ArticleMicrobial biotechnology2022
Identification of antibiotic resistance and virulence-encoding factors in Klebsiella pneumoniae by Raman spectroscopy and deep learning.
Article in Microbial biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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25 citing papers in PubMed, 53 citations in OpenAlex.
- Genotypic and Phenotypic Characterisation ofPathogens (Basel, Switzerland) · 2026Article
- Artificial Intelligence for Antimicrobial Resistance Detection and Prediction inInfection and drug resistance · 2026Review
- Exploiting Salmonella typhimurium as a novel cancer therapeutic agent: from tumor colonization to metabolic engineering.World journal of microbiology & biotechnology · 2025Review
- Machine Learning Prediction of Multidrug Resistance in Swine-DerivedVeterinary sciences · 2025Article
- Shared resistance and virulence ofOpen veterinary journal · 2025Article
- Accelerated detection of carbapenem resistance in Klebsiella pneumoniae via single-cell Raman spectroscopy.World journal of microbiology & biotechnology · 2025Article
- Leveraging the microbiome to combat antibiotic resistant gynecological infections.npj antimicrobials and resistance · 2025Review
- Multidrug-resistant hypervirulentFuture microbiology · 2025Review
- Postbiotics in the Bakery Products: Applications and Nutritional Values.Probiotics and antimicrobial proteins · 2025Review
- The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance.Computational and structural biotechnology journal · 2025Review
- Article
- Detecting antibiotic resistance: classical, molecular, advanced bioengineering, and AI-enhanced approaches.Frontiers in microbiology · 2025Review
- Implications of Artificial Intelligence in Addressing Antimicrobial Resistance: Innovations, Global Challenges, and Healthcare's Future.Antibiotics (Basel, Switzerland) · 2024Article
- Genome analyses of colistin-resistant high-risk blaBMC microbiology · 2024Article
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Siamese Networks for Clinically Relevant Bacteria Classification Based on Raman Spectroscopy.Molecules (Basel, Switzerland) · 2024Article
- Artificial intelligence applications in the diagnosis and treatment of bacterial infections.Frontiers in microbiology · 2024Review
- A study of machine learning to predict NRDS severity based on lung ultrasound score and clinical indicators.Frontiers in medicine · 2024Article
- HypervirulentPathogens & immunity · 2024Review
- Machine learning model of imipenem-resistantHealth science reports · 2023Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Klebsiella pneumoniae has become the number one bacterial pathogen that causes high mortality in clinical settings worldwide. Clinical K. pneumoniae strains with carbapenem resistance and/or hypervirulent phenotypes cause higher mortality comparing with classical K. pneumoniae strains. Rapid differentiation of clinical K. pneumoniae with high resistance/hypervirulence from classical K. pneumoniae would allow us to develop rational and timely treatment plans. In this study, we developed a convolution neural network (CNN) as a prediction method using Raman spectra raw data for rapid identification of ARGs, hypervirulence-encoding factors and resistance phenotypes from K. pneumoniae strains. A total of 71 K. pneumoniae strains were included in this study. The minimum inhibitory concentrations (MICs) of 15 commonly used antimicrobial agents on K. pneumoniae strains were determined. Seven thousand four hundred fifty-five spectra were obtained using the InVia Reflex confocal Raman microscope and used for deep learning-based and machine learning (ML) algorithms analyses. The quality of predictors was estimated in an independent data set. The results of antibiotic resistance and virulence-encoding factors identification showed that the CNN model not only simplified the classification system for Raman spectroscopy but also provided significantly higher accuracy to identify K. pneumoniae with high resistance and virulence when compared with the support vector machine (SVM) and logistic regression (LR) models. By back-testing the Raman-CNN platform on 71 K. pneumoniae strains, we found that Raman spectroscopy allows for highly accurate and rationally designed treatment plans against bacterial infections within hours. More importantly, this method could reduce healthcare costs and antibiotics misuse, limiting the development of antimicrobial resistance and improving patient outcomes.
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