Injury Metrics Research Articles

Effects of Standing, Upright Seated, vs. Reclined Seated Postures on Astronaut Injury Biomechanics for Lunar Landings.

Astronauts may pilot a future lunar lander in a standing or upright/reclined seated posture. This study compared kinematics and injury risk for the upright/reclined (30°; 60°) seated vs. standing postures for lunar launch/landing using human body modeling across 30 simulations. While head metrics for standing and upright seated postures were comparable to 30cm height jumps, those of reclined postures were closer to 60cm height jumps. Head linear acceleration for 60° reclined posture in the 5g/10ms pulse exceeded NASA's tolerance (10.1g; tolerance: 10g). Lower extremity metrics exceeding NASA's tolerance in the standing posture (revised tibia index: 0.36-0.53; tolerance: 0.43) were lowered in seated postures (0.00-0.04). Head displacement was higher in standing vs. seated (9.0cm vs. 2.4cm forward, 7.0cm vs. 1.3cm backward, 2.1cm vs. 1.2cm upward, 7.3cm vs. 0.8cm downward, 2.4cm vs. 3.2cm lateral). Higher arm movement was seen with seated vs. standing (40cm vs. 25cm forward, 60cm vs. 15cm upward, 30cm vs. 20cm downward). Pulse-nature contributed more than 40% to the injury metrics for seated postures compared to 80% in the standing posture. Seat recline angle contributed about 22% to the injury metrics in the seated posture. This study established a computational methodology to simulate the different postures of an astronaut for lunar landings and generated baseline injury risk and body kinematics data.

Approach for machine learning based design of experiments for occupant simulation

The complexity of crash scenarios in the context of vehicle safety is steadily increasing. This is especially the case on the way to mixed traffic challenges with non-automated and automated driving vehicles. The number of simulations required to design a robust restraint system is thus also increasing. The vast range of possible scenarios here is causing a huge parameter space. Simultaneously biofidelic simulation models are resulting in very high computational costs and therefore the number of simulations should be limited to a feasible operational range. In this study, a machine-learning based design of experiments algorithm is developed, which specifically addresses the issues when designing a safety system with a limited number of simulation samples taking diversity of the occupant and accident scenario into account. In contrast to an optimization task, where the aim is to meet a target function, our job has been to find the critical load case combinations to make sure that these are addressed and not missed. A combination of a space-filling approach and a metamodel has been established to find the critical scenarios in order to improve the system for those cases. It focuses specifically on the areas that are difficult to predict by the metamodel. The developed method was applied to iteratively generate a simulation matrix of a total of 208 simulations with a generic interior model and a detailed FE human body model. Kinematic and strain-based injury metrics were used as simulation output. These were used to train the metamodels after each iteration and derive the simulation matrix for the next iteration. In this paper we present a method that allows the training of a robust metamodel for the prediction of injury criteria, considering both varying load cases and varying restraint system parameters for individual anthropometries and seating postures. Based on that, restraint systems or metamodels can be optimized to achieve the best overall performance for a huge variety of possible scenarios with a specific focus on critical scenarios.

Injury Criteria for Vehicle Safety Assessment: A Review with a Focus Using Human Body Models

This paper aims at providing an overview of the most used injury criteria (IC) and injury metrics for the study of the passive safety of vehicles. In particular, the work is focused on the injury criteria that can be adopted when finite element simulations and Human Body Models (HBMs) are used. The HBMs will result in a fundamental instrument for studying the occupant’s safety in Autonomous Vehicles (AVs) since they allow the analysis of a larger variety of configurations compared to the limitations related to the traditional experimental dummies. In this work, the most relevant IC are reported and classified based on the body segments. In particular, the head, the torso, the spine, the internal organs, and the lower limbs are here considered. The applicability of the injury metrics to the analyses carried out with the HBMs is also discussed. The paper offers a global overview of the injury assessment useful to choose the injury criteria for the study of vehicle passive safety. To this aim, tables of the presented criteria are also reported to provide the available metrics for the considered body damage.

Finding the Spatial Co-Variation of Brain Deformation With Principal Component Analysis.

