On the Road to Personalized Medicine: Multiscale Computational Modeling of Bone Tissue

Podshivalov, Lev; Fischer, Anath; Bar‐Yoseph, P.

doi:10.1007/s11831-014-9120-1

Cited by 30 publications

(21 citation statements)

References 322 publications

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“…Properties such as elastic modulus (a measure of the stiffness of a solid material), tensile strength (capacity of a scaffold to cope with loads tending to reduce size), and fatigue strength (the highest stress that a material can hold for a given number of cycles without breaking) should be similar to those of natural bone in order to ensure bone mechanical strength [62]. However, bone presents diverse and dynamic mechanical properties, tightly related to its complex hierarchical structure [63]. Specifically, the elastic moduli of human bone tissue usually varies between 1 and 20 GPa (around 2.0 GPa and 14-18 GPa for trabecular and cortical bone, respectively) [54,64], and the tensile strength of cortical and cancellous bones is 50-150 MPa and 10-100 MPa, respectively [65].…”

Section: Scaffold Properties For Btementioning

confidence: 99%

A Multidisciplinary Journey towards Bone Tissue Engineering

2021

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Millions of patients suffer yearly from bone fractures and disorders such as osteoporosis or cancer, which constitute the most common causes of severe long-term pain and physical disabilities. The intrinsic capacity of bone to repair the damaged bone allows normal healing of most small bone injuries. However, larger bone defects or more complex diseases require additional stimulation to fully heal. In this context, the traditional routes to address bone disorders present several associated drawbacks concerning their efficacy and cost-effectiveness. Thus, alternative therapies become necessary to overcome these limitations. In recent decades, bone tissue engineering has emerged as a promising interdisciplinary strategy to mimic environments specifically designed to facilitate bone tissue regeneration. Approaches developed to date aim at three essential factors: osteoconductive scaffolds, osteoinduction through growth factors, and cells with osteogenic capability. This review addresses the biological basis of bone and its remodeling process, providing an overview of the bone tissue engineering strategies developed to date and describing the mechanisms that underlie cell–biomaterial interactions.

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Section: Scaffold Properties For Btementioning

confidence: 99%

A Multidisciplinary Journey towards Bone Tissue Engineering

2021

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“…The material design at the micro-scale assumes that the material is constructed by periodic unit cells (PUC), thus its effective properties can be calculated by the numerical homogenization theory [9]. The efficient material distribution on the micro-scale enriches the capacity of topology optimization method for more extensive advanced designs and applications [10,11]. However, whether it is structural-oriented or material-oriented topology optimization, it is difficult to obtain optimal design of structure and material at the same time.…”

Section: Introductionmentioning

confidence: 99%

Robust concurrent topology optimization of structure and its composite material considering uncertainty with imprecise probability

et al. 2020

Computer Methods in Applied Mechanics and Engineering

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This paper studied a robust concurrent topology optimization (RCTO) approach to design the structure and its composite materials simultaneously. For the first time, the material uncertainty with imprecise probability is integrated into the multi-scale concurrent topology optimization (CTO) framework. To describe the imprecise probabilistic uncertainty efficiently, the type I hybrid interval random model is adopted. An improved hybrid perturbation analysis (IHPA) method is formulated to estimate the expectation and stand variance of the objective function in the worst case. Combined with the bi-directional evolutionary structural optimization (BESO) framework, the robust designs of the structure and its composite material are carried out. Several 2D and 3D numerical examples are presented to illustrate the effectiveness of the proposed method. The results show that the proposed method has high efficiency and low precision loss. In addition, the proposed RCTO approach remains efficient in both of linear static and dynamic structures, which shows its extensive adaptability.where J Y and J Y denote the lower and upper bounds of interval vector J Y . The symbol m J Y is the mean value of J Y , which can be calculated by averaging the lower and upper bounds value as shown in Eq. (16b). I J Y denotes the variation interval of J Y , which depends on the difference of the lower and upper bound values as shown in Eq. (16c). The deviation J Y  of the symmetrical interval can be acquired by averaging the upper and lower bounds of J Y as shown in Eq. (16d). In the combined form, the J-th hybrid interval random parameter( ) JJ X Y can be formulated as RCTO Volume fraction of solid: 95.6% Volume fraction of Phase 1: 70.3% DCTO Volume fraction of solid: 95.1% Volume fraction of phase 1: 70.7% 33 (b) Macrostructure Micro base-cell Composite material Phase 1 only RCTO Volume fraction of solid: 93.0% Volume fraction of phase 1: 72.4% DCTO Volume fraction of solid: 92.7% Volume fraction of phase 1: 72.7% (c) Macrostructure Micro base-cell Composite material Phase 1 only RCTO Volume fraction of solid: 90.4% Volume fraction of phase 1: 74.9% DCTO Volume fraction of solid: 88.9% Volume fraction of phase 1: 76.3%JJ J ee JJ J J J J e J J e J J JJ J e e J J J J J J J J e J J e 2 2 J J J J JK J e e J J J J J J J J J J J J J e J J JJ JJ e J J J J JJ J J e J J J J e JJ e J J J J 3m d I e J J J J x X X e J J e J J e J J e J J e J J e J J e e e e J J J J e J J J J e J J J J H T A a J J J J H a J J J J pT A J J J J A e J J J J A e J J J J J J J J a i A Y J J J J p a i A Y J J J J a i J J J J a i A Y J J J J p a i A Y J J J J Ne ia A i J J J J a i J J J J Ne ia A i J J J J

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“…The layouts of materials can then be optimized to a large extent, and the structure performance can be expected to be the best. The multiscale lightweight cellular composites provide new opportunities in many advanced engineering applications (eg, bioengineering materials) and may enhance the structural performances (eg, thermal and thermoelastic effects) . Rodrigues et al developed a hierarchical computational procedure for optimizing the macrostructure material distribution as well as for designing the point‐wise material microstructures, which allows a unique microstructure in each macro finite element.…”

Section: Introductionmentioning

confidence: 99%

Robust topology optimization for cellular composites with hybrid uncertainties

Zheng

Luo

et al. 2018

Numerical Meth Engineering

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Summary This paper will develop a new robust topology optimization method for the concurrent design of cellular composites with an array of identical microstructures subject to random‐interval hybrid uncertainties. A concurrent topology optimization framework is formulated to optimize both the composite macrostructure and the material microstructure. The robust objective function is defined based on the interval mean and interval variance of the corresponding objective function. A new uncertain propagation approach, termed as a hybrid univariate dimension reduction method, is proposed to estimate the interval mean and variance. The sensitivity information of the robust objective function can be obtained after the uncertainty analysis. Several numerical examples are used to validate the effectiveness of the proposed robust topology optimization method.

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On the Road to Personalized Medicine: Multiscale Computational Modeling of Bone Tissue

Cited by 30 publications

References 322 publications

A Multidisciplinary Journey towards Bone Tissue Engineering

A Multidisciplinary Journey towards Bone Tissue Engineering

Robust concurrent topology optimization of structure and its composite material considering uncertainty with imprecise probability

Robust topology optimization for cellular composites with hybrid uncertainties

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