https://www.selleckchem.com/products/au-15330.html Determining the target population for the screening of Barrett's esophagus (BE), a precancerous condition of esophageal adenocarcinoma, remains a challenge in Asia. The aim of our study was to develop risk prediction models for BE using logistic regression (LR) and artificial neural network (ANN) methods. Their predictive performances were compared. We retrospectively analyzed 9646 adults aged ≥20 years undergoing upper gastrointestinal endoscopy at a health examinations center in Taiwan. Evaluated by using 10-fold cross-validation, both models exhibited good discriminative power, with comparable area under curve (AUC) for the LR and ANN models (Both AUC were 0.702). Our risk prediction models for BE were developed from individuals with or without clinical indications of upper gastrointestinal endoscopy. The models have the potential to serve as a practical tool for identifying high-risk individuals of BE among the general population for endoscopic screening.Synucleinopathies are diverse diseases with motor and cognitive dysfunction due to progressive neuronal loss or demyelination, due to oligodendrocyte loss in the brain. While the etiology of neurodegenerative disorders (NDDs) is likely multifactorial, mitochondrial injury is one of the most vital factors in neuronal loss and oligodendrocyte dysfunction, especially in Parkinson's disease, dementia with Lewy body, multiple system atrophy, and Krabbe disease. In recent years, the abnormal accumulation of highly neurotoxic α-synuclein in the mitochondrial membrane, which leads to mitochondrial dysfunction, was well studied. Furthermore, fatty acid-binding proteins (FABPs), which are members of a superfamily and are essential in fatty acid trafficking, were reported to trigger α-synuclein oligomerization in neurons and glial cells and to target the mitochondrial outer membrane, thereby causing mitochondrial loss. Here, we provide an updated overview of recent finding