ABSTRACT:The relationship between modern clinical diagnostic data, such as from radiographs or computed tomography, and the temporal biomechanical integrity of bone fracture healing has not been well-established. A diagnostic tool that could quantitatively describe the biomechanical stability of the fracture site in order to predict the course of healing would represent a paradigm shift in the way fracture healing is evaluated. This paper describes the development and evaluation of a wireless, biocompatible, implantable, microelectromechanical system (bioMEMS) sensor, and its implementation in a large animal (ovine) model, that utilized both normal and delayed healing variants. The in vivo data indicated that the bioMEMS sensor was capable of detecting statistically significant differences (p-value <0.04) between the two fracture healing groups as early as 21 days post-fracture. In addition, post-sacrifice microcomputed tomography, and histology data demonstrated that the two model variants represented significantly different fracture healing outcomes, providing explicit supporting evidence that the sensor has the ability to predict differential healing cascades. These data verify that the bioMEMS sensor can be used as a diagnostic tool for detecting the in vivo course of fracture healing in the acute post-treatment period. ß

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Although quasi-static and quasi-linear viscoelastic properties of the spinal cord have been reported previously, there are no published studies that have investigated the fully (strain-dependent) nonlinear viscoelastic properties of the spinal cord. In this study, stress relaxation experiments and dynamic cycling were performed on six fresh porcine lumbar cord specimens to examine their viscoelastic mechanical properties. The stress relaxation data were fitted to a modified superposition formulation and a novel finite ramp time correction technique was applied. The parameters obtained from this fitting methodology were used to predict the average dynamic cyclic viscoelastic behavior of the porcine cord. The data indicate that the porcine spinal cord exhibited fully nonlinear viscoelastic behavior. The average weighted RMSE for a Heaviside ramp fit was 2.8kPa, which was significantly greater (p < 0.001) than that of the nonlinear (comprehensive viscoelastic characterization (CVC) method) fit (0.365kPa). Further, the nonlinear mechanical parameters obtained were able to accurately predict the dynamic behavior, thus exemplifying the reliability of the obtained nonlinear parameters. These parameters will be important for future studies investigating various damage mechanisms of the spinal cord and studies developing high resolution finite elements models of the spine.

Finite element (FE) models of articular joint structures do not typically implement the fully nonlinear viscoelastic behavior of the soft connective tissue components. Instead, contemporary whole joint FE models usually represent the transient soft tissue behavior with significantly simplified formulations that are computationally tractable. The resultant fidelity of these models is greatly compromised with respect to predictions under temporally varying static and dynamic loading regimes. In addition, models based upon experimentally derived nonlinear viscoelastic coefficients that do not account for the transient behavior during the loading event(s) may further reduce the model's predictive accuracy. The current study provides the derivation and validation of a novel, phenomenological nonlinear viscoelastic formulation (based on the single integral nonlinear superposition formulation) that can be directly inputted into FE algorithms. This formulation and an accompanying experimental characterization technique, which incorporates relaxation manifested during the loading period of stress relaxation experiments, is compared to a previously published characterization method and validated against an independent analytical model. The results demonstrated that the static and dynamic FE approximations are in good agreement with the analytical solution. Additionally, the predictive accuracy of these approximations was observed to be highly dependent upon the experimental characterization technique. It is expected that implementation of the novel, computationally tractable nonlinear viscoelastic formulation and associated experimental characterization technique presented in the current study will greatly improve the predictive accuracy of the individual connective tissue components for whole joint FE simulations subjected to static and dynamic loading regimes.

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