BioMEMs Sensor for Monitoring Fracture Healing

描述（由申请人提供）：美国人口每年治疗近800万例长骨骨折。这些骨折中约有10%无法正常愈合。许多骨不连或假关节是由于严重或严重骨折（高度碎裂）而无法通过稳定的（膜内骨化）愈合途径进行。异常愈合的早期检测将允许使用更新类型的更无创的翻修策略，以及减轻更多开放式翻修手术的技术要求。不幸的是，异常骨折愈合的过程在早期不容易诊断，此时骨折部位的标准射线照相信息不能区分愈合途径。我们假设，通过监测植入的硬件力学，可以确定至关重要的早期愈合。这一假设利用了先前证明的现象，即软组织骨痂和新形成的骨随着愈合的进行逐渐承担部分载荷，从而减少了植入硬件上的负担（和相关应变）。因此，为了解决在早期识别异常骨折愈合的关键需求，我们开发了一种无线、感应供电（无植入式电源）、生物相容性微机电传感器（bioMEMS），该传感器能够监测植入骨折硬件上的表面应变，并使用射频（RF）技术报告这些数据。该开发提案寻求资金以进一步开展这些活动，并使用体外和动物模型建立传感器辨别正常骨愈合与异常骨愈合的能力。为了实现这些目标，我们提出了三个具体的目标：1-优化传感器的架构，用于检测应变，2-充分表征传感器在模拟骨折和生理环境中的响应，以及3-在正常和异常骨折愈合的动物模型中实施传感器，以测试我们的指导假设并进行严格的生物相容性分析。总之，该开发提案旨在将我们的bioMEMs传感器从台式发展到体内环境。研究计划代表了证明器械安全性和有效性所需的实验的逻辑进展。预计在本项目结束时，将获得足够的数据，以便提交人类患者临床试验申请。 公共卫生相关性：美国人口每年接受近800万例长骨骨折的治疗，其中约10%的骨折无法正常愈合。不幸的是，骨折畸形愈合的过程不容易在早期诊断，此时可以采用更多的无创翻修策略。因此，为了解决在早期识别异常骨折愈合的关键需求，我们开发了一种无线感应供电（无植入式电源）和生物相容性传感器，能够检测骨愈合过程并使用无线技术报告这些数据。

DESCRIPTION (provided by applicant): The American population is treated for nearly eight million long bone fractures per year. Approximately 10% of these fractures do not heal properly. Many of these non-unions or pseudoarthroses result when there is a severe or communited (highly fragmented) condition that does not proceed through a stabilized (intramembranous ossification) healing pathway. Early detection of aberrant healing would allow for newer classes of more non-invasive revision strategies to be utilized, as well as ease the technical demands of more open revision procedures. Unfortunately, the course of aberrant fracture healing is not easily diagnosed in the early time period when standard radiographic information of the fracture site is not capable of discriminating the healing pathway. We have hypothesized that healing in the critically important early time period can be determined by monitoring of the implanted hardware mechanics. This postulation leverages the previously demonstrated phenomena whereby the soft tissue callus and newly formed bone progressively assume part of the load as healing proceeds, thus reducing the burden (and associated strain) on the implanted hardware. Thus, to address the critical need of identifying aberrant fracture healing during the early time period, we have developed a wireless, inductively-powered (no implantable power source), biocompatible micro-electromechanical sensor (bioMEMS) that is capable of monitoring the surface strain on implanted bone fracture hardware and reports these data using radio frequency (RF) technology. This development proposal seeks funding to further these activities and to establish the sensor's ability to discern normal versus aberrant bone healing using in vitro and animal models. In order to achieve these goals, we propose three specific aims: 1 - to optimize the sensor's architecture for detecting strain, 2 - to fully characterize the sensor's response in a simulated fracture and physiological environments, and 3 - to implement the sensor in an animal model of normal and aberrant fracture healing to test our guiding hypothesis and perform rigorous biocompatibility analyses. In summary, this developmental proposal seeks to evolve our bioMEMs sensor from the benchtop to the in vivo environment. The research plan represents a logical progression of experiments that are required to demonstrate the safety and efficacy of the device. It is expected that at the end of this project that sufficient data will have been obtained in order to file an application for clinical trial in human patients. PUBLIC HEALTH RELEVANCE: The American population is treated for nearly eight million long bone fractures per year and approximately 10% of these fractures do not heal properly. Unfortunately, the course of aberrant fracture healing is not easily diagnosed in the early time period when more non- invasive revision strategies could be employed. Thus, to address the critical need of identifying aberrant fracture healing during the early time period, we have developed a wireless, inductively-powered (no implantable power source) and biocompatible sensor that is capable of detecting the course of bone healing and reports these data using wireless technology.