Serum cytokines are promising biomarkers of cancer staging and outcome prediction, including response to treatment. Serum samples were collected from 200 breast carcinoma patients prior to chemotherapy treatment. Luminex liquid protein chip technology was used to analyze 25 cytokines in serum. Linear regression was used to analyze the relationship of cytokine levels and tumor size. The independent sample T-test and Chi-square test methods were used to analyze the difference of cytokine levels between two groups. IL-12p40, sIL-2R, MMP-2 levels showed linear correlation with tumor size. Eotaxin, IL-10, IL-12p70, IL-7, IL-1ra, IP-10, MCP-1beta, MP-2 and MIP-1beta levels showed significant difference between different lymph node groups, but only Eotaxin, IP-10 and MCP-1 levels had an inverse correlation with the number of positive nodes. Fractalkine, G-CSF, MIP-1alpha, MIP-1beta levels showed significant differences between different ER+ groups. Eotaxin, Fractalkine, IL-6, IL-7, IL-10, MCP-1 and VEGF levels had significant differences between different HER-2 groups. Our study resulted in the identification of a serum cytokine profile with the potential to be clinically applicable to predict disease outcome and in monitoring of efficacy of treatment.

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The problem of the robust fault estimation and robust fault-tolerant control, for a class of nonlinear time-delayed Markov jump systems (MJSs) with both actuator and sensor faults, was studied. Firstly, by extending the system state, the actuator fault state vector, the sensor fault state vector and the original system state vector were extended to auxiliary state variables, and the original system was extended to a generalized system. Then, for the singular system, a generalized observer was designed to achieve the simultaneous estimation of its actuator faults, sensor faults and original system state. In addition, based on the observer estimation, a state feedback fault-tolerant controller was designed to make the closed-loop system stable and meet certain performance indicators. By solving linear matrix inequality, sufficient conditions for the existence of generalized observer and fault-tolerant controller were given. Finally, a numerical example and a practical example were given to demonstrate the effectiveness of the proposed approach.

For the trajectory tracking of the robot manipulators, the robust adaptive control based on iterative learning observer is designed. The robot manipulators model is given, and an iterable dynamic equation is obtained by linearizing the model. An iterative learning observer is designed, so that the output state of the observer can converge to the actual state of the system and the convergence analysis of the observer is also given. Based on Lyapunov stability theory, the robust adaptive controller is given to ensure that the trajectory tracking error of the robot manipulators gradually approaches zero. Finally, the double joint robot manipulators systems are simulated, and the results verify the effectiveness of the method.

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