bluuebunny/crossref_metadata_2025_split huggingface dataset:
White Matter Alterations in Young Children with Prenatal Alcohol Exposure
Authors: Kar Preeti , Reynolds Jess E. , Grohs Melody N. , Gibbard W. Ben , McMorris Carly , Tortorelli Christina , Lebel Catherine 
Published: January 2021

AbstractPrenatal alcohol exposure (PAE) can lead to cognitive, behavioural, and social-emotional challenges. Previous neuroimaging research has identified alterations to brain structure in newborns, older children, adolescents, and adults with PAE; however, little is known about brain structure in young children. Extensive brain development takes place during early childhood; therefore, understanding the neurological profiles of young children with PAE is critical for early identification and effective intervention. We studied 54 children (5.21±1.11 years; 27 males) with confirmed PAE compared to 54 age- and sex-matched children without PAE. Children underwent diffusion tensor imaging between 2 and 7 years of age. Mean fractional anisotropy (FA) and mean diffusivity (MD) were obtained for 10 major white matter tracts, along with tract volume, axial and radial diffusivity (AD, RD). A univariate analysis of covariance was conducted to test for group differences (PAE vs. control) controlling for age, sex and tract volume. Our results reveal white matter microstructural differences between young children with PAE and unexposed controls. The PAE group had higher FA and/or lower MD (as well as lower AD and RD) in the genu and the body of the corpus callosum, as well as the bilateral uncinate fasciculus and pyramidal tracts. Our findings align with studies of newborns with PAE finding lower AD, but contrast those in older populations with PAE, which consistently report lower FA and higher MD. These findings may reflect premature development of white matter that may then plateau too early, leading to the lower FA/higher MD observed at older ages.
eDNA in a bottleneck: obstacles to fish metabarcoding studies in megadiverse freshwater systems
Authors: Jackman Jake M. , Benvenuto Chiara , Coscia Ilaria , Carvalho Cintia Oliveira , Ready Jonathan S. , Boubli Jean P. , Magnusson William E. , McDevitt Allan D. , Sales Naiara Guimarães 
Published: January 2021

ABSTRACTThe current capacity of environmental DNA (eDNA) to provide accurate insights into the biodiversity of megadiverse regions (e.g., the Neotropics) requires further evaluation to ensure its reliability for long-term monitoring. In this study, we first evaluated the taxonomic resolution capabilities of a short fragment from the 12S rRNA gene widely used in fish eDNA metabarcoding studies, and then compared eDNA metabarcoding data from water samples with traditional sampling using nets. For the taxonomic discriminatory power analysis, we used a specifically curated reference dataset consisting of 373 sequences from 264 neotropical fish species (including 47 newly generated sequences) to perform a genetic distance-based analysis of the amplicons targeted by the MiFish primer set. We obtained an optimum delimitation threshold value of 0.5% due to lowest cumulative errors. The barcoding gap analysis revealed only a 50.38% success rate in species recovery (133/264), highlighting a poor taxonomic resolution from the targeted amplicon. To evaluate the empirical performance of this amplicon for biomonitoring, we assessed fish biodiversity using eDNA metabarcoding from water samples collected from the Amazon (Adolpho Ducke Forest Reserve and two additional locations outside the Reserve). From a total of 84 identified Molecular Operational Taxonomic Units (MOTUs), only four could be assigned to species level using a fixed threshold. Measures of α-diversity analyses within the Reserve showed similar patterns in each site between the number of MOTUs (eDNA dataset) and species (netting data) found. However, β-diversity revealed contrasting patterns between the methods. We therefore suggest that a new approach is needed, underpinned by sound taxonomic knowledge, and a more thorough evaluation of better molecular identification procedures such as multi-marker metabarcoding approaches and tailor-made (i.e., order-specific) taxonomic delimitation thresholds.
A Unifying Bayesian Framework for Merging X-ray Diffraction Data
Authors: Dalton Kevin M. , Greisman Jack B. , Hekstra Doeke R. 
Published: January 2021

