Research progress in biological activities and mechanisms of theabrownin

Tea is beneficial to human health, which is rich in tea pigments with important biological activities. Theabrownin, derived from theaflavins and thearubigins by oxidative polymerization, mainly distributes in semi-fermented oolong tea, and completely fermented black tea and dark tea. As a kind of macromolecular substance, theabrownin cannot be directly absorbed by the gut, but it can directly interact with intestinal microbiota to regulate and maintain the homeostasis of intestinal flora. Theabrownin has multiple physiological roles via modulating the gut microbiota, including inhibiting hepatic cholesterol production, promoting the catabolism of cholesterol and triglyceride, and promoting energy metabolism in adipose tissues, thereby improving lipid metabolism. Theabrownin can also directly influence the gut absorption of glucose to improve carbohydrate metabolism and maintain blood glucose homeostasis. Theabrownin plays an anti-tumor role by inducing apoptosis and regulating gene expression in tumor cells. Theabrownin also plays an anti-inflammatory role via participating in the regulation of the immune cell differentiation and the levels of inflammatory factors. This review summarizes the formation process, the extraction procedures, and the chemical structure of theabrownin, and reviews the effects and mechanisms of theabrownin on intestinal microbiota, lipid metabolism, blood glucose homeostasis, cancer and inflammation

Management of Hepatic Encephalopathy by Traditional Chinese Medicine

In spite of the impressive progress in the investigation of hepatic encephalopathy (HE), the complex mechanisms underlying the onset and deterioration of HE are still not fully understood. Currently, none of the existing theories provide conclusive explanations on the symptoms that link liver dysfunction to nervous system disorders and clinical manifestations. This paper summarized the diagnostic and therapeutic approaches used for HE in modern medicine and traditional Chinese medicine and provided future perspective in HE therapies from the viewpoint of holistic and personalized Chinese medicine

Management of Hepatic Encephalopathy by Traditional Chinese Medicine

In spite of the impressive progress in the investigation of hepatic encephalopathy (HE), the complex mechanisms underlying the onset and deterioration of HE are still not fully understood. Currently, none of the existing theories provide conclusive explanations on the symptoms that link liver dysfunction to nervous system disorders and clinical manifestations. This paper summarized the diagnostic and therapeutic approaches used for HE in modern medicine and traditional Chinese medicine and provided future perspective in HE therapies from the viewpoint of holistic and personalized Chinese medicine

Determination of 13 Free Fatty Acids in Pheretima Using Ultra-Performance LC-ESI-MS

Abstract: A simple and rapid ultra-performance liquid chromatography-electrospray ionization mass spectrometry method for the simultaneous determination of thirteen free fatty acids (FFAs) in Pheretima has been developed and validated. Measurements for each FFA were linear over a wide range (0.05-3.95 μg mL −1 ) with good correlation coefficients (>0.99). The limit of detection and limit of quantification for all the fatty acids were below 26 and 78 ng mL −1 , respectively. The intra-and inter-assay precision and accuracy for the thirteen FFAs fell well within the predefined limits of acceptability. Satisfactory recoveries were in the range of 96-103%. Article: INTROCUDTION Pheretima has been well known for its wide therapeutic properties such as anti-inflammatory, anti-oxidative [1], anti-asthmatic, thrombolytic, reducing symptoms of the central nervous system decline including memory loss in traditional Chinese medicine (TCM) for over 2,000 years Although fatty acids have measurable absorbance in the range of 190-215 nm, the interference of most solvents is a limiting factor for sensitive detection when analyzed directly by liqui

Pu-erh Tea Regulates Fatty Acid Metabolism in Mice Under High-Fat Diet

Pu-erh tea has been extensively reported to possess lipid lowering effects but the underlying mechanisms remained unclear. Free fatty acids (FFAs) are generally correlated with the development of obesity, leading to increased risk for type 2 diabetes mellitus and cardiovascular diseases. To investigate whether Pu-erh tea treatment alters FA metabolism, we treated HFD induced obese mice with Pu-erh tea for 22 weeks and analyzed FFA profiles of experimental mice using a UPLC-QTOF-MS platform. Results showed remarkable changes in metabolic phenotypes and FFA compositions in mice treated with or without Pu-erh tea. HFD induced a marked obese phenotype in mice as revealed by significantly increased body weight, liver and adipose tissue weight, lipid levels in serum and liver, and these parameters were markedly reduced by Pu-erh tea treatment. Several FFA or FFA ratios, such as DGLA, palmitoleic acid, and OA/SA ratio, were significantly increased while the levels of SA/PA and AA/DGLA were significantly reduced in HFD-induced obese mice. Interestingly, these differential FFAs or FFA ratios were previous identified as key markers in human obese subjects, and their changes observed in the HFD group were reversed by Pu-erh tea treatment. Moreover, a panel of FFA markers including C20:3 n6/C18:3 n6 and C20:3 n6/C20:2 n6, C18:3 n6/C18:2 n6, C18:3 n3/C18:2 n6 and C24:1 n9/C22:1 n9, which were previously identified as biomarkers in predicting the remission of obesity and diabetes in human subjects who underwent metabolic surgery procedures, were reversed by Pu-erh tea intervention. Pu-erh tea significantly improved glucose homeostasis and insulin tolerance compared to the HFD group. Additionally, Pu-erh tea treatment significantly decreased FFA synthesis genes and increased the expression of genes involved in FFA uptake and β-oxidation including FATP2, FATP5, PPARα, CPT1α, and ACOX-1. These finding confirmed the beneficial effects of Pu-erh tea on regulating lipid and glucose metabolism, and further validated a panel of FFA markers with diagnostic and prognostic value for obesity and diabetes

