Effective elastic modulus of an intact cornea related to indentation behavior: A comparison between the Hertz model and Johnson-Kendall-Roberts model

Ren, Qiaomei; Chen, Jing; Li, Xiaona; Lv, Ying; Niu, Xiaolian; Chen, Weiyi; Gao, Zhipeng

doi:10.1016/j.exer.2021.108670

Cited by 9 publications

(7 citation statements)

References 41 publications

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“…The intermediate area can be described by the Maugis‐Dugdale model (Dugdale, 1960; Maugis, 1992). More complex models combine the JKR approach with viscoelastic analysis techniques (Korayem, Sooha, & Rastgear, 2019; Ren et al., 2021). For these models, it is necessary to capture the whole force curve by measuring the entire indentation process including the tip approach, indentation and tip retraction (Ebenstein, 2011; Wahl, Asif, Greenwood, & Johnson, 2005).…”

Section: Methodsmentioning

confidence: 99%

Tissue‐Scale Biomechanical Testing of Brain Tissue for the Calibration of Nonlinear Material Models

et al. 2022

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Brain tissue is one of the most complex and softest tissues in the human body. Due to its ultrasoft and biphasic nature, it is difficult to control the deformation state during biomechanical testing and to quantify the highly nonlinear, time‐dependent tissue response. In numerous experimental studies that have investigated the mechanical properties of brain tissue over the last decades, stiffness values have varied significantly. One reason for the observed discrepancies is the lack of standardized testing protocols and corresponding data analyses. The tissue properties have been tested on different length and time scales depending on the testing technique, and the corresponding data have been analyzed based on simplifying assumptions. In this review, we highlight the advantage of using nonlinear continuum mechanics based modeling and finite element simulations to carefully design experimental setups and protocols as well as to comprehensively analyze the corresponding experimental data. We review testing techniques and protocols that have been used to calibrate material model parameters and discuss artifacts that might falsify the measured properties. The aim of this work is to provide standardized procedures to reliably quantify the mechanical properties of brain tissue and to more accurately calibrate appropriate constitutive models for computational simulations of brain development, injury and disease. Computational models can not only be used to predictively understand brain tissue behavior, but can also serve as valuable tools to assist diagnosis and treatment of diseases or to plan neurosurgical procedures. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. This article was corrected on 16 April 2022. See the end of the full text for details.

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Section: Methodsmentioning

confidence: 99%

Tissue‐Scale Biomechanical Testing of Brain Tissue for the Calibration of Nonlinear Material Models

et al. 2022

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“…After morphology scanning, five dots around the nucleus (an area that is rich in cytoplasm, chosen to avoid the influence of the substrate) were chosen to acquire force-distance curves at a 2 nN trigger force. The JKR model shown in Equation (1), which is based on the Hertz model and considers the viscoelasticity of cells, was used in this study, [56][57][58] and the elastic modulus E was calculated through analysis curves with the built-in AFM software Igor Pro 6.3.8.1…”

Section: Methodsmentioning

confidence: 99%

“…After morphology scanning, five dots around the nucleus (an area that is rich in cytoplasm, chosen to avoid the influence of the substrate) were chosen to acquire force‐distance curves at a 2 nN trigger force. The JKR model shown in Equation (), which is based on the Hertz model and considers the viscoelasticity of cells, was used in this study, [ 56–58 ] and the elastic modulus E was calculated through analysis curves with the built‐in AFM software Igor Pro 6.3.8.1

\left\{ \begin{array}{c}{a^3} = \frac{{3R\left( {1 - {\nu ^2}} \right)}}{{4E}}\left[ {F + 3\pi R\gamma + \sqrt {6\pi R\gamma F + {{\left( {3\pi R\gamma } \right)}^2}} } \right]\\\delta = \frac{{{a^2}}}{R} - \sqrt {\frac{{2\gamma \pi a\left( {1 - {\nu ^2}} \right)}}{E}} \end{array} \right.

…”

Section: Methodsmentioning

confidence: 99%

Magnetic Nanobubble Mechanical Stress Induces the Piezo1‐Ca²⁺‐BMP2/Smad Pathway to Modulate Neural Stem Cell Fate and MRI/Ultrasound Dual Imaging Surveillance for Ischemic Stroke

