Multiplex Detection and Quantification of Virus Co-Infections Using Label-free Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms.

ABSTRACT: Multiple respiratory viruses can concurrently or sequentially infect the respiratory tract, making their identification crucial for diagnosis, treatment, and disease management. We present a label-free diagnostic platform integrating surface-enhanced Raman scattering (SERS) with deep learning for rapid, quantitative detection of respiratory virus coinfections. Using sensitive silica-coated silver nanorod array substrates, over 1.2 million SERS spectra are collected from 11 viruses, nine two-virus mixtures, and four three-virus mixtures at various concentrations in saliva. A deep learning model, MultiplexCR, is developed to simultaneously predict virus species and concentrations from SERS spectra. It achieves an impressive 98.6% accuracy in classifying virus coinfections and a mean absolute

SUBMITTER: Yang Y 

PROVIDER: S-EPMC11877629 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature