bluuebunny/crossref_metadata_2025_split huggingface dataset:
Experience with Hydroxychloroquine and Azithromycin in the COVID-19 Pandemic: Implications for QT Interval Monitoring
Authors: Ramireddy Archana , Chugh Harpriya , Reinier Kyndaron , Ebinger Joseph , Park Eunice , Thompson Michael , Cingolani Eugenio , Cheng Susan , Marban Eduardo , Albert Christine M. , Chugh Sumeet S. 
Published: April 2020

ABSTRACTBackgroundDespite a paucity of clinical evidence, hydroxychloroquine and azithromycin are being administered widely to patients with verified or suspected COVID-19. Both drugs may increase risk of lethal arrhythmias associated with QT interval prolongation.MethodsWe performed a case series of COVID-19 positive/suspected patients admitted between 2/1/2020 and 4/4/2020 who were treated with azithromycin, hydroxychloroquine or a combination. We evaluated baseline and post-medication QT interval (QTc, Bazett’s) using 12-lead ECGs. Critical QTc prolongation was defined as: a) maximum QTc ≥500 ms (if QRS <120 ms) or QTc ≥550 (if QRS ≥120 ms) and b) increased QTc of ≥60 ms. Tisdale score and Elixhauser comorbidity index were calculated.ResultsOf 490 COVID-19 positive/suspected patients, 314 (64%) received either/both drugs, and 98 (73 COVID-19 positive, 25 suspected) met study criteria (age 62±17 yrs, 61% male). Azithromycin was prescribed in 28%, hydroxychloroquine in 10%, and both in 62%. Baseline mean QTc was 448±29 ms and increased to 459±36ms (p=0.005) with medications. Significant prolongation was observed only in men (18±43 ms vs -0.2±28 ms in women, p=0.02). 12% of patients reached critical QTc prolongation. In a multivariable logistic regression, age, sex, Tisdale score, Elixhauser score, and baseline QTc were not associated with critical QTc prolongation (p>0.14). Changes in QTc were highest with the combination compared to either drug, with many-fold greater prolongation with the combination vs. azithromycin alone (17±39 vs. 0.5±40 ms, p=0.07). No patients manifested torsades de pointes.ConclusionsOverall, 12% of patients manifested critical QTc interval prolongation, and traditional risk indices failed to flag these patients. With the drug combination, QTc prolongation was several-fold higher compared to azithromycin alone. The balance between uncertain benefit and potential risk when treating COVID-19 patients with these drugs should be carefully assessed prior to use.
Gut microbiota may underlie the predisposition of healthy individuals to COVID-19
Authors: Gou Wanglong , Fu Yuanqing , Yue Liang , Chen Geng-dong , Cai Xue , Shuai Menglei , Xu Fengzhe , Yi Xiao , Chen Hao , Zhu Yi , Xiao Mian-li , Jiang Zengliang , Miao Zelei , Xiao Congmei , Shen Bo , Wu Xiaomai , Zhao Haihong , Ling Wenhua , Wang Jun , Chen Yu-ming , Guo Tiannan , Zheng Ju-Sheng 
Published: April 2020

SUMMARYThe COVID-19 pandemic is spreading globally with high disparity in the susceptibility of the disease severity. Identification of the key underlying factors for this disparity is highly warranted. Here we describe constructing a proteomic risk score based on 20 blood proteomic biomarkers which predict the progression to severe COVID-19. We demonstrate that in our own cohort of 990 individuals without infection, this proteomic risk score is positively associated with proinflammatory cytokines mainly among older, but not younger, individuals. We further discovered that a core set of gut microbiota could accurately predict the above proteomic biomarkers among 301 individuals using a machine learning model, and that these gut microbiota features are highly correlated with proinflammatory cytokines in another set of 366 individuals. Fecal metabolomic analysis suggested potential amino acid-related pathways linking gut microbiota to inflammation. This study suggests that gut microbiota may underlie the predisposition of normal individuals to severe COVID-19.
Intermittent self-administration of fentanyl induces a multifaceted addiction state associated with persistent changes in the orexin system
Authors: Fragale Jennifer E. , James Morgan H. , Aston-Jones Gary 
Published: April 2020

