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Prognostic Value of T Cell Immunoglobulin Mucin-3 in Prostate Cancer
Piao, Yong-Rui,Piao, Long-Zhen,Zhu, Lian-Hua,Jin, Zhe-Hu,Dong, Xiu-Zhe Asian Pacific Journal of Cancer Prevention 2013 Asian Pacific journal of cancer prevention Vol.14 No.6
Background: Optimal treatment for prostate cancer remains a challenge worldwide. Recently, T cell immunoglobulin mucin-3 (TIM-3) has been implicated in tumor biology but its contribution prostate cancer remains unclear. The aim of this study was to investigate the role of TIM-3 as a prognostic marker in patients with prostate cancer. Methods: TIM-3 protein expression was determined by immunohistochemistry and Western blotting in 137 prostate cancer tumor samples and paired adjacent benign tissue. We also performed cell proliferation assays using 3-(4,5-dimethylthiazol-2yl)-2,5-diphenyl- 2H tetrazolium bromide (MTT) and cell invasion assays. The effects of small interfering RNA (siRNA)-mediated knockdown of TIM-3 (TIM-3 siRNA) in two human prostate cancer cell lines were also evaluated. Results: TIM-3 expression was higher in prostate cancer tissue than in the adjacent benign tissue (P<0.001). High TIM-3 expression was an independent predictor of both recurrence-free survival and progression-free survival. TIM-3 protein was expressed in both prostate cancer cell lines and knockdown suppressed their proliferation and invasion capacity. Conclusions: TIM-3 expression is associated with a poor prognosis in prostate cancer. Taken together, our resutlts indicate that TIM-3 is a potential prognostic marker in prostate cancer.
Hong-Yu Long,Cheng-Yong Zhu,Bi-Bin Huang,Chang-hao Piao,Ya-Qing Sun 대한전기학회 2019 Journal of Electrical Engineering & Technology Vol.14 No.4
The purpose of this paper is to discuss how to solve the problem of on-line identifi cation of model parameters of Li-ion battery and on-line estimation of SOC. Based on the matlab/simulink platform, a fi rst-order RC equivalent circuit model of the battery is built, and a joint estimation algorithm of the model parameters and SOC of the lithium ion battery is designed based on the dynamic model, which is compared with the single adaptive Kalman fi lter algorithm (AEKF). The simulation results show that the proposed joint estimation algorithm can make eff ective online estimation and update of the battery model parameters and SOC. The average estimation error of SOC is less than 2.8%, the estimation accuracy is higher than that of adaptive Kalman fi lter, and its robustness level is relatively high.