Engineering approaches for characterizing soft tissue mechanical properties: A review

Real-time haptic characterisation of Hunt-Crossley model based on radial basis function neural network for contact environment

Dynamic soft tissue characterisation is an important element in robotic minimally invasive surgery. This paper presents a novel method by combining neural network with recursive least square (RLS) estimation for dynamic soft tissue characterisation based on the nonlinear Hunt-Crossley (HC) model. It develops a radial basis function neural network (RBFNN) to compensate for the error caused by natural logarithmic factorisation (NLF) of the HC model for dynamic RLS estimation of soft tissue properties. The RBFNN weights are estimated according to the maximum likelihood principle to evaluate the probability distribution of the neural network modelling residual. Further, by using the linearisation error modelled by RBFNN to compensate for the linearised HC model, an RBFNN-based RLS algorithm is developed for dynamic soft tissue characterisation. Simulation and experimental results demonstrate that the proposed method can effectively model the natural logarithmic linearisation error, leading to improved accuracy for RLS estimation of the HC model parameters.

In vivo endoscopic optical coherence elastography based on a miniature probe

Optical coherence elastography (OCE) is a functional extension of optical coherence tomography (OCT). It offers high-resolution elasticity assessment with nanoscale tissue displacement sensitivity and high quantification accuracy, promising to enhance diagnostic precision. However, in vivo endoscopic OCE imaging has not been demonstrated yet, which needs to overcome key challenges related to probe miniaturization, high excitation efficiency and speed. This study presents a novel endoscopic OCE system, achieving the first endoscopic OCE imaging in vivo. The system features the smallest integrated OCE probe with an outer diameter of only 0.9 mm (with a 1.2-mm protective tube during imaging). Utilizing a single 38-MHz high-frequency ultrasound transducer, the system induced rapid deformation in tissues with enhanced excitation efficiency. In phantom studies, the OCE quantification results match well with compression testing results, showing the system's high accuracy. The in vivo imaging of the rat vagina demonstrated the system's capability to detect changes in tissue elasticity continually and distinguish between normal tissue, hematomas, and tissue with increased collagen fibers precisely. This research narrows the gap for the clinical implementation of the endoscopic OCE system, offering the potential for the early diagnosis of intraluminal diseases.

Material characterization of human middle ear using machine-learning-based surrogate models

This study aims to introduce a novel non-invasive method for rapid material characterization of middle-ear structures, taking into consideration the invaluable insights provided by the mechanical properties of ear tissues. Valuable insights into various ear pathologies can be gleaned from the mechanical properties of ear tissues, yet conventional techniques for assessing these properties often entail invasive procedures that preclude their use on living patients. In this study, in the first step, we developed machine-learning models of the middle ear to predict its responses with a significantly lower computational cost in comparison to finite-element models. Leveraging findings from prior research, we focused on the most influential model parameters: the Young's modulus and thickness of the tympanic membrane and the Young's modulus of the stapedial annular ligament. The eXtreme Gradient Boosting (XGBoost) method was implemented for creating the machine-learning models. Subsequently, we combined the created machine-learning models with Bayesian optimization (BoTorch) for fast and efficient estimation of the Young's moduli of the tympanic membrane and the stapedial annular ligament. We demonstrate that the resultant surrogate models can fairly represent the vibrational responses of the umbo, stapes footplate, and vibration patterns of the tympanic membrane at most frequencies. Also, our proposed material characterization approach successfully estimated the Young's moduli of the tympanic membrane and stapedial annular ligament (separately and simultaneously) with values of mean absolute percentage error of less than 7%. The remarkable accuracy achieved through the proposed material characterization method underscores its potential for eventual clinical applications of estimating mechanical properties of the middle-ear structures for diagnostic purposes.

