ArticleFrontiers in microbiology2022
Identification of pathogens and detection of antibiotic susceptibility at single-cell resolution by Raman spectroscopy combined with machine learning.
Article in Frontiers in microbiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 37 citations in OpenAlex.
- Multimodal High-Throughput Screening Raman Spectroscopy for Label-Free Single-Cell Characterization of Recombinant Protein Production in Baculovirus Expression Vector Systems.Analytical chemistry · 2026Article
- Establishing a competence model in discriminatingBiomedical reports · 2026Article
- Deep Learning-Enhanced Raman Microspectroscopy Enables Rapid Microbial Classification and Captures Phylogenetic Relationships.Microorganisms · 2026Article
- Early Sepsis Diagnosis as a Global Imperative: The Role of Raman Spectroscopy.Journal of clinical medicine · 2026Review
- Genotypic and Phenotypic Characterisation ofPathogens (Basel, Switzerland) · 2026Article
- Integrating artificial intelligence with genome sequencing against antimicrobial resistance: a narrative review.Frontiers in public health · 2026Review
- Antibiotic resistance inFrontiers in microbiology · 2026Review
- Rapid culture-free diagnosis of clinical pathogens via integrated microfluidic-Raman micro-spectroscopy.Nature communications · 2025Article
- Harnessing machine learning to predict antibiotic susceptibility in Pseudomonas aeruginosa biofilms.NPJ biofilms and microbiomes · 2025Article
- Population-Level Dynamics and Community-Mediated Resistance to Antimicrobial Peptides.Biomolecules · 2025Review
- A Pilot Study on Single-Cell Raman Spectroscopy Combined with Machine Learning for Phenotypic Characterization ofMicroorganisms · 2025Article
- Detecting and classifying metabolic activity ofBiosafety and health · 2025Article
- Broadband coherent anti-Stokes Raman scattering (BCARS) microscopy for rapid, label-free biological imaging.The Review of scientific instruments · 2025Article
- Detecting antibiotic resistance: classical, molecular, advanced bioengineering, and AI-enhanced approaches.Frontiers in microbiology · 2025Review
- Application of Raman spectroscopy and machine learning forApplied and environmental microbiology · 2024Article
- Improved Classification Performance of Bacteria in Interference Using Raman and Fourier-Transform Infrared Spectroscopy Combined with Machine Learning.Molecules (Basel, Switzerland) · 2024Article
- Raman-Activated Cell Ejection for Validating the Reliability of the Raman Fingerprint Database of Foodborne Pathogens.Foods (Basel, Switzerland) · 2024Article
- Differentiation of closely-related species within Acinetobacter baumannii-calcoaceticus complex via Raman spectroscopy: a comparative machine learning analysis.World journal of microbiology & biotechnology · 2024Article
- Electroacoustic Biosensor Systems for Evaluating Antibiotic Action on Microbial Cells.Sensors (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rapid, accurate, and label-free detection of pathogenic bacteria and antibiotic resistance at single-cell resolution is a technological challenge for clinical diagnosis. Overcoming the cumbersome culture process of pathogenic bacteria and time-consuming antibiotic susceptibility assays will significantly benefit early diagnosis and optimize the use of antibiotics in clinics. Raman spectroscopy can collect molecular fingerprints of pathogenic bacteria in a label-free and culture-independent manner, which is suitable for pathogen diagnosis at single-cell resolution. Here, we report a method based on Raman spectroscopy combined with machine learning to rapidly and accurately identify pathogenic bacteria and detect antibiotic resistance at single-cell resolution. Our results show that the average accuracy of identification of 12 species of common pathogenic bacteria by the machine learning method is 90.73 ± 9.72%. Antibiotic-sensitive and antibiotic-resistant strains of
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.