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 Jūn. 2023
First published
06 Jūl. 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

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. You can use material from this article in other publications, without requesting further permission from the RSC, provided that the correct acknowledgement is given and it is not used for commercial purposes.

To request permission to reproduce material from this article in a commercial publication, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party commercial publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements