Tanshinone IIA (TAN) is widely employed for handling cardiovascular disorders. The current study explored the potential role of miRs in the antifibrotic effect of TAN on heart. Fibrotic features were induced in cardiac fibroblasts (CFs) and in rat hearts, and then handled with TAN. MicroRNAs (miRs) responding to TAN were determined using a microarray assay. The selected miR was modulated to verify its role in antifibrotic effects of TAN. TAN suppressed the viability and the production of α-SMA in CFs, which was associated with 101 miR being upregulated and 223 miR being downregulated. MiR-618 was selected as the potential target of TAN. Ang II inhibited miR-618 level and resulted in the upregulation of pro-fibrosis factors, which was reversed by TAN. The antifibrotic effect of TAN was weakened by miR-618 inhibition. TAN inhibits hypertrophy and collagen deposition in heart tissues, which is associated with the increased level of miR-618. Practical applicationsThe findings outlined in the current study show that the antifibrotic function of TAN is closely related to the function of miRs: the induction of miR-618 is indispensable for the function of TAN against the fibrotic process after heart injury, which will promote the application of TAN as an adjuvant therapy for improving heart function.
Surface roughness parameters play a decisive role on grinding surface service performance. Among 26 surface roughness parameters, closely related to the surface performance and defined by ISO25178, in the 6 categories, numerical reconstruction of grinding surfaces can only control 7 height parameters and 2 spatial parameters with inability to achieve precise control of other parameter-defined features. Therefore, a novel numerical reconstruction method for grinding surfaces with specified rough parameter set (SRPS) was proposed to solve the problem of feature loss caused by other uncontrollable parameters. Combine three characteristic coefficients of height probability density function in Johnson transformation method with four characteristic coefficients of autocorrelation function expression to construct surface reconstruction coefficient set (SRCS) and build SRPS with number of 22 in consideration of the measurement error influence and industrial application frequency. Use BP neural network to establish quantitative mapping model between SRCS and SRPS for inversion. Through introducing genetic algorithm to invert SRPS corresponding SRCS, numerical reconstruction of grinding surfaces with SRPS will be realized. Experimental results show that compared with the measured grinding surfaces, the average error in SRPS of reconstructed surfaces is basically within 10%. The research provides a new means for the study of the surface performance, which solves the problem that the traditional rough surface modeling method based on random process theory could only associate 9 roughness parameters.
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