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。     

The relevance of Xenophon’s Anabasis and Plato’s Meno to nursing

The current situation in which the humanities are disparaged affects all university disciplines, including nursing, in whose historical evolution the humanities have always been present in one form or another. Looking beyond this disrepute, this study proposes that nursing renew its attention to classical philosophy. Specifically, it invites a close reading of Xenophon's Anabasis and Plato's Meno, to get three related goals: to show how the use of ancient texts are very valuable tools for the philosophical initiation of nursing students and can help them reflect on their choice of nursing as a practical activity; to reflect on the problem of virtue and the nature of the good life; and to show how the interaction with ancient texts allows students to reflect on questions and issues of life, theirs and others, that are not open to investigation through a purely scientific method. Consequently, both Anabasis and Meno readings strengthen the intellectual relationship between philosophy and nursing, enabling the latter to delve deeper into the key questions of its own thought as a discipline.     

模拟诊疗在留学生心血管内科实习带教中的应用研究

目的　探讨模拟诊疗在留学生心血管内科实习带教中的应用效果。方法　将35名留学生随机编号，分成试验组和对照组。试验组18名学生分成3组（每组6名）进行模拟诊疗教学，对照组17名学生进行常规实习。通过填写评价单和相关的临床能力测试对两组教学效果进行评价。选用SPSS 19.0软件进行t检验。结果　参与模拟诊疗教学的留学生均希望在各科实习中多采用此方法。在临床能力测试方面，试验组学生在病史采集[（22.16±2.17）vs.（20.33±2.03）]、体格检查[（23.05±1.79）vs.（19.78±2.05）]及回答问题[（23.50±1.47）vs.（19.56±1.92）]方面均优于对照组，差异有统计学意义（P<0.05）。但是在病历书写方面，两组差异无统计学意义（P>0.05）。结论　利用高仿真的模拟诊疗教学手段，弥补了留学生在实习期间不能充分接触患者的缺陷，有助于提高其临床操作技能、增强医患沟通能力等医学职业素养。     

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.