Strain and strain rate are effective traumatic brain injury metrics. In finite element (FE) head model, thousands of elements were used to represent the spatial distribution of these metrics. Owing that these metrics are resulted from brain inertia, their spatial distribution can be represented in more concise pattern. Since head kinematic features and brain deformation vary largely across head impact types (Zhan et al., 2021), we applied principal component analysis (PCA) to find the spatial co-variation of injury metrics (maximum principal strain (MPS), MPS rate (MPSR) and MPS × MPSR) in four impact types: simulation, football, mixed martial arts and car crashes, and used the PCA to find patterns in these metrics and improve the machine learning head model (MLHM). We applied PCA to decompose the injury metrics for all impacts in each impact type, and investigate the spatial co-variation using the first principal component (PC1). Furthermore, we developed a MLHM to predict PC1 and then inverse-transform to predict for all brain elements. The accuracy, the model complexity and the size of training dataset of PCA-MLHM are compared with previous MLHM (Zhan et al., 2021). PC1 explained variance on the datasets. Based on PC1 coefficients, the corpus callosum and midbrain exhibit high variance on all datasets. Finally, the PCA-MLHM reduced model parameters by 74% with a similar MPS estimation accuracy. The brain injury metric in a dataset can be decomposed into mean components and PC1 with high explained variance. The spatial co-variation analysis enables better interpretation of the patterns in brain injury metrics. It also improves the efficiency of MLHM.

Oblique impact responses of Hybrid III and a new headform with more biofidelic coefficient of friction and moments of inertia.

New oblique impact methods for evaluating head injury mitigation effects of helmets are emerging, which mandate measuring both translational and rotational kinematics of the headform. These methods need headforms with biofidelic mass, moments of inertia (MoIs), and coefficient of friction (CoF). To fulfill this need, working group 11 of the European standardization head protection committee (CEN/TC158) has been working on the development of a new headform with realistic MoIs and CoF, based on recent biomechanics research on the human head. In this study, we used a version of this headform (Cellbond) to test a motorcycle helmet under the oblique impact at 8 m/s at five different locations. We also used the Hybrid III headform, which is commonly used in the helmet oblique impact. We tested whether there is a difference between the predictions of the headforms in terms of injury metrics based on head kinematics, including peak translational and rotational acceleration, peak rotational velocity, and BrIC (brain injury criterion). We also used the Imperial College finite element model of the human head to predict the strain and strain rate across the brain and tested whether there is a difference between the headforms in terms of the predicted strain and strain rate. We found that the Cellbond headform produced similar or higher peak translational accelerations depending on the impact location (−3.2% in the front-side impact to 24.3% in the rear impact). The Cellbond headform, however, produced significantly lower peak rotational acceleration (−41.8% in a rear impact to −62.7% in a side impact), peak rotational velocity (−29.5% in a side impact to −47.6% in a rear impact), and BrIC (−29% in a rear-side impact to −45.3% in a rear impact). The 90th percentile values of the maximum brain strain and strain rate were also significantly lower using this headform. Our results suggest that MoIs and CoF have significant effects on headform rotational kinematics, and consequently brain deformation, during the helmeted oblique impact. Future helmet standards and rating methods should use headforms with realistic MoIs and CoF (e.g., the Cellbond headform) to ensure more accurate representation of the head in laboratory impact tests.

Sensitivity Analysis for Multidirectional Spaceflight Loading and Muscle Deconditioning on Astronaut Response.

A sensitivity analysis for loading conditions and muscle deconditioning on astronaut response for spaceflight transient accelerations was carried out using a mid-size male human body model with active musculature. The model was validated in spaceflight-relevant 2.5-15g loading magnitudes in seven volunteer tests, showing good biofidelity (CORA: 0.69). Sensitivity analysis was carried out in simulations varying pulse magnitude (5, 10, and 15g), rise time (32.5 and 120ms), and direction (10 directions: frontal, rear, vertical, lateral, and their combination) along with muscle size change (± 15% change) and responsiveness (pre-braced, relaxed, vs. delayed response) changes across 600 simulations. Injury metrics were most sensitive to the loading direction (50%, partial-R2) and least sensitive to muscle size changes (0.2%). The pulse magnitude also had significant effect on the injury metrics (16%), whereas muscle responsiveness (3%) and pulse rise time (2%) had only slight effects. Frontal and upward loading directions were the worst for neck, spine, and lower extremity injury metrics, whereas rear and downward directions were the worst for head injury metrics. Higher magnitude pulses and pre-bracing also increased the injury risk.

