Jian Chen

BACKGROUND Yiyi Fuzi Baijiang powder (YFB), a classic Chinese medicine, significantly affects ulcerative colitis (UC). However, it remains unclear whether YFB plays a therapeutic role by improving the intestinal flora of UC patients and its active ingredients. AIM To explore the mechanisms of action of YFB in treating UC. METHODS A mouse model of UC was established by drinking 2.5% dextran sulfate sodium (DSS). Mice were treated with YFB. 16S rDNA sequencing was used to detect changes in intestinal flora and perform functional predictions. Corresponding target genes of core active ingredients in YFB and UC were obtained using multiple database retrievals and then used to predict the mechanism of overlapping targets. After screening core ingredients and target genes, AutoDock software was used for molecular docking, and the best binding target was selected to verify binding activity. RESULTS YFB improved DSS-UC mice by restoring body weight, reducing disease activity index, increasing water and food intake, and alleviating diarrhea and local histopathological damage. YFB enhanced beta diversity, decreased pathogenic bacteria such as Turicibacter and Clostridium_sensu_stricto_1 , and increased probiotics such as unclassified_f_Lachnospiraceae and Akkermansia . However, it also reduced anaerobic probiotics such as Ruminococcus , Enterorhabdus and Bifidobacterium . Network pharmacology identified 17 pathways, with cancer and adipocytokine signaling pathways showing significant differences in predicting intestinal microbial function. Molecular docking revealed that nuclear factor kappa-B inhibitor A, RELA and NFKB1 , and colchamine, morusin and orotinin had docking scores > 5.0. CONCLUSION YFB treats UC by reducing harmful bacteria and boosting probiotics to restore intestinal balance, while potentially influencing signaling pathways.

Dried ginger (Gan-Jiang (GJ)) is the dried rhizome ofZingiber officinale Rosc., which has two different processed products, baked ginger (Pao-Jiang (PJ)) and ginger charcoal (Jiang-Tan (JT)). However, PJ and JT are still confused with each other in the market due to the absence of quality markers. In this study, metabolomics and chemometrics were used to detect metabolites and screen featured metabolites in GJ, PJ, and JT. Network pharmacology was employed to evaluate their activity strength. Machine learning algorithms were applied to distinguish GJ, PJ, and JT. Our findings illustrated that a total of 171 metabolites were identified. Among them, 20 featured metabolites were selected, including gingerenone C, methyl-6-gingerdiol, and gingerenone A. Significant changes in the content of featured metabolites were attributed to the conversion of gingerols and their degradation under high temperatures. Network pharmacology analysis suggested that featured metabolites mainly regulate the calcium signaling pathway, PI3K-Akt signaling pathway, and NF-κB signaling pathway, exerting different activity strengths. Furthermore, all 10 machine learning models exhibited a relatively good predictive performance in training sets (100%) and testing sets (97.83%). Overall, this study provides a theoretical and application basis for identifying and controlling the quality of GJ and its different processed products.

Rationale: Panax quinquefolius L. (PQ), a commonly used traditional Chinese medicine and a food, is usually processed into various products, including white PQ, red PQ (two‐ or three‐time steamed PQ), and black PQ (nine‐time steamed PQ). Previous studies demonstrated that volatile components (VOCs) were the important active substances of PQ, which had antibacterial, antiviral, and anti‐leukemia activities. However, most research had focused on ginsenosides, and few studies on the volatile components (VOCs) of PQ. Methods: This study used gas chromatography‐ion mobility spectrometry to analyze the variation of VOCs in PQ during steaming process. Further, machine learning algorithms were used to quickly identify the steaming degrees of PQ samples. Results: A total of 58 VOCs were identified, and 20 featured components with significant changes in the content were screened, including 2‐methylundecanal, n‐propanol, and n‐octanol. Based on these 20 featured components, six machine learning algorithms were used to predict PQ samples with different steaming degrees. Among them, naive Bayes (NB) and linear discriminant analysis (LDA) exhibited good predictive performance, demonstrating significant potential application. This study provided a reference for understanding the variation of VOCs in PQ during steaming and offered a simple, rapid, and low‐cost method for distinguishing the steaming degrees of PQ samples.

Background: An animal model of DSS-induced UC has been widely used in basic research, and the dysbiosis of gut microbiome is one of the important pathogenetic mechanisms of DSS-induced UC, but its dynamic changes and correlation with inflammatory factors are not clear yet. Methods: Clinical signs and tissue damage degree of C57BL/6 ulcerative colitis mice model induced by different concentrations of DSS were compared with that of normal mice, and finally the optimal concentration of DSS was determined. Then we analyzed the sequencing results of gut microbiome and inflammatory factors to determine the dynamic patterns of gut microbiome and their correlation with the inflammatory factors. Results: DSS at 2.5% and 3.0% concentration could cause intestinal injury and induce colitis. However, 3.0% DSS resulted in higher mortality. In addition, there were dynamic changes of gut microbiome in DSS-induced UC model: the relative abundance of intestinal flora increased first and then decreased in Bacteroides, Parabacteroides, Romboutsia, Clostridium_sensu_stricto_1, Lachnospiraceae_NK4A136_group, norank_f_norank_o_Clostridia_UCG-014, Parasutterella, and decreased first and then increased in Lactobacillus, Muribaculum, norank_f_Muribaculaceae, in addition, Bifidobacterium, Coriobacteriaceae_UCG-002 and Enterorhabdus did not change in the first 14 days but increased significantly on day 21. Moreover, inflammatory cytokines were closely associated with the imbalance of the intestinal microbiota in mice with UC: most pathogenic bacteria in the intestinal tract of the UC animal model were positively correlated with pro-inflammatory factors and negatively correlated with anti-inflammatory factors, while beneficial bacteria were the opposite. Conclusion: Intestinal microecology plays an important role in DSS-induced UC model, and the relative abundance of gut microbiome changes dynamically in the occurrence and development of ulcerative colitis.

Background/Aim: To use liquid chromatography-mass spectrometry (LC-MS) to identify endogenous differential metabolites in the urine of rats with chronic atrophic gastritis (CAG). Materials and Methods: Methylnitronitrosoguanidine (MNNG) was used to produce a CAG model in Wistar rats, and HE staining was used to determine the pathological model. LC-MS was used to detect the differential metabolic profiles in rat urine. Diversified analysis was performed by the statistical method. Results: Compared with the control group, the model group had 68 differential metabolites, 25 that were upregulated and 43 that were downregulated. The main metabolic pathways were D-glutamine and D-glutamic acid metabolism, histidine metabolism and purine metabolism. Conclusion: By searching for differential metabolites and metabolic pathways in the urine of CAG rats, this study provides effective experimental data for the pathogenesis and clinical diagnosis of CAG.