Zhang

Lou

et al. 2022

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Neural stem cells (NSCs) are used to treat various nervous system diseases because of their self‐renewal ability and multidirectional differentiation potential. However, an insufficient ability to track their migration in vivo and poor control over their survival and differentiation efficiency are two major critical challenges for clinical application. Here, it is shown that when magnetic nanobubbles (MNBs), which are assembled from magnetic nanoparticles, are internalized by NSCs, intramembrane volumetric oscillation of the MNBs induces an increase in intracellular hydrostatic pressure and cytoskeleton force, resulting in the activation of the Piezo1‐Ca2+ mechanosensory channel. This subsequently triggers the BMP2/Smad biochemical signaling pathway, leading to differentiation of NSCs into the neuronal phenotype. Signaling through the Piezo1‐Ca2+‐BMP2/Smad pathway can be further accelerated by application of an external shear stress force using low‐intensity pulsed ultrasound. More importantly, magnetic resonance imaging and ultrasound imaging surveillance of NSCs based on MNB labeling can be leveraged to provide NSC therapeutic outcomes. Both the in vitro and in vivo findings demonstrate that a bubble nanostructure‐induced physical force can modulate and control the mechanical signaling pathway regulating stem cell development.

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“…Prior to the indentation test, we removed the other tissues attached to the ocular surface including the extraocular muscles and fats with a pair of scissors and a tweezer at room temperature. As shown in Figure 1a, an eye specimen, with its cornea exposed to the air, was attached to a fixed chamber and fully filled with phosphate-buffered saline (PBS) by negative pressure through an injector, which could simultaneously tune the value of the IOP P. We measured the adjusted intraocular pressure with a rebound tonometer (FA800vet, Shanghai, China; resolution: ±1.5-2.0 mmHg) and then measured it again after standing for 30 s. If the intraocular pressure value did not change, the intraocular pressure value of this sample could be determined [10].…”

Section: Experimental Protocolmentioning

confidence: 99%

“…Adhesion behavior is usually determined through a pull-off test, that is, a rigid punch indents onto a solid material and then detaches from it [ 6 , 7 , 8 , 9 , 10 ]. To analyze the adhesion behavior of a solid, the work of adhesion γ , a characteristic of the material, must be considered.…”

Section: Introductionmentioning

confidence: 99%

Corneal Adhesion Possesses the Characteristics of Solid and Membrane

et al. 2022

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Adhesion behavior usually occurs in corneas associated with clinical treatments. Physiologically, an intact natural cornea is inflated by intraocular pressure. Due to the inflation, the physiological cornea has a mechanical property likeness to membrane. This characteristic is ignored by the classical theory used to analyze the adhesion behavior of soft solids, such as the Johnson–Kendall–Roberts (JKR) model. Performing the pull-off test, this work evidenced that the classical JKR solution was suitable for computing the corneal adhesion force corresponding to the submillimeter scale of contact. However, when the cornea was contacted at a millimeter scale, the JKR solutions were clearly smaller than the related experimental data. The reason was correlated with the membranous characteristic of the natural cornea was not considered in the JKR solid model. In this work, the modified JKR model was superimposed by the contribution from the surface tension related to the corneal inflation due to the intraocular pressure. It should be treated as a solid when the cornea is contacted at a submillimeter scale, whereas for the contact at a larger size, the characteristic of the membrane should be considered in analyzing the corneal adhesion. The modified JKR model successfully described the adhesion characteristics of the cornea from solid to membrane.

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Effective elastic modulus of an intact cornea related to indentation behavior: A comparison between the Hertz model and Johnson-Kendall-Roberts model

Cited by 9 publications

References 41 publications

Tissue‐Scale Biomechanical Testing of Brain Tissue for the Calibration of Nonlinear Material Models

Tissue‐Scale Biomechanical Testing of Brain Tissue for the Calibration of Nonlinear Material Models

Magnetic Nanobubble Mechanical Stress Induces the Piezo1‐Ca²⁺‐BMP2/Smad Pathway to Modulate Neural Stem Cell Fate and MRI/Ultrasound Dual Imaging Surveillance for Ischemic Stroke

Corneal Adhesion Possesses the Characteristics of Solid and Membrane

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Effective elastic modulus of an intact cornea related to indentation behavior: A comparison between the Hertz model and Johnson-Kendall-Roberts model

Cited by 9 publications

References 41 publications

Tissue‐Scale Biomechanical Testing of Brain Tissue for the Calibration of Nonlinear Material Models

Tissue‐Scale Biomechanical Testing of Brain Tissue for the Calibration of Nonlinear Material Models

Magnetic Nanobubble Mechanical Stress Induces the Piezo1‐Ca2+‐BMP2/Smad Pathway to Modulate Neural Stem Cell Fate and MRI/Ultrasound Dual Imaging Surveillance for Ischemic Stroke

Corneal Adhesion Possesses the Characteristics of Solid and Membrane

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Magnetic Nanobubble Mechanical Stress Induces the Piezo1‐Ca²⁺‐BMP2/Smad Pathway to Modulate Neural Stem Cell Fate and MRI/Ultrasound Dual Imaging Surveillance for Ischemic Stroke