The cartilaginous surfaces in ginglymus (hinge) joints such as the knee, elbow, and the ginglymoarthrodial temporomandibular joint (TMJ) primarily function under unidirectional shear and orthogonal compression. Regenerative medicine approaches to treat injured or arthritic joints include biphasic scaffolds, which must withstand the joint's biomechanical demands. In the current study, we leveraged computational modeling to design a 3D-printed biphasic scaffold with enhanced biomechanical performance for ginglymus joints. A sinusoidal hourglass tube geometry was introduced to support shear stresses at the hydrogel-substrate interface and to support orthogonal compression. Biphasic constructs were evaluated with both empirical and in silico interface shear experiments. A thermal extrusion 3D-printed polylactic acid (PLA) hourglass interface was infilled with a hydrogel, comprised of either (1) agarose or (2) pentenoate-modified hyaluronic acid (PHA), polyethylene glycol diacrylate (PEGDA), and devitalized cartilage (DVC). Shear loads were applied either parallel to the tube's long axis (i.e., 1-direction) or orthogonally (i.e., 2-direction). Additionally, the hourglass tube architecture without any hydrogel was evaluated in compression in the 1- and 3-directions. Empirically, ultimate interface shear stresses up to 51 ± 7kPa were observed for the infilled PHA-PEGDA-DVC hydrogels, with higher values in both loading directions compared to a crosshatch scaffold as a standard-of-comparison control (p < 0.05). The computer model suggested a geometry-dependent shear load transfer. The ultimate compressive stress for the hourglass architecture in the 3-direction reached 6.9 ± 1.8MPa, which was 39% higher than the crosshatch architecture. The hourglass design enhanced performance under shear in the 1-direction and compression in the 3-direction, which may add value for future designs employed for regenerating tissues in ginglymus joints that primarily function under unidirectional shear and orthogonal compression.

Modeling eukaryotic cell flow in microfluidic devices and capillary networks can be instrumental in assessing how cell mechanics influence its behavior. Due to the viscoelastic characteristics of cells and their capacity for substantial deformation, models that are both detailed and computationally efficient are necessary to explore cell rheology. We present a coarse-grained model for simulating the mechanics of eukaryotic cells in flow, with a focus on the modeling of cell membrane, nucleus, and cytoskeleton. The cell and nucleus membranes are represented using surface triangulation, capturing both viscous and elastic properties of the membranes. To maintain computational efficiency while retaining the ability to reproduce the viscoelastic behavior of the entire cell, the complexity of the cytoskeleton model is reduced through the use of the viscoelastic bonds. Dissipative Particle Dynamics is employed to facilitate flow simulations; however, the model is suitable for use in many existing continuum and particle-based methods. The cell model is calibrated and validated using experimental data from micropipette aspiration and microfluidic experiments involving breast epithelial cells (MCF-10A). We believe the balance between simplicity and accuracy makes the proposed model a valuable tool for simulating eukaryotic cell mechanics in flow, enabling faster simulations, while also simplifying the parameterization procedure.

In the planning of maxillofacial surgery, accurately evaluating the postoperative soft tissue area is crucial. This allows doctors to provide patients with better morphological recovery while ensuring the restoration of normal functional areas. This study aims to develop an advanced automatic algorithm for the completion of soft tissue defects, enhancing the accuracy and effectiveness of surgical planning. We introduce a point cloud completion method based on Generative Adversarial Networks. Firstly, we remove the defective regions, reserving the healthy tissues and converting them into point clouds. Then, using the soft tissue completion network, we reconstruct the defective areas and generate the corresponding point cloud images. Finally, we combine the healthy point cloud with the generated defective regions to produce a complete soft tissue image and model of the patient's face. To validate our approach, we conduct qualitative and quantitative experiments on 20 normal individuals (10 males and 10 females). Compared with several existing algorithms, our method shows superior significance in soft tissue prediction. The Root Mean Squared Error between the generated model and ground truth is 1.45 ± 0.25 mm, and the Surface Distance error is 0.69 ± 0.13 mm. The visualization results show that using our network to generate soft tissue at the defect site is more structurally consistent. This study introduces a novel point cloud generation network for reconstructing facial soft tissue images, which provides accurate and structurally consistent morphological outcomes. The proposed method shows great potential in improving the quality and accuracy of surgical planning in maxillofacial surgery.

Biodegradable implants are promising for fracture fixation but they have not been applied to the load-bearing skeletal sites. A critical issue is how implant degradation and fracture healing affect each other under mechanical loading. To address this issue, we first developed a finite element model of a long bone fracture fixed with a Zn alloy-based screw-plate system, where implant degradation and bone healing were simulated based upon the continuum damage mechanics and mechano-regulated tissue differentiation algorithm, respectively. For comparison, non-degradable Ti alloy implant with normal bone healing and non-healing fracture with normal implant degradation were served as two reference controls. In terms of the effect of implant degradation on bone healing, the results indicated that implant degradation resulted in a greater volume of newly formed bone within the callus (16% for the degradable implant vs 12% for the non-degradable implant) and a better biomechanical recovery of the fractured bone (bone stiffness fraction: 107% vs 95%) at week 8. Regarding the effect of bone healing on implant degradation, fracture healing led to a significant decrease in the degradation rate of the implant (implant stiffness fraction at week 4: 8% for non-healing vs 40% for healing) and an increase in the overall period from 4 to 8weeks for a complete degradation of the implant. These results together suggest that implant degradation and fracture healing significantly affect each other under mechanical loading. The in silico model developed here may provide a valuable platform to consider interactions between material degradation and bone healing when designing biodegradable implants for orthopaedic internal fixation at the load-bearing sites.