Assessment of brain injury characterization and influence of modeling approaches

In this study, using computational biomechanics models, we investigated influence of the skull-brain interface modeling approach and the material property of cerebrum on the kinetic, kinematic and injury outputs. Live animal head impact tests of different severities were reconstructed in finite element simulations and DAI and ASDH injury results were compared. We used the head/brain models of Total HUman Model for Safety (THUMS) and Global Human Body Models Consortium (GHBMC), which had been validated under several loading conditions. Four modeling approaches of the skull-brain interface in the head/brain models were evaluated. They were the original models from THUMS and GHBMC, the THUMS model with skull-brain interface changed to sliding contact, and the THUMS model with increased shear modulus of cerebrum, respectively. The results have shown that the definition of skull-brain interface would significantly influence the magnitude and distribution of the load transmitted to the brain. With sliding brain-skull interface, the brain had lower maximum principal stress compared to that with strong connected interface, while the maximum principal strain slightly increased. In addition, greater shear modulus resulted in slightly higher the maximum principal stress and significantly lower the maximum principal strain. This study has revealed that using models with different modeling approaches, the same value of injury metric may correspond to different injury severity.

Simulated Astronaut Kinematics and Injury Risk for Piloted Lunar Landings and Launches While Standing.

During future lunar missions, astronauts may be required to pilot vehicles while standing, and the associated kinematic and injury response is not well understood. In this study, we used human body modeling to predict unsuited astronaut kinematics and injury risk for piloted lunar launches and landings in the standing posture. Three pulses (2-5 g; 10-150 ms rise times) were applied in 10 directions (vertical; ± 10-degree offsets) for a total of 30 simulations. Across all simulations, motion envelopes were computed to quantify displacement of the astronaut's head (max 9.0 cm forward, 7.0 cm backward, 2.1 cm upward, 7.3 cm downward, 2.4 cm lateral) and arms (max 25 cm forward, 35 cm backward, 15 cm upward, 20 cm downward, 20 cm lateral). All head, neck, lumbar, and lower extremity injury metrics were within NASA's tolerance limits, except tibia compression forces (0-1543 N upper tibia; 0-1482 N lower tibia; tolerance-1350 N) and revised tibia index (0.04-0.58 upper tibia; 0.03-0.48 lower tibia; tolerance-0.43) for the 2.7 g/150 ms pulse. Pulse magnitude and duration contributed over 80% to the injury metric values, whereas loading direction contributed less than 3%. Overall, these simulations suggest piloting a lunar lander vehicle in the standing posture presents a tibia injury risk which is potentially outside NASA's acceptance limits and warrants further investigation.

A Review of Head Injury Metrics Used in Automotive Safety and Sports Protective Equipment.

Despite advances in the understanding of human tolerances to brain injury, injury metrics used in automotive safety and protective equipment standards have changed little since they were first implemented nearly a half-century ago. Although numerous metrics have been proposed as improvements over the ones currently used, evaluating the predictive capability of these metrics is challenging. The purpose of this review is to summarize existing head injury metrics that have been proposed for both severe head injuries, such as skull fractures and traumatic brain injuries (TBI), and mild traumatic brain injuries (mTBI) including concussions. Metrics have been developed based on head kinematics or intracranial parameters such as brain tissue stress and strain. Kinematic metrics are either based on translational motion, rotational motion, or a combination of the two. Tissue-based metrics are based on finite element model simulations or in vitro experiments. This review concludes with a discussion of the limitations of current metrics and how improvements can be made in the future.

Age-based differences in the disability of spine injuries in pediatric and adult motor vehicle crash occupants