AbstractNovel X-ray methods are transforming the study of the functional dynamics of biomolecules. Key to this revolution is detection of often subtle conformational changes from diffraction data. Diffraction data contain patterns of bright spots known as reflections. To compute the electron density of a molecule, the intensity of each reflection must be estimated, and redundant observations reduced to consensus intensities. Systematic effects, however, lead to the measurement of equivalent reflections on different scales, corrupting observation of changes in electron density. Here, we present a modern Bayesian solution to this problem, which uses deep learning and variational inference to simultaneously rescale and merge reflection observations. We successfully apply this method to monochromatic and polychromatic single-crystal diffraction data, as well as serial femtosecond crystallography data. We find that this approach is applicable to the analysis of many types of diffraction experiments, while accurately and sensitively detecting subtle dynamics and anomalous scattering.
Explaining Deep Neural Networks for Knowledge Discovery in Electrocardiogram Analysis
Authors: Hicks Steven A. , Isaksen Jonas L. , Thambawita Vajira , Ghouse Jonas , Ahlberg Gustav , Linneberg Allan , Grarup Niels , Strümke Inga , Ellervik Christina , Olesen Morten Salling , Hansen Torben , Graff Claus , Holstein-Rathlou Niels-Henrik , Halvorsen Pål , Maleckar Mary M. , Riegler Michael A. , Kanters Jørgen K. 
Published: January 2021

ABSTRACTDeep learning-based tools may annotate and interpret medical tests more quickly, consistently, and accurately than medical doctors. However, as medical doctors remain ultimately responsible for clinical decision-making, any deep learning-based prediction must necessarily be accompanied by an explanation that can be interpreted by a human. In this study, we present an approach, called ECGradCAM, which uses attention maps to explain the reasoning behind AI decision-making and how interpreting these explanations can be used to discover new medical knowledge. Attention maps are visualizations of how a deep learning network makes, which may be used in the clinic to aid diagnosis, and in research to identify novel features and characteristics of diagnostic medical tests. Here, we showcase the use of ECGradCAM attention maps using a novel deep learning model capable of measuring both amplitudes and intervals in 12-lead electrocardiograms.
Impact of B.1.1.7 variant mutations on antibody recognition of linear SARS-CoV-2 epitopes
Authors: Haynes Winston A. , Kamath Kathy , Lucas Carolina , Shon John , Iwasaki Akiko 
Published: January 2021

AbstractIn 579 COVID patients’ samples collected between March and July of 2020, we examined the effects of non-synonymous mutations harbored by the circulating B.1.1.7 strain on linear antibody epitope signal for spike glycoprotein and nucleoprotein. At the antigen level, the mutations only substantially reduced signal in 0.5% of the population. Although some epitope mutations reduce measured signal in up to 6% of the population, these are not the dominant epitopes for their antigens. Given dominant epitope patterns observed, our data suggest that the mutations would not result in immune evasion of linear epitopes for a large majority of these COVID patients.
SARS-CoV-2 patient self-testing with an antigen-detecting rapid test: a head-to-head comparison with professional testing
Authors: Lindner Andreas K. , Nikolai Olga , Rohardt Chiara , Kausch Franka , Wintel Mia , Gertler Maximilian , Burock Susen , Hörig Merle , Bernhard Julian , Tobian Frank , Gaeddert Mary , Lainati Federica , Corman Victor M. , Jones Terry C. , Sacks Jilian A. , Seybold Joachim , Denkinger Claudia M. , Mockenhaupt Frank P. 
Published: January 2021

AbstractBackgroundAntigen-detecting rapid diagnostic tests (Ag-RDTs) have been widely recommended as a complement to RT-PCR. Considering the possibility of nasal self-sampling and the ease-of-use in performing the test, self-testing may be an option.Methods and FindingsWe performed a manufacturer-independent, prospective diagnostic accuracy study of nasal mid-turbinate self-sampling and self-testing when using a WHO-listed SARS-CoV-2 Ag-RDT. Symptomatic participants suspected to have COVID-19 received written and illustrated instructions. Procedures were observed without intervention. For comparison, Ag-RDTs with nasopharyngeal sampling were professionally performed. Estimates of agreement, sensitivity, and specificity relative to RT-PCR on a combined oro-/nasopharyngeal sample were calculated. Feasibility was evaluated by observer and participant questionnaires.Among 146 symptomatic adults, 40 (27.4%) were RT-PCR-positive for SARS-CoV-2. Sensitivity with self-testing was 82.5% (33/40 RT-PCR positives detected; 95% CI 68.1-91.3), and 85.0% (34/40; 95% CI 70.9-92.9) with professional testing. The positive percent agreement between self-testing and professional testing on Ag-RDT was 91.4% (95% CI 77.6-97.0), and negative percent agreement 99.1% (95% CI 95.0-100). At high viral load (>7.0 log10 SARS-CoV-2 RNA copies/ml), sensitivity was 96.6% (28/29; 95% CI 82.8-99.8) for both self- and professional testing. Deviations in sampling and testing (incomplete self-sampling or extraction procedure, or imprecise volume applied on the test device) were observed in 25 out of the 40 PCR-positives. Participants were rather young (mean age 35 years) and educated (59.6% with higher education degree). Most participants (80.9%) considered the Ag-RDT as rather easy to perform.ConclusionsAmbulatory participants suspected for SARS-CoV-2 infection were able to reliably perform the Ag-RDT and test themselves. Procedural errors might be reduced by refinement of the Ag-RDTs for self-testing, such as modified instructions for use or product design/procedures. Self-testing may result in more wide-spread and more frequent testing. Paired with the appropriate information and education of the general public about the benefits and risks, self-testing may therefore have significant impact on the pandemic.
Folate (MTHFRC677T andMTRRA66G) gene polymorphisms and risk of prostate cancer: a case-control study with an updated meta-analysis
Authors: Yadav Upendra , Kumar Pradeep , Dwivedi Shailendra , Singh Bhupendra Pal , Rai Vandana 
Published: January 2021

