Journal of the Mechanical Behavior of Biomedical Materials最新文献

Recent advancements in biomaterial research conduct artificial intelligence for predicting diverse material properties. However, research predicting the mechanical properties of biomaterial based on amino acid sequences have been notably absent. This research pioneers the use of classification models to predict ultimate tensile strength from silk fiber amino acid sequences, employing logistic regression, support vector machines with various kernels, and a deep neural network (DNN). Remarkably, the model demonstrates a high accuracy of 0.83 during the generalization test. The study introduces an innovative approach to predicting biomaterial mechanical properties beyond traditional experimental methods. Recognizing the limitations of conventional linear prediction models, the research emphasizes the future trajectory toward DNNs that can adeptly capture non-linear relationships with high precision. Moreover, through comprehensive performance comparisons among diverse prediction models, the study offers insights into the effectiveness of specific models for predicting the mechanical properties of certain materials. In conclusion, this study serves as a pioneering contribution, laying the groundwork for future endeavors and advocating for the seamless integration of AI methodologies into materials research.

Background and objective

Tooth extraction is a common clinical procedure with biomechanical factors that can directly influence patient outcomes. Recent development in atraumatic extraction techniques have endeavoured to improve treatment outcomes, but the characterization of extraction biomechanics is sparse. An axisymmetric inverse finite element (FE) approach is presented to represent the biomechanics of vertical atraumatic tooth extraction in an ex-vivo swine model.

Methods

Geometry and boundary conditions from the model are determined to match the extraction of swine incisors in a self-aligning ex vivo extraction experiment. Material parameters for the periodontal ligament (PDL) model are determined by solving an inverse FE problem using clusters of data obtained from 10 highly-controlled mechanical experiments. A seven-parameter visco-hyperelastic damage model, based on an Arruda-Boyce framework, is used for curve fitting. Three loading schemes were fit to obtain a common set of material parameters.

Results

The inverse FE results demonstrate good predictions for overall force-time curve shape, peak force, and time to peak force. The fit model parameters are sufficiently consistent across all three cases that a coefficient-averaged model was taken that compares well to all three cases. Notably, the initial modulus $,u\text{,}$ converged across trials to an average value of 0.472 MPa with an average viscoelastic constant $g$ of 0.561.

Conclusions

The presented model is found to have consistent parameters across loading cases. The capability of this model to represent the fundamental mechanical characteristics of the dental complex during vertical extraction loading is a significant advancement in the modelling of extraction procedures. Future work will focus on verifying the model as a predictive design tool for assessing new loading schemes in addition to investigating its applications to subject-specific problems.

Micro finite-element (μFE) simulations serve as a crucial research tool to assist laboratory experiments in the biomechanical assessment of screw anchorage in bone. However, accurately modelling the interface between bone and screw threads at the microscale poses a significant challenge. Currently, the gold-standard approach involves employing computationally intensive physical contact models to simulate this interface. This study compared nonlinear μFE predictions of deformations, whole-construct stiffness, maximum force and damage patterns of three different computationally efficient simplified interface approaches to the general contact interface in Abaqus Explicit, which was defined as gold-standard and reference model. The μCT images (resolution: 32.8 μm) of two human radii with varying bone volume fractions were utilized and a screw was virtually inserted up to 50% and 100% of the volar-dorsal cortex distance. Materially nonlinear μFE models were generated and loaded in tension, compression and shear. In a first step, the common simplification of using a fully-bonded interface was compared to the general contact interface, revealing overestimations of whole-construct stiffness (19% on average) and maximum force (26% on average), along with inaccurate damage pattern replications. To enhance predictions, two additional simplified interface models were compared: tensionally strained element deletion (TED) and a novel modification of TED (TED-M). TED deletes interface elements strained in tension based on a linear-elastic simulation before the actual simulation. TED-M extends the remaining contact interface of TED by incorporating neighboring elements to the contact area. Both TED and TED-M reduced the errors in whole-construct stiffness and maximum force and improved the replication of the damage distributions in comparison to the fully-bonded approach. TED was better in predicting whole-construct stiffness (average error of 1%), while TED-M showed lowest errors in maximum force (1% on average). In conclusion, both TED and TED-M offer computationally efficient alternatives to physical contact modelling, although the fully-bonded interface may deliver sufficiently accurate predictions for many applications.

