Corpus overview


Overview

MeSH Disease

Pneumonia (970)

Disease (430)

Infections (413)

Coronavirus Infections (260)

Death (198)


Human Phenotype

Pneumonia (976)

Fever (162)

Cough (131)

Respiratory distress (72)

Hypertension (59)


Transmission

Seroprevalence
    displaying 1 - 10 records in total 1020
    records per page




    K18-hACE2 Mice for Studies of COVID-19 Treatments and Pathogenesis Including Anosmia HP

    Authors: Stanley Perlman; Jian Zheng; LOK YIN ROY WONG; Kun Li; Abhishek K Verma; Miguel E Ortiz Bezara; Christine Wohlford-Lenane; Mariah R. Leidinger; Michael C. Kundson; David K. Meyerholz; Paul B McCray Jr.

    doi:10.1101/2020.08.07.242073 Date: 2020-08-10 Source: bioRxiv

    The ongoing COVID-19 pandemic is associated with substantial morbidity and mortality. While much has been learned in the first months of the pandemic, many features of COVID-19 pathogenesis remain to be determined. For example, anosmia HP is a common presentation and many patients with this finding show no or only minor respiratory signs. Studies in animals experimentally infected with SARS-CoV-2, the cause of COVID-19, provide opportunities to study aspects of the disease MESHD not easily investigated in human patients. COVID-19 severity ranges from asymptomatic TRANS to lethal. Most experimental infections MESHD provide insights into mild disease MESHD. Here, using K18-hACE2 mice that we originally developed for SARS studies, we show that infection MESHD with SARS-CoV-2 causes severe disease in the lung MESHD, and in some mice, the brain. Evidence of thrombosis MESHD and vasculitis MESHD vasculitis HP was detected in mice with severe pneumonia MESHD pneumonia HP. Further, we show that infusion of convalescent plasma SERO (CP) from a recovered COVID-19 patient provided protection against lethal disease MESHD. Mice developed anosmia HP at early times after infection MESHD. Notably, while treatment with CP prevented significant clinical disease MESHD, it did not prevent anosmia HP. Thus K18-hACE2 mice provide a useful model for studying the pathological underpinnings of both mild and lethal COVID-19 and for assessing therapeutic interventions.

    Deep Learning Driven Automated Detection of COVID-19 from Radiography Images: A Comparative Analysis

    Authors: Sejuti Rahman; Sujan Sarker; Abdullah Al Miraj; Ragib Amin Nihal; A. K. M. Nadimul Haque; Abdullah Al Noman

    id:10.20944/preprints202008.0215.v1 Date: 2020-08-08 Source: Preprints.org

    The ravage of COVID-19 is not merely limited to taking its toll with half a million fatalities. It has halted the world economy, disrupting normalcy of lives with supervening severity than any other global catastrophe of the last few decades. The majority of the vaccine discovery attempts are still on trial, making early detection and containment the only feasible redress. The existing diagnostic technique with high accuracy has the setbacks of being expensive and sophisticated, requiring skilled individuals for specimen collection and screening resulting in lower outreach. So, methods excluding direct human intervention are much sought after, and artificial intelligence-driven automated diagnosis, especially with radiography images, captures the researchers' interest. This survey marks a detailed inspection of the deep-learning-based automated detection of COVID-19 works done to date, methodical challenges along with probable solutions, and scopes of future exploration in this arena. We also provided a comparative quantitative analysis of the performance SERO of 315 deep models in diagnosing COVID-19, Normal, and Pneumonia MESHD Pneumonia HP from x-ray images. Our results show that Densenet201 model with Quadratic SVM classifier performs the best (accuracy: 98.16\%, sensitivity SERO: 98.93\%, specificity: 98.77\%) and maintains high accuracies in other similar architectures as well. This proves that even though radiography images might not be conclusive for radiologists, but it is so for deep learning algorithms for detecting COVID-19. We hope this extensive review will provide a comprehensive guideline for researchers in this field.

    Clinical and intestinal histopathological findings in SARS-CoV-2/COVID-19 patients with hematochezia HP

    Authors: Margaret Cho; Weiguo Liu; Sophie Balzora; Yvelisse Suarez; Deepthi Hoskoppal; Neil D Theise; Wenqing Cao; Suparna A Sarkar