AbstractThe orexin (hypocretin) system plays a critical role in motivated drug-taking. Cocaine self-administration with the intermittent access (IntA) procedure produces a robust addiction-like state that is orexin-dependent. Here, we sought to determine the role of the orexin system in opioid addiction using IntA self-administration of fentanyl. Different groups of male rats were either given continuous access in 1h (short access; ShA), or 6h periods (long access, LgA), or IntA (5min of access separated by 25min of no-access) to fentanyl for 14 days. IntA produced a greater escalation of fentanyl intake, motivation for fentanyl on a behavioral economics task, persistent drug seeking during abstinence, and cued-induced reinstatement compared to rats given ShA or LgA. We found that addiction behaviors induced by IntA to fentanyl were reversed by the orexin-1 receptor antagonist SB-334867. IntA to fentanyl was also associated with a persistent increase in the number of orexin-expressing neurons. Together, results indicate that the IntA model is a useful tool in the study of opioid addiction, and that the orexin system is critical for the maintenance of addiction behaviors induced by IntA self-administration of fentanyl.
A mobile sex-determining region, male-specific haplotypes, and rearing environment influence age at maturity in Chinook salmon
Authors: McKinney Garrett J , Nichols Krista M , Ford Michael J 
Published: April 2020

AbstractVariation in age at maturity is an important contributor to life history and demographic variation within and among species. The optimal age at maturity can vary by sex, and the ability of each sex to evolve towards its fitness optimum depends on the genetic architecture of maturation. Using GWAS of RAD sequencing data, we show that age at maturity in Chinook salmon exhibits sex-specific genetic architecture, with age at maturity in males governed by large (up to 20Mb) male-specific haplotypes. These regions showed no such effect in females. We also provide evidence for translocation of the sex-determining gene between two different chromosomes. This has important implications for sexually antagonistic selection, particularly that sex-linkage of adaptive genes may differ within and among populations based on chromosomal location of the sex-determining gene. Our findings will facilitate research into the genetic causes of shifting demography in Chinook salmon as well as a better understanding of sex-determination in this species and Pacific salmon in general.
Open Access and Altmetrics in the pandemic age: Forescast analysis on COVID-19 literature
Authors: Torres-Salinas Daniel , Robinson-Garcia Nicolas , Castillo-Valdivieso Pedro A. 
Published: April 2020

AbstractWe present an analysis on the uptake of open access on COVID-19 related literature as well as the social media attention they gather when compared with non OA papers. We use a dataset of publications curated by Dimensions and analyze articles and preprints. Our sample includes 11,686 publications of which 67.5% are openly accessible. OA publications tend to receive the largest share of social media attention as measured by the Altmetric Attention Score. 37.6% of OA publications are bronze, which means toll journals are providing free access. MedRxiv contributes to 36.3% of documents in repositories but papers in BiorXiv exhibit on average higher AAS. We predict the growth of COVID-19 literature in the following 30 days estimating ARIMA models for the overall publications set, OA vs. non OA and by location of the document (repository vs. journal). We estimate that COVID-19 publications will double in the next 20 days, but non OA publications will grow at a higher rate than OA publications. We conclude by discussing the implications of such findings on the dissemination and communication of research findings to mitigate the coronavirus outbreak.
flowCut — An R package for precise and accurate automated removal of outlier events and flagging of files based on time versus fluorescence analysis
Authors: Meskas Justin , Wang Sherrie , Brinkman Ryan 
Published: April 2020

AbstractTechnical artifacts that occur during the data acquisition process of cytometry data can result in erroneous data. We showed the presence of these data leads to biased gating analysis. Common technical issues, such as clogging, can cause spurious events and fluorescence intensity shifting. These events should be identified and potentially removed before being passed to the next stage of the gating analysis. flowCut, an R package, automatically detects anomaly events and flags files for flow cytometry experiments. flowCut outperforms existing automated approaches in our evaluation.flowCut is available as an R package at:https://github.com/jmeskas/flowCut. Test data uploaded to FlowRepository (Repository ID: FR-FCM-ZYPD) along with the manual results.
Joint eQTL mapping and Inference of Gene Regulatory Network Improves Power of Detecting bothcis- andtrans-eQTLs
Authors: Zhou Xin , Cai Xiaodong 
Published: April 2020

