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

Journal: Analytical chemistry
PMID: 34155883

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.

Authors

  • Jinglin Huang
    Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China.
  • Jiaxing Wen
    Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China.
  • Minjie Zhou
    Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China.
  • Shuang Ni
    Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China.
  • Wei Le
    Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China.
  • Guo Chen
    Department of Orthopedics, West China Hospital, Sichuan University, Chengdu Sichuan, 610041, P.R.China.
  • Lai Wei
  • Yong Zeng
    a College of Pharmacy , Chengdu University of Traditional Chinese Medicine , Chengdu , P.R. China.
  • Daojian Qi
    Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China.
  • Ming Pan
    Sichuan Provincial Center for Disease Control and Prevention, 610041 Chengdu, China.
  • Jianan Xu
    Sichuan Provincial Center for Disease Control and Prevention, 610041 Chengdu, China.
  • Yan Wu
    Beijing Hui-Long-Guan Hospital, Peking University, Beijing, 100096, China.
  • Zeyu Li
    Department of automation, Harbin Engineering University, China. Electronic address: zyLee1@126.com.
  • Yuliang Feng
    Sichuan Provincial Center for Disease Control and Prevention, 610041 Chengdu, China.
  • Zongqing Zhao
    Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China.
  • Zhibing He
    Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China.
  • Bo Li
    Electric Power Research Institute, Yunnan Power Grid Co., Ltd., Kunming, Yunnan, China.
  • Songnan Zhao
    Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China.
  • Baohan Zhang
    Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China.
  • Peili Xue
    Sichuan Science City Hospital, 621000 Mianyang, China.
  • Shusen He
    Sichuan Provincial Center for Disease Control and Prevention, 610041 Chengdu, China.
  • Kun Fang
    Department of Surgery, Yinchuan Maternal and Child Health Hospital, Yinchuan, China.
  • Yuanyu Zhao
    Sichuan Science City Hospital, 621000 Mianyang, China.
  • Kai Du
    Beijing University of Chinese Medicine, Beijing, China.

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