Effects of ω-3 Fatty Acids and Catechins on Fatty Acid Synthase in the Prostate: A Randomized Controlled Trial

Animal and human studies suggest fish oil and green tea may have protective effect on prostate cancer. Fatty acid synthase (FAS) has been hypothesized to be linked to chemoprotective effects of both compounds. This study evaluated the independent and joint effects of fish oil (FO) and green tea supplement (epigallocatechin-3-gallate, EGCG) on FAS and Ki-67 levels in prostate tissue. Through a double-blinded, randomized controlled trial with 2 × 2 factorial design, 89 men scheduled for repeat prostate biopsy following an initial negative prostate biopsy were randomized into either FO alone (1.9 g DHA + EPA/day), EGCG alone (600 mg/day), a combination of FO and EGCG, or placebo. We used linear mixed-effects models to test the differences of prostate tissue FAS and Ki-67 by immunohistochemistry between pre- and post-intervention within each group, as well as between treatment groups. Results did not show significant difference among treatment groups in pre-to-post-intervention changes of FAS (P = 0.69) or Ki-67 (P = 0.26). Comparing placebo group with any of the treatment groups, we did not find significant difference in FAS or Ki-67 changes (all P > 0.05). Results indicate FO or EGCG supplementation for a short duration may not be sufficient to produce biologically meaningful changes in FAS or Ki-67 levels in prostate tissue.     

BCAN基因在肾透明细胞癌中的表达及临床意义

目的以基因表达数据集资料为研究对象,分析BCAN基因在肾透明细胞癌中的表达情况以及对患者预后的影响。方法在Oncomine数据库中挖掘BCAN在肾透明细胞癌(ccRCC)中的表达情况。从TCGA数据库中获取ccRCC患者临床资料和目的基因的表达信息并进行统计分析。利用GEO数据库中GSE73731数据集的ccRCC样本进行基因富集分析。利用String数据库分析与BCAN相关的蛋白。结果BCAN低表达组的ccRCC患者在病理分期及T分期方面低于高表达组(P<0.001;P=0.001);N分期及M分期差异无统计学意义(P>0.05)。BCAN低表达组患者的总生存期优于高表达组(P=0.033)。BCAN基因高表达组的样本主要富集在KRAS信号通路。结论BCAN可以通过多种途径来促进肿瘤细胞的侵袭能力,有望成为ccRCC不良预后的重要生物标志物之一。     

Optimal tracking control for non-zero-sum games of linear discrete-time systems via off-policy reinforcement learning

In this article, a model-free off-policy reinforcement learning algorithm is applied to address the optimal tracking problem based on multiplayer non-zero-sum games for discrete-time linear systems. In contrast to the traditional method and the policy iteration method for solving the optimal tracking problems, the proposed algorithm operates with the system data rather than the knowledge of the system dynamics. For performing the proposed algorithm, an auxiliary augmented system is constructed via assembling the original system and the reference trajectory while a discount factor is introduced into the performance indexes. It is analyzed that the solutions of the proposed algorithm converge to the Nash equilibrium and the result is not influenced by the probing noise. Two simulations are presented to verify the feasibility and effectiveness of the proposed algorithm.