Prescriptions

Learning to Super-Resolve Face Images via Dual-Domain Multi-scale Feature Interaction.

Face Super-Resolution (FSR), aiming to improve the quality of Low-Resolution (LR) facial images, has been greatly propelled by the deep learning techniques. However, existing approaches, whether based on Convolutional Neural Networks (CNNs) or Transformers, are either inherently damaging facial structures limited by their architectures or failing to capture essential multi-scale textures due to th...

Web-Based Gamified Auditory-Cognitive Dual-Task Training for Older Adults With Age-Related Hearing Loss: Pilot Randomized Controlled Trial.

BACKGROUND: Everyday listening ability is essential for individual health and well-being. Age-related hearing loss (ARHL) is associated with reduced communication engagement, social isolation, loneliness, cognitive decline, and increased dementia risk. Interventions that simultaneously target auditory processing and cognitive function, particularly within engaging and ecologically valid contexts, ...

Application of Language Models for the Analysis of Adverse Drug Events in Pharmaceutical Research and Development: Scoping Review.

BACKGROUND: Adverse drug events (ADEs) remain a critical safety issue in pharmaceutical research and development (Pharma R&D), necessitating robust me...

Social connectedness as a pathway towards climate resilience: exploring climate change scepticism with machine learning approaches and mediation regression models.

Climate resilience is vital for sustainable development, but vulnerable to doubt. Based on the data from the Chinese General Social Survey, this study...

drGT: Interpretable Drug Response Prediction with Attention-Guided Gene Attribution on a Drug-Cell-Gene Heterogeneous Graph.

BACKGROUND: For translational impact, both accurate drug response prediction and biological plausibility of predictive features are needed. We present...

Proteomics-Driven Strategies for Proximity-Inducing Drug Discovery.

In recent years, proximity-inducing drugs have emerged as a novel therapeutic modality that induces or stabilizes protein-protein interactions, especi...

Drug-induced multisystem syndromes: An overlooked challenge in pharmacovigilance.

BackgroundDrug-Induced Multisystem Syndromes (DIMS) represent a clinically significant yet under-recognized group of delayed immune-mediated adverse d...

Toward Precision Cardiac Rehabilitation: Current Limitations and Future Opportunities of Omics and Artificial Intelligence.

Cardiovascular disease involves complex molecular, cellular, and physiological derangements that present challenges for traditional diagnostic and the...

DrugDL: Dual-modal deep learning framework for multi-property drug prediction and targeted therapy discovery.

MOTIVATION: The accurate and robust representation of drug molecule features, the prediction of drug-target biomacromolecule interactions, and the det...

Variational autoencoder for interpretable seizure onset phases detection.

OBJECTIVE: In this study, we describe a deep learning framework for automated seizure annotation in stereo electroencephalography (SEEG) data of patie...

Phenotypic response surfaces in pharmacology: Mechanistic and translational perspectives on optimizing drug combination therapies.

Combination drug therapies are central to the treatment of diseases with multifactorial etiology, including cancer, infectious diseases, and autoimmun...

The Role of ChatGPT in Reducing Preoperative Anxiety in Patients Planning to Undergo Rhinoplasty.

OBJECTIVE: This study investigates the impact of an artificial intelligence (AI) chatbot (ChatGPT-3.5, OpenAI) on preoperative anxiety among patients ...

RPI-PLMGNN: Enhancing RNA-Protein Interaction Prediction with the Pretrained Large Language Models and Graph Neural Networks.

RNA-protein interactions play key roles in many life processes, and their study is significant for understanding gene regulation, revealing disease pa...

Principled XAI analysis of the deep learning-based landslide susceptibility prediction model.

Research on applying machine learning (ML) and deep learning (DL) techniques to landslide susceptibility analysis is widespread, with increasingly acc...

Hazard-portfolio patterns in US food recall severity reveal transferable pathogen signals and firm-specific compliance signals.

The U.S. FDA classifies food recalls into three severity tiers (Class I / II / III), a decision that drives public notification urgency and regulatory...

CME-KGDTI: integrating clustered mutations into knowledge graph embedding for drug-target interaction prediction.

BACKGROUND: Computational prediction of drug-target interaction (DTI) is critical for drug discovery and precision medicine. Herein, we constructed a ...

Radiomics Analysis for Predicting Medication-Related Osteonecrosis of the Jaw Using Computed Tomography.

OBJECTIVES: To determine whether pretreatment radiomics can predict medication-related osteonecrosis of the jaw (MRONJ). METHODS: Patients with mandib...

Leveraging administrative health records and machine learning for population-level prediction of preterm birth.

BACKGROUND: Preterm birth, defined as delivery before 37 weeks of gestation, is a major cause of neonatal morbidity and mortality and places a substan...

A Time-Frequency Decoupled Contrastive Learning Framework for Electroencephalography-Based Parkinson's Disease Diagnosis.

Parkinson's disease (PD), a prototypical neurodegenerative disorder, poses significant challenges for early diagnosis. Motivated by recent advances in...

DVNDTA: a dual virtual node based heterogeneous interaction model for accurate prediction of drug-target affinity.

Efficient prediction of drug-target affinity (DTA) is crucial for accelerating drug discovery. Recently, deep learning approaches leveraging 3D comple...