Suppr超能文献

[组织病理学颗粒算法。滑膜和SLIM中的颗粒识别]

[Histopathological particle algorithm. Particle identification in the synovia and the SLIM].

作者信息

Krenn V, Thomas P, Thomsen M, Kretzer J P, Usbeck S, Scheuber L, Perino G, Rüther W, v Welser R, Hopf F, Huber M

机构信息

MVZ-Zentrum für Histologie, Zytologie und Molekulare Diagnostik, Max-Planck-Str. 5, 54296, Trier, Deutschland,

出版信息

Z Rheumatol. 2014 Sep;73(7):639-49. doi: 10.1007/s00393-013-1315-6.

Abstract

BACKGROUND

In the histopathological diagnostics of synovitis and the synovium-like interface membrane (SLIM) the identification of crystals and crystal-like deposits and the associated inflammatory reactions play an important role. The multitude of endogenous crystals, the range of implant materials and material combinations, and the variability in the formation process of different particles explain the high morphological particle heterogeneity which complicates the diagnostic identification of diagnostic particles.

STUDY DESIGN AND METHODS

A simple histopathological particle algorithm has been designed which allows methodological particle identification based on (1) conventional transmitted light microscopy with a guide to particle size, shape and color, (2) optical polarization criteria and (3) enzyme histochemical properties (oil red staining and Prussian blue reaction). These methods, the importance for particle identification and the differential diagnostics from non-prosthetic materials are summarized in the so-called histopathological particle algorithm.

RESULTS

A total of 35 cases of synovitis and SLIM were analyzed and validated according to these criteria. Based on these criteria and a dichotomous differentiation the complete spectrum of particles in the SLIM and synovia can be defined histopathologically.

CONCLUSION

For histopathological diagnosis a particle score for synovitis and SLIM is recommended to evaluate (1) the predominant type of prothetic wear debris with differentiation between microparticles, and macroparticles, (2) the presence of non-prosthesis material particles and (3) the quantification of particle-association necrosis and lymphocytosis. An open, continuously updated web-based particle algorithm would be helpful to address the issue of particle heterogeneity and include all new particle materials generated in a rapidly changing field.

摘要

背景

在滑膜炎和滑膜样界面膜(SLIM)的组织病理学诊断中,晶体及类晶体沉积物的识别以及相关炎症反应起着重要作用。内源性晶体种类繁多、植入材料及材料组合各异,且不同颗粒形成过程存在差异,这些因素导致形态学上颗粒高度异质性,使得诊断性颗粒的鉴别诊断变得复杂。

研究设计与方法

设计了一种简单的组织病理学颗粒算法,该算法基于以下几点进行方法学上的颗粒识别:(1)传统透射光显微镜检查,同时给出颗粒大小、形状和颜色指南;(2)光学偏振标准;(3)酶组织化学特性(油红染色和普鲁士蓝反应)。这些方法、颗粒识别的重要性以及与非假体材料的鉴别诊断在所谓的组织病理学颗粒算法中进行了总结。

结果

根据这些标准对35例滑膜炎和SLIM病例进行了分析和验证。基于这些标准及二分法鉴别,可在组织病理学上定义SLIM和滑膜中颗粒的完整谱系。

结论

对于组织病理学诊断,建议采用滑膜炎和SLIM的颗粒评分来评估:(1)假体磨损碎片的主要类型,区分微粒和大颗粒;(2)非假体材料颗粒的存在情况;(3)颗粒相关坏死和淋巴细胞增多的量化。一个开放的、不断更新的基于网络的颗粒算法将有助于解决颗粒异质性问题,并纳入快速变化领域中产生的所有新颗粒材料。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验