Agomelatine protects heart and aorta against lipopolysaccharide-induced cardiovascular toxicity via inhibition of NF-kß phosphorylation.

The aim of this study was to investigate the possible ameliorating effects of agomelatine (AGO) on lipopolysaccharide (LPS)-induced endothelial and cardiac damage. Twenty-four female Wistar Albino rats divided into 3 groups as follows: Control, LPS and LPS + AGO. Total oxidant status (TOS), total antioxidant status (TAS), nuclear factor kappa beta (NF-kß)/p65, p-NF-kß, full caspase-8 (Cas-8) and cleaved cas-8 levels were measured in cardiac tissues and creatine kinase MB (CKMB), aspartate aminotransferase (AST), lactate dehydrogenase (LDH) levels in blood biochemically. In addition; cas-8, sirtuin-1 (SIRT-1), interleukin-4 (IL-4), interleukin-10 (IL-10), haptoglobin measured histopathologically in cardiac and aortic tissues. The levels of CKMB, AST, LDH and TOS were increased and TAS were decreased in the LPS group. In Western blot analyses NF-kß/p65, p-NF-kß/p65, full and cleaved cas-8 protein levels increased in cardiac tissues of LPS group. In histopathological and immunohistochemical evaluation of the heart sections; hyperemia, micro-hemorrhages and inflammatory cell infiltrations, increase of cas-8, haptoglobin, IL-4 and IL-10 and decrease of SIRT-1 levels were observed in cardiac and endothelial tissues of LPS groups. AGO treatment reversed all these parameters. It was shown that LPS-induced inflammation, oxidative stress and apoptosis via increasing of NF-kß/p65 signaling, decreasing of SIRT-1 levels and increase of cas-8 levels in heart and endothelial tissues respectively. AGO corrected all these parameters by its antioxidant, antiinflammatory and antiapoptotic activities.

Estimation of storage time of yogurt with artificial neural network modeling.

Changes in the physical, chemical, and microbiological structure of yogurt determine the storage and shelf life of the product. In this study, microbial counts and pH values of yogurt during storage were determined at d 1, 7, and 14. Simultaneously, image processing of yogurt was digitized by using a machine vision system (MVS) to determine color changes during storage, and the obtained data were modeled with an artificial neural network (ANN) for prediction of shelf life of set-type whole-fat and low-fat yogurts. The ANN models were developed using back-propagation networks with a single hidden layer and sigmoid activation functions. The input variables of the network were pH; total aerobic, yeast, mold, and coliform counts; and color analysis values measured by the machine vision system. The output variable was the storage time of the yogurt. The modeling results showed that there was excellent agreement between the experimental data and predicted values, with a high determination coefficient (R2 = 0.9996) showing that the developed model was able to analyze nonlinear multivariant data with very good performance, fewer parameters, and shorter calculation time. The model might be an alternative method to control the expiration date of yogurt shown in labeling and provide consumers with a safer food supply.