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Association Between Environmental Chemicals Phthalates and Stroke Among the US Adults: A Cross-Sectional Study.

Stroke has a multifactorial etiology, and phthalates, as widely used environmental chemicals, may play an underexplored role in cerebrovascular health. This study aimed to evaluate the association between urinary phthalate metabolites and stroke risk using data from the National Health and Nutrition Examination Survey (NHANES), complemented by network toxicology, molecular docking, and machine lea...

Application of machine learning algorithm for predicting acute malnutrition among under 5 children in east Africa using recent DHS.

Acute malnutrition remains a critical public health challenge across East Africa, contributing substantially to under-five morbidity and mortality. Early identification of at-risk children using predictive models could enhance timely intervention. This study aimed to develop and evaluate machine learning models for predicting acute malnutrition among under-five children in East Africa. A cross-sec...

AI-assisted learning: ChatGPT for anamnesis in women's health education.

UNLABELLED: Artificial intelligence (AI) tools such as ChatGPT are rapidly being adopted in medical education, yet evidence regarding their impact on ...

Prediction model for frailty risk in ischemic stroke patients: Application and validation of support vector machines and nomograms.

This study aimed to develop a prediction model based on nomograms and support vector machines (SVM) to assess frailty risk in ischemic stroke patients...

AI-assisted interpretation of Markush structures in pharmaceutical patents: a review of emerging tools, datasets, and challenges.

Automated interpretation of Markush structures widely used in pharmaceutical patents to claim large families of related compounds remains challenging ...

Artificial Intelligence and Machine Learning Resource Guide: The Academy of Nutrition and Dietetics and the American Society for Nutrition Joint Taskforce for Artificial Intelligence.

Artificial Intelligence and Machine Learning Resource Guide: The Academy of Nutrition and Dietetics and the American Society for Nutrition Joint Taskforce for Artificial Intelligence.

From Benefits to AI: How BJC Health Is Embracing Change While Strengthening Culture.

In a time of dramatic change on multiple fronts, health systems must evolve with a mindset of persistent flexibility, where change is treated as norma...

Development and Internal Validation of a Gradient Boosting Model for Pressure Injury Risk in the ICU.

Pressure injury (PI) is common in the ICU and not well captured by single-risk tools such as the Braden scale. We aimed to develop and internally vali...

Reimagining Nursing Theories in the Age of Artificial Intelligence: Preserving the Human Essence Amid Digital Transformation.

The exponential growth of artificial intelligence (AI) in healthcare has sparked both enthusiasm and apprehension within the nursing discipline. While...

Re: Artificial intelligence in the management of hospital malnutrition: A systematic review Clinical Nutrition ESPEN Volume 70, December 2025, Mancin et al.

The future of digital innovation in transforming food safety systems in the developing world.

Low- and middle-income countries bear the most significant burden of foodborne diseases, impacting their food and nutrition security, trade, and ultim...

Characterising oral microbial signatures for periodontal disease in the NHANES population.

OBJECTIVE: This study aims to characterise the relationship between the oral microbiota and periodontal disease (PD) by leveraging the latest and larg...

From reductionism to complex systems: Paradigm shift and knowledge evolution in competitive sports research using BERTopic.

BACKGROUND AND PURPOSE: With the exponential growth in the volume of competitive sports research, traditional bibliometric methods based on keyword co...

Association between triglyceride-glucose index and related indicators with heart failure-related mortality in individuals with early-stage cardiovascular-kidney-metabolic syndrome.

BACKGROUND: Insulin resistance is a central pathophysiological feature of cardiovascular-kidney-metabolic (CKM) syndrome and has been implicated in ad...

Multi-omics and machine learning reveal a critical mediator of PRKCA in quercetin-mediated protection against Sarcopenia.

BACKGROUND: Sarcopenia, the age-associated loss of skeletal muscle mass and function, poses a growing public health challenge. Although dietary flavon...

Development and validation of a tool for detecting misinformation risk in diet, nutrition, and health content (Diet-MisRAT).

High-density lipoprotein-related inflammatory ratios and coronary heart disease: A cross-sectional machine learning analysis of NHANES 2009 to 2020.

High-density lipoprotein (HDL)-related inflammatory ratios (monocyte-to-HDL cholesterol ratio [MHR], lymphocyte-to-HDL cholesterol ratio, neutrophil-t...

Syntaxin1A in synaptopathies: From molecular mechanisms to therapeutic implications in neurological disorders (Review).

Syntaxin1A (STX1A) is a presynaptic membrane protein that is abundantly expressed in the central nervous system. It is a key member of the soluble N-e...

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 tai...