Prescriptions

DisenKGE-DDI: A Knowledge Graph Embedding Framework Based on Disentangled Graph Attention Networks for Drug-Drug Interaction Prediction.

Combination therapy is an essential strategy for treating complex diseases. However, unintended drug-drug interactions (DDIs) can compromise therapeutic efficacy or even cause severe adverse reactions, posing significant challenges to clinical safety and drug development. Accurate DDI prediction is therefore crucial for ensuring drug safety and guiding rational drug use. Although deep learning-bas...

Combining cutting edge computational and experimental methods for targeting kras mutations in non-small cell lung cancer.

INTRODUCTION: Historically, KRAS mutations have been notoriously difficult to target despite their status as the most commonly mutated oncogene in the RAS gene family. However, pioneering work by Shokat and colleagues has led to the discovery of KRAS G12C-GDP mutant specific inhibitors, with two such inhibitors adagrasib and sotorasib now FDA approved for treatment of non-small cell lung cancer (N...

The role of social media influencer promotion in initiation and use of cigar, little cigar, cigarillo, and cannabis products among US youth and young adults: Using exogenous marketing exposure measures.

BACKGROUND: Influencer marketing on social media is a powerful, yet under-examined, driver of youth and young adult substance use. Yet, reliably ident...

Determinants of the Perceived Ageism Index using theory-driven and data-driven approaches: evidence from the Türkiye Elderly Profile Survey 2023.

OBJECTIVES: This study examines the multidimensional determinants of perceived ageism using a combined theory- and data-driven framework based on nati...

An interpretable stacking model for early warning of mastitis in dairy cows.

Early warning of mastitis in dairy cows is important for maintaining animal health and improving dairy production. However, existing early warning sys...

ASCENT: an active transfer learning paradigm for efficient drug-target interaction prediction.

Deep learning has made significant progress in drug-target interaction (DTI) prediction. However, most existing approaches are developed using fixed d...

Proteomics for precision nutrition: current evidence and future directions.

PURPOSE OF REVIEW: This review critically evaluates proteomics research applied to clinical nutrition and metabolism published between mid-2024 and ea...

Identifying neuroimaging-based biomarkers for obsessive-compulsive disorder: A resting-state fMRI and machine learning study in patients and unaffected first-degree relatives.

BACKGROUND: Identifying whether brain alterations in obsessive-compulsive disorder (OCD) represent the illness itself or an underlying genetic vulnera...

A hybrid dual-stream CNN framework with dynamic data augmentation and improved Manta Ray Foraging Optimization for robust glaucoma detection.

A progressive neurological condition, glaucoma is one of the main causes of irreversible blindness in the globe. Early detection is crucial to prevent...

Deep learning based instance segmentation of mandarin fruit slices for precision assessment and morphological quantification.

Accurate instance segmentation of mandarin fruit slices is essential for quantifying segment morphology and central core structure, which are key trai...

Multidimensional urban morphology and carbon intensity: Nonlinear drivers in dense cities.

Urban morphology (UM) significantly influences carbon emissions (CEs), yet fine-scale mechanisms involving three-dimensional (3D) structure and natura...

Ultra-Robust and Hyperelastic Triboelectric Webbing for Self-Powered Rehabilitation Sensing with Invisible and Embedded Design.

Driven by the rapid evolution of flexible electronics, rehabilitation healthcare is shifting toward devices that seamlessly interface with human body....

Design and first experimental results of an edgeless PET prototype for breast imaging: DeepBreast.

BACKGROUND: Breast cancer causes the largest number of cancer-related deaths among women worldwide. With the aim of improving Positron Emission Tomogr...

Reimagining human-centric drug development with new approach methodologies.

Despite unprecedented technological progress, most drug candidates continue to fail in clinical trials, reflecting a persistent gap between preclinica...

Nursing Perceptions of the Intended Use of Artificial Intelligence to Prevent Medication Errors: A Qualitative Descriptive Study.

AIM: To explore the perceptions of nursing professionals in high-demand healthcare services regarding the adoption of AI-based support systems for the...

AI-enhanced approaches for personalized cardiac treatment: insights from ECG data.

The analysis of drug-induced alterations in the electrocardiogram (ECG) is essential in measuring cardiac safety, but manual analysis is not always ac...

Personalizing In-Vehicle warnings: A causal machine learning approach to optimizing workload and risk perception.

The effectiveness of in-vehicle Connected Information (CI) is often limited by uniform warning strategies that overlook the interaction among warning ...

Speaking Patient's Language: Assessment of Readability and Fidelity of Artificial Intelligence-Optimized Consent Forms.

INTRODUCTION: Informed consent requires patients to fully comprehend the risks, benefits, and alternatives of an intervention. The American Medical As...

HP-DIL: Deep heterogeneity profiling with graph-informed disentangled interaction learning for MRI-based liver fibrosis staging.

Liver fibrosis staging (LFS) informs treatment decisions and prognostic assessment in liver disease. Multiparametric MRI enables non-invasive, quantit...

Fact-Checking Large Language Model Responses to a Health Care Prompt: Comparative Study.

BACKGROUND: Large language models use machine learning to produce natural language. These models have a range of potential applications in health care...