bluuebunny/crossref_metadata_2025_split huggingface dataset:
Scalable microbial strain inference in metagenomic data using StrainFacts
Authors: Smith Byron J. , Li Xiangpeng , Shi Zhou Jason , Abate Adam , Pollard Katherine S. 
Published: February 2022

AbstractWhile genome databases are nearing a complete catalog of species commonly inhabiting the human gut, their representation of intraspecific diversity is lacking for all but the most abundant and frequently studied taxa. Statistical deconvolution of allele frequencies from shotgun metagenomic data into strain genotypes and relative abundances is a promising approach, but existing methods are limited by computational scalability. Here we introduce StrainFacts, a method for strain deconvolution that enables inference across tens of thousands of metagenomes. We harness a “fuzzy” genotype approximation that makes the underlying graphical model fully differentiable, unlike existing methods. This allows parameter estimates to be optimized with gradient-based methods, speeding up model fitting by two orders of magnitude. A GPU implementation provides additional scalability. Extensive simulations show that StrainFacts can perform strain inference on thousands of metagenomes and has comparable accuracy to more computationally intensive tools. We further validate our strain inferences using single-cell genomic sequencing from a human stool sample. Applying StrainFacts to a collection of more than 10,000 publicly available human stool metagenomes, we quantify patterns of strain diversity, biogeography, and linkage-disequilibrium that agree with and expand on what is known based on existing reference genomes. StrainFacts paves the way for large-scale biogeography and population genetic studies of microbiomes using metagenomic data.
Single-Cell Transcriptome Analysis Identifies Novel Biomarkers Involved in Major Liver Cancer Subtypes
Authors: Swain Asish Kumar , Pandey Prashant , Sera Riddhi , Yadav Pankaj 
Published: February 2022

AbstractLiver cancers including hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) are leading cause of death worldwide. Single-cell transcriptomics studies have vast potential in advancing our understanding of cancers by defining the cellular composition of different solid tumor types. We peformed an integrated analysis using single-cell RNA sequencing (scRNA-seq) data from cancerous and healthy liver tissues in order to identify the molecular progression and intercellular heterogeneity across cell types in the liver cancer. Moreover, we performed a subtype specific analyses, separately for HCC and ICC, to identify any molecular drivers uniquely associated with these liver cancers. The scRNA-seq dataset comprising 5 healthy controls and 19 liver cancer patients were collected from Human Cell Atlas and Gene Expression Omnibus (GEO), respectively. Our analyses confirmed upregulation of four previously known malignant cell marker genes, namely, EPCAM, KRT19, KRT7 and S100P in the cancerous liver cells. Of these, KRT7 gene has been reported to be associated with ovarian cancer in the past studies. Noteworthy, four marker genes specific to the G1/S (MCM5 and PCNA) and G2/M phases (HMGB2 and CKS2) of the cell cycle were upregulated in the cancerous liver cells. This indicates that these four marker genes are actively dividing in these two phases in cancerous cells as compared to normal liver cells. Our differential expression analysis identified 2 upregulated genes (ATF3 and S100A11) and 2 downregulated genes (FCN3 and FGB) in the liver cancer. Our subtype based differential expression analysis identified 4 genes (HSPA6, LMNA, ATP1B1 and DCXR) specific to HCC and 3 genes (HSPB1, APOC3 and APOA1) specific to ICC. CD4+ T-cell, Hepatocyte, neutrophil, mesenchymal cells and liver bud hepatic cells are the predominant cell-types in liver cells. Our scRNA-seq study revealed the mesenchymal cells as potential malignant cell types in liver cancers. Our work suggests future research on developing liver cancer subtypes therapies could target these cell types and associated molecular markers.
Melanoma Skin Cancer Detection using Deep Learning
Authors: Santos Dheiver 
Published: February 2022

