• Deep Learning-Assisted Rapid B...

Treffer: Deep Learning-Assisted Rapid Bacterial Classification Based on Raman Spectroscopy of Bacteria Lysed by Acoustically Driven Fiber-Tip Vibration.

Title:
Deep Learning-Assisted Rapid Bacterial Classification Based on Raman Spectroscopy of Bacteria Lysed by Acoustically Driven Fiber-Tip Vibration.
Authors:
Liu Y; Key Laboratory of Intelligent Optical Sensing and Integration of the Ministry of Education, College of Engineering and Applied Sciences, Nanjing University, Nanjing, Jiangsu, 210023, P. R. China., Ji M; Key Laboratory of Intelligent Optical Sensing and Integration of the Ministry of Education, College of Engineering and Applied Sciences, Nanjing University, Nanjing, Jiangsu, 210023, P. R. China., Ren X; Key Laboratory of Intelligent Optical Sensing and Integration of the Ministry of Education, College of Engineering and Applied Sciences, Nanjing University, Nanjing, Jiangsu, 210023, P. R. China., Dong Z; Key Laboratory of Intelligent Optical Sensing and Integration of the Ministry of Education, College of Engineering and Applied Sciences, Nanjing University, Nanjing, Jiangsu, 210023, P. R. China., Wen T; NHC Key laboratory of Enteric Pathogenic Microbiology, Jiangsu Provincial Medical Key Laboratory of Pathogenic Microbiology in Emerging Major Infectious Diseases, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, Jiangsu, 210009, P. R. China., Dong Q; Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, 999077, P. R. China., Ho HP; Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, 999077, P. R. China., Cui L; NHC Key laboratory of Enteric Pathogenic Microbiology, Jiangsu Provincial Medical Key Laboratory of Pathogenic Microbiology in Emerging Major Infectious Diseases, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, Jiangsu, 210009, P. R. China., Lu Y; Key Laboratory of Intelligent Optical Sensing and Integration of the Ministry of Education, College of Engineering and Applied Sciences, Nanjing University, Nanjing, Jiangsu, 210023, P. R. China., Wang G; Key Laboratory of Intelligent Optical Sensing and Integration of the Ministry of Education, College of Engineering and Applied Sciences, Nanjing University, Nanjing, Jiangsu, 210023, P. R. China.
Source:
Advanced science (Weinheim, Baden-Wurttemberg, Germany) [Adv Sci (Weinh)] 2025 Aug; Vol. 12 (32), pp. e07724. Date of Electronic Publication: 2025 Jul 08.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: WILEY-VCH Country of Publication: Germany NLM ID: 101664569 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2198-3844 (Electronic) Linking ISSN: 21983844 NLM ISO Abbreviation: Adv Sci (Weinh) Subsets: MEDLINE
Imprint Name(s):
Original Publication: Weinheim : WILEY-VCH, [2014]-
MeSH Terms:
Spectrum Analysis, Raman*/methods , Deep Learning* , Bacteria*/classification , Bacteria*/isolation & purification, Vibration ; Neural Networks, Computer
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Grant Information:
62375121 National Natural Science Foundation of China; 61875083 National Natural Science Foundation of China; 61535005 National Natural Science Foundation of China; ZD2021060 Key Scientific Research Project of Jiangsu Provincial Health Commission; 2020CXGC011304 Key Research and Development Program of Shandong Province; KYCX25_0255 Postgraduate Research & Practice Innovation Program of Jiangsu Province; KYCX25_0257 Postgraduate Research & Practice Innovation Program of Jiangsu Province; KYCX24_0264 Postgraduate Research & Practice Innovation Program of Jiangsu Province
Contributed Indexing:
Keywords: acoustofluidic lysis; bacterial classification; deep learning; raman spectroscopy; vibrating fiber‐tip
Entry Date(s):
Date Created: 20250708 Date Completed: 20250901 Latest Revision: 20250905
Update Code:
20250905
PubMed Central ID:
PMC12407356
DOI:
10.1002/advs.202507724
PMID:
40625037
Database:
MEDLINE

Weitere Informationen

Rapid and accurate identification of bacterial pathogens is critical for effective clinical decision-making and combating antibiotic resistance. Surface-enhanced Raman spectroscopy (SERS) combined with machine learning (ML) offers a powerful method for rapid, label-free bacterial identification. Conventional methods rely on surface molecular structures for identification, yet the richer and unique spectral information from intracellular biomolecules is often masked by the bacterial envelope, limiting classification accuracy. Here, a novel bacterial classification method is demonstrated by introducing acoustofluidic lysis based on the vibrating fiber-tip, combined with Raman spectroscopy and deep learning. The fiber-tip oscillates in a torsional mode, generating a controlled single-vortex within a capillary to concentrate bacteria in high-shear regions, enhancing lysis efficiency. This process effectively exposes intracellular components such as nucleic acids, proteins, and lipids, significantly enhancing the expression of features in bacterial Raman spectra, improving both spectral resolution and information richness. A residual neural network (ResNet) model is further employed for automated classification, achieving 98.9% accuracy across seven bacterial samples, surpassing traditional classifiers like random forests. The clinical validation experiments highlight the method's potential for real-world applications, enabling direct, on-site detection of clinical samples and facilitating rapid diagnostics, thus offering a promising advancement in pathogen identification.
(© 2025 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)