Raman Imaging of Pathogenic Candida auris: Visualization of Structural Characteristics and Machine-Learning Identification
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 C...
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Frontiers Media S.A.
2021
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oai:doaj.org-article:31f168ccdaa0442497712b183394fe632021-11-12T06:48:10ZRaman Imaging of Pathogenic Candida auris: Visualization of Structural Characteristics and Machine-Learning Identification1664-302X10.3389/fmicb.2021.769597https://doaj.org/article/31f168ccdaa0442497712b183394fe632021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fmicb.2021.769597/fullhttps://doaj.org/toc/1664-302XInvasive 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.Giuseppe PezzottiGiuseppe PezzottiGiuseppe PezzottiGiuseppe PezzottiGiuseppe PezzottiMiyuki KobaraTenma AsaiTenma AsaiTamaki NakayaTamaki NakayaNao MiyamotoTetsuya AdachiToshiro YamamotoNarisato KanamuraEriko OhgitaniElia MarinElia MarinWenliang ZhuIchiro NishimuraOsam MazdaTetsuo NakataKoichi MakimuraFrontiers Media S.A.articleRaman imagingRaman spectroscopyCandida aurismachine-learningglucansergosterolMicrobiologyQR1-502ENFrontiers in Microbiology, Vol 12 (2021) |
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| language |
EN |
| topic |
Raman imaging Raman spectroscopy Candida auris machine-learning glucans ergosterol Microbiology QR1-502 |
| spellingShingle |
Raman imaging Raman spectroscopy Candida auris machine-learning glucans ergosterol Microbiology QR1-502 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 Raman Imaging of Pathogenic Candida auris: Visualization of Structural Characteristics and Machine-Learning Identification |
| description |
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. |
| format |
article |
| author |
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 |
| author_facet |
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 |
| author_sort |
Giuseppe Pezzotti |
| title |
Raman Imaging of Pathogenic Candida auris: Visualization of Structural Characteristics and Machine-Learning Identification |
| title_short |
Raman Imaging of Pathogenic Candida auris: Visualization of Structural Characteristics and Machine-Learning Identification |
| title_full |
Raman Imaging of Pathogenic Candida auris: Visualization of Structural Characteristics and Machine-Learning Identification |
| title_fullStr |
Raman Imaging of Pathogenic Candida auris: Visualization of Structural Characteristics and Machine-Learning Identification |
| title_full_unstemmed |
Raman Imaging of Pathogenic Candida auris: Visualization of Structural Characteristics and Machine-Learning Identification |
| title_sort |
raman imaging of pathogenic candida auris: visualization of structural characteristics and machine-learning identification |
| publisher |
Frontiers Media S.A. |
| publishDate |
2021 |
| url |
https://doaj.org/article/31f168ccdaa0442497712b183394fe63 |
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