Primary Care

PepPharmaHub: a cloud-based platform integrating multimodel language architectures with curated data resources for therapeutic peptide discovery.

BACKGROUND: Therapeutic peptides represent a rapidly expanding class of drug candidates due to their diverse biological activities and high specificity. However, accurately predicting peptide functions directly from sequence information remains a major challenge in computational peptidomics. Current tools, typically standalone applications or functionally constrained web servers, lack the flexibil...

Single-cell-based analysis establishes C1QA-mediated promotion of high glucose-induced tubular epithelial injury via ERS in DN.

BACKGROUND: Diabetic nephropathy (DN) poses a growing worldwide health challenge as a leading cause of end-stage renal disease, a condition that arises from a complex, poorly defined pathophysiology. Endoplasmic reticulum stress (ERS) is recognized as a pivotal contributor to renal cell injury in DN. While complement component C1q A chain (C1QA) is integral to innate immunity, its specific role an...

Development and Interpretability Analysis of a Stacking Ensemble Model for Early Prediction of Nutritional Risk in Intensive Care Unit Patients: Retrospective Cohort Study.

BACKGROUND: Malnutrition in critically ill patients is associated with increased morbidity and mortality, yet traditional screening tools such as the ...

Personalized Type 1 Diabetes Management: Reinforcement Learning-Based Insulin Dosing and Glucose Forecasting.

BACKGROUND: Optimizing insulin dosing and predicting future glucose levels for people with type 1 diabetes is challenging due to the dynamic nature of...

Early Deployment of an Integrated Digital Platform (shamiriOS) for Scalable Youth Mental Health Service Delivery in Kenya: Development and Usability Study.

BACKGROUND: Scaling youth mental health services in low-resource settings requires digital infrastructure that supports not just clinical delivery but...

Advances in the diagnosis and detection of chronic kidney disease.

Chronic kidney disease affects 788-844 million adults worldwide and is projected to become the fifth leading cause of death by 2040. Global burden est...

Clinical outcomes, patient satisfaction and operational efficiency of AI-powered chatbots in medicine and healthcare: protocol for an AI-aided scoping review.

INTRODUCTION: Artificial intelligence (AI)-powered chatbots are increasingly integrated into healthcare to support administrative processes, health ed...

Explainable machine-learning model for coronary artery disease diagnosis in non-AMI patients: integrating inflammatory and traditional risk factors.

OBJECTIVE: To develop and interpret an explainable machine learning (ML) model integrating inflammatory biomarkers and traditional risk factors for th...

Diabetes Management Through Glucose Dynamics Analysis Network: A Novel Approach for Accurate Blood Glucose Level Forecasting.

BACKGROUND: Accurate real-time prediction of blood glucose (BG) levels is essential for improving insulin-dosing decision support systems, including c...

Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications.

The integration of multiomics technologies with artificial intelligence (AI) has become a transformative force in modern precision medicine, particula...

Happiness and hypertension prevalence: A global analysis.

The prevalence of hypertension around the world is high while hypertension control is relatively low. The objective of this study is to investigate th...

Reliability assessment of key equipment for coal gasification using artificial intelligence technology.

To address the gap in quantitatively modeling dynamic failure mechanisms for Gasifier lock bucket valve system reliability, this study proposes an inn...

Cross-study interpretable machine learning for prediction of NH3 and H2S emissions and identification of operational windows during composting.

Accurate prediction of NH3 and H2S emissions is essential for odor-risk control and process management during composting, but remains challenging beca...

Advances in body composition imaging for diagnosis and monitoring: From radiologic assessment to precision medicine.

Body composition (BC) is a key determinant of metabolic risk, treatment tolerance, and long-term outcomes. The distribution of skeletal muscle, viscer...

Associations of serum per- and polyfluoroalkyl substances (PFAS) mixture with the risk of chronic diseases: evidence from single‑ , multi-pollutant, and machine learning models.

Per- and polyfluoroalkyl substances (PFAS) are persistent chemicals commonly found in consumer products. Despite growing concern over their health eff...

Multimodal Ultrasound Integration Pathways and Paradigm Innovations in Precision Diagnosis and Treatment of Thyroid Cancer.

RATIONALE AND OBJECTIVES: Thyroid cancer, the fastest-growing endocrine malignancy, is shifting from morphological evaluation to molecular-functional ...

Evaluating machine learning tools to assist title and abstract screening in systematic literature reviews: a report based on the EULAR RA Management Recommendations Task Force.

OBJECTIVES: Systematic literature reviews (SLRs) provide the scientific basis for European Alliance of Associations for Rheumatology (EULAR) task forc...

Integrating generalized linear mixed models and XGBoost for safety performance function development on urban arterials.

Urban arterial corridors present elevated crash risk due to high traffic volumes, complex geometries, and interactions among diverse road users. This ...

Predicting knowledge of cervical cancer screening among reproductive-age women in sub-saharan africa using machine learning algorithms.

Sub-Saharan Africa faces twice the incidence and up to fifteen times the fatality rate of cervical cancer compared to developed countries. Screening c...

Uncertainty-aware spatio-temporal contrastive graph neural networks for cyber financial fraud detection and risk management.

Financial fraud detection requires screening massive transaction networks where evolving topologies, extreme label sparsity, and asymmetric misclassif...