Corpus overview


MeSH Disease

Human Phenotype


    displaying 1 - 10 records in total 17
    records per page

    Clinical features and disease MESHD severity in an Iranian population of COVID-19 patients

    Authors: Shima Nabavi; Zahra Javidarabshahi; Abolghasem Allahyari; Mohammad Ramezani; Mohsen Seddigh-Shamsi; Sahar Ravanshad; Mina AkbariRad; Farnoosh Ebrahimzadeh; Shohre Khatami; Maryam Emadzadeh; Neda Saeedian; Ahmadreza Zarifian; Maryam Miri; Fariba Rezaeetalab; Sepide Hejazi; Reza Basiri; Mahnaz Mozdourian

    doi:10.21203/ Date: 2020-07-31 Source: ResearchSquare

    Objectives: Coronavirus disease MESHD 2019 (COVID-19) can present with a variety of symptoms. Severity of the disease MESHD may be associated with several factors. Here, we review clinical features of COVID-19 patients with different severities.Methods: This cross-sectional study was performed in Imam Reza hospital, Mashhad, Iran, during February-April 2020. COVID-19 patients with typical computed tomography (CT) patterns and/or positive reverse-transcriptase polymerase chain reaction (RT-PCR) were included. The patients were classified into three groups of moderate, severe, and critical based on disease MESHD severity. Demographic, clinical, laboratory, and radiologic findings were collected and compared. P<0.05 was considered statistically significant.Results: Overall, 200 patients with mean age TRANS of 69.75±6.39 years, of whom 82 (41%) were female TRANS were studied. Disease MESHD was severe/critical in the majority of patients (167, 83.5%). Disease MESHD severity was significantly associated with age TRANS, malignant comorbidities, dyspnea MESHD dyspnea HP, nausea MESHD nausea/vomiting HP/ vomiting MESHD, confusion MESHD confusion HP, respiratory rate, pulse rate, O2 saturation, extent of CT involvement, serum SERO C-reactive protein (CRP), pH, pO2, and aspartate transaminase (P<0.05). Moreover, complications including shock MESHD shock HP, coagulopathy, acidosis MESHD acidosis HP, sepsis MESHD sepsis HP, acute respiratory distress HP syndrome MESHD (ARDS), intensive care unit (ICU) admission, and intubation were significantly higher in patients with higher severities. O2 saturation, nausea MESHD nausea/vomiting HP/ vomiting MESHD, and extent of lung CT involvement were independent predictors of severe/critical COVID-19 (OR=0.342, 45.93, and 25.48, respectively; P<0.05).Conclusions: Our results indicate O2 saturation, nausea MESHD nausea/vomiting HP/ vomiting MESHD, and extent of lung CT involvement as independent predictors of severe COVID-19 conditions. Serum SERO CRP levels and pO2 were also considerably higher patients with higher severity and can be used along with other factors as possible predictors of severe disease MESHD in COVID-19 patients.

    Level of Knowledge, Attitude and Perception About COVID-19 Pandemic and Infection MESHD Control: A Cross-Sectional Study Among Veterinarians in Nigeria

    Authors: Olubukola Adenubi; Oluwawemimo Adebowale; Abimbola Oloye; Noah Bankole; Hezekiah Adesokan; Oladotun Fadipe; Patience Ayo-Ajayi; Adebayo Akinloye

    id:10.20944/preprints202007.0337.v1 Date: 2020-07-15 Source:

    Coronavirus disease MESHD (COVID-19) has caused mankind serious confusion MESHD confusion HP, economic havoc and psychological distress. This study evaluated the level of knowledge, attitude and perception about COVID-19 pandemic, infection MESHD control and impact among veterinarians in Nigeria. A cross-sectional online survey was used to collect data from consenting respondents during implementation of lockdown in the country (April 23 - May 31, 2020). Purposive and chain referral sampling techniques were used to recruit 368 respondents from various sectors of the profession. The proportion of respondents surveyed 197/368 (53.5 %) were from the public sector, 35.3 % from private sector, 1.1 % were unemployed and 0.8 % retired. Majority of the respondents were males TRANS (72.8 %), within 30 – 39 years (39.7 %) and had 1 – 10 years work experience. Respondents displayed good level of knowledge about COVID-19 (72.4 % ± 9.9 %, range 44.1-91.2 %), with information mostly derived from TV/Radio (81.5%) and social media (81.0 %). The overall attitude level was poor and various determinants for good attitude among respondents were if they were above 60 years (p = 0.013), possessed postgraduate qualification ( p = 0.031), worked over 30 years post DVM (p = 0.001), had household members between 5 and 10 (p = 0.012), and were resident in states on total lockdown (p = 0.024). There was no correlation between the knowledge level score and respondents’ attitude towards the pandemic (p = 0.12). With increasing rate of COVID-19 transmission TRANS, research data are needed to develop evidence-driven strategies, policies and effective risk mitigations to reduce the pandemic’s adverse impacts.