A Comparative Study of Machine Learning and Algorithmic Approaches to Automatically Identify the Yield Point in Normal and Aneurysmal Human Aortic Tissues

The stress-strain curve of biological soft tissues helps characterize their mechanical behavior. The yield point on this curve is when a specimen breaches its elastic range due to irreversible microstructural damage. The yield point is easily found using the offset yield method in traditional engineering materials. However, correctly identifying the yield point in soft tissues can be subjective due to its nonlinear material behavior. The typical method for yield point identification is visual inspection, which is investigator-dependent and does not lend itself to automation of the analysis pipeline. An automated algorithm to identify the yield point objectively assesses soft tissues' biomechanical properties. This study aimed to analyze data from uniaxial extension testing on biological soft tissue specimens and create a machine learning (ML) model to determine a tissue sample's yield point. We present a trained machine learning model from 279 uniaxial extension curves from testing aneurysmal/nonaneurysmal and longitudinal/circumferential oriented tissue specimens that multiple experts labeled through an adjudication process. The ML model showed a median error of 5% in its estimated yield stress compared to the expert picks. The study found that an ML model could accurately identify the yield point (as defined) in various aortic tissues. Future studies will be performed to validate this approach by visually inspecting when damage occurs and adjusting the model using the ML-based approach.

Noninvasive identification of directionally-dependent elastic properties of soft tissues using full-field optical data

This paper introduces an innovative approach for elastic property characterization of soft tissues, having directional-dependent material behavior, via their vibration response measurement and interpretation. The full-field time-dependent surface displacements as a result of externally excited soft tissues are collected through digital image correlation (DIC). A developed analytical model, capturing the low-amplitude vibration behavior of anisotropic layered human skin with the incorporation of the influence of subcutaneous elasticity and inertia, is employed to accurately predict its resonant frequencies and pertaining displacement field images. An efficient solution approach for the model is implemented into an inverse algorithm to rapidly characterize the anisotropic elastic properties based on importing the vibration characteristics. To show the merit of the approach, a 3-D finite element (FE) simulation model was used to generate full-field data, detected and matched with a set of specific vibration modes via modal assurance criterion (MAC). The validity of the model implemented into the inverse characterization algorithm is demonstrated through a comparison of predicted vibration frequencies and mode-shapes simulated via the 3-D FE model for different cases with anisotropic elastic properties in different layers of the skin. It is shown that modes are influenced differently when anisotropic properties are introduced to the model. Thus, the established inverse characterization algorithm is capable of rapidly predicting the elastic material properties of anisotropic soft sheets with adequate accuracy.

Material properties in regenerating axolotl limbs using inverse finite element analysis

BACKGROUND

The extracellular mechanical environment plays an important role in the skeletal development process. Characterization of the material properties of regenerating tissues that recapitulate development, provides insights into the mechanical environment experienced by the cells and the maturation of the matrix. In this study, we estimated the viscoelastic material properties of regenerating forelimbs in the axolotl (Ambystoma mexicanum) at three different regeneration stages: 27 days post-amputation (mid-late bud) and 41 days post-amputation (palette stage), and fully-grown time points. A stress-relaxation indentation test followed by two-term Prony series viscoelastic inverse finite element analysis was used to obtain material parameters. Glycosaminoglycan (GAG) content was estimated using a 1,9- dimethyl methylene blue assay.

RESULTS

The instantaneous and equilibrium shear moduli significantly increased with regeneration while the short-term stress relaxation time significantly decreased with limb regeneration. The long-term stress relaxation time in the fully-grown time point was significantly lower than 27 and 41 DPA groups. The GAG content was not significantly different between 27 and 41 DPA but the GAG content of cartilage in the fully-grown group was significantly greater than in 27 and 41 DPA.

CONCLUSIONS

The mechanical environment of the proliferating cells changes drastically during limb regeneration. Understanding how the tissue's mechanical properties change during limb regeneration is critical for linking molecular-level matrix production of the cells to tissue-level behavior and mechanical signals.

