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首页> 外文期刊>Osteoarthritis and cartilage >Subchondral bone micro-architectural alterations in osteoarthritis: a synchrotron micro-computed tomography study.
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Subchondral bone micro-architectural alterations in osteoarthritis: a synchrotron micro-computed tomography study.

机译:骨关节炎的软骨下骨微结构改变:同步加速器计算机断层扫描研究。

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OBJECTIVES: We evaluated the three-dimensional (3D) micro-architecture of subchondral trabecular (Tb) bone in osteoarthritis (OA). Due to high signal-to-noise ratio and high resolution, micro-computed tomography (micro-CT) by synchrotron radiation is considered as the gold standard for bone micro-architecture imaging. DESIGN: Subchondral bone were extracted from femoral heads in OA cases in areas without cartilage (OAc-; n=6) and in adjacent areas with cartilage (OAc+; n=6) and compared to eight subchondral bone cores from osteoporosis cases (OP). The voxel size of images was 10.13 microm. We measured the bone volume fraction (BV/TV) and morphological parameters: Tb thickness (TbTh), Tb spacing (TbSp), Tb number (TbN), and bone surface/bone volume (BS/BV). The degree of anisotropy (DA), the connectivity by the Euler number and the degree of mineralization (DM) were equally assessed. RESULTS: BV/TV and morphological parameters showed significant differences between OAc- and OP samples (P<0.01 except TbTh: P<0.05) and between OAc- and OAc+ (0.05
机译:目的:我们评估了骨关节炎(OA)中软骨下小梁(Tb)骨的三维(3D)微结构。由于高信噪比和高分辨率,通过同步加速器辐射进行的微计算机断层扫描(micro-CT)被认为是骨微结构成像的金标准。设计:在没有软骨的区域(OAc-; n = 6)和邻近有软骨的区域(OAc +; n = 6)的OA病例中,从股骨头中提取软骨下骨,并与骨质疏松症病例(OP)的八个软骨下骨核进行比较。图像的体素大小为10.13微米。我们测量了骨体积分数(BV / TV)和形态参数:Tb厚度(TbTh),Tb间距(TbSp),Tb数(TbN)和骨表面/骨体积(BS / BV)。对各向异性程度(DA),通过欧拉数的连通性和成矿程度(DM)进行了均等评估。结果:OAc-和OP样品之间的BV / TV和形态参数差异显着(TbTh:P <0.05,P <0.01); OAc-和OAc +之间的0.05(P <0.01),OAc +和OP之间的差异无统计学意义(TbN) (P <0.01)。与OAc +和OP相比,OAc-中的连接性更高。 OA和OP病例之间的DA差异显着(P <0.01),而OAc-和OAc +标本之间没有差异。对于OAc-,OAc +,DMs(平均值+/- SD)为0.817 +/- 0.142 g / cm(3),0.873 +/- 0.161 g / cm(3),0.906 +/- 0.156 g / cm(3), OP(P <0.01)。结论:软骨下骨变化主要发生在晚期OA中,此时软骨已被切除并保留在邻近区域。这些数据表明,软骨下骨的改变将是软骨退化的次要原因,而不是OA的原始机制。不过,纵向数据可以得出更准确的结论。

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