Corpus overview


Overview

MeSH Disease

Human Phenotype

Pneumonia (78)

Fever (23)

Hypertension (13)

Cough (12)

Fatigue (6)


Transmission

Seroprevalence
    displaying 1 - 10 records in total 450
    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.

    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.

    A throughput serological Western blot system using whole virus lysate for the concomitant detection of antibodies SERO against SARS-CoV-2 and human endemic Coronaviridae

    Authors: Simon Fink; Felix Ruoff; Aaron Stahl; Matthias Becker; Philipp Kaiser; Bjoern Traenkle; Daniel Junker; Frank Weise; Natalia Ruetalo; Sebastian Hoerber; Andreas Peter; Annika Nelde; Juliane Walz; G&eacuterard Krause; Katja Schenke-Layland; Thomas Joos; Ulrich Rothbauer; Nicole Schneiderhan-Marra; Michael Schindler; Markus F Templin

    doi:10.1101/2020.07.31.20165019 Date: 2020-08-04 Source: medRxiv

    BACKGROUND: Seroreactivity against human endemic coronaviruses has been linked to disease MESHD severity after SARS-CoV-2 infection MESHD. Assays that are capable of concomitantly detecting antibodies SERO against endemic coronaviridae such as OC43, 229E, NL63, and SARS-CoV-2 may help to elucidate this question. We set up a platform for serum SERO-screening and developed a bead-based Western blot system, namely DigiWest, capable of running hundreds of assays using microgram amounts of protein prepared directly from different viruses. METHODS: The parallelized and miniaturised DigiWest assay was adapted for detecting antibodies SERO using whole protein extract prepared from isolated SARS-CoV-2 virus particles. After characterisation and optimization of the newly established test, whole virus lysates of OC43, 229E, and NL63 were integrated into the system. RESULTS: The DigiWest-based immunoassay SERO system for detection of SARS-CoV-2 specific antibodies SERO shows a sensitivity SERO of 87.2 % and diagnostic specificity of 100 %. Concordance analysis with the SARS-CoV-2 immunoassays SERO available by Roche, Siemens, and Euroimmun indicates a comparable assay performance SERO (Cohen's Kappa ranging from 0.8799-0.9429). In the multiplexed assay, antibodies SERO against the endemic coronaviruses OC43, 229E, and NL63 were detected, displaying a high incidence of seroreactivity against these coronaviruses. CONCLUSION: The DigiWest-based immunoassay SERO, which uses authentic antigens from isolated virus particles, is capable of detecting individual serum SERO responses against SARS-CoV-2 with high specificity and sensitivity SERO in one multiplexed assay. It shows high concordance with other commercially available serologic assays. The DigiWest approach enables a concomitant detection of antibodies SERO against different endemic coronaviruses and will help to elucidate the role of these possibly cross-reactive antibodies SERO.

    TClustVID: A Novel Machine Learning Classification Model to Investigate Topics and Sentiment inCOVID-19 Tweets

    Authors: Md. Shahriare Satu; Md. Imran Khan; Mufti Mahmud; Shahadat Uddin; Matthew A Summers; Julian M. W. Quinn; Mohammad Ali Moni