Effect of the apron in the mechanical characterisation of hyperelastic materials by means of biaxial testing: A new method to improve accuracy

Biological soft tissues and polymers used in biomedical applications (e.g. in the cardiovascular area) are hyperelastic incompressible materials that commonly operate under multi-axial large deformation fields. Their characterisation requires biaxial tensile testing. Due to the typically small sample size, the gripping of the specimens commonly relies on rakes or sutures, where the specimen is punctured at the edges of the gauge area. This approach necessitates of an apron, excess of material around the gauge region. This work analyses the apron influence on the estimated mechanical response of biaxial tests performed by using a rakes gripping system, with the aim of verifying the test accuracy and propose improved solutions. In order to isolate the effect of the apron, avoiding the influence of anisotropy and inhomogeneity typical of most soft tissues, homogeneous and isotropic hyperplastic samples made from a uniform sheet of casted silicone were tested. The stress-strain response of specimens with different apron sizes/shapes was measured experimentally by means of biaxial testing and digital image correlation. Tests were replicated numerically, to interpret the experimental findings. The apron surrounding the gauge area acts as an additional annular constraint which stiffens the system, resulting in a significant overestimate in the stress values. This error can be avoided by introducing specific cuts in the apron. The study quantifies, for the first time, the correlation between the apron size/shape and the experimental stress overestimation, proposing a research protocol which, although identified on homogeneous hyperelastic materials, can be useful in providing more accurate characterisation of both, synthetic polymers and soft tissues.

Mechanical characterization of human skin-A non-invasive digital twin approach using vibration-response integrated with numerical methods

This paper proposes an innovative approach to identify elastic material properties and mass density of soft tissues based on interpreting their mechanical vibration response, externally excited by a mechanical indenter or acoustic waves. A vibration test is performed on soft sheets to measure their response to a continuous range of excitation frequencies. The frequency responses are collected with a pair of high-speed cameras in conjunction with 3-D digital image correlation (DIC). Two cases are considered, including suspended/fully-free rectangular neoprene sheets as artificial tissue cutout samples and continuous layered human skin vibrations. An efficient theoretical model is developed to analytically simulate the free vibrations of the neoprene artificial sheet samples as well as the continuous layered human skins. The high accuracy and validity of the presented analytical simulations are demonstrated through comparison with the DIC measurements and the conducted frequency tests, as well as a number of finite element (FE) modeling. The developed analytical approach is implemented into a numerical algorithm to perform an inverse calculation of the soft sheets' elastic properties using the imported experimental vibration results and the predicted system's mass via the system equivalent reduction/expansion process (SEREP) method. It is shown that the proposed frequency-dependent inverse approach is capable of rapidly predicting the material properties of the tested samples with high accuracy.

A Miniature Dual-Fiber Probe for Quantitative Optical Coherence Elastography

OBJECTIVE

Optical coherence elastography (OCE) allows for high resolution analysis of elastic tissue properties. However, due to the limited penetration of light into tissue, miniature probes are required to reach structures inside the body, e.g., vessel walls. Shear wave elastography relates shear wave velocities to quantitative estimates of elasticity. Generally, this is achieved by measuring the runtime of waves between two or multiple points. For miniature probes, optical fibers have been integrated and the runtime between the point of excitation and a single measurement point has been considered. This approach requires precise temporal synchronization and spatial calibration between excitation and imaging.

METHODS

We present a miniaturized dual-fiber OCE probe of 1 mm diameter allowing for robust shear wave elastography. Shear wave velocity is estimated between two optics and hence independent of wave propagation between excitation and imaging. We quantify the wave propagation by evaluating either a single or two measurement points. Particularly, we compare both approaches to ultrasound elastography.

RESULTS

Our experimental results demonstrate that quantification of local tissue elasticities is feasible. For homogeneous soft tissue phantoms, we obtain mean deviations of 0.15 ms-1 and 0.02 ms-1 for single-fiber and dual-fiber OCE, respectively. In inhomogeneous phantoms, we measure mean deviations of up to 0.54 ms-1 and 0.03 ms-1 for single-fiber and dual-fiber OCE, respectively.

