Please use this identifier to cite or link to this item: http://hdl.handle.net/10362/147350
Title: CRISPR Systems for COVID-19 Diagnosis
Author: Rahimi, Hossein
Salehiabar, Marziyeh
Barsbay, Murat
Ghaffarlou, Mohammadreza
Kavetskyy, Taras
Sharafi, Ali
Davaran, Soodabeh
Chauhan, Subhash C.
Danafar, Hossein
Kaboli, Saeed
Nosrati, Hamed
Yallapu, Murali M.
Conde, João
Keywords: COVID-19
CRISPR
diagnosis
RT-qPCR
SARS-CoV-2
Bioengineering
Instrumentation
Process Chemistry and Technology
Fluid Flow and Transfer Processes
Issue Date: 23-Apr-2021
Abstract: The emergence of the new coronavirus 2019 (COVID-19) was first seen in December 2019, which has spread rapidly and become a global pandemic. The number of cases of COVID-19 and its associated mortality have raised serious concerns worldwide. Early diagnosis of viral infection undoubtedly allows rapid intervention, disease management, and substantial control of the rapid spread of the disease. Currently, the standard approach for COVID-19 diagnosis globally is the RT-qPCR test; however, the limited access to kits and associated reagents, the need for specialized lab equipment, and the need for highly skilled personnel has led to a detection slowdown. Recently, the development of clustered regularly interspaced short palindromic repeats (CRISPR)-based diagnostic systems has reshaped molecular diagnosis. The benefits of the CRISPR system such as speed, precision, specificity, strength, efficiency, and versatility have inspired researchers to develop CRISPR-based diagnostic and therapeutic methods. With the global COVID-19 outbreak, different groups have begun to design and develop diagnostic and therapeutic programs based on the efficient CRISPR system. CRISPR-based COVID-19 diagnostic systems have advantages such as a high detection speed (i.e., 30 min from raw sample to reach a result), high sensitivity and precision, portability, and no need for specialized laboratory equipment. Here, we review contemporary studies on the detection of COVID-19 based on the CRISPR system.
Description: Funding: This work was supported by the NOVA University Lisbon, University of Texas Rio Grande Valley, and Zanjan University of Medical Sciences. J.C. acknowledges the European Research Council Starting Grant (ERC-StG-2019-848325).
Peer review: yes
URI: http://hdl.handle.net/10362/147350
DOI: https://doi.org/10.1021/acssensors.0c02312
ISSN: 2379-3694
Appears in Collections:NMS: ToxOmics - Artigos em revista internacional com arbitragem científica

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