Dietary health optimization models aim to create comprehensive frameworks that enhance individual health through tailored nutrition strategies. These models consider various factors, including genetics, lifestyle, preferences, and environmental influences. By utilizing data analytics and machine learning, researchers can predict the outcomes of dietary interventions, identifying the most effective combinations of foods and nutrients for improving health. This approach allows for the development of personalized nutrition plans that address specific health goals, promoting better health outcomes and disease prevention in diverse populations. Furthermore, these models can help public health officials design community-wide strategies that cater to specific dietary needs based on demographic data and health statistics.
Title : Assessment of a Metabolic Map 3.0 (MM3.0) in association with Cardio Metabolic-Renal Syndrome (CMR-S)
Antonio Claudio Goulart Duarte, Federal University of Rio de Janeiro, Brazil
Title : Brain health beyond cognition: Exploring the needs of an aging brain
Dilip Ghosh, Nutriconnect, Australia
Title : Beyond the apparent: Nutrition, perception, and resilience in contexts of cognitive vulnerability a transdisciplinary proposal inspired by the Volume Oltre l’Apparente (Conversano & irace, 2026)
Raffaella Conversano, University of Bari, Italy
Title : Nutrition, physical activity, mental health, and reproductive function in adolescent and young adult women: Neuroimmunometabolic perspectives
Malgorzata Mizgier, Poznan University of Physical Education, Poland
Title : Characterization of isolated strains of microorganisms from mineral, mountain and spring waters from France, Italy, England, South Korea, Japan, Netherlands, Austria, Spain, Singapore and Bulgaria
Nedyalka Naneva Valcheva, Vocational High School, Bulgaria
Title : Climate-smart legume composting and its influence on sweet potato yield, soil health, and nutrient quality
Topas M Peter, PNG University of Technology, Papua New Guinea