Different effects of high-fat and high-sucrose diets on the physiology of perivascular adipose tissues of the thoracic and abdominal aorta.

Vascular diseases such as atherosclerosis and aneurysms are associated with diet. Perivascular adipose tissue (PVAT) was reportedly involved in the regulation of vascular functions. It is suggested that imbalanced diets can cause PVAT inflammation and dysfunction as well as impaired vascular function. However, the association between diets and PVAT are not clearly understood. Here, we showed that a high-fat and a high-sucrose diet affected PVAT at different sites. A high-fat diet induced increased number of large-sized lipid droplets and increased CD (Cluster of differentiation) 68+ macrophage- and monocyte chemotactic protein (MCP)-1-positive areas in the abdominal aortic PVAT (aPVAT). In addition, a high-fat diet caused decreased collagen fibre-positive area and increased CD68+ macrophage- and MCP-1-positive areas in the abdominal aorta. In contrast, a high-sucrose diet induced increased number of large-sized lipid droplets, increased CD68+ macrophage- and MCP-1-positive areas, and decreased UCP-1 positive area in the thoracic aortic PVAT (tPVAT). A high-sucrose diet caused decreased collagen fibre-positive area and increased CD68+ macrophage- and MCP-1-positive areas in the thoracic aorta. These results could be attributed to the different adipocyte populations in the tPVAT and aPVAT. Our results provide pathological evidence to improve our understanding of the relationship between diet and vascular diseases.

Mixing Ginkgo biloba Extract with Sesame Extract and Turmeric Oil Increases Bioavailability of Ginkgolide A in Mice Brain.

Ginkgo biloba extract (GBE) is widely used as herbal medicine. Preventive effect of GBE against dementia, including Alzheimer's disease, has been reported. The bioactive compounds in GBE that impart these beneficial effects, flavonoids and terpene lactones, have poor bioavailability. Our previous study found distribution of bioactive compounds of sesame extract in mice brain after mixing it with turmeric oil. Here, we evaluate the distribution of bioactive compounds of GBE by combining it with the mixture of sesame extract and turmeric oil (MST). The content of terpene lactones in mice serum was significantly increased in a dose-dependent manner after administration of GBE. However, the contents of terpene lactones in mice brain were not significantly changed. Concentration of ginkgolide A in mice brain increased significantly when GBE was co-administrated with MST than when GBE was administered alone. These results suggest that MST may be effective in enhancing the bioavailability of ginkgolide A in GBE.

Network Anatomy Controlling Abrupt-like Percolation Transition.

We virtually dissect complex networks in order to understand their internal structure, just as doctors do with the bodies of animals. Our novel method classifies network links into four categories: bone, fat, cartilage, and muscle, based on network connectivity. We derive an efficient percolation strategy from this new viewpoint of network anatomy, which enables abrupt-like percolation transition through removal of a small amount of cartilage links, which play a crucial role in network connectivity. Furthermore, we find nontrivial scaling laws in the relationships between four types of links in each cluster and evaluate power exponents, which characterize network structures as seen in the real large-scale network of trading business firms and in the Erdos-Rényi network. Finally, we observe changes in the transition point for random bond percolation process, demonstrating that the addition of muscle links enhances network robustness, while fat links are irrelevant. These findings aid in controlling the percolation transition for an arbitrary network.

Precise calculation of a bond percolation transition and survival rates of nodes in a complex network.

Through precise numerical analysis, we reveal a new type of universal loopless percolation transition in randomly removed complex networks. As an example of a real-world network, we apply our analysis to a business relation network consisting of approximately 3,000,000 links among 300,000 firms and observe the transition with critical exponents close to the mean-field values taking into account the finite size effect. We focus on the largest cluster at the critical point, and introduce survival probability as a new measure characterizing the robustness of each node. We also discuss the relation between survival probability and k-shell decomposition.