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Ischemia-reperfusion injury is an important mechanism that results in pulmonary injury after liver transplantation. It is safe for portal hypertensive rats to tolerate 1 hour at the anhepatic phase. Pulmonary injury was the most severe within 12-24 hours after ischemia-reperfusion.

The aim of the present study was to investigate the characteristics and progression of intestinal injury at the anhepatic phase in portal hypertensive rats. A total of 120 healthy male Wistar rats were purchased, with 15 rats in the normal control group and 105 rats were assigned to establish a prehepatic portal hypertension model. The 105 model rats were further divided into seven treatment groups following ischemia-reperfusion. Meanwhile, portal vein pressure, the area of lower esophageal mucosal vein, endotoxin levels in portal vein blood and the level of malondialdehyde (MDA) and superoxide dismutase (SOD) were measured. Morphology changes of the intestine were observed using optical microscopy and transmission electron microscopy.

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This paper proposes a new graph convolutional neural architecture based on a depth-based representation of graph structure, called the depth-based subgraph convolutional neural networks (DS-CNNs), which integrates both the global topological and local connectivity structures within a graph. Our idea is to decompose a graph into a family of K-layer expansion subgraphs rooted at each vertex, and then a set of convolution filters are designed over these subgraphs to capture local connectivity structural information. Specifically, we commence by establishing a family of K-layer expansion subgraphs for each vertex of graph by mapping graph to tree procedures, which can provide global topological arrangement information contained within a graph. We then design a set of fixed-size convolution filters and integrate them with these subgraphs (depicted in Figure 1). The idea is to apply convolution filters sliding over the entire subgraphs of a vertex to extract the local features analogous to the standard convolution operation on grid data. In particular, the convolution operation captures the local structural information within the graph, and has the weight sharing property among different positions of subgraph; the pooling operation acts directly on the output of the preceding layer without any preprocessing scheme (e.g., clustering or other techniques). Experiments on three graph-structured datasets demonstrate that our model DS-CNNs are able to outperform six state-of-the-art methods at the task of node classification.