Skin Transcriptome of Middle-Aged Women Supplemented With Natural Herbo-mineral Shilajit Shows Induction of Microvascular and Extracellular Matrix Mechanisms

Objective: Shilajit is a pale-brown to blackish-brown organic mineral substance available from Himalayan rocks. We demonstrated that in type I obese humans, shilajit supplementation significantly upregulated extracellular matrix (ECM)–related genes in the skeletal muscle. Such an effect was highly synergistic with exercise. The present study (clinicaltrials.gov ) aimed to evaluate the effects of shilajit supplementation on skin gene expression profile and microperfusion in healthy adult females. Methods: The study design comprised six total study visits including a baseline visit (V1) and a final 14-week visit (V6) following oral shilajit supplementation (125 or 250 mg bid). A skin biopsy of the left inner upper arm of each subject was collected at visit 2 and visit 6 for gene expression profiling using Affymetrix Clariom™ D Assay. Skin perfusion was determined by MATLAB processing of dermascopic images. Transcriptome data were normalized and subjected to statistical analysis. The differentially regulated genes were subjected to Ingenuity Pathway Analysis (IPA®). The expression of the differentially regulated genes identified by IPA® were verified using real-time polymerasechain reaction (RT-PCR). Results: Supplementation with shilajit for 14 weeks was not associated with any reported adverse effect within this period. At a higher dose (250 mg bid), shilajit improved skin perfusion when compared to baseline or the placebo. Pathway analysis identified shilajit-inducible genes relevant to endothelial cell migration, growth of blood vessels, and ECM which were validated by quantitative real-time polymerasechain reaction (RT-PCR) analysis. Conclusions: This work provides maiden evidence demonstrating that oral shilajit supplementation in adult healthy women induced genes relevant to endothelial cell migration and growth of blood vessels. Shilajit supplementation improved skin microperfusion

A Geometric Variational Approach to Bayesian Inference

We propose a novel Riemannian geometric framework for variational inference in Bayesian models based on the nonparametric Fisher-Rao metric on the manifold of probability density functions. Under the square-root density representation, the manifold can be identified with the positive orthant of the unit hypersphere in L2, and the Fisher-Rao metric reduces to the standard L2 metric. Exploiting such a Riemannian structure, we formulate the task of approximating the posterior distribution as a variational problem on the hypersphere based on the alpha-divergence. This provides a tighter lower bound on the marginal distribution when compared to, and a corresponding upper bound unavailable with, approaches based on the Kullback-Leibler divergence. We propose a novel gradient-based algorithm for the variational problem based on Frechet derivative operators motivated by the geometry of the Hilbert sphere, and examine its properties. Through simulations and real-data applications, we demonstrate the utility of the proposed geometric framework and algorithm on several Bayesian models