Beneficial effect of vinegar consumption associated with regulating gut microbiome and metabolome.

Vinegar is used as fermented condiment and functional food worldwide. Vinegar contains many nutrients and bioactive components, which exhibits health benefits. In this study, the potential effects of Shanxi aged vinegar (SAV) on gut microbiome and metabolome were explored in normal mice. The levels of inflammatory factors were significantly decreased in SAV-treated mice. Immunoglobulin, NK cells and CD20 expression were significantly increased after SAV administration. In addition, SAV intake altered gut microbiota structure by up-regulating Verrucomicrobia, Akkermansia, Hungatella and Alistipes, and down-regulating Firmicutes, Lachnospiraceae_NK4A136_group and Oscillibacter. The differential metabolites were mainly included amino acids, carbohydrates and bile acids. Furthermore, after SAV intake, Verrucomicrobia, and Akkermansia closely impacted the related gut metabolites. These alterations of gut microbiota-related metabolism further modulated some immunoregulatory and inflammatory factors, and confer potential health benefits. Our results imply that vinegar consumption has beneficial effects on regulating gut microbiome and metabolome.

Hepatoprotective Mechanism of Ginsenoside Rg1 against Alcoholic Liver Damage Based on Gut Microbiota and Network Pharmacology.

Alcoholic liver disease (ALD) is a major public health problem worldwide, which needs to be effective prevention. Ginsenoside Rg1 (GRg1), a bioactive ingredient extracted from ginseng, has benefit effects on health. In this study, 11 potential targets of GRg1 against ALD were firstly obtained by network pharmacology. KEGG pathway enrichment showed that GRg1-target-ALD was closely related to Toll-like receptor (TLR) and nuclear factor-kappa B (NF-κB) signaling pathways. In addition, GRg1 decreased antioxidant levels and increased oxidative levels in alcohol-treated mice, which alleviated oxidative stress-induced hepatic damage. GRg1 enhanced intestinal barrier function via upregulating the levels of tight junction protein and immunoglobulin A. GRg1 also reduced alcohol-induced inflammation by suppressing TLR4/NF-κB pathway, which was consistent with the prediction of network targets. Moreover, GRg1 altered GM population, and Verrucomicrobia, Bacteroidetes, Akkermansia, Bacteroides, Lachnospiraceae_NK4A136_group, and Alloprevotella played positive association with intestinal barrier indicators and negative correlation with hepatic inflammation biomarkers. The results suggest that GRg1 administration might be a promising strategy for protection of alcohol-induced liver damage.

The anti-diabetic activity of polyphenols-rich vinegar extract in mice via regulating gut microbiota and liver inflammation.

Polyphenols in vinegar are benefit to human health. The purpose of this research was to identify the polyphenols-rich vinegar extract (VE) and evaluate the anti-diabetic mechanisms in vivo. The results showed that 29 polyphenols were identified by UPLC-Q/Trap-MS/MS analysis. 4-Hydroxybenzoic acid, ferulic acid, and ethyl ferulate were the main polyphenols. In addition, VE relieved the symptoms of type 2 diabetes mellitus (T2DM) by down-regulating blood glucose and lipemia. VE reduced inflammation by inhibiting TLR4/NF-κB signaling pathway. Furthermore, VE treatment restored gut microbiota dysbiosis (upregulating Bacteroidetes, Lactobacillus, Bifidobacterium, and Bacteroides and downregulating Firmicutes, Proteobacteria, and Enterorhabdus abundances), and increased short chain fatty acids contents in diabetic mice, which participated in anti-diabetic effect of VE by correlation analysis. These findings suggest that VE may be a candidate for T2DM intervention by regulating gut microbiota and inflammation.

Near-infrared spectroscopy and machine learning-based technique to predict quality-related parameters in instant tea.

The traditional method for analyzing the content of instant tea has disadvantages such as complicated operation and being time-consuming. In this study, a method for the rapid determination of instant tea components by near-infrared (NIR) spectroscopy was established and optimized. The NIR spectra of 118 instant tea samples were used to evaluate the modeling and prediction performance of a combination of binary particle swarm optimization (BPSO) with support vector regression (SVR), BPSO with partial least squares (PLS), and SVR and PLS without BPSO. Under optimal conditions, Rp for moisture, caffeine, tea polyphenols, and tea polysaccharides were 0.9678, 0.9757, 0.7569, and 0.8185, respectively. The values of SEP were less than 0.9302, and absolute values of Bias were less than 0.3667. These findings indicate that machine learning can be used to optimize the detection model of instant tea components based on NIR methods to improve prediction accuracy.