CONCLUSION

We present a dual-fiber OCE approach that is much more robust in inhomogeneous tissues. Moreover, we demonstrate the feasibility of elasticity quantification in ex-vivo coronary arteries.

SIGNIFICANCE

This study introduces an approach for robust elasticity quantification from within the tissue.

Vibro-acoustic sensing of tissue-instrument-interactions allows a differentiation of biological tissue in computerised palpation

BACKGROUND

The shift towards minimally invasive surgery is associated with a significant reduction of tactile information available to the surgeon, with compensation strategies ranging from vision-based techniques to the integration of sensing concepts into surgical instruments. Tactile information is vital for palpation tasks such as the differentiation of tissues or the characterisation of surfaces. This work investigates a new sensing approach to derive palpation-related information from vibration signals originating from instrument-tissue-interactions.

METHODS

We conducted a feasibility study to differentiate three non-animal and three animal tissue specimens based on palpation of the surface. A sensor configuration was mounted at the proximal end of a standard instrument opposite the tissue-interaction point. Vibro-acoustic signals of 1680 palpation events were acquired, and the time-varying spectrum was computed using Continuous-Wavelet-Transformation. For validation, nine spectral energy-related features were calculated for a subsequent classification using linear Support Vector Machine and k-Nearest-Neighbor.

RESULTS

Indicators derived from the vibration signal are highly stable in a set of palpations belonging to the same tissue specimen, regardless of the palpating subject. Differences in the surface texture of the tissue specimens reflect in those indicators and can serve as a basis for differentiation. The classification following a supervised learning approach shows an accuracy of >93.8% for the three-tissue classification tasks and decreases to 78.8% for a combination of all six tissues.

CONCLUSIONS

Simple features derived from the vibro-acoustic signals facilitate the differentiation between biological tissues, showing the potential of the presented approach to provide information related to the interacting tissue. The results encourage further investigation of a yet little-exploited source of information in minimally invasive surgery.

Micro-mechanical fingerprints of the rat bladder change in actinic cystitis and tumor presence

Tissue mechanics determines tissue homeostasis, disease development and progression. Bladder strongly relies on its mechanical properties to perform its physiological function, but these are poorly unveiled under normal and pathological conditions. Here we characterize the mechanical fingerprints at the micro-scale level of the three tissue layers which compose the healthy bladder wall, and identify modifications associated with the onset and progression of pathological conditions (i.e., actinic cystitis and bladder cancer). We use two indentation-based instruments (an Atomic Force Microscope and a nanoindenter) and compare the micromechanical maps with a comprehensive histological analysis. We find that the healthy bladder wall is a mechanically inhomogeneous tissue, with a gradient of increasing stiffness from the urothelium to the lamina propria, which gradually decreases when reaching the muscle outer layer. Stiffening in fibrotic tissues correlate with increased deposition of dense extracellular matrix in the lamina propria. An increase in tissue compliance is observed before the onset and invasion of the tumor. By providing high resolution micromechanical investigation of each tissue layer of the bladder, we depict the intrinsic mechanical heterogeneity of the layers of a healthy bladder as compared with the mechanical properties alterations associated with either actinic cystitis or bladder tumor.

