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。     

北京地方志医学资料挖掘浅析

地方志作为"地方性的百科全书",因其全面性而往往为专业研究如医学研究所忽略,而其中却不乏丰富的医学资料。通过对北京地方志中的医学资料进行挖掘,发现涉及太医院、官办医药机构、社会福利机构、医家、流行病、土产药物、药王庙等内容,并对相关文献进行分析,以展现北京地方志医学资料情况,为后期进一步研究奠定基础。     

Parental education and children's online health information seeking: Beyond the digital divide debate

Research has shown that increasing numbers of teenagers are going online to find health information, but it is unclear whether there are disparities in the prevalence of online health seeking among young Internet users associated with social and economic conditions. Existing literature on Internet uses by adults indicates that low income, less educated, and minority individuals are less likely to be online health seekers. Based on the analysis of data from the Pew Internet and American Life Project for the US, this study finds that teens of low education parents are either as likely as or even more likely than teens of high education parents to seek online health information. Multiple regression analysis shows that the higher engagement in health seeking by teens of low education parents is related to a lower prevalence of parental Internet use, suggesting that some of these teens may be seeking online health information on behalf of their low education parents. Implications of these findings are discussed in relation to the issues of the digital divide and digital empowerment.     

新生儿出生信息样本分析

目的:了解和探讨新生儿出生信息的分布态势及规律。方法:对本院2006年1~6月间所有新生儿出生信息进行样本分析。结果:半年间分娩的新生儿共1075例,其中死亡19例,双胞胎15例,以1028例单胎活产新生儿的出生体重、胎龄及产妇年龄作为研究样本进行分析,统计数据与传统意义上的正常胎龄和新生儿体重基本相符,胎龄、体重均值均有所提高。结论:孕产妇的健康状况和新生儿的体质均有所改善,随胎龄的增加,新生儿出生体重亦随之增加,并且,目前新生儿的整体统计体重要大于现有的临床指标,因此,应对现有的临床指标进行适当调整,重新确定低出生体重儿和巨大儿的临床指标。     

Bayesian estimation of a random effects heteroscedastic probit model

Summary Bayesian analysis is given of a random effects binary probit model that allows for heteroscedasticity. Real and simulated examples illustrate the approach and show that ignoring heteroscedasticity when it exists may lead to biased estimates and poor prediction. The computation is carried out by an efficient Markov chain Monte Carlo sampling scheme that generates the parameters in blocks. We use the Bayes factor, cross‐validation of the predictive density, the deviance information criterion and Receiver Operating Characteristic (ROC) curves for model comparison.     

Enhancing laboratory report contents to improve outpatient management of test results

In today''s environment, providers are extremely time-constrained. Assembling relevant contextual data to make decisions on laboratory results can take a significant amount of time from the day. The Regenstrief Institute has created a system which leverages data within Indiana Health Information Exchange''s (IHIE''s) repository, the Indiana Network for Patient Care (INPC), to provide well-organized and contextual information on returning laboratory results to outpatient providers. The system described here uses data extracted from INPC to add historical test results, medication-dispensing events, visit information, and clinical reminders to traditional laboratory result reports. These “Enhanced Laboratory Reports” (ELRs) are seamlessly delivered to outpatient practices connected through IHIE via the DOCS4DOCS clinical messaging service. All practices, including those without electronic medical record systems, can receive ELRs. In this paper, the design and implementation issues in creating this system are discussed, and generally favorable preliminary results of attitudes by providers towards ELRs are reported.     

Fisher information matrix for nonlinear mixed effects multiple response models: Evaluation of the appropriateness of the first order linearization using a pharmacokinetic/pharmacodynamic model

We focus on the Fisher information matrix used for design evaluation and optimization in nonlinear mixed effects multiple response models. We evaluate the appropriateness of its expression computed by linearization as proposed for a single response model. Using a pharmacokinetic–pharmacodynamic (PKPD) example, we first compare the computation of the Fisher information matrix with approximation to one derived from the observed matrix on a large simulation using the stochastic approximation expectation–maximization algorithm (SAEM). The expression of the Fisher information matrix for multiple responses is also evaluated by comparison with the empirical information obtained through a replicated simulation study using the first‐order linearization estimation methods implemented in the NONMEM software (first‐order (FO), first‐order conditional estimate (FOCE)) and the SAEM algorithm in the MONOLIX software. The predicted errors given by the approximated information matrix are close to those given by the information matrix obtained without linearization using SAEM and to the empirical ones obtained with FOCE and SAEM. The simulation study also illustrates the accuracy of both FOCE and SAEM estimation algorithms when jointly modelling multiple responses and the major limitations of the FO method. This study highlights the appropriateness of the approximated Fisher information matrix for multiple responses, which is implemented in PFIM 3.0, an extension of the R function PFIM dedicated to design evaluation and optimization. It also emphasizes the use of this computing tool for designing population multiple response studies, as for instance in PKPD studies or in PK studies including the modelling of the PK of a drug and its active metabolite. Copyright © 2009 John Wiley & Sons, Ltd.     

Working‐correlation‐structure identification in generalized estimating equations

Selecting an appropriate working correlation structure is pertinent to clustered data analysis using generalized estimating equations (GEE) because an inappropriate choice will lead to inefficient parameter estimation. We investigate the well‐known criterion of QIC for selecting a working correlation structure, and have found that performance of the QIC is deteriorated by a term that is theoretically independent of the correlation structures but has to be estimated with an error. This leads us to propose a correlation information criterion (CIC) that substantially improves the QIC performance. Extensive simulation studies indicate that the CIC has remarkable improvement in selecting the correct correlation structures. We also illustrate our findings using a data set from the Madras Longitudinal Schizophrenia Study. Copyright © 2008 John Wiley & Sons, Ltd.     

探究医院电子信息交换标准HL7

医药信息学在医学应用中的最大领域就是医院信息系统(Hospital Infornation System,HIS),随着HIS的快速发展,需要我们有一个开放的,统一的HIS开发和交换的标准.HL7是1987年开始发展起来的一个专门规范医疗机构用于临床信息,财务信息和管理信息电子信息交换的标准.它特别适合于解决不同厂商开发的医院信息系统、临床实验室系统及药学信息系统之间的互连问题.HL7在国内的发展刚刚起步,但是我国医疗卫生信息化发展很快,尤其是医疗保险、社区医疗和区域卫生信息系统等,都十分需要标准化的支持,HL7将成为建设这些系统的重要基础和关键技术.