Prediction of breast cancer Invasive Disease Events using transfer learning on clinical data as image-form.

Background And Objective: Detecting patients at high risk of occurrence of an Invasive Disease Event after a first diagnosis of breast cancer, such as recurrence, distant metastasis, contralateral tumor and second tumor, could support clinical decision-making processes in the treatment of this malignancy. Though several machine learning models analyzing both clinical and histopathological information have been developed in literature to address this task, these approaches turned out to be unsuitable for describing this problem.

Methods: In this study, we designed a novel artificial intelligence-based approach which converts clinical information into an image-form to be analyzed through Convolutional Neural Networks.

Efficacy of a Food Supplement Containing LA14, Peptides, and a Multivitamin Complex in Improving Gastroesophageal Reflux Disease-Related Outcomes and Quality of Life of Subjects Showing Mild-to-Moderate Gastroesophageal Reflux Disease.

Dietary interventions represent an interesting alternative to pharmacological treatments for improving the quality of life (QoL) of subjects suffering from gastroesophageal reflux disease (GERD). This randomized, double-blind, placebo-controlled study aimed to evaluate the efficacy of a food supplement (FS) containing a probiotic strain, bioactive peptides, and vitamins in relieving heartburn/dyspeptic symptoms in subjects with mild-to-moderate GERD. Fifty-six adult participants were randomly assigned to receive the placebo or the active FS for 28 days.

A Novel Metric for Alzheimer's Disease Detection Based on Brain Complexity Analysis via Multiscale Fuzzy Entropy.

Alzheimer's disease (AD) is a neurodegenerative brain disorder that affects cognitive functioning and memory. Current diagnostic tools, including neuroimaging techniques and cognitive questionnaires, present limitations such as invasiveness, high costs, and subjectivity. In recent years, interest has grown in using electroencephalography (EEG) for AD detection due to its non-invasiveness, low cost, and high temporal resolution.

Exploring Nutritional Influence on Blood Glucose Forecasting for Type 1 Diabetes Using Explainable AI.

Type 1 diabetes mellitus (T1DM) is characterized by insulin deficiency and blood sugar control issues. The state-of-the-art solution is the artificial pancreas (AP), which integrates basal insulin delivery and glucose monitoring. However, APs are unable to manage postprandial glucose response (PGR) due to limited knowledge of its determinants, requiring additional information for accurate bolus delivery, such as estimated carbohydrate intake.