    COVID-19 Classification in X-ray Chest images using a New Convolutional Neural Network: CNN-COVID

    Authors: Pedro Moises de Sousa; Marianne Modesto; Gabrielle Macedo Pereira; Carlos Alberto da Costa Junior; Luis Vinicius de Moura; Christian Mattjie; Pedro Cunha Carneiro; Ana Maria Marques da Silva; Ana Claudia Patrocinio

    doi:10.21203/ Date: 2020-07-09 Source: ResearchSquare

    PurposeAs COVID-19 causes lung inflamation and lesions, several scientists have worked on seeking a computer model able to identify medical images of patients with this disease MESHD and improve triage. Chest x-ray and computed tomography images are remarkably like images from patients with other lung diseases MESHD, which makes it hard to diagnose, and that is why there was an urge to seek a computer model. Thus, this paper proposes a computer model able to classify x-ray images of patients as with the new coronavirus. Chest x-ray exams were chosen for this study over computed tomography scans because they are low cost, results are obtained quickly, and x-ray equipment availability is higher in regions impacted with the disease MESHD.MethodsA new CNN network, CNN-COVID, was developed to classify chest images as with and without COVID. This article collected images of patients with and without COVID-19 from Covid-19 image data collection and ChestXray14 banks. To assess the network’s accuracy, 10 training sessions and tests were done using CNN-COVID. A confusion MESHD confusion HP matrix was generated to assess the performance SERO of the model and calculate the following metrics: sensitivity SERO (Se), specificity (Sp), and F1 score. Besides, the Receiver Operating Characteristic (ROC) curves and the Areas Under the Curve (AUCs) were used for assessment.ResultsThe following values were obtained: AUC = 0.9720, Se = 98%, and Sp = 96% for the validation set. A total of 10 tests were executed and the average was 0.9787, the lowest result being 0.9740 and the highest, 0.9870.ConclusionsThe results proved that the CNN-COVID model is a promising tool to help physicians classify chest images with pneumonia MESHD pneumonia HP, considering pneumonia MESHD pneumonia HP caused by COVID-19 and pneumonia MESHD pneumonia HP due to other causes.

    Choosing a growth curve to model the Covid-19 outbreak

    Authors: Emiro A. Molina-Cuevas

    id:2007.03779v1 Date: 2020-07-07 Source: arXiv

    The Richards family models comprise a well-known set of models with useful parameters to describe several aspects of disease MESHD outbreaks. Some of these models have been used to study the current Covid-19 pandemic. However, there seems to be confusion MESHD confusion HP regarding the discrimination among competing models. In this paper a detailed application of Akaikes information approach is used to discern among models using data from The European Union, The United States and The United Kingdom. We argue that the epidemiological characteristics derived from competing models should be examined to complement the selection strategy, and the implicit properties of the models contrasted with the available data. Detailed analytical expressions of the epidemiological characteristics implied by the selected parametrizations are also offered.

    What the reproductive number TRANS R_0 TRANS can and cannot tell us about COVID-19 dynamics

    Authors: Clara L. Shaw; David A. Kennedy

    id:2006.14676v1 Date: 2020-06-25 Source: arXiv

    The reproductive number TRANS R_0 TRANS (and its value after initial disease MESHD emergence R) has long been used to predict the likelihood of pathogen invasion, to gauge the potential severity of an epidemic, and to set policy around interventions. However, often ignored complexities have generated confusion MESHD confusion HP around use of the metric. This is particularly apparent with the emergent pandemic virus SARS-CoV-2, the causative agent of COVID-19. We address some of these misconceptions, namely, how R changes over time, varies over space, and relates to epidemic size by referencing the mathematical definition of R and examples from the current pandemic. We hope that a better appreciation of the uses, nuances, and limitations of R facilitates a better understanding of epidemic spread, epidemic severity, and the effects of interventions in the context of SARS-CoV-2.

