Cheong Vee San, Kadirkamanathan Visakan, Dall'Ara Enrico
Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, United Kingdom.
Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, United Kingdom.
Front Bioeng Biotechnol. 2021 Jun 11;9:676867. doi: 10.3389/fbioe.2021.676867. eCollection 2021.
The mouse tibial loading model is used to evaluate the effectiveness of mechanical loading treatment against skeletal diseases. Although studies have correlated bone adaptation with the induced mechanical stimulus, predictions of bone remodeling remained poor, and the interaction between external and physiological loading in engendering bone changes have not been determined. The aim of this study was to determine the effect of passive mechanical loading on the strain distribution in the mouse tibia and its predictions of bone adaptation. Longitudinal micro-computed tomography (micro-CT) imaging was performed over 2 weeks of cyclic loading from weeks 18 to 22 of age, to quantify the shape change, remodeling, and changes in densitometric properties. Micro-CT based finite element analysis coupled with an optimization algorithm for bone remodeling was used to predict bone adaptation under physiological loads, nominal 12N axial load and combined nominal 12N axial load superimposed to the physiological load. The results showed that despite large differences in the strain energy density magnitudes and distributions across the tibial length, the overall accuracy of the model and the spatial match were similar for all evaluated loading conditions. Predictions of densitometric properties were most similar to the experimental data for combined loading, followed closely by physiological loading conditions, despite no significant difference between these two predicted groups. However, all predicted densitometric properties were significantly different for the 12N and the combined loading conditions. The results suggest that computational modeling of bone's adaptive response to passive mechanical loading should include the contribution of daily physiological load.
小鼠胫骨加载模型用于评估机械加载治疗骨骼疾病的有效性。尽管已有研究将骨适应性与诱导的机械刺激相关联,但骨重塑的预测仍然较差,并且尚未确定外部加载与生理加载在引起骨变化方面的相互作用。本研究的目的是确定被动机械加载对小鼠胫骨应变分布及其骨适应性预测的影响。在18至22周龄的2周循环加载过程中进行纵向微计算机断层扫描(micro-CT)成像,以量化形状变化、重塑和密度测量特性的变化。基于micro-CT的有限元分析与用于骨重塑的优化算法相结合,用于预测生理负荷、标称12N轴向负荷以及叠加在生理负荷上的标称12N轴向负荷组合下的骨适应性。结果表明,尽管胫骨长度上的应变能密度大小和分布存在很大差异,但在所有评估的加载条件下,模型的总体准确性和空间匹配性相似。对于组合加载,密度测量特性的预测与实验数据最为相似,其次是生理加载条件,尽管这两个预测组之间没有显著差异。然而,对于12N和组合加载条件,所有预测的密度测量特性均存在显著差异。结果表明,骨骼对被动机械加载的适应性反应的计算模型应包括日常生理负荷的贡献。