Volume 2, 2023

Machine-learning-aided multiplexed nanoplasmonic biosensor for COVID-19 population immunity profiling

Abstract

Serological population surveillance plays a crucial role in monitoring the spread, evolution, and outbreak risks of infectious diseases, including COVID-19. However, current commercial rapid serological tests fall short of capturing complex humoral immune response from a diverse population. On the other hand, access to laboratory-based diagnostic tests can be challenging in pandemic settings. To address these issues, we report a machine-learning (ML)-aided nanoplasmonic biosensor that can simultaneously quantify antibodies against the ancestral strain and Omicron variants of SARS-CoV-2 with epitope resolution. Our approach is based on a multiplexed, rapid, and label-free nanoplasmonic biosensor, which can detect past infection and vaccination status and is sensitive to SARS-CoV-2 variants. After training an ML model with antigen-specific antibody datasets from four COVID-19 immunity groups (naïve, convalescent, vaccinated, and convalescent-vaccinated), we tested our approach on 100 blind blood samples collected in Dane County, WI. Our results are consistent with public epidemiological data, demonstrating that our user-friendly and field-deployable nanobiosensor can capture community-representative public health trends and help manage COVID-19 and future outbreaks.

Graphical abstract: Machine-learning-aided multiplexed nanoplasmonic biosensor for COVID-19 population immunity profiling

Supplementary files

Article information

Article type
Paper
Submitted
10 apr 2023
Accepted
21 iyn 2023
First published
06 iyl 2023
This article is Open Access
Creative Commons BY-NC license

Sens. Diagn., 2023,2, 1186-1198

Machine-learning-aided multiplexed nanoplasmonic biosensor for COVID-19 population immunity profiling

A. Beisenova, W. Adi, S. J. Bashar, M. Velmurugan, K. B. Germanson, M. A. Shelef and F. Yesilkoy, Sens. Diagn., 2023, 2, 1186 DOI: 10.1039/D3SD00081H

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