Invasive fungal infections caused by yeasts of the genus carry high morbidity and cause systemic infections with high mortality rate in both immunocompetent and immunosuppressed patients. Resistance rates against antifungal drugs vary among species, the most concerning specie being , which exhibits resistance to all major classes of available antifungal drugs. The presently available identification methods for 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 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.
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| http://dx.doi.org/10.3389/fmicb.2021.769597 | DOI Listing |
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