1. On-Site Detection of SARS-CoV-2 Antigen by Deep Learning-Based Surface-Enhanced Raman Spectroscopy and Its Biochemical Foundations
- Author
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Lai Wei, Li Bo, Daojian Qi, Shusen He, Jianan Xu, Kai Du, Wei Le, Li Zeyu, Zongqing Zhao, Yuanyu Zhao, Zhibing He, Jinglin Huang, Ni Shuang, Zhao Songnan, Baohan Zhang, Chen Guo, Peili Xue, Ming Pan, Wen Jiaxing, Yong Zeng, Kun Fang, Yuliang Feng, Minjie Zhou, and Yan Wu
- Subjects
2019-20 coronavirus outbreak ,Coronavirus disease 2019 (COVID-19) ,viruses ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,Computational biology ,Spectrum Analysis, Raman ,010402 general chemistry ,Sensitivity and Specificity ,01 natural sciences ,Article ,Analytical Chemistry ,symbols.namesake ,Deep Learning ,Antigen ,Humans ,skin and connective tissue diseases ,SARS-CoV-2 ,business.industry ,Chemistry ,Deep learning ,fungi ,010401 analytical chemistry ,Sputum ,COVID-19 ,Spike Protein ,Surface-enhanced Raman spectroscopy ,respiratory tract diseases ,0104 chemical sciences ,body regions ,symbols ,Artificial intelligence ,Raman spectroscopy ,business - Abstract
A rapid, on-site, and accurate SARS-CoV-2 detection method is crucial for the prevention and control of the COVID-19 epidemic. However, such an ideal screening technology has not yet been developed for the diagnosis of SARS-CoV-2. Here, we have developed a deep learning-based surface-enhanced Raman spectroscopy technique for the sensitive, rapid, and on-site detection of the SARS-CoV-2 antigen in the throat swabs or sputum from 30 confirmed COVID-19 patients. A Raman database based on the spike protein of SARS-CoV-2 was established from experiments and theoretical calculations. The corresponding biochemical foundation for this method is also discussed. The deep learning model could predict the SARS-CoV-2 antigen with an identification accuracy of 87.7%. These results suggested that this method has great potential for the diagnosis, monitoring, and control of SARS-CoV-2 worldwide.
- Published
- 2021