Recent advances in microsystem approaches for mechanical characterization of soft biological tissues

Microsystem technologies for evaluating the mechanical properties of soft biological tissues offer various capabilities relevant to medical research and clinical diagnosis of pathophysiologic conditions. Recent progress includes (1) the development of tissue-compliant designs that provide minimally invasive interfaces to soft, dynamic biological surfaces and (2) improvements in options for assessments of elastic moduli at spatial scales from cellular resolution to macroscopic areas and across depths from superficial levels to deep geometries. This review summarizes a collection of these technologies, with an emphasis on operational principles, fabrication methods, device designs, integration schemes, and measurement features. The core content begins with a discussion of platforms ranging from penetrating filamentary probes and shape-conformal sheets to stretchable arrays of ultrasonic transducers. Subsequent sections examine different techniques based on planar microelectromechanical system (MEMS) approaches for biocompatible interfaces to targets that span scales from individual cells to organs. One highlighted example includes miniature electromechanical devices that allow depth profiling of soft tissue biomechanics across a wide range of thicknesses. The clinical utility of these technologies is in monitoring changes in tissue properties and in targeting/identifying diseased tissues with distinct variations in modulus. The results suggest future opportunities in engineered systems for biomechanical sensing, spanning a broad scope of applications with relevance to many aspects of health care and biology research.

Assessment of alternative techniques to quantify the effect of injury on soft tissue in closed ankle and pilon fractures

Introduction

Local soft tissue status (STS) guides the timing for definitive surgical treatment strategies of fracture fixation around the ankle joint. The aim of this study was to assess different types of new technical devices in relation to the surgical treatment in closed ankle and pilon fractures.

Methods

This study was designed as a cohort study. Adult patients admitted between February 1, 2019 and December 31, 2020 presenting with closed ankle fracture requiring surgical treatment were eligible. The exclusion criteria were previous injuries to the lower extremity, acute deep venous thrombosis, skin diseases, and delayed presentation (admission >24 hours after injury). Moderate-energy trauma includes injuries sustained during team sports, biking, and running. The primary outcome was the assessment of the degree of soft tissue involvement following closed fractures by comparing different techniques focusing on the ankle region and including ankle and pilon fractures. The variables of interest included the circumference of soft tissue swelling around the ankle, determined within a 5-mm range in the area of the medial and lateral malleolus and the bone-skin distance on a plain radiograph, determined by the largest distance from the malleolus to the border of the soft-tissue shadow. STS assessment included optical measures of local perfusion (O2C, Lea Inc. Germany) and tactile measures of mechanical characteristics (Myoton^® tensiometer AS, Estonia). Measurements of Group Temp (temporary stabilization) and Group Def (definitive surgery) were taken on admission and prior to the treatment strategy decision. The contralateral non-injured ankle served as a control. The quality of assessment tools was quantified by calculating the smallest detectable change (SDC).

Results

In total, 38 patients with a mean age of 40.4 (SD 17.8) years were included. The SDC was 3.2% (95%CI 2.5 to 3.8) for local blood flow and 1.1% (95%CI 0.4 to 1.7) for soft tissue stiffness. The circumference of the injured area at admission was significantly higher than that of the healthy site (28.2 [SD 3.4] cm versus 23.9 [SD 2.4] cm, p < 0.001). The local perfusion (blood flow 107.5 (SD 40.79 A.U. vs. 80.1 [SD 13.8] A.U., p = 0.009), and local dynamic stiffness of the skin (668.1 (SD 148.0) N/m vs 449.5 (SD 87.7) N/m, p < 0.001) were significantly higher at the injured site. In Group Temp, the local blood flow was significantly higher when compared with Group Def (109.6 [SD 39.8] vs. 94.5 [SD 13.0], p = 0.023). The dynamic stiffness of the soft tissue was significantly higher in Group Temp (679.4 N/m [SD 147.0] N/m vs. 573.0 N/m (SD 93.8) N/m, p < 0.001). The physical properties of STS were comparable among the fracture types. None of the included patients had local soft tissue complications.

Conclusion

Closed fractures of the ankle and the pilon are associated with an increase in local circulation and local soft tissue stiffness and tension. These changes of the STS following injury can be quantified in a standardized and reproducible manner.

