Xiamen University

About: Xiamen University is a education organization based out in Amoy, Fujian, China. It is known for research contribution in the topics: Catalysis & Population. The organization has 50472 authors who have published 54480 publications receiving 1058239 citations. The organization is also known as: Amoy University & Xiàmén Dàxué.

Tumor Vasculature Targeted Photodynamic Therapy for Enhanced Delivery of Nanoparticles

Abstract: Delivery of nanoparticle drugs to tumors relies heavily on the enhanced permeability and retention (EPR) effect. While many consider the effect to be equally effective on all tumors, it varies drastically among the tumors’ origins, stages, and organs, owing much to differences in vessel leakiness. Suboptimal EPR effect represents a major problem in the translation of nanomedicine to the clinic. In the present study, we introduce a photodynamic therapy (PDT)-based EPR enhancement technology. The method uses RGD-modified ferritin (RFRT) as “smart” carriers that site-specifically deliver 1O2 to the tumor endothelium. The photodynamic stimulus can cause permeabilized tumor vessels that facilitate extravasation of nanoparticles at the sites. The method has proven to be safe, selective, and effective. Increased tumor uptake was observed with a wide range of nanoparticles by as much as 20.08-fold. It is expected that the methodology can find wide applications in the area of nanomedicine.

Dynamic Hypergraph Neural Networks.

Abstract: In recent years, graph/hypergraph-based deep learning methods have attracted much attention from researchers. These deep learning methods take graph/hypergraph structure as prior knowledge in the model. However, hidden and important relations are not directly represented in the inherent structure. To tackle this issue, we propose a dynamic hypergraph neural networks framework (DHGNN), which is composed of the stacked layers of two modules: dynamic hypergraph construction (DHG) and hypergrpah convolution (HGC). Considering initially constructed hypergraph is probably not a suitable representation for data, the DHG module dynamically updates hypergraph structure on each layer. Then hypergraph convolution is introduced to encode high-order data relations in a hypergraph structure. The HGC module includes two phases: vertex convolution and hyperedge convolution, which are designed to aggregate feature among vertices and hyperedges, respectively. We have evaluated our method on standard datasets, the Cora citation network and Microblog dataset. Our method outperforms state-of-the-art methods. More experiments are conducted to demonstrate the effectiveness and robustness of our method to diverse data distributions.

A Gigantic Molecular Wheel of {Gd140}: A New Member of the Molecular Wheel Family

Abstract: Nanoscale inorganic wheel-shaped structures are one of the most striking types of molecular aggregations. Here, we report the synthesis of a gigantic lanthanide wheel cluster containing 140 Gd3+ atoms. As the largest lanthanide cluster reported thus far, {Gd140} features an attractive wheel-like structure with 10-fold symmetry. The nanoscopic molecular wheel possesses the largest diameter of 6.0 nm and displays high stability in solution, which allows direct visualization by scanning transmission electron microscopy. The newly discovered lanthanide {Gd140} cluster represents a new member of the molecular wheel family.