AbstractData from the World Health Organization (WHO) indicate a worldwide occurrence of 2 to 3 million cases of non-melanoma skin cancer annually. The American Cancer Society estimates that the incidence reaches 5.4 million in the United States alone. In cases of fatal diseases, early detection received great attention from the population and the media due to the premise that the earlier a cancer is identified, the greater the chances of cure. It is to be believed that the application of automated methods will help in early diagnosis, especially with the set of images with a variety of diagnoses. Thus, this article presents a system for recognizing dermatological diseases through images with lesions, a machine intervention in contrast to conventional detection based on medical personnel. Our model is designed in three phases, committing to data collection and augmentation, model development, and finally, prediction. We used various AI algorithms such as ANN with image processing tools to form a better structure, leading to higher accuracy of 89%.Contactdheiver.santos@ictbridge.org, dheiver.santos@gmail.com
LGE-MRI for diagnosis of left atrial cardiomyopathy as identified in high-definition endocardial voltage and conduction velocity mapping
Authors: Nairn Deborah , Eichenlaub Martin , Müller-Edenborn Björn , Lehrmann Heiko , Nagel Claudia , Azzolin Luca , Luongo Giorgio , Figueras Ventura Rosa M. , Forcada Barbara Rubio , Colomer Anna Vallès , Arentz Thomas , Dössel Olaf , Loewe Axel , Jadidi Amir 
Published: February 2022

AbstractBackgroundElectro-anatomical voltage, conduction velocity (CV) mapping and late-gadolinium-enhancement-magnetic-resonance-imaging (LGE-MRI) are different diagnostic modalities for atrial cardiomyopathy (ACM). However, discordances remain in the location and extent of detected ACM.Objectives(1) Comparison of ACM extent and location between current modalities. (2) Development of new estimated optimised image-intensity-thresholds (EOIIT) for LGE-MRI identifying patients with ACM.MethodsThirty-six ablation-naive persistent AF patients underwent LGE-MRI and high-definition electro-anatomical mapping in sinus rhythm. Significant ACM was defined as low-voltage-substrate (LVS) extent≥5% of the left atrium (LA) surface at <0.5 mV. LGE areas were classified using the Utah, image-intensity-ratio (IIR>1.20) and new EOIIT method for comparison to LVS and slow-conductionareas <0.2 m/s. ROC-analysis determined the LGE-extent enabling accurate diagnosis of ACM.ResultsThe degree and distribution of detected pathological substrate varied significantly (p<0.001) across the mapping modalities: 3% (IQR 0-12%) of the LA displayed LVS<0.5 mV vs. 14% (3-25%) slow-conduction-areas<0.2 m/s vs. 16% (6-32%) LGE with Utah method vs. 17%(11-24%) using IIR>1.20, with enhanced discrepancies on posterior LA. A linear correlation was found between the OIIT and each patient’s mean blood pool intensity (R2=0.89, p<0.001). LGE-MRI-based ACM-diagnosis improved with the novel EOIIT (83% sensitivity, 88% specificity, AUC:0.94) in comparison to the Utah method (60% sensitivity, 75% specificity, AUC:0.76), and IIR>1.20 (58% sensitivity, 75% specificity, AUC:0.71)ConclusionImportant discordances in distribution of pathological substrate exist between LA-LVS, CV and LGE-MRI, irrespective of the LGE-detection protocol that is used. However, the new EOIIT method improves LGE-MRI-based ACM-diagnosis in ablation-naive AF-patients.
A large outbreak of COVID-19 in a UK prison, October 2020 to April 2021
Authors: Adamson James P. , Smith Christopher , Pacchiarini Nicole , Connor Thomas Richard , Wallsgrove Janet , Coles Ian , Frost Clare , Edwards Angharad , Sinha Jaisi , Moore Catherine , Perrett Steph , Craddock Christie , Sawyer Clare , Waldram Alison , Barrasa Alicia , Thomas Daniel Rh. , Daniels Philip , Lewis Heather 
Published: February 2022

