Meta-analysis: Effects of zinc supplementation alone or with multi-nutrients, on glucose control and lipid levels in patients with type 2 diabetes

The present study aims to assess the effects of zinc supplementation on metabolic parameters in patients with type 2 diabetes. A literature search was conducted in PubMedTM, Google ScholarTM, and ScopusTM up to March 2018. Twenty randomized controlled trials met the predefined inclusion criteria and were included in the meta-analysis. Weighted mean difference (WMD) with 95 confidence intervals (CIs) were calculated for net changes in glycemic indices including fasting blood glucose (FBG) and hemoglobin A1c (HbA1c), and in lipid markers including total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-c), and high density lipoprotein cholesterol (HDL-c). Subgroup analyses were performed based on intervention and study quality. Compared to controls, zinc supplementation significantly reduced the concentrations of both FBG and HbA1c (FBG WMD: �19.66 mg/dL, 95 CI: �33.71, �5.62; HbA1c WMD: �0.43 mg/dL, 95 CI: �0.80, �0.07). The pooled estimate showed a significant decrease in serum TC and LDL-c, and increase in serum HDL-c levels in treatment group compared with the control group (TC WMD: �18.51 mg/dL, 95 CI: �21.36, �15.66; LDL-c WMD: �4.80 mg/dL, 95 CI: �6.07, �3.53; HDL-c WMD: 1.45 mg/dL, 95 CI: 1.40, 1.51). Subgroup analysis of �no co-supplement� intervention demonstrated significant differences for mean changes in HDL-c and FBG levels, whereas subgroup analysis of high quality studies showed significant differences for mean changes of LDL-c, HDL-c, and FBG levels. Results suggested that zinc supplementation reduces FBG, HbA1c and LDL-c levels and increases HDL-C levels; however, these changes were related to intervention and quality of studies. Copyright © 2019 by The Korean Society of Food Science and Nutrition. All rights Reserved

The correlated optical and radio variability of BL Lacertae - WEBT data analysis 1994-2005

Since 1997, BL Lacertae has undergone a phase of high optical activity, with the occurrence of several prominent outbursts. Starting from 1999, the Whole Earth Blazar Telescope (WEBT) consortium has organized various multifrequency campaigns on this blazar, collecting tens of thousands of data points. One of the main issues in the study of this huge dataset has been the search for correlations between the optical and radio flux variations, and for possible periodicities in the light curves. The analysis of the data assembled during the first four campaigns (comprising also archival data to cover the period 1968-2003) revealed a fair optical-radio correlation in 1994-2003, with a delay of the hard radio events of ~100 days. Moreover, various statistical methods suggested the existence of a radio periodicity of ~8 years. In 2004 the WEBT started a new campaign to extend the dataset to the most recent observing seasons, in order to possibly confirm and better understand the previous results. In this campaign we have collected and assembled about 11000 new optical observations from twenty telescopes, plus near-IR and radio data at various frequencies. Here, we perform a correlation analysis on the long-term R-band and radio light curves. In general, we confirm the ~100-day delay of the hard radio events with respect to the optical ones, even if longer (~200-300 days) time lags are also found in particular periods. The radio quasi-periodicity is confirmed too, but the "period" seems to progressively lengthen from 7.4 to 9.3 years in the last three cycles. The optical and radio behaviour in the last forty years suggests a scenario where geometric effects play a major role. In particular, the alternation of enhanced and suppressed optical activity (accompanied by hard and soft radio events, respectively) ca

Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on 10^3 pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease