Artificial intelligence (AI) is transforming patient care, but it also raises ethical questions, such as bias and transparency. While a range of well-established frameworks exist to guide responsible AI practice, most were designed for academic or re... read more
BACKGROUND: The modified Rankin scale (mRS) is an important metric in stroke research, often used as a primary outcome in clinical trials and observational studies. The mRS can be assessed retrospectively from electronic health records (EHRs), but th... read more
We investigate the application of Federated Learning (FL) across heterogeneous, non-independent and identically distributed (non-IID) sleep data. We evaluate three algorithms-Federated Stochastic Gradient Descent (FedSGD), Federated Averaging (FedAvg... read more
Predictive tools are lacking for pain-related outcomes after endometriosis surgery. The objective of this study was to develop and validate a machine learning-based clinical model to predict poor pain-related quality of life after endometriosis surge... read more
The unprecedented extension of the human lifespan necessitates a parallel evolution in how we quantify the quality of aging and its socioeconomic impact. Traditional metrics focusing on Healthspan (years free of disease) overlook the gradual erosion ... read more
INTRODUCTION: In recent years, the evolving discourse surrounding Advanced Practice (AP) in radiography has highlighted potential need for a more harmonized educational approach across Europe. However, variations in healthcare systems, policies and c... read more
The rapid evolution of computational biology has transformed vaccine design from a largely empirical process into a rational, data-driven discipline. At the center of this transformation lies immunoinformatics, which integrates immunology, molecular ... read more
INTRODUCTION: Early identification of vertical skeletal discrepancies is essential for orthodontic diagnosis and treatment planning. Since panoramic radiographs (OPGs) are more routinely obtained than lateral cephalometric radiographs (LCR), this stu... read more
As an important neural network model, the continuous attractor neural network (CANN) demonstrates unique advantages in simulating and explaining the representation and storage mechanisms of continuous variables (such as position, direction, etc.) in ... read more
Current opinion in structural biology
Feb 25, 2026
Graph neural networks (GNNs) are emerging as powerful tools for advancing molecular dynamics (MD) simulations, providing data-driven frameworks to complement traditional physics-based approaches. By representing atoms and their interactions as graphs... read more
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