Banca de QUALIFICAÇÃO: RICARDO FERNANDES DOS SANTOS

Uma banca de QUALIFICAÇÃO de DOUTORADO foi cadastrada pelo programa.
STUDENT : RICARDO FERNANDES DOS SANTOS
DATE: 12/12/2022
TIME: 14:00
LOCAL: videoconferência
TITLE:

Molecular fluorescence (EEM) biospectroscopy coupled with 2ndorder classification algorithms for Alzheimer disease’s diagnosis.

KEY WORDS:

Alzheimer's disease; EEM fluorescence spectroscopy; multivariate classification; PARAFAC-QDA; Tucker3-QDA.

PAGES: 112
BIG AREA: Ciências Exatas e da Terra
AREA: Química
SUBÁREA: Química Analítica
SPECIALTY: Métodos Óticos de Análise
SUMMARY:

Alzheimer's disease (AD) is a neurodegenerative disease responsible for almost 70% of cases of dementia. Dementia, in turn, is the 7th leading cause of death in the world. In recent years there have been significant advances in research to identify AD, however, the methods traditionally used for diagnosis remain invasive, time-consuming and expensive. Studies with biospectroscopic techniques coupled with chemometrics have shown promising results in the AD diagnosis, with the possibility of offering a minimally invasive, rapid and inexpensive method. This thesis presents a new methodological approach for the diagnosis of AD through the analysis of blood plasma from 230 subjects (83 AD and 147 healthy controls) by molecular fluorescence in excitation-emission matrix (EEM) combined with second-order classification algorithms. The classification models were validated through the calculation of figures of merit commonly used in clinical studies (sensitivity, specificity and accuracy) and figures of merit that take into account the sample unbalance and the discriminatory power of the models (F2 - score (F2), Matthews correlation coefficient (MCC) and test effectiveness (δ)). The classification models performed in this study were Parallel Factor Analysis with Quadratic Discriminant Analysis (PARAFAC-QDA) and Tucker3 – QDA. The PARAFAC – QDA model obtained 83.33% sensitivity, 100% specificity and 86.21% F2. While the Tucker3- QDA model obtained 91.67% sensitivity, 95.45% specificity and 91.67% F2. Both models showed high overall performance with 94.12% accuracy and 0.87 MCC. The classifiers also showed high efficiency with the mean scores of the classes separated by three or more standard deviations. From the wavelength values obtained through the loading profiles of the two models, indications of possible AD biomarkers in the blood were made. The indicated biomarkers are just suggestions and future studies can work with these wavelength values and the spectral profiles of PARAFAC to correlate them with the real biomarkers or confirm our indication. The results achieved with the proposed new methodological approach point to a high-performance, blood-based method for the diagnosis/screening of Alzheimer's disease. This method has the advantages of being minimally invasive, fast, inexpensive, non-destructive and label-free. In addition, it requires a small aliquot of blood plasma and is performed in an easy-to-operate equipment.

COMMITTEE MEMBERS:
Externo ao Programa - 1913849 - EDGAR PERIN MORAES - nullPresidente - 1714946 - KASSIO MICHELL GOMES DE LIMA
Interna - 1805556 - LUCIENE DA SILVA SANTOS