Previous studies have indicated that propofol has immunomodulatory and antioxidative properties. However, the renoprotection effect and the precise mechanisms of propofol in sepsis-induced renal injury remain unclear. The purpose of the present study was to investigate the role of miR-290-5p/CCL-2 signaling in septic mice treatment with propofol. Mice were treated with propofol (50 mg/kg) twice within 24 h. Survival outcome was monitored within 48 h. The mRNA and protein levels were assayed by qRT-PCR and western blotting, respectively. Mouse podocytes (MPC5) were treated with lipopolysaccharide (LPS) to establish the cell model in vitro. The proliferation of MPC5 was monitored using the MTS assay. Cell apoptosis was analyzed by flow cytometry. Propofol improved survival outcome and alleviated acute kidney injury in cecal ligation and puncture-operated mice. Propofol increased miR-290-5p expression and decreased CCL-2 and inflammatory cytokines levels in the kidney for septic mice. We found that miR-290-5p was a direct regulator of CCL-2 in MPC5. Propofol could abrogate LPS-induced growth inhibition and apoptosis in MPC5. Meanwhile, propofol inhibited CCL-2 expression in LPS-treated MPC5, however, knockdown of miR-290-5p abrogated the inhibitory effect propofol on the mRNA and protein expressions of CCL-2. Propofol could serve as an effective therapeutic medication to suppress sepsis-induced renal injury in vivo and in vitro by regulating the miR-290-5p/CCL-2 signaling pathway.

Oleic acid needs to be heated when it is utilized for biodiesel production, but, as a low-loss solution, oleic acid is difficult to heat by microwave. An efficient heating method for oleic acid is designed. A high loss material porous media is placed in a quartz tube, and a microwave directly heats the porous medium of the high loss material. The oleic acid flows through the pores of porous media so that the oleic acid exchanges heat during this process and rapid heating of oleic acid is achieved. A coupling model, based on the finite element method, is used to analyze the microwave heating process. The multiphysics model is based on a single mode cavity operating at 2450 MHz. An elaborate experimental system is developed to validate the multiphysics model through temperature measurements carried out for different flow velocities of oleic acid and different microwave power levels. The computational results are in good agreement with the experimental data. Based on the validated model, the effects of different sizes, porosities, and materials on microwave heating efficiency are analyzed.

Microwave-assisted sintering materials have been proven to deliver improvements in the mechanical and physicochemical properties of the materials, compared with conventional sintering methods. Accurate values of dielectric properties of materials under high temperatures are essential for microwave-assisted sintering. In view of this, this paper, proposes an on-line system to measure the high temperature dielectric properties of materials under microwave processing at a frequency of 2450 MHz. A custom-designed ridge waveguide is utilized, where samples are heated and measured simultaneously. An artificial neural network (ANN) trained with the corresponding simulation data is integrated into this system to reverse the permittivity of the measured materials. This whole system is tested at room temperature with different materials. Accuracies of measuring dielectric property with an error lower than 9% with respect to theoretical data have been achieved even for high loss media. The functionality of the dielectric measurement system has also been demonstrated by heating and measuring Macor and Duran ceramic glass samples up to 800 °C. All the preliminary experiments prove the feasibility of this system. It provides another method for dielectric property measurement and improves the understanding of the mechanism between microwave and media under high temperatures, which is helpful for optimizing the microwave-assisted sintering of materials.

A ridged waveguide and heating system based on deep learning for dynamic measurement of dielectric properties at high temperatures has been developed. Firstly, specially designed ridged waveguide at the frequency of 915 MHz was utilized. The finite difference time domain simulation was then combined with the deep learning algorithm to construct the dielectric properties. In addition, the calibration method based on a two-point algorithm was involved in the network. The precision of the measured dielectric constant and loss tangent was improved by 4.4% and 1.9%, respectively.Simulated results indicated that the deep learning algorithm had higher accuracy than back propagation neural networks and support vector machine algorithms. The reliability of the system was verified by Si 3 N 4 and alcohols at room temperature. The dielectric properties of quartz particles were also tested over a temperature range up to 900 C. The experiment results agreed well with the reference values. This system can measure a wider range of dielectric properties and achieve higher accuracy using the advanced deep learning algorithm. It is more convenient to measure large size materials in industrial applications at high temperatures. K E Y W O R D S 915 MHz, deep learning, dielectric properties, FDTD