Corpus overview


Overview

MeSH Disease

Infections (499)

Disease (455)

Coronavirus Infections (263)

Pneumonia (174)

Death (171)


Human Phenotype

Pneumonia (196)

Fever (60)

Cough (32)

Hypertension (21)

Falls (20)


Transmission

Seroprevalence
    displaying 31 - 40 records in total 1389
    records per page




    Serology assessment of antibody SERO response to SARS-CoV-2 in patients with COVID-19 by rapid IgM/IgG antibody test SERO

    Authors: Yang De Marinis; Torgny Sunnerhagen; Pradeep Bompada; Anna Blackberg; Runtao Yang; Joel Svensson; Ola Ekstrom; Karl-Fredrik Eriksson; Ola Hansson; Leif Groop; Isabel Goncalves; Magnus Rasmussen

    doi:10.1101/2020.08.05.20168815 Date: 2020-08-06 Source: medRxiv

    The coronavirus disease MESHD 2019 (COVID-19) pandemic has created a global health- and economic crisis. Lifting confinement restriction and resuming to normality depends greatly on COVID-19 immunity screening. Detection of antibodies SERO to severe acute respiratory syndrome MESHD coronavirus 2 (SARS-CoV-2) which causes COVID-19 by serological methods is important to diagnose a current or resolved infection MESHD. In this study, we applied a rapid COVID-19 IgM/IgG antibody test SERO and performed serology assessment of antibody SERO response to SARS-CoV-2. In PCR-confirmed COVID-19 patients (n=45), the total antibody SERO detection rate is 92% in hospitalized patients and 79% in non-hospitalized patients. We also studied antibody SERO response in relation to time after symptom onset TRANS and disease MESHD severity, and observed an increase in antibody SERO reactivity and distinct distribution patterns of IgM and IgG following disease progression MESHD. The total IgM and IgG detection is 63% in patients with < 2 weeks from disease MESHD onset; 85% in non-hospitalized patients with > 2 weeks disease MESHD duration; and 91% in hospitalized patients with > 2 weeks disease MESHD duration. We also compared different blood SERO sample types and suggest a potentially higher sensitivity SERO by serum SERO/ plasma SERO comparing with whole blood SERO measurement. To study the specificity of the test, we used 69 sera/ plasma SERO samples collected between 2016-2018 prior to the COVID-19 pandemic, and obtained a test specificity of 97%. In summary, our study provides a comprehensive validation of the rapid COVID-19 IgM/IgG serology test, and mapped antibody SERO detection patterns in association with disease MESHD progress and hospitalization. Our study supports that the rapid COVID-19 IgM/IgG test may be applied to assess the COVID-19 status both at the individual and at a population level.

    Swab-Seq: A high-throughput platform for massively scaled up SARS-CoV-2 testing

    Authors: Joshua S. Bloom; Eric M. Jones; Molly Gasperini; Nathan B. Lubock; Laila Sathe; Chetan Munugala; A. Sina Booeshaghi; Oliver F. Brandenberg; Longhua Guo; Scott W. Simpkins; Isabella Lin; Nathan LaPierre; Duke Hong; Yi Zhang; Gabriel Oland; Bianca Judy Choe; Sukantha Chandrasekaran; Evann E. Hilt; Manish J. Butte; Robert Damoiseaux; Aaron R. Cooper; Yi Yin; Lior Pachter; Omai B. Garner; Jonathan Flint; Eleazar Eskin; Chongyuan Luo; Sriram Kosuri; Leonid Kruglyak; Valerie A. Arboleda

    doi:10.1101/2020.08.04.20167874 Date: 2020-08-06 Source: medRxiv

    The rapid spread of severe acute respiratory syndrome MESHD coronavirus 2 (SARS-CoV-2) is due to the high rates of transmission TRANS by individuals who are asymptomatic TRANS at the time of transmission TRANS. Frequent, widespread testing of the asymptomatic TRANS population for SARS-CoV-2 is essential to suppress viral transmission TRANS and is a key element in safely reopening society. Despite increases in testing capacity, multiple challenges remain in deploying traditional reverse transcription and quantitative PCR (RT-qPCR) tests at the scale required for population screening of asymptomatic TRANS individuals. We have developed SwabSeq, a high-throughput testing platform for SARS-CoV-2 that uses next-generation sequencing as a readout. SwabSeq employs sample-specific molecular barcodes to enable thousands of samples to be combined and simultaneously analyzed for the presence or absence of SARS-CoV-2 in a single run. Importantly, SwabSeq incorporates an in vitro RNA standard that mimics the viral amplicon, but can be distinguished by sequencing. This standard allows for end-point rather than quantitative PCR, improves quantitation, reduces requirements for automation and sample-to-sample normalization, enables purification-free detection, and gives better ability to call true negatives. We show that SwabSeq can test nasal and oral specimens for SARS-CoV-2 with or without RNA extraction while maintaining analytical sensitivity SERO better than or comparable to that of fluorescence-based RT-qPCR tests. SwabSeq is simple, sensitive, flexible, rapidly scalable, inexpensive enough to test widely and frequently, and can provide a turn around time of 12 to 24 hours.

