Is the callus shape an optimal response to a mechanobiological stimulus?

Ribeiro, Filomena; Folgado, João; Aznar, José Manuel García; Gómez‐Benito, María José; Fernandes, Paulo R.

doi:10.1016/j.medengphy.2014.07.015

Cited by 12 publications

(7 citation statements)

References 38 publications

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“…Isaksson et al, 2006a, Isaksson et al, 2006b reported that simultaneously utilizing both deviatoric strain and fluid velocity led to a better and more compatible result with experimental data. Ribeiro et al (2014) also found that combining interfragmentary strain and the second invariant of deviatoric gap strain tensor as mechanical stimuli with inflammatory factors as chemical stimuli, achieved the callus shape most compatible with histological data. In addition, when axial, shear, or torsion loading are applied to the bone during healing, a different set of mechanical stimuli should be utilized to predict bone healing that is consistent with experimental data.…”

Section: Mechanical Stimulussupporting

confidence: 52%

“…Ribeiro et al (2014) utilized optimization algorithms to investigate the effects of different mechanical and chemical stimulus on callus shape and stability. After studying different regions of the callus under each stimulus, they concluded that callus growth can be an optimal response to the local mechanical instability and inflammatory reaction.…”

Section: Mechanical Stimulusmentioning

confidence: 99%

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Bone fracture healing in mechanobiological modeling: A review of principles and methods

et al. 2017

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Bone fracture is a very common body injury. The healing process is physiologically complex, involving both biological and mechanical aspects. Following a fracture, cell migration, cell/tissue differentiation, tissue synthesis, and cytokine and growth factor release occur, regulated by the mechanical environment. Over the past decade, bone healing simulation and modeling has been employed to understand its details and mechanisms, to investigate specific clinical questions, and to design healing strategies. The goal of this effort is to review the history and the most recent work in bone healing simulations with an emphasis on both biological and mechanical properties. Therefore, we provide a brief review of the biology of bone fracture repair, followed by an outline of the key growth factors and mechanical factors influencing it. We then compare different methodologies of bone healing simulation, including conceptual modeling (qualitative modeling of bone healing to understand the general mechanisms), biological modeling (considering only the biological factors and processes), and mechanobiological modeling (considering both biological aspects and mechanical environment). Finally we evaluate different components and clinical applications of bone healing simulation such as mechanical stimuli, phases of bone healing, and angiogenesis.

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Section: Mechanical Stimulussupporting

confidence: 52%

Section: Mechanical Stimulusmentioning

confidence: 99%

Bone fracture healing in mechanobiological modeling: A review of principles and methods

et al. 2017

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“…Secondary bone healing involves callus formation, where callus size partially depends on the interfragmentary motion levels conducive to healing [23, 30–34]. Moreover, the callus geometry is shown to be an optimal shape to endure the mechanical loading during the healing process [35–37]. .…”

Section: Introductionmentioning

confidence: 99%

Computational modeling of human bone fracture healing affected by different conditions of initial healing stage

Ghiasi

Chen

Rodriguez

et al. 2019

BMC Musculoskelet Disord

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BackgroundBone healing process includes four phases: inflammatory response, soft callus formation, hard callus development, and remodeling. Mechanobiological models have been used to investigate the role of various mechanical and biological factors on bone healing. However, the effects of initial healing phase, which includes the inflammatory stage, the granulation tissue formation, and the initial callus formation during the first few days post-fracture, are generally neglected in such studies.MethodsIn this study, we developed a finite-element-based model to simulate different levels of diffusion coefficient for mesenchymal stem cell (MSC) migration, Young’s modulus of granulation tissue, callus thickness and interfragmentary gap size to understand the modulatory effects of these initial phase parameters on bone healing.ResultsThe results quantified how faster MSC migration, stiffer granulation tissue, thicker callus, and smaller interfragmentary gap enhanced healing to some extent. However, after a certain threshold, a state of saturation was reached for MSC migration rate, granulation tissue stiffness, and callus thickness. Therefore, a parametric study was performed to verify that the callus formed at the initial phase, in agreement with experimental observations, has an ideal range of geometry and material properties to have the most efficient healing time.ConclusionsFindings from this paper quantified the effects of the initial healing phase on healing outcome to better understand the biological and mechanobiological mechanisms and their utilization in the design and optimization of treatment strategies. It is also demonstrated through a simulation that for fractures, where bone segments are in close proximity, callus development is not required. This finding is consistent with the concepts of primary and secondary bone healing.

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“…This mesh is the initial point for a healing algorithm interpreting the segmented individual fracture part as possible bone healing area, i.e. a kind of callus shape taking the influence of the mechanobiological stimulus on the early callus formation into account [4]. The bone healing is simulated with finite element simulations of an elastic material model combined with a mechanoregulation algorithm to describe the cellular processes involved in the healing process, e.g.…”

Section: Methods and Resultsmentioning

confidence: 99%

An algorithmic strategy for the simulation of bone healing directly on computed tomography data

Roland

Tjardes

Dahmen

et al. 2015

Proc Appl Math and Mech

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An algorithmic strategy for the modelling and simulation of bone healing is presented. The algorithm works directly on the computed tomography data and simulates, after an appropriate volume meshing, a mechainically driven healing concept which is based on competitive and dynamical mechanical parameters. The finite element simulations are done with realistic boundary conditions from patient-specific OpenSim simulations.

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Is the callus shape an optimal response to a mechanobiological stimulus?

Cited by 12 publications

References 38 publications

Bone fracture healing in mechanobiological modeling: A review of principles and methods

Bone fracture healing in mechanobiological modeling: A review of principles and methods

Computational modeling of human bone fracture healing affected by different conditions of initial healing stage

An algorithmic strategy for the simulation of bone healing directly on computed tomography data

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