A study on the effect of resveratrol on lipid metabolism in hyperlipidemic mice

Background: The content of resveratrol is relatively high in Polygonum cuspidatum Sieb. et Zucc., and the resveratrol has the effect of blood vessel dilating, microcirculation improving, platelet aggregation inhibiting and anti-cancer. The objective of this paper was to study the effect of resveratrol on lipid metabolism in hyperlipidemia mice.Materials andMethods: Through the establishment of an experimental mouse model of hyperlipidemia, the effect of resveratrol on change in total cholesterol (TC), triglyceride (TG), high density lipoprotein cholesterol (HDL-c), and low-density lipoprotein cholesterol (LDL-c) levels in mouse serum were determined.Results: Resveratrol group can apparently reduce TC, TG, LDL-c and AI of hyperlipidemic mice in a dose effect manner.Conclusion: We concluded that resveratrol can effectively reduce blood lipid levels of hyperlipidemic mice.Keywords: Resveratrol; hyperlipidemia; TC; TG; HDL-c; LDL-

Weight management: a comparison of existing dietary approaches in a work-site setting

<b>OBJECTIVES:</b> (1) To compare the effectiveness a 2512 kJ (600 kcal) daily energy deficit diet (ED) with a 6279 kJ (1500 kcal) generalized low-calorie diet (GLC) over a 24 week period (12 weeks weight loss plus 12 weeks weight maintenance). (2) To determine if the inclusion of lean red meat at least five times per week as part of a slimming diet is compatible with weight loss in comparison with a diet that excludes lean red meat. DESIGN: Randomized controlled trial. <b>SETTING:</b> Large petrochemical work-site. <b>PARTICIPANTS:</b> One-hundred and twenty-two men aged between 18 and 55 y. <b>MAIN OUTCOME MEASURES:</b> Weight loss and maintenance of weight loss. <b>INTERVENTION:</b> Eligible volunteers were randomized to one of the four diet=meat combinations (ED meat, ED no meat, GLC meat, GLC no meat). One-third of subjects in each diet/meat combination were randomized to an initial control period prior to receiving dietary advice. All subjects attended for review every 2 weeks during the weight loss period. For the 12 week structured weight maintenance phase, individualized energy prescriptions were re-calculated for the ED group as 1.4 (activity factor)x basal metabolic rate. Healthy eating advice was reviewed with subjects in the GLC group. All subjects were contacted by electronic mail at 2 week intervals and anthropometric and dietary information requested. <b>RESULTS:</b> No difference was evident between diet groups in mean weight loss at 12 weeks (4.3 (s.d. 3.4) kg ED group vs 5.0 (s.d. 3.5) kg GLC group, P=0.34). Mean weight loss was closer to the intended weight loss in the 2512 kJ (600 kcal) ED group. The dropout rate was also lower than the GLC group. The inclusion of lean red meat in the diet on at least five occasions per week did not impair weight loss. Mean weight gain following 12 weeks weight maintenance was þ1.1 (s.d. 1.8) kg, P<0.0001. No differences were found between groups. <b>CONCLUSIONS:</b> This study has shown that the individualized 2512 kJ (600 kcal) ED approach was no more effective in terms of weight loss than the 6279 kJ (1500 kcal) GLC approach. However the ED approach might be considered preferable as compliance was better with this less demanding prescription. In terms of weight loss the elimination of red meat from the diet is unnecessary. The weight maintenance intervention was designed as a low-input approach, however weight regain was significant and weight maintenance strategies require further development

Immunolocalization of KATP channel subunits in mouse and rat cardiac myocytes and the coronary vasculature.

BACKGROUND: Electrophysiological data suggest that cardiac KATP channels consist of Kir6.2 and SUR2A subunits, but the distribution of these (and other KATP channel subunits) is poorly defined. We examined the localization of each of the KATP channel subunits in the mouse and rat heart. RESULTS: Immunohistochemistry of cardiac cryosections demonstrate Kir6.1 protein to be expressed in ventricular myocytes, as well as in the smooth muscle and endothelial cells of coronary resistance vessels. Endothelial capillaries also stained positive for Kir6.1 protein. Kir6.2 protein expression was found predominantly in ventricular myocytes and also in endothelial cells, but not in smooth muscle cells. SUR1 subunits are strongly expressed at the sarcolemmal surface of ventricular myocytes (but not in the coronary vasculature), whereas SUR2 protein was found to be localized predominantly in cardiac myocytes and coronary vessels (mostly in smaller vessels). Immunocytochemistry of isolated ventricular myocytes shows co-localization of Kir6.2 and SUR2 proteins in a striated sarcomeric pattern, suggesting t-tubular expression of these proteins. Both Kir6.1 and SUR1 subunits were found to express strongly at the sarcolemma. The role(s) of these subunits in cardiomyocytes remain to be defined and may require a reassessment of the molecular nature of ventricular KATP channels. CONCLUSIONS: Collectively, our data demonstrate unique cellular and subcellular KATP channel subunit expression patterns in the heart. These results suggest distinct roles for KATP channel subunits in diverse cardiac structures

A Recurrent Neural Network Survival Model: Predicting Web User Return Time

The size of a website's active user base directly affects its value. Thus, it is important to monitor and influence a user's likelihood to return to a site. Essential to this is predicting when a user will return. Current state of the art approaches to solve this problem come in two flavors: (1) Recurrent Neural Network (RNN) based solutions and (2) survival analysis methods. We observe that both techniques are severely limited when applied to this problem. Survival models can only incorporate aggregate representations of users instead of automatically learning a representation directly from a raw time series of user actions. RNNs can automatically learn features, but can not be directly trained with examples of non-returning users who have no target value for their return time. We develop a novel RNN survival model that removes the limitations of the state of the art methods. We demonstrate that this model can successfully be applied to return time prediction on a large e-commerce dataset with a superior ability to discriminate between returning and non-returning users than either method applied in isolation.Comment: Accepted into ECML PKDD 2018; 8 figures and 1 tabl