Application of Raman spectroscopy and machine learning for identification and characterization.

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.

IMPORTANCE

Currently, combating infections and transmission is challenging due to the lack of efficient identification and characterization methods for this species. To address these challenges, our study presents a novel approach that utilizes Raman spectroscopy and artificial intelligence to achieve precise identification and characterization of at the singe-cell level. It can accurately identify a single cell from the four clades. Additionally, we developed machine learning models designed to detect antifungal resistance in cells and differentiate between two distinct phenotypes based on the single-cell Raman spectrum. We also constructed prediction models for detecting aggregating and filamentous cells in , both of which are closely linked to its virulence. These results underscore the merits of Raman spectroscopy in the identification and characterization of , promising improved outcomes in the battle against infections and transmission.