Stiffness is associated with hepatic stellate cell heterogeneity during liver fibrosis

The fibrogenic wound-healing response in liver increases stiffness. Stiffness mechanotransduction, in turn, amplifies fibrogenesis. Here, we aimed to understand the distribution of stiffness in fibrotic liver, how it impacts hepatic stellate cell (HSC) heterogeneity, and identify mechanisms by which stiffness amplifies fibrogenic responses. Magnetic resonance elastography and atomic force microscopy demonstrated a heterogeneous distribution of liver stiffness at macroscopic and microscopic levels, respectively, in a carbon tetrachloride (CCl₄) mouse model of liver fibrosis as compared with controls. High stiffness was mainly attributed to extracellular matrix dense areas. To identify a stiffness-sensitive HSC subpopulation, we performed single-cell RNA sequencing (scRNA-seq) on primary HSCs derived from healthy versus CCl₄-treated mice. A subcluster of HSCs was matrix-associated with the most upregulated pathway in this subpopulation being focal adhesion signaling, including a specific protein termed four and a half LIM domains protein 2 (FHL2). In vitro, FHL2 expression was increased in primary human HSCs cultured on stiff matrix as compared with HSCs on soft matrix. Moreover, FHL2 knockdown inhibited fibronectin and collagen 1 expression, whereas its overexpression promoted matrix production. In summary, we demonstrate stiffness heterogeneity at the whole organ, lobular, and cellular level, which drives an amplification loop of fibrogenesis through specific focal adhesion molecular pathways.NEW & NOTEWORTHY The fibrogenic wound-healing response in liver increases stiffness. Here, macro and microheterogeneity of liver stiffness correlate with HSC heterogeneity in a hepatic fibrosis mouse model. Fibrogenic HSCs localized in stiff collagen-high areas upregulate the expression of focal adhesion molecule FHL2, which, in turn, promotes extracellular matrix protein expression. These results demonstrate that stiffness heterogeneity at the whole organ, lobular, and cellular level drives an amplification loop of fibrogenesis through specific focal adhesion molecular pathways.

Functional mechanical attributes of natural and synthetic gel-based scaffolds in tissue engineering: strain-stiffening effects on apparent elastic modulus and compressive toughness

The accurate identification and determination of elastic modulus and toughness, as well as other functional mechanical attributes of artificial tissues, are of paramount importance in several fields of tissue science, tissue engineering and technology, since biomechanical and biophysical behavior is strongly linked to biological features of the medical implants and tissue-engineering scaffolds. When soft or ultra-soft materials are investigated, a relevant dispersion of elastic modulus values can be achieved, due to the strain-stiffening effects, inducing a typical non-linear behavior of these materials, as a function of strain-range. In this short communication, the Apparent elastic modulus strain-range dependence is estimated from a segmentation of the strain stiffening curve, and the related compressive toughness is investigated and discussed, based on experimental evidence, for 6 different kinds of gels, used for artificial tissue fabrication; experimental results are compared to mechanical properties of native human tissues.

Biostable Shape Memory Polymer Foams for Smart Biomaterial Applications

Polyurethane foams provide a wide range of applications as a biomaterial system due to the ability to tune their physical, chemical, and biological properties to meet the requirements of the intended applications. Another key parameter that determines the usability of this biomaterial is its degradability under body conditions. Several current approaches focus on slowing the degradation rate for applications that require the implant to be present for a longer time frame (over 100 days). Here, biostable shape memory polymer (SMP) foams were synthesized with added ether-containing monomers to tune the degradation rates. The physical, thermal and shape memory properties of these foams were characterized along with their cytocompatibility and blood interactions. Degradation profiles were assessed in vitro in oxidative (3% H₂O₂; real-time) and hydrolytic media (0.1 M NaOH; accelerated) at 37 °C. The resulting foams had tunable degradation rates, with up 15% mass remaining after 108 days, and controlled erosion profiles. These easy-to-use, shape-filling SMP foams have the potential for various biomaterial applications where longer-term stability without the need for implant removal is desired.