    doi:10.1101/2020.08.04.20167973 Date: 2020-08-04 Source: medRxiv

    COVID-19, caused by the SARS-Cov2, varies greatly in its severity but represent serious respiratory symptoms with vascular and other complications, particularly in older adults TRANS. The disease MESHD disease can be spread TRANS can be spread by both symptomatic and asymptomatic TRANS infected individuals, and remains uncertainty over key aspects of its infectivity, no effective remedy yet exists and this disease MESHD causes severe economic effects globally. For these reasons, COVID-19 is the subject of intense and widespread discussion on social media platforms including Facebook and Twitter. These public forums substantially impact on public opinions in some cases and exacerbate widespread panic and misinformation spread during the crisis. Thus, this work aimed to design an intelligent clustering-based classification and topics extracting model (named TClustVID) that analyze COVID-19-related public tweets to extract significant sentiments with high accuracy. We gathered COVID-19 Twitter datasets from the IEEE Dataport repository and employed a range of data preprocessing methods to clean the raw data, then applied tokenization and produced a word-to-index dictionary. Thereafter, different classifications were employed to Twitter datasets which enabled exploration of the performance SERO of traditional and TClustVID classification methods. TClustVID showed higher performance SERO compared to the traditional classifiers determined by clustering criteria. Finally, we extracted significant topic clusters from TClustVID, split them into positive, neutral and negative clusters and implemented latent dirichlet allocation for extraction of popular COVID-19 topics. This approach identified common prevailing public opinions and concerns related to COVID-19, as well as attitudes to infection MESHD prevention strategies held by people from different countries concerning the current pandemic situation.

    SARS-CoV-2 antigens expressed in plants detect antibody SERO responses in COVID-19 patients

    Authors: Mohau S Makatsa; Marius B Tincho; Jerome M Wendoh; Sherazaan D Ismail; Rofhiwa Nesamari; Francisco Pera; Scott de Beer; Anura David; Sarika Jugwanth; Maemu P Gededzha; Nakampe Mampeule; Ian Sanne; Wendy Stevens; Lesley Scott; Jonathan Blackburn; Elizabeth S Mayne; Roanne S Keeton; Wendy A Burgers

    doi:10.1101/2020.08.04.20167940 Date: 2020-08-04 Source: medRxiv

    Background: The SARS-CoV-2 pandemic has swept the world and poses a significant global threat to lives and livelihoods, with over 16 million confirmed cases TRANS and at least 650 000 deaths MESHD from COVID-19 in the first 7 months of the pandemic. Developing tools to measure seroprevalence SERO and understand protective immunity to SARS-CoV-2 is a priority. We aimed to develop a serological assay SERO using plant-derived recombinant viral proteins, which represent important tools in less-resourced settings. Methods: We established an indirect enzyme-linked immunosorbent assay SERO ( ELISA SERO) using the S1 and receptor-binding domain (RBD) portions of the spike protein from SARS-CoV-2, expressed in Nicotiana benthamiana. We measured antibody SERO responses in sera from South African patients (n=77) who had tested positive by PCR for SARS-CoV-2. Samples were taken a median of six weeks after the diagnosis, and the majority of participants had mild and moderate COVID-19 disease MESHD. In addition, we tested the reactivity of pre-pandemic plasma SERO (n=58) and compared the performance SERO of our in-house ELISA SERO with a commercial assay. We also determined whether our assay could detect SARS-CoV-2-specific IgG and IgA in saliva. Results: We demonstrate that SARS-CoV-2-specific immunoglobulins are readily detectable using recombinant plant-derived viral proteins, in patients who tested positive for SARS-CoV-2 by PCR. Reactivity to S1 and RBD was detected in 51 (66%) and 48 (62%) of participants, respectively. Notably, we detected 100% of samples identified as having S1-specific antibodies SERO by a validated, high sensitivity SERO commercial ELISA SERO, and OD values were strongly and significantly correlated between the two assays. For the pre-pandemic plasma SERO, 1/58 (1.7%) of samples were positive, indicating a high specificity for SARS-CoV-2 in our ELISA SERO. SARS-CoV-2-specific IgG correlated significantly with IgA and IgM responses. Endpoint titers of S1- and RBD-specific immunoglobulins ranged from 1:50 to 1:3200. S1-specific IgG and IgA were found in saliva samples from convalescent volunteers. Conclusions: We demonstrate that recombinant SARS-CoV-2 proteins produced in plants enable robust detection of SARS-CoV-2 humoral responses. This assay can be used for seroepidemiological studies and to measure the strength and durability of antibody SERO responses to SARS-CoV-2 in infected patients in our setting.

