Prescriptions

Normalized QRS Duration by LV Dimension: Eliminating Sex-Specific Differences and Predicting Cardiac Resynchronization Therapy Outcomes.

BACKGROUND: Increasing evidence shows sex-specific differences in the efficacy of cardiac resynchronization therapy (CRT). Without a clear cardiac rationale, relying solely on sex to evaluate resynchronization outcomes is insufficient. OBJECTIVES: This study sought to assess whether QRS duration (QRSd) normalized to cardiac size (QRSd/left ventricular end-diastolic dimension [QRSd/LVEDD]) could un...

Multi-Objective Spray Drying Process Optimization via BBD-ANN-AGWO Framework: Case of Andrographolide Amorphous Solid Dispersions.

This study aims to establish a multi-objective spray drying process optimization framework, with andrographolide (ADG) amorphous solid dispersion serving as a model drug. The proposed framework integrates Box-Behnken design (BBD), artificial neural networks (ANN), and an adaptive grey wolf optimizer (AGWO). Molecular docking was employed to screen potential polymeric carriers, and in vitro dissolu...

Impact of GPT-4-Generated Discharge Letters on Patients' Medical Comprehension: Prospective Crossover Study.

BACKGROUND: Patients often struggle to understand standard hospital discharge letters, increasing the risk of medication errors and misunderstandings....

CPGPrompt: translating clinical guidelines into large language model-executable decision support.

OBJECTIVE: Clinical practice guidelines (CPGs) provide evidence-based recommendations for patient care; however, integrating them into artificial inte...

Retrieval-Augmented Generation for Medical Question Answering on a Heart Failure Dataset: Performance Analysis.

BACKGROUND: The integration of retrieval-augmented generation (RAG) systems into the domain of medical question-answering (QA) presents a significant ...

Explainable AI for mortality prediction: a comparative study using the MIMIC-III dataset.

OBJECTIVES: Predicting mortality is vital for tailoring treatments, improving care and reducing costs. Machine learning (ML) has shown strong potentia...

Hypnosis reshapes multilevel stress response and enhances executive performance in stressed medical students.

Stress and anxiety impair executive function and degrade performance, yet rapid and scalable interventions remain limited. This controlled study teste...

Explainable deep learning framework incorporating medical knowledge for insulin titration in diabetes.

BACKGROUND: Deep learning has shown promise in diabetes management but faces challenges in real-world application due to its "black-box" nature, chara...

AI-Enabled Smart Glasses for Active Aging: Scoping Review.

BACKGROUND: The daily use of digital technologies is transforming the day-to-day lives of older adults. Among these technologies, artificial intellige...

Ultrasound-Responsive Serotonylation and Hallmark Pathway Gene Signatures Reveal Tumor Microenvironment Vulnerabilities and Prognostic Subtypes in Colorectal Cancer.

BACKGROUND: Focused ultrasound, low-intensity focused ultrasound, and microbubble-enhanced sonoporation are examples of ultrasound-based cancer therap...

Antiviral drug discovery and development: challenges and future directions.

The coronavirus disease 2019 (COVID-19) pandemic has stimulated extensive endeavors toward the development of therapeutic interventions targeting seve...

Real-World Impact and Educational Effectiveness of an AI-Powered Medical History-Taking System: Retrospective Propensity Score-Matched Cohort Study.

BACKGROUND: Medical history-taking is a core clinical skill; yet, traditional teaching methods face challenges. We developed an artificial intelligenc...

Longitudinal multisource clinical model for early lung cancer risk stratification and screening.

OBJECTIVES: Lung cancer is the leading cause of cancer-related mortality worldwide, with poor prognosis largely due to late-stage diagnosis. Current s...

A switching-based deep learning framework for personalized and adaptive E-commerce recommendations.

In recommendation systems, it is bit challenging to address diverse user profiles, particularly when users demonstrate varied interaction histories an...

DVMMHGNN: A Dual View Multi-Modal Heterogeneous Graph Neural Network with Contrastive Learning for Microbe-Informed Drug Repurposing.

Drug repurposing (DR) offers an efficient and cost-effective strategy for pharmaceutical development by identifying new therapeutic applications for e...

Cardiorespiratory Markers of Type 2 Diabetes: Machine Learning-Based Analysis.

BACKGROUND: The global prevalence of type 2 diabetes mellitus (T2DM) poses significant challenges due to its association with increased cardiovascular...

CMCL-DDI: Pharmacophore-aware cross-view contrastive learning for drug-drug interaction prediction.

Accurate prediction of potential drug-drug interactions (DDIs) is vital for ensuring medication safety and efficacy. Existing graph-based methods typi...

Multi-relational knowledge graph for drug-drug interaction prediction via dual aggregation and collaborative optimization.

Identifying potential drug-drug interactions is crucial in clinical care and new drug development, as mutual interference between drugs can lead to ad...

Machine learning-assisted HS-GC-IMS for discrimination and traceability of baby bottles based on volatile fingerprints.

Ensuring the safety of food contact materials, particularly baby bottles, is crucial for infant health. In this study, a comprehensive analytical stra...

Beyond Partitioning: Using Force Field Science to Evaluate Electrostatics Models.

Accurate models for electrostatic and induction interactions are fundamental for computational molecular science, including drug discovery, studies of...