The Effect of Heat Treatment toward Glycerol-Based, Photocurable Polymeric Scaffold: Mechanical, Degradation and Biocompatibility

Photocurable polymers have become increasingly important for their quick prototyping and high accuracy when used in three dimensional (3D) printing. However, some of the common photocurable polymers are known to be brittle, cytotoxic and present low impact resistance, all of which limit their applications in medicine. In this study, thermal treatment was studied for its effect and potential applications on the mechanical properties, degradability and biocompatibility of glycerol-based photocurable polymers, poly(glycerol sebacate) acrylate (PGSA). In addition to the slight increase in elongation at break, a two-fold increase in both Young's modulus and ultimate tensile strength were also observed after thermal treatment for the production of thermally treated PGSA (tPGSA). Moreover, the degradation rate of tPGSA significantly decreased due to the increase in crosslinking density in thermal treatment. The significant increase in cell viability and metabolic activity on both flat films and 3D-printed scaffolds via digital light processing-additive manufacturing (DLP-AM) demonstrated high in vitro biocompatibility of tPGSA. The histological studies and immune staining indicated that tPGSA elicited minimum immune responses. In addition, while many scaffolds suffer from instability through sterilization processes, it was proven that once glycerol-based polymers have been treated thermally, the influence of autoclaving the scaffolds were minimized. Therefore, thermal treatment is considered an effective method for the overall enhancement and stabilization of photocurable glycerol-based polymeric scaffolds in medicine-related applications.

Predicting cell behaviour parameters from glioblastoma on a chip images. A deep learning approach

The broad possibilities offered by microfluidic devices in relation to massive data monitoring and acquisition open the door to the use of deep learning technologies in a very promising field: cell culture monitoring. In this work, we develop a methodology for parameter identification in cell culture from fluorescence images using Convolutional Neural Networks (CNN). We apply this methodology to the in vitro study of glioblastoma (GBM), the most common, aggressive and lethal primary brain tumour. In particular, the aim is to predict the three parameters defining the go or grow GBM behaviour, which is determinant for the tumour prognosis and response to treatment. The data used to train the network are obtained from a mathematical model, previously validated with in vitro experimental results. The resulting CNN provides remarkably accurate predictions (Pearson's ρ > 0.99 for all the parameters). Besides, it proves to be sound, to filter noise and to generalise. After training and validation with synthetic data, we predict the parameters corresponding to a real image of a microfluidic experiment. The obtained results show good performance of the CNN. The proposed technique may set the first steps towards patient-specific tools, able to predict in real-time the tumour evolution for each particular patient, thanks to a combined in vitro-in silico approach.

NUMERICAL COUPLING ANALYSIS OF THE INFLUENCE OF BLOOD FLOW ON THE MECHANICAL RESPONSE FOR LIVER

As an important basis for determining the state of the liver, the mechanical responses are associated with many factors, and belong to a complex coupling system. Liver tissue has significantly complicated vascular channels. The vascular diameter, vascular deflection angle and vascular depth are defined as the key characteristic parameters. The influences of these parameters on the mechanical responses were analyzed. On the basis of the real mechanical parameters, the coupled numerical model of blood vessel, blood flow and liver tissue was established. The corresponding mechanical responses are obtained by utilizing the different vascular parameters. The effects of vascular parameters on the differences among the mechanical response difference and high strain modulus were analyzed. It was found that the blood vessels in the central area could reduce the liver mechanical response. The inner diameter parameter had main influences on the regions where the stain was more than 0.1. The mechanical difference is greater with larger inner diameter. The influences of vascular depth are greatest when the vascular depth was in the intermediate value, which would increase the liver mechanical responses. With the increment of vascular deflection angle, the liver mechanical response would also increase, and exceed the mechanical response without blood vessels. The findings after analyzing the influence of vascular parameters will provide a basis for the quantitative studies on the influence of blood vessels.