Understanding the behaviour and material properties of bone is critical in predicting the failure and fracture of bones in humans. To address this, mechanical tests have traditionally been conducted to characterize bone material and this has resulted in large body of literature. However, there appears to be a lack of complete information regarding the storage protocols used for bone specimens prior to conducting mechanical tests. For example, while storage methods are well described, parameters such as the time between donor death and bone retrieval, as well as time between specimen machining and testing, are seldom reported. As biological materials undergo degradation in storage after being removed from the donor, a clear understanding of this degradation behaviour would identify critical time frames in which previously stored cortical bone specimens should be tested such that they can still be considered representative of an in-vivo condition. In this paper, the results of an investigation to determine the effects of long duration storage on the measured mechanical properties of bovine cortical bone are reported. Three different storage protocols are compared; namely machined-refrigerated, machined-frozen and frozen-machined-frozen. Degradation effects are evident for both refrigerated and frozen specimens and the results demonstrate that testing bone specimens after more than one week in storage may not provide representative in-vivo properties. In addition, specimens exhibit severe degradation after six months in storage regardless of the storage protocol.

After total hip arthroplasty, the stress shielding effect can occur due to the difference of stiffness between the metallic alloy of the stems and the host bone, which may cause a proximal bone loss. To overcome this problem, a low-modulus metastable β Ti-20Zr-3Mo-3Sn alloy composition has recently been designed to be potentially used for the cementless femoral hip stems. After having verified experimentally that the β alloy has a low modulus of around 50 GPa, a finite element analysis was performed on a Ti-20Zr-3Mo-3Sn alloy hip prosthesis model to evaluate the influence of a reduced modulus on stress shielding and stress fields in both stem and bone compared with the medical grade Ti-6Al-4V alloy whose elastic modulus reached 110 GPa. Our results show that the Ti-20Zr-3Mo-3Sn stem with low elastic modulus can effectively reduce the total stress shielding by 45.5% compared to the common Ti-6Al-4V prosthesis. Moreover, it is highlighted that the material elasticity affects the stress distribution in the implant, especially near the bone-stem interfaces.

Developing new low modulus structures is important for reducing the risk of aseptic loosening during loading of implant materials. However, an alloy that may also confer some advantage at preventing septic loosening could dramatically improve the outcomes for patients. Nevertheless, the predictive power of current models remains limited to common alloying additions. As such, this study considers the mechanical properties of a range of Ti–Nb–Au superelastic alloys to elucidate the composition range for which low modulus structures can be achieved. These modulus values are compared to other critical design parameters such as strain recovery and strength. It was found that Au additions are effective at suppressing the formation of the ω phase and allow alloys with lower moduli to be achieved. It was also shown that low β phase stability is critical for achieving the lowest modulus, and that this susceptibility to transform to a martensite may enable higher strengths to be achieved. However, this low β phase stability also limits the strain recovery that may be achieved meaning these two properties are not necessarily independently tuneable. These data provide important context for the design of new systems containing unusual alloying additions such as Au.

Superficial fascia is a fibrofatty tissue found throughout the body. Initially described in relation to hernias, it has only recently received attention from the scientific community due to new evidence on its role in force transmission and structural integrity of the body. Considering initial difficulties in its anatomical identification, to date, a characterization of the superficial fascia through mechanical tests is still lacking.

The mechanical properties of human superficial fasciae of abdominal and thoracic districts (back) of different subjects (n = 4) were then investigated, focusing on anisotropy and viscoelasticity. Experimental tests were performed on samples taken in two perpendicular directions according to body planes (cranio-caudal and latero-medial axes). Data collected from two different uniaxial tensile protocols, failure (i.e., ultimate tensile strength and strain at break, Young's modulus and toughness) and stress-relaxation (i.e., residual stress), were processed and then grouped for statistical analysis.

Failure tests confirmed tissue anisotropy, revealing the stiffer nature of the latero-medial direction compared to the cranio-caudal one, for both the districts (with a ratio of the respective Young's moduli close to 2). Furthermore, the thoracic region exhibited significantly greater strength and resultant Young's modulus compared to the abdomen (with greater results along the latero-medial direction, such as 6.13 ± 3.11 MPa versus 0.85 ± 0.39 MPa and 24.87 ± 15.23 MPa versus 3.19 ± 1.62 MPa, respectively). On the contrary, both regions displayed similar strain at break (varying between 38 and 47%), with no clear dependence from the loading directions. Stress-relaxation tests highlighted the viscous behavior of the superficial fascia, with no significant differences in the stress decay between directions and districts (35–38% of residual stress after 300 s).

