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Background/Aims: In this study, the molecular mechanisms of miR-27b and lipoprotein lipase (LPL) that regulate human adipose-derived mesenchymal stem cells (hASCs) adipogenic differentiation were detected. Methods: Microarray analysis was applied to screen for differentially expressed miRNAs and mRNA during hASCs adipocyte differentiation induction. MiR-27b and LPL were found to have abnormal expression. Then, a dual luciferase reporter assay was employed to validate the targeting relationship between miR-27b and LPL. We also utilized qRT-PCR, western blot, cellular immunofluorescence and an oil red O staining assay to analyze the regulation of miR-27b and LPL during adipogenic differentiation. Results: The microarray analysis demonstrated that, during adipogenic differentiation, miR-27b was down-regulated, while LPL was up-regulated but tended to become stable 14 days after induction. A dual luciferase reporter assay confirmed the negative targeting regulatory relationship between miR-27b and LPL. After overexpressing and silencing miR-27b, LPL was found to be reversely regulated by miR-27b according to qRT-PCR and western blot. The fat-formation-related biomarkers CCAAT-enhancer binding protein α (c/EBPα) and peroxisome proliferator-activated receptors γ (PPARγ) had decreasing levels after over-expressing miR-27b or knockdown of LPL followed by adipogenic differentiation. Meanwhile, the oil red O staining assay revealed that the accumulation of lipid droplets decreased. There was no change in the expression of c/EBPα, PPARγ, or lipid droplet accumulation when overexpressing miR-27b and LPL. Conclusion: During the adipogenic differentiation of hASCs, miR-27b expression decreased, and LPL expression increased. The abnormal expression of miR-27b and LPL effectively regulated the adipogenic differentiation of hASCs.

Long short-term memory (LSTM) is widely applied in both academic and industrial fields. However, there is no reliable criterion on selecting hyperparameters of LSTM. Currently, although some widely used classic methods such as random search and grid search have obtained success to some extent, the problems in local optimum and convergence still exist. In this research, we propose to use grey wolf optimizer (GWO) to search for the hyperparameters of LSTM. Through the method, the superiority of metaheuristic in global optimization and the strength of LSTM in predicting are combined. In this model, number of hidden layer nodes and learning rate of LSTM are set as preys, and grey wolf pack has a simple but efficient mechanism to search for the optimal hyperparameters. The benchmark tests on several basic functions were utilized, and the results were verified by a comparative study with random search, support vector regression and several other regression methods. Specifically, we applied this algorithm in predicting the degradation trend of the airborne fuel pump. As a result, the ergodicity and convergence of the algorithm are proved mathematically based on Markov processes theory. The benchmark tests show that the GWO-LSTM model holds for predicting data with low overall slope and high partial fluctuation. The application in airborne fuel pump shows that, trained by dataset with 5700 points, the proposed model could predict sequence of 300 points with root mean square error 0.617 after 30 iterations of optimizing, which is 2.512 previously. The result further demonstrates that the proposed algorithm is applicable to make prediction with high accuracy. Overall, the effectiveness of GWO-LSTM model is verified from theoretical proof to benchmark tests and then to actual product application.

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