For decades, the conversation around obesity has had a single protagonist: body weight. The classic formula “eat less and move more” may generate initial changes, but the problem arises when the script demands sustainable endings. Evidence shows that many individuals regain weight in the years that follow if lifestyle changes are not maintained, fueling a cycle of frustration, stigma and disengagement from healthcare. In response to this scenario the Health at Every Size (HAES) approach proposes a paradigm shift: focusing on health and well being behaviors without positioning weight loss as the primary goal.

Can you imagine anticipating the struggle between the immune system and a tumor using mathematical tools that understand each patient's uncertainty? Researchers at ESPOL developed an innovative model based on Type-3 Fuzzy Logic that simulates precisely that confrontation. Unlike traditional models, this proposal integrates individual biological variability and delays in the immune response, allowing highly accurate prediction of chaotic patterns and possible tumor relapses. In addition, the study generates clear and interpretable risk maps that facilitate the design of personalized and explainable therapies, aligning with the principles of Explainable Artificial Intelligence (XAI) to support clinical decision-making.