Increased deformations are dispensable for cell mechanoresponse in engineered bone analogs mimicking aging bone marrow.

Aged individuals and astronauts experience bone loss despite rigorous physical activity. Bone mechanoresponse is in-part regulated by mesenchymal stem cells (MSCs) that respond to mechanical stimuli. Direct delivery of low intensity vibration (LIV) recovers MSC proliferation in senescence and simulated microgravity models, indicating that age-related reductions in mechanical signal delivery within bone marrow may contribute to declining bone mechanoresponse. To answer this question, we developed a 3D bone marrow analog that controls trabecular geometry, marrow mechanics and external stimuli. Validated finite element (FE) models were developed to quantify strain environment within hydrogels during LIV. Bone marrow analogs with gyroid-based trabeculae of bone volume fractions (BV/TV) corresponding to adult (25%) and aged (13%) mice were printed using polylactic acid (PLA). MSCs encapsulated in migration-permissive hydrogels within printed trabeculae showed robust cell populations on both PLA surface and hydrogel within a week. Following 14 days of LIV treatment (1g, 100 Hz, 1 hour/day), type-I collagen and F-actin were quantified for the cells in the hydrogel fraction. While LIV increased all measured outcomes, FE models predicted higher von Mises strains for the 13% BV/TV groups (0.2%) when compared to the 25% BV/TV group (0.1%). Despite increased strains, collagen-I and F-actin measures remained lower in the 13% BV/TV groups when compared to 25% BV/TV counterparts, indicating that cell response to LIV does not depend on hydrogel strains and that bone volume fraction (i.e. available bone surface) directly affects cell behavior in the hydrogel phase independent of the external stimuli. Overall, bone marrow analogs offer a robust and repeatable platform to study bone mechanobiology.

Robust automatic hexahedral cartilage meshing framework enables population-based computational studies of the knee.

Osteoarthritis of the knee is increasingly prevalent as our population ages, representing an increasing financial burden, and severely impacting quality of life. The invasiveness of in vivo procedures and the high cost of cadaveric studies has left computational tools uniquely suited to study knee biomechanics. Developments in deep learning have great potential for efficiently generating large-scale datasets to enable researchers to perform population-sized investigations, but the time and effort associated with producing robust hexahedral meshes has been a limiting factor in expanding finite element studies to encompass a population. Here we developed a fully automated pipeline capable of taking magnetic resonance knee images and producing a working finite element simulation. We trained an encoder-decoder convolutional neural network to perform semantic image segmentation on the Imorphics dataset provided through the Osteoarthritis Initiative. The Imorphics dataset contained 176 image sequences with varying levels of cartilage degradation. Starting from an open-source swept-extrusion meshing algorithm, we further developed this algorithm until it could produce high quality meshes for every sequence and we applied a template-mapping procedure to automatically place soft-tissue attachment points. The meshing algorithm produced simulation-ready meshes for all 176 sequences, regardless of the use of provided (manually reconstructed) or predicted (automatically generated) segmentation labels. The average time to mesh all bones and cartilage tissues was less than 2 min per knee on an AMD Ryzen 5600X processor, using a parallel pool of three workers for bone meshing, followed by a pool of four workers meshing the four cartilage tissues. Of the 176 sequences with provided segmentation labels, 86% of the resulting meshes completed a simulated flexion-extension activity. We used a reserved testing dataset of 28 sequences unseen during network training to produce simulations derived from predicted labels. We compared tibiofemoral contact mechanics between manual and automated reconstructions for the 24 pairs of successful finite element simulations from this set, resulting in mean root-mean-squared differences under 20% of their respective min-max norms. In combination with further advancements in deep learning, this framework represents a feasible pipeline to produce population sized finite element studies of the natural knee from subject-specific models.

Emerging Gene-Editing Modalities for Osteoarthritis.

Osteoarthritis (OA) is a pathological degenerative condition of the joints that is widely prevalent worldwide, resulting in significant pain, disability, and impaired quality of life. The diverse etiology and pathogenesis of OA can explain the paucity of viable preventive and disease-modifying strategies to counter it. Advances in genome-editing techniques may improve disease-modifying solutions by addressing inherited predisposing risk factors and the activity of inflammatory modulators. Recent progress on technologies such as CRISPR/Cas9 and cell-based genome-editing therapies targeting the genetic and epigenetic alternations in OA offer promising avenues for early diagnosis and the development of personalized therapies. The purpose of this literature review was to concisely summarize the genome-editing options against chronic degenerative joint conditions such as OA with a focus on the more recently emerging modalities, especially CRISPR/Cas9. Future advancements in novel genome-editing therapies may improve the efficacy of such targeted treatments.

Development of a statistical shape-function model of the implanted knee for real-time prediction of joint mechanics.

Outcomes of total knee arthroplasty (TKA) are dependent on surgical technique, patient variability, and implant design. Non-optimal design or alignment choices may result in undesirable contact mechanics and joint kinematics, including poor joint alignment, instability, and reduced range of motion. Implant design and surgical alignment are modifiable factors with potential to improve patient outcomes, and there is a need for robust implant designs that can accommodate patient variability. Our objective was to develop a statistical shape-function model (SFM) of a posterior stabilized implanted knee to instantaneously predict joint mechanics in an efficient manner. Finite element methods were combined with Latin hypercube sampling and regression analyses to produce modeling equations relating nine implant design and six surgical alignment parameters to tibiofemoral (TF) joint mechanics outcomes during a deep knee bend. A SFM was developed and TF contact mechanics, kinematics, and soft tissue loads were instantaneously predicted from the model. Average normalized root-mean-square error predictions were between 2.79% and 9.42%, depending on the number of parameters included in the model. The statistical shape-function model generated instantaneous joint mechanics predictions using a maximum of 130 training simulations, making it ideally suited for integration into a patient-specific design and alignment optimization pipeline. Such a tool may be used to optimize kinematic function to achieve more natural motion or minimize implant wear, and may aid the engineering and clinical communities in improving patient satisfaction and surgical outcomes.