Herbal medicines (HMs) are a major subset of complementary and alternative medicine. They have been employed for the efficient clinical management of type 2 diabetes mellitus (T2DM) for centuries. However, the related underlying mechanisms still remain to be elucidated. It has been found out that microbiota is implicated in the pathogenesis and treatment of T2DM. An interplay between gut microbiota and host occurs mainly at the gastrointestinal mucosal barrier. The host movements influence the composition and abundance of gut microbiota, whereas gut microbiota in turn modulate the metabolic and immunological activities of the host. Intestinal dysbiosis, endotoxin-induced metabolic inflammation, immune response disorder, bacterial components and metabolites, and decreased production of short-chain fatty acids are considered significant pathogenic mechanisms underlying T2DM. The interaction between gut microbiota and HMs during T2DM treatment has been investigated in human, animal, and in vitro studies. HMs regulate the composition of beneficial and harmful bacteria and decrease the inflammation caused by gut microbiota. Furthermore, the metabolism of gut microbiota modulates HM biotransformation. In this review, we have summarized such research findings, with the aim to improve our understanding of the pathogenesis and potential therapeutic mechanisms of HMs in T2DM and to provide new insights into specific targeted HM-based therapies and drug discovery.

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Background Low-frequency electroacupuncture (EA) has been shown to ameliorate obesity and reproductive dysfunctions in patients with polycystic ovary syndrome (PCOS), and further explorations in PCOS-like rats showed that EA could affect white adipose tissue. However, the function and neuromodulation of brown adipose tissue (BAT) in PCOS and after EA treatment have remained unknown. The present study focused on the role of BAT in PCOS-like rats and its relationship with EA and characterized the three-dimensional (3D) innervation of BAT associated with activation molecules. Methods Female rats (21 days old) were implanted with dihydrotestosterone or fed with a high fat diet to establish PCOS-like and obesity models, respectively, and then EA treatment at “Guilai” (ST 29) and “Sanyinjiao” (SP 6) was carried out for 4 weeks. In the present study, morphological analysis, 3D imaging, molecular biology, and other experimental techniques were used to study the sympathetic nerves and activity of BAT. Results PCOS-like rats showed both obvious weight gain and reproductive dysfunction, similar to what was seen in obese rats except for the absence of reproductive dysfunction. The body weight gain was mainly caused by an increase in white adipose tissue, and there was an abnormal decrease in BAT. Because both the lipid metabolism and reproductive disorders could be improved with bilateral EA at “Guilai” (ST 29) and “Sanyinjiao” (SP 6), especially the restoration of BAT, we further investigated the neuromodulation and inflammation in BAT and identified the sympathetic marker tyrosine hydroxylase as one of the key factors of sympathetic nerves. Modified adipo-clearing technology and 3D high-resolution imaging showed that crooked or dispersed sympathetic nerves, but not the twisted vasculature, were reconstructed and associated with the activation of BAT and are likely to be the functional target for EA treatment. Conclusion Our study highlights the significant role of BAT and its sympathetic innervations in PCOS and in EA therapy.

Maximum Mean Discrepancy (MMD) has been widely used in the areas of machine learning and statistics to quantify the distance between two distributions in the p-dimensional Euclidean space. The asymptotic property of the sample MMD has been well studied when the dimension p is fixed using the theory of U-statistic. As motivated by the frequent use of MMD test for data of moderate/high dimension, we propose to investigate the behavior of the sample MMD in a high-dimensional environment and develop a new studentized test statistic. Specifically, we obtain the central limit theorems for the studentized sample MMD as both the dimension p and sample sizes n, m diverge to infinity. Our results hold for a wide range of kernels, including popular Gaussian and Laplacian kernels, and also cover energy distance as a special case. We also derive the explicit rate of convergence under mild assumptions and our results suggest that the accuracy of normal approximation can improve with dimensionality. Additionally, we provide a general theory on the power analysis under the alternative hypothesis and show that our proposed test can detect difference between two distributions in the moderately high dimensional regime. Numerical simulations demonstrate the effectiveness of our proposed test statistic and normal approximation.