Cervical vertebral and spinal cord injuries in rollover occupants.

BACKGROUND: Rollover crashes continue to be a substantial public health issue in North America. Previous research has shown that the cervical spine is the most injured spine segment in rollovers, but much of the past research has focused on risk factors rather than the actual cervical spine injuries. We sought to examine how different types of cervical spine injuries (vertebral and/or cord injury) vary with different occupant-related factors in rollovers and to compare these with non-rollovers. METHODS: We obtained crash and injury information from the National Automotive Sampling System-Crashworthiness Data System (NASS-CDS) for 2005-2015 and Crash Investigation Sampling System (CISS) for 2017-2022. Based on weighted data, we calculated relative risks to assess how occupant sex, seat belt use, ejection status, and fatal outcome relate to the rate of different cervical spine injuries in rollovers and non-rollovers. RESULTS: In NASS-CDS occupants with cervical spine injuries (N = 111,040 weighted cases), about 91.5% experienced at least one vertebral injury whereas only 11.3% experienced a spinal cord injury (most of which had a concomitant vertebral fracture). All types of cervical spine injuries we examined were 3.4-5.2 times more likely to occur in rollovers compared to non-rollovers. These relative risks were similar for both sexes, belted and unbelted, non-ejected, and non-fatal occupants. The number of weighted CISS occupants with cervical spine injuries (N = 42,003) was smaller than in the NASS analysis, but cervical spine injuries remained 6.25 to 6.36 times more likely in rollovers compared to non-rollovers despite a more modern vehicle fleet. CONCLUSIONS: These findings underscore the continued need for rollover-specific safety countermeasures, especially those focused on cervical spine injury prevention, and elucidate the frequency, severity and other characteristics of the specific vertebral and spinal cord injuries being sustained in rollovers. Our findings suggest that countermeasures focused on preventing cervical vertebral fractures will also effectively prevent most cervical spinal cord injuries.

Amphiphilic Polyelectrolyte Complexes for Fouling-Resistant and Easily Tunable Membranes.

Commercial membranes today are manufactured from a handful of membrane materials. While these systems are well-optimized, their capabilities remain constrained by limited chemistries and manufacturing methods available. As a result, membranes cannot address many relevant separations where precise selectivity is needed, especially with complex feeds. This constraint requires the development of novel membrane materials that offer customizable features to provide specific selectivity and durability requirements for each application, enabled by incorporating different functional chemistries into confined nanopores in a scalable process. This study introduces a new class of membrane materials, amphiphilic polyelectrolyte complexes (APECs), comprised of a blend two distinct amphiphilic polyelectrolytes of opposite charge that self-assemble to form a polymer selective layer. When coated on a porous support from a mixture in a nonaqueous solvent, APECs self-assemble to create ionic nanodomains acting as water-conducting nanochannels, enveloped within hydrophobic nanodomains, ensuring structural integrity of the layer in water. Notably, this approach allows precise control over selectivity without compromising pore size, permeability, or fouling resistance. For example, using only one pair of amphiphilic copolymers, sodium sulfate rejections can be varied from >95% to <10% with no change in effective pore size and fouling resistance. Given the wide range of amphiphilic polyelectrolytes (i.e., combinations of different hydrophobic, anionic, and cationic monomers), APECs can create membranes with many diverse chemistries and selectivities. Resultant membranes can potentially address precision separations in many applications, from wastewater treatment to chemical and biological manufacturing.

Growing three-dimensional objects with light.

Vat photopolymerization (VP) additive manufacturing enables fabrication of complex 3D objects by using light to selectively cure a liquid resin. Developed in the 1980s, this technique initially had few practical applications due to limitations in print speed and final part material properties. In the four decades since the inception of VP, the field has matured substantially due to simultaneous advances in light delivery, interface design, and materials chemistry. Today, VP materials are used in a variety of practical applications and are produced at industrial scale. In this perspective, we trace the developments that enabled this printing revolution by focusing on the enabling themes of light, interfaces, and materials. We focus on these fundamentals as they relate to continuous liquid interface production (CLIP), but provide context for the broader VP field. We identify the fundamental physics of the printing process and the key breakthroughs that have enabled faster and higher-resolution printing, as well as production of better materials. We show examples of how in situ print process monitoring methods such as optical coherence tomography can drastically improve our understanding of the print process. Finally, we highlight areas of recent development such as multimaterial printing and inorganic material printing that represent the next frontiers in VP methods.

