Precision Nutrition Unveiled: Gene-Nutrient Interactions, Microbiota Dynamics, and Lifestyle Factors in Obesity Management.

BACKGROUND: Obesity is a complex metabolic disorder that is associated with several diseases. Recently, precision nutrition (PN) has emerged as a tailored approach to provide individualised dietary recommendations. AIM: This review discusses the major intrinsic and extrinsic components considered when applying PN during the management of obesity and common associated chronic conditions. RESULTS: The review identified three main PN components: gene-nutrient interactions, intestinal microbiota, and lifestyle factors. Genetic makeup significantly contributes to inter-individual variations in dietary behaviours, with advanced genome sequencing and population genetics aiding in detecting gene variants associated with obesity. Additionally, PN-based host-microbiota evaluation emerges as an advanced therapeutic tool, impacting disease control and prevention. The gut microbiome's composition regulates diverse responses to nutritional recommendations. Several studies highlight PN's effectiveness in improving diet quality and enhancing adherence to physical activity among obese patients. PN is a key strategy for addressing obesity-related risk factors, encompassing dietary patterns, body weight, fat, blood lipids, glucose levels, and insulin resistance. CONCLUSION: PN stands out as a feasible tool for effectively managing obesity, considering its ability to integrate genetic and lifestyle factors. The application of PN-based approaches not only improves current obesity conditions but also holds promise for preventing obesity and its associated complications in the long term.

Medical Student Experiences and Perceptions of ChatGPT and Artificial Intelligence: Cross-Sectional Study.

BACKGROUND: Artificial intelligence (AI) has the potential to revolutionize the way medicine is learned, taught, and practiced, and medical education must prepare learners for these inevitable changes. Academic medicine has, however, been slow to embrace recent AI advances. Since its launch in November 2022, ChatGPT has emerged as a fast and user-friendly large language model that can assist health care professionals, medical educators, students, trainees, and patients. While many studies focus on the technology's capabilities, potential, and risks, there is a gap in studying the perspective of end users. OBJECTIVE: The aim of this study was to gauge the experiences and perspectives of graduating medical students on ChatGPT and AI in their training and future careers. METHODS: A cross-sectional web-based survey of recently graduated medical students was conducted in an international academic medical center between May 5, 2023, and June 13, 2023. Descriptive statistics were used to tabulate variable frequencies. RESULTS: Of 325 applicants to the residency programs, 265 completed the survey (an 81.5% response rate). The vast majority of respondents denied using ChatGPT in medical school, with 20.4% (n=54) using it to help complete written assessments and only 9.4% using the technology in their clinical work (n=25). More students planned to use it during residency, primarily for exploring new medical topics and research (n=168, 63.4%) and exam preparation (n=151, 57%). Male students were significantly more likely to believe that AI will improve diagnostic accuracy (n=47, 51.7% vs n=69, 39.7%; P=.001), reduce medical error (n=53, 58.2% vs n=71, 40.8%; P=.002), and improve patient care (n=60, 65.9% vs n=95, 54.6%; P=.007). Previous experience with AI was significantly associated with positive AI perception in terms of improving patient care, decreasing medical errors and misdiagnoses, and increasing the accuracy of diagnoses (P=.001, P<.001, P=.008, respectively). CONCLUSIONS: The surveyed medical students had minimal formal and informal experience with AI tools and limited perceptions of the potential uses of AI in health care but had overall positive views of ChatGPT and AI and were optimistic about the future of AI in medical education and health care. Structured curricula and formal policies and guidelines are needed to adequately prepare medical learners for the forthcoming integration of AI in medicine.

Targeting mGluR group III for the treatment of neurodegenerative diseases.

Glutamate, an excitatory neurotransmitter, is essential for neuronal function, and it acts on ionotropic or metabotropic glutamate receptors (mGluRs). A disturbance in glutamatergic signaling is a hallmark of many neurodegenerative diseases. Developing disease-modifying treatments for neurodegenerative diseases targeting glutamate receptors is a promising avenue. The understudied group III mGluR 4, 6-8 are commonly found in the presynaptic membrane, and their activation inhibits glutamate release. Thus, targeted mGluRs therapies could aid in treating neurodegenerative diseases. This review describes group III mGluRs and their pharmacological ligands in the context of amyotrophic lateral sclerosis, Parkinson's, Alzheimer's, and Huntington's diseases. Attempts to evaluate the efficacy of these drugs in clinical trials are also discussed. Despite a growing list of group III mGluR-specific pharmacological ligands, research on the use of these drugs in neurodegenerative diseases is limited, except for Parkinson's disease. Future efforts should focus on delineating the contribution of group III mGluR to neurodegeneration and developing novel ligands with superior efficacy and a favorable side effect profile for the treatment of neurodegenerative diseases.