Diet & Nutrition

Phenotypic Screening Coupled with AI-Driven Target Deconvolution Identifies α-Terthienyl as a Dual DPP-IV/HSD17β13 Modulator with Efficacy in a Mouse Model of MASLD

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a highly prevalent condition characterized by fat build-up in the liver and ranges from benign steatosis to progression to metabolic dysfunction-associated steatohepatitis (MASH), fibrosis, cirrhosis and end-stage liver disease including hepatocellular carcinoma, representing a significant cause of chronic liver disease globally.1...

OMNI: Optimized Multi-view Network Integration with Heterogeneous Graph Attention for Biomedical Interaction Prediction

Accurate prediction of biomedical relationships, such as chemical–gene interactions, is fundamental to understanding disease mechanisms and advancing drug discovery. With the rapid growth of heterogeneous biological data, modeling large-scale, multi-entity networks has become increasingly challenging. Traditional approaches, including homogeneous GNNs (e.g., GCN, GAT) and meta-path-based random wa...

NutriSighT: Interpretable Transformer Model for Dynamic Prediction of Hypocaloric Enteral Nutrition in Mechanically Ventilated Patients

Achieving adequate enteral nutrition among mechanically ventilated patients is challenging, yet critical. We developed NutriSighT, a transformer model...

Development and validation of a multivariable Prediction Model for Pre-diabetes and Diabetes using Easily Obtainable Clinical Data

In the US, pre-diabetes and diabetes are increasing in prevalence alongside other chronic diseases. Hemoglobin A1c is the most common diagnostic test ...

CT-based Osteoporosis Classification and Bone-Muscle Interaction Mapping Using Multiple Interpretable Machine Learning Models with the BMINet Framework

Osteoporosis progresses through stages characterized by declining bone mineral density, vertebral deterioration, and muscle atrophy, with bone-muscle ...

Predicting Levels of Anemia among Adolescents in Ethiopia Using homogeneous ensemble Machine Learning algorithm

Anemia significantly impacts adolescent girls’ health and quality of life in Ethiopia. Effective interventions require identifying key risk factors an...

AcuKG: a comprehensive knowledge graph for medical acupuncture

This study constructs an acupuncture knowledge graph (AcuKG) to systematically organize and represent acupuncture-related knowledge in a structured an...

NutriRAG: Unleashing the Power of Large Language Models for Food Identification and Classification through Retrieval Methods

This study explores the use of advanced Natural Language Processing (NLP) techniques to enhance food classification and dietary analysis using raw tex...

Healthome Polygon Framework: Comprehensive and Multi-dimensional Health Quantification Framework Using Artificial Intelligence and Multiomics Data

Quantifying human health and disease necessitates a transformative framework capable of integrating diverse biomedical data in a standardized manner. ...

A Combined Predictive and Causal Approach for Neighborhood-Level Diabetes Detection

Develop a neighborhood-level framework using machine learning and causal inference to identify socioeconomic and behavioral drivers of Type 2 diabetes...

Application of Generative Artificial Intelligence to Utilise Unstructured Clinical Data for Acceleration of Inflammatory Bowel Disease Research

Inflammatory bowel disease (IBD) research is a dynamic field. However, the growing volume of electronic health records (EHRs) and research data presen...

Development of a Machine Learning Tool for Home-Based Assessment of Periodontitis

According to an ADA report, approximately 15% of the US population requires dental care annually but does not receive it. Access to dental care, parti...

Predictive performance of wearable sensors for mortality risk in older adults: a model development and validation study

Many adults in high-income countries carry a device capable of measuring physical- activity behaviour. Thus, there is public health need to understand...

The gSOS Polygenic Score is Associated with Bone Density and Fracture Risk in Childhood

The polygenic risk score genetic quantitative ultrasound speed of sound (gSOS) was developed using machine learning algorithms in adults of European a...

Sex-based differences in imaging-derived body composition and their association with clinical malnutrition in abdominal surgery patients

Malnutrition significantly impacts surgical outcomes yet is difficult to identify preoperatively. Few studies have investigated the association betwee...

Machine learning models for the prediction of COVID-19 prognosis in the primary health care setting

This study aimed to identify prognostic factors associated with poor outcomes of COVID-19 at diagnosis in Primary Health Care (PHC). We conducted a re...

Key predictors of maternal mild depression and anxiety in low resource settings: A machine learning approach

Maternal mental health (MMH) disorders, particularly depression and anxiety, are major public health concerns in low- and middle-income countries (LMI...

A time-sequenced approach to machine learning prognostic modelling with implementation on running-related injury prediction

The use of machine learning (ML) methods in medical prognostic modelling is gaining popularity, yet all currently available source models were designe...

Automated Deep Learning Pipeline for Characterizing Left Ventricular Diastolic Function

Left ventricular diastolic dysfunction (LVDD) is most commonly evaluated by echocardiography. However, without a sole identifying metric, LVDD is asse...

Dense sampling of choices links high learning rates to obesity and low reward sensitivity to binge eating

Mounting evidence shows that obesity is associated with alterations in dopamine transmission. However, in humans, corresponding changes in dopamine-de...