    Analysis of COVID-19 and comorbidity co- infection MESHD Model with Optimal Control

    Authors: Dr. Andrew Omame; Nometa Ikenna

    doi:10.1101/2020.08.04.20168013 Date: 2020-08-04 Source: medRxiv

    The new coronavirus disease MESHD 2019 (COVID-19) infection MESHD is a double challenge for people infected with comorbidities such as cardiovascular and cerebrovascular diseases MESHD and diabetes. Comorbidities have been reported to be risk factors for the complications of COVID-19. In this work, we develop and analyze a mathematical model for the dynamics of COVID-19 infection MESHD in order to assess the impacts of prior comorbidity on COVID-19 complications and COVID-19 re- infection MESHD. The model is simulated using data relevant to the dynamics of the diseases MESHD in Lagos, Nigeria, making predictions for the attainment of peak periods in the presence or absence of comorbidity. The model is shown to undergo the phenomenon of backward bifurcation caused by the parameter accounting for increased susceptibility to COVID-19 infection MESHD by comorbid susceptibles as well as the rate of re- infection MESHD by those who have recovered from a previous COVID-19 infection MESHD. Sensitivity SERO analysis of the model when the population of individuals co-infected with COVID-19 and comorbidity is used as response function revealed that the top ranked parameters that drive the dynamics of the co- infection MESHD model are the effective contact rate for COVID-19 transmission TRANS, $\beta\sst{cv}$, the parameter accounting for increased susceptibility to COVID-19 by comorbid susceptibles, $\chi\sst{cm}$, the comorbidity development rate, $\theta\sst{cm}$, the detection rate for singly infected and co-infected individuals, $\eta_1$ and $\eta_2$, as well as the recovery rate from COVID-19 for co-infected individuals, $\varphi\sst{i2}$. Simulations of the model reveal that the cumulative confirmed cases TRANS (without comorbidity) may get up to 180,000 after 200 days, if the hyper susceptibility rate of comorbid susceptibles is as high as 1.2 per day. Also, the cumulative confirmed cases TRANS (including those co-infected with comorbidity) may be as high as 1000,000 cases by the end of November, 2020 if the re- infection MESHD rates for COVID-19 is 0.1 per day. It may be worse than this if the re- infection MESHD rates increase higher. Moreover, if policies are strictly put in place to step down the probability of COVID-19 infection MESHD by comorbid susceptibles to as low as 0.4 per day and step up the detection rate for singly infected individuals to 0.7 per day, then the reproduction number TRANS can be brought very low below one, and COVID-19 infection MESHD eliminated from the population. In addition, optimal control and cost-effectiveness analysis of the model reveal that the the strategy that prevents COVID-19 infection MESHD by comorbid susceptibles has the least ICER and is the most cost-effective of all the control strategies for the prevention of COVID-19.

    Interpretable Sequence Learning for COVID-19 Forecasting

    Authors: Sercan O. Arik; Chun-Liang Li; Jinsung Yoon; Rajarishi Sinha; Arkady Epshteyn; Long T. Le; Vikas Menon; Shashank Singh; Leyou Zhang; Nate Yoder; Martin Nikoltchev; Yash Sonthalia; Hootan Nakhost; Elli Kanal; Tomas Pfister

    id:2008.00646v1 Date: 2020-08-03 Source: arXiv

    We propose a novel approach that integrates machine learning into compartmental disease MESHD modeling to predict the progression of COVID-19. Our model is explainable by design as it explicitly shows how different compartments evolve and it uses interpretable encoders to incorporate covariates and improve performance SERO. Explainability is valuable to ensure that the model's forecasts are credible to epidemiologists and to instill confidence in end-users such as policy makers and healthcare institutions. Our model can be applied at different geographic resolutions, and here we demonstrate it for states and counties in the United States. We show that our model provides more accurate forecasts, in metrics averaged across the entire US, than state-of-the-art alternatives, and that it provides qualitatively meaningful explanatory insights. Lastly, we analyze the performance SERO of our model for different subgroups based on the subgroup distributions within the counties.

