An osteoinductive and biodegradable intramedullary implant accelerates bone healing and mitigates complications of bone transport in male rats.

Bone transport is a surgery-driven procedure for the treatment of large bone defects. However, challenging complications include prolonged consolidation, docking site nonunion and pin tract infection. Here, we develop an osteoinductive and biodegradable intramedullary implant by a hybrid tissue engineering construct technique to enable sustained delivery of bone morphogenetic protein-2 as an adjunctive therapy. In a male rat bone transport model, the eluting bone morphogenetic protein-2 from the implants accelerates bone formation and remodeling, leading to early bony fusion as shown by imaging, mechanical testing, histological analysis, and microarray assays. Moreover, no pin tract infection but tight osseointegration are observed. In contrast, conventional treatments show higher proportion of docking site nonunion and pin tract infection. The findings of this study demonstrate that the novel intramedullary implant holds great promise for advancing bone transport techniques by promoting bone regeneration and reducing complications in the treatment of bone defects.

Development and characterization of an automated active mixing platform for hydrogel bioink preparation.

Bioink preparation is an important yet challenging step for bioprinting with hydrogels, as it involves fast and homogeneous mixing of various viscous components. In this study, we have developed an automated active mixing platform (AAMP), which allows for high-quality preparation of hydrogel bioinks. The design of AAMP, adapted from syringe pumps, provides many advantages, including low cost, automated control, high precision, customizability, and great cytocompatibility, as well as the potential to intelligently detect the homogeneity. To demonstrate the capability of AAMP, mixing of different hydrogel components, including alginate and xanthan gum with and without Ca2+, alginate and Laponite, PEGDMA and xanthan gum, was performed to investigate an alginate hydrogel preparation process. Colorimetric analyses were carried out to evaluate the mixing outcome with AAMP. Result showed that AAMP can prepare homogeneous hydrogel mixing in a fast and automated fashion. A multiphysics COMSOL simulation is carried out to further validate the results. Moreover, cell viability and proliferation study were performed in a cell encapsulation mixing experiment to validate the cytocompatibility of the AAMP. The AAMP has demonstrated great capability in hydrogel bioink preparation and could therefore holds great promise and wide applications in bioprinting and tissue engineering.

Metabolic profile of mesenchymal stromal cells and macrophages in the presence of polyethylene particles in a 3D model.

BACKGROUND: Continuous cross talk between MSCs and macrophages is integral to acute and chronic inflammation resulting from contaminated polyethylene particles (cPE); however, the effect of this inflammatory microenvironment on mitochondrial metabolism has not been fully elucidated. We hypothesized that (a) exposure to cPE leads to impaired mitochondrial metabolism and glycolytic reprogramming and (b) macrophages play a key role in this pathway. METHODS: We cultured MSCs with/without uncommitted M0 macrophages, with/without cPE in 3-dimensional gelatin methacrylate (3D GelMA) constructs/scaffolds. We evaluated mitochondrial function (membrane potential and reactive oxygen species-ROS production), metabolic pathways for adenosine triphosphate (ATP) production (glycolysis or oxidative phosphorylation) and response to stress mechanisms. We also studied macrophage polarization toward the pro-inflammatory M1 or the anti-inflammatory M2 phenotype and the osteogenic differentiation of MSCs. RESULTS: Exposure to cPE impaired mitochondrial metabolism of MSCs; addition of M0 macrophages restored healthy mitochondrial function. Macrophages exposed to cPE-induced glycolytic reprogramming, but also initiated a response to this stress to restore mitochondrial biogenesis and homeostatic oxidative phosphorylation. Uncommitted M0 macrophages in coculture with MSC polarized to both M1 and M2 phenotypes. Osteogenesis was comparable among groups after 21 days. CONCLUSION: This work confirmed that cPE exposure triggers impaired mitochondrial metabolism and glycolytic reprogramming in a 3D coculture model of MSCs and macrophages and demonstrated that macrophages cocultured with MSCs undergo metabolic changes to maintain energy production and restore homeostatic metabolism.

