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Reviews of "Rapid 'mix and read' assay for scalable detection of SARS-CoV-2 antibodies in patient plasma"

Reviewers: Xi Chen, Simin Xia (The HIT Center for Life Sciences) | 📗📗📗📗 ◻️ • Thomas Moran, J. Andrew Duty, Thomas Kraus (Icahn School of Medicine at Mount Sinai) | 📗📗📗📗 ◻️

Published onNov 15, 2020
Reviews of "Rapid 'mix and read' assay for scalable detection of SARS-CoV-2 antibodies in patient plasma"
key-enterThis Pub is a Review of
Rapid 'mix and read' assay for scalable detection of SARS-CoV-2 antibodies in patient plasma
Description

The human beta coronavirus SARS-CoV-2, causative virus of COVID-19, has infected more than 15 million people globally and continues to spread. Widespread, population level testing to detect active and past infections is critical to curb the COVID-19 pandemic. Antibody (serological) testing is the only option for detecting past infections outside the narrow window accessible to nucleic acid-based tests. However, currently available serological assays commonly lack scalability. Here, we describe the development of a rapid homogenous serological assay for the detection of antibodies to SARS-CoV-2 in patient plasma. We show that the fluorescence-based assay accurately detects seroconversion in COVID-19 patients from less than 1 microliter of plasma. Using a cohort of samples from COVID-19 infected or healthy individuals, we demonstrate detection with 100% sensitivity and specificity. This assay addresses an important need for a robust, low barrier to implementation, and scalable serological assay with complementary strengths to currently available serological platforms.

To read the original manuscript, click the link above.

Summary of Reviews: This pre-print offers a FRET-based diagnostic platform that is faster and less labor-intensive than ELISA-based diagnostics. The claims should be considered reliable.

Reviewer 1 (Xi Chen, Simin Xia) | 📗📗📗📗◻️

Reviewer 2 (Thomas Moran, J. Andrew Duty, Thomas Kraus) | 📗📗📗📗◻️

RR:C19 Strength of Evidence Scale Key

📕 ◻️◻️◻️◻️ = Misleading

📙📙 ◻️◻️◻️ = Not Informative

📒📒📒 ◻️◻️ = Potentially Informative

📗📗📗📗◻️ = Reliable

📘📘📘📘📘 = Strong

To read the reviews, click the links below.

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