AbstractIntroductionMethylenetetrahydrofolate reductase (MTHFR) and methionine synthase reductase (MTRR) are the key enzymes of the folate pathway, which involved in the DNA methylation. DNA methylation may affect the stability and integrity of DNA, that supposed to play a pivotal role in carcinogenesis. So, we aimed to investigate the association ofMTHFRC677T andMTRRA66G gene polymorphisms with susceptibility to prostate cancer in North Indian population. We also performed meta-analyses of published literatures on these polymorphisms to evaluate their association with prostate cancer.MethodsWe genotypedMTHFRC677T andMTRRA66G gene polymorphisms in 147 prostate cancer cases and 147 healthy controls using PCR-RFLP methods. Odds ratios (ORs) with 95% confidence intervals (CIs) were estimated for risk estimation. For meta-analysis different databases were searched and all statistical analysis were performed using Open Meta-Analyst software.ResultsThe present case control study revealed that the T allele (OR= 1.67; 95% CI: 0.99-2.84, p= 0.05), CT genotype (OR= 1.92; 95% CI: 1.06-3.48, p= 0.02), and dominant (TT+CT) model (OR= 1.85; 95% CI: 1.05-3.30, p= 0.03) ofMTHFRC677T gene polymorphism and G allele (OR= 1.92; 95% CI: 1.35-2.73, p= 0.0002) ofMTRRA66G gene polymorphism were significantly associated with prostate cancer susceptibility. Meta-analyses ofMTHFRC677T andMTRRA66G gene polymorphisms showed no significant association between these polymorphisms and prostate cancer risk in overall or in subgroup meta-analysis stratified by ethnicity.ConclusionMTHFRC677T andMTRRA66G gene polymorphisms seem to play a significant role in prostate cancer susceptibility in North Indian population, while results of meta-analysis revealed no association betweenMTHFRC677T andMTRRA66G gene polymorphisms and prostate cancer susceptibility.
Mobilefuge: A low-cost, portable, open source, 3D-printed centrifuge that can be used for purification of saliva samples for SARS-CoV2 detection
Authors: Bhupathi Mohini , Devarapu Ganga Chinna Rao 
Published: January 2021

AbstractOne of the best ways to contain the spread of COVID-19 is frequent testing of as many people as possible and timely isolation of uninfected personnel from infected personnel. However, the cost of massive testing is affordable in many countries. The existing technologies might not be scalable to offer affordable testing for millions of people. To address this issue, novel testing methods based on Loop-Mediated Isothermal Amplification (LAMP) were proposed that are more sensitive, require less reagents and can work with saliva samples instead of more tedious nasal swabs. As a result, LAMP based protocols can make it possible to drive the cost down to one dollar per test. These LAMP based methods require a centrifuge device, mostly for separation of viral particles from reaction inhibitors in saliva samples. However, centrifuge is neither accessible nor affordable in many resource limited settings, especially during this pandemic situation when normal supply chains are heavily disrupted. To overcome these challenges, we invented a low-cost centrifuge that can be useful for carrying out low-cost LAMP based detection of SARS-Cov2 virus in saliva. The 3D printed centrifuge (Mobilefuge) is portable, robust, stable, safe, easy to build and operate. The Mobilefuge doesn’t require soldering or programming skills and can be built without any specialised equipment, yet practical enough for high throughput use. More importantly, Mobilefuge can be powered from widely available USB ports, including mobile phones and associated power supplies. This allows the Mobilefuge to be used even in off-grid and resource limited settings. We believe that our invention will aid the efforts to contain the spread of COVID-19 by lowering the costs of testing equipment. Apart from the COVID-19 testing, the Mobilefuge can have applications in the field of biomedical research and diagnostics.
COVID-19 Rapid Antigen Test at hospital admission associated to the knowledge of individual risk factors allow overcoming the difficulty of managing suspected patients in hospitals COVID-19 Rapid Antigen Test facilitates the management of suspected patients on hospital admission
Authors: Filgueiras Priscilla S , Corsini Camila A , Almeida Nathalie BF , Assis Jessica V , Pedrosa Maria Luysa C , de Oliveira Alana K , Amorim Raquel NH , de Miranda Daniel AP , Coutinho Lucélia A , Gomes Sarah VC , Custódio Natália G , da Silva Douglas H , Santos Gabriela PV , Silva Raphael A , Medeiros Maria Izabella VARC , Reis Priscila VCC , Lourenço Adelina J , Bicalho Cecília MF , Vilela Raquel VR , Neves Hércules P , Fernandes Gabriel R , Grenfell Rafaella FQ 
Published: January 2021

