Corpus overview


Overview

MeSH Disease

Human Phenotype

Transmission

Seroprevalence
    displaying 1 - 2 records in total 2
    records per page




    The involvement of Central Nervous System and sequence variability of Severe Adult Respiratory Syndrome MESHD Adult TRANS Respiratory Syndrome – Coronavirus-2 revealed in autopsy tissue samples: a case report.

    Authors: Lis Høy Marbjerg; Christina Jacobsen; Jannik Fonager; Claus Bøgelund; Morten Rasmussen; Anders Fomsgaard; Jytte Banner; Veronika Vorobieva Solholm Jensen

    doi:10.21203/rs.3.rs-61471/v1 Date: 2020-08-18 Source: ResearchSquare

    Background: The case presented here illustrates that interdisciplinary teamwork can be essential for the understanding of the COVID-19 disease presentation and enlightening of the pathophysiology. Case presentation: A 60-years-old overweight HP woman without any comorbidities was found dead in her apartment after 14 days of home isolation due to suspicion on the Coronavirus disease MESHD 2019 (COVID-19). She had reported symptoms of tachycardia HP tachycardia MESHD, fever HP fever MESHD, and increasing respiratory difficulty one day before her death MESHD. Due to the Danish legal act on sudden deaths a forensic autopsy was performed including a thorough examination and biosampling. The results of the forensic autopsy displayed sever densified, almost airless, firm lungs, and an unspecific reactive minimal focal perivascular inflammation MESHD consisting of macrophages of the brain tissue. The final diagnosis, COVID-19 with involvement of the central nervous system was established by use of the RT-RNA analysis on cerebrospinal fluid, as well as by serologic detection of the specific antibodies for SARS-CoV-2 SERO in cerebrospinal fluid and serum SERO. The genetic analysis displayed a 2 % variation between SARS-CoV-2 isolates recovered from the tracheal sample, cerebrospinal fluid, and tissues from both lungs.Conclusion: The combination of all available results revealed that the cause of death MESHD was COVID-19 with severe pulmonary disease MESHD and neuroinvasion, as well as renal affection resulting in hyponatremia HP hyponatremia MESHD. To our knowledge, it was not shown previously that neuroinvasion could be confirmed by the detection of specific antibodies for SARS-CoV-2 SERO and SARS-CoV-2 specific RNA in cerebrospinal fluid. This case supports hypotheses that SARS-CoV-2 may cause central nervous system infection MESHD. The genetic distinction between SARS-CoV-2 isolates was done by whole-genome sequencing, where the isolate recovered from the cerebrospinal fluid was the most different. 

    Establishment of a clinical nomogram model to predict the progress to severe COVID-19 

    Authors: Changli Tu; Guojie Wang; Cuiyan Tan; Meizhu Chen; Hu Peng; Ying Wang; Yingjian Liang; Yiying Huang; Zhenguo Wang; Jian Wu; Kongqiu Wang; Qinhuan Huang; Jin Huang; Xiaobin Zheng; Qiuyue Chen; Yayuan Geng; Na Guo; Xiaorong Zhou; Xinran Liu; Jing Liu; Hong Shan

    doi:10.21203/rs.3.rs-17574/v1 Date: 2020-03-14 Source: ResearchSquare

    Background: Severe acute r espiratory syndrome coronavirus 2 (SARS-CoV-2) infection MESHDis the leading cause of a public health emergency in the world, accompanying with high mortality in severe c orona virus disease MESHD2019(COVID-19 ), thereby early detection and stopping the progress to severe COVID-19 is important. Our aim is to establish a clinical nomogram model to calculate and predict the progress to severe COVID-19 timely and efficiently.Methods: In this study, 65 patients with COVID-19 had been included retrospectively in the Fifth Affiliated Hospital of Sun Yat-sen University from January 17, to February 11, 2020. Patients were randomly assigned to train dataset (n=51 with 15 progressing to severe COVID-19) and test dataset (n=14 with 4 progressing to severe COVID-19). Lasso algorithm was applied to filter the most classification relevant clinical factors. Based on selected factors, logistic regression model was fit to predict the severe from mild/common. Meanwhile in nomogram sensitivity SERO, specificity, AUC (Area under Curve), and calibration curve were depicted and calculated by R language, to evaluate the prediction performance SERO to severe COVID-19.Results:High ratio of sever COVID-19 patients (26.5%) had been found in our retrospective study, and 84% of these cases progress to severe or critical after 5 days from their first clinical examination. In these 65 patients with COVID-19, 77 clinical characteristics in first examination were collected and analyzed, and 37 ones had been found different between non-severe and severe COVID-19. But when all these factors were analyzed in establishment of prediction model, six factors are crucial for predicting progress of severe COVID-19 via Lasso algorithm. Based on these six factors, including increased fibrinogen, hyponatremia HP yponatremia, MESHD decreased PaO2,multiple lung lobes involved, down-regulated CD3(+)T-lymphocyte and fever HP ever, MESHD a logistic regression model was fit to discriminate severe and common COVID-19 patients. The sensitivity SERO, specificity and AUC were 0.93, 0.86, 0.96 in the train dataset and 0.9, 1.0, 1.0 in test dataset respectively. Nomogram-predicted probability was more consistent with actual probability by R language.Conclusions:In summary, an efficient and reliable clinical nomogram model had been established, which indicate increased fibrinogen, hyponatremia HP yponatremia, MESHD decreased PaO2, multiple lung lobes involved, down-regulated CD3(+)T-lymphocyte and fever HP ever MESHDat the first clinical examination, could predict progress of patients to severe COVID-19.

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).
The web page can also be accessed via API.

Sources


Annotations

All
None
MeSH Disease
Human Phenotype
Transmission
Seroprevalence


Export subcorpus as...

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.