    doi:10.1101/2020.07.29.20164558 Date: 2020-08-07 Source: medRxiv

    Gastrointestinal (GI) symptoms of SARS-CoV2/COVID-19 in the form of anorexia MESHD anorexia HP, nausea MESHD nausea, vomiting HP, vomiting MESHD, abdominal pain MESHD abdominal pain HP and diarrhea MESHD diarrhea HP are usually preceeded by respiratory manifestations and are associated with a poor prognosis. Hematochezia HP is an uncommon clinical presentation of COVID-19 disease MESHD and we hypothesize that older patients with significant comorbidites ( obesity MESHD obesity HP and cardiovascular) and prolonged hospitalization are suspectible to ischemic injury to the bowel. We reviewed the clinical course, key laboratory data including acute phase reactants, drug/medication history in two elderly TRANS male TRANS patients admitted for COVID-19 respiratory failure HP. Both patients had a complicated clinical course and suffered from hematochezia HP and acute blood SERO loss anemia MESHD anemia HP requiring blood SERO transfusion around day 40 of their hospitalization. Colonoscopic impressions were correlated with the histopathological findings in the colonic biopies and changes compatible with ischemia MESHD to nonspecific acute inflammation MESHD, edema MESHD edema HP and increased eosinophils in the lamina propria were noted.Both patients were on anticoagulants, multiple antibiotics and antifungal agents due to respiratory infections MESHD at the time of lower GI bleeding. Hematochezia HP resolved spontaneously with supportive care. Both patients eventually recovered and were discharged. Elderly TRANS patients with significant comorbid conditions are uniquely at risk for ischemic injury to the bowel. Hypoxic conditions due to COVID-19 pneumonia MESHD pneumonia HP and respiratory failure HP, compounded by preexisting cardiovascular complications, and/or cytokine storm orchestrated by the viral infection MESHD leading to alteration in coagulation profile and/or drug/medication injury can be difficult to distinguish in these critically ill patients. Presentation of hematochezia HP may further increase the mortality and morbidity of COVID-19 patients, and prompt consultation and management by gastroenterology is therefore warranted.

    Predictive Parameters for the Worsening Clinical Course of Mild COVID-19 Pneumonia MESHD Pneumonia HP

    Authors: Cho Rom Hahm; Young Kyung Lee; Dong Hyun Oh; Mi Young Ahn; Jae-Phil Choi; Na Ree Kang; Jungkyun Oh; Hanzo Choi; Suhyun Kim

    doi:10.21203/rs.3.rs-54860/v1 Date: 2020-08-06 Source: ResearchSquare

    Background: This study aimed to determine parameters for worsening oxygenation in mild COVID-19 pneumonia MESHD pneumonia HP.Methods: This retrospective cohort study included confirmed COVID-19 pneumonia MESHD pneumonia HP in a single public hospital in South Korea from January to April 2020. Parameters were compared between the two groups on the basis of clinical course: the desaturation group was defined as those with oxygen saturation ≤ 94% on ambient air, or received oxygen or mechanical ventilation (MV) throughout the clinical course versus the nonevent group who were without any respiratory event up to 28 days. The severity and extent of viral pneumonia MESHD pneumonia HP from an initial single chest CT were calculated using artificial intelligence (AI) algorithms and measured visually by a radiologist. Results: We included 136 patients with 32 (23.5%) in the desaturation group, of whom two needed MV and one died. Initial vital signs and duration of symptoms showed no difference between the two groups, however, univariate logistic regression analysis revealed that a variety of parameters at admission were associated with an increased risk of a desaturation event. In a sex-, age TRANS-, and comorbid illness-matched case-control study, ferritin ≥ 280 μg/L (OR 3.600, 95% CI 1.142-11.346; p=0.029), LDH≥ 240 U/L (OR 3.600, 95% CI 1.142-11.346; p=0.029), pneumonia MESHD pneumonia HP burden (OR 1.010, 95% CI 1.002-1.019; p=0.021), and extent (OR 1.194, 95% CI 1.017-1.401; p=0.030) by AI, and visual severity scores (OR 1.146, 95% CI 1.005-1.307; p=0.042) were the predictive parameters for worsening clinical course with desaturation. Conclusion: Our study presents initial CT parameters measured by AI or visual severity scoring as well as serum SERO markers of inflammation MESHD at admission as the best parameters for predicting worsening oxygenation in the COVID-19 pneumonia MESHD pneumonia HP cohort. Initial chest CT scans may help clinicians diagnose viral pneumonia MESHD pneumonia HP and evaluate the prognosis in mild 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:2008.02866v1 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.

    Early warnings of COVID-19 outbreaks across Europe from social media?

    Authors: Milena Lopreite; Pietro Panzarasa; Michelangelo Puliga; Massimo Riccaboni

    id:2008.02649v1 Date: 2020-08-06 Source: arXiv

    We analyze social network data from Twitter to uncover early-warning signals of COVID-19 outbreaks in Europe in the winter season 2020, before the first public announcements of local sources of infection MESHD were made. We show evidence that unexpected levels of concerns about cases of pneumonia MESHD pneumonia HP were raised across a number of European countries. Whistleblowing came primarily from the geographical regions that eventually turned out to be the key breeding grounds for infections MESHD. These findings point to the urgency of setting up an integrated digital surveillance system in which social media can help geo-localize chains of contagion that would otherwise proliferate almost completely undetected.

