bluuebunny/crossref_metadata_2025_split huggingface dataset:
Association Between ACEIs or ARBs Use and Clinical Outcomes in COVID-19 Patients: A Systematic Review and Meta-analysis
Authors: Diaz-Arocutipa Carlos , Saucedo-Chinchay Jose , Hernandez Adrian V. 
Published: June 2020

AbstractImportanceThere is a controversy regarding whether or not to continue angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) in patients with coronavirus disease 2019 (COVID-19).ObjectiveTo evaluate the association between ACEIs or ARBs use and clinical outcomes in COVID-19 patients.Data SourcesSystematic search of the PubMed, Embase, Scopus, Web of Science, and Cochrane Central Register of Controlled Trials from database inception to May 31, 2020. We also searched the preprint servers medRxiv and SSNR for additional studies.Study SelectionObservational studies and randomized controlled trials reporting the effect of ACEIs or ARBs use on clinical outcomes of adult patients with COVID-19.Data Extraction and SynthesisRisk of bias of observational studies were evaluated using the Newcastle-Ottawa Scale. Meta-analyses were performed using a random-effects models and effects expressed as Odds ratios (OR) and mean differences with their 95% confidence interval (95%CI). If available, adjusted effects were pooled.Main Outcomes and MeasuresThe primary outcome was all-cause mortality and secondary outcomes were COVID-19 severity, hospital discharge, hospitalization, intensive care unit admission, mechanical ventilation, length of hospital stay, and troponin, creatinine, procalcitonin, C-reactive protein (CRP), interleukin-6 (IL-6), and D-dimer levels.Results40 studies (21 cross-sectional, two case-control, and 17 cohorts) involving 50615 patients were included. ACEIs or ARBs use was not associated with all-cause mortality overall (OR 1.11, 95%CI 0.77-1.60, p=0.56), in subgroups by study design and using adjusted effects. ACEI or ARB use was independently associated with lower COVID-19 severity (aOR 0.56, 95%CI 0.37-0.87, p<0.01). No significant associations were found between ACEIs or ARBs use and hospital discharge, hospitalization, mechanical ventilation, length of hospital stay, and biomarkers.Conclusions and RelevanceACEIs or ARBs use was not associated with higher all-cause mortality in COVID-19. However, ACEI or ARB use was independently associated with lower COVID-19 severity. Our results support the current international guidelines to continue the use of ACEIs and ARBs in COVID-19 patients with hypertension.Key pointsQuestionWhat is the association between angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin receptor blockers (ARBs) use and clinical outcomes in coronavirus disease 2019 (COVID-19) patients?FindingsIn this systematic review and meta-analysis of 40 observational studies, the use of ACEIs or ARBs was not associated with higher all-cause mortality in COVID-19 patients. Additionally, ACEIs or ARBs use was independently associated with lower COVID-19 severity.MeaningThese results support the current international guidelines to continue the use of ACEIs and ARBs in COVID-19 patients with hypertension.
Drug-Drug interactions between COVID-19 treatments and antipsychotics drugs: integrated evidence from 4 databases and a systematic review
Authors: Oda Plasencia-García Beatriz , Gonzalo Rodriguez-Menendez , Isabel Rico-Rangel María , Ana Rubio-Garcia , Jaime Torelló-Iserte , Benedicto Crespo-Facorro 
Published: June 2020

