Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Identification and antimicrobial resistance profiling of Pseudomonas aeruginosa using multi-excitation Raman spectroscopy and computational analytics

25 Aug 2025

Abstract excerpt

Antimicrobial resistance (AMR) poses a global healthcare challenge, where overprescription of antibiotics contributes to its prevalence. We have developed a rapid multi-excitation Raman spectroscopy methodology (MX-Raman) that outperforms conventional Raman spectroscopy and enhances specificity. A support vector machine (SVM) model was used to identify 20 clinical isolates of Pseudomonas aeruginosa with an...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Antibiotics Pharmacokinetics and Efficacy
Share to Bacterial Identification and Susceptibility Testing

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 40854982
DOI: 10.1038/s44259-025-00141-z

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 2 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Identification and antimicrobial resistance profiling of Pseudomonas aeruginosa using multi-excitation Raman spectroscopy and computational analyticsPMID 40854982Read ↗

Multi-excitation Raman Spectroscopy Complements Whole Genome Sequencing for Rapid Detection of Bacterial Infection and Resistance in WHO Priority PathogensSemantic proximity Read ↗

Multimodal Raman-Gene Framework for Interpretable Phenotypic Antibiotic Resistance and Health Risk Stratification.Semantic proximity Read ↗

Accurate and fast identification of minimally prepared bacteria phenotypes using Raman spectroscopy assisted by machine learningSemantic proximity Read ↗

Accurate and fast identification of minimally prepared bacteria phenotypes using Raman spectroscopy assisted by machine learning.Semantic proximity Read ↗

Phenotypic profiling of antibiotic response signatures in Escherichia coli using Raman spectroscopy.Semantic proximity Read ↗

Automated and rapid identification of multidrug resistant Escherichia coli against the lead drugs of acylureidopenicillins, cephalosporins, and fluoroquinolones using specific Raman marker bandsSemantic proximity Read ↗

Genotypic and Phenotypic Characterisation of Staphylococcus aureus Enterotoxins Using Single-Cell Raman Spectroscopy and Metabolomics.Semantic proximity Read ↗

Keeping up with the pathogens: improved antimicrobial resistance detection and prediction from Pseudomonas aeruginosa genomes.Semantic proximity Read ↗

Fighting antimicrobial resistance in <i>Pseudomonas aeruginosa</i> with machine learning-enabled molecular diagnosticsSemantic proximity Read ↗

Application of Raman spectroscopy and machine learning for Candida auris identification and characterizationSemantic proximity Read ↗

Integrated FTIR and Whole-Genome Sequencing Reveal Scale-Dependent Genotype-Phenotype Relationships in Multidrug-Resistant Pseudomonas aeruginosa.Semantic proximity Read ↗

Identification of fluoroquinolone-resistant Mycobacterium tuberculosis through high-level data fusion of Raman and laser-induced breakdown spectroscopy.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.