ArticleMicrobiology spectrum2022
Discrimination between Carbapenem-Resistant and Carbapenem-Sensitive Klebsiella pneumoniae Strains through Computational Analysis of Surface-Enhanced Raman Spectra: a Pilot Study.
Article in Microbiology spectrum, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 52 citations in OpenAlex.
- Recent advances in surface enhanced Raman spectroscopy for bacterial pathogen identifications.Journal of advanced research · 2023Pooled it
- Untargeted metabolomics reveals organism specific biomarkers of carbapenem resistance in Klebsiella pneumoniae and Escherichia coli.NPJ systems biology and applications · 2026Article
- Artificial Intelligence for Antimicrobial Resistance Detection and Prediction inInfection and drug resistance · 2026Review
- Explainable AI-SERS approach for highly accurate discrimination ofCurrent research in microbial sciences · 2026Article
- Review
- Comparison of Raman spectroscopy with mass spectrometry for sequence typing ofMicrobiology spectrum · 2025Article
- Next-Gen Nano Biosensor Technologies to Monitor Carbapenem Resistance for Personalized Medicine.Indian journal of microbiology · 2025Article
- Rapid diagnosis of bacterial vaginosis using machine-learning-assisted surface-enhanced Raman spectroscopy of human vaginal fluids.mSystems · 2025Article
- Involvement of theFrontiers in cellular and infection microbiology · 2025Article
- Review
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Raman spectrum combined with deep learning for precise recognition of Carbapenem-resistant Enterobacteriaceae.Analytical and bioanalytical chemistry · 2024Article
- Rapid discrimination of four Salmonella enterica serovars: A performance comparison between benchtop and handheld Raman spectrometers.Journal of cellular and molecular medicine · 2024Article
- Siamese Networks for Clinically Relevant Bacteria Classification Based on Raman Spectroscopy.Molecules (Basel, Switzerland) · 2024Article
- Rapid identification of pathogens in blood serum via Raman tweezers in combination with advanced processing methods.Biomedical optics express · 2023Article
- Dense Convolutional Neural Network for Identification of Raman Spectra.Sensors (Basel, Switzerland) · 2023Article
- Rapid Identification of Pathogens Causing Bloodstream Infections by Raman Spectroscopy and Raman Tweezers.Microbiology spectrum · 2023Article
- Recent advances of Raman spectroscopy for the analysis of bacteria.Analytical science advances · 2023Review
- SERS-Based Biosensors Combined with Machine Learning for Medical Application.ChemistryOpen · 2023Review
- Rapid discrimination ofFrontiers in microbiology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In clinical settings, rapid and accurate diagnosis of antibiotic resistance is essential for the efficient treatment of bacterial infections. Conventional methods for antibiotic resistance testing are time consuming, while molecular methods such as PCR-based testing might not accurately reflect phenotypic resistance. Thus, fast and accurate methods for the analysis of bacterial antibiotic resistance are in high demand for clinical applications. In this pilot study, we isolated 7 carbapenem-sensitive Klebsiella pneumoniae (CSKP) strains and 8 carbapenem-resistant Klebsiella pneumoniae (CRKP) strains from clinical samples. Surface-enhanced Raman spectroscopy (SERS) as a label-free and noninvasive method was employed for discriminating CSKP strains from CRKP strains through computational analysis. Eight supervised machine learning algorithms were applied for sample analysis. According to the results, all supervised machine learning methods could successfully predict carbapenem sensitivity and resistance in K. pneumoniae, with a convolutional neural network (CNN) algorithm on top of all other methods. Taken together, this pilot study confirmed the application potentials of surface-enhanced Raman spectroscopy in fast and accurate discrimination of Klebsiella pneumoniae strains with different antibiotic resistance profiles.
Indexed as
Identifiers
What OpenQuestion holds
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.