    Alveolitis in severe SARS-CoV-2 pneumonia MESHD pneumonia HP is driven by self-sustaining circuits between infected alveolar macrophages and T cells

    Authors: Rogan A Grant; Luisa Morales-Nebreda; Nikolay S Markov; Suchitra Swaminathan; Estefany R Guzman; Darryl A Abbott; Helen K Donnelly; Alvaro Donayre; Isaac A Goldberg; Zasu M Klug; Nicole Borkowski; Ziyan Lu; Hermon Kihshen; Yuliya Politanska; Lango Sichizya; Mengjia Kang; Ali Shilatifard; Chao Qi; A Christine Argento; Jacqueline M Kruser; Elizabeth S Malsin; Chiagozie O Pickens; Sean Smith; James M Walter; Anna E Pawlowski; Daniel Schneider; Prasanth Nannapaneni; Hiam Abdala-Valencia; Ankit Bharat; Cara J Gottardi; GR Scott Budinger; Alexander A Misharin; Benjamin David Singer; Richard G Wunderink; - The NU SCRIPT Study Investigators

    doi:10.1101/2020.08.05.238188 Date: 2020-08-05 Source: bioRxiv

    Some patients infected with Severe Acute Respiratory Syndrome MESHD Coronavirus-2 (SARS-CoV-2) develop severe pneumonia MESHD pneumonia HP and the acute respiratory distress HP syndrome MESHD (ARDS). Distinct clinical features in these patients have led to speculation that the immune response to virus in the SARS-CoV-2-infected alveolus differs from other types of pneumonia MESHD pneumonia HP. We collected bronchoalveolar lavage fluid samples from 86 patients with SARS-CoV-2-induced respiratory failure HP and 252 patients with known or suspected pneumonia MESHD pneumonia HP from other pathogens and subjected them to flow cytometry and bulk transcriptomic profiling. We performed single cell RNA-Seq in 5 bronchoalveolar lavage fluid samples collected from patients with severe COVID-19 within 48 hours of intubation. In the majority of patients with SARS-CoV-2 infection MESHD at the onset of mechanical ventilation, the alveolar space is persistently enriched in alveolar macrophages and T cells without neutrophilia HP. Bulk and single cell transcriptomic profiling suggest SARS-CoV-2 infects alveolar macrophages that respond by recruiting T cells. These T cells release interferon-gamma to induce inflammatory cytokine release from alveolar macrophages and further promote T cell recruitment. Our results suggest SARS-CoV-2 causes a slowly unfolding, spatially-limited alveolitis in which alveolar macrophages harboring SARS-CoV-2 transcripts and T cells form a positive feedback loop that drives progressive alveolar inflammation MESHD.

    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.

    Concurrent cavitary pulmonary tuberculosisand COVID-19 pneumonia MESHD pneumonia HP with in vitro immune cell anergy:a case report.

    Authors: Maria Musso; Francesco Di Gennaro; Gina Gualano; Silvia Mosti; Carlotta Cerva; Saeid Najafi Fard; Raffaella Libertone; Virginia Di Bari; Massimo Cristofaro; Roberto Tonnarini; Delia Goletti; Fabrizio Palmieri

    doi:10.21203/rs.3.rs-54297/v1 Date: 2020-08-05 Source: ResearchSquare

    Tuberculosis MESHD (TB) is top infectious disease MESHD killer caused by a single organismresponsible for 1.5 million deaths MESHD in 2018. Both COVID 19 and the pandemic responseare risking to affect control measures for TB and continuity of essential services forpeople affected by this infection MESHD in western countries and even more in developingcountries. Knowledges about concomitant pulmonary TB and COVID-19 are extremelylimited. The double burden of these two diseases MESHD can have devastating effects. Herewe describe from both the clinical and the immunological point of view a case of apatient with in vitro immune cell anergy affected by bilateral cavitary pulmonary TB andsubsequent COVID-19-associated pneumonia MESHD pneumonia HP with a worst outcome. COVID-19 can bea precipitating factor in TB respiratory failure HP and, during ongoing SARS COV 2 pandemic, clinicians must be aware of this possible coinfection MESHD in differential diagnosisof patients with active TB and new or worsening chest imaging

    COVID-19 in CXR: from Detection and Severity Scoring to Patient Disease MESHD Monitoring

    Authors: Rula Amer; Maayan Frid-Adar; Ophir Gozes; Jannette Nassar; Hayit Greenspan

    id:2008.02150v1 Date: 2020-08-04 Source: arXiv

    In this work, we estimate the severity of pneumonia MESHD pneumonia HP in COVID-19 patients and conduct a longitudinal study of disease progression MESHD. To achieve this goal, we developed a deep learning model for simultaneous detection and segmentation of pneumonia MESHD pneumonia HP in chest Xray (CXR) images and generalized to COVID-19 pneumonia MESHD pneumonia HP. The segmentations were utilized to calculate a " Pneumonia MESHD Pneumonia HP Ratio" which indicates the disease MESHD severity. The measurement of disease MESHD severity enables to build a disease MESHD extent profile over time for hospitalized patients. To validate the model relevance to the patient monitoring task, we developed a validation strategy which involves a synthesis of Digital Reconstructed Radiographs (DRRs - synthetic Xray) from serial CT scans; we then compared the disease progression MESHD profiles that were generated from the DRRs to those that were generated from CT volumes.

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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