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Laboratory diagnosis of coronavirus disease-2019 (COVID-19)

descriptionPublicationkeyboard_double_arrow_right Article , Other literature type 01 Nov 2020Publisher:Elsevier BVJournal:Clinica Chimica Acta, volume 510, pages 35-46 (issn: 0009-8981,

Authors: Li, Chenxi; Zhao, Chengxue; Bao, Jingfeng; Tang, Bo; Wang, Yunfeng; Gu, Bing;

doi: 10.1016/j.cca.2020.06.045

pmid: 32621814

pmc: PMC7329657

Laboratory diagnosis of coronavirus disease-2019 (COVID-19)

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Abstract

The outbreak of Coronavirus Disease-2019 (COVID-19) caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) has threatened health worldwide. As of the end of 2020, there were nearly 10 million confirmed cases and nearly 5 million deaths associated with COVID-19. Rapid and early laboratory diagnosis of COVID-19 is the main focus of treatment and control. Molecular tests are the basis for confirmation of COVID-19, but serological tests for SARS-CoV-2 are widely available and play an increasingly important role in understanding the epidemiology of the virus and in identifying populations at higher risk for infection. Point-of-care tests have the advantage of rapid, accurate, portable, low cost and non-specific device requirements, which provide great help for disease diagnosis and detection. This review will discuss the performance of different laboratory diagnostic tests and platforms, as well as suitable clinical samples for testing, and related biosafety protection. This review shall guide for the diagnosis of COVID-19 caused by SARS-CoV-2.

Related Organizations

Emory University
United States
Xuzhou University of Technology
China (People's Republic of)
Xuzhou Medical College
China (People's Republic of)

Keywords

Biochemistry, medical, Clinical Laboratory Techniques, Clinical Biochemistry, Pneumonia, Viral, COVID-19, General Medicine, Review, Genomics, Biochemistry, COVID-19 Testing, Virology, Humans, Coronavirus Infections, Pandemics

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	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 1%