The effect of zinc and vitamin E cosupplementation on metabolic status and its related gene expression in patients with gestational diabetes

Objective: The aim of this study was to determine the effects of zinc and vitamin E cosupplementation on metabolic status and gene expression related to insulin and lipid metabolism in women with gestational diabetes mellitus (GDM). Methods: Fifty-four women, in the age range of 18�40 years, diagnosed with GDM were recruited for this randomized, double-blinded, placebo-controlled trial. Subjects were randomly allocated into two intervention groups to either taking 233�mg/day Zinc Gluconate plus 400-IU/day vitamin E supplements or placebo (n�=�27 each group) for 6 weeks. Gene expression related to insulin and lipid metabolism was evaluated in peripheral blood mononuclear cells (PBMCs) of women with GDM using RT-PCR method. Results: Participants who received zinc plus vitamin E supplements had significantly lower serum insulin levels (β�=��3.81; 95 CI, �5.90, �1.72; p�=�.001), homeostasis model of assessment-insulin resistance (β�=��0.96; 95 CI, �1.54, �0.38; p�=�.002), serum total-cholesterol (β�=��8.56; 95 CI, �16.69, �0.43; p�=�.03) and low density lipoprotein-cholesterol (LDL)-cholesterol (β�=��8.72; 95 CI, �15.27, �2.16; p�=�.01), and higher quantitative insulin sensitivity check index (β�=�0.01; 95 CI, 0.005, 0.02; p�=�.007) compared with the placebo. Moreover, zinc and vitamin E cosupplementation upregulated gene expression of peroxisome proliferator-activated receptor gamma (PPAR-γ; p�=�.03) and low-density lipoprotein receptor (LDLR; p�=�.04) compared with the placebo. Though, zinc and vitamin E combination did not affect other metabolic parameters. Conclusions: Overall, zinc and vitamin E cosupplementation for 6 weeks in women with GDM significantly improved insulin metabolism, lipid profile, and the gene expression levels of PPAR-γ and LDLR. © 2018 Informa UK Limited, trading as Taylor & Francis Grou

Generative models of online discussion threads: state of the art and research challenges

Online discussion in form of written comments is a core component of many social media platforms. It has attracted increasing attention from academia, mainly because theories from social sciences can be explored at an unprecedented scale. This interest has led to the development of statistical models which are able to characterize the dynamics of threaded online conversations. In this paper, we review research on statistical modeling of online discussions, in particular, we describe current generative models of the structure and growth of discussion threads. These are parametrized network formation models that are able to generate synthetic discussion threads that reproduce certain features of the real discussions present in different online platforms. We aim to provide a clear overview of the state of the art and to motivate future work in this relevant research field.This work has been supported by the Spanish Ministry of Economy and Competitiveness under the María de Maeztu Units of Excellence Programme (MDM-2015-0502); the Marie Curie FP7-PEOPLE-2012-COFUND Action (grant agreement no: 600387); and the CIEN LPS-BIGGER project (UCTR150175, IDI-20141259), co-funded by Centro para el Desarrollo Tecnológico Industrial (CDTI) and Fondo Europeo de Desarrollo Regional (FEDER)