Effects of exposure to cigarette smoke prior to pregnancy in diabetic rats

<p>Abstract</p> <p>Background</p> <p>The purpose of this study was to evaluate the effects of cigarette smoke exposure before pregnancy on diabetic rats and their offspring development.</p> <p>Methods</p> <p>Diabetes was induced by streptozotocin and cigarette smoke exposure was conducted by mainstream smoke generated by a mechanical smoking device and delivered into a chamber. Diabetic female Wistar rats were randomly distributed in four experimental groups (n minimum = 13/group): nondiabetic (ND) and diabetic rats exposed to filtered air (D), diabetic rats exposed to cigarette smoke prior to and into the pregnancy period (DS) and diabetic rats exposed to cigarette smoke prior to pregnancy period (DSPP). At day 21 of pregnancy, rats were killed for maternal biochemical determination and reproductive outcomes.</p> <p>Results</p> <p>The association of diabetes and cigarette smoke in DSPP group caused altered glycemia at term, reduced number of implantation and live fetuses, decreased litter and maternal weight, increased pre and postimplantation loss rates, reduced triglyceride and VLDL-c concentrations, increased levels of thiol groups and MDA. Besides, these dams presented increased SOD and GSH-Px activities. However, the increased antioxidant status was not sufficient to prevent the lipid peroxidation observed in these animals.</p> <p>Conclusion</p> <p>Despite the benefits stemming from smoking interruption during the pregnancy of diabetic rats, such improvement was insufficient to avoid metabolic alterations and provide an adequate intrauterine environment for embryofetal development. Therefore, these results suggest that it is necessary to cease smoking extensive time before planning pregnancy, since stopping smoking only when pregnancy is detected may not contribute effectively to fully adequate embryofetal development.</p

A fuzzy clustering approach toward Hidden Markov random field models for enhanced spatially constrained image segmentation

Hidden Markov random field (HMRF) models have been widely used for image segmentation, as they appear naturally in problems where a spatially constrained clustering scheme, taking into account the mutual influences of neighboring sites, is asked for. Fuzzy c-means (FCM) clustering has also been successfully applied in several image segmentation applications. In this paper, we combine the benefits of these two approaches, by proposing a novel treatment of HMRF models, formulated on the basis of a fuzzy clustering principle. We approach the HMRF model treatment problem as an FCM-type clustering problem, effected by introducing the explicit assumptions of the HMRF model into the fuzzy clustering procedure. Our approach utilizes a fuzzy objective function regularized by Kullback--Leibler divergence information, and is facilitated by application of a mean-field-like approximation of the MRF prior. We experimentally demonstrate the superiority of the proposed approach over competing methodologies, considering a series of synthetic and real-world image segmentation application