Department of Mechanical Engineering & Materials Science, and NSF Center for Engineering MechanoBiology, Washington University in St. Louis, St. Louis, MO, USA.
Department of Bioengineering, Rice University,Houston, TX, USA.
J Mech Behav Biomed Mater. 2017 May;69:193-202. doi: 10.1016/j.jmbbm.2016.12.013. Epub 2016 Dec 22.
The time- and frequency-dependent properties of connective tissue define their physiological function, but are notoriously difficult to characterize. Well-established tools such as linear viscoelasticity and the Fung quasi-linear viscoelastic (QLV) model impose forms on responses that can mask true tissue behavior. Here, we applied a more general discrete quasi-linear viscoelastic (DQLV) model to identify the static and dynamic time- and frequency-dependent behavior of rabbit medial collateral ligaments. Unlike the Fung QLV approach, the DQLV approach revealed that energy dissipation is elevated at a loading period of ∼10s. The fitting algorithm was applied to the entire loading history on each specimen, enabling accurate estimation of the material's viscoelastic relaxation spectrum from data gathered from transient rather than only steady states. The application of the DQLV method to cyclically loading regimens has broad applicability for the characterization of biological tissues, and the results suggest a mechanistic basis for the stretching regimens most favored by athletic trainers.
结缔组织的时频依赖特性决定了其生理功能,但它们的特性很难被准确描述。线性粘弹性和 Fung 拟线性粘弹性(QLV)模型等成熟工具对响应施加了形式,这可能掩盖了真实组织的行为。在这里,我们应用了一种更通用的离散拟线性粘弹性(DQLV)模型来确定兔内侧副韧带的静态和动态时频相关行为。与 Fung QLV 方法不同,DQLV 方法揭示了在约 10 秒的加载周期内能量耗散会增加。拟合算法应用于每个样本的整个加载历史,从而可以从瞬态而不仅仅是稳态收集的数据中准确估计材料的粘弹性松弛谱。DQLV 方法在循环加载方案中的应用具有广泛的生物组织特性描述适用性,并且结果为运动训练师最青睐的拉伸方案提供了力学基础。