Ginseng, as a functional food, is widely used worldwide because of its multifarious benefits. Studies have verified that 25-hydroxyl-protopanaxatriol (T19) is a new ginsenoside from ginseng, which had an important inhibitory effect on α-glucosidase and protein tyrosine phosphatase 1B in vitro. This study aims to assess the regulation of T19 against glycolipid metabolism by insulin-resistant HepG2 cells and diabetes mice induced with high-fat diet combined with streptozotocin (STZ). T19 effectively lowered the levels of blood glucose and lipid, alleviated insulin resistance, and improved histological pathology of liver and pancreas. Further study demonstrated that regulation of AMP-activated protein kinase-and phosphoinositide-3-kinase-signaling pathways was involved in the potential mechanism of T19 efficiency. Simultaneously, high-throughput sequencing of 16S rDNA revealed that T19 remarkably ameliorated the high-fat diet/STZ-induced disorders of intestinal microbiota by decreasing the value of Firmicutes/ Bacteroidetes, and remarkably raised the relative abundance of the Lachnospiraceae family, which are the beneficial bacteria that can regulate glucose and lipid metabolism. The results may provide clues for further understanding the mechanism of T19 in regulating glycolipid metabolism, and may provide a scientific basis for ginseng as a potential dietary food to prevent metabolic diseases.

Type 2 diabetes mellitus (T2DM) is a type of metabolic illness based on relatively insufficient insulin secretion and insulin resistance (IR) as pathophysiological bases. Currently, it is the main type of diabetes. Hypoglycemic and hypolipidemic effects of licochalcone A (LicA) on high-fat diet and streptozocin-caused T2DM were studied. LicA can remarkably decline the IR index and blood glucose and serum lipid levels. Also, the treatment of LicA can improve the "three more and one less" phenomenon in T2DM mice, such as excessive drinking, eating, urine, and weight loss. In addition, LicA can improve oral glucose tolerance, pancreatic injury, and liver enlargement in T2DM mice. Network pharmacology analysis demonstrated that the observed pharmacological effects were mediated by regulating the insulin signal transduction pathway. Therefore, the PI3K/Akt-signaling pathway was selected for verification; it was demonstrated that LicA could improve the insulin-signaling pathway, protect islet cells, improve IR, reduce blood glucose levels, and alleviate lipid metabolism disorder. Its mechanism of influence may be closely related to LicA up-regulating the liver and pancreas IRS-2/PI3K/AKT-signaling pathway. Among them, the high-dose group of LicA had the best effect, which provided an idea for the use of LicA as a nutritional agent in the cure of T2DM.

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Heterogeneous graph convolutional networks have gained great popularity in tackling various network analytical tasks on heterogeneous network data, ranging from link prediction to node classification. However, most existing works ignore the relation heterogeneity with multiplex network between multi-typed nodes and different importance of relations in meta-paths for node embedding, which can hardly capture the heterogeneous structure signals across different relations. To tackle this challenge, this work proposes a Multiplex Heterogeneous Graph Convolutional Network (MHGCN) for heterogeneous network embedding. Our MHGCN can automatically learn the useful heterogeneous metapath interactions of different lengths in multiplex heterogeneous networks through multi-layer convolution aggregation. Additionally, we effectively integrate both multi-relation structural signals and attribute semantics into the learned node embeddings with both unsupervised and semi-supervised learning paradigms. Extensive experiments on five real-world datasets with various network analytical tasks demonstrate the significant superiority of MHGCN against state-of-the-art embedding baselines in terms of all evaluation metrics. The source code of our method is available at: https://github.com/NSSSJSS/MHGCN. CCS CONCEPTS• Mathematics of computing → Graph algorithms; • Computing methodologies → Learning latent representations.