    Household Representative Sample Strategy for COVID-19 Large-Scale Population Screening

    Authors: John Takyi-Williams

    id:10.20944/preprints202008.0030.v1 Date: 2020-08-02 Source: preprints.org

    In the advent of COVID-19 pandemic, testing is highly essential to be able to isolate, treat infected persons, and finally curb transmission TRANS of this infectious respiratory disease MESHD. Group testing has been used previously for various infectious diseases MESHD and recently reported for large-scale population testing of COVID-19. However, possible sample dilution as a result of large pool sizes has been reported, limiting testing methods’ detection sensitivity SERO. Moreover, the need to sample all individuals prior to pooling overburden the limited resources such as test kits. An alternative proposed strategy where test is performed on pooled samples from individuals representing different households is presented here. This strategy intends to improve group testing method through the reduction in the number of samples collected and pooled during large-scale population testing. Moreover, it introduces database system which enables continuous monitoring of the population’s virus exposure for better decision making.

    Balancing Common Treatment and Epidemic Control in Medical Procurement during COVID-19: Transform-and-Divide Evolutionary Optimization

    Authors: Yu-Jun Zheng; Xin Chen; Tie-Er Gan; Min-Xia Zhang; Wei-Guo Sheng; Ling Wang

    id:2008.00395v1 Date: 2020-08-02 Source: arXiv

    Balancing common disease MESHD treatment and epidemic control is a key objective of medical supplies procurement in hospitals during a pandemic such as COVID-19. This problem can be formulated as a bi-objective optimization problem for simultaneously optimizing the effects of common disease MESHD treatment and epidemic control. However, due to the large number of supplies, difficulties in evaluating the effects, and the strict budget constraint, it is difficult for existing evolutionary multiobjective algorithms to efficiently approximate the Pareto front of the problem. In this paper, we present an approach that first transforms the original high-dimensional, constrained multiobjective optimization problem to a low-dimensional, unconstrained multiobjective optimization problem, and then evaluates each solution to the transformed problem by solving a set of simple single-objective optimization subproblems, such that the problem can be efficiently solved by existing evolutionary multiobjective algorithms. We applied the transform-and-divide evolutionary optimization approach to six hospitals in Zhejiang Province, China, during the peak of COVID-19. Results showed that the proposed approach exhibits significantly better performance SERO than that of directly solving the original problem. Our study has also shown that transform-and-divide evolutionary optimization based on problem-specific knowledge can be an efficient solution approach to many other complex problems and, therefore, enlarge the application field of evolutionary algorithms.

    Contact Classification in COVID-19 Tracing TRANS

    Authors: Christoph Günther; Daniel Günther

    id:2008.00431v1 Date: 2020-08-02 Source: arXiv

    The present paper addresses the task of reliably identifying critical contacts by using COVID-19 tracing TRANS apps. A reliable classification is crucial to ensure a high level of protection, and at the same time to prevent many people from being sent to quarantine by the app. Tracing TRANS apps are based on the capabilities of current smartphones to enable a broadest possible availability. Existing capabilities of smartphones include the exchange of Bluetooth Low Energy (BLE) signals and of audio signals, as well as the use of gyroscopes and magnetic sensors. The Bluetooth power measurements, which are often used today, may be complemented by audio ranging and attitude estimation in the future. Smartphones are worn in different ways, often in pockets and bags, which makes the propagation of signals and thus the classification rather unpredictable. Relying on the cooperation of users to wear their phones hanging from their neck would change the situation considerably. In this case the performance SERO, achievable with BLE and audio measurements, becomes predictable. Our analysis identifies parameters that result in accurate warnings, at least within the scope of validity of the models. A significant reduction of the spreading of the disease TRANS disease MESHD can then be achieved by the apps, without causing many people to unduly go to quarantine. The present paper is the first of three papers which analyze the situation in some detail.

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).

Sources


Annotations

All
None
MeSH Disease
Human Phenotype
Transmission
Seroprevalence


Export subcorpus as Endnote

This service is developed in the project nfdi4health task force covid-19 which is a part of nfdi4health.

nfdi4health is one of the funded consortia of the National Research Data Infrastructure programme of the DFG.