ABSTRACTRelevanceManagement of symptoms like anxiety, delirium and agitation cannot be neglected in COVID-19 patients. Antipsychotics are usually used for the pharmacological management of delirium, and confusion and behavioral disturbances. The selection of concomitant COVID-19 medications and antipsychotics should be evidence-based and closely monitoredObjectiveTo systematically review evidence-based available on drug-drug interactions between COVID-19 treatments and antipsychotics.Evidence ReviewThree databases were consulted: (a) Lexicomp® Drug Interactions, (b) Micromedex® Solutions Drugs Interactions, (c) Liverpool © Drug Interaction Group for COVID-19 therapies. To acquire more information on QT prolongation and TdP, the CredibleMeds® QTDrugs List was searched. Based on the information collected, the authors made a recommendation agreed to by consensus. In addition, a systematic review was conducted to find the clinical outcomes of drug-drug interactions between COVID-19 treatments and antipsychoticsResultsThe main interaction between COVID-19 drugs and antipsychotics are the risk of QT prolongation and TdP, and CYP interactions. Remdesivir, favipiravir, baricinitib, and anakinra can be used concomitantly with antipsychotics with no risk of drug-drug interaction (except for hematological risk with clozapine and baricinitib). Tocilizumab is rather safe to use in combination with antipsychotics, although it can restore the activity of CYP3A4 and therefore its substrate metabolism may increase. The most demanding COVID-19 treatments for co-administration with antipsychotics are chloroquine, hydroxychloroquine, azithromycin (all prolong QT interval) and lopinavir / ritonavir (CYP interaction and risk of QT prolongation).ConclusionsWe urge to development of evidence-based guidelines that can help clinicians decide the safest treatment combination and monitoring necessary for each particular patient. The selection of concomitant COVID-19 medications and antipsychotics should be evidence-based and closely monitored.
Policy Implications of an Approximate Linear Infection Model for SARS-CoV-2
Authors: McCarthy John E. , Dumas Bob A. 
Published: June 2020

AbstractWe propose a linear model of infection probability, and prove that this is a good approximation to a more refined model in which we assume infections come from a series of independent risks. We argue that the linearity assumption makes interpreting and using the model much easier, without significantly diminishing the reliability of the model.
Postural constraints recruit shorter-timescale processes into the non-Gaussian cascade processes
Authors: Furmanek Mariusz P. , Mangalam Madhur , Kelty-Stephen Damian G. , Juras Grzegorz 
Published: June 2020

AbstractHealthy human postural sway exhibits strong intermittency, reflecting a richly interactive foundation of postural control. From a linear perspective, intermittent fluctuations might be interpreted as engagement and disengagement of complementary control processes at distinct timescales or from a nonlinear perspective, as cascade-like interactions across many timescales at once. The diverse control processes entailed by cascade-like multiplicative dynamics suggest specific non-Gaussian distributional properties at different timescales. Multiscale probability density function (PDF) analysis showed that when standing quietly while balancing a sand-filled tube with the two arms elicited non-Gaussianity profiles showing a negative-quadratic crossover between short and long timescales. A more stringent task of balancing a water-filled tube elicited simpler monotonic decreases in non-Gaussianity, that is, a positive-quadratic cancellation of the negative-quadratic crossover. Multiple known indices of postural sway governed the appearance or disappearance of the crossover. Finally, both tasks elicited lognormal distributions over progressively larger timescales. These results provide the first evidence that more stringent postural constraints recruit shorter-timescale processes into the non-Gaussian cascade processes, that indices of postural sway moderate this recruitment, and that more stringent postural constraints show stronger statistical hallmarks of cascade structure.
Phenotype-guided subpopulation identification from single-cell sequencing data
Authors: Sun Duanchen , Guan Xiangnan , Moran Amy E. , Qian David Z. , Schedin Pepper , Adey Andrew , Spellman Paul T. , Xia Zheng 
Published: June 2020

AbstractSingle-cell sequencing yields novel discoveries by distinguishing cell types, states and lineages within the context of heterogeneous tissues. However, interpreting complex single-cell data from highly heterogeneous cell populations remains challenging. Currently, most existing single-cell data analyses focus on cell type clusters defined by unsupervised clustering methods, which cannot directly link cell clusters with specific biological and clinical phenotypes. Here we present Scissor, a novel approach that utilizes disease phenotypes to identify cell subpopulations from single-cell data that most highly correlate with a given phenotype. This “phenotype-to-cell within a single step” strategy enables the utilization of a large amount of clinical information that has been collected for bulk assays to identify the most highly phenotype-associated cell subpopulations. When applied to a lung cancer single-cell RNA-seq (scRNA-seq) dataset, Scissor identified a subset of cells exhibiting high hypoxia activities, which predicted worse survival outcomes in lung cancer patients. Furthermore, in a melanoma scRNA-seq dataset, Scissor discerned a T cell subpopulation with lowPDCD1/CTLA4and highTCF7expressions, which is associated with a favorable immunotherapy response. Thus, Scissor provides a novel framework to identify the biologically and clinically relevant cell subpopulations from single-cell assays by leveraging the wealth of phenotypes and bulk-omics datasets.
Rapid implementation of SARS-CoV-2 emergency use authorization RT-PCR testing and experience at an academic medical institution
Authors: Velu P. , Craney A. , Ruggiero P. , Sipley J. , Cong L. , Hissong Erika M. , Loda M. , Westblade L. F. , Cushing M. , Rennert H. 
Published: June 2020

