No abstract

Early reperfusion is the most effective and important treatment for acute myocardial infarction. However, reperfusion therapy often leads to a certain degree of myocardial damage. The aim of the present study was to identify the role of klotho, and the molecular mechanism underlying its effects, in myocardial damage using a model of myocardial hypoxia injury. Hypoxia/reoxygenation (H/R) was used to mimic ischemia/reperfusion (I/R) injury in vitro . The expression and distribution of klotho in H9c2(2-1) cells was observed by fluorogenic scanning, and the apoptotic rate was determined by Annexin V-FITC/propidium iodide dual staining. Cell viability was determined by MTT assay, and caspase-3, cleaved caspase-3, Bcl-2, Bax, heat shock protein (Hsp) 70 and Akt levels were assessed by western blotting. A lactate dehydrogenase test was performed to determine the degree of H9c2(2-1) cell damage. The results revealed that klotho was primarily located in the cytoplasm of H9c2(2-1) cells. Klotho overexpression markedly suppressed H/R-induced H9c2(2-1) cell apoptosis. Furthermore, cell viability increased, and injury decreased in response to klotho. Klotho also suppressed the activation of caspase-3, upregulated Bcl2 and decreased Bax levels following H/R injury, as well as alleviating H/R injury by upregulating the expression of Hsp70 and increasing the levels of phosphorylated (p-)Akt and Bad. In conclusion, these results indicate that klotho suppressed H/R-induced H9c2(2-1) cell apoptosis by regulating the levels of Hsp70, p-Akt and p-Bad, which suggest that klotho could be a novel agent for the treatment of coronary disease.

Cyclic steam stimulation (CSS) is one of the main offshore heavy oil recovery methods used. Predicting the production of horizontal CSS wells is significant for developing offshore heavy oil reservoirs. Currently, the existing reservoir numerical simulation and analytical models are the two major methods to predict the production of horizontal CSS wells. The reservoir numerical simulation method is tedious and time-consuming, while the analytical models need many assumptions, decreasing models’ accuracy. Therefore, in this study, a novel methodology combining the particle swarm optimization algorithm (PA) and long short-term memory (LM) model was developed to predict the production of horizontal CSS wells. First, a simulation model was established to calculate the cumulative oil production (COP) of horizontal CSS wells under different well, geological, and operational parameters, and then the correlations between the calculated COP and parameters were analyzed by Pearson correlation coefficient to select the input variables and to generate the initial data set. Then, a PA-LM model for the COP of horizontal CSS wells was developed by utilizing the PA to determine the optimal hyperparameters of the LM model. Finally, the accuracy of the PA-LM model was validated by the initial data set and actual production data. The results showed that, compared with the LM model, the mean absolute percentage error (MAPE) of the testing set for the PA-LM model decreased by 4.27%, and the percentage of the paired points in zone A increased by 2.8% in the Clarke error grids. In addition, the MAPEs of the training set for the PA-LM and LM models stabilized at 267 and 304 epochs, respectively. Therefore, the proposed PA-LM model had a higher accuracy, a stronger generalization ability, and a faster convergence rate. The MAPEs of the actual and predicted COP of the wells B1H and B5H by the optimized PA-LM model were 8.66% and 5.93%, respectively, satisfying the requirements in field applications.