Hyperbaric oxygen via mediating SIRT1-induced deacetylation of HMGB1 improved cReperfusion inj/reperfusion injury.

Ischemic stroke leads to severe neurological dysfunction in adults. Hyperbaric oxygen (HBO) induces tolerance to cReperfusion inj/reperfusion (I/R) injury. Therefore, our aims were to investigate whether SIRT1 participates in regulatingin the neuro-protective effect of HBO in a cerebral I/R model and its mechanism. Mice N2a cells were used to construct an oxygen deprivation/reperfusion (OGD/R) model to simulate in vitro brain I/R injury and to evaluate the role of HBO in OGD/R stimulated cells. Cell proliferation was detected using MTT, and apoptosis was determined by flow cytometry. ELISA was used to measure the concentration of TNF-α, IL-1ß and IL-6 related inflammatory factors. RT-qPCR and western blot assays were performed to test the expression of SIRT1. Immunoprecipitation was used to detect acetylation of HMGB1. Expression of SIRT1 was obviously reduced after OGD/R treatment in N2a cells, while SIRT1 was obviously enhanced in HBO treated cells. Moreover, knockdown of SIRT1 induced neuro-inflammation damage in cells and HBO effectively improved the inflammatory response in OGD/R treated cells by affecting SIRT1 levels. Furthermore, HBO induced the deacetylation of HMGB1 via regulating SIRT1. Interestingly, HBO via regulating the SIRT1-induced HMGB1 deacetylation and suppressing MMP-9 improved ischemic brain injury. HBO regulated ischemic brain injury via regulation of SIRT1-induced HMGB1 deacetylation, making it a potential treatment for ischemic brain injury treatment.

Predicting Short-term Survival after Liver Transplantation using Machine Learning.

Liver transplantation is one of the most effective treatments for end-stage liver disease, but the demand for livers is much higher than the available donor livers. Model for End-stage Liver Disease (MELD) score is a commonly used approach to prioritize patients, but previous studies have indicated that MELD score may fail to predict well for the postoperative patients. This work proposes to use data-driven approach to devise a predictive model to predict postoperative survival within 30 days based on patient's preoperative physiological measurement values. We use random forest (RF) to select important features, including clinically used features and new features discovered from physiological measurement values. Moreover, we propose a new imputation method to deal with the problem of missing values and the results show that it outperforms the other alternatives. In the predictive model, we use patients' blood test data within 1-9 days before surgery to construct the model to predict postoperative patients' survival. The experimental results on a real data set indicate that RF outperforms the other alternatives. The experimental results on the temporal validation set show that our proposed model achieves area under the curve (AUC) of 0.771 and specificity of 0.815, showing superior discrimination power in predicting postoperative survival.