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- W3212956906 abstract "Invasive fungal infections caused by yeasts of the genus Candida carry high morbidity and cause systemic infections with high mortality rate in both immunocompetent and immunosuppressed patients. Resistance rates against antifungal drugs vary among Candida species, the most concerning specie being Candida auris, which exhibits resistance to all major classes of available antifungal drugs. The presently available identification methods for Candida species face a severe trade-off between testing speed and accuracy. Here, we propose and validate a machine-learning approach adapted to Raman spectroscopy as a rapid, precise, and labor-efficient method of clinical microbiology for C. auris identification and drug efficacy assessments. This paper demonstrates that the combination of Raman spectroscopy and machine learning analyses can provide an insightful and flexible mycology diagnostic tool, easily applicable on-site in the clinical environment." @default.
- W3212956906 created "2021-11-22" @default.
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- W3212956906 date "2021-11-12" @default.
- W3212956906 modified "2023-10-18" @default.
- W3212956906 title "Raman Imaging of Pathogenic Candida auris: Visualization of Structural Characteristics and Machine-Learning Identification" @default.
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- W3212956906 doi "https://doi.org/10.3389/fmicb.2021.769597" @default.
- W3212956906 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34867902" @default.
- W3212956906 hasPublicationYear "2021" @default.
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