Background:This meta-analysis aimed to systematically evaluate the effects of probiotics on blood lipid and blood pressure among patients with type 2 diabetes mellitus (T2DM) based on the randomized controlled studies.Methods:PubMed, Cochrane, Embase, Wanfang, China National Knowledge Infrastructure, and VIP database were searched by the index words to identify the qualified randomized control trial. The latest research was done in the January 2017. Mean difference (MD) along with 95% confidence interval (CI) was used to analyze the included outcomes.Results:Ten trials were included at last with 297 patients in the treatment group and 294 patients in the control group. Probiotics significantly decreased the value of total cholesterol (SMD −0.57, 95% CI −0.92 to 0.21), triglyceride (SMD −0.66, 95% CI −0.93 to 0.39), low-density lipoprotein (SMD −0.40, 95% CI −0.79 to 0.01), systolic blood pressure (WMD −5.04, 95% CI −8.8 to 1.20), diastolic blood pressure (SMD −0.39, 95% CI −0.62 to 0.17), fasting blood glucose (FBG) (SMD 3.54, 95% CI 1.94–5.15) compared with the placebo treatment. Apart from this, probiotics could significantly improve the value of high-density lipoprotein (SMD 0.38, 95% CI 0.03–0.73).Conclusion:Probiotics may decrease the indexes of lipid profile, blood pressure, and FBG in patients with T2DM; application of probiotics might be a new method for lipid profiles and blood pressure management in T2DM.

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We present a novel global 3D coronal MHD model called COCONUT, polytropic in its first stage and based on a time-implicit backward Euler scheme. Our model boosts run-time performance in comparison with contemporary MHD-solvers based on explicit schemes, which is particularly important when later employed in an operational setting for space-weather forecasting. It is data-driven in the sense that we use synoptic maps as inner boundary inputs for our potential-field initialization as well as an inner boundary condition in the further MHD time evolution. The coronal model is developed as part of the EUropean Heliospheric FORecasting Information Asset (EUHFORIA) and will replace the currently employed, more simplistic, empirical Wang–Sheeley–Arge (WSA) model. At 21.5 R ⊙ where the solar wind is already supersonic, it is coupled to EUHFORIA’s heliospheric model. We validate and benchmark our coronal simulation results with the explicit-scheme Wind-Predict model and find good agreement for idealized limit cases as well as real magnetograms, while obtaining a computational time reduction of up to a factor 3 for simple idealized cases, and up to 35 for realistic configurations, and we demonstrate that the time gained increases with the spatial resolution of the input synoptic map. We also use observations to constrain the model and show that it recovers relevant features such as the position and shape of the streamers (by comparison with eclipse white-light images), the coronal holes (by comparison with EUV images), and the current sheet (by comparison with WSA model at 0.1 au).

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