An efficient statistical approach to design 3D-printed metamaterials for mimicking mechanical properties of soft biological tissues

2018 ◽  
Vol 24 ◽  
pp. 341-352
Author(s):  
Jialei Chen ◽  
Kan Wang ◽  
Chuck Zhang ◽  
Ben Wang
Author(s):  
Kristy Tan ◽  
Shaokoon Cheng ◽  
Lynne E. Bilston

The mechanical properties of soft biological tissues have been widely investigated over the past five decades [1–5]. Reported measurements of soft biological tissues such as the brain, spinal cord, liver and muscle vary by orders of magnitude, depending on the sample preparation, anisotropy and loading regime. Knowing the accurate mechanical properties of biological tissues is important for many applications, for example car crash testing and simulations require accurate information on how different parts of the body deform due to a combination of loads. Deformation of tissues around prosthetics and artificial limbs are critical in understanding load transfer at interfaces with the body. The recent use of Magnetic Resonance Elastography (MRE) in diagnostic imaging has resulted in a surge of interest in accurate measurements of mechanical properties of tissues [6].


2021 ◽  
Vol 18 (182) ◽  
pp. 20210411
Author(s):  
Gerhard A. Holzapfel ◽  
Kevin Linka ◽  
Selda Sherifova ◽  
Christian J. Cyron

The constitutive modelling of soft biological tissues has rapidly gained attention over the last 20 years. Current constitutive models can describe the mechanical properties of arterial tissue. Predicting these properties from microstructural information, however, remains an elusive goal. To address this challenge, we are introducing a novel hybrid modelling framework that combines advanced theoretical concepts with deep learning. It uses data from mechanical tests, histological analysis and images from second-harmonic generation. In this first proof of concept study, our hybrid modelling framework is trained with data from 27 tissue samples only. Even such a small amount of data is sufficient to be able to predict the stress–stretch curves of tissue samples with a median coefficient of determination of R 2 = 0.97 from microstructural information, as long as one limits the scope to tissue samples whose mechanical properties remain in the range commonly encountered. This finding suggests that deep learning could have a transformative impact on the way we model the constitutive properties of soft biological tissues.


2013 ◽  
Vol 05 (04) ◽  
pp. 1330002 ◽  
Author(s):  
Hadi Taghizadeh ◽  
Mohammad Tafazzoli Shadpour

Mechanical characteristics of soft biological tissues mostly depend on their hierarchy at different scales from nano- to macro-structure. It is shown that arterial wall tissue is highly sensitive to its mechanical environment and any alteration in mechanical factors such as blood pressure, triggers physio- pathological processes within arterial wall. Quantification of these mechanical properties will provide us with deeper insights of ongoing biological events. In this context, mechanical contributions of wall constituents in health and diseases are of growing interest. Hence, this review is concerned with mechanical models of arterial wall tissue with a focus on microstructurally motivated representations.


2021 ◽  
pp. 1-18
Author(s):  
N. Vinoth Babu ◽  
N. Venkateshwaran ◽  
N. Rajini ◽  
Sikiru Oluwarotimi Ismail ◽  
Faruq Mohammad ◽  
...  

Nanomaterials ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1656
Author(s):  
Carla Huerta-López ◽  
Jorge Alegre-Cebollada

Biomaterials are dynamic tools with many applications: from the primitive use of bone and wood in the replacement of lost limbs and body parts, to the refined involvement of smart and responsive biomaterials in modern medicine and biomedical sciences. Hydrogels constitute a subtype of biomaterials built from water-swollen polymer networks. Their large water content and soft mechanical properties are highly similar to most biological tissues, making them ideal for tissue engineering and biomedical applications. The mechanical properties of hydrogels and their modulation have attracted a lot of attention from the field of mechanobiology. Protein-based hydrogels are becoming increasingly attractive due to their endless design options and array of functionalities, as well as their responsiveness to stimuli. Furthermore, just like the extracellular matrix, they are inherently viscoelastic in part due to mechanical unfolding/refolding transitions of folded protein domains. This review summarizes different natural and engineered protein hydrogels focusing on different strategies followed to modulate their mechanical properties. Applications of mechanically tunable protein-based hydrogels in drug delivery, tissue engineering and mechanobiology are discussed.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Mary Beth Wandel ◽  
Craig A. Bell ◽  
Jiayi Yu ◽  
Maria C. Arno ◽  
Nathan Z. Dreger ◽  
...  

AbstractComplex biological tissues are highly viscoelastic and dynamic. Efforts to repair or replace cartilage, tendon, muscle, and vasculature using materials that facilitate repair and regeneration have been ongoing for decades. However, materials that possess the mechanical, chemical, and resorption characteristics necessary to recapitulate these tissues have been difficult to mimic using synthetic resorbable biomaterials. Herein, we report a series of resorbable elastomer-like materials that are compositionally identical and possess varying ratios of cis:trans double bonds in the backbone. These features afford concomitant control over the mechanical and surface eroding degradation properties of these materials. We show the materials can be functionalized post-polymerization with bioactive species and enhance cell adhesion. Furthermore, an in vivo rat model demonstrates that degradation and resorption are dependent on succinate stoichiometry in the elastomers and the results show limited inflammation highlighting their potential for use in soft tissue regeneration and drug delivery.


Polymers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1394
Author(s):  
Yong Sang Cho ◽  
So-Jung Gwak ◽  
Young-Sam Cho

In this study, we investigated the dual-pore kagome-structure design of a 3D-printed scaffold with enhanced in vitro cell response and compared the mechanical properties with 3D-printed scaffolds with conventional or offset patterns. The compressive modulus of the 3D-printed scaffold with the proposed design was found to resemble that of the 3D-printed scaffold with a conventional pattern at similar pore sizes despite higher porosity. Furthermore, the compressive modulus of the proposed scaffold surpassed that of the 3D-printed scaffold with conventional and offset patterns at similar porosities owing to the structural characteristics of the kagome structure. Regarding the in vitro cell response, cell adhesion, cell growth, and ALP concentration of the proposed scaffold for 14 days was superior to those of the control group scaffolds. Consequently, we found that the mechanical properties and in vitro cell response of the 3D-printed scaffold could be improved by kagome and dual-pore structures through DfAM. Moreover, we revealed that the dual-pore structure is effective for the in vitro cell response compared to the structures possessing conventional and offset patterns.


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