University of Catania

About: University of Catania is a education organization based out in Catania, Italy. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 14599 authors who have published 41195 publications receiving 1032705 citations. The organization is also known as: Università degli Studi di Catania & Universita degli Studi di Catania.

Endocannabinoids and β-amyloid-induced neurotoxicity in vivo: effect of pharmacological elevation of endocannabinoid levels

Abstract: We investigated the involvement of endocannabinoids in the control of neuronal damage and memory retention loss in rodents treated with the beta-amyloid peptide (1-42) (BAP). Twelve days after stereotaxic injection of BAP into the rat cortex, and concomitant with the appearance in the hippocampus of markers of neuronal damage, 2-arachidonoyl glycerol, but not anandamide, levels were enhanced in the hippocampus. VDM-11 (5 mg/kg, i.p.), an inhibitor of endocannabinoid cellular reuptake, significantly enhanced rat hippocampal and mouse brain endocannabinoid levels when administered sub-chronically starting either 3 or 7 days after BAP injection and until the 12-14th day. VDM-11 concomitantly reversed hippocampal damage in rats, and loss of memory retention in the passive avoidance test in mice, but only when administered from the 3rd day after BAP injection. We suggest that early, as opposed to late, pharmacological enhancement of brain endocannabinoid levels might protect against beta-amyloid neurotoxicity and its consequences.

Analysing information flows and key mediators through temporal centrality metrics

Abstract: The study of influential members of human networks is an important research question in social network analysis. However, the current state-of-the-art is based on static or aggregated representation of the network topology. We argue that dynamically evolving network topologies are inherent in many systems, including real online social and technological networks: fortunately the nature of these systems is such that they allow the gathering of large quantities of finegrained temporal data on interactions amongst the network members.In this paper we propose novel temporal centrality metrics which take into account such dynamic interactions over time. Using a real corporate email dataset we evaluate the important individuals selected by means of static and temporal analysis taking two perspectives: firstly, from a semantic level, we investigate their corporate role in the organisation; and secondly, from a dynamic process point of view, we measure information dissemination and the role of information mediators. We find that temporal analysis provides a better understanding of dynamic processes and a more accurate identification of important people compared to traditional static methods.