Near-infrared spectroscopy and multivariate analysis as effective, fast, and cost-effective methods to discriminate Candida auris from Candida haemulonii .

Autor: Nascimento ALF; Laboratório de Química Biológica e Quimiometria, Instituto de Química, Universidade Federal do Rio Grande do Norte, Natal, Brazil., de Medeiros AGJ; Laboratório de Uso Comum, Centro de Biociências, Universidade Federal do Rio Grande do Norte, Natal, Brazil., Neves ACO; Laboratório de Química Biológica e Quimiometria, Instituto de Química, Universidade Federal do Rio Grande do Norte, Natal, Brazil., de Macedo ABN; Laboratório de Uso Comum, Centro de Biociências, Universidade Federal do Rio Grande do Norte, Natal, Brazil., Rossato L; Laboratório de Pesquisa em Ciências da Saúde, Universidade Federal da Grande Dourados, Dourados, Brazil., Assis Santos D; Laboratório de Micologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.; National Institute of Science and Technology in Human Pathogenic Fungi, Ribeirão Preto, Brazil., Dos Santos ALS; Instituto de Microbiologia Paulo de Góes, Universidade Federal do Rio de Janeiro, Rio de Janeiro-RJ, Brazil., Lima KMG; Laboratório de Química Biológica e Quimiometria, Instituto de Química, Universidade Federal do Rio Grande do Norte, Natal, Brazil., Bastos RW; Laboratório de Uso Comum, Centro de Biociências, Universidade Federal do Rio Grande do Norte, Natal, Brazil.; National Institute of Science and Technology in Human Pathogenic Fungi, Ribeirão Preto, Brazil.
Jazyk: angličtina
Zdroj: Frontiers in chemistry [Front Chem] 2024 Jul 10; Vol. 12, pp. 1412288. Date of Electronic Publication: 2024 Jul 10 (Print Publication: 2024).
DOI: 10.3389/fchem.2024.1412288
Abstrakt: Candida auris and Candida haemulonii are two emerging opportunistic pathogens that have caused an increase in clinical cases in the recent years worldwide. The differentiation of some Candida species is highly laborious, difficult, costly, and time-consuming depending on the similarity between the species. Thus, this study aimed to develop a new, faster, and less expensive methodology for differentiating between C . auris and C. haemulonii based on near-infrared (NIR) spectroscopy and multivariate analysis. C. auris CBS10913 and C. haemulonii CH02 were separated in 15 plates per species, and three isolated colonies of each plate were selected for Fourier transform near-infrared (FT-NIR) analysis, totaling 90 spectra. Subsequently, principal component analysis (PCA) and variable selection algorithms, including the successive projections algorithm (SPA) and genetic algorithm (GA) coupled with linear discriminant analysis (LDA), were employed to discern distinctive patterns among the samples. The use of PCA, SPA, and GA algorithms associated with LDA achieved 100% sensitivity and specificity for the discriminations. The SPA-LDA and GA-LDA algorithms were essential in selecting the variables (infrared wavelengths) of most importance for the models, which could be attributed to binding of cell wall structures such as polysaccharides, peptides, proteins, or molecules resulting from yeasts' metabolism. These results show the high potential of combined FT-NIR and multivariate analysis techniques for the classification of Candida -like fungi, which can contribute to faster and more effective diagnosis and treatment of patients affected by these microorganisms.
Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
(Copyright © 2024 Nascimento, de Medeiros, Neves, de Macedo, Rossato, Assis Santos, dos Santos, Lima and Bastos.)
Databáze: MEDLINE
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