Physics-Informed Machine Learning Improves Detection of Head Impacts.

In this work we present a new physics-informed machine learning model that can be used to analyze kinematic data from an instrumented mouthguard and detect impacts to the head. Monitoring player impacts is vitally important to understanding and protecting from injuries like concussion. Typically, to analyze this data, a combination of video analysis and sensor data is used to ascertain the recorded events are true impacts and not false positives. In fact, due to the nature of using wearable devices in sports, false positives vastly outnumber the true positives. Yet, manual video analysis is time-consuming. This imbalance leads traditional machine learning approaches to exhibit poor performance in both detecting true positives and preventing false negatives. Here, we show that by simulating head impacts numerically using a standard Finite Element head-neck model, a large dataset of synthetic impacts can be created to augment the gathered, verified, impact data from mouthguards. This combined physics-informed machine learning impact detector reported improved performance on test datasets compared to traditional impact detectors with negative predictive value and positive predictive values of 88 and 87% respectively. Consequently, this model reported the best results to date for an impact detection algorithm for American football, achieving an F1 score of 0.95. In addition, this physics-informed machine learning impact detector was able to accurately detect true and false impacts from a test dataset at a rate of 90% and 100% relative to a purely manual video analysis workflow. Saving over 12 h of manual video analysis for a modest dataset, at an overall accuracy of 92%, these results indicate that this model could be used in place of, or alongside, traditional video analysis to allow for larger scale and more efficient impact detection in sports such as American Football.

Predictive Factors of Kinematics in Traumatic Brain Injury from Head Impacts Based on Statistical Interpretation.

Brain tissue deformation resulting from head impacts is primarily caused by rotation and can lead to traumatic brain injury. To quantify brain injury risk based on measurements of kinematics on the head, finite element (FE) models and various brain injury criteria based on different factors of these kinematics have been developed, but the contribution of different kinematic factors has not been comprehensively analyzed across different types of head impacts in a data-driven manner. To better design brain injury criteria, the predictive power of rotational kinematics factors, which are different in (1) the derivative order (angular velocity, angular acceleration, angular jerk), (2) the direction and (3) the power (e.g., square-rooted, squared, cubic) of the angular velocity, were analyzed based on different datasets including laboratory impacts, American football, mixed martial arts (MMA), NHTSA automobile crashworthiness tests and NASCAR crash events. Ordinary least squares regressions were built from kinematics factors to the 95% maximum principal strain (MPS95), and we compared zero-order correlation coefficients, structure coefficients, commonality analysis, and dominance analysis. The angular acceleration, the magnitude and the first power factors showed the highest predictive power for the majority of impacts including laboratory impacts, American football impacts, with few exceptions (angular velocity for MMA and NASCAR impacts). The predictive power of rotational kinematics about three directions (x: posterior-to-anterior, y: left-to-right, z: superior-to-inferior) of kinematics varied with different sports and types of head impacts.

The relationship between brain injury criteria and brain strain across different types of head impacts can be different.

Multiple brain injury criteria (BIC) are developed to quickly quantify brain injury risks after head impacts. These BIC originated from different head impact types (e.g. sports and car crashes) are widely used in risk evaluation. However, the accuracy of using the BIC on brain injury risk estimation across head impact types has not been evaluated. Physiologically, brain strain is often considered the key parameter of brain injury. To evaluate the BIC's risk estimation accuracy across five datasets comprising different head impact types, linear regression was used to model 95% maximum principal strain, 95% maximum principal strain at the corpus callosum and cumulative strain damage (15%) on 18 BIC. The results show significantly different relationships between BIC and brain strain across datasets, indicating the same BIC value may suggest different brain strain across head impact types. The accuracy of brain strain regression is generally decreasing if the BIC regression models are fitted on a dataset with a different type of head impact rather than on the dataset with the same type. Given this finding, this study raises concerns for applying BIC to estimate the brain injury risks for head impacts different from the head impacts on which the BIC was developed.