Prescriptions

Physics-based machine learning for enhanced drug formulation development.

Formulation design is constrained by scarce and heterogeneous experimental data, which limits the accuracy and generalizability of conventional AI models. Here, we introduce a physics-based machine learning (PBML) approach that integrates physics-based modeling with data-driven learning to improve drug formulation development. Our approach predicts key formulation properties across two different s...

Digital twins to accelerate target identification and drug development for immune-mediated disorders.

Immune-related/mediated disorders (IDs) comprise a very diverse group of diseases affecting millions worldwide. The complexity and heterogeneity of IDs, coupled with individual variability in immune system responses, create multiple challenges for developing targeted therapies. These challenges often result in prolonged diagnostic timelines, higher treatment costs, and frequent failures in clinica...

TCR recognition of peptide-HLA class II complexes in autoimmunity: Structural insights for therapeutic targeting.

Autoimmune diseases encompass a broad spectrum of disorders in which self-reactive T and B cells breach immune tolerance and drive chronic tissue infl...

Assessing the accuracy of 4 generative AI tools to guide dosing of long-acting injectable antipsychotics.

PURPOSE: The purpose of this study was to review the accuracy of 4 different artificial intelligence (AI) tools in providing dosing recommendations fo...

Deep learning model for automated identification of ventrally positioned right hepatic artery in contrast-enhanced computed tomography of pediatric congenital biliary dilatation: development and clinical application.

BACKGROUND: Preoperative identification of a ventrally positioned right hepatic artery (vRHA) is critical in congenital biliary dilatation (CBD), as u...

Predicting drug-target interactions and binding affinity using an optimized deep learning approach.

The accurate prediction of Drug-Target Interactions (DTIs) and Drug-Target Affinity (DTA) is crucial for reducing experimental costs and time, thereby...

Digital twin technologies for supporting self-care in adults with diet-related chronic conditions: a systematic review.

BACKGROUND: Diet-related chronic conditions are major contributors to global morbidity and mortality. Effective management of these conditions require...

Machine learning-based prediction of drug lactation risk: Bridging molecular features and breastfeeding safety.

BACKGROUND: Breastfeeding medication safety assessment presents critical challenges due to limited clinical evidence and ethical constraints on lactat...

Teaching activity design and psychological practice of music majors under a convolutional neural network and transformer module.

The purpose of this study is to explore a new mode of teaching activity design and psychological practice for music majors supported by the concept of...

High diagnostic accuracy of a resnet50-based deep learning model for osteochondral lesions of the talus on magnetic resonance imaging.

OBJECTIVES: This study aims to evaluate the diagnostic performance of a ResNet50-based convolutional neural network (CNN) in detecting osteochondral l...

Strengthening Pharmacovigilance in Latin America: Multidisciplinary Insights from Two ISoP Latin American Chapter Symposia.

Pharmacovigilance in Latin America has witnessed notable progress in recent years, marked by advancements in regulatory frameworks, regional cooperati...

BGC-LiteNet: BeiDou grid code embedded lightweight neural architecture for real-time UAV fire detection and localization.

Real-time fire detection and precise geographic localization using unmanned aerial vehicles (UAVs) are critical for early forest-fire warning. However...

Transfer learning is the electrocardiogram reconstruction capstone.

Electrocardiogram (ECG) reconstruction from reduced-lead configurations is essential for improving patient comfort and enabling wearable cardiac monit...

High-precision apple classification and traceability based on enhanced CBAM for near-infrared spectroscopy.

Apple origin traceability is crucial in modern agriculture and the food industry for ensuring food safety, protecting consumer rights, and enhancing b...

Machine learning-augmented finite element modeling for transient hemodynamics in human arteries.

This work introduces a machine learning enhanced finite element model for the study of blood flow in a stretching artery with the presence of sulfonat...

AI-assisted tumor board decision-making in pancreatic oncology.

BACKGROUND: Pancreatic cancer requires nuanced, multidisciplinary treatment planning typically conducted within tumor boards. While Large Language Mod...

A Therapeutic Conversational Agent (Solace) for Management of Chronic Pain: Acceptability and Usability Study.

BACKGROUND: Chronic pain is a critical cause of personal suffering and societal concern. However, treatment options remain inadequate, and access to e...

Artificial intelligence models for osteoporosis risk prediction in postmenopausal women with CKD: A cross-sectional analysis of NHANES 2005 to 2018.

Artificial intelligence (AI) can transform osteoporosis (OP) screening, but its application in high-risk, complex populations like postmenopausal wome...

Ontology-Based Medication Named Entity Recognition Using Pretrained Transformer Models From a Thai Hospital: Model Fine-Tuning and Validation Study.

BACKGROUND: Extracting accurate medication information from Thai hospital records presents challenges due to the narrative style of medical notes, whi...

Deep learning for Evaluation and Prediction of TecHnical Skills in robotic-assisted vaginal cuff closure (DEPTHS) study.

BACKGROUND: To support surgical education, there has been an increasing focus on integrating surgical data, including surgical motion, activity and pr...