After stroke, walking is characterized by hemiparetic patterns, quantified with force sensitive walkways and motion capture systems. Some joint-level kinematic patterns of walking also can be obtained with wearable sensors. The purpose of this project was to measure joint-level kinematic patterns during walking with wearable sensors and determine the association with walking speed and endurance in individuals with chronic stroke. In this cross-sectional observational study, participants donned APDM Opal wearable sensors during walking tests (10-meter walk test or 6-min walk test). We extracted joint-level kinematic variables of elevation at midswing, circumduction, foot strike angle, and toe-off angle. Associations of each variable with walking speed and endurance were tested, and significantly associated variables were entered into a regression model. 68 individuals with chronic stroke were included. We found that the less affected foot strike angle, less affected toe-off angle, and more affected toe-off angle were significant predictors of walking speed (R2 ≥ 0.71, p < 0.001). Less affected toe-off angle, more affected foot strike angle, and more affected toe-off angle were significant predictors of walking endurance (R2 ≥ 0.67, p < 0.001). We found consistent evidence that greater toe-off angle (may reflect greater push-off) and lesser foot strike angle (may reflect lesser foot drop) were important predictors of greater walking speed and endurance. Our results suggest that wearable sensors can provide important information about joint-level kinematic patterns that are important for walking function. This information could help therapists target interventions toward specific deficits or compensatory patterns to improve walking.

The translation of biomedical devices and drug research is an expensive and long process with a low probability of receiving FDA approval. Developing physiologically relevant in vitro models with human cells offers a solution to not only improving the odds of FDA approval but also to expand our ability to study complex in vivo systems in a simpler fashion. Animal models remain the standard for pre-clinical testing; however, the data from animal models is an unreliable extrapolation when anticipating a human response in clinical trials, thus contributing to the low rates of translation. In this review, we focus on in vitro vascular or angiogenic models because of the incremental role that the vascular system plays in the translation of biomedical research. The first section of this review discusses the most common angiogenic cytokines that are used in vitro to initiate angiogenesis, followed by angiogenic inhibitors where both initiators and inhibitors work to maintain vascular homeostasis. Next, we evaluate previously published in vitro models, where we evaluate capturing the physical environment for biomimetic in vitro modeling. These topics provide a foundation of parameters that must be considered to improve and achieve vascular biomimicry. Finally, we summarize these topics to suggest a path forward with the goal of engineering human in vitro models that emulate the in vivo environment and provide a platform for biomedical device and drug screening that produces data to support clinical translation.

The rigorous regulatory landscape for medical devices demands meticulous chemical characterization to ensure safety and compliance. This review examines the critical role of chemical characterization within regulatory frameworks, emphasizing its importance in the approval and market entry of medical devices. Key challenges, including the complexity of sample matrices, trace-level impurity detection, and the necessity of method validation, are thoroughly explored. In addition, the review addresses the dynamic nature of regulatory requirements, analyzing how updates in international standards, such as those from the International Organization for Standardization (ISO), the American National Standards Institute (ANSI), and the Association for the Advancement of Medical Instrumentation (AAMI), or the American Society for Testing and Materials (ASTM), shape the chemical characterization process. The review discusses future directions, including advancements in analytical technologies, the potential for increased automation and standardization, and the growing significance of managing emerging contaminants. By offering a comprehensive analysis of current practices and future trends, this review highlights the essential role of chemical characterization in ensuring the development and regulation of safe and effective medical devices.

Multiple cell types interact within the aortic wall to control development, homeostasis, and adaptation as well as to drive disease progression. Given the complexity of these interactions and their manifestations at the tissue level, there is a pressing need for a new class of computational models that integrate data across scales. We meld logic-based cell signaling models of vascular smooth muscle cells, adventitial fibroblasts, and macrophages and couple this multi-cell model with a tissue level-constrained mixture model of aortic growth and remodeling. The coupled multi-scale model is parameterized using data from the literature and then specialized for the case of angiotensin II-induced hypertensive remodeling of the descending thoracic aorta in wild-type mice. We contrast important contributions of chemo- and mechano-stimulation of cell responses and identify critical roles of recruited macrophages in driving the non-homeostatic thickening of the adventitial layer that reduces biaxial wall stress below setpoint values. We show the utility of a multi-scale, multi-cell model in delineating effects of different chemo-mechanical stimuli in aortic remodeling in hypertension.