Author: Torun, H.; Bilgin, B.; Ilgu, M.; Yanik, C.; Batur, S. N.; Celik, S.; Ozturk, M.; Dogan, O.; Ergonul, O.; Solaroglu, I.; Can, F.; Onbasli, M. C.
Title: Machine learning detects SARS-CoV-2 and variants rapidly on DNA aptamer metasurfaces Cord-id: tl98e8qj Document date: 2021_8_8
ID: tl98e8qj
Snippet: COVID-19 is detected using reverse transcription polymerase chain reaction (RT-PCR) of nasal swabs. A very sensitive and rapid detection technique using easily-collected fluids like saliva must be developed for safe and precise mass testing. Here, we introduce a metasurface platform for direct sensing of COVID-19 from unprocessed saliva. We computationally screen gold metasurfaces out of a pattern space of 2100 combinations for strongly-enhanced light-virus interaction with machine learning and
Document: COVID-19 is detected using reverse transcription polymerase chain reaction (RT-PCR) of nasal swabs. A very sensitive and rapid detection technique using easily-collected fluids like saliva must be developed for safe and precise mass testing. Here, we introduce a metasurface platform for direct sensing of COVID-19 from unprocessed saliva. We computationally screen gold metasurfaces out of a pattern space of 2100 combinations for strongly-enhanced light-virus interaction with machine learning and use it to investigate the presence and concentration of the SARS-CoV-2. We use machine learning to identify the virus from Raman spectra with 95.2% sensitivity and specificity on 36 PCR positive and 33 negative clinical samples and to distinguish wild-type, alpha, and beta variants. Our results could pave the way for effective, safe and quantitative preventive screening and identification of variants.
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