Prescriptions

Real-world evidence in metabolic dysfunction-associated steatotic liver disease (MASLD): insights, challenges, and future directions.

Real-world evidence (RWE), derived from real-world data, offers key insights into metabolic dysfunction-associated steatotic liver disease (MASLD). RWE complements traditional randomised controlled trials by capturing large-scale, diverse patient populations and long-term outcomes. This review interrogates the role of RWE in understanding MASLD epidemiology, natural history, hepatic and extra-hepa...

FRET-SAM: SAM_Med2D-based automatic FRET two-hybrid analysis.

BACKGROUND AND OBJECTIVE: The fluorescence resonance energy transfer (FRET) two-hybrid assay enables quantification of the stoichiometry and binding affinity of protein interactions directly in living cells, but its broader application remains constrained by labor-intensive manual image analysis and high computational complexity. This study leverages deep learning to accurately extract FRET two-hy...

Mid-infrared spectroscopy combined with machine learning for rapid detection of caffeine in creatine supplements: Supporting food safety and product integrity.

Creatine (CR) and Caffeine (CAF) are popular and extensively consumed dietary supplements (DS) on a global scale. Although the concomitant use of thes...

Development of a deep learning classification model using a codeless platform for orthodontic extraction decision-making: Impact of image type on model performance.

OBJECTIVES: To evaluate the effect of image type on the performance of a deep learning classification model for decision-making in orthodontic treatme...

Exploiting colloidal drug aggregation for drug delivery: From promise to prediction using computational tools.

Colloidal drug aggregates are amorphous nanoparticles formed by the self-assembly of hydrophobic small molecule drugs. They can be leveraged as drug-r...

Computer-vision based recognition of cervical spine stabilization during trauma resuscitation.

BACKGROUND: Cervical spine (c-spine) injuries can lead to significant disability and mortality. Although stabilization is the primary management for s...

Prediction model for the risk of vitreous haemorrhage after vitrectomy combined with intraocular injection for the treatment of proliferative diabetic retinopathy.

OBJECTIVE: The purpose of this study was to develop a lightweight multimodal deep learning model for accurately predicting the risk of postoperative v...

Applying machine learning in predicting medication treatment outcomes for opioid use disorder.

BACKGROUND: While medication for opioid use disorder (MOUD) is effective for a significant proportion of patients, many return to using opioids during...

The engagement behaviors and treatment barriers for depressed patients in an online health community: a pre-/post-treatment comparison.

The treatment of depression involves numerous barriers, both before and after individuals seek professional care. Online health communities (OHCs) hav...

Toward Scalable Electromyography in Oncology: A Narrative Review of Normalization Challenges and Machine Learning Innovations.

OBJECTIVES: Electromyography (EMG) is increasingly applied in oncology to monitor neuromuscular impairment, treatment toxicities, and rehabilitation o...

Precision engineering of anti-atherosclerotic herbal nanomedicine: from machine learning-aided active components screening to optimized metal-phenolic network codelivery.

Atherosclerosis (AS), a chronic inflammatory disease linked to oxidative stress and lipid imbalance, remains a major cardiovascular threat. Traditiona...

Machine learning methods for predicting adverse drug events: A systematic review.

Predicting adverse drug events (ADEs) in outpatient settings is crucial for improving medication safety, identifying high-risk patients and reducing h...

Recent advances in numerical simulation of magnetically guided drug delivery systems and applications.

Magnetically guided drug delivery (MGDD) employs magnetic forces acting on magnetically responsive drug delivery systems (DDS) to direct therapeutic a...

Application of machine learning to create a prediction model for the need of aggressive nutritional intervention during head & neck cancer radiotherapy.

BACKGROUND: Curative-intent radiotherapy (RT) or chemoradiotherapy (CRT) for head and neck squamous cell carcinoma (HNSCC) frequently leads to mucosit...

Assessment of Artificial Intelligence (AI)-Powered Self-Care Recommendations for Management of Minor Ailments: A Comparative Analysis.

INTRODUCTION: Self-care and self-medication are increasingly viewed as helpful approaches to managing minor ailments; however, patients are often not ...

Graph-based deep learning approach for high-throughput protein-DNA interaction scoring.

Accurately quantifying protein-DNA interactions (PDIs) is critical for understanding biological processes and facilitating drug design. However, the i...

AI in ethnopharmacology, the pharmaceutical industry, and its applications.

Traditional knowledge from medicinal plants receives substantial analysis through the field of ethnopharmacology in its role for drug discovery. AI te...

Developing a Statistical Modeling-Based Machine Learning Approach for Capturing Drug Dosing Using a Proton Pump Inhibitor Case.

OBJECTIVE: To develop a statistical model to capture medication dosing for proton pump inhibitors (PPIs) using structured data from electronic health ...

Fully Automated Plane Prescription in Cardiac MRI: A Prospective Cohort Study.

BACKGROUND: Accurate plane positioning is important for high-quality cardiac MRI images but requires specialized training, limiting accessibility. PUR...

Use of Expected Utility to Evaluate Artificial Intelligence-Enabled Rule-out Devices for Mammography Screening.

BackgroundAn artificial intelligence (AI)-enabled rule-out device may autonomously remove patient images unlikely to have cancer from radiologist revi...