All these collected results represent the starting point for a more in-depth knowledge of the mechanical characterization of the superficial fascia, which can have direct implications in the design, implementation, and effectiveness of site-specific treatments.

Background

Despite its success in the mechanical characterization of biological tissues, magnetic resonance elastography (MRE) uses ill-posed wave inversions to estimate tissue stiffness. 1-Norm has been recently introduced as a mathematical measure for the scattering of mechanical waves due to inhomogeneities based on an analysis of the delineated contours of wave displacement.

Purpose

To investigate 1-Norm as an MRE-based quantitative biomarker of mechanical inhomogeneities arising from microscopic structural tissue alterations caused by the freeze-thaw cycle (FTC) or Alzheimer's disease (AD).

Methods

In this proof-of-concept study, we prospectively investigated excised porcine kidney (n = 6), liver (n = 6), and muscle (n = 6) before vs. after the FTC at 500–2000 Hz and excised murine brain of healthy controls (n = 3) vs. 5xFAD species with AD (n = 3) at 1200–1800 Hz using 0.5 T tabletop MRE. 1-Norm analysis was compared with conventional wave inversion.

Results

While the FTC reduced both stiffness and inhomogeneity in kidney, liver, and muscle tissue, AD led to lower brain stiffness but more pronounced mechanical inhomogeneity.

Conclusion

Our preliminary results show that 1-Norm is sensitive to tissue mechanical inhomogeneity due to FTC and AD without relying on ill-posed wave inversion techniques. 1-Norm has the potential to be used as an MRE-based diagnostic biomarker independent of stiffness to characterize abnormal conditions that involve changes in tissue mechanical inhomogeneity.

Background

Surgical correction of unicoronal craniosynostosis (UCS) is highly complex due to its asymmetric appearance. Although fronto-orbital advancement (FOA) is a versatile technique for craniosynostosis correction, harmonization of the orbital bandeau in UCS is difficult to predict. This study evaluates the biomechanics of the orbital bandeau using different patterns and varying characteristics of inner cortical bone layer osteotomies in a finite element (FE) analysis.

Method

An FE model was created using the computed tomography (CT) scan of a 6.5-month-old male infant with a right-sided UCS. The unaffected side of the orbital bandeau was virtually mirrored, and anatomical correction of the orbital bandeau was simulated. Different combinations of osteotomy patterns, numbers, depths, and widths were examined (n = 48) and compared to an uncut model.

Results

Reaction forces and maximum stress values differed significantly (p < 0.01) among osteotomy patterns and between each osteotomy characteristic. Regardless of the osteotomy pattern, higher numbers of osteotomies significantly (p < 0.05) correlated with reductions in reaction force and maximum stress. An X-shaped configuration with three osteotomies deep and wide to the bone was biomechanically the most favorable model.

Conclusion

Inner cortical bone layer osteotomy might be an effective modification to the conventional FOA approach in terms of predictable shaping of the orbital bandeau.

Currently, the use of autografts is the gold standard for the replacement of many damaged biological tissues. However, this practice presents disadvantages that can be mitigated through tissue-engineered implants. The aim of this study is to explore how machine learning can mechanically evaluate 2D and 3D polyvinyl alcohol (PVA) electrospun scaffolds (one twisted filament, 3 twisted filament and 3 twisted/braided filament scaffolds) for their use in different tissue engineering applications. Crosslinked and non-crosslinked scaffolds were fabricated and mechanically characterised, in dry/wet conditions and under longitudinal/transverse loading, using tensile testing. 28 machine learning models (ML) were used to predict the mechanical properties of the scaffolds. 4 exogenous variables (structure, environmental condition, crosslinking and direction of the load) were used to predict 2 endogenous variables (Young’s modulus and ultimate tensile strength). ML models were able to identify 6 structures and testing conditions with comparable Young’s modulus and ultimate tensile strength to ligamentous tissue, skin tissue, oral and nasal tissue, and renal tissue. This novel study proved that Classification and Regression Trees (CART) models were an innovative and easy to interpret tool to identify biomimetic electrospun structures; however, Cubist and Support Vector Machine (SVM) models were the most accurate, with R² of 0.93 and 0.8, to predict the ultimate tensile strength and Young’s modulus, respectively. This approach can be implemented to optimise the manufacturing process in different applications.