Transforming growth factor-β1 (TGF-β1), secreted by main components of tumor microenvironment, is considered to be closely associated with cancer development and chemoresistance. The present study aimed to analyze the effects and mechanisms underlying TGF-β1-induced chemoresistance to oxaliplatin (LOH) in human colorectal cancer (CRC) cell lines. The cytotoxic effects of LOH subsequent to TGF-β1 treatment were assessed in three CRC cell lines by MTT assay. In addition, epithelial to mesenchymal transition (EMT), DNA damage and apoptosis assays were performed to evaluate the mechanisms of drug resistance in vitro. It was revealed that an exposure of CRC cells to TGF-β1 induced EMT. This was followed by a decrease in the levels of DNA damage and LOH-induced apoptotic cell death at certain TGF-β1 concentrations compared with untreated cells, which was responsible for LOH resistance. TGF-β1 leads to resistance to LOH in CRC cells, primarily through EMT. These data not only provide insight into the understanding of the chemoresistant mechanisms, but also may guide the clinical applications of reducing EMT to enhance the sensitivity to chemotherapy, by targeting TGF-β1.
Our goal in this paper is to improve the computational performance of the proximity algorithms for the L1/TV denoising model. This leads us to a new characterization of all solutions to the L1/TV model via fixed-point equations expressed in terms of the proximity operators. Based upon this observation we develop an algorithm for solving the model and establish its convergence. Furthermore, we demonstrate that the proposed algorithm can be accelerated through the use of the componentwise Gauss-Seidel iteration so that the CPU time consumed is significantly reduced. Numerical experiments using the proposed algorithm for impulsive noise removal are included, with a comparison to three recently developed algorithms. The numerical results show that while the proposed algorithm enjoys a high quality of the restored images, as the other three known algorithms do, it performs significantly better in terms of computational efficiency measured in the CPU time consumed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.