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ERI: A Computational and Experimental Approach to Establishing Multiscale and Multiphasic Structure-Function Mechanisms of Muscle Stiffness

ERI: A Computational and Experimental Approach to Establishing Multiscale and Multiphasic Structure-Function Mechanisms of Muscle Stiffness
ERI:建立肌肉僵硬多尺度、多相结构功能机制的计算和实验方法
批准号:
2301653
负责人:
Benjamin Wheatley
金额:
$20.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-06-01 至 2025-05-31

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中文摘要
翻译
这项工程研究启动(ERI)奖支持将在多个尺度上创建肌肉行为模型的研究。肌肉负责为我们的日常生活提供必要的力量——走路、交流和呼吸都要感谢骨骼肌。然而,肌肉损伤、神经肌肉疾病和其他损伤会减少一个人的活动能力,并引起严重的疼痛,因为肌肉太僵硬了。本项目旨在研究健康肌肉的僵硬度和生物学特性与受损肌肉的不同。特别是,目前尚不清楚组成骨骼肌的分子如何影响机械性能,特别是肌肉硬度。该项目将研究骨骼肌的三个关键部分是如何导致肌肉僵硬的:a)肌肉细胞,b)称为细胞外基质的网状组织,以及c)肌肉中的细胞液体。研究小组将使用实验测试和计算机模型来生成数据。这些数据将帮助科学家和临床医生更好地了解疾病改变肌肉僵硬的具体机制。研究结果将有助于改善患有这种肌肉疾病的人的治疗方法。这笔拨款还将支持教育推广,以指导不同群体的本科生研究人员,并支持他们在科学职业生涯中的专业发展。这项工作将通过利用多尺度材料测试和有限元分析来建立被动骨骼肌的关键结构-功能机制。虽然损伤引起的肌肉僵硬变化与胶原蛋白含量和类型等测量值相关,但预测体内肌肉的超弹性、各向异性材料特性是不可能的。研究小组将在拉伸和压缩条件下对单个肌肉纤维、肌肉组织和整个肌肉进行多轴材料测试。这些实验数据将包括迄今为止收集的最全面的肌肉材料特性集,并将用于校准和验证肌肉组织的多尺度、双相、均质有限元模型,该模型将微观结构形式与宏观功能联系起来。该模型可用于未来研究肌肉组织的微观结构变化如何改变肌肉材料特性,从而改变体内功能。该奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
This Engineering Research Initiation (ERI) award supports research that will create a model of muscle behavior at multiple scales. Muscle is responsible for providing the power that is necessary to live our daily lives – walking, communicating, and breathing are all possible thanks to skeletal muscle. However, muscle injury, neuromuscular disease, and other impairments can reduce a person’s mobility and cause significant pain because the muscles are too stiff. This project is to investigate how the stiffness and biology of healthy muscle is different from that of impaired muscle. In particular, it is currently unknown how molecules that compose skeletal muscle contribute to the mechanical properties, particularly to muscle stiffness. The project will investigate how three key parts of skeletal muscle contribute to muscle stiffness – a) muscle cells, b) a web-like tissue called the extracellular matrix, and c) the cellular liquids in muscle. The research team will use experimental testing complemented by computer models to generate data. This data will help scientists and clinicians better to understand what specific mechanisms of a disease change muscle stiffness. The results will contribute to improving treatments for people who suffer from such muscle conditions. This grant also will support educational outreach to mentor a diverse group of undergraduate student researchers and support their professional development in careers in the sciences. This work will establish critical structure-function mechanisms in passive skeletal muscle by leveraging multiscale materials testing and finite element analysis. While changes to muscle stiffness from impairments correlate to measurements such as collagen content and type, predicting the hyperelastic, anisotropic material properties of muscle in vivo is not possible. The research team will perform multi-axial materials testing on individual muscle fibers, muscle tissue, and whole muscle under both tensile and compressive conditions. These experimental data will comprise the most comprehensive set of muscle material properties collected to date and will be used for the calibration and validation of a multiscale, biphasic, homogenized finite element model of muscle tissue that links microstructural form to macro function. This model can then be used in future efforts to study how microstructural changes to muscle tissue alter muscle material properties and thus in vivo function.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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会议论文
MRI: Acquisition of a Planar Biaxial Material Testing System for Enhancement of Research and Teaching at Bucknell University
  • 批准号:
    1828082
  • 项目类别:
    Standard Grant
  • 资助金额:
    $12.38万
  • 财政年份:
    2018
  • 负责人:
    Benjamin Wheatley
  • 依托单位:
国内基金
海外基金
Computational Methods for Analyzing Toponome Data