The role of the Mediterranean diet in reducing the risk of cognitive impairement, dementia, and Alzheimer's disease: a meta-analysis.

Age-related cognitive impairment and dementia pose a significant global health, social, and economic challenge. While Alzheimer's disease (AD) has historically been viewed as the leading cause of dementia, recent evidence reveals the considerable impact of vascular cognitive impairment and dementia (VCID), which now accounts for nearly half of all dementia cases. The Mediterranean diet-characterized by high consumption of fruits, vegetables, whole grains, fish, and olive oil-has been widely recognized for its cardiovascular benefits and may also reduce the risk of cognitive decline and dementia.

Lifestyle Factors Associated with Children's and Adolescents' Adherence to the Mediterranean Diet Living in Mediterranean Countries: The DELICIOUS Project.

Traditional dietary patterns are being abandoned in Mediterranean countries, especially among younger generations. This study aimed to investigate the potential lifestyle determinants that can increase adherence to the Mediterranean diet in children and adolescents. This study is a cross-sectional analysis of data from five Mediterranean countries (Italy, Spain, Portugal, Egypt, and Lebanon) within the context of the EU-funded project DELICIOUS (UnDErstanding consumer food choices & promotion of healthy and sustainable Mediterranean Diet and LIfestyle in Children and adolescents through behavIOUral change actionS).

Impact of adherence to the Mediterranean diet on stroke risk.

Stroke is a leading cause of morbidity and mortality worldwide, and dietary patterns have emerged as a significant modifiable factor in stroke prevention. The Mediterranean diet, characterized by high intake of fruits, vegetables, whole grains, nuts, olive oil, and fish, has been widely recognized for its cardiovascular benefits. However, its specific impact on stroke risk requires further elucidation.

Multiple testing for signal-agnostic searches for new physics with machine learning.

In this work, we address the question of how to enhance signal-agnostic searches by leveraging multiple testing strategies. Specifically, we consider hypothesis tests relying on machine learning, where model selection can introduce a bias towards specific families of new physics signals. Focusing on the New Physics Learning Machine, a methodology to perform a signal-agnostic likelihood-ratio test, we explore a number of approaches to multiple testing, such as combining -values and aggregating test statistics.