Prescriptions

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 patients with focal epilepsy. We use a one-dimensional Variational Autoencoder (VAE) for feature extraction of single-channel temporal series and a linear classifier for segment classification. METHODS: We trained the network using data from 37 patients c...

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 autoimmune disorders. Despite their widespread use, optimizing drug combinations remains challenging because of complex pharmacokinetic and pharmacodynamic interactions and substantial inter-patient variability. Phenotypic Response Surfaces (PRS) have emerged...

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...

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...

Analytic Misjudgment of Drug Safety Evidence and Causality: From the Prosecutor's Fallacy and Simpson's Paradox to Artificial Intelligence.

Drug safety assessment, particularly in the post-marketing setting, is especially vulnerable to analytic misjudgment because it relies on heterogeneou...

Machine learning prediction of childhood nephrotic syndrome outcomes.

BACKGROUND: Children with steroid-resistant, frequently relapsing, and steroid-dependent nephrotic syndrome experience high disease and treatment-rela...

Transcending Structural Dependencies: A Tunable Mass Spectrometry-Driven Machine Learning Framework for Genotoxicity Prediction.

The escalating global challenge of genotoxic compounds (GCs) in environmental, pharmaceutical, and food contexts necessitates analytical approaches th...

Patient Cognitive Bias in Large Language Model-Supported Health Consultations: Simulation-Based Comparative Study.

BACKGROUND: Large language models (LLMs) are increasingly used by patients for health information and preliminary medical advice. In patient-facing co...

Oral and Maxillofacial Surgery research hotspots and emerging trends in 2025: A targeted bibliometric analysis.

OBJECTIVES: To perform a targeted bibliometric analysis of the Oral and Maxillofacial Surgery (OMFS) literature from 2025 to map its current intellect...

Development and prospective evaluation of a real-time deep learning model for inpatient hypoglycemia prediction.

Inpatient hypoglycemia is associated with increased morbidity, mortality, length of stay, and healthcare costs, yet current management remains reactiv...

Transforming hemodialysis care: a tripartite collaboration model among medical staff, AI agents, and robots.

Hemodialysis demand is rising as populations age and the chronic kidney disease burden increases, yet dialysis units face persistent workforce constra...

Peripheral and central immune features in insomnia: Integrative bulk and single-cell transcriptomic analyses reveal core hub genes and candidate compounds.

Insomnia is closely associated with immune dysregulation, yet the overall pattern of peripheral-central immune disequilibrium and its underlying molec...

Identifying the relationship between exosome genes and breast cancer risk using bioinformatics and machine learning methods.

BACKGROUND: Breast cancer (BC) is the most prevalent cancer among women globally, with a high mortality rate. The treatment and prevention of this dis...

Performance evaluation of deep learning based YOLOv5 and YOLOv8 models for real time breast cancer detection in mammographic images.

Breast cancer detection remains a significant challenge in medical diagnostics. Traditional diagnostic methods are time-consuming, unable to detect co...