    Transient dynamics of SARS-CoV-2 as England exited national lockdown

    Authors: Steven Riley; Kylie E. C. Ainslie; Oliver Eales; Caroline E Walters; Haowei Wang; Christina J Atchison; Peter Diggle; Deborah Ashby; Christl A. Donnelly; Graham Cooke; Wendy Barclay; Helen Ward; Ara Darzi; Paul Elliott

    doi:10.1101/2020.08.05.20169078 Date: 2020-08-06 Source: medRxiv

    Control of the COVID-19 pandemic requires a detailed understanding of prevalence SERO of SARS-CoV-2 virus in the population. Case-based surveillance is necessarily biased towards symptomatic individuals and sensitive to varying patterns of reporting in space and time. The real-time assessment of community transmission TRANS antigen study (REACT-1) is designed to overcome these limitations by obtaining prevalence SERO data based on a nose and throat swab RT-PCR test among a representative community-based sample in England, including asymptomatic TRANS individuals. Here, we describe results comparing rounds 1 and 2 carried out during May and mid June / early July 2020 respectively across 315 lower tier local authority areas. In round 1 we found 159 positive samples from 120,620 tested swabs while round 2 there were 123 positive samples from 159,199 tested swabs, indicating a downwards trend in prevalence SERO from 0.13% (95% CI, 0.11%, 0.15%) to 0.077% (0.065%, 0.092%), a halving time of 38 (28, 58) days, and an R of 0.89 (0.86, 0.93). The proportion of swab-positive participants who were asymptomatic TRANS at the time of sampling increased from 69% (61%, 76%) in round 1 to 81% (73%, 87%) in round 2. Although health care and care home workers were infected far more frequently than other workers in round 1, the odds were markedly reduced in round 2. Age TRANS patterns of infection MESHD changed between rounds, with a reduction by a factor of five in prevalence SERO in 18 to 24 year olds. Our data were suggestive of increased risk of infection TRANS risk of infection TRANS infection MESHD in Black and Asian (mainly South Asian) ethnicities. Using regional and detailed case location data, we detected increased infection MESHD intensity in and near London. Under multiple sensitivity SERO analyses, our results were robust to the possibility of false positives. At the end of the initial lockdown in England, we found continued decline in prevalence SERO and a shift in the pattern of infection MESHD by age TRANS and occupation. Community-based sampling, including asymptomatic TRANS individuals, is necessary to fully understand the nature of ongoing transmission TRANS.

    Improving Explainability of Image Classification in Scenarios with Class Overlap: Application to COVID-19 and Pneumonia MESHD Pneumonia HP

    Authors: Edward Verenich; Alvaro Velasquez; Nazar Khan; Faraz Hussain

    id:2008.02866v1 Date: 2020-08-06 Source: arXiv

    Trust in predictions made by machine learning models is increased if the model generalizes well on previously unseen samples and when inference is accompanied by cogent explanations of the reasoning behind predictions. In the image classification domain, generalization can also be assessed through accuracy, sensitivity SERO, and specificity, and one measure to assess explainability is how well the model localizes the object of interest within an image. However, in multi-class settings, both generalization and explanation through localization are degraded when available training data contains features with significant overlap between classes. We propose a method to enhance explainability of image classification through better localization by mitigating the model uncertainty induced by class overlap. Our technique performs discriminative localization on images that contain features with significant class overlap, without explicitly training for localization. Our method is particularly promising in real-world class overlap scenarios, such as COVID19 vs pneumonia MESHD pneumonia HP, where expertly labeled data for localization is not available. This can be useful for early, rapid, and trustworthy screening for COVID-19.

