AUTHOR=Pezzotti Giuseppe , Kobara Miyuki , Asai Tenma , Nakaya Tamaki , Miyamoto Nao , Adachi Tetsuya , Yamamoto Toshiro , Kanamura Narisato , Ohgitani Eriko , Marin Elia , Zhu Wenliang , Nishimura Ichiro , Mazda Osam , Nakata Tetsuo , Makimura Koichi 

TITLE=Raman Imaging of Pathogenic Candida auris: Visualization of Structural Characteristics and Machine-Learning Identification

JOURNAL=Frontiers in Microbiology

VOLUME=12

YEAR=2021

URL=https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2021.769597

DOI=10.3389/fmicb.2021.769597

ISSN=1664-302X

ABSTRACT=<p>Invasive fungal infections caused by yeasts of the genus <italic>Candida</italic> carry high morbidity and cause systemic infections with high mortality rate in both immunocompetent and immunosuppressed patients. Resistance rates against antifungal drugs vary among <italic>Candida</italic> species, the most concerning specie being <italic>Candida auris</italic>, which exhibits resistance to all major classes of available antifungal drugs. The presently available identification methods for <italic>Candida</italic> 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 <italic>C. auris</italic> 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.</p>