Finite element modeling of finite deformable, biphasic biological tissues with transversely isotropic statistically distributed fibers: toward a practical solution

Wu, John Z.; Herzog, Walter; Federico, Salvatore

doi:10.1007/s00033-015-0598-7

Cited by 6 publications

(3 citation statements)

References 60 publications

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“…The cervical discs are complex in mechanical properties; they are not only biphasic, composed of a solid and a fluid phase, but also anisotropic due to the reinforce effects of the distributed collagen fibers within the tissues. 39 If the major concern is the injury mechanism of the cervical discs, it is necessary to know the loading share between the fluid and solid phases; in that case, the interstitial fluid and collagen become non-negligible effects. However, in this study, we need to only know the mechanical response of the cervical discs when subjected to shock load; we are not interested in the detailed stress/strain distributions within the tissues.…”

Section: Discussionmentioning

confidence: 99%

Finite element simulations of the head–brain responses to the top impacts of a construction helmet: Effects of the neck and body mass

Pan

Wimer

et al. 2016

Proc Inst Mech Eng H

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Traumatic brain injuries are among the most common severely disabling injuries in the United States. Construction helmets are considered essential personal protective equipment for reducing traumatic brain injury risks at work sites. In this study, we proposed a practical finite element modeling approach that would be suitable for engineers to optimize construction helmet design. The finite element model includes all essential anatomical structures of a human head (i.e. skin, scalp, skull, cerebrospinal fluid, brain, medulla, spinal cord, cervical vertebrae, and discs) and all major engineering components of a construction helmet (i.e. shell and suspension system). The head finite element model has been calibrated using the experimental data in the literature. It is technically difficult to precisely account for the effects of the neck and body mass on the dynamic responses, because the finite element model does not include the entire human body. An approximation approach has been developed to account for the effects of the neck and body mass on the dynamic responses of the head–brain. Using the proposed model, we have calculated the responses of the head–brain during a top impact when wearing a construction helmet. The proposed modeling approach would provide a tool to improve the helmet design on a biomechanical basis.

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Section: Discussionmentioning

confidence: 99%

Finite element simulations of the head–brain responses to the top impacts of a construction helmet: Effects of the neck and body mass

Pan

Wimer

et al. 2016

Proc Inst Mech Eng H

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“…Their results concluded that the fibers realigned themselves differently in each zone of the construct to resist the applied load. Anisotropic representation of the fibers is more realistic as the fiber distribution in articular cartilage changes between zones [36]. Therefore, any FEA of scaffold design should include orientation and depth-dependent fiber distribution.…”

Section: Components Of Finite Element Analysismentioning

confidence: 99%

Utilization of Finite Element Analysis for Articular Cartilage Tissue Engineering

et al. 2019

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Scaffold design plays an essential role in tissue engineering of articular cartilage by providing the appropriate mechanical and biological environment for chondrocytes to proliferate and function. Optimization of scaffold design to generate tissue-engineered cartilage has traditionally been conducted using in-vitro and in-vivo models. Recent advances in computational analysis allow us to significantly decrease the time and cost of scaffold optimization using finite element analysis (FEA). FEA is an in-silico analysis technique that allows for scaffold design optimization by predicting mechanical responses of cells and scaffolds under applied loads. Finite element analyses can potentially mimic the morphology of cartilage using mesh elements (tetrahedral, hexahedral), material properties (elastic, hyperelastic, poroelastic, composite), physiological loads by applying loading conditions (static, dynamic), and constitutive stress–strain equations (linear, porous–elastic, biphasic). Furthermore, FEA can be applied to the study of the effects of dynamic loading, material properties cell differentiation, cell activity, scaffold structure optimization, and interstitial fluid flow, in isolated or combined multi-scale models. This review covers recent studies and trends in the use of FEA for cartilage tissue engineering and scaffold design.

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“…These models leverage enhanced understanding of the local mechanical environment in cartilage tissue under injurious loading and around structural defects as well as its contribution to cartilage damage. To this end, several in silico models were introduced in the literature ( Keenan et al, 2013 ; Wu et al, 2016 ; Koh et al, 2019 ; Wang et al, 2019 ). Among the proposed models, a fibril-reinforced poro-viscoelastic swelling (FRPVES) finite element (FE) model introduced by Wilson et al (2005) accounts for different ECM constituents (i.e., collagen content, fibril orientation, PG content, and water content) and therefore allows studying the effect of variations in cartilage composition due to the altered mechanical environment.…”

Section: Introductionmentioning

confidence: 99%

An in silico Framework of Cartilage Degeneration That Integrates Fibril Reorientation and Degradation Along With Altered Hydration and Fixed Charge Density Loss

Elahi

Tanska

Korhonen

et al. 2021

Front. Bioeng. Biotechnol.

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Injurious mechanical loading of articular cartilage and associated lesions compromise the mechanical and structural integrity of joints and contribute to the onset and progression of cartilage degeneration leading to osteoarthritis (OA). Despite extensive in vitro and in vivo research, it remains unclear how the changes in cartilage composition and structure that occur during cartilage degeneration after injury, interact. Recently, in silico techniques provide a unique integrated platform to investigate the causal mechanisms by which the local mechanical environment of injured cartilage drives cartilage degeneration. Here, we introduce a novel integrated Cartilage Adaptive REorientation Degeneration (CARED) algorithm to predict the interaction between degenerative variations in main cartilage constituents, namely collagen fibril disorganization and degradation, proteoglycan (PG) loss, and change in water content. The algorithm iteratively interacts with a finite element (FE) model of a cartilage explant, with and without variable depth to full-thickness defects. In these FE models, intact and injured explants were subjected to normal (2 MPa unconfined compression in 0.1 s) and injurious mechanical loading (4 MPa unconfined compression in 0.1 s). Depending on the mechanical response of the FE model, the collagen fibril orientation and density, PG and water content were iteratively updated. In the CARED model, fixed charge density (FCD) loss and increased water content were related to decrease in PG content. Our model predictions were consistent with earlier experimental studies. In the intact explant model, minimal degenerative changes were observed under normal loading, while the injurious loading caused a reorientation of collagen fibrils toward the direction perpendicular to the surface, intense collagen degradation at the surface, and intense PG loss in the superficial and middle zones. In the injured explant models, normal loading induced intense collagen degradation, collagen reorientation, and PG depletion both on the surface and around the lesion. Our results confirm that the cartilage lesion depth is a crucial parameter affecting tissue degeneration, even under physiological loading conditions. The results suggest that potential fibril reorientation might prevent or slow down fibril degradation under conditions in which the tissue mechanical homeostasis is perturbed like the presence of defects or injurious loading.

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Finite element modeling of finite deformable, biphasic biological tissues with transversely isotropic statistically distributed fibers: toward a practical solution

Cited by 6 publications

References 60 publications

Finite element simulations of the head–brain responses to the top impacts of a construction helmet: Effects of the neck and body mass

Finite element simulations of the head–brain responses to the top impacts of a construction helmet: Effects of the neck and body mass

Utilization of Finite Element Analysis for Articular Cartilage Tissue Engineering

An in silico Framework of Cartilage Degeneration That Integrates Fibril Reorientation and Degradation Along With Altered Hydration and Fixed Charge Density Loss

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