A Sensitive and Fast Fiber Bragg Grating-Based Investigation of the Biomechanical Dynamics of In Vitro Spinal Cord Injuries

To better understand the real-time biomechanics of soft tissues under sudden mechanical loads such as traumatic spinal cord injury (SCI), it is important to improve in vitro models. During a traumatic SCI, the spinal cord suffers high-velocity compression. The evaluation of spinal canal occlusion with a sensor is required in order to investigate the degree of spinal compression and the fast biomechanical processes involved. Unfortunately, available techniques suffer with drawbacks such as the inability to measure transverse compression and impractically large response times. In this work, an optical pressure sensing scheme based on a fiber Bragg grating and a narrow-band filter was designed to detect and demonstrate the transverse compression inside a spinal cord surrogate in real-time. The response time of the proposed scheme was 20 microseconds; a five orders of magnitude enhancement over comparable schemes that depend on costly and slower optical spectral analyzers. We further showed that this improvement in speed comes with a negligible loss in sensitivity. This study is another step towards better understanding the complex biomechanics involved during a traumatic SCI, using a method capable of probing the related internal strains with high-spatiotemporal resolution.

In vitro and in vivo evaluations of nano-hydroxyapatite/polyamide 66/yttria-stabilized zirconia as a novel bioactive material for bone screws: Biocompatibility and bioactivity

Zirconia and its derivatives have been receiving increased levels of attention with regard to their potential application in bone tissue engineering. These materials are of particular interest because of their excellent characteristics, such as superior biological and mechanical properties. In this study, yttria-stabilized tetragonal zirconia (YTZ)-reinforced nanohydroxyapatite/polyamide 66 (nHA/PA66) bone screws were prepared. The biocompatibility and bioactivity of nHA/PA66/YTZ were evaluated in vitro using MC3T3-E1 cells. Biocompatibility and bioactivity experiments (cell counting kit-8 tests, cell immunofluorescence analysis, and polymerase chain reaction) showed that nHA/PA66/YTZ could facilitate the biological functions of MC3T3-E1 cells. The attachment, proliferation, spreading, and expression of genes associated with osteogenesis (collagen 1, osteopontin, and osteocalcin) in cells cultured with the nHA/PA66/YTZ composite were all superior compared with the control groups (P < 0.05). In addition, nHA/PA66/YTZ bone screws were implanted into the femoral condyles of rabbits, and titanium screws were employed as a control group; postoperative histology and blood analysis revealed no obvious damage to the liver, kidneys, or any other major organs in either of the experimental groups. Moreover, nHA/PA66/YTZ screws resulted in significantly better bone-implant contact interfaces and enhanced formation of trabecular bone (P < 0.05); these characteristics were markedly better than those in the group that received titanium screws. These observations indicate that YTZ-reinforced nHA/PA66 composites have significant potential for applications in bone tissue engineering.

Depth-Sensing Indentation as a Micro- and Nanomechanical Approach to Characterisation of Mechanical Properties of Soft, Biological, and Biomimetic Materials

Classical methods of material testing become extremely complicated or impossible at micro-/nanoscale. At the same time, depth-sensing indentation (DSI) can be applied without much change at various length scales. However, interpretation of the DSI data needs to be done carefully, as length-scale dependent effects, such as adhesion, should be taken into account. This review paper is focused on different DSI approaches and factors that can lead to erroneous results, if conventional DSI methods are used for micro-/nanomechanical testing, or testing soft materials. We also review our recent advances in the development of a method that intrinsically takes adhesion effects in DSI into account: the Borodich-Galanov (BG) method, and its extended variant (eBG). The BG/eBG methods can be considered a framework made of the experimental part (DSI by means of spherical indenters), and the data processing part (data fitting based on the mathematical model of the experiment), with such distinctive features as intrinsic model-based account of adhesion, the ability to simultaneously estimate elastic and adhesive properties of materials, and non-destructive nature.