The purpose of this study is to analyze prospective approaches that emphasize the beneficial biofunctional and metabolic effects of different anatomic parts of the Prunus spinosa L. on maintaining human health and improving some pathophysiological processes. Our research is based on recent data from literature on the biology and ecology of P. spinosa L., focusing on its bioactive compounds. Based on such bifunctional parameters, we aim to conceptualize path-breaking approaches that have as a goal the incorporation of P. spinosa L. fruits into certain food products to make use of their potential metabolic benefits in cardiovascular pathologies and other disorders that are prevalent at present and respond to nutritional intake of antioxidants. It is well known that dietary interventions allow the search for mechanisms leading to the development of effective nutritional therapies. This review has mainly focused on the identification of bioactive and functional nutrients of P. spinosa L. that can be incorporated into diverse food formulations. This is because nutrition plays a pivotal role in the development, validation, and recommendation of the nutritional composition of food, with demonstrated impacts on metabolic processes in specific diet-related pathologies.

Background: Esophageal varices, dilated submucosal veins in the lower esophagus, are commonly associated with portal hypertension, particularly due to liver cirrhosis. The high morbidity and mortality linked to variceal hemorrhage underscore the need for accurate diagnosis and effective management. The traditional method of assessing esophageal varices is esophagogastroduodenoscopy (EGD), which, despite its diagnostic and therapeutic capabilities, presents limitations such as interobserver variability and invasiveness. This review aims to explore the role of artificial intelligence (AI) in enhancing the management of esophageal varices, focusing on its applications in diagnosis, risk stratification, and treatment optimization. Methods: This systematic review focuses on the capabilities of AI algorithms to analyze clinical scores, laboratory data, endoscopic images, and imaging modalities like CT scans. Results: AI-based systems, particularly machine learning (ML) and deep learning (DL) algorithms, have demonstrated the ability to improve risk stratification and diagnosis of esophageal varices, analyzing vast amounts of data, identifying patterns, and providing individualized recommendations. However, despite these advancements, clinical scores based on laboratory data still show low specificity for esophageal varices, often requiring confirmatory endoscopic or imaging studies. Conclusions: AI integration in managing esophageal varices offers significant potential for advancing diagnosis, risk assessment, and treatment strategies. While promising, AI systems should complement rather than replace traditional methods, ensuring comprehensive patient evaluation. Further research is needed to refine these technologies and validate their efficacy in clinical practice.