Results 121 to 130 of about 928,791 (254)
Student Learning Outcome Prediction via Sheaflet-Based Graph Learning and LLM
Accurately modeling the interactions between students and learning content is a central challenge in achieving personalized and adaptive learning in online education.
Dongmei Zhang +3 more
doaj +1 more source
Subjective Cognitive Concerns and Cognitive Trajectories in Parkinson's Disease: Biomarker Impact
ABSTRACT Objective To examine the relationship of subjective cognitive concerns (SCC) with biomarkers of Alzheimer's disease (AD), neurodegeneration, and Parkinson's disease (PD) and domain‐specific cognitive trajectories among cognitively unimpaired persons with de novo PD. Method Cognitively unimpaired participants with SCC (n = 294) and without SCC (
Francesca V. Lopez +5 more
wiley +1 more source
Graph-based prediction of Protein-protein interactions with attributed signed graph embedding. [PDF]
Yang F, Fan K, Song D, Lin H.
europepmc +1 more source
Investigation of Complement Activation and Cytokine Signatures in AQP4+ NMOSD, MOGAD, and MS
ABSTRACT Objective Aquaporin‐4‐IgG seropositive neuromyelitis optica spectrum disorder (AQP4+ NMOSD), myelin oligodendrocyte glycoprotein antibody‐associated disease (MOGAD), and multiple sclerosis (MS) are pathophysiologically distinct neuroinflammatory disorders. Complement activation is a major mechanism of injury in AQP4+ NMOSD, whereas its role in
Angeliki G. Filippatou +23 more
wiley +1 more source
Stage‐Specific Patterns of Imaging and Non‐Motor Biomarkers in Spinocerebellar Ataxia Type 3
ABSTRACT Objective Advancements in disease‐modifying therapies for spinocerebellar ataxia Type 3 (SCA3) underscore the need for robust biomarkers to monitor disease progression and evaluate treatment efficacy. Although motor phenotypes have been well characterized, less is known about non‐motor and imaging‐based features across different disease stages.
Chun Li +17 more
wiley +1 more source
Adversarially Robust Signed Graph Contrastive Learning from Balance Augmentation [PDF]
Signed graphs consist of edges and signs, which can be separated into structural information and balance-related information, respectively. Existing signed graph neural networks (SGNNs) typically rely on balance-related information to generate embeddings.
Zhou, Jialong +3 more
core +1 more source
Objective Although the definition of a gout flare is well established, the state of gout flare resolution has not yet been defined. This study aimed to explore patients’ experiences and perceptions of gout flare resolution. Methods Semistructured interviews were conducted with 24 people with gout, guided by open‐ended questions exploring their ...
Sarah Stewart +5 more
wiley +1 more source
Objective We describe the frequency, risk factors, severity, and management of actionable and serious adverse events (AAE and SAE) in children with newly diagnosed Juvenile Idiopathic Arthritis (JIA) in Canada. Methods We enrolled patients within 3 months of JIA diagnosis in the Canadian Alliance of Pediatric Rheumatology Investigators (CAPRI) Registry,
Bashayer Alnuaimi +10 more
wiley +1 more source
Generative AI in clinical practice: how should rheumatologists evaluate and monitor emerging tools?
Generative artificial intelligence (AI) is increasingly being used in rheumatology practice, to assist with tasks such as documentation, record summarization, and clinical decision support. Rheumatologists need a practical approach to determining what evidence and oversight each application requires.
Koushan Kouranloo +2 more
wiley +1 more source
A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions
This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice +2 more
wiley +1 more source

