Author: Tripathi, Siddhartha; Agrawal, Amit
Title: Blood Plasma Microfluidic Device: Aiming for the Detection of COVID-19 Antibodies Using an On-Chip ELISA Platform Cord-id: xx52u01h Document date: 2020_6_10
ID: xx52u01h
Snippet: COVID-19 is a public health emergency of international concern. Detection of SARS-CoV-2 virus is an important step towards containing the virus spread. Although viral detection using molecular diagnostic methods is quite common and efficient, these methods are prone to errors, laborious and time consuming. There is an urgent need for blood-based tests which are simple to use, accurate, less time consuming, portable and cost-effective. Human blood plasma contains water, proteins, organic and in-o
Document: COVID-19 is a public health emergency of international concern. Detection of SARS-CoV-2 virus is an important step towards containing the virus spread. Although viral detection using molecular diagnostic methods is quite common and efficient, these methods are prone to errors, laborious and time consuming. There is an urgent need for blood-based tests which are simple to use, accurate, less time consuming, portable and cost-effective. Human blood plasma contains water, proteins, organic and in-organic substances including bacteria and viruses. Blood plasma can be effectively used to detect COVID-19 antibodies. The immune system generates antibodies (IgM/IgG proteins) in response to the virus and identification of these antibodies is related to the presence of the infection in the patient in the past. Therefore, detecting and testing the presence of these antibodies will be extremely useful for monitoring and surveillance of the population (Petherick, Lancet 395:1101–1102, 2020). Herein, we describe and propose a microfluidic ELISA (enzyme-linked immunosorbent assay) system to detect COVID-19 antibodies on a lab-on-chip platform. We propose to first separate plasma from whole human blood using a microfluidic device and subsequently perform the detection of antibodies in the separated plasma using a semi-automated on-chip ELISA.
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