AbstractMotivationGenetic variations of expression quantitative trait loci (eQTLs) play a critical role in influencing complex traits and diseases development. Two main factors that affect the statistical power of detecting eQTLs are: 1) relatively small size of samples available, and 2) heavy burden of multiple testing due to a very large number of variants to be tested. The later issue is particularly severe when one tries to identifytrans-eQTLs that are far away from the genes they influence. If one can exploit co-expressed genes jointly in eQTL-mapping, effective sample size can be increased. Furthermore, using the structure of the gene regulatory network (GRN) may help to identifytrans-eQTLs without increasing multiple testing burden.ResultsIn this paper, we employ the structure equation model (SEM) to model both GRN and effect of eQTLs on gene expression, and then develop a novel algorithm, named sparse SEM, for eQTL mapping (SSEMQ) to conduct joint eQTL mapping and GRN inference. The SEM can exploit co-expressed genes jointly in eQTL mapping and also use GRN to determinetrans-eQTLs. Computer simulations demonstrate that our SSEMQ significantly outperforms eight existing eQTL mapping methods. SSEMQ is further employed to analyze a real dataset of human breast tissues, yielding a number ofcis- andtrans-eQTLs.AvailabilityR package ssemQr is available onhttps://github.com/Ivis4ml/ssemQr.git.
Role of the atmospheric pollution in the Covid-19 outbreak risk in Italy
Authors: Fattorini Daniele , Regoli Francesco 
Published: April 2020

AbstractAfter the initial outbreak in China, the diffusion in Italy of SARS-CoV-2 is exhibiting a clear regional trend with Northern areas being the most affected in terms of both frequency and severity of cases. Among multiple factors possibly involved in such geographical differences, a role has been hypothesized for atmospheric pollution. We provide additional evidence on the possible influence of air quality, particularly in terms of chronicity of exposure on the spread viral infection in Italian regions. Actual data on COVID-19 outbreak in Italian provinces and corresponding long-term air quality evaluations, were obtained from Italian and European agencies, elaborated and tested for possible interactions. Our elaborations reveal that, beside concentrations, the chronicity of exposure may influence the anomalous variability of SARS-CoV-2 in Italy. Data on distribution of atmospheric pollutants (NO2, O3, PM2.5 and PM10) in Italian regions during the last 4 years, days exceeding regulatory limits, and years of the last decade (2010-2019) in which the limits have been exceeded for at least 35 days, confirmed that Northern Italy has been constantly exposed to chronic air pollution. Long-term air-quality data significantly correlated with cases of Covid-19 in up to 71 Italian provinces (updated 27 April 2020) providing further evidence that chronic exposure to atmospheric contamination may represent a favourable context for the spread of the virus. Pro-inflammatory responses and high incidence of respiratory and cardiac affections are well known, while the capability of this coronavirus to bind particulate matters remains to be established. Atmospheric and environmental pollution should be considered as part of an integrated approach for sustainable development, human health protection and prevention of epidemic spreads but in a long-term and chronic perspective, since adoption of mitigation actions during a viral outbreak could be of limited utility.CapsuleChronic exposure to air pollutants might have a role in the spread of COVID-19 in Italian regions. Diffusion of Covid-19 in 71 Italian provinces correlated with long-term air-quality data.
MiBiOmics: An interactive web application for multi-omics data exploration and integration
Authors: Zoppi Johanna , Guillaume Jean-François , Neunlist Michel , Chaffron Samuel 
Published: April 2020

AbstractBackgroundMulti-omics experimental approaches are becoming common practice in biological and medical sciences underlying the need to design new integrative techniques and applications to enable the holistic characterization of biological systems. The integrative analysis of heterogeneous datasets generally allows us to acquire additional insights and generate novel hypotheses about a given biological system. However, it can often become challenging given the large size of omics datasets and the diversity of existing techniques. Moreover, visualization tools for interpretation are usually non-accessible to biologists without programming skills.ResultsHere, we present MiBiOmics, a web-based and standalone application that facilitates multi-omics data visualization, exploration, integration, and analysis by providing easy access to dedicated and interactive protocols. It implements advanced ordination techniques and the inference of omics-based (multi-layer) networks to mine complex biological systems, and identify robust biomarkers linked to specific contextual parameters or biological states.ConclusionsThrough an intuitive and interactive interface, MiBiOmics provides easy-access to ordination techniques and to a network-based approach for integrative multi-omics analyses. MiBiOmics is currently available as a Shiny app athttps://shiny-bird.univ-nantes.fr/app/Mibiomicsand as a standalone application athttps://gitlab.univ-nantes.fr/combi-ls2n/mibiomics.
Somatomotor cortical representations are predicted by levels of short-interval intracortical inhibition
Authors: Sigurdsson Hilmar P. , Molloy Katherine , Jackson Stephen R. 
Published: April 2020

