, a division of  ToxStrategies, Inc. , is acknowledged for providing medical writing support. Portions of these results were presented in abstract form at the 8th International Conference on Nutrition and Growth, August 2021.
Following a non-targeted approach aiming to detect a maximum of compounds in breast milk, the mass spectrometer was used in TIC (total ionic current) mode to obtain qualitative information on the identity of the detected compounds. However, data from the flame ionization detector (FID) were preferred for a semi-quantitative approach, in accordance with good practices  [45, 46]  and standard recommendations  [47]. Indeed, although the response factors are different from one molecule to another for FID, the disparities observed in the response factors for different compounds are smaller than for the mass spectrometer and generate less distortion of the chromatographic profiles with respect to the real compositional profile of the extract or product analyzed. The resulting profiles of the six methods reveal that the recovery of volatile compounds and their chemical classes differ significantly according to the extraction method, as shown by the FID area and mass spectrometry responses (Table  1 ).
Data Processing and Statistical Analyses Peak areas were integrated using the MSD Chemstation software (Agilent). Compounds were identified by comparison of their mass spectra with those of reference databases (Wiley version 11, National Institute of Standards and Technology-NIST version 17 and internal laboratory database). Authentic compounds, when available, were also injected under the same conditions. Retention times and mass spectra thus obtained were compared with those of compounds detected. Linear retention indices (LRIs) of compounds were calculated using n-alkane injections (C5 to C26) and compared with values from the literature. One-way analyses of variance (ANOVA) and least significant difference (LSD) multiple comparison tests (95% confidence level) were performed on FID peak areas obtained with the different extraction methods.
Coefficients of variation (CVs in %) were determined to compare the repeatability of each extraction method. CVs were calculated as the ratio of the standard deviation to the mean of the FID peak area values obtained (n = 3) for each compound and each extraction method. Values are expressed as a percentage. The evaluation of the discriminative ability of the D-HS-MultiSPME/Tenax method was displayed through the analysis of four individual milk samples. A principal component analysis (PCA) was applied to the FID peak areas of compounds (active variables) obtained from the analysis of individual milk samples collected from four women. In this analysis, the sum of the peak area of each chemical class was added as illustrative variables. XLSTAT software (version 011. 2. 08, Addinsoft, New York, NY, USA) was used to perform statistical analyses.
Acknowledgments: The authors are grateful to the mothers who donated milk. This work is part of the Ph. D. Thesis of Sarah Le Roy, who was a Ph. D. student on the MILKODOR project. The authors and the MILKODOR Consortium want to pay tribute to the memory of Sarah through this publication. The MILKODOR Consortium is composed of Fabrice Damon, Karine Durand, Magali Klaey-Tassone, Bruno Patris, Benoist Schaal, and Robert Soussignan (Centre for Smell, Taste and.
Acknowledgements We acknowledge the support of the  IdEx PhD program ,  University of Strasbourg  for funding MO. Grants from the  Agence Nationale pour la Recherche  ( ANR-19-CE44-0019 ) to N. V. and  ERC StG aLzINK  to  C. H. INSERM  to NV are acknowledged. We thank  Dr Fabienne Burlina  and  Dr Sandrine Sagan  for the synthesis protocol of  RW9 ,  Dr Silvie Ferlay  for access to the uorimeter,  Dr Thomas Hermans  and  Dr George Formon  for access to the atomic absorption spectroscope and  Dr Charlène Esmieu  for fruitful discussions.
The picture taken is then analyzed through Image J software to measure the fluorescence intensity and determine the methylxanthine concentration (mM). This graphical scheme was created in BioRender. We evaluated the efficiency of the IPMA system for detecting and quantifying caffeine in Ilex guayusa leaves. Nevertheless, we first validated the system with theophylline and caffeine in solution assays. This research demonstrates the versatility of the IPMA system and presents it as a useful, inexpensive, and reliable metabolite quantification method with the potential to be translated into an agricultural tool. Vol.
After incubation for 72 h at 37 • C, plates were maintained at room temperature, the culture medium was removed, and 100 µL volume of CellTiter-Glo reagent was added to each well. The bioluminescence was measured using a microplate reader Filtermax F5 Multi-Mode instrument (Molecular Devices) and Softmax software. CC 50 data were obtained from at least two independent experiments for each cell line and analyzed using Prism version 5. 01. The selectivity index (S. I. ) was estimated using the IC 50 s from mammalian cells divided by the IC 50 obtained against the asexual blood stage P. falciparum.
