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Objective: To establish a mouse model of breast cancer lung metastasis with miR-155 knockout (miR155-/-) mice, and to compare the difference of peripheral blood immune cell typing between miR155-/- mice and C57BL/6J wide-type (WT) mice. Methods: Bioinformatics analysis was used to explore the expression level of miR-155 in breast cancer tissues and peripheral serum, and its relationship with prognosis. Mouse model of lung metastasis of breast cancer was established by tail vein injection; peripheral blood was collected for flow cytometry, and the immune cell typing was analyzed; the lung tissues were collected for immunohisto-chemical detection to observe the tumor metastasis. Results: Percentage of T lymphocytes and monocytes in peripheral blood of miR155-/- mice was significantly decreased compared with WT mice (P<0.05), percentage of myeloid inhibitory cells (MDSCs) was increased significantly (P<0.05), in which the proportion of monocyte subsets (M-MDSC) was significantly decreased (P<0.05), while the proportion of granulocyte subsets (G-MDSC) was significantly increased (P<0.05). In lung metastasis model of breast cancer, percentage of T lymphocytes in peripheral blood of miR155-/- mice was significantly higher compared with WT mice, while percentage of NK cells was decreased significantly (P<0.05), percentage of neutrophil was significantly decreased (P<0.001), proportion of Th cells in T lymphocytes was significantly decreased (P<0.05), proportion of M-MDSCs was significantly decreased (P< 0.01), while proportion of G-MDSCs was significantly increased (P<0.01). Conclusion: Deletion of miR-155 gene leads to significant differences in peripheral immune cell typing, making mice more susceptible to lung metastasis of breast cancer. [ABSTRACT FROM AUTHOR]

Bearingless induction motors, which were multivariable, were strongly coupled, along with a higher order nonlinear system. To obtain the stable suspension control of a bearingless induction motor, a new control strategy based on Adaptive Neuro Fuzzy Inference System was proposed. First, in the analysis of the generation mechanism of a bearingless induction motor's radial suspension force, the mathematical model of a bearingless induction motor was achieved. Based on the control principle of an Adaptive Neuro Fuzzy Inference System, the Adaptive Neuro Fuzzy Inference System had been built to design the controller, including the option of control variables and membership functions. By the PID control, the input data and output data could be collected. The selected criterion of error was set to correct the membership function parameters. In addition, the Fuzzy Inference System (FIS) model was trained by a Sugeno type Adaptive Neuro Fuzzy Inference System controller. Then, aiming at the performances of rotor suspending, speed, and torque response, the simulation and analysis of the control system for bearingless induction motors had been carried out on the basis of MATLAB/Simulink simulation platform. Moreover, the motor speed was set to 6000r/min. The simulation results showed that the stable suspension of a bearingless induction motor can be quickly achieved by this presented control strategy. Through the comparison with PID control, the speed response was faster, and the speed overshoot was smaller in the Adaptive Neuro Fuzzy Inference System control. Further, the suspension performance of the rotor was not affected by the sudden change in the load torque. When the rotor speed suddenly changed from 6000r/min to 3000r/min at the time of 0.5 seconds, the speed response of the control system could track the given speed well, and with a very small steady state error. The control system has a fine dynamic and static performance. Finally, the control system test platform of a bearingless induction motor was built based on Adaptive Neuro Fuzzy Inference System controller. The experimental results of the control system also showed that this control strategy could achieve the stable suspension of a bearingless induction motor. The control system has a quickly response, a high control precision, and the strong robustness to load torque disturbance. The correctness and effectiveness of the Adaptive Neuro Fuzzy Inference System control method was verified in this paper. [ABSTRACT FROM AUTHOR]