Mineral content and biomechanical properties of fibrolamellar bone

editorial reviewe

A Multimodal Analysis of Cement Lines in Human Osteonal Bone

peer reviewe

Mineralization and Mechanical Insights of Cement Lines in Human Osteonal Bone

peer reviewe

Rational positioning of 3D-printed voxels to realize high-fidelity multifunctional soft-hard interfaces

Living organisms use functional gradients (FGs) to interface hard and soft materials (e.g., bone and tendon), a strategy with engineering potential. Past attempts involving hard (or soft) phase ratio variation have led to mechanical property inaccuracies because of microscale-material macroscale-property nonlinearity. This study examines 3D-printed voxels from either hard or soft phase to decode this relationship. Combining micro/macroscale experiments and finite element simulations, a power law model emerges, linking voxel arrangement to composite properties. This model guides the creation of voxel-level FG structures, resulting in two biomimetic constructs mimicking specific bone-soft tissue interfaces with superior mechanical properties. Additionally, the model studies the FG influence on murine preosteoblast and human bone marrow-derived mesenchymal stromal cell (hBMSC) morphology and protein expression, driving rational design of soft-hard interfaces in biomedical applications.</p

A Molecular-Scale Understanding of Misorientation Toughening in Corals and Seashells.

peer reviewedBiominerals are organic-mineral composites formed by living organisms. They are the hardest and toughest tissues in those organisms, are often polycrystalline, and their mesostructure (which includes nano- and microscale crystallite size, shape, arrangement, and orientation) can vary dramatically. Marine biominerals may be aragonite, vaterite, or calcite, all calcium carbonate (CaCO3 ) polymorphs, differing in crystal structure. Unexpectedly, diverse CaCO3 biominerals such as coral skeletons and nacre share a similar characteristic: Adjacent crystals are slightly misoriented. This observation is documented quantitatively at the micro- and nanoscales, using polarization-dependent imaging contrast mapping (PIC mapping), and the slight misorientations are consistently between 1° and 40°. Nanoindentation shows that both polycrystalline biominerals and abiotic synthetic spherulites are tougher than single-crystalline geologic aragonite. Molecular dynamics (MD) simulations of bicrystals at the molecular scale reveal that aragonite, vaterite, and calcite exhibit toughness maxima when the bicrystals are misoriented by 10°, 20°, and 30°, respectively, demonstrating that slight misorientation alone can increase fracture toughness. Slight-misorientation-toughening can be harnessed for synthesis of bioinspired materials that only require one material, are not limited to specific top-down architecture, and are easily achieved by self-assembly of organic molecules (e.g., aspirin, chocolate), polymers, metals, and ceramics well beyond biominerals

Rational positioning of 3D printed micro-bricks to realize high-fidelity, multi-functional soft-hard interfaces

peer reviewedLiving organisms have developed design principles, such as functional gradients (FGs), to interface hard materials with soft ones (e.g., bone and tendon). Mimicking such design principles can address the challenges faced when developing engineered constructs with soft-hard interfaces. To date, implementing these FG design principles has been primarily performed by varying the ratio of the hard phase to that of the soft phase. Such design approaches, however, lead to inaccurate mechanical properties within the transition zone. That is due to the highly nonlinear relationship between the material distribution at the microscale and the macroscale mechanical properties. Here, we 3D print micro-bricks from either a soft or a hard phase and study the nonlinear relationship between their arrangements within the transition zone and the resulting macroscale properties. We carry out experiments at the micro- and macroscales as well as finite element simulations at both scales. Based on the obtained results, we develop a co-continuous power-law model relating the arrangement of the micro-bricks to the local mechanical properties of the micro-brick composites. We then use this model to rationally design FGs at the individual micro-brick level and create two types of biomimetic soft-hard constructs, including a specimen modeling bone-ligament junctions in the knee and another modeling the nucleus pulposus-annulus fibrosus interface in intervertebral discs. We show that the implemented FGs drastically enhance the stiffness, strength, and toughness of both types of specimens as compared to non-graded designs. Furthermore, we hypothesize that our soft-hard FGs regulate the behavior of murine preosteoblasts and primary human bone marrow-derived mesenchymal stromal cells (hBMSCc). We culture those cells to confirm the effects of soft-hard interfaces on cell morphology as well as on regulating the expression of focal adhesion kinase, subcellular localization, and YAP nuclear translocation of hBMSCs. Taken together, our results pave the way for the rational design of soft-hard interfaces at the micro-brick level and (biomedical) applications of such designs

Évaluation du résultat d'une arthroplastie de genou à l'aide de capteurs inertiels

