Prescriptions

Dihydroergotamine alleviates circadian rhythm disorders through predicted interaction with to CRY1.

Circadian rhythm disorders (CRDs) significantly affect human health, yet therapeutic options remain limited. This study employed a multi-scale virtual screening approach to identify novel drug candidates from FDA-approved small molecules for the treatment of CRDs. We screened 1429 FDA-approved small molecules against five key circadian rhythm-related proteins using molecular docking techniques. Th...

Evaluating human-AI interaction in optical diagnosis of early colorectal carcinoma through endoscopist responses to simulated artificial intelligence: a multinational online study.

Background Suboptimal human - artificial intelligence (AI) interaction is a potential roadblock for implementation of AI in clinical practice. We aimed to evaluate interaction between AI and endoscopists in optical diagnosis of colorectal carcinoma (CRC). Methods International endoscopists were invited to diagnose colorectal lesions online. After a pretest of 15 videos, the diagnosis could be adju...

The use of artificial intelligence (AI) in neuropsychiatric drug discovery: current challenges and future directions.

Artificial intelligence (AI) has emerged as a powerful tool for solving real world problems across a wide range of industries and is increasingly bein...

Utility of Artificial Intelligence Image Recognition Technology in Identifying Prescription Pills.

Machine learning for predicting emergency department visits in patients with type 2 diabetes: A real-world, multi-institutional study.

BACKGROUND: Patients with type 2 diabetes mellitus (T2DM) prone to acute diabetic complications are at high risk for emergency department (ED) visits,...

A Multitask Learning framework for E-commerce time series analytics: Operational risk, demand forecasting, and anomaly detection.

The study presents MT-PyraRisk, a multi-task learning framework that integrates pyramidal attention mechanisms for cross-border e-commerce risk predic...

Trigger tool methodologies in hospital settings: reappraising their role in pharmacovigilance and digital safety surveillance.

INTRODUCTION: Trigger tool methodologies have become important approaches for detecting adverse events in hospital care because they identify more har...

Context-Aware Multilevel Classification of Semantic Relations in Drug-Adverse Drug Reaction (ADR) Networks-Predicting Drug-Induced Liver Injury (DILI) as a Case Study.

Adverse drug reactions (ADRs) are a major concern for public health and patient safety and a burden in drug discovery, with drug-induced liver injury ...

Slope length thresholds and factor interactions drive nonlinear transitions in bare slope soil erosion.

Soil erosion is a major global environmental threat, and unraveling the complex, non-linear interactions among its drivers is crucial for effective mi...

Machine learning-based identification of medication adherence predictors in two Spanish primary care cohorts.

Non-adherence to medication represents an important global challenge that compromises patient outcomes and increases healthcare costs, particularly in...

An explainable AfroXLMR approach for multi-label emotion classification of Amharic social media text with dataset release.

Emotion detection from social media is crucial for understanding human emotions across languages. However, for low-resourced languages such as Amharic...

Assessment of the knowledge, attitudes, and practices of pharmacy students toward self-medication using artificial intelligence: a cross-sectional study.

BACKGROUND: The objectives of our study were to assess self-reported knowledge, attitudes and practices (KAP) among pharmacy students regarding self-m...

Teacher and peer support in AI-assisted Chinese EFL learning: A phenomenological study of emotional support and classroom atmosphere.

The impact of artificial intelligence (AI) on educational practices presents new opportunities and challenges for learners and teachers. Although AI t...

Multi-method statistical signal aggregation with machine learning for severity classification of neonatal adverse drug reactions.

Neonates represent one of the most pharmacologically vulnerable patient populations, yet they remain systematically underrepresented in clinical drug ...

Medication-Related Errors Among Nurses by Unit Adaptation Levels: Bayesian Network-Based Exploratory Study.

BACKGROUND: Medical errors occur more frequently in health care than in other industries due to challenges in patient safety education for nurses and ...

Are T1-weighted and T2-weighted volumetric pipelines interchangeable methodologies for investigating amyotrophic lateral sclerosis pathology in vivo?

PURPOSE: To test the hypothesis that T1-w and T2-w volumetric pipelines are not interchangeable, particularly regarding their differential sensitivity...

TACTIC: A transfer learning framework to predict drug interactions in emerging pathogens.

Machine learning (ML) is necessary to efficiently identify potent drug combinations within a large candidate space to combat drug resistance. However,...

An online cutaneous melanoma risk screen tool.

To develop and deploy a publicly accessible online risk estimation tool for cutaneous melanoma that prioritizes high recall to minimize missed diagnos...

Current Landscape and Future Perspectives of Diabetic Retinopathy Therapy: Pharmacological Targets, Precision Laser Technology, and Clinical Evidence.

Diabetic retinopathy (DR), a major cause of blindness worldwide, poses a substantial and escalating burden on public health. The limitations of curren...

Automated detection of lumbar disc herniation at L4-L5 and L5-S1 levels on sagittal MRI using a YOLO-based deep learning model.

OBJECTIVE: To conduct a preliminary single-center feasibility study of a YOLO-based deep-learning model for automated detection of lumbar disc herniat...