Model to Predict Probability of Significant Skin Burn Injury From a Directed-Energy Source.

INTRODUCTION: Millimeter wave directed energy in the frequency regime of 94-95 GHz has potential for use in numerous military applications including crowd control and area denial. Although proven to be very safe, millimeter wave energy has the potential, because of accidental over exposure, to produce significant injuries. Currently, the Dynamic Thermal Model (DTM), developed by Beason and colleagues, is used to calculate the temperature profile in skin undergoing (millimeter wave) heating, using an all-or-nothing threshold of injury. Risk of significant injury (RSI) is determined by product of the probability of an injury outcome on a region of the body times the probability of that the injury will occur. Thus, a threshold injury determination may over predict burn probability and fail safety requirements. This work augments the DTM, replacing the current threshold value of injury with a probabilistic risk of injury to better quantify the risk of significant injury. MATERIALS AND METHODS: In this study, continuous probabilistic dose-response models using logistic regression analysis have been developed to account for mild second-degree, deep second-degree, and third-degree burn injuries based on a historic experimental burn dataset. Statistical analysis methods such as Hosmer-Lemeshow statistics, McFadden's pseudo R2 and receiver operator characteristic were used to validate the models against an independent experimental burn dataset. RESULTS: Comparison of logistic models fit using damage coefficients from the literature showed that Mehta and Wong provided the best fits historic burn data, which was corroborated by the McFadden pseudo R2 statistic for mild second-degree, deep second-degree, and third-degree severity. CONCLUSION: The dose-response models developed in this study are shown to be an excellent predictor of burn injury for each severity. The DTM was repackaged with the probabilistic burn models to more accurately determine the risk of significant burn injury.

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.

Callosal injury-induced working memory impairment: a computational network modeling study.

Mild traumatic brain injury (mTBI) often results in working memory (WM) impairment, but the mechanistic relationship between the two remains elusive. We used a computational model of two cortical neuronal networks linked by myelinated callosal axons with distance-dependent conduction delays to simulate callosal dysfunction in mTBI and quantify its impact on WM. WM maintenance and termination in the model network depended on short-term synaptic plasticity. In injured networks, WM duration depended on the extent of callosal injury, consistent with clinical data. The model provides a framework for studying callosal injury-induced neurobehavioral alterations following mTBI, and, to the best of our knowledge, is the first computational model to address mTBI-induced WM impairment.

A Mechanistic End-to-End Concussion Model That Translates Head Kinematics to Neurologic Injury.

Past concussion studies have focused on understanding the injury processes occurring on discrete length scales (e.g., tissue-level stresses and strains, cell-level stresses and strains, or injury-induced cellular pathology). A comprehensive approach that connects all length scales and relates measurable macroscopic parameters to neurological outcomes is the first step toward rationally unraveling the complexity of this multi-scale system, for better guidance of future research. This paper describes the development of the first quantitative end-to-end (E2E) multi-scale model that links gross head motion to neurological injury by integrating fundamental elements of tissue and cellular mechanical response with axonal dysfunction. The model quantifies axonal stretch (i.e., tension) injury in the corpus callosum, with axonal functionality parameterized in terms of axonal signaling. An internal injury correlate is obtained by calculating a neurological injury measure (the average reduction in the axonal signal amplitude) over the corpus callosum. By using a neurologically based quantity rather than externally measured head kinematics, the E2E model is able to unify concussion data across a range of exposure conditions and species with greater sensitivity and specificity than correlates based on external measures. In addition, this model quantitatively links injury of the corpus callosum to observed specific neurobehavioral outcomes that reflect clinical measures of mild traumatic brain injury. This comprehensive modeling framework provides a basis for the systematic improvement and expansion of this mechanistic-based understanding, including widening the range of neurological injury estimation, improving concussion risk correlates, guiding the design of protective equipment, and setting safety standards.

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.

Callosal dysfunction explains injury sequelae in a computational network model of axonal injury.

