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HGFK1 is Associated with a Better Prognostis and Reverses Inhibition by Gefitinib in NSCLC Cases
Zhou, Xiao-Hui,Tang, Li-Na,Yue, Lu,Min, Da-Liu,Yang, Yi,Huang, Jian-An,Shen, Zan Asian Pacific Journal of Cancer Prevention 2012 Asian Pacific journal of cancer prevention Vol.13 No.4
Purpose: Non small cell lung cancer (NSCLC) is the leading worldwide source of cancer-related deaths. Although some drugs targeting EGFR mutations have been developed, most advanced cases are still incurable. New targets for anticancer drugs are demanded. The kringle 1 domain of hepatocellular growth factor alpha chain (HGFK1) is a potent anti-angiogenesis factor. It has also emerged as a potential anticancer factor in hepatocellular carcinoma (HCC). The expression of HGFK1 protein in patients with NSCLC has not been reported to date. Method: Here, we assessed HGFK1 expression by Western blotting in 103 cases with advanced NSCLC to investigate the impact of HGFK1 on survival. Results: Results revealed 33 (30.1%) patients were classified as high expressors, this being significantly associated with less remote metastasis (P = 0.002) but not with lymph node metastasis (P = 0.062). There was also a significant association between HGFK1 expression and tumor size (P = 0.025) as well as clinical stage (P = 0.012). Kaplan-Meier survival analysis showed that both overall survival (OS) and progression free survival (PFS) of patients with HGFK1 expression were longer than those of patients without HGFK1 expression (P = 0.004 and P = 0.001 respectively). HGFK1 reversed gefitinib inhibition in the resistent NSCLC cell line A431/GR but did not inhibit the proliferation of NSCLC cells A431 and A431/GR directly. Reversion of gefitinib inhibition in A431/GR cells by HGFK1 was related to decreased phosphorylation of ERK and STAT5. Conclusions: HGFK1 may be a useful prognostic factor of advanced NSCLC patients and a potential drug for gefitinib resistant patients.
Minghe Li,Saifei Hu,Jianwei Xia,Jing Wang,Xiao-Na Song,Hao Shen 제어·로봇·시스템학회 2020 International Journal of Control, Automation, and Vol.18 No.9
In this work, the problem of predictive control of dissolved oxygen for the activated sludge process model with high nonlinearity and strong coupling is addressed. Firstly, the determination of the structure of fuzzy rules is displayed established upon Activated sludge model 1 (ASM1). Besides, the fuzzy space is divided through the clustering algorithm of fuzzy C-means. The corresponding parameters are estimated by means of the wellknown least squares method. Subsequently, a fuzzy predictive model of dissolved oxygen is established by using the historical data. The aim is to design a predictive controller that is capable of performing the online track of dissolved oxygen attributed to better dynamic response and steadier output in different weather. Ultimately, the availability and validity of the developed technique are verified by a comparison example.