AbstractIntroductionPrisons are susceptible to outbreaks. Control measures focusing on isolation and cohorting negatively affect wellbeing. We present an outbreak of COVID-19 in a large male prison in Wales, UK, October 2020 to April 2021, and discuss control measures.MethodsWe gathered case-information, including demographics, staff-residence postcode, resident cell number, work areas/dates, test results, staff interview dates/notes and resident prison-transfer dates. Epidemiological curves were mapped by prison location. Control measures included isolation (exclusion from work or cell-isolation), cohorting (new admissions and work-area groups), asymptomatic testing (case-finding), removal of communal dining and movement restrictions. Facemask use and enhanced hygiene were already in place. Whole genome sequencing (WGS) and interviews determined genetic relationship between cases plausibility of transmission.ResultsOf 453 cases, 53% (n=242) were staff, most aged 25-34 years (11.5% females, 27.15% males) and symptomatic (64%). Crude attack-rate was higher in staff (29%, 95%CI: 26-64%) than in residents (12%, 95%CI: 9-15%).ConclusionsWhole genome sequencing can help differentiate multiple introductions from person-to-person transmission in prisons. It should be introduced alongside asymptomatic testing as soon as possible to control prison outbreaks. Timely epidemiological investigation, including data visualisation, allowed dynamic risk assessment and proportionate control measures, minimising reduction in resident welfare.
Evaluating binary classifiers: extending the Efficiency Index
Authors: Larner Andrew J 
Published: February 2022

AbstractAn “efficiency index” (EI) for the evaluation of binary classifiers was recently characterised, where EI is the ratio of classifier accuracy to inaccuracy. The purpose of this study was to further develop EI by substituting balanced accuracy and unbiased accuracy in place of accuracy, and their respective complements in place of inaccuracy, to construct balanced EI and unbiased EI measures. Additional investigations, using the dataset of a prospective pragmatic test accuracy study of a cognitive screening instrument, explored use of the log method to calculate confidence intervals for the various EI formulations; the dependence of EI formulations on prevalence; and comparison of EI formulations with analogous formulations based on the Identification Index (II), a previously described metric which is also based on accuracy and inaccuracy, where II is accuracy minus inaccuracy. EI formulations are shown to have advantages over II formulations, in particular their boundary values (0 and ∞) mean that negative values never occur, unlike the case for II, and the inflection point of 1 demarcates likelihood of correct versus incorrect classification.
Risk factors for severe COVID-19 differ by age: a retrospective study of hospitalized adults
Authors: Molani Sevda , Hernandez Patricia V. , Roper Ryan T. , Duvvuri Venkata R. , Baumgartner Andrew M. , Goldman Jason D. , Ertekin-Taner Nilüfer , Funk Cory C. , Price Nathan D. , Rappaport Noa , Hadlock Jennifer J. 
Published: February 2022

AbstractBackgroundRisk stratification for hospitalized adults with COVID-19 is essential to inform decisions for individual patients and allocation of potentially scarce resources. So far, risk models for severe COVID outcomes have included age but have not been optimized to best serve the needs of either older or younger adults. Additionally, existing risk models have been limited to either small sample sizes, or modeling mortality over an entire hospital admission. Further, previous models were developed on data from early in the pandemic, before improvements in COVID-19 treatment, the SARS-CoV-2 delta variant, and vaccination. There remains a need for early, accurate identification of patients who may need invasive mechanical ventilation (IMV) or die, considering multiple time horizons.MethodsThis retrospective study analyzed data from 6,906 hospitalized adults with COVID-19 from a community health system with 51 hospitals and 1085 clinics across five states in the western United States. Risk models were developed to predict mechanical ventilation illness or death across one to 56 days of hospitalization, using clinical data collected available within the first hour after either admission with COVID-19 or a first positive SARS-CoV-2 test. The relative importance of predictive risk factors features for all models was determined using Shapley additive explanations.FindingsThe percentage of patients who required mechanical ventilation or died within seven days of admission to the hospital due to COVID-19 was 10.82%. For the seven-day interval, models for age ≥ 18 and < 50 years reached AUROC 0.80 (95% CI: 0.70-0.89) and models for age ≥ 50 years reached AUROC 0.83 (95% CI: 0.79-0.88). Models revealed differences in the statistical significance and relative predictive value of risk factors between older and younger patients, including age, BMI, vital signs, and laboratory results. In addition, sex and chronic comorbidities had lower predictive value than vital signs and laboratory results.InterpretationFor hospitalized adults, baseline data that is readily available within one hour after hospital admission or a first positive inpatient SARS-CoV-2 test can predict critical illness within one day, and up to 56 days later. Further, the relative importance of risk factors differs between older and younger patients.
Recovery Time and Its Predictors among Women Admitted with Obstetric Fistula in Jimma University Medical Center Southwest, Ethiopia: A Retrospective Cohort Study
Authors: Areba Abriham Shiferaw , Akessa Geremew Muleta , Tadesse Megersa , Haile Arega , Abire Belayneh Genoro , Eritero Aneleay Cherinet , Gondol Belay Negassa , Akiso Denebo Ersulo , Kanno Girum Gebremeskel , Abame Desta Erkalo , Soboksa Negasa Eshete 
Published: February 2022

