bluuebunny/crossref_metadata_2025_split huggingface dataset:
Genomics and transcriptomics of epizoic Seisonidea (Rotifera, syn. Syndermata) reveal strain formation and gradual gene loss with growing ties to the host
Authors: Mauer Katharina M. , Schmidt Hanno , Dittrich Marco , Fröbius Andreas C. , Hellmann Sören Lukas , Zischler Hans , Hankeln Thomas , Herlyn Holger 
Published: August 2021

AbstractBackgroundSeisonidea (also Seisonacea or Seisonidae) is a group of small animals living on marine crustaceans (Nebaliaspec.) with only four species described so far. Its monophyletic origin with mostly free-living wheel animals (Monogononta, Bdelloidea) and endoparasitic thorny-headed worms (Acanthocephala) is widely accepted. However, the phylogenetic relationships inside the Rotifera-Acanthocephala clade (Rotiferasensulatoor Syndermata) are subject to ongoing debate, with consequences for our understanding of how genomes and lifestyles might have evolved. To gain new insights, we analyzed first drafts of the genome and transcriptome of the key taxon Seisonidea.ResultsAnalyses of gDNA-Seq and mRNA-Seq data uncovered two genetically distinct lineages inSeison nebaliaeGrube, 1861 off the French Channel coast. Their mitochondrial haplotypes shared only 82% sequence identity despite identical gene order. In the nuclear genome, distinct linages were reflected in different gene compactness, GC content and codon usage. The haploid nuclear genome spans ca. 46 Mb, of which 96% were reconstructed. According to ~ 23,000 SuperTranscripts, gene number inS. nebaliaeshould be within the range published for other members of Rotifera-Acanthocephala. Consistent with this, numbers of metazoan core orthologues and ANTP-type transcriptional regulatory genes in theS. nebaliaegenome assembly were between the corresponding numbers in the other assemblies analyzed. We additionally provide evidence that a basal branching of Seisonidea within Rotifera-Acanthocephala could reflect attraction to the outgroup. Accordingly, rooting via a reconstructed ancestral sequence led to monophyletic Pararotatoria (Seisonidea+Acanthocephala) within Hemirotifera (Bdelloidea+Pararotatoria).ConclusionMatching genome/transcriptome metrics with the above phylogenetic hypothesis suggests that a haploid nuclear genome of about 50 Mb represents the plesiomorphic state for Rotifera-Acanthocephala. Smaller genome size inS. nebaliaeprobably results from subsequent reduction. In contrast, genome size should have increased independently in monogononts as well as bdelloid and acanthocephalan stem lines. The present data additionally indicate a decrease in gene repertoire from free-living to epizoic and endoparasitic lifestyles. Potentially, this reflects corresponding steps from the root of Rotifera-Acanthocephala via the last common ancestors of Hemirotifera and Pararotatoria to the one of Acanthocephala. Lastly, rooting via a reconstructed ancestral sequence may prove useful in phylogenetic analyses of other deep splits.
Integrated miRNA and transcriptome profiling to explore the molecular determinism of convergent adaptation to corn in two lepidopteran pests of agriculture
Authors: Gimenez Sylvie , Seninet Imène , Orsucci Marion , Audiot Philippe , Nègre Nicolas , Nam Kiwoong , Streiff Réjane , d’Alençon Emmanuelle 
Published: August 2021

Abstract
Background
The degree to which adaptation to same environment is determined by similar molecular mechanisms, is a topic of broad interest in evolutionary biology, as an indicator of evolutionary predictability. We wished to address if adaptation to the same host plant in phytophagous insects involved related gene expression patterns. We compared sRNA-Seq and RNA-Seq data between two pairs of taxa of Ostrinia and Spodoptera frugiperda sharing maize as host-plant. For the latter, we had previously carried out a reciprocal transplant experiment by feeding of the larvae of the Corn strain (Sf-C) and the Rice strain (Sf-R) on corn versus rice and characterized the mRNA and miRNA responses.