AbstractEarly diagnosis of SARS-CoV-2 is essential to limiting the spread of the virus and managing infected patients during hospitalization. The sensitivity of RT-qPCR is contested by the fact that it is time-consuming, executed by trained technicians in proper environment for material extraction. Here, we evaluated the first SARS-CoV-2 antigen test recommended by the World Health Organization at September, 2020 as an alternative for immediate diagnosis of symptomatic and suspected patients at a hospital in Brazil during the epidemic peak. All patients were submitted to RT-qPCR and rapid antigen test using nasopharyngeal swabs rigorously collected at the same time. Demographics, baseline comorbidities, symptoms and outcomes were considered. Prediction analysis revealed that previous stroke, chronic obstructive pulmonary disease, desaturation and tachypnea were the most relevant determinants of the death of COVID-19 patients. Comparison between the rapid antigen test and RT-qPCR revealed an overall PPV of 97%, extended to 100% when performed between 4 and 15 days of symptoms, with an accuracy of 90-91% from days 1 to 7 and a ‘Substantial’ agreement. The rapid antigen test presented no inconclusive result. Among the discordant results and RT-qPCR inconclusives, 72% presented bilateral multifocal ground-glass opacities on imaging and other exams alterations. The median time to obtain RT-qPCR results was 83.6 hours, against 15 minutes for the rapid test, precious time for deciding on patient isolation and management. Knowledge of the risk factors and a rapid diagnosis upon patient admission is critical to reduce mortality of COVID-19 patients, hospital internal costs and in-hospital transmission.
Clinical Validation of a Novel T-cell Receptor Sequencing Assay for Identification of Recent or Prior SARS-CoV-2 Infection
Authors: Dalai Sudeb C. , Dines Jennifer N. , Snyder Thomas M. , Gittelman Rachel M. , Eerkes Tera , Vaney Pashmi , Howard Sally , Akers Kipp , Skewis Lynell , Monteforte Anthony , Witte Pam , Wolf Cristina , Nesse Hans , Herndon Megan , Qadeer Jia , Duffy Sarah , Svejnoha Emily , Taromino Caroline , Kaplan Ian M. , Alsobrook John , Manley Thomas , Baldo Lance 
Published: January 2021

