Suppr超能文献

一种经过验证的被动骨骼肌模型,用于预测力量和肌肉内压力。

A validated model of passive skeletal muscle to predict force and intramuscular pressure.

作者信息

Wheatley Benjamin B, Odegard Gregory M, Kaufman Kenton R, Haut Donahue Tammy L

机构信息

Department of Mechanical Engineering, Colorado State University, 1374 Campus Delivery, Fort Collins, CO, 80523, USA.

Department of Mechanical Engineering - Engineering Mechanics, Michigan Technological University, 1400 Townsend Drive, Houghton, MI, 49931, USA.

出版信息

Biomech Model Mechanobiol. 2017 Jun;16(3):1011-1022. doi: 10.1007/s10237-016-0869-z. Epub 2016 Dec 31.

Abstract

The passive properties of skeletal muscle are often overlooked in muscle studies, yet they play a key role in tissue function in vivo. Studies analyzing and modeling muscle passive properties, while not uncommon, have never investigated the role of fluid content within the tissue. Additionally, intramuscular pressure (IMP) has been shown to correlate with muscle force in vivo and could be used to predict muscle force in the clinic. In this study, a novel model of skeletal muscle was developed and validated to predict both muscle stress and IMP under passive conditions for the New Zealand White Rabbit tibialis anterior. This model is the first to include fluid content within the tissue and uses whole muscle geometry. A nonlinear optimization scheme was highly effective at fitting model stress output to experimental stress data (normalized mean square error or NMSE fit value of 0.993) and validation showed very good agreement to experimental data (NMSE fit values of 0.955 and 0.860 for IMP and stress, respectively). While future work to include muscle activation would broaden the physiological application of this model, the passive implementation could be used to guide surgeries where passive muscle is stretched.

摘要

骨骼肌的被动特性在肌肉研究中常常被忽视,然而它们在体内组织功能中起着关键作用。分析和模拟肌肉被动特性的研究虽并不罕见,但从未探究过组织内液体含量的作用。此外,肌内压(IMP)已被证明在体内与肌肉力量相关,可用于临床预测肌肉力量。在本研究中,开发并验证了一种新型骨骼肌模型,用于预测新西兰白兔胫骨前肌在被动条件下的肌肉应力和IMP。该模型首次将组织内的液体含量纳入考虑,并采用了全肌肉几何形状。一种非线性优化方案在使模型应力输出拟合实验应力数据方面非常有效(归一化均方误差或NMSE拟合值为0.993),验证结果表明与实验数据非常吻合(IMP和应力的NMSE拟合值分别为0.955和0.860)。虽然未来纳入肌肉激活的工作将拓宽该模型的生理应用范围,但被动模型可用于指导被动肌肉被拉伸的手术。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验