Abstract: The proposed approach involves the development of a low-cost non-invasive diagnostic tool for the detection and differentiation between patients with AD, FTD, and ALS. This diagnosis is based on the interpretation and analysis of a panel of exosomal and plasma miRNAs. Using a Machine Learning algorithm based on the expression levels of these miRNAs, and including neuropsychological and neuroimaging information from the patients, we aim to recognize, differentiate, and perform an accurate diagnosis of AD, FTD, and ALS.
Director: Claudia Duran-Aniotz
PIs: Alejandro Bisquertt, Jose Manuel Matamala, Agustín Ibañez, Andrea Slachevsky & Rolando de la Cruz
Support: Alzheimer’s Drug Discovery Foundation (ADDF) & Target Amyotrophic Lateral Sclerosis (ALS)
