Alzheimer’s disease (AD) is a neurodegenerative disease, characterized histopathologically by intra-neuronal tau-related lesions and by the accumulation of amyloid β-peptide (Aβ) in the brain parenchyma and around cerebral blood vessels. According to the vascular hypothesis of AD, an alteration in the neurovascular unit (NVU) could lead to Aβ vascular accumulation and promote neuronal dysfunction, accelerating neurodegeneration and dementia. To date, the effects of insoluble vascular Aβ deposits on the NVU and the blood–brain barrier (BBB) are unknown. In this study, we analyze different Aβ species and their association with the cells that make up the NVU. We evaluated post-mortem AD brain tissue. Multiple immunofluorescence assays were performed against different species of Aβ and the main elements that constitute the NVU. Our results showed that there are insoluble vascular deposits of both full-length and truncated Aβ species. Besides, insoluble aggregates are associated with a decrease in the phenotype of the cellular components that constitute the NVU and with BBB disruption. This approach could help identify new therapeutic targets against key molecules and receptors in the NVU that can prevent the accumulation of vascular fibrillar Aβ in AD.

Neurodegenerative diseases, tauopathies, constitute a serious global health problem. The etiology of these diseases is unclear and an increase in their incidence has been projected in the next 30 years. Therefore, the study of the molecular mechanisms that might stop these neurodegenerative processes is very relevant. Classification of neurodegenerative diseases using Machine and Deep Learning algorithms has been widely studied for medical imaging such as Magnetic Resonance Imaging. However, post-mortem immunofluorescence imaging studies of the brains of patients have not yet been used for this purpose. These studies may represent a valuable tool for monitoring aberrant chemical changes or pathological post-translational modifications of the Tau polypeptide. We propose a Convolutional Neural Network pipeline for the classification of Tau pathology of Alzheimer’s disease and Progressive Supranuclear Palsy by analyzing post-mortem immunofluorescence images with different Tau biomarkers performed with models generated with the architecture ResNet-IFT using Transfer Learning. These models’ outputs were interpreted with interpretability algorithms such as Guided Grad-CAM and Occlusion Analysis. To determine the best classifier, four different architectures were tested. We demonstrated that our design was able to classify diseases with an accuracy of 98.41% on average whilst providing an interpretation concerning the proper classification involving different structural patterns in the immunoreactivity of the Tau protein in NFTs present in the brains of patients with Progressive Supranuclear Palsy and Alzheimer’s disease.

We recently developed the National Dementia Biobank in México (BioBanco Nacional de Demencias, BND) as a unit for diagnosis, research, and tissue transfer for research purposes. BND is associated with the Facultad de Estudios Superiores Cuautitlán, Universidad Nacional Autónoma de Mexico (UNAM), Mexico. The donation of fluids, brain, and other organs of deceased donors is crucial for understanding the underlying mechanisms of neurodegenerative diseases and for the development of successful treatment. Our laboratory research focuses on 1) analysis of the molecular processing of the proteins involved in those neurodegenerative diseases termed tauopathies and 2) the search for biomarkers for the non-invasive and early diagnosis of Alzheimer’s disease.

Background/Objectives: Antibiotic resistance is a serious public health problem threatening the treatment of infectious diseases caused by Escherichia coli, the main source of food contamination and responsible for many infectious diseases with high indices of AR profiles. Our objective was to study the presence of Escherichia coli in foods that are distributed and prepared on the street, characterizing its sensitivity profile and resistance to antibiotic drugs commonly prescribed in this geographical area. Methods: Standard procedures were performed to identify and isolate E. coli colonies from food samples collected during a three-year study. Susceptibility assays were conducted to determine the antibiotic resistance profile, and Colony PCR assays were performed to determine the pathogenic and antibiotic resistance genes. Results: A total of 189 food samples were collected, and 100% of the samples were positive for E. coli, with higher percentages of contamination for vegetables and fruits. ETEC (lt) and UPEC (vat, cnf1, hylA) genes were identified in 100% of the samples and DAEC (afa) in 27%. E. coli exhibited high percentages of resistance against ampicillin and amoxicillin/clavulanic acid (100%) and cephalexin (45%). The most effective antibiotics were tetracycline, TMP-SMX, polymyxin, and quinolones. The AR genes tetA, sul1, catA1, strA, qnrS, and floR were identified among the samples. Conclusions: Food prepared and marketed on the streets seriously threatens human health. Ampicillin and amoxicillin/clavulanic acid should not be used to treat infections caused by the multidrug-resistant ETEC and UPEC identified in this area. To our knowledge, this is the first study that explores the status of AR in this geographical area.

Amyloidosis is a pathological condition which consists on the accumulation of fibrillar proteins. This disease is characterized by extracellular amyloid deposits with a clinical variability depending on the affected tissue. Histopathological evidence indicates that diabetes mellitus type 2 (DM2) induces dementia development, specifically Alzheimer’s disease (AD). It has been demonstrated in animal subjects that there is a possibility that aberrant signaling of insulin is a key factor in the induction of the pathology of AD. Recently, there has been newly emerged evidence regarding the relationship between the pathogenesis of Parkinson’s disease (PD) and insulin resistance. On another note, the importance of the amyloid deposits in the patients’ pancreas with DM2 was evidenced by the discovery of islets of amyloid polypeptide. This has generated interest in the search of the etiopathogenic role of DM2 in the carbohydrates’ metabolism. Finally, it is important to consider DM2 as a risk factor essential for the formation of deposits of amyloid-β in patients’ brains with dementia.

Efforts have been made to diagnose and predict the course of different neurodegenerative diseases through various imaging techniques. Particularly tauopathies, where the tau polypeptide is a key participant in molecular pathogenesis, have significantly increased their morbidity and mortality in the human population over the years. However, the standard approach to exploring the phenomenon of neurodegeneration in tauopathies has not been directed at understanding the molecular mechanism that causes the aberrant polymeric and fibrillar behavior of the tau protein, which forms neurofibrillary tangles that replace neuronal populations in the hippocampal and cortical regions. The main objective of this work is to implement a novel quantification protocol for different biomarkers based on pathological post-translational modifications undergone by tau in the brains of patients with tauopathies. The quantification protocol consists of an adaptation of the U-Net neural network architecture. We used the resulting segmentation masks for the quantification of combined fluorescent signals of the different molecular changes tau underwent in neurofibrillary tangles. The quantification considers the neurofibrillary tangles as an individual study structure separated from the rest of the quadrant present in the images. This allows us to detect unconventional interaction signals between the different biomarkers. Our algorithm provides information that will be fundamental to understanding the pathogenesis of dementias with another computational analysis approach in subsequent studies.