Collaborative Research: Integration of Implantable MEMS Sensors and Computational Modeling to Assess Mechanical Regulation of Bone Regeneration

合作研究：集成植入式 MEMS 传感器和计算模型来评估骨再生的机械调节

• 批准号: 1400065
• 负责人: Robert Guldberg
• 金额: $ 26.58万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2018-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1400065&HistoricalAwards=false
• 关键词: Collaborative; Research; Integration; Implantable; MEMS

These studies will provide a detailed characterization of the bone healing mechanical environment in vivo at both the tissue level and the local cell level. Additionally, the small scale and wireless properties of sensors advanced in this study will provide a platform to enable non-invasive quantification of the local mechanical environment for a broad range of regenerative engineering and medicine applications. Ultimately, these sensors could be utilized to transform the methods, modes, and costs of tissue replacements and enable the real time assessment of such cases as graft rejection and spinal fixation. This project will also combine an educational plan with three routes for broadening participation: (1) outreach programs in micro- and nanotechnology to K-12 participants; (2) GT-ENGAGE, a program which immerses high-school students from underrepresented groups in engineering research projects; and (3) the GT-SURE undergraduate research opportunity program. Through this approach, students across age ranges from K-college will be provided both a broad and detailed immersion in the research and technology development experience. The research objective of this award is to develop a multi-scale mechanics approach to comprehensively characterize the in vivo mechanical environment within a tissue engineered construct during regeneration. Studies conducted under this award will utilize implantable wireless microelectromechanical (MEMS) sensors, biomechanical testing, and image-based finite element modeling. Tissue engineered constructs and dynamic fixation plates will be instrumented with wireless MEMS sensors; these will then be implemented in a well-established in vivo critically sized segmental bone defect model. In vivo measurements of the local strain gradients within the engineered constructs will be measured in real time during the course of regeneration to obtain a loading history. Image-based finite element models of the local mechanical strains in regenerating bone will be created and validated using the in vivo data from the implantable MEMS sensors.

这些研究将在组织水平和局部细胞水平上提供体内骨愈合力学环境的详细表征。此外，本研究中先进的传感器的小规模和无线特性将提供一个平台，以实现对广泛的再生工程和医学应用的局部机械环境的非侵入性量化。最终，这些传感器可用于改变组织置换的方法、模式和成本，并能够对移植物排斥和脊柱固定等情况进行真实的时间评估。该项目还将联合收割机教育计划与扩大参与的三条路线相结合：（1）面向K-12参与者的微型和纳米技术推广计划;（2）GT-ENGAGE，一个让来自代表性不足群体的高中生沉浸在工程研究项目中的计划;以及（3）GT-SURE本科生研究机会计划。通过这种方法，K学院各个年龄段的学生将获得广泛而详细的研究和技术开发体验。该奖项的研究目标是开发一种多尺度力学方法，以全面表征组织工程结构再生过程中的体内力学环境。根据该奖项进行的研究将利用可植入的无线微机电（MEMS）传感器，生物力学测试和基于图像的有限元建模。组织工程结构和动态固定板将配备无线MEMS传感器;然后将在完善的体内临界尺寸节段性骨缺损模型中实施。在再生过程中，将在真实的时间内测量工程结构内局部应变梯度的体内测量值，以获得加载历史。再生骨中局部机械应变的基于图像的有限元模型将使用来自植入式MEMS传感器的体内数据来创建和验证。