紫杉醇联合卡铂化疗治疗卵巢癌的效果分析

目的探讨紫杉醇联合卡铂化疗治疗卵巢癌的效果分析。方法选自2018年5月-2019年5月132名符合入组以及排除标准的患者,随后研究人员将患者分为对照组以及研究组,分组应按照科学、合理、随机的标准,两组患者分别均有67例,对照组患者按照研究要求,接受卡铂化疗治疗,研究组患者按照研究要求,接受紫杉醇联合卡铂化疗治疗,两组患者分别接受不同的治疗,评估患者的临床效果,对比两组患者的观察指标,如:T细胞亚群、临床疗效。结果治疗后,CD8、CD4、CD3、CD4/CD8,研究组高于对照组;研究组中,CR+PR+SD人数多于对照组患者,且治疗总有效率高于对照组患者(P<0.05)。结论紫杉醇联合卡铂化疗治疗卵巢癌,能够提升患者自身的免疫功能,促进疾病的好转。     

miR-200、 miR -155及血管新生因子与复发性流产的相关性分析

目的 研究miR-200、miR-155及血管新生因子与原因不明复发性流产(unexplained recurrent spontaneousabortion,URSA)的相关性分析。 方法 选择2015年3月—2018年1月在青岛市妇女儿童医院妇产科就诊的URSA患者作为URSA组、要求终止妊娠的正常早孕患者作为对照组,检测绒毛组织中微小RNA(microRNA, miR)miR-200、miR-155、血管内皮生长因子(vascular endothelial growth factor,VEGF)、可溶性FMS样酪氨酸激酶1(soluble FMS like tyrosine kinase-1,sFlt-1)的表达量及血清中VEGF、sFlt-1的含量,对miR-200、miR-155靶向结合VEGF、sFlt-1进行生物信息学分析。 结果 URSA组的绒毛组织中miR-200(1.78±0.32 vs. 0.91±0.15)、sFlt-1(1.87±0.35 vs. 1.06±0.21)的相对表达量及血清中sFlt-1的含量[(12.39±2.31)ng/ml vs. (6.51±0.95)ng/ml]均高于对照组,差异有统计学意义(均P<0.05)。绒毛组织中miR-155相对表达量(0.60±0.10 vs. 0.93±0.16)、VEGF mRNA相对表达量(0.59±0.09 vs. 1.02±0.16)及蛋白表达量(0.62±0.07 vs. 1.04±0.18)、血清中VEGF的含量[(601.25±94.39)ng/ml vs. (935.12±132.47)ng/ml]低于对照组,差异有统计学意义(均P<0.05);URSA组患者绒毛组织中miR-200的表达量与血清中VEGF的含量、绒毛组织中VEGF的表达量均呈负相关,绒毛组织中miR-155的表达量与血清中sFlt-1的含量和绒毛组织中sFlt-1的表达量均呈负相关;miR-200、miR-155分别靶向结合VEGF、sFlt-1基因的3’UTR。 结论 miR-200表达增多、miR-155表达减少与URSA发生有关,miR-200靶向VEGF、miR-155靶向sFlt-1是介导该过程的可能机制。     

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。     

Meta-Analysis of Saturated Fatty Acid Intake and Breast Cancer Risk

The associations between saturated fatty acid (SFA) consumption and risk of breast cancer (BC) remains inconclusive. Therefore, we conducted this meta-analysis to determine the quantitative relations between dietary SFA intake and incidence of BC.Literatures published up to April 2015 were systematically screened through Pubmed and Web of Science. Relevant publication quality was evaluated by conducting the Newcastle-Ottawa scale. We used fixed effects models or random effect models to calculate the summary relative risks (RRs) and odds ratios (ORs), and conducted sensitivity analyses and evaluated the publication bias.We identified a total of 52 studies (24 cohort studies and 28 case–control studies), with over 50,000 females diagnosed with BC. The associations between dietary SFA intake and risk of BC were 1.18 for case–control studies (high vs low intake, 95% confidence interval [CI] = 1.03–1.34) and 1.04 for cohort studies (95% CI = 0.97–1.11). When restricted analyses to population-based studies, positive associations were observed for both cohort (RR [95% CI] = 1.11 [1.01–1.21]) and case–control studies (OR [95% CI] = 1.26 [1.03–1.53]). Additionally, for case–control studies, significant positive associations between higher SFA intake and BC risk were observed for Asian (OR [95% CI] = 1.17 [1.02–1.34]) and Caucasian (OR [95% CI] = 1.19 [1.00–1.41]), as well as for postmenopausal women (OR = 1.33, 95% CI: 1.02–1.73). In contrast, higher dietary SFA intake was not associated with risk of BC among premenopausal women, in cohort studies or hospital-based studies.A positive association between higher dietary SFA intake and postmenopausal BC risk was observed in case–control but not in cohort studies. More studies are warranted to confirm these findings.     

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.     

贵阳地区儿童重症社区获得性肺炎肺泡灌洗液病原及药敏分析

目的分析贵阳地区儿童重症社区获得性肺炎支气管肺泡灌洗液（BALF）病原学分布及耐药特点。方法收集989例重症社区获得性肺炎患儿临床资料，将支气管肺泡灌液采用支气管镜取出进行细菌培养、病毒以及肺炎支原体(MP)检测。结果（1）989例重症社区获得性肺炎患儿病原检出阳性716例，阳性率72.40%，细菌、病毒、支原体检出率分别33.27% （329例）、22.45%（222例）、31.45%（311例）。（2）细菌感染中的肺炎链球菌、金黄色葡萄球菌为最为常见的革兰阳性菌株(G+)；而肺炎克雷伯菌、大肠埃希菌为最为常见的革兰阴性菌株(G-)。培养菌株对青霉素类、红霉素、第1、2、3代头孢类抗生素有较高的耐药性，对头孢吡肟、拉氧头孢、哌拉西林、左氧氟沙星有较高的敏感性，对亚胺培南、万古霉素、利奈唑烷均无耐药发生。（3）病毒感染检出222例，其中呼吸道合胞病毒131例，腺病毒检测49例，流感病毒6例（A型2例，B型4例），副流感病毒36例（1型3例、2型4例、3型29例），病毒检出率以0～12月龄组最高，RSV、ADV感染主要集中在冬春季节。（4）肺炎支原体检出阳性率31.45%（311例），肺炎支原体检出率以3～5岁组最高。结论贵阳地区重症肺炎中肺炎克雷伯杆菌、肺炎链球菌、大肠埃希菌、金黄色葡萄球菌为重要的细菌病原。重要病毒为腺病毒和呼吸道病毒为主，1～12月龄组的病毒感染检出率比较高。     

六手操作技术在口腔微螺钉种植支抗术中的应用

目的:探讨六手操作技术在口腔正畸微螺钉种植支抗术中的应用效果。方法:选取158例运用微螺钉种植支抗术的患者,在手术过程中采用六手操作护理配合。结果:六手操作护理配合明显缩短了手术时间,避免了交叉感染的发生,提高了患者满意度。结论:六手操作护理技术有一定的先进性,值得在临床推广。     

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.