1. A Paper-Based Multiplexed Serological Test to Monitor Immunity against SARS-COV-2 Using Machine Learning.
- Author
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Eryilmaz, Merve, Goncharov, Artem, Han, Gyeo-Re, Joung, Hyou-Arm, Ballard, Zachary, Ghosh, Rajesh, Zhang, Yijie, Di Carlo, Dino, and Ozcan, Aydogan
- Subjects
COVID-19 ,machine learning ,paper-based assays ,serology ,vertical flow assays ,Humans ,COVID-19 ,SARS-CoV-2 ,Machine Learning ,Antibodies ,Viral ,Immunoglobulin G ,Immunoglobulin M ,Paper ,COVID-19 Serological Testing ,Serologic Tests - Abstract
The rapid spread of SARS-CoV-2 caused the COVID-19 pandemic and accelerated vaccine development to prevent the spread of the virus and control the disease. Given the sustained high infectivity and evolution of SARS-CoV-2, there is an ongoing interest in developing COVID-19 serology tests to monitor population-level immunity. To address this critical need, we designed a paper-based multiplexed vertical flow assay (xVFA) using five structural proteins of SARS-CoV-2, detecting IgG and IgM antibodies to monitor changes in COVID-19 immunity levels. Our platform not only tracked longitudinal immunity levels but also categorized COVID-19 immunity into three groups: protected, unprotected, and infected, based on the levels of IgG and IgM antibodies. We operated two xVFAs in parallel to detect IgG and IgM antibodies using a total of 40 μL of human serum sample in
- Published
- 2024