Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Assessment of Alzheimer's disease-related blood and urine biomarkers for wastewater-based epidemiological studies(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-11) Armenta Castro, A.; Aguilar Jiménez, Osear Alejandro; emimmayorquin; Montesinos Castellanos, Alejandro; Flores Tlacuahuac, Antonio; School of Engineering and Sciences; Campus Monterrey; de la Rosa Flores, Orlando DanielIncidence of Alzheimer's disease, the leading cause of dementia and the fifth cause of death among elderly patients, has been rapidly increasing in recent years due to continued demographic aging. However, access to diagnosis and adequate care remains limited, especially in low-to-middle income countries, leaving an approximate 41 million cases currently undiagnosed. Such limitations can crucially compromise the quality and availability of care that can be provided to those in need. Wastewater surveillance, which is based on the detection and quantification of biomarkers in wastewater samples, has emerged as a promising tool to assess public health in a time and resource-efficient manner, providing important information for public health authorities and healthcare providers when used in tandem with relevant socioeconomic data and clinical reports. While its potential for monitoring infectious diseases has been proven, efforts towards the integration of biomarkers of chronic and degenerative diseases into such surveillance platforms are still needed. This dissertation aims to evaluate the main biomarkers related to Alzheimer’s disease, including proteins, long non-coding RNAs, and oxidative stress biomarkers, for their integration into wastewater surveillance biomarkers. Moreover, machine learning-based algorithms to correlate the concentration of biomarkers in wastewater to the clinical reports of incidence of a disease were developed using SARS-CoV-2 surveillance in university campuses across Mexico as a relevant case study, to develop effective data analysis strategies to integrate wastewater surveillance data into epidemiological models that allow for public health risk assessment and forecasting. This dissertation contributes to the consolidation of wastewater surveillance as a tool for comprehensive public health risk assessment and data-driven decision-making by demonstrating a pipeline for the integration of new biomarkers into surveillance platforms and effective, easily-interpretable data integration.
- Deep Learning Approach for Alzheimer’s Disease Classification: Integrating Multimodal MRI and FDG- PET Imaging Through Dual Feature Extractors and Shared Neural Network Processing(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024) Vega Guzmán, Sergio Eduardo; Alfaro Ponce, Mariel; emimmayorquin; Ochoa Ruíz, Gilberto; Chairez Oria, Jorge Isaac; Hernandez Sanchez, Alejandra; School of Engineering and Sciences; Campus Monterrey; Ramírez Nava, Gerardo JuliánAlzheimer’s disease (AD) is a progressive neurodegenerative disorder whose incidence is expected to grow in the coming years. Traditional diagnostic methods, such as MRI and FDG-PET, each provide valuable but limited insights into the disease’s pathology. This thesis researches the potential of a multimodal deep learning classifier to improve the diagnostic accuracy of AD by integrating MRI and FDG-PET imaging data in comparison to single modality implementations. The study proposes a lightweight neural architecture that uses the strengths of both imaging modalities, aiming to reduce computational costs while maintaining state-of-the-art diagnostic performance. The proposed model utilizes two pre-trained feature extractors, one for each imaging modality, fine-tuned to capture the relevant features from the dataset. The outputs of these extractors are fused into a single vector to form an enriched feature map that better describes the brain. Experimental results demonstrate that the multimodal classifier outperforms single modality classifiers, achieving an overall accuracy of 90% on the test dataset. The VGG19 model was the best feature extractor for both MRI and PET data since it showed superior performance when compared to the other experimental models, with an accuracy of 71.9% for MRI and 80.3% for PET images. The multimodal implementation also exhibited higher precision, recall, and F1 scores than the single-modality implementations. For instance, it achieved a precision of 0.90, recall of 0.94, and F1-score of 0.92 for the AD class and a precision of 0.89, recall of 0.82, and F1-score of 0.86 for the CN class. Furthermore, explainable AI techniques provided insights into the model’s decisionmaking process, revealing that it effectively utilizes both structural and metabolic information to distinguish between AD and cognitively normal (CN) subjects. This research adds supporting evidence into the potential of multimodal imaging and machine learning to enhance early detection and diagnosis of Alzheimer’s disease, offering a cost-effective solution suitable for widespread clinical applications.