| Autor: |
Yanjun Yang, Hao Li, Les Jones, Jackelyn Murray, Hemant Naikare, Yung‐Yi C. Mosley, Teddy Spikes, Sebastian Hülck, Ralph A. Tripp, Bin Ai, Yiping Zhao |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Advanced Materials Interfaces, Vol 11, Iss 18, Pp n/a-n/a (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2196-7350 |
| DOI: |
10.1002/admi.202400013 |
| Popis: |
Abstract This study presents an integrated approach combining surface‐enhanced Raman spectroscopy (SERS) with a specialized deep learning algorithm, SFNet, to offer a rapid, accurate, and label‐free alternative for COVID‐19 diagnosis and viral load quantification. The SiO2‐coated silver nanorod arrays are employed as the SERS substrates, fabricated using a reliable and effective glancing angle deposition technique. A dataset of 4800 SERS spectra from 120 positive and 120 negative inactivated clinical human nasopharyngeal swabs are collected directly on the SERS substrates without any labels. A SFNet algorithm is tailored to adapt to the unique spectral features inherent to SERS data, achieving a test accuracy of 98.5% and a blind test accuracy of 99.04%. Moreover, an optimized SFNet algorithm unveils the capability of estimating SARS‐CoV‐2 viral loads, accurately predicting the cycle threshold values (Ct values) of the three vital gene fragments with a root mean square error (RMSE) of 1.627 (1.3 for blind test). The methodology is substantiated using actual clinical specimens and completed in |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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