A targeted metabolomic protocol for short-chain fatty acids and branched-chain amino acids

Research in obesity and metabolic disorders that involve intestinal microbiota demands reliable methods for the precise measurement of the short-chain fatty acids (SCFAs) and branched-chain amino acids (BCAAs) concentration. Here, we report a rapid method of simultaneously determining SCFAs and BCAAs in biological samples using propyl chloroformate (PCF) derivatization followed by gas chromatography mass spectrometry (GC-MS) analysis. A one-step derivatization using 100 µL of PCF in a reaction system of water, propanol, and pyridine (v/v/v = 8:3:2) at pH 8 provided the optimal derivatization efficiency. The best extraction efficiency of the derivatized products was achieved by a two-step extraction with hexane. The method exhibited good derivatization efficiency and recovery for a wide range of concentrations with a low limit of detection for each compound. The relative standard deviations (RSDs) of all targeted compounds showed good intra- and inter-day (within 7 days) precision (< 10%), and good stability (< 20%) within 4 days at room temperature (23–25 °C), or 7 days when stored at −20 °C. We applied our method to measure SCFA and BCAA levels in fecal samples from rats administrated with different diet. Both univariate and multivariate statistics analysis of the concentrations of these target metabolites could differentiate three groups with ethanol intervention and different oils in diet. This method was also successfully employed to determine SCFA and BCAA in the feces, plasma and urine from normal humans, providing important baseline information of the concentrations of these metabolites. This novel metabolic profile study has great potential for translational research

Imitation of a Pre-Designed Irregular 3D Yarn in Given Fabric Structures

The 3D CAD software has obvious advantages in appearance imitating and geometric structure modeling for fabrics. In contemporary 3D CAD fabric systems, only uniform yarns are involved in studies on fabric geometric structures, due to technological limitations, whereas objectives such as irregular/uneven 3D yarns have not been considered much. As the fabric structure or the central curve of the yarn changes, it is difficult to reflect the changed positions of the effect spots of the pre-designed uneven 3D yarns accordingly. In this paper, a key-point-mapping algorithm between the source yarn and the target curve is proposed to reflect the position change in effect spots when the fabric structure changes. By using the shape-preserving quasi-uniform cubic B-spline curve, a simple 3D irregular source yarn is designed using key points and setting their corresponding base cross-sections. The mapping is based on the principle that the lengths of the curve between the key points and the contours of the corresponding base cross-sections of the source yarn remain unchanged. Finally, the control grid of the new 3D yarn in the fabric structure is automatically generated. According to the examples and error analysis, the mapping technique can be applied to arbitrary given fabric structures, and the effect spots of the irregular 3D yarn are reasonably distributed as expected

Using "Umbrella Deconstruction & Energy Dispersive Spectrometer (UD-EDS)" technique to quantify the anisotropic elements distribution of "Chang 7" shale and its significance

This study utilizes the experimental technique named "Umbrella Deconstruction & Energy Dispersive Spectrometer (UD-EDS)" method to quantify the anisotropic element distribution of shale which has been proved significant in the stimulation of shale. Direct quantification of anisotropic distribution of element in shale from the Triassic Yanchang formation in the HJS" district was carried out to ensure both observational resolution and sample representativeness. Results show that many types of elements distribution vary in eight directions. The element contents are similar in three directions - 90 degrees (270 degrees), 112.5 degrees (292.5 degrees) and 135 degrees (315 degrees), they are quite different from which in other five directions, which has obvious significance in shale stimulation. Results also prove that the evidence of dominant fracture direction in fracturing can be found in brittle minerals. As to "Chang 7" shale, the dominant fracture direction of shale reservoir is distributed in a specific area instead of overall extending along a single direction. In this specific area the best dominant fracture direction can be found. The subsequent results would offer the microscopic evidences for the shale fracturing and point to an innovative direction for research on exploration and development of the unconventional oil and gas. (C) 2019 Elsevier Ltd. All rights reserved

Predicting the components and types of kerogen in shale by combining machine learning with NMR spectra

This study aims to develop a new method that combines machine learning with nuclear magnetic resonance (NMR) spectra to predict the kemgen components and types. Kerogen is the primary hydrocarbon source of shale oil/gas, and nearly half of the hydrocarbons in shale are adsorbed in kemgen. The adsorption and hydrocarbon generation capacity of kerogen is directly related to its types, molecular components, and structures. Fruitful researches studying kerogen at the molecular level have been conducted. Unfortunately, these methods are complicated, time-consuming, and labor-intensive. Our method has the advantages of high-throughput prediction, high accuracy, and time savings compared with the existing methods. Additionally, this method simplifies the operations from repetitive trial and error. This study proposes a solution to convert non-uniform two-dimensional (2D) graph into a uniform one-dimensional (1D) matrix, which makes 2D graph data available for machine learning models. An automatic labeling platform is constructed that annotated over 22,000 groups of organic matter molecules and their NMR spectra. The results show that the carbon, hydrogen, and oxygen element prediction accuracy reach 96.1%, 94.8%, and 81.7%, respectively. In addition, the accuracy of the three kerogen types is approximately 90% in total. These results reflect the excellent performance of the machine learning method. Therefore, our work provides an automated and intelligent prediction and analysis method, which is a powerful and superior tool in kerogen studies at the molecular level