The role of melatonin in preventing amiodarone-induced rat liver damage.

Long-term exposure to amiodarone, an antiarrhythmic drug, can induce different organ damage, including liver. Cell damage included by amiodarone is a consequence of mitochondrial damage, reactive oxygen species production, and cell energy depletion leading to programmed cell death. In the present study, hepatoprotective potential of neurohormone melatonin (50 mg/kg/day) was evaluated in a chronic experimental model of liver damage induced by a 4-week application of amiodarone (70 mg/kg/day). The obtained results indicate that amiodarone induces an increase in xanthine oxidase activity, as well as the content of the lipid and protein oxidatively modified products and p53 levels. Microscopic analysis further corroborated the biochemical findings revealing hepatocyte degeneration, apoptosis, and occasional necrosis, with the activation of Kupffer cells. Coadministration of melatonin and amiodaron prevented an increase in certain damage associated parameters, due to its multiple targets. In conclusion, the application of melatonin together with amiodarone prevented an increase in tissue oxidative damage parameters and moderately prevented liver cell apoptosis, indicating that the damage of hepatocytes provoked by amiodarone supersedes the protective properties of melatonin in a given dose.

Thermal sensation prediction by soft computing methodology.

Thermal comfort in open urban areas is very factor based on environmental point of view. Therefore it is need to fulfill demands for suitable thermal comfort during urban planning and design. Thermal comfort can be modeled based on climatic parameters and other factors. The factors are variables and they are changed throughout the year and days. Therefore there is need to establish an algorithm for thermal comfort prediction according to the input variables. The prediction results could be used for planning of time of usage of urban areas. Since it is very nonlinear task, in this investigation was applied soft computing methodology in order to predict the thermal comfort. The main goal was to apply extreme leaning machine (ELM) for forecasting of physiological equivalent temperature (PET) values. Temperature, pressure, wind speed and irradiance were used as inputs. The prediction results are compared with some benchmark models. Based on the results ELM can be used effectively in forecasting of PET.