Results of an interlaboratory study on the working curve in vat photopolymerization.

The working curve informs resin properties and print parameters for stereolithography, digital light processing, and other photopolymer additive manufacturing (PAM) technologies. First demonstrated in 1992, the working curve measurement of cure depth vs radiant exposure of light is now a foundational measurement in the field of PAM. Despite its widespread use in industry and academia, there is no formal method or procedure for performing the working curve measurement, raising questions about the utility of reported working curve parameters. Here, an interlaboratory study (ILS) is described in which 24 individual laboratories performed a working curve measurement on an aliquot from a single batch of PAM resin. The ILS reveals that there is enormous scatter in the working curve data and the key fit parameters derived from it. The measured depth of light penetration Dp varied by as much as 7x between participants, while the critical radiant exposure for gelation Ec varied by as much as 70x. This significant scatter is attributed to a lack of common procedure, variation in light engines, epistemic uncertainties from the Jacobs equation, and the use of measurement tools with insufficient precision. The ILS findings highlight an urgent need for procedural standardization and better hardware characterization in this rapidly growing field.

Zwitterionic Polymer Ionogel Electrolytes Supported by Coulombic Cross-Links: Impacts of Alkali Metal Cation Identity.

Zwitterionic (ZI) polymers enable the formation of noncovalent cross-links within ionic liquid electrolytes (ILEs) to create nonflammable, mechanically robust, and highly conductive ionogel electrolytes. In this study, ZI homopolymer poly(2-methacryloyloxyethyl phosphorylcholine) [poly(MPC)] scaffolds are synthesized in situ within lithium and/or sodium salt-based ILEs to construct a series of ionogels that contain between 3 and 15 wt % poly(MPC). Room-temperature ionic conductivity values of these ionogels are found to vary between approximately 1.3 and 2.2 mS cm-1. For sodium only and 1:1 lithium/sodium equimolar mixed salt ionogels containing 6 wt % poly(MPC), the ionic conductivity is found to improve by 14% compared to the neat ILE due to the presence of the ZI scaffold. Moreover, comparing the elastic modulus values of lithium- versus sodium-containing ionogels revealed a difference of up to 1 order of magnitude [10.6 vs 111 kPa, respectively, for 3 wt % poly(MPC)]. Molecular dynamics simulations of ionogel precursor solutions corroborate the experimental results by demonstrating differences in the lithium/ZI monomer and sodium/ZI monomer cluster size distributions formed, which is hypothesized to influence the scaffold network cross-link density obtained upon photopolymerization. This work provides insights into why ZI polymer-supported ionogel properties that are relevant for the development of safer electrolytes for lithium-ion and sodium-ion batteries depend upon the chemical identity of the alkali metal cation.

Development of a biofidelic computational model of human pelvis for predicting biomechanical responses and pelvic fractures.

BACKGROUND AND OBJECTIVE: The pelvis, a crucial structure for human locomotion, is susceptible to injuries resulting in significant morbidity and disability. This study aims to introduce and validate a biofidelic computational pelvis model, enhancing our understanding of pelvis injury mechanisms under lateral loading conditions. METHODS: The Finite Element (FE) pelvic model, representing a mid-sized male, was developed with variable cortical thickness in pelvis bones. Material properties were determined through a synthesis of existing constitutive models, parametric studies, and multiple validations. Comprehensive validation included various tests, such as load-displacement assessments of sacroiliac joints, quasi-static and dynamic lateral compression on the acetabulum, dynamic side impacts on the acetabulum and iliac wing using defleshed pelvis, and lateral impacts by a rigid plate on the full body's pelvis region. RESULTS: Simulation results demonstrated a reasonable correlation between the pelvis model's overall response and cadaveric testing data. Predicted fracture patterns of the isolated pelvis exhibited fair agreement with experimental results. CONCLUSIONS: This study introduces a credible computational model, providing valuable biomechanical insights into the pelvis' response under diverse lateral loading conditions and fracture patterns. The work establishes a robust framework for developing and enhancing the biofidelity of pelvis FE models through a multi-level validation approach, stimulating further research in modeling, validation, and experimental studies related to pelvic injuries. The findings are expected to offer critical perspectives for predicting, preventing, and mitigating pelvic injuries from vehicular accidents, contributing to advancements in clinical research on medical treatments for pelvic fractures.

