A paper-based multiplexed serological test to monitor immunity against SARS-CoV-2 using machine learning
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910493929635840 |
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| author | Eryilmaz, Merve Goncharov, Artem Han, Gyeo-Re Joung, Hyou-Arm Ballard, Zachary S. Ghosh, Rajesh Zhang, Yijie Di Carlo, Dino Ozcan, Aydogan |
| author_facet | Eryilmaz, Merve Goncharov, Artem Han, Gyeo-Re Joung, Hyou-Arm Ballard, Zachary S. Ghosh, Rajesh Zhang, Yijie Di Carlo, Dino Ozcan, Aydogan |
| contents | 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 uL of human serum sample in <20 min per test. After the assay, images of the paper-based sensor panel were captured using a mobile phone-based custom-designed optical reader and then processed by a neural network-based serodiagnostic algorithm. The trained serodiagnostic algorithm was blindly tested with serum samples collected before and after vaccination or infection, achieving an accuracy of 89.5%. The competitive performance of the xVFA, along with its portability, cost-effectiveness, and rapid operation, makes it a promising computational point-of-care (POC) serology test for monitoring COVID-19 immunity, aiding in timely decisions on the administration of booster vaccines and general public health policies to protect vulnerable populations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_17774 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A paper-based multiplexed serological test to monitor immunity against SARS-CoV-2 using machine learning Eryilmaz, Merve Goncharov, Artem Han, Gyeo-Re Joung, Hyou-Arm Ballard, Zachary S. Ghosh, Rajesh Zhang, Yijie Di Carlo, Dino Ozcan, Aydogan Medical Physics Biological Physics Quantitative Methods 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 uL of human serum sample in <20 min per test. After the assay, images of the paper-based sensor panel were captured using a mobile phone-based custom-designed optical reader and then processed by a neural network-based serodiagnostic algorithm. The trained serodiagnostic algorithm was blindly tested with serum samples collected before and after vaccination or infection, achieving an accuracy of 89.5%. The competitive performance of the xVFA, along with its portability, cost-effectiveness, and rapid operation, makes it a promising computational point-of-care (POC) serology test for monitoring COVID-19 immunity, aiding in timely decisions on the administration of booster vaccines and general public health policies to protect vulnerable populations. |
| title | A paper-based multiplexed serological test to monitor immunity against SARS-CoV-2 using machine learning |
| topic | Medical Physics Biological Physics Quantitative Methods |
| url | https://arxiv.org/abs/2402.17774 |