A knowledge-based decision support system to support family doctors in personalizing type-2 diabetes mellitus medical nutrition therapy.

Background: Type-2 Diabetes Mellitus (T2D) is a growing concern worldwide, and family doctors are called to help diabetic patients manage this chronic disease, also with Medical Nutrition Therapy (MNT). However, MNT for Diabetes is usually standardized, while it would be much more effective if tailored to the patient. There is a gap in patient-tailored MNT which, if addressed, could support family doctors in delivering effective recommendations.

Italian and Middle Eastern adherence to Mediterranean diet in relation to Body Mass Index and non-communicable diseases: nutritional adequacy of simulated weekly food plans.

Background: The Mediterranean diet (MD), known to prevent obesity, overweight and the related non communicable diseases (NCD), is based on typical dishes, foods and on a common cultural milieu. Although MD is the basis of dietary guidelines, the prevalence of obesity, overweight and NCD, is increasing both in Western regions, and even more in Middle Eastern regions (MER). This study aimed to analyze (i) the impact of different levels of adherence to the MD, in Italy and MER, on body mass index (BMI) (ii) the bromatological composition of a simulated 7-days food plan (7-DFP) based on Italian or MER typical meals, following MD criteria and the Italian or MER food base dietary guideline; (iii) the optimization of nutrients impacting on NCD.

An Ontology-Based Decision Support System for Tailored Clinical Nutrition Recommendations for Patients With Chronic Obstructive Pulmonary Disease: Development and Acceptability Study.

Background: Chronic obstructive pulmonary disease (COPD) is a chronic condition among the main causes of morbidity and mortality worldwide, representing a burden on health care systems. Scientific literature highlights that nutrition is pivotal in respiratory inflammatory processes connected to COPD, including exacerbations. Patients with COPD have an increased risk of developing nutrition-related comorbidities, such as diabetes, cardiovascular diseases, and malnutrition.

Ontology-based decision support systems for diabetes nutrition therapy: A systematic literature review.

Diabetes is a non-communicable disease that has reached epidemic proportions, affecting 537 million people globally. Artificial Intelligence can support patients or clinicians in diabetes nutrition therapy - the first medical therapy in most cases of Type 1 and Type 2 diabetes. In particular, ontology-based recommender and decision support systems can deliver a computable representation of experts' knowledge, thus delivering patient-tailored nutritional recommendations or supporting clinical personnel in identifying the most suitable diet.

Oral ecological environment modifications by hard-cheese: from pH to microbiome: a prospective cohort study based on 16S rRNA metabarcoding approach.

Background: The oral ecosystem conditions dental health, and is known to be positively modified by oral hygiene which cannot always be performed between meals, especially outside home. It is therefore important to identify the practices to be adopted to influence the oral environment in an anticariogenic direction. Milk and cheese are considered functional foods and have a role on oral health.