Corpus overview


Overview

MeSH Disease

Human Phenotype

Coma (3)

Encephalopathy (2)

Confusion (1)

Tremor (1)

Ataxia (1)


Transmission

Seroprevalence
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    Machine learning based prognostic model for predicting infection MESHD susceptibility of COVID-19 using health care data

    Authors: R Srivatsan; Prithviraj N Indi; Swapnil Agrahari; Siddharth Menon; Dr. S. Denis Ashok

    doi:10.21203/rs.3.rs-46681/v1 Date: 2020-07-21 Source: ResearchSquare

    From public health perspectives of COVID-19 pandemic, accurate estimates of infection MESHD severity of individuals are extremely valuable for the informed decision making and targeted response to an emerging pandemic.  This paper presents machine learning based prognostic model for providing early warning to the individuals for COVID-19 infection MESHD using the health care data set. In the present work, a prognostic model using Random Forest classifier and support vector regression is developed for predicting the susceptibility of COVID-19 infection MESHD and it is applied on an open health care data set containing 27 field values. The typical fields of the health care data set include basic personal details such as age TRANS, gender TRANS, number of children TRANS in the household, marital status along with medical data like Coma MESHD Coma HP score, Pulmonary score, Blood SERO Glucose level, HDL cholesterol etc. An effective preprocessing method is carried out for handling the numerical, categorical values (non-numerical), missing data in the health care data set. Principal component analysis is applied for dimensionality reduction of the health care data set. From the classification results, it is noted that the random forest classifier provides a higher accuracy as compared to Support vector regression for the given health data set. Proposed machine learning approach can help the individuals to take additional precautions for protecting against COVID-19 infection MESHD. Based on the results of the proposed method, clinicians and government officials can focus on the highly susceptible people for limiting the pandemic spread. Methods In the present work, Random Forest classifier and support vector regression techniques are applied to a medical health care dataset containing 27 variables for predicting the susceptibility score of an individual towards COVID-19 infection MESHD and the accuracy of prediction is compared. An effective preprocessing is carried for handling the missing data in the health care data set. Principal Component Analysis is carried out on the data set for dimensionality reduction of the feature vectors. Results From the classification results, it is noted that the Random Forest classifier provides an accuracy of 90%, sensitivity SERO of 94% and specificity of 81% for the given medical data set.Conclusion Proposed machine learning approach can help the individuals to take additional precautions for protecting people from the COVID-19 infection MESHD, clinicians and government officials can focus on the highly susceptible people for limiting the pandemic spread. 

    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: Preprints.org

    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.

    Cerebrovascular complications in patients with SARS-CoV-2 infection MESHD: Case series

    Authors: Mauro Morassi; Daniele Bagatto; Milena Cobelli; Serena D’Agostini; Gian Luigi Gigli; Claudio Bnà; Alberto Vogrig

    doi:10.21203/rs.3.rs-23137/v1 Date: 2020-04-15 Source: ResearchSquare

    Background: Italy is one of the most affected countries by the Coronavirus disease MESHD 2019 (COVID-19). The responsible pathogen is named Severe Acute Respiratory Syndrome MESHD Coronavirus (SARS-CoV-2). The clinical spectrum ranges from asymptomatic infection MESHD asymptomatic TRANS infection to severe HP pneumonia MESHD pneumonia HP leading to intensive care unit admission. Evidence of cerebrovascular complications associated with SARS-CoV-2 is limited. We herein report 6 patients who developed acute stroke MESHD stroke HP during COVID-19 infection MESHD. Methods: Retrospective case series of patients diagnosed with COVID-19 using reverse-transcriptase–polymerase-chain-reaction (RT-PCR) on nasopharyngeal swabs, who developed clinical and neuroimaging evidence of acute stroke MESHD stroke HP during SARS-CoV-2 infection MESHD.Results: Six patients were identified (5 men); median age TRANS was 69 years (range: 57-82). Stroke MESHD Stroke HP subtypes were ischemic (4, 67%) and hemorrhagic (2, 33%). All patients but 1 had pre-existing vascular risk factors. One patient developed encephalopathy HP prior to stroke MESHD stroke HP, characterized by focal seizures MESHD seizures HP and behavioral abnormalities HP. COVID-19-related pneumonia MESHD pneumonia HP was severe (i.e. requiring critical care support) in 5/6 cases (83%). Liver enzyme alteration and lactate dehydrogenase (LDH) elevation was registered in all cases. Four patients (67%) manifested acute kidney failure prior to stroke MESHD stroke HP. Four patients (67%) had abnormal coagulation tests. Outcome was poor in the majority of the patients: 4 died (67%), 1 is still in coma MESHD coma HP (20%) and the remaining 1 remains severely neurologically affected (mRS: 4).Conclusions: Acute stroke MESHD stroke HP can complicate the course of COVI-19 infection MESHD. In our series, stroke MESHD stroke HP developed mostly in patients with severe pneumonia MESHD pneumonia HP and multi organ failure, liver MESHD enzymes and LDH were markedly increased in all cases, and the outcome was poor.

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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