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关键词： 生化检验   肾上腺皮质疾病   诊断   作用  

摘要： 目的探讨生化检验在肾上腺皮质疾病诊断中的作用。方法选取我院2010年6月至2012年6月收治过的肾上腺皮质疾病患者70例,抽取其临床资料,进行回顾性分析。其中,40例患者采用生化检验法进行检测,将其例入观察组,另外30例行普通检验的患者则例入对照组,对两组患者的临床资料进行比较与分析。结果观察组40例患者中,37例检查结果为有效,2例检验结果为无效,检验有效率为92.5%;对照组30例患者中,21例检查结果为有效,9例检验效果为无效,检验有效率为70.0%。两组间的该项差异经统计学方法的分析,P<0.05,差异具有统计学意义。结论生化检验对肾上腺皮质病症的诊断更准确,在对肾上腺皮质病症的诊断检查中可作为例行检查进行。

关键词： 计价货币   国际贸易   竞争力   人民币   经验   国际经济活动   货币职能   货币国际化  

摘要： 一国货币国际化的过程即为该货币的货币职能不断外化的过程，具体来讲，就是一国货币被他国私人部门和公共部门接受作为国际经济活动的计价手段、交易媒介和储藏手段等职能。

关键词： 地市级药品检验所   药品不良反应监测   构想  

摘要： 通过分析目前药品不良反应监测工作的现状,提出了加强地市级药品检验所在药品不良反应监测工作的几点构想,为各级药品检验所更好的发挥保障人民用药安全作用提供一些建议。

关键词： 血液病   网织红细胞   血涂片   中性粒细胞   形态异常   红细胞生成素   原发性血小板增多症   非造血系统疾病   淋巴瘤   地中海贫血  

摘要： 众所周知,血细胞计数异常或形态异常不一定是原发性血液病,有些是因为非造血系统疾病或介入治疗所致。譬如,很多疾病会引起贫血,而原发性血液病应考虑患者有脾肿大、淋巴结肿大、出血倾向或血栓、白血病/淋巴瘤的非特异性症状(如不舒服、出汗或体重减轻等)。任何临床诊断,首先需考虑全面的体格检查。

关键词： 技能竞赛   分析检验   专业建设  

摘要： 组队参加全国工业分析与检验技能竞赛,对促进学校管理方式改革,改善工业分析与检验专业办学条件,优化该专业教学方式方法和内容,增强师资队伍建设等方面有良好的促进作用,并对学校管理层、教师队伍和学生等多方面产生多层次的深刻影响,是常规教育教学培训和学习等方式不可比拟的。

关键词： 增值服务   评估值   专业管理  

摘要： 检验医学是医疗卫生中的一项医学专业。医疗卫生的未来发展趋势表明对诊断性检查提供的信息的依赖程度将越来越高,这意味着检验医学的重要作用将得到巩固。检验医学领导者的职责在于从当地医疗卫生环境中发展检验医学的中心地位,进而使患者能最大获益。临床实验室负责人的首要责任是通过国际标准实验室认可(例如ISO 15189)来确保各方面质量的持续改进。在此基础上,地区、国家及国际检验医学领导者需要为检验医学提供增值服务,来确保为患者个人或群体提供最适宜的传递、使用、开发和评估服务。对附加价值的简要诠释的缩写是"SCIENCE",包括7个方面:标准化和一致性(standardisation and harmonisation)、临床效益(clinical effectiveness)、创新(innovation)、循证实践(evidence-based practice)、新项目应用(novel applications)、成本效益(cost effectiveness)和教育培训(education of others)。另一简单方法可用于评估实验室医学的发展,即通过三方面的框架结构(运营效率、患者管理及患者行为)对任何发展进行评估。这2种方法均可使复杂问题转变为简单且可衡量的结果。专业人员和患者则可从良好实践中分享未来检验医学发展的益处。

关键词： K-means   Support vector machines (SVMs)   Close-to-cluster-border sampling   Near-cluster-center sampling   Reduced convex hulls (RCHs)   Spectral angular mapper (SAM)   Remote sensing images   MACHINE  

摘要： The definition of valuable training samples and automatic classification of land cover with remote sensing data are both classical problems, which are known to be difficult and have attracted major research efforts. In this paper, a method of modified K-means-based support vector machine (SVM) classification is proposed to use a hybrid sample selection that leverages the informativeness and representativeness of training samples to classify real multi/hyperspectral images. The hybrid sample selection (close-to-cluster-border sampling and near-cluster-center sampling) is constructed on the reduced convex hulls (RCHs) of clustering structure and can reduce the risk of overtraining caused by active sample selection of active learning methods. Numerical results obtained on the classification of three challenging remote sensing images (Landsat-7 ETM+, AVIRIS Indian pines, and KSC) by comparing the proposed technique with random sampling (RS) and margin sampling (MS) demonstrate the good efficiency and high accuracy of our approach.

关键词： Supervised learning   Error estimation   High dimension   Small sample setting   Confidence interval   CROSS-VALIDATION   MICROARRAY CLASSIFICATION   PREDICTION ERROR   PERFORMANCE   CLASSIFIERS   INFERENCE   CANCER  

摘要： Error estimation accuracy is the salient issue regarding the validity of a classifier model. When samples are small, training-data-based error estimates tend to suffer from inaccuracy and quantification of error estimation accuracy is difficult. Numerous methods have been proposed for estimating confidence intervals for the true error based on the estimated error. This paper surveys proposed methods and quantifies their performance. Monte Carlo methods are used to obtain accurate estimates of the true confidence intervals and compare these to the intervals estimated from samples. We consider different error estimators and several proposed confidence-bound estimators. Both synthetic and real genomic data are employed. Our simulations show the majority of the confidence intervals methods have poor performance because of the difference of shape between true and estimated intervals. According to our results, the best estimation strategy is to use the 10-time 10-fold cross-validation with a confidence interval based on the standard deviation. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Cognitive style   Computer modeling tools   Problem solving   Complex problem   WORKING-MEMORY   ABILITY   STYLES  

摘要： An investigation was carried out to examine the effects of cognitive style on learners' performance and interaction during complex problem solving with a computer modeling tool. One hundred and nineteen undergraduates volunteered to participate in the study. Participants were first administered a test, and based on their test scores they were classified into three groups, namely field-dependent, field-mixed, and field-independent learners. Participants then received the same set of integrated-format materials and were asked to use a computer modeling tool to solve a complex problem about immigration policy. A multivariate analysis of variance was performed with field type as the independent variable, and cognitive load, problem-solving performance, and learner interaction with the computer tool as the dependent variables. The results indicated that there was no significant difference in terms of the amount of cognitive load reported. However, there was a significant difference in terms of learner problem-solving performance. Specifically, field-independent learners outperformed field-dependent learners, and field-mixed learners outperformed field-dependent learners. The results also indicated significant differences in computer interaction between field-independent and field-dependent learners, and between field-mixed and field-dependent learners. The qualitative findings of the study showed that students who interacted poorly with the software were unsure about how to systematically use the affordances of the computer tool to solve the problem, did not have a goal-directed plan or strategy in mind about how to investigate the issue at hand, and had difficulty with testing the immigration policies by appropriately controlling variables in order to collect data to inform decision making. Implications are discussed in terms of designing computer systems that scaffold learners' complex problem solving by considering the cognitive demands of the task. (C) 2012 Elsevier