ABSTRACTAn epidemic caused by an outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in China in December 2019 has since rapidly spread internationally, requiring urgent response from the clinical diagnostics community. We present a detailed overview of the clinical validation and implementation of the first laboratory-developed real-time reverse-transcription-PCR (rRT-PCR) test offered in the NewYork-Presbyterian Hospital system following the emergency use authority (EUA) guidance issued by the US Food and Drug Administration. Validation was performed on nasopharyngeal and sputum specimens (n=124) using newly designed dual-target rRT-PCR (altona RealStar® SARS-CoV-2 Reagent) for detecting of SARS-CoV-2 in upper respiratory and lower respiratory tract specimens, including bronchoalveolar lavage and tracheal aspirates. Accuracy testing demonstrated excellent assay agreement between expected and observed values. The limit of detection (LOD) was 2.7 and 23.0 gene copies/reaction for nasopharyngeal and sputum specimens, respectively. Retrospective analysis of 1,694 tests from 1,571 patients revealed increased positivity in older patients and males compared to females, and an increasing positivity rate from approximately 20% at the start of testing to 50% at the end of testing three weeks later. Our findings demonstrate that the assay accurately and sensitively identifies SARS-CoV-2 in multiple specimen types in the clinical setting and summarizes clinical data from early in the epidemic in New York City.
Genome-Wide Sequencing as a First-Tier Screening Test for Short Tandem Repeat Expansions
Authors: Rajan-Babu Indhu-Shree , Peng Junran , Chiu Readman ,   IMAGINE Study,   CAUSES Study, Mohajeri Arezoo , Dolzhenko Egor , Eberle Michael A. , Birol Inanc , Friedman Jan M. 
Published: June 2020

ABSTRACTShort tandem repeat (STR) expansions cause several neurological and neuromuscular disorders. Screening for STR expansions in genome-wide (exome and genome) sequencing data can enable diagnosis, optimal clinical management/treatment, and accurate genetic counselling of patients with repeat expansion disorders. We assessed the performance of lobSTR, HipSTR, RepeatSeq, ExpansionHunter, TREDPARSE, GangSTR, STRetch, and exSTRa – bioinformatics tools that have been developed to detect and/or genotype STR expansions – on experimental and simulated genome sequence data with known STR expansions aligned using two different aligners, Isaac and BWA. We then adjusted the parameter settings to optimize the sensitivity and specificity of the STR tools and fed the optimized results into a machine-learning decision tree classifier to determine the best combination of tools to detect full mutation expansions with high diagnostic sensitivity and specificity. The decision tree model supported using ExpansionHunter’s full mutation calls with those of either STRetch or exSTRa for detection of full mutations with precision, recall, and F1-score of 90%, 100%, and 95%, respectively.We used this pipeline to screen the BWA-aligned exome or genome sequence data of 306 families of children with suspected genetic disorders for pathogenic expansions of known disease STR loci. We identified 27 samples, 17 with an apparent full-mutation expansion of theAR,ATXN1,ATXN2,ATXN8,DMPK,FXN,HTT, orTBPlocus, nine with an intermediate or premutation allele in theFMR1locus, and one with a borderline allele in theATXN2locus. We report the concordance between our bioinformatics findings and the clinical PCR results in a subset of these samples. Implementation of our bioinformatics workflow can improve the detection of disease STR expansions in exome and genome sequence diagnostics and enhance clinical outcomes for patients with repeat expansion disorders.
Identification of unique mutations in SARS-CoV-2 strains isolated from India suggests its attenuated pathotype
Authors: Gaurav Shubham , Pandey Shambhavi , Puvar Apurvasinh , Shah Tejas , Joshi Madhvi , Joshi Chaitanya , Kumar Sachin 
Published: June 2020

