Ki-67表达与原发性肝癌根治性切除术后行预防性肝动脉化疗栓塞患者预后的关系

目的:探讨Ki-67表达与原发性肝癌根治性切除术后行预防性TACE患者预后的关系。方法:采用回顾性队列研究,收集2014年12月—2016年1月福建医科大学孟超肝胆医院150例行原发性肝癌根治性切除术并在术后2个月内行预防性TACE患者的临床病理资料,根据术后肝癌组织病理Ki-67评分分为为低表达组(Ki-67评分≤20%,44例)和高表达组(Ki-67评分20%,106例);分析Ki-67表达量与患者临床病理因素及复发与生存的关系。结果:高表达组肿瘤多发、肿瘤包膜不完整及合并微血管癌栓患者比例明显高于低表达组(均P0.05)。Ki-67高表达与肿瘤多发、肿瘤直径大为影响无瘤生存期的独立危险因素(均P0.05);Ki-67高表达与肿瘤多发、肿瘤直径大、肿瘤包膜不完整、合并微血管癌栓为影响总生存期的独立危险因素(均P0.05);高表达组患者复发率明显高于低表达组(57.9%vs. 37.7%,χ~2=6.777,P0.05),总生存率明显低于低表达组(45.6%vs. 75.9%,χ~2=8.447,P0.05)。结论:Ki-67的表达量对肝癌根治性切除术后行预防性TACE患者的预后有显著影响,高表达者预后不良。     

探析新型冠状病毒肺炎的眼表损害及中西医治疗对策＊

新型冠状病毒肺炎（corona virus disease 2019，COVID-19）自2019年12月爆发以来，由于具有高传染性，迅速在世界各地蔓延，国内外疫情防治形势空前严峻。COVID-19不仅造成肺部、肠道、肾脏等多脏器损害，且部分患者以眼表损害为首发或伴发症状出现，临床上很容易被忽视。COVID-19的眼表损害归属于祖国医学“天行赤眼”范畴，本文结合国内外最新的文献报道，探讨新型冠状病毒（2019 novel corona virus，2019-nCoV）对眼表的损害，阐明其可能机制，并从中西医角度提出可行的治疗措施。     

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.     

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.