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关键词： 检验科   医院感染   生物安全   实验室  

摘要： [目的]从相关法律法规角度探讨检验科在医院感染管理中的作用以及建设要求。[方法]对照医院感染相关法律法规,了解医院感染管理中涉及的各种微生物学检测;从生物安全相关法规规范中了解医院感染管理中涉及的各种微生物的实验活动应配备的实验室生物安全级别及其硬件配备要求。[结果]医院感染管理中的消毒灭菌监测、耐药性分析、院感病例监测等都需要检验科的配合,检验科应建设符合要求的二级生物安全实验室和洁净实验室。[结论]检验科在医院感染管理中起到重要作用,应加强检验科的生物安全建设及管理。

关键词： 乙型肝炎病毒   丙型肝炎病毒   检验医师   质量保证  

摘要： HBV和HCV检测复杂,常会出现一些看似相互矛盾难以解释的结果,造成临床医生对检测结果的不理解及临床与实验室间的矛盾,产生医患纠纷。目前在HBV和HCV的检测过程中,检测前和检测后的质量保证是检验科较为薄弱的环节。本文从保证HBV和HCV检测前和检测后的过程质量的角度,探讨了检验医师应当如何成为实验室和临床之间沟通的桥梁,从而使得检验结果更加准确、可靠,并为病毒性肝炎的诊疗提供充分有效的信息。

关键词： 尿糖   微量白蛋白   糖尿病肾病   早期   诊断  

摘要： 目的探讨尿糖(uGlu)与微量白蛋白(mAlb)检测指标在早期诊断糖尿病肾病中的意义。方法将该院100例糖尿病肾病患者作为观察组,采用干化学试纸检测尿糖,采用免疫胶体金技术检测微量白蛋白在晨尿中的水平,同时与对照组100例健康体检者的相关体检指标进行对比。结果糖尿病肾病患者中尿微量白蛋白含量呈阳性的有56例,尿糖呈阳性的有81例,明显高于健康体检者,且与发展为糖尿病肾脏的患者数相符。结论尿糖和微量白蛋白检验同时呈阳性是早期确诊糖尿病肾病存在的有效手段。

关键词： 交通肇事   物证   检验技术   作用  

摘要： 随着经济的发展交通技术的进步,随之而来的也遇到了一些难题,比如交通事故的发生。然而据资料分析,交警部门在处理交通肇事案件中,在运用痕迹检验技术勘验此类案件时,更多的时候是根据特殊的痕迹来检验肇事案件,即通过对整体分离痕迹和轮胎印痕来认定肇事车辆,很少关注其他痕迹在事件中的作用,更是很少注意到侦查方向的特殊作用。该文通过一系列具体的交通肇事案件,通过在交通肇事案件痕迹检验技术的运用以及现场侦查方向的运用,从而以最有效的方式侦破交通肇事案件。

关键词： 临床护士   检验质量   重要作用  

摘要： 检验分析的质量管理包括分析前、分析中和分析后的质量控制,多数人认为判断一个检验结果是否准确是实验室分析的结果,这是不全面的。分析前质量控制是保证检验质量的前提,而临床护士在分析前质量控制中扮演着重要的角色,护理工作与医院临床工作联系密切[1]。经过多年的护理工作,

关键词： Imidazolium based ionic liquid   Apparent molar volume   Apparent molar isentropic compression   Density   Speed of sound  

摘要： Densities and speeds of sound for aqueous solutions of ionic liquids having 1-butyl-3-methylimidazolium as cation and chloride, bromide, iodide, acetate, tetrafluoroborate, and trifluoromethanesulfonate as anions were accurately measured at various concentrations and temperatures. The data were used in evaluating thermodynamic properties as apparent molar volumes and apparent molar isentropic compressions. Infinite dilution values of these properties were determined using appropriate extrapolation procedures utilizing Debye-Huckel limiting law for electrolyte solutions. Apparent molar isobaric expansions at infinite dilutions were also evaluated from the temperature dependence of apparent molar volumes. The results were interpreted in terms of ionic liquid-water interactions. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： metagenomics   taxonomic binning   next-generation sequencing  

摘要： Metagenome research uses random shotgun sequencing of microbial community DNA to study the genetic sequences of its members without cultivation. This development has been strongly supported by improvements in sequencing technologies, which have rendered sequencing cheaper than before. As a consequence, downstream computational analysis of metagenome sequence samples is now faced with large amounts of complex data. One of the essential steps in metagenome analysis is reconstruction of draft genomes for populations of a community or of draft 'pan-genomes' for higher level clades. 'Taxonomic binning' corresponds to the process of assigning a taxonomic identifier to sequence fragments, based on information such as sequence similarity, sequence composition or read coverage. This is used for draft genome reconstruction, if sequencing coverage is insufficient for reconstruction based on assembly information alone. Subsequent functional and metabolic annotation of draft genomes allows a genome-level analysis of novel uncultured microbial species and even inference of their cultivation requirements.