AbstractSevere Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), which was first reported in Wuhan, China in November 2019 has developed into a pandemic since March 2020, causing substantial human casualties and economic losses. Studies on SARS-CoV-2 are being carried out at an unprecedented rate to tackle this threat. Genomics studies, in particular, are indispensable to elucidate the dynamic nature of the RNA genome of SARS-CoV-2. RNA viruses are marked by their unique ability to undergo high rates of mutation in their genome, much more frequently than their hosts, which diversifies their strengths qualifying them to elude host immune response and amplify drug resistance. In this study, we sequenced and analyzed the genomic information of the SARS-CoV-2 isolates from two infected Indian patients and explored the possible implications of point mutations in its biology. In addition to multiple point mutations, we found a remarkable similarity between relatively common mutations of 36-nucleotide deletion in ORF8 of SARS-CoV-2. Our results corroborate with the earlier reported 29-nucleotide deletion in SARS, which was frequent during the early stage of human-to-human transmission. The results will be useful to understand the biology of SARS-CoV-2 and itsattenuation for vaccine development.
Bioengineering a light-responsive encapsulin nanoreactor: a potential tool for photodynamic therapy
Authors: Diaz Dennis , Vidal Xavier , Sunna Anwar , Care Andrew 
Published: June 2020

AbstractEncapsulins, a prokaryotic class of self-assembling protein nanocompartments, are being re-engineered to serve as ‘nanoreactors’ for the augmentation or creation of key biochemical reactions. However, approaches that allow encapsulin nanoreactors to be functionally activated with spatial and temporal precision is lacking. We report the construction of a light-responsive encapsulin nanoreactor for “on-demand” production of reactive oxygen species (ROS). Herein, encapsulins were loaded with the fluorescent flavoprotein mini-Singlet Oxygen Generator (miniSOG), a biological photosensitizer that is activated by blue-light to generate ROS, primarily singlet oxygen (1O2). We established that the nanocompartments stably encased miniSOG, and in response to blue-light were able to mediate the photoconversion of molecular oxygen into ROS. Using anin vitromodel of lung cancer, ROS generated by the nanoreactor was shown to trigger photosensitized oxidation reactions that exerted a toxic effect on tumour cells, suggesting utility in photodynamic therapy. This encapsulin nanoreactor thus represents a platform for the light-controlled initiation and/or modulation of ROS-driven processes in biomedicine and biotechnology.
Feasibility and visualization of deep learning detection and classification of inferior vena cava filters
Authors: Park Brian J. , Sotirchos Vlasios S. , Adleberg Jason , Stavropoulos S. William , Cook Tessa S. , Hunt Stephen J. 
Published: June 2020

AbstractPurposeThis study assesses the feasibility of deep learning detection and classification of 3 retrievable inferior vena cava filters with similar radiographic appearances and emphasizes the importance of visualization methods to confirm proper detection and classification.Materials and MethodsThe fast.ai library with ResNet-34 architecture was used to train a deep learning classification model. A total of 442 fluoroscopic images (N=144 patients) from inferior vena cava filter placement or removal were collected. Following image preprocessing, the training set included 382 images (110 Celect, 149 Denali, 123 Günther Tulip), of which 80% were used for training and 20% for validation. Data augmentation was performed for regularization. A random test set of 60 images (20 images of each filter type), not included in the training or validation set, was used for evaluation. Total accuracy and receiver operating characteristic area under the curve were used to evaluate performance. Feature heatmaps were visualized using guided backpropagation and gradient-weighted class activation mapping.ResultsThe overall accuracy was 80.2% with mean receiver operating characteristic area under the curve of 0.96 for the validation set (N=76), and 85.0% with mean receiver operating characteristic area under the curve of 0.94 for the test set (N=60). Two visualization methods were used to assess correct filter detection and classification.ConclusionsA deep learning model can be used to automatically detect and accurately classify inferior vena cava filters on radiographic images. Visualization techniques should be utilized to ensure deep learning models function as intended.
Assessing the Impact of Imputation on the Interpretations of Prediction Models: A Case Study on Mortality Prediction for Patients with Acute Myocardial Infarction
Authors: Payrovnaziri Seyedeh Neelufar , Xing Aiwen , Salman Shaeke , Liu Xiuwen , Bian Jiang , He Zhe 
Published: June 2020