Mild traumatic brain injury (mTBI) often results in neurobehavioral aberrations such as impaired attention and increased reaction time. Diffusion imaging and postmortem analysis studies suggest that mTBI primarily affects myelinated axons in white matter tracts. In particular, corpus callosum, mediating interhemispheric information exchange, has been shown to be affected in mTBI. Yet little is known about the mechanisms linking the injury of myelinated callosal axons to the neurobehavioral sequelae of mTBI. To address this issue, we devised and studied a large, biologically plausible neuronal network model of cortical tissue. Importantly, the model architecture incorporated intra- and interhemispheric organization, including myelinated callosal axons and distance-dependent axonal conduction delays. In the resting state, the intact model network exhibited several salient features, including alpha-band (8-12 Hz) collective activity with low-frequency irregular spiking of individual neurons. The network model of callosal injury captured several clinical observations, including 1) "slowing down" of the network rhythms, manifested as an increased resting-state theta-to-alpha power ratio, 2) reduced response to attention-like network stimulation, manifested as a reduced spectral power of collective activity, and 3) increased population response time in response to stimulation. Importantly, these changes were positively correlated with injury severity, supporting proposals to use neurobehavioral indices as biomarkers for determining the severity of injury. Our modeling effort helps to understand the role played by the injury of callosal myelinated axons in defining the neurobehavioral sequelae of mTBI.

Perinodal glial swelling mitigates axonal degradation in a model of axonal injury.

Mild traumatic brain injury (mTBI) has been associated with the damage to myelinated axons in white matter tracts. Animal models and in vitro studies suggest that axonal degradation develops during a latent period following a traumatic event. This delay has been attributed to slowly developing axonal membrane depolarization that is initiated by injury-induced ionic imbalance and in turn, leads to the activation of Ca(2+) proteases via pathological accumulation of Ca(2+). However, the mechanisms mitigating the transition to axonal degradation after injury remain elusive. We addressed this question in a detailed biophysical model of axonal injury that incorporated ion exchange and glial swelling mechanisms. We show that glial swelling, which often co-occurs with mTBI, promotes axonal survival by regulating extracellular K(+) dynamics, extending the range of injury parameters in which axons exhibit stable membrane potential postinjury. In addition, glial swelling was instrumental in reducing axonal sensitivity to repetitive stretch injury that occurred several minutes following the first one. Results of this study suggest that acute post-traumatic swelling of perinodal astrocytes helps prevent or postpone axonal degradation by maintaining physiologically relevant levels of extracellular K(+).

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.

An integrated physiology model to study regional lung damage effects and the physiologic response.

BACKGROUND: This work expands upon a previously developed exercise dynamic physiology model (DPM) with the addition of an anatomic pulmonary system in order to quantify the impact of lung damage on oxygen transport and physical performance decrement. METHODS: A pulmonary model is derived with an anatomic structure based on morphometric measurements, accounting for heterogeneous ventilation and perfusion observed experimentally. The model is incorporated into an existing exercise physiology model; the combined system is validated using human exercise data. Pulmonary damage from blast, blunt trauma, and chemical injury is quantified in the model based on lung fluid infiltration (edema) which reduces oxygen delivery to the blood. The pulmonary damage component is derived and calibrated based on published animal experiments; scaling laws are used to predict the human response to lung injury in terms of physical performance decrement. RESULTS: The augmented dynamic physiology model (DPM) accurately predicted the human response to hypoxia, altitude, and exercise observed experimentally. The pulmonary damage parameters (shunt and diffusing capacity reduction) were fit to experimental animal data obtained in blast, blunt trauma, and chemical damage studies which link lung damage to lung weight change; the model is able to predict the reduced oxygen delivery in damage conditions. The model accurately estimates physical performance reduction with pulmonary damage. CONCLUSIONS: We have developed a physiologically-based mathematical model to predict performance decrement endpoints in the presence of thoracic damage; simulations can be extended to estimate human performance and escape in extreme situations.

Primary paranode demyelination modulates slowly developing axonal depolarization in a model of axonal injury.

Neurological sequelae of mild traumatic brain injury are associated with the damage to white matter myelinated axons. In vitro models of axonal injury suggest that the progression to pathological ruin is initiated by the mechanical damage to tetrodotoxin-sensitive voltage-gated sodium channels that breaches the ion balance through alteration in kinetic properties of these channels. In myelinated axons, sodium channels are concentrated at nodes of Ranvier, making these sites vulnerable to mechanical injury. Nodal damage can also be inflicted by injury-induced partial demyelination of paranode/juxtaparanode compartments that flank the nodes and contain high density of voltage-gated potassium channels. Demyelination-induced potassium deregulation can further aggravate axonal damage; however, the role of paranode/juxtaparanode demyelination in immediate impairment of axonal function, and its contribution to the development of axonal depolarization remain elusive. A biophysically realistic computational model of myelinated axon that incorporates ion exchange mechanisms and nodal/paranodal/juxtaparanodal organization was developed and used to study the impact of injury-induced demyelination on axonal signal transmission. Injured axons showed alterations in signal propagation that were consistent with the experimental findings and with the notion of reduced axonal excitability immediately post trauma. Injury-induced demyelination strongly modulated the rate of axonal depolarization, suggesting that trauma-induced damage to paranode myelin can affect axonal transition to degradation. Results of these studies clarify the contribution of paranode demyelination to immediate post trauma alterations in axonal function and suggest that partial paranode demyelination should be considered as another "injury parameter" that is likely to determine the stability of axonal function.