AbstractBackgroundObstetric fistula is one of the most shocking medical disabilities that women face as a result of problems caused by a lack of surgical intervention.MethodsFrom January 2011 to February 2017, a retrospective analysis was undertaken on obstetric fistula participants who received undergone surgical repair at the Jimma University Medical Center.Results81.4% of fistula patients were reported to have recovered. The results of lognormal inverse-Gaussian frailty model revealed that weight ≥50kg (Ф= 0.86, 95% CI: 0.747, 0.99), divorced (Ф=1.2, 95% CI: 1.028, 1.413), urban residence (Ф= 1.56, 95% CI: 01.046, 2.317), urine incontinence >3 months (Ф=1.2, 95% CI: 1.056, 1.371), no antenatal care (Ф=1.189, 95% CI:1.023, 1.382), duration of labor <2days (Ф= 0.86, 95% CI: 0.758, 0.986), health center delivery (Ф= 0.76, 95% CI: 0.576, 0.989), vaginal delivery (Ф= 1.5, 95% CI: 1.125, 1.996), and partially damaged urethra (Ф=1.59, 95% CI: 1.168, 2.166) and recto-vaginal fistula(Ф= 0.735, 95%CI: 0.6045, 0.894) were associated with the recovery time.ConclusionPatients with a weight < 50 kg, delivery at a health center, labor lasting <2 days, urinary incontinence lasting <3 months, antenatal care follow-up, cesarean section delivery, urethra status not damaged, and recto-vaginal fistula had a faster recovery period.
Machine learning-based prediction of cognitive outcomes in de novo Parkinson’s disease
Authors: Harvey Joshua , Reijnders Rick A , Cavill Rachel , Duits Annelien , Köhler Sebastian , Eijssen Lars , Rutten Bart PF , Shireby Gemma , Torkamani Ali , Creese Byron , Leentjens Albert FG , Lunnon Katie , Pishva Ehsan 
Published: February 2022

AbstractCognitive impairment is a debilitating symptom in Parkinson’s disease (PD). We aimed to establish an accurate multivariate machine learning (ML) model to predict cognitive outcome in newly diagnosed PD cases from the Parkinson’s Progression Markers Initiative (PPMI). Annual cognitive assessments over an eight-year time span were used to define two cognitive outcomes of i) cognitive impairment, and ii) dementia conversion. Selected baseline variables were organized into three subsets of clinical, biofluid and genetic/epigenetic measures and tested using four different ML algorithms. Irrespective of the ML algorithm used, the models consisting of the clinical variables performed best and showed better prediction of cognitive impairment outcome over dementia conversion. We observed a marginal improvement in the prediction performance when clinical, biofluid, and epigenetic/genetic variables were all included in one model. Several cerebrospinal fluid measures and an epigenetic marker showed high predictive weighting in multiple models when included alongside clinical variables.
Idiopathic subglottic stenosis arises at the interface of host and pathogen
Authors: Gelbard Alexander , Shilts Meghan H. , Strickland Britton , Motz Kevin , Tsai Hsiu-Wen , Boone Helen , Drake Wonder P. , Wanjalla Celestine , Smith Paula Marincola , Brown Hunter , Ramierez Marisol , Atkinson James B. , Powell Jason , Simpson A John , Rajagopala Seesandra V. , Mallal Simon , Sheng Quanhu , Hillel Alexander T. , Das Suman R. 
Published: February 2022

