Maternal exercise before and during pregnancy protects against genotoxicity and promotes offspring hippocampal health in mice prenatally exposed to high fructose.

The fetal brain is susceptible to programming effects during pregnancy, potentially leading to long-term consequences for offspring's cognitive health. Fructose intake is thought to adversely affect fetal brain development, whereas physical exercise before and during pregnancy may be protective. Therefore, this study aimed to assess biochemical and genotoxic changes in maternal hippocampi and behavioral, genotoxic, and biochemical alterations in offspring hippocampi.

Neural mobilisation effects in nerve function and nerve structure of patients with peripheral neuropathic pain: A systematic review with meta-analysis.

Objective: To assess the effects of neural mobilisation on nerve function and nerve structure of patients with peripheral neuropathic pain.

Methods: A systematic review with meta-analysis was conducted. Medline, Embase, CINAHL, Cochrane Library, and World Health Organization International Clinical Trials Registry Platform were searched without restrictions.

Leveraging genomes to support conservation and bioeconomy policies in a megadiverse country.

The biodiversity crisis is a global phenomenon, and measures to monitor, stop, and revert the impacts on species' extinction risk are urgently needed. Megadiverse countries, especially in the Global South, are responsible for managing and protecting Earth's biodiversity. Various initiatives have started to sequence reference-level genomes or perform large-scale species detection and monitoring through environmental DNA.

A method to promote safe cycling powered by large language models and AI agents.

This paper presents a novel information generation methodology to support safer cycling patterns in urban environments, leveraging for that Large Language Models (LLMs), AI-based agents, and open geospatial data. By processing multiple files containing previously computed urban risk levels and existing mobility infrastructure, which are generated by exploiting open data sources, our method exploits multi-layer data preprocessing procedures and prompt engineering to create easy-to-use, user-friendly assistive systems that are able to provide useful information concerning cycling safety. Through a well-defined processing pipeline based on Data Ingestion and Preparation, Agents Orchestration, and Decision Execution methodological steps, our method shows how to integrate open-source tools and datasets, ensuring reproducibility and accessibility for urban planners and cyclists.