Objective The objective was to develop a disability-based metric for quantifying disability rates as a result of motor vehicle crash (MVC) spine injuries and compare functional outcomes between pediatric and adult subgroups. Methods Disability rate was quantified using Functional Independence Measure (FIM) scores within the National Trauma Data Bank—Research Data System for the top 95% most frequent Abbreviated Injury Scale (AIS) 3 spine injuries (14 unique injuries). Pediatric (7–18 years), young adult (19–45 years), middle-aged adult (46–65 years), and older adult (66+ years) MVC occupants with FIM scores available and at least one of the 14 spine injuries were included. FIM scores of 1 or 2 at time of discharge were used to define disability and correspond to full functional or modified dependence in self-feeding, locomotion, and/or verbal expression. Disability rate was evaluated on a per injury basis for each AIS 3 spine injury and calculated as the proportion of cases associated with disability (i.e. FIM of 1 or 2) out of the total cases of that particular injury. Disability rates were calculated with and without the exclusion of cases with severe co-injuries (AIS 4+) to minimize bias from additional non-spinal injuries that could have contributed to disability. Associations between adjusted disability rates and existing mortality rates were investigated. Results Locomotion impairment alone was the most frequent disability type for the top 14 AIS 3 spine injuries (7 cervical, 4 thoracic, and 3 lumbar) across all age groups and spine regions. Adjusted and unadjusted disability rates ranged from 0-69%. Adjusted disability rates increased with age: 14.8 ± 10% (mean ± SD) in pediatrics to 16.2 ± 6.6% (young adults), 29.2 ± 10.9% (middle-aged adults), and 45.0 ± 12.2% (older adults). Among all adult populations, adjusted mortality and disability rates were positively correlated (R 2>0.24), with disability rates consistently greater than corresponding mortality rates. Conclusions Older adults had significantly greater disability rates associated with MVC spine injuries across all spinal regions. MVC disability rates for pediatrics were considerably lower. Overall, rates of mortality were significantly lower than rates of disability. The adjusted disability rates developed can supplement existing injury metrics by accounting for age- and location-specific functional implications of MVC spine injuries.

Instantaneous Brain Strain Estimation for Automotive Head Impacts via Deep Learning.

Efficient brain strain estimation is critical for routine application of a head injury model. Lately, a convolutional neural network (CNN) has been successfully developed to estimate spatially detailed brain strains instantly and accurately in contact sports. Here, we extend its application to automotive head impacts, where impact profiles are typically more complex with longer durations. Head impact kinematics (N=458) from two public databases were used to generate augmented impacts (N=2694). They were simulated using the anisotropic Worcester Head Injury Model (WHIM) V1.0, which provided baseline elementwise peak maximum principal strain (MPS). For each augmented impact, rotational velocity (vrot) and the corresponding rotational acceleration (arot) profiles were concatenated as static images to serve as CNN input. Three training strategies were evaluated: 1) "baseline", using random initial weights; 2) "transfer learning", using weight transfer from a previous CNN model trained on head impacts drawn from contact sports; and 3) "combined training", combining previous training data from contact sports (N=5661) for training. The combined training achieved the best performances. For peak MPS, the CNN achieved a coefficient of determination (R2) of 0.932 and root mean squared error (RMSE) of 0.031 for the real-world testing dataset. It also achieved a success rate of 60.5% and 94.8% for elementwise MPS, where the linear regression slope, k, and correlation coefficient, r, between estimated and simulated MPS did not deviate from 1.0 (when identical) by more than 0.1 and 0.2, respectively. Cumulative strain damage measure (CSDM) from the CNN estimation was also highly accurate compared to those from direct simulation across a range of thresholds (R2 of 0.899-0.943 with RMSE of 0.054-0.069). Finally, the CNN achieved an average k and r of 0.98±0.12 and 0.90±0.07, respectively, for six reconstructed car crash impacts drawn from two other sources independent of the training dataset. Importantly, the CNN is able to efficiently estimate elementwise MPS with sufficient accuracy while conventional kinematic injury metrics cannot. Therefore, the CNN has the potential to supersede current kinematic injury metrics that can only approximate a global peak MPS or CSDM. The CNN technique developed here may offer enhanced utility in the design and development of head protective countermeasures, including in the automotive industry. This is the first study aimed at instantly estimating spatially detailed brain strains for automotive head impacts, which employs >8.8 thousand impact simulations generated from ~1.5 years of nonstop computations on a high-performance computing platform.

Interactions between rearward-facing child restraint systems and the front row seatback in frontal impact sled tests

Objective Some child restraint system (CRS) manufacturers specify a minimum distance between the CRS and the seatback, whereas others require that the CRS may contact the seatback but cannot be “braced”; however, few studies have investigated these interactions. Therefore, the aim was to investigate the interactions between the front row seat and rearward-facing CRS models with and without a support leg during frontal crashes. Methods Sled tests using the FMVSS 213 frontal crash pulse were performed with the Q1.5 and Q3 anthropomorphic test devices (ATDs) seated in rearward-facing infant and convertible CRS models, respectively. A front row vehicle seat was in front of the test bench in three track positions: brace, touch and gap. For the touch condition, the front row seat was translated aftward until the seatback contacted the CRS. For the brace condition, the front row seat was translated 20 mm aftward. For the gap condition, the front row seat was translated 50 mm forward. Each condition was tested with and without the support leg of the CRS. Results The tests with a support leg were associated with significantly (p = 0.007) lower resultant linear head acceleration 3 ms clip compared to the tests without a support leg, but the reduction of head injury criterion 15 ms (HIC15) was not significant (p = 0.057). The Q1.5 ATD in the rearward-facing infant CRS with a support leg had the lowest injury metrics for the touch and gap conditions, whereas the Q3 in the rearward-facing convertible CRS had the lowest head injury metrics for the brace condition. Conclusions The use of a support leg provided a clear benefit in terms of reducing head injury metrics for the Q1.5 in the rearward-facing infant CRS, especially for the touch and gap conditions. The rearward-facing convertible CRS in the current study appears to benefit from being braced against the front row seat. However, further tests are required to allow further statistical comparisons and determine if these preliminary findings extend to other rearward-facing CRS models.

