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Compound Raman microscopy for rapid diagnosis and antimicrobial susceptibility testing of pathogenic bacteria in urine.
Zhang, Weifeng; Sun, Hongyi; He, Shipei; Chen, Xun; Yao, Lin; Zhou, Liqun; Wang, Yi; Wang, Pu; Hong, Weili.
Affiliation
  • Zhang W; Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
  • Sun H; Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
  • He S; Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
  • Chen X; Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
  • Yao L; School of Engineering Medicine, Beihang University, Beijing, China.
  • Zhou L; Department of Urology, Peking University First Hospital, Beijing, China.
  • Wang Y; Department of Urology, Peking University First Hospital, Beijing, China.
  • Wang P; Department of Clinical Laboratory, China Rehabilitation Research Center, Capital Medical University, Beijing, China.
  • Hong W; Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
Front Microbiol ; 13: 874966, 2022.
Article in En | MEDLINE | ID: mdl-36090077
Rapid identification and antimicrobial susceptibility testing (AST) of bacteria are key interventions to curb the spread and emergence of antimicrobial resistance. The current gold standard identification and AST methods provide comprehensive diagnostic information but often take 3 to 5 days. Here, a compound Raman microscopy (CRM), which integrates Raman spectroscopy and stimulated Raman scattering microscopy in one system, is presented and demonstrated for rapid identification and AST of pathogens in urine. We generated an extensive bacterial Raman spectral dataset and applied deep learning to identify common clinical bacterial pathogens. In addition, we employed stimulated Raman scattering microscopy to quantify bacterial metabolic activity to determine their antimicrobial susceptibility. For proof-of-concept, we demonstrated an integrated assay to diagnose urinary tract infection pathogens, S. aureus and E. coli. Notably, the CRM system has the unique ability to provide Gram-staining classification and AST results within ~3 h directly from urine samples and shows great potential for clinical applications.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Diagnostic_studies Language: En Journal: Front Microbiol Year: 2022 Document type: Article Affiliation country: China Country of publication: Switzerland

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Diagnostic_studies Language: En Journal: Front Microbiol Year: 2022 Document type: Article Affiliation country: China Country of publication: Switzerland