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量化神经退行性疾病的病理学:定量测量、采样策略与数据分析。

Quantifying the pathology of neurodegenerative disorders: quantitative measurements, sampling strategies and data analysis.

作者信息

Armstrong R A

机构信息

Vision Sciences, Aston University, Birmingham B4 7ET, UK.

出版信息

Histopathology. 2003 Jun;42(6):521-9. doi: 10.1046/j.1365-2559.2003.01601.x.

Abstract

The use of quantitative methods has become increasingly important in the study of neurodegenerative disease. Disorders such as Alzheimer's disease (AD) are characterized by the formation of discrete, microscopic, pathological lesions which play an important role in pathological diagnosis. This article reviews the advantages and limitations of the different methods of quantifying the abundance of pathological lesions in histological sections, including estimates of density, frequency, coverage, and the use of semiquantitative scores. The major sampling methods by which these quantitative measures can be obtained from histological sections, including plot or quadrat sampling, transect sampling, and point-quarter sampling, are also described. In addition, the data analysis methods commonly used to analyse quantitative data in neuropathology, including analyses of variance (anova) and principal components analysis (PCA), are discussed. These methods are illustrated with reference to particular problems in the pathological diagnosis of AD and dementia with Lewy bodies (DLB).

摘要

定量方法在神经退行性疾病研究中的应用变得越来越重要。诸如阿尔茨海默病(AD)等疾病的特征是形成离散的、微观的病理病变,这些病变在病理诊断中起着重要作用。本文综述了在组织学切片中量化病理病变丰度的不同方法的优缺点,包括密度估计、频率估计、覆盖范围估计以及半定量评分的使用。还描述了从组织学切片中获取这些定量测量值的主要抽样方法,包括样方抽样、断面抽样和点四分法抽样。此外,还讨论了神经病理学中常用于分析定量数据的数据分析方法,包括方差分析(anova)和主成分分析(PCA)。文中结合AD和路易体痴呆(DLB)病理诊断中的具体问题对这些方法进行了说明。

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