关键词： Amyotrophic Lateral Sclerosis   Partial Little Square   Random Forest   Duchenne Muscular Dystrophy   True Positive Rate  

摘要： Background: A feature selection method in microarray gene expression data should be independent of platform, disease and dataset size. Our hypothesis is that among the statistically significant ranked genes in a gene list, there should be clusters of genes that share similar biological functions related to the investigated disease. Thus, instead of keeping N top ranked genes, it would be more appropriate to define and keep a number of gene cluster exemplars. Results: We propose a hybrid FS method (mAP-KL), which combines multiple hypothesis testing and affinity propagation (AP)-clustering algorithm along with the Krzanowski & Lai cluster quality index, to select a small yet informative subset of genes. We applied mAP-KL on real microarray data, as well as on simulated data, and compared its performance against 13 other feature selection approaches. Across a variety of diseases and number of samples, mAP-KL presents competitive classification results, particularly in neuromuscular diseases, where its overall AUC score was 0.91. Furthermore, mAP-KL generates concise yet biologically relevant and informative N-gene expression signatures, which can serve as a valuable tool for diagnostic and prognostic purposes, as well as a source of potential disease biomarkers in a broad range of diseases. Conclusions: mAP-KL is a data-driven and classifier-independent hybrid feature selection method, which applies to any disease classification problem based on microarray data, regardless of the available samples. Combining multiple hypothesis testing and AP leads to subsets of genes, which classify unknown samples from both, small and large patient cohorts with high accuracy.

关键词： Microarrays   Algorithms   Breast cancer   Data reduction   Acute myeloid leukemia   Lung and intrathoracic tumors   Optimization   Support vector machines  

摘要： Background: Using hybrid approach for gene selection and classification is common as results obtained are generally better than performing the two tasks independently. Yet, for some microarray datasets, both classification accuracy and stability of gene sets obtained still have rooms for improvement. This may be due to the presence of samples with wrong class labels (i.e. outliers). Outlier detection algorithms proposed so far are either not suitable for microarray data, or only solve the outlier detection problem on their own. Results: We tackle the outlier detection problem based on a previously proposed Multiple-Filter-Multiple-Wrapper (MFMW) model, which was demonstrated to yield promising results when compared to other hybrid approaches (Leung and Hung, 2010). To incorporate outlier detection and overcome limitations of the existing MFMW model, three new features are introduced in our proposed MFMW-outlier approach: 1) an unbiased external Leave-One-Out Cross-Validation framework is developed to replace internal cross-validation in the previous MFMW model;2) wrongly labeled samples are identified within the MFMW-outlier model;and 3) a stable set of genes is selected using an L1-norm SVM that removes any redundant genes present. Six binary-class microarray datasets were tested. Comparing with outlier detection studies on the same datasets, MFMW-outlier could detect all the outliers found in the original paper (for which the data was provided for analysis), and the genes selected after outlier removal were proven to have biological relevance. We also compared MFMW-outlier with PRAPIV (Zhang et al., 2006) based on same synthetic datasets. MFMW-outlier gave better average precision and recall values on three different settings. Lastly, artificially flipped microarray datasets were created by removing our detected outliers and flipping some of the remaining samples' labels. Almost all the 'wrong' (artificially flipped) samples were detected, suggesting that MFMW-out

关键词： Fire debris   Factor analysis   Bayesian decision theory   Pattern classification   Chemometrics  

摘要： A Bayesian soft classification method combined with target factor analysis (TFA) is described and tested for the analysis of fire debris data. The method relies on analysis of the average mass spectrum across the chromatographic profile (i.e., the total ion spectrum, TIS) from multiple samples taken from a single fire scene. A library of TIS from reference ignitable liquids with assigned ASTM classification is used as the target factors in TFA. The class-conditional distributions of correlations between the target and predicted factors for each ASTM class are represented by kernel functions and analyzed by Bayesian decision theory. The soft classification approach assists in assessing the probability that ignitable liquid residue from a specific ASTM E1618 class, is present in a set of samples from a single fire scene, even in the presence of unspecified background contributions from pyrolysis products. The method is demonstrated with sample data sets and then tested on laboratory-scale burn data and large-scale field test burns. The overall performance achieved in laboratory and field test of the method is approximately 80% correct classification of fire debris samples. (C) 2012 Elsevier Ireland Ltd. All rights reserved.