AbstractTranscranial magnetic stimulation (TMS) can be used to probe for the location of cortical somatomotor representations in humans. These somatomotor representations are dynamic and are perturbed following motor training, systematic intervention, and in disease. Evidence suggests that these representations are maintained by the inhibitory neurotransmitter gamma-Aminobutyric acid (GABA). In the current study, we quantified the location, outline, and variability of the first dorsal interosseous (FDI) hand muscle somatomotor representation using a novel rapid-acquisition TMS method in 14 healthy young volunteers. In addition, resting motor thresholds were measured using established protocols. TMS was also used to examine short-interval intracortical inhibition (SICI), which is thought to measure transiently activated cortical gamma-Aminobutyric acid (GABA) interneurons. Using stepwise regression, our results showed that the level of intracortical inhibition was a significant predictor of the FDI somatomotor representation suggesting that greater excitability of the hand area representation is possibly governed by greater activation of transient GABA interneurons.
Primary and recurrent glioma patient-derived orthotopic xenografts (PDOX) represent relevant patient avatars for precision medicine
Authors: Golebiewska Anna , Hau Ann-Christin , Oudin Anaïs , Stieber Daniel , Yabo Yahaya A. , Baus Virginie , Barthelemy Vanessa , Klein Eliane , Bougnaud Sébastien , Keunen Olivier , Wantz May , Michelucci Alessandro , Neirinckx Virginie , Muller Arnaud , Kaoma Tony , Nazarov Petr V. , Azuaje Francisco , De Falco Alfonso , Flies Ben , Richart Lorraine , Poovathingal Suresh , Arns Thais , Grzyb Kamil , Mock Andreas , Herold-Mende Christel , Steino Anne , Brown Dennis , May Patrick , Miletic Hrvoje , Malta Tathiane M. , Noushmehr Houtan , Kwon Yong-Jun , Jahn Winnie , Klink Barbara , Tanner Georgette , Stead Lucy F. , Mittelbronn Michel , Skupin Alexander , Hertel Frank , Bjerkvig Rolf , Niclou Simone P. 
Published: April 2020

ABSTRACTPatient-derived cancer models are essential tools for studying tumor biology and preclinical interventions. Here, we show that glioma patient-derived orthotopic xenografts (PDOXs) enable long-term propagation of patient tumors and represent clinically relevant patient avatars. We created a large collection of PDOXs from primary and recurrent gliomas with and without mutations inIDH1, which retained histopathological, genetic, epigenetic and transcriptomic features of patient tumors with no mouse-specific clonal evolution. Longitudinal PDOX models recapitulate the limited genetic evolution of gliomas observed in patient tumors following treatment. PDOX-derived standardized tumor organoid cultures enabled assessment of drug responses, which were validated in mice. PDOXs showed clinically relevant responses to Temozolomide and to targeted treatments such as EGFR and CDK4/6 inhibitors in (epi)genetically defined groups, according toMGMTpromoter andEGFR/CDKstatus respectively. Dianhydrogalactitol, a bifunctional alkylating agent, showed promising potential against glioblastoma. Our study underlines the clinical relevance of glioma PDOX models for translational research and personalized treatment studies.
Leveraging UMLS-driven NLP to enhance identification of influenza predictors derived from electronic medical record data
Authors: Stephens Kari A. , Au Margaret A. , Yetisgen Meliha , Lutz Barry , Suchsland Monica Zigman , Ebell Mark H. , Thompson Matthew 
Published: April 2020

