Prescriptions

When brand prominence mutes uniqueness: Deconstructing the prominence paradox with network science and machine learning.

This study investigates the "Prominence Paradox": how market prominence paradoxically suppresses brand uniqueness signals. Grounded in dual-process theory, we posit a "shift in the locus of judgment" as the core mechanism. Using a large-scale survey (N = 38,986), we employ computational network analysis to derive prominence and use a dual-analysis strategy (LMM and XGBoost with SHAP) to test the f...

Identification of tolerogenic dendritic cells-related prognostic biomarkers in Wilms tumor via machine learning integration.

Wilms tumor (WT) is the most common pediatric kidney cancer. Tolerogenic dendritic cells (TolDCs) promote tumor immune evasion in the tumor microenvironment. Therefore, establishing a TolDC-based prognostic model for WT holds significant clinical value. We analyzed WT-related genes from The Cancer Genome Atlas and TolDC-associated datasets to identify shared differentially expressed genes using Ve...

Improving Long-Term Adherence to Endocrine Therapy Among Breast Cancer Survivors: Development of a Multiscale Modeling and Intervention System.

BACKGROUND: Breast cancer is a significant public health burden. Despite its critical role in preventing the recurrence of breast cancer, rates of lon...

Capturing induced-fit effects: A geometry-aware and interpretable framework for robust drug-target affinity prediction.

Accurate drug-target affinity (DTA) prediction is pivotal for virtual screening, yet practical reliability is often limited by the static treatment of...

Predicting early and complete drug release from long-acting injectables using explainable machine learning.

Polymer-based long-acting injectables (LAIs) have transformed the treatment of chronic diseases by enabling controlled drug delivery, thus reducing do...

A multi-target drug design method based on target feature fusion.

BACKGROUND: Targeted drugs are medications designed to treat diseases by targeting specific sites on cancerous or diseased cells. Multi-target drugs c...

Evolution and forecasting of antiretroviral therapy utilisation in British Columbia: a population-level analysis of provincial drug treatment programme data, 2014-2024.

OBJECTIVE: To characterise temporal trends in antiretroviral therapy (ART) utilisation and forecast short-term changes in regimen distribution within ...

Deep learning-based breast cancer detection with customized ensemble attention.

Breast cancer remains one of the leading malignancies globally, and accurate diagnostic decisions at the early stages of the disease can significantly...

A Qualitative Exploration of Ethical Aspects of Using AI in Parkinson Disease: Patient Panel Study.

BACKGROUND: As Parkinson disease (PD) rates increase, so does interest in finding new technological solutions for PD management. Despite substantial e...

Patient Concerns Regarding Artificial Intelligence Applications in Health Care: Systematic Review and Meta-Synthesis Based on Social Ecological Theory.

BACKGROUND: The use of artificial intelligence (AI) in health care is growing quickly, but there is not enough research that looks at patient concerns...

Ensemble machine learning framework with SHAP and LIME for accurate early prediction of student success in online learning environments.

The development of digital learning environments has generated rich educational data capable of supporting early prediction of student outcomes. In th...

How host-guest interaction types shape value co-creation in urban tourism: The moderating role of AI technology stimuli.

This study examines how different types of host-guest interaction relate to tourists' value co-creation intention in urban tourism and whether artific...

Performance evaluation of large language models in real-world perinatal medication consultations: a cross-sectional study.

INTRODUCTION: Perinatal medication consultation is a core clinical pharmacy service that involves a complex benefit-risk assessment for both maternal ...

Opportunities for artificial intelligence and synthetic biology in designing living drug delivery systems.

Advances in artificial intelligence (AI) and synthetic biology are transforming biological research and biotechnology. These fields are for the first ...

Taste-active peptides, end-to-end: from generation and sensory validation to AI-driven discovery.

Taste-active peptides (TAPs) are food-protein-derived peptides that elicit or modulate gustatory sensations; presently gaining interest as natural uma...

LEP-AD: language embedding of proteins and attention to drugs predicts drug-target interactions.

INTRODUCTION: Predicting drug-target interactions remains a significant challenge in drug development and lead optimization. Recent advances have leve...

Artificial intelligence and computerized decision support in adult intensive care: A systematic review of randomized controlled trials.

BACKGROUND: Artificial intelligence (AI) and computerized clinical decision support systems (CDSS) are increasingly applied in intensive care, yet the...

DrugGPS: Attention-guided multimodal fusion for intelligent exploration of drug-target and drug-disease interactions.

BACKGROUND AND PURPOSE: Accurate prediction of drug-target interactions (DTIs) and drug-disease interactions (DDIs) are critical for accelerating the ...

Dual-Sabatier Optima: How Reaction Mechanism Determines Activity Volcano Map of Dual-Atom Catalysts for Oxygen Reduction Reaction.

Dual-atom catalysts (DACs) have demonstrated superior potential in the oxygen reduction reaction (ORR). However, the single-peak activity volcano deri...

Algometric assessment of sensitization and habituation as predictive markers for OnabotulinumtoxinA response in chronic migraine.

BACKGROUND: Chronic migraine is a debilitating disorder characterized by central sensitization and impaired habituation. Although OnabotulinumtoxinA (...