The Development of a Tympanic Membrane Model and Probabilistic Dose-Response Risk Assessment of Rupture Because of Blast.

INTRODUCTION: There is no dose-response model available for the assessment of the risk of tympanic membrane rupture (TMR), commonly known as eardrum rupture, from exposures to blast from nonlethal flashbangs, which can occur concurrently with temporary threshold shift. Therefore, the objective of this work was to develop a fast-running, lumped parameter model of the tympanic membrane (TM) with probabilistic dose-dependent prediction of injury risk. MATERIALS AND METHODS: The lumped parameter model was first benchmarked with a finite element model of the middle ear. To develop the dose-response curves, TMR data from a historic cadaver study were utilized. From these data, the binary probability response was constructed and logistic regression was applied to generate the respective dose-response curves at moderate and severe eardrum rupture severity. RESULTS: Hosmer-Lemeshow statistical and receiver operation characteristic analyses showed that maximum stored TM energy was the overall best dose metric or injury correlate when compared with total work and peak TM pressure. CONCLUSIONS: Dose-response curves are needed for probabilistic risk assessments of unintended effects like TMR. For increased functionality, the lumped parameter model was packaged as a software library that predicts eardrum rupture for a given blast loading condition.

Deriving pQCT-Type Spatial Density from DXA Images.

INTRODUCTION: Musculoskeletal overuse injuries are a serious problem in the military, particularly in basic combat training, resulting in hundreds of millions of dollars lost because of limited duty days, medical treatment, and high rates of reinjury. Injury risk models have been developed using peripheral computed tomography (pQCT)-based injury correlates. However, pQCT image capture on large number of recruits is not practical for military settings. Thus, this article presents a method to derive spatial density pQCT images from much lower resolution but more accessible dual-energy X-ray absorptiometry (DXA). MATERIALS AND METHODS: Whole-body DXA images and lower leg pQCT images for 51 male military recruits were collected before a 40-day School of Infantry. An artificial neural network model was constructed to relate the DXA density profiles to spatial pQCT density at the 38% and 66% tibial locations. RESULTS: Strong correlation, R2 = 0.993 and R2 = 0.990 for the 38% and 66% pQCT slices, respectively, was shown between spatial density predicted by the artificial neural network model and raw data. CONCLUSIONS: High potential exists to create a practical protocol using DXA in place of pQCT to assess stress fracture risk and aid in mitigating musculoskeletal injuries seen in military recruits.

3D Tibia Reconstruction Using 2D Computed Tomography Images.

OBJECTIVE: Skeletal stress fracture of the lower limbs remains a significant problem for the military. The objective of this study was to develop a subject-specific 3D reconstruction of the tibia using only a few CT images for the prediction of peak stresses and locations. METHODS: Full bilateral tibial CT scans were recorded for 63 healthy college male participants. A 3D finite element (FE) model of the tibia for each subject was generated from standard CT cross-section data (i.e., 4%, 14%, 38%, and 66% of the tibial length) via a transformation matrix. The final reconstructed FE models were used to calculate peak stress and location on the tibia due to a simulated walking load (3,700 N), and compared to the raw models. RESULTS: The density-weighted, spatially-normalized errors between the raw and reconstructed CT models were small. The mean percent difference between the raw and reconstructed models for peak stress (0.62%) and location (-0.88%) was negligible. CONCLUSIONS: Subject-specific tibia models can provide even great insights into the mechanisms of stress fracture injury, which are common in military and athletic settings. Rapid development of 3D tibia models allows for the future work of determining peak stress-related injury correlates to stress fracture outcomes.

Modeling of Gender Differences in Thermoregulation.

In January 2013, the Department of Defense lifted a ban that had prevented women from holding combat positions in the military. However, innate differences in physical traits and physiology between men and women likely will result in differences in physical performance. Sex differences in thermoregulation is a key area that needs to be examined due to the potential impact on physical performance. Therefore, we expanded our previously developed thermoregulation model (TRM) to include the effects of gender. Women have been found to have a lower sweat output in heat stress and lesser shivering in cold stress than men; therefore, the equations for sweat mass loss rate and shivering heat generation were modified for women accordingly. The updated TRM showed good agreement with female data collected from exercise in cool to hot conditions, cold air exposure, and cold water immersion. Gender differences in sweat evaporation appear minimal except for sufficiently high exercise-heat combinations. Gender differences in core temperature and heat generation during cold stress are significant. The expanded TRM can be used to assess gender-specific thermal response with future application to predicting performance differences and optimizing warfighter effectiveness for a wide range of military relevant tasks.

Prediction of performance reduction in self-paced exercise as modulated by the rating of perceived exertion.

PURPOSE: Rating of perceived exertion (RPE) is a scale of exercise difficulty and has been hypothesized to be a regulator of work rate during self-pacing. The goal of this work was to develop a dynamic prediction of RPE and to characterize the control strategy employed to reduce work rate during self-paced exercise using RPE as feedback. METHODS: Training and test data were acquired from the literature to develop a linear regression of RPE as a function of four physiological variables: core temperature, mean-weighted skin temperature, metabolic rate, and integral of relative oxygen consumption (R (2) = 0.85). A thermoregulatory model was used to predict core and mean-weighted skin temperature. Utilizing self-paced cycling and running data from the literature, we characterized reductions in work rate with a proportional-derivative control algorithm with RPE as feedback. RESULTS: Bland-Altman analysis revealed the necessity to parameterize RPE equations for untrained and endurance-trained individuals. Afterwards, dynamic predictions of RPE were accurate for a wide range of activity levels and air temperatures for walking, running, and cycling (LoA and bias of 2.3 and -0.03, respectively). For self-paced exercise, the control algorithm characterized the trend and magnitude of work rate reductions for cycling and running, and showed regulated RPE to be less conservative for shorter vs. longer duration exercise. CONCLUSIONS: A novel methodology to characterize self-paced work intensity, based upon dynamic physiologic response, is provided. The complete model is a useful tool that estimates performance decrements during self-paced exercise and predicts tolerance time for exhaustive fixed-rate exercise.

Assessing the application of acute toxic gas standards.

OBJECTIVE: In this paper, we compare acute toxic gas standards developed for occupational, military, and civilian use that predict or establish guidelines for limiting exposure to inhaled toxic gases. CONTEXT: Large disparities between guidelines exist for similar exposure scenarios, raising questions about why differences exist, as well as the applicability of each standard. The motivation and rationale behind the development of the standards is explored with emphasis on the experimental data used to set the standards. METHODS: The Toxic Gas Assessment Software (TGAS) is used to quantitatively compare current acute exposure standards, such as: Acute Exposure Guidelines (AEGL), Immediate Danger to Life or Health (IDLH), Purser, International Organization for Standardization (ISO 13571), and Federal Aviation Administration (FAA). The TGAS software does this by calculating the body-mass-normalized internal doses of each gas exposure in each standard, which is then plotted on a cumulative distribution function for a normal or susceptible population to visualize the relationship of the standards to each other. To focus the comparison, acute toxic gas standards for five common fire gases, carbon monoxide (CO), hydrogen cyanide (HCN), hydrogen chloride (HCl), nitrogen dioxide (NO2), and acrolein (C3H4O), are explored. RESULTS: It was found that differences between standards can be reconciled when the target population, effect endpoint, and incidence level are taken into account. CONCLUSION: By analyzing the standards with respect to these factors, we can acquire a better understanding of the applicability of each.