ABSTRACTIdiopathic subglottic stenosis (iSGS) is a rare fibrotic disease of the proximal airway affecting adult Caucasian women nearly exclusively. Life-threatening ventilatory obstruction occurs secondary to pernicious subglottic mucosal scar. Diverse diseases in divergent organ systems are associated with fibrosis, suggesting common biologic mechanisms. One well characterized pathway is chronic inflammation secondary to pathogen. In the present study, we explored the role of the proximal airway microbiome in iSGS pathogenesis. In human samples, abundant bacteria are detectable in iSGS scar as well as in health subglottic controls or patients that developed subglottic stenosis following endotracheal intubation. Interestingly, the community structure of the iSGS proximal airway microbiome does not appear disrupted. Rather, in iSGS defects in the airway epithelial barrier allow displacement of the native microbiome into the immunoprivileged lamina propria and are associated with adaptive immune activation. Animal models of iSGS confirm both bacteria and an adaptive immune response are necessary for pathologic proximal airway fibrosis. Single cell RNA sequencing of the affected airway in iSGS offers an unbiased characterization of the observed epithelial barrier dysfunction. The airway scar in iSGS patients demonstrates basal cell depletion and epithelial acquisition of a mesenchymal phenotype. The epithelial alterations are associated with the observed microbiome displacement, dysregulated immune activation, and localized fibrosis. These results refine our understanding of iSGS and implicate shared pathogenic mechanisms with distal airway fibrotic diseases.
Single-cell genomics improves the discovery of risk variants and genes of Atrial Fibrillation
Authors: Selewa Alan , Luo Kaixuan , Wasney Michael , Smith Linsin , Sun Xiaotong , Tang Chenwei , Eckart Heather , Moskowitz Ivan P. , Basu Anindita , He Xin , Pott Sebastian 
Published: February 2022

AbstractGenome-wide association studies (GWAS) have linked hundreds of loci to cardiac diseases. However, in most loci the causal variants and their target genes remain unknown. We developed a combined experimental and analytical approach that integrates single cell epigenomics with GWAS to prioritize risk variants and genes. We profiled accessible chromatin in single cells obtained from human hearts and leveraged the data to study genetics of Atrial Fibrillation (AF), the most common cardiac arrhythmia. Enrichment analysis of AF risk variants using cell-type-resolved open chromatin regions (OCRs) implicated cardiomyocytes as the main mediator of AF risk. We then performed statistical fine-mapping, leveraging the information in OCRs, and identified putative causal variants in 122 AF-associated loci. Taking advantage of the fine-mapping results, our novel statistical procedure for gene discovery prioritized 46 high-confidence risk genes, highlighting transcription factors and signal transduction pathways important for heart development. In summary, our analysis provides a comprehensive map of AF risk variants and genes, and a general framework to integrate single-cell genomics with genetic studies of complex traits.
Clustering analysis based on PiCCO parameters in patients with septic shock
Authors: Shen Yanfei , Hu Caibao , Cai Guolong , Yan Jing 
Published: February 2022

AbstractThe current study identified three septic shock phenotypes according to the pulse index continuous cardiac output parameters: Phenotype-1: normal cardiac output and vascular resistance, high blood volume; Phenotype-2: low cardiac output, normal blood volume, and high vascular resistance. Phenotype-3: normal vascular resistance and blood volume, high cardiac output. The mortality was significantly high in phenotype-1, but comparable between phenotype-2 and phenotype-3 (13/17 (76.4) vs. 13/35 (37.1) vs. 16/46 (34.7), p <0.001). Compared to phenotype 1 and 3, phenotype 2 had a higher AKI incidence and higher creatinine level at discharge.
Estimation and interpretation of vaccine efficacy in COVID-19 randomized clinical trials
Authors: Michiels Hege , Vandebosch An , Vansteelandt Stijn 
Published: February 2022

AbstractAn exceptional effort by the scientific community has led to the development of multiple vaccines against COVID-19. Efficacy estimates for these vaccines have been widely communicated to the general public, but are nonetheless challenging to compare because they are based on phase 3 trials that differ in study design, definition of vaccine efficacy and in the handling of cases arising shortly after vaccination. In this work, we investigate the impact of these choices on vaccine efficacy estimates, both theoretically and by re-analyzing the Janssen and Pfizer COVID-19 trial data under a uniform protocol. We moreover study the causal interpretation that can be assigned to per-protocol analyses typically performed in vaccine trials. Finally, we propose alternative estimands to measure the intrinsic vaccine efficacy in settings with delayed immune response.