ABSTRACTObjectiveMultiple clinical prediction rules have been developed, but lack validation. This study aims to identify a set of prediction algorithms for influenza, based on electronic health record (EHR) structured data and clinical notes derived data using Unified Medical Language System (UMLS) driven natural language processing (NLP).Materials and MethodsData were extracted from an enterprise-wide data warehouse for all patients who tested positive for influenza and were seen in ambulatory care between 2009 and 2019 (N= 7,278). A text processing pipeline was used to analyze chart notes for UMLS terms for symptoms of interest to improve data quality completeness. Three models, which step up complexity of the dataset and predictors, were tested with least absolute shrinkage and selection operator (LASSO)-selected parameters to identify predictors for influenza. Receiver operating characteristic (ROC) curves compared test accuracy across the three models.ResultsThree models identified 7, 8, and 10 predictors, and the most complex model performed best. The addition of the UMLS-driven NLP symptoms data improved data quality (false negatives) and increased the number of significant predictors. NLP also increased the strength of the models, as did the addition of two-way predictor interactions.DiscussionThe EHR is a feasible source for offering rapidly accessible datasets for influenza related prediction research that was used to produce a prediction model for influenza. Combining data collected in routine care with data science methods improved a prediction model for influenza, and in the future, could be used to drive diagnostics at the point of care.
AlphaPart - R implementation of the method for partitioning genetic trends
Authors: Obšteter Jana , Holl Justin , Hickey John M. , Gorjanc Gregor 
Published: April 2020

AbstractBackgroundIn this paper we present the AlphaPart R package, an open-source software that implements a method for partitioning breeding values and genetic trends to identify sources of genetic gain. Breeding programmes improve populations for a set of traits, which can be measured with a genetic trend calculated from averaged year of birth estimated breeding values of selection candidates. While sources of the overall genetic gain are generally known, their realised contributions are hard to quantify in complex breeding programmes. The aim of this paper is to present the AlphaPart R package and demonstrate it with a simulated pig breeding example.ResultsThe package includes the main partitioning function AlphaPart, that partitions the breeding values and genetic trends by analyst defined paths, and a set of functions for handling data and results. The package is freely available from CRAN repository athttp://CRAN.R-project.org/package=AlphaPart. We demonstrate the use of the package by examining the genetic gain in a pig breeding example, in which the multiplier achieved higher breeding values than the nucleus for traits measured and selected in the multiplier. The partitioning analysis revealed that these higher values depended on the accuracy and intensity of selection in the multiplier and the extent of gene flow from the nucleus. For traits measured only in the nucleus, the multiplier achieved comparable or smaller genetic gain than the nucleus depending on the amount of gene flow.ConclusionsAlphaPart implements a method for partitioning breeding values and genetic trends and provides a useful tool for quantifying the sources of genetic gain in breeding programmes. The use of AlphaPart will help breeders to better understand or improve their breeding programmes.
Gene polymorphism associated with Angiotensinogen(M235T), Endothelial lipase (584C/T) and susceptibility to coronary artery disease: A meta-analysis
Authors: Zhao Hongyan , Hu Shan , Rong Jidong 
Published: April 2020

AbstractObjectiveTo explore the association between the variant M235 locus of angiotensinogen (AGT) gene, 584C/T locus of Endothelial lipase (EL) gene, and coronary artery disease (CAD) by meta-analysis.MethodsThe case-control studies on the association between AGT/EL gene polymorphism and CAD were collected through searching PubMed, EMbase, Web of Science, CNKI and Wanfang database up to March 1, 2020. Stata 15.0 software was used for analysis.ResultsA total of 29 articles met the inclusion criteria. After analyzing, it was found that the M235T polymorphism of AGT gene was associated with the occurrence of CAD. In the allele model (T vs. M), OR=1.38 (P < 0.05). In other heredity, there was also statistically significant. Subgroup analysis indicated that except the heterozygous genetic model of the Chinese population, other genetic models of the Caucasian and Chinese population were also statistically significant. The 584C/T polymorphism of EL gene was associated with the occurrence of CAD, with OR=0.83 (P < 0.05) in the allele model (T vs. C) and OR=0.80 (P < 0.05) in the dominant gene model. Also, in the allele model of Caucasian subgroup, OR=0.83 (P < 0.05), while in Asian subgroup, there was no statistically significant genetic model.ConclusionAGT M235 and EL 584C/T polymorphisms are associated with CAD susceptibility. The genotype TT, TC or allele T of AGT M235T and genotype CC or allele C of EL 584C/T might be the genetic risk factors for the development of CAD.