Banca de QUALIFICAÇÃO: AYRTON LUCAS FIRMINO DO NASCIMENTO

Uma banca de QUALIFICAÇÃO de MESTRADO foi cadastrada pelo programa.
STUDENT : AYRTON LUCAS FIRMINO DO NASCIMENTO
DATE: 12/07/2024
TIME: 14:00
LOCAL: Auditório do Química III
TITLE:

Near infrared spectroscopy and multivariate analysis as an effective, fast and cost-effective method to discriminate between Candida auris and Candida haemulonii

KEY WORDS:

Candida auris; Candida haemulonii; Near Infrared Spectroscopy; PCA- LDA; SPA-LDA; GA-LDA; Multivariate Analysis

PAGES: 52
BIG AREA: Ciências Exatas e da Terra
AREA: Química
SUBÁREA: Química Analítica
SPECIALTY: Métodos Óticos de Análise
SUMMARY:

Candida auris and Candida haemulonii are two emerging opportunistic pathogens that have been increasing in clinical cases worldwide in recent years. Differentiating someCandidaspecies can be very laborious, difficult, financially costly and time consuming, depending on their similarity. Thus, the objective of this work is to develop a new, faster and cost-effective methodology for differentiating between C. auris and C. haemulonii based on near-infrared spectroscopy (NIR) 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 NearInfrared (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 most important variables (infrared wavelengths) for the models, which could be attributed to the binding of cell wall structures 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.

COMMITTEE MEMBERS:
Presidente - 1714946 - KASSIO MICHELL GOMES DE LIMA
Interna - 3420591 - ANA CAROLINA DE OLIVEIRA NEVES MENEZES
Externo ao Programa - 1143204 - RAFAEL WESLEY BASTOS - UFRNExterno à Instituição - CAMILO DE LELIS MEDEIROS DE MORAIS - UECE