Thioredoxin interacting protein (Txnip) is one of the most abundantly up-regulated genes in response to hyperglycemia. The increased renal expression of Txnip was associated with type IV collagen accumulation in streptozotocin-induced diabetic mice. As the mechanism of action of high glucose is unknown, we undertook the investigation of the signaling pathway on the upregulation of Txnip expression induced by high glucose in rat mesangial cells. Rat mesangial cells were exposed to normal (5.5 mM) or high (25 mM) glucose at different time points. Txnip expression was determined using real-time RT-PCR and western-blotting at transcription and translation level, respectively. Intracellular reactive oxygen species (ROS) was detected by FACS Calibur flow cytometer using fluorescent probe (DCFH-DA).The treatment with high glucose resulted in an increase of Txnip mRNA from 4 h to 12 h and Txnip protein from 12 to 24 h in comparison with normal glucose condition. In addition, N-acetyl-cysteine (NAC) was found to decrease Txnip protein expression under high glucose condition. Furthermore, p38MAPK inhibitor SB203580 suppressed Txnip expression at transcription and protein level significantly to high glucose exposure. These results suggest that high glucose exposure improves Txnip mRNA and protein expression level by ROS/MEK/MAPK signaling pathway.

Graph neural networks provide a powerful toolkit for embedding real-world graphs into low-dimensional spaces according to specific tasks. Up to now, there have been several surveys on this topic. However, they usually lay emphasis on different angles so that the readers cannot see a panorama of the graph neural networks. This survey aims to overcome this limitation and provide a systematic and comprehensive review on the graph neural networks. First of all, we provide a novel taxonomy for the graph neural networks, and then refer to up to 327 relevant literatures to show the panorama of the graph neural networks. All of them are classified into the corresponding categories. In order to drive the graph neural networks into a new stage, we summarize four future research directions so as to overcome the challenges faced. It is expected that more and more scholars can understand and exploit the graph neural networks and use them in their research community.

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