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This study evaluated the effect of 3-(4-fluorophenylselenyl)-2,5-diphenylselenophene (DPS) in the mouse forced swim test (FST) and tail suspension test (TST), two assays predictive of depressant activity. The involvement of serotonergic system in the effect caused by DPS was studied. The antidepressant-like effect of combined treatment with subeffetive(More)
We investigated the antidepressant-like action of 5 compounds belonging to the selenophene class. The involvement of ERK and CREB activation in this action was also demonstrated. In the experiment 1, time-course and dose-response effect of H-DPS, CH3-DPS, Cl-DPS, F-DPS and CF3-DPS were accompanied in the mouse forced swimming test (FST). Firstly, animals(More)
Clinically, it is suggested that chronic pain might induce mood disorders like depression and anxiety. Based on this antidepressant drugs have emerged as a new therapy for pain. In this study, the effect of acute and subchronic treatments with 3-(4-fluorophenylselenyl)-2,5-diphenylselenophene (F-DPS) on behavioral changes induced by partial sciatic nerve(More)
Isoquinolines are formed endogenously as metabolites of neurotransmitters and are studied because they have structures similar to neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine and selegiline, a selective inhibitor of MAO-B. This study investigated a possible in vitro inhibitory activity of new 4-organochalcogen-isoquinoline derivatives, containing(More)
3-(4-Fluorophenylselenyl)-2,5-diphenylselenophene (F-DPS) is a promising organoselenium compound that shows antidepressant-like properties related to interaction with the serotonergic system. In this study, a mouse model of anxiety/depressant-like behavior induced by long-term corticosterone treatment was used to evaluate behavioral, endocrinal, and(More)
—In the past, many clustering algorithms for ad-hoc networks have been proposed. Their main objective is to solve the scalability issue of ad-hoc networks by grouping nodes into clusters. The challenge in MANETs for those clustering algorithms is to cope with the high node mobility which affects the stability of the cluster structures. Wireless mesh(More)
This paper proposes an automatic adaptive $k$-distance dominating set-based clustering scheme for service discovery in Wireless Mesh Networks. Supernodes and clients form a so-called virtual backbone where clients are at most k hops away from a supernode. The scheme automatically adapts the hop distance between supernodes and clients based on(More)
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