Diet & Nutrition

Charting the Evolution of Personalized Nutrition: A Comprehensive Bibliometric Survey of Global Research Landscapes and Future Trajectories.

Personalized nutrition has emerged as a revolutionary paradigm in nutritional science, shifting from traditional "one-size-fits-all" approaches to tailored dietary recommendations. We conducted a comprehensive bibliometric analysis through a systematic search strategy capturing various conceptualizations of personalized nutrition. The analysis identified 3,159 publications demonstrating three dist...

SEMASEARCH Study Design: Real-World Evaluation of Semaglutide 2.4 mg in Adults With Severe Obesity Underrepresented in Clinical Trials.

BACKGROUND: Although semaglutide 2.4 mg has demonstrated significant weight loss efficacy in clinical trials, real-world data, particularly with regard to clinically complex and underrepresented populations, remain limited. OBJECTIVES: The study aims to assess the real-world effectiveness and patient-reported outcomes associated with the use of semaglutide 2.4 mg in individuals with severe and com...

Diet-microbiota-kidney axis reprogramming: mechanistic insights into microbial metabolite-driven precision nutrition for chronic kidney disease.

Medical nutrition therapy serves as the cornerstone in the management of chronic kidney disease (CKD). While conventional approaches emphasize macronu...

Identifying Undiagnosed COPD Using Machine Learning-Based Classification.

BACKGROUND: COPD is a leading cause of morbidity and mortality worldwide. However, 80% of individuals with COPD are undiagnosed, leading to accelerate...

Improving the composition of donor milk using machine learning and optimisation techniques.

BACKGROUND AND AIMS: The macronutrient composition of donor human milk (DHM) can vary substantially due to several factors such as maternal age, diet,...

Predicting suicidal ideation from depression screening data: A network-augmented machine learning approach.

BACKGROUND: Suicidal ideation is often assessed using a single self-report item in routine screening. We developed a model that combines machine learn...

Ameliorative effect of Rhus verniciflua Stokes on high-fat diet-induced obesity and advanced analysis with machine learning and deep learning-based network pharmacology.

AIMS: Obesity and metabolic dysfunction-associated steatotic liver disease (MASLD) arise from impaired redox and energy homeostasis, yet current thera...

Using artificial intelligence to predict sexual health outcomes in endometriosis: a decision tree model algorithm.

BACKGROUND: Endometriosis profoundly impairs sexual function through complex interactions between pain, hormonal disturbances, psychological distress,...

From Guidelines to Clicklists: GPT-5-Generated ERAS Checklists Improve Guideline Coverage for Bariatric and Gastrointestinal Cancer Surgery-A STROBE-Compatible Cross-Sectional Evaluation.

BACKGROUND: Enhanced Recovery After Surgery (ERAS) pathways improve outcomes after bariatric and gastrointestinal (GI) cancer surgery, yet real-world ...

Severe COVID-19 in the Republic of Korea: Epidemiology, Risk Factors, Therapeutics, and Prognostic Models From Nationwide Data.

Severe coronavirus disease 2019 (COVID-19) has posed ongoing clinical and public health challenges worldwide, with Korea providing a unique perspectiv...

FTO-CHRM3 axis regulates multiple myeloma progression: a machine learning-based identification.

BACKGROUND: Fat Mass and Obesity-Associated (FTO) is linked to multiple myeloma (MM) progression, but its action mechanisms are poorly understood. MET...

AI and Robotics Advancement in Analytical Mineral Characterization and Mining Processes: A Review and Research Trends Analysis.

The mining sector is undergoing a major transformation, as it moves shifting from traditional, labor-intensive methods to adopting digital technologie...

Combined variations in sleep, physical activity, and nutrition and the risk of major adverse cardiovascular events.

AIMS: Sleep, physical activity, and nutrition (SPAN) are major lifestyle behaviours that influence cardiovascular disease risk. We examined the multi-...

Digital twin technologies for supporting self-care in adults with diet-related chronic conditions: a systematic review.

BACKGROUND: Diet-related chronic conditions are major contributors to global morbidity and mortality. Effective management of these conditions require...

Methodological workflow for assessing heterogeneity in immature organic-rich carbonate reservoirs.

Understanding the vertical and lateral heterogeneity of immature organic-rich carbonate reservoirs is essential for predicting reservoir quality, main...

Temporal dynamics of mercury exposure and associated health and economic burdens in China.

Mercury is a global highly toxic pollutant linked to neurodevelopmental impairments and cardiovascular risks. However, despite being the world's large...

A sequential deep learning model for predicting people with obesity in adults aged 18-64 using physical fitness variables.

BACKGROUND/OBJECTIVES: Health of people with obesity is a global concern. We developed an explainable sequential deep learning model using nationally ...

Artificial intelligence models for osteoporosis risk prediction in postmenopausal women with CKD: A cross-sectional analysis of NHANES 2005 to 2018.

Artificial intelligence (AI) can transform osteoporosis (OP) screening, but its application in high-risk, complex populations like postmenopausal wome...

Modification strategies and multi-scene applications for plant proteins in 3D food printing: A review.

3D printing technology reveals considerable advantages in production of personalized foods. Plant proteins have become a popular material for 3D print...

Advancing diagnostic equity through artificial intelligence chest radiograph screening for osteoporosis in Asian populations.

Early identification of abnormal bone mineral density (BMD) through opportunistic screening is critical for preventing osteoporotic fractures. We vali...