Zwitterionic Materials for Enhanced Battery Electrolytes.

Zwitterions (ZIs), which are molecules bearing an equal number of positive and negative charges and typically possessing large dipole moments, can play an important role in improving the characteristics of a wide variety of novel battery electrolytes. Significant Coulombic interactions among ZI charged groups and any mobile ions present can lead to several beneficial phenomena within electrolytes, such as increased salt dissociation, the formation of ordered pathways for ion transport, and enhanced mechanical robustness. In some cases, ZI additives can also boost electrochemical stability at the electrolyte/electrode interface and enable longer battery cycling. Here, a brief summary of selected key historical and recent advances in the use of ZI materials to enrich the performance of three distinct classes of battery electrolytes is presented. These include: ionic liquid-based, conventional solvent-based, and solid matrix-based (non-ceramic) electrolytes. Exploring a greater chemical diversity of ZI types and electrolyte pairings will likely lead to more discoveries that can empower next-generation battery designs in the years to come.

Investigating the Effect of Brain Size on Deformation Magnitude Using Subject-Specific Finite Element Models.

Abstract In the last decade, computational models of the brain have become the gold standard tool for investigating traumatic brain injury (TBI) mechanisms and developing novel protective equipment and other safety countermeasures. However, most studies utilizing finite element (FE) models of the brain have been conducted using models developed to represent the average neuroanatomy of a target demographic, such as the 50th percentile male. Although this is an efficient strategy, it neglects normal anatomical variations present within the population and their contributions on the brain's deformation response. As a result, the contributions of structural characteristics of the brain, such as brain volume, on brain deformation are not well understood. The objective of this study was to develop a set of statistical regression models relating measures of the size and shape of the brain to the resulting brain deformation. This was performed using a database of 125 subject-specific models, simulated under six independent head kinematic boundary conditions, spanning a range of impact modes (frontal, oblique, side), severity (non-injurious and injurious), and environments (volunteer, automotive, and American football). Two statistical regression techniques were utilized. First, simple linear regression (SLR) models were trained to relate intracranial volume (ICV) and the 95th percentile of maximum principal strain (MPS-95) for each of the impact cases. Second, a partial least squares regression model was constructed to predict MPS-95 based on the affine transformation parameters from each subject, representing the size and shape of their brain, considering the six impact conditions collectively. Both techniques indicated a strong linear relationship between ICV and MPS-95, with MPS-95 varying by approximately 5% between the smallest and largest brains. This difference represented up to 40% of the mean strain across all subjects. This study represents a comprehensive assessment of the relationships between brain anatomy and deformation, which is crucial for the development of personalized protective equipment, identifying individuals at higher risk of injury, and using computational models to aid clinical diagnostics of TBI.

A quantitative thermodynamic metric for identifying deep eutectic solvents.

Deep eutectic solvents (DESs) are a rapidly expanding class of liquid phase mixtures that offer many useful features. However, there currently exists no widely accepted criterion to identify whether or not a particular mixture is, in fact, a DES. This study introduces a quantitative metric based on the molar excess Gibbs energy of a eutectic mixture and proposes a threshold value in order to classify a eutectic system as a DES.

Understanding Lithium Dendrite Suppression by Hybrid Composite Separators: Indentation Measurements Informed by Operando X-ray Computed Tomography.

This study investigates the significance of the mechanics of hybrid particle-polymer separators in the stabilization of lithium metal interfaces by probing these properties in realistic conditions informed by X-ray microcomputed tomography (micro-CT). Elastic properties and viscoelastic behavior of inorganic microparticle-filled poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) films are characterized using a nanoindentation experiment whose displacement simulates the interfacial response seen in operando micro-CT. It is determined that the dominating mechanical behavior in this hybrid separator relevant to lithium metal cell conditions is comprised of viscoelasticity. Consistent with this finding, along with correlations across other physicochemical properties, a mechanism describing the improvement of lithium metal cycling performance according to inorganic filler type and content is proposed.

Brain architecture-based vulnerability to traumatic injury.