Computer modeling of mild axonal injury: implications for axonal signal transmission.

Diffusion imaging and postmortem studies of patients with mild traumatic brain injury (mTBI) of the concussive type are consistent with the observations of diffuse axonal injury to the white matter axons. Mechanical trauma to axons affects the properties of tetrodotoxin-sensitive sodium channels at the nodes of Ranvier, leading to axonal degeneration through intra-axonal accumulation of calcium ions and activation of calcium proteases; however, the immediate implications of axonal trauma regarding axonal functionality and their relevance to transient impairment of function as observed in concussion remain elusive. A biophysically realistic computational model of a myelinated axon was developed to investigate how mTBI could immediately affect axonal function. Traumatized axons showed alterations in signal propagation properties that nonlinearly depended on the level of trauma; subthreshold traumatized axons had decreased spike propagation time, whereas suprathreshold traumatized axons exhibited a slowdown of spike propagation and spike propagation failure. Trauma had consistently reduced axonal spike amplitude. The susceptibility of an axon to trauma could be modulated by the function of an ATP-dependent sodium-potassium pump. The results suggest a mechanism by which concussive mTBI could lead to the immediate impairment of signal propagation through the axon and the emerging dysfunctional neuronal information exchange.

An integrated exercise response and muscle fatigue model for performance decrement estimates of workloads in oxygen-limiting environments.

The performance dynamic physiology model (DPM-PE) integrates a modified muscle fatigue model with an exercise physiology model that calculates the transport and delivery of oxygen to working muscles during exposures of oxygen-limiting environments. This mathematical model implements a number of physiologic processes (respiration, circulation, tissue metabolism, diffusion-limited gas transfer at the blood/gas lung interface, and ventilatory control with afferent feedback, central command and humoral chemoreceptor feedback) to replicate the three phases of ventilatory response to a variety of exertion patterns, predict the delivery and transport of oxygen and carbon dioxide from the lungs to tissues, and calculate the amount of aerobic and anaerobic work performed. The ventilatory patterns from passive leg movement, unloaded work, and stepped and ramping loaded work compare well against data. The model also compares well against steady-state ventilation, cardiac output, blood oxygen levels, oxygen consumption, and carbon dioxide generation against a range of exertion levels at sea level and at altitude, thus demonstrating the range of applicability of the exercise model. With the ability to understand and predict gas transport and delivery of oxygen to working muscle tissue for different workloads and environments, the correlation between blood oxygen measures and the recovery factor of the muscle fatigue model was explored. Endurance data sets in normoxia and hypoxia were best replicated using arterial oxygen saturation as the correlate with the recovery factor. This model provides a physiologically based method for predicting physical performance decrement due to oxygen-limiting environments.

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.

Incorporation of acute dynamic ventilation changes into a standardized physiologically based pharmacokinetic model.

A seven-compartment physiologically based pharmacokinetic (PBPK) model incorporating a dynamic ventilation response has been developed to predict normalized internal dose from inhalation exposure to a large range of volatile gases. The model uses a common set of physiologic parameters, including standardized ventilation rates and cardiac outputs for rat and human. This standardized model is validated against experimentally measured blood and tissue concentrations for 21 gases. For each of these gases, body-mass-normalized critical internal dose (blood concentration) is established, as calculated using exposure concentration and time duration specified by the lowest observed adverse effect level (LOAEL) or the acute exposure guideline level (AEGL). The dynamic ventilation changes are obtained by combining the standardized PBPK model with the Toxic Gas Assessment Software 2.0 (TGAS-2), a validated acute ventilation response model. The combined TGAS-2P model provides a coupled, transient ventilation and pharmacokinetic response that predicts body mass normalized internal dose that is correlated with deleterious outcomes. The importance of ventilation in pharmacokinetics is illustrated in a simulation of the introduction of Halon 1301 into an environment of fire gases.