Results
First, we predicted the genes encoding miRNA in Ostrinia nubilalis (On) and O. scapulalis (Os). Respectively 67 and 65 known miRNA genes, as well as 196 and 190 novel ones were predicted with Os genome using sncRNAs extracted from whole larvae feeding on corn or mugwort. In On, a read counts analysis showed that 37 (55.22%) known miRNAs and 19 (9.84%) novel miRNAs were differentially expressed (DE) on mugwort compared to corn (in Os, 25 known miRs (38.46%) and 8 novel ones (4.34%)). Between species on corn, 8 (12.5%) known miRNAs and 8 (6.83%) novel ones were DE while only one novel miRNA showed expression variation between species on mugwort. Gene target prediction led to the identification of 2953 unique target genes in On and 2719 in Os, among which 11.6% (344) were DE when comparing species on corn. 1.8% (54) of On miR targets showed expression variation upon a change of host-plant.
We found molecular changes matching convergent phenotype, i.e., a set of nine miRNAs that are regulated either according to the host-plant both in On and Sf-C or between them on the same plant, corn. Among DE miR target genes between taxa, 13.7% shared exactly the same annotation between the two pairs of taxa and had function related to insect host-plant interaction.

Conclusion
There is some similarity in underlying genetic mechanisms of convergent evolution of two distant Lepidopteran species having adopted corn in their host range, highlighting possible adaptation genes.
Factors associated with IgG levels in adults with IgG subclass deficiency
Authors: Barton James C. , Barton Jackson Clayborn , Bertoli Luigi F. , Acton Ronald T. 
Published: August 2021

Abstract
Background
Factors associated with IgG levels in adults with IgG subclass deficiency (IgGSD) are incompletely understood. We studied adults with IgGSD with subnormal IgG1 only, subnormal IgG1/IgG3, or subnormal IgG3 only without other subnormal IgG subclasses, IgA, or IgM. We compiled: age; sex; autoimmune condition(s) (AC); atopy; IgG, IgG subclasses, IgA, IgM; IgGsum (IgG1 + IgG2 + IgG3 + IgG4); and D (percentage difference between IgGsum and IgG). We compared attributes of patients with/without subnormal IgG (< 7.00 g/L; subnormal IgG1 subclass groups only) and analyzed IgGsum and IgG relationships. We performed backward stepwise regressions on IgG using independent variables IgG subclasses, age, and sex and on D using independent variables age and sex.

Results
There were 39 patients with subnormal IgG1 only (89.7% women), 53 with subnormal IgG1/IgG3 (88.7% women), and 115 with subnormal IgG3 only (91.3% women). Fifteen patients (38.5%) and 32 patients (60.4%) in the respective subnormal IgG1 subclass groups had subnormal IgG. Attributes of patients with/without IgG < 7.00 g/L were similar, except that AC prevalence was lower in patients with subnormal IgG1 only and IgG < 7.00 g/L than ≥ 7.00 g/L (p = 0.0484). Mean/median IgG1 and IgG2 were significantly lower in patients with IgG < 7.00 g/L in both subnormal IgG1 subclass groups (p < 0.0001, all comparisons). Regressions on IgG in three subclass groups revealed positive associations with IgG1 and IgG2 (p < 0.0001 each association). Regressions on D revealed no significant association. IgG1 percentages of IgGsum were lower and IgG2 percentages were higher in patients with subnormal IgG1 subclass levels than subnormal IgG3 only (p < 0.0001 all comparisons).

Conclusions
We conclude that both IgG1 and IgG2 are major determinants of IgG in patients with subnormal IgG1, combined subnormal IgG1/IgG3, or subnormal IgG3 and that in patients with subnormal IgG1 or combined subnormal IgG1/IgG3, median IgG2 levels are significantly lower in those with IgG < 7.00 g/L than those with IgG ≥ 7.00 g/L.
Association between eNOS rs1799983 polymorphism and hypertension: a meta-analysis involving 14,185 cases and 13,407 controls
Authors: Shi Jikang , Liu Siyu , Guo Yanbo , Liu Sainan , Xu Jiayi , Pan Lingfeng , Hu Yueyang , Liu Yawen , Cheng Yi 
Published: August 2021

Abstract
Background
Essential hypertension is a complex disease determined by the interaction of genetic and environmental factors, eNOS is considered to be one of the susceptible genes for hypertension. Our study aimed to evaluate the association between eNOS rs1799983 polymorphism and hypertension, and to provide evidence for the etiology of hypertension.

Methods
Case–control studies of eNOS rs1799983 polymorphism and hypertension were included by searching PubMed, Embase, Web of Science, Medline, Scopus, WanFang datebase, Vip datebase, and CNKI database according to PRISMA guideline. Eligible data were extracted and pooled, and were analyzed using R software based on five different genetic models.