The white matter tracts forming the intricate wiring of the brain are subject-specific; this heterogeneity can complicate studies of brain function and disease. Here we collapse tractography data from the Human Connectome Project (HCP) into structural connectivity (SC) matrices and identify groups of similarly wired brains from both sexes. To characterize the significance of these architectural groupings, we examined how similarly wired brains led to distinct groupings of neural activity dynamics estimated with Kuramoto oscillator models (KMs). We then lesioned our networks to simulate traumatic brain injury (TBI) and finally we tested whether these distinct architecture groups' dynamics exhibited differing responses to simulated TBI. At each of these levels we found that brain structure, simulated dynamics, and injury susceptibility were all related to brain grouping. We found four primary brain architecture groupings (two male and two female), with similar architectures appearing across both sexes. Among these groupings of brain structure, two architecture types were significantly more vulnerable than the remaining two architecture types to lesions. These groups suggest that mesoscale brain architecture types exist, and these architectural differences may contribute to differential risks to TBI and clinical outcomes across the population.

Generating Human Arm Kinematics Using Reinforcement Learning to Train Active Muscle Behavior in Automotive Research.

Computational human body models (HBMs) are important tools for predicting human biomechanical responses under automotive crash environments. In many scenarios, the prediction of the occupant response will be improved by incorporating active muscle control into the HBMs to generate biofidelic kinematics during different vehicle maneuvers. In this study, we have proposed an approach to develop an active muscle controller based on reinforcement learning (RL). The RL muscle activation control (RL-MAC) approach is a shift from using traditional closed-loop feedback controllers, which can mimic accurate active muscle behavior under a limited range of loading conditions for which the controller has been tuned. Conversely, the RL-MAC uses an iterative training approach to generate active muscle forces for desired joint motion and is analogous to how a child develops gross motor skills. In this study, the ability of a deep deterministic policy gradient (DDPG) RL controller to generate accurate human kinematics is demonstrated using a multibody model of the human arm. The arm model was trained to perform goal-directed elbow rotation by activating the responsible muscles and investigated using two recruitment schemes: as independent muscles or as antagonistic muscle groups. Simulations with the trained controller show that the arm can move to the target position in the presence or absence of externally applied loads. The RL-MAC trained under constant external loads was able to maintain the desired elbow joint angle under a simplified automotive impact scenario, implying the robustness of the motor control approach.

Concussion Prone Scenarios: A Multi-Dimensional Exploration in Impact Directions, Brain Morphology, and Network Architectures Using Computational Models.

While individual susceptibility to traumatic brain injury (TBI) has been speculated, past work does not provide an analysis considering how physical features of an individual's brain (e.g., brain size, shape), impact direction, and brain network features can holistically contribute to the risk of suffering a TBI from an impact. This work investigated each of these features simultaneously using computational modeling and analyses of simulated functional connectivity. Unlike the past studies that assess the severity of TBI based on the quantification of brain tissue damage (e.g., principal strain), we approached the brain as a complex network in which neuronal oscillations orchestrate to produce normal brain function (estimated by functional connectivity) and, to this end, both the anatomical damage location and its topological characteristics within the brain network contribute to the severity of brain function disruption and injury. To represent the variations in the population, we analyzed a publicly available database of brain imaging data and selected five distinct network architectures, seven different brain sizes, and three uniaxial head rotational conditions to study the consequences of 74 virtual impact scenarios. Results show impact direction produces the most significant change in connections across brain areas (structural connectome) and the functional coupling of activity across these brain areas (functional connectivity). Axial rotations were more injurious than those with sagittal and coronal rotations when the head kinematics were the same for each condition. When the impact direction was held constant, brain network architecture showed a significantly different vulnerability across axial and sagittal, but not coronal rotations. As expected, brain size significantly affected the expected change in structural and functional connectivity after impact. Together, these results provided groupings of predicted vulnerability to impact-a subgroup of male brain architectures exposed to axial impacts were most vulnerable, while a subgroup of female brain architectures was the most tolerant to the sagittal impacts studied. These findings lay essential groundwork for subject-specific analyses of concussion and provide invaluable guidance for designing personalized protection equipment.

Quantifying the Effect of Sex and Neuroanatomical Biomechanical Features on Brain Deformation Response in Finite Element Brain Models.

