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Aim: To investigate the protective effect of dantonic in ischemic myocardial damage by evaluating the expression of circulating microvesicles (MVs) and microRNA-1 (miR-1) in two animal models. Methods: Two animal models of myocardial ischemia were established that were isoproterenol-induced myocardial ischemia (ISO-AMI) rat model and the acute myocardial infarction rat model induced by ligation of the left anterior descending coronary artery (LAD-AMI) of rat. To investigate the protective effect of dantonic, we observed the myocardial infarction size, creatine kinase (CK), lactate dehydrogenase (LDH), aspartate aminotransferase (AST) activities, cardiac troponin I (cTnI) level in serum, and the plasma levels of miR-1 and MVs. Results: The results showed that pretreatment with dantonic significantly attenuated the LAD-AMI induced myocardial damage by decreasing the size of myocardial infarction, CK, LDH, AST activities, and cTnI level in serum. High dose dantonic treatment could significantly abrogate the increased plasma levels of miR-1 and MVs as compared to the LAD rat model. In addition, pretreatment with dantonic also showed a significant myocardial protective effect through reducing the expression levels of CK, LDH, and AST as compared to the ISO-AMI model. Whereas the cTnI level was no significant difference between model group and control group, suggesting that the model caused less myocardial damage. In the ISO-induced myocardial ischemia model, there is no significant difference between the model group with the control group of MVs and miR-1 levels. This may be that miR-1 is reported as a biomarker of acute myocardial infarction. The pathological changes of IOS-induced acute myocardial ischemia model are also different from those of acute myocardial infarction. Conclusion: Dantonic showed the protective effect in these two ischemic myocardial injury rat models, whereas the circulating miR-1 and MVs levels were only ameliorated in the LAD rat model. [ABSTRACT FROM AUTHOR]

The widespread application of metal-organic frameworks (MOFs) in wastewater and gas treatment has created an increasing demand for accurate and rapid assessment of their BET specific surface area. However, experimental methods for acquiring sufficient statistical data are often costly and time-consuming. Therefore, this study proposes a dual-stage stacking model with Gaussian mixture model-virtual sample generation (GMM-VSG) technology for the BET specific surface area prediction. In this study, 90 real samples were selected from the MOF database and 300 virtual samples were generated. The performance on both real and virtual samples was evaluated by using four machine learning models, including Bayesian regression (Bayes), adaptive boosting (AdaBoost), random forest (RF), and extreme gradient boosting (XGBoost). Subsequently, three best-performing models and a linear regression model were selected for constructing a two-stage stacking model, with R 2 value of 0.974. Finally, experimental conditions were adjusted based on feature importance analysis during the validation process, and the result shows that the prediction accuracy of the BET specific surface area is 0.943. This study contributes to the development of more efficient and accurate evaluation methods.

Lung cancer is one of the most harmful cancers in the world, endangering the lives and health of many people. Although there are various methods to treat lung cancer at present, but lung cancer is asymptomatic in the early stages and has a high recurrence rate after late treatment which make it difficult to cure with conventional treatments. Drug combinations for the treatment of lung cancer have been used in many clinical studies. In this study, we constructed a recurrence model of Non-Small Cell Lung Cancer (NSCLC) lung cancer and used a combination of Ginsenoside H dripping pills (GH) and vinorelbine (NVB) to treat the recurrence of lung cancer. The results showed that the inhibition rate of the combined treatment of GH and NVB is 74.81% which is significantly higher than the therapeutic effect of separate use. We also used GC-TOF/MS-based metabolomics to identify differentially abundant metabolites in relapse models and explore biomarker trends. We found that there are 12 metabolite differences in the abundance of metabolites between the treatment groups (GH group, NVB group and GH-NVB group) and the model group, such as glucose 6-phosphate, palmitoleic acid, linoleic acid, guanine, allantoic acid. The differences in these metabolites involve glucose and lipid metabolism, amino acid metabolism, and purine metabolism. We further analyzed the changes in the content of these metabolites and found that the combined use of GH and NVB can regulate purine metabolism, folate synthesis, and thiamine metabolism, ultimately reducing the abnormal increase in alkaline phosphatase (AP). This study provides a method for the treatment of lung cancer and some biomarkers for the detection of lung cancer., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)