Abstract
The rapid and accurate sequence typing (ST) of bacterial pathogens is pivotal in controlling transmission within healthcare settings. Acinetobacter baumannii infection, known for its high transmissibility and drug resistance, presents a major challenge in nosocomial infection control. In this study, surface-enhanced Raman spectroscopy (SERS) was used to differentiate A. baumannii strains with distinct STs based on unique Raman spectral profiles. We then constructed and compared eight machine-learning models on SERS spectra to quickly identify bacterial STs. The results showed that the support vector machine model outperformed matrix-assisted laser desorption/ionization time-of-flight mass spectrometer in determining A. baumannii STs. This approach enables rapid identification of A. baumannii variants with different STs, supporting the early detection and control of nosocomial infections by this multidrug-resistant pathogen.
Published Version
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