Sparse graphs-based dynamic attention networks.

In previous research, the prevailing assumption was that Graph Neural Networks (GNNs) precisely depicted the interconnections among nodes within the graph's architecture. Nonetheless, real-world graph datasets are often rife with noise, elements that can disseminate through the network and ultimately affect the outcome of the downstream tasks. Facing the complex fabric of real-world graphs and the myriad potential disturbances, we introduce the Sparse Graph Dynamic Attention Networks (SDGAT) in this research.

Effect of fine particulate matter exposure on gestational diabetes mellitus risk: a retrospective cohort study.

Background: The literature on the association between fine particulate matter (PM2.5) exposure and gestational diabetes mellitus (GDM) risk has focused mainly on exposure during the first and second trimesters, and the research results are inconsistent. Therefore, this study aimed to investigate the associations between PM2.

Umbilical cord blood cell characteristics in very preterm neonates for autologous cell therapy of preterm-associated complications.

Background: There are emerging clinical evidence for umbilical cord blood mononuclear cells (UCBMNCs) intervention to improve preterm complications. The first critical step in cell therapy is to obtain high-quality cells. This retrospective study aimed to investigate the quantity and quality of UCBMNCs from very preterm infants (VPIs) for the purpose of autologous cell therapy in prevention and treatment of preterm complications.