Results
A total of 60 eligible articles involving 14,185 cases and 13,407 controls were finally selected. We found significant association between eNOS rs1799983 polymorphism and hypertension under any genetic model (T vs G: OR = 1.44, 95% CI 1.26–1.63; GT vs GG: OR 1.34, 95% CI 1.18–1.52; TT vs GG: OR 1.80, 95% CI 1.41–2.31; GT + TT vs GG: OR 1.42, 95% CI 1.25–1.63; TT vs GG + GT: OR 1.68, 95% CI 1.35–2.08; GT vs GG + TT: OR 1.24, 95% CI 1.11–1.40).

Conclusions
We found that eNOS rs1799983 polymorphism is associated with the increased risk of hypertension under any genetic model. Moreover, investigations of gene–gene and gene-environment interactions are needed to give more insight into the association between eNOS rs1799983 polymorphism and hypertension.
Circulating lymphocyte subsets as promising biomarkers to identify septic patients at higher risk of unfavorable outcome
Authors: Polilli Ennio , Esposito Jessica Elisabetta , Frattari Antonella , Trave Francesca , Sozio Federica , Ferrandu Giovanna , Di Iorio Giancarlo , Parruti Giustino 
Published: August 2021

Abstract
Background
Early recognition of patients hospitalized for sepsis at higher risk of poor clinical outcome is a mandatory task and many studies suggested that indicators of the immune status may be useful for this purpose. We performed a retrospective, monocentric cohort study to evaluate whether lymphocyte subsets may be useful in predicting in-hospital mortality of septic patients.

Methods
Data of all consecutive patients with a diagnosis of sepsis at discharge and an available peripherical blood lymphocyte subset (CD4, CD8, CD16/CD56 and CD19) analysis at hospital entry were retrospectively collected between January 2015 and August 2018. Clinical characteristics of patients, past medical history and other laboratory parameters were also considered.

Results
Two-hundred-seventy-eight septic patients, 171 (61.5%) males, mean age 63.2 ± 19.6 years, were enrolled. Total counts of lymphocytes, CD4 T cells, CD8 T cells and B cells were found significantly lower in deceased than in surviving patients. At univariate analyses, CD4 T cells/µL (OR 0.99 for each incremental unit, 95%CI 0.99–1.10, p < 0.0001), age (OR 1.06, 95%CI 1.04–1.09, p < 0.0001), procalcitonin (OR 1.01, 95%CI 1.01–1.02, p < 0.0001) and female gender (OR 2.81, 95%CI 1.49–5.28, p = 0.001) were associated with in-hospital mortality. When a dichotomic threshold of < 400/µL for CD4 T cells as a dependent variable was considered in multivariate models, age (OR 1.04; 95%CI 1.01–1.09, p = 0.018); female gender (OR 3.18; 95%CI 1.40–7.20, p = 0.006), qSOFA (OR 4.00, 95%CI 1.84–8.67, p < 0.001) and CD4 T cells < 400/µL (OR 5.3; 95%CI 1.65–17.00, p = 0.005) were the independent predictors.

Conclusions
In adjunct to biomarkers routinely determined for the prediction of prognosis in sepsis, CD4 T lymphocytes, measured at hospital entry, may be useful in identifying patients at higher risk of in-hospital death.
A study to design minimum data set of COVID-19 registry system
Authors: Zarei Javad , Badavi Mohammad , Karandish Majid , Haddadzadeh Shoushtari Maryam , Dastoorpoor Maryam , Yousefi Farid , Raji Hanieh , Cheraghi Maria 
Published: August 2021

Abstract
Background
From the beginning of the COVID-19 pandemic, the development of infrastructures to record, collect and report COVID-19‏ ‏data has become a fundamental necessity in the world. The disease registry system can help build an infrastructure to collect data systematically. The study aimed to design a minimum data set for the COVID-19 registry system.

Methods
A qualitative study to design an MDS for the COVID-19 registry system was performed in five phases at Ahvaz University of Medical Sciences in Khuzestan Province in southwestern Iran, 2020–2021. In the first phase, assessing the information requirements was performed for the COVID-19 registry system. Data elements were identified in the second phase. In the third phase, the MDS was selected, and in the four phases, the COVID-19 registry system was implemented as a pilot study to test the MDS. Finally, based on the experiences gained from the COVID-19 registry system implementation, the MDS were evaluated, and corrections were made.