Osteoarthritis is a frequent and debilitating disease whose burden is set to increase given our ageing population. Arthroplasty is the only curative treatment for end-stage arthritis and can greatly improve joint function, control pain and enhance quality of life. However, surgery is only part of the picture, and ensuring successful outcomes requires both extensive tailored physiotherapy and close patient monitoring for complications. Currently, patient reported outcome measures (PROMs) are used with minimal clinical follow-up. Not only does this allow for limited opportunities to assess postoperative function, but PROMs are also inherently subjective. As such, the orthopaedic clinic lacks of quantitative information with which to actively monitor a patient’s progress. In addition, due to resource limitations, it struggles to closely monitor patients during the first six weeks following surgery, a key period for ensuring adequate long-term joint function. Inertial measurement units (IMUs) provide an opportunity to objectively measure important biomechanical gait variables in both clinic and home settings. This allows clinicians and physiotherapists to remotely monitor patients through cloud-computing technologies. The aim of this thesis, which is part of a larger research project at the Auckland Bioengineering Institute (Auckland, New Zealand), is to develop and assess a new workflow based on machine learning algorithm to quantitatively evaluate joint function during walking gait of patients following knee arthroplasty using only two ankle-worn IMUs. To evaluate this algorithm, predictions of joint kinematics were compared to ‘ground truth’ joint kinematics recorded from optical motion capture. Twelve patients undergoing knee arthroplasty were recruited. They participated in two gait sessions before and around six weeks after their surgery during which optical marker trajectories and acceleration and angular velocity from IMUs were recorded. However, in view of the issues encountered with their quantity and quality, two other datasets, previously collected for other studies, were also exploited. One involved ten healthy volunteers performing treadmill walking and the second was composed of four overground walking healthy participants. Two types of models were generated and evaluated: a personalised model, trained on a portion of a subject’s data and predicting the remaining part, and a generalised model, trained on every individual of the cohort but one used for prediction. Moreover, a sensitivity analysis was performed to select the most optimal combination of parameters and data processing ways. Our method enables to predict knee kinematics with more than 95\% accuracy for personalised models. This also holds for the treadmill generalised model. However, the poor performance of the overground generalised model was due to limited number of steps per person which could not capture the variability within the dataset. The continuation of this study should increase the patient dataset and include other motions than walking. Moreover, information obtained about the outcome recorded in patients’ environment will be contrasted with other metrics (PROMs, range of motion) collected during their clinical follow-up. Ultimately, this may help clinicians to identify potential complications during recovery and provide the opportunity for early intervention.Evaluating outcome following knee arthroplasty using inertial measurement unit

Personalized Machine Learning Approach to Estimating Knee Kinematics Using Only Shank-Mounted IMU

peer reviewedKnee kinematics is a valuable measure of knee joint function. However, collecting that data outside the clinic is difficult, especially with a limited number of wearable sensors and when you only use an ankle-mounted inertial measurement unit (IMU) to estimate knee kinematics. Due to the cyclic nature of gait, it is possible to use machine learning to extract joint angles from only ankle-mounted sensors. This study aimed to use time-series feature extraction and a random forest regressor to generate a person-specific surrogate model for estimating knee joint flexion angles from a single-mounted IMU above the ankle. Optical motion capture (OMC) and inertial data from ten healthy participants walking on a treadmill were collected to create ten personalized surrogate models for estimating right knee flexion angles during gait. An additional ten models were created for a leave-one-out analysis to test the generalisability of the models. Temporal cross validation of the personalized models and a leave-one-out analysis was performed on the selected feature set. The personalized models achieved an average root-mean-square error (RMSE) of 2.45 \pm 0.65 ( R2 of 0.98) compared to a gold-standard OMC. The generalized models achieved an average RMSE of 6.77 \pm 3.38 ( R2 of 0.83) in the leave-one-out analysis. Time-series feature-based personalized surrogate models could be used to accurately estimate knee kinematics by using a single ankle-mounted sensor. However, more data are required to train a generalized model using the presented method

Mineral Content and Mechanical Properties of Cement Lines in Human Osteonal Bone

editorial reviewe

Deep learning virtual indenter maps nanoscale hardness rapidly and non-destructively, revealing mechanism and enhancing bioinspired design

Over evolution, organisms develop complex material structures fit to their environments. Based on these time-tested designs, human-engineered bioinspired structures offer exciting possible materials configurations. However, navigating diverse structure spaces for attaining desired properties remains non-trivial. We focus on the hardest biological tissue in humans, tooth enamel, to examine the structure-property relationship. While typical hardness measurements are time consuming and destructive, we propose that artificial intelligence models can predict properties directly and enable high-throughput, non-destructive characterization. We train a deep image regression neural network as a surrogate model and visualize with gradient ascent and saliency maps to identify structural features contributing most to hardness. This model demonstrates improved spatial resolution and sensitivity compared with experimental hardness maps. Using this rapid hardness testing model, a generative adversarial model, and a genetic algorithm that operates in latent space, allows for guided materials design, yielding proposed designs for bioinspired structures with precisely controlled hardness