Impact of Ex Vivo Bisphenol A Exposure on Gut Microbiota Dysbiosis and Its Association with Childhood Obesity.

Dietary exposure to the plasticiser bisphenol A (BPA), an obesogenic and endocrine disruptor from plastic and epoxy resin industries, remains prevalent despite regulatory restriction and food safety efforts. BPA can be accumulated in humans and animals, potentially exerting differential health effects based on individual metabolic capacity. This pilot study examines the impact of direct ex vivo BPA exposure on the gut microbiota of obese and normal-weight children, using 16S rRNA amplicon sequencing and anaerobic culturing combined methods.

Interactions between live attenuated influenza vaccine and nasopharyngeal microbiota among children aged 24-59 months in The Gambia: a phase 4, open-label, randomised controlled trial.

Background: Live attenuated influenza vaccines (LAIVs) alter nasopharyngeal microbiota in adults. It is poorly understood why LAIV immunogenicity varies across populations, but it could be linked to the microbiome. We aimed to investigate the interactions between intranasal immunisation with LAIV and nasopharyngeal microbiota composition in children from The Gambia.

Multi-omics approach in gut and environmental microbiota research under the One Health concept.

The One Health concept, although formulated two decades ago, remains challenging to implement. It necessitates the integration of numerous scientific disciplines, diverse techniques and various professional expertise. Furthermore, it often requires the collaboration of different institutions, encompassing both scientific and administrative entities.

Detecting Honey Adulteration: Advanced Approach Using UF-GC Coupled with Machine Learning.

This article introduces a novel approach to detecting honey adulteration by combining ultra-fast gas chromatography (UF-GC) with advanced machine learning techniques. Machine learning models, particularly support vector regression (SVR) and least absolute shrinkage and selection operator (LASSO), were applied to predict adulteration in orange blossom (OB) and sunflower (SF) honeys. The SVR model achieved R values above 0.

Action observation with motor simulation of reactive stepping: A randomized study in older adults with a history of falls.

Background: Perturbation-based training improves reactive stepping responses to prevent falling following a loss-of-balance. As there is currently no safe and feasible method for home-based practice, this randomized study investigated whether action observation with motor simulation (AOMS) of balance recovery improves reactive stepping in older adults with a history of falls. Additionally, we evaluated whether effects differ between AOMS of a human actor in the same experimental context or of an avatar in a virtual world.