Results
MDS of the COVID-19 registry system contains eight top groups including administrative (34 data elements), disease exposure (61 data elements), medical history and physical examination (138 data elements), findings of clinical diagnostic tests (101 data elements), disease progress and outcome of treatment (55 data elements), medical diagnosis and cause of death (12 data elements), follow-up (14 data elements), and COVID-19 vaccination (19 data elements) data, respectively.

Conclusion
Creating a standard and comprehensive MDS can help to design any national data dictionary for COVID-19 and improve the quality of COVID-19 data.
Association between bacterial finding, antibiotic treatment and clinical course in patients with pharyngotonsillitis: a registry-based study in primary healthcare in Sweden
Authors: Pallon Jon , Sundqvist Martin , Rööst Mattias , Hedin Katarina 
Published: August 2021

Abstract
Background
The role of non-group A streptococci and Fusobacterium necrophorum in pharyngotonsillitis has been disputed and few prospective studies have evaluated the effect of antibiotic treatment. This study uses registry data to investigate the relation between antibiotic prescription for pharyngotonsillitis in primary healthcare and return visits for pharyngotonsillitis, complications, and tonsillectomy.

Methods
Retrospective data were extracted from the regional electronic medical record system in Kronoberg County, Sweden, for all patients diagnosed with pharyngotonsillitis between 2012 and 2016. From these data, two cohorts were formed: one based on rapid antigen detection tests (RADT) for group A streptococci (GAS) and one based on routine throat cultures for β-haemolytic streptococci and F. necrophorum. The 90 days following the inclusion visit were assessed for new visits for pharyngotonsillitis, complications, and tonsillectomy, and related to bacterial aetiology and antibiotic prescriptions given at inclusion.

Results
In the RADT cohort (n = 13,781), antibiotic prescription for patients with a positive RADT for GAS was associated with fewer return visits for pharyngotonsillitis within 30 days compared with no prescription (8.7% vs. 12%; p = 0.02), but not with the complication rate within 30 days (1.5% vs. 1.8%; p = 0.7) or with the tonsillectomy rate within 90 days (0.27% vs. 0.26%; p = 1). In contrast, antibiotic prescription for patients with a negative RADT was associated with more return visits for pharyngotonsillitis within 30 days (9.7% vs. 7.0%; p = 0.01). In the culture cohort (n = 1 370), antibiotic prescription for patients with Streptococcus dysgalactiae ssp. equisimilis was associated with fewer return visits for pharyngotonsillitis within 30 days compared with no prescription (15% vs. 29%; p = 0.03).

Conclusions
Antibiotic prescription was associated with fewer return visits for pharyngotonsillitis in patients with a positive RADT for GAS but with more return visits in patients with a negative RADT for GAS. There were no differences in purulent complications related to antibiotic prescription.
Bacteriology and mortality of necrotizing fasciitis in a tertiary coastal hospital with comparing risk indicators of methicillin-resistant Staphylococcus aureus and Vibrio vulnificus infections: a prospective study
Authors: Tsai Yao-Hung , Huang Tsung-Yu , Chen Jiun-Liang , Hsiao Cheng-Ting , Kuo Liang-Tseng , Huang Kuo-Chin 
Published: August 2021

Abstract
Background
Vibrio vulnificus has been reported as the leading causative pathogen of necrotizing fasciitis (NF) and related fatality in the coastal area. Necrotizing fasciitis caused by methicillin-resistant Staphylococcus aureus (MRSA) and V. vulnificus have high mortality rates. The purpose of this prospective study was to clarify the clinical characteristics between death and survival NF patients, to investigate bacteriologic profile and mortality of NF patients, and to compare risk indicators of MRSA and V. vulnificus NF patients.

Methods
This prospective study was conducted in 184 consecutive NF patients over a period of three years in a tertiary coastal hospital. Differences in mortality, laboratory findings, microbiology and clinical outcomes were compared between the death and survival groups, and the V. vulnificus and MRSA subgroups.

Results
Twenty patients died, resulting in a mortality rate of 10.9%, and there were 108 patients with a monomicrobial infection (58.7%). The death group had a significantly higher incidence of shock at emergency room and bacteremia than did the survival group. Vibrio species (40 cases) and S. aureus (31 cases) were the two major pathogens. Significant differences with respect to hepatic dysfunction, shock, the event with seawater or seafood contact, bacteremia, C-reactive protein, mean platelet counts, and the Laboratory Risk Indicator for Necrotizing Fasciitis (LRINEC) score were observes between V. vulnificus and MRSA groups.

