Orthopaedic Research, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA ; Tissue Engineering, VA Boston Healthcare System, Boston, MA, USA ; Orthopaedic Research Lab, Institute for Clinical Medicine, Aarhus University Hospital, Aarhus, Denmark.
Stereology and EM Laboratory, Centre for Stochastic Geometry and Advanced Bioimaging, Aarhus University, Aarhus, Denmark.
Cartilage. 2015 Apr;6(2):123-32. doi: 10.1177/1947603514560655.
To implement stereological principles to develop an easy applicable algorithm for unbiased and quantitative evaluation of cartilage repair.
Design-unbiased sampling was performed by systematically sectioning the defect perpendicular to the joint surface in parallel planes providing 7 to 10 hematoxylin-eosin stained histological sections. Counting windows were systematically selected and converted into image files (40-50 per defect). The quantification was performed by two-step point counting: (1) calculation of defect volume and (2) quantitative analysis of tissue composition. Step 2 was performed by assigning each point to one of the following categories based on validated and easy distinguishable morphological characteristics: (1) hyaline cartilage (rounded cells in lacunae in hyaline matrix), (2) fibrocartilage (rounded cells in lacunae in fibrous matrix), (3) fibrous tissue (elongated cells in fibrous tissue), (4) bone, (5) scaffold material, and (6) others. The ability to discriminate between the tissue types was determined using conventional or polarized light microscopy, and the interobserver variability was evaluated.
We describe the application of the stereological method. In the example, we assessed the defect repair tissue volume to be 4.4 mm(3) (CE = 0.01). The tissue fractions were subsequently evaluated. Polarized light illumination of the slides improved discrimination between hyaline cartilage and fibrocartilage and increased the interobserver agreement compared with conventional transmitted light.
We have applied a design-unbiased method for quantitative evaluation of cartilage repair, and we propose this algorithm as a natural supplement to existing descriptive semiquantitative scoring systems. We also propose that polarized light is effective for discrimination between hyaline cartilage and fibrocartilage.
运用体视学原理,开发一种简单适用的算法,对软骨修复进行无偏且定量的评估。
通过系统地在与关节面垂直的平面上对缺损进行平行切片,对设计进行无偏采样,以提供 7 至 10 个苏木精-伊红染色的组织学切片。系统地选择计数窗口,并将其转换为图像文件(每个缺损 40-50 个)。通过两步点计数进行量化:(1)计算缺损体积;(2)组织成分的定量分析。第二步通过基于验证和易于区分的形态特征,将每个点分配到以下类别之一来完成:(1)透明软骨(透明基质中陷窝中的圆形细胞);(2)纤维软骨(纤维基质中陷窝中的圆形细胞);(3)纤维组织(纤维组织中的长形细胞);(4)骨;(5)支架材料;(6)其他。使用传统或偏振光显微镜确定区分组织类型的能力,并评估观察者间的变异性。
我们描述了体视学方法的应用。在该示例中,我们评估缺损修复组织体积为 4.4mm3(CE=0.01)。随后评估了组织分数。与传统透射光相比,偏振光照射载玻片可改善透明软骨和纤维软骨之间的区分,并提高观察者间的一致性。
我们已经应用了一种无偏设计的方法来定量评估软骨修复,并且我们提出了这种算法作为现有描述性半定量评分系统的自然补充。我们还建议偏振光可有效区分透明软骨和纤维软骨。