Acute effect of rosiglitazone on relaxation responses in hypercholesterolemic corpus cavernosum.

Thiazolidinediones (TZDs) improve vascular endothelial dysfunction through non-genomic effects of peroxisomal proliferator-activated receptor γ. This study investigated the acute effect of one of the TZD, rosiglitazone, on endothelium-dependent relaxation response of corpus cavernosum (CC) in hypercholesterolemic rabbits. New Zealand rabbits were divided into two groups randomly as control and cholesterol groups. Hypercholesterolemia was induced by feeding rabbits with 2% cholesterol diet (w/w) for 6 weeks. Endothelium-dependent and -independent relaxation response of CC were evaluated in the presence of rosiglitazone by organ bath studies with cumulative doses of acetylcholine (Ach) and sodium nitroprusside (SNP). Maximal relaxation (Emax) response to Ach significantly decreased owing to hypercholesterolemia in CC tissues. However, in vitro incubation of rosiglitazone with different concentrations (0.1, 1 and 10 µm) did not improve the Ach-dependent Emax responses in hypercholesterolemic rabbit CC. Surprisingly, rosiglitazone caused a significant decrease in Ach-dependent relaxation in healthy CC. Emax responses to SNP did not differ in the presence of rosiglitazone in both the control and hypercholesterolemic groups. Rosiglitazone does not improve hypercholesterolemia-induced endothelial dysfunction in CC tissues while it dose-dependently impairs endothelium-dependent relaxation in healthy CC tissue.

Evaluation of the effect of selective serotonin reuptake inhibitors on bone mineral density: an observational cross-sectional study.

UNLABELLED: Sixty patients diagnosed with generalized anxiety disorder and treated with either paroxetine, sertraline, or citalopram for at least 12 months were enrolled in this study, and the bone mineral density (BMD) of the patients was compared with that of 40 healthy volunteers. Selective serotonin reuptake inhibitor (SSRI) therapy in generalized anxiety disorder was found to be related with decreased BMD values. INTRODUCTION: The objectives of this study were to evaluate the effect of SSRI therapy on BMD in postmenopausal women diagnosed with generalized anxiety disorder (GAD) and to identify the effects of the duration of disease and treatment on risk factors for osteoporosis. METHODS: Sixty patients diagnosed with GAD and treated with paroxetine, sertraline, or citalopram from the SSRI group for at least 12 months were enrolled. Social demographic features, the Hamilton Anxiety Scale (HAS) results, and the Hamilton Depression Scale (HDS) scores of all the patients were assessed. The BMD of the patients was measured by dual-energy X-ray absorptiometry (DXA) at the femoral and lumbar regions. The patients were divided into three groups which are the paroxetine, sertraline, and citalopram groups. The BMD of the patients was compared with that of 40 healthy volunteers. RESULTS: The L2-L4, total lumbar vertebrae, femoral intertrochanteric, total femoral Z-scores, and femoral Ward's region T-scores of the treatment group were lower than the median T- and Z-scores of the control group (p < 0.05). Of the treatment groups, the femoral neck, trochanteric and intertrochanteric T- and Z-scores, total femoral T- and Z-scores, and femoral Ward's T- and Z-scores of the sertraline group were significantly lower than the BMD values measured at the identical regions in the paroxetine and citalopram groups (p < 0.05).There was a significant negative correlation between the duration of treatment and the BMD values. CONCLUSION: SSRI therapy in GAD was found to be related with decreased BMD values. Further randomized controlled studies are warranted to determine whether SSRI use is a risk factor for osteoporosis; such studies should investigate these factors by performing BMD assessments before treatment.

The labeled systems of multiple neural networks.

This paper proposes an implementation scheme of K-class classification problem using systems of multiple neural networks. Usually, a multi-class problem is decomposed into simple sub-problems solved independently using similar single neural networks. For the reason that these sub-problems are not equivalent in their complexity, we propose a system that includes reinforced networks destined to solve complicated parts of the entire problem. Our approach is inspired from principles of the multi-classifiers systems and the labeled classification, which aims to improve performances of the networks trained by the Back-Propagation algorithm. We propose two implementation schemes based on both OAO (one-against-all) and OAA (one-against-one). The proposed models are evaluated using iris and human thigh databases.