ABSTRACTBackgroundWhile diagnostic, therapeutic, and vaccine development in the COVID-19 pandemic has proceeded at unprecedented speed and scale, critical gaps remain in our understanding of the immune response to SARS-CoV-2. Current diagnostic strategies, including serology, have numerous limitations in addressing these gaps. Here we describe clinical performance of T- Detect™ COVID, the first reported assay to determine recent or prior SARS-CoV-2 infection based on T-cell receptor (TCR) sequencing and immune repertoire profiling from whole blood samples.MethodsMethods for high-throughput immunosequencing of the TCRβ gene from blood specimens have been described1. We developed a statistical classifier showing high specificity for identifying prior SARS-CoV-2 infection2, utilizing >4,000 SARS-CoV-2-associated TCR sequences from 784 cases and 2,447 controls across 5 independent cohorts. The T-Detect COVID Assay comprises immunosequencing and classifier application to yield a qualitative positive or negative result. Several retrospective and prospective cohorts were enrolled to assess assay performance including primary and secondary Positive Percent Agreement (PPA; N=205, N=77); primary and secondary Negative Percent Agreement (NPA; N=87, N=79); PPA compared to serology (N=55); and pathogen cross-reactivity (N=38).ResultsT-Detect COVID demonstrated high PPA in subjects with prior PCR-confirmed SARS-CoV-2 infection (97.1% 15+ days from diagnosis; 94.5% 15+ days from symptom onset), high NPA (∼100%) in presumed or confirmed SARS-CoV-2 negative cases, equivalent or higher PPA than two commercial EUA serology tests, and no evidence of pathogen cross-reactivity.ConclusionT-Detect COVID is a novel T-cell immunosequencing assay demonstrating high clinical performance to identify recent or prior SARS-CoV-2 infection from standard blood samples. This assay can provide critical insights on the SARS-CoV-2 immune response, with potential implications for clinical management, risk stratification, surveillance, assessing protective immunity, and understanding long-term sequelae.
Automatic Gender Detection in Twitter Profiles for Health-related Cohort Studies
Authors: Yang Yuan-Chi , Al-Garadi Mohammed Ali , Love Jennifer S. , Perrone Jeanmarie , Sarker Abeed 
Published: January 2021

AbstractObjectiveBiomedical research involving social media (SM) data is gradually moving from population-level to targeted, cohort-level data analysis. Though crucial for biomedical studies, SM user’s demographic information (e.g., gender) is often not explicitly known from profiles. Here we present an automatic gender classification system for SM and we illustrate how gender information can be incorporated into a SM-based health-related study.Materials and MethodsWe used two large Twitter datasets: (i) public, gender-labeled users (Dataset-1), and (ii) users who have self-reported nonmedical use of prescription medications (Dataset-2). Dataset-1 was used to train and evaluate the gender detection pipeline. We experimented with machine-learning algorithms including support vector machines (SVMs) and deep-learning models, and released packages including M3. We considered user’s information including profile and tweets for classification. We also developed a meta-classifier ensemble that strategically uses the predicted scores from the classifiers. We applied the best-performing pipeline to Dataset-2 to assess the system’s utility.Results and DiscussionWe collected 67,181 and 176,683 users for Dataset-1 and Dataset-2, respectively. A meta-classifier involving SVM and M3 performed the best (Dataset-1 accuracy: 94.4% [95%-CI: 94.0%-94.8%]; Dataset-2: 94.4% [95%-CI: 92.0%-96.6%]. Including automatically-classified information in the analyses of Dataset-2 revealed gender-specific trends— proportions of females closely resemble data from the National Survey of Drug Use and Health 2018 (tranquilizers: 0.50 vs. 0.50; stimulants: 0.50 vs. 0.45), and the overdose Emergency Room Visit due to Opioids by CDC (pain relievers: 0.38 vs. 0.37).ConclusionOur publicly-available, automated gender detection pipeline may aid cohort-specific social media data analyses (https://bitbucket.org/sarkerlab/gender-detection-for-public).
Assessment of fractional anisotropy outcomes in combat sport athletes with mild traumatic brain injury
Authors: Fonseca Jessica Humara , Inguanzo Joe M. Lopez , Hernández Janet Perodin , González Dalmau Evelio 
Published: January 2021

AbstractThe practice of combat sports increases the risk of suffering white matter injuries. That is why, it is required the early damage detection to determine to what extent the athlete may be active preserving their performance and health status. The integrity of the white matter can be quantitatively characterized in diffusion tensor images, using fractional anisotropy. This study aims at characterizing the fractional anisotropy of white matter injuries in combat athletes that are exposed to repetitive trauma and also, to detect changes in fractional anisotropy between cerebral hemispheres with and without lesions. It is proposed a global and structural analysis of the hemispheres, as well as the selection of ROI in the lesions. 14 athletes, from Boxing, Karate and Taekwondo sports, participated. The sample was divided into two groups of seven subjects each: Injured (23.428±4.157 years old) and Healthy (24.285±5.023 years old) paired by sport denomination. Diffusion tensor images were used to obtain FA values in the analysis of the hemispheres and lesions. Global and structural analysis of the hemispheres did not detect the presence of white matter lesions; however, the use of ROI selection permitted maximum approximation of the injuries location. It also improved the breakdown of FA values as it allows a local analysis of the lesion. As an additional result, there were found ROIs values, FAmed = 0.454±0.062, which exceed the average fractional anisotropy of the white matter. The cohesion of acute and chronic phase lesions were found in the same subject. The apparently contradictory results in FA values are related to the stage of the lesions.