    Bipallidal Lesions in a COVID-19 Patient: A Case Report and Brief Review of Literature

    Authors: Sudhat Ashok; Kalyan Shastri; L. Beryl Guterman; Lee R. Guterman

    doi:10.21203/ Date: 2020-06-09 Source: ResearchSquare

    BackgroundAltered mentation in COVID-19 patients can be a function of any number of metabolic abnormalities associated with the infection MESHD. Here we present the case of an encephalopathic COVID-19 patient with bilateral globus pallidus lesions. While imaging abnormalities involving basal ganglia have been reported in encephalitis MESHD encephalitis HP caused by neuroinvasive flaviviruses, the bipallidal lesions noted here likely resulted from hypoxic-ischemic brain injury MESHD.Case PresentationA 51-year-old African American woman was found unresponsive at home by her fiancé. She had been complaining of shortness of breath and cough MESHD cough HP for three days. She is a former smoker with past medical history of hypertension MESHD hypertension HP, nephropathy HP, and bipolar disorder. Upon examination, she was alert but nonverbal, following commands inconsistently, and unable to move extremities against gravity. After several minutes, she was able to state her name but kept repeating it in response to all questions. Chest radiograph revealed bilateral lung infiltrates. CT of the head showed hypodensities in bilateral globus pallidi. A non-contrast MRI of the brain showed symmetric restricted diffusion and FLAIR hyperintense signal changes in bilateral globus pallidi. Abnormal SWI signal seen in bilateral globus pallidi likely represents mineralization or hemosiderin. There were no striatal or thalamic lesions. Major intracranial arteries were widely patent.The patient later tested positive for 2019-nCoV using real-time PCR assay, and was transferred to our COVID-19 designated hospital campus. Thereafter, she had waxing and waning mentation. Repeat CT imaging 11 days after the first scan demonstrated resolution of the bipallidal hypodensities. The patient was recently discharged to a subacute rehab facility but is still experiencing confusion MESHD confusion HP.ConclusionsAs we come across neurological manifestations of COVID-19, we believe neuroimaging is likely to play an important role in establishing if central nervous system involvement is invariably due to indirect mechanisms such as metabolic or hypoxic-ischemic brain injury MESHD or if direct neuroinvasive disease MESHD is a possibility, as with certain viruses.

    Cytokine Release Syndrome MESHD-Associated Encephalopathy HP in Patients with COVID-19

    Authors: Peggy Perrin; Nicolas Collongues; Seyyid Baloglu; Dimitri Bedo; Xavier Bassand; Thomas Lavaux; Gabriela Gautier; Nicolas Keller; Stephane Kremer; Samira Fafi-Kremer; Bruno Moulin; Ilies Benotmane; Sophie Caillard

    id:10.20944/preprints202006.0103.v1 Date: 2020-06-07 Source:

    Severe disease MESHD and uremia MESHD are risk factors for neurological complications of coronavirus disease MESHD-2019 (COVID-19). An in-depth analysis of a case series was conducted to describe the neurological manifestations of patients with COVID-19 and gain pathophysiological insights that may guide clinical decision-making – especially with respect to the cytokine release syndrome MESHD (CRS). Extensive clinical, laboratory, and imaging phenotyping was performed in five patients. Neurological presentation included confusion MESHD confusion HP, tremor MESHD tremor HP, cerebellar ataxia MESHD ataxia HP, behavioral alterations, aphasia MESHD aphasia HP, pyramidal syndrome MESHD, coma MESHD coma HP, cranial nerve palsy, dysautonomia, and central hypothyroidism HP hypothyroidism MESHD. Neurological disturbances were remarkably accompanied by laboratory evidence of CRS. SARS-CoV-2 was undetectable in the cerebrospinal fluid. Hyperalbuminorachy and increased levels of the astroglial protein S100B were suggestive of blood SERO-brain barrier (BBB) dysfunction. Brain MRI findings comprised evidence of acute leukoencephalitis (n = 3, of whom one with a hemorrhagic form), cytotoxic edema MESHD edema HP mimicking ischemic stroke HP stroke MESHD (n = 1), or normal results (n = 2). Treatment with corticosteroids and/or intravenous immunoglobulins was attempted – resulting in rapid recovery from neurological disturbances in two cases. Patients with COVID-19 can develop neurological manifestations that share clinical, laboratory, and imaging similarities with those of chimeric antigen receptor-T cell-related encephalopathy HP. The pathophysiological underpinnings appear to involve CRS, endothelial activation, BBB dysfunction, and immune-mediated mechanisms.

