AKR7A3在肺腺癌中异常表达及临床意义

目的:探讨醛-酮还原酶家族7成员A3（AKR7A3）在肺腺癌中的异常表达及与临床病理特征的关系，并探究其临床意义。方法:采用生物信息学数据库分析、免疫组化、Western Blot、Real-time PCR等方法对肺腺癌组织及不同细胞中AKR7A3的表达进行检测与分析。结果:Oncomine数据库分析结果显示，在肺腺癌中，AKR7A3的表达普遍高于正常肺组织，分别为正常肺组织的1.811倍（P=0.022）、1.356倍（P<0.01）、1.413倍（P=0.002）。Kaplan-Meier Plotter数据库分析结果显示，AKR7A3高表达的患者较低表达的患者生存时间缩短，差异具有统计学意义（P=0.003 7）。免疫组化染色显示肺腺癌组织中AKR7A3的表达较癌旁增高，在与临床病理特征的相关性分析中，发现其与肿瘤分化程度（P<0.01）、淋巴结转移情况（P=0.029）以及TNM分期（P<0.01）相关，且会造成患者生存时间缩短（P=0.031）。Cox多因素分析表明AKR7A3可能是影响肺腺癌患者预后的独立危险因素(P=0.012)。Western Blot及Real-time PCR实验提示不同肺腺癌细胞中AKR7A3蛋白及mRNA表达普遍增高。结论:AKR7A3在肺腺癌中表达增高，对预后有不良影响，有促进肿瘤发生发展的作用。     

Monitoring Hepatocyte Dysfunction and Biliary Complication After Liver Transplantation Using Quantitative Hepatobiliary Scintigraphy

The significance of hepatobiliary scintigraphy (HBS) for hepatic graft function assessment was established mostly on retrospective studies and was not widely recognized due to the lack of quantitative data and variation in accuracy. This prospective study was performed to investigate the effectiveness of quantitative HBS for assessing hepatocyte dysfunction and biliary complication in liver transplant recipients.In 57 recipients who had undergone orthotopic liver transplantation, a total of 67 dynamic ^99mTc-EHIDA scans were performed and quantitative parameters including the hepatocyte extraction fraction (HEF), time to maximum hepatic radioactivity (T_max), and time for peak activity to decrease by 50% (T_1/2) were calculated. The scintigraphic results based on the 3 parameters were compared against the final diagnosis. A ROC curve analysis was carried out to identify the cutoff value of T_max for diagnosis of biliary stricture. Correlation between the parameters of postoperative HBS and conventional biochemical liver function indices were also analyzed.Quantitative ^99mTc-EHIDA HBS had an overall sensitivity of 94.12% (16/17), specificity of 93.33% (42/45), and diagnostic accuracy of 93.55% (58/62) for detecting hepatocyte dysfunction and biliary complication in liver transplant recipients. The recommended cutoff value of T_max for diagnosis of post-transplant biliary stricture was set at 15.75 min with a sensitivity of 100.0% and a specificity of 94.0%. The scintigraphic parameters (HEF, T_max) were statistically significantly associated with the conventional liver function parameters.Quantitative ^99mTc-EHIDA HBS offers a noninvasive imaging modality with high sensitivity and specificity to diagnose hepatocyte dysfunction as well as distinguish between patients with or without biliary stricture following liver transplantation. Furthermore, HEF and T_max values obtained from dynamic HBS show good correlation with conventional liver function parameters.     

AXL表达与乳腺癌预后及临床病理特征关系的Meta分析

目的 探讨受体酪氨酸激酶AXL蛋白的表达对乳腺癌患者预后及临床病理特征的意义。方法 在中国知网(CNKI)、万方医学网(Wanfang Data)、 Cochrane Library、Embase、PubMed和Medline数据库中,搜索国内外公开发表的有关AXL与乳腺癌预后关系的文献,检索时限均为从建库至2019年10月。2位研究者按纳入和排除标准独立筛选文献、提取资料和评价纳入研究的偏倚风险,并进行Meta分析。结果 共纳入7篇文献,包括933例乳腺癌患者。AXL高表达组的乳腺癌患者总生存期(overall survival,OS)明显低于低表达组(HR=2.36,95%CI 1.60~3.46,P<0.000 1),但其表达在无复发生存期(relapse free survival,RFS)和无疾病生存期(disease free survival,DFS)中差异无统计学意义,且与乳腺癌的年龄、临床分期、组织分级、淋巴结转移及受体表达的相关性分析无统计学意义。结论 AXL的阳性表达与乳腺癌患者预后不良有关,是乳腺癌个体化治疗的潜在分子标志物。     

Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning

Conservation laws are considered to be fundamental laws of nature. It has broad applications in many fields, including physics, chemistry, biology, geology, and engineering. Solving the differential equations associated with conservation laws is a major branch in computational mathematics. The recent success of machine learning, especially deep learning in areas such as computer vision and natural language processing, has attracted a lot of attention from the community of computational mathematics and inspired many intriguing works in combining machine learning with traditional methods. In this paper, we are the first to view numerical PDE solvers as an MDP and to use (deep) RL to learn new solvers. As proof of concept, we focus on 1-dimensional scalar conservation laws. We deploy the machinery of deep reinforcement learning to train a policy network that can decide on how the numerical solutions should be approximated in a sequential and spatial-temporal adaptive manner. We will show that the problem of solving conservation laws can be naturally viewed as a sequential decision-making process, and the numerical schemes learned in such a way can easily enforce long-term accuracy. Furthermore, the learned policy network is carefully designed to determine a good local discrete approximation based on the current state of the solution, which essentially makes the proposed method a meta-learning approach. In other words, the proposed method is capable of learning how to discretize for a given situation mimicking human experts. Finally, we will provide details on how the policy network is trained, how well it performs compared with some state-of-the-art numerical solvers such as WENO schemes, and supervised learning based approach L3D and PINN, and how well it generalizes.