    Improving Explainability of Image Classification in Scenarios with Class Overlap: Application to COVID-19 and Pneumonia MESHD Pneumonia HP

    Authors: Edward Verenich; Alvaro Velasquez; Nazar Khan; Faraz Hussain

    id:2008.02866v2 Date: 2020-08-06 Source: arXiv

    Trust in predictions made by machine learning models is increased if the model generalizes well on previously unseen samples and when inference is accompanied by cogent explanations of the reasoning behind predictions. In the image classification domain, generalization can be assessed through accuracy, sensitivity SERO, and specificity. Explainability can be assessed by how well the model localizes the object of interest within an image. However, both generalization and explainability through localization are degraded in scenarios with significant overlap between classes. We propose a method based on binary expert networks that enhances the explainability of image classifications through better localization by mitigating the model uncertainty induced by class overlap. Our technique performs discriminative localization on images that contain features with significant class overlap, without explicitly training for localization. Our method is particularly promising in real-world class overlap scenarios, such as COVID-19 and pneumonia MESHD pneumonia HP, where expertly labeled data for localization is not readily available. This can be useful for early, rapid, and trustworthy screening for COVID-19.

    A Machine Learning based approach to unleash the impact of COVID-19 on Indian Stock Market

    Authors: Nusrat Rouf; Majid Bashir Malik; Tasleem Arif

    doi:10.21203/rs.3.rs-54882/v1 Date: 2020-08-06 Source: ResearchSquare

    Introduction: Advancement in information technology, be it hardware, software or communication technology, over few decades has rapidly impacted almost every field of study. Machine learning tools and techniques are nowadays applied to every field. It has opened the ways for interdisciplinary research by promising effective analyzation and decision-making strategies. COVID-19 has badly affected more than 200 countries within a short span of time. It has drastically affected both daily activities as well as economic activities. Herd behavior of investors has triggered panic selling. As a result, stock markets around the world have plunged down.Methods: In this paper, we analyze the impact of COVID-19 on NSE (National Stock Exchange) index Nifty50. We employ Pearson Correlation and investigate the impact of total confirmed cases TRANS and daily cases on Nifty50 closing price. We use various machine learning regression models for predictive analysis viz, linear regression with polynomial terms (quadratic, cubic), Decision Tree Regression and Random Forest Regression. Model performance SERO is measured using MSE (Mean Square Error), RMSE (Root Mean Square Error) and R2 (R Squared) evaluators. Results: Correlation analysis reveals that total confirmed cases TRANS and daily cases in both India and the World have negative correlation with Nifty50 closing prices. Moreover, Nifty50 closing prices are more negatively correlated with total confirmed and daily cases in India. Predictive analysis shows that the Random Forest Regression model outperforms all other models. Conclusion: We analyze and predict the impact of COVID-19 on closing price of Nifty50 index. We employ Pearson Correlation and investigate the impact of COVID-19 on Nifty50 closing prices. We use various machine learning regression models to predict the closing price of Nifty50 index. Results reveal that the market volatility is directly proportional to increase in number of COVID-19 cases. Random Forest Regression model has comparatively shown better RMSE and R2 values.

    MultiCheXNet: A Multi-Task Learning Deep Network For Pneumonia MESHD Pneumonia HP-like Diseases MESHD Diagnosis From X-ray Scans

    Authors: Abdullah Tarek Farag; Ahmed Raafat Abd El-Wahab; Mahmoud Nada; Mohamed Yasser Abd El-Hakeem; Omar Sayed Mahmoud; Reem Khaled Rashwan; Ahmad El Sallab

    id:2008.01973v1 Date: 2020-08-05 Source: arXiv

    We present MultiCheXNet, an end-to-end Multi-task learning model, that is able to take advantage of different X-rays data sets of Pneumonia MESHD Pneumonia HP-like diseases MESHD in one neural architecture, performing three tasks at the same time; diagnosis, segmentation and localization. The common encoder in our architecture can capture useful common features present in the different tasks. The common encoder has another advantage of efficient computations, which speeds up the inference time compared to separate models. The specialized decoders heads can then capture the task-specific features. We employ teacher forcing to address the issue of negative samples that hurt the segmentation and localization performance SERO. Finally,we employ transfer learning to fine tune the classifier on unseen pneumonia MESHD pneumonia HP-like diseases MESHD. The MTL architecture can be trained on joint or dis-joint labeled data sets. The training of the architecture follows a carefully designed protocol, that pre trains different sub-models on specialized datasets, before being integrated in the joint MTL model. Our experimental setup involves variety of data sets, where the baseline performance SERO of the 3 tasks is compared to the MTL architecture performance SERO. Moreover, we evaluate the transfer learning mode to COVID-19 data set,both from individual classifier model, and from MTL architecture classification head.

