Renal effects of SGLT2 inhibitors in cardiovascular patients with and without chronic kidney disease: focus on heart failure and renal outcomes

The kidney has a prominent role in maintaining glucose homeostasis by using glucose as a metabolic substrate. This occurs by generating glucose through gluconeogenesis, and by reuptaking filtered glucose through the sodium–glucose cotransporters SGLT1 and SGLT2 located in the proximal tubule. In recent studies, the administration of sodium–glucose cotransporters inhibitors demonstrated that inhibition of renal glucose reabsorption significantly reduces adverse renal events and heart failure exacerbations, in type 2 diabetic patients with and without cardiovascular damage as well as in advanced chronic kidney disease and heart failure patients with reduced ejection fraction with and without diabetes. The benefit was consistent throughout the different investigated clinical conditions, ameliorating overall patient outcome. The efficacy of sodium glucose cotransporters inhibitors was prominently linked to the limitation of renal damage as highlighted by the significant reduction on global mortality achieved in the studies investigating diabetic and not diabetic populations with advanced chronic kidney disease. Both studies were halted at the interim analysis because of unquestionable evidence of treatment benefit. In current review, we examine the role of SGLT2 and SGLT1 in the regulation of renal glucose reabsorption in health and disease and the effect of SGLT2 inhibition on clinical outcomes of populations with different cardiovascular conditions investigated with large-scale outcome trials

Sharing knowledge in digital ecosystems using semantic multimedia big data

The use of formal representations has a basic importance in the era of big data. This need is more evident in the context of multimedia big data due to the intrinsic complexity of this type of data. Furthermore, the relationships between objects should be clearly expressed and formalized to give the right meaning to the correlation of data. For this reason the design of formal models to represent and manage information is a necessary task to implement intelligent information systems. Approaches based on the semantic web need to improve the data models that are the basis for implementing big data applications. Using these models, data and information visualization becomes an intrinsic and strategic task for the analysis and exploration of multimedia Big Data. In this article we propose the use of a semantic approach to formalize the structure of a multimedia Big Data model. Moreover, the identification of multimodal features to represent concepts and linguistic-semantic properties to relate them is an effective way to bridge the gap between target semantic classes and low-level multimedia descriptors. The proposed model has been implemented in a NoSQL graph database populated by different knowledge sources. We explore a visualization strategy of this large knowledge base and we present and discuss a case study for sharing information represented by our model according to a peer-to-peer(P2P) architecture. In this digital ecosystem, agents (e.g. machines, intelligent systems, robots,..) act like interconnected peers exchanging and delivering knowledge with each other