Corpus overview


Overview

MeSH Disease

HGNC Genes

SARS-CoV-2 proteins

There are no SARS-CoV-2 protein terms in the subcorpus


Filter

Genes
Diseases
SARS-CoV-2 Proteins
    displaying 1 - 2 records in total 2
    records per page




    COVID-19 MESHD Kaggle Literature Organization

    Authors: Maksim Ekin Eren; Nick Solovyev; Edward Raff; Charles Nicholas; Ben Johnson

    id:2008.13542v3 Date: 2020-08-04 Source: arXiv

    The world has faced the devastating outbreak of Severe Acute Respiratory Syndrome Coronavirus-2 MESHD (SARS-CoV-2), or COVID-19 MESHD, in 2020. Research in the subject matter was fast-tracked to such a point that scientists were struggling to keep up with new findings. With this increase in the scientific literature, there arose a need for organizing those documents. We describe an approach to organize and visualize the scientific literature on or related to COVID-19 MESHD using machine learning techniques so that papers on similar topics are grouped together. By doing so, the navigation of topics and related papers is simplified. We implemented this approach using the widely recognized CORD-19 HGNC dataset to present a publicly available proof of concept.

    Tuberculosis and COVID-19 MESHD in 2020: lessons from the past viral outbreaks and possible future outcomes

    Authors: Radu Crisan-Dabija; Cristina Grigorescu; Cristina Alice Pavel; Bogdan Artene; Iolanda Valentina Popa; Andrei Cernomaz; Alexandru Burlacu

    doi:10.1101/2020.04.28.20082917 Date: 2020-05-01 Source: medRxiv

    Background. The threat of contagious infectious diseases MESHD is constantly evolving, as demographic explosion, travel globalization and changes in human lifestyle increase the risk of spreading pathogens, leading to accelerated changes in disease landscape. Of particular interest is the aftermath of superimposing viral epidemics (especially SARS-CoV-2) over long-standing diseases, such as tuberculosis MESHD ( TB MESHD), which remains a significant disease for public health worldwide and especially in emerging economies. Methods and Results. PubMed electronic database was requested for relevant articles linking TB MESHD, influenza and SARS-CoV viruses MESHD and subsequently assessed eligibility according to inclusion criteria. Using a data mining approach, we also queried the COVID-19 MESHD Open Research Dataset ( CORD-19 HGNC). We aimed to answer the following questions: What can be learned from other coronavirus outbreaks (with a focus on TB MESHD patients)? Is coinfection ( TB MESHD and SARS-CoV-2) more severe? Is there a vaccine for SARS-CoV-2? How does the TB MESHD vaccine affect COVID-19 MESHD? How does one diagnosis affect the other? Discussions. Few essential elements about TB MESHD and SARS-CoV coinfections MESHD were discussed. First, lessons from the past outbreaks (other coronaviruses), as well as influenza pandemic / seasonal outbreaks have taught the importance of infection control to avoid the severe impact on TB MESHD patients. Second, although challenging due to data scarcity, investigating the pathological pathways linking TB MESHD and SARS-CoV-2 leads to the idea that their coexistence might yield a more severe clinical evolution. Finally, we addressed the issues of vaccination and diagnostic reliability in the context of coinfection. Conclusions. Because viral respiratory infections MESHD and TB MESHD impede the host's immune responses, it can be assumed that their harmful synergism may contribute to more severe clinical evolution. Despite the rapidly growing number of cases, the data needed to predict the impact of the COVID-19 pandemic MESHD on patients with latent TB MESHD and TB MESHD sequelae still lies ahead.

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
HGNC Genes
SARS-CoV-2 Proteins


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.