Prescriptions

A Scoping Review of Digital Behavioral Approaches to Improve Medication Adherence in Type 2 Diabetes.

PURPOSE OF REVIEW: Consistent medication adherence is fundamental for achieving favorable health outcomes in diabetes care; however, attaining optimal adherence remains a challenge. This review summarized recent evidence on digital behavioral interventions designed to improve medication adherence in type 2 diabetes (T2D) and identified key components commonly incorporated in these interventions. R...

Deep learning-based multi-class misbehavior classification in fog-enabled vehicular ad hoc networks.

Traffic collisions and congestion represent significant challenges within intelligent transportation systems (ITS). Consequently, a vehicular ad-hoc network (VANET) has been established. Numerous architectures have been incorporated into VANETs to manage the extensive data generated by vehicles. Collaboration with fog computing is vital, particularly for applications requiring real-time processing...

Emerging Trends in the Integration of Nanotechnology and 3D Printing for Next-Generation Drug Delivery Systems.

OBJECTIVE: To provide a comprehensive overview of three-dimensional (3D) printing as an emerging manufacturing approach in pharmaceutical and biomedic...

Machine Learning-based Early Detection of Intraoperative Anaphylaxis Among Patients with Hypotension Using Real-World Physiological Time Series Data.

BACKGROUND: Intraoperative anaphylaxis remains a rare yet fatal condition that has been a challenge due to its unpredictability. The unique characteri...

Real-Time Nanoscale Multiparametric Biophysical Phenotyping of Single Cells by Surface Plasmon Resonance Microscopy.

How cell physical state relates to function and stimulus response remains difficult to resolve because most methods measure only one biophysical prope...

MAIDTA: an interpretable attention-based multi-modal model for drug-target affinity prediction.

High-performance computer-aided drug design is a promising field, in which drug-target affinity (DTA) prediction serves as a core step to reduce R&D c...

Beyond the Single Pose: Perturbation-Resolved Mapping of Host-CO2 Interaction Landscapes with Interpretable Machine Learning.

Screening carbon-dioxide capture media requires models that examine diverse molecular environments while retaining interpretable host-CO2 descriptions...

Progress in nanoparticle-mediated photothermal therapy for Acne vulgaris: Recent clinical advances and potential AI integration.

Acne vulgaris is a chronic inflammatory skin condition that affects everyone at least once in their life. Notably, acne vulgaris predominantly negativ...

Explainability in Forecasting Hospital Readmissions with Machine Learning.

Predictive modeling of healthcare needs to strike a balance between performance and interpretability-especially when used to guide decisions regarding...

Deep Learning Method Based on Multimodal Medical Image Fusion for Predicting Acute Vertebral Fracture.

RATIONALE AND OBJECTIVES: The study aimed to develop and validate a deep learning (DL) model based on X-ray and computed tomography (CT) to diagnose a...

Status, challenges, and future directions of machine learning in the management of epilepsy: a systematic review and meta-analysis.

BACKGROUND: Despite advances in epilepsy treatment options, selecting the appropriate therapy for an individual with epilepsy is a process of trial an...

A Clinically Grounded Review of Medical Image Classification: Quantitative Insights into CNNs, Vision Transformers, and Hybrid CNN-ViT Models.

Medical image classification has advanced substantially with convolutional neural networks (CNNs), Vision Transformers (ViTs), and hybrid CNN-ViT arch...

Internal validation of THINKERS-PA for outcome prediction after pituitary adenoma radiosurgery.

BACKGROUND: Outcome prediction after Gamma Knife radiosurgery (GKRS) for pituitary adenomas remains guided by tumor anatomy, functional status, prior ...

Advances in New Approach Methodologies at FDA's Division of Applied Regulatory Science.

New Approach Methodologies (NAMs) represent a paradigm shift in drug development and regulatory science, offering human-relevant alternatives to tradi...

Implementation of an opioid use disorder (OUD) machine-learning phenotype in real-time for the ADAPT clinical trial.

We developed and deploy a real‑time, electronic health record‑integrated machine learning phenotype to identify emergency department patients with opi...

Risk assessment method for power communication network based on LTA-EN.

As the nerve center of power systems, power communication networks require risk assessment methods with lightweight architecture and high prediction a...

Multimodal Graph Neural Networks and Evolutionary Knowledge Fusion for Secure and Explainable Governance in Intelligent IoT Interactive Media Systems.

This research proposes a secure, explainable, and context-aware governance framework for blockchain-based digital media contracts in multimodal artifi...

Digital Twin-Enabled Toxicology: Current Applications, Opportunities, and Future Directions.

Toxicology has long depended on animal studies and static computational models to evaluate the safety of chemical substances and pharmaceutical compou...

Insights into the use of artificial intelligence in the drug discovery and delivery of nanosystems for the treatment of infectious diseases and neoplasms.

Artificial Intelligence (AI) has become a fundamental driver of scientific progress, particularly in disease diagnosis, drug development, and drug del...

Two‑tier prediction framework with OR‑voting for real‑time freeway secondary-crash prediction: Balancing sensitivity and specificity.

OBJECTIVES: Secondary crashes on freeways pose significant safety risks and are often preventable with timely intervention. This study aims to develop...