AbstractAcute myocardial infarction poses significant health risks and financial burden on healthcare and families. Prediction of mortality risk among AMI patients using rich electronic health record (EHR) data can potentially save lives and healthcare costs. Nevertheless, EHR-based prediction models usually use a missing data imputation method without considering its impact on the performance and interpretability of the model, hampering its real-world applicability in the healthcare setting. This study examines the impact of different methods for imputing missing values in EHR data on both the performance and the interpretations of predictive models. Our results showed that a small standard deviation in root mean squared error across different runs of an imputation method does not necessarily imply a small standard deviation in the prediction models’ performance and interpretation. We also showed that the level of missingness and the imputation method used can have a significant impact on the interpretation of the models.
Neuropilin-1 facilitates SARS-CoV-2 cell entry and provides a possible pathway into the central nervous system
Authors: Cantuti-Castelvetri Ludovico , Ojha Ravi , Pedro Liliana D. , Djannatian Minou , Franz Jonas , Kuivanen Suvi , Kallio Katri , Kaya Tuğberk , Anastasina Maria , Smura Teemu , Levanov Lev , Szirovicza Leonora , Tobi Allan , Kallio-Kokko Hannimari , Österlund Pamela , Joensuu Merja , Meunier Frédéric A. , Butcher Sarah , Winkler Martin Sebastian , Mollenhauer Brit , Helenius Ari , Gokce Ozgun , Teesalu Tambet , Hepojoki Jussi , Vapalahti Olli , Stadelmann Christine , Balistreri Giuseppe , Simons Mikael 
Published: June 2020

SUMMARYThe causative agent of the current pandemic and coronavirus disease 2019 (COVID-19) is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)1. Understanding how SARS-CoV-2 enters and spreads within human organs is crucial for developing strategies to prevent viral dissemination. For many viruses, tissue tropism is determined by the availability of virus receptors on the surface of host cells2. Both SARS-CoV and SARS-CoV-2 use angiotensin-converting enzyme 2 (ACE2) as a host receptor, yet, their tropisms differ3-5. Here, we found that the cellular receptor neuropilin-1 (NRP1), known to bind furin-cleaved substrates, significantly potentiates SARS-CoV-2 infectivity, which was inhibited by a monoclonal blocking antibody against the extracellular b1b2 domain of NRP1. NRP1 is abundantly expressed in the respiratory and olfactory epithelium, with highest expression in endothelial cells and in the epithelial cells facing the nasal cavity. Neuropathological analysis of human COVID-19 autopsies revealed SARS-CoV-2 infected NRP1-positive cells in the olfactory epithelium and bulb. In the olfactory bulb infection was detected particularly within NRP1-positive endothelial cells of small capillaries and medium-sized vessels. Studies in mice demonstrated, after intranasal application, NRP1-mediated transport of virus-sized particles into the central nervous system. Thus, NRP1 could explain the enhanced tropism and spreading of SARS-CoV-2.
IP-MS Analysis of ESX-5 and ESX-1 Substrates Enables Mycobacterial Species Identification
Authors: Shu Qingbo , Rajagopal Meena , Fan Jia , Zhan Lingpeng , Kong Xiangxing , He Yifan , Rotcheewaphan Suwatchareeporn , Lyon Christopher J. , Sha Wei , Zelazny Adrian M. , Hu Tony 
Published: June 2020

AbstractPulmonary disease resulting from non-tuberculous mycobacteria (NTM) infection has emerged as an increasingly prevalent clinical entity in the past two to three decades, but there are no standardized, commercial assays available for the molecular diagnosis of NTM infections from clinical samples. Herein we discuss the development of an assay that employs immunoprecipitation coupled with mass spectrometry (IP-MS) to rapidly and accurately discriminate prevalent slow-growing mycobacterial species (i.e.,M. aviumandM. intracellulare, M. kansasii, M. gordonae, M. marinumandM. tuberculosis) during early growth in mycobacterial growth indicator tube (MGIT) cultures. This IP-MS assay employs antibodies specific for conserved tryptic peptides of M. tuberculosis EsxN (AQAASLEAEHQAIVR) and CFP-10 (TQIDQVESTAGSLQGQWR) to capture and identify specific mycobacterial species and allows species-specific mycobacterium identification at the first sign of MGIT culture positivity.