Frontiers in Microbiology (Nov 2021)

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

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

DOI
https://doi.org/10.3389/fmicb.2021.769597
Journal volume & issue
Vol. 12

Abstract

Read online

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.

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