On-Site Detection of SARS-CoV-2 Antigen by Deep Learning-Based Surface-Enhanced Raman Spectroscopy and Its Biochemical Foundations

被引:75
作者
Huang, Jinglin [1 ]
Wen, Jiaxing [1 ,2 ]
Zhou, Minjie [1 ]
Ni, Shuang [1 ]
Le, Wei [1 ]
Chen, Guo [1 ]
Wei, Lai [1 ]
Zeng, Yong [1 ]
Qi, Daojian [1 ]
Pan, Ming [3 ]
Xu, Jianan [3 ]
Wu, Yan [4 ]
Li, Zeyu [1 ]
Feng, Yuliang [3 ]
Zhao, Zongqing [1 ]
He, Zhibing [1 ]
Li, Bo [1 ]
Zhao, Songnan [1 ]
Zhang, Baohan [1 ]
Xue, Peili [4 ]
He, Shusen [3 ]
Fang, Kun [4 ]
Zhao, Yuanyu [4 ]
Du, Kai [1 ]
机构
[1] China Acad Engn Phys, Laser Fus Res Ctr, Mianyang 621900, Sichuan, Peoples R China
[2] Tsinghua Univ, Dept Engn Phys, Beijing 100084, Peoples R China
[3] Sichuan Prov Ctr Dis Control & Prevent, Chengdu 610041, Peoples R China
[4] Sichuan Sci City Hosp, Mianyang 621000, Sichuan, Peoples R China
基金
中国国家自然科学基金;
关键词
NANOPARTICLES; SPECTRA; PCR;
D O I
10.1021/acs.analchem.1c01061
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
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. Her; 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.
引用
收藏
页码:9174 / 9182
页数:9
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