Associations between the prevalence of asthma and dietary exposure to food contaminants in children: CHASER study.

Background: Asthma is a common, multifactorial chronic disease with a major impact on children and adolescents. Exposure to food contaminants is one of the contributing factors to asthma. This study aims to evaluate the intake and exposure of different contaminants and explore their association with childhood asthma.

Revisiting the impact of Schistosoma mansoni regulating mechanisms on transmission dynamics using SchiSTOP, a novel modelling framework.

Background: The transmission cycle of Schistosoma is remarkably complex, including sexual reproduction in human hosts and asexual reproduction in the intermediate host (freshwater snails). Patterns of rapid recrudescence after treatment and stable low transmission are often observed, hampering the achievement of control targets. Current mathematical models commonly assume regulation of transmission to occur at worm level through density-dependent egg production.

Assessing external exposome by implementing an Environmental Data Management System using Open Data.

Due to the increasing importance of exposome in environmental epidemiology, feasibility and usefulness of an Environmental Data Management System (EDMS) using Open Data was evaluated. The EDMS includes data from 10 European cities (Celje (Slovenia), Łódź (Poland), Manchester (UK), Palermo (Italy), Paris (France), Porto (Portugal), Regensburg (Germany), Reus (Spain), Rijeka (Croatia), Thessaloniki (Greece)) about external non-specific and specific exposome factors at the city or country level (2017-2020). Findings showed that the highest values of life expectancy were in Reus females (86 years) and Palermo males (81 years).

Machine learning-enhanced HRCT analysis for diagnosis and severity assessment in pediatric asthma.

Objectives: Chest high-resolution computed tomography (HRCT) is conditionally recommended to rule out conditions that mimic or coexist with severe asthma in children. However, it may provide valuable insights into identifying structural airway changes in pediatric patients. This study aims to develop a machine learning-based chest HRCT image analysis model to aid pediatric pulmonologists in identifying features of severe asthma.