Kaempferol and ginsenoside Rg1 ameliorate acute hypobaric hypoxia induced lung injury based on network pharmacology analysis.

Acute hypobaric hypoxia at high altitude can cause fatal non-cardiogenic high altitude pulmonary edema. Anti-inflammatory and anti-oxidant treatments appear to be a prospective way to alleviate acute hypoxia lung injury. Kaempferol (KA) and ginsenoside Rg1 (GRg1) can be isolated and purified from ginseng with anti-inflammatory, antioxidant, anti-carcinogenic, neuroprotective, and antiaging effects. However, their effects and pharmacological mechanisms on lung injury remains unclear. Network pharmacology analyses were used to explore potential targets of KA and GRg1 against acute hypobaric hypoxia induced lung injury. Rat lung tissues were further used for animal experiment verification. Among the putative targets of KA and GRg1 for inhibition of acute hypobaric hypoxia induced lung injury, AKT1, PIK3R1, PTK2, STAT3, HSP90AA1 and AKT2 were recognized as higher interrelated targets. And PI3K-AKT signaling pathway is considered to be the most important and relevant pathway. The rat experimental results showed that KA and GRg1 significantly improved histopathological changes and decreased pulmonary edema in rats with lung injury caused by acute hypobaric hypoxia. The concentrations of IL-6, TNF-α, MDA, SOD and CAT in rats treated with KA and GRg1 were significantly ameliorated. Protein and mRNA levels of PI3K and AKTI were significantly inhibited after KA administration. KA and GRg1 can lower lung water content, improve lung tissue damage, reduce the production of pro-inflammatory cytokines and the oxidative stress level.

A Retrospective Cohort Study: Predicting 90-Day Mortality for ICU Trauma Patients with a Machine Learning Algorithm Using XGBoost Using MIMIC-III Database.

Objective: The aim of this study was to develop and validate a machine learning-based predictive model that predicts 90-day mortality in ICU trauma patients. Methods: Data of patients with severe trauma were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. The performances of mortality prediction models generated using nine machine learning extreme gradient boosting (XGBoost), logistic regression, random forest, AdaBoost, multilayer perceptron (MLP) neural networks, support vector machine (SVM), light gradient boosting machine (GBM), k nearest neighbors (KNN) and gaussian naive bayes (GNB). The performance of the model was evaluated in terms of discrimination, calibration and clinical application. Results: We found that the accuracy, sensitivity, specificity, PPV, NPV and F1 score of our proposed XGBoost model were 82.8%, 79.7%, 77.6%, 51.2%, 91.5% and 0.624, respectively. Among the nine models, the XGBoost model performed best. Compared with traditional logistic regression, the calibration curves of the XGBoost model and decision curve analysis (DCA) performed well. Conclusion: Our study shows that the XGBoost model outperforms other machine learning models in predicting 90-day mortality in trauma patients. It can be used to assist clinicians in the early identification of mortality risk factors and early intervention to reduce mortality.

Isolation of biogenic amine-negative lactic acid bacteria for Chinese rice wine fermentation based on molecular marker reverse screening.

BACKGROUND: Chinese rice wine (CRW; a traditional alcoholic beverage in China with unique flavor and high nutritional value) containing high level of biogenic amines (BAs) may be deleterious to human health. The processes of rice soaking, primary fermentation and secondary fermentation were found to be the major factors for accumulation of BAs during industrial CRW production. RESULTS: To reduce the risk of the formation of BAs in CRW production, Enterococcus durans AR315, a BA-negative lactic acid bacterium, was isolated from CRW samples by PCR-based molecular marker reverse screening in this work. With addition of AR315 during steeping rice phase, the level of total BAs was significantly decreased by 45.1% in comparison with the control. Moreover, the final BA concentration with the addition of AR315 was 27.6% lower than that of the control during fermentation phase. CONCLUSIONS: To our knowledge, this is the first report of decreased accumulation of BAs in CRW production using a BA-negative lactic acid bacterium. Hence, using a BA-negative lactic acid bacterium as a starter culture could be an efficient strategy for significantly reducing the formation of BAs, which has the potential for industrial application in CRW production. © 2020 Society of Chemical Industry.