Apelin signaling modulates splanchnic angiogenesis and portosystemic collateral vessel formation in rats with portal hypertension

BACKGROUND/AIMS: Angiogenesis is a pathological hallmark of portal hypertension. Although VEGF is considered to be the most important proangiogenic factor in neoangiogenesis, this process requires the coordinated action of a variety of factors. Identification of novel molecules involved in angiogenesis is highly relevant, since they may represent potential new targets to suppress pathological neovascularization in angiogenesis-related diseases like portal hypertension. The apelin/APJ signaling pathway plays a crucial role in angiogenesis. Therefore, we determined whether the apelin system modulates angiogenesis-driven processes in portal hypertension. METHODS: Partial portal vein-ligated rats were treated with the APJ antagonist F13A for seven days. Splanchnic neovascularization and expression of angiogenesis mediators (Western blotting) was determined. Portosystemic collateral formation (microspheres), and hemodynamic parameters (flowmetry) were also assessed. RESULTS: Apelin and its receptor APJ were overexpressed in the splanchnic vasculature of portal hypertensive rats. F13A effectively decreased, by 52%, splanchnic neovascularization and expression of proangiogenic factors VEGF, PDGF and angiopoietin-2 in portal hypertensive rats. F13A also reduced, by 35%, the formation of portosystemic collateral vessels. CONCLUSIONS: This study provides the first experimental evidence showing that the apelin/APJ system contributes to portosystemic collateralization and splanchnic neovascularization in portal hypertensive rats, presenting a potential novel therapeutic target for portal hypertension.

Mobility and Bandwidth Prediction as a Service in Virtualized LTE Systems

Recently telecommunication industry benefits from infrastructure sharing, one of the most fundamental enablers of cloud computing, leading to emergence of the Mobile Virtual Network Operator (MVNO) concept. The most momentous intents by this approach are the support of on-demand provisioning and elasticity of virtualized mobile network components, based on data traffic load. To realize it, during operation and management procedures, the virtualized services need be triggered in order to scale-up/down or scale-out/in an instance. In this paper we propose an architecture called MOBaaS (Mobility and Bandwidth Availability Prediction as a Service), comprising two algorithms in order to predict user(s) mobility and network link bandwidth availability, that can be implemented in cloud based mobile network structure and can be used as a support service by any other virtualized mobile network services. MOBaaS can provide prediction information in order to generate required triggers for on-demand deploying, provisioning, disposing of virtualized network components. This information can be used for self-adaptation procedures and optimal network function configuration during run-time operation, as well. Through the preliminary experiments with the prototype implementation on the OpenStack platform, we evaluated and confirmed the feasibility and the effectiveness of the prediction algorithms and the proposed architecture.

A Demonstration of Mobility Prediction as a Service in Cloudified LTE Networks

Location prediction has attracted a significant amount of research effort. Being able to predict users’ movement benefits a wide range of communication systems, including location-based service/applications, mobile access control, mobile QoS provision, and resource management for mobile computation and storage management. In this demo, we present MOBaaS, which is a cloudified Mobility and Bandwidth prediction services that can be instantiated, deployed, and disposed on-demand. Mobility prediction of MOBaaS provides location predictions of a single/group user equipments (UEs) in a future moment. This information can be used for self-adaptation procedures and optimal network function configuration during run-time operations. We demonstrate an example of real-time mobility prediction service deployment running on OpenStack platform, and the potential benefits it bring to other invoking services.