Application of Raman spectroscopy and machine learning for identification and characterization.
Journal:
Applied and environmental microbiology
PMID:
39470219
Abstract
UNLABELLED: an emerging fungal pathogen characterized by multidrug resistance and high-mortality nosocomial infections, poses a serious global health threat. However, the precise and rapid identification and characterization of remain a challenge. Here, we employed Raman spectroscopy combined with machine learning to identify isolates and its closely related species as well as to predict antifungal resistance and key virulence factors at the single-cell level. The average accuracy of identification among all species was 93.33%, with an accuracy of 98% for the clinically simulated samples. The drug susceptibility of to fluconazole and amphotericin B was 99% and 94%, respectively. Furthermore, the phenotypic prediction of yielded an accuracy of 100% for aggregating cells and 97% for filamentous cells. This proof-of-concept methodology not only precisely identifies at the clade-specific level but also rapidly predicts the antifungal resistance and biological characteristics, promising a valuable medical diagnostic tool to combat this multidrug-resistant pathogen in the future.