BackgroundEpidemiological literature regarding the effect of polycystic ovary syndrome (PCOS) as a risk factor for non-alcoholic fatty liver disease (NAFLD) remains inconsistent. Furthermore, it remains debatable whether NAFLD is associated with PCOS as a consequence of shared risk factors or whether PCOS contributes to NAFLD in an independent fashion. Therefore, this meta-analysis was conducted.MethodsThis meta-analysis was conducted in accordance with the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Relevant studies published before May 2017 were identified and retrieved from PubMed and Web of Science databases. The data were extracted, and the pooled odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated.ResultsA total of 17 studies were included into the present analysis. Compared to the control group, the risk of NAFLD in the PCOS group was higher (OR = 2.25, 95% CI = 1.95–2.60). When stratified by BMI and geographic location, the results indicated that the frequency of NAFLD risk was significantly higher in obese subjects (OR = 3.01, 95% CI = 1.88–4.82), non-obese subjects (OR = 2.07, 95% CI = 1.12–3.85), subjects from Europe (OR = 2.00, 95% CI = 1.58–2.52), subjects from the Asia-Pacific Region, (OR = 2.32, 95% CI = 1.89–2.84) and subjects from America (OR = 2.96, 95% CI = 1.93–4.55). In addition, PCOS patients with hyperandrogenism (HA) had a significantly higher risk of NAFLD, compared with controls (OR = 3.31, 95% CI = 2.58–4.24). However, there was no association between PCOS patients without HA and higher risk of NAFLD (OR = 1.46; 95% CI =0.55–3.87). The results of this meta-analysis should be interpreted with caution due to the small number of observational studies and possible confounding factors.ConclusionThe meta-analysis results suggest that PCOS is significantly associated with high risk of NAFLD. Although this association was independent of obesity and geographic region, it might be correlated with HA.

Abstract:The genus Gaultheria, comprised of approximately 134 species, is mostly used in ethnic drugs to cure rheumatism and relieve pain. Phytochemical investigations of the genus Gaultheria have revealed the presence of methyl salicylate derivatives, C 6 -C 3 constituents, organic acids, terpenoids, steroids, and other compounds. Methyl salicylate glycoside is considered as a characteristic ingredient in this genus, whose anti-rheumatic effects may have a new mechanism of action. In this review, comprehensive information on the phytochemistry, volatile components and the pharmacology of the genus Gaultheria is provided to explore its potential and advance research.

Human brains perform tasks via complex functional networks consisting of separated brain regions. A popular approach to characterize brain functional networks in fMRI studies is independent component analysis (ICA), which is a powerful method to reconstruct latent source signals from their linear mixtures. In many fMRI studies, an important goal is to investigate how brain functional networks change according to specific clinical and demographic variabilities. Existing ICA methods, however, cannot directly incorporate covariate effects in ICA decomposition. Heuristic post-ICA analysis to address this need can be inaccurate and inefficient. In this paper, we propose a hierarchical covariate-adjusted ICA (hc-ICA) model that provides a formal statistical framework for estimating covariate effects and testing differences between brain functional networks. Our method provides a more reliable and powerful statistical tool for evaluating group differences in brain functional networks while appropriately controlling for potential confounding factors. We present an analytically tractable EM algorithm to obtain maximum likelihood estimates of our model. We also develop a subspace-based approximate EM that runs significantly faster while retaining high accuracy. To test the differences in functional networks, we introduce a voxel-wise approximate inference procedure which eliminates the need of computationally expensive covariance matrix estimation and inversion. We demonstrate the advantages of our methods over the existing method via simulation studies. We apply our method to an fMRI study to investigate differences in brain functional networks associated with post-traumatic stress disorder (PTSD).

Significant charge delocalization in the Dion–Jacobson (4AMP)(MA)Pb2I7 perovskite enhances non-adiabatic coupling and accelerates non-radiative electron–hole recombination.