Presentation + Paper
5 October 2023 Rapid COVID-19 immunity screening by machine learning aided multiplexed nanoplasmonic biosensor
Author Affiliations +
Abstract
Surveillance of population immunity against infectious diseases is critical for pandemic management and risk assessment of future outbreaks. However, current serological tests fall short in accurately identifying immunity profiles from diverse populations. We present a label-free, rapid, multiplexed, and variant-sensitive nanoplasmonic biosensor to quantify antibodies against multiple SARS-CoV-2 antigens from small human blood samples. We combined a machine learning model with antigen-specific antibody patterns measured from four different cohorts with known COVID-19 immunity. Subsequently, we tested our model on 100 blind human samples and determine that our findings are consistent with public epidemiological data showing that our nanobiosensor can help monitor population health during a pandemic.
Conference Presentation
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Aidana Beisenova, Wihan Adi, S. Janna Bashar, Monniiesh Velmurugan, Kenzie B. Germanson, Miriam A. Shelef, and Filiz Yesilkoy "Rapid COVID-19 immunity screening by machine learning aided multiplexed nanoplasmonic biosensor", Proc. SPIE 12648, Plasmonics: Design, Materials, Fabrication, Characterization, and Applications XXI, 1264808 (5 October 2023); https://doi.org/10.1117/12.2676173
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KEYWORDS
Antibodies

COVID 19

Multiplexing

Biosensors

Machine learning

Nanoplasmonics

Proteins

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