After frozen and thawing the plates, hemolyzed cultures were homogenized by vortexing the plates. The antiparasitic activity was determined using the HRP2 ELISA-based assay Malaria Ag Celisa kit (ref KM2159, Cellabs PTY LDT, Brookvale, Australia). The concentration at which the drugs were able to inhibit 50% of parasite growth (IC 50 ) was calculated with the inhibitory sigmoid E max model, with estimation of the IC 50 through nonlinear regression using a standard function of the R software (ICEstimator version 1. 2). IC 50 s are expressed as means of three to five experiments.
Statistical Analysis Data are presented as the median ± standard error of the mean (S. E. M. ) or the mean ± standard deviation (S. D. ). Statistical analysis was performed using the Prism version 5. 01 software (GraphPad Software, La Jolla, CA, USA). Statistical significance was assessed by performing the one-way analysis of variance (ANOVA) followed by post-test for multiple comparisons as indicated in each figure. Differences with p values < 0. 05 were considered significant.
Acknowledgments: The authors are thankful to  Rodrigo M. Justo  ( UFJF, Brazil ) for the valuable assistance in the determination of the elemental analyses and to  Olívia Moreira  and  Marcone A. L. de Oliveira  ( UFJF, Brazil ) for the valuable assistance in the determination of  ESI-MS , supported through the  MCT/FINEP/CT-INFRA 01/2013-REF 0633/13S  grant. S. D. -A. and N. B. thank the  Transfusion Medicine Service, Department of Laboratory Medicine, ASST Grande Ospedale Metropolitano Niguarda (Milan, Italy)  for providing blood samples for the parasite culture. H. C. Q. and D. R. M. M. thank the flow cytometry facility at  Fiocruz (Brazil)  for data acquisition.
Animals were fed twice per day using controlled amounts of food sufficient to induce an average body weight gain of ~ 1 and ~ 0. 4 kg (as recommended per Ellegaard Göttingen Minipigs for adequate development) per week in IR-O and Lean groups, respectively. Only the technical staff looking at the minipigs and the statistical consultant were aware of the minipigs assignment. For the exploratory outcome reported here, the data analyst and statistician that analyzed the data were blinded and only one scientist was aware of the treatments. Clamps and tissue collection. At 47 weeks of age, both a hyperinsulinemic-euglycemic (HIEC) and a hyperglycemic clamp (HGC) were performed in sequence in each minipig. Jugular and carotid permanent catheters were inserted surgically 4-7 days to the clamp procedures as described by Koopmans et al.
Study design This study is a multi-country prospective cohort study following "post-SAM" children (defined as children following anthropometric recovery from SAM) and matched community controls (defined as children who did not previously experience AM) monthly for six months. During the follow-up period, children are assessed for AM as well as linear growth, morbidity, and mortality outcomes. Follow-up procedures and data collection are identical across both post-SAM and control groups. This study design enables the quantification of relapse among post-SAM children, but also to determine the relative risk for, and excess burden of, AM between post-SAM children and their matched community controls. Individual-, household-, and community-level information will be analyzed to identify potential risk-factors for relapse, with a specific focus on associations between WASH-related exposures and post-discharge outcomes.
Main study Recruitment of the post-SAM cohort occurs at OTP or SFP sites supported by Action Against Hunger. Recruitment of the control children occurs through referrals from trained community health workers (CHWs) performing regular nutrition screening in the communities. Each week, these CHWs provide a list of age ranges and genders that match the post-SAM children who were enrolled in the study the previous week. The CHWs then use these age and gender parameters to guide the recruitment of matched controls. The timing of control children enrollment matches as closely as possible to the timing of matched post-SAM children enrollment, which is occurring on a rolling basis. Once enrollment criteria are confirmed and informed consent has been taken, trained data collectors evaluate the child's acute malnutrition status by measuring anthropometry and assessing edema.
For post-SAM children, medical records during initial SAM treatment prior to enrollment are obtained to collect all information regarding each child's health and nutrition parameters throughout SAM treatment. This includes weekly anthropometrics measurements (weight, height, MUAC, and edema), length of treatment, and symptoms of co-morbidities present during treatment. All caregivers, including both post-SAM and control groups, are asked to bring children back to the nutrition clinic site for a monthly follow-up visits post-discharge for a period of six months. Data collection schedules and procedures are identical across both study groups. At each follow-up visit, the child's anthropometric measurements (weight, length, and MUAC) are collected and edema reassessed. An interview with the caregiver is conducted to collect information regarding child illness symptoms, receipt of any medical care, participation in any other assistance programs, household food security, household WASH, etc.
At each follow-up point, the child is classified with one of the following outcomes: •Without acute malnutrition, defined as having a MUAC ≥ 125 mm, WHZ ≥ -2, and without edema •Relapsed to SAM, defined as having MUAC < 115 mm, WHZ < -3, and/or edema •Relapsed to MAM, defined as having a MUAC of 115-124 mm or WHZ > -3 and ≤ -2 without edema •Having died •Defaulted, defined as failure to complete the followup visit within 3 weeks of the scheduled visit If a child is identified to have either SAM or MAM, he/ she is referred and treated accordingly. Clinical treatment data for all relapses is retrieved from the corresponding clinic. Regardless of whether the child relapses, the child will remain in the study for the full 6-month follow-up period. Caregivers who miss scheduled follow-up visits are sought by CHWs at their homes and encouraged to return on the next day to complete the child's scheduled follow-up visit.
Costs sub-study To gather data regarding the financial costs of relapse, financial data regarding the CMAM programmatic costs will be collected through a review of program financial records and interviews with relevant staff. All estimates will be confirmed with receipts, invoices, and other financial records when possible. Details of collecting and analyzing costing data will be detailed in a separate publication. Ultimately, this process will lead to the development of cost-efficiency results that will provide information related to the costs of re-treating children for relapse as an indication for the cost-efficiency of current CMAM protocols in achieving sustained recovery.
WASH sub-study The WASH sub-study involves additional data collection from the post-SAM study participants only (Table  2 ). In all three sites (Mali, Somalia and South Sudan), data collection includes: (1) a household WASH questionnaire and (2) collection and analysis of household drinking water samples, at three time-points across the follow-up period (enrollment, 3-months, and 6-months post-discharge). In only one site (South Sudan), the study collects: (1) child food samples at the household at time of SAM recovery from the CMAM program; (2) stool samples by rectal swab at time of SAM recovery from the CMAM program; and (3) dried blood spots (DBS) at the time of admission into OTP, four weeks into treatment, at the time of SAM recovery from the CMAM program, and at month one and month four post-discharge.
05, a beta of 0. 80, assuming an incidence rate in the control group of 5%, a rho of 0. 007 (to account for some clustering in South Sudan and Mali, based on previous relapse studies  [30, 31] ), and a maximum 5% LTFU, 614 post-SAM and 306 control children will be enrolled in each country, which is sufficient to detect a RR of 2. 3 in South Sudan and Mali, and a RR of 2. 0 in Somalia (no clustering given that all data is coming from one clinic. ), and a RR of 1. 7 in a combined country analysis. Given the calculations, the total number of participants enrolled in the study will be 2,760. The sample size was calculated with the use of Stata Version 13. 0 software (StataCorp LP, College Station, Texas, USA). Regarding the WASH sub-study analysis, the sub-study is considered exploratory as there is a lack of published work concerning the influence of WASH conditions, drinking water and food contamination, and enteric pathogen detection on SAM relapse rates upon which to base our estimates for calculations.
Using the same conditions as for the main sample size calculation described above-a power of 0. 8, an alpha of 0. 05, and assuming the true proportion relapsing within 6 months of discharge to be 12%, the minimum detectable differences (MDD) between exposed and unexposed groups (e. g. , proportion with/without adequate household WASH, proportion with/without enteric pathogens detected, and proportion with/without contaminated food and drinking water) under different exposure scenarios (i. e. proportion exposed from 10% -80%) was calculated. Calculations were performed using the R package "powerMediation", function "SSizeLogisticBin". A sample size of 614 in each country would be adequate to detect statistically significant differences in the proportion relapsing of approximately at least 20 percentage points between groups and under different exposure scenarios.
Data analysis Anthropometric indices will be computed using the WHO's 2006 Child Growth Standards  [32]. In binary analyses examining differences in participant enrollment characteristics between the post-SAM and control groups, measurements will be compared using Chi-square tests for categorical parameters and paired sample t-test and Wilcoxon sign rank test for matched continuous parameters. The analytical approach will include analyses on an individual country-level as well as using a combined dataset from all three country contexts. Relapse rates (including relapse to SAM, MAM, and AM) will be calculated as 1) a cumulative incidence, defined as the total number of children who experience at least one episode of relapse divided by the total number of children at risk; 2) an incidence rate, defined as the total number of episodes divided by the total person-time (expressed in 100 person months); and 3) point prevalence of SAM, MAM, and AM at the different follow-up points across the six-month follow-up period.