Recent automotive epidemiology studies have concluded that females have significantly higher odds of sustaining a moderate brain injury or concussion than males in a frontal crash after controlling for multiple crash and occupant variables. Differences in neuroanatomical features, such as intracranial volume (ICV), have been shown between male and female subjects, but how these sex-specific neuroanatomical differences affect brain deformation is unknown. This study used subject-specific finite element brain models, generated via registration-based morphing using both male and female magnetic resonance imaging scans, to investigate sex differences of a variety of neuroanatomical features and their effect on brain deformation; additionally, this study aimed to determine the relative importance of these neuroanatomical features and sex on brain deformation metrics for a single automotive loading environment. Based on the Bayesian linear mixed models, sex had a significant effect on ICV, white matter volume and gray matter volume, as well as a section of cortical gray matter regions' thicknesses and volumes; however, after these neuroanatomical features were accounted for in the statistical model, sex was not a significant factor in predicting brain deformation. ICV had the highest relative effect on the brain deformation metrics assessed. Therefore, ICV should be considered when investigating both brain injury biomechanics and injury risk.

Helmet Technology, Head Impact Exposure, and Cortical Thinning Following a Season of High School Football.

The purpose of this study was to compare the effects of wearing older, lower-ranked football helmets (LRank) to wearing newer, higher-ranked football helmets (HRank) on pre- to post-season changes in cortical thickness in response to repetitive head impacts and assess whether changes in cortical thickness are associated with head impact exposure for either helmet type. 105 male high-school athletes (NHRank = 52, NLRank = 53) wore accelerometers affixed behind the left mastoid during all practices and games for one regular season of American football to monitor head impact exposure. Pre- and post-season magnetic resonance imaging (MRI) were completed to assess longitudinal changes in cortical thickness. Significant reductions in cortical thickness (i.e., cortical thinning) were observed pre- to post-season for each group, but these longitudinal alterations were not significantly different between the LRank and HRank groups. Further, significant group-by-head impact exposure interactions were observed when predicting changes in cortical thickness. Specifically, a greater frequency of high magnitude head impacts during the football season resulted in greater cortical thinning for the LRank group, but not for the HRank group. These data provide preliminary in vivo evidence that HRank helmets may provide a buffer between the specific effect of high magnitude head impacts on regional thinning by dissipating forces more evenly throughout the cortex. However, future research with larger sample sizes, increased longitudinal measures and additional helmet technologies is warranted to both expand upon and further validate the present study findings.

Use of Brain Biomechanical Models for Monitoring Impact Exposure in Contact Sports.

Head acceleration measurement sensors are now widely deployed in the field to monitor head kinematic exposure in contact sports. The wealth of impact kinematics data provides valuable, yet challenging, opportunities to study the biomechanical basis of mild traumatic brain injury (mTBI) and subconcussive kinematic exposure. Head impact kinematics are translated into brain mechanical responses through physics-based computational simulations using validated brain models to study the mechanisms of injury. First, this article reviews representative legacy and contemporary brain biomechanical models primarily used for blunt impact simulation. Then, it summarizes perspectives regarding the development and validation of these models, and discusses how simulation results can be interpreted to facilitate injury risk assessment and head acceleration exposure monitoring in the context of contact sports. Recommendations and consensus statements are presented on the use of validated brain models in conjunction with kinematic sensor data to understand the biomechanics of mTBI and subconcussion. Mainly, there is general consensus that validated brain models have strong potential to improve injury prediction and interpretation of subconcussive kinematic exposure over global head kinematics alone. Nevertheless, a major roadblock to this capability is the lack of sufficient data encompassing different sports, sex, age and other factors. The authors recommend further integration of sensor data and simulations with modern data science techniques to generate large datasets of exposures and predicted brain responses along with associated clinical findings. These efforts are anticipated to help better understand the biomechanical basis of mTBI and improve the effectiveness in monitoring kinematic exposure in contact sports for risk and injury mitigation purposes.

An interdisciplinary computational model for predicting traumatic brain injury: Linking biomechanics and functional neural networks.

