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基于适配体的生物传感器及其在 COVID-19 诊断中的应用

Aptamer-based biosensors and their implications in COVID-19 diagnosis.

作者信息

Mandal Mukti, Dutta Nirmita, Dutta Gorachand

机构信息

School of Medical Science and Technology (SMST), Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India.

出版信息

Anal Methods. 2021 Nov 25;13(45):5400-5417. doi: 10.1039/d1ay01519b.

DOI:10.1039/d1ay01519b
PMID:34751684
Abstract

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), a novel infectious member of the coronavirus family, has caused millions of cases of infection and deaths all over the world, and been declared a pandemic by the World Health Organization. Conventional laboratory-based diagnostic testing has faced extreme difficulties in meeting the overwhelming demand for testing worldwide, and this has brought about a pressing need for cost-effective rapid diagnosis. There has been a surge in the number of prototypes of diagnostic kits developed, although many of these have been found to be lacking in terms of their accuracy and sensitivity. One type of chip-based diagnostic platform is the aptamer-based biosensor. Aptamers are artificially synthesized oligonucleotides that are capable of specifically binding to a target antigen. As of now, some aptamers have been reported for SARS-CoV-2. Although many ultrasensitive aptasensors have been developed for viruses, few have been successfully adapted for SARS-CoV-2 detection. Our review discusses the recent developments in the domain of SARS-CoV-2 specific aptamer isolation, the design of electrochemical and optical aptasensors, and the implications of aptasensor-based COVID-19 diagnosis.

摘要

严重急性呼吸综合征冠状病毒2(SARS-CoV-2)是冠状病毒家族的一种新型传染性病毒,已在全球范围内导致数百万例感染和死亡病例,并被世界卫生组织宣布为大流行病。基于传统实验室的诊断检测在满足全球对检测的巨大需求方面面临极大困难,这迫切需要具有成本效益的快速诊断方法。尽管许多诊断试剂盒原型在准确性和灵敏度方面存在不足,但开发的诊断试剂盒原型数量仍大幅增加。一种基于芯片的诊断平台是基于适配体的生物传感器。适配体是能够特异性结合靶抗原的人工合成寡核苷酸。截至目前,已经报道了一些针对SARS-CoV-2的适配体。尽管已经开发了许多用于病毒检测的超灵敏适配体传感器,但很少有成功用于SARS-CoV-2检测的。我们的综述讨论了SARS-CoV-2特异性适配体分离领域的最新进展、电化学和光学适配体传感器的设计以及基于适配体传感器的COVID-19诊断的意义。

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