MP05-12 DEPENDENT ANTIBIOTIC AND OXALATE INTERACTIONS IMPACT ATHEROSCLEROSIS AND CALCIUM OXALATE STONES IN THE KIDNEYS

You have accessJournal of UrologyCME1 May 2022MP05-12 DEPENDENT ANTIBIOTIC AND OXALATE INTERACTIONS IMPACT ATHEROSCLEROSIS AND CALCIUM OXALATE STONES IN THE KIDNEYS Jose Agudelo, Lucas Osborn, Beckey DeLucia, Mark Brown, Ava Adler, Jan Claesen, Jane Nguyen, and Aaron Miller Jose AgudeloJose Agudelo More articles by this author , Lucas OsbornLucas Osborn More articles by this author , Beckey DeLuciaBeckey DeLucia More articles by this author , Mark BrownMark Brown More articles by this author , Ava AdlerAva Adler More articles by this author , Jan ClaesenJan Claesen More articles by this author , Jane NguyenJane Nguyen More articles by this author , and Aaron MillerAaron Miller More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002522.12AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Previous studies have shown a positive relationship between cardiometabolic syndrome and increased risk of urinary stone disease (USD). However, the underlying mechanisms that leads to these co-morbidities are unknown. In the current study, we aimed to determine microbiome and diet dependent effects on an atherosclerosis and calcium oxalate stone formation in a mouse model. METHODS: Sixty-six female ApoE null mice were randomly assigned to either a basal diet or 3% oxalate diet. Half of the animals received antibiotic water and the other half sterile water. Mice consumed diets ad libitum for 12-weeks. At the end of the trial, fecal pellets were collected for 16S rRNA quantification. Mice were necropsied, and kidneys, aortas and liver were collected. Kidneys and aortas underwent polarized light quantification of oxalate content. Kidneys were also assessed for oxalate nephropathy after Von Kossa and Alizarin red staining and aortas were assessed for atherosclerosis plaque quantification. Livers were used for bile acid content assessment. Results were compared on a one-way and two-way ANOVA analyses. RESULTS: Bile acid production was significantly impacted by both oxalate and antibiotics. Renal histopathological analysis revealed that oxalate led to an increase in dilated tubules, oxalate nephropathy and tubular injury metrics, which was exacerbated with antibiotic use. Total atherosclerotic plaque lesions in the aortas and the presence of calcium oxalate in the plaque was significantly higher in the group that received dietary oxalate, which again was exacerbated by antibiotic use. CONCLUSIONS: Our results support the hypothesis that exogenous sources of oxalate may lead to both kidney stones and atherosclerosis through the formation of calcium oxalate plaques, which is exacerbated by a loss of function dysbiosis driven by long-term antibiotic use. Importantly, given the clear and significant formation of kidney stones, the APOE mice may serve as a more appropriate mouse model for kidney stones that what has previously been used. Source of Funding: Seed Funding Lerner Research Institute. Urology Care Foundation Research Scholar Award © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e72 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Jose Agudelo More articles by this author Lucas Osborn More articles by this author Beckey DeLucia More articles by this author Mark Brown More articles by this author Ava Adler More articles by this author Jan Claesen More articles by this author Jane Nguyen More articles by this author Aaron Miller More articles by this author Expand All Advertisement PDF DownloadLoading ...

