ArticleACS sensors2025
Multiplex Detection and Quantification of Virus Co-Infections Using Label-free Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms.
Article in ACS sensors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Surface-Enhanced Raman Spectroscopy for Viral Diagnostics: Principles, Strategies, Clinical Challenges, and Future Directions.Chemical reviews · 2026Review
- Research progress, translation-related challenges, and clinical prospects of surface-enhanced Raman spectroscopy in respiratory virus detection: a narrative review.Journal of thoracic disease · 2026Review
- Comprehensive Open-Source Ecosystem for Raman and SERS Spectroscopy: Introducing SpectraGuru.Analytical chemistry · 2026Article
- Immunochromatographic SERS sensor-deep learning combined strategy for intelligent diagnosis of chronic kidney disease.Mikrochimica acta · 2026Article
- Chemometric Methods Applied to Infrared and Raman Spectroscopy for Arboviruses Diagnosis: A Systematic Review With Meta-Analysis.Analytical science advances · 2025Review
- Quantitative Analysis of Virus Adsorption and Co-adsorption Behavior Using BET Modeling and SERS Spectroscopy.The journal of physical chemistry. A · 2025Article
- Review
- Recent advances in applications of artificial intelligence-assisted Raman spectroscopy in diagnosis of cancers.Frontiers in molecular biosciences · 2025Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
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 error of 0.028 for concentration regression. In blind tests, the model demonstrates consistent high accuracy and precise concentration predictions. This SERS-MultiplexCR platform completes the entire detection process in just 15 min, offering significant potential for rapid, point-of-care diagnostics in infection detection, as well as applications in food safety and environmental monitoring.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.