Conclusions
NF patients with both hepatic dysfunction and diabetes mellitus, bacteremia and shock have significantly higher mortality. We should be aware of the increasing incidence of monomicrobial NF and higher mortality rates of Gram-negative pathogens in the warm coastal area. LRINEC score is not a suitable diagnostic indicator for V. vulnificus NF, which is more rapidly progressive and fulminant than MRSA NF. NF needed team works by early suspicion, immediate surgical intervention and aggressive care, which can successfully decrease mortality.
Chronic kidney disease diagnosis using decision tree algorithms
Authors: Ilyas Hamida , Ali Sajid , Ponum Mahvish , Hasan Osman , Mahmood Muhammad Tahir , Iftikhar Mehwish , Malik Mubasher Hussain 
Published: August 2021

Abstract
Background
Chronic Kidney Disease (CKD), i.e., gradual decrease in the renal function spanning over a duration of several months to years without any major symptoms, is a life-threatening disease. It progresses in six stages according to the severity level. It is categorized into various stages based on the Glomerular Filtration Rate (GFR), which in turn utilizes several attributes, like age, sex, race and Serum Creatinine. Among multiple available models for estimating GFR value, Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), which is a linear model, has been found to be quite efficient because it allows detecting all CKD stages.

Methods
Early detection and cure of CKD is extremely desirable as it can lead to the prevention of unwanted consequences. Machine learning methods are being extensively advocated for early detection of symptoms and diagnosis of several diseases recently. With the same motivation, the aim of this study is to predict the various stages of CKD using machine learning classification algorithms on the dataset obtained from the medical records of affected people. Specifically, we have used the Random Forest and J48 algorithms to obtain a sustainable and practicable model to detect various stages of CKD with comprehensive medical accuracy.

Results
Comparative analysis of the results revealed that J48 predicted CKD in all stages better than random forest with an accuracy of 85.5%. The study also showed that J48 shows improved performance over Random Forest.

Conclusions
The study concluded that it may be used to build an automated system for the detection of severity of CKD.
Real-time prediction of intradialytic relative blood volume: a proof-of-concept for integrated cloud computing infrastructure
Authors: Chaudhuri Sheetal , Han Hao , Monaghan Caitlin , Larkin John , Waguespack Peter , Shulman Brian , Kuang Zuwen , Bellamkonda Srikanth , Brzozowski Jane , Hymes Jeffrey , Black Mike , Kotanko Peter , Kooman Jeroen P. , Maddux Franklin W. , Usvyat Len 
Published: August 2021

AbstractBackgroundInadequate refilling from extravascular compartments during hemodialysis can lead to intradialytic symptoms, such as hypotension, nausea, vomiting, and cramping/myalgia. Relative blood volume (RBV) plays an important role in adapting the ultrafiltration rate which in turn has a positive effect on intradialytic symptoms. It has been clinically challenging to identify changes RBV in real time to proactively intervene and reduce potential negative consequences of volume depletion. Leveraging advanced technologies to process large volumes of dialysis and machine data in real time and developing prediction models using machine learning (ML) is critical in identifying these signals.MethodWe conducted a proof-of-concept analysis to retrospectively assess near real-time dialysis treatment data from in-center patients in six clinics using Optical Sensing Device (OSD), during December 2018 to August 2019. The goal of this analysis was to use real-time OSD data to predict if a patient’s relative blood volume (RBV) decreases at a rate of at least − 6.5 % per hour within the next 15 min during a dialysis treatment, based on 10-second windows of data in the previous 15 min. A dashboard application was constructed to demonstrate how reporting structures may be developed to alert clinicians in real time of at-risk cases. Data was derived from three sources: (1) OSDs, (2) hemodialysis machines, and (3) patient electronic health records.ResultsTreatment data from 616 in-center dialysis patients in the six clinics was curated into a big data store and fed into a Machine Learning (ML) model developed and deployed within the cloud. The threshold for classifying observations as positive or negative was set at 0.08. Precision for the model at this threshold was 0.33 and recall was 0.94. The area under the receiver operating curve (AUROC) for the ML model was 0.89 using test data.ConclusionsThe findings from our proof-of concept analysis demonstrate the design of a cloud-based framework that can be used for making real-time predictions of events during dialysis treatments. Making real-time predictions has the potential to assist clinicians at the point of care during hemodialysis.