Peripheral anti-inflammatory effects explain the ginsenosides paradox between poor brain distribution and anti-depression efficacy

<p>Abstract</p> <p>Background</p> <p>The effectiveness of ginseng in preventing and treating various central nervous system (CNS) diseases has been widely confirmed. However, ginsenosides, the principal components of ginseng, are characterized by poor accessibility to the brain, and this pharmacokinetic-pharmacological paradox remains poorly explained. Anti-inflammatory approaches are becoming promising therapeutic strategies for depression and other CNS diseases; however, previous studies have focused largely on anti-inflammatory therapies directed at the central nervous system. It is thus of interest to determine whether ginsenosides, characterized by poor brain distribution, are also effective in treating lipopolysaccharide- (LPS) induced depression-like behavior and neuroinflammation.</p> <p>Methods</p> <p>In an LPS-induced depression-like behavior model, the antidepressant effects of ginseng total saponins (GTS) were assessed using a forced swimming test, a tail suspension test, and a sucrose preference test. The anti-inflammatory efficacies of GTS in brain, plasma, and LPS-challenged RAW264.7 cells were validated using ELISA and quantitative real-time PCR. Moreover, indoleamine 2,3-dioxygenase (IDO) activity in the periphery and brain were also determined by measuring levels of kynurenine/tryptophan.</p> <p>Results</p> <p>GTS significantly attenuated LPS-induced depression-like behavior. Moreover, LPS-induced increases in 5-HT and tryptophane turnover in the brain were significantly reduced by GTS. IDO activities in brain and periphery were also suppressed after pretreatment with GTS. Furthermore, GTS-associated recovery from LPS-induced depression-like behavior was paralleled with reduced mRNA levels for IL-1β, IL-6, TNF-α, and IDO in hippocampus. Poor brain distribution of ginsenosides was confirmed in LPS-challenged mice. GTS treatment significantly decreased production of various proinflammatory cytokines in both LPS-challenged mice and RAW264.7 cells.</p> <p>Conclusion</p> <p>This study suggests that the anti-depression efficacy of GTS may be largely attributable to its peripheral anti-inflammatory activity. Our study also strengthens an important notion that peripheral anti-inflammation strategies may be useful in the therapy of inflammation-related depression and possibly other CNS diseases.</p

Rise of the Wenzhou Model and its influence on China's development strategy

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DeepHistone: a deep learning approach to predicting histone modifications

Abstract Motivation Quantitative detection of histone modifications has emerged in the recent years as a major means for understanding such biological processes as chromosome packaging, transcriptional activation, and DNA damage. However, high-throughput experimental techniques such as ChIP-seq are usually expensive and time-consuming, prohibiting the establishment of a histone modification landscape for hundreds of cell types across dozens of histone markers. These disadvantages have been appealing for computational methods to complement experimental approaches towards large-scale analysis of histone modifications. Results We proposed a deep learning framework to integrate sequence information and chromatin accessibility data for the accurate prediction of modification sites specific to different histone markers. Our method, named DeepHistone, outperformed several baseline methods in a series of comprehensive validation experiments, not only within an epigenome but also across epigenomes. Besides, sequence signatures automatically extracted by our method was consistent with known transcription factor binding sites, thereby giving insights into regulatory signatures of histone modifications. As an application, our method was shown to be able to distinguish functional single nucleotide polymorphisms from their nearby genetic variants, thereby having the potential to be used for exploring functional implications of putative disease-associated genetic variants. Conclusions DeepHistone demonstrated the possibility of using a deep learning framework to integrate DNA sequence and experimental data for predicting epigenomic signals. With the state-of-the-art performance, DeepHistone was expected to shed light on a variety of epigenomic studies. DeepHistone is freely available in https://github.com/QijinYin/DeepHistone

Plasma-produced vertical carbonous nanoflakes for Li-Ion batteries

An effective method to produce <i>vertically-oriented carbonous nanoflakes</i> (VCNFs) on copper foils using an advanced custom-designed plasma-enhanced horizontal tube furnace deposition system under different RF powers at 850 °C without using any binder or catalyst materials is demonstrated. The morphology and structure of the synthesized carbonous materials are investigated using Raman spectroscopy, scanning electron microscopy, X-ray photoelectron spectroscopy, and high-resolution transmission electron microscopy. It is shown that the VCNFs can be effectively synthesized at moderate RF powers. When the RF power is 660 W, VCNFs which are only 2 atomic carbon layers thin, can be grown. The thickness and level of structural defects in the VCNFs can also be controlled by changing the RF power. A plausible growth mechanism for VCNFs under the plasma conditions is proposed. The synthesized VCNFs are applied as a cathode material for Li-ion batteries. The coin-type batteries demonstrate stable efficiency (∼99%) and specific capacity (110 mAh g⁻¹) over 100 charge-discharge cycles