Accumulation of head impacts may contribute to acute and long-term brain trauma. Wearable sensors can measure impact exposure, yet current sensors do not have validated impact detection methods for accurate exposure monitoring. Here we demonstrate a head impact detection method that can be implemented on a wearable sensor for detecting field football head impacts. Our method incorporates a support vector machine classifier that uses biomechanical features from the time domain and frequency domain, as well as model predictions of head-neck motions. The classifier was trained and validated using instrumented mouthguard data from collegiate football games and practices, with ground truth data labels established from video review. We found that low frequency power spectral density and wavelet transform features (10~30 Hz) were the best performing features. From forward feature selection, fewer than ten features optimized classifier performance, achieving 87.2% sensitivity and 93.2% precision in cross-validation on the collegiate dataset (n = 387), and over 90% sensitivity and precision on an independent youth dataset (n = 32). Accurate head impact detection is essential for studying and monitoring head impact exposure on the field, and the approach in the current paper may help to improve impact detection performance on wearable sensors.

Bioprinting is an emerging additive manufacturing approach to the fabrication of patient-specific, implantable three-dimensional (3D) constructs for regenerative medicine. However, developing cell-compatible bioinks with high printability, structural stability, biodegradability, and bioactive characteristics is still a primary challenge for translating 3D bioprinting technology to preclinical and clinal models. To overcome this challenge, we developed a nanoengineered ionic covalent entanglement (NICE) bioink formulation for 3D bone bioprinting. The NICE bioinks allow precise control over printability, mechanical properties, and degradation characteristics, enabling custom 3D fabrication of mechanically resilient, cellularized structures. We demonstrate cell-induced remodeling of 3D bioprinted scaffolds over 60 days, demonstrating deposition of nascent extracellular matrix proteins. Interestingly, the bioprinted constructs induce endochondral differentiation of encapsulated human mesenchymal stem cells (hMSCs) in the absence of osteoinducing agent. Using next-generation transcriptome sequencing (RNA-seq) technology, we establish the role of nanosilicates, a bioactive component of NICE bioink, to stimulate endochondral differentiation at the transcriptome level. Overall, the osteoinductive bioink has the ability to induce formation of osteo-related mineralized extracellular matrix by encapsulated hMSCs in growth factor-free conditions. Furthermore, we demonstrate the ability of NICE bioink to fabricate patient-specific, implantable 3D scaffolds for repair of craniomaxillofacial bone defects. We envision development of this NICE bioink technology toward a realistic clinical process for 3D bioprinting patient-specific bone tissue for regenerative medicine.

Traumatic brain injury is a leading cause of disability and injury-related death. To enhance our ability to prevent such injuries, brain response can be studied using validated finite element (FE) models. In the current study, a high-resolution, anatomically accurate FE model was developed from the International Consortium for Brain Mapping brain atlas. Due to wide variation in published brain material parameters, optimal brain properties were identified using a technique called Latin hypercube sampling, which optimized material properties against three experimental cadaver tests to achieve ideal biomechanics. Additionally, falx pretension and thickness were varied in a lateral impact variation. The atlas-based brain model (ABM) was subjected to the boundary conditions from three high-rate experimental cadaver tests with different material parameter combinations. Local displacements, determined experimentally using neutral density targets, were compared to displacements predicted by the ABM at the same locations. Error between the observed and predicted displacements was quantified using CORrelation and Analysis (CORA), an objective signal rating method that evaluates the correlation of two curves. An average CORA score was computed for each variation and maximized to identify the optimal combination of parameters. The strongest relationships between CORA and material parameters were observed for the shear parameters. Using properties obtained through the described multiobjective optimization, the ABM was validated in three impact configurations and shows good agreement with experimental data. The final model developed in this study consists of optimized brain material properties and was validated in three cadaver impacts against local brain displacement data.

Approximately 5,000,000 athletes play organized football in the United States, and youth athletes constitute the largest proportion with ∼3,500,000 participants. Investigations of head impact exposure (HIE) in youth football have been limited in size and duration. The objective of this study was to evaluate HIE of athletes participating in three age- and weight-based levels of play within a single youth football organization over four seasons. Head impact data were collected using the Head Impact Telemetry (HIT) System. Mixed effects linear models were fitted, and Wald tests were used to assess differences in head accelerations and number of impacts among levels and session type (competitions vs. practices). The three levels studied were levels A (n = 39, age = 10.8 ± 0.7 years, weight = 97.5 ± 11.8 lb), B (n = 48, age = 11.9 ± 0.5 years, weight = 106.1 ± 13.8 lb), and C (n = 32, age = 13.0 ± 0.5 years, weight = 126.5 ± 18.6 lb). A total of 40,538 head impacts were measured. The median/95th percentile linear head acceleration for levels A, B, and C was 19.8/49.4g, 20.6/51.0g, and 22.0/57.9g, respectively. Level C had significantly greater mean linear acceleration than both levels A (p = 0.005) and B (p = 0.02). There were a significantly greater number of impacts per player in a competition than in a practice session for all levels (A, p = 0.0005, B, p = 0.0019, and C, p < 0.0001). Athletes at lower levels experienced a greater percentage of their high magnitude impacts (≥ 80g) in practice, whereas those at the highest level experienced a greater percentage of their high magnitude impacts in competition. These data improve our understanding of HIE within youth football and are an important step in making evidence-based decisions to reduce HIE.