Effect of different helmets against ground impact based on the in-depth reconstruction of electric two-wheeler accidents

Skull fracture and brain injury are frequent head injuries in electric two-wheeler (ETW) accidents, and the type of helmet and impact conditions affect the effectiveness of the helmet in protecting the rider’s head. The purpose of this study was to conduct in-depth reconstructions of rider’s head-to-ground impacts in ten ETW accidents by using a multi-body system combined with a finite element approach and to evaluate the effect of two typical full-face helmets (FFH) and one half-coverage helmet (HCH) through head accelerations and intracranial biomechanics injury metrics in ground impacts. The results showed that all three helmets reduced the risk of skull fracture in most cases, however, FFH performed better due to its wider protection area. In addition, three helmets showed varying degrees of overall reduction in measuring all indicators of brain injury. Although the effectiveness of the helmets on angular acceleration was largely influenced by the angle and location of impact, it was certain that wearing an FFH was more likely to reduce rotational head movements than an HCH, and that the FFH also offered the better advantage in reducing diffuse axonal injury (DAI) risk due to its better resistance to ejection in a crash.

The Protective Performance of Modern Motorcycle Helmets Under Oblique Impacts

Motorcyclists are at high risk of head injuries, including skull fractures, focal brain injuries, intracranial bleeding and diffuse brain injuries. New helmet technologies have been developed to mitigate head injuries in motorcycle collisions, but there is limited information on their performance under commonly occurring oblique impacts. We used an oblique impact method to assess the performance of seven modern motorcycle helmets at five impact locations. Four helmets were fitted with rotational management technologies: a low friction layer (MIPS), three-layer liner system (Flex) and dampers-connected liner system (ODS). Helmets were dropped onto a 45° anvil at 8 m/s at five locations. We determined peak translational and rotational accelerations (PTA and PRA), peak rotational velocity (PRV) and brain injury criteria (BrIC). In addition, we used a human head finite element model to predict strain distribution across the brain and in corpus callosum and sulci. We found that the impact location affected the injury metrics and brain strain, but this effect was not consistent. The rear impact produced lowest PTAs but highest PRAs. This impact produced highest strain in corpus callosum. The front impact produced the highest PRV and BrIC. The side impact produced the lowest PRV, BrIC and strain across the brain, sulci and corpus callosum. Among helmet technologies, MIPS reduced all injury metrics and brain strain compared with conventional helmets. Flex however was effective in reducing PRA only and ODS was not effective in reducing any injury metrics in comparison with conventional helmets. This study shows the importance of using different impact locations and injury metrics when assessing head protection effects of helmets. It also provides new data on the performance of modern motorcycle helmets. These results can help with improving helmet design and standard and rating test methods.

Return to Play and Player Performance After Foot Fracture in UEFA Soccer Players.

Background:There is a paucity of information on rate and time to return to play (RTP) in elite-level soccer players who have sustained foot fractures.Purpose:To (1) determine the rate and timing of RTP after foot fracture (eg, tarsal, metatarsal, or phalangeal), (2) investigate foot fracture reinjury incidence after RTP, and (3) evaluate performance after foot fracture as compared with matched, uninjured controls.Study Design:Descriptive epidemiology study.Methods:Athletes sustaining foot fractures were identified across the 5 major European soccer leagues (English Premier League, Bundesliga, La Liga, Ligue 1, and Serie A) between 2000 and 2016. Injured athletes were matched to controls (1:1) using demographic characteristics and performance metrics from 1 season before injury. The authors recorded RTP rate, reinjury incidence, player characteristics associated with RTP within 2 seasons of injury, player availability, field time, and performance metrics during the 4 seasons after injury.Results:A total of 192 elite soccer players sustaining a foot fracture were identified; 40 players (20.8%) underwent operative treatment. Athletes missed an average of 69.41 ± 59.43 days and 5.15 ± 23.28 games. In the 4 seasons after injury, 80% of players returned to play, with 72% returning to play within 1 season of injury. Nine players (5%) sustained a subsequent foot fracture. Athletes with a foot fracture demonstrated significantly longer league retention compared with uninjured controls (P < .001). Elite soccer players older than 30 years of age were less likely to RTP (odds ratio, 0.67; P = .002), whereas career experience, field position, and baseline performance showed no significant association with RTP rates. Injured athletes demonstrated similar performance to controls during the 4 years after injury, and there were no position-dependent differences on subgroup analysis. The players who underwent operative treatment had more assists per 90 minutes and more team points per game during the first season after injury compared with athletes treated nonoperatively.Conclusion:Foot fractures in elite soccer players resulted in moderate loss of play time (69.41 days). RTP rates were high at 80%, although players older than 30 years of age were less likely to RTP. On RTP, athletes who sustained a foot fracture maintained performance similar to preinjury levels and to uninjured controls.