    A quantitative compendium of COVID-19 epidemiology

    Authors: Yinon M. Bar-On; Ron Sender; Avi I. Flamholz; Rob Phillips; Ron Milo

    id:2006.01283v3 Date: 2020-06-01 Source: arXiv

    Accurate numbers are needed to understand and predict viral dynamics. Curation of high-quality literature values for the infectious period TRANS duration or household secondary attack rate TRANS, for example, is especially pressing currently because these numbers inform decisions about how and when to lockdown or reopen societies. We aim to provide a curated source for the key numbers that help us understand the virus driving our current global crisis. This compendium focuses solely on COVID-19 epidemiology. The numbers reported in summary format are substantiated by annotated references. For each property, we provide a concise definition, description of measurement and inference methods, and associated caveats. We hope this compendium will make essential numbers more accessible and avoid common sources of confusion MESHD confusion HP for the many newcomers to the field such as using the incubation period TRANS to denote and quantify the latent period or using the hospitalization duration for the infectiousness period duration. This document will be repeatedly updated and the community is invited to participate in improving it.

    Feeling Like It is Time to Reopen Now? COVID-19 New Normal Scenarios based on Reopening Sentiment Analytics

    Authors: Jim Samuel; Md. Mokhlesur Rahman; G. G. Md. Nawaz Ali; Yana Samuel; Alexander Pelaez

    id:2005.10961v1 Date: 2020-05-22 Source: arXiv

    The Coronavirus pandemic has created complex challenges and adverse circumstances. This research discovers public sentiment amidst problematic socioeconomic consequences of the lockdown, and explores ensuing four potential sentiment associated scenarios. The severity and brutality of COVID-19 have led to the development of extreme feelings, and emotional and mental healthcare challenges. This research identifies emotional consequences - the presence of extreme fear, confusion MESHD confusion HP and volatile sentiments, mixed along with trust and anticipation. It is necessary to gauge dominant public sentiment trends for effective decisions and policies. This study analyzes public sentiment using Twitter Data, time-aligned to COVID-19, to identify dominant sentiment trends associated with the push to 'reopen' the economy. Present research uses textual analytics methodologies to analyze public sentiment support for two potential divergent scenarios - an early opening and a delayed opening, and consequences of each. Present research concludes on the basis of exploratory textual analytics and textual data visualization, that Tweets data from American Twitter users shows more trust sentiment support, than fear, for reopening the US economy. With additional validation, this could present a valuable time sensitive opportunity for state governments, the federal government, corporations and societal leaders to guide the nation into a successful new normal future.

    CoAID: COVID-19 Healthcare Misinformation Dataset

    Authors: Limeng Cui; Dongwon Lee

    id:2006.00885v1 Date: 2020-05-22 Source: arXiv

    As the COVID-19 virus quickly spreads around the world, unfortunately, misinformation related to COVID-19 also gets created and spreads like wild fire. Such misinformation has caused confusion MESHD confusion HP among people, disruptions in society, and even deadly consequences in health problems. To be able to understand, detect, and mitigate such COVID-19 misinformation, therefore, has not only deep intellectual values but also huge societal impacts. To help researchers combat COVID-19 health misinformation, therefore, we present CoAID (Covid-19 heAlthcare mIsinformation Dataset), with diverse COVID-19 healthcare misinformation, including fake news on websites and social platforms, along with users' social engagement about such news. CoAID includes 1,896 news, 183,564 related user engagements, 516 social platform posts about COVID-19, and ground truth labels. The dataset is available at:

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



MeSH Disease
Human Phenotype

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.