Your browser doesn't support javascript.
Integrated System for On-Site Rapid and Safe Screening of COVID-19.
Zhang, Dongheyu; Guo, Yuntao; Zhang, Liyang; Wang, Yao; Peng, Siqi; Duan, Simeng; Geng, Lin; Zhang, Xiao; Wang, Wei; Yang, Mengjie; Wu, Guizhen; Chen, Jiayi; Feng, Zihao; Wang, Xinyuan; Wu, Yue; Jiang, Haotian; Zhang, Qikang; Sun, Jingjun; Li, Shenwei; He, Yuping; Xiao, Meng; Xu, Yingchun; Wang, Hongqiu; Liu, Peipei; Zhou, Qun; Luo, Haiyun.
  • Zhang D; Department of Electrical Engineering, Tsinghua University, Beijing100084, China.
  • Guo Y; Department of Electrical Engineering, Tsinghua University, Beijing100084, China.
  • Zhang L; Department of Electrical Engineering, Tsinghua University, Beijing100084, China.
  • Wang Y; Department of Clinical Laboratory, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing100730, China.
  • Peng S; Department of Electrical Engineering, Tsinghua University, Beijing100084, China.
  • Duan S; Department of Clinical Laboratory, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing100730, China.
  • Geng L; JINSP Co., Ltd., Beijing100083, China.
  • Zhang X; JINSP Co., Ltd., Beijing100083, China.
  • Wang W; Shanghai Customs Port Clinic, Shanghai International Travel Healthcare Center, Shanghai200335, China.
  • Yang M; Chinese Center for Disease Control and Prevention, National Institute for Viral Disease Control and Prevention, Beijing102206, China.
  • Wu G; Chinese Center for Disease Control and Prevention, National Institute for Viral Disease Control and Prevention, Beijing102206, China.
  • Chen J; Department of Electrical Engineering, Tsinghua University, Beijing100084, China.
  • Feng Z; Department of Electrical Engineering, Tsinghua University, Beijing100084, China.
  • Wang X; Holy-shine Technology Co., Ltd., Beijing100045, China.
  • Wu Y; Holy-shine Technology Co., Ltd., Beijing100045, China.
  • Jiang H; Department of Electrical Engineering, Tsinghua University, Beijing100084, China.
  • Zhang Q; Department of Electrical Engineering, Tsinghua University, Beijing100084, China.
  • Sun J; Department of Electrical Engineering, Tsinghua University, Beijing100084, China.
  • Li S; Shanghai Customs Port Clinic, Shanghai International Travel Healthcare Center, Shanghai200335, China.
  • He Y; Shanghai Customs Port Clinic, Shanghai International Travel Healthcare Center, Shanghai200335, China.
  • Xiao M; Department of Clinical Laboratory, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing100730, China.
  • Xu Y; Department of Clinical Laboratory, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing100730, China.
  • Wang H; JINSP Co., Ltd., Beijing100083, China.
  • Liu P; Chinese Center for Disease Control and Prevention, National Institute for Viral Disease Control and Prevention, Beijing102206, China.
  • Zhou Q; Department of Chemistry, Tsinghua University, Beijing100084, China.
  • Luo H; Department of Electrical Engineering, Tsinghua University, Beijing100084, China.
Anal Chem ; 94(40): 13810-13819, 2022 10 11.
Article in English | MEDLINE | ID: covidwho-2050235
ABSTRACT
Since the outbreak of coronavirus disease 2019 (COVID-19), the epidemic has been spreading around the world for more than 2 years. Rapid, safe, and on-site detection methods of COVID-19 are in urgent demand for the control of the epidemic. Here, we established an integrated system, which incorporates a machine-learning-based Fourier transform infrared spectroscopy technique for rapid COVID-19 screening and air-plasma-based disinfection modules to prevent potential secondary infections. A partial least-squares discrimination analysis and a convolutional neural network model were built using the collected infrared spectral dataset containing 857 training serum samples. Furthermore, the sensitivity, specificity, and prediction accuracy could all reach over 94% from the results of the field test regarding 968 blind testing samples. Additionally, the disinfection modules achieved an inactivation efficiency of 99.9% for surface and airborne tested bacteria. The proposed system is conducive and promising for point-of-care and on-site COVID-19 screening in the mass population.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Diagnostic study / Prognostic study Limits: Humans Language: English Journal: Anal Chem Year: 2022 Document Type: Article Affiliation country: Acs.analchem.2c02337

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Diagnostic study / Prognostic study Limits: Humans Language: English Journal: Anal Chem Year: 2022 Document Type: Article Affiliation country: Acs.analchem.2c02337