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.

Assessment of brain injury biomechanics in soccer heading using finite element analysis

This study presents an in silico finite element (FE) model-based biomechanical analysis of brain injury metrics and associated risks of a soccer ball impact to the head for aware and unaware athletes, considering ball impact velocity and direction. The analysis presented herein implements a validated soccer ball and 50th percentile human head computational FE model for quantifying traumatic brain injury (TBI) metrics. The brain's mechanical properties are designated using a viscoelastic-viscoplastic constitutive material model for the white and gray matter within the human head FE model. FE results show a dynamic human head-soccer ball peak contact area of approximately seven times greater than those documented for helmet-to-helmet hits in American Football. Due to the deformable nature of the soccer ball, the impact dynamics are unique depending on the location and velocity of impact. TBI injury risks also depend on the location of impact and the impact velocity. Impacts to the rear (BrIC:0.48, HIC15:180.7), side (BrIC:0.52, HIC15:176.5), and front (BrIC:0.37, HIC15:129.0) are associated with the highest injury risks. Furthermore, the FE results indicate when an athlete is aware of an incoming ball, HIC15-based Abbreviated Injury Scale 1 (AIS 1) injury risks for the front, side, and rear impacts decrease from 10.5%, 18.5%, and 19.3%, respectively, to approximately 1% in front and side impacts and under 6% in a rear impact. Lastly, the unique contact area between the head and soccer ball produces pressure gradients in the ball that translate into distinguishable stress waves in the skull and the cerebral cortex. Statement of significanceMild traumatic brain injuries (mTBI) are a worrisome aspect of participation in most sports due to difficulties in their diagnosis in competitions and the potential of long-term neurological defects. These types of injuries are not well understood for athletes playing soccer, specifically pertaining to the risks of heading a soccer ball. Studies are warranted which investigate impacts in this game to improve current knowledge. Our computational study uses finite element modeling to investigate contact between a player's head and the soccer ball. The results of this study present potential injury mechanisms and risks caused by this contact interaction to contribute to the current understanding of brain injuries in soccer and the promotion of athlete safety.

Injury Metrics for Assessing the Risk of Acute Subdural Hematoma in Traumatic Events.

Worldwide, the ocurrence of acute subdural hematomas (ASDHs) in road traffic crashes is a major public health problem. ASDHs are usually produced by loss of structural integrity of one of the cerebral bridging veins (CBVs) linking the parasagittal sinus to the brain. Therefore, to assess the risk of ASDH it is important to know the mechanical conditions to which the CBVs are subjected during a potentially traumatic event (such as a traffic accident or a fall from height). Recently, new studies on CBVs have been published allowing much more accurate prediction of the likelihood of mechanical failure of CBVs. These new data can be used to propose new damage metrics, which make more accurate predictions about the probability of occurrence of ASDH in road crashes. This would allow a better assessement of the effects of passive safety countermeasures and, consequently, to improve vehicle restraint systems. Currently, some widely used damage metrics are based on partially obsolete data and measurements of the mechanical behavior of CBVs that have not been confirmed by subsequent studies. This paper proposes a revision of some existing metrics and constructs a new metric based on more accurate recent data on the mechanical failure of human CBVs.

The effect of blood cells retained in rat livers during static cold storage on viability outcomes during normothermic machine perfusion

In transplantation, livers are transported to recipients using static cold storage (SCS), whereby livers are exposed to cold ischemic injury that contribute to post-transplant risk factors. We hypothesized that flushing organs during procurement with cold preservation solutions could influence the number of donor blood cells retained in the allograft thereby exacerbating cold ischemic injury. We present the results of rat livers that underwent 24 h SCS after being flushed with a cold University of Wisconsin (UW) solution versus room temperature (RT) lactated ringers (LR) solution. These results were compared to livers that were not flushed prior to SCS and thoroughly flushed livers without SCS. We used viability and injury metrics collected during normothermic machine perfusion (NMP) and the number of retained peripheral cells (RPCs) measured by histology to compare outcomes. Compared to the cold UW flush group, livers flushed with RT LR had lower resistance, lactate, AST, and ALT at 6 h of NMP. The number of RPCs also had significant positive correlations with resistance, lactate, and potassium levels and a negative correlation with energy charge. In conclusion, livers exposed to cold UW flush prior to SCS appear to perform worse during NMP, compared to RT LR flush.