    Risk, Trust, and Bias: Causal Regulators of Biometric-Enabled Decision Support

    Authors: Kenneth Lai; Helder C. R. Oliveira; Ming Hou; Svetlana N. Yanushkevich; Vlad P. Shmerko

    id:2008.02359v1 Date: 2020-08-05 Source: arXiv

    Biometrics and biometric-enabled decision support systems (DSS) have become a mandatory part of complex dynamic systems such as security checkpoints, personal health monitoring systems, autonomous robots, and epidemiological surveillance. Risk, trust, and bias (R-T-B) are emerging measures of performance SERO of such systems. The existing studies on the R-T-B impact on system performance SERO mostly ignore the complementary nature of R-T-B and their causal relationships, for instance, risk of trust, risk of bias, and risk of trust over biases. This paper offers a complete taxonomy of the R-T-B causal performance SERO regulators for the biometric-enabled DSS. The proposed novel taxonomy links the R-T-B assessment to the causal inference mechanism for reasoning in decision making. Practical details of the R-T-B assessment in the DSS are demonstrated using the experiments of assessing the trust in synthetic biometric and the risk of bias in face biometrics. The paper also outlines the emerging applications of the proposed approach beyond biometrics, including decision support for epidemiological surveillance such as for COVID-19 pandemics.

    Optimal Pooling Matrix Design for Group Testing with Dilution (Row Degree) Constraints

    Authors: Jirong Yi; Myung Cho; Xiaodong Wu; Raghu Mudumbai; Weiyu Xu

    id:2008.01944v1 Date: 2020-08-05 Source: arXiv

    In this paper, we consider the problem of designing optimal pooling matrix for group testing (for example, for COVID-19 virus testing) with the constraint that no more than $r>0$ samples can be pooled together, which we call "dilution constraint". This problem translates to designing a matrix with elements being either 0 or 1 that has no more than $r$ '1's in each row and has a certain performance SERO guarantee of identifying anomalous elements. We explicitly give pooling matrix designs that satisfy the dilution constraint and have performance SERO guarantees of identifying anomalous elements, and prove their optimality in saving the largest number of tests, namely showing that the designed matrices have the largest width-to-height ratio among all constraint-satisfying 0-1 matrices.

    Trove: Ontology-driven weak supervision for medical entity classification

    Authors: Jason A. Fries; Ethan Steinberg; Saelig Khattar; Scott L. Fleming; Jose Posada; Alison Callahan; Nigam H. Shah

    id:2008.01972v1 Date: 2020-08-05 Source: arXiv

    Motivation: Recognizing named entities (NER) and their associated attributes like negation are core tasks in natural language processing. However, manually labeling data for entity tasks is time consuming and expensive, creating barriers to using machine learning in new medical applications. Weakly supervised learning, which automatically builds imperfect training sets from low cost, less accurate labeling rules, offers a potential solution. Medical ontologies are compelling sources for generating labels, however combining multiple ontologies without ground truth data creates challenges due to label noise introduced by conflicting entity definitions. Key questions remain on the extent to which weakly supervised entity classification can be automated using ontologies, or how much additional task-specific rule engineering is required for state-of-the-art performance SERO. Also unclear is how pre-trained language models, such as BioBERT, improve the ability to generalize from imperfectly labeled data. Results: We present Trove, a framework for weakly supervised entity classification using medical ontologies. We report state-of-the-art, weakly supervised performance SERO on two NER benchmark datasets and establish new baselines for two entity classification tasks in clinical text. We perform within an average of 3.5 F1 points (4.2%) of NER classifiers trained with hand-labeled data. Automatically learning label source accuracies to correct for label noise provided an average improvement of 3.9 F1 points. BioBERT provided an average improvement of 0.9 F1 points. We measure the impact of combining large numbers of ontologies and present a case study on rapidly building classifiers for COVID-19 clinical tasks. Our framework demonstrates how a wide range of medical entity classifiers can be quickly constructed using weak supervision and without requiring manually-labeled training data.

The ZB MED preprint Viewer preVIEW includes all COVID-19 related preprints from medRxiv and bioRxiv, from ChemRxiv, from ResearchSquare, from arXiv and from Preprints.org and is updated on a daily basis (7am CET/CEST).

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MeSH Disease
Human Phenotype
Transmission
Seroprevalence


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