The brain is a complex network consisting of neuron cell bodies in the gray matter and their axonal projections, forming the white matter tracts. These neurons are supported by an equally complex vascular network as well as glial cells. Traumatic brain injury (TBI) can lead to the disruption of the structural and functional brain networks due to disruption of both neuronal cell bodies in the gray matter as well as their projections and supporting cells. To explore how an impact can alter the function of brain networks, we integrated a finite element (FE) brain mechanics model with linked models of brain dynamics (Kuramoto oscillator) and vascular perfusion (Balloon-Windkessel) in this study. We used empirical resting-state functional magnetic resonance imaging (MRI) data to optimize the fit of our brain dynamics and perfusion models to clinical data. Results from the FE model were used to mimic injury in these optimized brain dynamics models: injury to the nodes (gray matter) led to a decrease in the nodal oscillation frequency, while damage to the edges (axonal connections/white matter) progressively decreased coupling among connected nodes. A total of 53 cases, including 33 non-injurious and 20 concussive head impacts experienced by professional American football players were simulated using this integrated model. We examined the correlation of injury outcomes with global measures of structural connectivity, neural dynamics, and functional connectivity of the brain networks when using different lesion methods. Results show that injurious head impacts cause significant alterations in global network topology regardless of lesion methods. Changes between the disrupted and healthy functional connectivity (measured by Pearson correlation) consistently correlated well with injury outcomes (AUC≥0.75), although the predictive performance is not significantly different (p>0.05) to that of traditional kinematic measures (angular acceleration). Intriguingly, our lesion model for gray matter damage predicted increases in global efficiency and clustering coefficient with increases in injury risk, while disrupting axonal connections led to lower network efficiency and clustering. When both injury mechanisms were combined into a single injury prediction model, the injury prediction performance depended on the thresholds used to determine neurodegeneration and mechanical tolerance for axonal injury. Together, these results point towards complex effects of mechanical trauma to the brain and provide a new framework for understanding brain injury at a causal mechanistic level and developing more effective diagnostic methods and therapeutic interventions.

Integrating Human and Nonhuman Primate Data to Estimate Human Tolerances for Traumatic Brain Injury.

Traumatic brain injury (TBI) contributes to a significant portion of the injuries resulting from motor vehicle crashes, falls, and sports collisions. The development of advanced countermeasures to mitigate these injuries requires a complete understanding of the tolerance of the human brain to injury. In this study, we developed a new method to establish human injury tolerance levels using an integrated database of reconstructed football impacts, subinjurious human volunteer data, and nonhuman primate data. The human tolerance levels were analyzed using tissue-level metrics determined using harmonized species-specific finite element (FE) brain models. Kinematics-based metrics involving complete characterization of angular motion (e.g., diffuse axonal multi-axial general evaluation (DAMAGE)) showed better power of predicting tissue-level deformation in a variety of impact conditions and were subsequently used to characterize injury tolerance. The proposed human brain tolerances for mild and severe TBI were estimated and presented in the form of injury risk curves based on selected tissue-level and kinematics-based injury metrics. The application of the estimated injury tolerances was finally demonstrated using real-world automotive crash data.

Evaluation of the Effectiveness of Newer Helmet Designs with Emergent Shell and Padding Technologies Versus Older Helmet Models for Preserving White Matter Following a Season of High School Football.

We aimed to objectively compare the effects of wearing newer, higher-ranked football helmets (HRank) vs. wearing older, lower-ranked helmets (LRank) on pre- to post-season alterations to neuroimaging-derived metrics of athletes' white matter. Fifty-four high-school athletes wore an HRank helmet, and 62 athletes wore an LRank helmet during their competitive football season and completed pre- and post-season diffusion tensor imaging (DTI). Longitudinal within- and between-group DTI metrics [fractional anisotropy (FA) and mean/axial/radial diffusivity (MD, AD, RD)] were analyzed using tract-based spatial statistics. The LRank helmet group exhibited significant pre- to post-season reductions in MD, AD, and RD, the HRank helmet group displayed significant pre- to post-season increases in FA, and both groups showed significant pre- to post-season increases in AD (p's < .05 [corrected]). Between-group analyses revealed the pre- to post-season increase in AD was significantly less for athletes wearing HRank compared to LRank (p < .05 [corrected]). These data provide in vivo evidence that wearing an HRank helmet may be efficacious for preserving white matter from head impact exposure during high school football. Future prospective longitudinal investigations with complimentary imaging and behavioral outcomes are warranted to corroborate these initial in vivo findings.