Marmor, M. | bioRxiv Science

bioRxiv2018

Smart bone plates can monitor fracture healing

One Sentence SummaryElectrical impedance measurements using microscale sensors implanted in two mouse fracture models tracked longitudinal differences between individual mice with proper healing and mice experiencing poor healing, laying the groundwork for translation to the clinic through integration into fracture fixation implants (i.e. instrumented bone plates).\n\nAbstractThere are currently no standardized methods for monitoring fracture healing. While histological studies can clearly identify the tissues found in the four stages of repair, in practice surgeons rely on X-ray, which is only useful at later stages of healing after mineralization has occurred. As electrical impedance spectroscopy (EIS) has previously been used to distinguish tissue types during healing, we hypothesized that microscale sensors embedded in the fracture callus could track the changing tissue with high sensitivity. Using in vivo mouse fracture models, we present the first evidence that microscale instrumented implants provide a route for post-operative fracture monitoring. In this study, we implanted sensors in mouse long bone fractures fixed with either external fixators or bone plates. EIS measurements taken across two electrodes implanted in the fracture gap were able to track longitudinal differences between individual mice with proper healing and mice experiencing poor healing. We additionally present an equivalent circuit model that combines the EIS data in order to classify healing states of fractures. Lastly, we show that EIS measures are strongly correlated with standard {micro}CT measures of healing and that these correlations validate clinically-relevant operating frequencies for implementation of this technique. The data from these two models demonstrate that this technique can be translated to the clinic through integration into current fracture management strategies such as bone plating, providing physicians with quantitative information about the state of a fracture to guide clinical decision-making for patients.

bioengineering↗

bioRxiv2017

Simulation Enabled Search for Explanatory Mechanisms of the Fracture Healing Process

A significant portion of bone fractures fail to heal properly, increasing healthcare costs. Advances in fracture management have slowed because translational barriers have limited generation of mechanism-based explanations for the healing process. When uncertainties are numerous, analogical modeling can be an effective strategy for developing plausible explanations of complex phenomena. We demonstrate the feasibility of engineering analogical models in software to provide plausible biomimetic explanations for how fracture healing may progress. Concrete analogical models - Callus Analogs - were created using the MASON simulation toolkit. We designated a Target Region initial state within a characteristic tissue section of mouse tibia fracture at day-7 and posited a corresponding day-10 Target Region final state. The goal was to discover a coarse-grain analog mechanism that would enable the discretized initial state to transform itself into the corresponding Target Region final state, thereby providing a new way to study the healing process. One of nine quasi-autonomous Tissue Unit types is assigned to each grid space, which maps to an 80x80 {micro}m region of the tissue section. All Tissue Units have an opportunity each time step to act based on individualized logic, probabilities, and information about adjacent neighbors. Action causes transition from one Tissue Unit type to another, and simulation through several thousand time steps generates a coarse-grain analog - a theory - of the healing process. We prespecified a minimum measure of success: simulated and actual Target Region states achieve [≥] 70% Similarity. We used an iterative protocol to explore many combinations of Tissue Unit logic and action constraints. Workflows progressed through four stages of analog mechanisms. Similarities of 73-90% were achieved for Mechanisms 2-4. The range of Upper-Level similarities increased to 83-94% when we allowed for uncertainty about two Tissue Unit designations. We have demonstrated how Callus Analog experiments provide domain experts with a new medium and tools for thinking about and understanding the fracture healing process.\n\nAuthor summaryTranslational barriers have limited the generation of mechanism-based explanations of fracture healing processes. Those barriers help explain why, to date, biological therapeutics have had only a minor impact on fracture management. New approaches are needed, and we present one that is intended to overcome those barriers incrementally. We created virtual Callus Analogs to simulate how the histologic appearance of a mouse fracture callus may transition from day-7 to day-10. Callus Analogs use software-based model mechanisms. Simulation experiments enable challenging and improving those model mechanisms. During execution, model mechanism operation provides a coarse-grain explanation (a theory) of a four-day portion of the healing process. Simulated day-10 callus histologic images achieved 73-94% similarity to a corresponding day-10 fracture callus image, thus demonstrating feasibility. Simulated healing provides a new perspective on the actual healing process and a new way of thinking about plausible fracture-healing processes. Our working hypothesis is that the approach can be extended to cover more of the healing process while making features of simulated and actual fracture healing increasingly analogous. The methods presented are intended to be extensible to other research areas that use histologic analysis to investigate and explain tissue level phenomena.

physiology↗