Dietary Exposure to Pesticide and Veterinary Drug Residues and Their Effects on Human Fertility and Embryo Development: A Global Overview.

Drug residues that contaminate food and water represent a serious concern for human health. The major concerns regard the possible irrational use of these contaminants, since this might increase the amplitude of exposure. Multiple sources contribute to the overall exposure to contaminants, including agriculture, domestic use, personal, public and veterinary healthcare, increasing the possible origin of contamination.

Well-being among university students in the post-COVID-19 era: a cross-country survey.

University students have to handle crucial challenges for their future lives, such as succeeding in academic studies and finding attachment figures. These processes could potentially involve their well-being and mental health, with possible sociocultural differences based on the country of study. In order to explore such potential differences, a cross-sectional, multi-center survey was performed involving students from the University of Torino (Italy), Sevilla (Spain), and Lusòfona (Portugal).

Identification and Characterization of Chemotherapy-Resistant High-Risk Neuroblastoma Persister Cells.

Relapse rates in high-risk neuroblastoma remain exceedingly high. The malignant cells that are responsible for relapse have not been identified, and mechanisms of therapy resistance remain poorly understood. In this study, we used single-nucleus RNA sequencing and bulk whole-genome sequencing to identify and characterize the residual malignant persister cells that survive chemotherapy from a cohort of 20 matched diagnosis and definitive surgery tumor samples from patients treated with high-risk neuroblastoma induction chemotherapy.

Information extraction from medical case reports using OpenAI InstructGPT.

Background And Objective: Researchers commonly use automated solutions such as Natural Language Processing (NLP) systems to extract clinical information from large volumes of unstructured data. However, clinical text's poor semantic structure and domain-specific vocabulary can make it challenging to develop a one-size-fits-all solution. Large Language Models (LLMs), such as OpenAI's Generative Pre-Trained Transformer 3 (GPT-3), offer a promising solution for capturing and standardizing unstructured clinical information.