CircKMT2E Participates in Osteoarthritis through Promotes Apoptosis of Chondrocytes Via Sponging miR-140-5p to Activate TLR4.

Objective: To explore the latent pathogenesis of circRNAs in osteoarthritis (OA), as well as their function mechanism. Methods: The murine chondrocytes with and without OA were involved and used for in-depth sequencing. Herein, we carried out subsequent bioinformatics analysis to disclose the expression pattern, characteristics of circRNAs based on gene ontology, and the KEGG pathway analyses. Then sequencing data were used to deduce the interaction between circRNA and miRNA. The potential miRNA response elements for the annotated circRNAs and relevant target genes were forecasted on the basis of TargetScan and miRanda. For chondrocytes, the effect of the overexpression of the screened circRNA for apoptosis was spotted by flow cytometry as well as Western Blot. Results: 466 diverse circRNAs in the 23,787 spotted circRNAs were both significantly and differentially transcribed. CircKMT2E was upregulated more than two folds in chondrocytes with OA compared with normal tissues, exhibiting an expression trend opposite to miR-140-5p. We disclosed that circKMT2E could possess mutual effect with miR-140-5p by way of AGO proteins. Thus, circKMT2E was verified to have functioned as a molecular sponge targeting miR-140-5p. Therefore, circKMT2E may be at work in the pathogenesis of OA. Further, the sponge connection between circKMT2E and miR-140-5p was proved on the basis of a dual-luciferase reporter assay. Besides, miR-140-5p was speculated can bind TLR4 by bioinformatics analysis. Further PCR analysis found the relative expression level of TLR4, caspase-3, and Bax in the OA groups presented significant upregulation. Overexpression of circKMT2E can promote apoptosis of chondrocytes. Conclusion: The upregulation of circKMT2E is involved in the chondrocyte apoptosis of the pathogenesis of OA through activation of TLR4 by the sponge function of miR-140-5p.

Semi-Empirical Prediction of Residual Stress Distributions Introduced by Turning Inconel 718 Alloy Based on Lorentz Function.

The residual stress of machined surface has a crucial influence on the performance of parts. It results in large deviations in terms of the position accuracy, dimension accuracy and service life. The purpose of the present study is to provide a novel semi-empirical residual stress prediction approach for turning Inconel 718. In the method, the bimodal Lorentz function was originally applied to express the residual stress distribution. A statistical model between the coefficients of the bimodal Lorentz function and cutting parameters was established by the random forest regression, in order to predict the residual stress distribution along the depth direction. Finally, the turning experiments, electrolytic corrosion peeling, residual stress measurement and correlation analysis were carried out to verify the accuracy of predicted residual stress. The results show that the bimodal Lorentz function has a great fitting accuracy. The adjusted R2 (Ad-R2) are ranging from 95.4% to 99.4% and 94.7% to 99.6% in circumferential and axial directions, respectively. The maximum and minimum errors of the surface residual tensile stress (SRTS) are 124.564 MPa and 18.082 MPa, those of the peak residual compressive stress (PRCS) are 84.649 MPa and 3.009 MPa and those of the depth of the peak residual compressive stress (DPRCS) are 0.00875 mm and 0.00155 mm, comparing three key feature indicators of predicted and simulated residual stress. The predicted residual stress is highly correlated with the measured residual stress, with correlation coefficients greater than 0.8. In the range of experimental measurement error, the research in the present work provides a quite accurate method for predicting the residual stress in turning Inconel 718, and plays a vital role in controlling the machining deformation of parts.

Semi-Empirical Prediction of Residual Stress Profiles in Machining IN718 Alloy Using Bimodal Gaussian Curve.

Residual stresses are often imposed on the end-product due to mechanical and thermal loading during the machining process, influencing the distortion and fatigue life. This paper proposed an original semi-empirical method to predict the residual stress distribution along the depth direction. In the statistical model of the method, the bimodal Gaussian function was innovatively used to fit Inconel 718 alloy residual stress profiles obtained from the finite element model, achieving a great fit precision from 89.0% to 99.6%. The coefficients of the bimodal Gaussian function were regressed with cutting parameters by the random forest algorithm. The regression precision was controlled between 80% and 85% to prevent overfitting. Experiments, compromising cylindrical turning and residual stress measurements, were conducted to modify the finite element results. The finite element results were convincing after the experiment modification, ensuring the rationality of the statistical model. It turns out that predicted residual stresses are consistent with simulations and predicted data points are within the range of error bars. The max error of predicted surface residual stress (SRS) is 113.156 MPa, while the min error is 23.047 MPa. As for the maximum compressive residual stress (MCRS), the max error is 93.025 MPa, and the min error is 22.233 MPa. Considering the large residual stress value of Inconel 718, the predicted error is acceptable. According to the semi-empirical model, the influence of cutting parameters on the residual stress distribution was investigated. It shows that the cutting speed influences circumferential and axial MCRS, circumferential and axial depth of settling significantly, and thus has the most considerable influence on the residual stress distribution. Meanwhile, the depth of cut has the least impact because it only affects axial MCRS and axial depth of settling significantly.

Sensitivity Analysis of Johnson-Cook Material Constants and Friction Coefficient Influence on Finite Element Simulation of Turning Inconel 718.

The Johnson-Cook (J-C) constitutive model, including five material constants (A, B, n, C, m), and the Coulomb friction coefficient (µ) are critical preprocessed data in machining simulations. Before they become reliable preprocessed data, investigating these parameters' effect on simulation results benefits parameter-selecting. This paper aims to investigate the different influence of five settings of the J-C constitutive equation and Coulomb friction coefficient on the turning simulation results of Inconel 718 under low-high cutting conditions, including residual stress, chip morphology, cutting force and temperature. A three-dimensional (3-D) finite element model was built, meanwhile, the reliability of the model was verified by comparing the experiment with the simulation. Sensitivity analysis of J-C parameters and friction coefficient on simulation results at low-high cutting conditions was carried out by the hybrid orthogonal test. The results demonstrate that the simulation accuracy of Inconel 718 is more susceptible to strain hardening and thermal softening in the J-C constitutive model. The friction coefficient only has significant effects on axial and radial forces in the high cutting condition. The influences of the coefficient A, n, and m on the residual stress, chip thickness, cutting force and temperature are especially significant. As the cutting parameters increase, the effect of the three coefficients will change visibly. This paper provides direction for controlling simulation results through the adjustment of the J-C constitutive model of Inconel 718 and the friction coefficient.