Background: The aim of this study was to explore the estrogenic effects of the extracts from Chinese yam and its effective compounds. Methods: The activity of the yam was investigated by the uterine weight gain of mice and a proliferation assay of breast cancer cell lines (MCF-7 cell); the estrogenic activity was comprehensively evaluated by a serum pharmacology experiment. The levels of estradiol (E2), follicle stimulating hormone (FSH), and luteinizing hormone (LH) were also measured. Western blot analysis and antagonist assays with faslodex (ICI182,780), methylpiperidino-pyrazole (MPP), Delta (9) –tetrahydrocannabinol (THC), and G-15 were used to explore the mechanism of the effects of the yam. To find the effective compounds of the yam which play a role in its estrogen-like effects, we used the same methods to study the effects of adenosine and arbutin. Results: The Chinese yam and two main compounds, adenosine and arbutin, have estrogen-like effects. The mechanism of the yam which plays a role in its estrogen-like effects was mainly mediated by the estrogen receptors ERα, ERβ, and GPR30; that of adenosine was mainly mediated by estrogen receptors ERα and ERβ, and that of arbutin was mainly mediated by estrogen receptors ERβ and GPR30. Conclusions: The Chinese yam has estrogen-like effects; adenosine and arbutin are two of the effective compounds in the yam which play a role in its estrogen-like effects.

Background. The prognosis is poor when acute pancreatitis (AP) progresses to sepsis; therefore, it is necessary to accurately predict the probability of sepsis and develop a personalized treatment plan to reduce the disease burden of AP patients. Methods. A total of 1295 patients with AP and 43 variables were extracted from the Medical Information Mart for Intensive Care (MIMIC) IV database. The included patients were randomly assigned to the training set and to the validation set at a ratio of 7 : 3. The chi‐square test or Fisher’s exact test was used to test the distribution of categorical variables, and Student’s t‐test was used for continuous variables. Multivariate logistic regression was used to establish a prognostic model for predicting the occurrence of sepsis in AP patients. The indicators to verify the overall performance of the model included the area under the receiver operating characteristic curve (AUC), calibration curves, the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), and a decision curve analysis (DCA). Results. The multifactor analysis results showed that temperature, phosphate, calcium, lactate, the mean blood pressure (MBP), urinary output, Glasgow Coma Scale (GCS), Charlson Comorbidity Index (CCI), sodium, platelet count, and albumin were independent risk factors. All of the indicators proved that the prediction performance and clinical profitability of the newly established nomogram were better than those of other common indicators (including SIRS, BISAP, SOFA, and qSOFA). Conclusions. The new risk‐prediction system that was established in this research can accurately predict the probability of sepsis in patients with acute pancreatitis, and this helps clinicians formulate personalized treatment plans for patients. The new model can reduce the disease burden of patients and can contribute to the reasonable allocation of medical resources, which is significant for tertiary prevention.

A UPLC‐QTOF‐MS based metabolomics research was conducted to explore potential biomarkers which would increase our understanding of the model and to assess the integral efficacy of Descurainia sophia seeds extract (DS‐A). Additionally, DS‐A was split into five fractions in descending order of polarity, which were utilized to illustrate the mechanism together. The 26 identified biomarkers were mainly related to disturbances in phenylalanine, tyrosine, tryptophan, purine, arginine, and proline metabolism. Furthermore, heat map, hierarchical cluster analysis (HCA), and correlation network diagram of biomarkers perturbed by modeling were all conducted. The results of heat map and HCA suggested that fat oil fraction could reverse the abnormal metabolism in the model to some extent; meanwhile the metabolic inhibitory effect produced by the other four fractions helped to relieve cardiac load and compensate the insufficient energy supplement induced by the existing heart and lung injury in model rats. Briefly, the split fractions interfered with the model from different aspects and ultimately constituted the overall effects of extract. In conclusion, the metabolomics method, combined with split fractions of extract, is a powerful approach for illustrating pathologic changes of Chinese medicine syndrome and action mechanisms of traditional Chinese medicine.

Iridoid components have been reported to have significant neuroprotective effects. However, it is not yet clear whether the efficacy and mechanisms of iridoid components with similar structures are also similar. This study aimed to compare the neuroprotective effects and mechanisms of eight iridoid components (catalpol (CAT), genipin (GE), geniposide (GEN), geniposidic acid (GPA), aucubin (AU), ajugol (AJU), rehmannioside C (RC), and rehmannioside D (RD)) based on corticosterone (CORT)-induced injury in PC12 cells. PC12 cells were randomly divided into a normal control group (NC), model group (M), positive drug group (FLX), and eight iridoid administration groups. Firstly, PC12 cells were induced with CORT to simulate neuronal injury. Then, the MTT method and flow cytometry were applied to evaluate the protective effects of eight iridoid components on PC12 cell damage. Thirdly, a cell metabolomics study based on ultra-performance liquid chromatography–quadrupole–time-of-flight mass spectrometry (UPLC-Q/TOF-MS) was performed to explore changes in relevant biomarkers and metabolic pathways following the intervention of administration. The MTT assay and flow cytometry analysis showed that the eight iridoid components can improve cell viability, inhibit cell apoptosis, reduce intracellular ROS levels, and elevate MMP levels. In the PCA score plots, the sample points of the treatment groups showed a trend towards approaching the NC group. Among them, AU, AJU, and RC had a weaker effect. There were 38 metabolites (19 metabolites each in positive and negative ion modes, respectively) identified as potential biomarkers during the experiment, among which 23 metabolites were common biomarkers of the eight iridoid groups. Pathway enrichment analysis revealed that the eight iridoid components regulated the metabolism mainly in relation to D-glutamine and D-glutamate metabolism, arginine biosynthesis, the TCA cycle, purine metabolism, and glutathione metabolism. In conclusion, the eight iridoid components could reverse an imbalanced metabolic state by regulating amino acid neurotransmitters, interfering with amino acid metabolism and energy metabolism, and harmonizing the level of oxidized substances to exhibit neuroprotective effects.